diff --git a/AutoMxL/Explore/Explore.py b/AutoMxL/Explore/Explore.py index e31d0d0..fc880af 100644 --- a/AutoMxL/Explore/Explore.py +++ b/AutoMxL/Explore/Explore.py @@ -1,60 +1,12 @@ -""" Global dataset information functions : - - - explore (func): Identify variables types and gives global information about the dataset (NA, low variance features) - - low variance features (func): identify features with low variance - - get_features_type (func): get all features per type -""" from sklearn.preprocessing import MinMaxScaler from AutoMxL.Explore.Features_Type import * from AutoMxL.Utils.Display import * - def explore(df, verbose=False): - """Identify variables types and gives global information about the dataset - - - Variables type : - - date - - identifier - - verbatim - - boolean - - categorical - - numerical - - variables containing NA values - - low variance and unique values variables - - See get_features_type function doc for type identification heuristics - - Parameters - ---------- - df : DataFrame - input dataset - verbose : boolean (Default False) - Get logging information - - Returns - ------- - dict - {x : variables names list } - - - date : date features - - identifier : identifier features - - verbatim : verbatim features - - boolean : boolean features - - categorical : categorical features - - numerical : numerical features - - categorical : categorical features - - date : date features - - NA : features which contains NA values - - low_variance : list of the features with low variance - """ - # dataset dimensions if verbose: color_print("Dimensions :") print(" > row number :", df.shape[0], "\n > col number :", df.shape[1]) - ######################### - # Low variance features - ######################### if verbose: color_print('Low variance features') @@ -62,16 +14,12 @@ def explore(df, verbose=False): low_variance_features(df, var_list=df._get_numeric_data().columns.tolist(), threshold=0, rescale=True, verbose=verbose).index.tolist() - # categorical features with unique values l_unique = [col for col in df.columns.tolist() if df[col].dtype == 'object' and df[col].nunique(dropna=True) == 1] l_low_var = l_low_var + l_unique df_valid = df.drop(l_low_var, axis=1).copy() - ################# - # features type # - ################# d_features = get_features_type(df_valid, l_var=None, th=0.95) if verbose: @@ -81,13 +29,9 @@ def explore(df, verbose=False): round(len(d_features[typ]) / df_valid.shape[1] * 100)) + '%)'), d_features.keys())) - ###################### - # NA values analysis - ###################### df_col = pd.DataFrame(df_valid.columns.values, columns=['variables']) df_col['Nbr NA'] = df_valid.isna().sum().tolist() df_col['Taux NA'] = df_col['Nbr NA'] / df_valid.shape[0] - # features containing NA values NA_columns = df_col.loc[df_col['Nbr NA'] > 0].sort_values('Nbr NA', ascending=False).variables.tolist() col_des = df_col['Taux NA'].describe() @@ -97,50 +41,16 @@ def explore(df, verbose=False): '\n > min : ' + str(round(col_des['min'] * 100, 2)) + '%', '\n > max : ' + str(round(col_des['max'] * 100, 2)) + '%') - # store into DataFrame d_features['NA'] = NA_columns d_features['low_variance'] = l_low_var return d_features - """ ------------------------------------------------------------------------------------------------------------------------- """ - def get_features_type(df, l_var=None, th=0.95): - """ Get all features per type : - - - date : try to apply to_datetime - - identifier : - - #(unique values)/#(total values) > threshold (default 0.95) - - AND length is the same for all values (for non NA) - - verbatim : - - #(unique values)/#(total values) >= threshold (default 0.95) - - AND length is NOT the same for all values (for non NA) - - boolean : #(distinct values) = 2 - - categorical : - - not a date - - #(unique values)/#(total values) < threshold (default 0.95) - - AND #(uniques values)>2 - - AND for num values #(unique values)<30 - - numerical : others - - Parameters - ---------- - df : DataFrame - input dataset - l_var : list (Default : None) - variable names - th : float (Default : 0.95) - threshold used to identify identifiers/verbatims variables - - Returns - ------- - dict - { type : variables name list} - """ d_output = {} if l_var is None: @@ -158,37 +68,11 @@ def get_features_type(df, l_var=None, th=0.95): return d_output - """ ------------------------------------------------------------------------------------------------------------------------- """ - def low_variance_features(df, var_list=None, threshold=0, rescale=True, verbose=False): - """Identify numerical features with low variance : (< threshold). - Possible to rescale feature before computing. - - Parameters - ---------- - df : DataFrame - input DataFrame - var_list : list (default : None) - names of the variables to check variance - if None : all the numerical features - threshold : float (default : 0) - variance threshold - rescale : bool (default : true) - enable MinMaxScaler before computing variance - verbose : boolean (Default False) - Get logging information - - Returns - ------- - list - Names of the variables with low variance - """ - # if var_list = None, get all num features - # else, remove features from var_list whose type is not num l_num = df._get_numeric_data().columns.tolist() if var_list is None: @@ -205,7 +89,6 @@ def low_variance_features(df, var_list=None, threshold=0, rescale=True, verbose= selected_var = df_bis[var_list].var().loc[df_bis.var() <= threshold] if verbose: - # print('features : ',list(var_list)) if rescale: print(' **MinMaxScaler [0,1]') print(' ', str(len(selected_var)) + ' feature(s) with variance <= threshold (' + str(threshold) + ')') diff --git a/AutoMxL/Explore/Features_Type.py b/AutoMxL/Explore/Features_Type.py index a3299ff..1e01d25 100644 --- a/AutoMxL/Explore/Features_Type.py +++ b/AutoMxL/Explore/Features_Type.py @@ -1,57 +1,8 @@ -"""Variables type identification function - -- features_from_type (func): get all features for a selected type -- is_date (func): test if a variable is a date -- is_identifier (func): test if a variable is an identifier -- is_verbatim (func): test if a variable is a verbatim -- is_boolean (func): test if a variable is a boolean -- is_categorical (func): test if a variable is a categorical one (with more than 2 categories) -""" import pandas as pd from time import time from AutoMxL.Utils.Decorators import timer - def features_from_type(df, typ, l_var=None, th=0.95): - """Get features of a selected type : - - - date : try to apply to_datetime - - identifier : - - #(unique values)/#(total values) > threshold (default 0.95) - - AND length is the same for all values (for non NA) - - verbatim : - - #(unique values)/#(total values) >= threshold (default 0.95) - - AND length is NOT the same for all values (for non NA) - - boolean : #(distinct values) = 2 - - categorical : - - not a date - - #(unique values)/#(total values) < threshold (default 0.95) - - AND #(uniques values)>2 - - AND for num values #(unique values)<30 - - Parameters - ---------- - df : DataFrame - input dataset - typ : string - selected type to get features: - - - 'date' - - 'identifier' - - 'verbatim' - - 'boolean' - - categorical - - l_var : list (Default : None) - variables names. If None, all dataset columns - th : float (Default : 0.95) - threshold used to identify identifiers/verbatims variables - - Returns - ------- - list - identified variables names - """ assert typ in ['date', 'identifier', 'verbatim', 'boolean', 'categorical'], 'Invalid type' if l_var is None: @@ -72,35 +23,16 @@ def features_from_type(df, typ, l_var=None, th=0.95): return l_var - """ ------------------------------------------------------------------------------------------------------------------------- """ - def is_date(df, col): - """Test if a variable is a date. - - Method : try to apply to_datetime - - Parameters - ---------- - df : DataFrame - input dataset - col : string - variable name - - Returns - ------- - res : boolean - test result - """ sample_size = 10 full_col = df[col].loc[~df[col].isna()] smpl_size = min(sample_size, len(full_col)) smpl = full_col.sample(smpl_size).copy() - # if col is numerical/object type, try apply to_datetime if df[col].dtype != 'datetime64[ns]': try: if smpl.dtype == 'object': @@ -113,39 +45,15 @@ def is_date(df, col): pass except TypeError: pass - # if col is datetime type, res = True return smpl.dtype == 'datetime64[ns]' - """ ------------------------------------------------------------------------------------------------------------------------- """ - def is_identifier(df, col, th=0.95): - """Test if a variable is an identifier. - - - #(unique values)/#(total values) > threshold (default 0.95) - - AND length is the same for all values (for non NA) - - AND not date - - Parameters - ---------- - df : DataFrame - input dataset - col : string - variable name - th : float (Default : 0.95) - threshold rate - - Returns - ------- - res : boolean - test result - """ full_col = df[col].loc[~df[col].isna()] - # test if #(v unique values)/#(v,total,values) >= threshold (default 0.95) if full_col.nunique() / full_col.count() >= th: if df[col].dtype != 'object': try: @@ -157,7 +65,6 @@ def is_identifier(df, col, th=0.95): except TypeError: return False - # test if all (non NA) values have the same length if full_col.apply(lambda x: len(x)).nunique() == 1: if not is_date(df, col): return True @@ -168,41 +75,17 @@ def is_identifier(df, col, th=0.95): else: return False - """ ------------------------------------------------------------------------------------------------------------------------- """ - def is_verbatim(df, col, th=0.95): - """Test if a variable is a verbatim. - - - #(unique values)/#(total values) >= threshold (default 0.95) - - AND length is NOT the same for all values (for non NA) - - Parameters - ---------- - df : DataFrame - input dataset - col : string - variable name - th : float (Default : 0.95) - threshold rate - - Returns - ------- - res : boolean - test result - """ - # get variable serie with non NA values if df[col].dtype == 'object': full_col = df[col].loc[~df[col].isna()] else: return False - # test if #(v unique values)/#(v,total,values) > threshold (default 0.95) if full_col.nunique() / full_col.count() >= th: - # test if all (non NA) values have the same length if full_col.apply(lambda x: len(x)).nunique() > 1: return True else: @@ -210,31 +93,12 @@ def is_verbatim(df, col, th=0.95): else: return False - """ ------------------------------------------------------------------------------------------------------------------------- """ - def is_boolean(df, col): - """Test if a variable is a boolean. - - - #(distinct values) = 2 - - Parameters - ---------- - df : DataFrame - input dataset - col : string - variable name - - Returns - ------- - res : boolean - test result - """ full_col = df[col].loc[~df[col].isna()] - # get variable serie with non NA values if full_col.nunique() == 2: if len(full_col) > 2: @@ -245,35 +109,11 @@ def is_boolean(df, col): else: return False - """ ------------------------------------------------------------------------------------------------------------------------- """ - def is_categorical(df, col, th=0.95): - """Test if a variable is a categorical one (with more than 2 categories). - - - not a date - - #(unique values)/#(total values) < threshold (default 0.95 - - AND #(uniques values)>2 - - AND for num values #(unique values)<30 - - Parameters - ---------- - df : DataFrame - input dataset - col : string - variable name - th : float (Default : 0.95) - threshold - - Returns - ------- - res : boolean - test result - """ - # get variable serie with non NA values full_col = df[col].loc[~df[col].isna()] if full_col.nunique() > 2: if (full_col.nunique() / full_col.count()) < th: diff --git a/AutoMxL/Explore/__init__.py b/AutoMxL/Explore/__init__.py index 679a962..761587d 100644 --- a/AutoMxL/Explore/__init__.py +++ b/AutoMxL/Explore/__init__.py @@ -1,12 +1,3 @@ -""" -Contains modules to get and store informations about the data. - -Modules : -- Audit_Dataset -- Get_Outliers - -""" - __all__ = ['Explore', 'Features_Type'] \ No newline at end of file diff --git a/AutoMxL/Modelisation/Bagging.py b/AutoMxL/Modelisation/Bagging.py index 0e3b65a..b1a2952 100644 --- a/AutoMxL/Modelisation/Bagging.py +++ b/AutoMxL/Modelisation/Bagging.py @@ -1,9 +1,3 @@ -""" Bagging algorithm class. Methods : - -- Bagging (class) : generate new training more balanced and train model for each -- Bagging_sample (func) : generate bagging sample - -""" from sklearn.ensemble import RandomForestClassifier from AutoMxL.Modelisation.Utils import * import pandas as pd @@ -15,34 +9,7 @@ 'pos_sample_size': 1.0, 'replace': False} - class Bagging(object): - """Meta-algo designed to improve the stability and accuracy of ML classif/regression algos - or to face an "imbalanced target distribution" issue. - - Bagging generates m new training sets more balanced. Then, a model is fitted on each - sample and outputs are combined by averaging (for regression) or voting (for classification). - - Available classifiers : Random Forest and XGBOOST - - Parameters - ---------- - clf : Model fitted on samples (Default : RandomForestClassifier(n_estimators=100, max_leaf_nodes=100) - Model fitted on the samples - n_sample : int (Default : 5) - number a samples - pos_sample_size : int/float (Default : 1.0) - Number/rate of target=1 observations in each sample (filled with 3 times more target=0 ) - - - if int : number of target=1 - - if float : rate of total target=1 - - replace : Boolean (Default : False) - Enable sampling with replacement - - list_model : list (Default : None) - Fitted models (created with fit method) - """ def __init__(self, clf=RandomForestClassifier(n_estimators=100, max_leaf_nodes=100), @@ -62,13 +29,6 @@ def __init__(self, """ def get_params(self): - """Get bagging object parameters - - Returns - ------- - dict - {param : value} - """ return {'classifier': self.classifier, 'niter': self.niter, 'pos_sample_size': self.pos_sample_size, @@ -80,42 +40,22 @@ def get_params(self): """ def fit(self, df_train, target): - """Create bagging samples from a DataFrame and fit the model (self.clf) on each sample - - Parameters - ---------- - df_train : DataFrame - Training dataset - target : String - Target name - - Returns - ------- - self.list_model : list - Fitted models - """ - # list_model init self.list_model = [None] * self.niter - # get number of target=1 in bagging samples if isinstance(self.pos_sample_size, int): N = self.pos_sample_size else: N = int(self.pos_sample_size * df_train.loc[df_train[target] == 1].shape[0]) for i in range(self.niter): - # Sample creation df_train_bag = create_sample(df_train, target, N, replace=self.replace) - # X_train / y_train X_train_bag = df_train_bag.copy() y_train_bag = X_train_bag[target] del X_train_bag[target] - # Create and store model self.list_model[i] = self.classifier - # fit model for each sample self.list_model[i].fit(X_train_bag, y_train_bag) self.is_fitted = True @@ -127,35 +67,14 @@ def fit(self, df_train, target): """ def predict(self, df): - """Apply models fitted on sample to a dataset. - Combine models by averaging the outputs (for regression) or voting (for classification) - - Parameters - ---------- - df : DataFrame - Dataset to apply the model - - Returns - ------- - numpy.ndarray (float) - Averaged classification probabilities - numpy.ndarray (int) - Predictions for each observation - """ assert self.is_fitted, "Fit first !" - # Init probs storage matrix mat_prob = np.zeros((self.niter, df.shape[0])) - # for each fitted models for j in range(self.niter): - # apply the model on test set y_prob_rf = self.list_model[j].predict_proba(df) - # probabilities storage in matrix mat_prob[j] = y_prob_rf[:, 1] - # probas averaging list_prob_pred = mat_prob.sum(axis=0) / self.niter - # voting list_pred = [round(elem, 0) for elem in list_prob_pred] return list_prob_pred, list_pred @@ -165,66 +84,27 @@ def predict(self, df): """ def bag_feature_importance(self, X): - """Get features importance of the model by averaging importance of models fitted on the samples - - Parameters - ---------- - X : DataFrame - Input Dataset - - Returns - ------- - dict - {feature : importance} - - """ - # Init importance storage matrix mat_feat_imp = np.zeros((self.niter, len(X.columns))) - # for each fitted models for i in range(self.niter): - # importances storage in matrix mat_feat_imp[i] = self.list_model[i].feature_importances_ - # Averaging importances list_feat_imp_moy = mat_feat_imp.sum(axis=0) / self.niter features_dict = dict(zip(X.columns, list_feat_imp_moy)) return features_dict - """ ------------------------------------------------------------------------------------------------------------- """ - def create_sample(df, target, pos_target_nb, replace=False): - """Generate a DataFrame sample with selected number of target=1 - - Parameters - ---------- - df : DataFrame - Input dataset - target : String - Target name - pos_target_nb : int - Number of target=1 observations in the sample - replace : Boolean (défaut : False) - If True, create samples with replacement - - Returns - ------- - DataFrame - sample dataset - """ - # split target = 1 / 0 df_pos = df.loc[(df[target] == 1)] df_neg = df.loc[(df[target] == 0)] n_size = min(3 * pos_target_nb, df_neg.shape[0]) - # sample creation df_bag = pd.concat( (df_pos.sample(n=pos_target_nb, replace=replace), df_neg.sample(n=n_size, replace=replace)), axis=0) diff --git a/AutoMxL/Modelisation/HyperOpt.py b/AutoMxL/Modelisation/HyperOpt.py index 4e59699..41bb015 100644 --- a/AutoMxL/Modelisation/HyperOpt.py +++ b/AutoMxL/Modelisation/HyperOpt.py @@ -1,46 +1,13 @@ -""" Hyperopt class : -Model hyper-optimisation with random search - -- Hyperopt (class) : Model hyper-optimisation with random search - -""" import xgboost import random import itertools as it -# import datetime from AutoMxL.Modelisation.Bagging import * from AutoMxL.Modelisation.Utils import * from AutoMxL.Utils.Display import color_print from datetime import datetime from AutoMxL.param_config import default_bagging_param, default_RF_grid_param, default_XGB_grid_param - class HyperOpt(object): - """Model hyper-optimisation with random search : - - - From a hyper-parameters grid, creates random HPs combinations - - train a model for each combination - - apply the model - - Parameters - ---------- - classifier : string (Default : 'RF') - classifier for modelisation - grid_param : dict (Default : Default_RF_grid_param) - HP grid - n_param_comb : int (Default : 10) - number of HP combinations - bagging : Boolean (Default = False) - use bagging method - bagging_param : n-uple - bagging parameters (Default : default_bagging_param (Bagging module)) - train_model_dict (created with fit method) : dict - {model_index : {'HP', 'probas', 'model', 'features_importance', 'train_metrics'} - bagging_object : Bagging - bagging object - comb_seed : int - seed for randomized HP combinations - """ def __init__(self, classifier='RF', @@ -50,7 +17,6 @@ def __init__(self, bagging_param=default_bagging_param, comb_seed=None): - # parameters if grid_param is None: if classifier == 'RF': self.grid_param = default_RF_grid_param @@ -63,7 +29,6 @@ def __init__(self, self.bagging = bagging self.bagging_param = bagging_param self.comb_seed = comb_seed - # attributes self.d_train_model = {} self.d_bagging = {} self.is_fitted = False @@ -73,13 +38,6 @@ def __init__(self, """ def get_params(self): - """Return Hyperopt object parameters - - Returns - ------- - dict - {param : value} - """ return {'classifier': self.classifier, 'grid_param': self.grid_param, 'n_param_comb': self.n_param_comb, @@ -92,30 +50,10 @@ def get_params(self): """ def fit(self, df_train, target, verbose=False): - """Fit a model for each HP combination - - Parameters - ---------- - df_train : DataFrame - Training dataset - target : string - Target name - verbose : boolean (Default False) - Get logging information - - Returns - ------- - self.train_model_dict (created with fit method) : dict - {model_index : {'HP', 'probas', 'model', 'features_importance', 'train_metrics'} - """ - # X / y y_train = df_train[target] X_train = df_train.drop(target, axis=1) - # Sort HPs grid dict by param name (a->z) grid_names = sorted(self.grid_param) - # random sampling : 'n_param_comb' HPS combinations - # list(it.product(*(self.grid_param[Name] for Name in grid_names))) create all the possible combinations if self.comb_seed is not None: random.seed(self.comb_seed) @@ -126,59 +64,42 @@ def fit(self, df_train, target, verbose=False): print('\033[34m' + 'Random search:', self.n_param_comb, 'HP combs', '\033[0m') print('\033[34m' + 'Model : ', self.classifier, '\033[0m') - # for each HP combination : for model_idx in range(len(sample_combinations)): t_ini_model = datetime.now() - # Model params in dict HP_dict = dict(zip(grid_names, sample_combinations[model_idx])) - # instantiate model - if self.classifier == 'RF': # Classifier Random Forest + if self.classifier == 'RF': clf = RandomForestClassifier(**HP_dict) - # elif self.classifier == 'XGBOOST': else: clf = xgboost.XGBClassifier(**HP_dict) - # disabling bagging if not self.bagging: - # model training clf_fit = clf.fit(X_train, y_train) - # features importance features_dict = dict(zip(X_train.columns, clf.feature_importances_)) - # outputs y_proba = clf_fit.predict_proba(X_train)[:, 1] y_pred = clf_fit.predict(X_train) - # enabling bagging else: - # init bagging object with default params bag = Bagging(clf, **self.bagging_param) - # model training bag.fit(df_train, target) clf_fit = bag.list_model - # features importance features_dict = bag.bag_feature_importance(X_train) - # classification probas y_proba, y_pred = bag.predict(df_train.drop(target, axis=1)) self.d_bagging[model_idx] = bag - # Model evaluation eval_dict = classifier_evaluate(y_train, y_pred, y_proba, verbose=0) - # store train_model = {'HP': HP_dict, 'model': clf_fit, 'features_importance': features_dict, 'train_output': {'y_proba': y_proba, 'y_pred': y_pred}, 'train_metrics': eval_dict} - # store model results for each combination self.d_train_model[model_idx] = train_model - # Fitted ! self.is_fitted = True if verbose: @@ -193,66 +114,37 @@ def fit(self, df_train, target, verbose=False): """ def predict(self, df, target, delta_auc, verbose=False): - """Apply the models - - Parameters - ---------- - df : DataFrame - Dataset to apply the models - target : string - Target name - delta_auc_th : float - Threshold for valid models : abs(auc(train) - auc(test)) - verbose : boolean (Default False) - Get logging information - - Returns - ------- - dict - {model_index : {'HP', 'probas', 'model', 'features_importance', 'train_metrics', 'metrics', 'output'} - """ assert self.is_fitted, 'fit first' d_apply_model = self.d_train_model - # X / y y = df[target] X = df.drop(target, axis=1) - # For each HPs combination for key, value in self.d_train_model.items(): t_ini_model = datetime.now() modl = value['model'] - # Without bagging if not self.bagging: - # classification probas y_proba = modl.predict_proba(X)[:, 1] - # classification votes y_pred = modl.predict(X) - # With bagging elif self.bagging: - # classification probs and votes y_proba, y_pred = self.d_bagging[key].predict(X) - # store outputs d_output = {'y_proba': y_proba, 'y_pred': y_pred} - # compute model metrics eval_dict = classifier_evaluate(y, y_pred, y_proba, verbose=0) eval_dict['delta_auc'] = abs(self.d_train_model[key]['train_metrics']['Roc_auc'] - eval_dict["Roc_auc"]) - # store d_apply_model[key]['outputs'] = d_output d_apply_model[key]['metrics'] = eval_dict - # print metrics if verbose: print(value['HP']) if eval_dict['delta_auc'] <= delta_auc: @@ -277,34 +169,11 @@ def predict(self, df, target, delta_auc, verbose=False): """ def get_best_model(self, d_model_info, metric='F1', delta_auc_th=0.03, verbose=False): - """Identify valid models according to delta auc (test/train). - Get the best model in respect of a selected metric among valid model - - Parameters - ---------- - d_model_info : dict - {model_index : {'HP', 'probas', 'model', 'features_importance', 'train_metrics', 'metrics', 'output'} - metric : string (default = F1-score) - Metric used to get the best model - delta_auc_th : float - Threshold for valid models : abs(auc(train) - auc(test)) - verbose : boolean (Default False) - Get logging information - - Returns - ------- - int - Best model index - list - Valid model indexes - """ - # select valid models (abs(auc_train - auc_test)<0.03) valid_model = {} for key, param in d_model_info.items(): if param['metrics']['delta_auc'] <= delta_auc_th: valid_model[key] = param - # Best model according to selected metric if len(valid_model.keys()) > 0: best_model_idx = max(valid_model, key=lambda x: valid_model[x].get('metrics').get(metric)) if verbose: @@ -321,21 +190,6 @@ def get_best_model(self, d_model_info, metric='F1', delta_auc_th=0.03, verbose=F """ def model_res_to_df(self, d_model_infos, sort_metric='F1'): - """Store models summary in DataFrame - - Parameters - ---------- - d_model_info : dict - {model_index : {'HP', 'probas', 'model', 'features_importance', 'train_metrics', 'metrics', 'output'} - sort_metric : string (default = 'F1') - metric to sort models (descendant) - - Returns - ------- - DataFrame - model infos and metrics - """ - # dataFrame columns names model_col = ['model_index'] HP_col = list(self.d_train_model[0]['HP'].keys()) bagging_col = ['bagging'] @@ -344,7 +198,6 @@ def model_res_to_df(self, d_model_infos, sort_metric='F1'): df_local = pd.DataFrame(columns=model_col + HP_col + bagging_col + metrics_col + feat_imp_col) - # store informations in df for key, value in self.d_train_model.items(): dict_tmp = {'model_index': key} dict_tmp.update(value['HP'].copy()) diff --git a/AutoMxL/Modelisation/Utils.py b/AutoMxL/Modelisation/Utils.py index 6cc8cee..f6b35bc 100644 --- a/AutoMxL/Modelisation/Utils.py +++ b/AutoMxL/Modelisation/Utils.py @@ -2,29 +2,7 @@ from sklearn.metrics import roc_curve, auc, log_loss, f1_score, precision_score, recall_score from sklearn import metrics - def classifier_evaluate(y, y_pred, y_proba_pred, verbose=False): - """ - store fitted model metrics - - Parameters - ---------- - y : pandas.Series - real outputs - y_pred : numpy.ndarray - classification outputs - y_proba_pred : numpy.ndarray - probs - verbose : boolean (Default False) - Get logging information - - Returns - ------- - dict - {metric : value} - - """ - # Calcul des métriques fpr, tpr, thresholds = roc_curve(y, y_proba_pred) acc = metrics.accuracy_score(y, y_pred) roc_auc = auc(fpr, tpr) @@ -33,7 +11,6 @@ def classifier_evaluate(y, y_pred, y_proba_pred, verbose=False): recall = recall_score(y, y_pred) precision = precision_score(y, y_pred) - # Stockage des métriques dans eval_dict eval_dict = { "fpr tpr": (fpr, tpr), "Accuracy": acc, @@ -44,7 +21,6 @@ def classifier_evaluate(y, y_pred, y_proba_pred, verbose=False): "Recall": recall } - # Affichage des métriques selon top_print l_metrics = ["Accuracy", "Roc_auc", "F1", "Logloss", "Precision", "Recall"] if verbose: @@ -53,33 +29,13 @@ def classifier_evaluate(y, y_pred, y_proba_pred, verbose=False): return eval_dict - """ ------------------------------------------------------------------------------------------------------------- """ - def train_test(df, test_size=0.2, seed=None): - """Split train and test sets - - Parameters - ----- - df : DataFrame - input dataset - test_size : float (Default 0.2) - proportion of the dataset to include in test set - seed : int (Default None) - random seed - - Returns - ------ - DataFrame : train set - DataFrame : test set - """ - # Liste des index list_index_df = df.index - # Tirage aléatoire dans la liste des index (en fonction de train_size) if seed is not None : np.random.seed(seed) chosen_idx = np.random.choice(list_index_df, replace=False, size=int(len(list_index_df) * test_size)) diff --git a/AutoMxL/Modelisation/__init__.py b/AutoMxL/Modelisation/__init__.py index 907c321..0d98339 100644 --- a/AutoMxL/Modelisation/__init__.py +++ b/AutoMxL/Modelisation/__init__.py @@ -1,14 +1,4 @@ -""" -Contains modules related to modelisation - -Modules : -- Bagging -- Classifiers -- Hyperopt - -""" __all__ = ['Bagging', 'HyperOpt', 'Utils'] - diff --git a/AutoMxL/Preprocessing/Categorical.py b/AutoMxL/Preprocessing/Categorical.py index 081c46d..ae670bc 100644 --- a/AutoMxL/Preprocessing/Categorical.py +++ b/AutoMxL/Preprocessing/Categorical.py @@ -1,35 +1,10 @@ -""" Categorical features processing - - - CategoricalEncoder (class) : Encode categorical features - - dummy_all_var (func) : get one hot encoded vector for each category of a categorical features list - - get_embedded_cat (func) : get embedding representation with NN - - mca (func) : to do - -""" import pandas as pd from AutoMxL.Preprocessing.Deep_Encoder import * from sklearn.preprocessing import LabelEncoder from AutoMxL.param_config import batch_size, n_epoch, learning_rate from AutoMxL.Explore.Features_Type import is_categorical, is_boolean - class CategoricalEncoder(object): - """Encode categorical features - - Available encoding methods : - - - one hot encoding - - deep_encoder : Build and train a Neural Network for the creation of embeddings for categorical variables. - (https://www.fast.ai/2018/04/29/categorical-embeddings/) - - Default NN model parameters are stored in param_config.py file - - Parameters - ---------- - method : string (Default : deep_encoder) - method used to get categorical encoding - Available methods : "one_hot", "deep_encoder" - """ def __init__(self, method='deep_encoder' @@ -51,28 +26,12 @@ def __init__(self, """ def fit(self, df, l_var=None, target=None, verbose=False): - """ Fit encoder on dataset following method - - Parameters - ---------- - df : DataFrame - input dataset - l_var : list (Default None) - names of the variables to encode. - If None, all the categorical and boolean features - target : string (Default None) - name of the target for deep_encoder method - verbose : boolean (Default False) - Get logging information - """ if self.method == 'deep_encoder': assert target is not None, 'fill target parameter to use deep encoder' - # get categorical and boolean features (see Features_Type module doc) l_cat = [col for col in df.columns.tolist() if (is_categorical(df, col) or is_boolean(df, col)) and col != target] - # list of features to encode if l_var is None: self.l_var2encode = l_cat else: @@ -80,7 +39,6 @@ def fit(self, df, l_var=None, target=None, verbose=False): df_local = df.copy() - # store target self.target = target if verbose: @@ -92,16 +50,13 @@ def fit(self, df, l_var=None, target=None, verbose=False): print(" ", self.l_var2encode) if len(self.l_var2encode) > 0: - # deep learning embedded representation method if self.method == 'deep_encoder': self.d_int_encoders, self.d_embeddings, self.d_metrics = \ get_embedded_cat(df_local, self.l_var2encode, target, batch_size, n_epoch, learning_rate, verbose=False) - # Fitted ! self.is_fitted = True - # verbose if verbose: if (self.method == "deep_encoder") and len(self.l_var2encode) > 0: print(" NN Loss:", round(self.d_metrics['loss'], 4), "/ Accuracy:", @@ -113,42 +68,22 @@ def fit(self, df, l_var=None, target=None, verbose=False): """ def transform(self, df, verbose=False): - """ transform dataset categorical features using the encoder. - Can be done only if encoder has been fitted - - Parameters - ---------- - df : DataFrame - dataset to transform - verbose : boolean (Default False) - Get logging information - - Returns - ------- - DataFrame : modified dataset - """ assert self.is_fitted, 'fit the encoding first using .fit method' df_local = df.copy() - # if list of features to encode is not empty if len(self.l_var2encode) > 0: - # one hot encoding method if self.method == 'one_hot': return dummy_all_var(df_local, var_list=self.l_var2encode, prefix_list=None, keep=False, verbose=verbose) - # Deep learning embedding method elif self.method == 'deep_encoder': - # features not to encode self.l_var_other = [col for col in df_local.columns.tolist() if col not in self.l_var2encode] - # transform data with int encoder for col in self.l_var2encode: df_local[col] = self.d_int_encoders[col].fit_transform(df_local[col].astype('str')) - # get embedding if verbose: print(' Deep Encoder Embedding dim:') @@ -156,20 +91,16 @@ def transform(self, df, verbose=False): for col, d_level in self.d_embeddings.items(): for i in range(len(d_level[0])): - # replace int values with new embedding df_embedded[col + '_' + str(i)] = df_embedded[col].replace( {k: v[i] for k, v in d_level.items()}) - # drop raw feature df_embedded = df_embedded.drop(col, axis=1) - # verbose if verbose: print(" > " + col + ":", len(d_level[0])) return pd.concat([df[self.l_var_other], df_embedded], axis=1) - # if no feature to encode else: print(" No variable to encode") @@ -180,75 +111,28 @@ def transform(self, df, verbose=False): """ def fit_transform(self, df, l_var=None, target=None, verbose=False): - """fit and transform dataset categorical features - - Parameters - ---------- - df : DataFrame - input dataset - l_var : list (Default None) - names of the variables to encode. - If None, all the categorical and boolean features - target : string (Default None) - name of the target for deep_encoder method - verbose : boolean (Default False) - Get logging information - - Returns - ------- - DataFrame : modified dataset - """ df_local = df.copy() - # fit self.fit(df_local, l_var, target, verbose) df_local = self.transform(df_local, verbose) return df_local - """ ---------------------------------------------------------------------------------------------- """ - def dummy_all_var(df, var_list=None, prefix_list=None, keep=False, verbose=False): - """Get one hot encoded vector for selected/all categorical features - - Parameters - ---------- - df : DatraFrame - Input dataset - var_list : list (Default : None) - Names of the features to dummify - If None, all the num features - prefix_list : list (default : None) - Prefix to add before new features name (prefix+'_'+cat). - If None, prefix=variable name - keep : boolean (Default = False) - If True, delete the original feature - verbose : boolean (Default False) - Get logging information - - Returns - ------- - DataFrame - Modified dataset - """ df_local = df.copy() for col in var_list: - # if prefix_list == None, add column name as prefix, else add prefix_list if prefix_list is None: pref = col else: pref = prefix_list[var_list.index(col)] - # dummify df_cat = pd.get_dummies(df_local[col], prefix=pref, drop_first=True) - # concat source DataFrame and new features df_local = pd.concat((df_local, df_cat), axis=1) - # if keep = False, remove original features if not keep: df_local = df_local.drop(col, axis=1) if verbose: @@ -256,52 +140,18 @@ def dummy_all_var(df, var_list=None, prefix_list=None, keep=False, verbose=False return df_local - """ ---------------------------------------------------------------------------------------------- """ - def get_embedded_cat(df, var_list, target, batchsize, n_epochs, lr, verbose=False): - """Get embedded representation for categorical features using NN encoder - - Parameters - ---------- - df : DataFrame - input Dataset - var_list : list of strings - features names - target : string - target name - batchsize : int - batch size for encoder training - n_epochs : int - number of epoch for encoder training - lr : float - encoder learning rate - verbose : boolean (Default False) - Get logging information - - Returns - ------- - DataFrame : modified dataset - """ - ###################### - # Get list to encode # - ###################### df_local = df[var_list + [target]].copy() - ############################ - # Categories to int labels # - ############################ d_int_encoders = {} for cat_col in var_list: d_int_encoders[cat_col] = LabelEncoder() df_local[cat_col] = d_int_encoders[cat_col].fit_transform(df_local[cat_col].astype('str')) - ################### - # Get layer sizes # - ################### d_exp = {col: np.exp(-df_local[col].nunique() * 0.05) for col in var_list} d_tmp = {col: np.int(5 * (1 - exp) + 1) for col, exp in d_exp.items()} @@ -313,10 +163,6 @@ def get_embedded_cat(df, var_list, target, batchsize, n_epochs, lr, verbose=Fals emb_dims = [(df_local[col].nunique(), d_tmp[col]) for col in var_list] - ##################### - # Train the encoder # - ##################### - # Create Torch_Dataset df_to_encoder = Torch_Dataset(data=df_local, cat_cols=var_list, output_col=target) model = Deep_Cat_Encoder(emb_dims, layer_sizes=[nlayer1, nlayer2], output_size=1) @@ -327,9 +173,6 @@ def get_embedded_cat(df, var_list, target, batchsize, n_epochs, lr, verbose=Fals d_metrics = {'loss': loss, 'accuracy': accuracy} - ############################################ - # Store embedding and get output DataFrame # - ############################################ i = 0 d_embeddings = {} for param in fit_model.emb_layers.parameters(): diff --git a/AutoMxL/Preprocessing/Date.py b/AutoMxL/Preprocessing/Date.py index 787c641..7e0cf18 100644 --- a/AutoMxL/Preprocessing/Date.py +++ b/AutoMxL/Preprocessing/Date.py @@ -1,30 +1,8 @@ -""" Date Features processing functions: - - - DateEncoder (class) : encode date features - - all_to_date (func): detect dates from num/cat features and transform them to datetime format. - - date_to_anc (func): transform datetime features to timedelta according to a ref date -""" import pandas as pd from datetime import datetime from AutoMxL.Explore.Features_Type import features_from_type - class DateEncoder(object): - """Encode categorical features - - Available methods : - - - timedelta : compute time between date feature and parameter date_ref - - Parameters - ---------- - method : string (Default : timedelta) - method used to encode dates - Available methods : "timedelta" - date_ref : string '%d/%m/%y' (Default : None) - Date to compute timedelta. - If None, today date - """ def __init__(self, method='timedelta', @@ -35,7 +13,6 @@ def __init__(self, self.method = method self.is_fitted = False self.l_var2encode = [] - # if date_ref not filled, set to today's date if date_ref is None: self.date_ref = datetime.now() else: @@ -46,31 +23,15 @@ def __init__(self, """ def fit(self, df, l_var=None, verbose=False): - """fit encoder - - Parameters - ---------- - df : DataFrame - input dataset - l_var : list - features to encode. - If None, contains all features identified as dates (see Features_Type module) - verbose : boolean (Default False) - Get logging information - """ - # get date features l_date_var = features_from_type(df, typ='date', l_var=None) - # list of features to encode (in l_var and l_date_var) if l_var is None: self.l_var2encode = l_date_var else: self.l_var2encode = [col for col in l_var if col in l_date_var] - # Fitted !!!! self.is_fitted = True - # verbose if verbose: if self.method == 'timedelta': print(" **method " + self.method + " / date ref : ", self.date_ref) @@ -84,30 +45,16 @@ def fit(self, df, l_var=None, verbose=False): """ def transform(self, df, verbose=False): - """ transform dataset date features using the encoder. - Can be done only if encoder has been fitted - - Parameters - ---------- - df : DataFrame - dataset to transform - verbose : boolean (Default False) - Get logging information - """ assert self.is_fitted, 'fit the encoding first using .fit method' df_local = df.copy() - # if list of features to encode not empty if len(self.l_var2encode) > 0: - # transform features to datetime df_local = all_to_date(df_local, l_var=self.l_var2encode, verbose=verbose) - # method timedelta if self.method == 'timedelta': df_local, _ = date_to_anc(df_local, l_var=self.l_var2encode, date_ref=self.date_ref, verbose=verbose) - # if no features to transform elif verbose: print(" > No date to transform") @@ -118,52 +65,17 @@ def transform(self, df, verbose=False): """ def fit_transform(self, df, l_var=None, verbose=False): - """fit and transform dataset with encoder - - Parameters - ---------- - df : DataFrame - input dataset - l_var : list - features to encode. - If None, all features identified as dates (see Features_Type module) - verbose : boolean (Default False) - Get logging information - """ df_local = df.copy() - # fit self.fit(df_local, l_var=l_var, verbose=verbose) - # transform df_local = self.transform(df_local, verbose=verbose) return df_local - """ ---------------------------------------------------------------------------------------------- """ - def all_to_date(df, l_var=None, verbose=False): - """Detect dates from selected/all features and transform them to datetime format. - - Parameters - ---------- - df : DataFrame - Input dataset - l_var : list (Default : None) - Names of the features - If None, all the features - verbose : boolean (Default False) - Get logging information - - Return - ------- - DataFrame - Modified dataset - """ - # if var_list = None, get all df features - # else, exclude features if not in df if l_var is None: l_var = df.columns.tolist() else: @@ -175,7 +87,6 @@ def all_to_date(df, l_var=None, verbose=False): print(' > features : ', l_var) print(' > features conversion to date using "try .to_datetime') - # for each feature in var_list, try to convert to datetime for col in l_var: try: if df_local[col].dtype == 'object': @@ -193,37 +104,11 @@ def all_to_date(df, l_var=None, verbose=False): return df_local - """ ------------------------------------------------------------------------------------------------------------------------- """ - def date_to_anc(df, l_var=None, date_ref=None, verbose=False): - """Transform selected/all datetime features to timedelta according to a ref date - - Parameters - ---------- - df : DataFrame - Input dataset - l_var : list (Default : None) - List of the features to analyze. - If None, contains all the datetime features - date_ref : string '%d/%m/%y' (Default : None) - Date to compute timedelta. - If None, today date - verbose : boolean (Default False) - Get logging information - - Returns - ------- - DataFrame - Modified dataset - - list - New timedelta features names - """ - # if date_ref is None, use today date if date_ref is None: date_ref = datetime.now() else: @@ -232,8 +117,6 @@ def date_to_anc(df, l_var=None, date_ref=None, verbose=False): else: date_ref = datetime.strptime(date_ref, '%d/%m/%Y') - # if var_list = None, get all datetime features - # else, exclude features from var_list whose type is not datetime l_date = df.dtypes[df.dtypes == 'datetime64[ns]'].index.tolist() if l_var is None: l_var = l_date @@ -242,11 +125,8 @@ def date_to_anc(df, l_var=None, date_ref=None, verbose=False): df_local = df.copy() - # new variables names l_new_var_names = ['anc_' + col for col in l_var] - # compute time delta for selected dates variables df_local = df_local.apply(lambda x: (date_ref - x).dt.days / 365 if x.name in l_var else x) - # rename columns df_local = df_local.rename(columns=dict(zip(l_var, l_new_var_names))) if verbose: diff --git a/AutoMxL/Preprocessing/Deep_Encoder.py b/AutoMxL/Preprocessing/Deep_Encoder.py index 6c93eae..a5c3e06 100644 --- a/AutoMxL/Preprocessing/Deep_Encoder.py +++ b/AutoMxL/Preprocessing/Deep_Encoder.py @@ -1,11 +1,3 @@ -""" Label encoder for categorical features : - -- Torch_Dataset (class) : Prepare dataset for encoder -- Categorical encoder (class) : Build categorical encoder -- train_label_encoder : Train encoder for categorical features - -https://yashuseth.blog/2018/07/22/pytorch-neural-network-for-tabular-data-with-categorical-embeddings/ -""" import torch.nn as nn import torch.nn.functional as F import numpy as np @@ -14,43 +6,19 @@ from matplotlib import pyplot as plt from AutoMxL.Utils.Display import color_print - -# parameters config -# from AutoMxL.config import n_epoch, learning_rate, batch_size, crit, optim - - class Torch_Dataset(Dataset): - """Prepare Dataset for categorical encoder - - - creates batches from the dataset - - shuffles the data - - loads the data in parallel - - Parameters - ---------- - data : DataFrame - input dataset, containes numerical/categorical/output features - cat_cols : list of strings - Names of the categorical columns - output_col : String - Name of the output variable - """ def __init__(self, data, cat_cols=None, output_col=None): - # data rows number self.nrow = data.shape[0] - # target if output_col: self.y = data[output_col].astype(np.float32).values.reshape(-1, 1) else: self.y = np.zeros((self.nrow, 1)) - # categorical features self.cat_cols = cat_cols if cat_cols else [] if self.cat_cols: self.cat_X = data[cat_cols].astype(np.int64).values else: self.cat_X = np.zeros((self.nrow, 1)) - # (real) numerical features self.cont_cols = [col for col in data.columns if col not in self.cat_cols + [output_col]] if self.cont_cols: self.cont_X = data[self.cont_cols].astype(np.float32).values @@ -62,9 +30,6 @@ def __init__(self, data, cat_cols=None, output_col=None): """ def __len__(self): - """ - Return total number of samples - """ return self.nrow """ @@ -72,50 +37,29 @@ def __len__(self): """ def __getitem__(self, idx): - """ - Generates one sample of data - """ return [self.y[idx], self.cat_X[idx]] - """ ------------------------------------------------------------------------------------------------------ """ - class Deep_Cat_Encoder(nn.Module): - """Build categorical encoder - - Parameters - ---------- - l_levels : list of tuples (int, int)] - for each col (#unique values, embedding dim) - layer_sizes : list of int - encoder layers sizes - output_size : int - output dim (binary : 1, multi-class : n) - """ def __init__(self, l_levels, layer_sizes, output_size, layer_dropout=0.1): super(Deep_Cat_Encoder, self).__init__() - # Embedding layers self.emb_layers = nn.ModuleList([nn.Embedding(x, y) for x, y in l_levels]) self.emb_layer_size = sum([y for x, y in l_levels]) - # layer 1 self.linear1 = nn.Linear(self.emb_layer_size, layer_sizes[0]) nn.init.kaiming_uniform_(self.linear1.weight.data) - # layer 2 self.linear2 = nn.Linear(layer_sizes[0], layer_sizes[-1]) nn.init.kaiming_uniform_(self.linear2.weight.data) - # output_layer self.output_layer = nn.Linear(layer_sizes[-1], output_size) nn.init.kaiming_uniform_(self.output_layer.weight.data) - # dropout if layer_dropout: self.dropout = nn.Dropout(p=layer_dropout) @@ -124,100 +68,50 @@ def __init__(self, l_levels, layer_sizes, output_size, layer_dropout=0.1): """ def forward(self, cat_data): - """Execute the forward propagation fed with categorical data input - - Parameters - ---------- - cat_data : list - categorical features sample - - Returns - ------- - model : updated model - """ - # embedding layer if self.emb_layer_size > 0: x = [emb_layer(cat_data[:, i]) for i, emb_layer in enumerate(self.emb_layers)] x = torch.cat(x, 1) - # layer 1 (+ dropout) x = F.relu(self.linear1(x)) if self.dropout: x = self.dropout(x) - # layer 2 (+ dropout) x = F.relu(self.linear2(x)) if self.dropout: x = self.dropout(x) - # output layer x = self.output_layer(x) x = torch.sigmoid(x) return x - """ ------------------------------------------------------------------------------------------------------ """ - def train_deep_encoder(torch_dataset, model, optimizer, criterion, lr, n_epochs, batchsize, verbose=False): - """Train label encoder for categorical features - - Parameters - ---------- - torch_dataset : Torch_Dataset - Dataset to feed the NN containing categorical features and target - model : Deep_Cat_Encoder - encoder - crit : string - model loss to optimize - optimizer : string - NN optimizer - n_epochs : int (default = 20) - batchsize : int - batch size - verbose : boolean (Default False) - Get logging information - - Returns - ------- - Categorical_encoder : fitted model - float : loss - float : accuracy - """ assert criterion == 'MSE', 'invalid criterion : select MSE' assert optimizer == 'Adam', "invalid optimizer : select 'Adam'" - # device device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') - # optimizer and criterion if optimizer == 'Adam': optimizer = torch.optim.Adam(model.parameters(), lr=lr) if criterion == 'MSE': criterion = nn.MSELoss() - # create DataLoader object to feed the model dataloader = DataLoader(torch_dataset, batch_size=batchsize, shuffle=True, num_workers=0) - #################### - # encoder training # - #################### l_loss, l_accuracy = [], [] ep = 0 for epoch in range(n_epochs): ep += 1 - # batch training for y, cat_x in dataloader: - # to device cat_x = cat_x.to(device) y = y.to(device) - # outputs preds = model(cat_x) loss = criterion(preds, y) output = (preds > 0.5).float() @@ -230,12 +124,10 @@ def train_deep_encoder(torch_dataset, model, optimizer, criterion, lr, n_epochs, print("loss : ", loss.item()) print("accuracy : ", accuracy.item() / y.shape[0]) - # Backward Pass and Optimization optimizer.zero_grad() loss.backward() optimizer.step() - # plot if verbose: plt.plot(l_loss) plt.title('Loss') diff --git a/AutoMxL/Preprocessing/Missing_Values.py b/AutoMxL/Preprocessing/Missing_Values.py index 99128e8..db4167c 100644 --- a/AutoMxL/Preprocessing/Missing_Values.py +++ b/AutoMxL/Preprocessing/Missing_Values.py @@ -1,29 +1,7 @@ -""" Missing values handling functions : - - - NAEncoder (class): encoder that replaces missing values - - fill_numerical (func): replace missing values for numerical features - - fill_categorical (func): replace missing values for categorical features - - get_NA_features (func): get features containing NA values -""" import pandas as pd import numpy as np - class NAEncoder(object): - """ Missing values filling - - Available methods to replace missing values - - - num : metdian/mean/zero - - cat : 'NR' - - Parameters - ---------- - replace_num_with: string - method used to replace numerical missing values - replace_cat_with: string - method used to replace categorical missing values - """ def __init__(self, replace_num_with='median', @@ -45,23 +23,9 @@ def __init__(self, """ def fit(self, df, l_var, verbose=False): - """fit encoder - - Parameters - ---------- - df : DataFrame - input dataset - l_var : list - features to encode. - If None, all features - verbose : boolean (Default False) - Get logging information - """ - # get num and categorical columns l_num = [col for col in df.columns.tolist() if df[col].dtype != 'object'] l_str = [col for col in df.columns.tolist() if df[col].dtype == 'object'] - # get list of valid features (containing NA) if l_var is None: self.l_var_cat = [col for col in l_str if df[col].isna().sum() > 0] self.l_var_num = [col for col in l_num if df[col].isna().sum() > 0] @@ -69,10 +33,8 @@ def fit(self, df, l_var, verbose=False): self.l_var_cat = [col for col in l_var if col in l_str and df[col].isna().sum() > 0] self.l_var_num = [col for col in l_var if col in l_num and df[col].isna().sum() > 0] - # Fitted ! self.is_fitted = True - # verbose if verbose: print(" **method cat:", self.replace_cat_with, " / num:", self.replace_num_with) print(" >", len(self.l_var_cat) + len(self.l_var_num), "features to fill") @@ -86,31 +48,18 @@ def fit(self, df, l_var, verbose=False): """ def transform(self, df, verbose=False): - """ transform dataset categorical features using the encoder. - Can be done only if encoder has been fitted - - Parameters - ---------- - df : DataFrame - dataset to transform - verbose : boolean (Default False) - Get logging information - """ assert self.is_fitted, 'fit the encoding first using .fit method' df_local = df.copy() - # categorical features filling if len(self.l_var_cat) > 0: df_local = fill_categorical(df_local, l_var=self.l_var_cat, method=self.replace_cat_with, verbose=verbose) - # numerical features filling if len(self.l_var_num) > 0: df_local = fill_numerical(df_local, l_var=self.l_var_num, method=self.replace_num_with, track_num_NA=self.track_num_NA, verbose=verbose) - # if no feature to fill if len(self.l_var_cat) + len(self.l_var_num) == 0 and verbose: print(" > no transformation to apply") @@ -121,22 +70,8 @@ def transform(self, df, verbose=False): """ def fit_transform(self, df, l_var=None, verbose=False): - """fit and transform dataset with encoder - - Parameters - ---------- - df : DataFrame - input dataset - l_var : list - features to encode. - If None, all features identified as dates (see Features_Type module) - verbose : boolean (Default False) - Get logging information - """ df_local = df.copy() - # fit self.fit(df_local, l_var=l_var, verbose=verbose) - # transform df_local = self.transform(df_local, verbose=verbose) return df_local @@ -145,41 +80,9 @@ def fit_transform(self, df, l_var=None, verbose=False): ---------------------------------------------------------------------------------------------- """ - def fill_numerical(df, l_var=None, method='median', track_num_NA=True, verbose=False): - """Fill missing values for selected/all numerical features. - top_var_NA parameter allows to create a variable to keep track of missing values. - - Available methods : replace with zero, median or mean (Default = median) - - Parameters - ---------- - df : DataFrame - Input dataset - l_var : list (Default : None) - names of the features to fill. - If None, all the numerical features - method : string (Default : 'median') - Method used to fill the NA values : - - - zero : replace with zero - - median : replace with median - - mean : replace with mean - - track_num_NA : boolean (Defaut : True) - If True, create a boolean column to keep track of missing values - verbose : boolean (Default False) - Get logging information - - Returns - ------- - DataFrame - Modified dataset - """ assert method in ['zero', 'median', 'mean'], method + ' invalid method : choose zero, median or mean' - # if var_list = None, get all num features - # else, remove features from var_list whose type is not num l_num = df._get_numeric_data().columns.tolist() if l_var is None: @@ -189,7 +92,6 @@ def fill_numerical(df, l_var=None, method='median', track_num_NA=True, verbose=F df_local = df.copy() - # values to fill NA if method == 'median': fill_value = df_local[l_var].mean() elif method == 'mean': @@ -199,9 +101,7 @@ def fill_numerical(df, l_var=None, method='median', track_num_NA=True, verbose=F for var in l_var: if track_num_NA: - # keep track of NA values in Top_var_NA df_local['top_NA_' + var] = df_local.apply(lambda x: 1 if np.isnan(x[var]) else 0, axis=1) - # fill NA df_local[var] = df_local[var].fillna(fill_value[var]) if verbose: @@ -210,39 +110,13 @@ def fill_numerical(df, l_var=None, method='median', track_num_NA=True, verbose=F return df_local - """ ------------------------------------------------------------------------------------------------------------------------- """ - def fill_categorical(df, l_var=None, method='NR', verbose=False): - """Fill missing values for selected/all categorical features. - - Parameters - ---------- - df : DataFrame - Input dataset - l_var : list (Default : None) - list of the features to fill. - If None, contains all the categorical features - method : string (Default : 'NR') - Method used to fill the NA values : - - - NR : replace NA with 'NR' - - verbose : boolean (Default False) - Get logging information - - Returns - ------- - DataFrame - Modified dataset - """ assert method in ['NR'], method + ' invalid method : choose NR ' - # if var_list = None, get all categorical features - # else, remove features from var_list whose type is not categorical l_cat = [col for col in df.columns.tolist() if df[col].dtype == 'object'] if l_var is None: @@ -252,7 +126,6 @@ def fill_categorical(df, l_var=None, method='NR', verbose=False): df_local = df.copy() - # values to fill NA if method in ['NR']: fill_value = 'NR' @@ -265,17 +138,5 @@ def fill_categorical(df, l_var=None, method='NR', verbose=False): return df_local - def get_NA_features(df): - """identify features containing NA values - - Parameters - ---------- - df : DataFrame - input dataset - - Returns - ------- - list : features containing missing values - """ return df.isna().sum()[df.isna().sum() > 0].index.tolist() diff --git a/AutoMxL/Preprocessing/Outliers.py b/AutoMxL/Preprocessing/Outliers.py index 0961e3d..ff4cece 100644 --- a/AutoMxL/Preprocessing/Outliers.py +++ b/AutoMxL/Preprocessing/Outliers.py @@ -1,30 +1,8 @@ -""" Outliers handling functions - - - OutliersEncoding (class) : identify and replace outliers - - get_cat_outliers (funct): identify categorical features containing outliers - - get_num_outliers (func): identify numerical features containing outliers - - replace_category (func): replace categories of a categorical variable - - replace_extreme_values (func): replace extreme values (oh!) -""" import pandas as pd import numpy as np from AutoMxL.Utils.Display import * - class OutliersEncoder(object): - """Identify et replace outliers for categorical dang numerical features - - - num : x outlier <=> abs(x - mean) > xstd * var - - cat : x outlier category <=> with frequency 2] self.l_var_num = [col for col in l_num if df[col].nunique() > 2] @@ -68,20 +28,15 @@ def fit(self, df, l_var, verbose=False): self.l_var_cat = [col for col in l_var if col in l_str and df[col].nunique() > 2] self.l_var_num = [col for col in l_var if col in l_num and df[col].nunique() > 2] - - # cat outliers if len(self.l_var_cat) > 0: self.d_cat_outliers = get_cat_outliers(df, l_var=self.l_var_cat, threshold=self.cat_threshold, verbose=False) - # num outliers if len(self.l_var_num) > 0: self.d_num_outliers = get_num_outliers(df, l_var=self.l_var_num, xstd=self.num_xstd, verbose=False) - # Fitted ! self.is_fitted = True - # verbose if verbose: print(" **method cat: frequency<" + str(self.cat_threshold) + " / num:( x: |x - mean| > " + str(self.num_xstd) + "* var)") @@ -91,25 +46,10 @@ def fit(self, df, l_var, verbose=False): if len(self.d_num_outliers.keys()) > 0: print(" - num", list(self.d_num_outliers.keys())) - """ - ---------------------------------------------------------------------------------------------- - """ - def transform(self, df, verbose=False): - """Transform dataset features using the encoder. - Can be done only if encoder has been fitted - - Parameters - ---------- - df : DataFrame - dataset to transform - verbose : boolean (Default False) - Get logging information - """ assert self.is_fitted, 'fit the encoding first using .fit method' df_local = df.copy() - # cat features if len(list(self.d_cat_outliers.keys())) > 0: if verbose: print(" - cat aggregated values:") @@ -117,7 +57,6 @@ def transform(self, df, verbose=False): df_local = replace_category(df_local, col, self.d_cat_outliers[col], replace_with='outliers', verbose=verbose) - # num features if len(list(self.d_num_outliers.keys())) > 0: if verbose: print(" - num values replaces:") @@ -125,67 +64,19 @@ def transform(self, df, verbose=False): df_local = replace_extreme_values(df_local, col, self.d_num_outliers[col][0], self.d_num_outliers[col][1], verbose=verbose) - # if no features with outliers if len(list(self.d_cat_outliers.keys())) + len(list(self.d_num_outliers.keys())) == 0: print(" > no outlier to replace") return df_local - """ - ---------------------------------------------------------------------------------------------- - """ - def fit_transform(self, df, l_var=None, verbose=False): - """Fit and transform dataset with encoder - - Parameters - ---------- - df : DataFrame - input dataset - l_var : list - features to encode. - If None, all features identified as dates (see Features_Type module) - verbose : boolean (Default False) - Get logging information - """ df_local = df.copy() - # fit self.fit(df_local, l_var=l_var, verbose=False) - # transform df_local = self.transform(df_local, verbose=verbose) return df_local - -""" ----------------------------------------------------------------------------------------------- -""" - - def get_cat_outliers(df, l_var=None, threshold=0.05, verbose=False): - """Outliers detection for selected/all categorical features. - - Method : Modalities with frequency 1} @@ -211,36 +99,7 @@ def get_cat_outliers(df, l_var=None, threshold=0.05, verbose=False): return d_outliers - -""" -------------------------------------------------------------------------------------------------------------------------- -""" - - def get_num_outliers(df, l_var=None, xstd=3, verbose=False): - """Outliers detection for selected/all numerical features. - - Method : x outlier <=> abs(x - mean) > xstd * var - - Parameters - ---------- - df : DataFrame - Input dataset - l_var : list (Default : None) - Names of the features - If None, all the num features - xstd : int (Default : 3) - Variance gap coef - verbose : boolean (Default False) - Get logging information - - Returns - ------- - dict - {variable : [lower_limit, upper_limit]} - """ - # if var_list = None, get all num features - # else, remove features from var_list whose type is not num l_num = df._get_numeric_data().columns.tolist() if l_var is None: @@ -250,7 +109,6 @@ def get_num_outliers(df, l_var=None, xstd=3, verbose=False): df_local = df[l_var].copy() - # compute features upper and lower limit (abs(x - mean) > xstd * var (x=3 by default)) data_std = np.std(df_local) data_mean = np.mean(df_local) anomaly_cut_off = data_std * xstd @@ -259,7 +117,6 @@ def get_num_outliers(df, l_var=None, xstd=3, verbose=False): data_min = np.min(df_local) data_max = np.max(df_local) - # store variables and lower/upper limits d_outliers = {col: [lower_limit[col], upper_limit[col]] for col in df_local.columns.tolist() if (data_min[col] < lower_limit[col] or data_max[col] > upper_limit[col])} @@ -271,36 +128,9 @@ def get_num_outliers(df, l_var=None, xstd=3, verbose=False): return d_outliers - -""" -------------------------------------------------------------------------------------------------------------------------- -""" - - def replace_category(df, var, categories, replace_with='outliers', verbose=False): - """Replace categories of a categorical variable - - Parameters - ---------- - df : DataFrame - Input dataset - var : string - variable to modify - categories : list(string) - categories to replace - replace_with : string (Default : 'outliers') - word to replace categories with - verbose : boolean (Default False) - Get logging information - - Returns - ------- - DataFrame - Modified dataset - """ df_local = df.copy() - # replace categories df_local.loc[df_local[var].isin(categories), var] = replace_with if verbose: @@ -308,37 +138,10 @@ def replace_category(df, var, categories, replace_with='outliers', verbose=False return df_local - -""" -------------------------------------------------------------------------------------------------------------------------- -""" - - def replace_extreme_values(df, var, lower_th=None, upper_th=None, verbose=False): - """Replace extrem values : > upper threshold or < lower threshold - - Parameters - ---------- - df : DataFrame - Input dataset - var : string - variable to modify - lower_th : int/float (Default=None) - lower threshold - upper_th : int/float (Default=None) - upper threshold - verbose : boolean (Default False) - Get logging information - - Returns - ------- - DataFrame - Modified dataset - """ assert (lower_th is not None or upper_th is not None), 'specify at least one limit value' df_local = df.copy() - # replace values with upper_limit and lower_limit if upper_th is not None: df_local.loc[df_local[var] > upper_th, var] = upper_th if lower_th is not None: diff --git a/AutoMxL/Preprocessing/__init__.py b/AutoMxL/Preprocessing/__init__.py index 4c476af..7ba9ef2 100644 --- a/AutoMxL/Preprocessing/__init__.py +++ b/AutoMxL/Preprocessing/__init__.py @@ -1,19 +1,6 @@ -""" -Contains modules related to cleaning and features processing - -Modules : -- Categorical_Data -- Date_Data -- Label encoder -- Missing_Values -- Process_Outliers -- Scaling - -""" __all__ = ['Categorical', 'Date', 'Deep_Encoder', 'Missing_Values', 'Outliers'] - diff --git a/AutoMxL/Select_Features/Select_Features.py b/AutoMxL/Select_Features/Select_Features.py index 8187d15..a158e2e 100644 --- a/AutoMxL/Select_Features/Select_Features.py +++ b/AutoMxL/Select_Features/Select_Features.py @@ -1,25 +1,9 @@ -""" Features selection - -- select_features (func) : features selection following method - -""" from sklearn.decomposition import PCA from sklearn.preprocessing import StandardScaler import pandas as pd import numpy as np - class FeatSelector(object): - """features selection following method - - - pca : use pca to reduce dataset dimensions - - no_rescale_pca : use pca without rescaling data - - Parameters - ---------- - method : string (Default pca) - method use to select features - """ def __init__(self, method='pca' @@ -32,34 +16,15 @@ def __init__(self, self.selector = None self.scaler = None - """ - ---------------------------------------------------------------------------------------------- - """ - def fit(self, df, l_var=None, verbose=False): - """fit selector - - Parameters - ---------- - df : DataFrame - input dataset - l_var : list - features to encode. - If None, all features identified as numerical - verbose : boolean (Default False) - Get logging information - """ - # get categorical and boolean features (see Features_Type module doc) l_num = [col for col in df.columns.tolist() if df[col].dtype != 'object'] - # list of features to encode if l_var is None: self.l_select_var = l_num else: self.l_select_var = [col for col in l_var if col in l_num] if len(self.l_select_var) > 1: - # PCA method if self.method in ['pca', 'no_rescale_pca']: if self.method == 'pca': @@ -69,17 +34,13 @@ def fit(self, df, l_var=None, verbose=False): else: df_local = df[self.l_select_var].copy() - # init pca object pca = PCA() - # fit and transform with pca pca.fit(df_local) self.selector = pca - # Fitted ! self.is_fitted = True - # verbose if verbose: print(" **method : " + self.method) print(" >", len(self.l_select_var), "features to encode") @@ -87,30 +48,12 @@ def fit(self, df, l_var=None, verbose=False): else: print('not enough features !') - """ - ---------------------------------------------------------------------------------------------- - """ - def transform(self, df, verbose=False): - """ apply features selection on a dataset - - Parameters - ---------- - df : DataFrame - dataset to transform - verbose : boolean (Default False) - Get logging information - - Returns - ------- - DataFrame : modified dataset - """ assert self.is_fitted, 'fit the encoding first using .fit method' l_var_other = [col for col in df.columns.tolist() if col not in self.l_select_var] df_local = df[self.l_select_var].copy() - # pca methods if self.method in ['pca', 'no_rescale_pca']: if self.scaler is not None: df_local = self.scaler.transform(df_local) @@ -120,87 +63,33 @@ def transform(self, df, verbose=False): df_local = df_local.rename( columns=dict(zip(df_local.columns.tolist(), ['Dim' + str(v) for v in df_local.columns.tolist()]))) - # find argmin to get 90% of variance n_dim = np.argwhere(np.cumsum(pca.explained_variance_ratio_) > 0.95)[0][0] - # concat with other dataset features if len(l_var_other) > 0: df_reduced = pd.concat((df[l_var_other].reset_index(drop=True), df_local.iloc[:, :n_dim + 1]), axis=1) else: df_reduced = df_local.iloc[:, :n_dim + 1] - # verbose if verbose: print("Numerical Dimensions reduction : " + str(len(self.l_select_var)) + " - > " + str(n_dim + 1)) print("explained inertia : " + str(round(np.cumsum(pca.explained_variance_ratio_)[n_dim], 4))) return df_reduced - """ - ---------------------------------------------------------------------------------------------- - """ - def fit_transform(self, df, l_var, verbose=False): - """ fit and apply features selection - - Parameters - ---------- - df : DataFrame - input dataset - l_var : list - features to encode. - If None, all features identified as dates (see Features_Type module) - verbose : boolean (Default False) - Get logging information - - Returns - ------- - DataFrame : modified dataset - """ df_local = df.copy() self.fit(df_local, l_var=l_var, verbose=verbose) df_reduced = self.transform(df_local, verbose=verbose) return df_reduced - -""" ----------------------------------------------------------------------------------------------- -""" - - def select_features(df, target, method='pca', verbose=False): - """features selection following method - - - pca : use pca to reduce dataset dimensions - - no_rescale_pca : use pca without rescaling data - - Parameters - ---------- - df : DataFrame - input dataset containing features - target : string - target name - method : string (Default pca) - method use to select features - verbose : boolean (Default False) - Get logging information - - Returns - ------- - DataFrame - modified dataset - """ - # assert valid method assert method in ['pca', 'no_rescale_pca'], method + " invalid method : select pca, no_rescale_pca" - # get numerical features (except target) and others l_num = [col for col in df._get_numeric_data().columns.tolist() if col != target] l_other = [col for col in df.columns.tolist() if col not in l_num] - # prepare dataset to apply PCA df_num = df[l_num].copy() - # PCA method if method in ['pca', 'no_rescale_pca']: if method == 'pca': @@ -209,25 +98,20 @@ def select_features(df, target, method='pca', verbose=False): else: X = df_num.copy() - # init pca object pca = PCA() - # fit and transform with pca X_transform = pd.DataFrame(pca.fit_transform(X)) X_transform = X_transform.rename( columns=dict(zip(X_transform.columns.tolist(), ['Dim' + str(v) for v in X_transform.columns.tolist()]))) - # find argmin to get 90% of variance n_dim = np.argwhere(np.cumsum(pca.explained_variance_ratio_) > 0.95)[0][0] - # concat with other dataset features if len(l_other) > 0: df_pca = pd.concat((df[l_other].reset_index(drop=True), X_transform.iloc[:, :n_dim + 1]), axis=1) else: df_pca = X_transform.iloc[:, :n_dim + 1] - # verbose if verbose: print("Numerical Dimensions reduction : " + str(len(l_num)) + " - > " + str(n_dim + 1)) print("explained inertia : " + str(round(np.cumsum(pca.explained_variance_ratio_)[n_dim], 4))) diff --git a/AutoMxL/Select_Features/__init__.py b/AutoMxL/Select_Features/__init__.py index b41bd1a..3392466 100644 --- a/AutoMxL/Select_Features/__init__.py +++ b/AutoMxL/Select_Features/__init__.py @@ -1,9 +1 @@ -""" -Contains modules related to features selection - -Modules : -- Categorical_Data -- Select_Features -""" - __all__ = ['Select_Features'] diff --git a/AutoMxL/Start/Encode_Target.py b/AutoMxL/Start/Encode_Target.py index 0fca73b..a20d123 100644 --- a/AutoMxL/Start/Encode_Target.py +++ b/AutoMxL/Start/Encode_Target.py @@ -1,104 +1,43 @@ -"""Target encoding functions : - -- category_to_target : create a target variable (1/0) from a selected category -- range_to_target : create a target variable (1/0) from a selected range -""" import pandas as pd import numpy as np - def category_to_target(df, var, cat): - """Create a target variable (1/0) from a selected category - - Parameters - ---------- - df : DataFrame - input dataset - var : string - variable containing the target category - cat : string - target category - - Returns - ------- - DataFrame : modified dataset - string : new target name (var+'_'+cat) - """ df_local = df.copy() - # transform variable to string if numerical if var in df._get_numeric_data().columns: df_local[var] = df_local[var].apply(str) cat = str(cat) - # one hot encoding target_dummies = pd.get_dummies(df_local[var]) - # select cat feature target_dummies[var + '_' + cat] = target_dummies[cat] - # add encoded cat feature to dataset df_local = pd.concat((df_local, target_dummies[var + '_' + cat]), axis=1) - # remove var del df_local[var] return df_local, var + '_' + cat - -""" ------------------------------------------------------------------------------------------------------ -""" - - def range_to_target(df, var, min=None, max=None, verbose=False): - """Create a target variable (1/0) from a selected range - - Parameters - ---------- - df : DataFrame - input dataset - var : string - variable containing the target range - min : float - lower limit. - If None, no min - max : float - upper limit. - If None, no max - verbose : boolean (Default False) - Get logging information - - Returns - ------- - DataFrame : modified dataset - string : new target name (var+'_'+lower+'_'+upper) - """ assert min is not None or max is not None, 'fill at least one limit parameter (lower,upper)' df_local = df.copy() - # transform variable to numeric if string if var not in df_local._get_numeric_data().columns: df_local[var] = pd.to_numeric(df_local[var], errors='coerce') - # handle None limits : replace by infinity if min is None: min = -float("inf") if max is None: max = float("inf") - # define target name, using lower and upper values target_name = var + '_' + str(min) + '_' + str(max) - # encode target df_local[target_name] = np.where((df_local[var] >= min) & (df_local[var] <= max), 1, 0) if verbose: print("Created target : ", target_name) print(df_local[target_name].value_counts().rename_axis('values').to_frame('counts')) - # remove var del df_local[var] return df_local, target_name - diff --git a/AutoMxL/Start/Load.py b/AutoMxL/Start/Load.py index 0ff8618..8fc8bcf 100644 --- a/AutoMxL/Start/Load.py +++ b/AutoMxL/Start/Load.py @@ -1,31 +1,10 @@ -"""Data_handling import functions : - -- get_delimiter : identify delimiter for a .csv/.txt file -- load_data : import dataset file into dataframe -""" import pandas as pd - def get_delimiter(file): - """Identify the delimiter for a csv/txt file - - Parameters - ---------- - file : string - Path and name of the file (Ex : "data/file.csv") - - Returns - ------- - string - identified delimiter - """ if file.endswith('.csv') or file.endswith('.txt'): - # file reading with open(file, 'r') as myCsvfile: - # Reads one entire line from the file header = myCsvfile.readline() - # Returns the lowest index of the substring if it is found in given string. (-1 = not found) if header.find(";") != -1: delimiter = ";" elif header.find(",") != -1: @@ -36,47 +15,15 @@ def get_delimiter(file): else: print('Please use a .csv or .txt file') - -""" -------------------------------------------------------------------------------------------------------------------------- -""" - - def import_data(file, index_col=None, verbose=False): - """Import dataset as a DataFrame (identify delimiter for txt and csv files) - - Available files : .txt, .csv, .xlsx, .xls files - - Parameters - ---------- - file : string - Path and name of the file (Ex : "data/file.csv") - If file is .csv, automatically identify delimiter - index_col : int, str, sequence of int / str, or False (Default None) - Column(s) to use as the row labels of the DataFrame, either given as string name or column index. - If a sequence of int / str is given, a MultiIndex is used. - verbose : boolean (Default False) - Get logging information - - Returns - ------- - DataFrame - imported dataset - """ - # CSV if file.endswith('.csv') or file.endswith('.txt'): - # Find file delimiter file_sep = get_delimiter(file) - # import df = pd.read_csv(file, encoding="iso-8859-1", sep=file_sep, index_col=index_col) - # Excel elif (file.endswith('.xlsx')) or (file.endswith('.xsl')): df = pd.read_excel(file) - # JSON elif file.endswith('.json'): - # to-do pass else: diff --git a/AutoMxL/Start/__init__.py b/AutoMxL/Start/__init__.py index 88e5787..2e51ea2 100644 --- a/AutoMxL/Start/__init__.py +++ b/AutoMxL/Start/__init__.py @@ -1,8 +1 @@ -""" -Contains modules related to the import of the data and formatting of the target - -Modules : -- Start -- Encode_Target -""" __all__ = ['Load', 'Encode_Target'] diff --git a/AutoMxL/Utils/Decorators.py b/AutoMxL/Utils/Decorators.py index 0308e49..86cb7e3 100644 --- a/AutoMxL/Utils/Decorators.py +++ b/AutoMxL/Utils/Decorators.py @@ -1,20 +1,6 @@ from time import time - -# Timer def timer(func): - """Function decorator to get the execution time - - Parameters - ---------- - func : function - input function - - Returns - ------- - function - wrapped function - """ def f(*args, **kwargs): before = time() diff --git a/AutoMxL/Utils/Display.py b/AutoMxL/Utils/Display.py index 1ead3df..703351f 100644 --- a/AutoMxL/Utils/Display.py +++ b/AutoMxL/Utils/Display.py @@ -1,67 +1,16 @@ def print_title1(titre, color_code=34): - """ - print text as a title - - input - ----- - titre : string - text - - color_code : int - code couleur (voir http://ascii-table.com/ansi-escape-sequences.php) - - return - ------ - print formatted text - """ col = '\033[' + str(color_code) + 'm' print(col + '-------------------') print(' ' + '\033[1m' + titre + '\033[0m' + col) print('-------------------' + '\033[0m') - -""" -------------------------------------------------------------------------------------------------------------------------- -""" - - def bold_print(text): - """ - print bold text - - input - ----- - text : string - text to print bold - """ print('\033[1m' + text + '\033[0m') - -""" -------------------------------------------------------------------------------------------------------------------------- -""" - - def color_print(text, color_code=34): - """ - print colorized text - - input - ----- - text : string - text to print crolorized - """ col = '\033[' + str(color_code) + 'm' print(col + text + '\033[0m') - -""" -------------------------------------------------------------------------------------------------------------------------- -""" - - def print_dict(dic): - """ - """ for key, value in dic.items(): print(key, ' : ', value) diff --git a/AutoMxL/Utils/Utils.py b/AutoMxL/Utils/Utils.py index 0da5cad..8221595 100644 --- a/AutoMxL/Utils/Utils.py +++ b/AutoMxL/Utils/Utils.py @@ -1,26 +1,10 @@ import random - def random_from_dict(dic, verbose=False): - """ pick random item for each dict key if value is a list - - Parameters - ---------- - dic : dict - input dict - verbose : bool (Default False) - Get logging information - - Returns - ------- - dict with picked values - """ d_res = {} - # for each key of input dc for k in dic.keys(): - # if value is a list, pick random, else pick keep item if isinstance(dic[k], list): d_res[k] = random.choice(dic[k]) else: diff --git a/AutoMxL/Utils/__init__.py b/AutoMxL/Utils/__init__.py index 2374c70..89d147d 100644 --- a/AutoMxL/Utils/__init__.py +++ b/AutoMxL/Utils/__init__.py @@ -1,12 +1,3 @@ -""" -Utilis functions. - -Modules : -- Utils -- Display -- Decorators - -""" __all__ = [ 'Decorators', - 'Display'] \ No newline at end of file + 'Display'] diff --git a/AutoMxL/__init__.py b/AutoMxL/__init__.py index 67e6b5c..7e27814 100644 --- a/AutoMxL/__init__.py +++ b/AutoMxL/__init__.py @@ -1,19 +1,5 @@ -""" The purpose of this package is to provide a Python library that automate -the different steps of a ML classification project. -This package contains following sections : -- Start -- Explore -- Preprocessing (clear and process) -- Select features -- Modelisation - -It can be use as a catalog of functions to ease and speed up repetitive aswell -Data_handling Scientists tasks - -""" from __future__ import absolute_import -# Version of the package (à modifier également dans setup.py __version__ = "1.0.0" from . import Start diff --git a/AutoMxL/__main__.py b/AutoMxL/__main__.py index 49ece6c..718790b 100644 --- a/AutoMxL/__main__.py +++ b/AutoMxL/__main__.py @@ -9,46 +9,12 @@ from AutoMxL.Select_Features.Select_Features import FeatSelector from time import time - class AML(pd.DataFrame): - """Covers the complete pipeline of a classification project from a raw dataset to a deployable model. - - AML is built as a class inherited from pandas DataFrame. Each Machine Learning step corresponds to method that - can be called with default or filled parameters. - - - explore: explore dataset and identify features types - - preprocess: clean and prepare data (optional : outliers processing). - - select_features: features selection (optional) - - model_train_predict : split AML in train/test sets to fits/apply models with random search. - Returns the list of the valid models (without overfitting) and the best one. - - deployment methods: - - - preprocess_apply : apply fitted preprocessing transformation to a new dataset - - select_features_apply : idem - - model_apply : apply fitted models to a new dataset - - - Notes : - - - A method requires that the former one has been applied (actuel step is given by "step" attribute) - - Target has to be binary and encoded as int (1/0) (see MLGB59.Start.Encode_Target module if you need help) - - don't call your target "target" please :> - - Parameters - ---------- - _obj : DataFrame - Source Dataset - target : string - target name - """ def __init__(self, *args, target=None, **kwargs): super(AML, self).__init__(*args, **kwargs) assert target != 'target', 'target name cannot be "target"' - # parameters self.target = target - # attributes self.step = 'None' self.d_features = None self.d_preprocess = None @@ -79,25 +45,6 @@ def duplicate(self): """ def explore(self, verbose=False): - """data exploration and features type identification - - Note : if you disagree with automated identification, you can directly modify d_features attribute - - Create self.d_features : dict {x : list of variables names} - - date: date features - - identifier: identifier features - - verbatim: verbatim features - - boolean: boolean features - - categorical: categorical features - - numerical: numerical features - - NA: features which contains NA values - - low_variance: list of the features with low variance and unique values - - Parameters - ---------- - verbose : boolean (Default False) - Get logging information - """ if verbose: start_time = time() print_title1('Explore') @@ -106,13 +53,11 @@ def explore(self, verbose=False): if self.target is not None: df_local = df_local.drop(self.target, axis=1) - # call std_audit_dataset function self.d_features = explore( df_local, verbose=verbose) self.step = 'explore' - # created attributes display if verbose: color_print("\nCreated attributes : d_features (dict) ") print("Keys :") @@ -133,42 +78,9 @@ def explore(self, verbose=False): def preprocess(self, date_ref=None, process_outliers=False, cat_method='deep_encoder', verbose=False): - """Prepare the data before feeding it to the model : - - - remove low variance features - - remove identifiers and verbatims features - - transform date features to timedelta - - fill missing values - - process categorical and boolean data (one-hot-encoding or Pytorch NN encoder) - - replace outliers (optional) - - create self.d_preprocess : dict {step : transformation} - - remove: list of the features to remove - - date: fitted DateEncoder object - - NA: fitted NAEncoder object - - categorical: fitted CategoricalEncoder object - - outlier: fitted OutlierEncoder object - - Parameters - ---------- - date_ref : string '%d/%m/%y' (Default : None) - ref date to compute date features timedelta. - If None, today date - process_outliers : boolean (Default : False) - Enable outliers replacement - cat_method : string (Default : 'deep_encoder') - Categorical features encoding method - verbose : boolean (Default False) - Get logging information - - """ - # check pipe step assert self.step in ['explore'], 'apply explore method first' assert not self.is_fitted_preprocessing, 'preprocessing encoders already fitted' - ############################### - # Fit and apply preprocessing # - ############################### if verbose: start_time = time() print_title1('Fit and apply preprocessing') @@ -176,7 +88,6 @@ def preprocess(self, date_ref=None, process_outliers=False, target = self.target df_local = self.copy() - # Features Removing 'zero variance / verbatims / identifiers) if verbose: color_print("Features removing (zero variance / verbatims / identifiers)") @@ -189,7 +100,6 @@ def preprocess(self, date_ref=None, process_outliers=False, if len(l_remove) > 0: print(" ", l_remove) - # Transform date -> time between date and date_ref if verbose: color_print("Transform date") @@ -197,7 +107,6 @@ def preprocess(self, date_ref=None, process_outliers=False, date_encoder.fit(self, l_var=self.d_features['date'], verbose=False) df_local = date_encoder.transform(df_local, verbose=verbose) - # Missing Values if verbose: color_print('Missing values') @@ -205,7 +114,6 @@ def preprocess(self, date_ref=None, process_outliers=False, NA_encoder.fit(df_local, l_var=None, verbose=False) df_local = NA_encoder.transform(df_local, verbose=verbose) - # replace outliers if process_outliers: if verbose: color_print('Outliers') @@ -215,22 +123,18 @@ def preprocess(self, date_ref=None, process_outliers=False, else: out_encoder = None - # categorical processing if verbose: color_print('Encode Categorical and boolean') cat_col = self.d_features['categorical'] + self.d_features['boolean'] - # apply one-hot encoding if target not filled in class parameters if self.target is None: cat_method = 'one_hot' color_print('No target -> one_hot encoding !', 31) - # get embedding cat_encoder = CategoricalEncoder(method=cat_method) cat_encoder.fit(self, l_var=cat_col, target=self.target, verbose=verbose) df_local = cat_encoder.transform(df_local, verbose=verbose) - # store preprocessing params self.d_preprocess = {'remove': l_remove, 'date': date_encoder, 'NA': NA_encoder, 'categorical': cat_encoder} if out_encoder is not None: self.d_preprocess['outlier'] = out_encoder @@ -244,10 +148,8 @@ def preprocess(self, date_ref=None, process_outliers=False, print(" -> categorical") print(" -> outlier (optional)") - # is_fitted self.is_fitted_preprocessing = True - # update self self.__dict__.update(df_local.__dict__) self.target = target self.step = 'preprocess' @@ -262,32 +164,14 @@ def preprocess(self, date_ref=None, process_outliers=False, """ def preprocess_apply(self, df, verbose=False): - """Apply preprocessing. - - Requires preprocess method to have been applied (so that all encoder are fitted). - - Parameters - ---------- - df : DataFrame - dataset to apply preprocessing on - verbose : boolean (Default False) - Get logging information - - Returns - ------- - DataFrame : Preprocessed dataset - """ if verbose: start_time = time() print_title1('Apply Preprocessing') - # check pipe step and is_fitted assert self.is_fitted_preprocessing, "fit first (please)" - # df_local = df.copy() - # Remove features with zero variance / verbatims and identifiers if verbose: color_print("Remove features (zero variance, verbatims and identifiers") @@ -299,23 +183,19 @@ def preprocess_apply(self, df, verbose=False): if verbose: print(" > No features to remove") - # Transform date -> time between date and date_ref if verbose: color_print("Transform date") df_local = self.d_preprocess['date'].transform(df_local, verbose=verbose) - # Missing Values if verbose: color_print('Missing values') df_local = self.d_preprocess['NA'].transform(df_local, verbose=verbose) - # replace outliers if 'outlier' in list(self.d_preprocess.keys()): if verbose: color_print('Outliers') df_local = self.d_preprocess['outlier'].transform(df_local, verbose=verbose) - # categorical processing if verbose: color_print('Encode categorical and boolean') print('\n\t\t>>>', 'preprocess_apply execution time:', round(time() - start_time, 4), 'secs. <<<') @@ -328,16 +208,6 @@ def preprocess_apply(self, df, verbose=False): """ def select_features(self, method='pca', verbose=False): - """ fit and apply features selection (optional) - - Parameters - ---------- - method : string (Default pca) - method use to select features - verbose : boolean (Default False) - Get logging information - - """ assert self.step in ['preprocess'], 'apply preprocess method' target = self.target @@ -350,8 +220,6 @@ def select_features(self, method='pca', verbose=False): l_select_var = [col for col in df_local.columns.tolist() if col != self.target] - # df_local = select_features(df=df_local, target=self.target, method=method, verbose=verbose) - features_selector = FeatSelector(method=method) features_selector.fit(df_local, l_var=l_select_var, verbose=verbose) df_local = features_selector.transform(df_local, verbose=verbose) @@ -370,22 +238,6 @@ def select_features(self, method='pca', verbose=False): """ def select_features_apply(self, df, verbose=False): - """Apply features selection. - - Requires Select_Features method to have been applied - - Parameters - ---------- - df : DataFrame - dataset to apply selection on - verbose : boolean (Default False) - Get logging information - - Returns - ------- - DataFrame : reduced dataset - """ - # check pipe step and is_fitted assert self.is_fitted_selector, "fit first (please)" if verbose: @@ -408,73 +260,27 @@ def select_features_apply(self, df, verbose=False): def model_train_test(self, clf='XGBOOST', grid_param=None, metric='F1', delta_auc=0.03, top_bagging=False, n_comb=10, comb_seed=None, verbose=False): - """train and test models with random search - - - creates models with random hyper-parameters combinations from HP grid - - splits (random 80/20) train/test sets to fit/apply models - - identifies valid models (auc(train)-auc(test)<0.03 - - gets the best model in respect of a selected metric among valid model - - - Notes : - - - Available classifiers : Random Forest, XGBOOST - - can enable bagging algo with top_bagging parameter - - Parameters - ---------- - clf : string (Default : 'XGBOOST') - classifier used for modelisation - grid_param : dict - random search grid {Hyperparameter name : values list} - metric : string (Default : 'F1') - objective metric - top_bagging : boolean (Default : False) - enable Bagging - n_comb : int (Default : 10) - HP combination number - comb_seed : int (Default : None) - random combination seed - verbose : boolean (Default False) - Get logging information - - Returns - ------- - dict - {model_index : {'HP', 'probas', 'model', 'features_importance', 'train_metrics', 'metrics', 'output'} - list - valid models indexes - int - best model index - DataFrame - models summary - """ assert self.step in ['preprocess', 'features_selection'], 'apply preprocess method' if verbose: start_time = time() print_title1('Train predict') - # Train/Test split df_train, df_test = train_test(self, 0.2) - # Create Hyperopt object hyperopt = HyperOpt(classifier=clf, grid_param=grid_param, n_param_comb=n_comb, bagging=top_bagging, comb_seed=comb_seed) - # fit model on train set if verbose: color_print('training models') hyperopt.fit(df_train, self.target, verbose=verbose) - # Apply model on test set if verbose: color_print('\napplying models') d_fitted_models = hyperopt.predict(df_test, self.target, delta_auc=delta_auc, verbose=verbose) - # model selection if verbose: color_print('\nbest model selection') best_model_idx, l_valid_models = hyperopt.get_best_model(d_fitted_models, metric=metric, delta_auc_th=delta_auc, @@ -500,51 +306,21 @@ def model_train_test(self, clf='XGBOOST', grid_param=None, metric='F1', delta_au """ def model_train(self, clf='XGBOOST', grid_param=None, top_bagging=False, n_comb=10, comb_seed=None, verbose=False): - """train models with random search - - - creates models with random hyper-parameters combinations from HP grid - - fits models on self - - Notes : - - - Available classifiers : Random Forest, XGBOOST - - can enable bagging algo with top_bagging parameter - - Parameters - ---------- - clf : string (Default : 'XGBOOST') - classifier used for modelisation - grid_param : dict - random search grid {Hyperparameter name : values list} - top_bagging : boolean (Default : False) - enable Bagging - n_comb : int (Default : 10) - HP combination number - comb_seed : int (Default : None) - random combination seed - verbose : boolean (Default False) - Get logging information - - """ assert self.step in ['preprocess', 'features_selection'], 'apply preprocess method' df_train = self.copy() target = self.target - if verbose: start_time = time() print_title1('Train Models') - # instantiate Hyperopt object hyperopt = HyperOpt(classifier=clf, grid_param=grid_param, n_param_comb=n_comb, bagging=top_bagging, comb_seed=comb_seed) - # fit model on train set if verbose: color_print('training models') - # fit hyperopt on self hyperopt.fit(df_train, self.target, verbose=verbose) self.d_hyperopt = hyperopt @@ -560,45 +336,19 @@ def model_train(self, clf='XGBOOST', grid_param=None, top_bagging=False, n_comb= """ def model_predict(self, df, metric='F1', delta_auc=0.03, verbose=False): - """apply fitted models on a dataset - - - identifies valid models (auc(train)-auc(test)<0.03 - - gets the best model in respect of a selected metric among valid model - - Parameters - ---------- - metric : string (Default : 'F1') - objective metric - verbose : boolean (Default False) - Get logging information - - Returns - ------- - dict - {model_index : {'HP', 'probas', 'model', 'features_importance', 'train_metrics', 'metrics', 'output'} - list - valid models indexes - int - best model index - DataFrame - models summary - """ assert self.is_fitted_model, "model is not fitted yet, apply model_train_predict or model_train methods" if verbose: start_time = time() color_print('\napplying models') - # apply models on dataset d_fitted_models = self.d_hyperopt.predict(df, self.target, delta_auc=delta_auc, verbose=verbose) - # model selection if verbose: color_print('\nbest model selection') best_model_idx, l_valid_models = self.d_hyperopt.get_best_model(d_fitted_models, metric=metric, delta_auc_th=delta_auc, verbose=False) - # store model results df_model_res = self.d_hyperopt.model_res_to_df(d_fitted_models, sort_metric=metric) if best_model_idx is not None: diff --git a/AutoMxL/param_config.py b/AutoMxL/param_config.py index 4148de0..4711001 100644 --- a/AutoMxL/param_config.py +++ b/AutoMxL/param_config.py @@ -1,43 +1,21 @@ -""" -Default values for -""" import numpy as np -########### -# Explore # -########### - -################# -# Preprocessing # -################# -# categorical encoder batch_size = 124 n_epoch = 20 learning_rate = 0.001 crit = 'MSE' optim = 'Adam' -###################### -# Features Selection # -###################### - - -################ -# Modelisation # -################ -# Default params for Bagging default_bagging_param = {'n_sample': 5, 'pos_sample_size': 1.0, 'replace': False} -# Defaults HP grid for RF default_RF_grid_param = { 'n_estimators': np.random.uniform(low=20, high=500, size=40).astype(int), 'max_features': ['auto', 'log2'], 'max_depth': [3, 4, 5, 6, 7, 8], 'min_samples_split': [5, 10, 15, 20]} -# Defaults HP grid for XGBOOST default_XGB_grid_param = { 'n_estimators': np.random.uniform(low=100, high=300, size=40).astype(int), 'max_features': ['auto', 'log2'], diff --git a/README.md b/README.md deleted file mode 100644 index 99e82bb..0000000 --- a/README.md +++ /dev/null @@ -1,144 +0,0 @@ - - - -# Presentation - -The main purpose of this package is to provide a Python AutoML class named AML that covers the complete pipeline of a binary classification project -from a raw dataset to a deployable model. -It can be used as a functions/classes catalogue to ease and speed-up Data Scientists repetitive dev tasks aswell. - -You can find the whole code documentation on [MLBG59 Readthedoc documentation](https://mlbg59.readthedocs.io/en/latest/) - -# Getting Started -### Prerequisites -- Python 3.7 -- pandas 1.0.1 -- torch -- xgboost - -### Installation -Since this package is uploaded to PyPI, it can be installed with pip using the terminal : -``` -$ pip install AutoMxL -``` - -# AML class tutorial -AML is built as a class inherited from pandas DataFrame. Each Machine Learning step corresponds to a method that can be called with default or filled parameters. - -Note : -For each method, verbose parameter allows you to get logging informations. - -### Import and target encoding - -If needed, you can find in Start sub-package functions that facilitate data loading and target encoding. -```python -# import package -from AutoMxL import * - -# import data into DataFrame with delimiter auto-detection for csv and txt files -df_raw = import_data('data/bank-additional-full.csv', verbose=False) - -# set "yes" category from variable "y" as the classification target. -# => get modified dataset and new target name -df, target = category_to_target(df_raw, var='y' , cat='yes') - -# instantiate AML object with dataset and target name -auto_df = AML(df, target=target) -``` - -### Explore - -explore method gives you global information about the dataset and automatically -identify features types (booleans, dates, verbatims, categoricals, numericals). This information is stored in "d_features" attribute. - -```python -auto_df.explore(verbose=False) - -print(auto_df.d_features.keys()) -> output : dict_keys(['date', 'identifier', 'verbatim', 'boolean', 'categorical', 'numerical', 'NA', 'low_variance']) -``` - -### Preprocess -preprocess method prepares the data before feeding it to the model : - -- removes features with low variance and features identified as verbatims and identifiers -- transforms date features to numeric data (timedelta, ...) -- fills missing values -- processes categorical data (using one hot encoding or Pytorch §NN embedding encoder) -- processes outliers (optional) - -```python -auto_df.preprocess(process_outliers=False, cat_method='encoder', verbose=False) -``` - -### Select Features (optional) -select_features method reduces the features dimension to speed up the modelisation execution time -(may increase model performance aswell). - -```python - -auto_df.select_features(verbose=False) -``` - -### Model Train Test -model_train_test method trains and test models with random search. - -- creates models with random hyper-parameters combinations from HP grid -- splits (random 80/20) train/test sets to fit/apply models -- identifies valid models |(auc(train)-auc(test)|<0.03 -- gets the best model in respect of a selected metric among valid model - -Available classifiers : Random Forest, XGBOOST (and bagging). - -```python -d_fitted_models, l_valid_models, best_model_idx, df_model_res = auto_df.model_train_test(verbose=False) -``` -output : - -- d_fitted_models: dict containing models and information on test set -- l_valid_models: valid model indexes -- best_model_idx: best model index -- df_model_res: models information and metrics stored in DataFrame - -\ -Note : if you prefer to train and test your model separately, you can also use the following modelisation methods: -```python -auto_df.model_train(verbose=False) -d_fitted_models, l_valid_models, best_model_idx, df_model_res = auto_df.model_apply(df_sel, verbose=False) -``` - -### Application methods -Once you have applied preprocess and select_features, you can apply the same transformations to any iso-structure dataset using following methods: - -```python -df_prep = auto_df.preprocess_apply(df, verbose=False) -df_sel = auto_df.select_features_apply(df_prep, verbose=False) -``` - -### Other methods -Since AML is pandas DataFrame inherited class, you can apply any DataFrame methods on it. - -Note : copy() method applied on AML object will return a DataFrame. If you need to make a copy of AML object, use duplicate() method instead. - - -# Information -#### Release History -- 1.0.0 : First proper release - -#### Next steps -- Regression and multi-class classification - -#### Licence -Distributed under the MIT license. See License.txt for more information - -#### Author -Maxence Labesse - maxence.labesse@yahoo.fr - -https://github.com/Maxence-Labesse/AutoMxL - -#### Contributors - diff --git a/docs/Makefile b/docs/Makefile deleted file mode 100644 index 298ea9e..0000000 --- a/docs/Makefile +++ /dev/null @@ -1,19 +0,0 @@ -# Minimal makefile for Sphinx documentation -# - -# You can set these variables from the command line. -SPHINXOPTS = -SPHINXBUILD = sphinx-build -SOURCEDIR = . -BUILDDIR = _build - -# Put it first so that "make" without argument is like "make help". -help: - @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) - -.PHONY: help Makefile - -# Catch-all target: route all unknown targets to Sphinx using the new -# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). -%: Makefile - @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) \ No newline at end of file diff --git a/docs/_build.doctrees/autoML.doctree b/docs/_build.doctrees/autoML.doctree deleted file mode 100644 index e2b6462..0000000 Binary files a/docs/_build.doctrees/autoML.doctree and /dev/null differ diff --git a/docs/_build.doctrees/docstring_test.doctree b/docs/_build.doctrees/docstring_test.doctree deleted file mode 100644 index 19f217c..0000000 Binary files a/docs/_build.doctrees/docstring_test.doctree and /dev/null differ diff --git a/docs/_build.doctrees/environment.pickle b/docs/_build.doctrees/environment.pickle deleted file mode 100644 index e60ae49..0000000 Binary files a/docs/_build.doctrees/environment.pickle and /dev/null differ diff --git a/docs/_build.doctrees/features.doctree b/docs/_build.doctrees/features.doctree deleted file mode 100644 index aa5d9f2..0000000 Binary files a/docs/_build.doctrees/features.doctree and /dev/null differ diff --git a/docs/_build.doctrees/index.doctree b/docs/_build.doctrees/index.doctree deleted file mode 100644 index eb7eeaf..0000000 Binary files a/docs/_build.doctrees/index.doctree and /dev/null differ diff --git a/docs/_build.html/.buildinfo b/docs/_build.html/.buildinfo deleted file mode 100644 index b06c3e0..0000000 --- a/docs/_build.html/.buildinfo +++ /dev/null @@ -1,4 +0,0 @@ -# Sphinx build info version 1 -# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done. -config: 0d9202b8681f8712829cb0d6418c886b -tags: 645f666f9bcd5a90fca523b33c5a78b7 diff --git a/docs/_build.html/_modules/AutoMxL/Audit/Audit_Dataset.html b/docs/_build.html/_modules/AutoMxL/Audit/Audit_Dataset.html deleted file mode 100644 index ffe9fc1..0000000 --- a/docs/_build.html/_modules/AutoMxL/Audit/Audit_Dataset.html +++ /dev/null @@ -1,386 +0,0 @@ - - - - - - - - - - - MLBG59.Audit.Audit_Dataset — MLBG59 1.0.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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Source code for MLBG59.Audit.Audit_Dataset

-""" Dataset Features analysis :
-
- - audit dataset  : get informations on the data (NA, features type, low variance features, ...)
- - is_date : detect if an object/num feature is a date
- - get_all_dates : identify all dates features in a DataFrame and store their names in a list
- - low variance features : identify all features with a low variance (<threshold) and sotre their name in a list
-"""
-import pandas as pd
-from sklearn.preprocessing import MinMaxScaler
-from MLBG59.Utils.Display import *
-from MLBG59.Utils.Utils import get_type_features
-
-
-
[docs]def audit_dataset(df, verbose=1):
-    """Achieve a short audit of the dataset
-
-    Identify features of each type (num, cat, date), features containing NA and features whose variance is null
-        
-    Parameters
-    ----------
-    df : DataFrame
-        input dataset
-    target : string (Default : None)
-        target name
-    verbose : int (0/1) (Default : 1)
-        get more operations information
-            
-    Returns
-    -------
-    list of the x features names
-        - x = num : numerical features
-        - x = cat : categorical features
-        - x = date : date features
-        - x = NA : features which contains NA values
-        - x = low_var : list of the features with low variance
-    """
-
-    # dataset dimensions
-    if verbose > 0:
-        color_print("Dimensions : ")
-        print("  > row number : ", df.shape[0], "\n  > col number : ", df.shape[1])
-
-    #################
-    # features type #
-    #################
-    # numerical
-    num_columns = df._get_numeric_data().columns.tolist()
-    # date
-    date_columns = get_all_dates(df)
-    # categorical
-    cat_columns = [x for x in df.columns if (x not in num_columns) and (x not in date_columns)]
-
-    if verbose > 0:
-        color_print("Features type identification : ")
-        print("  > cat : " + str(len(cat_columns)) + ' (' + str(round(len(cat_columns) / df.shape[1] * 100)) + '%)',
-              '\n  > num : ' + str(len(num_columns)) + ' (' + str(round(len(num_columns) / df.shape[1] * 100)) + '%)',
-              '\n  > dates: ' + str(len(date_columns)) + ' (' + str(
-                  round(len(date_columns) / df.shape[1] * 100)) + ' %)')
-
-    ######################
-    # NA values analysis
-    ######################
-    df_col = pd.DataFrame(df.columns.values, columns=['variables'])
-    df_col['Nbr NA'] = df.isna().sum().tolist()
-    df_col['Taux NA'] = df_col['Nbr NA'] / df.shape[0]
-    # features containing NA values
-    NA_columns = df_col.loc[df_col['Nbr NA'] > 0].sort_values('Nbr NA', ascending=False).variables.tolist()
-    col_des = df_col['Taux NA'].describe()
-
-    if verbose > 0:
-        color_print(str(len(NA_columns)) + " features containing NA")
-        print('  > Taux NA moyen : ' + str(round(col_des['mean'] * 100, 2)) + '%',
-              '\n  >           min : ' + str(round(col_des['min'] * 100, 2)) + '%',
-              '\n  >           max : ' + str(round(col_des['max'] * 100, 2)) + '%')
-
-    #########################
-    # Low variance features
-    #########################
-    if verbose > 0:
-        color_print('Low variance features')
-    low_var_columns = \
-        low_variance_features(df, var_list=num_columns, threshold=0, rescale=True, verbose=verbose).index.tolist()
-
-    return num_columns, date_columns, cat_columns, NA_columns, low_var_columns

-
-
-"""
--------------------------------------------------------------------------------------------------------------------------
-"""
-
-
-
[docs]def is_date(df, col):
-    """Test if a DataFrame feature is recognized as a date (using to_datetime)
-
-    Parameters
-    ----------
-     df : DataFrame
-        input dataset
-     col : string
-        feature name
-
-    Returns
-    -------
-    res : boolean
-        True if the col is recognized as a date
-    """
-    # if col is datetime type, res = True
-    if df[col].dtype == 'datetime64[ns]':
-        return True
-
-    # if col is object type, try apply to_datetime
-    elif df[col].dtype == 'object':
-        try:
-            df_smpl = df.sample(100).copy()
-            pd.to_datetime(df_smpl[col])
-            return True
-        except ValueError:
-            return False
-        except OverflowError:
-            return False

-
-
-"""
--------------------------------------------------------------------------------------------------------------------------
-"""
-
-
-
[docs]def get_all_dates(df):
-    """Identify all date features of a DataFrame
-    
-    Parameters
-    ----------
-     df : DataFrame
-        input DataFrame
-
-    Returns
-    -------
-     list
-        list of features recognized as date
-    """
-    date_list = list()
-
-    for col in df.columns:
-        # if col is recognized as date
-        if is_date(df, col): date_list.append(col)
-
-    return date_list

-
-
-"""
--------------------------------------------------------------------------------------------------------------------------
-"""
-
-
-
[docs]def low_variance_features(df, var_list=None, threshold=0, rescale=True, verbose=1):
-    """identify  features with low variance (<= threshold)
-
-    Parameters
-    ----------
-     df : DataFrame
-        input DataFrame
-     var_list : list (default : None)
-        features to check variance
-     threshold : float (default : 0
-        variance threshold
-     rescale : bool (default : true)
-        if yes : use MinMaxScaler on data before computing variance
-
-    Returns
-    -------
-    list
-        list of the variable with low variance
-    """
-    # if var_list = None, get all numerical features
-    # else, exclude features from var_list whose type is not numerical
-    var_list = get_type_features(df, 'num', var_list)
-
-    df_bis = df.copy()
-
-    if rescale:
-        scler = MinMaxScaler()
-        df_bis[var_list] = scler.fit_transform(df_bis[var_list].astype('float64'))
-
-    selected_var = df_bis[var_list].var().loc[df_bis.var() <= threshold]
-
-    if verbose > 0:
-        # print('features : ',list(var_list))
-        if rescale: print('  **MinMaxScaler [0,1]')
-        print('  ', str(len(selected_var)) + ' feature(s) with  variance <= threshold (' + str(threshold) + ')')
-
-    return selected_var.sort_values(ascending=True)

-
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- -
-
- - -
- -
-

- © Copyright 2020, Maxence LABESSE - -

-
- Built with Sphinx using a theme provided by Read the Docs. - -
- -
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- - - - - - - - - - - - \ No newline at end of file diff --git a/docs/_build.html/_modules/AutoMxL/Audit/Get_Outliers.html b/docs/_build.html/_modules/AutoMxL/Audit/Get_Outliers.html deleted file mode 100644 index bd7dca9..0000000 --- a/docs/_build.html/_modules/AutoMxL/Audit/Get_Outliers.html +++ /dev/null @@ -1,314 +0,0 @@ - - - - - - - - - - - MLBG59.Audit.Get_Outliers — MLBG59 1.0.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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Source code for MLBG59.Audit.Get_Outliers

-""" Outliers detection :
-
- - get_cat_outliers : identify categorical features containing outliers and store their names in a list
- - get_num_outliers : identify numerical features containing outliers and store their names in a list data
-"""
-import pandas as pd
-import numpy as np
-from MLBG59.Utils.Display import *
-from MLBG59.Utils.Utils import get_type_features
-
-
-
[docs]def get_cat_outliers(df, var_list=None, threshold=0.05, verbose=1):
-    """outliers detection for categorical features
-
-    Parameters
-    ----------
-     df : DataFrame
-        Input dataset
-     var_list : list (Default : None)
-        list of the features to analyze.
-        If None, contains all the categorical features
-     threshold : float (Default : 0.05)
-        Minimum modality frequency
-     verbose : int (0/1) (Default : 1)
-        Get more operations information
-
-    Returns
-    -------
-     outlier_dict : dict
-          {feature : list of modalities considered as outliers}
-    """
-    # if var_list = None, get all categorical features
-    # else, exclude features from var_list whose type is not categorical
-    var_list = get_type_features(df, 'cat', var_list)
-
-    df_local = df[var_list].copy()
-
-    if verbose > 0:
-        color_print('cat features outliers identification (frequency<' + str(threshold) + ')')
-        print('  > features : ', var_list,)
-
-    # initialize output dict
-    outlier_dict = {}
-
-    # value count (frequency as number and percent for each modality) for features in var_list
-    for col in df_local.columns:
-        # percent
-        freq_perc = pd.value_counts(df[col], dropna=False) / len(df[col])
-
-        # if feature contain modalities with frequency < trehshold, store in outlier_dict
-        if len(freq_perc.loc[freq_perc < threshold]) > 0:
-            outlier_dict[col] = freq_perc.loc[freq_perc < threshold].index.tolist()
-
-    if verbose > 0:
-        print("  > containing outliers", list(outlier_dict.keys()))
-
-    return outlier_dict

-
-
-"""
--------------------------------------------------------------------------------------------------------------------------
-"""
-
-
-
[docs]def get_num_outliers(df, var_list=None, xstd=3, verbose=0):
-    """outliers detection for num features
-    
-    Parameters
-    ----------
-     df : DataFrame
-        Input dataset
-     var_list : list (Default : None)
-        List of the features to analyze.
-        If None, contains all the num features
-     xstd : int (Default : 3)
-        coefficient (TODO)
-     verbose : int (0/1) (Default : 1)
-        Get more operations information
-            
-    Returns
-    -------
-     outlier_dict : dict
-         {feature : index of outliers}
-    """
-    # if var_list = None, get all num features
-    # else, exclude features from var_list whose type is not num
-    var_list = get_type_features(df, 'num', var_list)
-
-    df_bis = df[var_list].copy()
-
-    if verbose > 0:
-        color_print('num features outliers identification ( x: |x - mean| > '+str(xstd)+' * var)')
-        print('  > features : ', var_list, )
-
-    # initialize output dict
-    outlier_dict = {}
-
-    # compute features upper and lower limit (deviation from the mean > x*std dev (x=3 by default))
-    data_std = np.std(df_bis)
-    data_mean = np.mean(df_bis)
-    anomaly_cut_off = data_std * xstd
-    lower_limit = data_mean - anomaly_cut_off
-    upper_limit = data_mean + anomaly_cut_off
-
-    df_outliers = pd.DataFrame()
-
-    # mask (1 if outlier, else 0)
-    for col in df_bis.columns:
-        df_outliers[col] = np.where((df_bis[col] < lower_limit[col]) | (df_bis[col] > upper_limit[col]), 1, 0)
-
-    # for features containing outliers
-    for col in df_outliers.sum().loc[df_outliers.sum() > 0].index.tolist():
-        # store features and outliers index in outlierèdict
-        outlier_dict[col] = [lower_limit[col], upper_limit[col]]
-
-    if verbose > 0:
-        print("  > containing outliers", list(outlier_dict.keys()))
-
-    return outlier_dict

-
- -
- -
-
- - -
- -
-

- © Copyright 2020, Maxence LABESSE - -

-
- Built with Sphinx using a theme provided by Read the Docs. - -
- -
-
- -
- -
- - - - - - - - - - - - \ No newline at end of file diff --git a/docs/_build.html/_modules/AutoMxL/Explore/Audit_Dataset.html b/docs/_build.html/_modules/AutoMxL/Explore/Audit_Dataset.html deleted file mode 100644 index 6d84f22..0000000 --- a/docs/_build.html/_modules/AutoMxL/Explore/Audit_Dataset.html +++ /dev/null @@ -1,386 +0,0 @@ - - - - - - - - - - - MLBG59.Explore.Audit_Dataset — MLBG59 1.0.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
- - - -
- - - - - -
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    - -
  • Docs »
    • - -
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    • - -
  • MLBG59.Explore.Audit_Dataset
    • - - -
  • - -
    • - -
- - -
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-
- -

Source code for MLBG59.Explore.Audit_Dataset

-""" Dataset Features analysis :
-
- - audit dataset  : get informations on the data (NA, features type, low variance features, ...)
- - is_date : detect if an object/num feature is a date
- - get_all_dates : identify all dates features in a DataFrame and store their names in a list
- - low variance features : identify all features with a low variance (<threshold) and sotre their name in a list
-"""
-import pandas as pd
-from sklearn.preprocessing import MinMaxScaler
-from MLBG59.Utils.Display import *
-from MLBG59.Utils.Utils import get_type_features
-
-
-
[docs]def audit_dataset(df, verbose=1):
-    """Achieve a short audit of the dataset
-
-    Identify features of each type (num, cat, date), features containing NA and features whose variance is null
-        
-    Parameters
-    ----------
-    df : DataFrame
-        input dataset
-    target : string (Default : None)
-        target name
-    verbose : int (0/1) (Default : 1)
-        get more operations information
-            
-    Returns
-    -------
-    list of the x features names
-        - x = num : numerical features
-        - x = cat : categorical features
-        - x = date : date features
-        - x = NA : features which contains NA values
-        - x = low_var : list of the features with low variance
-    """
-
-    # dataset dimensions
-    if verbose > 0:
-        color_print("Dimensions : ")
-        print("  > row number : ", df.shape[0], "\n  > col number : ", df.shape[1])
-
-    #################
-    # features type #
-    #################
-    # numerical
-    num_columns = df._get_numeric_data().columns.tolist()
-    # date
-    date_columns = get_all_dates(df)
-    # categorical
-    cat_columns = [x for x in df.columns if (x not in num_columns) and (x not in date_columns)]
-
-    if verbose > 0:
-        color_print("Features type identification : ")
-        print("  > cat : " + str(len(cat_columns)) + ' (' + str(round(len(cat_columns) / df.shape[1] * 100)) + '%)',
-              '\n  > num : ' + str(len(num_columns)) + ' (' + str(round(len(num_columns) / df.shape[1] * 100)) + '%)',
-              '\n  > dates: ' + str(len(date_columns)) + ' (' + str(
-                  round(len(date_columns) / df.shape[1] * 100)) + ' %)')
-
-    ######################
-    # NA values analysis
-    ######################
-    df_col = pd.DataFrame(df.columns.values, columns=['variables'])
-    df_col['Nbr NA'] = df.isna().sum().tolist()
-    df_col['Taux NA'] = df_col['Nbr NA'] / df.shape[0]
-    # features containing NA values
-    NA_columns = df_col.loc[df_col['Nbr NA'] > 0].sort_values('Nbr NA', ascending=False).variables.tolist()
-    col_des = df_col['Taux NA'].describe()
-
-    if verbose > 0:
-        color_print(str(len(NA_columns)) + " features containing NA")
-        print('  > Taux NA moyen : ' + str(round(col_des['mean'] * 100, 2)) + '%',
-              '\n  >           min : ' + str(round(col_des['min'] * 100, 2)) + '%',
-              '\n  >           max : ' + str(round(col_des['max'] * 100, 2)) + '%')
-
-    #########################
-    # Low variance features
-    #########################
-    if verbose > 0:
-        color_print('Low variance features')
-    low_var_columns = \
-        low_variance_features(df, var_list=num_columns, threshold=0, rescale=True, verbose=verbose).index.tolist()
-
-    return num_columns, date_columns, cat_columns, NA_columns, low_var_columns

-
-
-"""
--------------------------------------------------------------------------------------------------------------------------
-"""
-
-
-
[docs]def is_date(df, col):
-    """Test if a DataFrame feature is recognized as a date (using to_datetime)
-
-    Parameters
-    ----------
-     df : DataFrame
-        input dataset
-     col : string
-        feature name
-
-    Returns
-    -------
-    res : boolean
-        True if the col is recognized as a date
-    """
-    # if col is datetime type, res = True
-    if df[col].dtype == 'datetime64[ns]':
-        return True
-
-    # if col is object type, try apply to_datetime
-    elif df[col].dtype == 'object':
-        try:
-            df_smpl = df.sample(100).copy()
-            pd.to_datetime(df_smpl[col])
-            return True
-        except ValueError:
-            return False
-        except OverflowError:
-            return False

-
-
-"""
--------------------------------------------------------------------------------------------------------------------------
-"""
-
-
-
[docs]def get_all_dates(df):
-    """Identify all date features of a DataFrame
-    
-    Parameters
-    ----------
-     df : DataFrame
-        input DataFrame
-
-    Returns
-    -------
-     list
-        list of features recognized as date
-    """
-    date_list = list()
-
-    for col in df.columns:
-        # if col is recognized as date
-        if is_date(df, col): date_list.append(col)
-
-    return date_list

-
-
-"""
--------------------------------------------------------------------------------------------------------------------------
-"""
-
-
-
[docs]def low_variance_features(df, var_list=None, threshold=0, rescale=True, verbose=1):
-    """identify  features with low variance (<= threshold)
-
-    Parameters
-    ----------
-     df : DataFrame
-        input DataFrame
-     var_list : list (default : None)
-        features to check variance
-     threshold : float (default : 0
-        variance threshold
-     rescale : bool (default : true)
-        if yes : use MinMaxScaler on data before computing variance
-
-    Returns
-    -------
-    list
-        list of the variable with low variance
-    """
-    # if var_list = None, get all numerical features
-    # else, exclude features from var_list whose type is not numerical
-    var_list = get_type_features(df, 'num', var_list)
-
-    df_bis = df.copy()
-
-    if rescale:
-        scler = MinMaxScaler()
-        df_bis[var_list] = scler.fit_transform(df_bis[var_list].astype('float64'))
-
-    selected_var = df_bis[var_list].var().loc[df_bis.var() <= threshold]
-
-    if verbose > 0:
-        # print('features : ',list(var_list))
-        if rescale: print('  **MinMaxScaler [0,1]')
-        print('  ', str(len(selected_var)) + ' feature(s) with  variance <= threshold (' + str(threshold) + ')')
-
-    return selected_var.sort_values(ascending=True)

-
- -
- -
-
- - -
- -
-

- © Copyright 2020, Maxence LABESSE - -

-
- Built with Sphinx using a theme provided by Read the Docs. - -
- -
-
- -
- -
- - - - - - - - - - - - \ No newline at end of file diff --git a/docs/_build.html/_modules/AutoMxL/Explore/Explore.html b/docs/_build.html/_modules/AutoMxL/Explore/Explore.html deleted file mode 100644 index bdd6629..0000000 --- a/docs/_build.html/_modules/AutoMxL/Explore/Explore.html +++ /dev/null @@ -1,408 +0,0 @@ - - - - - - - - - - - MLBG59.Explore.Explore — MLBG59 1.0.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
- - - -
- - - - - -
- -
- - - - - - - - - - - - - - - - - -
- - - - -
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-
-
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Source code for MLBG59.Explore.Explore

-""" Global dataset information functions :
-
- - explore (func): Identify variables types and gives global information about the dataset (NA, low variance features)
- - low variance features (func): identify features with low variance
- - - get_features_type (func): get all features per type
-"""
-from sklearn.preprocessing import MinMaxScaler
-from MLBG59.Explore.Features_Type import *
-from MLBG59.Utils.Display import *
-
-
-
[docs]def explore(df, verbose=False):
-    """Identify variables types and gives global information about the dataset
-
-    - Variables type :
-        - date
-        - identifier
-        - verbatim
-        - boolean
-        - categorical
-        - numerical
-    - variables containing NA values
-    - low variance and unique values variables
-
-    See get_features_type function doc for type identification heuristics
-
-    Parameters
-    ----------
-    df : DataFrame
-        input dataset
-    verbose : boolean (Default False)
-        Get logging information
-
-    Returns
-    -------
-    dict
-        {x : variables names list }
-
-        - date : date features
-        - identifier : identifier features
-        - verbatim : verbatim features
-        - boolean : boolean features
-        - categorical : categorical features
-        - numerical : numerical features
-        - categorical : categorical features
-        - date : date features
-        - NA : features which contains NA values
-        - low_variance : list of the features with low variance
-    """
-    # dataset dimensions
-    if verbose:
-        color_print("Dimensions :")
-        print("  > row number :", df.shape[0], "\n  > col number : ", df.shape[1])
-
-    #########################
-    # Low variance features
-    #########################
-    if verbose:
-        color_print('Low variance features')
-
-    l_low_var = \
-        low_variance_features(df, var_list=df._get_numeric_data().columns.tolist(), threshold=0, rescale=True,
-                              verbose=verbose).index.tolist()
-
-    # categorical features with unique values
-    l_unique = [col for col in df.columns.tolist() if df[col].dtype == 'object' and df[col].nunique(dropna=True) == 1]
-
-    l_low_var = l_low_var + l_unique
-
-    df_valid = df.drop(l_low_var, axis=1).copy()
-
-    #################
-    # features type #
-    #################
-    d_features = get_features_type(df_valid, l_var=None, th=0.95)
-
-    if verbose:
-        color_print("Features type identification : ")
-        list(map(lambda typ:
-                 print("  > " + typ + " : " + str(len(d_features[typ])) + ' (' + str(
-                     round(len(d_features[typ]) / df_valid.shape[1] * 100)) + '%)'),
-                 d_features.keys()))
-
-    ######################
-    # NA values analysis
-    ######################
-    df_col = pd.DataFrame(df_valid.columns.values, columns=['variables'])
-    df_col['Nbr NA'] = df_valid.isna().sum().tolist()
-    df_col['Taux NA'] = df_col['Nbr NA'] / df_valid.shape[0]
-    # features containing NA values
-    NA_columns = df_col.loc[df_col['Nbr NA'] > 0].sort_values('Nbr NA', ascending=False).variables.tolist()
-    col_des = df_col['Taux NA'].describe()
-
-    if verbose:
-        color_print(str(len(NA_columns)) + " features containing NA")
-        print('  > Taux NA moyen : ' + str(round(col_des['mean'] * 100, 2)) + '%',
-              '\n  >           min : ' + str(round(col_des['min'] * 100, 2)) + '%',
-              '\n  >           max : ' + str(round(col_des['max'] * 100, 2)) + '%')
-
-    # store into DataFrame
-    d_features['NA'] = NA_columns
-    d_features['low_variance'] = l_low_var
-
-    return d_features

-
-
-"""
--------------------------------------------------------------------------------------------------------------------------
-"""
-
-
-
[docs]def get_features_type(df, l_var=None, th=0.95):
-    """ Get all features per type :
-
-    - date : try to apply to_datetime
-    - identifier :
-        - #(unique values)/#(total values) > threshold (default 0.95)
-        - AND length is the same for all values (for non NA)
-    - verbatim :
-        - #(unique values)/#(total values) >= threshold (default 0.95)
-        - AND length is NOT the same for all values (for non NA)
-    - boolean : #(distinct values) = 2
-    - categorical :
-        - not a date
-        - #(unique values)/#(total values) < threshold (default 0.95)
-        - AND #(uniques values)>2
-        - AND for num values #(unique values)<30
-    - numerical : others
-
-    Parameters
-    ----------
-    df : DataFrame
-        input dataset
-    l_var : list (Default  : None)
-        variable names
-    th : float (Default : 0.95)
-        threshold used to identify identifiers/verbatims variables
-
-    Returns
-    -------
-    dict
-        { type : variables name list}
-    """
-    d_output = {}
-
-    if l_var is None:
-        df_local = df.copy()
-    else:
-        df_local = df[l_var].copy()
-
-    l_col = df_local.columns.tolist()
-
-    for typ in ['date', 'identifier', 'verbatim', 'boolean', 'categorical']:
-        d_output[typ] = features_from_type(df_local, typ, l_var=l_col, th=th)
-        l_col = [x for x in l_col if (x not in d_output[typ])]
-
-    d_output['numerical'] = l_col
-
-    return d_output

-
-
-"""
--------------------------------------------------------------------------------------------------------------------------
-"""
-
-
-
[docs]def low_variance_features(df, var_list=None, threshold=0, rescale=True, verbose=False):
-    """Identify numerical features with low variance : (< threshold).
-    Possible to rescale feature before computing.
-
-    Parameters
-    ----------
-     df : DataFrame
-        input DataFrame
-     var_list : list (default : None)
-        names of the variables to check variance
-        if None : all the numerical features
-     threshold : float (default : 0)
-        variance threshold
-     rescale : bool (default : true)
-        enable  MinMaxScaler before computing variance
-    verbose : boolean (Default False)
-        Get logging information
-
-    Returns
-    -------
-    list
-       Names of the variables with low variance
-    """
-    # if var_list = None, get all num features
-    # else, remove features from var_list whose type is not num
-    l_num = df._get_numeric_data().columns.tolist()
-
-    if var_list is None:
-        var_list = l_num
-    else:
-        var_list = [col for col in var_list if col in l_num]
-
-    df_bis = df.copy()
-
-    if rescale:
-        scler = MinMaxScaler()
-        df_bis[var_list] = scler.fit_transform(df_bis[var_list].astype('float64'))
-
-    selected_var = df_bis[var_list].var().loc[df_bis.var() <= threshold]
-
-    if verbose:
-        # print('features : ',list(var_list))
-        if rescale:
-            print('  **MinMaxScaler [0,1]')
-        print('  ', str(len(selected_var)) + ' feature(s) with  variance <= threshold (' + str(threshold) + ')')
-
-    return selected_var.sort_values(ascending=True)

-
- -
- -
-
- - -
- -
-

- © Copyright 2020, Maxence LABESSE - -

-
- Built with Sphinx using a theme provided by Read the Docs. - -
- -
-
- -
- -
- - - - - - - - - - - - \ No newline at end of file diff --git a/docs/_build.html/_modules/AutoMxL/Explore/Features_Type.html b/docs/_build.html/_modules/AutoMxL/Explore/Features_Type.html deleted file mode 100644 index 0f05089..0000000 --- a/docs/_build.html/_modules/AutoMxL/Explore/Features_Type.html +++ /dev/null @@ -1,485 +0,0 @@ - - - - - - - - - - - MLBG59.Explore.Features_Type — MLBG59 1.0.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
- - - -
- - - - - -
- -
- - - - - - - - - - - - - - - - - -
- -
    - -
  • Docs »
    • - -
  • Module code »
    • - -
  • MLBG59.Explore.Features_Type
    • - - -
  • - -
    • - -
- - -
-
-
-
- -

Source code for MLBG59.Explore.Features_Type

-"""Variables type identification function
-
-- features_from_type (func): get all features for a selected type
-- is_date (func): test if a variable is a date
-- is_identifier (func): test if a variable is an identifier
-- is_verbatim (func): test if a variable is a verbatim
-- is_boolean (func): test if a variable is a boolean
-- is_categorical (func): test if a variable is a categorical one (with more than 2 categories)
-"""
-import pandas as pd
-from time import time
-from MLBG59.Utils.Decorators import timer
-
-
-
[docs]def features_from_type(df, typ, l_var=None, th=0.95):
-    """Get features of a selected type :
-
-    - date : try to apply to_datetime
-    - identifier :
-        - #(unique values)/#(total values) > threshold (default 0.95)
-        - AND length is the same for all values (for non NA)
-    - verbatim :
-        - #(unique values)/#(total values) >= threshold (default 0.95)
-        - AND length is NOT the same for all values (for non NA)
-    - boolean : #(distinct values) = 2
-    - categorical :
-        - not a date
-        - #(unique values)/#(total values) < threshold (default 0.95)
-        - AND #(uniques values)>2
-        - AND for num values #(unique values)<30
-
-    Parameters
-    ----------
-    df : DataFrame
-        input dataset
-    typ : string
-        selected type to get features:
-
-        - 'date'
-        - 'identifier'
-        - 'verbatim'
-        - 'boolean'
-        - categorical
-        
-    l_var : list (Default : None)
-        variables names. If None, all dataset columns
-    th : float (Default : 0.95)
-        threshold used to identify identifiers/verbatims variables
-
-    Returns
-    -------
-    list
-        identified variables names
-    """
-    assert typ in ['date', 'identifier', 'verbatim', 'boolean', 'categorical'], 'Invalid type'
-
-    if l_var is None:
-        df_local = df.copy()
-    else:
-        df_local = df[l_var].copy()
-
-    if typ == 'date':
-        l_var = [col for col in df_local.columns if is_date(df_local, col)]
-    elif typ == 'identifier':
-        l_var = [col for col in df_local.columns if is_identifier(df_local, col, th)]
-    elif typ == 'verbatim':
-        l_var = [col for col in df_local.columns if is_verbatim(df_local, col, th)]
-    elif typ == 'boolean':
-        l_var = [col for col in df_local.columns if is_boolean(df_local, col)]
-    elif typ == 'categorical':
-        l_var = [col for col in df_local.columns if is_categorical(df_local, col, th)]
-
-    return l_var

-
-
-"""
--------------------------------------------------------------------------------------------------------------------------
-"""
-
-
-
[docs]def is_date(df, col):
-    """Test if a variable is a date.
-
-    Method : try to apply to_datetime
-
-    Parameters
-    ----------
-    df : DataFrame
-        input dataset
-    col : string
-        variable name
-
-    Returns
-    -------
-    res : boolean
-        test result
-    """
-    sample_size = 10
-    full_col = df[col].loc[~df[col].isna()]
-
-    smpl_size = min(sample_size, len(full_col))
-    smpl = full_col.sample(smpl_size).copy()
-    # if col is numerical/object type, try apply to_datetime
-    if df[col].dtype != 'datetime64[ns]':
-        try:
-            if smpl.dtype == 'object':
-                smpl = pd.to_datetime(smpl, errors='raise')
-            else:
-                smpl = pd.to_datetime(smpl.astype('Int32').astype(str), errors='raise')
-        except ValueError:
-            pass
-        except OverflowError:
-            pass
-        except TypeError:
-            pass
-    # if col is datetime type, res = True
-    return smpl.dtype == 'datetime64[ns]'

-
-
-"""
--------------------------------------------------------------------------------------------------------------------------
-"""
-
-
-
[docs]def is_identifier(df, col, th=0.95):
-    """Test if a variable is an identifier.
-
-    - #(unique values)/#(total values) > threshold (default 0.95)
-    - AND length is the same for all values (for non NA)
-    - AND not date
-
-    Parameters
-    ----------
-    df : DataFrame
-        input dataset
-    col : string
-        variable name
-    th : float (Default : 0.95)
-        threshold rate
-
-    Returns
-    -------
-    res : boolean
-        test result
-    """
-    full_col = df[col].loc[~df[col].isna()]
-
-    # test if #(v unique values)/#(v,total,values) >= threshold (default 0.95)
-    if full_col.nunique() / full_col.count() >= th:
-        if df[col].dtype != 'object':
-            try:
-                full_col = full_col.astype('Int32').astype(str)
-            except ValueError:
-                return False
-            except OverflowError:
-                return False
-            except TypeError:
-                return False
-
-        # test if all (non NA) values have the same length
-        if full_col.apply(lambda x: len(x)).nunique() == 1:
-            if not is_date(df, col):
-                return True
-            else:
-                return False
-        else:
-            return False
-    else:
-        return False

-
-
-"""
--------------------------------------------------------------------------------------------------------------------------
-"""
-
-
-
[docs]def is_verbatim(df, col, th=0.95):
-    """Test if a variable is a verbatim.
-
-    - #(unique values)/#(total values) >= threshold (default 0.95)
-    - AND length is NOT the same for all values (for non NA)
-
-    Parameters
-    ----------
-    df : DataFrame
-        input dataset
-    col : string
-        variable name
-    th : float (Default : 0.95)
-        threshold rate
-
-    Returns
-    -------
-    res : boolean
-        test result
-    """
-    # get variable serie with non NA values
-    if df[col].dtype == 'object':
-        full_col = df[col].loc[~df[col].isna()]
-    else:
-        return False
-
-    # test if #(v unique values)/#(v,total,values) > threshold (default 0.95)
-    if full_col.nunique() / full_col.count() >= th:
-        # test if all (non NA) values have the same length
-        if full_col.apply(lambda x: len(x)).nunique() > 1:
-            return True
-        else:
-            return False
-    else:
-        return False

-
-
-"""
--------------------------------------------------------------------------------------------------------------------------
-"""
-
-
-
[docs]def is_boolean(df, col):
-    """Test if a variable is a boolean.
-
-    - #(distinct values) = 2
-
-    Parameters
-    ----------
-    df : DataFrame
-        input dataset
-    col : string
-        variable name
-
-    Returns
-    -------
-    res : boolean
-        test result
-    """
-    full_col = df[col].loc[~df[col].isna()]
-    # get variable serie with non NA values
-
-    if full_col.nunique() == 2:
-        if len(full_col) > 2:
-            return True
-        else:
-            return False
-
-    else:
-        return False

-
-
-"""
--------------------------------------------------------------------------------------------------------------------------
-"""
-
-
-
[docs]def is_categorical(df, col, th=0.95):
-    """Test if a variable is a categorical one (with more than 2 categories).
-
-    - not a date
-    - #(unique values)/#(total values) < threshold (default 0.95
-    - AND #(uniques values)>2
-    - AND for num values #(unique values)<30
-
-    Parameters
-    ----------
-    df : DataFrame
-        input dataset
-    col : string
-        variable name
-    th : float (Default : 0.95)
-        threshold
-
-    Returns
-    -------
-    res : boolean
-        test result
-    """
-    # get variable serie with non NA values
-    full_col = df[col].loc[~df[col].isna()]
-    if full_col.nunique() > 2:
-        if (full_col.nunique() / full_col.count()) < th:
-            if df[col].dtype == 'object':
-                return True
-            else:
-                if full_col.nunique() < 5:
-                    return True
-                else:
-                    return False
-        else:
-            return False
-    else:
-        return False

-
- -
- -
-
- - -
- -
-

- © Copyright 2020, Maxence LABESSE - -

-
- Built with Sphinx using a theme provided by Read the Docs. - -
- -
-
- -
- -
- - - - - - - - - - - - \ No newline at end of file diff --git a/docs/_build.html/_modules/AutoMxL/Explore/Get_Info.html b/docs/_build.html/_modules/AutoMxL/Explore/Get_Info.html deleted file mode 100644 index e7964e5..0000000 --- a/docs/_build.html/_modules/AutoMxL/Explore/Get_Info.html +++ /dev/null @@ -1,399 +0,0 @@ - - - - - - - - - - - MLBG59.Explore.Get_Info — MLBG59 1.0.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
- - - -
- - - - - -
- -
- - - - - - - - - - - - - - - - - -
- - - - -
-
-
-
- -

Source code for MLBG59.Explore.Get_Info

-""" Global dataset information functions :
-
- - recap : get global information about the dataset (NA, features type, low variance features, ...)
- - is_date : test if a variable is as date
- - get_all_dates : identify date features
- - low variance features : identify features with low variance
-"""
-import pandas as pd
-from sklearn.preprocessing import MinMaxScaler
-from MLBG59.Utils.Display import *
-from MLBG59.Utils.Utils import get_type_features
-
-
-
[docs]def recap(df, verbose=False):
-    """Get global information about the dataset
-
-    - Variables type (num, cat, date)
-    - NA values
-    - low variance variables
-        
-    Parameters
-    ----------
-    df : DataFrame
-        input dataset
-    verbose : boolean (Default False)
-        Get logging information
-            
-    Returns
-    -------
-    dict
-        {x : list of variables names}
-
-        - x = numerical : numerical features
-        - x = categorical : categorical features
-        - x = date : date features
-        - x = NA : features which contains NA values
-        - x = low_variance : list of the features with low variance
-    """
-    # dataset dimensions
-    if verbose:
-        color_print("Dimensions : ")
-        print("  > row number : ", df.shape[0], "\n  > col number : ", df.shape[1])
-
-    #################
-    # features type #
-    #################
-    # numerical
-    num_columns = df._get_numeric_data().columns.tolist()
-    # date
-    date_columns = get_all_dates(df)
-    # categorical
-    cat_columns = [x for x in df.columns if (x not in num_columns) and (x not in date_columns)]
-
-    if verbose:
-        color_print("Features type identification : ")
-        print("  > cat : " + str(len(cat_columns)) + ' (' + str(round(len(cat_columns) / df.shape[1] * 100)) + '%)',
-              '\n  > num : ' + str(len(num_columns)) + ' (' + str(round(len(num_columns) / df.shape[1] * 100)) + '%)',
-              '\n  > dates: ' + str(len(date_columns)) + ' (' + str(
-                  round(len(date_columns) / df.shape[1] * 100)) + ' %)')
-
-    ######################
-    # NA values analysis
-    ######################
-    df_col = pd.DataFrame(df.columns.values, columns=['variables'])
-    df_col['Nbr NA'] = df.isna().sum().tolist()
-    df_col['Taux NA'] = df_col['Nbr NA'] / df.shape[0]
-    # features containing NA values
-    NA_columns = df_col.loc[df_col['Nbr NA'] > 0].sort_values('Nbr NA', ascending=False).variables.tolist()
-    col_des = df_col['Taux NA'].describe()
-
-    if verbose:
-        color_print(str(len(NA_columns)) + " features containing NA")
-        print('  > Taux NA moyen : ' + str(round(col_des['mean'] * 100, 2)) + '%',
-              '\n  >           min : ' + str(round(col_des['min'] * 100, 2)) + '%',
-              '\n  >           max : ' + str(round(col_des['max'] * 100, 2)) + '%')
-
-    #########################
-    # Low variance features
-    #########################
-    if verbose:
-        color_print('Low variance features')
-    low_var_columns = \
-        low_variance_features(df, var_list=num_columns, threshold=0, rescale=True, verbose=verbose).index.tolist()
-
-    # store into DataFrame
-    d_features = {'numerical': num_columns,
-                  'date': date_columns,
-                  'categorical': cat_columns,
-                  'NA': NA_columns,
-                  'low_variance': low_var_columns}
-
-    return d_features

-
-
-"""
--------------------------------------------------------------------------------------------------------------------------
-"""
-
-
-
[docs]def is_date(df, col):
-    """Test if a variable is as date.
-
-    Method : try to apply to_datetime
-
-    Parameters
-    ----------
-    df : DataFrame
-        input dataset
-    col : string
-        variable name
-
-    Returns
-    -------
-    res : boolean
-        test result
-    """
-    # if col is datetime type, res = True
-    if df[col].dtype == 'datetime64[ns]':
-        return True
-
-    # if col is object type, try apply to_datetime
-    elif df[col].dtype == 'object':
-        try:
-            df_smpl = df.sample(100).copy()
-            pd.to_datetime(df_smpl[col])
-            return True
-        except ValueError:
-            return False
-        except OverflowError:
-            return False

-
-
-"""
--------------------------------------------------------------------------------------------------------------------------
-"""
-
-
-
[docs]def get_all_dates(df):
-    """Identify dates variables.
-
-    Method : try to apply to_datetime
-    
-    Parameters
-    ----------
-    df : DataFrame
-        input DataFrame
-
-    Returns
-    -------
-    list
-        features identified as date
-    """
-    date_list = list()
-
-    for col in df.columns:
-        # if col is recognized as date
-        if is_date(df, col): date_list.append(col)
-
-    return date_list

-
-
-"""
--------------------------------------------------------------------------------------------------------------------------
-"""
-
-
-
[docs]def low_variance_features(df, var_list=None, threshold=0, rescale=True, verbose=1):
-    """Identify  features with low variance (< threshold).
-    Possible to rescale feature before computing.
-
-    Parameters
-    ----------
-     df : DataFrame
-        input DataFrame
-     var_list : list (default : None)
-        names of the variables to test variance
-     threshold : float (default : 0)
-        variance threshold
-     rescale : bool (default : true)
-        enable  MinMaxScaler before computing variance
-
-    Returns
-    -------
-    list
-       Names of the variables with low variance
-    """
-    # if var_list = None, get all numerical features
-    # else, exclude features from var_list whose type is not numerical
-    var_list = get_type_features(df, 'num', var_list)
-
-    df_bis = df.copy()
-
-    if rescale:
-        scler = MinMaxScaler()
-        df_bis[var_list] = scler.fit_transform(df_bis[var_list].astype('float64'))
-
-    selected_var = df_bis[var_list].var().loc[df_bis.var() <= threshold]
-
-    if verbose > 0:
-        # print('features : ',list(var_list))
-        if rescale: print('  **MinMaxScaler [0,1]')
-        print('  ', str(len(selected_var)) + ' feature(s) with  variance <= threshold (' + str(threshold) + ')')
-
-    return selected_var.sort_values(ascending=True)

-
- -
- -
-
- - -
- -
-

- © Copyright 2020, Maxence LABESSE - -

-
- Built with Sphinx using a theme provided by Read the Docs. - -
- -
-
- -
- -
- - - - - - - - - - - - \ No newline at end of file diff --git a/docs/_build.html/_modules/AutoMxL/Explore/Get_Infos.html b/docs/_build.html/_modules/AutoMxL/Explore/Get_Infos.html deleted file mode 100644 index 4495d65..0000000 --- a/docs/_build.html/_modules/AutoMxL/Explore/Get_Infos.html +++ /dev/null @@ -1,397 +0,0 @@ - - - - - - - - - - - MLBG59.Explore.Get_Infos — MLBG59 1.0.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
- - - -
- - - - - -
- -
- - - - - - - - - - - - - - - - - -
- -
    - -
  • Docs »
    • - -
  • Module code »
    • - -
  • MLBG59.Explore.Get_Infos
    • - - -
  • - -
    • - -
- - -
-
-
-
- -

Source code for MLBG59.Explore.Get_Infos

-""" Dataset Features analysis :
-
- - recap : get and store informations related to the dataset (NA, features type, low variance features, ...)
- - is_date : Test if a variable can be considered as date
- - get_all_dates : identify all dates features
- - low variance features : identify all features with low variance (<threshold)
-"""
-import pandas as pd
-from sklearn.preprocessing import MinMaxScaler
-from MLBG59.Utils.Display import *
-from MLBG59.Utils.Utils import get_type_features
-
-
-
[docs]def recap(df, verbose=False):
-    """get and store global informations about the dataset :
-
-    - Variables type (num, cat, date)
-    - NA values
-    - low variance variables
-        
-    Parameters
-    ----------
-    df : DataFrame
-        input dataset
-    target : string (Default : None)
-        target name
-    verbose : boolean (Default False)
-        Get logging information
-            
-    Returns
-    -------
-    dict
-        {x : list of variables names}
-
-        - x = numerical : numerical features
-        - x = categorical : categorical features
-        - x = date : date features
-        - x = NA : features which contains NA values
-        - x = low_variance : list of the features with low variance
-    """
-    # dataset dimensions
-    if verbose:
-        color_print("Dimensions : ")
-        print("  > row number : ", df.shape[0], "\n  > col number : ", df.shape[1])
-
-    #################
-    # features type #
-    #################
-    # numerical
-    num_columns = df._get_numeric_data().columns.tolist()
-    # date
-    date_columns = get_all_dates(df)
-    # categorical
-    cat_columns = [x for x in df.columns if (x not in num_columns) and (x not in date_columns)]
-
-    if verbose:
-        color_print("Features type identification : ")
-        print("  > cat : " + str(len(cat_columns)) + ' (' + str(round(len(cat_columns) / df.shape[1] * 100)) + '%)',
-              '\n  > num : ' + str(len(num_columns)) + ' (' + str(round(len(num_columns) / df.shape[1] * 100)) + '%)',
-              '\n  > dates: ' + str(len(date_columns)) + ' (' + str(
-                  round(len(date_columns) / df.shape[1] * 100)) + ' %)')
-
-    ######################
-    # NA values analysis
-    ######################
-    df_col = pd.DataFrame(df.columns.values, columns=['variables'])
-    df_col['Nbr NA'] = df.isna().sum().tolist()
-    df_col['Taux NA'] = df_col['Nbr NA'] / df.shape[0]
-    # features containing NA values
-    NA_columns = df_col.loc[df_col['Nbr NA'] > 0].sort_values('Nbr NA', ascending=False).variables.tolist()
-    col_des = df_col['Taux NA'].describe()
-
-    if verbose:
-        color_print(str(len(NA_columns)) + " features containing NA")
-        print('  > Taux NA moyen : ' + str(round(col_des['mean'] * 100, 2)) + '%',
-              '\n  >           min : ' + str(round(col_des['min'] * 100, 2)) + '%',
-              '\n  >           max : ' + str(round(col_des['max'] * 100, 2)) + '%')
-
-    #########################
-    # Low variance features
-    #########################
-    if verbose:
-        color_print('Low variance features')
-    low_var_columns = \
-        low_variance_features(df, var_list=num_columns, threshold=0, rescale=True, verbose=verbose).index.tolist()
-
-    # store into DataFrame
-    d_features = {'numerical': num_columns,
-                  'date': date_columns,
-                  'categorical': cat_columns,
-                  'NA': NA_columns,
-                  'low_variance': low_var_columns}
-
-    return d_features

-
-
-"""
--------------------------------------------------------------------------------------------------------------------------
-"""
-
-
-
[docs]def is_date(df, col):
-    """Test if a DataFrame feature can be considered as a date (using to_datetime)
-
-    Parameters
-    ----------
-     df : DataFrame
-        input dataset
-     col : string
-        feature name
-
-    Returns
-    -------
-    res : boolean
-        Test result
-    """
-    # if col is datetime type, res = True
-    if df[col].dtype == 'datetime64[ns]':
-        return True
-
-    # if col is object type, try apply to_datetime
-    elif df[col].dtype == 'object':
-        try:
-            df_smpl = df.sample(100).copy()
-            pd.to_datetime(df_smpl[col])
-            return True
-        except ValueError:
-            return False
-        except OverflowError:
-            return False

-
-
-"""
--------------------------------------------------------------------------------------------------------------------------
-"""
-
-
-
[docs]def get_all_dates(df):
-    """Identify all date features of a DataFrame
-    
-    Parameters
-    ----------
-     df : DataFrame
-        input DataFrame
-
-    Returns
-    -------
-     list
-        list of features identified as date
-    """
-    date_list = list()
-
-    for col in df.columns:
-        # if col is recognized as date
-        if is_date(df, col): date_list.append(col)
-
-    return date_list

-
-
-"""
--------------------------------------------------------------------------------------------------------------------------
-"""
-
-
-
[docs]def low_variance_features(df, var_list=None, threshold=0, rescale=True, verbose=1):
-    """Identify  features with low variance (<= threshold).
-    Possible to rescale feature before computing.
-
-    Parameters
-    ----------
-     df : DataFrame
-        input DataFrame
-     var_list : list (default : None)
-        names of the variables to test variance
-     threshold : float (default : 0)
-        variance threshold
-     rescale : bool (default : true)
-        enable  MinMaxScaler before computing variance
-
-    Returns
-    -------
-    list
-       Names of the variables with low variance
-    """
-    # if var_list = None, get all numerical features
-    # else, exclude features from var_list whose type is not numerical
-    var_list = get_type_features(df, 'num', var_list)
-
-    df_bis = df.copy()
-
-    if rescale:
-        scler = MinMaxScaler()
-        df_bis[var_list] = scler.fit_transform(df_bis[var_list].astype('float64'))
-
-    selected_var = df_bis[var_list].var().loc[df_bis.var() <= threshold]
-
-    if verbose > 0:
-        # print('features : ',list(var_list))
-        if rescale: print('  **MinMaxScaler [0,1]')
-        print('  ', str(len(selected_var)) + ' feature(s) with  variance <= threshold (' + str(threshold) + ')')
-
-    return selected_var.sort_values(ascending=True)

-
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-

- © Copyright 2020, Maxence LABESSE - -

-
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- - - - - - - - - - - - \ No newline at end of file diff --git a/docs/_build.html/_modules/AutoMxL/Explore/Get_Outliers.html b/docs/_build.html/_modules/AutoMxL/Explore/Get_Outliers.html deleted file mode 100644 index 64fd686..0000000 --- a/docs/_build.html/_modules/AutoMxL/Explore/Get_Outliers.html +++ /dev/null @@ -1,318 +0,0 @@ - - - - - - - - - - - MLBG59.Explore.Get_Outliers — MLBG59 1.0.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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Source code for MLBG59.Explore.Get_Outliers

-""" Outliers detection functions :
-
- - get_cat_outliers : identify categorical features containing outliers
- - get_num_outliers : identify numerical features containing outliers
-"""
-import pandas as pd
-import numpy as np
-from MLBG59.Utils.Display import *
-from MLBG59.Utils.Utils import get_type_features
-
-
-
[docs]def get_cat_outliers(df, var_list=None, threshold=0.05, verbose=False):
-    """Outliers detection for selected/all categorical features.
-
-    Method : Modalities with frequency <x% (Default 5%)
-
-    Parameters
-    ----------
-     df : DataFrame
-        Input dataset
-     var_list : list (Default : None)
-        Names of the features
-        If None, all the categorical features
-     threshold : float (Default : 0.05)
-        Minimum modality frequency
-     verbose : boolean (Default False)
-        Get logging information
-
-    Returns
-    -------
-    dict
-        {variable : list of categories considered as outliers}
-    """
-    # if var_list = None, get all categorical features
-    # else, exclude features from var_list whose type is not categorical
-    var_list = get_type_features(df, 'cat', var_list)
-
-    df_local = df[var_list].copy()
-
-    if verbose:
-        color_print('cat features outliers identification (frequency<' + str(threshold) + ')')
-        print('  > features : ', var_list, )
-
-    # initialize output dict
-    outlier_dict = {}
-
-    # value count (frequency as number and percent for each modality) for features in var_list
-    for col in df_local.columns:
-        # percent
-        freq_perc = pd.value_counts(df[col], dropna=False) / len(df[col])
-
-        # if feature contain modalities with frequency < trehshold, store in outlier_dict
-        if len(freq_perc.loc[freq_perc < threshold]) > 0:
-            outlier_dict[col] = freq_perc.loc[freq_perc < threshold].index.tolist()
-
-    if verbose:
-        print("  > containing outliers", list(outlier_dict.keys()))
-
-    return outlier_dict

-
-
-"""
--------------------------------------------------------------------------------------------------------------------------
-"""
-
-
-
[docs]def get_num_outliers(df, var_list=None, xstd=3, verbose=False):
-    """Outliers detection for selected/all numerical features.
-
-    Method : x outlier <=> abs(x - mean) > xstd * var
-    
-    Parameters
-    ----------
-     df : DataFrame
-        Input dataset
-     var_list : list (Default : None)
-        Names of the features
-        If None, all the num features
-     xstd : int (Default : 3)
-        Variance gap coef
-     verbose : boolean (Default False)
-        Get logging information
-            
-    Returns
-    -------
-    dict
-        {variable : [lower_limit, upper_limit]}
-    """
-    # if var_list = None, get all num features
-    # else, exclude features from var_list whose type is not num
-    var_list = get_type_features(df, 'num', var_list)
-
-    df_bis = df[var_list].copy()
-
-    if verbose:
-        color_print('num features outliers identification ( x: |x - mean| > ' + str(xstd) + ' * var)')
-        print('  > features : ', var_list, )
-
-    # initialize output dict
-    outlier_dict = {}
-
-    # compute features upper and lower limit (abs(x - mean) > xstd * var (x=3 by default))
-    data_std = np.std(df_bis)
-    data_mean = np.mean(df_bis)
-    anomaly_cut_off = data_std * xstd
-    lower_limit = data_mean - anomaly_cut_off
-    upper_limit = data_mean + anomaly_cut_off
-
-    df_outliers = pd.DataFrame()
-
-    # mask (1 if outlier, else 0)
-    for col in df_bis.columns:
-        df_outliers[col] = np.where((df_bis[col] < lower_limit[col]) | (df_bis[col] > upper_limit[col]), 1, 0)
-
-    # for features containing outliers
-    for col in df_outliers.sum().loc[df_outliers.sum() > 0].index.tolist():
-        # store features and outliers index in outlier_dict
-        outlier_dict[col] = [lower_limit[col], upper_limit[col]]
-
-    if verbose:
-        print("  > containing outliers", list(outlier_dict.keys()))
-
-    return outlier_dict

-
- -
- -
-
- - -
- -
-

- © Copyright 2020, Maxence LABESSE - -

-
- Built with Sphinx using a theme provided by Read the Docs. - -
- -
-
- -
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- - - - - - - - - - - - \ No newline at end of file diff --git a/docs/_build.html/_modules/AutoMxL/Load/Load.html b/docs/_build.html/_modules/AutoMxL/Load/Load.html deleted file mode 100644 index ce44af3..0000000 --- a/docs/_build.html/_modules/AutoMxL/Load/Load.html +++ /dev/null @@ -1,326 +0,0 @@ - - - - - - - - - - - MLBG59.Load.Load — MLBG59 1.0.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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Source code for MLBG59.Load.Load

-""" Data importation :
-
- - get_delimiter : identify csv file delimiter
- - load data
-"""
-import pandas as pd
-
-
-
[docs]def get_delimiter(csvfile):
-    """Identify the delimiter of a .csv file
-
-    Parameters
-    ----------
-    csvfile : string
-        Path and name of the file (Ex : "data/file.csv")
-
-    Returns
-    -------
-    string
-        Identified delimiter
-
-    """
-    # csv file reading
-    with open(csvfile, 'r') as myCsvfile:
-        # Reads one entire line from the file
-        header = myCsvfile.readline()
-
-        # Returns the lowest index of the substring if it is found in given string. (-1 = not found)
-        if header.find(";") != -1:
-            delimiter = ";"
-        elif header.find(",") != -1:
-            delimiter = ","
-
-    return delimiter

-
-
-"""
--------------------------------------------------------------------------------------------------------------------------
-"""
-
-
-
[docs]def load_data(file, index_col=None, verbose=1):
-    """Import dataset as a DataFrame
-    accept .csv, .xlsx, .xls files
-
-    Parameters
-    ----------
-    file : string
-        Path and name of the file (Ex : "data/file.csv")
-        If file is .csv, automatically identify delimiter
-    index_col : int, str, sequence of int / str, or False, default None
-        Column(s) to use as the row labels of the DataFrame, either given as string name or column index.
-        If a sequence of int / str is given, a MultiIndex is used.
-    verbose : int (0/1) (Default : 1)
-        Get more operations information
-
-    Returns
-    -------
-    DataFrame :
-        dataset imported as DataFrame
-
-    """
-    # CSV
-    if file.endswith('.csv') or file.endswith('.txt'):
-        # Find file delimiter
-        file_sep = get_delimiter(file)
-        # import 
-        df = pd.read_csv(file, encoding="iso-8859-1", sep=file_sep, index_col=index_col)
-
-    # Excel
-    elif (file.endswith('.xlsx')) or (file.endswith('.xsl')):
-        df = pd.read_excel(file)
-
-    # JSON
-    elif file.endswith('.json'):
-        # to-do
-        pass
-
-    else:
-        df = None
-
-    if verbose == 1:
-        if df is not None:
-            print('-> File ' + file + ' successfully imported as DataFrame')
-            print('-> DataFrame size  : ', df.shape)
-        else:
-            print("File couldn't be imported")
-
-    return df

-
-
-"""
--------------------------------------------------------------------------------------------------------------
-"""
-
-
-
[docs]def parse_target(df, target, modalite):
-    """Transform target to boolean (1/0), choosing the reference modality
-
-    Parameters
-    ----------
-    df : dataframe
-        input dataset
-    target : string
-        target feature
-    modalite : string
-         modality name
-
-    Returns
-    -------
-    dataframe
-        modified dataframe
-    string
-        new target name
-    """
-    # Si la variable cible est numérique, transformation en string (nécessaire pour dichotomiser)
-    if target in df._get_numeric_data().columns:
-        df[target] = df[target].apply(str)
-
-    # Dichotomisation
-    target_dummies = pd.get_dummies(df[target])
-    # Choix de la nouvelle variable cible et renommage
-    target_dummies[target + '_' + modalite] = target_dummies[modalite]
-
-    # Intégration de la nouvelle variable cible dans le dataset
-    df_bis = pd.concat((df, target_dummies[target + '_' + modalite]), axis=1)
-
-    # suppresion de l'ancienne variable cible
-    del df_bis[target]
-
-    return df_bis, target + '_' + modalite

-
- -
- -
-
- - -
- -
-

- © Copyright 2020, Maxence LABESSE - -

-
- Built with Sphinx using a theme provided by Read the Docs. - -
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- - - - - - - - - - - - \ No newline at end of file diff --git a/docs/_build.html/_modules/AutoMxL/Modelisation/Bagging.html b/docs/_build.html/_modules/AutoMxL/Modelisation/Bagging.html deleted file mode 100644 index d0a82ed..0000000 --- a/docs/_build.html/_modules/AutoMxL/Modelisation/Bagging.html +++ /dev/null @@ -1,426 +0,0 @@ - - - - - - - - - - - MLBG59.Modelisation.Bagging — MLBG59 1.0.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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- -

Source code for MLBG59.Modelisation.Bagging

-""" Bagging algorithm class. Methods :
-
-- Bagging (class) : generate new training more balanced and train model for each
-- Bagging_sample (func) : generate bagging sample
-
-"""
-from sklearn.ensemble import RandomForestClassifier
-from MLBG59.Modelisation.Utils import *
-import pandas as pd
-
-"""
-Default bagging parameters
-"""
-default_bagging_param = {'n_sample': 5,
-                         'pos_sample_size': 1.0,
-                         'replace': False}
-
-
-
[docs]class Bagging(object):
-    """Meta-algo designed to improve the stability and accuracy of ML classif/regression algos
-    or to face an "imbalanced target distribution" issue.
-    
-    Bagging generates m new training sets more balanced. Then, a model is fitted on each
-    sample and outputs are combined by averaging (for regression) or voting (for classification).
-
-    Available classifiers : Random Forest and XGBOOST
-    
-    Parameters
-    ----------
-    clf : Model fitted on samples (Default  : RandomForestClassifier(n_estimators=100, max_leaf_nodes=100)
-        Model fitted on the samples
-    n_sample : int (Default : 5)
-        number a samples
-    pos_sample_size : int/float (Default : 1.0)
-        Number/rate of target=1 observations in each sample (filled with 3 times more target=0 )
-
-        - if int : number of target=1
-        - if float : rate of total target=1
-
-    replace : Boolean (Default : False)
-        Enable sampling with replacement
-
-    list_model : list (Default : None)
-        Fitted models (created with fit method)
-    """
-
-    def __init__(self,
-                 clf=RandomForestClassifier(n_estimators=100, max_leaf_nodes=100),
-                 n_sample=5,
-                 pos_sample_size=1.0,
-                 replace=True):
-
-        self.classifier = clf
-        self.niter = n_sample
-        self.pos_sample_size = pos_sample_size
-        self.replace = replace
-        self.list_model = list()
-        self.is_fitted = False
-
-    """
-    -------------------------------------------------------------------------------------------------------------
-    """
-
-
[docs]    def get_params(self):
-        """Get bagging object parameters
-
-        Returns
-        -------
-        dict
-            {param : value}
-        """
-        return {'classifier': self.classifier,
-                'niter': self.niter,
-                'pos_sample_size': self.pos_sample_size,
-                'replace': self.replace,
-                'list_model': self.list_model}

-
-    """
-    -------------------------------------------------------------------------------------------------------------
-    """
-
-
[docs]    def fit(self, df_train, target):
-        """Create bagging samples from a DataFrame and fit the model (self.clf) on each sample
-        
-        Parameters
-        ----------
-        df_train : DataFrame
-            Training dataset
-        target : String
-            Target name
-
-        Returns
-        -------
-         self.list_model : list
-            Fitted models
-        """
-        # list_model init
-        self.list_model = [None] * self.niter
-
-        # get number of target=1 in bagging samples
-        if isinstance(self.pos_sample_size, int):
-            N = self.pos_sample_size
-        else:
-            N = int(self.pos_sample_size * df_train.loc[df_train[target] == 1].shape[0])
-
-        for i in range(self.niter):
-            # Sample creation
-            df_train_bag = create_sample(df_train, target, N, replace=self.replace)
-
-            # X_train / y_train
-            X_train_bag = df_train_bag.copy()
-            y_train_bag = X_train_bag[target]
-            del X_train_bag[target]
-
-            # Create and store model
-            self.list_model[i] = self.classifier
-
-            # fit model for each sample
-            self.list_model[i].fit(X_train_bag, y_train_bag)
-
-            self.is_fitted = True
-
-        return self

-
-    """
-    -------------------------------------------------------------------------------------------------------------
-    """
-
-
[docs]    def predict(self, df):
-        """Apply models fitted on sample to a  dataset.
-        Combine models by averaging the outputs (for regression) or voting (for classification)
-
-        Parameters
-        ----------
-        df : DataFrame
-            Dataset to apply the model
-
-        Returns
-        -------
-        numpy.ndarray (float)
-            Averaged classification probabilities
-        numpy.ndarray (int)
-            Predictions for each observation
-        """
-        assert self.is_fitted, "Fit first !"
-        # Init probs storage matrix
-        mat_prob = np.zeros((self.niter, df.shape[0]))
-
-        # for each fitted models
-        for j in range(self.niter):
-            # apply the model on test set
-            y_prob_rf = self.list_model[j].predict_proba(df)
-            # probabilities storage in matrix
-            mat_prob[j] = y_prob_rf[:, 1]
-
-        # probas averaging
-        list_prob_pred = mat_prob.sum(axis=0) / self.niter
-        # voting
-        list_pred = [round(elem, 0) for elem in list_prob_pred]
-
-        return list_prob_pred, list_pred

-
-    """
-    -------------------------------------------------------------------------------------------------------------
-    """
-
-
[docs]    def bag_feature_importance(self, X):
-        """Get features importance of the model by averaging importance of models fitted on the samples
-        
-        Parameters
-        ----------
-        X : DataFrame
-            Input Dataset
-            
-        Returns
-        -------
-        dict
-            {feature : importance}
-            
-        """
-        # Init importance storage matrix
-        mat_feat_imp = np.zeros((self.niter, len(X.columns)))
-
-        #  for each fitted models
-        for i in range(self.niter):
-            # importances storage in matrix
-            mat_feat_imp[i] = self.list_model[i].feature_importances_
-
-        # Averaging importances
-        list_feat_imp_moy = mat_feat_imp.sum(axis=0) / self.niter
-
-        features_dict = dict(zip(X.columns, list_feat_imp_moy))
-
-        return features_dict

-
-
-"""
--------------------------------------------------------------------------------------------------------------
-"""
-
-
-
[docs]def create_sample(df, target, pos_target_nb, replace=False):
-    """Generate a DataFrame sample with selected number of target=1
-        
-    Parameters
-    ----------
-    df : DataFrame
-        Input dataset
-    target : String
-        Target name
-    pos_target_nb : int
-        Number of target=1 observations in the sample
-    replace : Boolean (défaut : False)
-        If True, create samples with replacement
-            
-    Returns
-    -------
-    DataFrame
-        sample dataset
-    """
-    # split target = 1 / 0
-    df_pos = df.loc[(df[target] == 1)]
-    df_neg = df.loc[(df[target] == 0)]
-
-    n_size = min(3 * pos_target_nb, df_neg.shape[0])
-
-    # sample creation
-    df_bag = pd.concat(
-        (df_pos.sample(n=pos_target_nb, replace=replace), df_neg.sample(n=n_size, replace=replace)), axis=0)
-
-    return df_bag

-
- -
- -
-
- - -
- -
-

- © Copyright 2020, Maxence LABESSE - -

-
- Built with Sphinx using a theme provided by Read the Docs. - -
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- - - - - - - - - - - - \ No newline at end of file diff --git a/docs/_build.html/_modules/AutoMxL/Modelisation/Classifiers.html b/docs/_build.html/_modules/AutoMxL/Modelisation/Classifiers.html deleted file mode 100644 index 6e7cd7b..0000000 --- a/docs/_build.html/_modules/AutoMxL/Modelisation/Classifiers.html +++ /dev/null @@ -1,233 +0,0 @@ - - - - - - - - - - - MLBG59.Modelisation.Classifiers — MLBG59 1.0.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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Source code for MLBG59.Modelisation.Classifiers

-""" Classifiers utils functions :
-
-- features_importance_select : select top features according to model importance
-"""
-
-
[docs]def features_importance_select(eval_dict, treshold):
-    """Get most important features according to a threshold
-
-    Parameters
-    ----------
-    eval_dict : dict
-        Model evaluation dict
-    threshold : int/float
-
-        - if int : number of top important features to get
-        - if float : cumulative importance rate of top features
-
-    Returns
-    -------
-     list
-        most important features
-    """
-    rf_top_feat = eval_dict['feature_importances']
-    rf_top_feat['Features'] = rf_top_feat.index
-    rf_top_feat = rf_top_feat.reset_index(drop=True)
-    rf_top_feat = rf_top_feat.sort_values('importance', ascending=False)
-
-    if isinstance(treshold, int):
-        n_feat_list = rf_top_feat['Features'].tolist()[0:treshold]
-
-    elif isinstance(treshold, float):
-        rf_top_feat['cum_importance'] = rf_top_feat['importance'].cumsum()
-        val_ref = rf_top_feat['cum_importance'].loc[rf_top_feat['cum_importance'] >= treshold].min()
-        n_feat_list = rf_top_feat['Features'].loc[rf_top_feat['cum_importance'] <= val_ref].tolist()
-    else:
-        print("il faut que le seuil soit un entier ou un décimal !")
-
-    return n_feat_list

-
- -
- -
-
- - -
- -
-

- © Copyright 2020, Maxence LABESSE - -

-
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- - - - - - - - - - - - \ No newline at end of file diff --git a/docs/_build.html/_modules/AutoMxL/Modelisation/HyperOpt.html b/docs/_build.html/_modules/AutoMxL/Modelisation/HyperOpt.html deleted file mode 100644 index e395778..0000000 --- a/docs/_build.html/_modules/AutoMxL/Modelisation/HyperOpt.html +++ /dev/null @@ -1,557 +0,0 @@ - - - - - - - - - - - MLBG59.Modelisation.HyperOpt — MLBG59 1.0.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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Source code for MLBG59.Modelisation.HyperOpt

-""" Hyperopt class :
-Model hyper-optimisation with random search
-
-- Hyperopt (class) : Model hyper-optimisation with random search
-
-"""
-import xgboost
-import random
-import itertools as it
-# import datetime
-from MLBG59.Modelisation.Bagging import *
-from MLBG59.Modelisation.Utils import *
-from MLBG59.Utils.Display import color_print
-from datetime import datetime
-from MLBG59.param_config import default_bagging_param, default_RF_grid_param, default_XGB_grid_param
-
-
-
[docs]class HyperOpt(object):
-    """Model hyper-optimisation with random search :
-
-    - From a hyper-parameters grid, creates random HPs combinations
-    - train a model for each combination
-    - apply the model
-    
-    Parameters
-    ----------
-    classifier : string (Default : 'RF')
-        classifier for modelisation
-    grid_param : dict (Default : Default_RF_grid_param)
-        HP grid
-    n_param_comb : int (Default : 10)
-        number of HP combinations
-    bagging : Boolean (Default = False)
-        use bagging method
-    bagging_param : n-uple
-        bagging parameters (Default : default_bagging_param (Bagging module))
-    train_model_dict (created with fit method) : dict
-        {model_index : {'HP', 'probas', 'model', 'features_importance', 'train_metrics'}
-    bagging_object : Bagging
-        bagging object
-    comb_seed : int
-        seed for randomized HP combinations
-    """
-
-    def __init__(self,
-                 classifier='RF',
-                 grid_param=None,
-                 n_param_comb=10,
-                 bagging=False,
-                 bagging_param=default_bagging_param,
-                 comb_seed=None):
-
-        # parameters
-        if grid_param is None:
-            if classifier == 'RF':
-                self.grid_param = default_RF_grid_param
-            elif classifier == 'XGBOOST':
-                self.grid_param = default_XGB_grid_param
-        else:
-            self.grid_param = grid_param
-        self.classifier = classifier
-        self.n_param_comb = n_param_comb
-        self.bagging = bagging
-        self.bagging_param = bagging_param
-        self.comb_seed = comb_seed
-        # attributes
-        self.d_train_model = {}
-        self.d_bagging = {}
-        self.is_fitted = False
-
-    """
-    -------------------------------------------------------------------------------------------------------------
-    """
-
-
[docs]    def get_params(self):
-        """Return Hyperopt object parameters
-
-        Returns
-        -------
-        dict
-            {param : value}
-        """
-        return {'classifier': self.classifier,
-                'grid_param': self.grid_param,
-                'n_param_comb': self.n_param_comb,
-                'top_bagging': self.bagging,
-                'bagging_param': self.bagging_param,
-                'comb_seed': self.comb_seed}

-
-    """
-    -------------------------------------------------------------------------------------------------------------
-    """
-
-
[docs]    def fit(self, df_train, target, verbose=False):
-        """Fit a model for each HP combination
-        
-        Parameters
-        ----------
-        df_train : DataFrame
-            Training dataset
-        target : string
-            Target name
-        verbose : boolean (Default False)
-            Get logging information
-            
-        Returns
-        -------
-        self.train_model_dict (created with fit method) : dict
-            {model_index : {'HP', 'probas', 'model', 'features_importance', 'train_metrics'}
-        """
-        # X / y
-        y_train = df_train[target]
-        X_train = df_train.drop(target, axis=1)
-
-        # Sort HPs grid dict by param name (a->z)
-        grid_names = sorted(self.grid_param)
-        # random sampling : 'n_param_comb' HPS combinations
-        # list(it.product(*(self.grid_param[Name] for Name in grid_names))) create all the possible combinations
-        if self.comb_seed is not None:
-            random.seed(self.comb_seed)
-
-        sample_combinations = random.sample(list(it.product(*(self.grid_param[Name] for Name in grid_names))),
-                                            k=self.n_param_comb)
-
-        if verbose :
-            print('\033[34m' + 'Random search:', self.n_param_comb, 'HP combs', '\033[0m')
-            print('\033[34m' + 'Model : ', self.classifier, '\033[0m')
-
-        # for each HP combination :
-        for model_idx in range(len(sample_combinations)):
-            t_ini_model = datetime.now()
-
-            # Model params in dict
-            HP_dict = dict(zip(grid_names, sample_combinations[model_idx]))
-
-            # instantiate model
-            if self.classifier == 'RF':  # Classifier Random Forest
-                clf = RandomForestClassifier(**HP_dict)
-            # elif self.classifier == 'XGBOOST':
-            else:
-                clf = xgboost.XGBClassifier(**HP_dict)
-
-            # disabling bagging
-            if not self.bagging:
-
-                # model training
-                clf_fit = clf.fit(X_train, y_train)
-                # features importance
-                features_dict = dict(zip(X_train.columns, clf.feature_importances_))
-                # outputs
-                y_proba = clf_fit.predict_proba(X_train)[:, 1]
-                y_pred = clf_fit.predict(X_train)
-
-            # enabling bagging
-            else:
-                # init bagging object with default params
-                bag = Bagging(clf, **self.bagging_param)
-                # model training
-                bag.fit(df_train, target)
-                clf_fit = bag.list_model
-                # features importance
-                features_dict = bag.bag_feature_importance(X_train)
-                # classification probas
-                y_proba, y_pred = bag.predict(df_train.drop(target, axis=1))
-
-                self.d_bagging[model_idx] = bag
-
-            # Model evaluation
-            eval_dict = classifier_evaluate(y_train, y_pred, y_proba, verbose=0)
-
-            # store
-            train_model = {'HP': HP_dict,
-                           'model': clf_fit,
-                           'features_importance': features_dict,
-                           'train_output': {'y_proba': y_proba, 'y_pred': y_pred},
-                           'train_metrics': eval_dict}
-
-            # store model results for each combination
-            self.d_train_model[model_idx] = train_model
-
-            # Fitted !
-            self.is_fitted = True
-
-            if verbose:
-                t_fin_model = datetime.now()
-                print(str(model_idx + 1) + '/' + str(len(sample_combinations)) +
-                      ' >> {} Sec.'.format((t_fin_model - t_ini_model).total_seconds()))
-
-        return self

-
-    """
-    -------------------------------------------------------------------------------------------------------------
-    """
-
-
[docs]    def predict(self, df, target, delta_auc, verbose=False):
-        """Apply the models
-        
-        Parameters
-        ----------
-        df : DataFrame
-            Dataset to apply the models
-        target : string
-            Target name
-        delta_auc_th : float
-            Threshold for valid models : abs(auc(train) - auc(test))
-        verbose : boolean (Default False)
-            Get logging information
-
-        Returns
-        -------
-        dict
-            {model_index : {'HP', 'probas', 'model', 'features_importance', 'train_metrics', 'metrics', 'output'}
-        """
-        assert self.is_fitted, 'fit first'
-
-        d_apply_model = self.d_train_model
-
-        # X / y
-        y = df[target]
-        X = df.drop(target, axis=1)
-
-        # For each HPs combination
-        for key, value in self.d_train_model.items():
-            t_ini_model = datetime.now()
-
-            modl = value['model']
-
-            # Without bagging
-            if not self.bagging:
-
-                # classification probas
-                y_proba = modl.predict_proba(X)[:, 1]
-
-                # classification votes
-                y_pred = modl.predict(X)
-
-            # With bagging
-            elif self.bagging:
-
-                # classification probs and votes
-                y_proba, y_pred = self.d_bagging[key].predict(X)
-
-            # store outputs
-            d_output = {'y_proba': y_proba,
-                        'y_pred': y_pred}
-
-            # compute model metrics
-            eval_dict = classifier_evaluate(y, y_pred, y_proba, verbose=0)
-            eval_dict['delta_auc'] = abs(self.d_train_model[key]['train_metrics']['Roc_auc'] - eval_dict["Roc_auc"])
-
-            # store 
-            d_apply_model[key]['outputs'] = d_output
-            d_apply_model[key]['metrics'] = eval_dict
-
-            # print metrics
-            if verbose:
-                print(value['HP'])
-                if eval_dict['delta_auc'] <= delta_auc:
-                    c_code = 32
-                else:
-                    c_code = 31
-
-                color_print(
-                    ' > AUC test: ' + str(round(eval_dict["Roc_auc"], 3)) + ' train: ' + str(
-                        round(self.d_train_model[key]['train_metrics']['Roc_auc'], 3)) +
-                    ' / F1: ' + str(round(eval_dict['F1'], 3)) +
-                    ' / prec: ' + str(round(eval_dict['Precision'], 3)) +
-                    ' / recall: ' + str(round(eval_dict['Recall'], 3)), color_code=c_code)
-
-                t_fin_model = datetime.now()
-                print('{} Sec.'.format((t_fin_model - t_ini_model).total_seconds()))
-
-        return d_apply_model

-
-    """
-    -------------------------------------------------------------------------------------------------------------
-    """
-
-
[docs]    def get_best_model(self, d_model_info, metric='F1', delta_auc_th=0.03, verbose=False):
-        """Identify valid models according to delta auc (test/train).
-        Get the best model in respect of a selected metric among valid model
-
-        Parameters
-        ----------
-        d_model_info : dict
-            {model_index : {'HP', 'probas', 'model', 'features_importance', 'train_metrics', 'metrics', 'output'}
-        metric : string (default = F1-score)
-            Metric used to get the best model
-        delta_auc_th : float
-            Threshold for valid models : abs(auc(train) - auc(test))
-        verbose : boolean (Default False)
-            Get logging information
-
-        Returns
-        -------
-        int
-            Best model index
-        list
-            Valid model indexes
-        """
-        # select valid models (abs(auc_train - auc_test)<0.03)
-        valid_model = {}
-        for key, param in d_model_info.items():
-            if param['metrics']['delta_auc'] <= delta_auc_th:
-                valid_model[key] = param
-
-        # Best model according to selected metric
-        if len(valid_model.keys()) > 0:
-            best_model_idx = max(valid_model, key=lambda x: valid_model[x].get('metrics').get(metric))
-            if verbose:
-                print(' >', len(valid_model.keys()), ' valid models |auc(train)-auc(test)|<=' + str(delta_auc_th))
-                print(' > best model : ' + str(best_model_idx))
-        else:
-            best_model_idx = None
-            print('0 valid model')
-
-        return best_model_idx, list(valid_model.keys())

-
-    """
-    ---------------------------------------------------------------------------------------------------------------
-    """
-
-
[docs]    def model_res_to_df(self, d_model_infos, sort_metric='F1'):
-        """Store models summary in DataFrame
-
-        Parameters
-        ----------
-        d_model_info : dict
-            {model_index : {'HP', 'probas', 'model', 'features_importance', 'train_metrics', 'metrics', 'output'}
-        sort_metric : string (default = 'F1')
-            metric to sort models (descendant)
-
-        Returns
-        -------
-        DataFrame
-            model infos and metrics
-        """
-        # dataFrame columns names
-        model_col = ['model_index']
-        HP_col = list(self.d_train_model[0]['HP'].keys())
-        bagging_col = ['bagging']
-        metrics_col = ['Accuracy', 'Roc_auc', 'F1', 'Logloss', 'Precision', 'Recall', 'delta_auc']
-        feat_imp_col = ['TOP_feat1', 'TOP_feat2', 'TOP_feat3', 'TOP_feat4', 'TOP_feat5']
-
-        df_local = pd.DataFrame(columns=model_col + HP_col + bagging_col + metrics_col + feat_imp_col)
-
-        # store informations in df
-        for key, value in self.d_train_model.items():
-            dict_tmp = {'model_index': key}
-            dict_tmp.update(value['HP'].copy())
-            dict_tmp.update({x: d_model_infos[key]['metrics'][x] for x in metrics_col})
-
-            dict_tmp.update({'bagging': self.bagging})
-            df_tmp = pd.DataFrame.from_dict(self.d_train_model[key]['features_importance'],
-                                            orient='index').reset_index().rename(
-                columns={'index': 'feat', 0: 'importance'}).sort_values(by='importance', ascending=False).head(5)
-            serie_tmp = df_tmp['feat'] + ' ' + round(df_tmp['importance'], 5).astype(str)
-            dict_tmp.update(dict(zip(feat_imp_col, serie_tmp.tolist())))
-
-            df_local = df_local.append(dict_tmp, ignore_index=True)
-
-        return df_local.loc[df_local['delta_auc'] <= 0.03].sort_values(by=sort_metric, ascending=False)

-
- -
- -
-
- - -
- -
-

- © Copyright 2020, Maxence LABESSE - -

-
- Built with Sphinx using a theme provided by Read the Docs. - -
- -
-
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- - - - - - - - - - - - \ No newline at end of file diff --git a/docs/_build.html/_modules/AutoMxL/Preprocessing/Categorical.html b/docs/_build.html/_modules/AutoMxL/Preprocessing/Categorical.html deleted file mode 100644 index d0686c9..0000000 --- a/docs/_build.html/_modules/AutoMxL/Preprocessing/Categorical.html +++ /dev/null @@ -1,538 +0,0 @@ - - - - - - - - - - - MLBG59.Preprocessing.Categorical — MLBG59 1.0.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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Source code for MLBG59.Preprocessing.Categorical

-from MLBG59.Explore.Features_Type import is_categorical, is_boolean
-
-""" Categorical features processing
-
- - CategoricalEncoder (class) : Encode categorical features
- - dummy_all_var (func) : get one hot encoded vector for each category of a categorical features list
- - get_embedded_cat (func) : get embedding representation with NN
- - mca (func) : TODO
-
-"""
-import pandas as pd
-from MLBG59.Preprocessing.Deep_Encoder import *
-from sklearn.preprocessing import LabelEncoder
-from MLBG59.param_config import batch_size, n_epoch, learning_rate
-
-
-
[docs]class CategoricalEncoder(object):
-    """Encode categorical features
-
-    Available encoding methods :
-
-    - one hot encoding
-    - deep_encoder : Build and train a Neural Network for the creation of embeddings for categorical variables. (https://www.fast.ai/2018/04/29/categorical-embeddings/)
-
-    Default NN model parameters are stored in param_config.py file
-
-    Parameters
-    ----------
-    method : string (Default : deep_encoder)
-        method used to get categorical encoding
-        Available methods : "one_hot", "deep_encoder"
-    """
-
-    def __init__(self,
-                 method='deep_encoder'
-                 ):
-
-        assert method in ['deep_encoder', 'one_hot'], 'invalid method : select deep_encoder / one_hot'
-
-        self.method = method
-        self.is_fitted = False
-        self.l_var2encode = []
-        self.l_var_other = []
-        self.target = None
-        self.d_embeddings = {}
-        self.d_int_encoders = {}
-        self.d_metrics = {}
-
-    """
-    ----------------------------------------------------------------------------------------------
-    """
-
-
[docs]    def fit(self, df, l_var=None, target=None, verbose=False):
-        """ Fit encoder on dataset following method
-
-        Parameters
-        ----------
-        df : DataFrame
-            input dataset
-        l_var : list (Default None)
-            names of the variables to encode.
-            If None, all the categorical and boolean features
-        target : string (Default None)
-            name of the target for deep_encoder method
-        verbose : boolean (Default False)
-            Get logging information
-        """
-        if self.method == 'deep_encoder':
-            assert target is not None, 'fill target parameter to use deep encoder'
-
-        # get categorical and boolean features (see Features_Type module doc)
-        l_cat = [col for col in df.columns.tolist() if
-                 (is_categorical(df, col) or is_boolean(df, col)) and col != target]
-
-        # list of features to encode
-        if l_var is None:
-            self.l_var2encode = l_cat
-        else:
-            self.l_var2encode = [col for col in l_var if col in l_cat]
-
-        df_local = df.copy()
-
-        for col in self.l_var2encode:
-            if df_local[col].dtype != 'object':
-                df_local[col] = df_local[col].astype('str')
-
-        # store target
-        self.target = target
-
-        if len(self.l_var2encode) > 0:
-            # deep learning embedded representation method
-            if self.method == 'deep_encoder':
-                self.d_int_encoders, self.d_embeddings, self.d_metrics = \
-                    get_embedded_cat(df_local, self.l_var2encode, target, batch_size, n_epoch, learning_rate,
-                                     verbose=False)
-
-        # Fitted !
-        self.is_fitted = True
-
-        # verbose
-        if verbose:
-            print(" **method : " + self.method)
-            print("  >", len(self.l_var2encode), "features to encode")
-            if len(self.l_var2encode) > 0:
-                print(" ", self.l_var2encode)
-            if (self.method == "deep_encoder") and len(self.l_var2encode) > 0:
-                print("  NN Loss:", round(self.d_metrics['loss'], 4), "/ Accuracy:",
-                      round(self.d_metrics['accuracy'], 4))
-                print("  Epoch:", n_epoch, "/ batch:", batch_size, "/ l_rate:", learning_rate)

-
-    """
-    ----------------------------------------------------------------------------------------------
-    """
-
-
[docs]    def transform(self, df, verbose=False):
-        """ transform dataset categorical features using the encoder.
-        Can be done only if encoder has been fitted
-
-        Parameters
-        ----------
-        df : DataFrame
-            dataset to transform
-        verbose : boolean (Default False)
-            Get logging information
-
-        Returns
-        -------
-        DataFrame : modified dataset
-        """
-        assert self.is_fitted, 'fit the encoding first using .fit method'
-
-        df_local = df.copy()
-
-        # if list of features to encode is not empty
-        if len(self.l_var2encode) > 0:
-            # one hot encoding method
-            if self.method == 'one_hot':
-
-                return dummy_all_var(df_local, var_list=self.l_var2encode, prefix_list=None, keep=False,
-                                     verbose=verbose)
-
-            # Deep learning embedding method
-            elif self.method == 'deep_encoder':
-                # features not to encode
-                self.l_var_other = [col for col in df_local.columns.tolist() if col not in self.l_var2encode]
-
-                # transform data with int encoder
-                for col in self.l_var2encode:
-                    if df_local[col].dtype != 'object':
-                        df_local[col] = df_local[col].astype('str')
-                    df_local[col] = self.d_int_encoders[col].fit_transform(df_local[col])
-
-                # get embedding
-                if verbose:
-                    print(' Deep Encoder Embedding dim:')
-
-                df_embedded = df_local[self.l_var2encode].copy()
-
-                for col, d_level in self.d_embeddings.items():
-                    for i in range(len(d_level[0])):
-                        # replace int values with new embedding
-                        df_embedded[col + '_' + str(i)] = df_embedded[col].replace(
-                            {k: v[i] for k, v in d_level.items()})
-
-                    # drop raw feature
-                    df_embedded = df_embedded.drop(col, axis=1)
-
-                    # verbose
-                    if verbose:
-                        print("  > " + col + ":", len(d_level[0]))
-
-                return pd.concat([df[self.l_var_other], df_embedded], axis=1)
-
-        # if no feature to encode
-        else:
-            print(" No variable to encode")
-
-            return df_local

-
-    """
-    ----------------------------------------------------------------------------------------------
-    """
-
-
[docs]    def fit_transform(self, df, l_var=None, target=None, verbose=False):
-        """fit and transform dataset categorical features
-
-        Parameters
-        ----------
-        df : DataFrame
-            input dataset
-        l_var : list (Default None)
-            names of the variables to encode.
-            If None, all the categorical and boolean features
-        target : string (Default None)
-            name of the target for deep_encoder method
-        verbose : boolean (Default False)
-            Get logging information
-
-        Returns
-        -------
-        DataFrame : modified dataset
-        """
-        df_local = df.copy()
-        # fit
-        self.fit(df_local, l_var, target, verbose)
-        df_local = self.transform(df_local, verbose)
-
-        return df_local

-
-
-"""
-----------------------------------------------------------------------------------------------
-"""
-
-
-
[docs]def dummy_all_var(df, var_list=None, prefix_list=None, keep=False, verbose=False):
-    """Get one hot encoded vector for selected/all categorical features
-
-    Parameters
-    ----------
-     df : DatraFrame
-        Input dataset
-     var_list : list (Default : None)
-        Names of the features to dummify
-        If None, all the num features
-     prefix_list : list (default : None)
-        Prefix to add before new features name (prefix+'_'+cat).
-        If None, prefix=variable name
-     keep : boolean (Default = False)
-        If True, delete the original feature
-     verbose : boolean (Default False)
-        Get logging information
-
-    Returns
-    -------
-    DataFrame
-          Modified dataset
-    """
-    df_local = df.copy()
-
-    for col in var_list:
-        # if prefix_list == None, add column name as prefix, else add prefix_list
-        if prefix_list is None:
-            pref = col
-        else:
-            pref = prefix_list[var_list.index(col)]
-
-        # dummify
-        df_cat = pd.get_dummies(df_local[col], prefix=pref)
-        # concat source DataFrame and new features
-        df_local = pd.concat((df_local, df_cat), axis=1)
-
-        # if keep = False, remove original features
-        if not keep:
-            df_local = df_local.drop(col, axis=1)
-        if verbose:
-            print('  > ' + col + ' ->', df_cat.columns.tolist())
-
-    return df_local

-
-
-"""
-----------------------------------------------------------------------------------------------
-"""
-
-
-
[docs]def get_embedded_cat(df, var_list, target, batchsize, n_epochs, lr, verbose=False):
-    """Get embedded representation for categorical features using NN encoder
-
-    Parameters
-    ----------
-    df : DataFrame
-        input Dataset
-    var_list : list of strings
-        features names
-    target : string
-        target name
-    batchsize : int
-        batch size for encoder training
-    n_epochs : int
-        number of epoch for encoder training
-    lr : float
-        encoder learning rate
-    verbose : boolean (Default False)
-        Get logging information
-
-    Returns
-    -------
-        DataFrame : modified dataset
-    """
-    ######################
-    # Get list to encode #
-    ######################
-    df_local = df[var_list + [target]].copy()
-
-    ############################
-    # Categories to int labels #
-    ############################
-    d_int_encoders = {}
-    for cat_col in var_list:
-        print(cat_col)
-        print(df_local[cat_col].dtype)
-        d_int_encoders[cat_col] = LabelEncoder()
-        df_local[cat_col] = d_int_encoders[cat_col].fit_transform(df_local[cat_col])
-
-    ###################
-    # Get layer sizes #
-    ###################
-    d_exp = {col: np.exp(-df_local[col].nunique() * 0.05) for col in var_list}
-    d_tmp = {col: np.int(5 * (1 - exp) + 1) for col, exp in d_exp.items()}
-
-    sum_ = sum([1. * np.log(k) for k in d_tmp.values()])
-
-    A, B = 10, 5
-    nlayer1 = min(1000, int(A * (len(d_tmp) ** 0.5) * sum_ + 1))
-    nlayer2 = int(nlayer1 / B) + 2
-
-    emb_dims = [(df_local[col].nunique(), d_tmp[col]) for col in var_list]
-
-    #####################
-    # Train the encoder #
-    #####################
-    # Create Torch_Dataset
-    df_to_encoder = Torch_Dataset(data=df_local, cat_cols=var_list, output_col=target)
-
-    model = Deep_Cat_Encoder(emb_dims, layer_sizes=[nlayer1, nlayer2], output_size=1)
-
-    fit_model, loss, accuracy = train_deep_encoder(df_to_encoder, model=model, optimizer='Adam', criterion='MSE',
-                                                   lr=lr, n_epochs=n_epochs, batchsize=batchsize,
-                                                   verbose=verbose)
-
-    d_metrics = {'loss': loss, 'accuracy': accuracy}
-
-    ############################################
-    # Store embedding and get output DataFrame #
-    ############################################
-    i = 0
-    d_embeddings = {}
-    for param in fit_model.emb_layers.parameters():
-        d_embeddings[var_list[i]] = dict(zip(list(range(len(param.data[:, 0]))), param.data.tolist()))
-        i += 1
-
-    return d_int_encoders, d_embeddings, d_metrics

-
- -
- -
-
- - -
- -
-

- © Copyright 2020, Maxence LABESSE - -

-
- Built with Sphinx using a theme provided by Read the Docs. - -
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- - - - - - - - - - - - \ No newline at end of file diff --git a/docs/_build.html/_modules/AutoMxL/Preprocessing/Categorical_Data.html b/docs/_build.html/_modules/AutoMxL/Preprocessing/Categorical_Data.html deleted file mode 100644 index 9126580..0000000 --- a/docs/_build.html/_modules/AutoMxL/Preprocessing/Categorical_Data.html +++ /dev/null @@ -1,255 +0,0 @@ - - - - - - - - - - - MLBG59.Preprocessing.Categorical_Data — MLBG59 1.0.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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Source code for MLBG59.Preprocessing.Categorical_Data

-""" Categorical features processing
-
- - dummy_all_var : get one hot encoded vector for each category of a categorical features list
- - label encoding : coming soon
-"""
-import pandas as pd
-from MLBG59.Utils.Utils import get_type_features
-
-
-
[docs]def dummy_all_var(df, var_list=None, prefix_list=None, keep=False, verbose=1):
-    """Get one hot encoded vector for selected/all categorical features
-    
-    Parameters
-    ----------
-     df : DatraFrame
-        Input dataset
-     var_list : list (Default : None)
-        Names of the features to dummify
-        If None, all the num features
-     prefix_list : list (default : None)
-        Prefix to add before new features name (prefix+'_'+cat).
-        It None, prefix=variable name
-     Keep : boolean (Default = False)
-        If True, delete the original feature
-     verbose : boolean (Default False)
-        Get logging information
-        
-    Returns
-    -------
-    DataFrame
-          Modified dataset
-    """
-    # if var_list = None, get all categorical features
-    # else, exclude features from var_list whose type is not categorical
-    var_list = get_type_features(df, 'cat', var_list)
-
-    df_local = df.copy()
-
-    if verbose:
-        print('  ** method : one hot encoding')
-
-    for col in var_list:
-        # if prefix_list == None, add column name as prefix, else add prefix_list
-        if prefix_list == None:
-            pref = col
-        else:
-            pref = prefix_list[var_list.index(col)]
-
-        # dummify
-        df_cat = pd.get_dummies(df_local[col], prefix=pref)
-        # concat source DataFrame and new features
-        df_local = pd.concat((df_local, df_cat), axis=1)
-
-        # if keep = False, delete original features
-        if keep == False:
-            df_local = df_local.drop(col, axis=1)
-        if verbose:
-            print('  > ' + col + ' ->', df_cat.columns.tolist())
-
-    return df_local

-
- -
- -
-
- - -
- -
-

- © Copyright 2020, Maxence LABESSE - -

-
- Built with Sphinx using a theme provided by Read the Docs. - -
- -
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- - - - - - - - - - - - \ No newline at end of file diff --git a/docs/_build.html/_modules/AutoMxL/Preprocessing/Date.html b/docs/_build.html/_modules/AutoMxL/Preprocessing/Date.html deleted file mode 100644 index 1383c4c..0000000 --- a/docs/_build.html/_modules/AutoMxL/Preprocessing/Date.html +++ /dev/null @@ -1,451 +0,0 @@ - - - - - - - - - - - MLBG59.Preprocessing.Date — MLBG59 1.0.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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Source code for MLBG59.Preprocessing.Date

-""" Date Features processing functions:
-
- - DateEncoder (class) : encode date features
- - all_to_date (func): detect dates from num/cat features and transform them to datetime format.
- - date_to_anc (func): transform datetime features to timedelta according to a ref date
-"""
-import pandas as pd
-from datetime import datetime
-from MLBG59.Explore.Features_Type import features_from_type
-
-
-
[docs]class DateEncoder(object):
-    """Encode categorical features
-
-    Available methods :
-
-    - timedelta : compute time between date feature and parameter date_ref
-
-    Parameters
-    ----------
-    method : string (Default : timedelta)
-        method used to encode dates
-        Available methods : "timedelta"
-    date_ref : string '%d/%m/%y' (Default : None)
-        Date to compute timedelta.
-        If None, today date
-    """
-
-    def __init__(self,
-                 method='timedelta',
-                 date_ref=None,):
-
-        assert method in ['timedelta'], "invalid method : select timedelta"
-
-        self.method = method
-        self.is_fitted = False
-        self.l_var2encode = []
-        # if date_ref not filled, set to today's date
-        if date_ref is None:
-            self.date_ref = datetime.now()
-        else:
-            self.date_ref = date_ref
-
-    """
-    ----------------------------------------------------------------------------------------------
-    """
-
-
[docs]    def fit(self, df, l_var=None, verbose=False):
-        """fit encoder
-
-        Parameters
-        ----------
-        df : DataFrame
-            input dataset
-        l_var : list
-            features to encode.
-            If None, contains all features identified as dates (see Features_Type module)
-        verbose : boolean (Default False)
-            Get logging information
-        """
-        # get date features
-        l_date_var = features_from_type(df, typ='date', l_var=None)
-
-        # list of features to encode (in l_var and l_date_var)
-        if l_var is None:
-            self.l_var2encode = l_date_var
-        else:
-            self.l_var2encode = [col for col in l_var if col in l_date_var]
-
-        # Fitted !!!!
-        self.is_fitted = True
-
-        # verbose
-        if verbose:
-            if self.method == 'timedelta':
-                print(" **method " + self.method + " / date ref : ", self.date_ref)
-
-            print("  >", len(self.l_var2encode), "features to transform")
-            if len(self.l_var2encode) > 0:
-                print(" ", self.l_var2encode)

-
-    """
-    ----------------------------------------------------------------------------------------------
-    """
-
-
[docs]    def transform(self, df, verbose=False):
-        """ transform dataset date features using the encoder.
-        Can be done only if encoder has been fitted
-
-        Parameters
-        ----------
-        df : DataFrame
-            dataset to transform
-        verbose : boolean (Default False)
-            Get logging information
-        """
-        assert self.is_fitted, 'fit the encoding first using .fit method'
-
-        df_local = df.copy()
-
-        # if list of features to encode not empty
-        if len(self.l_var2encode) > 0:
-            # transform features to datetime
-            df_local = all_to_date(df_local, l_var=self.l_var2encode, verbose=verbose)
-
-            # method timedelta
-            if self.method == 'timedelta':
-                df_local, _ = date_to_anc(df_local, l_var=self.l_var2encode, date_ref=self.date_ref, verbose=verbose)
-
-        # if no features to transform
-        elif verbose:
-            print("  > No date to transform")
-
-        return df_local

-
-    """
-    ----------------------------------------------------------------------------------------------
-    """
-
-
[docs]    def fit_transform(self, df, l_var=None, verbose=False):
-        """fit and transform dataset with encoder
-
-        Parameters
-        ----------
-        df : DataFrame
-            input dataset
-        l_var : list
-            features to encode.
-            If None, all features identified as dates (see Features_Type module)
-        verbose : boolean (Default False)
-            Get logging information
-        """
-        df_local = df.copy()
-        # fit
-        self.fit(df_local, l_var=l_var, verbose=verbose)
-        # transform
-        df_local = self.transform(df_local, verbose=verbose)
-
-        return df_local

-
-
-"""
-----------------------------------------------------------------------------------------------
-"""
-
-
-
[docs]def all_to_date(df, l_var=None, verbose=False):
-    """Detect dates from selected/all features and transform them to datetime format.
-    
-    Parameters
-    ----------
-    df : DataFrame
-        Input dataset
-    l_var : list (Default : None)
-        Names of the features
-        If None, all the features
-    verbose : boolean (Default False)
-        Get logging information
-        
-    Return
-    -------
-    DataFrame
-        Modified dataset
-    """
-    # if var_list = None, get all df features
-    # else, exclude features if not in df
-    if l_var is None:
-        l_var = df.columns.tolist()
-    else:
-        l_var = [col for col in l_var if col in df.columns.tolist()]
-
-    df_local = df.copy()
-
-    if verbose:
-        print('  > features : ', l_var)
-        print('  > features conversion to date using "try .to_datetime')
-
-    # for each feature in var_list, try to convert to datetime
-    for col in l_var:
-        try:
-            if df_local[col].dtype == 'object':
-                df_local[col] = pd.to_datetime(df_local[col], errors='raise')
-            else:
-                df_smpl = df.loc[~df[col].isna()].copy()
-                df_smpl[col] = pd.to_datetime(df_smpl[col].astype('Int32').astype(str), errors='raise')
-                df_local[col] = pd.to_datetime(df_local[col].astype('Int32').astype(str), errors='coerce')
-        except ValueError:
-            pass
-        except OverflowError:
-            pass
-        except TypeError:
-            pass
-
-    return df_local

-
-
-"""
--------------------------------------------------------------------------------------------------------------------------
-"""
-
-
-
[docs]def date_to_anc(df, l_var=None, date_ref=None, verbose=False):
-    """Transform selected/all datetime features to timedelta according to a ref date
-    
-    Parameters
-    ----------
-    df : DataFrame
-        Input dataset
-    l_var : list (Default : None)
-        List of the features to analyze.
-        If None, contains all the datetime features
-    date_ref : string '%d/%m/%y' (Default : None)
-        Date to compute timedelta.
-        If None, today date
-    verbose : boolean (Default False)
-        Get logging information
-
-    Returns
-    -------
-    DataFrame
-        Modified dataset
-        
-    list
-        New timedelta features names
-    """
-    # if date_ref is None, use today date
-    if date_ref is None:
-        date_ref = datetime.now()
-    else:
-        if isinstance(date_ref, datetime):
-            pass
-        else:
-            date_ref = datetime.strptime(date_ref, '%d/%m/%Y')
-
-    # if var_list = None, get all datetime features
-    # else, exclude features from var_list whose type is not datetime
-    l_date = df.dtypes[df.dtypes == 'datetime64[ns]'].index.tolist()
-    if l_var is None:
-        l_var = l_date
-    else:
-        l_var = [col for col in l_var if col in l_date]
-
-    df_local = df.copy()
-
-    # new variables names
-    l_new_var_names = ['anc_' + col for col in l_var]
-    # compute time delta for selected dates variables
-    df_local = df_local.apply(lambda x: (date_ref - x).dt.days / 365 if x.name in l_var else x)
-    # rename columns
-    df_local = df_local.rename(columns=dict(zip(l_var, l_new_var_names)))
-
-    if verbose:
-        print('  ** Reference date for timelapse computing : ', date_ref)
-        list(map(lambda x, y: print("  >", x + ' -> ' + y), l_var, l_new_var_names))
-
-    return df_local, l_new_var_names

-
- -
- -
-
- - -
- -
-

- © Copyright 2020, Maxence LABESSE - -

-
- Built with Sphinx using a theme provided by Read the Docs. - -
- -
-
- -
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- - - - - - - - - - - - \ No newline at end of file diff --git a/docs/_build.html/_modules/AutoMxL/Preprocessing/Date_Data.html b/docs/_build.html/_modules/AutoMxL/Preprocessing/Date_Data.html deleted file mode 100644 index 9206231..0000000 --- a/docs/_build.html/_modules/AutoMxL/Preprocessing/Date_Data.html +++ /dev/null @@ -1,303 +0,0 @@ - - - - - - - - - - - MLBG59.Preprocessing.Date_Data — MLBG59 1.0.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
- - - -
- - - - - -
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Source code for MLBG59.Preprocessing.Date_Data

-""" Date Features processing functions:
-
- - all_to_date : detect dates from num/cat features and transform them to datetime format.
- - date_to_anc : Transform datetime features to timedelta according to a ref date
-"""
-import pandas as pd
-from datetime import datetime
-from MLBG59.Utils.Utils import get_type_features
-
-
-
[docs]def all_to_date(df, var_list=None, verbose=1):
-    """Detect dates from selected/all features and transform them to datetime format.
-    
-    Parameters
-    ----------
-    df : DataFrame
-        Input dataset
-    var_list : list (Default : None)
-        Names of the features
-        If None, all the features
-    verbose : boolean (Default False)
-        Get logging information
-        
-    Return
-    -------
-    DataFrame
-        Modified dataset
-    """
-    # if var_list = None, get all df features
-    # else, exclude features if not in df
-    var_list = get_type_features(df, 'all', var_list)
-    df_local = df.copy()
-
-    if verbose:
-        print('  > features : ', var_list)
-        print('  > features conversion to date using "try .to_datetime')
-
-    # for each feature in var_list, try to convert to datetime
-    for col in var_list:
-        try:
-            if df_local[col].dtype == 'object':
-                df_local[col] = pd.to_datetime(df_local[col], errors='coerce')
-            else:
-                df_local[col] = pd.to_datetime(df_local[col].astype('Int32').astype(str), errors='coerce')
-        except:
-            pass
-
-    return df_local

-
-
-"""
--------------------------------------------------------------------------------------------------------------------------
-"""
-
-
-
[docs]def date_to_anc(df, var_list=None, date_ref=None, verbose=1):
-    """Transform selected/all datetime features to timedelta according to a ref date
-    
-    Parameters
-    ----------
-    df : DataFrame
-        Input dataset
-    var_list : list (Default : None)
-        List of the features to analyze.
-        If None, contains all the datetime features
-    date_ref : string '%d/%m/%y' (Default : None)
-        Date to compute timedelta.
-        If None, today date
-    verbose : boolean (Default False)
-        Get logging information
-
-    Returns
-    -------
-    DataFrame
-        Modified dataset
-        
-    list
-        New timedelta features names
-    """
-    # if date_ref is None, use today date
-    if date_ref is None:
-        date_ref = datetime.now()
-    else:
-        date_ref = datetime.strptime(date_ref, '%d/%m/%Y')
-
-    # if var_list = None, get all datetime features
-    # else, exclude features from var_list whose type is not datetime
-    var_list = get_type_features(df, 'date', var_list)
-
-    df_local = df.copy()
-
-    if verbose > 0:
-        print('  ** Reference date for timelapse computing : ', date_ref)
-
-    # initialisation
-    new_var_list = []
-
-    for col in var_list:
-        # new feature name
-        var_name = 'anc_' + col
-        df_local[var_name] = (date_ref - df_local[col]).dt.days / 365
-        del df_local[col]
-        new_var_list.append(var_name)
-
-        if verbose > 0:
-            print("  >", col + ' -> ' + var_name)
-
-    return df_local, new_var_list

-
- -
- -
-
- - -
- -
-

- © Copyright 2020, Maxence LABESSE - -

-
- Built with Sphinx using a theme provided by Read the Docs. - -
- -
-
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- -
- - - - - - - - - - - - \ No newline at end of file diff --git a/docs/_build.html/_modules/AutoMxL/Preprocessing/Missing_Values.html b/docs/_build.html/_modules/AutoMxL/Preprocessing/Missing_Values.html deleted file mode 100644 index 59dc7bb..0000000 --- a/docs/_build.html/_modules/AutoMxL/Preprocessing/Missing_Values.html +++ /dev/null @@ -1,476 +0,0 @@ - - - - - - - - - - - MLBG59.Preprocessing.Missing_Values — MLBG59 1.0.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
- - - -
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- - -
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-
- -

Source code for MLBG59.Preprocessing.Missing_Values

-""" Missing values handling functions :
-
- - NAEncoder (class): encoder that replaces missing values
- - fill_numerical (func): replace missing values for numerical features
- - fill_categorical (func): replace missing values for categorical features
- - get_NA_features (func): get features containing NA values
-"""
-import pandas as pd
-import numpy as np
-
-
-
[docs]class NAEncoder(object):
-    """ Missing values filling
-
-    Available methods to replace missing values
-
-    - num : metdian/mean/zero
-    - cat : 'NR'
-
-    Parameters
-    ----------
-    replace_num_with: string
-        method used to replace numerical missing values
-    replace_cat_with: string
-        method used to replace categorical missing values
-    """
-
-    def __init__(self,
-                 replace_num_with='median',
-                 replace_cat_with='NR',
-                 track_num_NA=True
-                 ):
-
-        assert replace_num_with in ['median', 'mean', 'zero'], 'invalid method, select median/mean/zero'
-        assert replace_cat_with in ['NR'], 'invalid method, select NR'
-        self.replace_num_with = replace_num_with
-        self.replace_cat_with = replace_cat_with
-        self.track_num_NA = track_num_NA
-        self.l_var_cat = []
-        self.l_var_num = []
-        self.is_fitted = False
-
-    """
-    ----------------------------------------------------------------------------------------------
-    """
-
-
[docs]    def fit(self, df, l_var, verbose=False):
-        """fit encoder
-
-        Parameters
-        ----------
-        df : DataFrame
-            input dataset
-        l_var : list
-            features to encode.
-            If None, all features
-        verbose : boolean (Default False)
-            Get logging information
-        """
-        # get num and categorical columns
-        l_num = [col for col in df.columns.tolist() if df[col].dtype != 'object']
-        l_str = [col for col in df.columns.tolist() if df[col].dtype == 'object']
-
-        # get list of valid features (containing NA)
-        if l_var is None:
-            self.l_var_cat = [col for col in l_str if df[col].isna().sum() > 0]
-            self.l_var_num = [col for col in l_num if df[col].isna().sum() > 0]
-        else:
-            self.l_var_cat = [col for col in l_var if col in l_str and df[col].isna().sum() > 0]
-            self.l_var_num = [col for col in l_var if col in l_num and df[col].isna().sum() > 0]
-
-        # Fitted !
-        self.is_fitted = True
-
-        # verbose
-        if verbose:
-            print(" **method cat:", self.replace_cat_with, " / num:", self.replace_num_with)
-            print("  >", len(self.l_var_cat) + len(self.l_var_num), "features to fill")
-            if len(self.l_var_cat) > 0:
-                print("  - cat", self.l_var_cat)
-            if len(self.l_var_num) > 0:
-                print("  - num", self.l_var_num)

-
-    """
-    ----------------------------------------------------------------------------------------------
-    """
-
-
[docs]    def transform(self, df, verbose=False):
-        """ transform dataset categorical features using the encoder.
-        Can be done only if encoder has been fitted
-
-        Parameters
-        ----------
-        df : DataFrame
-            dataset to transform
-        verbose : boolean (Default False)
-            Get logging information
-        """
-        assert self.is_fitted, 'fit the encoding first using .fit method'
-
-        df_local = df.copy()
-
-        # categorical features filling
-        if len(self.l_var_cat) > 0:
-            df_local = fill_categorical(df_local, l_var=self.l_var_cat, method=self.replace_cat_with,
-                                        verbose=verbose)
-
-        # numerical features filling
-        if len(self.l_var_num) > 0:
-            df_local = fill_numerical(df_local, l_var=self.l_var_num, method=self.replace_num_with,
-                                      track_num_NA=self.track_num_NA, verbose=verbose)
-
-        # if no feature to fill
-        if len(self.l_var_cat) + len(self.l_var_num) == 0 and verbose:
-            print("  > no transformation to apply")
-
-        return df_local

-
-    """
-    ----------------------------------------------------------------------------------------------
-    """
-
-
[docs]    def fit_transform(self, df, l_var=None, verbose=False):
-        """fit and transform dataset with encoder
-
-        Parameters
-        ----------
-        df : DataFrame
-            input dataset
-        l_var : list
-            features to encode.
-            If None, all features identified as dates (see Features_Type module)
-        verbose : boolean (Default False)
-            Get logging information
-        """
-        df_local = df.copy()
-        # fit
-        self.fit(df_local, l_var=l_var, verbose=verbose)
-        # transform
-        df_local = self.transform(df_local, verbose=verbose)
-
-        return df_local

-
-    """
-    ----------------------------------------------------------------------------------------------
-    """

-
-
-
[docs]def fill_numerical(df, l_var=None, method='median', track_num_NA=True, verbose=False):
-    """Fill missing values for selected/all numerical features.
-    top_var_NA parameter allows to create a variable to keep track of missing values.
-
-    Available methods : replace with zero, median or mean (Default = median)
-
-    Parameters
-    ----------
-    df : DataFrame
-        Input dataset
-    l_var : list (Default : None)
-        names of the features to fill.
-        If None, all the numerical features
-    method : string (Default : 'median')
-        Method used to fill the NA values :
-
-        - zero : replace with zero
-        - median : replace with median
-        - mean : replace with mean
-
-    track_num_NA : boolean (Defaut : True)
-        If True, create a boolean column to keep track of missing values
-    verbose : boolean (Default False)
-        Get logging information
-
-    Returns
-    -------
-    DataFrame
-        Modified dataset
-    """
-    assert method in ['zero', 'median', 'mean'], method + ' invalid method : choose zero, median or mean'
-
-    # if var_list = None, get all num features
-    # else, remove features from var_list whose type is not num
-    l_num = df._get_numeric_data().columns.tolist()
-
-    if l_var is None:
-        l_var = l_num
-    else:
-        l_var = [col for col in l_var if col in l_num]
-
-    df_local = df.copy()
-
-    # values to fill NA
-    if method == 'median':
-        fill_value = df_local[l_var].mean()
-    elif method == 'mean':
-        fill_value = df_local[l_var].mean()
-    elif method == 'zero':
-        fill_value = pd.Series([0] * len(l_var), index=l_var)
-
-    for var in l_var:
-        if track_num_NA:
-            # keep track of NA values in Top_var_NA
-            df_local['top_NA_' + var] = df_local.apply(lambda x: 1 if np.isnan(x[var]) else 0, axis=1)
-        # fill NA
-        df_local[var] = df_local[var].fillna(fill_value[var])
-
-    if verbose:
-        print('  > method: ' + method)
-        print('  > filled features:', df[l_var].isna().sum().loc[df[l_var].isna().sum() > 0].index.tolist())
-
-    return df_local

-
-
-"""
--------------------------------------------------------------------------------------------------------------------------
-"""
-
-
-
[docs]def fill_categorical(df, l_var=None, method='NR', verbose=False):
-    """Fill missing values for selected/all categorical features.
-
-    Parameters
-    ----------
-    df : DataFrame
-        Input dataset
-    l_var : list (Default : None)
-        list of the features to fill.
-        If None, contains all the categorical features
-    method : string (Default : 'NR')
-        Method used to fill the NA values :
-
-        - NR : replace NA with 'NR'
-
-    verbose : boolean (Default False)
-        Get logging information
-
-    Returns
-    -------
-    DataFrame
-        Modified dataset
-    """
-    assert method in ['NR'], method + ' invalid method : choose NR '
-
-    # if var_list = None, get all categorical features
-    # else, remove features from var_list whose type is not categorical
-    l_cat = [col for col in df.columns.tolist() if df[col].dtype == 'object']
-
-    if l_var is None:
-        l_var = l_cat
-    else:
-        l_var = [col for col in l_var if col in l_cat]
-
-    df_local = df.copy()
-
-    # values to fill NA
-    if method in ['NR']:
-        fill_value = 'NR'
-
-    for var in l_var:
-        df_local[var] = df_local[var].fillna(fill_value)
-
-    if verbose:
-        print('  > method: ' + method)
-        print('  > filled features:', df[l_var].isna().sum().loc[df[l_var].isna().sum() > 0].index.tolist())
-
-    return df_local

-
-
-
[docs]def get_NA_features(df):
-    """identify features containing NA values
-
-    Parameters
-    ----------
-    df : DataFrame
-        input dataset
-
-    Returns
-    -------
-    list : features containing missing values
-    """
-    return df.isna().sum()[df.isna().sum() > 0].index.tolist()

-
- -
- -
-
- - -
- -
-

- © Copyright 2020, Maxence LABESSE - -

-
- Built with Sphinx using a theme provided by Read the Docs. - -
- -
-
- -
- -
- - - - - - - - - - - - \ No newline at end of file diff --git a/docs/_build.html/_modules/AutoMxL/Preprocessing/Outliers.html b/docs/_build.html/_modules/AutoMxL/Preprocessing/Outliers.html deleted file mode 100644 index 2d93f5b..0000000 --- a/docs/_build.html/_modules/AutoMxL/Preprocessing/Outliers.html +++ /dev/null @@ -1,546 +0,0 @@ - - - - - - - - - - - MLBG59.Preprocessing.Outliers — MLBG59 1.0.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
- - - -
- - - - - -
- -
- - - - - - - - - - - - - - - - - -
- -
    - -
  • Docs »
    • - -
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    • - -
  • MLBG59.Preprocessing.Outliers
    • - - -
  • - -
    • - -
- - -
-
-
-
- -

Source code for MLBG59.Preprocessing.Outliers

-""" Outliers handling functions
-
- - OutliersEncoding (class) : identify and replace outliers
- - get_cat_outliers (funct): identify categorical features containing outliers
- - get_num_outliers (func): identify numerical features containing outliers
- - replace_category (func): replace categories of a categorical variable
- - replace_extreme_values (func): replace extreme values (oh!)
-"""
-import pandas as pd
-import numpy as np
-from MLBG59.Utils.Display import *
-
-
-
[docs]class OutliersEncoder(object):
-    """Identify et replace outliers for categorical dang numerical features
-
-    - num : x outlier <=> abs(x - mean) > xstd * var
-    - cat : x outlier category <=> with frequency <x% (Default 5%)
-
-    Parameters
-    ----------
-    cat_threshold : float (default 0.02)
-        Minimum modality frequency
-    num_xstd : int (Default : 3)
-        Variance gap coef
-
-    """
-
-    def __init__(self,
-                 cat_threshold=0.02,
-                 num_xstd=4
-                 ):
-
-        self.cat_threshold = cat_threshold,
-        self.num_xstd = num_xstd
-        self.is_fitted = False
-        self.l_var_num = []
-        self.l_var_cat = []
-        self.d_num_outliers = {}
-        self.d_cat_outliers = {}
-
-    """
-    ----------------------------------------------------------------------------------------------
-    """
-
-
[docs]    def fit(self, df, l_var, verbose=False):
-        """Fit encoder
-
-        Parameters
-        ----------
-        df : DataFrame
-            input dataset
-        l_var : list
-            features to encode.
-            If None, all features
-        verbose : boolean (Default False)
-            Get logging information
-        """
-        # get num and cat features
-        l_num = [col for col in df.columns.tolist() if df[col].dtype != 'object']
-        l_str = [col for col in df.columns.tolist() if df[col].dtype == 'object']
-
-        # get valid values (not boolean)
-        if l_var is None:
-            self.l_var_cat = [col for col in l_str if df[col].nunique() > 2]
-            self.l_var_num = [col for col in l_num if df[col].nunique() > 2]
-        else:
-            self.l_var_cat = [col for col in l_var if col in l_str and df[col].nunique() > 2]
-            self.l_var_num = [col for col in l_var if col in l_num and df[col].nunique() > 2]
-
-
-        # cat outliers
-        if len(self.l_var_cat) > 0:
-            self.d_cat_outliers = get_cat_outliers(df, l_var=self.l_var_cat, threshold=self.cat_threshold,
-                                                   verbose=False)
-
-        # num outliers
-        if len(self.l_var_num) > 0:
-            self.d_num_outliers = get_num_outliers(df, l_var=self.l_var_num, xstd=self.num_xstd, verbose=False)
-
-        # Fitted !
-        self.is_fitted = True
-
-        # verbose
-        if verbose:
-            print(" **method cat: frequency<" + str(self.cat_threshold)
-                  + " / num:( x: |x - mean| > " + str(self.num_xstd) + "* var)")
-            print("  >", len(self.d_cat_outliers.keys()) + len(self.d_num_outliers.keys()), "features with outliers")
-            if len(self.d_cat_outliers.keys()) > 0:
-                print("  - cat", list(self.d_cat_outliers.keys()))
-            if len(self.d_num_outliers.keys()) > 0:
-                print("  - num", list(self.d_num_outliers.keys()))

-
-    """
-    ----------------------------------------------------------------------------------------------
-    """
-
-
[docs]    def transform(self, df, verbose=False):
-        """Transform dataset  features using the encoder.
-        Can be done only if encoder has been fitted
-
-        Parameters
-        ----------
-        df : DataFrame
-            dataset to transform
-        verbose : boolean (Default False)
-            Get logging information
-        """
-        assert self.is_fitted, 'fit the encoding first using .fit method'
-        df_local = df.copy()
-
-        # cat features
-        if len(list(self.d_cat_outliers.keys())) > 0:
-            if verbose:
-                print(" - cat aggregated values:")
-            for col in self.d_cat_outliers.keys():
-                df_local = replace_category(df_local, col, self.d_cat_outliers[col], replace_with='outliers',
-                                            verbose=verbose)
-
-        # num features
-        if len(list(self.d_num_outliers.keys())) > 0:
-            if verbose:
-                print(" - num values replaces:")
-            for col in self.d_num_outliers.keys():
-                df_local = replace_extreme_values(df_local, col, self.d_num_outliers[col][0],
-                                                  self.d_num_outliers[col][1], verbose=verbose)
-
-        # if no features with outliers
-        if len(list(self.d_cat_outliers.keys())) + len(list(self.d_num_outliers.keys())) == 0:
-            print("  > no outlier to replace")
-
-        return df_local

-
-    """
-    ----------------------------------------------------------------------------------------------
-    """
-
-
[docs]    def fit_transform(self, df, l_var=None, verbose=False):
-        """Fit and transform dataset with encoder
-
-        Parameters
-        ----------
-        df : DataFrame
-            input dataset
-        l_var : list
-            features to encode.
-            If None, all features identified as dates (see Features_Type module)
-        verbose : boolean (Default False)
-            Get logging information
-        """
-        df_local = df.copy()
-        # fit
-        self.fit(df_local, l_var=l_var, verbose=False)
-        # transform
-        df_local = self.transform(df_local, verbose=verbose)
-
-        return df_local

-
-
-"""
-----------------------------------------------------------------------------------------------
-"""
-
-
-
[docs]def get_cat_outliers(df, l_var=None, threshold=0.05, verbose=False):
-    """Outliers detection for selected/all categorical features.
-
-    Method : Modalities with frequency <x% (Default 5%)
-
-    Parameters
-    ----------
-     df : DataFrame
-        Input dataset
-     l_var : list (Default : None)
-        Names of the features
-        If None, all the categorical features
-     threshold : float (Default : 0.05)
-        Minimum modality frequency
-     verbose : boolean (Default False)
-        Get logging information
-
-    Returns
-    -------
-    dict
-        {variable : list of categories considered as outliers}
-    """
-    # if var_list = None, get all categorical features
-    # else, remove features from var_list whose type is not categorical
-    l_cat = [col for col in df.columns.tolist() if df[col].dtype == 'object']
-
-    if l_var is None:
-        l_var = l_cat
-    else:
-        l_var = [col for col in l_var if col in l_cat]
-
-    df_local = df[l_var].copy()
-
-    # dict containing value_counts for each variable
-    d_freq = {col: pd.value_counts(df[col], dropna=False, normalize=True) for col in l_var}
-
-    # if features contain at least 1 outlier category (frequency <threshold)
-    # store outliers categories in dict
-    d_outliers = {k: v[v < threshold].index.tolist()
-                  for k, v in d_freq.items()
-                  if len(v[v < threshold]) > 1}
-
-    if verbose:
-        color_print('cat features outliers identification (frequency<' + str(threshold) + ')')
-        print('  > features : ', df_local.columns, )
-        print("  > containing outliers", list(d_outliers.keys()))
-
-    return d_outliers

-
-
-"""
--------------------------------------------------------------------------------------------------------------------------
-"""
-
-
-
[docs]def get_num_outliers(df, l_var=None, xstd=3, verbose=False):
-    """Outliers detection for selected/all numerical features.
-
-    Method : x outlier <=> abs(x - mean) > xstd * var
-
-    Parameters
-    ----------
-     df : DataFrame
-        Input dataset
-     l_var : list (Default : None)
-        Names of the features
-        If None, all the num features
-     xstd : int (Default : 3)
-        Variance gap coef
-     verbose : boolean (Default False)
-        Get logging information
-
-    Returns
-    -------
-    dict
-        {variable : [lower_limit, upper_limit]}
-    """
-    # if var_list = None, get all num features
-    # else, remove features from var_list whose type is not num
-    l_num = df._get_numeric_data().columns.tolist()
-
-    if l_var is None:
-        l_var = l_num
-    else:
-        l_var = [col for col in l_var if col in l_num]
-
-    df_local = df[l_var].copy()
-
-    # compute features upper and lower limit (abs(x - mean) > xstd * var (x=3 by default))
-    data_std = np.std(df_local)
-    data_mean = np.mean(df_local)
-    anomaly_cut_off = data_std * xstd
-    lower_limit = data_mean - anomaly_cut_off
-    upper_limit = data_mean + anomaly_cut_off
-    data_min = np.min(df_local)
-    data_max = np.max(df_local)
-
-    # store variables and lower/upper limits
-    d_outliers = {col: [lower_limit[col], upper_limit[col]]
-                  for col in df_local.columns.tolist()
-                  if (data_min[col] < lower_limit[col] or data_max[col] > upper_limit[col])}
-
-    if verbose:
-        color_print('num features outliers identification ( x: |x - mean| > ' + str(xstd) + ' * var)')
-        print('  > features : ', l_var)
-        print("  > containing outliers", list(d_outliers.keys()))
-
-    return d_outliers

-
-
-"""
--------------------------------------------------------------------------------------------------------------------------
-"""
-
-
-
[docs]def replace_category(df, var, categories, replace_with='outliers', verbose=False):
-    """Replace categories of a categorical variable
-    
-    Parameters
-    ----------
-    df : DataFrame
-        Input dataset
-    var : string
-        variable to modify
-    categories : list(string)
-        categories to replace
-    replace_with : string (Default : 'outliers')
-        word to replace categories with
-    verbose : boolean (Default False)
-        Get logging information
-        
-    Returns
-    -------
-    DataFrame
-        Modified dataset
-    """
-    df_local = df.copy()
-
-    # replace categories
-    df_local.loc[df_local[var].isin(categories), var] = replace_with
-
-    if verbose:
-        print('  > ' + var + ' ', categories)
-
-    return df_local

-
-
-"""
--------------------------------------------------------------------------------------------------------------------------
-"""
-
-
-
[docs]def replace_extreme_values(df, var, lower_th=None, upper_th=None, verbose=False):
-    """Replace extrem values : > upper threshold or < lower threshold
-    
-    Parameters
-    ----------
-    df : DataFrame
-        Input dataset
-    var : string
-        variable to modify
-    lower_th : int/float (Default=None)
-        lower threshold
-    upper_th : int/float (Default=None)
-        upper threshold
-    verbose : boolean (Default False)
-        Get logging information
-
-    Returns
-    -------
-    DataFrame
-        Modified dataset
-    """
-    assert (lower_th is not None or upper_th is not None), 'specify at least one limit value'
-    df_local = df.copy()
-
-    # replace values with upper_limit and lower_limit
-    if upper_th is not None:
-        df_local.loc[df_local[var] > upper_th, var] = upper_th
-    if lower_th is not None:
-        df_local.loc[df_local[var] < lower_th, var] = lower_th
-
-    if verbose:
-        print('  > ' + var + ' < ' + str(round(lower_th, 4)) + ' or > ' + str(
-            round(upper_th, 4)))
-
-    return df_local

-
- -
- -
-
- - -
- -
-

- © Copyright 2020, Maxence LABESSE - -

-
- Built with Sphinx using a theme provided by Read the Docs. - -
- -
-
- -
- -
- - - - - - - - - - - - \ No newline at end of file diff --git a/docs/_build.html/_modules/AutoMxL/Preprocessing/Process_Outliers.html b/docs/_build.html/_modules/AutoMxL/Preprocessing/Process_Outliers.html deleted file mode 100644 index c56b436..0000000 --- a/docs/_build.html/_modules/AutoMxL/Preprocessing/Process_Outliers.html +++ /dev/null @@ -1,273 +0,0 @@ - - - - - - - - - - - MLBG59.Preprocessing.Process_Outliers — MLBG59 1.0.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
- - - -
- - - - - -
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    • - -
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    • - -
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    • - - -
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    • - -
- - -
-
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-
- -

Source code for MLBG59.Preprocessing.Process_Outliers

-""" Outliers handling functions
-
- - replace_category : replace categories of a categorical variable
- - replace_extreme_values : replace extreme values (oh!)
-"""
-
-
-
[docs]def replace_category(df, var, categories, replace_with='outliers', verbose=False):
-    """Replace categories of a categorical variable
-    
-    Parameters
-    ----------
-    df : DataFrame
-        Input dataset
-    var : string
-        variable to modify
-    categories : list(string)
-        categories to replace
-    replace_with : string (Default : 'outliers')
-        word to replace categories with
-    verbose : boolean (Default False)
-        Get logging information
-        
-    Returns
-    -------
-    DataFrame
-        Modified dataset
-    """
-    df_local = df.copy()
-
-    # replace categories
-    df_local.loc[df_local[var].isin(categories), var] = replace_with
-
-    if verbose:
-        print('  > ' + var + ' ' + replace_with + ' ->', categories, )
-
-    return df_local

-
-
-"""
--------------------------------------------------------------------------------------------------------------------------
-"""
-
-
-
[docs]def replace_extreme_values(df, var, lower_th=None, upper_th=None, verbose=False):
-    """Replace extrem values : > upper threshold or < lower threshold
-    
-    Parameters
-    ----------
-    df : DataFrame
-        Input dataset
-    var : string
-        variable to modify
-    lower_th : int/float (Default=None)
-        lower threshold
-    upper_th : int/float (Default=None)
-        upper threshold
-    verbose : boolean (Default False)
-        Get logging information
-
-    Returns
-    -------
-    DataFrame
-        Modified dataset
-    """
-    df_local = df.copy()
-
-    # replace values with upper_limit and lower_limit
-    if upper_th is not None:
-        df_local.loc[df_local[var] > upper_th, var] = upper_th
-    if lower_th is not None:
-        df_local.loc[df_local[var] < lower_th, var] = lower_th
-
-    if verbose:
-        print('  > Values replaced for variable ' + var + ' : <' + str(round(lower_th, 4)) + ' or >' + str(
-            round(upper_th, 4)))
-
-    return df_local

-
- -
- -
-
- - -
- -
-

- © Copyright 2020, Maxence LABESSE - -

-
- Built with Sphinx using a theme provided by Read the Docs. - -
- -
-
- -
- -
- - - - - - - - - - - - \ No newline at end of file diff --git a/docs/_build.html/_modules/AutoMxL/Select_Features/Select_Features.html b/docs/_build.html/_modules/AutoMxL/Select_Features/Select_Features.html deleted file mode 100644 index 080f57e..0000000 --- a/docs/_build.html/_modules/AutoMxL/Select_Features/Select_Features.html +++ /dev/null @@ -1,430 +0,0 @@ - - - - - - - - - - - MLBG59.Select_Features.Select_Features — MLBG59 1.0.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    • - -
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    • - - -
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    • - -
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-
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-
- -

Source code for MLBG59.Select_Features.Select_Features

-""" Features selection
-
-- select_features (func) : features selection following method
-
-"""
-from sklearn.decomposition import PCA
-from sklearn.preprocessing import StandardScaler
-import pandas as pd
-import numpy as np
-
-
-
[docs]class FeatSelector(object):
-    """features selection following  method
-
-    - pca : use pca to reduce dataset dimensions
-    - no_rescale_pca : use pca without rescaling data
-
-    Parameters
-    ----------
-    method : string (Default pca)
-        method use to select features
-    """
-
-    def __init__(self,
-                 method='pca'
-                 ):
-        assert method in ['pca', 'no_rescale_pca'], 'invalid method : select pca / no_rescale_pca'
-
-        self.method = method
-        self.is_fitted = False
-        self.l_select_var = []
-        self.selector = None
-        self.scaler = None
-
-    """
-    ----------------------------------------------------------------------------------------------
-    """
-
-
[docs]    def fit(self, df, l_var=None, verbose=False):
-        """fit selector
-
-        Parameters
-        ----------
-        df : DataFrame
-            input dataset
-        l_var : list
-            features to encode.
-            If None, all features identified as numerical
-        verbose : boolean (Default False)
-            Get logging information
-        """
-        # get categorical and boolean features (see Features_Type module doc)
-        l_num = [col for col in df.columns.tolist() if df[col].dtype != 'object']
-
-        # list of features to encode
-        if l_var is None:
-            self.l_select_var = l_num
-        else:
-            self.l_select_var = [col for col in l_var if col in l_num]
-
-        if len(self.l_select_var) > 1:
-            # PCA method
-            if self.method in ['pca', 'no_rescale_pca']:
-
-                if self.method == 'pca':
-                    scaler = StandardScaler()
-                    df_local = scaler.fit_transform(df[self.l_select_var])
-                    self.scaler = scaler
-                else:
-                    df_local = df['l_select_var'].copy()
-
-            # init pca object
-            pca = PCA()
-
-            # fit and transform with pca
-            pca.fit(df_local)
-            self.selector = pca
-
-            # Fitted !
-            self.is_fitted = True
-
-            # verbose
-            if verbose:
-                print(" **method : " + self.method)
-                print("  >", len(self.l_select_var), "features to encode")
-
-        else:
-            print('not enough features !')

-
-    """
-    ----------------------------------------------------------------------------------------------
-    """
-
-
[docs]    def transform(self, df, verbose=False):
-        """ apply features selection on a dataset
-
-        Parameters
-        ----------
-        df : DataFrame
-            dataset to transform
-        verbose : boolean (Default False)
-            Get logging information
-
-        Returns
-        -------
-        DataFrame : modified dataset
-        """
-        assert self.is_fitted, 'fit the encoding first using .fit method'
-
-        l_var_other = [col for col in df.columns.tolist() if col not in self.l_select_var]
-        df_local = df[self.l_select_var].copy()
-
-        # pca methods
-        if self.method in ['pca', 'no_rescale_pca']:
-            if self.scaler is not None:
-                df_local = self.scaler.transform(df_local)
-
-            pca = self.selector
-            df_local = pd.DataFrame(pca.transform(df_local))
-            df_local = df_local.rename(
-                columns=dict(zip(df_local.columns.tolist(), ['Dim' + str(v) for v in df_local.columns.tolist()])))
-
-        # find argmin to get 90% of variance
-        n_dim = np.argwhere(np.cumsum(pca.explained_variance_ratio_) > 0.90)[0][0]
-
-        # concat with other dataset features
-        if len(l_var_other) > 0:
-            df_reduced = pd.concat((df[l_var_other].reset_index(drop=True), df_local.iloc[:, :n_dim + 1]), axis=1)
-        else:
-            df_reduced = df_local.iloc[:, :n_dim + 1]
-
-        # verbose
-        if verbose:
-            print("Numerical Dimensions reduction : " + str(len(self.l_select_var)) + " - > " + str(n_dim + 1))
-            print("explained inertia : " + str(round(np.cumsum(pca.explained_variance_ratio_)[n_dim], 4)))
-        return df_reduced

-
-    """
-    ----------------------------------------------------------------------------------------------
-    """
-
-
[docs]    def fit_transform(self, df, l_var, verbose=False):
-        """ fit and apply features selection
-
-        Parameters
-        ----------
-        df : DataFrame
-            input dataset
-        l_var : list
-            features to encode.
-            If None, all features identified as dates (see Features_Type module)
-        verbose : boolean (Default False)
-            Get logging information
-
-        Returns
-        -------
-        DataFrame : modified dataset
-        """
-        df_local = df.copy()
-        self.fit(df_local, l_var=l_var, verbose=verbose)
-        df_reduced = self.transform(df_local, verbose=verbose)
-
-        return df_reduced

-
-
-"""
-----------------------------------------------------------------------------------------------
-"""
-
-
-
[docs]def select_features(df, target, method='pca', verbose=False):
-    """features selection following  method
-
-    - pca : use pca to reduce dataset dimensions
-    - no_rescale_pca : use pca without rescaling data
-
-    Parameters
-    ----------
-    df : DataFrame
-        input dataset containing features
-    target : string
-        target name
-    method : string (Default pca)
-        method use to select features
-    verbose : boolean (Default False)
-        Get logging information
-
-    Returns
-    -------
-    DataFrame
-        modified dataset
-    """
-    # assert valid method
-    assert method in ['pca', 'no_rescale_pca'], method + " invalid method : select pca, no_rescale_pca"
-
-    # get numerical features (except target) and others
-    l_num = [col for col in df._get_numeric_data().columns.tolist() if col != target]
-    l_other = [col for col in df.columns.tolist() if col not in l_num]
-
-    # prepare dataset to apply PCA
-    df_num = df[l_num].copy()
-
-    # PCA method
-    if method in ['pca', 'no_rescale_pca']:
-
-        if method == 'pca':
-            scaler = StandardScaler()
-            X = scaler.fit_transform(df_num)
-        else:
-            X = df_num.copy()
-
-        # init pca object
-        pca = PCA()
-
-        # fit and transform with pca
-        X_transform = pd.DataFrame(pca.fit_transform(X))
-        X_transform = X_transform.rename(
-            columns=dict(zip(X_transform.columns.tolist(), ['Dim' + str(v) for v in X_transform.columns.tolist()])))
-
-        # find argmin to get 90% of variance
-        n_dim = np.argwhere(np.cumsum(pca.explained_variance_ratio_) > 0.90)[0][0]
-
-    # concat with other dataset features
-    if len(l_other) > 0:
-
-        df_pca = pd.concat((df[l_other].reset_index(drop=True), X_transform.iloc[:, :n_dim + 1]), axis=1)
-    else:
-        df_pca = X_transform.iloc[:, :n_dim + 1]
-
-    # verbose
-    if verbose:
-        print("Numerical Dimensions reduction : " + str(len(l_num)) + " - > " + str(n_dim + 1))
-        print("explained inertia : " + str(round(np.cumsum(pca.explained_variance_ratio_)[n_dim], 4)))
-
-    return df_pca

-
- -
- -
-
- - -
- -
-

- © Copyright 2020, Maxence LABESSE - -

-
- Built with Sphinx using a theme provided by Read the Docs. - -
- -
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- -
- -
- - - - - - - - - - - - \ No newline at end of file diff --git a/docs/_build.html/_modules/AutoMxL/Start/Encode_Target.html b/docs/_build.html/_modules/AutoMxL/Start/Encode_Target.html deleted file mode 100644 index 4ed8c7c..0000000 --- a/docs/_build.html/_modules/AutoMxL/Start/Encode_Target.html +++ /dev/null @@ -1,297 +0,0 @@ - - - - - - - - - - - MLBG59.Start.Encode_Target — MLBG59 1.0.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
- - - -
- - - - - -
- -
- - - - - - - - - - - - - - - - - -
- -
    - -
  • Docs »
    • - -
  • Module code »
    • - -
  • MLBG59.Start.Encode_Target
    • - - -
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    • - -
- - -
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-
-
- -

Source code for MLBG59.Start.Encode_Target

-"""Target encoding functions :
-
-- category_to_target : create a target variable (1/0) from a selected category
-- range_to_target : create a target variable (1/0) from a selected range
-"""
-import pandas as pd
-import numpy as np
-
-
-
[docs]def category_to_target(df, var, cat):
-    """Create a target variable (1/0) from a selected category
-
-    Parameters
-    ----------
-    df : DataFrame
-        input dataset
-    var : string
-        variable containing the target category
-    cat : string
-         target category
-
-    Returns
-    -------
-    DataFrame : modified dataset
-    string : new target name (var+'_'+cat)
-    """
-    # transform variable to string if numerical
-    if var in df._get_numeric_data().columns:
-        df[var] = df[var].apply(str)
-        cat = str(cat)
-
-    # one hot encoding
-    target_dummies = pd.get_dummies(df[var])
-    # select cat feature
-    target_dummies[var + '_' + cat] = target_dummies[cat]
-
-    # add encoded cat feature to dataset
-    df_local = pd.concat((df, target_dummies[var + '_' + cat]), axis=1)
-
-    # remove var
-    del df_local[var]
-
-    return df_local, var + '_' + cat

-
-
-"""
------------------------------------------------------------------------------------------------------
-"""
-
-
-
[docs]def range_to_target(df, var, min=None, max=None, verbose=False):
-    """Create a target variable (1/0) from a selected range
-
-    Parameters
-    ----------
-    df : DataFrame
-        input dataset
-    var : string
-        variable containing the target range
-    min : float
-        lower limit.
-        If None, no min
-    max : float
-        upper limit.
-        If None, no max
-    verbose : boolean (Default False)
-        Get logging information
-
-    Returns
-    -------
-    DataFrame : modified dataset
-    string : new target name (var+'_'+lower+'_'+upper)
-    """
-    assert min is not None or max is not None, 'fill at least one limit parameter (lower,upper)'
-
-    df_local = df.copy()
-
-    # transform variable to numeric if string
-    if var not in df_local._get_numeric_data().columns:
-        df_local[var] = pd.to_numeric(df_local[var], errors='coerce')
-
-    # handle None limits : replace by infinity
-    if min is None:
-        min = -float("inf")
-    if max is None:
-        max = float("inf")
-
-    # define target name, using lower and upper values
-    target_name = var + '_' + str(min) + '_' + str(max)
-
-    # encode target
-    df_local[target_name] = np.where((df_local[var] >= min) & (df_local[var] <= max), 1, 0)
-
-    if verbose:
-        print("Created target : ", target_name)
-        print(df_local[target_name].value_counts().rename_axis('values').to_frame('counts'))
-
-    # remove var
-    del df_local[var]
-
-    return df_local, target_name

-
-
- -
- -
-
- - -
- -
-

- © Copyright 2020, Maxence LABESSE - -

-
- Built with Sphinx using a theme provided by Read the Docs. - -
- -
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- -
- -
- - - - - - - - - - - - \ No newline at end of file diff --git a/docs/_build.html/_modules/AutoMxL/Start/Load.html b/docs/_build.html/_modules/AutoMxL/Start/Load.html deleted file mode 100644 index 0721888..0000000 --- a/docs/_build.html/_modules/AutoMxL/Start/Load.html +++ /dev/null @@ -1,287 +0,0 @@ - - - - - - - - - - - MLBG59.Start.Load — MLBG59 1.0.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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- -
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- -

Source code for MLBG59.Start.Load

-"""Data import functions :
-
-- get_delimiter : identify delimiter for a .csv/.txt file
-- load_data : import dataset file into dataframe
-"""
-import pandas as pd
-
-
-
[docs]def get_delimiter(file):
-    """Identify the delimiter for a csv/txt file
-
-    Parameters
-    ----------
-    file : string
-        Path and name of the file (Ex : "data/file.csv")
-
-    Returns
-    -------
-    string
-        identified delimiter
-    """
-    if file.endswith('.csv') or file.endswith('.txt'):
-        # file reading
-        with open(file, 'r') as myCsvfile:
-            # Reads one entire line from the file
-            header = myCsvfile.readline()
-
-            # Returns the lowest index of the substring if it is found in given string. (-1 = not found)
-            if header.find(";") != -1:
-                delimiter = ";"
-            elif header.find(",") != -1:
-                delimiter = ","
-
-        return delimiter
-
-    else:
-        print('Please use a .csv or .txt file')

-
-
-"""
--------------------------------------------------------------------------------------------------------------------------
-"""
-
-
-
[docs]def import_data(file, index_col=None, verbose=False):
-    """Import dataset as a DataFrame (identify delimiter for txt and csv files)
-
-    Available files : .txt, .csv, .xlsx, .xls files
-
-    Parameters
-    ----------
-    file : string
-        Path and name of the file (Ex : "data/file.csv")
-        If file is .csv, automatically identify delimiter
-    index_col : int, str, sequence of int / str, or False (Default None)
-        Column(s) to use as the row labels of the DataFrame, either given as string name or column index.
-        If a sequence of int / str is given, a MultiIndex is used.
-    verbose : boolean (Default False)
-        Get logging information
-
-    Returns
-    -------
-    DataFrame
-        imported dataset
-    """
-    # CSV
-    if file.endswith('.csv') or file.endswith('.txt'):
-        # Find file delimiter
-        file_sep = get_delimiter(file)
-        # import 
-        df = pd.read_csv(file, encoding="iso-8859-1", sep=file_sep, index_col=index_col)
-
-    # Excel
-    elif (file.endswith('.xlsx')) or (file.endswith('.xsl')):
-        df = pd.read_excel(file)
-
-    # JSON
-    elif file.endswith('.json'):
-        # to-do
-        pass
-
-    else:
-        df = None
-
-    if verbose:
-        if df is not None:
-            print('-> File ' + file + ' successfully imported as DataFrame')
-            print('-> DataFrame size  : ', df.shape)
-        else:
-            print("File couldn't be imported")
-
-    return df

-
- -
- -
-
- - -
- -
-

- © Copyright 2020, Maxence LABESSE - -

-
- Built with Sphinx using a theme provided by Read the Docs. - -
- -
-
- -
- -
- - - - - - - - - - - - \ No newline at end of file diff --git a/docs/_build.html/_modules/AutoMxL/Utils/Decorators.html b/docs/_build.html/_modules/AutoMxL/Utils/Decorators.html deleted file mode 100644 index a91b723..0000000 --- a/docs/_build.html/_modules/AutoMxL/Utils/Decorators.html +++ /dev/null @@ -1,221 +0,0 @@ - - - - - - - - - - - MLBG59.Utils.Decorators — MLBG59 1.0.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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- -

Source code for MLBG59.Utils.Decorators

-from time import time
-
-# Timer
-def timer(func):
-    """Function decorator to get the execution time
-
-    Parameters
-    ----------
-    func : function
-        input function
-
-    Returns
-    -------
-    function
-        wrapped function
-    """
-    def f(*args, **kwargs):
-        before = time()
-        rv = func(*args, **kwargs)
-        after = time()
-        print('\t\t>>>',func.__name__,'execution time:', round(after - before, 4),'secs. <<<')
-        return rv
-    f.__name__ = func.__name__
-    f.__doc__ = func.__doc__
-
-    return f
-
- -
- -
-
- - -
- -
-

- © Copyright 2020, Maxence LABESSE - -

-
- Built with Sphinx using a theme provided by Read the Docs. - -
- -
-
- -
- -
- - - - - - - - - - - - \ No newline at end of file diff --git a/docs/_build.html/_modules/AutoMxL/__main__.html b/docs/_build.html/_modules/AutoMxL/__main__.html deleted file mode 100644 index cede0a4..0000000 --- a/docs/_build.html/_modules/AutoMxL/__main__.html +++ /dev/null @@ -1,803 +0,0 @@ - - - - - - - - - - - MLBG59.__main__ — MLBG59 1.0.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
- - - -
- - - - - -
- -
- - - - - - - - - - - - - - - - - -
- - - - -
-
-
-
- -

Source code for MLBG59.__main__

-from MLBG59.Utils.Display import print_title1
-from MLBG59.Utils.Decorators import timer
-from MLBG59.Explore.Explore import explore
-from MLBG59.Preprocessing.Date import DateEncoder
-from MLBG59.Preprocessing.Missing_Values import NAEncoder
-from MLBG59.Preprocessing.Outliers import OutliersEncoder
-from MLBG59.Preprocessing.Categorical import CategoricalEncoder
-from MLBG59.Modelisation.HyperOpt import *
-from MLBG59.Select_Features.Select_Features import FeatSelector
-from time import time
-
-
-
[docs]class AML(pd.DataFrame):
-    """Covers the complete pipeline of a classification project from a raw dataset to a deployable model.
-
-    AML is built as a class inherited from pandas DataFrame. Each Machine Learning step corresponds to method that
-    can be called with default or filled parameters.
-
-    - explore: explore dataset and identify features types
-    - preprocess: clean and prepare data (optional : outliers processing).
-    - select_features: features selection (optional)
-    - model_train_predict : split AML in train/test sets to fits/apply models with random search.
-      Returns the list of the valid models (without overfitting) and the best one.
-
-    deployment methods:
-
-    - preprocess_apply : apply fitted preprocessing transformation to a new dataset
-    - select_features_apply : idem
-    - model_apply : apply fitted models to a new dataset
-
-
-    Notes :
-
-    - A method requires that the former one has been applied (actuel step is given by "step" attribute)
-    - Target has to be binary and encoded as int (1/0) (see MLGB59.Start.Encode_Target module if you need help)
-    - don't call your target "target" please :>
-
-    Parameters
-    ----------
-    _obj : DataFrame
-        Source Dataset
-    target : string
-        target name
-  """
-
-    def __init__(self, *args, target=None, **kwargs):
-        super(AML, self).__init__(*args, **kwargs)
-        assert target != 'target', 'target name cannot be "target"'
-        # parameters
-        self.target = target
-        # attributes
-        self.step = 'None'
-        self.d_features = None
-        self.d_preprocess = None
-        self.features_selector = None
-        self.d_hyperopt = None
-        self.is_fitted_preprocessing = False
-        self.is_fitted_selector = False
-        self.is_fitted_model = False
-
-    """
-    --------------------------------------------------------------------------------------------------------------------
-    """
-
-    def __repr__(self):
-        return 'MLBG59 instance'
-
-    """
-    --------------------------------------------------------------------------------------------------------------------
-    """
-
-    def duplicate(self):
-        res = AML(self)
-        res.__dict__.update(self.__dict__)
-        return res
-
-    """
-    --------------------------------------------------------------------------------------------------------------------
-    """
-
-
[docs]    def explore(self, verbose=False):
-        """data exploration and features type identification
-
-        Note :  if you disagree with automated identification, you can directly modify d_features attribute
-
-        Create self.d_features : dict {x : list of variables names}
-            - date: date features
-            - identifier: identifier features
-            - verbatim: verbatim features
-            - boolean: boolean features
-            - categorical: categorical features
-            - numerical: numerical features
-            - NA: features which contains NA values
-            - low_variance: list of the features with low variance and unique values
-
-        Parameters
-        ----------
-        verbose : boolean (Default False)
-            Get logging information
-        """
-        if verbose:
-            start_time = time()
-            print_title1('Explore')
-
-        df_local = self.copy()
-        if self.target is not None:
-            df_local = df_local.drop(self.target, axis=1)
-
-        # call std_audit_dataset function
-        self.d_features = explore(
-            df_local, verbose=verbose)
-
-        self.step = 'explore'
-
-        # created attributes display
-        if verbose:
-            color_print("\nCreated attributes :  d_features (dict) ")
-            print("Keys :")
-            print("  -> date")
-            print("  -> identifier")
-            print("  -> verbatim")
-            print("  -> boolean")
-            print("  -> categorical")
-            print("  -> numerical")
-            print("  -> date")
-            print("  -> NA")
-            print("  -> low_variance")
-            print('\n\t\t>>>', 'explore execution time:', round(time() - start_time, 4), 'secs. <<<')

-
-    """
-    --------------------------------------------------------------------------------------------------------------------
-    """
-
-
[docs]    def preprocess(self, date_ref=None, process_outliers=False,
-                   cat_method='deep_encoder', verbose=False):
-        """Prepare the data before feeding it to the model :
-
-            - remove low variance features
-            - remove identifiers and verbatims features
-            - transform date features to timedelta
-            - fill missing values
-            - process categorical and boolean data (one-hot-encoding or Pytorch NN encoder)
-            - replace outliers (optional)
-
-            create self.d_preprocess : dict {step : transformation}
-                - remove: list of the features to remove
-                - date: fitted DateEncoder object
-                - NA: fitted NAEncoder object
-                - categorical: fitted CategoricalEncoder object
-                - outlier: fitted OutlierEncoder object
-
-        Parameters
-        ----------
-        date_ref : string '%d/%m/%y' (Default : None)
-            ref date to compute date features timedelta.
-            If None, today date
-        process_outliers : boolean (Default : False)
-            Enable outliers replacement
-        cat_method : string (Default : 'deep_encoder')
-            Categorical features encoding method
-        verbose : boolean (Default False)
-            Get logging information
-
-        """
-        # check pipe step
-        assert self.step in ['explore'], 'apply explore method first'
-        assert not self.is_fitted_preprocessing, 'preprocessing encoders already fitted'
-
-        ###############################
-        # Fit and apply preprocessing #
-        ###############################
-        if verbose:
-            start_time = time()
-            print_title1('Fit and apply preprocessing')
-
-        target = self.target
-        df_local = self.copy()
-
-        # Features Removing 'zero variance / verbatims / identifiers)
-        if verbose:
-            color_print("Features removing (zero variance / verbatims / identifiers)")
-
-        l_remove = self.d_features['low_variance'] + self.d_features['verbatim'] + self.d_features['identifier']
-        if len(l_remove) > 0:
-            df_local = df_local.drop(l_remove, axis=1)
-
-        if verbose:
-            print("  >", len(l_remove), "features to remove")
-            if len(l_remove) > 0:
-                print(" ", l_remove)
-
-        # Transform date -> time between date and date_ref
-        if verbose:
-            color_print("Transform date")
-
-        date_encoder = DateEncoder(method='timedelta', date_ref=date_ref)
-        date_encoder.fit(self, l_var=self.d_features['date'], verbose=False)
-        df_local = date_encoder.transform(df_local, verbose=verbose)
-
-        # Missing Values
-        if verbose:
-            color_print('Missing values')
-
-        NA_encoder = NAEncoder()
-        NA_encoder.fit(df_local, l_var=None, verbose=False)
-        df_local = NA_encoder.transform(df_local, verbose=verbose)
-
-        # replace outliers
-        if process_outliers:
-            if verbose:
-                color_print('Outliers')
-            out_encoder = OutliersEncoder()
-            out_encoder.fit(df_local, l_var=None, verbose=False)
-            df_local = out_encoder.transform(df_local, verbose=verbose)
-        else:
-            out_encoder = None
-
-        # categorical processing
-        if verbose:
-            color_print('Encode Categorical and boolean')
-
-        cat_col = self.d_features['categorical'] + self.d_features['boolean']
-        # apply one-hot encoding if target not filled in class parameters
-        if self.target is None:
-            cat_method = 'one_hot'
-            color_print('No target -> one_hot encoding !', 31)
-
-        # get embedding
-        cat_encoder = CategoricalEncoder(method=cat_method)
-        cat_encoder.fit(self, l_var=cat_col, target=self.target, verbose=verbose)
-        df_local = cat_encoder.transform(df_local, verbose=verbose)
-
-        # store preprocessing params
-        self.d_preprocess = {'remove': l_remove, 'date': date_encoder, 'NA': NA_encoder, 'categorical': cat_encoder}
-        if out_encoder is not None:
-            self.d_preprocess['outlier'] = out_encoder
-
-        if verbose:
-            color_print("\nCreated attributes :  d_preprocess (dict) ")
-            print("Keys :")
-            print("  -> remove")
-            print("  -> date")
-            print("  -> NA")
-            print("  -> categorical")
-            print("  -> outlier (optional)")
-
-        # is_fitted
-        self.is_fitted_preprocessing = True
-
-        # update self
-        self.__dict__.update(df_local.__dict__)
-        self.target = target
-        self.step = 'preprocess'
-
-        if verbose:
-            color_print("New DataFrame size ")
-            print("  > row number : ", self.shape[0], "\n  > col number : ", self.shape[1])
-            print('\n\t\t>>>', 'proprocess execution time:', round(time() - start_time, 4), 'secs. <<<')

-
-    """
-    --------------------------------------------------------------------------------------------------------------------
-    """
-
-
[docs]    def preprocess_apply(self, df, verbose=False):
-        """Apply preprocessing.
-
-        Requires preprocess method to have been applied (so that all encoder are fitted).
-
-        Parameters
-        ----------
-        df : DataFrame
-            dataset to apply preprocessing on
-        verbose : boolean (Default False)
-            Get logging information
-
-        Returns
-        -------
-        DataFrame : Preprocessed dataset
-        """
-        if verbose:
-            start_time = time()
-            print_title1('Apply Preprocessing')
-
-        # check pipe step and is_fitted
-        assert self.is_fitted_preprocessing, "fit first (please)"
-
-        #
-        df_local = df.copy()
-
-        # Remove features with zero variance / verbatims and identifiers
-        if verbose:
-            color_print("Remove features (zero variance, verbatims and identifiers")
-
-        if len(self.d_preprocess['remove']) > 0:
-            df_local = df_local.drop(self.d_preprocess['remove'], axis=1)
-            if verbose:
-                print("  >", len(self.d_preprocess['remove']), 'removed features')
-        else:
-            if verbose:
-                print("  > No features to remove")
-
-        # Transform date -> time between date and date_ref
-        if verbose:
-            color_print("Transform date")
-        df_local = self.d_preprocess['date'].transform(df_local, verbose=verbose)
-
-        # Missing Values
-        if verbose:
-            color_print('Missing values')
-        df_local = self.d_preprocess['NA'].transform(df_local, verbose=verbose)
-
-        # replace outliers
-        if 'outlier' in list(self.d_preprocess.keys()):
-            if verbose:
-                color_print('Outliers')
-            df_local = self.d_preprocess['outlier'].transform(df_local, verbose=verbose)
-
-        # categorical processing
-        if verbose:
-            color_print('Encode categorical and boolean')
-            print('\n\t\t>>>', 'preprocess_apply execution time:', round(time() - start_time, 4), 'secs. <<<')
-        df_local = self.d_preprocess['categorical'].transform(df_local, verbose=verbose)
-
-        return df_local

-
-    """
-    --------------------------------------------------------------------------------------------------------------------
-    """
-
-
[docs]    def select_features(self, method='pca', verbose=False):
-        """ fit and apply features selection (optional)
-
-        Parameters
-        ----------
-        method : string (Default pca)
-            method use to select features
-        verbose : boolean (Default False)
-            Get logging information
-
-        """
-        assert self.step in ['preprocess'], 'apply preprocess method'
-
-        target = self.target
-
-        if verbose:
-            start_time = time()
-            print_title1('Features Selection')
-
-        df_local = self.copy()
-
-        l_select_var = [col for col in df_local.columns.tolist() if col != self.target]
-
-        # df_local = select_features(df=df_local, target=self.target, method=method, verbose=verbose)
-
-        features_selector = FeatSelector(method=method)
-        features_selector.fit(df_local, l_var=l_select_var, verbose=verbose)
-        df_local = features_selector.transform(df_local, verbose=verbose)
-
-        self.__dict__.update(df_local.__dict__)
-        self.target = target
-        self.features_selector = features_selector
-        self.is_fitted_selector = True
-        self.step = 'features_selection'
-
-        if verbose :
-            print('\n\t\t>>>', 'select_features execution time:', round(time() - start_time, 4), 'secs. <<<')

-
-    """
-        --------------------------------------------------------------------------------------------------------------------
-    """
-
-
[docs]    def select_features_apply(self, df, verbose=False):
-        """Apply features selection.
-
-         Requires Select_Features method to have been applied
-
-         Parameters
-         ----------
-         df : DataFrame
-             dataset to apply selection on
-         verbose : boolean (Default False)
-             Get logging information
-
-         Returns
-         -------
-         DataFrame : reduced dataset
-         """
-        # check pipe step and is_fitted
-        assert self.is_fitted_selector, "fit first (please)"
-
-        if verbose:
-            start_time = time()
-            print_title1('Apply select_features')
-
-        df_local = df.copy()
-
-        df_local = self.features_selector.transform(df_local, verbose=verbose)
-
-        if verbose:
-            print('\n\t\t>>>', 'select_features_apply execution time:', round(time() - start_time, 4), 'secs. <<<')
-
-        return df_local

-
-    """
-        --------------------------------------------------------------------------------------------------------------------
-    """
-
-
[docs]    def model_train_test(self, clf='XGBOOST', grid_param=None, metric='F1', top_bagging=False, n_comb=10,
-                         comb_seed=None,
-                         verbose=False):
-        """train and test models with random search
-
-        - creates models with random hyper-parameters combinations from HP grid
-        - splits (random 80/20) train/test sets to fit/apply models
-        - identifies valid models |(auc(train)-auc(test)|<0.03
-        - gets the best model in respect of a selected metric among valid model
-
-
-        Notes :
-
-        - Available classifiers : Random Forest, XGBOOST
-        - can enable bagging algo with top_bagging parameter
-
-        Parameters
-        ----------
-        clf : string (Default : 'XGBOOST')
-            classifier used for modelisation
-        grid_param : dict
-            random search grid {Hyperparameter name : values list}
-        metric : string (Default : 'F1')
-            objective metric
-        top_bagging : boolean (Default : False)
-            enable Bagging
-        n_comb : int (Default : 10)
-            HP combination number
-        comb_seed : int (Default : None)
-            random combination seed
-        verbose : boolean (Default False)
-            Get logging information
-
-        Returns
-        -------
-        dict
-            {model_index : {'HP', 'probas', 'model', 'features_importance', 'train_metrics', 'metrics', 'output'}
-        list
-            valid models indexes
-        int
-            best model index
-        DataFrame
-            models summary
-        """
-        assert self.step in ['preprocess', 'features_selection'], 'apply preprocess method'
-
-        if verbose:
-            start_time = time()
-            print_title1('Train predict')
-
-        # Train/Test split
-        df_train, df_test = train_test(self, 0.2)
-
-        # Create Hyperopt object
-        hyperopt = HyperOpt(classifier=clf, grid_param=grid_param, n_param_comb=n_comb,
-                            bagging=top_bagging, comb_seed=comb_seed)
-
-        # fit model on train set
-        if verbose:
-            color_print('training models')
-
-        hyperopt.fit(df_train, self.target, verbose=verbose)
-
-        # Apply model on test set
-        if verbose:
-            color_print('\napplying models')
-
-        d_fitted_models = hyperopt.predict(df_test, self.target, delta_auc=0.03, verbose=verbose)
-
-        # model selection
-        if verbose:
-            color_print('\nbest model selection')
-        best_model_idx, l_valid_models = hyperopt.get_best_model(d_fitted_models, metric=metric, delta_auc_th=0.03,
-                                                                 verbose=False)
-
-        df_model_res = hyperopt.model_res_to_df(d_fitted_models, sort_metric=metric)
-
-        if best_model_idx is not None:
-            print_title1('best model : ' + str(best_model_idx))
-            print(metric + ' : ' + str(round(d_fitted_models[best_model_idx]['metrics'][metric], 4)))
-            print('AUC : ' + str(round(d_fitted_models[best_model_idx]['metrics']['Roc_auc'], 4)))
-            if round(d_fitted_models[best_model_idx]['metrics'][metric], 4) == 1.0:
-                color_print("C'était pas qu'un physique finalement hein ?", 32)
-            print('\n\t\t>>>', 'model_train_test execution time:', round(time() - start_time, 4), 'secs. <<<')
-
-        self.d_hyperopt = hyperopt
-        self.is_fitted_model = True
-
-        return d_fitted_models, l_valid_models, best_model_idx, df_model_res

-
-    """
-    ------------------------------------------------------------------------------------------------------------------------
-    """
-
-
[docs]    def model_train(self, clf='XGBOOST', grid_param=None, top_bagging=False, n_comb=10, comb_seed=None, verbose=False):
-        """train models with random search
-
-        - creates models with random hyper-parameters combinations from HP grid
-        - fits models on self
-
-        Notes :
-
-        - Available classifiers : Random Forest, XGBOOST
-        - can enable bagging algo with top_bagging parameter
-
-        Parameters
-        ----------
-        clf : string (Default : 'XGBOOST')
-            classifier used for modelisation
-        grid_param : dict
-            random search grid {Hyperparameter name : values list}
-        top_bagging : boolean (Default : False)
-            enable Bagging
-        n_comb : int (Default : 10)
-            HP combination number
-        comb_seed : int (Default : None)
-            random combination seed
-        verbose : boolean (Default False)
-            Get logging information
-
-        """
-        assert self.step in ['preprocess', 'features_selection'], 'apply preprocess method'
-
-        df_train = self.copy()
-
-        if verbose:
-            start_time = time()
-            print_title1('Train Models')
-
-        # instantiate Hyperopt object
-        hyperopt = HyperOpt(classifier=clf, grid_param=grid_param, n_param_comb=n_comb,
-                            bagging=top_bagging, comb_seed=comb_seed)
-
-        # fit model on train set
-        if verbose:
-            color_print('training models')
-
-        # fit hyperopt on self
-        hyperopt.fit(df_train, self.target, verbose=verbose)
-
-        self.d_hyperopt = hyperopt
-        self.is_fitted_model = True
-
-        if verbose:
-            print('\n\t\t>>>', 'model_train execution time:', round(time() - start_time, 4), 'secs. <<<')

-
-    """
-    ------------------------------------------------------------------------------------------------------------------------
-    """
-
-
[docs]    def model_predict(self, df, metric='F1', verbose=False):
-        """apply fitted models on a dataset
-
-        - identifies valid models |(auc(train)-auc(test)|<0.03
-        - gets the best model in respect of a selected metric among valid model
-
-        Parameters
-        ----------
-        metric : string (Default : 'F1')
-            objective metric
-        verbose : boolean (Default False)
-            Get logging information
-
-        Returns
-        -------
-        dict
-            {model_index : {'HP', 'probas', 'model', 'features_importance', 'train_metrics', 'metrics', 'output'}
-        list
-            valid models indexes
-        int
-            best model index
-        DataFrame
-            models summary
-        """
-        assert self.is_fitted_model, "model is not fitted yet, apply model_train_predict or model_train methods"
-
-        if verbose:
-            start_time = time()
-            color_print('\napplying models')
-
-        # apply models on dataset
-        d_fitted_models = self.d_hyperopt.predict(df, self.target, delta_auc=0.03, verbose=verbose)
-
-        # model selection
-        if verbose:
-            color_print('\nbest model selection')
-        best_model_idx, l_valid_models = self.d_hyperopt.get_best_model(d_fitted_models, metric=metric,
-                                                                        delta_auc_th=0.03,
-                                                                        verbose=False)
-        # store model results
-        df_model_res = self.d_hyperopt.model_res_to_df(d_fitted_models, sort_metric=metric)
-
-        if best_model_idx is not None:
-            print_title1('best model : ' + str(best_model_idx))
-            print(metric + ' : ' + str(round(d_fitted_models[best_model_idx]['metrics'][metric], 4)))
-            print('AUC : ' + str(round(d_fitted_models[best_model_idx]['metrics']['Roc_auc'], 4)))
-            if round(d_fitted_models[best_model_idx]['metrics'][metric], 4) == 1.0:
-                color_print("C'était pas qu'un physique finalement hein ?", 32)
-            print('\n\t\t>>>', 'model_predict execution time:', round(time() - start_time, 4), 'secs. <<<')
-
-        return d_fitted_models, l_valid_models, best_model_idx, df_model_res

-
- -
- -
-
- - -
- -
-

- © Copyright 2020, Maxence LABESSE - -

-
- Built with Sphinx using a theme provided by Read the Docs. - -
- -
-
- -
- -
- - - - - - - - - - - - \ No newline at end of file diff --git a/docs/_build.html/_modules/Load/Load.html b/docs/_build.html/_modules/Load/Load.html deleted file mode 100644 index 73aa7a2..0000000 --- a/docs/_build.html/_modules/Load/Load.html +++ /dev/null @@ -1,275 +0,0 @@ - - - - - - - - - - - Load.Load — MLBG59 1.0.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
- - - -
- - - - - -
- -
- - - - - - - - - - - - - - - - - -
- - - - -
-
-
-
- -

Source code for Load.Load

-""" contains objets related to data importation :
-- get_delimiter : identify csv file delimiter
-- load data
-
-"""
-import pandas as pd
-
-
-
[docs]def get_delimiter(csvfile):
-    """
-    Identify the delimiter of a .csv file
-
-    Parameters
-    ----------
-    csvfile : string
-        path and name of the file (Ex : "data/file.csv")
-
-    Returns
-    -------
-    string
-        identified delimiter
-    """
-    # csv file reading
-    with open(csvfile, 'r') as myCsvfile:
-        # Reads one entire line from the file
-        header = myCsvfile.readline()
-        
-        # Returns the lowest index of the substring if it is found in given string. (-1 = not found)
-        if header.find(";") != -1:
-            delimiter = ";"
-        elif header.find(",") != -1:
-            delimiter = ","
-
-    return delimiter

-
-
-"""
--------------------------------------------------------------------------------------------------------------------------
-"""
-
-
-
[docs]def import_data(file, index_col=None, verbose=1):
-    """
-    Import dataset as a DataFrame
-    accept .csv, .xlsx, .xls files
-
-    Parameters
-    ----------
-    file : string
-        path and name of the file (Ex : "data/file.csv")
-        if file is .csv, automatically identify delimiter
-    index_col : int, str, sequence of int / str, or False, default None
-        Column(s) to use as the row labels of the DataFrame, either given as string name or column index.
-        If a sequence of int / str is given, a MultiIndex is used.
-    verbose : int (0/1) (Default : 1)
-        get more operations information
-
-    Returns
-    -------
-    DataFrame :
-        dataset imported as dataset
-    """
-    # CSV
-    if file.endswith('.csv'):
-        # Find separator
-        file_sep = get_delimiter(file)
-        # import 
-        df = pd.read_csv(file, encoding="iso-8859-1", sep=file_sep, index_col=index_col)
-    
-    # Excel
-    elif (file.endswith('.xlsx')) or (file.endswith('.xsl')):
-        df = pd.read_excel(file)
-
-    # JSON
-    elif file.endswith('.json'):
-        pass
-
-    else:
-        df = None
-
-    if verbose==1:
-        if df is not None:
-            print('-> Fichier '+file+' importé avec succès')
-            print('-> Taille du dataframe créé : ', df.shape)
-        else:
-            print("Le Fichier n'a pas pu être importé (dommage)")
-
-    return df

-
- -
- -
-
- - -
- -
-

- © Copyright 2020, Maxence LABESSE - -

-
- Built with Sphinx using a theme provided by Read the Docs. - -
- -
-
- -
- -
- - - - - - - - - - - - \ No newline at end of file diff --git a/docs/_build.html/_modules/index.html b/docs/_build.html/_modules/index.html deleted file mode 100644 index 2a77745..0000000 --- a/docs/_build.html/_modules/index.html +++ /dev/null @@ -1,218 +0,0 @@ - - - - - - - - - - - Overview: module code — MLBG59 1.0.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
- - - -
- - - - - -
- -
- - - - - - - - - - - - - - - - - -
- -
    - -
  • Docs »
    • - -
  • Overview: module code
    • - - -
  • - -
    • - -
- - -
-
-
- - -
-
- - -
- -
-

- © Copyright 2020, Maxence LABESSE - -

-
- Built with Sphinx using a theme provided by Read the Docs. - -
- -
-
- -
- -
- - - - - - - - - - - - \ No newline at end of file diff --git a/docs/_build.html/_sources/autoML.rst.txt b/docs/_build.html/_sources/autoML.rst.txt deleted file mode 100644 index 66ad2d8..0000000 --- a/docs/_build.html/_sources/autoML.rst.txt +++ /dev/null @@ -1,4 +0,0 @@ -AML class -============ -.. autoclass:: MLBG59.__main__.AML - :members: diff --git a/docs/_build.html/_sources/docstring_test.rst.txt b/docs/_build.html/_sources/docstring_test.rst.txt deleted file mode 100644 index a822dcd..0000000 --- a/docs/_build.html/_sources/docstring_test.rst.txt +++ /dev/null @@ -1,6 +0,0 @@ -Test -==== -Features_type -------------- -.. automodule:: dev.Features_type - :members: diff --git a/docs/_build.html/_sources/features.rst.txt b/docs/_build.html/_sources/features.rst.txt deleted file mode 100644 index b86f400..0000000 --- a/docs/_build.html/_sources/features.rst.txt +++ /dev/null @@ -1,64 +0,0 @@ -Start -===== -Load ----- -.. automodule:: MLBG59.Start.Load - :members: - -Encode_Target -------------- -.. automodule:: MLBG59.Start.Encode_Target - :members: - -Explore -======= -Explore --------- -.. automodule:: MLBG59.Explore.Explore - :members: - -Features_Type ------------------- -.. automodule:: MLBG59.Explore.Features_Type - :members: - - -Preprocessing -============= -Missing_Values --------------- -.. automodule:: MLBG59.Preprocessing.Missing_Values - :members: - -Categorical Data ----------------- -.. automodule:: MLBG59.Preprocessing.Categorical - :members: - -Date Data --------------------------------------- -.. automodule:: MLBG59.Preprocessing.Date - :members: - -Process Outliers ------------------ -.. automodule:: MLBG59.Preprocessing.Outliers - :members: - -Features Selection -================== -.. automodule:: MLBG59.Select_Features.Select_Features - :members: - - -Modelisation -============ -Bagging -------- -.. automodule:: MLBG59.Modelisation.Bagging - :members: - -Hyperoptimisation ------------------ -.. automodule:: MLBG59.Modelisation.HyperOpt - :members: diff --git a/docs/_build.html/_sources/index.rst.txt b/docs/_build.html/_sources/index.rst.txt deleted file mode 100644 index 08ee273..0000000 --- a/docs/_build.html/_sources/index.rst.txt +++ /dev/null @@ -1,22 +0,0 @@ -.. MLBG59 documentation master file, created by - sphinx-quickstart on Mon Feb 10 14:44:06 2020. - You can adapt this file completely to your liking, but it should at least - contain the root `toctree` directive. - -Welcome to MLBG59's documentation! -================================== - - -.. toctree:: - :maxdepth: 3 - :caption: AutoML class - :hidden: - - autoML - -.. toctree:: - :maxdepth: 3 - :caption: Features - :hidden: - - features diff --git a/docs/_build.html/_static/ajax-loader.gif b/docs/_build.html/_static/ajax-loader.gif deleted file mode 100644 index 61faf8c..0000000 Binary files a/docs/_build.html/_static/ajax-loader.gif and /dev/null differ diff --git a/docs/_build.html/_static/basic.css b/docs/_build.html/_static/basic.css deleted file mode 100644 index 104f076..0000000 --- a/docs/_build.html/_static/basic.css +++ /dev/null @@ -1,676 +0,0 @@ -/* - * basic.css - * ~~~~~~~~~ - * - * Sphinx stylesheet -- basic theme. - * - * :copyright: Copyright 2007-2018 by the Sphinx team, see AUTHORS. - * :license: BSD, see LICENSE for details. - * - */ - -/* -- main layout ----------------------------------------------------------- */ - -div.clearer { - clear: both; -} - -/* -- relbar ---------------------------------------------------------------- */ - -div.related { - width: 100%; - font-size: 90%; -} - -div.related h3 { - display: none; -} - -div.related ul { - margin: 0; - padding: 0 0 0 10px; - list-style: none; -} - -div.related li { - display: inline; -} - -div.related li.right { - float: right; - margin-right: 5px; -} - -/* -- sidebar --------------------------------------------------------------- */ - -div.sphinxsidebarwrapper { - padding: 10px 5px 0 10px; -} - -div.sphinxsidebar { - float: left; - width: 230px; - margin-left: -100%; - font-size: 90%; - word-wrap: break-word; - overflow-wrap : break-word; -} - -div.sphinxsidebar ul { - list-style: none; -} - -div.sphinxsidebar ul ul, -div.sphinxsidebar ul.want-points { - margin-left: 20px; - list-style: square; -} - -div.sphinxsidebar ul ul { - margin-top: 0; - margin-bottom: 0; -} - -div.sphinxsidebar form { - margin-top: 10px; -} - -div.sphinxsidebar input { - border: 1px solid #98dbcc; - font-family: sans-serif; - font-size: 1em; -} - -div.sphinxsidebar #searchbox form.search { - overflow: hidden; -} - -div.sphinxsidebar #searchbox input[type="text"] { - float: left; - width: 80%; - padding: 0.25em; - box-sizing: border-box; -} - -div.sphinxsidebar #searchbox input[type="submit"] { - float: left; - width: 20%; - border-left: none; - padding: 0.25em; - box-sizing: border-box; -} - - -img { - border: 0; - max-width: 100%; -} - -/* -- search page ----------------------------------------------------------- */ - -ul.search { - margin: 10px 0 0 20px; - padding: 0; -} - -ul.search li { - padding: 5px 0 5px 20px; - background-image: url(file.png); - background-repeat: no-repeat; - background-position: 0 7px; -} - -ul.search li a { - font-weight: bold; -} - -ul.search li div.context { - color: #888; - margin: 2px 0 0 30px; - text-align: left; -} - -ul.keywordmatches li.goodmatch a { - font-weight: bold; -} - -/* -- index page ------------------------------------------------------------ */ - -table.contentstable { - width: 90%; - margin-left: auto; - margin-right: auto; -} - -table.contentstable p.biglink { - line-height: 150%; -} - -a.biglink { - font-size: 1.3em; -} - -span.linkdescr { - font-style: italic; - padding-top: 5px; - font-size: 90%; -} - -/* -- general index --------------------------------------------------------- */ - -table.indextable { - width: 100%; -} - -table.indextable td { - text-align: left; - vertical-align: top; -} - -table.indextable ul { - margin-top: 0; - margin-bottom: 0; - list-style-type: none; -} - -table.indextable > tbody > tr > td > ul { - padding-left: 0em; -} - -table.indextable tr.pcap { - height: 10px; -} - -table.indextable tr.cap { - margin-top: 10px; - background-color: #f2f2f2; -} - -img.toggler { - margin-right: 3px; - 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debugger -if (!window.console || !console.firebug) { - var names = ["log", "debug", "info", "warn", "error", "assert", "dir", - "dirxml", "group", "groupEnd", "time", "timeEnd", "count", "trace", - "profile", "profileEnd"]; - window.console = {}; - for (var i = 0; i < names.length; ++i) - window.console[names[i]] = function() {}; -} - */ - -/** - * small helper function to urldecode strings - */ -jQuery.urldecode = function(x) { - return decodeURIComponent(x).replace(/\+/g, ' '); -}; - -/** - * small helper function to urlencode strings - */ -jQuery.urlencode = encodeURIComponent; - -/** - * This function returns the parsed url parameters of the - * current request. Multiple values per key are supported, - * it will always return arrays of strings for the value parts. - */ -jQuery.getQueryParameters = function(s) { - if (typeof s === 'undefined') - s = document.location.search; - var parts = s.substr(s.indexOf('?') + 1).split('&'); - var result = {}; - for (var i = 0; i < parts.length; i++) { - var tmp = parts[i].split('=', 2); - var key = jQuery.urldecode(tmp[0]); - var value = jQuery.urldecode(tmp[1]); - if (key in result) - result[key].push(value); - else - result[key] = [value]; - } - return result; -}; - -/** - * highlight a given string on a jquery object by wrapping it in - * span elements with the given class name. - */ -jQuery.fn.highlightText = function(text, className) { - function highlight(node, addItems) { - if (node.nodeType === 3) { - var val = node.nodeValue; - var pos = val.toLowerCase().indexOf(text); - if (pos >= 0 && - !jQuery(node.parentNode).hasClass(className) && - !jQuery(node.parentNode).hasClass("nohighlight")) { - var span; - var isInSVG = jQuery(node).closest("body, svg, foreignObject").is("svg"); - if (isInSVG) { - span = document.createElementNS("http://www.w3.org/2000/svg", "tspan"); - } else { - span = document.createElement("span"); - span.className = className; - } - span.appendChild(document.createTextNode(val.substr(pos, text.length))); - node.parentNode.insertBefore(span, node.parentNode.insertBefore( - document.createTextNode(val.substr(pos + text.length)), - node.nextSibling)); - node.nodeValue = val.substr(0, pos); - if (isInSVG) { - var bbox = span.getBBox(); - var rect = document.createElementNS("http://www.w3.org/2000/svg", "rect"); - rect.x.baseVal.value = bbox.x; - rect.y.baseVal.value = bbox.y; - rect.width.baseVal.value = bbox.width; - rect.height.baseVal.value = bbox.height; - rect.setAttribute('class', className); - var parentOfText = node.parentNode.parentNode; - addItems.push({ - "parent": node.parentNode, - "target": rect}); - } - } - } - else if (!jQuery(node).is("button, select, textarea")) { - jQuery.each(node.childNodes, function() { - highlight(this, addItems); - }); - } - } - var addItems = []; - var result = this.each(function() { - highlight(this, addItems); - }); - for (var i = 0; i < addItems.length; ++i) { - jQuery(addItems[i].parent).before(addItems[i].target); - } - return result; -}; - -/* - * backward compatibility for jQuery.browser - * This will be supported until firefox bug is fixed. - */ -if (!jQuery.browser) { - jQuery.uaMatch = function(ua) { - ua = ua.toLowerCase(); - - var match = /(chrome)[ \/]([\w.]+)/.exec(ua) || - /(webkit)[ \/]([\w.]+)/.exec(ua) || - /(opera)(?:.*version|)[ \/]([\w.]+)/.exec(ua) || - /(msie) ([\w.]+)/.exec(ua) || - ua.indexOf("compatible") < 0 && /(mozilla)(?:.*? rv:([\w.]+)|)/.exec(ua) || - []; - - return { - browser: match[ 1 ] || "", - version: match[ 2 ] || "0" - }; - }; - jQuery.browser = {}; - jQuery.browser[jQuery.uaMatch(navigator.userAgent).browser] = true; -} - -/** - * Small JavaScript module for the documentation. - */ -var Documentation = { - - init : function() { - this.fixFirefoxAnchorBug(); - this.highlightSearchWords(); - this.initIndexTable(); - if (DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) { - this.initOnKeyListeners(); - } - }, - - /** - * i18n support - */ - TRANSLATIONS : {}, - PLURAL_EXPR : function(n) { return n === 1 ? 0 : 1; }, - LOCALE : 'unknown', - - // gettext and ngettext don't access this so that the functions - // can safely bound to a different name (_ = Documentation.gettext) - gettext : function(string) { - var translated = Documentation.TRANSLATIONS[string]; - if (typeof translated === 'undefined') - return string; - return (typeof translated === 'string') ? translated : translated[0]; - }, - - ngettext : function(singular, plural, n) { - var translated = Documentation.TRANSLATIONS[singular]; - if (typeof translated === 'undefined') - return (n == 1) ? singular : plural; - return translated[Documentation.PLURALEXPR(n)]; - }, - - addTranslations : function(catalog) { - for (var key in catalog.messages) - this.TRANSLATIONS[key] = catalog.messages[key]; - this.PLURAL_EXPR = new Function('n', 'return +(' + catalog.plural_expr + ')'); - this.LOCALE = catalog.locale; - }, - - /** - * add context elements like header anchor links - */ - addContextElements : function() { - $('div[id] > :header:first').each(function() { - $('\u00B6'). - attr('href', '#' + this.id). - attr('title', _('Permalink to this headline')). - appendTo(this); - }); - $('dt[id]').each(function() { - $('\u00B6'). - attr('href', '#' + this.id). - attr('title', _('Permalink to this definition')). - appendTo(this); - }); - }, - - /** - * workaround a firefox stupidity - * see: https://bugzilla.mozilla.org/show_bug.cgi?id=645075 - */ - fixFirefoxAnchorBug : function() { - if (document.location.hash && $.browser.mozilla) - window.setTimeout(function() { - document.location.href += ''; - }, 10); - }, - - /** - * highlight the search words provided in the url in the text - */ - highlightSearchWords : function() { - var params = $.getQueryParameters(); - var terms = (params.highlight) ? params.highlight[0].split(/\s+/) : []; - if (terms.length) { - var body = $('div.body'); - if (!body.length) { - body = $('body'); - } - window.setTimeout(function() { - $.each(terms, function() { - body.highlightText(this.toLowerCase(), 'highlighted'); - }); - }, 10); - $('

' + _('Hide Search Matches') + '

') - .appendTo($('#searchbox')); - } - }, - - /** - * init the domain index toggle buttons - */ - initIndexTable : function() { - var togglers = $('img.toggler').click(function() { - var src = $(this).attr('src'); - var idnum = $(this).attr('id').substr(7); - $('tr.cg-' + idnum).toggle(); - if (src.substr(-9) === 'minus.png') - $(this).attr('src', src.substr(0, src.length-9) + 'plus.png'); - else - $(this).attr('src', src.substr(0, src.length-8) + 'minus.png'); - }).css('display', ''); - if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) { - togglers.click(); - } - }, - - /** - * helper function to hide the search marks again - */ - hideSearchWords : function() { - $('#searchbox .highlight-link').fadeOut(300); - $('span.highlighted').removeClass('highlighted'); - }, - - /** - * make the url absolute - */ - makeURL : function(relativeURL) { - return DOCUMENTATION_OPTIONS.URL_ROOT + '/' + relativeURL; - }, - - /** - * get the current relative url - */ - getCurrentURL : function() { - var path = document.location.pathname; - var parts = path.split(/\//); - $.each(DOCUMENTATION_OPTIONS.URL_ROOT.split(/\//), function() { - if (this === '..') - parts.pop(); - }); - var url = parts.join('/'); - return path.substring(url.lastIndexOf('/') + 1, path.length - 1); - }, - - initOnKeyListeners: function() { - $(document).keyup(function(event) { - var activeElementType = document.activeElement.tagName; - // don't navigate when in search box or textarea - if (activeElementType !== 'TEXTAREA' && activeElementType !== 'INPUT' && activeElementType !== 'SELECT') { - switch (event.keyCode) { - case 37: // left - var prevHref = $('link[rel="prev"]').prop('href'); - if (prevHref) { - window.location.href = prevHref; - return false; - } - case 39: // right - var nextHref = $('link[rel="next"]').prop('href'); - if (nextHref) { - window.location.href = nextHref; - return false; - } - } - } - }); - } -}; - -// quick alias for translations -_ = Documentation.gettext; - -$(document).ready(function() { - Documentation.init(); -}); diff --git a/docs/_build.html/_static/documentation_options.js b/docs/_build.html/_static/documentation_options.js deleted file mode 100644 index b81190b..0000000 --- a/docs/_build.html/_static/documentation_options.js +++ /dev/null @@ -1,296 +0,0 @@ -var DOCUMENTATION_OPTIONS = { - URL_ROOT: document.getElementById("documentation_options").getAttribute('data-url_root'), - VERSION: '1.0.0', - LANGUAGE: 'None', - COLLAPSE_INDEX: false, - FILE_SUFFIX: '.html', - HAS_SOURCE: true, - SOURCELINK_SUFFIX: '.txt', - NAVIGATION_WITH_KEYS: false, - SEARCH_LANGUAGE_STOP_WORDS: ["a","and","are","as","at","be","but","by","for","if","in","into","is","it","near","no","not","of","on","or","such","that","the","their","then","there","these","they","this","to","was","will","with"] -}; - - - -/* Non-minified version JS is _stemmer.js if file is provided */ -/** - * Porter Stemmer - */ -var Stemmer = function() { - - var step2list = { - ational: 'ate', - tional: 'tion', - enci: 'ence', - anci: 'ance', - izer: 'ize', - bli: 'ble', - alli: 'al', - entli: 'ent', - eli: 'e', - ousli: 'ous', - ization: 'ize', - ation: 'ate', - ator: 'ate', - alism: 'al', - iveness: 'ive', - fulness: 'ful', - ousness: 'ous', - aliti: 'al', - iviti: 'ive', - biliti: 'ble', - logi: 'log' - }; - - var step3list = { - icate: 'ic', - ative: '', - alize: 'al', - iciti: 'ic', - ical: 'ic', - ful: '', - ness: '' - }; - - var c = "[^aeiou]"; // consonant - var v = "[aeiouy]"; // vowel - var C = c + "[^aeiouy]*"; // consonant sequence - var V = v + "[aeiou]*"; // vowel sequence - - var mgr0 = "^(" + C + ")?" + V + C; // [C]VC... is m>0 - var meq1 = "^(" + C + ")?" + V + C + "(" + V + ")?$"; // [C]VC[V] is m=1 - var mgr1 = "^(" + C + ")?" + V + C + V + C; // [C]VCVC... is m>1 - var s_v = "^(" + C + ")?" + v; // vowel in stem - - this.stemWord = function (w) { - var stem; - var suffix; - var firstch; - var origword = w; - - if (w.length < 3) - return w; - - var re; - var re2; - var re3; - var re4; - - firstch = w.substr(0,1); - if (firstch == "y") - w = firstch.toUpperCase() + w.substr(1); - - // Step 1a - re = /^(.+?)(ss|i)es$/; - re2 = /^(.+?)([^s])s$/; - - if (re.test(w)) - w = w.replace(re,"$1$2"); - else if (re2.test(w)) - w = w.replace(re2,"$1$2"); - - // Step 1b - re = /^(.+?)eed$/; - re2 = /^(.+?)(ed|ing)$/; - if (re.test(w)) { - var fp = re.exec(w); - re = new RegExp(mgr0); - if (re.test(fp[1])) { - re = /.$/; - w = w.replace(re,""); - } - } - else if (re2.test(w)) { - var fp = re2.exec(w); - stem = fp[1]; - re2 = new RegExp(s_v); - if (re2.test(stem)) { - w = stem; - re2 = /(at|bl|iz)$/; - re3 = new RegExp("([^aeiouylsz])\\1$"); - re4 = new RegExp("^" + C + v + "[^aeiouwxy]$"); - if (re2.test(w)) - w = w + "e"; - else if (re3.test(w)) { - re = /.$/; - w = w.replace(re,""); - } - else if (re4.test(w)) - w = w + "e"; - } - } - - // Step 1c - re = /^(.+?)y$/; - if (re.test(w)) { - var fp = re.exec(w); - stem = fp[1]; - re = new RegExp(s_v); - if (re.test(stem)) - w = stem + "i"; - } - - // Step 2 - re = /^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/; - if (re.test(w)) { - var fp = re.exec(w); - stem = fp[1]; - suffix = fp[2]; - re = new RegExp(mgr0); - if (re.test(stem)) - w = stem + step2list[suffix]; - } - - // Step 3 - re = /^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/; - if (re.test(w)) { - var fp = re.exec(w); - stem = fp[1]; - suffix = fp[2]; - re = new RegExp(mgr0); - if (re.test(stem)) - w = stem + step3list[suffix]; - } - - // Step 4 - re = /^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/; - re2 = /^(.+?)(s|t)(ion)$/; - if (re.test(w)) { - var fp = re.exec(w); - stem = fp[1]; - re = new RegExp(mgr1); - if (re.test(stem)) - w = stem; - } - else if (re2.test(w)) { - var fp = re2.exec(w); - stem = fp[1] + fp[2]; - re2 = new RegExp(mgr1); - if (re2.test(stem)) - w = stem; - } - - // Step 5 - re = /^(.+?)e$/; - if (re.test(w)) { - var fp = re.exec(w); - stem = fp[1]; - re = new RegExp(mgr1); - re2 = new RegExp(meq1); - re3 = new RegExp("^" + C + v + "[^aeiouwxy]$"); - if (re.test(stem) || (re2.test(stem) && !(re3.test(stem)))) - w = stem; - } - re = /ll$/; - re2 = new RegExp(mgr1); - if (re.test(w) && re2.test(w)) { - re = /.$/; - w = w.replace(re,""); - } - - // and turn initial Y back to y - if (firstch == "y") - w = firstch.toLowerCase() + w.substr(1); - return w; - } -} - - - - - -var splitChars = (function() { - var result = {}; - var singles = [96, 180, 187, 191, 215, 247, 749, 885, 903, 907, 909, 930, 1014, 1648, - 1748, 1809, 2416, 2473, 2481, 2526, 2601, 2609, 2612, 2615, 2653, 2702, - 2706, 2729, 2737, 2740, 2857, 2865, 2868, 2910, 2928, 2948, 2961, 2971, - 2973, 3085, 3089, 3113, 3124, 3213, 3217, 3241, 3252, 3295, 3341, 3345, - 3369, 3506, 3516, 3633, 3715, 3721, 3736, 3744, 3748, 3750, 3756, 3761, - 3781, 3912, 4239, 4347, 4681, 4695, 4697, 4745, 4785, 4799, 4801, 4823, - 4881, 5760, 5901, 5997, 6313, 7405, 8024, 8026, 8028, 8030, 8117, 8125, - 8133, 8181, 8468, 8485, 8487, 8489, 8494, 8527, 11311, 11359, 11687, 11695, - 11703, 11711, 11719, 11727, 11735, 12448, 12539, 43010, 43014, 43019, 43587, - 43696, 43713, 64286, 64297, 64311, 64317, 64319, 64322, 64325, 65141]; - var i, j, start, end; - for (i = 0; i < singles.length; i++) { - result[singles[i]] = true; - } - var ranges = [[0, 47], [58, 64], [91, 94], [123, 169], [171, 177], [182, 184], [706, 709], - [722, 735], [741, 747], [751, 879], [888, 889], [894, 901], [1154, 1161], - [1318, 1328], [1367, 1368], [1370, 1376], [1416, 1487], [1515, 1519], [1523, 1568], - [1611, 1631], [1642, 1645], [1750, 1764], [1767, 1773], [1789, 1790], [1792, 1807], - [1840, 1868], [1958, 1968], [1970, 1983], [2027, 2035], [2038, 2041], [2043, 2047], - [2070, 2073], [2075, 2083], [2085, 2087], [2089, 2307], [2362, 2364], [2366, 2383], - [2385, 2391], [2402, 2405], [2419, 2424], [2432, 2436], [2445, 2446], [2449, 2450], - [2483, 2485], [2490, 2492], [2494, 2509], [2511, 2523], [2530, 2533], [2546, 2547], - [2554, 2564], [2571, 2574], [2577, 2578], [2618, 2648], [2655, 2661], [2672, 2673], - [2677, 2692], [2746, 2748], [2750, 2767], [2769, 2783], [2786, 2789], [2800, 2820], - [2829, 2830], [2833, 2834], [2874, 2876], [2878, 2907], [2914, 2917], [2930, 2946], - [2955, 2957], [2966, 2968], [2976, 2978], [2981, 2983], [2987, 2989], [3002, 3023], - [3025, 3045], [3059, 3076], [3130, 3132], [3134, 3159], [3162, 3167], [3170, 3173], - [3184, 3191], [3199, 3204], [3258, 3260], [3262, 3293], [3298, 3301], [3312, 3332], - [3386, 3388], [3390, 3423], [3426, 3429], [3446, 3449], [3456, 3460], [3479, 3481], - [3518, 3519], [3527, 3584], [3636, 3647], [3655, 3663], [3674, 3712], [3717, 3718], - [3723, 3724], [3726, 3731], [3752, 3753], [3764, 3772], [3774, 3775], [3783, 3791], - [3802, 3803], [3806, 3839], [3841, 3871], [3892, 3903], [3949, 3975], [3980, 4095], - [4139, 4158], [4170, 4175], [4182, 4185], [4190, 4192], [4194, 4196], [4199, 4205], - [4209, 4212], [4226, 4237], [4250, 4255], [4294, 4303], [4349, 4351], [4686, 4687], - [4702, 4703], [4750, 4751], [4790, 4791], [4806, 4807], [4886, 4887], [4955, 4968], - [4989, 4991], [5008, 5023], [5109, 5120], [5741, 5742], [5787, 5791], [5867, 5869], - [5873, 5887], [5906, 5919], [5938, 5951], [5970, 5983], [6001, 6015], [6068, 6102], - [6104, 6107], [6109, 6111], [6122, 6127], [6138, 6159], [6170, 6175], [6264, 6271], - [6315, 6319], [6390, 6399], [6429, 6469], [6510, 6511], [6517, 6527], [6572, 6592], - [6600, 6607], [6619, 6655], [6679, 6687], [6741, 6783], [6794, 6799], [6810, 6822], - [6824, 6916], [6964, 6980], [6988, 6991], [7002, 7042], [7073, 7085], [7098, 7167], - [7204, 7231], [7242, 7244], [7294, 7400], [7410, 7423], [7616, 7679], [7958, 7959], - [7966, 7967], [8006, 8007], [8014, 8015], [8062, 8063], [8127, 8129], [8141, 8143], - [8148, 8149], [8156, 8159], [8173, 8177], [8189, 8303], [8306, 8307], [8314, 8318], - [8330, 8335], [8341, 8449], [8451, 8454], [8456, 8457], [8470, 8472], [8478, 8483], - [8506, 8507], [8512, 8516], [8522, 8525], [8586, 9311], [9372, 9449], [9472, 10101], - [10132, 11263], [11493, 11498], [11503, 11516], [11518, 11519], [11558, 11567], - [11622, 11630], [11632, 11647], [11671, 11679], [11743, 11822], [11824, 12292], - [12296, 12320], [12330, 12336], [12342, 12343], [12349, 12352], [12439, 12444], - [12544, 12548], [12590, 12592], [12687, 12689], [12694, 12703], [12728, 12783], - [12800, 12831], [12842, 12880], [12896, 12927], [12938, 12976], [12992, 13311], - [19894, 19967], [40908, 40959], [42125, 42191], [42238, 42239], [42509, 42511], - [42540, 42559], [42592, 42593], [42607, 42622], [42648, 42655], [42736, 42774], - [42784, 42785], [42889, 42890], [42893, 43002], [43043, 43055], [43062, 43071], - [43124, 43137], [43188, 43215], [43226, 43249], [43256, 43258], [43260, 43263], - [43302, 43311], [43335, 43359], [43389, 43395], [43443, 43470], [43482, 43519], - [43561, 43583], [43596, 43599], [43610, 43615], [43639, 43641], [43643, 43647], - [43698, 43700], [43703, 43704], [43710, 43711], [43715, 43738], [43742, 43967], - [44003, 44015], [44026, 44031], [55204, 55215], [55239, 55242], [55292, 55295], - [57344, 63743], [64046, 64047], [64110, 64111], [64218, 64255], [64263, 64274], - [64280, 64284], [64434, 64466], [64830, 64847], [64912, 64913], [64968, 65007], - [65020, 65135], [65277, 65295], [65306, 65312], [65339, 65344], [65371, 65381], - [65471, 65473], [65480, 65481], [65488, 65489], [65496, 65497]]; - for (i = 0; i < ranges.length; i++) { - start = ranges[i][0]; - end = ranges[i][1]; - for (j = start; j <= end; j++) { - result[j] = true; - } - } - return result; -})(); - -function splitQuery(query) { - var result = []; - var start = -1; - for (var i = 0; i < query.length; i++) { - if (splitChars[query.charCodeAt(i)]) { - if (start !== -1) { - result.push(query.slice(start, i)); - start = -1; - } - } else if (start === -1) { - start = i; - } - } - if 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All rights reserved. - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - diff --git a/docs/_build.html/_static/fonts/fontawesome-webfont.ttf b/docs/_build.html/_static/fonts/fontawesome-webfont.ttf deleted file mode 100644 index 35acda2..0000000 Binary files a/docs/_build.html/_static/fonts/fontawesome-webfont.ttf and /dev/null differ diff --git a/docs/_build.html/_static/fonts/fontawesome-webfont.woff b/docs/_build.html/_static/fonts/fontawesome-webfont.woff deleted file mode 100644 index 400014a..0000000 Binary files a/docs/_build.html/_static/fonts/fontawesome-webfont.woff and /dev/null differ diff --git a/docs/_build.html/_static/fonts/fontawesome-webfont.woff2 b/docs/_build.html/_static/fonts/fontawesome-webfont.woff2 deleted file mode 100644 index 4d13fc6..0000000 Binary files a/docs/_build.html/_static/fonts/fontawesome-webfont.woff2 and /dev/null differ diff --git a/docs/_build.html/_static/jquery-3.2.1.js b/docs/_build.html/_static/jquery-3.2.1.js deleted file mode 100644 index d2d8ca4..0000000 --- a/docs/_build.html/_static/jquery-3.2.1.js +++ /dev/null @@ -1,10253 +0,0 @@ -/*! - * jQuery JavaScript Library v3.2.1 - * https://jquery.com/ - * - * Includes Sizzle.js - * https://sizzlejs.com/ - * - * Copyright JS Foundation and other contributors - * Released under the MIT license - * https://jquery.org/license - * - * Date: 2017-03-20T18:59Z - */ -( function( global, factory ) { - - "use strict"; - - if ( typeof module === "object" && typeof module.exports === "object" ) { - - // For CommonJS and CommonJS-like environments where a proper `window` - // is present, execute the factory and get jQuery. - // For environments that do not have a `window` with a `document` - // (such as Node.js), expose a factory as module.exports. - // This accentuates the need for the creation of a real `window`. - // e.g. var jQuery = require("jquery")(window); - // See ticket #14549 for more info. - module.exports = global.document ? - factory( global, true ) : - function( w ) { - if ( !w.document ) { - throw new Error( "jQuery requires a window with a document" ); - } - return factory( w ); - }; - } else { - factory( global ); - } - -// Pass this if window is not defined yet -} )( typeof window !== "undefined" ? window : this, function( window, noGlobal ) { - -// Edge <= 12 - 13+, Firefox <=18 - 45+, IE 10 - 11, Safari 5.1 - 9+, iOS 6 - 9.1 -// throw exceptions when non-strict code (e.g., ASP.NET 4.5) accesses strict mode -// arguments.callee.caller (trac-13335). But as of jQuery 3.0 (2016), strict mode should be common -// enough that all such attempts are guarded in a try block. -"use strict"; - -var arr = []; - -var document = window.document; - -var getProto = Object.getPrototypeOf; - -var slice = arr.slice; - -var concat = arr.concat; - -var push = arr.push; - -var indexOf = arr.indexOf; - -var class2type = {}; - -var toString = class2type.toString; - -var hasOwn = class2type.hasOwnProperty; - -var fnToString = hasOwn.toString; - -var ObjectFunctionString = fnToString.call( Object ); - -var support = {}; - - - - function DOMEval( code, doc ) { - doc = doc || document; - - var script = doc.createElement( "script" ); - - script.text = code; - doc.head.appendChild( script ).parentNode.removeChild( script ); - } -/* global Symbol */ -// Defining this global in .eslintrc.json would create a danger of using the global -// unguarded in another place, it seems safer to define global only for this module - - - -var - version = "3.2.1", - - // Define a local copy of jQuery - jQuery = function( selector, context ) { - - // The jQuery object is actually just the init constructor 'enhanced' - // Need init if jQuery is called (just allow error to be thrown if not included) - return new jQuery.fn.init( selector, context ); - }, - - // Support: Android <=4.0 only - // Make sure we trim BOM and NBSP - rtrim = /^[\s\uFEFF\xA0]+|[\s\uFEFF\xA0]+$/g, - - // Matches dashed string for camelizing - rmsPrefix = /^-ms-/, - rdashAlpha = /-([a-z])/g, - - // Used by jQuery.camelCase as callback to replace() - fcamelCase = function( all, letter ) { - return letter.toUpperCase(); - }; - -jQuery.fn = jQuery.prototype = { - - // The current version of jQuery being used - jquery: version, - - constructor: jQuery, - - // The default length of a jQuery object is 0 - length: 0, - - toArray: function() { - return slice.call( this ); - }, - - // Get the Nth element in the matched element set OR - // Get the whole matched element set as a clean array - get: function( num ) { - - // Return all the elements in a clean array - if ( num == null ) { - return slice.call( this ); - } - - // Return just the one element from the set - return num < 0 ? this[ num + this.length ] : this[ num ]; - }, - - // Take an array of elements and push it onto the stack - // (returning the new matched element set) - pushStack: function( elems ) { - - // Build a new jQuery matched element set - var ret = jQuery.merge( this.constructor(), elems ); - - // Add the old object onto the stack (as a reference) - ret.prevObject = this; - - // Return the newly-formed element set - return ret; - }, - - // Execute a callback for every element in the matched set. - each: function( callback ) { - return jQuery.each( this, callback ); - }, - - map: function( callback ) { - return this.pushStack( jQuery.map( this, function( elem, i ) { - return callback.call( elem, i, elem ); - } ) ); - }, - - slice: function() { - return this.pushStack( slice.apply( this, arguments ) ); - }, - - first: function() { - return this.eq( 0 ); - }, - - last: function() { - return this.eq( -1 ); - }, - - eq: function( i ) { - var len = this.length, - j = +i + ( i < 0 ? len : 0 ); - return this.pushStack( j >= 0 && j < len ? [ this[ j ] ] : [] ); - }, - - end: function() { - return this.prevObject || this.constructor(); - }, - - // For internal use only. - // Behaves like an Array's method, not like a jQuery method. - push: push, - sort: arr.sort, - splice: arr.splice -}; - -jQuery.extend = jQuery.fn.extend = function() { - var options, name, src, copy, copyIsArray, clone, - target = arguments[ 0 ] || {}, - i = 1, - length = arguments.length, - deep = false; - - // Handle a deep copy situation - if ( typeof target === "boolean" ) { - deep = target; - - // Skip the boolean and the target - target = arguments[ i ] || {}; - i++; - } - - // Handle case when target is a string or something (possible in deep copy) - if ( typeof target !== "object" && !jQuery.isFunction( target ) ) { - target = {}; - } - - // Extend jQuery itself if only one argument is passed - if ( i === length ) { - target = this; - i--; - } - - for ( ; i < length; i++ ) { - - // Only deal with non-null/undefined values - if ( ( options = arguments[ i ] ) != null ) { - - // Extend the base object - for ( name in options ) { - src = target[ name ]; - copy = options[ name ]; - - // Prevent never-ending loop - if ( target === copy ) { - continue; - } - - // Recurse if we're merging plain objects or arrays - if ( deep && copy && ( jQuery.isPlainObject( copy ) || - ( copyIsArray = Array.isArray( copy ) ) ) ) { - - if ( copyIsArray ) { - copyIsArray = false; - clone = src && Array.isArray( src ) ? src : []; - - } else { - clone = src && jQuery.isPlainObject( src ) ? src : {}; - } - - // Never move original objects, clone them - target[ name ] = jQuery.extend( deep, clone, copy ); - - // Don't bring in undefined values - } else if ( copy !== undefined ) { - target[ name ] = copy; - } - } - } - } - - // Return the modified object - return target; -}; - -jQuery.extend( { - - // Unique for each copy of jQuery on the page - expando: "jQuery" + ( version + Math.random() ).replace( /\D/g, "" ), - - // Assume jQuery is ready without the ready module - isReady: true, - - error: function( msg ) { - throw new Error( msg ); - }, - - noop: function() {}, - - isFunction: function( obj ) { - return jQuery.type( obj ) === "function"; - }, - - isWindow: function( obj ) { - return obj != null && obj === obj.window; - }, - - isNumeric: function( obj ) { - - // As of jQuery 3.0, isNumeric is limited to - // strings and numbers (primitives or objects) - // that can be coerced to finite numbers (gh-2662) - var type = jQuery.type( obj ); - return ( type === "number" || type === "string" ) && - - // parseFloat NaNs numeric-cast false positives ("") - // ...but misinterprets leading-number strings, particularly hex literals ("0x...") - // subtraction forces infinities to NaN - !isNaN( obj - parseFloat( obj ) ); - }, - - isPlainObject: function( obj ) { - var proto, Ctor; - - // Detect obvious negatives - // Use toString instead of jQuery.type to catch host objects - if ( !obj || toString.call( obj ) !== "[object Object]" ) { - return false; - } - - proto = getProto( obj ); - - // Objects with no prototype (e.g., `Object.create( null )`) are plain - if ( !proto ) { - return true; - } - - // Objects with prototype are plain iff they were constructed by a global Object function - Ctor = hasOwn.call( proto, "constructor" ) && proto.constructor; - return typeof Ctor === "function" && fnToString.call( Ctor ) === ObjectFunctionString; - }, - - isEmptyObject: function( obj ) { - - /* eslint-disable no-unused-vars */ - // See https://github.com/eslint/eslint/issues/6125 - var name; - - for ( name in obj ) { - return false; - } - return true; - }, - - type: function( obj ) { - if ( obj == null ) { - return obj + ""; - } - - // Support: Android <=2.3 only (functionish RegExp) - return typeof obj === "object" || typeof obj === "function" ? - class2type[ toString.call( obj ) ] || "object" : - typeof obj; - }, - - // Evaluates a script in a global context - globalEval: function( code ) { - DOMEval( code ); - }, - - // Convert dashed to camelCase; used by the css and data modules - // Support: IE <=9 - 11, Edge 12 - 13 - // Microsoft forgot to hump their vendor prefix (#9572) - camelCase: function( string ) { - return string.replace( rmsPrefix, "ms-" ).replace( rdashAlpha, fcamelCase ); - }, - - each: function( obj, callback ) { - var length, i = 0; - - if ( isArrayLike( obj ) ) { - length = obj.length; - for ( ; i < length; i++ ) { - if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) { - break; - } - } - } else { - for ( i in obj ) { - if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) { - break; - } - } - } - - return obj; - }, - - // Support: Android <=4.0 only - trim: function( text ) { - return text == null ? - "" : - ( text + "" ).replace( rtrim, "" ); - }, - - // results is for internal usage only - makeArray: function( arr, results ) { - var ret = results || []; - - if ( arr != null ) { - if ( isArrayLike( Object( arr ) ) ) { - jQuery.merge( ret, - typeof arr === "string" ? - [ arr ] : arr - ); - } else { - push.call( ret, arr ); - } - } - - return ret; - }, - - inArray: function( elem, arr, i ) { - return arr == null ? -1 : indexOf.call( arr, elem, i ); - }, - - // Support: Android <=4.0 only, PhantomJS 1 only - // push.apply(_, arraylike) throws on ancient WebKit - merge: function( first, second ) { - var len = +second.length, - j = 0, - i = first.length; - - for ( ; j < len; j++ ) { - first[ i++ ] = second[ j ]; - } - - first.length = i; - - return first; - }, - - grep: function( elems, callback, invert ) { - var callbackInverse, - matches = [], - i = 0, - length = elems.length, - callbackExpect = !invert; - - // Go through the array, only saving the items - // that pass the validator function - for ( ; i < length; i++ ) { - callbackInverse = !callback( elems[ i ], i ); - if ( callbackInverse !== callbackExpect ) { - matches.push( elems[ i ] ); - } - } - - return matches; - }, - - // arg is for internal usage only - map: function( elems, callback, arg ) { - var length, value, - i = 0, - ret = []; - - // Go through the array, translating each of the items to their new values - if ( isArrayLike( elems ) ) { - length = elems.length; - for ( ; i < length; i++ ) { - value = callback( elems[ i ], i, arg ); - - if ( value != null ) { - ret.push( value ); - } - } - - // Go through every key on the object, - } else { - for ( i in elems ) { - value = callback( elems[ i ], i, arg ); - - if ( value != null ) { - ret.push( value ); - } - } - } - - // Flatten any nested arrays - return concat.apply( [], ret ); - }, - - // A global GUID counter for objects - guid: 1, - - // Bind a function to a context, optionally partially applying any - // arguments. - proxy: function( fn, context ) { - var tmp, args, proxy; - - if ( typeof context === "string" ) { - tmp = fn[ context ]; - context = fn; - fn = tmp; - } - - // Quick check to determine if target is callable, in the spec - // this throws a TypeError, but we will just return undefined. - if ( !jQuery.isFunction( fn ) ) { - return undefined; - } - - // Simulated bind - args = slice.call( arguments, 2 ); - proxy = function() { - return fn.apply( context || this, args.concat( slice.call( arguments ) ) ); - }; - - // Set the guid of unique handler to the same of original handler, so it can be removed - proxy.guid = fn.guid = fn.guid || jQuery.guid++; - - return proxy; - }, - - now: Date.now, - - // jQuery.support is not used in Core but other projects attach their - // properties to it so it needs to exist. - support: support -} ); - -if ( typeof Symbol === "function" ) { - jQuery.fn[ Symbol.iterator ] = arr[ Symbol.iterator ]; -} - -// Populate the class2type map -jQuery.each( "Boolean Number String Function Array Date RegExp Object Error Symbol".split( " " ), -function( i, name ) { - class2type[ "[object " + name + "]" ] = name.toLowerCase(); -} ); - -function isArrayLike( obj ) { - - // Support: real iOS 8.2 only (not reproducible in simulator) - // `in` check used to prevent JIT error (gh-2145) - // hasOwn isn't used here due to false negatives - // regarding Nodelist length in IE - var length = !!obj && "length" in obj && obj.length, - type = jQuery.type( obj ); - - if ( type === "function" || jQuery.isWindow( obj ) ) { - return false; - } - - return type === "array" || length === 0 || - typeof length === "number" && length > 0 && ( length - 1 ) in obj; -} -var Sizzle = -/*! - * Sizzle CSS Selector Engine v2.3.3 - * https://sizzlejs.com/ - * - * Copyright jQuery Foundation and other contributors - * Released under the MIT license - * http://jquery.org/license - * - * Date: 2016-08-08 - */ -(function( window ) { - -var i, - support, - Expr, - getText, - isXML, - tokenize, - compile, - select, - outermostContext, - sortInput, - hasDuplicate, - - // Local document vars - setDocument, - document, - docElem, - documentIsHTML, - rbuggyQSA, - rbuggyMatches, - matches, - contains, - - // Instance-specific data - expando = "sizzle" + 1 * new Date(), - preferredDoc = window.document, - dirruns = 0, - done = 0, - classCache = createCache(), - tokenCache = createCache(), - compilerCache = createCache(), - sortOrder = function( a, b ) { - if ( a === b ) { - hasDuplicate = true; - } - return 0; - }, - - // Instance methods - hasOwn = ({}).hasOwnProperty, - arr = [], - pop = arr.pop, - push_native = arr.push, - push = arr.push, - slice = arr.slice, - // Use a stripped-down indexOf as it's faster than native - // https://jsperf.com/thor-indexof-vs-for/5 - indexOf = function( list, elem ) { - var i = 0, - len = list.length; - for ( ; i < len; i++ ) { - if ( list[i] === elem ) { - return i; - } - } - return -1; - }, - - booleans = "checked|selected|async|autofocus|autoplay|controls|defer|disabled|hidden|ismap|loop|multiple|open|readonly|required|scoped", - - // Regular expressions - - // http://www.w3.org/TR/css3-selectors/#whitespace - whitespace = "[\\x20\\t\\r\\n\\f]", - - // http://www.w3.org/TR/CSS21/syndata.html#value-def-identifier - identifier = "(?:\\\\.|[\\w-]|[^\0-\\xa0])+", - - // Attribute selectors: http://www.w3.org/TR/selectors/#attribute-selectors - attributes = "\\[" + whitespace + "*(" + identifier + ")(?:" + whitespace + - // Operator (capture 2) - "*([*^$|!~]?=)" + whitespace + - // "Attribute values must be CSS identifiers [capture 5] or strings [capture 3 or capture 4]" - "*(?:'((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\"|(" + identifier + "))|)" + whitespace + - "*\\]", - - pseudos = ":(" + identifier + ")(?:\\((" + - // To reduce the number of selectors needing tokenize in the preFilter, prefer arguments: - // 1. quoted (capture 3; capture 4 or capture 5) - "('((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\")|" + - // 2. simple (capture 6) - "((?:\\\\.|[^\\\\()[\\]]|" + attributes + ")*)|" + - // 3. anything else (capture 2) - ".*" + - ")\\)|)", - - // Leading and non-escaped trailing whitespace, capturing some non-whitespace characters preceding the latter - rwhitespace = new RegExp( whitespace + "+", "g" ), - rtrim = new RegExp( "^" + whitespace + "+|((?:^|[^\\\\])(?:\\\\.)*)" + whitespace + "+$", "g" ), - - rcomma = new RegExp( "^" + whitespace + "*," + whitespace + "*" ), - rcombinators = new RegExp( "^" + whitespace + "*([>+~]|" + whitespace + ")" + whitespace + "*" ), - - rattributeQuotes = new RegExp( "=" + whitespace + "*([^\\]'\"]*?)" + whitespace + "*\\]", "g" ), - - rpseudo = new RegExp( pseudos ), - ridentifier = new RegExp( "^" + identifier + "$" ), - - matchExpr = { - "ID": new RegExp( "^#(" + identifier + ")" ), - "CLASS": new RegExp( "^\\.(" + identifier + ")" ), - "TAG": new RegExp( "^(" + identifier + "|[*])" ), - "ATTR": new RegExp( "^" + attributes ), - "PSEUDO": new RegExp( "^" + pseudos ), - "CHILD": new RegExp( "^:(only|first|last|nth|nth-last)-(child|of-type)(?:\\(" + whitespace + - "*(even|odd|(([+-]|)(\\d*)n|)" + whitespace + "*(?:([+-]|)" + whitespace + - "*(\\d+)|))" + whitespace + "*\\)|)", "i" ), - "bool": new RegExp( "^(?:" + booleans + ")$", "i" ), - // For use in libraries implementing .is() - // We use this for POS matching in `select` - "needsContext": new RegExp( "^" + whitespace + "*[>+~]|:(even|odd|eq|gt|lt|nth|first|last)(?:\\(" + - whitespace + "*((?:-\\d)?\\d*)" + whitespace + "*\\)|)(?=[^-]|$)", "i" ) - }, - - rinputs = /^(?:input|select|textarea|button)$/i, - rheader = /^h\d$/i, - - rnative = /^[^{]+\{\s*\[native \w/, - - // Easily-parseable/retrievable ID or TAG or CLASS selectors - rquickExpr = /^(?:#([\w-]+)|(\w+)|\.([\w-]+))$/, - - rsibling = /[+~]/, - - // CSS escapes - // http://www.w3.org/TR/CSS21/syndata.html#escaped-characters - runescape = new RegExp( "\\\\([\\da-f]{1,6}" + whitespace + "?|(" + whitespace + ")|.)", "ig" ), - funescape = function( _, escaped, escapedWhitespace ) { - var high = "0x" + escaped - 0x10000; - // NaN means non-codepoint - // Support: Firefox<24 - // Workaround erroneous numeric interpretation of +"0x" - return high !== high || escapedWhitespace ? - escaped : - high < 0 ? - // BMP codepoint - String.fromCharCode( high + 0x10000 ) : - // Supplemental Plane codepoint (surrogate pair) - String.fromCharCode( high >> 10 | 0xD800, high & 0x3FF | 0xDC00 ); - }, - - // CSS string/identifier serialization - // https://drafts.csswg.org/cssom/#common-serializing-idioms - rcssescape = /([\0-\x1f\x7f]|^-?\d)|^-$|[^\0-\x1f\x7f-\uFFFF\w-]/g, - fcssescape = function( ch, asCodePoint ) { - if ( asCodePoint ) { - - // U+0000 NULL becomes U+FFFD REPLACEMENT CHARACTER - if ( ch === "\0" ) { - return "\uFFFD"; - } - - // Control characters and (dependent upon position) numbers get escaped as code points - return ch.slice( 0, -1 ) + "\\" + ch.charCodeAt( ch.length - 1 ).toString( 16 ) + " "; - } - - // Other potentially-special ASCII characters get backslash-escaped - return "\\" + ch; - }, - - // Used for iframes - // See setDocument() - // Removing the function wrapper causes a "Permission Denied" - // error in IE - unloadHandler = function() { - setDocument(); - }, - - disabledAncestor = addCombinator( - function( elem ) { - return elem.disabled === true && ("form" in elem || "label" in elem); - }, - { dir: "parentNode", next: "legend" } - ); - -// Optimize for push.apply( _, NodeList ) -try { - push.apply( - (arr = slice.call( preferredDoc.childNodes )), - preferredDoc.childNodes - ); - // Support: Android<4.0 - // Detect silently failing push.apply - arr[ preferredDoc.childNodes.length ].nodeType; -} catch ( e ) { - push = { apply: arr.length ? - - // Leverage slice if possible - function( target, els ) { - push_native.apply( target, slice.call(els) ); - } : - - // Support: IE<9 - // Otherwise append directly - function( target, els ) { - var j = target.length, - i = 0; - // Can't trust NodeList.length - while ( (target[j++] = els[i++]) ) {} - target.length = j - 1; - } - }; -} - -function Sizzle( selector, context, results, seed ) { - var m, i, elem, nid, match, groups, newSelector, - newContext = context && context.ownerDocument, - - // nodeType defaults to 9, since context defaults to document - nodeType = context ? context.nodeType : 9; - - results = results || []; - - // Return early from calls with invalid selector or context - if ( typeof selector !== "string" || !selector || - nodeType !== 1 && nodeType !== 9 && nodeType !== 11 ) { - - return results; - } - - // Try to shortcut find operations (as opposed to filters) in HTML documents - if ( !seed ) { - - if ( ( context ? context.ownerDocument || context : preferredDoc ) !== document ) { - setDocument( context ); - } - context = context || document; - - if ( documentIsHTML ) { - - // If the selector is sufficiently simple, try using a "get*By*" DOM method - // (excepting DocumentFragment context, where the methods don't exist) - if ( nodeType !== 11 && (match = rquickExpr.exec( selector )) ) { - - // ID selector - if ( (m = match[1]) ) { - - // Document context - if ( nodeType === 9 ) { - if ( (elem = context.getElementById( m )) ) { - - // Support: IE, Opera, Webkit - // TODO: identify versions - // getElementById can match elements by name instead of ID - if ( elem.id === m ) { - results.push( elem ); - return results; - } - } else { - return results; - } - - // Element context - } else { - - // Support: IE, Opera, Webkit - // TODO: identify versions - // getElementById can match elements by name instead of ID - if ( newContext && (elem = newContext.getElementById( m )) && - contains( context, elem ) && - elem.id === m ) { - - results.push( elem ); - return results; - } - } - - // Type selector - } else if ( match[2] ) { - push.apply( results, context.getElementsByTagName( selector ) ); - return results; - - // Class selector - } else if ( (m = match[3]) && support.getElementsByClassName && - context.getElementsByClassName ) { - - push.apply( results, context.getElementsByClassName( m ) ); - return results; - } - } - - // Take advantage of querySelectorAll - if ( support.qsa && - !compilerCache[ selector + " " ] && - (!rbuggyQSA || !rbuggyQSA.test( selector )) ) { - - if ( nodeType !== 1 ) { - newContext = context; - newSelector = selector; - - // qSA looks outside Element context, which is not what we want - // Thanks to Andrew Dupont for this workaround technique - // Support: IE <=8 - // Exclude object elements - } else if ( context.nodeName.toLowerCase() !== "object" ) { - - // Capture the context ID, setting it first if necessary - if ( (nid = context.getAttribute( "id" )) ) { - nid = nid.replace( rcssescape, fcssescape ); - } else { - context.setAttribute( "id", (nid = expando) ); - } - - // Prefix every selector in the list - groups = tokenize( selector ); - i = groups.length; - while ( i-- ) { - groups[i] = "#" + nid + " " + toSelector( groups[i] ); - } - newSelector = groups.join( "," ); - - // Expand context for sibling selectors - newContext = rsibling.test( selector ) && testContext( context.parentNode ) || - context; - } - - if ( newSelector ) { - try { - push.apply( results, - newContext.querySelectorAll( newSelector ) - ); - return results; - } catch ( qsaError ) { - } finally { - if ( nid === expando ) { - context.removeAttribute( "id" ); - } - } - } - } - } - } - - // All others - return select( selector.replace( rtrim, "$1" ), context, results, seed ); -} - -/** - * Create key-value caches of limited size - * @returns {function(string, object)} Returns the Object data after storing it on itself with - * property name the (space-suffixed) string and (if the cache is larger than Expr.cacheLength) - * deleting the oldest entry - */ -function createCache() { - var keys = []; - - function cache( key, value ) { - // Use (key + " ") to avoid collision with native prototype properties (see Issue #157) - if ( keys.push( key + " " ) > Expr.cacheLength ) { - // Only keep the most recent entries - delete cache[ keys.shift() ]; - } - return (cache[ key + " " ] = value); - } - return cache; -} - -/** - * Mark a function for special use by Sizzle - * @param {Function} fn The function to mark - */ -function markFunction( fn ) { - fn[ expando ] = true; - return fn; -} - -/** - * Support testing using an element - * @param {Function} fn Passed the created element and returns a boolean result - */ -function assert( fn ) { - var el = document.createElement("fieldset"); - - try { - return !!fn( el ); - } catch (e) { - return false; - } finally { - // Remove from its parent by default - if ( el.parentNode ) { - el.parentNode.removeChild( el ); - } - // release memory in IE - el = null; - } -} - -/** - * Adds the same handler for all of the specified attrs - * @param {String} attrs Pipe-separated list of attributes - * @param {Function} handler The method that will be applied - */ -function addHandle( attrs, handler ) { - var arr = attrs.split("|"), - i = arr.length; - - while ( i-- ) { - Expr.attrHandle[ arr[i] ] = handler; - } -} - -/** - * Checks document order of two siblings - * @param {Element} a - * @param {Element} b - * @returns {Number} Returns less than 0 if a precedes b, greater than 0 if a follows b - */ -function siblingCheck( a, b ) { - var cur = b && a, - diff = cur && a.nodeType === 1 && b.nodeType === 1 && - a.sourceIndex - b.sourceIndex; - - // Use IE sourceIndex if available on both nodes - if ( diff ) { - return diff; - } - - // Check if b follows a - if ( cur ) { - while ( (cur = cur.nextSibling) ) { - if ( cur === b ) { - return -1; - } - } - } - - return a ? 1 : -1; -} - -/** - * Returns a function to use in pseudos for input types - * @param {String} type - */ -function createInputPseudo( type ) { - return function( elem ) { - var name = elem.nodeName.toLowerCase(); - return name === "input" && elem.type === type; - }; -} - -/** - * Returns a function to use in pseudos for buttons - * @param {String} type - */ -function createButtonPseudo( type ) { - return function( elem ) { - var name = elem.nodeName.toLowerCase(); - return (name === "input" || name === "button") && elem.type === type; - }; -} - -/** - * Returns a function to use in pseudos for :enabled/:disabled - * @param {Boolean} disabled true for :disabled; false for :enabled - */ -function createDisabledPseudo( disabled ) { - - // Known :disabled false positives: fieldset[disabled] > legend:nth-of-type(n+2) :can-disable - return function( elem ) { - - // Only certain elements can match :enabled or :disabled - // https://html.spec.whatwg.org/multipage/scripting.html#selector-enabled - // https://html.spec.whatwg.org/multipage/scripting.html#selector-disabled - if ( "form" in elem ) { - - // Check for inherited disabledness on relevant non-disabled elements: - // * listed form-associated elements in a disabled fieldset - // https://html.spec.whatwg.org/multipage/forms.html#category-listed - // https://html.spec.whatwg.org/multipage/forms.html#concept-fe-disabled - // * option elements in a disabled optgroup - // https://html.spec.whatwg.org/multipage/forms.html#concept-option-disabled - // All such elements have a "form" property. - if ( elem.parentNode && elem.disabled === false ) { - - // Option elements defer to a parent optgroup if present - if ( "label" in elem ) { - if ( "label" in elem.parentNode ) { - return elem.parentNode.disabled === disabled; - } else { - return elem.disabled === disabled; - } - } - - // Support: IE 6 - 11 - // Use the isDisabled shortcut property to check for disabled fieldset ancestors - return elem.isDisabled === disabled || - - // Where there is no isDisabled, check manually - /* jshint -W018 */ - elem.isDisabled !== !disabled && - disabledAncestor( elem ) === disabled; - } - - return elem.disabled === disabled; - - // Try to winnow out elements that can't be disabled before trusting the disabled property. - // Some victims get caught in our net (label, legend, menu, track), but it shouldn't - // even exist on them, let alone have a boolean value. - } else if ( "label" in elem ) { - return elem.disabled === disabled; - } - - // Remaining elements are neither :enabled nor :disabled - return false; - }; -} - -/** - * Returns a function to use in pseudos for positionals - * @param {Function} fn - */ -function createPositionalPseudo( fn ) { - return markFunction(function( argument ) { - argument = +argument; - return markFunction(function( seed, matches ) { - var j, - matchIndexes = fn( [], seed.length, argument ), - i = matchIndexes.length; - - // Match elements found at the specified indexes - while ( i-- ) { - if ( seed[ (j = matchIndexes[i]) ] ) { - seed[j] = !(matches[j] = seed[j]); - } - } - }); - }); -} - -/** - * Checks a node for validity as a Sizzle context - * @param {Element|Object=} context - * @returns {Element|Object|Boolean} The input node if acceptable, otherwise a falsy value - */ -function testContext( context ) { - return context && typeof context.getElementsByTagName !== "undefined" && context; -} - -// Expose support vars for convenience -support = Sizzle.support = {}; - -/** - * Detects XML nodes - * @param {Element|Object} elem An element or a document - * @returns {Boolean} True iff elem is a non-HTML XML node - */ -isXML = Sizzle.isXML = function( elem ) { - // documentElement is verified for cases where it doesn't yet exist - // (such as loading iframes in IE - #4833) - var documentElement = elem && (elem.ownerDocument || elem).documentElement; - return documentElement ? documentElement.nodeName !== "HTML" : false; -}; - -/** - * Sets document-related variables once based on the current document - * @param {Element|Object} [doc] An element or document object to use to set the document - * @returns {Object} Returns the current document - */ -setDocument = Sizzle.setDocument = function( node ) { - var hasCompare, subWindow, - doc = node ? node.ownerDocument || node : preferredDoc; - - // Return early if doc is invalid or already selected - if ( doc === document || doc.nodeType !== 9 || !doc.documentElement ) { - return document; - } - - // Update global variables - document = doc; - docElem = document.documentElement; - documentIsHTML = !isXML( document ); - - // Support: IE 9-11, Edge - // Accessing iframe documents after unload throws "permission denied" errors (jQuery #13936) - if ( preferredDoc !== document && - (subWindow = document.defaultView) && subWindow.top !== subWindow ) { - - // Support: IE 11, Edge - if ( subWindow.addEventListener ) { - subWindow.addEventListener( "unload", unloadHandler, false ); - - // Support: IE 9 - 10 only - } else if ( subWindow.attachEvent ) { - subWindow.attachEvent( "onunload", unloadHandler ); - } - } - - /* Attributes - ---------------------------------------------------------------------- */ - - // Support: IE<8 - // Verify that getAttribute really returns attributes and not properties - // (excepting IE8 booleans) - support.attributes = assert(function( el ) { - el.className = "i"; - return !el.getAttribute("className"); - }); - - /* getElement(s)By* - ---------------------------------------------------------------------- */ - - // Check if getElementsByTagName("*") returns only elements - support.getElementsByTagName = assert(function( el ) { - el.appendChild( document.createComment("") ); - return !el.getElementsByTagName("*").length; - }); - - // Support: IE<9 - support.getElementsByClassName = rnative.test( document.getElementsByClassName ); - - // Support: IE<10 - // Check if getElementById returns elements by name - // The broken getElementById methods don't pick up programmatically-set names, - // so use a roundabout getElementsByName test - support.getById = assert(function( el ) { - docElem.appendChild( el ).id = expando; - return !document.getElementsByName || !document.getElementsByName( expando ).length; - }); - - // ID filter and find - if ( support.getById ) { - Expr.filter["ID"] = function( id ) { - var attrId = id.replace( runescape, funescape ); - return function( elem ) { - return elem.getAttribute("id") === attrId; - }; - }; - Expr.find["ID"] = function( id, context ) { - if ( typeof context.getElementById !== "undefined" && documentIsHTML ) { - var elem = context.getElementById( id ); - return elem ? [ elem ] : []; - } - }; - } else { - Expr.filter["ID"] = function( id ) { - var attrId = id.replace( runescape, funescape ); - return function( elem ) { - var node = typeof elem.getAttributeNode !== "undefined" && - elem.getAttributeNode("id"); - return node && node.value === attrId; - }; - }; - - // Support: IE 6 - 7 only - // getElementById is not reliable as a find shortcut - Expr.find["ID"] = function( id, context ) { - if ( typeof context.getElementById !== "undefined" && documentIsHTML ) { - var node, i, elems, - elem = context.getElementById( id ); - - if ( elem ) { - - // Verify the id attribute - node = elem.getAttributeNode("id"); - if ( node && node.value === id ) { - return [ elem ]; - } - - // Fall back on getElementsByName - elems = context.getElementsByName( id ); - i = 0; - while ( (elem = elems[i++]) ) { - node = elem.getAttributeNode("id"); - if ( node && node.value === id ) { - return [ elem ]; - } - } - } - - return []; - } - }; - } - - // Tag - Expr.find["TAG"] = support.getElementsByTagName ? - function( tag, context ) { - if ( typeof context.getElementsByTagName !== "undefined" ) { - return context.getElementsByTagName( tag ); - - // DocumentFragment nodes don't have gEBTN - } else if ( support.qsa ) { - return context.querySelectorAll( tag ); - } - } : - - function( tag, context ) { - var elem, - tmp = [], - i = 0, - // By happy coincidence, a (broken) gEBTN appears on DocumentFragment nodes too - results = context.getElementsByTagName( tag ); - - // Filter out possible comments - if ( tag === "*" ) { - while ( (elem = results[i++]) ) { - if ( elem.nodeType === 1 ) { - tmp.push( elem ); - } - } - - return tmp; - } - return results; - }; - - // Class - Expr.find["CLASS"] = support.getElementsByClassName && function( className, context ) { - if ( typeof context.getElementsByClassName !== "undefined" && documentIsHTML ) { - return context.getElementsByClassName( className ); - } - }; - - /* QSA/matchesSelector - ---------------------------------------------------------------------- */ - - // QSA and matchesSelector support - - // matchesSelector(:active) reports false when true (IE9/Opera 11.5) - rbuggyMatches = []; - - // qSa(:focus) reports false when true (Chrome 21) - // We allow this because of a bug in IE8/9 that throws an error - // whenever `document.activeElement` is accessed on an iframe - // So, we allow :focus to pass through QSA all the time to avoid the IE error - // See https://bugs.jquery.com/ticket/13378 - rbuggyQSA = []; - - if ( (support.qsa = rnative.test( document.querySelectorAll )) ) { - // Build QSA regex - // Regex strategy adopted from Diego Perini - assert(function( el ) { - // Select is set to empty string on purpose - // This is to test IE's treatment of not explicitly - // setting a boolean content attribute, - // since its presence should be enough - // https://bugs.jquery.com/ticket/12359 - docElem.appendChild( el ).innerHTML = "" + - ""; - - // Support: IE8, Opera 11-12.16 - // Nothing should be selected when empty strings follow ^= or $= or *= - // The test attribute must be unknown in Opera but "safe" for WinRT - // https://msdn.microsoft.com/en-us/library/ie/hh465388.aspx#attribute_section - if ( el.querySelectorAll("[msallowcapture^='']").length ) { - rbuggyQSA.push( "[*^$]=" + whitespace + "*(?:''|\"\")" ); - } - - // Support: IE8 - // Boolean attributes and "value" are not treated correctly - if ( !el.querySelectorAll("[selected]").length ) { - rbuggyQSA.push( "\\[" + whitespace + "*(?:value|" + booleans + ")" ); - } - - // Support: Chrome<29, Android<4.4, Safari<7.0+, iOS<7.0+, PhantomJS<1.9.8+ - if ( !el.querySelectorAll( "[id~=" + expando + "-]" ).length ) { - rbuggyQSA.push("~="); - } - - // Webkit/Opera - :checked should return selected option elements - // http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked - // IE8 throws error here and will not see later tests - if ( !el.querySelectorAll(":checked").length ) { - rbuggyQSA.push(":checked"); - } - - // Support: Safari 8+, iOS 8+ - // https://bugs.webkit.org/show_bug.cgi?id=136851 - // In-page `selector#id sibling-combinator selector` fails - if ( !el.querySelectorAll( "a#" + expando + "+*" ).length ) { - rbuggyQSA.push(".#.+[+~]"); - } - }); - - assert(function( el ) { - el.innerHTML = "" + - ""; - - // Support: Windows 8 Native Apps - // The type and name attributes are restricted during .innerHTML assignment - var input = document.createElement("input"); - input.setAttribute( "type", "hidden" ); - el.appendChild( input ).setAttribute( "name", "D" ); - - // Support: IE8 - // Enforce case-sensitivity of name attribute - if ( el.querySelectorAll("[name=d]").length ) { - rbuggyQSA.push( "name" + whitespace + "*[*^$|!~]?=" ); - } - - // FF 3.5 - :enabled/:disabled and hidden elements (hidden elements are still enabled) - // IE8 throws error here and will not see later tests - if ( el.querySelectorAll(":enabled").length !== 2 ) { - rbuggyQSA.push( ":enabled", ":disabled" ); - } - - // Support: IE9-11+ - // IE's :disabled selector does not pick up the children of disabled fieldsets - docElem.appendChild( el ).disabled = true; - if ( el.querySelectorAll(":disabled").length !== 2 ) { - rbuggyQSA.push( ":enabled", ":disabled" ); - } - - // Opera 10-11 does not throw on post-comma invalid pseudos - el.querySelectorAll("*,:x"); - rbuggyQSA.push(",.*:"); - }); - } - - if ( (support.matchesSelector = rnative.test( (matches = docElem.matches || - docElem.webkitMatchesSelector || - docElem.mozMatchesSelector || - docElem.oMatchesSelector || - docElem.msMatchesSelector) )) ) { - - assert(function( el ) { - // Check to see if it's possible to do matchesSelector - // on a disconnected node (IE 9) - support.disconnectedMatch = matches.call( el, "*" ); - - // This should fail with an exception - // Gecko does not error, returns false instead - matches.call( el, "[s!='']:x" ); - rbuggyMatches.push( "!=", pseudos ); - }); - } - - rbuggyQSA = rbuggyQSA.length && new RegExp( rbuggyQSA.join("|") ); - rbuggyMatches = rbuggyMatches.length && new RegExp( rbuggyMatches.join("|") ); - - /* Contains - ---------------------------------------------------------------------- */ - hasCompare = rnative.test( docElem.compareDocumentPosition ); - - // Element contains another - // Purposefully self-exclusive - // As in, an element does not contain itself - contains = hasCompare || rnative.test( docElem.contains ) ? - function( a, b ) { - var adown = a.nodeType === 9 ? a.documentElement : a, - bup = b && b.parentNode; - return a === bup || !!( bup && bup.nodeType === 1 && ( - adown.contains ? - adown.contains( bup ) : - a.compareDocumentPosition && a.compareDocumentPosition( bup ) & 16 - )); - } : - function( a, b ) { - if ( b ) { - while ( (b = b.parentNode) ) { - if ( b === a ) { - return true; - } - } - } - return false; - }; - - /* Sorting - ---------------------------------------------------------------------- */ - - // Document order sorting - sortOrder = hasCompare ? - function( a, b ) { - - // Flag for duplicate removal - if ( a === b ) { - hasDuplicate = true; - return 0; - } - - // Sort on method existence if only one input has compareDocumentPosition - var compare = !a.compareDocumentPosition - !b.compareDocumentPosition; - if ( compare ) { - return compare; - } - - // Calculate position if both inputs belong to the same document - compare = ( a.ownerDocument || a ) === ( b.ownerDocument || b ) ? - a.compareDocumentPosition( b ) : - - // Otherwise we know they are disconnected - 1; - - // Disconnected nodes - if ( compare & 1 || - (!support.sortDetached && b.compareDocumentPosition( a ) === compare) ) { - - // Choose the first element that is related to our preferred document - if ( a === document || a.ownerDocument === preferredDoc && contains(preferredDoc, a) ) { - return -1; - } - if ( b === document || b.ownerDocument === preferredDoc && contains(preferredDoc, b) ) { - return 1; - } - - // Maintain original order - return sortInput ? - ( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) : - 0; - } - - return compare & 4 ? -1 : 1; - } : - function( a, b ) { - // Exit early if the nodes are identical - if ( a === b ) { - hasDuplicate = true; - return 0; - } - - var cur, - i = 0, - aup = a.parentNode, - bup = b.parentNode, - ap = [ a ], - bp = [ b ]; - - // Parentless nodes are either documents or disconnected - if ( !aup || !bup ) { - return a === document ? -1 : - b === document ? 1 : - aup ? -1 : - bup ? 1 : - sortInput ? - ( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) : - 0; - - // If the nodes are siblings, we can do a quick check - } else if ( aup === bup ) { - return siblingCheck( a, b ); - } - - // Otherwise we need full lists of their ancestors for comparison - cur = a; - while ( (cur = cur.parentNode) ) { - ap.unshift( cur ); - } - cur = b; - while ( (cur = cur.parentNode) ) { - bp.unshift( cur ); - } - - // Walk down the tree looking for a discrepancy - while ( ap[i] === bp[i] ) { - i++; - } - - return i ? - // Do a sibling check if the nodes have a common ancestor - siblingCheck( ap[i], bp[i] ) : - - // Otherwise nodes in our document sort first - ap[i] === preferredDoc ? -1 : - bp[i] === preferredDoc ? 1 : - 0; - }; - - return document; -}; - -Sizzle.matches = function( expr, elements ) { - return Sizzle( expr, null, null, elements ); -}; - -Sizzle.matchesSelector = function( elem, expr ) { - // Set document vars if needed - if ( ( elem.ownerDocument || elem ) !== document ) { - setDocument( elem ); - } - - // Make sure that attribute selectors are quoted - expr = expr.replace( rattributeQuotes, "='$1']" ); - - if ( support.matchesSelector && documentIsHTML && - !compilerCache[ expr + " " ] && - ( !rbuggyMatches || !rbuggyMatches.test( expr ) ) && - ( !rbuggyQSA || !rbuggyQSA.test( expr ) ) ) { - - try { - var ret = matches.call( elem, expr ); - - // IE 9's matchesSelector returns false on disconnected nodes - if ( ret || support.disconnectedMatch || - // As well, disconnected nodes are said to be in a document - // fragment in IE 9 - elem.document && elem.document.nodeType !== 11 ) { - return ret; - } - } catch (e) {} - } - - return Sizzle( expr, document, null, [ elem ] ).length > 0; -}; - -Sizzle.contains = function( context, elem ) { - // Set document vars if needed - if ( ( context.ownerDocument || context ) !== document ) { - setDocument( context ); - } - return contains( context, elem ); -}; - -Sizzle.attr = function( elem, name ) { - // Set document vars if needed - if ( ( elem.ownerDocument || elem ) !== document ) { - setDocument( elem ); - } - - var fn = Expr.attrHandle[ name.toLowerCase() ], - // Don't get fooled by Object.prototype properties (jQuery #13807) - val = fn && hasOwn.call( Expr.attrHandle, name.toLowerCase() ) ? - fn( elem, name, !documentIsHTML ) : - undefined; - - return val !== undefined ? - val : - support.attributes || !documentIsHTML ? - elem.getAttribute( name ) : - (val = elem.getAttributeNode(name)) && val.specified ? - val.value : - null; -}; - -Sizzle.escape = function( sel ) { - return (sel + "").replace( rcssescape, fcssescape ); -}; - -Sizzle.error = function( msg ) { - throw new Error( "Syntax error, unrecognized expression: " + msg ); -}; - -/** - * Document sorting and removing duplicates - * @param {ArrayLike} results - */ -Sizzle.uniqueSort = function( results ) { - var elem, - duplicates = [], - j = 0, - i = 0; - - // Unless we *know* we can detect duplicates, assume their presence - hasDuplicate = !support.detectDuplicates; - sortInput = !support.sortStable && results.slice( 0 ); - results.sort( sortOrder ); - - if ( hasDuplicate ) { - while ( (elem = results[i++]) ) { - if ( elem === results[ i ] ) { - j = duplicates.push( i ); - } - } - while ( j-- ) { - results.splice( duplicates[ j ], 1 ); - } - } - - // Clear input after sorting to release objects - // See https://github.com/jquery/sizzle/pull/225 - sortInput = null; - - return results; -}; - -/** - * Utility function for retrieving the text value of an array of DOM nodes - * @param {Array|Element} elem - */ -getText = Sizzle.getText = function( elem ) { - var node, - ret = "", - i = 0, - nodeType = elem.nodeType; - - if ( !nodeType ) { - // If no nodeType, this is expected to be an array - while ( (node = elem[i++]) ) { - // Do not traverse comment nodes - ret += getText( node ); - } - } else if ( nodeType === 1 || nodeType === 9 || nodeType === 11 ) { - // Use textContent for elements - // innerText usage removed for consistency of new lines (jQuery #11153) - if ( typeof elem.textContent === "string" ) { - return elem.textContent; - } else { - // Traverse its children - for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) { - ret += getText( elem ); - } - } - } else if ( nodeType === 3 || nodeType === 4 ) { - return elem.nodeValue; - } - // Do not include comment or processing instruction nodes - - return ret; -}; - -Expr = Sizzle.selectors = { - - // Can be adjusted by the user - cacheLength: 50, - - createPseudo: markFunction, - - match: matchExpr, - - attrHandle: {}, - - find: {}, - - relative: { - ">": { dir: "parentNode", first: true }, - " ": { dir: "parentNode" }, - "+": { dir: "previousSibling", first: true }, - "~": { dir: "previousSibling" } - }, - - preFilter: { - "ATTR": function( match ) { - match[1] = match[1].replace( runescape, funescape ); - - // Move the given value to match[3] whether quoted or unquoted - match[3] = ( match[3] || match[4] || match[5] || "" ).replace( runescape, funescape ); - - if ( match[2] === "~=" ) { - match[3] = " " + match[3] + " "; - } - - return match.slice( 0, 4 ); - }, - - "CHILD": function( match ) { - /* matches from matchExpr["CHILD"] - 1 type (only|nth|...) - 2 what (child|of-type) - 3 argument (even|odd|\d*|\d*n([+-]\d+)?|...) - 4 xn-component of xn+y argument ([+-]?\d*n|) - 5 sign of xn-component - 6 x of xn-component - 7 sign of y-component - 8 y of y-component - */ - match[1] = match[1].toLowerCase(); - - if ( match[1].slice( 0, 3 ) === "nth" ) { - // nth-* requires argument - if ( !match[3] ) { - Sizzle.error( match[0] ); - } - - // numeric x and y parameters for Expr.filter.CHILD - // remember that false/true cast respectively to 0/1 - match[4] = +( match[4] ? match[5] + (match[6] || 1) : 2 * ( match[3] === "even" || match[3] === "odd" ) ); - match[5] = +( ( match[7] + match[8] ) || match[3] === "odd" ); - - // other types prohibit arguments - } else if ( match[3] ) { - Sizzle.error( match[0] ); - } - - return match; - }, - - "PSEUDO": function( match ) { - var excess, - unquoted = !match[6] && match[2]; - - if ( matchExpr["CHILD"].test( match[0] ) ) { - return null; - } - - // Accept quoted arguments as-is - if ( match[3] ) { - match[2] = match[4] || match[5] || ""; - - // Strip excess characters from unquoted arguments - } else if ( unquoted && rpseudo.test( unquoted ) && - // Get excess from tokenize (recursively) - (excess = tokenize( unquoted, true )) && - // advance to the next closing parenthesis - (excess = unquoted.indexOf( ")", unquoted.length - excess ) - unquoted.length) ) { - - // excess is a negative index - match[0] = match[0].slice( 0, excess ); - match[2] = unquoted.slice( 0, excess ); - } - - // Return only captures needed by the pseudo filter method (type and argument) - return match.slice( 0, 3 ); - } - }, - - filter: { - - "TAG": function( nodeNameSelector ) { - var nodeName = nodeNameSelector.replace( runescape, funescape ).toLowerCase(); - return nodeNameSelector === "*" ? - function() { return true; } : - function( elem ) { - return elem.nodeName && elem.nodeName.toLowerCase() === nodeName; - }; - }, - - "CLASS": function( className ) { - var pattern = classCache[ className + " " ]; - - return pattern || - (pattern = new RegExp( "(^|" + whitespace + ")" + className + "(" + whitespace + "|$)" )) && - classCache( className, function( elem ) { - return pattern.test( typeof elem.className === "string" && elem.className || typeof elem.getAttribute !== "undefined" && elem.getAttribute("class") || "" ); - }); - }, - - "ATTR": function( name, operator, check ) { - return function( elem ) { - var result = Sizzle.attr( elem, name ); - - if ( result == null ) { - return operator === "!="; - } - if ( !operator ) { - return true; - } - - result += ""; - - return operator === "=" ? result === check : - operator === "!=" ? result !== check : - operator === "^=" ? check && result.indexOf( check ) === 0 : - operator === "*=" ? check && result.indexOf( check ) > -1 : - operator === "$=" ? check && result.slice( -check.length ) === check : - operator === "~=" ? ( " " + result.replace( rwhitespace, " " ) + " " ).indexOf( check ) > -1 : - operator === "|=" ? result === check || result.slice( 0, check.length + 1 ) === check + "-" : - false; - }; - }, - - "CHILD": function( type, what, argument, first, last ) { - var simple = type.slice( 0, 3 ) !== "nth", - forward = type.slice( -4 ) !== "last", - ofType = what === "of-type"; - - return first === 1 && last === 0 ? - - // Shortcut for :nth-*(n) - function( elem ) { - return !!elem.parentNode; - } : - - function( elem, context, xml ) { - var cache, uniqueCache, outerCache, node, nodeIndex, start, - dir = simple !== forward ? "nextSibling" : "previousSibling", - parent = elem.parentNode, - name = ofType && elem.nodeName.toLowerCase(), - useCache = !xml && !ofType, - diff = false; - - if ( parent ) { - - // :(first|last|only)-(child|of-type) - if ( simple ) { - while ( dir ) { - node = elem; - while ( (node = node[ dir ]) ) { - if ( ofType ? - node.nodeName.toLowerCase() === name : - node.nodeType === 1 ) { - - return false; - } - } - // Reverse direction for :only-* (if we haven't yet done so) - start = dir = type === "only" && !start && "nextSibling"; - } - return true; - } - - start = [ forward ? parent.firstChild : parent.lastChild ]; - - // non-xml :nth-child(...) stores cache data on `parent` - if ( forward && useCache ) { - - // Seek `elem` from a previously-cached index - - // ...in a gzip-friendly way - node = parent; - outerCache = node[ expando ] || (node[ expando ] = {}); - - // Support: IE <9 only - // Defend against cloned attroperties (jQuery gh-1709) - uniqueCache = outerCache[ node.uniqueID ] || - (outerCache[ node.uniqueID ] = {}); - - cache = uniqueCache[ type ] || []; - nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ]; - diff = nodeIndex && cache[ 2 ]; - node = nodeIndex && parent.childNodes[ nodeIndex ]; - - while ( (node = ++nodeIndex && node && node[ dir ] || - - // Fallback to seeking `elem` from the start - (diff = nodeIndex = 0) || start.pop()) ) { - - // When found, cache indexes on `parent` and break - if ( node.nodeType === 1 && ++diff && node === elem ) { - uniqueCache[ type ] = [ dirruns, nodeIndex, diff ]; - break; - } - } - - } else { - // Use previously-cached element index if available - if ( useCache ) { - // ...in a gzip-friendly way - node = elem; - outerCache = node[ expando ] || (node[ expando ] = {}); - - // Support: IE <9 only - // Defend against cloned attroperties (jQuery gh-1709) - uniqueCache = outerCache[ node.uniqueID ] || - (outerCache[ node.uniqueID ] = {}); - - cache = uniqueCache[ type ] || []; - nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ]; - diff = nodeIndex; - } - - // xml :nth-child(...) - // or :nth-last-child(...) or :nth(-last)?-of-type(...) - if ( diff === false ) { - // Use the same loop as above to seek `elem` from the start - while ( (node = ++nodeIndex && node && node[ dir ] || - (diff = nodeIndex = 0) || start.pop()) ) { - - if ( ( ofType ? - node.nodeName.toLowerCase() === name : - node.nodeType === 1 ) && - ++diff ) { - - // Cache the index of each encountered element - if ( useCache ) { - outerCache = node[ expando ] || (node[ expando ] = {}); - - // Support: IE <9 only - // Defend against cloned attroperties (jQuery gh-1709) - uniqueCache = outerCache[ node.uniqueID ] || - (outerCache[ node.uniqueID ] = {}); - - uniqueCache[ type ] = [ dirruns, diff ]; - } - - if ( node === elem ) { - break; - } - } - } - } - } - - // Incorporate the offset, then check against cycle size - diff -= last; - return diff === first || ( diff % first === 0 && diff / first >= 0 ); - } - }; - }, - - "PSEUDO": function( pseudo, argument ) { - // pseudo-class names are case-insensitive - // http://www.w3.org/TR/selectors/#pseudo-classes - // Prioritize by case sensitivity in case custom pseudos are added with uppercase letters - // Remember that setFilters inherits from pseudos - var args, - fn = Expr.pseudos[ pseudo ] || Expr.setFilters[ pseudo.toLowerCase() ] || - Sizzle.error( "unsupported pseudo: " + pseudo ); - - // The user may use createPseudo to indicate that - // arguments are needed to create the filter function - // just as Sizzle does - if ( fn[ expando ] ) { - return fn( argument ); - } - - // But maintain support for old signatures - if ( fn.length > 1 ) { - args = [ pseudo, pseudo, "", argument ]; - return Expr.setFilters.hasOwnProperty( pseudo.toLowerCase() ) ? - markFunction(function( seed, matches ) { - var idx, - matched = fn( seed, argument ), - i = matched.length; - while ( i-- ) { - idx = indexOf( seed, matched[i] ); - seed[ idx ] = !( matches[ idx ] = matched[i] ); - } - }) : - function( elem ) { - return fn( elem, 0, args ); - }; - } - - return fn; - } - }, - - pseudos: { - // Potentially complex pseudos - "not": markFunction(function( selector ) { - // Trim the selector passed to compile - // to avoid treating leading and trailing - // spaces as combinators - var input = [], - results = [], - matcher = compile( selector.replace( rtrim, "$1" ) ); - - return matcher[ expando ] ? - markFunction(function( seed, matches, context, xml ) { - var elem, - unmatched = matcher( seed, null, xml, [] ), - i = seed.length; - - // Match elements unmatched by `matcher` - while ( i-- ) { - if ( (elem = unmatched[i]) ) { - seed[i] = !(matches[i] = elem); - } - } - }) : - function( elem, context, xml ) { - input[0] = elem; - matcher( input, null, xml, results ); - // Don't keep the element (issue #299) - input[0] = null; - return !results.pop(); - }; - }), - - "has": markFunction(function( selector ) { - return function( elem ) { - return Sizzle( selector, elem ).length > 0; - }; - }), - - "contains": markFunction(function( text ) { - text = text.replace( runescape, funescape ); - return function( elem ) { - return ( elem.textContent || elem.innerText || getText( elem ) ).indexOf( text ) > -1; - }; - }), - - // "Whether an element is represented by a :lang() selector - // is based solely on the element's language value - // being equal to the identifier C, - // or beginning with the identifier C immediately followed by "-". - // The matching of C against the element's language value is performed case-insensitively. - // The identifier C does not have to be a valid language name." - // http://www.w3.org/TR/selectors/#lang-pseudo - "lang": markFunction( function( lang ) { - // lang value must be a valid identifier - if ( !ridentifier.test(lang || "") ) { - Sizzle.error( "unsupported lang: " + lang ); - } - lang = lang.replace( runescape, funescape ).toLowerCase(); - return function( elem ) { - var elemLang; - do { - if ( (elemLang = documentIsHTML ? - elem.lang : - elem.getAttribute("xml:lang") || elem.getAttribute("lang")) ) { - - elemLang = elemLang.toLowerCase(); - return elemLang === lang || elemLang.indexOf( lang + "-" ) === 0; - } - } while ( (elem = elem.parentNode) && elem.nodeType === 1 ); - return false; - }; - }), - - // Miscellaneous - "target": function( elem ) { - var hash = window.location && window.location.hash; - return hash && hash.slice( 1 ) === elem.id; - }, - - "root": function( elem ) { - return elem === docElem; - }, - - "focus": function( elem ) { - return elem === document.activeElement && (!document.hasFocus || document.hasFocus()) && !!(elem.type || elem.href || ~elem.tabIndex); - }, - - // Boolean properties - "enabled": createDisabledPseudo( false ), - "disabled": createDisabledPseudo( true ), - - "checked": function( elem ) { - // In CSS3, :checked should return both checked and selected elements - // http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked - var nodeName = elem.nodeName.toLowerCase(); - return (nodeName === "input" && !!elem.checked) || (nodeName === "option" && !!elem.selected); - }, - - "selected": function( elem ) { - // Accessing this property makes selected-by-default - // options in Safari work properly - if ( elem.parentNode ) { - elem.parentNode.selectedIndex; - } - - return elem.selected === true; - }, - - // Contents - "empty": function( elem ) { - // http://www.w3.org/TR/selectors/#empty-pseudo - // :empty is negated by element (1) or content nodes (text: 3; cdata: 4; entity ref: 5), - // but not by others (comment: 8; processing instruction: 7; etc.) - // nodeType < 6 works because attributes (2) do not appear as children - for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) { - if ( elem.nodeType < 6 ) { - return false; - } - } - return true; - }, - - "parent": function( elem ) { - return !Expr.pseudos["empty"]( elem ); - }, - - // Element/input types - "header": function( elem ) { - return rheader.test( elem.nodeName ); - }, - - "input": function( elem ) { - return rinputs.test( elem.nodeName ); - }, - - "button": function( elem ) { - var name = elem.nodeName.toLowerCase(); - return name === "input" && elem.type === "button" || name === "button"; - }, - - "text": function( elem ) { - var attr; - return elem.nodeName.toLowerCase() === "input" && - elem.type === "text" && - - // Support: IE<8 - // New HTML5 attribute values (e.g., "search") appear with elem.type === "text" - ( (attr = elem.getAttribute("type")) == null || attr.toLowerCase() === "text" ); - }, - - // Position-in-collection - "first": createPositionalPseudo(function() { - return [ 0 ]; - }), - - "last": createPositionalPseudo(function( matchIndexes, length ) { - return [ length - 1 ]; - }), - - "eq": createPositionalPseudo(function( matchIndexes, length, argument ) { - return [ argument < 0 ? argument + length : argument ]; - }), - - "even": createPositionalPseudo(function( matchIndexes, length ) { - var i = 0; - for ( ; i < length; i += 2 ) { - matchIndexes.push( i ); - } - return matchIndexes; - }), - - "odd": createPositionalPseudo(function( matchIndexes, length ) { - var i = 1; - for ( ; i < length; i += 2 ) { - matchIndexes.push( i ); - } - return matchIndexes; - }), - - "lt": createPositionalPseudo(function( matchIndexes, length, argument ) { - var i = argument < 0 ? argument + length : argument; - for ( ; --i >= 0; ) { - matchIndexes.push( i ); - } - return matchIndexes; - }), - - "gt": createPositionalPseudo(function( matchIndexes, length, argument ) { - var i = argument < 0 ? argument + length : argument; - for ( ; ++i < length; ) { - matchIndexes.push( i ); - } - return matchIndexes; - }) - } -}; - -Expr.pseudos["nth"] = Expr.pseudos["eq"]; - -// Add button/input type pseudos -for ( i in { radio: true, checkbox: true, file: true, password: true, image: true } ) { - Expr.pseudos[ i ] = createInputPseudo( i ); -} -for ( i in { submit: true, reset: true } ) { - Expr.pseudos[ i ] = createButtonPseudo( i ); -} - -// Easy API for creating new setFilters -function setFilters() {} -setFilters.prototype = Expr.filters = Expr.pseudos; -Expr.setFilters = new setFilters(); - -tokenize = Sizzle.tokenize = function( selector, parseOnly ) { - var matched, match, tokens, type, - soFar, groups, preFilters, - cached = tokenCache[ selector + " " ]; - - if ( cached ) { - return parseOnly ? 0 : cached.slice( 0 ); - } - - soFar = selector; - groups = []; - preFilters = Expr.preFilter; - - while ( soFar ) { - - // Comma and first run - if ( !matched || (match = rcomma.exec( soFar )) ) { - if ( match ) { - // Don't consume trailing commas as valid - soFar = soFar.slice( match[0].length ) || soFar; - } - groups.push( (tokens = []) ); - } - - matched = false; - - // Combinators - if ( (match = rcombinators.exec( soFar )) ) { - matched = match.shift(); - tokens.push({ - value: matched, - // Cast descendant combinators to space - type: match[0].replace( rtrim, " " ) - }); - soFar = soFar.slice( matched.length ); - } - - // Filters - for ( type in Expr.filter ) { - if ( (match = matchExpr[ type ].exec( soFar )) && (!preFilters[ type ] || - (match = preFilters[ type ]( match ))) ) { - matched = match.shift(); - tokens.push({ - value: matched, - type: type, - matches: match - }); - soFar = soFar.slice( matched.length ); - } - } - - if ( !matched ) { - break; - } - } - - // Return the length of the invalid excess - // if we're just parsing - // Otherwise, throw an error or return tokens - return parseOnly ? - soFar.length : - soFar ? - Sizzle.error( selector ) : - // Cache the tokens - tokenCache( selector, groups ).slice( 0 ); -}; - -function toSelector( tokens ) { - var i = 0, - len = tokens.length, - selector = ""; - for ( ; i < len; i++ ) { - selector += tokens[i].value; - } - return selector; -} - -function addCombinator( matcher, combinator, base ) { - var dir = combinator.dir, - skip = combinator.next, - key = skip || dir, - checkNonElements = base && key === "parentNode", - doneName = done++; - - return combinator.first ? - // Check against closest ancestor/preceding element - function( elem, context, xml ) { - while ( (elem = elem[ dir ]) ) { - if ( elem.nodeType === 1 || checkNonElements ) { - return matcher( elem, context, xml ); - } - } - return false; - } : - - // Check against all ancestor/preceding elements - function( elem, context, xml ) { - var oldCache, uniqueCache, outerCache, - newCache = [ dirruns, doneName ]; - - // We can't set arbitrary data on XML nodes, so they don't benefit from combinator caching - if ( xml ) { - while ( (elem = elem[ dir ]) ) { - if ( elem.nodeType === 1 || checkNonElements ) { - if ( matcher( elem, context, xml ) ) { - return true; - } - } - } - } else { - while ( (elem = elem[ dir ]) ) { - if ( elem.nodeType === 1 || checkNonElements ) { - outerCache = elem[ expando ] || (elem[ expando ] = {}); - - // Support: IE <9 only - // Defend against cloned attroperties (jQuery gh-1709) - uniqueCache = outerCache[ elem.uniqueID ] || (outerCache[ elem.uniqueID ] = {}); - - if ( skip && skip === elem.nodeName.toLowerCase() ) { - elem = elem[ dir ] || elem; - } else if ( (oldCache = uniqueCache[ key ]) && - oldCache[ 0 ] === dirruns && oldCache[ 1 ] === doneName ) { - - // Assign to newCache so results back-propagate to previous elements - return (newCache[ 2 ] = oldCache[ 2 ]); - } else { - // Reuse newcache so results back-propagate to previous elements - uniqueCache[ key ] = newCache; - - // A match means we're done; a fail means we have to keep checking - if ( (newCache[ 2 ] = matcher( elem, context, xml )) ) { - return true; - } - } - } - } - } - return false; - }; -} - -function elementMatcher( matchers ) { - return matchers.length > 1 ? - function( elem, context, xml ) { - var i = matchers.length; - while ( i-- ) { - if ( !matchers[i]( elem, context, xml ) ) { - return false; - } - } - return true; - } : - matchers[0]; -} - -function multipleContexts( selector, contexts, results ) { - var i = 0, - len = contexts.length; - for ( ; i < len; i++ ) { - Sizzle( selector, contexts[i], results ); - } - return results; -} - -function condense( unmatched, map, filter, context, xml ) { - var elem, - newUnmatched = [], - i = 0, - len = unmatched.length, - mapped = map != null; - - for ( ; i < len; i++ ) { - if ( (elem = unmatched[i]) ) { - if ( !filter || filter( elem, context, xml ) ) { - newUnmatched.push( elem ); - if ( mapped ) { - map.push( i ); - } - } - } - } - - return newUnmatched; -} - -function setMatcher( preFilter, selector, matcher, postFilter, postFinder, postSelector ) { - if ( postFilter && !postFilter[ expando ] ) { - postFilter = setMatcher( postFilter ); - } - if ( postFinder && !postFinder[ expando ] ) { - postFinder = setMatcher( postFinder, postSelector ); - } - return markFunction(function( seed, results, context, xml ) { - var temp, i, elem, - preMap = [], - postMap = [], - preexisting = results.length, - - // Get initial elements from seed or context - elems = seed || multipleContexts( selector || "*", context.nodeType ? [ context ] : context, [] ), - - // Prefilter to get matcher input, preserving a map for seed-results synchronization - matcherIn = preFilter && ( seed || !selector ) ? - condense( elems, preMap, preFilter, context, xml ) : - elems, - - matcherOut = matcher ? - // If we have a postFinder, or filtered seed, or non-seed postFilter or preexisting results, - postFinder || ( seed ? preFilter : preexisting || postFilter ) ? - - // ...intermediate processing is necessary - [] : - - // ...otherwise use results directly - results : - matcherIn; - - // Find primary matches - if ( matcher ) { - matcher( matcherIn, matcherOut, context, xml ); - } - - // Apply postFilter - if ( postFilter ) { - temp = condense( matcherOut, postMap ); - postFilter( temp, [], context, xml ); - - // Un-match failing elements by moving them back to matcherIn - i = temp.length; - while ( i-- ) { - if ( (elem = temp[i]) ) { - matcherOut[ postMap[i] ] = !(matcherIn[ postMap[i] ] = elem); - } - } - } - - if ( seed ) { - if ( postFinder || preFilter ) { - if ( postFinder ) { - // Get the final matcherOut by condensing this intermediate into postFinder contexts - temp = []; - i = matcherOut.length; - while ( i-- ) { - if ( (elem = matcherOut[i]) ) { - // Restore matcherIn since elem is not yet a final match - temp.push( (matcherIn[i] = elem) ); - } - } - postFinder( null, (matcherOut = []), temp, xml ); - } - - // Move matched elements from seed to results to keep them synchronized - i = matcherOut.length; - while ( i-- ) { - if ( (elem = matcherOut[i]) && - (temp = postFinder ? indexOf( seed, elem ) : preMap[i]) > -1 ) { - - seed[temp] = !(results[temp] = elem); - } - } - } - - // Add elements to results, through postFinder if defined - } else { - matcherOut = condense( - matcherOut === results ? - matcherOut.splice( preexisting, matcherOut.length ) : - matcherOut - ); - if ( postFinder ) { - postFinder( null, results, matcherOut, xml ); - } else { - push.apply( results, matcherOut ); - } - } - }); -} - -function matcherFromTokens( tokens ) { - var checkContext, matcher, j, - len = tokens.length, - leadingRelative = Expr.relative[ tokens[0].type ], - implicitRelative = leadingRelative || Expr.relative[" "], - i = leadingRelative ? 1 : 0, - - // The foundational matcher ensures that elements are reachable from top-level context(s) - matchContext = addCombinator( function( elem ) { - return elem === checkContext; - }, implicitRelative, true ), - matchAnyContext = addCombinator( function( elem ) { - return indexOf( checkContext, elem ) > -1; - }, implicitRelative, true ), - matchers = [ function( elem, context, xml ) { - var ret = ( !leadingRelative && ( xml || context !== outermostContext ) ) || ( - (checkContext = context).nodeType ? - matchContext( elem, context, xml ) : - matchAnyContext( elem, context, xml ) ); - // Avoid hanging onto element (issue #299) - checkContext = null; - return ret; - } ]; - - for ( ; i < len; i++ ) { - if ( (matcher = Expr.relative[ tokens[i].type ]) ) { - matchers = [ addCombinator(elementMatcher( matchers ), matcher) ]; - } else { - matcher = Expr.filter[ tokens[i].type ].apply( null, tokens[i].matches ); - - // Return special upon seeing a positional matcher - if ( matcher[ expando ] ) { - // Find the next relative operator (if any) for proper handling - j = ++i; - for ( ; j < len; j++ ) { - if ( Expr.relative[ tokens[j].type ] ) { - break; - } - } - return setMatcher( - i > 1 && elementMatcher( matchers ), - i > 1 && toSelector( - // If the preceding token was a descendant combinator, insert an implicit any-element `*` - tokens.slice( 0, i - 1 ).concat({ value: tokens[ i - 2 ].type === " " ? "*" : "" }) - ).replace( rtrim, "$1" ), - matcher, - i < j && matcherFromTokens( tokens.slice( i, j ) ), - j < len && matcherFromTokens( (tokens = tokens.slice( j )) ), - j < len && toSelector( tokens ) - ); - } - matchers.push( matcher ); - } - } - - return elementMatcher( matchers ); -} - -function matcherFromGroupMatchers( elementMatchers, setMatchers ) { - var bySet = setMatchers.length > 0, - byElement = elementMatchers.length > 0, - superMatcher = function( seed, context, xml, results, outermost ) { - var elem, j, matcher, - matchedCount = 0, - i = "0", - unmatched = seed && [], - setMatched = [], - contextBackup = outermostContext, - // We must always have either seed elements or outermost context - elems = seed || byElement && Expr.find["TAG"]( "*", outermost ), - // Use integer dirruns iff this is the outermost matcher - dirrunsUnique = (dirruns += contextBackup == null ? 1 : Math.random() || 0.1), - len = elems.length; - - if ( outermost ) { - outermostContext = context === document || context || outermost; - } - - // Add elements passing elementMatchers directly to results - // Support: IE<9, Safari - // Tolerate NodeList properties (IE: "length"; Safari: ) matching elements by id - for ( ; i !== len && (elem = elems[i]) != null; i++ ) { - if ( byElement && elem ) { - j = 0; - if ( !context && elem.ownerDocument !== document ) { - setDocument( elem ); - xml = !documentIsHTML; - } - while ( (matcher = elementMatchers[j++]) ) { - if ( matcher( elem, context || document, xml) ) { - results.push( elem ); - break; - } - } - if ( outermost ) { - dirruns = dirrunsUnique; - } - } - - // Track unmatched elements for set filters - if ( bySet ) { - // They will have gone through all possible matchers - if ( (elem = !matcher && elem) ) { - matchedCount--; - } - - // Lengthen the array for every element, matched or not - if ( seed ) { - unmatched.push( elem ); - } - } - } - - // `i` is now the count of elements visited above, and adding it to `matchedCount` - // makes the latter nonnegative. - matchedCount += i; - - // Apply set filters to unmatched elements - // NOTE: This can be skipped if there are no unmatched elements (i.e., `matchedCount` - // equals `i`), unless we didn't visit _any_ elements in the above loop because we have - // no element matchers and no seed. - // Incrementing an initially-string "0" `i` allows `i` to remain a string only in that - // case, which will result in a "00" `matchedCount` that differs from `i` but is also - // numerically zero. - if ( bySet && i !== matchedCount ) { - j = 0; - while ( (matcher = setMatchers[j++]) ) { - matcher( unmatched, setMatched, context, xml ); - } - - if ( seed ) { - // Reintegrate element matches to eliminate the need for sorting - if ( matchedCount > 0 ) { - while ( i-- ) { - if ( !(unmatched[i] || setMatched[i]) ) { - setMatched[i] = pop.call( results ); - } - } - } - - // Discard index placeholder values to get only actual matches - setMatched = condense( setMatched ); - } - - // Add matches to results - push.apply( results, setMatched ); - - // Seedless set matches succeeding multiple successful matchers stipulate sorting - if ( outermost && !seed && setMatched.length > 0 && - ( matchedCount + setMatchers.length ) > 1 ) { - - Sizzle.uniqueSort( results ); - } - } - - // Override manipulation of globals by nested matchers - if ( outermost ) { - dirruns = dirrunsUnique; - outermostContext = contextBackup; - } - - return unmatched; - }; - - return bySet ? - markFunction( superMatcher ) : - superMatcher; -} - -compile = Sizzle.compile = function( selector, match /* Internal Use Only */ ) { - var i, - setMatchers = [], - elementMatchers = [], - cached = compilerCache[ selector + " " ]; - - if ( !cached ) { - // Generate a function of recursive functions that can be used to check each element - if ( !match ) { - match = tokenize( selector ); - } - i = match.length; - while ( i-- ) { - cached = matcherFromTokens( match[i] ); - if ( cached[ expando ] ) { - setMatchers.push( cached ); - } else { - elementMatchers.push( cached ); - } - } - - // Cache the compiled function - cached = compilerCache( selector, matcherFromGroupMatchers( elementMatchers, setMatchers ) ); - - // Save selector and tokenization - cached.selector = selector; - } - return cached; -}; - -/** - * A low-level selection function that works with Sizzle's compiled - * selector functions - * @param {String|Function} selector A selector or a pre-compiled - * selector function built with Sizzle.compile - * @param {Element} context - * @param {Array} [results] - * @param {Array} [seed] A set of elements to match against - */ -select = Sizzle.select = function( selector, context, results, seed ) { - var i, tokens, token, type, find, - compiled = typeof selector === "function" && selector, - match = !seed && tokenize( (selector = compiled.selector || selector) ); - - results = results || []; - - // Try to minimize operations if there is only one selector in the list and no seed - // (the latter of which guarantees us context) - if ( match.length === 1 ) { - - // Reduce context if the leading compound selector is an ID - tokens = match[0] = match[0].slice( 0 ); - if ( tokens.length > 2 && (token = tokens[0]).type === "ID" && - context.nodeType === 9 && documentIsHTML && Expr.relative[ tokens[1].type ] ) { - - context = ( Expr.find["ID"]( token.matches[0].replace(runescape, funescape), context ) || [] )[0]; - if ( !context ) { - return results; - - // Precompiled matchers will still verify ancestry, so step up a level - } else if ( compiled ) { - context = context.parentNode; - } - - selector = selector.slice( tokens.shift().value.length ); - } - - // Fetch a seed set for right-to-left matching - i = matchExpr["needsContext"].test( selector ) ? 0 : tokens.length; - while ( i-- ) { - token = tokens[i]; - - // Abort if we hit a combinator - if ( Expr.relative[ (type = token.type) ] ) { - break; - } - if ( (find = Expr.find[ type ]) ) { - // Search, expanding context for leading sibling combinators - if ( (seed = find( - token.matches[0].replace( runescape, funescape ), - rsibling.test( tokens[0].type ) && testContext( context.parentNode ) || context - )) ) { - - // If seed is empty or no tokens remain, we can return early - tokens.splice( i, 1 ); - selector = seed.length && toSelector( tokens ); - if ( !selector ) { - push.apply( results, seed ); - return results; - } - - break; - } - } - } - } - - // Compile and execute a filtering function if one is not provided - // Provide `match` to avoid retokenization if we modified the selector above - ( compiled || compile( selector, match ) )( - seed, - context, - !documentIsHTML, - results, - !context || rsibling.test( selector ) && testContext( context.parentNode ) || context - ); - return results; -}; - -// One-time assignments - -// Sort stability -support.sortStable = expando.split("").sort( sortOrder ).join("") === expando; - -// Support: Chrome 14-35+ -// Always assume duplicates if they aren't passed to the comparison function -support.detectDuplicates = !!hasDuplicate; - -// Initialize against the default document -setDocument(); - -// Support: Webkit<537.32 - Safari 6.0.3/Chrome 25 (fixed in Chrome 27) -// Detached nodes confoundingly follow *each other* -support.sortDetached = assert(function( el ) { - // Should return 1, but returns 4 (following) - return el.compareDocumentPosition( document.createElement("fieldset") ) & 1; -}); - -// Support: IE<8 -// Prevent attribute/property "interpolation" -// https://msdn.microsoft.com/en-us/library/ms536429%28VS.85%29.aspx -if ( !assert(function( el ) { - el.innerHTML = ""; - return el.firstChild.getAttribute("href") === "#" ; -}) ) { - addHandle( "type|href|height|width", function( elem, name, isXML ) { - if ( !isXML ) { - return elem.getAttribute( name, name.toLowerCase() === "type" ? 1 : 2 ); - } - }); -} - -// Support: IE<9 -// Use defaultValue in place of getAttribute("value") -if ( !support.attributes || !assert(function( el ) { - el.innerHTML = ""; - el.firstChild.setAttribute( "value", "" ); - return el.firstChild.getAttribute( "value" ) === ""; -}) ) { - addHandle( "value", function( elem, name, isXML ) { - if ( !isXML && elem.nodeName.toLowerCase() === "input" ) { - return elem.defaultValue; - } - }); -} - -// Support: IE<9 -// Use getAttributeNode to fetch booleans when getAttribute lies -if ( !assert(function( el ) { - return el.getAttribute("disabled") == null; -}) ) { - addHandle( booleans, function( elem, name, isXML ) { - var val; - if ( !isXML ) { - return elem[ name ] === true ? name.toLowerCase() : - (val = elem.getAttributeNode( name )) && val.specified ? - val.value : - null; - } - }); -} - -return Sizzle; - -})( window ); - - - -jQuery.find = Sizzle; -jQuery.expr = Sizzle.selectors; - -// Deprecated -jQuery.expr[ ":" ] = jQuery.expr.pseudos; -jQuery.uniqueSort = jQuery.unique = Sizzle.uniqueSort; -jQuery.text = Sizzle.getText; -jQuery.isXMLDoc = Sizzle.isXML; -jQuery.contains = Sizzle.contains; -jQuery.escapeSelector = Sizzle.escape; - - - - -var dir = function( elem, dir, until ) { - var matched = [], - truncate = until !== undefined; - - while ( ( elem = elem[ dir ] ) && elem.nodeType !== 9 ) { - if ( elem.nodeType === 1 ) { - if ( truncate && jQuery( elem ).is( until ) ) { - break; - } - matched.push( elem ); - } - } - return matched; -}; - - -var siblings = function( n, elem ) { - var matched = []; - - for ( ; n; n = n.nextSibling ) { - if ( n.nodeType === 1 && n !== elem ) { - matched.push( n ); - } - } - - return matched; -}; - - -var rneedsContext = jQuery.expr.match.needsContext; - - - -function nodeName( elem, name ) { - - return elem.nodeName && elem.nodeName.toLowerCase() === name.toLowerCase(); - -}; -var rsingleTag = ( /^<([a-z][^\/\0>:\x20\t\r\n\f]*)[\x20\t\r\n\f]*\/?>(?:<\/\1>|)$/i ); - - - -var risSimple = /^.[^:#\[\.,]*$/; - -// Implement the identical functionality for filter and not -function winnow( elements, qualifier, not ) { - if ( jQuery.isFunction( qualifier ) ) { - return jQuery.grep( elements, function( elem, i ) { - return !!qualifier.call( elem, i, elem ) !== not; - } ); - } - - // Single element - if ( qualifier.nodeType ) { - return jQuery.grep( elements, function( elem ) { - return ( elem === qualifier ) !== not; - } ); - } - - // Arraylike of elements (jQuery, arguments, Array) - if ( typeof qualifier !== "string" ) { - return jQuery.grep( elements, function( elem ) { - return ( indexOf.call( qualifier, elem ) > -1 ) !== not; - } ); - } - - // Simple selector that can be filtered directly, removing non-Elements - if ( risSimple.test( qualifier ) ) { - return jQuery.filter( qualifier, elements, not ); - } - - // Complex selector, compare the two sets, removing non-Elements - qualifier = jQuery.filter( qualifier, elements ); - return jQuery.grep( elements, function( elem ) { - return ( indexOf.call( qualifier, elem ) > -1 ) !== not && elem.nodeType === 1; - } ); -} - -jQuery.filter = function( expr, elems, not ) { - var elem = elems[ 0 ]; - - if ( not ) { - expr = ":not(" + expr + ")"; - } - - if ( elems.length === 1 && elem.nodeType === 1 ) { - return jQuery.find.matchesSelector( elem, expr ) ? [ elem ] : []; - } - - return jQuery.find.matches( expr, jQuery.grep( elems, function( elem ) { - return elem.nodeType === 1; - } ) ); -}; - -jQuery.fn.extend( { - find: function( selector ) { - var i, ret, - len = this.length, - self = this; - - if ( typeof selector !== "string" ) { - return this.pushStack( jQuery( selector ).filter( function() { - for ( i = 0; i < len; i++ ) { - if ( jQuery.contains( self[ i ], this ) ) { - return true; - } - } - } ) ); - } - - ret = this.pushStack( [] ); - - for ( i = 0; i < len; i++ ) { - jQuery.find( selector, self[ i ], ret ); - } - - return len > 1 ? jQuery.uniqueSort( ret ) : ret; - }, - filter: function( selector ) { - return this.pushStack( winnow( this, selector || [], false ) ); - }, - not: function( selector ) { - return this.pushStack( winnow( this, selector || [], true ) ); - }, - is: function( selector ) { - return !!winnow( - this, - - // If this is a positional/relative selector, check membership in the returned set - // so $("p:first").is("p:last") won't return true for a doc with two "p". - typeof selector === "string" && rneedsContext.test( selector ) ? - jQuery( selector ) : - selector || [], - false - ).length; - } -} ); - - -// Initialize a jQuery object - - -// A central reference to the root jQuery(document) -var rootjQuery, - - // A simple way to check for HTML strings - // Prioritize #id over to avoid XSS via location.hash (#9521) - // Strict HTML recognition (#11290: must start with <) - // Shortcut simple #id case for speed - rquickExpr = /^(?:\s*(<[\w\W]+>)[^>]*|#([\w-]+))$/, - - init = jQuery.fn.init = function( selector, context, root ) { - var match, elem; - - // HANDLE: $(""), $(null), $(undefined), $(false) - if ( !selector ) { - return this; - } - - // Method init() accepts an alternate rootjQuery - // so migrate can support jQuery.sub (gh-2101) - root = root || rootjQuery; - - // Handle HTML strings - if ( typeof selector === "string" ) { - if ( selector[ 0 ] === "<" && - selector[ selector.length - 1 ] === ">" && - selector.length >= 3 ) { - - // Assume that strings that start and end with <> are HTML and skip the regex check - match = [ null, selector, null ]; - - } else { - match = rquickExpr.exec( selector ); - } - - // Match html or make sure no context is specified for #id - if ( match && ( match[ 1 ] || !context ) ) { - - // HANDLE: $(html) -> $(array) - if ( match[ 1 ] ) { - context = context instanceof jQuery ? context[ 0 ] : context; - - // Option to run scripts is true for back-compat - // Intentionally let the error be thrown if parseHTML is not present - jQuery.merge( this, jQuery.parseHTML( - match[ 1 ], - context && context.nodeType ? context.ownerDocument || context : document, - true - ) ); - - // HANDLE: $(html, props) - if ( rsingleTag.test( match[ 1 ] ) && jQuery.isPlainObject( context ) ) { - for ( match in context ) { - - // Properties of context are called as methods if possible - if ( jQuery.isFunction( this[ match ] ) ) { - this[ match ]( context[ match ] ); - - // ...and otherwise set as attributes - } else { - this.attr( match, context[ match ] ); - } - } - } - - return this; - - // HANDLE: $(#id) - } else { - elem = document.getElementById( match[ 2 ] ); - - if ( elem ) { - - // Inject the element directly into the jQuery object - this[ 0 ] = elem; - this.length = 1; - } - return this; - } - - // HANDLE: $(expr, $(...)) - } else if ( !context || context.jquery ) { - return ( context || root ).find( selector ); - - // HANDLE: $(expr, context) - // (which is just equivalent to: $(context).find(expr) - } else { - return this.constructor( context ).find( selector ); - } - - // HANDLE: $(DOMElement) - } else if ( selector.nodeType ) { - this[ 0 ] = selector; - this.length = 1; - return this; - - // HANDLE: $(function) - // Shortcut for document ready - } else if ( jQuery.isFunction( selector ) ) { - return root.ready !== undefined ? - root.ready( selector ) : - - // Execute immediately if ready is not present - selector( jQuery ); - } - - return jQuery.makeArray( selector, this ); - }; - -// Give the init function the jQuery prototype for later instantiation -init.prototype = jQuery.fn; - -// Initialize central reference -rootjQuery = jQuery( document ); - - -var rparentsprev = /^(?:parents|prev(?:Until|All))/, - - // Methods guaranteed to produce a unique set when starting from a unique set - guaranteedUnique = { - children: true, - contents: true, - next: true, - prev: true - }; - -jQuery.fn.extend( { - has: function( target ) { - var targets = jQuery( target, this ), - l = targets.length; - - return this.filter( function() { - var i = 0; - for ( ; i < l; i++ ) { - if ( jQuery.contains( this, targets[ i ] ) ) { - return true; - } - } - } ); - }, - - closest: function( selectors, context ) { - var cur, - i = 0, - l = this.length, - matched = [], - targets = typeof selectors !== "string" && jQuery( selectors ); - - // Positional selectors never match, since there's no _selection_ context - if ( !rneedsContext.test( selectors ) ) { - for ( ; i < l; i++ ) { - for ( cur = this[ i ]; cur && cur !== context; cur = cur.parentNode ) { - - // Always skip document fragments - if ( cur.nodeType < 11 && ( targets ? - targets.index( cur ) > -1 : - - // Don't pass non-elements to Sizzle - cur.nodeType === 1 && - jQuery.find.matchesSelector( cur, selectors ) ) ) { - - matched.push( cur ); - break; - } - } - } - } - - return this.pushStack( matched.length > 1 ? jQuery.uniqueSort( matched ) : matched ); - }, - - // Determine the position of an element within the set - index: function( elem ) { - - // No argument, return index in parent - if ( !elem ) { - return ( this[ 0 ] && this[ 0 ].parentNode ) ? this.first().prevAll().length : -1; - } - - // Index in selector - if ( typeof elem === "string" ) { - return indexOf.call( jQuery( elem ), this[ 0 ] ); - } - - // Locate the position of the desired element - return indexOf.call( this, - - // If it receives a jQuery object, the first element is used - elem.jquery ? elem[ 0 ] : elem - ); - }, - - add: function( selector, context ) { - return this.pushStack( - jQuery.uniqueSort( - jQuery.merge( this.get(), jQuery( selector, context ) ) - ) - ); - }, - - addBack: function( selector ) { - return this.add( selector == null ? - this.prevObject : this.prevObject.filter( selector ) - ); - } -} ); - -function sibling( cur, dir ) { - while ( ( cur = cur[ dir ] ) && cur.nodeType !== 1 ) {} - return cur; -} - -jQuery.each( { - parent: function( elem ) { - var parent = elem.parentNode; - return parent && parent.nodeType !== 11 ? parent : null; - }, - parents: function( elem ) { - return dir( elem, "parentNode" ); - }, - parentsUntil: function( elem, i, until ) { - return dir( elem, "parentNode", until ); - }, - next: function( elem ) { - return sibling( elem, "nextSibling" ); - }, - prev: function( elem ) { - return sibling( elem, "previousSibling" ); - }, - nextAll: function( elem ) { - return dir( elem, "nextSibling" ); - }, - prevAll: function( elem ) { - return dir( elem, "previousSibling" ); - }, - nextUntil: function( elem, i, until ) { - return dir( elem, "nextSibling", until ); - }, - prevUntil: function( elem, i, until ) { - return dir( elem, "previousSibling", until ); - }, - siblings: function( elem ) { - return siblings( ( elem.parentNode || {} ).firstChild, elem ); - }, - children: function( elem ) { - return siblings( elem.firstChild ); - }, - contents: function( elem ) { - if ( nodeName( elem, "iframe" ) ) { - return elem.contentDocument; - } - - // Support: IE 9 - 11 only, iOS 7 only, Android Browser <=4.3 only - // Treat the template element as a regular one in browsers that - // don't support it. - if ( nodeName( elem, "template" ) ) { - elem = elem.content || elem; - } - - return jQuery.merge( [], elem.childNodes ); - } -}, function( name, fn ) { - jQuery.fn[ name ] = function( until, selector ) { - var matched = jQuery.map( this, fn, until ); - - if ( name.slice( -5 ) !== "Until" ) { - selector = until; - } - - if ( selector && typeof selector === "string" ) { - matched = jQuery.filter( selector, matched ); - } - - if ( this.length > 1 ) { - - // Remove duplicates - if ( !guaranteedUnique[ name ] ) { - jQuery.uniqueSort( matched ); - } - - // Reverse order for parents* and prev-derivatives - if ( rparentsprev.test( name ) ) { - matched.reverse(); - } - } - - return this.pushStack( matched ); - }; -} ); -var rnothtmlwhite = ( /[^\x20\t\r\n\f]+/g ); - - - -// Convert String-formatted options into Object-formatted ones -function createOptions( options ) { - var object = {}; - jQuery.each( options.match( rnothtmlwhite ) || [], function( _, flag ) { - object[ flag ] = true; - } ); - return object; -} - -/* - * Create a callback list using the following parameters: - * - * options: an optional list of space-separated options that will change how - * the callback list behaves or a more traditional option object - * - * By default a callback list will act like an event callback list and can be - * "fired" multiple times. - * - * Possible options: - * - * once: will ensure the callback list can only be fired once (like a Deferred) - * - * memory: will keep track of previous values and will call any callback added - * after the list has been fired right away with the latest "memorized" - * values (like a Deferred) - * - * unique: will ensure a callback can only be added once (no duplicate in the list) - * - * stopOnFalse: interrupt callings when a callback returns false - * - */ -jQuery.Callbacks = function( options ) { - - // Convert options from String-formatted to Object-formatted if needed - // (we check in cache first) - options = typeof options === "string" ? - createOptions( options ) : - jQuery.extend( {}, options ); - - var // Flag to know if list is currently firing - firing, - - // Last fire value for non-forgettable lists - memory, - - // Flag to know if list was already fired - fired, - - // Flag to prevent firing - locked, - - // Actual callback list - list = [], - - // Queue of execution data for repeatable lists - queue = [], - - // Index of currently firing callback (modified by add/remove as needed) - firingIndex = -1, - - // Fire callbacks - fire = function() { - - // Enforce single-firing - locked = locked || options.once; - - // Execute callbacks for all pending executions, - // respecting firingIndex overrides and runtime changes - fired = firing = true; - for ( ; queue.length; firingIndex = -1 ) { - memory = queue.shift(); - while ( ++firingIndex < list.length ) { - - // Run callback and check for early termination - if ( list[ firingIndex ].apply( memory[ 0 ], memory[ 1 ] ) === false && - options.stopOnFalse ) { - - // Jump to end and forget the data so .add doesn't re-fire - firingIndex = list.length; - memory = false; - } - } - } - - // Forget the data if we're done with it - if ( !options.memory ) { - memory = false; - } - - firing = false; - - // Clean up if we're done firing for good - if ( locked ) { - - // Keep an empty list if we have data for future add calls - if ( memory ) { - list = []; - - // Otherwise, this object is spent - } else { - list = ""; - } - } - }, - - // Actual Callbacks object - self = { - - // Add a callback or a collection of callbacks to the list - add: function() { - if ( list ) { - - // If we have memory from a past run, we should fire after adding - if ( memory && !firing ) { - firingIndex = list.length - 1; - queue.push( memory ); - } - - ( function add( args ) { - jQuery.each( args, function( _, arg ) { - if ( jQuery.isFunction( arg ) ) { - if ( !options.unique || !self.has( arg ) ) { - list.push( arg ); - } - } else if ( arg && arg.length && jQuery.type( arg ) !== "string" ) { - - // Inspect recursively - add( arg ); - } - } ); - } )( arguments ); - - if ( memory && !firing ) { - fire(); - } - } - return this; - }, - - // Remove a callback from the list - remove: function() { - jQuery.each( arguments, function( _, arg ) { - var index; - while ( ( index = jQuery.inArray( arg, list, index ) ) > -1 ) { - list.splice( index, 1 ); - - // Handle firing indexes - if ( index <= firingIndex ) { - firingIndex--; - } - } - } ); - return this; - }, - - // Check if a given callback is in the list. - // If no argument is given, return whether or not list has callbacks attached. - has: function( fn ) { - return fn ? - jQuery.inArray( fn, list ) > -1 : - list.length > 0; - }, - - // Remove all callbacks from the list - empty: function() { - if ( list ) { - list = []; - } - return this; - }, - - // Disable .fire and .add - // Abort any current/pending executions - // Clear all callbacks and values - disable: function() { - locked = queue = []; - list = memory = ""; - return this; - }, - disabled: function() { - return !list; - }, - - // Disable .fire - // Also disable .add unless we have memory (since it would have no effect) - // Abort any pending executions - lock: function() { - locked = queue = []; - if ( !memory && !firing ) { - list = memory = ""; - } - return this; - }, - locked: function() { - return !!locked; - }, - - // Call all callbacks with the given context and arguments - fireWith: function( context, args ) { - if ( !locked ) { - args = args || []; - args = [ context, args.slice ? args.slice() : args ]; - queue.push( args ); - if ( !firing ) { - fire(); - } - } - return this; - }, - - // Call all the callbacks with the given arguments - fire: function() { - self.fireWith( this, arguments ); - return this; - }, - - // To know if the callbacks have already been called at least once - fired: function() { - return !!fired; - } - }; - - return self; -}; - - -function Identity( v ) { - return v; -} -function Thrower( ex ) { - throw ex; -} - -function adoptValue( value, resolve, reject, noValue ) { - var method; - - try { - - // Check for promise aspect first to privilege synchronous behavior - if ( value && jQuery.isFunction( ( method = value.promise ) ) ) { - method.call( value ).done( resolve ).fail( reject ); - - // Other thenables - } else if ( value && jQuery.isFunction( ( method = value.then ) ) ) { - method.call( value, resolve, reject ); - - // Other non-thenables - } else { - - // Control `resolve` arguments by letting Array#slice cast boolean `noValue` to integer: - // * false: [ value ].slice( 0 ) => resolve( value ) - // * true: [ value ].slice( 1 ) => resolve() - resolve.apply( undefined, [ value ].slice( noValue ) ); - } - - // For Promises/A+, convert exceptions into rejections - // Since jQuery.when doesn't unwrap thenables, we can skip the extra checks appearing in - // Deferred#then to conditionally suppress rejection. - } catch ( value ) { - - // Support: Android 4.0 only - // Strict mode functions invoked without .call/.apply get global-object context - reject.apply( undefined, [ value ] ); - } -} - -jQuery.extend( { - - Deferred: function( func ) { - var tuples = [ - - // action, add listener, callbacks, - // ... .then handlers, argument index, [final state] - [ "notify", "progress", jQuery.Callbacks( "memory" ), - jQuery.Callbacks( "memory" ), 2 ], - [ "resolve", "done", jQuery.Callbacks( "once memory" ), - jQuery.Callbacks( "once memory" ), 0, "resolved" ], - [ "reject", "fail", jQuery.Callbacks( "once memory" ), - jQuery.Callbacks( "once memory" ), 1, "rejected" ] - ], - state = "pending", - promise = { - state: function() { - return state; - }, - always: function() { - deferred.done( arguments ).fail( arguments ); - return this; - }, - "catch": function( fn ) { - return promise.then( null, fn ); - }, - - // Keep pipe for back-compat - pipe: function( /* fnDone, fnFail, fnProgress */ ) { - var fns = arguments; - - return jQuery.Deferred( function( newDefer ) { - jQuery.each( tuples, function( i, tuple ) { - - // Map tuples (progress, done, fail) to arguments (done, fail, progress) - var fn = jQuery.isFunction( fns[ tuple[ 4 ] ] ) && fns[ tuple[ 4 ] ]; - - // deferred.progress(function() { bind to newDefer or newDefer.notify }) - // deferred.done(function() { bind to newDefer or newDefer.resolve }) - // deferred.fail(function() { bind to newDefer or newDefer.reject }) - deferred[ tuple[ 1 ] ]( function() { - var returned = fn && fn.apply( this, arguments ); - if ( returned && jQuery.isFunction( returned.promise ) ) { - returned.promise() - .progress( newDefer.notify ) - .done( newDefer.resolve ) - .fail( newDefer.reject ); - } else { - newDefer[ tuple[ 0 ] + "With" ]( - this, - fn ? [ returned ] : arguments - ); - } - } ); - } ); - fns = null; - } ).promise(); - }, - then: function( onFulfilled, onRejected, onProgress ) { - var maxDepth = 0; - function resolve( depth, deferred, handler, special ) { - return function() { - var that = this, - args = arguments, - mightThrow = function() { - var returned, then; - - // Support: Promises/A+ section 2.3.3.3.3 - // https://promisesaplus.com/#point-59 - // Ignore double-resolution attempts - if ( depth < maxDepth ) { - return; - } - - returned = handler.apply( that, args ); - - // Support: Promises/A+ section 2.3.1 - // https://promisesaplus.com/#point-48 - if ( returned === deferred.promise() ) { - throw new TypeError( "Thenable self-resolution" ); - } - - // Support: Promises/A+ sections 2.3.3.1, 3.5 - // https://promisesaplus.com/#point-54 - // https://promisesaplus.com/#point-75 - // Retrieve `then` only once - then = returned && - - // Support: Promises/A+ section 2.3.4 - // https://promisesaplus.com/#point-64 - // Only check objects and functions for thenability - ( typeof returned === "object" || - typeof returned === "function" ) && - returned.then; - - // Handle a returned thenable - if ( jQuery.isFunction( then ) ) { - - // Special processors (notify) just wait for resolution - if ( special ) { - then.call( - returned, - resolve( maxDepth, deferred, Identity, special ), - resolve( maxDepth, deferred, Thrower, special ) - ); - - // Normal processors (resolve) also hook into progress - } else { - - // ...and disregard older resolution values - maxDepth++; - - then.call( - returned, - resolve( maxDepth, deferred, Identity, special ), - resolve( maxDepth, deferred, Thrower, special ), - resolve( maxDepth, deferred, Identity, - deferred.notifyWith ) - ); - } - - // Handle all other returned values - } else { - - // Only substitute handlers pass on context - // and multiple values (non-spec behavior) - if ( handler !== Identity ) { - that = undefined; - args = [ returned ]; - } - - // Process the value(s) - // Default process is resolve - ( special || deferred.resolveWith )( that, args ); - } - }, - - // Only normal processors (resolve) catch and reject exceptions - process = special ? - mightThrow : - function() { - try { - mightThrow(); - } catch ( e ) { - - if ( jQuery.Deferred.exceptionHook ) { - jQuery.Deferred.exceptionHook( e, - process.stackTrace ); - } - - // Support: Promises/A+ section 2.3.3.3.4.1 - // https://promisesaplus.com/#point-61 - // Ignore post-resolution exceptions - if ( depth + 1 >= maxDepth ) { - - // Only substitute handlers pass on context - // and multiple values (non-spec behavior) - if ( handler !== Thrower ) { - that = undefined; - args = [ e ]; - } - - deferred.rejectWith( that, args ); - } - } - }; - - // Support: Promises/A+ section 2.3.3.3.1 - // https://promisesaplus.com/#point-57 - // Re-resolve promises immediately to dodge false rejection from - // subsequent errors - if ( depth ) { - process(); - } else { - - // Call an optional hook to record the stack, in case of exception - // since it's otherwise lost when execution goes async - if ( jQuery.Deferred.getStackHook ) { - process.stackTrace = jQuery.Deferred.getStackHook(); - } - window.setTimeout( process ); - } - }; - } - - return jQuery.Deferred( function( newDefer ) { - - // progress_handlers.add( ... ) - tuples[ 0 ][ 3 ].add( - resolve( - 0, - newDefer, - jQuery.isFunction( onProgress ) ? - onProgress : - Identity, - newDefer.notifyWith - ) - ); - - // fulfilled_handlers.add( ... ) - tuples[ 1 ][ 3 ].add( - resolve( - 0, - newDefer, - jQuery.isFunction( onFulfilled ) ? - onFulfilled : - Identity - ) - ); - - // rejected_handlers.add( ... ) - tuples[ 2 ][ 3 ].add( - resolve( - 0, - newDefer, - jQuery.isFunction( onRejected ) ? - onRejected : - Thrower - ) - ); - } ).promise(); - }, - - // Get a promise for this deferred - // If obj is provided, the promise aspect is added to the object - promise: function( obj ) { - return obj != null ? jQuery.extend( obj, promise ) : promise; - } - }, - deferred = {}; - - // Add list-specific methods - jQuery.each( tuples, function( i, tuple ) { - var list = tuple[ 2 ], - stateString = tuple[ 5 ]; - - // promise.progress = list.add - // promise.done = list.add - // promise.fail = list.add - promise[ tuple[ 1 ] ] = list.add; - - // Handle state - if ( stateString ) { - list.add( - function() { - - // state = "resolved" (i.e., fulfilled) - // state = "rejected" - state = stateString; - }, - - // rejected_callbacks.disable - // fulfilled_callbacks.disable - tuples[ 3 - i ][ 2 ].disable, - - // progress_callbacks.lock - tuples[ 0 ][ 2 ].lock - ); - } - - // progress_handlers.fire - // fulfilled_handlers.fire - // rejected_handlers.fire - list.add( tuple[ 3 ].fire ); - - // deferred.notify = function() { deferred.notifyWith(...) } - // deferred.resolve = function() { deferred.resolveWith(...) } - // deferred.reject = function() { deferred.rejectWith(...) } - deferred[ tuple[ 0 ] ] = function() { - deferred[ tuple[ 0 ] + "With" ]( this === deferred ? undefined : this, arguments ); - return this; - }; - - // deferred.notifyWith = list.fireWith - // deferred.resolveWith = list.fireWith - // deferred.rejectWith = list.fireWith - deferred[ tuple[ 0 ] + "With" ] = list.fireWith; - } ); - - // Make the deferred a promise - promise.promise( deferred ); - - // Call given func if any - if ( func ) { - func.call( deferred, deferred ); - } - - // All done! - return deferred; - }, - - // Deferred helper - when: function( singleValue ) { - var - - // count of uncompleted subordinates - remaining = arguments.length, - - // count of unprocessed arguments - i = remaining, - - // subordinate fulfillment data - resolveContexts = Array( i ), - resolveValues = slice.call( arguments ), - - // the master Deferred - master = jQuery.Deferred(), - - // subordinate callback factory - updateFunc = function( i ) { - return function( value ) { - resolveContexts[ i ] = this; - resolveValues[ i ] = arguments.length > 1 ? slice.call( arguments ) : value; - if ( !( --remaining ) ) { - master.resolveWith( resolveContexts, resolveValues ); - } - }; - }; - - // Single- and empty arguments are adopted like Promise.resolve - if ( remaining <= 1 ) { - adoptValue( singleValue, master.done( updateFunc( i ) ).resolve, master.reject, - !remaining ); - - // Use .then() to unwrap secondary thenables (cf. gh-3000) - if ( master.state() === "pending" || - jQuery.isFunction( resolveValues[ i ] && resolveValues[ i ].then ) ) { - - return master.then(); - } - } - - // Multiple arguments are aggregated like Promise.all array elements - while ( i-- ) { - adoptValue( resolveValues[ i ], updateFunc( i ), master.reject ); - } - - return master.promise(); - } -} ); - - -// These usually indicate a programmer mistake during development, -// warn about them ASAP rather than swallowing them by default. -var rerrorNames = /^(Eval|Internal|Range|Reference|Syntax|Type|URI)Error$/; - -jQuery.Deferred.exceptionHook = function( error, stack ) { - - // Support: IE 8 - 9 only - // Console exists when dev tools are open, which can happen at any time - if ( window.console && window.console.warn && error && rerrorNames.test( error.name ) ) { - window.console.warn( "jQuery.Deferred exception: " + error.message, error.stack, stack ); - } -}; - - - - -jQuery.readyException = function( error ) { - window.setTimeout( function() { - throw error; - } ); -}; - - - - -// The deferred used on DOM ready -var readyList = jQuery.Deferred(); - -jQuery.fn.ready = function( fn ) { - - readyList - .then( fn ) - - // Wrap jQuery.readyException in a function so that the lookup - // happens at the time of error handling instead of callback - // registration. - .catch( function( error ) { - jQuery.readyException( error ); - } ); - - return this; -}; - -jQuery.extend( { - - // Is the DOM ready to be used? Set to true once it occurs. - isReady: false, - - // A counter to track how many items to wait for before - // the ready event fires. See #6781 - readyWait: 1, - - // Handle when the DOM is ready - ready: function( wait ) { - - // Abort if there are pending holds or we're already ready - if ( wait === true ? --jQuery.readyWait : jQuery.isReady ) { - return; - } - - // Remember that the DOM is ready - jQuery.isReady = true; - - // If a normal DOM Ready event fired, decrement, and wait if need be - if ( wait !== true && --jQuery.readyWait > 0 ) { - return; - } - - // If there are functions bound, to execute - readyList.resolveWith( document, [ jQuery ] ); - } -} ); - -jQuery.ready.then = readyList.then; - -// The ready event handler and self cleanup method -function completed() { - document.removeEventListener( "DOMContentLoaded", completed ); - window.removeEventListener( "load", completed ); - jQuery.ready(); -} - -// Catch cases where $(document).ready() is called -// after the browser event has already occurred. -// Support: IE <=9 - 10 only -// Older IE sometimes signals "interactive" too soon -if ( document.readyState === "complete" || - ( document.readyState !== "loading" && !document.documentElement.doScroll ) ) { - - // Handle it asynchronously to allow scripts the opportunity to delay ready - window.setTimeout( jQuery.ready ); - -} else { - - // Use the handy event callback - document.addEventListener( "DOMContentLoaded", completed ); - - // A fallback to window.onload, that will always work - window.addEventListener( "load", completed ); -} - - - - -// Multifunctional method to get and set values of a collection -// The value/s can optionally be executed if it's a function -var access = function( elems, fn, key, value, chainable, emptyGet, raw ) { - var i = 0, - len = elems.length, - bulk = key == null; - - // Sets many values - if ( jQuery.type( key ) === "object" ) { - chainable = true; - for ( i in key ) { - access( elems, fn, i, key[ i ], true, emptyGet, raw ); - } - - // Sets one value - } else if ( value !== undefined ) { - chainable = true; - - if ( !jQuery.isFunction( value ) ) { - raw = true; - } - - if ( bulk ) { - - // Bulk operations run against the entire set - if ( raw ) { - fn.call( elems, value ); - fn = null; - - // ...except when executing function values - } else { - bulk = fn; - fn = function( elem, key, value ) { - return bulk.call( jQuery( elem ), value ); - }; - } - } - - if ( fn ) { - for ( ; i < len; i++ ) { - fn( - elems[ i ], key, raw ? - value : - value.call( elems[ i ], i, fn( elems[ i ], key ) ) - ); - } - } - } - - if ( chainable ) { - return elems; - } - - // Gets - if ( bulk ) { - return fn.call( elems ); - } - - return len ? fn( elems[ 0 ], key ) : emptyGet; -}; -var acceptData = function( owner ) { - - // Accepts only: - // - Node - // - Node.ELEMENT_NODE - // - Node.DOCUMENT_NODE - // - Object - // - Any - return owner.nodeType === 1 || owner.nodeType === 9 || !( +owner.nodeType ); -}; - - - - -function Data() { - this.expando = jQuery.expando + Data.uid++; -} - -Data.uid = 1; - -Data.prototype = { - - cache: function( owner ) { - - // Check if the owner object already has a cache - var value = owner[ this.expando ]; - - // If not, create one - if ( !value ) { - value = {}; - - // We can accept data for non-element nodes in modern browsers, - // but we should not, see #8335. - // Always return an empty object. - if ( acceptData( owner ) ) { - - // If it is a node unlikely to be stringify-ed or looped over - // use plain assignment - if ( owner.nodeType ) { - owner[ this.expando ] = value; - - // Otherwise secure it in a non-enumerable property - // configurable must be true to allow the property to be - // deleted when data is removed - } else { - Object.defineProperty( owner, this.expando, { - value: value, - configurable: true - } ); - } - } - } - - return value; - }, - set: function( owner, data, value ) { - var prop, - cache = this.cache( owner ); - - // Handle: [ owner, key, value ] args - // Always use camelCase key (gh-2257) - if ( typeof data === "string" ) { - cache[ jQuery.camelCase( data ) ] = value; - - // Handle: [ owner, { properties } ] args - } else { - - // Copy the properties one-by-one to the cache object - for ( prop in data ) { - cache[ jQuery.camelCase( prop ) ] = data[ prop ]; - } - } - return cache; - }, - get: function( owner, key ) { - return key === undefined ? - this.cache( owner ) : - - // Always use camelCase key (gh-2257) - owner[ this.expando ] && owner[ this.expando ][ jQuery.camelCase( key ) ]; - }, - access: function( owner, key, value ) { - - // In cases where either: - // - // 1. No key was specified - // 2. A string key was specified, but no value provided - // - // Take the "read" path and allow the get method to determine - // which value to return, respectively either: - // - // 1. The entire cache object - // 2. The data stored at the key - // - if ( key === undefined || - ( ( key && typeof key === "string" ) && value === undefined ) ) { - - return this.get( owner, key ); - } - - // When the key is not a string, or both a key and value - // are specified, set or extend (existing objects) with either: - // - // 1. An object of properties - // 2. A key and value - // - this.set( owner, key, value ); - - // Since the "set" path can have two possible entry points - // return the expected data based on which path was taken[*] - return value !== undefined ? value : key; - }, - remove: function( owner, key ) { - var i, - cache = owner[ this.expando ]; - - if ( cache === undefined ) { - return; - } - - if ( key !== undefined ) { - - // Support array or space separated string of keys - if ( Array.isArray( key ) ) { - - // If key is an array of keys... - // We always set camelCase keys, so remove that. - key = key.map( jQuery.camelCase ); - } else { - key = jQuery.camelCase( key ); - - // If a key with the spaces exists, use it. - // Otherwise, create an array by matching non-whitespace - key = key in cache ? - [ key ] : - ( key.match( rnothtmlwhite ) || [] ); - } - - i = key.length; - - while ( i-- ) { - delete cache[ key[ i ] ]; - } - } - - // Remove the expando if there's no more data - if ( key === undefined || jQuery.isEmptyObject( cache ) ) { - - // Support: Chrome <=35 - 45 - // Webkit & Blink performance suffers when deleting properties - // from DOM nodes, so set to undefined instead - // https://bugs.chromium.org/p/chromium/issues/detail?id=378607 (bug restricted) - if ( owner.nodeType ) { - owner[ this.expando ] = undefined; - } else { - delete owner[ this.expando ]; - } - } - }, - hasData: function( owner ) { - var cache = owner[ this.expando ]; - return cache !== undefined && !jQuery.isEmptyObject( cache ); - } -}; -var dataPriv = new Data(); - -var dataUser = new Data(); - - - -// Implementation Summary -// -// 1. Enforce API surface and semantic compatibility with 1.9.x branch -// 2. Improve the module's maintainability by reducing the storage -// paths to a single mechanism. -// 3. Use the same single mechanism to support "private" and "user" data. -// 4. _Never_ expose "private" data to user code (TODO: Drop _data, _removeData) -// 5. Avoid exposing implementation details on user objects (eg. expando properties) -// 6. Provide a clear path for implementation upgrade to WeakMap in 2014 - -var rbrace = /^(?:\{[\w\W]*\}|\[[\w\W]*\])$/, - rmultiDash = /[A-Z]/g; - -function getData( data ) { - if ( data === "true" ) { - return true; - } - - if ( data === "false" ) { - return false; - } - - if ( data === "null" ) { - return null; - } - - // Only convert to a number if it doesn't change the string - if ( data === +data + "" ) { - return +data; - } - - if ( rbrace.test( data ) ) { - return JSON.parse( data ); - } - - return data; -} - -function dataAttr( elem, key, data ) { - var name; - - // If nothing was found internally, try to fetch any - // data from the HTML5 data-* attribute - if ( data === undefined && elem.nodeType === 1 ) { - name = "data-" + key.replace( rmultiDash, "-$&" ).toLowerCase(); - data = elem.getAttribute( name ); - - if ( typeof data === "string" ) { - try { - data = getData( data ); - } catch ( e ) {} - - // Make sure we set the data so it isn't changed later - dataUser.set( elem, key, data ); - } else { - data = undefined; - } - } - return data; -} - -jQuery.extend( { - hasData: function( elem ) { - return dataUser.hasData( elem ) || dataPriv.hasData( elem ); - }, - - data: function( elem, name, data ) { - return dataUser.access( elem, name, data ); - }, - - removeData: function( elem, name ) { - dataUser.remove( elem, name ); - }, - - // TODO: Now that all calls to _data and _removeData have been replaced - // with direct calls to dataPriv methods, these can be deprecated. - _data: function( elem, name, data ) { - return dataPriv.access( elem, name, data ); - }, - - _removeData: function( elem, name ) { - dataPriv.remove( elem, name ); - } -} ); - -jQuery.fn.extend( { - data: function( key, value ) { - var i, name, data, - elem = this[ 0 ], - attrs = elem && elem.attributes; - - // Gets all values - if ( key === undefined ) { - if ( this.length ) { - data = dataUser.get( elem ); - - if ( elem.nodeType === 1 && !dataPriv.get( elem, "hasDataAttrs" ) ) { - i = attrs.length; - while ( i-- ) { - - // Support: IE 11 only - // The attrs elements can be null (#14894) - if ( attrs[ i ] ) { - name = attrs[ i ].name; - if ( name.indexOf( "data-" ) === 0 ) { - name = jQuery.camelCase( name.slice( 5 ) ); - dataAttr( elem, name, data[ name ] ); - } - } - } - dataPriv.set( elem, "hasDataAttrs", true ); - } - } - - return data; - } - - // Sets multiple values - if ( typeof key === "object" ) { - return this.each( function() { - dataUser.set( this, key ); - } ); - } - - return access( this, function( value ) { - var data; - - // The calling jQuery object (element matches) is not empty - // (and therefore has an element appears at this[ 0 ]) and the - // `value` parameter was not undefined. An empty jQuery object - // will result in `undefined` for elem = this[ 0 ] which will - // throw an exception if an attempt to read a data cache is made. - if ( elem && value === undefined ) { - - // Attempt to get data from the cache - // The key will always be camelCased in Data - data = dataUser.get( elem, key ); - if ( data !== undefined ) { - return data; - } - - // Attempt to "discover" the data in - // HTML5 custom data-* attrs - data = dataAttr( elem, key ); - if ( data !== undefined ) { - return data; - } - - // We tried really hard, but the data doesn't exist. - return; - } - - // Set the data... - this.each( function() { - - // We always store the camelCased key - dataUser.set( this, key, value ); - } ); - }, null, value, arguments.length > 1, null, true ); - }, - - removeData: function( key ) { - return this.each( function() { - dataUser.remove( this, key ); - } ); - } -} ); - - -jQuery.extend( { - queue: function( elem, type, data ) { - var queue; - - if ( elem ) { - type = ( type || "fx" ) + "queue"; - queue = dataPriv.get( elem, type ); - - // Speed up dequeue by getting out quickly if this is just a lookup - if ( data ) { - if ( !queue || Array.isArray( data ) ) { - queue = dataPriv.access( elem, type, jQuery.makeArray( data ) ); - } else { - queue.push( data ); - } - } - return queue || []; - } - }, - - dequeue: function( elem, type ) { - type = type || "fx"; - - var queue = jQuery.queue( elem, type ), - startLength = queue.length, - fn = queue.shift(), - hooks = jQuery._queueHooks( elem, type ), - next = function() { - jQuery.dequeue( elem, type ); - }; - - // If the fx queue is dequeued, always remove the progress sentinel - if ( fn === "inprogress" ) { - fn = queue.shift(); - startLength--; - } - - if ( fn ) { - - // Add a progress sentinel to prevent the fx queue from being - // automatically dequeued - if ( type === "fx" ) { - queue.unshift( "inprogress" ); - } - - // Clear up the last queue stop function - delete hooks.stop; - fn.call( elem, next, hooks ); - } - - if ( !startLength && hooks ) { - hooks.empty.fire(); - } - }, - - // Not public - generate a queueHooks object, or return the current one - _queueHooks: function( elem, type ) { - var key = type + "queueHooks"; - return dataPriv.get( elem, key ) || dataPriv.access( elem, key, { - empty: jQuery.Callbacks( "once memory" ).add( function() { - dataPriv.remove( elem, [ type + "queue", key ] ); - } ) - } ); - } -} ); - -jQuery.fn.extend( { - queue: function( type, data ) { - var setter = 2; - - if ( typeof type !== "string" ) { - data = type; - type = "fx"; - setter--; - } - - if ( arguments.length < setter ) { - return jQuery.queue( this[ 0 ], type ); - } - - return data === undefined ? - this : - this.each( function() { - var queue = jQuery.queue( this, type, data ); - - // Ensure a hooks for this queue - jQuery._queueHooks( this, type ); - - if ( type === "fx" && queue[ 0 ] !== "inprogress" ) { - jQuery.dequeue( this, type ); - } - } ); - }, - dequeue: function( type ) { - return this.each( function() { - jQuery.dequeue( this, type ); - } ); - }, - clearQueue: function( type ) { - return this.queue( type || "fx", [] ); - }, - - // Get a promise resolved when queues of a certain type - // are emptied (fx is the type by default) - promise: function( type, obj ) { - var tmp, - count = 1, - defer = jQuery.Deferred(), - elements = this, - i = this.length, - resolve = function() { - if ( !( --count ) ) { - defer.resolveWith( elements, [ elements ] ); - } - }; - - if ( typeof type !== "string" ) { - obj = type; - type = undefined; - } - type = type || "fx"; - - while ( i-- ) { - tmp = dataPriv.get( elements[ i ], type + "queueHooks" ); - if ( tmp && tmp.empty ) { - count++; - tmp.empty.add( resolve ); - } - } - resolve(); - return defer.promise( obj ); - } -} ); -var pnum = ( /[+-]?(?:\d*\.|)\d+(?:[eE][+-]?\d+|)/ ).source; - -var rcssNum = new RegExp( "^(?:([+-])=|)(" + pnum + ")([a-z%]*)$", "i" ); - - -var cssExpand = [ "Top", "Right", "Bottom", "Left" ]; - -var isHiddenWithinTree = function( elem, el ) { - - // isHiddenWithinTree might be called from jQuery#filter function; - // in that case, element will be second argument - elem = el || elem; - - // Inline style trumps all - return elem.style.display === "none" || - elem.style.display === "" && - - // Otherwise, check computed style - // Support: Firefox <=43 - 45 - // Disconnected elements can have computed display: none, so first confirm that elem is - // in the document. - jQuery.contains( elem.ownerDocument, elem ) && - - jQuery.css( elem, "display" ) === "none"; - }; - -var swap = function( elem, options, callback, args ) { - var ret, name, - old = {}; - - // Remember the old values, and insert the new ones - for ( name in options ) { - old[ name ] = elem.style[ name ]; - elem.style[ name ] = options[ name ]; - } - - ret = callback.apply( elem, args || [] ); - - // Revert the old values - for ( name in options ) { - elem.style[ name ] = old[ name ]; - } - - return ret; -}; - - - - -function adjustCSS( elem, prop, valueParts, tween ) { - var adjusted, - scale = 1, - maxIterations = 20, - currentValue = tween ? - function() { - return tween.cur(); - } : - function() { - return jQuery.css( elem, prop, "" ); - }, - initial = currentValue(), - unit = valueParts && valueParts[ 3 ] || ( jQuery.cssNumber[ prop ] ? "" : "px" ), - - // Starting value computation is required for potential unit mismatches - initialInUnit = ( jQuery.cssNumber[ prop ] || unit !== "px" && +initial ) && - rcssNum.exec( jQuery.css( elem, prop ) ); - - if ( initialInUnit && initialInUnit[ 3 ] !== unit ) { - - // Trust units reported by jQuery.css - unit = unit || initialInUnit[ 3 ]; - - // Make sure we update the tween properties later on - valueParts = valueParts || []; - - // Iteratively approximate from a nonzero starting point - initialInUnit = +initial || 1; - - do { - - // If previous iteration zeroed out, double until we get *something*. - // Use string for doubling so we don't accidentally see scale as unchanged below - scale = scale || ".5"; - - // Adjust and apply - initialInUnit = initialInUnit / scale; - jQuery.style( elem, prop, initialInUnit + unit ); - - // Update scale, tolerating zero or NaN from tween.cur() - // Break the loop if scale is unchanged or perfect, or if we've just had enough. - } while ( - scale !== ( scale = currentValue() / initial ) && scale !== 1 && --maxIterations - ); - } - - if ( valueParts ) { - initialInUnit = +initialInUnit || +initial || 0; - - // Apply relative offset (+=/-=) if specified - adjusted = valueParts[ 1 ] ? - initialInUnit + ( valueParts[ 1 ] + 1 ) * valueParts[ 2 ] : - +valueParts[ 2 ]; - if ( tween ) { - tween.unit = unit; - tween.start = initialInUnit; - tween.end = adjusted; - } - } - return adjusted; -} - - -var defaultDisplayMap = {}; - -function getDefaultDisplay( elem ) { - var temp, - doc = elem.ownerDocument, - nodeName = elem.nodeName, - display = defaultDisplayMap[ nodeName ]; - - if ( display ) { - return display; - } - - temp = doc.body.appendChild( doc.createElement( nodeName ) ); - display = jQuery.css( temp, "display" ); - - temp.parentNode.removeChild( temp ); - - if ( display === "none" ) { - display = "block"; - } - defaultDisplayMap[ nodeName ] = display; - - return display; -} - -function showHide( elements, show ) { - var display, elem, - values = [], - index = 0, - length = elements.length; - - // Determine new display value for elements that need to change - for ( ; index < length; index++ ) { - elem = elements[ index ]; - if ( !elem.style ) { - continue; - } - - display = elem.style.display; - if ( show ) { - - // Since we force visibility upon cascade-hidden elements, an immediate (and slow) - // check is required in this first loop unless we have a nonempty display value (either - // inline or about-to-be-restored) - if ( display === "none" ) { - values[ index ] = dataPriv.get( elem, "display" ) || null; - if ( !values[ index ] ) { - elem.style.display = ""; - } - } - if ( elem.style.display === "" && isHiddenWithinTree( elem ) ) { - values[ index ] = getDefaultDisplay( elem ); - } - } else { - if ( display !== "none" ) { - values[ index ] = "none"; - - // Remember what we're overwriting - dataPriv.set( elem, "display", display ); - } - } - } - - // Set the display of the elements in a second loop to avoid constant reflow - for ( index = 0; index < length; index++ ) { - if ( values[ index ] != null ) { - elements[ index ].style.display = values[ index ]; - } - } - - return elements; -} - -jQuery.fn.extend( { - show: function() { - return showHide( this, true ); - }, - hide: function() { - return showHide( this ); - }, - toggle: function( state ) { - if ( typeof state === "boolean" ) { - return state ? this.show() : this.hide(); - } - - return this.each( function() { - if ( isHiddenWithinTree( this ) ) { - jQuery( this ).show(); - } else { - jQuery( this ).hide(); - } - } ); - } -} ); -var rcheckableType = ( /^(?:checkbox|radio)$/i ); - -var rtagName = ( /<([a-z][^\/\0>\x20\t\r\n\f]+)/i ); - -var rscriptType = ( /^$|\/(?:java|ecma)script/i ); - - - -// We have to close these tags to support XHTML (#13200) -var wrapMap = { - - // Support: IE <=9 only - option: [ 1, "" ], - - // XHTML parsers do not magically insert elements in the - // same way that tag soup parsers do. So we cannot shorten - // this by omitting or other required elements. - thead: [ 1, "", "
" ], - col: [ 2, "", "
" ], - tr: [ 2, "", "
" ], - td: [ 3, "", "
" ], - - _default: [ 0, "", "" ] -}; - -// Support: IE <=9 only -wrapMap.optgroup = wrapMap.option; - -wrapMap.tbody = wrapMap.tfoot = wrapMap.colgroup = wrapMap.caption = wrapMap.thead; -wrapMap.th = wrapMap.td; - - -function getAll( context, tag ) { - - // Support: IE <=9 - 11 only - // Use typeof to avoid zero-argument method invocation on host objects (#15151) - var ret; - - if ( typeof context.getElementsByTagName !== "undefined" ) { - ret = context.getElementsByTagName( tag || "*" ); - - } else if ( typeof context.querySelectorAll !== "undefined" ) { - ret = context.querySelectorAll( tag || "*" ); - - } else { - ret = []; - } - - if ( tag === undefined || tag && nodeName( context, tag ) ) { - return jQuery.merge( [ context ], ret ); - } - - return ret; -} - - -// Mark scripts as having already been evaluated -function setGlobalEval( elems, refElements ) { - var i = 0, - l = elems.length; - - for ( ; i < l; i++ ) { - dataPriv.set( - elems[ i ], - "globalEval", - !refElements || dataPriv.get( refElements[ i ], "globalEval" ) - ); - } -} - - -var rhtml = /<|&#?\w+;/; - -function buildFragment( elems, context, scripts, selection, ignored ) { - var elem, tmp, tag, wrap, contains, j, - fragment = context.createDocumentFragment(), - nodes = [], - i = 0, - l = elems.length; - - for ( ; i < l; i++ ) { - elem = elems[ i ]; - - if ( elem || elem === 0 ) { - - // Add nodes directly - if ( jQuery.type( elem ) === "object" ) { - - // Support: Android <=4.0 only, PhantomJS 1 only - // push.apply(_, arraylike) throws on ancient WebKit - jQuery.merge( nodes, elem.nodeType ? [ elem ] : elem ); - - // Convert non-html into a text node - } else if ( !rhtml.test( elem ) ) { - nodes.push( context.createTextNode( elem ) ); - - // Convert html into DOM nodes - } else { - tmp = tmp || fragment.appendChild( context.createElement( "div" ) ); - - // Deserialize a standard representation - tag = ( rtagName.exec( elem ) || [ "", "" ] )[ 1 ].toLowerCase(); - wrap = wrapMap[ tag ] || wrapMap._default; - tmp.innerHTML = wrap[ 1 ] + jQuery.htmlPrefilter( elem ) + wrap[ 2 ]; - - // Descend through wrappers to the right content - j = wrap[ 0 ]; - while ( j-- ) { - tmp = tmp.lastChild; - } - - // Support: Android <=4.0 only, PhantomJS 1 only - // push.apply(_, arraylike) throws on ancient WebKit - jQuery.merge( nodes, tmp.childNodes ); - - // Remember the top-level container - tmp = fragment.firstChild; - - // Ensure the created nodes are orphaned (#12392) - tmp.textContent = ""; - } - } - } - - // Remove wrapper from fragment - fragment.textContent = ""; - - i = 0; - while ( ( elem = nodes[ i++ ] ) ) { - - // Skip elements already in the context collection (trac-4087) - if ( selection && jQuery.inArray( elem, selection ) > -1 ) { - if ( ignored ) { - ignored.push( elem ); - } - continue; - } - - contains = jQuery.contains( elem.ownerDocument, elem ); - - // Append to fragment - tmp = getAll( fragment.appendChild( elem ), "script" ); - - // Preserve script evaluation history - if ( contains ) { - setGlobalEval( tmp ); - } - - // Capture executables - if ( scripts ) { - j = 0; - while ( ( elem = tmp[ j++ ] ) ) { - if ( rscriptType.test( elem.type || "" ) ) { - scripts.push( elem ); - } - } - } - } - - return fragment; -} - - -( function() { - var fragment = document.createDocumentFragment(), - div = fragment.appendChild( document.createElement( "div" ) ), - input = document.createElement( "input" ); - - // Support: Android 4.0 - 4.3 only - // Check state lost if the name is set (#11217) - // Support: Windows Web Apps (WWA) - // `name` and `type` must use .setAttribute for WWA (#14901) - input.setAttribute( "type", "radio" ); - input.setAttribute( "checked", "checked" ); - input.setAttribute( "name", "t" ); - - div.appendChild( input ); - - // Support: Android <=4.1 only - // Older WebKit doesn't clone checked state correctly in fragments - support.checkClone = div.cloneNode( true ).cloneNode( true ).lastChild.checked; - - // Support: IE <=11 only - // Make sure textarea (and checkbox) defaultValue is properly cloned - div.innerHTML = ""; - support.noCloneChecked = !!div.cloneNode( true ).lastChild.defaultValue; -} )(); -var documentElement = document.documentElement; - - - -var - rkeyEvent = /^key/, - rmouseEvent = /^(?:mouse|pointer|contextmenu|drag|drop)|click/, - rtypenamespace = /^([^.]*)(?:\.(.+)|)/; - -function returnTrue() { - return true; -} - -function returnFalse() { - return false; -} - -// Support: IE <=9 only -// See #13393 for more info -function safeActiveElement() { - try { - return document.activeElement; - } catch ( err ) { } -} - -function on( elem, types, selector, data, fn, one ) { - var origFn, type; - - // Types can be a map of types/handlers - if ( typeof types === "object" ) { - - // ( types-Object, selector, data ) - if ( typeof selector !== "string" ) { - - // ( types-Object, data ) - data = data || selector; - selector = undefined; - } - for ( type in types ) { - on( elem, type, selector, data, types[ type ], one ); - } - return elem; - } - - if ( data == null && fn == null ) { - - // ( types, fn ) - fn = selector; - data = selector = undefined; - } else if ( fn == null ) { - if ( typeof selector === "string" ) { - - // ( types, selector, fn ) - fn = data; - data = undefined; - } else { - - // ( types, data, fn ) - fn = data; - data = selector; - selector = undefined; - } - } - if ( fn === false ) { - fn = returnFalse; - } else if ( !fn ) { - return elem; - } - - if ( one === 1 ) { - origFn = fn; - fn = function( event ) { - - // Can use an empty set, since event contains the info - jQuery().off( event ); - return origFn.apply( this, arguments ); - }; - - // Use same guid so caller can remove using origFn - fn.guid = origFn.guid || ( origFn.guid = jQuery.guid++ ); - } - return elem.each( function() { - jQuery.event.add( this, types, fn, data, selector ); - } ); -} - -/* - * Helper functions for managing events -- not part of the public interface. - * Props to Dean Edwards' addEvent library for many of the ideas. - */ -jQuery.event = { - - global: {}, - - add: function( elem, types, handler, data, selector ) { - - var handleObjIn, eventHandle, tmp, - events, t, handleObj, - special, handlers, type, namespaces, origType, - elemData = dataPriv.get( elem ); - - // Don't attach events to noData or text/comment nodes (but allow plain objects) - if ( !elemData ) { - return; - } - - // Caller can pass in an object of custom data in lieu of the handler - if ( handler.handler ) { - handleObjIn = handler; - handler = handleObjIn.handler; - selector = handleObjIn.selector; - } - - // Ensure that invalid selectors throw exceptions at attach time - // Evaluate against documentElement in case elem is a non-element node (e.g., document) - if ( selector ) { - jQuery.find.matchesSelector( documentElement, selector ); - } - - // Make sure that the handler has a unique ID, used to find/remove it later - if ( !handler.guid ) { - handler.guid = jQuery.guid++; - } - - // Init the element's event structure and main handler, if this is the first - if ( !( events = elemData.events ) ) { - events = elemData.events = {}; - } - if ( !( eventHandle = elemData.handle ) ) { - eventHandle = elemData.handle = function( e ) { - - // Discard the second event of a jQuery.event.trigger() and - // when an event is called after a page has unloaded - return typeof jQuery !== "undefined" && jQuery.event.triggered !== e.type ? - jQuery.event.dispatch.apply( elem, arguments ) : undefined; - }; - } - - // Handle multiple events separated by a space - types = ( types || "" ).match( rnothtmlwhite ) || [ "" ]; - t = types.length; - while ( t-- ) { - tmp = rtypenamespace.exec( types[ t ] ) || []; - type = origType = tmp[ 1 ]; - namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort(); - - // There *must* be a type, no attaching namespace-only handlers - if ( !type ) { - continue; - } - - // If event changes its type, use the special event handlers for the changed type - special = jQuery.event.special[ type ] || {}; - - // If selector defined, determine special event api type, otherwise given type - type = ( selector ? special.delegateType : special.bindType ) || type; - - // Update special based on newly reset type - special = jQuery.event.special[ type ] || {}; - - // handleObj is passed to all event handlers - handleObj = jQuery.extend( { - type: type, - origType: origType, - data: data, - handler: handler, - guid: handler.guid, - selector: selector, - needsContext: selector && jQuery.expr.match.needsContext.test( selector ), - namespace: namespaces.join( "." ) - }, handleObjIn ); - - // Init the event handler queue if we're the first - if ( !( handlers = events[ type ] ) ) { - handlers = events[ type ] = []; - handlers.delegateCount = 0; - - // Only use addEventListener if the special events handler returns false - if ( !special.setup || - special.setup.call( elem, data, namespaces, eventHandle ) === false ) { - - if ( elem.addEventListener ) { - elem.addEventListener( type, eventHandle ); - } - } - } - - if ( special.add ) { - special.add.call( elem, handleObj ); - - if ( !handleObj.handler.guid ) { - handleObj.handler.guid = handler.guid; - } - } - - // Add to the element's handler list, delegates in front - if ( selector ) { - handlers.splice( handlers.delegateCount++, 0, handleObj ); - } else { - handlers.push( handleObj ); - } - - // Keep track of which events have ever been used, for event optimization - jQuery.event.global[ type ] = true; - } - - }, - - // Detach an event or set of events from an element - remove: function( elem, types, handler, selector, mappedTypes ) { - - var j, origCount, tmp, - events, t, handleObj, - special, handlers, type, namespaces, origType, - elemData = dataPriv.hasData( elem ) && dataPriv.get( elem ); - - if ( !elemData || !( events = elemData.events ) ) { - return; - } - - // Once for each type.namespace in types; type may be omitted - types = ( types || "" ).match( rnothtmlwhite ) || [ "" ]; - t = types.length; - while ( t-- ) { - tmp = rtypenamespace.exec( types[ t ] ) || []; - type = origType = tmp[ 1 ]; - namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort(); - - // Unbind all events (on this namespace, if provided) for the element - if ( !type ) { - for ( type in events ) { - jQuery.event.remove( elem, type + types[ t ], handler, selector, true ); - } - continue; - } - - special = jQuery.event.special[ type ] || {}; - type = ( selector ? special.delegateType : special.bindType ) || type; - handlers = events[ type ] || []; - tmp = tmp[ 2 ] && - new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" ); - - // Remove matching events - origCount = j = handlers.length; - while ( j-- ) { - handleObj = handlers[ j ]; - - if ( ( mappedTypes || origType === handleObj.origType ) && - ( !handler || handler.guid === handleObj.guid ) && - ( !tmp || tmp.test( handleObj.namespace ) ) && - ( !selector || selector === handleObj.selector || - selector === "**" && handleObj.selector ) ) { - handlers.splice( j, 1 ); - - if ( handleObj.selector ) { - handlers.delegateCount--; - } - if ( special.remove ) { - special.remove.call( elem, handleObj ); - } - } - } - - // Remove generic event handler if we removed something and no more handlers exist - // (avoids potential for endless recursion during removal of special event handlers) - if ( origCount && !handlers.length ) { - if ( !special.teardown || - special.teardown.call( elem, namespaces, elemData.handle ) === false ) { - - jQuery.removeEvent( elem, type, elemData.handle ); - } - - delete events[ type ]; - } - } - - // Remove data and the expando if it's no longer used - if ( jQuery.isEmptyObject( events ) ) { - dataPriv.remove( elem, "handle events" ); - } - }, - - dispatch: function( nativeEvent ) { - - // Make a writable jQuery.Event from the native event object - var event = jQuery.event.fix( nativeEvent ); - - var i, j, ret, matched, handleObj, handlerQueue, - args = new Array( arguments.length ), - handlers = ( dataPriv.get( this, "events" ) || {} )[ event.type ] || [], - special = jQuery.event.special[ event.type ] || {}; - - // Use the fix-ed jQuery.Event rather than the (read-only) native event - args[ 0 ] = event; - - for ( i = 1; i < arguments.length; i++ ) { - args[ i ] = arguments[ i ]; - } - - event.delegateTarget = this; - - // Call the preDispatch hook for the mapped type, and let it bail if desired - if ( special.preDispatch && special.preDispatch.call( this, event ) === false ) { - return; - } - - // Determine handlers - handlerQueue = jQuery.event.handlers.call( this, event, handlers ); - - // Run delegates first; they may want to stop propagation beneath us - i = 0; - while ( ( matched = handlerQueue[ i++ ] ) && !event.isPropagationStopped() ) { - event.currentTarget = matched.elem; - - j = 0; - while ( ( handleObj = matched.handlers[ j++ ] ) && - !event.isImmediatePropagationStopped() ) { - - // Triggered event must either 1) have no namespace, or 2) have namespace(s) - // a subset or equal to those in the bound event (both can have no namespace). - if ( !event.rnamespace || event.rnamespace.test( handleObj.namespace ) ) { - - event.handleObj = handleObj; - event.data = handleObj.data; - - ret = ( ( jQuery.event.special[ handleObj.origType ] || {} ).handle || - handleObj.handler ).apply( matched.elem, args ); - - if ( ret !== undefined ) { - if ( ( event.result = ret ) === false ) { - event.preventDefault(); - event.stopPropagation(); - } - } - } - } - } - - // Call the postDispatch hook for the mapped type - if ( special.postDispatch ) { - special.postDispatch.call( this, event ); - } - - return event.result; - }, - - handlers: function( event, handlers ) { - var i, handleObj, sel, matchedHandlers, matchedSelectors, - handlerQueue = [], - delegateCount = handlers.delegateCount, - cur = event.target; - - // Find delegate handlers - if ( delegateCount && - - // Support: IE <=9 - // Black-hole SVG instance trees (trac-13180) - cur.nodeType && - - // Support: Firefox <=42 - // Suppress spec-violating clicks indicating a non-primary pointer button (trac-3861) - // https://www.w3.org/TR/DOM-Level-3-Events/#event-type-click - // Support: IE 11 only - // ...but not arrow key "clicks" of radio inputs, which can have `button` -1 (gh-2343) - !( event.type === "click" && event.button >= 1 ) ) { - - for ( ; cur !== this; cur = cur.parentNode || this ) { - - // Don't check non-elements (#13208) - // Don't process clicks on disabled elements (#6911, #8165, #11382, #11764) - if ( cur.nodeType === 1 && !( event.type === "click" && cur.disabled === true ) ) { - matchedHandlers = []; - matchedSelectors = {}; - for ( i = 0; i < delegateCount; i++ ) { - handleObj = handlers[ i ]; - - // Don't conflict with Object.prototype properties (#13203) - sel = handleObj.selector + " "; - - if ( matchedSelectors[ sel ] === undefined ) { - matchedSelectors[ sel ] = handleObj.needsContext ? - jQuery( sel, this ).index( cur ) > -1 : - jQuery.find( sel, this, null, [ cur ] ).length; - } - if ( matchedSelectors[ sel ] ) { - matchedHandlers.push( handleObj ); - } - } - if ( matchedHandlers.length ) { - handlerQueue.push( { elem: cur, handlers: matchedHandlers } ); - } - } - } - } - - // Add the remaining (directly-bound) handlers - cur = this; - if ( delegateCount < handlers.length ) { - handlerQueue.push( { elem: cur, handlers: handlers.slice( delegateCount ) } ); - } - - return handlerQueue; - }, - - addProp: function( name, hook ) { - Object.defineProperty( jQuery.Event.prototype, name, { - enumerable: true, - configurable: true, - - get: jQuery.isFunction( hook ) ? - function() { - if ( this.originalEvent ) { - return hook( this.originalEvent ); - } - } : - function() { - if ( this.originalEvent ) { - return this.originalEvent[ name ]; - } - }, - - set: function( value ) { - Object.defineProperty( this, name, { - enumerable: true, - configurable: true, - writable: true, - value: value - } ); - } - } ); - }, - - fix: function( originalEvent ) { - return originalEvent[ jQuery.expando ] ? - originalEvent : - new jQuery.Event( originalEvent ); - }, - - special: { - load: { - - // Prevent triggered image.load events from bubbling to window.load - noBubble: true - }, - focus: { - - // Fire native event if possible so blur/focus sequence is correct - trigger: function() { - if ( this !== safeActiveElement() && this.focus ) { - this.focus(); - return false; - } - }, - delegateType: "focusin" - }, - blur: { - trigger: function() { - if ( this === safeActiveElement() && this.blur ) { - this.blur(); - return false; - } - }, - delegateType: "focusout" - }, - click: { - - // For checkbox, fire native event so checked state will be right - trigger: function() { - if ( this.type === "checkbox" && this.click && nodeName( this, "input" ) ) { - this.click(); - return false; - } - }, - - // For cross-browser consistency, don't fire native .click() on links - _default: function( event ) { - return nodeName( event.target, "a" ); - } - }, - - beforeunload: { - postDispatch: function( event ) { - - // Support: Firefox 20+ - // Firefox doesn't alert if the returnValue field is not set. - if ( event.result !== undefined && event.originalEvent ) { - event.originalEvent.returnValue = event.result; - } - } - } - } -}; - -jQuery.removeEvent = function( elem, type, handle ) { - - // This "if" is needed for plain objects - if ( elem.removeEventListener ) { - elem.removeEventListener( type, handle ); - } -}; - -jQuery.Event = function( src, props ) { - - // Allow instantiation without the 'new' keyword - if ( !( this instanceof jQuery.Event ) ) { - return new jQuery.Event( src, props ); - } - - // Event object - if ( src && src.type ) { - this.originalEvent = src; - this.type = src.type; - - // Events bubbling up the document may have been marked as prevented - // by a handler lower down the tree; reflect the correct value. - this.isDefaultPrevented = src.defaultPrevented || - src.defaultPrevented === undefined && - - // Support: Android <=2.3 only - src.returnValue === false ? - returnTrue : - returnFalse; - - // Create target properties - // Support: Safari <=6 - 7 only - // Target should not be a text node (#504, #13143) - this.target = ( src.target && src.target.nodeType === 3 ) ? - src.target.parentNode : - src.target; - - this.currentTarget = src.currentTarget; - this.relatedTarget = src.relatedTarget; - - // Event type - } else { - this.type = src; - } - - // Put explicitly provided properties onto the event object - if ( props ) { - jQuery.extend( this, props ); - } - - // Create a timestamp if incoming event doesn't have one - this.timeStamp = src && src.timeStamp || jQuery.now(); - - // Mark it as fixed - this[ jQuery.expando ] = true; -}; - -// jQuery.Event is based on DOM3 Events as specified by the ECMAScript Language Binding -// https://www.w3.org/TR/2003/WD-DOM-Level-3-Events-20030331/ecma-script-binding.html -jQuery.Event.prototype = { - constructor: jQuery.Event, - isDefaultPrevented: returnFalse, - isPropagationStopped: returnFalse, - isImmediatePropagationStopped: returnFalse, - isSimulated: false, - - preventDefault: function() { - var e = this.originalEvent; - - this.isDefaultPrevented = returnTrue; - - if ( e && !this.isSimulated ) { - e.preventDefault(); - } - }, - stopPropagation: function() { - var e = this.originalEvent; - - this.isPropagationStopped = returnTrue; - - if ( e && !this.isSimulated ) { - e.stopPropagation(); - } - }, - stopImmediatePropagation: function() { - var e = this.originalEvent; - - this.isImmediatePropagationStopped = returnTrue; - - if ( e && !this.isSimulated ) { - e.stopImmediatePropagation(); - } - - this.stopPropagation(); - } -}; - -// Includes all common event props including KeyEvent and MouseEvent specific props -jQuery.each( { - altKey: true, - bubbles: true, - cancelable: true, - changedTouches: true, - ctrlKey: true, - detail: true, - eventPhase: true, - metaKey: true, - pageX: true, - pageY: true, - shiftKey: true, - view: true, - "char": true, - charCode: true, - key: true, - keyCode: true, - button: true, - buttons: true, - clientX: true, - clientY: true, - offsetX: true, - offsetY: true, - pointerId: true, - pointerType: true, - screenX: true, - screenY: true, - targetTouches: true, - toElement: true, - touches: true, - - which: function( event ) { - var button = event.button; - - // Add which for key events - if ( event.which == null && rkeyEvent.test( event.type ) ) { - return event.charCode != null ? event.charCode : event.keyCode; - } - - // Add which for click: 1 === left; 2 === middle; 3 === right - if ( !event.which && button !== undefined && rmouseEvent.test( event.type ) ) { - if ( button & 1 ) { - return 1; - } - - if ( button & 2 ) { - return 3; - } - - if ( button & 4 ) { - return 2; - } - - return 0; - } - - return event.which; - } -}, jQuery.event.addProp ); - -// Create mouseenter/leave events using mouseover/out and event-time checks -// so that event delegation works in jQuery. -// Do the same for pointerenter/pointerleave and pointerover/pointerout -// -// Support: Safari 7 only -// Safari sends mouseenter too often; see: -// https://bugs.chromium.org/p/chromium/issues/detail?id=470258 -// for the description of the bug (it existed in older Chrome versions as well). -jQuery.each( { - mouseenter: "mouseover", - mouseleave: "mouseout", - pointerenter: "pointerover", - pointerleave: "pointerout" -}, function( orig, fix ) { - jQuery.event.special[ orig ] = { - delegateType: fix, - bindType: fix, - - handle: function( event ) { - var ret, - target = this, - related = event.relatedTarget, - handleObj = event.handleObj; - - // For mouseenter/leave call the handler if related is outside the target. - // NB: No relatedTarget if the mouse left/entered the browser window - if ( !related || ( related !== target && !jQuery.contains( target, related ) ) ) { - event.type = handleObj.origType; - ret = handleObj.handler.apply( this, arguments ); - event.type = fix; - } - return ret; - } - }; -} ); - -jQuery.fn.extend( { - - on: function( types, selector, data, fn ) { - return on( this, types, selector, data, fn ); - }, - one: function( types, selector, data, fn ) { - return on( this, types, selector, data, fn, 1 ); - }, - off: function( types, selector, fn ) { - var handleObj, type; - if ( types && types.preventDefault && types.handleObj ) { - - // ( event ) dispatched jQuery.Event - handleObj = types.handleObj; - jQuery( types.delegateTarget ).off( - handleObj.namespace ? - handleObj.origType + "." + handleObj.namespace : - handleObj.origType, - handleObj.selector, - handleObj.handler - ); - return this; - } - if ( typeof types === "object" ) { - - // ( types-object [, selector] ) - for ( type in types ) { - this.off( type, selector, types[ type ] ); - } - return this; - } - if ( selector === false || typeof selector === "function" ) { - - // ( types [, fn] ) - fn = selector; - selector = undefined; - } - if ( fn === false ) { - fn = returnFalse; - } - return this.each( function() { - jQuery.event.remove( this, types, fn, selector ); - } ); - } -} ); - - -var - - /* eslint-disable max-len */ - - // See https://github.com/eslint/eslint/issues/3229 - rxhtmlTag = /<(?!area|br|col|embed|hr|img|input|link|meta|param)(([a-z][^\/\0>\x20\t\r\n\f]*)[^>]*)\/>/gi, - - /* eslint-enable */ - - // Support: IE <=10 - 11, Edge 12 - 13 - // In IE/Edge using regex groups here causes severe slowdowns. - // See https://connect.microsoft.com/IE/feedback/details/1736512/ - rnoInnerhtml = /\s*$/g; - -// Prefer a tbody over its parent table for containing new rows -function manipulationTarget( elem, content ) { - if ( nodeName( elem, "table" ) && - nodeName( content.nodeType !== 11 ? content : content.firstChild, "tr" ) ) { - - return jQuery( ">tbody", elem )[ 0 ] || elem; - } - - return elem; -} - -// Replace/restore the type attribute of script elements for safe DOM manipulation -function disableScript( elem ) { - elem.type = ( elem.getAttribute( "type" ) !== null ) + "/" + elem.type; - return elem; -} -function restoreScript( elem ) { - var match = rscriptTypeMasked.exec( elem.type ); - - if ( match ) { - elem.type = match[ 1 ]; - } else { - elem.removeAttribute( "type" ); - } - - return elem; -} - -function cloneCopyEvent( src, dest ) { - var i, l, type, pdataOld, pdataCur, udataOld, udataCur, events; - - if ( dest.nodeType !== 1 ) { - return; - } - - // 1. Copy private data: events, handlers, etc. - if ( dataPriv.hasData( src ) ) { - pdataOld = dataPriv.access( src ); - pdataCur = dataPriv.set( dest, pdataOld ); - events = pdataOld.events; - - if ( events ) { - delete pdataCur.handle; - pdataCur.events = {}; - - for ( type in events ) { - for ( i = 0, l = events[ type ].length; i < l; i++ ) { - jQuery.event.add( dest, type, events[ type ][ i ] ); - } - } - } - } - - // 2. Copy user data - if ( dataUser.hasData( src ) ) { - udataOld = dataUser.access( src ); - udataCur = jQuery.extend( {}, udataOld ); - - dataUser.set( dest, udataCur ); - } -} - -// Fix IE bugs, see support tests -function fixInput( src, dest ) { - var nodeName = dest.nodeName.toLowerCase(); - - // Fails to persist the checked state of a cloned checkbox or radio button. - if ( nodeName === "input" && rcheckableType.test( src.type ) ) { - dest.checked = src.checked; - - // Fails to return the selected option to the default selected state when cloning options - } else if ( nodeName === "input" || nodeName === "textarea" ) { - dest.defaultValue = src.defaultValue; - } -} - -function domManip( collection, args, callback, ignored ) { - - // Flatten any nested arrays - args = concat.apply( [], args ); - - var fragment, first, scripts, hasScripts, node, doc, - i = 0, - l = collection.length, - iNoClone = l - 1, - value = args[ 0 ], - isFunction = jQuery.isFunction( value ); - - // We can't cloneNode fragments that contain checked, in WebKit - if ( isFunction || - ( l > 1 && typeof value === "string" && - !support.checkClone && rchecked.test( value ) ) ) { - return collection.each( function( index ) { - var self = collection.eq( index ); - if ( isFunction ) { - args[ 0 ] = value.call( this, index, self.html() ); - } - domManip( self, args, callback, ignored ); - } ); - } - - if ( l ) { - fragment = buildFragment( args, collection[ 0 ].ownerDocument, false, collection, ignored ); - first = fragment.firstChild; - - if ( fragment.childNodes.length === 1 ) { - fragment = first; - } - - // Require either new content or an interest in ignored elements to invoke the callback - if ( first || ignored ) { - scripts = jQuery.map( getAll( fragment, "script" ), disableScript ); - hasScripts = scripts.length; - - // Use the original fragment for the last item - // instead of the first because it can end up - // being emptied incorrectly in certain situations (#8070). - for ( ; i < l; i++ ) { - node = fragment; - - if ( i !== iNoClone ) { - node = jQuery.clone( node, true, true ); - - // Keep references to cloned scripts for later restoration - if ( hasScripts ) { - - // Support: Android <=4.0 only, PhantomJS 1 only - // push.apply(_, arraylike) throws on ancient WebKit - jQuery.merge( scripts, getAll( node, "script" ) ); - } - } - - callback.call( collection[ i ], node, i ); - } - - if ( hasScripts ) { - doc = scripts[ scripts.length - 1 ].ownerDocument; - - // Reenable scripts - jQuery.map( scripts, restoreScript ); - - // Evaluate executable scripts on first document insertion - for ( i = 0; i < hasScripts; i++ ) { - node = scripts[ i ]; - if ( rscriptType.test( node.type || "" ) && - !dataPriv.access( node, "globalEval" ) && - jQuery.contains( doc, node ) ) { - - if ( node.src ) { - - // Optional AJAX dependency, but won't run scripts if not present - if ( jQuery._evalUrl ) { - jQuery._evalUrl( node.src ); - } - } else { - DOMEval( node.textContent.replace( rcleanScript, "" ), doc ); - } - } - } - } - } - } - - return collection; -} - -function remove( elem, selector, keepData ) { - var node, - nodes = selector ? jQuery.filter( selector, elem ) : elem, - i = 0; - - for ( ; ( node = nodes[ i ] ) != null; i++ ) { - if ( !keepData && node.nodeType === 1 ) { - jQuery.cleanData( getAll( node ) ); - } - - if ( node.parentNode ) { - if ( keepData && jQuery.contains( node.ownerDocument, node ) ) { - setGlobalEval( getAll( node, "script" ) ); - } - node.parentNode.removeChild( node ); - } - } - - return elem; -} - -jQuery.extend( { - htmlPrefilter: function( html ) { - return html.replace( rxhtmlTag, "<$1>" ); - }, - - clone: function( elem, dataAndEvents, deepDataAndEvents ) { - var i, l, srcElements, destElements, - clone = elem.cloneNode( true ), - inPage = jQuery.contains( elem.ownerDocument, elem ); - - // Fix IE cloning issues - if ( !support.noCloneChecked && ( elem.nodeType === 1 || elem.nodeType === 11 ) && - !jQuery.isXMLDoc( elem ) ) { - - // We eschew Sizzle here for performance reasons: https://jsperf.com/getall-vs-sizzle/2 - destElements = getAll( clone ); - srcElements = getAll( elem ); - - for ( i = 0, l = srcElements.length; i < l; i++ ) { - fixInput( srcElements[ i ], destElements[ i ] ); - } - } - - // Copy the events from the original to the clone - if ( dataAndEvents ) { - if ( deepDataAndEvents ) { - srcElements = srcElements || getAll( elem ); - destElements = destElements || getAll( clone ); - - for ( i = 0, l = srcElements.length; i < l; i++ ) { - cloneCopyEvent( srcElements[ i ], destElements[ i ] ); - } - } else { - cloneCopyEvent( elem, clone ); - } - } - - // Preserve script evaluation history - destElements = getAll( clone, "script" ); - if ( destElements.length > 0 ) { - setGlobalEval( destElements, !inPage && getAll( elem, "script" ) ); - } - - // Return the cloned set - return clone; - }, - - cleanData: function( elems ) { - var data, elem, type, - special = jQuery.event.special, - i = 0; - - for ( ; ( elem = elems[ i ] ) !== undefined; i++ ) { - if ( acceptData( elem ) ) { - if ( ( data = elem[ dataPriv.expando ] ) ) { - if ( data.events ) { - for ( type in data.events ) { - if ( special[ type ] ) { - jQuery.event.remove( elem, type ); - - // This is a shortcut to avoid jQuery.event.remove's overhead - } else { - jQuery.removeEvent( elem, type, data.handle ); - } - } - } - - // Support: Chrome <=35 - 45+ - // Assign undefined instead of using delete, see Data#remove - elem[ dataPriv.expando ] = undefined; - } - if ( elem[ dataUser.expando ] ) { - - // Support: Chrome <=35 - 45+ - // Assign undefined instead of using delete, see Data#remove - elem[ dataUser.expando ] = undefined; - } - } - } - } -} ); - -jQuery.fn.extend( { - detach: function( selector ) { - return remove( this, selector, true ); - }, - - remove: function( selector ) { - return remove( this, selector ); - }, - - text: function( value ) { - return access( this, function( value ) { - return value === undefined ? - jQuery.text( this ) : - this.empty().each( function() { - if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) { - this.textContent = value; - } - } ); - }, null, value, arguments.length ); - }, - - append: function() { - return domManip( this, arguments, function( elem ) { - if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) { - var target = manipulationTarget( this, elem ); - target.appendChild( elem ); - } - } ); - }, - - prepend: function() { - return domManip( this, arguments, function( elem ) { - if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) { - var target = manipulationTarget( this, elem ); - target.insertBefore( elem, target.firstChild ); - } - } ); - }, - - before: function() { - return domManip( this, arguments, function( elem ) { - if ( this.parentNode ) { - this.parentNode.insertBefore( elem, this ); - } - } ); - }, - - after: function() { - return domManip( this, arguments, function( elem ) { - if ( this.parentNode ) { - this.parentNode.insertBefore( elem, this.nextSibling ); - } - } ); - }, - - empty: function() { - var elem, - i = 0; - - for ( ; ( elem = this[ i ] ) != null; i++ ) { - if ( elem.nodeType === 1 ) { - - // Prevent memory leaks - jQuery.cleanData( getAll( elem, false ) ); - - // Remove any remaining nodes - elem.textContent = ""; - } - } - - return this; - }, - - clone: function( dataAndEvents, deepDataAndEvents ) { - dataAndEvents = dataAndEvents == null ? false : dataAndEvents; - deepDataAndEvents = deepDataAndEvents == null ? dataAndEvents : deepDataAndEvents; - - return this.map( function() { - return jQuery.clone( this, dataAndEvents, deepDataAndEvents ); - } ); - }, - - html: function( value ) { - return access( this, function( value ) { - var elem = this[ 0 ] || {}, - i = 0, - l = this.length; - - if ( value === undefined && elem.nodeType === 1 ) { - return elem.innerHTML; - } - - // See if we can take a shortcut and just use innerHTML - if ( typeof value === "string" && !rnoInnerhtml.test( value ) && - !wrapMap[ ( rtagName.exec( value ) || [ "", "" ] )[ 1 ].toLowerCase() ] ) { - - value = jQuery.htmlPrefilter( value ); - - try { - for ( ; i < l; i++ ) { - elem = this[ i ] || {}; - - // Remove element nodes and prevent memory leaks - if ( elem.nodeType === 1 ) { - jQuery.cleanData( getAll( elem, false ) ); - elem.innerHTML = value; - } - } - - elem = 0; - - // If using innerHTML throws an exception, use the fallback method - } catch ( e ) {} - } - - if ( elem ) { - this.empty().append( value ); - } - }, null, value, arguments.length ); - }, - - replaceWith: function() { - var ignored = []; - - // Make the changes, replacing each non-ignored context element with the new content - return domManip( this, arguments, function( elem ) { - var parent = this.parentNode; - - if ( jQuery.inArray( this, ignored ) < 0 ) { - jQuery.cleanData( getAll( this ) ); - if ( parent ) { - parent.replaceChild( elem, this ); - } - } - - // Force callback invocation - }, ignored ); - } -} ); - -jQuery.each( { - appendTo: "append", - prependTo: "prepend", - insertBefore: "before", - insertAfter: "after", - replaceAll: "replaceWith" -}, function( name, original ) { - jQuery.fn[ name ] = function( selector ) { - var elems, - ret = [], - insert = jQuery( selector ), - last = insert.length - 1, - i = 0; - - for ( ; i <= last; i++ ) { - elems = i === last ? this : this.clone( true ); - jQuery( insert[ i ] )[ original ]( elems ); - - // Support: Android <=4.0 only, PhantomJS 1 only - // .get() because push.apply(_, arraylike) throws on ancient WebKit - push.apply( ret, elems.get() ); - } - - return this.pushStack( ret ); - }; -} ); -var rmargin = ( /^margin/ ); - -var rnumnonpx = new RegExp( "^(" + pnum + ")(?!px)[a-z%]+$", "i" ); - -var getStyles = function( elem ) { - - // Support: IE <=11 only, Firefox <=30 (#15098, #14150) - // IE throws on elements created in popups - // FF meanwhile throws on frame elements through "defaultView.getComputedStyle" - var view = elem.ownerDocument.defaultView; - - if ( !view || !view.opener ) { - view = window; - } - - return view.getComputedStyle( elem ); - }; - - - -( function() { - - // Executing both pixelPosition & boxSizingReliable tests require only one layout - // so they're executed at the same time to save the second computation. - function computeStyleTests() { - - // This is a singleton, we need to execute it only once - if ( !div ) { - return; - } - - div.style.cssText = - "box-sizing:border-box;" + - "position:relative;display:block;" + - "margin:auto;border:1px;padding:1px;" + - "top:1%;width:50%"; - div.innerHTML = ""; - documentElement.appendChild( container ); - - var divStyle = window.getComputedStyle( div ); - pixelPositionVal = divStyle.top !== "1%"; - - // Support: Android 4.0 - 4.3 only, Firefox <=3 - 44 - reliableMarginLeftVal = divStyle.marginLeft === "2px"; - boxSizingReliableVal = divStyle.width === "4px"; - - // Support: Android 4.0 - 4.3 only - // Some styles come back with percentage values, even though they shouldn't - div.style.marginRight = "50%"; - pixelMarginRightVal = divStyle.marginRight === "4px"; - - documentElement.removeChild( container ); - - // Nullify the div so it wouldn't be stored in the memory and - // it will also be a sign that checks already performed - div = null; - } - - var pixelPositionVal, boxSizingReliableVal, pixelMarginRightVal, reliableMarginLeftVal, - container = document.createElement( "div" ), - div = document.createElement( "div" ); - - // Finish early in limited (non-browser) environments - if ( !div.style ) { - return; - } - - // Support: IE <=9 - 11 only - // Style of cloned element affects source element cloned (#8908) - div.style.backgroundClip = "content-box"; - div.cloneNode( true ).style.backgroundClip = ""; - support.clearCloneStyle = div.style.backgroundClip === "content-box"; - - container.style.cssText = "border:0;width:8px;height:0;top:0;left:-9999px;" + - "padding:0;margin-top:1px;position:absolute"; - container.appendChild( div ); - - jQuery.extend( support, { - pixelPosition: function() { - computeStyleTests(); - return pixelPositionVal; - }, - boxSizingReliable: function() { - computeStyleTests(); - return boxSizingReliableVal; - }, - pixelMarginRight: function() { - computeStyleTests(); - return pixelMarginRightVal; - }, - reliableMarginLeft: function() { - computeStyleTests(); - return reliableMarginLeftVal; - } - } ); -} )(); - - -function curCSS( elem, name, computed ) { - var width, minWidth, maxWidth, ret, - - // Support: Firefox 51+ - // Retrieving style before computed somehow - // fixes an issue with getting wrong values - // on detached elements - style = elem.style; - - computed = computed || getStyles( elem ); - - // getPropertyValue is needed for: - // .css('filter') (IE 9 only, #12537) - // .css('--customProperty) (#3144) - if ( computed ) { - ret = computed.getPropertyValue( name ) || computed[ name ]; - - if ( ret === "" && !jQuery.contains( elem.ownerDocument, elem ) ) { - ret = jQuery.style( elem, name ); - } - - // A tribute to the "awesome hack by Dean Edwards" - // Android Browser returns percentage for some values, - // but width seems to be reliably pixels. - // This is against the CSSOM draft spec: - // https://drafts.csswg.org/cssom/#resolved-values - if ( !support.pixelMarginRight() && rnumnonpx.test( ret ) && rmargin.test( name ) ) { - - // Remember the original values - width = style.width; - minWidth = style.minWidth; - maxWidth = style.maxWidth; - - // Put in the new values to get a computed value out - style.minWidth = style.maxWidth = style.width = ret; - ret = computed.width; - - // Revert the changed values - style.width = width; - style.minWidth = minWidth; - style.maxWidth = maxWidth; - } - } - - return ret !== undefined ? - - // Support: IE <=9 - 11 only - // IE returns zIndex value as an integer. - ret + "" : - ret; -} - - -function addGetHookIf( conditionFn, hookFn ) { - - // Define the hook, we'll check on the first run if it's really needed. - return { - get: function() { - if ( conditionFn() ) { - - // Hook not needed (or it's not possible to use it due - // to missing dependency), remove it. - delete this.get; - return; - } - - // Hook needed; redefine it so that the support test is not executed again. - return ( this.get = hookFn ).apply( this, arguments ); - } - }; -} - - -var - - // Swappable if display is none or starts with table - // except "table", "table-cell", or "table-caption" - // See here for display values: https://developer.mozilla.org/en-US/docs/CSS/display - rdisplayswap = /^(none|table(?!-c[ea]).+)/, - rcustomProp = /^--/, - cssShow = { position: "absolute", visibility: "hidden", display: "block" }, - cssNormalTransform = { - letterSpacing: "0", - fontWeight: "400" - }, - - cssPrefixes = [ "Webkit", "Moz", "ms" ], - emptyStyle = document.createElement( "div" ).style; - -// Return a css property mapped to a potentially vendor prefixed property -function vendorPropName( name ) { - - // Shortcut for names that are not vendor prefixed - if ( name in emptyStyle ) { - return name; - } - - // Check for vendor prefixed names - var capName = name[ 0 ].toUpperCase() + name.slice( 1 ), - i = cssPrefixes.length; - - while ( i-- ) { - name = cssPrefixes[ i ] + capName; - if ( name in emptyStyle ) { - return name; - } - } -} - -// Return a property mapped along what jQuery.cssProps suggests or to -// a vendor prefixed property. -function finalPropName( name ) { - var ret = jQuery.cssProps[ name ]; - if ( !ret ) { - ret = jQuery.cssProps[ name ] = vendorPropName( name ) || name; - } - return ret; -} - -function setPositiveNumber( elem, value, subtract ) { - - // Any relative (+/-) values have already been - // normalized at this point - var matches = rcssNum.exec( value ); - return matches ? - - // Guard against undefined "subtract", e.g., when used as in cssHooks - Math.max( 0, matches[ 2 ] - ( subtract || 0 ) ) + ( matches[ 3 ] || "px" ) : - value; -} - -function augmentWidthOrHeight( elem, name, extra, isBorderBox, styles ) { - var i, - val = 0; - - // If we already have the right measurement, avoid augmentation - if ( extra === ( isBorderBox ? "border" : "content" ) ) { - i = 4; - - // Otherwise initialize for horizontal or vertical properties - } else { - i = name === "width" ? 1 : 0; - } - - for ( ; i < 4; i += 2 ) { - - // Both box models exclude margin, so add it if we want it - if ( extra === "margin" ) { - val += jQuery.css( elem, extra + cssExpand[ i ], true, styles ); - } - - if ( isBorderBox ) { - - // border-box includes padding, so remove it if we want content - if ( extra === "content" ) { - val -= jQuery.css( elem, "padding" + cssExpand[ i ], true, styles ); - } - - // At this point, extra isn't border nor margin, so remove border - if ( extra !== "margin" ) { - val -= jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles ); - } - } else { - - // At this point, extra isn't content, so add padding - val += jQuery.css( elem, "padding" + cssExpand[ i ], true, styles ); - - // At this point, extra isn't content nor padding, so add border - if ( extra !== "padding" ) { - val += jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles ); - } - } - } - - return val; -} - -function getWidthOrHeight( elem, name, extra ) { - - // Start with computed style - var valueIsBorderBox, - styles = getStyles( elem ), - val = curCSS( elem, name, styles ), - isBorderBox = jQuery.css( elem, "boxSizing", false, styles ) === "border-box"; - - // Computed unit is not pixels. Stop here and return. - if ( rnumnonpx.test( val ) ) { - return val; - } - - // Check for style in case a browser which returns unreliable values - // for getComputedStyle silently falls back to the reliable elem.style - valueIsBorderBox = isBorderBox && - ( support.boxSizingReliable() || val === elem.style[ name ] ); - - // Fall back to offsetWidth/Height when value is "auto" - // This happens for inline elements with no explicit setting (gh-3571) - if ( val === "auto" ) { - val = elem[ "offset" + name[ 0 ].toUpperCase() + name.slice( 1 ) ]; - } - - // Normalize "", auto, and prepare for extra - val = parseFloat( val ) || 0; - - // Use the active box-sizing model to add/subtract irrelevant styles - return ( val + - augmentWidthOrHeight( - elem, - name, - extra || ( isBorderBox ? "border" : "content" ), - valueIsBorderBox, - styles - ) - ) + "px"; -} - -jQuery.extend( { - - // Add in style property hooks for overriding the default - // behavior of getting and setting a style property - cssHooks: { - opacity: { - get: function( elem, computed ) { - if ( computed ) { - - // We should always get a number back from opacity - var ret = curCSS( elem, "opacity" ); - return ret === "" ? "1" : ret; - } - } - } - }, - - // Don't automatically add "px" to these possibly-unitless properties - cssNumber: { - "animationIterationCount": true, - "columnCount": true, - "fillOpacity": true, - "flexGrow": true, - "flexShrink": true, - "fontWeight": true, - "lineHeight": true, - "opacity": true, - "order": true, - "orphans": true, - "widows": true, - "zIndex": true, - "zoom": true - }, - - // Add in properties whose names you wish to fix before - // setting or getting the value - cssProps: { - "float": "cssFloat" - }, - - // Get and set the style property on a DOM Node - style: function( elem, name, value, extra ) { - - // Don't set styles on text and comment nodes - if ( !elem || elem.nodeType === 3 || elem.nodeType === 8 || !elem.style ) { - return; - } - - // Make sure that we're working with the right name - var ret, type, hooks, - origName = jQuery.camelCase( name ), - isCustomProp = rcustomProp.test( name ), - style = elem.style; - - // Make sure that we're working with the right name. We don't - // want to query the value if it is a CSS custom property - // since they are user-defined. - if ( !isCustomProp ) { - name = finalPropName( origName ); - } - - // Gets hook for the prefixed version, then unprefixed version - hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ]; - - // Check if we're setting a value - if ( value !== undefined ) { - type = typeof value; - - // Convert "+=" or "-=" to relative numbers (#7345) - if ( type === "string" && ( ret = rcssNum.exec( value ) ) && ret[ 1 ] ) { - value = adjustCSS( elem, name, ret ); - - // Fixes bug #9237 - type = "number"; - } - - // Make sure that null and NaN values aren't set (#7116) - if ( value == null || value !== value ) { - return; - } - - // If a number was passed in, add the unit (except for certain CSS properties) - if ( type === "number" ) { - value += ret && ret[ 3 ] || ( jQuery.cssNumber[ origName ] ? "" : "px" ); - } - - // background-* props affect original clone's values - if ( !support.clearCloneStyle && value === "" && name.indexOf( "background" ) === 0 ) { - style[ name ] = "inherit"; - } - - // If a hook was provided, use that value, otherwise just set the specified value - if ( !hooks || !( "set" in hooks ) || - ( value = hooks.set( elem, value, extra ) ) !== undefined ) { - - if ( isCustomProp ) { - style.setProperty( name, value ); - } else { - style[ name ] = value; - } - } - - } else { - - // If a hook was provided get the non-computed value from there - if ( hooks && "get" in hooks && - ( ret = hooks.get( elem, false, extra ) ) !== undefined ) { - - return ret; - } - - // Otherwise just get the value from the style object - return style[ name ]; - } - }, - - css: function( elem, name, extra, styles ) { - var val, num, hooks, - origName = jQuery.camelCase( name ), - isCustomProp = rcustomProp.test( name ); - - // Make sure that we're working with the right name. We don't - // want to modify the value if it is a CSS custom property - // since they are user-defined. - if ( !isCustomProp ) { - name = finalPropName( origName ); - } - - // Try prefixed name followed by the unprefixed name - hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ]; - - // If a hook was provided get the computed value from there - if ( hooks && "get" in hooks ) { - val = hooks.get( elem, true, extra ); - } - - // Otherwise, if a way to get the computed value exists, use that - if ( val === undefined ) { - val = curCSS( elem, name, styles ); - } - - // Convert "normal" to computed value - if ( val === "normal" && name in cssNormalTransform ) { - val = cssNormalTransform[ name ]; - } - - // Make numeric if forced or a qualifier was provided and val looks numeric - if ( extra === "" || extra ) { - num = parseFloat( val ); - return extra === true || isFinite( num ) ? num || 0 : val; - } - - return val; - } -} ); - -jQuery.each( [ "height", "width" ], function( i, name ) { - jQuery.cssHooks[ name ] = { - get: function( elem, computed, extra ) { - if ( computed ) { - - // Certain elements can have dimension info if we invisibly show them - // but it must have a current display style that would benefit - return rdisplayswap.test( jQuery.css( elem, "display" ) ) && - - // Support: Safari 8+ - // Table columns in Safari have non-zero offsetWidth & zero - // getBoundingClientRect().width unless display is changed. - // Support: IE <=11 only - // Running getBoundingClientRect on a disconnected node - // in IE throws an error. - ( !elem.getClientRects().length || !elem.getBoundingClientRect().width ) ? - swap( elem, cssShow, function() { - return getWidthOrHeight( elem, name, extra ); - } ) : - getWidthOrHeight( elem, name, extra ); - } - }, - - set: function( elem, value, extra ) { - var matches, - styles = extra && getStyles( elem ), - subtract = extra && augmentWidthOrHeight( - elem, - name, - extra, - jQuery.css( elem, "boxSizing", false, styles ) === "border-box", - styles - ); - - // Convert to pixels if value adjustment is needed - if ( subtract && ( matches = rcssNum.exec( value ) ) && - ( matches[ 3 ] || "px" ) !== "px" ) { - - elem.style[ name ] = value; - value = jQuery.css( elem, name ); - } - - return setPositiveNumber( elem, value, subtract ); - } - }; -} ); - -jQuery.cssHooks.marginLeft = addGetHookIf( support.reliableMarginLeft, - function( elem, computed ) { - if ( computed ) { - return ( parseFloat( curCSS( elem, "marginLeft" ) ) || - elem.getBoundingClientRect().left - - swap( elem, { marginLeft: 0 }, function() { - return elem.getBoundingClientRect().left; - } ) - ) + "px"; - } - } -); - -// These hooks are used by animate to expand properties -jQuery.each( { - margin: "", - padding: "", - border: "Width" -}, function( prefix, suffix ) { - jQuery.cssHooks[ prefix + suffix ] = { - expand: function( value ) { - var i = 0, - expanded = {}, - - // Assumes a single number if not a string - parts = typeof value === "string" ? value.split( " " ) : [ value ]; - - for ( ; i < 4; i++ ) { - expanded[ prefix + cssExpand[ i ] + suffix ] = - parts[ i ] || parts[ i - 2 ] || parts[ 0 ]; - } - - return expanded; - } - }; - - if ( !rmargin.test( prefix ) ) { - jQuery.cssHooks[ prefix + suffix ].set = setPositiveNumber; - } -} ); - -jQuery.fn.extend( { - css: function( name, value ) { - return access( this, function( elem, name, value ) { - var styles, len, - map = {}, - i = 0; - - if ( Array.isArray( name ) ) { - styles = getStyles( elem ); - len = name.length; - - for ( ; i < len; i++ ) { - map[ name[ i ] ] = jQuery.css( elem, name[ i ], false, styles ); - } - - return map; - } - - return value !== undefined ? - jQuery.style( elem, name, value ) : - jQuery.css( elem, name ); - }, name, value, arguments.length > 1 ); - } -} ); - - -function Tween( elem, options, prop, end, easing ) { - return new Tween.prototype.init( elem, options, prop, end, easing ); -} -jQuery.Tween = Tween; - -Tween.prototype = { - constructor: Tween, - init: function( elem, options, prop, end, easing, unit ) { - this.elem = elem; - this.prop = prop; - this.easing = easing || jQuery.easing._default; - this.options = options; - this.start = this.now = this.cur(); - this.end = end; - this.unit = unit || ( jQuery.cssNumber[ prop ] ? "" : "px" ); - }, - cur: function() { - var hooks = Tween.propHooks[ this.prop ]; - - return hooks && hooks.get ? - hooks.get( this ) : - Tween.propHooks._default.get( this ); - }, - run: function( percent ) { - var eased, - hooks = Tween.propHooks[ this.prop ]; - - if ( this.options.duration ) { - this.pos = eased = jQuery.easing[ this.easing ]( - percent, this.options.duration * percent, 0, 1, this.options.duration - ); - } else { - this.pos = eased = percent; - } - this.now = ( this.end - this.start ) * eased + this.start; - - if ( this.options.step ) { - this.options.step.call( this.elem, this.now, this ); - } - - if ( hooks && hooks.set ) { - hooks.set( this ); - } else { - Tween.propHooks._default.set( this ); - } - return this; - } -}; - -Tween.prototype.init.prototype = Tween.prototype; - -Tween.propHooks = { - _default: { - get: function( tween ) { - var result; - - // Use a property on the element directly when it is not a DOM element, - // or when there is no matching style property that exists. - if ( tween.elem.nodeType !== 1 || - tween.elem[ tween.prop ] != null && tween.elem.style[ tween.prop ] == null ) { - return tween.elem[ tween.prop ]; - } - - // Passing an empty string as a 3rd parameter to .css will automatically - // attempt a parseFloat and fallback to a string if the parse fails. - // Simple values such as "10px" are parsed to Float; - // complex values such as "rotate(1rad)" are returned as-is. - result = jQuery.css( tween.elem, tween.prop, "" ); - - // Empty strings, null, undefined and "auto" are converted to 0. - return !result || result === "auto" ? 0 : result; - }, - set: function( tween ) { - - // Use step hook for back compat. - // Use cssHook if its there. - // Use .style if available and use plain properties where available. - if ( jQuery.fx.step[ tween.prop ] ) { - jQuery.fx.step[ tween.prop ]( tween ); - } else if ( tween.elem.nodeType === 1 && - ( tween.elem.style[ jQuery.cssProps[ tween.prop ] ] != null || - jQuery.cssHooks[ tween.prop ] ) ) { - jQuery.style( tween.elem, tween.prop, tween.now + tween.unit ); - } else { - tween.elem[ tween.prop ] = tween.now; - } - } - } -}; - -// Support: IE <=9 only -// Panic based approach to setting things on disconnected nodes -Tween.propHooks.scrollTop = Tween.propHooks.scrollLeft = { - set: function( tween ) { - if ( tween.elem.nodeType && tween.elem.parentNode ) { - tween.elem[ tween.prop ] = tween.now; - } - } -}; - -jQuery.easing = { - linear: function( p ) { - return p; - }, - swing: function( p ) { - return 0.5 - Math.cos( p * Math.PI ) / 2; - }, - _default: "swing" -}; - -jQuery.fx = Tween.prototype.init; - -// Back compat <1.8 extension point -jQuery.fx.step = {}; - - - - -var - fxNow, inProgress, - rfxtypes = /^(?:toggle|show|hide)$/, - rrun = /queueHooks$/; - -function schedule() { - if ( inProgress ) { - if ( document.hidden === false && window.requestAnimationFrame ) { - window.requestAnimationFrame( schedule ); - } else { - window.setTimeout( schedule, jQuery.fx.interval ); - } - - jQuery.fx.tick(); - } -} - -// Animations created synchronously will run synchronously -function createFxNow() { - window.setTimeout( function() { - fxNow = undefined; - } ); - return ( fxNow = jQuery.now() ); -} - -// Generate parameters to create a standard animation -function genFx( type, includeWidth ) { - var which, - i = 0, - attrs = { height: type }; - - // If we include width, step value is 1 to do all cssExpand values, - // otherwise step value is 2 to skip over Left and Right - includeWidth = includeWidth ? 1 : 0; - for ( ; i < 4; i += 2 - includeWidth ) { - which = cssExpand[ i ]; - attrs[ "margin" + which ] = attrs[ "padding" + which ] = type; - } - - if ( includeWidth ) { - attrs.opacity = attrs.width = type; - } - - return attrs; -} - -function createTween( value, prop, animation ) { - var tween, - collection = ( Animation.tweeners[ prop ] || [] ).concat( Animation.tweeners[ "*" ] ), - index = 0, - length = collection.length; - for ( ; index < length; index++ ) { - if ( ( tween = collection[ index ].call( animation, prop, value ) ) ) { - - // We're done with this property - return tween; - } - } -} - -function defaultPrefilter( elem, props, opts ) { - var prop, value, toggle, hooks, oldfire, propTween, restoreDisplay, display, - isBox = "width" in props || "height" in props, - anim = this, - orig = {}, - style = elem.style, - hidden = elem.nodeType && isHiddenWithinTree( elem ), - dataShow = dataPriv.get( elem, "fxshow" ); - - // Queue-skipping animations hijack the fx hooks - if ( !opts.queue ) { - hooks = jQuery._queueHooks( elem, "fx" ); - if ( hooks.unqueued == null ) { - hooks.unqueued = 0; - oldfire = hooks.empty.fire; - hooks.empty.fire = function() { - if ( !hooks.unqueued ) { - oldfire(); - } - }; - } - hooks.unqueued++; - - anim.always( function() { - - // Ensure the complete handler is called before this completes - anim.always( function() { - hooks.unqueued--; - if ( !jQuery.queue( elem, "fx" ).length ) { - hooks.empty.fire(); - } - } ); - } ); - } - - // Detect show/hide animations - for ( prop in props ) { - value = props[ prop ]; - if ( rfxtypes.test( value ) ) { - delete props[ prop ]; - toggle = toggle || value === "toggle"; - if ( value === ( hidden ? "hide" : "show" ) ) { - - // Pretend to be hidden if this is a "show" and - // there is still data from a stopped show/hide - if ( value === "show" && dataShow && dataShow[ prop ] !== undefined ) { - hidden = true; - - // Ignore all other no-op show/hide data - } else { - continue; - } - } - orig[ prop ] = dataShow && dataShow[ prop ] || jQuery.style( elem, prop ); - } - } - - // Bail out if this is a no-op like .hide().hide() - propTween = !jQuery.isEmptyObject( props ); - if ( !propTween && jQuery.isEmptyObject( orig ) ) { - return; - } - - // Restrict "overflow" and "display" styles during box animations - if ( isBox && elem.nodeType === 1 ) { - - // Support: IE <=9 - 11, Edge 12 - 13 - // Record all 3 overflow attributes because IE does not infer the shorthand - // from identically-valued overflowX and overflowY - opts.overflow = [ style.overflow, style.overflowX, style.overflowY ]; - - // Identify a display type, preferring old show/hide data over the CSS cascade - restoreDisplay = dataShow && dataShow.display; - if ( restoreDisplay == null ) { - restoreDisplay = dataPriv.get( elem, "display" ); - } - display = jQuery.css( elem, "display" ); - if ( display === "none" ) { - if ( restoreDisplay ) { - display = restoreDisplay; - } else { - - // Get nonempty value(s) by temporarily forcing visibility - showHide( [ elem ], true ); - restoreDisplay = elem.style.display || restoreDisplay; - display = jQuery.css( elem, "display" ); - showHide( [ elem ] ); - } - } - - // Animate inline elements as inline-block - if ( display === "inline" || display === "inline-block" && restoreDisplay != null ) { - if ( jQuery.css( elem, "float" ) === "none" ) { - - // Restore the original display value at the end of pure show/hide animations - if ( !propTween ) { - anim.done( function() { - style.display = restoreDisplay; - } ); - if ( restoreDisplay == null ) { - display = style.display; - restoreDisplay = display === "none" ? "" : display; - } - } - style.display = "inline-block"; - } - } - } - - if ( opts.overflow ) { - style.overflow = "hidden"; - anim.always( function() { - style.overflow = opts.overflow[ 0 ]; - style.overflowX = opts.overflow[ 1 ]; - style.overflowY = opts.overflow[ 2 ]; - } ); - } - - // Implement show/hide animations - propTween = false; - for ( prop in orig ) { - - // General show/hide setup for this element animation - if ( !propTween ) { - if ( dataShow ) { - if ( "hidden" in dataShow ) { - hidden = dataShow.hidden; - } - } else { - dataShow = dataPriv.access( elem, "fxshow", { display: restoreDisplay } ); - } - - // Store hidden/visible for toggle so `.stop().toggle()` "reverses" - if ( toggle ) { - dataShow.hidden = !hidden; - } - - // Show elements before animating them - if ( hidden ) { - showHide( [ elem ], true ); - } - - /* eslint-disable no-loop-func */ - - anim.done( function() { - - /* eslint-enable no-loop-func */ - - // The final step of a "hide" animation is actually hiding the element - if ( !hidden ) { - showHide( [ elem ] ); - } - dataPriv.remove( elem, "fxshow" ); - for ( prop in orig ) { - jQuery.style( elem, prop, orig[ prop ] ); - } - } ); - } - - // Per-property setup - propTween = createTween( hidden ? dataShow[ prop ] : 0, prop, anim ); - if ( !( prop in dataShow ) ) { - dataShow[ prop ] = propTween.start; - if ( hidden ) { - propTween.end = propTween.start; - propTween.start = 0; - } - } - } -} - -function propFilter( props, specialEasing ) { - var index, name, easing, value, hooks; - - // camelCase, specialEasing and expand cssHook pass - for ( index in props ) { - name = jQuery.camelCase( index ); - easing = specialEasing[ name ]; - value = props[ index ]; - if ( Array.isArray( value ) ) { - easing = value[ 1 ]; - value = props[ index ] = value[ 0 ]; - } - - if ( index !== name ) { - props[ name ] = value; - delete props[ index ]; - } - - hooks = jQuery.cssHooks[ name ]; - if ( hooks && "expand" in hooks ) { - value = hooks.expand( value ); - delete props[ name ]; - - // Not quite $.extend, this won't overwrite existing keys. - // Reusing 'index' because we have the correct "name" - for ( index in value ) { - if ( !( index in props ) ) { - props[ index ] = value[ index ]; - specialEasing[ index ] = easing; - } - } - } else { - specialEasing[ name ] = easing; - } - } -} - -function Animation( elem, properties, options ) { - var result, - stopped, - index = 0, - length = Animation.prefilters.length, - deferred = jQuery.Deferred().always( function() { - - // Don't match elem in the :animated selector - delete tick.elem; - } ), - tick = function() { - if ( stopped ) { - return false; - } - var currentTime = fxNow || createFxNow(), - remaining = Math.max( 0, animation.startTime + animation.duration - currentTime ), - - // Support: Android 2.3 only - // Archaic crash bug won't allow us to use `1 - ( 0.5 || 0 )` (#12497) - temp = remaining / animation.duration || 0, - percent = 1 - temp, - index = 0, - length = animation.tweens.length; - - for ( ; index < length; index++ ) { - animation.tweens[ index ].run( percent ); - } - - deferred.notifyWith( elem, [ animation, percent, remaining ] ); - - // If there's more to do, yield - if ( percent < 1 && length ) { - return remaining; - } - - // If this was an empty animation, synthesize a final progress notification - if ( !length ) { - deferred.notifyWith( elem, [ animation, 1, 0 ] ); - } - - // Resolve the animation and report its conclusion - deferred.resolveWith( elem, [ animation ] ); - return false; - }, - animation = deferred.promise( { - elem: elem, - props: jQuery.extend( {}, properties ), - opts: jQuery.extend( true, { - specialEasing: {}, - easing: jQuery.easing._default - }, options ), - originalProperties: properties, - originalOptions: options, - startTime: fxNow || createFxNow(), - duration: options.duration, - tweens: [], - createTween: function( prop, end ) { - var tween = jQuery.Tween( elem, animation.opts, prop, end, - animation.opts.specialEasing[ prop ] || animation.opts.easing ); - animation.tweens.push( tween ); - return tween; - }, - stop: function( gotoEnd ) { - var index = 0, - - // If we are going to the end, we want to run all the tweens - // otherwise we skip this part - length = gotoEnd ? animation.tweens.length : 0; - if ( stopped ) { - return this; - } - stopped = true; - for ( ; index < length; index++ ) { - animation.tweens[ index ].run( 1 ); - } - - // Resolve when we played the last frame; otherwise, reject - if ( gotoEnd ) { - deferred.notifyWith( elem, [ animation, 1, 0 ] ); - deferred.resolveWith( elem, [ animation, gotoEnd ] ); - } else { - deferred.rejectWith( elem, [ animation, gotoEnd ] ); - } - return this; - } - } ), - props = animation.props; - - propFilter( props, animation.opts.specialEasing ); - - for ( ; index < length; index++ ) { - result = Animation.prefilters[ index ].call( animation, elem, props, animation.opts ); - if ( result ) { - if ( jQuery.isFunction( result.stop ) ) { - jQuery._queueHooks( animation.elem, animation.opts.queue ).stop = - jQuery.proxy( result.stop, result ); - } - return result; - } - } - - jQuery.map( props, createTween, animation ); - - if ( jQuery.isFunction( animation.opts.start ) ) { - animation.opts.start.call( elem, animation ); - } - - // Attach callbacks from options - animation - .progress( animation.opts.progress ) - .done( animation.opts.done, animation.opts.complete ) - .fail( animation.opts.fail ) - .always( animation.opts.always ); - - jQuery.fx.timer( - jQuery.extend( tick, { - elem: elem, - anim: animation, - queue: animation.opts.queue - } ) - ); - - return animation; -} - -jQuery.Animation = jQuery.extend( Animation, { - - tweeners: { - "*": [ function( prop, value ) { - var tween = this.createTween( prop, value ); - adjustCSS( tween.elem, prop, rcssNum.exec( value ), tween ); - return tween; - } ] - }, - - tweener: function( props, callback ) { - if ( jQuery.isFunction( props ) ) { - callback = props; - props = [ "*" ]; - } else { - props = props.match( rnothtmlwhite ); - } - - var prop, - index = 0, - length = props.length; - - for ( ; index < length; index++ ) { - prop = props[ index ]; - Animation.tweeners[ prop ] = Animation.tweeners[ prop ] || []; - Animation.tweeners[ prop ].unshift( callback ); - } - }, - - prefilters: [ defaultPrefilter ], - - prefilter: function( callback, prepend ) { - if ( prepend ) { - Animation.prefilters.unshift( callback ); - } else { - Animation.prefilters.push( callback ); - } - } -} ); - -jQuery.speed = function( speed, easing, fn ) { - var opt = speed && typeof speed === "object" ? jQuery.extend( {}, speed ) : { - complete: fn || !fn && easing || - jQuery.isFunction( speed ) && speed, - duration: speed, - easing: fn && easing || easing && !jQuery.isFunction( easing ) && easing - }; - - // Go to the end state if fx are off - if ( jQuery.fx.off ) { - opt.duration = 0; - - } else { - if ( typeof opt.duration !== "number" ) { - if ( opt.duration in jQuery.fx.speeds ) { - opt.duration = jQuery.fx.speeds[ opt.duration ]; - - } else { - opt.duration = jQuery.fx.speeds._default; - } - } - } - - // Normalize opt.queue - true/undefined/null -> "fx" - if ( opt.queue == null || opt.queue === true ) { - opt.queue = "fx"; - } - - // Queueing - opt.old = opt.complete; - - opt.complete = function() { - if ( jQuery.isFunction( opt.old ) ) { - opt.old.call( this ); - } - - if ( opt.queue ) { - jQuery.dequeue( this, opt.queue ); - } - }; - - return opt; -}; - -jQuery.fn.extend( { - fadeTo: function( speed, to, easing, callback ) { - - // Show any hidden elements after setting opacity to 0 - return this.filter( isHiddenWithinTree ).css( "opacity", 0 ).show() - - // Animate to the value specified - .end().animate( { opacity: to }, speed, easing, callback ); - }, - animate: function( prop, speed, easing, callback ) { - var empty = jQuery.isEmptyObject( prop ), - optall = jQuery.speed( speed, easing, callback ), - doAnimation = function() { - - // Operate on a copy of prop so per-property easing won't be lost - var anim = Animation( this, jQuery.extend( {}, prop ), optall ); - - // Empty animations, or finishing resolves immediately - if ( empty || dataPriv.get( this, "finish" ) ) { - anim.stop( true ); - } - }; - doAnimation.finish = doAnimation; - - return empty || optall.queue === false ? - this.each( doAnimation ) : - this.queue( optall.queue, doAnimation ); - }, - stop: function( type, clearQueue, gotoEnd ) { - var stopQueue = function( hooks ) { - var stop = hooks.stop; - delete hooks.stop; - stop( gotoEnd ); - }; - - if ( typeof type !== "string" ) { - gotoEnd = clearQueue; - clearQueue = type; - type = undefined; - } - if ( clearQueue && type !== false ) { - this.queue( type || "fx", [] ); - } - - return this.each( function() { - var dequeue = true, - index = type != null && type + "queueHooks", - timers = jQuery.timers, - data = dataPriv.get( this ); - - if ( index ) { - if ( data[ index ] && data[ index ].stop ) { - stopQueue( data[ index ] ); - } - } else { - for ( index in data ) { - if ( data[ index ] && data[ index ].stop && rrun.test( index ) ) { - stopQueue( data[ index ] ); - } - } - } - - for ( index = timers.length; index--; ) { - if ( timers[ index ].elem === this && - ( type == null || timers[ index ].queue === type ) ) { - - timers[ index ].anim.stop( gotoEnd ); - dequeue = false; - timers.splice( index, 1 ); - } - } - - // Start the next in the queue if the last step wasn't forced. - // Timers currently will call their complete callbacks, which - // will dequeue but only if they were gotoEnd. - if ( dequeue || !gotoEnd ) { - jQuery.dequeue( this, type ); - } - } ); - }, - finish: function( type ) { - if ( type !== false ) { - type = type || "fx"; - } - return this.each( function() { - var index, - data = dataPriv.get( this ), - queue = data[ type + "queue" ], - hooks = data[ type + "queueHooks" ], - timers = jQuery.timers, - length = queue ? queue.length : 0; - - // Enable finishing flag on private data - data.finish = true; - - // Empty the queue first - jQuery.queue( this, type, [] ); - - if ( hooks && hooks.stop ) { - hooks.stop.call( this, true ); - } - - // Look for any active animations, and finish them - for ( index = timers.length; index--; ) { - if ( timers[ index ].elem === this && timers[ index ].queue === type ) { - timers[ index ].anim.stop( true ); - timers.splice( index, 1 ); - } - } - - // Look for any animations in the old queue and finish them - for ( index = 0; index < length; index++ ) { - if ( queue[ index ] && queue[ index ].finish ) { - queue[ index ].finish.call( this ); - } - } - - // Turn off finishing flag - delete data.finish; - } ); - } -} ); - -jQuery.each( [ "toggle", "show", "hide" ], function( i, name ) { - var cssFn = jQuery.fn[ name ]; - jQuery.fn[ name ] = function( speed, easing, callback ) { - return speed == null || typeof speed === "boolean" ? - cssFn.apply( this, arguments ) : - this.animate( genFx( name, true ), speed, easing, callback ); - }; -} ); - -// Generate shortcuts for custom animations -jQuery.each( { - slideDown: genFx( "show" ), - slideUp: genFx( "hide" ), - slideToggle: genFx( "toggle" ), - fadeIn: { opacity: "show" }, - fadeOut: { opacity: "hide" }, - fadeToggle: { opacity: "toggle" } -}, function( name, props ) { - jQuery.fn[ name ] = function( speed, easing, callback ) { - return this.animate( props, speed, easing, callback ); - }; -} ); - -jQuery.timers = []; -jQuery.fx.tick = function() { - var timer, - i = 0, - timers = jQuery.timers; - - fxNow = jQuery.now(); - - for ( ; i < timers.length; i++ ) { - timer = timers[ i ]; - - // Run the timer and safely remove it when done (allowing for external removal) - if ( !timer() && timers[ i ] === timer ) { - timers.splice( i--, 1 ); - } - } - - if ( !timers.length ) { - jQuery.fx.stop(); - } - fxNow = undefined; -}; - -jQuery.fx.timer = function( timer ) { - jQuery.timers.push( timer ); - jQuery.fx.start(); -}; - -jQuery.fx.interval = 13; -jQuery.fx.start = function() { - if ( inProgress ) { - return; - } - - inProgress = true; - schedule(); -}; - -jQuery.fx.stop = function() { - inProgress = null; -}; - -jQuery.fx.speeds = { - slow: 600, - fast: 200, - - // Default speed - _default: 400 -}; - - -// Based off of the plugin by Clint Helfers, with permission. -// https://web.archive.org/web/20100324014747/http://blindsignals.com/index.php/2009/07/jquery-delay/ -jQuery.fn.delay = function( time, type ) { - time = jQuery.fx ? jQuery.fx.speeds[ time ] || time : time; - type = type || "fx"; - - return this.queue( type, function( next, hooks ) { - var timeout = window.setTimeout( next, time ); - hooks.stop = function() { - window.clearTimeout( timeout ); - }; - } ); -}; - - -( function() { - var input = document.createElement( "input" ), - select = document.createElement( "select" ), - opt = select.appendChild( document.createElement( "option" ) ); - - input.type = "checkbox"; - - // Support: Android <=4.3 only - // Default value for a checkbox should be "on" - support.checkOn = input.value !== ""; - - // Support: IE <=11 only - // Must access selectedIndex to make default options select - support.optSelected = opt.selected; - - // Support: IE <=11 only - // An input loses its value after becoming a radio - input = document.createElement( "input" ); - input.value = "t"; - input.type = "radio"; - support.radioValue = input.value === "t"; -} )(); - - -var boolHook, - attrHandle = jQuery.expr.attrHandle; - -jQuery.fn.extend( { - attr: function( name, value ) { - return access( this, jQuery.attr, name, value, arguments.length > 1 ); - }, - - removeAttr: function( name ) { - return this.each( function() { - jQuery.removeAttr( this, name ); - } ); - } -} ); - -jQuery.extend( { - attr: function( elem, name, value ) { - var ret, hooks, - nType = elem.nodeType; - - // Don't get/set attributes on text, comment and attribute nodes - if ( nType === 3 || nType === 8 || nType === 2 ) { - return; - } - - // Fallback to prop when attributes are not supported - if ( typeof elem.getAttribute === "undefined" ) { - return jQuery.prop( elem, name, value ); - } - - // Attribute hooks are determined by the lowercase version - // Grab necessary hook if one is defined - if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) { - hooks = jQuery.attrHooks[ name.toLowerCase() ] || - ( jQuery.expr.match.bool.test( name ) ? boolHook : undefined ); - } - - if ( value !== undefined ) { - if ( value === null ) { - jQuery.removeAttr( elem, name ); - return; - } - - if ( hooks && "set" in hooks && - ( ret = hooks.set( elem, value, name ) ) !== undefined ) { - return ret; - } - - elem.setAttribute( name, value + "" ); - return value; - } - - if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) { - return ret; - } - - ret = jQuery.find.attr( elem, name ); - - // Non-existent attributes return null, we normalize to undefined - return ret == null ? undefined : ret; - }, - - attrHooks: { - type: { - set: function( elem, value ) { - if ( !support.radioValue && value === "radio" && - nodeName( elem, "input" ) ) { - var val = elem.value; - elem.setAttribute( "type", value ); - if ( val ) { - elem.value = val; - } - return value; - } - } - } - }, - - removeAttr: function( elem, value ) { - var name, - i = 0, - - // Attribute names can contain non-HTML whitespace characters - // https://html.spec.whatwg.org/multipage/syntax.html#attributes-2 - attrNames = value && value.match( rnothtmlwhite ); - - if ( attrNames && elem.nodeType === 1 ) { - while ( ( name = attrNames[ i++ ] ) ) { - elem.removeAttribute( name ); - } - } - } -} ); - -// Hooks for boolean attributes -boolHook = { - set: function( elem, value, name ) { - if ( value === false ) { - - // Remove boolean attributes when set to false - jQuery.removeAttr( elem, name ); - } else { - elem.setAttribute( name, name ); - } - return name; - } -}; - -jQuery.each( jQuery.expr.match.bool.source.match( /\w+/g ), function( i, name ) { - var getter = attrHandle[ name ] || jQuery.find.attr; - - attrHandle[ name ] = function( elem, name, isXML ) { - var ret, handle, - lowercaseName = name.toLowerCase(); - - if ( !isXML ) { - - // Avoid an infinite loop by temporarily removing this function from the getter - handle = attrHandle[ lowercaseName ]; - attrHandle[ lowercaseName ] = ret; - ret = getter( elem, name, isXML ) != null ? - lowercaseName : - null; - attrHandle[ lowercaseName ] = handle; - } - return ret; - }; -} ); - - - - -var rfocusable = /^(?:input|select|textarea|button)$/i, - rclickable = /^(?:a|area)$/i; - -jQuery.fn.extend( { - prop: function( name, value ) { - return access( this, jQuery.prop, name, value, arguments.length > 1 ); - }, - - removeProp: function( name ) { - return this.each( function() { - delete this[ jQuery.propFix[ name ] || name ]; - } ); - } -} ); - -jQuery.extend( { - prop: function( elem, name, value ) { - var ret, hooks, - nType = elem.nodeType; - - // Don't get/set properties on text, comment and attribute nodes - if ( nType === 3 || nType === 8 || nType === 2 ) { - return; - } - - if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) { - - // Fix name and attach hooks - name = jQuery.propFix[ name ] || name; - hooks = jQuery.propHooks[ name ]; - } - - if ( value !== undefined ) { - if ( hooks && "set" in hooks && - ( ret = hooks.set( elem, value, name ) ) !== undefined ) { - return ret; - } - - return ( elem[ name ] = value ); - } - - if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) { - return ret; - } - - return elem[ name ]; - }, - - propHooks: { - tabIndex: { - get: function( elem ) { - - // Support: IE <=9 - 11 only - // elem.tabIndex doesn't always return the - // correct value when it hasn't been explicitly set - // https://web.archive.org/web/20141116233347/http://fluidproject.org/blog/2008/01/09/getting-setting-and-removing-tabindex-values-with-javascript/ - // Use proper attribute retrieval(#12072) - var tabindex = jQuery.find.attr( elem, "tabindex" ); - - if ( tabindex ) { - return parseInt( tabindex, 10 ); - } - - if ( - rfocusable.test( elem.nodeName ) || - rclickable.test( elem.nodeName ) && - elem.href - ) { - return 0; - } - - return -1; - } - } - }, - - propFix: { - "for": "htmlFor", - "class": "className" - } -} ); - -// Support: IE <=11 only -// Accessing the selectedIndex property -// forces the browser to respect setting selected -// on the option -// The getter ensures a default option is selected -// when in an optgroup -// eslint rule "no-unused-expressions" is disabled for this code -// since it considers such accessions noop -if ( !support.optSelected ) { - jQuery.propHooks.selected = { - get: function( elem ) { - - /* eslint no-unused-expressions: "off" */ - - var parent = elem.parentNode; - if ( parent && parent.parentNode ) { - parent.parentNode.selectedIndex; - } - return null; - }, - set: function( elem ) { - - /* eslint no-unused-expressions: "off" */ - - var parent = elem.parentNode; - if ( parent ) { - parent.selectedIndex; - - if ( parent.parentNode ) { - parent.parentNode.selectedIndex; - } - } - } - }; -} - -jQuery.each( [ - "tabIndex", - "readOnly", - "maxLength", - "cellSpacing", - "cellPadding", - "rowSpan", - "colSpan", - "useMap", - "frameBorder", - "contentEditable" -], function() { - jQuery.propFix[ this.toLowerCase() ] = this; -} ); - - - - - // Strip and collapse whitespace according to HTML spec - // https://html.spec.whatwg.org/multipage/infrastructure.html#strip-and-collapse-whitespace - function stripAndCollapse( value ) { - var tokens = value.match( rnothtmlwhite ) || []; - return tokens.join( " " ); - } - - -function getClass( elem ) { - return elem.getAttribute && elem.getAttribute( "class" ) || ""; -} - -jQuery.fn.extend( { - addClass: function( value ) { - var classes, elem, cur, curValue, clazz, j, finalValue, - i = 0; - - if ( jQuery.isFunction( value ) ) { - return this.each( function( j ) { - jQuery( this ).addClass( value.call( this, j, getClass( this ) ) ); - } ); - } - - if ( typeof value === "string" && value ) { - classes = value.match( rnothtmlwhite ) || []; - - while ( ( elem = this[ i++ ] ) ) { - curValue = getClass( elem ); - cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " ); - - if ( cur ) { - j = 0; - while ( ( clazz = classes[ j++ ] ) ) { - if ( cur.indexOf( " " + clazz + " " ) < 0 ) { - cur += clazz + " "; - } - } - - // Only assign if different to avoid unneeded rendering. - finalValue = stripAndCollapse( cur ); - if ( curValue !== finalValue ) { - elem.setAttribute( "class", finalValue ); - } - } - } - } - - return this; - }, - - removeClass: function( value ) { - var classes, elem, cur, curValue, clazz, j, finalValue, - i = 0; - - if ( jQuery.isFunction( value ) ) { - return this.each( function( j ) { - jQuery( this ).removeClass( value.call( this, j, getClass( this ) ) ); - } ); - } - - if ( !arguments.length ) { - return this.attr( "class", "" ); - } - - if ( typeof value === "string" && value ) { - classes = value.match( rnothtmlwhite ) || []; - - while ( ( elem = this[ i++ ] ) ) { - curValue = getClass( elem ); - - // This expression is here for better compressibility (see addClass) - cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " ); - - if ( cur ) { - j = 0; - while ( ( clazz = classes[ j++ ] ) ) { - - // Remove *all* instances - while ( cur.indexOf( " " + clazz + " " ) > -1 ) { - cur = cur.replace( " " + clazz + " ", " " ); - } - } - - // Only assign if different to avoid unneeded rendering. - finalValue = stripAndCollapse( cur ); - if ( curValue !== finalValue ) { - elem.setAttribute( "class", finalValue ); - } - } - } - } - - return this; - }, - - toggleClass: function( value, stateVal ) { - var type = typeof value; - - if ( typeof stateVal === "boolean" && type === "string" ) { - return stateVal ? this.addClass( value ) : this.removeClass( value ); - } - - if ( jQuery.isFunction( value ) ) { - return this.each( function( i ) { - jQuery( this ).toggleClass( - value.call( this, i, getClass( this ), stateVal ), - stateVal - ); - } ); - } - - return this.each( function() { - var className, i, self, classNames; - - if ( type === "string" ) { - - // Toggle individual class names - i = 0; - self = jQuery( this ); - classNames = value.match( rnothtmlwhite ) || []; - - while ( ( className = classNames[ i++ ] ) ) { - - // Check each className given, space separated list - if ( self.hasClass( className ) ) { - self.removeClass( className ); - } else { - self.addClass( className ); - } - } - - // Toggle whole class name - } else if ( value === undefined || type === "boolean" ) { - className = getClass( this ); - if ( className ) { - - // Store className if set - dataPriv.set( this, "__className__", className ); - } - - // If the element has a class name or if we're passed `false`, - // then remove the whole classname (if there was one, the above saved it). - // Otherwise bring back whatever was previously saved (if anything), - // falling back to the empty string if nothing was stored. - if ( this.setAttribute ) { - this.setAttribute( "class", - className || value === false ? - "" : - dataPriv.get( this, "__className__" ) || "" - ); - } - } - } ); - }, - - hasClass: function( selector ) { - var className, elem, - i = 0; - - className = " " + selector + " "; - while ( ( elem = this[ i++ ] ) ) { - if ( elem.nodeType === 1 && - ( " " + stripAndCollapse( getClass( elem ) ) + " " ).indexOf( className ) > -1 ) { - return true; - } - } - - return false; - } -} ); - - - - -var rreturn = /\r/g; - -jQuery.fn.extend( { - val: function( value ) { - var hooks, ret, isFunction, - elem = this[ 0 ]; - - if ( !arguments.length ) { - if ( elem ) { - hooks = jQuery.valHooks[ elem.type ] || - jQuery.valHooks[ elem.nodeName.toLowerCase() ]; - - if ( hooks && - "get" in hooks && - ( ret = hooks.get( elem, "value" ) ) !== undefined - ) { - return ret; - } - - ret = elem.value; - - // Handle most common string cases - if ( typeof ret === "string" ) { - return ret.replace( rreturn, "" ); - } - - // Handle cases where value is null/undef or number - return ret == null ? "" : ret; - } - - return; - } - - isFunction = jQuery.isFunction( value ); - - return this.each( function( i ) { - var val; - - if ( this.nodeType !== 1 ) { - return; - } - - if ( isFunction ) { - val = value.call( this, i, jQuery( this ).val() ); - } else { - val = value; - } - - // Treat null/undefined as ""; convert numbers to string - if ( val == null ) { - val = ""; - - } else if ( typeof val === "number" ) { - val += ""; - - } else if ( Array.isArray( val ) ) { - val = jQuery.map( val, function( value ) { - return value == null ? "" : value + ""; - } ); - } - - hooks = jQuery.valHooks[ this.type ] || jQuery.valHooks[ this.nodeName.toLowerCase() ]; - - // If set returns undefined, fall back to normal setting - if ( !hooks || !( "set" in hooks ) || hooks.set( this, val, "value" ) === undefined ) { - this.value = val; - } - } ); - } -} ); - -jQuery.extend( { - valHooks: { - option: { - get: function( elem ) { - - var val = jQuery.find.attr( elem, "value" ); - return val != null ? - val : - - // Support: IE <=10 - 11 only - // option.text throws exceptions (#14686, #14858) - // Strip and collapse whitespace - // https://html.spec.whatwg.org/#strip-and-collapse-whitespace - stripAndCollapse( jQuery.text( elem ) ); - } - }, - select: { - get: function( elem ) { - var value, option, i, - options = elem.options, - index = elem.selectedIndex, - one = elem.type === "select-one", - values = one ? null : [], - max = one ? index + 1 : options.length; - - if ( index < 0 ) { - i = max; - - } else { - i = one ? index : 0; - } - - // Loop through all the selected options - for ( ; i < max; i++ ) { - option = options[ i ]; - - // Support: IE <=9 only - // IE8-9 doesn't update selected after form reset (#2551) - if ( ( option.selected || i === index ) && - - // Don't return options that are disabled or in a disabled optgroup - !option.disabled && - ( !option.parentNode.disabled || - !nodeName( option.parentNode, "optgroup" ) ) ) { - - // Get the specific value for the option - value = jQuery( option ).val(); - - // We don't need an array for one selects - if ( one ) { - return value; - } - - // Multi-Selects return an array - values.push( value ); - } - } - - return values; - }, - - set: function( elem, value ) { - var optionSet, option, - options = elem.options, - values = jQuery.makeArray( value ), - i = options.length; - - while ( i-- ) { - option = options[ i ]; - - /* eslint-disable no-cond-assign */ - - if ( option.selected = - jQuery.inArray( jQuery.valHooks.option.get( option ), values ) > -1 - ) { - optionSet = true; - } - - /* eslint-enable no-cond-assign */ - } - - // Force browsers to behave consistently when non-matching value is set - if ( !optionSet ) { - elem.selectedIndex = -1; - } - return values; - } - } - } -} ); - -// Radios and checkboxes getter/setter -jQuery.each( [ "radio", "checkbox" ], function() { - jQuery.valHooks[ this ] = { - set: function( elem, value ) { - if ( Array.isArray( value ) ) { - return ( elem.checked = jQuery.inArray( jQuery( elem ).val(), value ) > -1 ); - } - } - }; - if ( !support.checkOn ) { - jQuery.valHooks[ this ].get = function( elem ) { - return elem.getAttribute( "value" ) === null ? "on" : elem.value; - }; - } -} ); - - - - -// Return jQuery for attributes-only inclusion - - -var rfocusMorph = /^(?:focusinfocus|focusoutblur)$/; - -jQuery.extend( jQuery.event, { - - trigger: function( event, data, elem, onlyHandlers ) { - - var i, cur, tmp, bubbleType, ontype, handle, special, - eventPath = [ elem || document ], - type = hasOwn.call( event, "type" ) ? event.type : event, - namespaces = hasOwn.call( event, "namespace" ) ? event.namespace.split( "." ) : []; - - cur = tmp = elem = elem || document; - - // Don't do events on text and comment nodes - if ( elem.nodeType === 3 || elem.nodeType === 8 ) { - return; - } - - // focus/blur morphs to focusin/out; ensure we're not firing them right now - if ( rfocusMorph.test( type + jQuery.event.triggered ) ) { - return; - } - - if ( type.indexOf( "." ) > -1 ) { - - // Namespaced trigger; create a regexp to match event type in handle() - namespaces = type.split( "." ); - type = namespaces.shift(); - namespaces.sort(); - } - ontype = type.indexOf( ":" ) < 0 && "on" + type; - - // Caller can pass in a jQuery.Event object, Object, or just an event type string - event = event[ jQuery.expando ] ? - event : - new jQuery.Event( type, typeof event === "object" && event ); - - // Trigger bitmask: & 1 for native handlers; & 2 for jQuery (always true) - event.isTrigger = onlyHandlers ? 2 : 3; - event.namespace = namespaces.join( "." ); - event.rnamespace = event.namespace ? - new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" ) : - null; - - // Clean up the event in case it is being reused - event.result = undefined; - if ( !event.target ) { - event.target = elem; - } - - // Clone any incoming data and prepend the event, creating the handler arg list - data = data == null ? - [ event ] : - jQuery.makeArray( data, [ event ] ); - - // Allow special events to draw outside the lines - special = jQuery.event.special[ type ] || {}; - if ( !onlyHandlers && special.trigger && special.trigger.apply( elem, data ) === false ) { - return; - } - - // Determine event propagation path in advance, per W3C events spec (#9951) - // Bubble up to document, then to window; watch for a global ownerDocument var (#9724) - if ( !onlyHandlers && !special.noBubble && !jQuery.isWindow( elem ) ) { - - bubbleType = special.delegateType || type; - if ( !rfocusMorph.test( bubbleType + type ) ) { - cur = cur.parentNode; - } - for ( ; cur; cur = cur.parentNode ) { - eventPath.push( cur ); - tmp = cur; - } - - // Only add window if we got to document (e.g., not plain obj or detached DOM) - if ( tmp === ( elem.ownerDocument || document ) ) { - eventPath.push( tmp.defaultView || tmp.parentWindow || window ); - } - } - - // Fire handlers on the event path - i = 0; - while ( ( cur = eventPath[ i++ ] ) && !event.isPropagationStopped() ) { - - event.type = i > 1 ? - bubbleType : - special.bindType || type; - - // jQuery handler - handle = ( dataPriv.get( cur, "events" ) || {} )[ event.type ] && - dataPriv.get( cur, "handle" ); - if ( handle ) { - handle.apply( cur, data ); - } - - // Native handler - handle = ontype && cur[ ontype ]; - if ( handle && handle.apply && acceptData( cur ) ) { - event.result = handle.apply( cur, data ); - if ( event.result === false ) { - event.preventDefault(); - } - } - } - event.type = type; - - // If nobody prevented the default action, do it now - if ( !onlyHandlers && !event.isDefaultPrevented() ) { - - if ( ( !special._default || - special._default.apply( eventPath.pop(), data ) === false ) && - acceptData( elem ) ) { - - // Call a native DOM method on the target with the same name as the event. - // Don't do default actions on window, that's where global variables be (#6170) - if ( ontype && jQuery.isFunction( elem[ type ] ) && !jQuery.isWindow( elem ) ) { - - // Don't re-trigger an onFOO event when we call its FOO() method - tmp = elem[ ontype ]; - - if ( tmp ) { - elem[ ontype ] = null; - } - - // Prevent re-triggering of the same event, since we already bubbled it above - jQuery.event.triggered = type; - elem[ type ](); - jQuery.event.triggered = undefined; - - if ( tmp ) { - elem[ ontype ] = tmp; - } - } - } - } - - return event.result; - }, - - // Piggyback on a donor event to simulate a different one - // Used only for `focus(in | out)` events - simulate: function( type, elem, event ) { - var e = jQuery.extend( - new jQuery.Event(), - event, - { - type: type, - isSimulated: true - } - ); - - jQuery.event.trigger( e, null, elem ); - } - -} ); - -jQuery.fn.extend( { - - trigger: function( type, data ) { - return this.each( function() { - jQuery.event.trigger( type, data, this ); - } ); - }, - triggerHandler: function( type, data ) { - var elem = this[ 0 ]; - if ( elem ) { - return jQuery.event.trigger( type, data, elem, true ); - } - } -} ); - - -jQuery.each( ( "blur focus focusin focusout resize scroll click dblclick " + - "mousedown mouseup mousemove mouseover mouseout mouseenter mouseleave " + - "change select submit keydown keypress keyup contextmenu" ).split( " " ), - function( i, name ) { - - // Handle event binding - jQuery.fn[ name ] = function( data, fn ) { - return arguments.length > 0 ? - this.on( name, null, data, fn ) : - this.trigger( name ); - }; -} ); - -jQuery.fn.extend( { - hover: function( fnOver, fnOut ) { - return this.mouseenter( fnOver ).mouseleave( fnOut || fnOver ); - } -} ); - - - - -support.focusin = "onfocusin" in window; - - -// Support: Firefox <=44 -// Firefox doesn't have focus(in | out) events -// Related ticket - https://bugzilla.mozilla.org/show_bug.cgi?id=687787 -// -// Support: Chrome <=48 - 49, Safari <=9.0 - 9.1 -// focus(in | out) events fire after focus & blur events, -// which is spec violation - http://www.w3.org/TR/DOM-Level-3-Events/#events-focusevent-event-order -// Related ticket - https://bugs.chromium.org/p/chromium/issues/detail?id=449857 -if ( !support.focusin ) { - jQuery.each( { focus: "focusin", blur: "focusout" }, function( orig, fix ) { - - // Attach a single capturing handler on the document while someone wants focusin/focusout - var handler = function( event ) { - jQuery.event.simulate( fix, event.target, jQuery.event.fix( event ) ); - }; - - jQuery.event.special[ fix ] = { - setup: function() { - var doc = this.ownerDocument || this, - attaches = dataPriv.access( doc, fix ); - - if ( !attaches ) { - doc.addEventListener( orig, handler, true ); - } - dataPriv.access( doc, fix, ( attaches || 0 ) + 1 ); - }, - teardown: function() { - var doc = this.ownerDocument || this, - attaches = dataPriv.access( doc, fix ) - 1; - - if ( !attaches ) { - doc.removeEventListener( orig, handler, true ); - dataPriv.remove( doc, fix ); - - } else { - dataPriv.access( doc, fix, attaches ); - } - } - }; - } ); -} -var location = window.location; - -var nonce = jQuery.now(); - -var rquery = ( /\?/ ); - - - -// Cross-browser xml parsing -jQuery.parseXML = function( data ) { - var xml; - if ( !data || typeof data !== "string" ) { - return null; - } - - // Support: IE 9 - 11 only - // IE throws on parseFromString with invalid input. - try { - xml = ( new window.DOMParser() ).parseFromString( data, "text/xml" ); - } catch ( e ) { - xml = undefined; - } - - if ( !xml || xml.getElementsByTagName( "parsererror" ).length ) { - jQuery.error( "Invalid XML: " + data ); - } - return xml; -}; - - -var - rbracket = /\[\]$/, - rCRLF = /\r?\n/g, - rsubmitterTypes = /^(?:submit|button|image|reset|file)$/i, - rsubmittable = /^(?:input|select|textarea|keygen)/i; - -function buildParams( prefix, obj, traditional, add ) { - var name; - - if ( Array.isArray( obj ) ) { - - // Serialize array item. - jQuery.each( obj, function( i, v ) { - if ( traditional || rbracket.test( prefix ) ) { - - // Treat each array item as a scalar. - add( prefix, v ); - - } else { - - // Item is non-scalar (array or object), encode its numeric index. - buildParams( - prefix + "[" + ( typeof v === "object" && v != null ? i : "" ) + "]", - v, - traditional, - add - ); - } - } ); - - } else if ( !traditional && jQuery.type( obj ) === "object" ) { - - // Serialize object item. - for ( name in obj ) { - buildParams( prefix + "[" + name + "]", obj[ name ], traditional, add ); - } - - } else { - - // Serialize scalar item. - add( prefix, obj ); - } -} - -// Serialize an array of form elements or a set of -// key/values into a query string -jQuery.param = function( a, traditional ) { - var prefix, - s = [], - add = function( key, valueOrFunction ) { - - // If value is a function, invoke it and use its return value - var value = jQuery.isFunction( valueOrFunction ) ? - valueOrFunction() : - valueOrFunction; - - s[ s.length ] = encodeURIComponent( key ) + "=" + - encodeURIComponent( value == null ? "" : value ); - }; - - // If an array was passed in, assume that it is an array of form elements. - if ( Array.isArray( a ) || ( a.jquery && !jQuery.isPlainObject( a ) ) ) { - - // Serialize the form elements - jQuery.each( a, function() { - add( this.name, this.value ); - } ); - - } else { - - // If traditional, encode the "old" way (the way 1.3.2 or older - // did it), otherwise encode params recursively. - for ( prefix in a ) { - buildParams( prefix, a[ prefix ], traditional, add ); - } - } - - // Return the resulting serialization - return s.join( "&" ); -}; - -jQuery.fn.extend( { - serialize: function() { - return jQuery.param( this.serializeArray() ); - }, - serializeArray: function() { - return this.map( function() { - - // Can add propHook for "elements" to filter or add form elements - var elements = jQuery.prop( this, "elements" ); - return elements ? jQuery.makeArray( elements ) : this; - } ) - .filter( function() { - var type = this.type; - - // Use .is( ":disabled" ) so that fieldset[disabled] works - return this.name && !jQuery( this ).is( ":disabled" ) && - rsubmittable.test( this.nodeName ) && !rsubmitterTypes.test( type ) && - ( this.checked || !rcheckableType.test( type ) ); - } ) - .map( function( i, elem ) { - var val = jQuery( this ).val(); - - if ( val == null ) { - return null; - } - - if ( Array.isArray( val ) ) { - return jQuery.map( val, function( val ) { - return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) }; - } ); - } - - return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) }; - } ).get(); - } -} ); - - -var - r20 = /%20/g, - rhash = /#.*$/, - rantiCache = /([?&])_=[^&]*/, - rheaders = /^(.*?):[ \t]*([^\r\n]*)$/mg, - - // #7653, #8125, #8152: local protocol detection - rlocalProtocol = /^(?:about|app|app-storage|.+-extension|file|res|widget):$/, - rnoContent = /^(?:GET|HEAD)$/, - rprotocol = /^\/\//, - - /* Prefilters - * 1) They are useful to introduce custom dataTypes (see ajax/jsonp.js for an example) - * 2) These are called: - * - BEFORE asking for a transport - * - AFTER param serialization (s.data is a string if s.processData is true) - * 3) key is the dataType - * 4) the catchall symbol "*" can be used - * 5) execution will start with transport dataType and THEN continue down to "*" if needed - */ - prefilters = {}, - - /* Transports bindings - * 1) key is the dataType - * 2) the catchall symbol "*" can be used - * 3) selection will start with transport dataType and THEN go to "*" if needed - */ - transports = {}, - - // Avoid comment-prolog char sequence (#10098); must appease lint and evade compression - allTypes = "*/".concat( "*" ), - - // Anchor tag for parsing the document origin - originAnchor = document.createElement( "a" ); - originAnchor.href = location.href; - -// Base "constructor" for jQuery.ajaxPrefilter and jQuery.ajaxTransport -function addToPrefiltersOrTransports( structure ) { - - // dataTypeExpression is optional and defaults to "*" - return function( dataTypeExpression, func ) { - - if ( typeof dataTypeExpression !== "string" ) { - func = dataTypeExpression; - dataTypeExpression = "*"; - } - - var dataType, - i = 0, - dataTypes = dataTypeExpression.toLowerCase().match( rnothtmlwhite ) || []; - - if ( jQuery.isFunction( func ) ) { - - // For each dataType in the dataTypeExpression - while ( ( dataType = dataTypes[ i++ ] ) ) { - - // Prepend if requested - if ( dataType[ 0 ] === "+" ) { - dataType = dataType.slice( 1 ) || "*"; - ( structure[ dataType ] = structure[ dataType ] || [] ).unshift( func ); - - // Otherwise append - } else { - ( structure[ dataType ] = structure[ dataType ] || [] ).push( func ); - } - } - } - }; -} - -// Base inspection function for prefilters and transports -function inspectPrefiltersOrTransports( structure, options, originalOptions, jqXHR ) { - - var inspected = {}, - seekingTransport = ( structure === transports ); - - function inspect( dataType ) { - var selected; - inspected[ dataType ] = true; - jQuery.each( structure[ dataType ] || [], function( _, prefilterOrFactory ) { - var dataTypeOrTransport = prefilterOrFactory( options, originalOptions, jqXHR ); - if ( typeof dataTypeOrTransport === "string" && - !seekingTransport && !inspected[ dataTypeOrTransport ] ) { - - options.dataTypes.unshift( dataTypeOrTransport ); - inspect( dataTypeOrTransport ); - return false; - } else if ( seekingTransport ) { - return !( selected = dataTypeOrTransport ); - } - } ); - return selected; - } - - return inspect( options.dataTypes[ 0 ] ) || !inspected[ "*" ] && inspect( "*" ); -} - -// A special extend for ajax options -// that takes "flat" options (not to be deep extended) -// Fixes #9887 -function ajaxExtend( target, src ) { - var key, deep, - flatOptions = jQuery.ajaxSettings.flatOptions || {}; - - for ( key in src ) { - if ( src[ key ] !== undefined ) { - ( flatOptions[ key ] ? target : ( deep || ( deep = {} ) ) )[ key ] = src[ key ]; - } - } - if ( deep ) { - jQuery.extend( true, target, deep ); - } - - return target; -} - -/* Handles responses to an ajax request: - * - finds the right dataType (mediates between content-type and expected dataType) - * - returns the corresponding response - */ -function ajaxHandleResponses( s, jqXHR, responses ) { - - var ct, type, finalDataType, firstDataType, - contents = s.contents, - dataTypes = s.dataTypes; - - // Remove auto dataType and get content-type in the process - while ( dataTypes[ 0 ] === "*" ) { - dataTypes.shift(); - if ( ct === undefined ) { - ct = s.mimeType || jqXHR.getResponseHeader( "Content-Type" ); - } - } - - // Check if we're dealing with a known content-type - if ( ct ) { - for ( type in contents ) { - if ( contents[ type ] && contents[ type ].test( ct ) ) { - dataTypes.unshift( type ); - break; - } - } - } - - // Check to see if we have a response for the expected dataType - if ( dataTypes[ 0 ] in responses ) { - finalDataType = dataTypes[ 0 ]; - } else { - - // Try convertible dataTypes - for ( type in responses ) { - if ( !dataTypes[ 0 ] || s.converters[ type + " " + dataTypes[ 0 ] ] ) { - finalDataType = type; - break; - } - if ( !firstDataType ) { - firstDataType = type; - } - } - - // Or just use first one - finalDataType = finalDataType || firstDataType; - } - - // If we found a dataType - // We add the dataType to the list if needed - // and return the corresponding response - if ( finalDataType ) { - if ( finalDataType !== dataTypes[ 0 ] ) { - dataTypes.unshift( finalDataType ); - } - return responses[ finalDataType ]; - } -} - -/* Chain conversions given the request and the original response - * Also sets the responseXXX fields on the jqXHR instance - */ -function ajaxConvert( s, response, jqXHR, isSuccess ) { - var conv2, current, conv, tmp, prev, - converters = {}, - - // Work with a copy of dataTypes in case we need to modify it for conversion - dataTypes = s.dataTypes.slice(); - - // Create converters map with lowercased keys - if ( dataTypes[ 1 ] ) { - for ( conv in s.converters ) { - converters[ conv.toLowerCase() ] = s.converters[ conv ]; - } - } - - current = dataTypes.shift(); - - // Convert to each sequential dataType - while ( current ) { - - if ( s.responseFields[ current ] ) { - jqXHR[ s.responseFields[ current ] ] = response; - } - - // Apply the dataFilter if provided - if ( !prev && isSuccess && s.dataFilter ) { - response = s.dataFilter( response, s.dataType ); - } - - prev = current; - current = dataTypes.shift(); - - if ( current ) { - - // There's only work to do if current dataType is non-auto - if ( current === "*" ) { - - current = prev; - - // Convert response if prev dataType is non-auto and differs from current - } else if ( prev !== "*" && prev !== current ) { - - // Seek a direct converter - conv = converters[ prev + " " + current ] || converters[ "* " + current ]; - - // If none found, seek a pair - if ( !conv ) { - for ( conv2 in converters ) { - - // If conv2 outputs current - tmp = conv2.split( " " ); - if ( tmp[ 1 ] === current ) { - - // If prev can be converted to accepted input - conv = converters[ prev + " " + tmp[ 0 ] ] || - converters[ "* " + tmp[ 0 ] ]; - if ( conv ) { - - // Condense equivalence converters - if ( conv === true ) { - conv = converters[ conv2 ]; - - // Otherwise, insert the intermediate dataType - } else if ( converters[ conv2 ] !== true ) { - current = tmp[ 0 ]; - dataTypes.unshift( tmp[ 1 ] ); - } - break; - } - } - } - } - - // Apply converter (if not an equivalence) - if ( conv !== true ) { - - // Unless errors are allowed to bubble, catch and return them - if ( conv && s.throws ) { - response = conv( response ); - } else { - try { - response = conv( response ); - } catch ( e ) { - return { - state: "parsererror", - error: conv ? e : "No conversion from " + prev + " to " + current - }; - } - } - } - } - } - } - - return { state: "success", data: response }; -} - -jQuery.extend( { - - // Counter for holding the number of active queries - active: 0, - - // Last-Modified header cache for next request - lastModified: {}, - etag: {}, - - ajaxSettings: { - url: location.href, - type: "GET", - isLocal: rlocalProtocol.test( location.protocol ), - global: true, - processData: true, - async: true, - contentType: "application/x-www-form-urlencoded; charset=UTF-8", - - /* - timeout: 0, - data: null, - dataType: null, - username: null, - password: null, - cache: null, - throws: false, - traditional: false, - headers: {}, - */ - - accepts: { - "*": allTypes, - text: "text/plain", - html: "text/html", - xml: "application/xml, text/xml", - json: "application/json, text/javascript" - }, - - contents: { - xml: /\bxml\b/, - html: /\bhtml/, - json: /\bjson\b/ - }, - - responseFields: { - xml: "responseXML", - text: "responseText", - json: "responseJSON" - }, - - // Data converters - // Keys separate source (or catchall "*") and destination types with a single space - converters: { - - // Convert anything to text - "* text": String, - - // Text to html (true = no transformation) - "text html": true, - - // Evaluate text as a json expression - "text json": JSON.parse, - - // Parse text as xml - "text xml": jQuery.parseXML - }, - - // For options that shouldn't be deep extended: - // you can add your own custom options here if - // and when you create one that shouldn't be - // deep extended (see ajaxExtend) - flatOptions: { - url: true, - context: true - } - }, - - // Creates a full fledged settings object into target - // with both ajaxSettings and settings fields. - // If target is omitted, writes into ajaxSettings. - ajaxSetup: function( target, settings ) { - return settings ? - - // Building a settings object - ajaxExtend( ajaxExtend( target, jQuery.ajaxSettings ), settings ) : - - // Extending ajaxSettings - ajaxExtend( jQuery.ajaxSettings, target ); - }, - - ajaxPrefilter: addToPrefiltersOrTransports( prefilters ), - ajaxTransport: addToPrefiltersOrTransports( transports ), - - // Main method - ajax: function( url, options ) { - - // If url is an object, simulate pre-1.5 signature - if ( typeof url === "object" ) { - options = url; - url = undefined; - } - - // Force options to be an object - options = options || {}; - - var transport, - - // URL without anti-cache param - cacheURL, - - // Response headers - responseHeadersString, - responseHeaders, - - // timeout handle - timeoutTimer, - - // Url cleanup var - urlAnchor, - - // Request state (becomes false upon send and true upon completion) - completed, - - // To know if global events are to be dispatched - fireGlobals, - - // Loop variable - i, - - // uncached part of the url - uncached, - - // Create the final options object - s = jQuery.ajaxSetup( {}, options ), - - // Callbacks context - callbackContext = s.context || s, - - // Context for global events is callbackContext if it is a DOM node or jQuery collection - globalEventContext = s.context && - ( callbackContext.nodeType || callbackContext.jquery ) ? - jQuery( callbackContext ) : - jQuery.event, - - // Deferreds - deferred = jQuery.Deferred(), - completeDeferred = jQuery.Callbacks( "once memory" ), - - // Status-dependent callbacks - statusCode = s.statusCode || {}, - - // Headers (they are sent all at once) - requestHeaders = {}, - requestHeadersNames = {}, - - // Default abort message - strAbort = "canceled", - - // Fake xhr - jqXHR = { - readyState: 0, - - // Builds headers hashtable if needed - getResponseHeader: function( key ) { - var match; - if ( completed ) { - if ( !responseHeaders ) { - responseHeaders = {}; - while ( ( match = rheaders.exec( responseHeadersString ) ) ) { - responseHeaders[ match[ 1 ].toLowerCase() ] = match[ 2 ]; - } - } - match = responseHeaders[ key.toLowerCase() ]; - } - return match == null ? null : match; - }, - - // Raw string - getAllResponseHeaders: function() { - return completed ? responseHeadersString : null; - }, - - // Caches the header - setRequestHeader: function( name, value ) { - if ( completed == null ) { - name = requestHeadersNames[ name.toLowerCase() ] = - requestHeadersNames[ name.toLowerCase() ] || name; - requestHeaders[ name ] = value; - } - return this; - }, - - // Overrides response content-type header - overrideMimeType: function( type ) { - if ( completed == null ) { - s.mimeType = type; - } - return this; - }, - - // Status-dependent callbacks - statusCode: function( map ) { - var code; - if ( map ) { - if ( completed ) { - - // Execute the appropriate callbacks - jqXHR.always( map[ jqXHR.status ] ); - } else { - - // Lazy-add the new callbacks in a way that preserves old ones - for ( code in map ) { - statusCode[ code ] = [ statusCode[ code ], map[ code ] ]; - } - } - } - return this; - }, - - // Cancel the request - abort: function( statusText ) { - var finalText = statusText || strAbort; - if ( transport ) { - transport.abort( finalText ); - } - done( 0, finalText ); - return this; - } - }; - - // Attach deferreds - deferred.promise( jqXHR ); - - // Add protocol if not provided (prefilters might expect it) - // Handle falsy url in the settings object (#10093: consistency with old signature) - // We also use the url parameter if available - s.url = ( ( url || s.url || location.href ) + "" ) - .replace( rprotocol, location.protocol + "//" ); - - // Alias method option to type as per ticket #12004 - s.type = options.method || options.type || s.method || s.type; - - // Extract dataTypes list - s.dataTypes = ( s.dataType || "*" ).toLowerCase().match( rnothtmlwhite ) || [ "" ]; - - // A cross-domain request is in order when the origin doesn't match the current origin. - if ( s.crossDomain == null ) { - urlAnchor = document.createElement( "a" ); - - // Support: IE <=8 - 11, Edge 12 - 13 - // IE throws exception on accessing the href property if url is malformed, - // e.g. http://example.com:80x/ - try { - urlAnchor.href = s.url; - - // Support: IE <=8 - 11 only - // Anchor's host property isn't correctly set when s.url is relative - urlAnchor.href = urlAnchor.href; - s.crossDomain = originAnchor.protocol + "//" + originAnchor.host !== - urlAnchor.protocol + "//" + urlAnchor.host; - } catch ( e ) { - - // If there is an error parsing the URL, assume it is crossDomain, - // it can be rejected by the transport if it is invalid - s.crossDomain = true; - } - } - - // Convert data if not already a string - if ( s.data && s.processData && typeof s.data !== "string" ) { - s.data = jQuery.param( s.data, s.traditional ); - } - - // Apply prefilters - inspectPrefiltersOrTransports( prefilters, s, options, jqXHR ); - - // If request was aborted inside a prefilter, stop there - if ( completed ) { - return jqXHR; - } - - // We can fire global events as of now if asked to - // Don't fire events if jQuery.event is undefined in an AMD-usage scenario (#15118) - fireGlobals = jQuery.event && s.global; - - // Watch for a new set of requests - if ( fireGlobals && jQuery.active++ === 0 ) { - jQuery.event.trigger( "ajaxStart" ); - } - - // Uppercase the type - s.type = s.type.toUpperCase(); - - // Determine if request has content - s.hasContent = !rnoContent.test( s.type ); - - // Save the URL in case we're toying with the If-Modified-Since - // and/or If-None-Match header later on - // Remove hash to simplify url manipulation - cacheURL = s.url.replace( rhash, "" ); - - // More options handling for requests with no content - if ( !s.hasContent ) { - - // Remember the hash so we can put it back - uncached = s.url.slice( cacheURL.length ); - - // If data is available, append data to url - if ( s.data ) { - cacheURL += ( rquery.test( cacheURL ) ? "&" : "?" ) + s.data; - - // #9682: remove data so that it's not used in an eventual retry - delete s.data; - } - - // Add or update anti-cache param if needed - if ( s.cache === false ) { - cacheURL = cacheURL.replace( rantiCache, "$1" ); - uncached = ( rquery.test( cacheURL ) ? "&" : "?" ) + "_=" + ( nonce++ ) + uncached; - } - - // Put hash and anti-cache on the URL that will be requested (gh-1732) - s.url = cacheURL + uncached; - - // Change '%20' to '+' if this is encoded form body content (gh-2658) - } else if ( s.data && s.processData && - ( s.contentType || "" ).indexOf( "application/x-www-form-urlencoded" ) === 0 ) { - s.data = s.data.replace( r20, "+" ); - } - - // Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode. - if ( s.ifModified ) { - if ( jQuery.lastModified[ cacheURL ] ) { - jqXHR.setRequestHeader( "If-Modified-Since", jQuery.lastModified[ cacheURL ] ); - } - if ( jQuery.etag[ cacheURL ] ) { - jqXHR.setRequestHeader( "If-None-Match", jQuery.etag[ cacheURL ] ); - } - } - - // Set the correct header, if data is being sent - if ( s.data && s.hasContent && s.contentType !== false || options.contentType ) { - jqXHR.setRequestHeader( "Content-Type", s.contentType ); - } - - // Set the Accepts header for the server, depending on the dataType - jqXHR.setRequestHeader( - "Accept", - s.dataTypes[ 0 ] && s.accepts[ s.dataTypes[ 0 ] ] ? - s.accepts[ s.dataTypes[ 0 ] ] + - ( s.dataTypes[ 0 ] !== "*" ? ", " + allTypes + "; q=0.01" : "" ) : - s.accepts[ "*" ] - ); - - // Check for headers option - for ( i in s.headers ) { - jqXHR.setRequestHeader( i, s.headers[ i ] ); - } - - // Allow custom headers/mimetypes and early abort - if ( s.beforeSend && - ( s.beforeSend.call( callbackContext, jqXHR, s ) === false || completed ) ) { - - // Abort if not done already and return - return jqXHR.abort(); - } - - // Aborting is no longer a cancellation - strAbort = "abort"; - - // Install callbacks on deferreds - completeDeferred.add( s.complete ); - jqXHR.done( s.success ); - jqXHR.fail( s.error ); - - // Get transport - transport = inspectPrefiltersOrTransports( transports, s, options, jqXHR ); - - // If no transport, we auto-abort - if ( !transport ) { - done( -1, "No Transport" ); - } else { - jqXHR.readyState = 1; - - // Send global event - if ( fireGlobals ) { - globalEventContext.trigger( "ajaxSend", [ jqXHR, s ] ); - } - - // If request was aborted inside ajaxSend, stop there - if ( completed ) { - return jqXHR; - } - - // Timeout - if ( s.async && s.timeout > 0 ) { - timeoutTimer = window.setTimeout( function() { - jqXHR.abort( "timeout" ); - }, s.timeout ); - } - - try { - completed = false; - transport.send( requestHeaders, done ); - } catch ( e ) { - - // Rethrow post-completion exceptions - if ( completed ) { - throw e; - } - - // Propagate others as results - done( -1, e ); - } - } - - // Callback for when everything is done - function done( status, nativeStatusText, responses, headers ) { - var isSuccess, success, error, response, modified, - statusText = nativeStatusText; - - // Ignore repeat invocations - if ( completed ) { - return; - } - - completed = true; - - // Clear timeout if it exists - if ( timeoutTimer ) { - window.clearTimeout( timeoutTimer ); - } - - // Dereference transport for early garbage collection - // (no matter how long the jqXHR object will be used) - transport = undefined; - - // Cache response headers - responseHeadersString = headers || ""; - - // Set readyState - jqXHR.readyState = status > 0 ? 4 : 0; - - // Determine if successful - isSuccess = status >= 200 && status < 300 || status === 304; - - // Get response data - if ( responses ) { - response = ajaxHandleResponses( s, jqXHR, responses ); - } - - // Convert no matter what (that way responseXXX fields are always set) - response = ajaxConvert( s, response, jqXHR, isSuccess ); - - // If successful, handle type chaining - if ( isSuccess ) { - - // Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode. - if ( s.ifModified ) { - modified = jqXHR.getResponseHeader( "Last-Modified" ); - if ( modified ) { - jQuery.lastModified[ cacheURL ] = modified; - } - modified = jqXHR.getResponseHeader( "etag" ); - if ( modified ) { - jQuery.etag[ cacheURL ] = modified; - } - } - - // if no content - if ( status === 204 || s.type === "HEAD" ) { - statusText = "nocontent"; - - // if not modified - } else if ( status === 304 ) { - statusText = "notmodified"; - - // If we have data, let's convert it - } else { - statusText = response.state; - success = response.data; - error = response.error; - isSuccess = !error; - } - } else { - - // Extract error from statusText and normalize for non-aborts - error = statusText; - if ( status || !statusText ) { - statusText = "error"; - if ( status < 0 ) { - status = 0; - } - } - } - - // Set data for the fake xhr object - jqXHR.status = status; - jqXHR.statusText = ( nativeStatusText || statusText ) + ""; - - // Success/Error - if ( isSuccess ) { - deferred.resolveWith( callbackContext, [ success, statusText, jqXHR ] ); - } else { - deferred.rejectWith( callbackContext, [ jqXHR, statusText, error ] ); - } - - // Status-dependent callbacks - jqXHR.statusCode( statusCode ); - statusCode = undefined; - - if ( fireGlobals ) { - globalEventContext.trigger( isSuccess ? "ajaxSuccess" : "ajaxError", - [ jqXHR, s, isSuccess ? success : error ] ); - } - - // Complete - completeDeferred.fireWith( callbackContext, [ jqXHR, statusText ] ); - - if ( fireGlobals ) { - globalEventContext.trigger( "ajaxComplete", [ jqXHR, s ] ); - - // Handle the global AJAX counter - if ( !( --jQuery.active ) ) { - jQuery.event.trigger( "ajaxStop" ); - } - } - } - - return jqXHR; - }, - - getJSON: function( url, data, callback ) { - return jQuery.get( url, data, callback, "json" ); - }, - - getScript: function( url, callback ) { - return jQuery.get( url, undefined, callback, "script" ); - } -} ); - -jQuery.each( [ "get", "post" ], function( i, method ) { - jQuery[ method ] = function( url, data, callback, type ) { - - // Shift arguments if data argument was omitted - if ( jQuery.isFunction( data ) ) { - type = type || callback; - callback = data; - data = undefined; - } - - // The url can be an options object (which then must have .url) - return jQuery.ajax( jQuery.extend( { - url: url, - type: method, - dataType: type, - data: data, - success: callback - }, jQuery.isPlainObject( url ) && url ) ); - }; -} ); - - -jQuery._evalUrl = function( url ) { - return jQuery.ajax( { - url: url, - - // Make this explicit, since user can override this through ajaxSetup (#11264) - type: "GET", - dataType: "script", - cache: true, - async: false, - global: false, - "throws": true - } ); -}; - - -jQuery.fn.extend( { - wrapAll: function( html ) { - var wrap; - - if ( this[ 0 ] ) { - if ( jQuery.isFunction( html ) ) { - html = html.call( this[ 0 ] ); - } - - // The elements to wrap the target around - wrap = jQuery( html, this[ 0 ].ownerDocument ).eq( 0 ).clone( true ); - - if ( this[ 0 ].parentNode ) { - wrap.insertBefore( this[ 0 ] ); - } - - wrap.map( function() { - var elem = this; - - while ( elem.firstElementChild ) { - elem = elem.firstElementChild; - } - - return elem; - } ).append( this ); - } - - return this; - }, - - wrapInner: function( html ) { - if ( jQuery.isFunction( html ) ) { - return this.each( function( i ) { - jQuery( this ).wrapInner( html.call( this, i ) ); - } ); - } - - return this.each( function() { - var self = jQuery( this ), - contents = self.contents(); - - if ( contents.length ) { - contents.wrapAll( html ); - - } else { - self.append( html ); - } - } ); - }, - - wrap: function( html ) { - var isFunction = jQuery.isFunction( html ); - - return this.each( function( i ) { - jQuery( this ).wrapAll( isFunction ? html.call( this, i ) : html ); - } ); - }, - - unwrap: function( selector ) { - this.parent( selector ).not( "body" ).each( function() { - jQuery( this ).replaceWith( this.childNodes ); - } ); - return this; - } -} ); - - -jQuery.expr.pseudos.hidden = function( elem ) { - return !jQuery.expr.pseudos.visible( elem ); -}; -jQuery.expr.pseudos.visible = function( elem ) { - return !!( elem.offsetWidth || elem.offsetHeight || elem.getClientRects().length ); -}; - - - - -jQuery.ajaxSettings.xhr = function() { - try { - return new window.XMLHttpRequest(); - } catch ( e ) {} -}; - -var xhrSuccessStatus = { - - // File protocol always yields status code 0, assume 200 - 0: 200, - - // Support: IE <=9 only - // #1450: sometimes IE returns 1223 when it should be 204 - 1223: 204 - }, - xhrSupported = jQuery.ajaxSettings.xhr(); - -support.cors = !!xhrSupported && ( "withCredentials" in xhrSupported ); -support.ajax = xhrSupported = !!xhrSupported; - -jQuery.ajaxTransport( function( options ) { - var callback, errorCallback; - - // Cross domain only allowed if supported through XMLHttpRequest - if ( support.cors || xhrSupported && !options.crossDomain ) { - return { - send: function( headers, complete ) { - var i, - xhr = options.xhr(); - - xhr.open( - options.type, - options.url, - options.async, - options.username, - options.password - ); - - // Apply custom fields if provided - if ( options.xhrFields ) { - for ( i in options.xhrFields ) { - xhr[ i ] = options.xhrFields[ i ]; - } - } - - // Override mime type if needed - if ( options.mimeType && xhr.overrideMimeType ) { - xhr.overrideMimeType( options.mimeType ); - } - - // X-Requested-With header - // For cross-domain requests, seeing as conditions for a preflight are - // akin to a jigsaw puzzle, we simply never set it to be sure. - // (it can always be set on a per-request basis or even using ajaxSetup) - // For same-domain requests, won't change header if already provided. - if ( !options.crossDomain && !headers[ "X-Requested-With" ] ) { - headers[ "X-Requested-With" ] = "XMLHttpRequest"; - } - - // Set headers - for ( i in headers ) { - xhr.setRequestHeader( i, headers[ i ] ); - } - - // Callback - callback = function( type ) { - return function() { - if ( callback ) { - callback = errorCallback = xhr.onload = - xhr.onerror = xhr.onabort = xhr.onreadystatechange = null; - - if ( type === "abort" ) { - xhr.abort(); - } else if ( type === "error" ) { - - // Support: IE <=9 only - // On a manual native abort, IE9 throws - // errors on any property access that is not readyState - if ( typeof xhr.status !== "number" ) { - complete( 0, "error" ); - } else { - complete( - - // File: protocol always yields status 0; see #8605, #14207 - xhr.status, - xhr.statusText - ); - } - } else { - complete( - xhrSuccessStatus[ xhr.status ] || xhr.status, - xhr.statusText, - - // Support: IE <=9 only - // IE9 has no XHR2 but throws on binary (trac-11426) - // For XHR2 non-text, let the caller handle it (gh-2498) - ( xhr.responseType || "text" ) !== "text" || - typeof xhr.responseText !== "string" ? - { binary: xhr.response } : - { text: xhr.responseText }, - xhr.getAllResponseHeaders() - ); - } - } - }; - }; - - // Listen to events - xhr.onload = callback(); - errorCallback = xhr.onerror = callback( "error" ); - - // Support: IE 9 only - // Use onreadystatechange to replace onabort - // to handle uncaught aborts - if ( xhr.onabort !== undefined ) { - xhr.onabort = errorCallback; - } else { - xhr.onreadystatechange = function() { - - // Check readyState before timeout as it changes - if ( xhr.readyState === 4 ) { - - // Allow onerror to be called first, - // but that will not handle a native abort - // Also, save errorCallback to a variable - // as xhr.onerror cannot be accessed - window.setTimeout( function() { - if ( callback ) { - errorCallback(); - } - } ); - } - }; - } - - // Create the abort callback - callback = callback( "abort" ); - - try { - - // Do send the request (this may raise an exception) - xhr.send( options.hasContent && options.data || null ); - } catch ( e ) { - - // #14683: Only rethrow if this hasn't been notified as an error yet - if ( callback ) { - throw e; - } - } - }, - - abort: function() { - if ( callback ) { - callback(); - } - } - }; - } -} ); - - - - -// Prevent auto-execution of scripts when no explicit dataType was provided (See gh-2432) -jQuery.ajaxPrefilter( function( s ) { - if ( s.crossDomain ) { - s.contents.script = false; - } -} ); - -// Install script dataType -jQuery.ajaxSetup( { - accepts: { - script: "text/javascript, application/javascript, " + - "application/ecmascript, application/x-ecmascript" - }, - contents: { - script: /\b(?:java|ecma)script\b/ - }, - converters: { - "text script": function( text ) { - jQuery.globalEval( text ); - return text; - } - } -} ); - -// Handle cache's special case and crossDomain -jQuery.ajaxPrefilter( "script", function( s ) { - if ( s.cache === undefined ) { - s.cache = false; - } - if ( s.crossDomain ) { - s.type = "GET"; - } -} ); - -// Bind script tag hack transport -jQuery.ajaxTransport( "script", function( s ) { - - // This transport only deals with cross domain requests - if ( s.crossDomain ) { - var script, callback; - return { - send: function( _, complete ) { - script = jQuery( " - - - - - - - - - - - - - - - - - - - - - - - -
- - - -
- - - - - -
- -
- - - - - - - - - - - - - - - - - -
- - - - -
-
-
-
- -
-

AML class

-
-
-class MLBG59.__main__.AML(*args, target=None, **kwargs)[source]
-

Covers the complete pipeline of a classification project from a raw dataset to a deployable model.

-

AML is built as a class inherited from pandas DataFrame. Each Machine Learning step corresponds to method that -can be called with default or filled parameters.

-
    -
  • explore: explore dataset and identify features types
    • -
  • preprocess: clean and prepare data (optional : outliers processing).
    • -
  • select_features: features selection (optional)
    • -
  • model_train_predict : split AML in train/test sets to fits/apply models with random search. -Returns the list of the valid models (without overfitting) and the best one.
    • -
-

deployment methods:

-
    -
  • preprocess_apply : apply fitted preprocessing transformation to a new dataset
    • -
  • select_features_apply : idem
    • -
  • model_apply : apply fitted models to a new dataset
    • -
-

Notes :

-
    -
  • A method requires that the former one has been applied (actuel step is given by “step” attribute)
    • -
  • Target has to be binary and encoded as int (1/0) (see MLGB59.Start.Encode_Target module if you need help)
    • -
  • don’t call your target “target” please :>
    • -
- --- - - - -
Parameters:
    -
  • _obj (DataFrame) – Source Dataset
    • -
  • target (string) – target name
    • -
-
-
-
-explore(verbose=False)[source]
-

data exploration and features type identification

-

Note : if you disagree with automated identification, you can directly modify d_features attribute

-
-
Create self.d_features : dict {x : list of variables names}
-
    -
  • date: date features
    • -
  • identifier: identifier features
    • -
  • verbatim: verbatim features
    • -
  • boolean: boolean features
    • -
  • categorical: categorical features
    • -
  • numerical: numerical features
    • -
  • NA: features which contains NA values
    • -
  • low_variance: list of the features with low variance and unique values
    • -
-
-
- --- - - - -
Parameters:verbose (boolean (Default False)) – Get logging information
-
- -
-
-model_predict(df, metric='F1', verbose=False)[source]
-

apply fitted models on a dataset

-
    -
  • identifies valid models |(auc(train)-auc(test)|<0.03
    • -
  • gets the best model in respect of a selected metric among valid model
    • -
- --- - - - - - -
Parameters:
    -
  • metric (string (Default : 'F1')) – objective metric
    • -
  • verbose (boolean (Default False)) – Get logging information
    • -
-
Returns:

    -
  • dict – {model_index : {‘HP’, ‘probas’, ‘model’, ‘features_importance’, ‘train_metrics’, ‘metrics’, ‘output’}
    • -
  • list – valid models indexes
    • -
  • int – best model index
    • -
  • DataFrame – models summary
    • -
-

-
-
- -
-
-model_train(clf='XGBOOST', grid_param=None, top_bagging=False, n_comb=10, comb_seed=None, verbose=False)[source]
-

train models with random search

-
    -
  • creates models with random hyper-parameters combinations from HP grid
    • -
  • fits models on self
    • -
-

Notes :

-
    -
  • Available classifiers : Random Forest, XGBOOST
    • -
  • can enable bagging algo with top_bagging parameter
    • -
- --- - - - -
Parameters:
    -
  • clf (string (Default : 'XGBOOST')) – classifier used for modelisation
    • -
  • grid_param (dict) – random search grid {Hyperparameter name : values list}
    • -
  • top_bagging (boolean (Default : False)) – enable Bagging
    • -
  • n_comb (int (Default : 10)) – HP combination number
    • -
  • comb_seed (int (Default : None)) – random combination seed
    • -
  • verbose (boolean (Default False)) – Get logging information
    • -
-
-
- -
-
-model_train_test(clf='XGBOOST', grid_param=None, metric='F1', top_bagging=False, n_comb=10, comb_seed=None, verbose=False)[source]
-

train and test models with random search

-
    -
  • creates models with random hyper-parameters combinations from HP grid
    • -
  • splits (random 80/20) train/test sets to fit/apply models
    • -
  • identifies valid models |(auc(train)-auc(test)|<0.03
    • -
  • gets the best model in respect of a selected metric among valid model
    • -
-

Notes :

-
    -
  • Available classifiers : Random Forest, XGBOOST
    • -
  • can enable bagging algo with top_bagging parameter
    • -
- --- - - - - - -
Parameters:
    -
  • clf (string (Default : 'XGBOOST')) – classifier used for modelisation
    • -
  • grid_param (dict) – random search grid {Hyperparameter name : values list}
    • -
  • metric (string (Default : 'F1')) – objective metric
    • -
  • top_bagging (boolean (Default : False)) – enable Bagging
    • -
  • n_comb (int (Default : 10)) – HP combination number
    • -
  • comb_seed (int (Default : None)) – random combination seed
    • -
  • verbose (boolean (Default False)) – Get logging information
    • -
-
Returns:

    -
  • dict – {model_index : {‘HP’, ‘probas’, ‘model’, ‘features_importance’, ‘train_metrics’, ‘metrics’, ‘output’}
    • -
  • list – valid models indexes
    • -
  • int – best model index
    • -
  • DataFrame – models summary
    • -
-

-
-
- -
-
-preprocess(date_ref=None, process_outliers=False, cat_method='deep_encoder', verbose=False)[source]
-

Prepare the data before feeding it to the model :

-
-
    -
  • remove low variance features
    • -
  • remove identifiers and verbatims features
    • -
  • transform date features to timedelta
    • -
  • fill missing values
    • -
  • process categorical and boolean data (one-hot-encoding or Pytorch NN encoder)
    • -
  • replace outliers (optional)
    • -
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create self.d_preprocess : dict {step : transformation}
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  • remove: list of the features to remove
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  • date: fitted DateEncoder object
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  • NA: fitted NAEncoder object
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  • categorical: fitted CategoricalEncoder object
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  • outlier: fitted OutlierEncoder object
    • -
-
-
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Parameters:
    -
  • date_ref (string '%d/%m/%y' (Default : None)) – ref date to compute date features timedelta. -If None, today date
    • -
  • process_outliers (boolean (Default : False)) – Enable outliers replacement
    • -
  • cat_method (string (Default : 'deep_encoder')) – Categorical features encoding method
    • -
  • verbose (boolean (Default False)) – Get logging information
    • -
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-preprocess_apply(df, verbose=False)[source]
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Apply preprocessing. -Requires preprocess method to have been applied (so that all encoder are fitted)

- --- - - - - - - - -
Parameters:
    -
  • df (DataFrame) – dataset to apply preprocessing on
    • -
  • verbose (boolean (Default False)) – Get logging information
    • -
-
Returns:

DataFrame

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Return type:

Preprocessed dataset

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-select_features(method='pca', verbose=False)[source]
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fit and apply features selection (optional)

- --- - - - -
Parameters:
    -
  • method (string (Default pca)) – method use to select features
    • -
  • verbose (boolean (Default False)) – Get logging information
    • -
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-select_features_apply(df, verbose=False)[source]
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Apply features selection.

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Requires Select_Features method to have been applied

- --- - - - - - - - -
Parameters:
    -
  • df (DataFrame) – dataset to apply selection on
    • -
  • verbose (boolean (Default False)) – Get logging information
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Returns:

DataFrame

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Return type:

reduced dataset

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Test

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Features_type

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Variables type identification function

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  • is_date : test if a variable is a date
    • -
  • is_identifier : test if a variable is an identifier
    • -
  • is_verbatim : test if a variable is a verbatim
    • -
  • is_boolean : test if a variable is a boolean
    • -
  • is_categorical : test if a variable is a categorical one (with more than 2 categories)
    • -
  • features_from_type : get all
    • -
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-dev.Features_type.features_from_type(df, type, var_list=None, th=0.95)[source]
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Get features of a selected type :

-
    -
  • date : try to apply to_datetime
    • -
  • -
    identifier :
    -
      -
    • #(unique values)/#(total values) > threshold (default 0.95)
      • -
    • AND length is the same for all values (for non NA)
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    verbatim :
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    • #(unique values)/#(total values) >= threshold (default 0.95)
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    • AND length is NOT the same for all values (for non NA)
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    -
    -
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    • -
  • boolean : #(distinct values) = 2
    • -
  • categorical : #(unique values)/#(total values) < threshold (default 0.95)
    • -
- --- - - - - - - - -
Parameters:
    -
  • df (DataFrame) – input dataset
    • -
  • var_list (list) – variables names
    • -
  • type – selected type to get features
    • -
  • th (float (Default : 0.90)) – threshold used to identify identifiers/verbatims/categorcial variables
    • -
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Returns:

identified variables names

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Return type:

list

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-dev.Features_type.is_boolean(df, col)[source]
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Test if a variable is a boolean.

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    -
  • #(distinct values) = 2
    • -
- --- - - - - - - - -
Parameters:
    -
  • df (DataFrame) – input dataset
    • -
  • col (string) – variable name
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Returns:

res – test result

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Return type:

boolean

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- -
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-dev.Features_type.is_categorical(df, col, th=0.95)[source]
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Test if a variable is a categorical one (with more than 2 categories).

-
    -
  • #(unique values)/#(total values) < threshold (default 0.95)
    • -
- --- - - - - - - - -
Parameters:
    -
  • df (DataFrame) – input dataset
    • -
  • col (string) – variable name
    • -
  • th (float (Default : 0.95)) – threshold rate
    • -
-
Returns:

res – test result

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Return type:

boolean

-
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- -
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-dev.Features_type.is_date(df, col)[source]
-

Test if a variable is a date.

-

Method : try to apply to_datetime

- --- - - - - - - - -
Parameters:
    -
  • df (DataFrame) – input dataset
    • -
  • col (string) – variable name
    • -
-
Returns:

res – test result

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Return type:

boolean

-
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- -
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-dev.Features_type.is_identifier(df, col, th=0.95)[source]
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Test if a variable is an identifier.

-
    -
  • #(unique values)/#(total values) > threshold (default 0.95)
    • -
  • AND length is the same for all values (for non NA)
    • -
  • AND not date
    • -
- --- - - - - - - - -
Parameters:
    -
  • df (DataFrame) – input dataset
    • -
  • col (string) – variable name
    • -
  • th (float (Default : 0.95)) – threshold rate
    • -
-
Returns:

res – test result

-
Return type:

boolean

-
-
- -
-
-dev.Features_type.is_verbatim(df, col, th=0.95)[source]
-

Test if a variable is a verbatim.

-
    -
  • #(unique values)/#(total values) >= threshold (default 0.95)
    • -
  • AND length is NOT the same for all values (for non NA)
    • -
- --- - - - - - - - -
Parameters:
    -
  • df (DataFrame) – input dataset
    • -
  • col (string) – variable name
    • -
  • th (float (Default : 0.95)) – threshold rate
    • -
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Returns:

res – test result

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Return type:

boolean

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- © Copyright 2020, Maxence LABESSE - -

-
- Built with Sphinx using a theme provided by Read the Docs. - -
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- -
- - - - - - - - - - - - \ No newline at end of file diff --git a/docs/_build.html/features.html b/docs/_build.html/features.html deleted file mode 100644 index eccc9c4..0000000 --- a/docs/_build.html/features.html +++ /dev/null @@ -1,1840 +0,0 @@ - - - - - - - - - - - Start — MLBG59 1.0.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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Start

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Load

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Data import functions :

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    -
  • get_delimiter : identify delimiter for a .csv/.txt file
    • -
  • load_data : import dataset file into dataframe
    • -
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-MLBG59.Start.Load.get_delimiter(file)[source]
-

Identify the delimiter for a csv/txt file

- --- - - - - - - - -
Parameters:file (string) – Path and name of the file (Ex : “data/file.csv”)
Returns:identified delimiter
Return type:string
-
- -
-
-MLBG59.Start.Load.import_data(file, index_col=None, verbose=False)[source]
-

Import dataset as a DataFrame (identify delimiter for txt and csv files)

-

Available files : .txt, .csv, .xlsx, .xls files

- --- - - - - - - - -
Parameters:
    -
  • file (string) – Path and name of the file (Ex : “data/file.csv”) -If file is .csv, automatically identify delimiter
    • -
  • index_col (int, str, sequence of int / str, or False (Default None)) – Column(s) to use as the row labels of the DataFrame, either given as string name or column index. -If a sequence of int / str is given, a MultiIndex is used.
    • -
  • verbose (boolean (Default False)) – Get logging information
    • -
-
Returns:

imported dataset

-
Return type:

DataFrame

-
-
- -
-
-

Encode_Target

-

Target encoding functions :

-
    -
  • category_to_target : create a target variable (1/0) from a selected category
    • -
  • range_to_target : create a target variable (1/0) from a selected range
    • -
-
-
-MLBG59.Start.Encode_Target.category_to_target(df, var, cat)[source]
-

Create a target variable (1/0) from a selected category

- --- - - - - - -
Parameters:
    -
  • df (DataFrame) – input dataset
    • -
  • var (string) – variable containing the target category
    • -
  • cat (string) – target category
    • -
-
Returns:

    -
  • DataFrame (modified dataset)
    • -
  • string (new target name (var+’_’+cat))
    • -
-

-
-
- -
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-MLBG59.Start.Encode_Target.range_to_target(df, var, min=None, max=None, verbose=False)[source]
-

Create a target variable (1/0) from a selected range

- --- - - - - - -
Parameters:
    -
  • df (DataFrame) – input dataset
    • -
  • var (string) – variable containing the target range
    • -
  • min (float) – lower limit. -If None, no min
    • -
  • max (float) – upper limit. -If None, no max
    • -
  • verbose (boolean (Default False)) – Get logging information
    • -
-
Returns:

    -
  • DataFrame (modified dataset)
    • -
  • string (new target name (var+’_’+lower+’_’+upper))
    • -
-

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Explore

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Explore

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Global dataset information functions :

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  • explore (func): Identify variables types and gives global information about the dataset (NA, low variance features)
    • -
  • low variance features (func): identify features with low variance
    • -
    • -
    • get_features_type (func): get all features per type
      • -
    -
    • -
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-MLBG59.Explore.Explore.explore(df, verbose=False)[source]
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Identify variables types and gives global information about the dataset

-
    -
  • -
    Variables type :
    -
      -
    • date
      • -
    • identifier
      • -
    • verbatim
      • -
    • boolean
      • -
    • categorical
      • -
    • numerical
      • -
    -
    -
    -
    • -
  • variables containing NA values
    • -
  • low variance and unique values variables
    • -
-

See get_features_type function doc for type identification heuristics

- --- - - - - - - - -
Parameters:
    -
  • df (DataFrame) – input dataset
    • -
  • verbose (boolean (Default False)) – Get logging information
    • -
-
Returns:

{x : variables names list }

-
    -
  • date : date features
    • -
  • identifier : identifier features
    • -
  • verbatim : verbatim features
    • -
  • boolean : boolean features
    • -
  • categorical : categorical features
    • -
  • numerical : numerical features
    • -
  • categorical : categorical features
    • -
  • date : date features
    • -
  • NA : features which contains NA values
    • -
  • low_variance : list of the features with low variance
    • -
-

-
Return type:

dict

-
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- -
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-MLBG59.Explore.Explore.get_features_type(df, l_var=None, th=0.95)[source]
-

Get all features per type :

-
    -
  • date : try to apply to_datetime
    • -
  • -
    identifier :
    -
      -
    • #(unique values)/#(total values) > threshold (default 0.95)
      • -
    • AND length is the same for all values (for non NA)
      • -
    -
    -
    -
    • -
  • -
    verbatim :
    -
      -
    • #(unique values)/#(total values) >= threshold (default 0.95)
      • -
    • AND length is NOT the same for all values (for non NA)
      • -
    -
    -
    -
    • -
  • boolean : #(distinct values) = 2
    • -
  • -
    categorical :
    -
      -
    • not a date
      • -
    • #(unique values)/#(total values) < threshold (default 0.95)
      • -
    • AND #(uniques values)>2
      • -
    • AND for num values #(unique values)<30
      • -
    -
    -
    -
    • -
  • numerical : others
    • -
- --- - - - - - - - -
Parameters:
    -
  • df (DataFrame) – input dataset
    • -
  • l_var (list (Default : None)) – variable names
    • -
  • th (float (Default : 0.95)) – threshold used to identify identifiers/verbatims variables
    • -
-
Returns:

{ type : variables name list}

-
Return type:

dict

-
-
- -
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-MLBG59.Explore.Explore.low_variance_features(df, var_list=None, threshold=0, rescale=True, verbose=False)[source]
-

Identify numerical features with low variance : (< threshold). -Possible to rescale feature before computing.

- --- - - - -
Parameters:
    -
  • df (DataFrame) – input DataFrame
    • -
  • var_list (list (default : None)) – names of the variables to check variance -if None : all the numerical features
    • -
  • threshold (float (default : 0)) – variance threshold
    • -
  • rescale (bool (default : true)) – enable MinMaxScaler before computing variance
    • -
-
-
-
verbose : boolean (Default False)
-
Get logging information
-
- --- - - - - - -
Returns:Names of the variables with low variance
Return type:list
-
- -
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Features_Type

-

Variables type identification function

-
    -
  • features_from_type (func): get all features for a selected type
    • -
  • is_date (func): test if a variable is a date
    • -
  • is_identifier (func): test if a variable is an identifier
    • -
  • is_verbatim (func): test if a variable is a verbatim
    • -
  • is_boolean (func): test if a variable is a boolean
    • -
  • is_categorical (func): test if a variable is a categorical one (with more than 2 categories)
    • -
-
-
-MLBG59.Explore.Features_Type.features_from_type(df, typ, l_var=None, th=0.95)[source]
-

Get features of a selected type :

-
    -
  • date : try to apply to_datetime
    • -
  • -
    identifier :
    -
      -
    • #(unique values)/#(total values) > threshold (default 0.95)
      • -
    • AND length is the same for all values (for non NA)
      • -
    -
    -
    -
    • -
  • -
    verbatim :
    -
      -
    • #(unique values)/#(total values) >= threshold (default 0.95)
      • -
    • AND length is NOT the same for all values (for non NA)
      • -
    -
    -
    -
    • -
  • boolean : #(distinct values) = 2
    • -
  • -
    categorical :
    -
      -
    • not a date
      • -
    • #(unique values)/#(total values) < threshold (default 0.95)
      • -
    • AND #(uniques values)>2
      • -
    • AND for num values #(unique values)<30
      • -
    -
    -
    -
    • -
- --- - - - - - - - -
Parameters:
    -
  • df (DataFrame) – input dataset
    • -
  • typ (string) –

    selected type to get features:

    -
      -
    • ’date’
      • -
    • ’identifier’
      • -
    • ’verbatim’
      • -
    • ’boolean’
      • -
    • categorical
      • -
    -
    • -
  • l_var (list (Default : None)) – variables names. If None, all dataset columns
    • -
  • th (float (Default : 0.95)) – threshold used to identify identifiers/verbatims variables
    • -
-
Returns:

identified variables names

-
Return type:

list

-
-
- -
-
-MLBG59.Explore.Features_Type.is_boolean(df, col)[source]
-

Test if a variable is a boolean.

-
    -
  • #(distinct values) = 2
    • -
- --- - - - - - - - -
Parameters:
    -
  • df (DataFrame) – input dataset
    • -
  • col (string) – variable name
    • -
-
Returns:

res – test result

-
Return type:

boolean

-
-
- -
-
-MLBG59.Explore.Features_Type.is_categorical(df, col, th=0.95)[source]
-

Test if a variable is a categorical one (with more than 2 categories).

-
    -
  • not a date
    • -
  • #(unique values)/#(total values) < threshold (default 0.95
    • -
  • AND #(uniques values)>2
    • -
  • AND for num values #(unique values)<30
    • -
- --- - - - - - - - -
Parameters:
    -
  • df (DataFrame) – input dataset
    • -
  • col (string) – variable name
    • -
  • th (float (Default : 0.95)) – threshold
    • -
-
Returns:

res – test result

-
Return type:

boolean

-
-
- -
-
-MLBG59.Explore.Features_Type.is_date(df, col)[source]
-

Test if a variable is a date.

-

Method : try to apply to_datetime

- --- - - - - - - - -
Parameters:
    -
  • df (DataFrame) – input dataset
    • -
  • col (string) – variable name
    • -
-
Returns:

res – test result

-
Return type:

boolean

-
-
- -
-
-MLBG59.Explore.Features_Type.is_identifier(df, col, th=0.95)[source]
-

Test if a variable is an identifier.

-
    -
  • #(unique values)/#(total values) > threshold (default 0.95)
    • -
  • AND length is the same for all values (for non NA)
    • -
  • AND not date
    • -
- --- - - - - - - - -
Parameters:
    -
  • df (DataFrame) – input dataset
    • -
  • col (string) – variable name
    • -
  • th (float (Default : 0.95)) – threshold rate
    • -
-
Returns:

res – test result

-
Return type:

boolean

-
-
- -
-
-MLBG59.Explore.Features_Type.is_verbatim(df, col, th=0.95)[source]
-

Test if a variable is a verbatim.

-
    -
  • #(unique values)/#(total values) >= threshold (default 0.95)
    • -
  • AND length is NOT the same for all values (for non NA)
    • -
- --- - - - - - - - -
Parameters:
    -
  • df (DataFrame) – input dataset
    • -
  • col (string) – variable name
    • -
  • th (float (Default : 0.95)) – threshold rate
    • -
-
Returns:

res – test result

-
Return type:

boolean

-
-
- -
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-

Preprocessing

-
-

Missing_Values

-

Missing values handling functions :

-
    -
  • NAEncoder (class): encoder that replaces missing values
    • -
  • fill_numerical (func): replace missing values for numerical features
    • -
  • fill_categorical (func): replace missing values for categorical features
    • -
  • get_NA_features (func): get features containing NA values
    • -
-
-
-class MLBG59.Preprocessing.Missing_Values.NAEncoder(replace_num_with='median', replace_cat_with='NR', track_num_NA=True)[source]
-

Missing values filling

-

Available methods to replace missing values

-
    -
  • num : metdian/mean/zero
    • -
  • cat : ‘NR’
    • -
- --- - - - -
Parameters:
    -
  • replace_num_with (string) – method used to replace numerical missing values
    • -
  • replace_cat_with (string) – method used to replace categorical missing values
    • -
-
-
-
-fit(df, l_var, verbose=False)[source]
-

fit encoder

- --- - - - -
Parameters:
    -
  • df (DataFrame) – input dataset
    • -
  • l_var (list) – features to encode. -If None, all features
    • -
  • verbose (boolean (Default False)) – Get logging information
    • -
-
-
- -
-
-fit_transform(df, l_var=None, verbose=False)[source]
-

fit and transform dataset with encoder

- --- - - - -
Parameters:
    -
  • df (DataFrame) – input dataset
    • -
  • l_var (list) – features to encode. -If None, all features identified as dates (see Features_Type module)
    • -
  • verbose (boolean (Default False)) – Get logging information
    • -
-
-
- -
-
-transform(df, verbose=False)[source]
-

transform dataset categorical features using the encoder. -Can be done only if encoder has been fitted

- --- - - - -
Parameters:
    -
  • df (DataFrame) – dataset to transform
    • -
  • verbose (boolean (Default False)) – Get logging information
    • -
-
-
- -
- -
-
-MLBG59.Preprocessing.Missing_Values.fill_categorical(df, l_var=None, method='NR', verbose=False)[source]
-

Fill missing values for selected/all categorical features.

- --- - - - - - - - -
Parameters:
    -
  • df (DataFrame) – Input dataset
    • -
  • l_var (list (Default : None)) – list of the features to fill. -If None, contains all the categorical features
    • -
  • method (string (Default : 'NR')) –

    Method used to fill the NA values :

    -
      -
    • NR : replace NA with ‘NR’
      • -
    -
    • -
  • verbose (boolean (Default False)) – Get logging information
    • -
-
Returns:

Modified dataset

-
Return type:

DataFrame

-
-
- -
-
-MLBG59.Preprocessing.Missing_Values.fill_numerical(df, l_var=None, method='median', track_num_NA=True, verbose=False)[source]
-

Fill missing values for selected/all numerical features. -top_var_NA parameter allows to create a variable to keep track of missing values.

-

Available methods : replace with zero, median or mean (Default = median)

- --- - - - - - - - -
Parameters:
    -
  • df (DataFrame) – Input dataset
    • -
  • l_var (list (Default : None)) – names of the features to fill. -If None, all the numerical features
    • -
  • method (string (Default : 'median')) –

    Method used to fill the NA values :

    -
      -
    • zero : replace with zero
      • -
    • median : replace with median
      • -
    • mean : replace with mean
      • -
    -
    • -
  • track_num_NA (boolean (Defaut : True)) – If True, create a boolean column to keep track of missing values
    • -
  • verbose (boolean (Default False)) – Get logging information
    • -
-
Returns:

Modified dataset

-
Return type:

DataFrame

-
-
- -
-
-MLBG59.Preprocessing.Missing_Values.get_NA_features(df)[source]
-

identify features containing NA values

- --- - - - - - - - -
Parameters:df (DataFrame) – input dataset
Returns:list
Return type:features containing missing values
-
- -
-
-

Categorical Data

-
-
-class MLBG59.Preprocessing.Categorical.CategoricalEncoder(method='deep_encoder')[source]
-

Encode categorical features

-

Available encoding methods :

- -

Default NN model parameters are stored in param_config.py file

- --- - - - -
Parameters:method (string (Default : deep_encoder)) – method used to get categorical encoding -Available methods : “one_hot”, “deep_encoder”
-
-
-fit(df, l_var=None, target=None, verbose=False)[source]
-

Fit encoder on dataset following method

- --- - - - -
Parameters:
    -
  • df (DataFrame) – input dataset
    • -
  • l_var (list (Default None)) – names of the variables to encode. -If None, all the categorical and boolean features
    • -
  • target (string (Default None)) – name of the target for deep_encoder method
    • -
  • verbose (boolean (Default False)) – Get logging information
    • -
-
-
- -
-
-fit_transform(df, l_var=None, target=None, verbose=False)[source]
-

fit and transform dataset categorical features

- --- - - - - - - - -
Parameters:
    -
  • df (DataFrame) – input dataset
    • -
  • l_var (list (Default None)) – names of the variables to encode. -If None, all the categorical and boolean features
    • -
  • target (string (Default None)) – name of the target for deep_encoder method
    • -
  • verbose (boolean (Default False)) – Get logging information
    • -
-
Returns:

DataFrame

-
Return type:

modified dataset

-
-
- -
-
-transform(df, verbose=False)[source]
-

transform dataset categorical features using the encoder. -Can be done only if encoder has been fitted

- --- - - - - - - - -
Parameters:
    -
  • df (DataFrame) – dataset to transform
    • -
  • verbose (boolean (Default False)) – Get logging information
    • -
-
Returns:

DataFrame

-
Return type:

modified dataset

-
-
- -
- -
-
-MLBG59.Preprocessing.Categorical.dummy_all_var(df, var_list=None, prefix_list=None, keep=False, verbose=False)[source]
-

Get one hot encoded vector for selected/all categorical features

- --- - - - - - - - -
Parameters:
    -
  • df (DatraFrame) – Input dataset
    • -
  • var_list (list (Default : None)) – Names of the features to dummify -If None, all the num features
    • -
  • prefix_list (list (default : None)) – Prefix to add before new features name (prefix+’_’+cat). -If None, prefix=variable name
    • -
  • keep (boolean (Default = False)) – If True, delete the original feature
    • -
  • verbose (boolean (Default False)) – Get logging information
    • -
-
Returns:

Modified dataset

-
Return type:

DataFrame

-
-
- -
-
-MLBG59.Preprocessing.Categorical.get_embedded_cat(df, var_list, target, batchsize, n_epochs, lr, verbose=False)[source]
-

Get embedded representation for categorical features using NN encoder

- --- - - - - - - - -
Parameters:
    -
  • df (DataFrame) – input Dataset
    • -
  • var_list (list of strings) – features names
    • -
  • target (string) – target name
    • -
  • batchsize (int) – batch size for encoder training
    • -
  • n_epochs (int) – number of epoch for encoder training
    • -
  • lr (float) – encoder learning rate
    • -
  • verbose (boolean (Default False)) – Get logging information
    • -
-
Returns:

DataFrame

-
Return type:

modified dataset

-
-
- -
-
-

Date Data

-

Date Features processing functions:

-
    -
  • DateEncoder (class) : encode date features
    • -
  • all_to_date (func): detect dates from num/cat features and transform them to datetime format.
    • -
  • date_to_anc (func): transform datetime features to timedelta according to a ref date
    • -
-
-
-class MLBG59.Preprocessing.Date.DateEncoder(method='timedelta', date_ref=None)[source]
-

Encode categorical features

-

Available methods :

-
    -
  • timedelta : compute time between date feature and parameter date_ref
    • -
- --- - - - -
Parameters:
    -
  • method (string (Default : timedelta)) – method used to encode dates -Available methods : “timedelta”
    • -
  • date_ref (string '%d/%m/%y' (Default : None)) – Date to compute timedelta. -If None, today date
    • -
-
-
-
-fit(df, l_var=None, verbose=False)[source]
-

fit encoder

- --- - - - -
Parameters:
    -
  • df (DataFrame) – input dataset
    • -
  • l_var (list) – features to encode. -If None, contains all features identified as dates (see Features_Type module)
    • -
  • verbose (boolean (Default False)) – Get logging information
    • -
-
-
- -
-
-fit_transform(df, l_var=None, verbose=False)[source]
-

fit and transform dataset with encoder

- --- - - - -
Parameters:
    -
  • df (DataFrame) – input dataset
    • -
  • l_var (list) – features to encode. -If None, all features identified as dates (see Features_Type module)
    • -
  • verbose (boolean (Default False)) – Get logging information
    • -
-
-
- -
-
-transform(df, verbose=False)[source]
-

transform dataset date features using the encoder. -Can be done only if encoder has been fitted

- --- - - - -
Parameters:
    -
  • df (DataFrame) – dataset to transform
    • -
  • verbose (boolean (Default False)) – Get logging information
    • -
-
-
- -
- -
-
-MLBG59.Preprocessing.Date.all_to_date(df, l_var=None, verbose=False)[source]
-

Detect dates from selected/all features and transform them to datetime format.

- --- - - - - - - - -
Parameters:
    -
  • df (DataFrame) – Input dataset
    • -
  • l_var (list (Default : None)) – Names of the features -If None, all the features
    • -
  • verbose (boolean (Default False)) – Get logging information
    • -
-
Returns:

Modified dataset

-
Return type:

DataFrame

-
-
- -
-
-MLBG59.Preprocessing.Date.date_to_anc(df, l_var=None, date_ref=None, verbose=False)[source]
-

Transform selected/all datetime features to timedelta according to a ref date

- --- - - - - - -
Parameters:
    -
  • df (DataFrame) – Input dataset
    • -
  • l_var (list (Default : None)) – List of the features to analyze. -If None, contains all the datetime features
    • -
  • date_ref (string '%d/%m/%y' (Default : None)) – Date to compute timedelta. -If None, today date
    • -
  • verbose (boolean (Default False)) – Get logging information
    • -
-
Returns:

    -
  • DataFrame – Modified dataset
    • -
  • list – New timedelta features names
    • -
-

-
-
- -
-
-

Process Outliers

-

Outliers handling functions

-
    -
  • OutliersEncoding (class) : identify and replace outliers
    • -
  • get_cat_outliers (funct): identify categorical features containing outliers
    • -
  • get_num_outliers (func): identify numerical features containing outliers
    • -
  • replace_category (func): replace categories of a categorical variable
    • -
  • replace_extreme_values (func): replace extreme values (oh!)
    • -
-
-
-class MLBG59.Preprocessing.Outliers.OutliersEncoder(cat_threshold=0.02, num_xstd=4)[source]
-

Identify et replace outliers for categorical dang numerical features

-
    -
  • num : x outlier <=> abs(x - mean) > xstd * var
    • -
  • cat : x outlier category <=> with frequency <x% (Default 5%)
    • -
- --- - - - -
Parameters:
    -
  • cat_threshold (float (default 0.02)) – Minimum modality frequency
    • -
  • num_xstd (int (Default : 3)) – Variance gap coef
    • -
-
-
-
-fit(df, l_var, verbose=False)[source]
-

Fit encoder

- --- - - - -
Parameters:
    -
  • df (DataFrame) – input dataset
    • -
  • l_var (list) – features to encode. -If None, all features
    • -
  • verbose (boolean (Default False)) – Get logging information
    • -
-
-
- -
-
-fit_transform(df, l_var=None, verbose=False)[source]
-

Fit and transform dataset with encoder

- --- - - - -
Parameters:
    -
  • df (DataFrame) – input dataset
    • -
  • l_var (list) – features to encode. -If None, all features identified as dates (see Features_Type module)
    • -
  • verbose (boolean (Default False)) – Get logging information
    • -
-
-
- -
-
-transform(df, verbose=False)[source]
-

Transform dataset features using the encoder. -Can be done only if encoder has been fitted

- --- - - - -
Parameters:
    -
  • df (DataFrame) – dataset to transform
    • -
  • verbose (boolean (Default False)) – Get logging information
    • -
-
-
- -
- -
-
-MLBG59.Preprocessing.Outliers.get_cat_outliers(df, l_var=None, threshold=0.05, verbose=False)[source]
-

Outliers detection for selected/all categorical features.

-

Method : Modalities with frequency <x% (Default 5%)

- --- - - - - - - - -
Parameters:
    -
  • df (DataFrame) – Input dataset
    • -
  • l_var (list (Default : None)) – Names of the features -If None, all the categorical features
    • -
  • threshold (float (Default : 0.05)) – Minimum modality frequency
    • -
  • verbose (boolean (Default False)) – Get logging information
    • -
-
Returns:

{variable : list of categories considered as outliers}

-
Return type:

dict

-
-
- -
-
-MLBG59.Preprocessing.Outliers.get_num_outliers(df, l_var=None, xstd=3, verbose=False)[source]
-

Outliers detection for selected/all numerical features.

-

Method : x outlier <=> abs(x - mean) > xstd * var

- --- - - - - - - - -
Parameters:
    -
  • df (DataFrame) – Input dataset
    • -
  • l_var (list (Default : None)) – Names of the features -If None, all the num features
    • -
  • xstd (int (Default : 3)) – Variance gap coef
    • -
  • verbose (boolean (Default False)) – Get logging information
    • -
-
Returns:

{variable : [lower_limit, upper_limit]}

-
Return type:

dict

-
-
- -
-
-MLBG59.Preprocessing.Outliers.replace_category(df, var, categories, replace_with='outliers', verbose=False)[source]
-

Replace categories of a categorical variable

- --- - - - - - - - -
Parameters:
    -
  • df (DataFrame) – Input dataset
    • -
  • var (string) – variable to modify
    • -
  • categories (list(string)) – categories to replace
    • -
  • replace_with (string (Default : 'outliers')) – word to replace categories with
    • -
  • verbose (boolean (Default False)) – Get logging information
    • -
-
Returns:

Modified dataset

-
Return type:

DataFrame

-
-
- -
-
-MLBG59.Preprocessing.Outliers.replace_extreme_values(df, var, lower_th=None, upper_th=None, verbose=False)[source]
-

Replace extrem values : > upper threshold or < lower threshold

- --- - - - - - - - -
Parameters:
    -
  • df (DataFrame) – Input dataset
    • -
  • var (string) – variable to modify
    • -
  • lower_th (int/float (Default=None)) – lower threshold
    • -
  • upper_th (int/float (Default=None)) – upper threshold
    • -
  • verbose (boolean (Default False)) – Get logging information
    • -
-
Returns:

Modified dataset

-
Return type:

DataFrame

-
-
- -
-
-
-

Features Selection

-

Features selection

-
    -
  • select_features (func) : features selection following method
    • -
-
-
-class MLBG59.Select_Features.Select_Features.FeatSelector(method='pca')[source]
-

features selection following method

-
    -
  • pca : use pca to reduce dataset dimensions
    • -
  • no_rescale_pca : use pca without rescaling data
    • -
- --- - - - -
Parameters:method (string (Default pca)) – method use to select features
-
-
-fit(df, l_var=None, verbose=False)[source]
-

fit selector

- --- - - - -
Parameters:
    -
  • df (DataFrame) – input dataset
    • -
  • l_var (list) – features to encode. -If None, all features identified as numerical
    • -
  • verbose (boolean (Default False)) – Get logging information
    • -
-
-
- -
-
-fit_transform(df, l_var, verbose=False)[source]
-

fit and apply features selection

- --- - - - - - - - -
Parameters:
    -
  • df (DataFrame) – input dataset
    • -
  • l_var (list) – features to encode. -If None, all features identified as dates (see Features_Type module)
    • -
  • verbose (boolean (Default False)) – Get logging information
    • -
-
Returns:

DataFrame

-
Return type:

modified dataset

-
-
- -
-
-transform(df, verbose=False)[source]
-

apply features selection on a dataset

- --- - - - - - - - -
Parameters:
    -
  • df (DataFrame) – dataset to transform
    • -
  • verbose (boolean (Default False)) – Get logging information
    • -
-
Returns:

DataFrame

-
Return type:

modified dataset

-
-
- -
- -
-
-MLBG59.Select_Features.Select_Features.select_features(df, target, method='pca', verbose=False)[source]
-

features selection following method

-
    -
  • pca : use pca to reduce dataset dimensions
    • -
  • no_rescale_pca : use pca without rescaling data
    • -
- --- - - - - - - - -
Parameters:
    -
  • df (DataFrame) – input dataset containing features
    • -
  • target (string) – target name
    • -
  • method (string (Default pca)) – method use to select features
    • -
  • verbose (boolean (Default False)) – Get logging information
    • -
-
Returns:

modified dataset

-
Return type:

DataFrame

-
-
- -
-
-

Modelisation

-
-

Bagging

-

Bagging algorithm class. Methods :

-
    -
  • Bagging (class) : generate new training more balanced and train model for each
    • -
  • Bagging_sample (func) : generate bagging sample
    • -
-
-
-class MLBG59.Modelisation.Bagging.Bagging(clf=<sphinx.ext.autodoc.importer._MockObject object>, n_sample=5, pos_sample_size=1.0, replace=True)[source]
-

Meta-algo designed to improve the stability and accuracy of ML classif/regression algos -or to face an “imbalanced target distribution” issue.

-

Bagging generates m new training sets more balanced. Then, a model is fitted on each -sample and outputs are combined by averaging (for regression) or voting (for classification).

-

Available classifiers : Random Forest and XGBOOST

- --- - - - -
Parameters:
    -
  • clf (Model fitted on samples (Default : RandomForestClassifier(n_estimators=100, max_leaf_nodes=100)) – Model fitted on the samples
    • -
  • n_sample (int (Default : 5)) – number a samples
    • -
  • pos_sample_size (int/float (Default : 1.0)) –

    Number/rate of target=1 observations in each sample (filled with 3 times more target=0 )

    -
      -
    • if int : number of target=1
      • -
    • if float : rate of total target=1
      • -
    -
    • -
  • replace (Boolean (Default : False)) – Enable sampling with replacement
    • -
  • list_model (list (Default : None)) – Fitted models (created with fit method)
    • -
-
-
-
-bag_feature_importance(X)[source]
-

Get features importance of the model by averaging importance of models fitted on the samples

- --- - - - - - - - -
Parameters:X (DataFrame) – Input Dataset
Returns:{feature : importance}
Return type:dict
-
- -
-
-fit(df_train, target)[source]
-

Create bagging samples from a DataFrame and fit the model (self.clf) on each sample

- --- - - - - - - - -
Parameters:
    -
  • df_train (DataFrame) – Training dataset
    • -
  • target (String) – Target name
    • -
-
Returns:

self.list_model – Fitted models

-
Return type:

list

-
-
- -
-
-get_params()[source]
-

Get bagging object parameters

- --- - - - - - -
Returns:{param : value}
Return type:dict
-
- -
-
-predict(df)[source]
-

Apply models fitted on sample to a dataset. -Combine models by averaging the outputs (for regression) or voting (for classification)

- --- - - - - - -
Parameters:df (DataFrame) – Dataset to apply the model
Returns:
    -
  • numpy.ndarray (float) – Averaged classification probabilities
    • -
  • numpy.ndarray (int) – Predictions for each observation
    • -
-
-
- -
- -
-
-MLBG59.Modelisation.Bagging.create_sample(df, target, pos_target_nb, replace=False)[source]
-

Generate a DataFrame sample with selected number of target=1

- --- - - - - - - - -
Parameters:
    -
  • df (DataFrame) – Input dataset
    • -
  • target (String) – Target name
    • -
  • pos_target_nb (int) – Number of target=1 observations in the sample
    • -
  • replace (Boolean (défaut : False)) – If True, create samples with replacement
    • -
-
Returns:

sample dataset

-
Return type:

DataFrame

-
-
- -
-
-

Hyperoptimisation

-

Hyperopt class : -Model hyper-optimisation with random search

-
    -
  • Hyperopt (class) : Model hyper-optimisation with random search
    • -
-
-
-class MLBG59.Modelisation.HyperOpt.HyperOpt(classifier='RF', grid_param=None, n_param_comb=10, bagging=False, bagging_param={'n_sample': 5, 'pos_sample_size': 1.0, 'replace': False}, comb_seed=None)[source]
-

Model hyper-optimisation with random search :

-
    -
  • From a hyper-parameters grid, creates random HPs combinations
    • -
  • train a model for each combination
    • -
  • apply the model
    • -
- --- - - - -
Parameters:
    -
  • classifier (string (Default : 'RF')) – classifier for modelisation
    • -
  • grid_param (dict (Default : Default_RF_grid_param)) – HP grid
    • -
  • n_param_comb (int (Default : 10)) – number of HP combinations
    • -
  • bagging (Boolean (Default = False)) – use bagging method
    • -
  • bagging_param (n-uple) – bagging parameters (Default : default_bagging_param (Bagging module))
    • -
  • (created with fit method) (train_model_dict) – {model_index : {‘HP’, ‘probas’, ‘model’, ‘features_importance’, ‘train_metrics’}
    • -
  • bagging_object (Bagging) – bagging object
    • -
  • comb_seed (int) – seed for randomized HP combinations
    • -
-
-
-
-fit(df_train, target, verbose=False)[source]
-

Fit a model for each HP combination

- --- - - - - - - - -
Parameters:
    -
  • df_train (DataFrame) – Training dataset
    • -
  • target (string) – Target name
    • -
  • verbose (boolean (Default False)) – Get logging information
    • -
-
Returns:

self.train_model_dict (created with fit method) – {model_index : {‘HP’, ‘probas’, ‘model’, ‘features_importance’, ‘train_metrics’}

-
Return type:

dict

-
-
- -
-
-get_best_model(d_model_info, metric='F1', delta_auc_th=0.03, verbose=False)[source]
-

Identify valid models according to delta auc (test/train). -Get the best model in respect of a selected metric among valid model

- --- - - - - - -
Parameters:
    -
  • d_model_info (dict) – {model_index : {‘HP’, ‘probas’, ‘model’, ‘features_importance’, ‘train_metrics’, ‘metrics’, ‘output’}
    • -
  • metric (string (default = F1-score)) – Metric used to get the best model
    • -
  • delta_auc_th (float) – Threshold for valid models : abs(auc(train) - auc(test))
    • -
  • verbose (boolean (Default False)) – Get logging information
    • -
-
Returns:

    -
  • int – Best model index
    • -
  • list – Valid model indexes
    • -
-

-
-
- -
-
-get_params()[source]
-

Return Hyperopt object parameters

- --- - - - - - -
Returns:{param : value}
Return type:dict
-
- -
-
-model_res_to_df(d_model_infos, sort_metric='F1')[source]
-

Store models summary in DataFrame

- --- - - - - - - - -
Parameters:
    -
  • d_model_info (dict) – {model_index : {‘HP’, ‘probas’, ‘model’, ‘features_importance’, ‘train_metrics’, ‘metrics’, ‘output’}
    • -
  • sort_metric (string (default = 'F1')) – metric to sort models (descendant)
    • -
-
Returns:

model infos and metrics

-
Return type:

DataFrame

-
-
- -
-
-predict(df, target, delta_auc, verbose=False)[source]
-

Apply the models

- --- - - - - - - - -
Parameters:
    -
  • df (DataFrame) – Dataset to apply the models
    • -
  • target (string) – Target name
    • -
  • delta_auc_th (float) – Threshold for valid models : abs(auc(train) - auc(test))
    • -
  • verbose (boolean (Default False)) – Get logging information
    • -
-
Returns:

{model_index : {‘HP’, ‘probas’, ‘model’, ‘features_importance’, ‘train_metrics’, ‘metrics’, ‘output’}

-
Return type:

dict

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When it is not found, a full rebuild will be done. -config: b8d7bea55dfdd04a2c6676e410b9a3ea -tags: 645f666f9bcd5a90fca523b33c5a78b7 diff --git a/docs/_build/html/Features.html b/docs/_build/html/Features.html deleted file mode 100644 index bc1b392..0000000 --- a/docs/_build/html/Features.html +++ /dev/null @@ -1,1636 +0,0 @@ - - - - - - - - - - Start — AutoMxL 1.0.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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Start

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Load

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Data_handling import functions :

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  • get_delimiter : identify delimiter for a .csv/.txt file

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  • load_data : import dataset file into dataframe

    • -
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-AutoMxL.Start.Load.get_delimiter(file)[source]
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Identify the delimiter for a csv/txt file

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Parameters
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file (string) – Path and name of the file (Ex : “data/file.csv”)

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Returns
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identified delimiter

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Return type
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string

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-AutoMxL.Start.Load.import_data(file, index_col=None, verbose=False)[source]
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Import dataset as a DataFrame (identify delimiter for txt and csv files)

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Available files : .txt, .csv, .xlsx, .xls files

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Parameters
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    -

  • file (string) – Path and name of the file (Ex : “data/file.csv”) -If file is .csv, automatically identify delimiter

    • -

  • index_col (int, str, sequence of int / str, or False (Default None)) – Column(s) to use as the row labels of the DataFrame, either given as string name or column index. -If a sequence of int / str is given, a MultiIndex is used.

    • -

  • verbose (boolean (Default False)) – Get logging information

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Returns
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imported dataset

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Return type
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DataFrame

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Encode_Target

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Target encoding functions :

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  • category_to_target : create a target variable (1/0) from a selected category

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  • range_to_target : create a target variable (1/0) from a selected range

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-AutoMxL.Start.Encode_Target.category_to_target(df, var, cat)[source]
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Create a target variable (1/0) from a selected category

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Parameters
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  • df (DataFrame) – input dataset

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  • var (string) – variable containing the target category

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  • cat (string) – target category

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Returns
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  • DataFrame (modified dataset)

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  • string (new target name (var+’_’+cat))

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-AutoMxL.Start.Encode_Target.range_to_target(df, var, min=None, max=None, verbose=False)[source]
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Create a target variable (1/0) from a selected range

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Parameters
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  • df (DataFrame) – input dataset

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  • var (string) – variable containing the target range

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  • min (float) – lower limit. -If None, no min

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  • max (float) – upper limit. -If None, no max

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  • verbose (boolean (Default False)) – Get logging information

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Returns
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  • DataFrame (modified dataset)

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  • string (new target name (var+’_’+lower+’_’+upper))

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Explore

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Explore

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Global dataset information functions :

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  • explore (func): Identify variables types and gives global information about the dataset (NA, low variance features)

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  • low variance features (func): identify features with low variance

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  • get_features_type (func): get all features per type

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-AutoMxL.Explore.Explore.explore(df, verbose=False)[source]
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Identify variables types and gives global information about the dataset

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    Variables type :
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    • date

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    • identifier

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    • verbatim

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    • boolean

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    • categorical

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    • numerical

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  • variables containing NA values

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  • low variance and unique values variables

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See get_features_type function doc for type identification heuristics

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Parameters
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  • df (DataFrame) – input dataset

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  • verbose (boolean (Default False)) – Get logging information

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Returns
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{x : variables names list }

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    -

  • date : date features

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  • identifier : identifier features

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  • verbatim : verbatim features

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  • boolean : boolean features

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  • categorical : categorical features

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  • numerical : numerical features

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  • categorical : categorical features

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  • date : date features

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  • NA : features which contains NA values

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  • low_variance : list of the features with low variance

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Return type
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dict

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-AutoMxL.Explore.Explore.get_features_type(df, l_var=None, th=0.95)[source]
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Get all features per type :

-
    -

  • date : try to apply to_datetime

    • -
  • -
    identifier :
      -

    • #(unique values)/#(total values) > threshold (default 0.95)

      • -

    • AND length is the same for all values (for non NA)

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    verbatim :
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    • #(unique values)/#(total values) >= threshold (default 0.95)

      • -

    • AND length is NOT the same for all values (for non NA)

      • -
    -
    -
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    • -

  • boolean : #(distinct values) = 2

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  • -
    categorical :
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    • not a date

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    • #(unique values)/#(total values) < threshold (default 0.95)

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    • AND #(uniques values)>2

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    • AND for num values #(unique values)<30

      • -
    -
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  • numerical : others

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Parameters
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  • df (DataFrame) – input dataset

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  • l_var (list (Default : None)) – variable names

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  • th (float (Default : 0.95)) – threshold used to identify identifiers/verbatims variables

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Returns
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{ type : variables name list}

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Return type
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dict

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-AutoMxL.Explore.Explore.low_variance_features(df, var_list=None, threshold=0, rescale=True, verbose=False)[source]
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Identify numerical features with low variance : (< threshold). -Possible to rescale feature before computing.

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Parameters
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  • df (DataFrame) – input DataFrame

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  • var_list (list (default : None)) – names of the variables to check variance -if None : all the numerical features

    • -

  • threshold (float (default : 0)) – variance threshold

    • -

  • rescale (bool (default : true)) – enable MinMaxScaler before computing variance

    • -
-
-
-
-
verboseboolean (Default False)

Get logging information

-
-
-
-
Returns
-

Names of the variables with low variance

-
-
Return type
-

list

-
-
-
- -
-
-

Features_Type

-

Variables type identification function

-
    -

  • features_from_type (func): get all features for a selected type

    • -

  • is_date (func): test if a variable is a date

    • -

  • is_identifier (func): test if a variable is an identifier

    • -

  • is_verbatim (func): test if a variable is a verbatim

    • -

  • is_boolean (func): test if a variable is a boolean

    • -

  • is_categorical (func): test if a variable is a categorical one (with more than 2 categories)

    • -
-
-
-AutoMxL.Explore.Features_Type.features_from_type(df, typ, l_var=None, th=0.95)[source]
-

Get features of a selected type :

-
    -

  • date : try to apply to_datetime

    • -
  • -
    identifier :
      -

    • #(unique values)/#(total values) > threshold (default 0.95)

      • -

    • AND length is the same for all values (for non NA)

      • -
    -
    -
    -
    • -
  • -
    verbatim :
      -

    • #(unique values)/#(total values) >= threshold (default 0.95)

      • -

    • AND length is NOT the same for all values (for non NA)

      • -
    -
    -
    -
    • -

  • boolean : #(distinct values) = 2

    • -
  • -
    categorical :
      -

    • not a date

      • -

    • #(unique values)/#(total values) < threshold (default 0.95)

      • -

    • AND #(uniques values)>2

      • -

    • AND for num values #(unique values)<30

      • -
    -
    -
    -
    • -
-
-
Parameters
-
    -

  • df (DataFrame) – input dataset

    • -

  • typ (string) –

    selected type to get features:

    -
      -

    • ’date’

      • -

    • ’identifier’

      • -

    • ’verbatim’

      • -

    • ’boolean’

      • -

    • categorical

      • -
    -

    • -

  • l_var (list (Default : None)) – variables names. If None, all dataset columns

    • -

  • th (float (Default : 0.95)) – threshold used to identify identifiers/verbatims variables

    • -
-
-
Returns
-

identified variables names

-
-
Return type
-

list

-
-
-
- -
-
-AutoMxL.Explore.Features_Type.is_boolean(df, col)[source]
-

Test if a variable is a boolean.

-
    -

  • #(distinct values) = 2

    • -
-
-
Parameters
-
    -

  • df (DataFrame) – input dataset

    • -

  • col (string) – variable name

    • -
-
-
Returns
-

res – test result

-
-
Return type
-

boolean

-
-
-
- -
-
-AutoMxL.Explore.Features_Type.is_categorical(df, col, th=0.95)[source]
-

Test if a variable is a categorical one (with more than 2 categories).

-
    -

  • not a date

    • -

  • #(unique values)/#(total values) < threshold (default 0.95

    • -

  • AND #(uniques values)>2

    • -

  • AND for num values #(unique values)<30

    • -
-
-
Parameters
-
    -

  • df (DataFrame) – input dataset

    • -

  • col (string) – variable name

    • -

  • th (float (Default : 0.95)) – threshold

    • -
-
-
Returns
-

res – test result

-
-
Return type
-

boolean

-
-
-
- -
-
-AutoMxL.Explore.Features_Type.is_date(df, col)[source]
-

Test if a variable is a date.

-

Method : try to apply to_datetime

-
-
Parameters
-
    -

  • df (DataFrame) – input dataset

    • -

  • col (string) – variable name

    • -
-
-
Returns
-

res – test result

-
-
Return type
-

boolean

-
-
-
- -
-
-AutoMxL.Explore.Features_Type.is_identifier(df, col, th=0.95)[source]
-

Test if a variable is an identifier.

-
    -

  • #(unique values)/#(total values) > threshold (default 0.95)

    • -

  • AND length is the same for all values (for non NA)

    • -

  • AND not date

    • -
-
-
Parameters
-
    -

  • df (DataFrame) – input dataset

    • -

  • col (string) – variable name

    • -

  • th (float (Default : 0.95)) – threshold rate

    • -
-
-
Returns
-

res – test result

-
-
Return type
-

boolean

-
-
-
- -
-
-AutoMxL.Explore.Features_Type.is_verbatim(df, col, th=0.95)[source]
-

Test if a variable is a verbatim.

-
    -

  • #(unique values)/#(total values) >= threshold (default 0.95)

    • -

  • AND length is NOT the same for all values (for non NA)

    • -
-
-
Parameters
-
    -

  • df (DataFrame) – input dataset

    • -

  • col (string) – variable name

    • -

  • th (float (Default : 0.95)) – threshold rate

    • -
-
-
Returns
-

res – test result

-
-
Return type
-

boolean

-
-
-
- -
-
-
-

Preprocessing

-
-

Missing_Values

-

Missing values handling functions :

-
    -

  • NAEncoder (class): encoder that replaces missing values

    • -

  • fill_numerical (func): replace missing values for numerical features

    • -

  • fill_categorical (func): replace missing values for categorical features

    • -

  • get_NA_features (func): get features containing NA values

    • -
-
-
-class AutoMxL.Preprocessing.Missing_Values.NAEncoder(replace_num_with='median', replace_cat_with='NR', track_num_NA=True)[source]
-

Missing values filling

-

Available methods to replace missing values

-
    -

  • num : metdian/mean/zero

    • -

  • cat : ‘NR’

    • -
-
-
Parameters
-
    -

  • replace_num_with (string) – method used to replace numerical missing values

    • -

  • replace_cat_with (string) – method used to replace categorical missing values

    • -
-
-
-
-
-fit(df, l_var, verbose=False)[source]
-

fit encoder

-
-
Parameters
-
    -

  • df (DataFrame) – input dataset

    • -

  • l_var (list) – features to encode. -If None, all features

    • -

  • verbose (boolean (Default False)) – Get logging information

    • -
-
-
-
- -
-
-fit_transform(df, l_var=None, verbose=False)[source]
-

fit and transform dataset with encoder

-
-
Parameters
-
    -

  • df (DataFrame) – input dataset

    • -

  • l_var (list) – features to encode. -If None, all features identified as dates (see Features_Type module)

    • -

  • verbose (boolean (Default False)) – Get logging information

    • -
-
-
-
- -
-
-transform(df, verbose=False)[source]
-

transform dataset categorical features using the encoder. -Can be done only if encoder has been fitted

-
-
Parameters
-
    -

  • df (DataFrame) – dataset to transform

    • -

  • verbose (boolean (Default False)) – Get logging information

    • -
-
-
-
- -
- -
-
-AutoMxL.Preprocessing.Missing_Values.fill_categorical(df, l_var=None, method='NR', verbose=False)[source]
-

Fill missing values for selected/all categorical features.

-
-
Parameters
-
    -

  • df (DataFrame) – Input dataset

    • -

  • l_var (list (Default : None)) – list of the features to fill. -If None, contains all the categorical features

    • -

  • method (string (Default : 'NR')) –

    Method used to fill the NA values :

    -
      -

    • NR : replace NA with ‘NR’

      • -
    -

    • -

  • verbose (boolean (Default False)) – Get logging information

    • -
-
-
Returns
-

Modified dataset

-
-
Return type
-

DataFrame

-
-
-
- -
-
-AutoMxL.Preprocessing.Missing_Values.fill_numerical(df, l_var=None, method='median', track_num_NA=True, verbose=False)[source]
-

Fill missing values for selected/all numerical features. -top_var_NA parameter allows to create a variable to keep track of missing values.

-

Available methods : replace with zero, median or mean (Default = median)

-
-
Parameters
-
    -

  • df (DataFrame) – Input dataset

    • -

  • l_var (list (Default : None)) – names of the features to fill. -If None, all the numerical features

    • -

  • method (string (Default : 'median')) –

    Method used to fill the NA values :

    -
      -

    • zero : replace with zero

      • -

    • median : replace with median

      • -

    • mean : replace with mean

      • -
    -

    • -

  • track_num_NA (boolean (Defaut : True)) – If True, create a boolean column to keep track of missing values

    • -

  • verbose (boolean (Default False)) – Get logging information

    • -
-
-
Returns
-

Modified dataset

-
-
Return type
-

DataFrame

-
-
-
- -
-
-AutoMxL.Preprocessing.Missing_Values.get_NA_features(df)[source]
-

identify features containing NA values

-
-
Parameters
-

df (DataFrame) – input dataset

-
-
Returns
-

list

-
-
Return type
-

features containing missing values

-
-
-
- -
-
-

Categorical Data

-

Categorical features processing

-
    -

  • CategoricalEncoder (class) : Encode categorical features

    • -

  • dummy_all_var (func) : get one hot encoded vector for each category of a categorical features list

    • -

  • get_embedded_cat (func) : get embedding representation with NN

    • -

  • mca (func) : to do

    • -
-
-
-class AutoMxL.Preprocessing.Categorical.CategoricalEncoder(method='deep_encoder')[source]
-

Encode categorical features

-

Available encoding methods :

-
    -

  • one hot encoding

    • -

  • deep_encoder : Build and train a Neural Network for the creation of embeddings for categorical variables.

    • -
-

(https://www.fast.ai/2018/04/29/categorical-embeddings/)

-

Default NN model parameters are stored in param_config.py file

-
-
Parameters
-

method (string (Default : deep_encoder)) – method used to get categorical encoding -Available methods : “one_hot”, “deep_encoder”

-
-
-
-
-fit(df, l_var=None, target=None, verbose=False)[source]
-

Fit encoder on dataset following method

-
-
Parameters
-
    -

  • df (DataFrame) – input dataset

    • -

  • l_var (list (Default None)) – names of the variables to encode. -If None, all the categorical and boolean features

    • -

  • target (string (Default None)) – name of the target for deep_encoder method

    • -

  • verbose (boolean (Default False)) – Get logging information

    • -
-
-
-
- -
-
-fit_transform(df, l_var=None, target=None, verbose=False)[source]
-

fit and transform dataset categorical features

-
-
Parameters
-
    -

  • df (DataFrame) – input dataset

    • -

  • l_var (list (Default None)) – names of the variables to encode. -If None, all the categorical and boolean features

    • -

  • target (string (Default None)) – name of the target for deep_encoder method

    • -

  • verbose (boolean (Default False)) – Get logging information

    • -
-
-
Returns
-

DataFrame

-
-
Return type
-

modified dataset

-
-
-
- -
-
-transform(df, verbose=False)[source]
-

transform dataset categorical features using the encoder. -Can be done only if encoder has been fitted

-
-
Parameters
-
    -

  • df (DataFrame) – dataset to transform

    • -

  • verbose (boolean (Default False)) – Get logging information

    • -
-
-
Returns
-

DataFrame

-
-
Return type
-

modified dataset

-
-
-
- -
- -
-
-AutoMxL.Preprocessing.Categorical.dummy_all_var(df, var_list=None, prefix_list=None, keep=False, verbose=False)[source]
-

Get one hot encoded vector for selected/all categorical features

-
-
Parameters
-
    -

  • df (DatraFrame) – Input dataset

    • -

  • var_list (list (Default : None)) – Names of the features to dummify -If None, all the num features

    • -

  • prefix_list (list (default : None)) – Prefix to add before new features name (prefix+’_’+cat). -If None, prefix=variable name

    • -

  • keep (boolean (Default = False)) – If True, delete the original feature

    • -

  • verbose (boolean (Default False)) – Get logging information

    • -
-
-
Returns
-

Modified dataset

-
-
Return type
-

DataFrame

-
-
-
- -
-
-AutoMxL.Preprocessing.Categorical.get_embedded_cat(df, var_list, target, batchsize, n_epochs, lr, verbose=False)[source]
-

Get embedded representation for categorical features using NN encoder

-
-
Parameters
-
    -

  • df (DataFrame) – input Dataset

    • -

  • var_list (list of strings) – features names

    • -

  • target (string) – target name

    • -

  • batchsize (int) – batch size for encoder training

    • -

  • n_epochs (int) – number of epoch for encoder training

    • -

  • lr (float) – encoder learning rate

    • -

  • verbose (boolean (Default False)) – Get logging information

    • -
-
-
Returns
-

DataFrame

-
-
Return type
-

modified dataset

-
-
-
- -
-
-

Date Data

-

Date Features processing functions:

-
    -

  • DateEncoder (class) : encode date features

    • -

  • all_to_date (func): detect dates from num/cat features and transform them to datetime format.

    • -

  • date_to_anc (func): transform datetime features to timedelta according to a ref date

    • -
-
-
-class AutoMxL.Preprocessing.Date.DateEncoder(method='timedelta', date_ref=None)[source]
-

Encode categorical features

-

Available methods :

-
    -

  • timedelta : compute time between date feature and parameter date_ref

    • -
-
-
Parameters
-
    -

  • method (string (Default : timedelta)) – method used to encode dates -Available methods : “timedelta”

    • -

  • date_ref (string '%d/%m/%y' (Default : None)) – Date to compute timedelta. -If None, today date

    • -
-
-
-
-
-fit(df, l_var=None, verbose=False)[source]
-

fit encoder

-
-
Parameters
-
    -

  • df (DataFrame) – input dataset

    • -

  • l_var (list) – features to encode. -If None, contains all features identified as dates (see Features_Type module)

    • -

  • verbose (boolean (Default False)) – Get logging information

    • -
-
-
-
- -
-
-fit_transform(df, l_var=None, verbose=False)[source]
-

fit and transform dataset with encoder

-
-
Parameters
-
    -

  • df (DataFrame) – input dataset

    • -

  • l_var (list) – features to encode. -If None, all features identified as dates (see Features_Type module)

    • -

  • verbose (boolean (Default False)) – Get logging information

    • -
-
-
-
- -
-
-transform(df, verbose=False)[source]
-

transform dataset date features using the encoder. -Can be done only if encoder has been fitted

-
-
Parameters
-
    -

  • df (DataFrame) – dataset to transform

    • -

  • verbose (boolean (Default False)) – Get logging information

    • -
-
-
-
- -
- -
-
-AutoMxL.Preprocessing.Date.all_to_date(df, l_var=None, verbose=False)[source]
-

Detect dates from selected/all features and transform them to datetime format.

-
-
Parameters
-
    -

  • df (DataFrame) – Input dataset

    • -

  • l_var (list (Default : None)) – Names of the features -If None, all the features

    • -

  • verbose (boolean (Default False)) – Get logging information

    • -
-
-
Returns
-

Modified dataset

-
-
Return type
-

DataFrame

-
-
-
- -
-
-AutoMxL.Preprocessing.Date.date_to_anc(df, l_var=None, date_ref=None, verbose=False)[source]
-

Transform selected/all datetime features to timedelta according to a ref date

-
-
Parameters
-
    -

  • df (DataFrame) – Input dataset

    • -

  • l_var (list (Default : None)) – List of the features to analyze. -If None, contains all the datetime features

    • -

  • date_ref (string '%d/%m/%y' (Default : None)) – Date to compute timedelta. -If None, today date

    • -

  • verbose (boolean (Default False)) – Get logging information

    • -
-
-
Returns
-

    -

  • DataFrame – Modified dataset

    • -

  • list – New timedelta features names

    • -
-

-
-
-
- -
-
-

Process Outliers

-

Outliers handling functions

-
    -

  • OutliersEncoding (class) : identify and replace outliers

    • -

  • get_cat_outliers (funct): identify categorical features containing outliers

    • -

  • get_num_outliers (func): identify numerical features containing outliers

    • -

  • replace_category (func): replace categories of a categorical variable

    • -

  • replace_extreme_values (func): replace extreme values (oh!)

    • -
-
-
-class AutoMxL.Preprocessing.Outliers.OutliersEncoder(cat_threshold=0.02, num_xstd=4)[source]
-

Identify et replace outliers for categorical dang numerical features

-
    -

  • num : x outlier <=> abs(x - mean) > xstd * var

    • -

  • cat : x outlier category <=> with frequency <x% (Default 5%)

    • -
-
-
Parameters
-
    -

  • cat_threshold (float (default 0.02)) – Minimum modality frequency

    • -

  • num_xstd (int (Default : 3)) – Variance gap coef

    • -
-
-
-
-
-fit(df, l_var, verbose=False)[source]
-

Fit encoder

-
-
Parameters
-
    -

  • df (DataFrame) – input dataset

    • -

  • l_var (list) – features to encode. -If None, all features

    • -

  • verbose (boolean (Default False)) – Get logging information

    • -
-
-
-
- -
-
-fit_transform(df, l_var=None, verbose=False)[source]
-

Fit and transform dataset with encoder

-
-
Parameters
-
    -

  • df (DataFrame) – input dataset

    • -

  • l_var (list) – features to encode. -If None, all features identified as dates (see Features_Type module)

    • -

  • verbose (boolean (Default False)) – Get logging information

    • -
-
-
-
- -
-
-transform(df, verbose=False)[source]
-

Transform dataset features using the encoder. -Can be done only if encoder has been fitted

-
-
Parameters
-
    -

  • df (DataFrame) – dataset to transform

    • -

  • verbose (boolean (Default False)) – Get logging information

    • -
-
-
-
- -
- -
-
-AutoMxL.Preprocessing.Outliers.get_cat_outliers(df, l_var=None, threshold=0.05, verbose=False)[source]
-

Outliers detection for selected/all categorical features.

-

Method : Modalities with frequency <x% (Default 5%)

-
-
Parameters
-
    -

  • df (DataFrame) – Input dataset

    • -

  • l_var (list (Default : None)) – Names of the features -If None, all the categorical features

    • -

  • threshold (float (Default : 0.05)) – Minimum modality frequency

    • -

  • verbose (boolean (Default False)) – Get logging information

    • -
-
-
Returns
-

{variable : list of categories considered as outliers}

-
-
Return type
-

dict

-
-
-
- -
-
-AutoMxL.Preprocessing.Outliers.get_num_outliers(df, l_var=None, xstd=3, verbose=False)[source]
-

Outliers detection for selected/all numerical features.

-

Method : x outlier <=> abs(x - mean) > xstd * var

-
-
Parameters
-
    -

  • df (DataFrame) – Input dataset

    • -

  • l_var (list (Default : None)) – Names of the features -If None, all the num features

    • -

  • xstd (int (Default : 3)) – Variance gap coef

    • -

  • verbose (boolean (Default False)) – Get logging information

    • -
-
-
Returns
-

{variable : [lower_limit, upper_limit]}

-
-
Return type
-

dict

-
-
-
- -
-
-AutoMxL.Preprocessing.Outliers.replace_category(df, var, categories, replace_with='outliers', verbose=False)[source]
-

Replace categories of a categorical variable

-
-
Parameters
-
    -

  • df (DataFrame) – Input dataset

    • -

  • var (string) – variable to modify

    • -

  • categories (list(string)) – categories to replace

    • -

  • replace_with (string (Default : 'outliers')) – word to replace categories with

    • -

  • verbose (boolean (Default False)) – Get logging information

    • -
-
-
Returns
-

Modified dataset

-
-
Return type
-

DataFrame

-
-
-
- -
-
-AutoMxL.Preprocessing.Outliers.replace_extreme_values(df, var, lower_th=None, upper_th=None, verbose=False)[source]
-

Replace extrem values : > upper threshold or < lower threshold

-
-
Parameters
-
    -

  • df (DataFrame) – Input dataset

    • -

  • var (string) – variable to modify

    • -

  • lower_th (int/float (Default=None)) – lower threshold

    • -

  • upper_th (int/float (Default=None)) – upper threshold

    • -

  • verbose (boolean (Default False)) – Get logging information

    • -
-
-
Returns
-

Modified dataset

-
-
Return type
-

DataFrame

-
-
-
- -
-
-
-

Features Selection

-

Features selection

-
    -

  • select_features (func) : features selection following method

    • -
-
-
-class AutoMxL.Select_Features.Select_Features.FeatSelector(method='pca')[source]
-

features selection following method

-
    -

  • pca : use pca to reduce dataset dimensions

    • -

  • no_rescale_pca : use pca without rescaling data

    • -
-
-
Parameters
-

method (string (Default pca)) – method use to select features

-
-
-
-
-fit(df, l_var=None, verbose=False)[source]
-

fit selector

-
-
Parameters
-
    -

  • df (DataFrame) – input dataset

    • -

  • l_var (list) – features to encode. -If None, all features identified as numerical

    • -

  • verbose (boolean (Default False)) – Get logging information

    • -
-
-
-
- -
-
-fit_transform(df, l_var, verbose=False)[source]
-

fit and apply features selection

-
-
Parameters
-
    -

  • df (DataFrame) – input dataset

    • -

  • l_var (list) – features to encode. -If None, all features identified as dates (see Features_Type module)

    • -

  • verbose (boolean (Default False)) – Get logging information

    • -
-
-
Returns
-

DataFrame

-
-
Return type
-

modified dataset

-
-
-
- -
-
-transform(df, verbose=False)[source]
-

apply features selection on a dataset

-
-
Parameters
-
    -

  • df (DataFrame) – dataset to transform

    • -

  • verbose (boolean (Default False)) – Get logging information

    • -
-
-
Returns
-

DataFrame

-
-
Return type
-

modified dataset

-
-
-
- -
- -
-
-AutoMxL.Select_Features.Select_Features.select_features(df, target, method='pca', verbose=False)[source]
-

features selection following method

-
    -

  • pca : use pca to reduce dataset dimensions

    • -

  • no_rescale_pca : use pca without rescaling data

    • -
-
-
Parameters
-
    -

  • df (DataFrame) – input dataset containing features

    • -

  • target (string) – target name

    • -

  • method (string (Default pca)) – method use to select features

    • -

  • verbose (boolean (Default False)) – Get logging information

    • -
-
-
Returns
-

modified dataset

-
-
Return type
-

DataFrame

-
-
-
- -
-
-

Modelisation

-
-

Bagging

-

Bagging algorithm class. Methods :

-
    -

  • Bagging (class) : generate new training more balanced and train model for each

    • -

  • Bagging_sample (func) : generate bagging sample

    • -
-
-
-class AutoMxL.Modelisation.Bagging.Bagging(clf=sklearn.ensemble.RandomForestClassifier, n_sample=5, pos_sample_size=1.0, replace=True)[source]
-

Meta-algo designed to improve the stability and accuracy of ML classif/regression algos -or to face an “imbalanced target distribution” issue.

-

Bagging generates m new training sets more balanced. Then, a model is fitted on each -sample and outputs are combined by averaging (for regression) or voting (for classification).

-

Available classifiers : Random Forest and XGBOOST

-
-
Parameters
-
    -

  • clf (Model fitted on samples (Default : RandomForestClassifier(n_estimators=100, max_leaf_nodes=100)) – Model fitted on the samples

    • -

  • n_sample (int (Default : 5)) – number a samples

    • -

  • pos_sample_size (int/float (Default : 1.0)) –

    Number/rate of target=1 observations in each sample (filled with 3 times more target=0 )

    -
      -

    • if int : number of target=1

      • -

    • if float : rate of total target=1

      • -
    -

    • -

  • replace (Boolean (Default : False)) – Enable sampling with replacement

    • -

  • list_model (list (Default : None)) – Fitted models (created with fit method)

    • -
-
-
-
-
-bag_feature_importance(X)[source]
-

Get features importance of the model by averaging importance of models fitted on the samples

-
-
Parameters
-

X (DataFrame) – Input Dataset

-
-
Returns
-

{feature : importance}

-
-
Return type
-

dict

-
-
-
- -
-
-fit(df_train, target)[source]
-

Create bagging samples from a DataFrame and fit the model (self.clf) on each sample

-
-
Parameters
-
    -

  • df_train (DataFrame) – Training dataset

    • -

  • target (String) – Target name

    • -
-
-
Returns
-

self.list_model – Fitted models

-
-
Return type
-

list

-
-
-
- -
-
-get_params()[source]
-

Get bagging object parameters

-
-
Returns
-

{param : value}

-
-
Return type
-

dict

-
-
-
- -
-
-predict(df)[source]
-

Apply models fitted on sample to a dataset. -Combine models by averaging the outputs (for regression) or voting (for classification)

-
-
Parameters
-

df (DataFrame) – Dataset to apply the model

-
-
Returns
-

    -

  • numpy.ndarray (float) – Averaged classification probabilities

    • -

  • numpy.ndarray (int) – Predictions for each observation

    • -
-

-
-
-
- -
- -
-
-AutoMxL.Modelisation.Bagging.create_sample(df, target, pos_target_nb, replace=False)[source]
-

Generate a DataFrame sample with selected number of target=1

-
-
Parameters
-
    -

  • df (DataFrame) – Input dataset

    • -

  • target (String) – Target name

    • -

  • pos_target_nb (int) – Number of target=1 observations in the sample

    • -

  • replace (Boolean (défaut : False)) – If True, create samples with replacement

    • -
-
-
Returns
-

sample dataset

-
-
Return type
-

DataFrame

-
-
-
- -
-
-

Hyperoptimisation

-

Hyperopt class : -Model hyper-optimisation with random search

-
    -

  • Hyperopt (class) : Model hyper-optimisation with random search

    • -
-
-
-class AutoMxL.Modelisation.HyperOpt.HyperOpt(classifier='RF', grid_param=None, n_param_comb=10, bagging=False, bagging_param={'n_sample': 5, 'pos_sample_size': 1.0, 'replace': False}, comb_seed=None)[source]
-

Model hyper-optimisation with random search :

-
    -

  • From a hyper-parameters grid, creates random HPs combinations

    • -

  • train a model for each combination

    • -

  • apply the model

    • -
-
-
Parameters
-
    -

  • classifier (string (Default : 'RF')) – classifier for modelisation

    • -

  • grid_param (dict (Default : Default_RF_grid_param)) – HP grid

    • -

  • n_param_comb (int (Default : 10)) – number of HP combinations

    • -

  • bagging (Boolean (Default = False)) – use bagging method

    • -

  • bagging_param (n-uple) – bagging parameters (Default : default_bagging_param (Bagging module))

    • -

  • (created with fit method) (train_model_dict) – {model_index : {‘HP’, ‘probas’, ‘model’, ‘features_importance’, ‘train_metrics’}

    • -

  • bagging_object (Bagging) – bagging object

    • -

  • comb_seed (int) – seed for randomized HP combinations

    • -
-
-
-
-
-fit(df_train, target, verbose=False)[source]
-

Fit a model for each HP combination

-
-
Parameters
-
    -

  • df_train (DataFrame) – Training dataset

    • -

  • target (string) – Target name

    • -

  • verbose (boolean (Default False)) – Get logging information

    • -
-
-
Returns
-

self.train_model_dict (created with fit method) – {model_index : {‘HP’, ‘probas’, ‘model’, ‘features_importance’, ‘train_metrics’}

-
-
Return type
-

dict

-
-
-
- -
-
-get_best_model(d_model_info, metric='F1', delta_auc_th=0.03, verbose=False)[source]
-

Identify valid models according to delta auc (test/train). -Get the best model in respect of a selected metric among valid model

-
-
Parameters
-
    -

  • d_model_info (dict) – {model_index : {‘HP’, ‘probas’, ‘model’, ‘features_importance’, ‘train_metrics’, ‘metrics’, ‘output’}

    • -

  • metric (string (default = F1-score)) – Metric used to get the best model

    • -

  • delta_auc_th (float) – Threshold for valid models : abs(auc(train) - auc(test))

    • -

  • verbose (boolean (Default False)) – Get logging information

    • -
-
-
Returns
-

    -

  • int – Best model index

    • -

  • list – Valid model indexes

    • -
-

-
-
-
- -
-
-get_params()[source]
-

Return Hyperopt object parameters

-
-
Returns
-

{param : value}

-
-
Return type
-

dict

-
-
-
- -
-
-model_res_to_df(d_model_infos, sort_metric='F1')[source]
-

Store models summary in DataFrame

-
-
Parameters
-
    -

  • d_model_info (dict) – {model_index : {‘HP’, ‘probas’, ‘model’, ‘features_importance’, ‘train_metrics’, ‘metrics’, ‘output’}

    • -

  • sort_metric (string (default = 'F1')) – metric to sort models (descendant)

    • -
-
-
Returns
-

model infos and metrics

-
-
Return type
-

DataFrame

-
-
-
- -
-
-predict(df, target, delta_auc, verbose=False)[source]
-

Apply the models

-
-
Parameters
-
    -

  • df (DataFrame) – Dataset to apply the models

    • -

  • target (string) – Target name

    • -

  • delta_auc_th (float) – Threshold for valid models : abs(auc(train) - auc(test))

    • -

  • verbose (boolean (Default False)) – Get logging information

    • -
-
-
Returns
-

{model_index : {‘HP’, ‘probas’, ‘model’, ‘features_importance’, ‘train_metrics’, ‘metrics’, ‘output’}

-
-
Return type
-

dict

-
-
-
- -
- -
-
- - -
- -
- - -
-
- -
- -
- - - - - - - - - - - \ No newline at end of file diff --git a/docs/_build/html/_modules/AutoMxL/Audit/Audit_Dataset.html b/docs/_build/html/_modules/AutoMxL/Audit/Audit_Dataset.html deleted file mode 100644 index ffe9fc1..0000000 --- a/docs/_build/html/_modules/AutoMxL/Audit/Audit_Dataset.html +++ /dev/null @@ -1,386 +0,0 @@ - - - - - - - - - - - MLBG59.Audit.Audit_Dataset — MLBG59 1.0.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
- - - -
- - - - - -
- -
- - - - - - - - - - - - - - - - - -
- -
    - -
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    • - -
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    • - -
  • MLBG59.Audit.Audit_Dataset
    • - - -
  • - -
    • - -
- - -
-
-
-
- -

Source code for MLBG59.Audit.Audit_Dataset

-""" Dataset Features analysis :
-
- - audit dataset  : get informations on the data (NA, features type, low variance features, ...)
- - is_date : detect if an object/num feature is a date
- - get_all_dates : identify all dates features in a DataFrame and store their names in a list
- - low variance features : identify all features with a low variance (<threshold) and sotre their name in a list
-"""
-import pandas as pd
-from sklearn.preprocessing import MinMaxScaler
-from MLBG59.Utils.Display import *
-from MLBG59.Utils.Utils import get_type_features
-
-
-
[docs]def audit_dataset(df, verbose=1):
-    """Achieve a short audit of the dataset
-
-    Identify features of each type (num, cat, date), features containing NA and features whose variance is null
-        
-    Parameters
-    ----------
-    df : DataFrame
-        input dataset
-    target : string (Default : None)
-        target name
-    verbose : int (0/1) (Default : 1)
-        get more operations information
-            
-    Returns
-    -------
-    list of the x features names
-        - x = num : numerical features
-        - x = cat : categorical features
-        - x = date : date features
-        - x = NA : features which contains NA values
-        - x = low_var : list of the features with low variance
-    """
-
-    # dataset dimensions
-    if verbose > 0:
-        color_print("Dimensions : ")
-        print("  > row number : ", df.shape[0], "\n  > col number : ", df.shape[1])
-
-    #################
-    # features type #
-    #################
-    # numerical
-    num_columns = df._get_numeric_data().columns.tolist()
-    # date
-    date_columns = get_all_dates(df)
-    # categorical
-    cat_columns = [x for x in df.columns if (x not in num_columns) and (x not in date_columns)]
-
-    if verbose > 0:
-        color_print("Features type identification : ")
-        print("  > cat : " + str(len(cat_columns)) + ' (' + str(round(len(cat_columns) / df.shape[1] * 100)) + '%)',
-              '\n  > num : ' + str(len(num_columns)) + ' (' + str(round(len(num_columns) / df.shape[1] * 100)) + '%)',
-              '\n  > dates: ' + str(len(date_columns)) + ' (' + str(
-                  round(len(date_columns) / df.shape[1] * 100)) + ' %)')
-
-    ######################
-    # NA values analysis
-    ######################
-    df_col = pd.DataFrame(df.columns.values, columns=['variables'])
-    df_col['Nbr NA'] = df.isna().sum().tolist()
-    df_col['Taux NA'] = df_col['Nbr NA'] / df.shape[0]
-    # features containing NA values
-    NA_columns = df_col.loc[df_col['Nbr NA'] > 0].sort_values('Nbr NA', ascending=False).variables.tolist()
-    col_des = df_col['Taux NA'].describe()
-
-    if verbose > 0:
-        color_print(str(len(NA_columns)) + " features containing NA")
-        print('  > Taux NA moyen : ' + str(round(col_des['mean'] * 100, 2)) + '%',
-              '\n  >           min : ' + str(round(col_des['min'] * 100, 2)) + '%',
-              '\n  >           max : ' + str(round(col_des['max'] * 100, 2)) + '%')
-
-    #########################
-    # Low variance features
-    #########################
-    if verbose > 0:
-        color_print('Low variance features')
-    low_var_columns = \
-        low_variance_features(df, var_list=num_columns, threshold=0, rescale=True, verbose=verbose).index.tolist()
-
-    return num_columns, date_columns, cat_columns, NA_columns, low_var_columns

-
-
-"""
--------------------------------------------------------------------------------------------------------------------------
-"""
-
-
-
[docs]def is_date(df, col):
-    """Test if a DataFrame feature is recognized as a date (using to_datetime)
-
-    Parameters
-    ----------
-     df : DataFrame
-        input dataset
-     col : string
-        feature name
-
-    Returns
-    -------
-    res : boolean
-        True if the col is recognized as a date
-    """
-    # if col is datetime type, res = True
-    if df[col].dtype == 'datetime64[ns]':
-        return True
-
-    # if col is object type, try apply to_datetime
-    elif df[col].dtype == 'object':
-        try:
-            df_smpl = df.sample(100).copy()
-            pd.to_datetime(df_smpl[col])
-            return True
-        except ValueError:
-            return False
-        except OverflowError:
-            return False

-
-
-"""
--------------------------------------------------------------------------------------------------------------------------
-"""
-
-
-
[docs]def get_all_dates(df):
-    """Identify all date features of a DataFrame
-    
-    Parameters
-    ----------
-     df : DataFrame
-        input DataFrame
-
-    Returns
-    -------
-     list
-        list of features recognized as date
-    """
-    date_list = list()
-
-    for col in df.columns:
-        # if col is recognized as date
-        if is_date(df, col): date_list.append(col)
-
-    return date_list

-
-
-"""
--------------------------------------------------------------------------------------------------------------------------
-"""
-
-
-
[docs]def low_variance_features(df, var_list=None, threshold=0, rescale=True, verbose=1):
-    """identify  features with low variance (<= threshold)
-
-    Parameters
-    ----------
-     df : DataFrame
-        input DataFrame
-     var_list : list (default : None)
-        features to check variance
-     threshold : float (default : 0
-        variance threshold
-     rescale : bool (default : true)
-        if yes : use MinMaxScaler on data before computing variance
-
-    Returns
-    -------
-    list
-        list of the variable with low variance
-    """
-    # if var_list = None, get all numerical features
-    # else, exclude features from var_list whose type is not numerical
-    var_list = get_type_features(df, 'num', var_list)
-
-    df_bis = df.copy()
-
-    if rescale:
-        scler = MinMaxScaler()
-        df_bis[var_list] = scler.fit_transform(df_bis[var_list].astype('float64'))
-
-    selected_var = df_bis[var_list].var().loc[df_bis.var() <= threshold]
-
-    if verbose > 0:
-        # print('features : ',list(var_list))
-        if rescale: print('  **MinMaxScaler [0,1]')
-        print('  ', str(len(selected_var)) + ' feature(s) with  variance <= threshold (' + str(threshold) + ')')
-
-    return selected_var.sort_values(ascending=True)

-
- -
- -
-
- - -
- -
-

- © Copyright 2020, Maxence LABESSE - -

-
- Built with Sphinx using a theme provided by Read the Docs. - -
- -
-
- -
- -
- - - - - - - - - - - - \ No newline at end of file diff --git a/docs/_build/html/_modules/AutoMxL/Audit/Get_Outliers.html b/docs/_build/html/_modules/AutoMxL/Audit/Get_Outliers.html deleted file mode 100644 index bd7dca9..0000000 --- a/docs/_build/html/_modules/AutoMxL/Audit/Get_Outliers.html +++ /dev/null @@ -1,314 +0,0 @@ - - - - - - - - - - - MLBG59.Audit.Get_Outliers — MLBG59 1.0.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
- - - -
- - - - - -
- -
- - - - - - - - - - - - - - - - - -
- -
    - -
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    • - -
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    • - -
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    • - - -
  • - -
    • - -
- - -
-
-
-
- -

Source code for MLBG59.Audit.Get_Outliers

-""" Outliers detection :
-
- - get_cat_outliers : identify categorical features containing outliers and store their names in a list
- - get_num_outliers : identify numerical features containing outliers and store their names in a list data
-"""
-import pandas as pd
-import numpy as np
-from MLBG59.Utils.Display import *
-from MLBG59.Utils.Utils import get_type_features
-
-
-
[docs]def get_cat_outliers(df, var_list=None, threshold=0.05, verbose=1):
-    """outliers detection for categorical features
-
-    Parameters
-    ----------
-     df : DataFrame
-        Input dataset
-     var_list : list (Default : None)
-        list of the features to analyze.
-        If None, contains all the categorical features
-     threshold : float (Default : 0.05)
-        Minimum modality frequency
-     verbose : int (0/1) (Default : 1)
-        Get more operations information
-
-    Returns
-    -------
-     outlier_dict : dict
-          {feature : list of modalities considered as outliers}
-    """
-    # if var_list = None, get all categorical features
-    # else, exclude features from var_list whose type is not categorical
-    var_list = get_type_features(df, 'cat', var_list)
-
-    df_local = df[var_list].copy()
-
-    if verbose > 0:
-        color_print('cat features outliers identification (frequency<' + str(threshold) + ')')
-        print('  > features : ', var_list,)
-
-    # initialize output dict
-    outlier_dict = {}
-
-    # value count (frequency as number and percent for each modality) for features in var_list
-    for col in df_local.columns:
-        # percent
-        freq_perc = pd.value_counts(df[col], dropna=False) / len(df[col])
-
-        # if feature contain modalities with frequency < trehshold, store in outlier_dict
-        if len(freq_perc.loc[freq_perc < threshold]) > 0:
-            outlier_dict[col] = freq_perc.loc[freq_perc < threshold].index.tolist()
-
-    if verbose > 0:
-        print("  > containing outliers", list(outlier_dict.keys()))
-
-    return outlier_dict

-
-
-"""
--------------------------------------------------------------------------------------------------------------------------
-"""
-
-
-
[docs]def get_num_outliers(df, var_list=None, xstd=3, verbose=0):
-    """outliers detection for num features
-    
-    Parameters
-    ----------
-     df : DataFrame
-        Input dataset
-     var_list : list (Default : None)
-        List of the features to analyze.
-        If None, contains all the num features
-     xstd : int (Default : 3)
-        coefficient (TODO)
-     verbose : int (0/1) (Default : 1)
-        Get more operations information
-            
-    Returns
-    -------
-     outlier_dict : dict
-         {feature : index of outliers}
-    """
-    # if var_list = None, get all num features
-    # else, exclude features from var_list whose type is not num
-    var_list = get_type_features(df, 'num', var_list)
-
-    df_bis = df[var_list].copy()
-
-    if verbose > 0:
-        color_print('num features outliers identification ( x: |x - mean| > '+str(xstd)+' * var)')
-        print('  > features : ', var_list, )
-
-    # initialize output dict
-    outlier_dict = {}
-
-    # compute features upper and lower limit (deviation from the mean > x*std dev (x=3 by default))
-    data_std = np.std(df_bis)
-    data_mean = np.mean(df_bis)
-    anomaly_cut_off = data_std * xstd
-    lower_limit = data_mean - anomaly_cut_off
-    upper_limit = data_mean + anomaly_cut_off
-
-    df_outliers = pd.DataFrame()
-
-    # mask (1 if outlier, else 0)
-    for col in df_bis.columns:
-        df_outliers[col] = np.where((df_bis[col] < lower_limit[col]) | (df_bis[col] > upper_limit[col]), 1, 0)
-
-    # for features containing outliers
-    for col in df_outliers.sum().loc[df_outliers.sum() > 0].index.tolist():
-        # store features and outliers index in outlierèdict
-        outlier_dict[col] = [lower_limit[col], upper_limit[col]]
-
-    if verbose > 0:
-        print("  > containing outliers", list(outlier_dict.keys()))
-
-    return outlier_dict

-
- -
- -
-
- - -
- -
-

- © Copyright 2020, Maxence LABESSE - -

-
- Built with Sphinx using a theme provided by Read the Docs. - -
- -
-
- -
- -
- - - - - - - - - - - - \ No newline at end of file diff --git a/docs/_build/html/_modules/AutoMxL/Explore/Audit_Dataset.html b/docs/_build/html/_modules/AutoMxL/Explore/Audit_Dataset.html deleted file mode 100644 index 6d84f22..0000000 --- a/docs/_build/html/_modules/AutoMxL/Explore/Audit_Dataset.html +++ /dev/null @@ -1,386 +0,0 @@ - - - - - - - - - - - MLBG59.Explore.Audit_Dataset — MLBG59 1.0.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
- - - -
- - - - - -
- -
- - - - - - - - - - - - - - - - - -
- -
    - -
  • Docs »
    • - -
  • Module code »
    • - -
  • MLBG59.Explore.Audit_Dataset
    • - - -
  • - -
    • - -
- - -
-
-
-
- -

Source code for MLBG59.Explore.Audit_Dataset

-""" Dataset Features analysis :
-
- - audit dataset  : get informations on the data (NA, features type, low variance features, ...)
- - is_date : detect if an object/num feature is a date
- - get_all_dates : identify all dates features in a DataFrame and store their names in a list
- - low variance features : identify all features with a low variance (<threshold) and sotre their name in a list
-"""
-import pandas as pd
-from sklearn.preprocessing import MinMaxScaler
-from MLBG59.Utils.Display import *
-from MLBG59.Utils.Utils import get_type_features
-
-
-
[docs]def audit_dataset(df, verbose=1):
-    """Achieve a short audit of the dataset
-
-    Identify features of each type (num, cat, date), features containing NA and features whose variance is null
-        
-    Parameters
-    ----------
-    df : DataFrame
-        input dataset
-    target : string (Default : None)
-        target name
-    verbose : int (0/1) (Default : 1)
-        get more operations information
-            
-    Returns
-    -------
-    list of the x features names
-        - x = num : numerical features
-        - x = cat : categorical features
-        - x = date : date features
-        - x = NA : features which contains NA values
-        - x = low_var : list of the features with low variance
-    """
-
-    # dataset dimensions
-    if verbose > 0:
-        color_print("Dimensions : ")
-        print("  > row number : ", df.shape[0], "\n  > col number : ", df.shape[1])
-
-    #################
-    # features type #
-    #################
-    # numerical
-    num_columns = df._get_numeric_data().columns.tolist()
-    # date
-    date_columns = get_all_dates(df)
-    # categorical
-    cat_columns = [x for x in df.columns if (x not in num_columns) and (x not in date_columns)]
-
-    if verbose > 0:
-        color_print("Features type identification : ")
-        print("  > cat : " + str(len(cat_columns)) + ' (' + str(round(len(cat_columns) / df.shape[1] * 100)) + '%)',
-              '\n  > num : ' + str(len(num_columns)) + ' (' + str(round(len(num_columns) / df.shape[1] * 100)) + '%)',
-              '\n  > dates: ' + str(len(date_columns)) + ' (' + str(
-                  round(len(date_columns) / df.shape[1] * 100)) + ' %)')
-
-    ######################
-    # NA values analysis
-    ######################
-    df_col = pd.DataFrame(df.columns.values, columns=['variables'])
-    df_col['Nbr NA'] = df.isna().sum().tolist()
-    df_col['Taux NA'] = df_col['Nbr NA'] / df.shape[0]
-    # features containing NA values
-    NA_columns = df_col.loc[df_col['Nbr NA'] > 0].sort_values('Nbr NA', ascending=False).variables.tolist()
-    col_des = df_col['Taux NA'].describe()
-
-    if verbose > 0:
-        color_print(str(len(NA_columns)) + " features containing NA")
-        print('  > Taux NA moyen : ' + str(round(col_des['mean'] * 100, 2)) + '%',
-              '\n  >           min : ' + str(round(col_des['min'] * 100, 2)) + '%',
-              '\n  >           max : ' + str(round(col_des['max'] * 100, 2)) + '%')
-
-    #########################
-    # Low variance features
-    #########################
-    if verbose > 0:
-        color_print('Low variance features')
-    low_var_columns = \
-        low_variance_features(df, var_list=num_columns, threshold=0, rescale=True, verbose=verbose).index.tolist()
-
-    return num_columns, date_columns, cat_columns, NA_columns, low_var_columns

-
-
-"""
--------------------------------------------------------------------------------------------------------------------------
-"""
-
-
-
[docs]def is_date(df, col):
-    """Test if a DataFrame feature is recognized as a date (using to_datetime)
-
-    Parameters
-    ----------
-     df : DataFrame
-        input dataset
-     col : string
-        feature name
-
-    Returns
-    -------
-    res : boolean
-        True if the col is recognized as a date
-    """
-    # if col is datetime type, res = True
-    if df[col].dtype == 'datetime64[ns]':
-        return True
-
-    # if col is object type, try apply to_datetime
-    elif df[col].dtype == 'object':
-        try:
-            df_smpl = df.sample(100).copy()
-            pd.to_datetime(df_smpl[col])
-            return True
-        except ValueError:
-            return False
-        except OverflowError:
-            return False

-
-
-"""
--------------------------------------------------------------------------------------------------------------------------
-"""
-
-
-
[docs]def get_all_dates(df):
-    """Identify all date features of a DataFrame
-    
-    Parameters
-    ----------
-     df : DataFrame
-        input DataFrame
-
-    Returns
-    -------
-     list
-        list of features recognized as date
-    """
-    date_list = list()
-
-    for col in df.columns:
-        # if col is recognized as date
-        if is_date(df, col): date_list.append(col)
-
-    return date_list

-
-
-"""
--------------------------------------------------------------------------------------------------------------------------
-"""
-
-
-
[docs]def low_variance_features(df, var_list=None, threshold=0, rescale=True, verbose=1):
-    """identify  features with low variance (<= threshold)
-
-    Parameters
-    ----------
-     df : DataFrame
-        input DataFrame
-     var_list : list (default : None)
-        features to check variance
-     threshold : float (default : 0
-        variance threshold
-     rescale : bool (default : true)
-        if yes : use MinMaxScaler on data before computing variance
-
-    Returns
-    -------
-    list
-        list of the variable with low variance
-    """
-    # if var_list = None, get all numerical features
-    # else, exclude features from var_list whose type is not numerical
-    var_list = get_type_features(df, 'num', var_list)
-
-    df_bis = df.copy()
-
-    if rescale:
-        scler = MinMaxScaler()
-        df_bis[var_list] = scler.fit_transform(df_bis[var_list].astype('float64'))
-
-    selected_var = df_bis[var_list].var().loc[df_bis.var() <= threshold]
-
-    if verbose > 0:
-        # print('features : ',list(var_list))
-        if rescale: print('  **MinMaxScaler [0,1]')
-        print('  ', str(len(selected_var)) + ' feature(s) with  variance <= threshold (' + str(threshold) + ')')
-
-    return selected_var.sort_values(ascending=True)

-
- -
- -
-
- - -
- -
-

- © Copyright 2020, Maxence LABESSE - -

-
- Built with Sphinx using a theme provided by Read the Docs. - -
- -
-
- -
- -
- - - - - - - - - - - - \ No newline at end of file diff --git a/docs/_build/html/_modules/AutoMxL/Explore/Explore.html b/docs/_build/html/_modules/AutoMxL/Explore/Explore.html deleted file mode 100644 index 3b046d2..0000000 --- a/docs/_build/html/_modules/AutoMxL/Explore/Explore.html +++ /dev/null @@ -1,419 +0,0 @@ - - - - - - - - - - AutoMxL.Explore.Explore — AutoMxL 1.0.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
- - - -
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    • - -
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    • - -
  • AutoMxL.Explore.Explore
    • - - -
  • - -
    • - -
- - -
-
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-
- -

Source code for AutoMxL.Explore.Explore

-""" Global dataset information functions :
-
- - explore (func): Identify variables types and gives global information about the dataset (NA, low variance features)
- - low variance features (func): identify features with low variance
- - get_features_type (func): get all features per type
-"""
-from sklearn.preprocessing import MinMaxScaler
-from AutoMxL.Explore.Features_Type import *
-from AutoMxL.Utils.Display import *
-
-
-
[docs]def explore(df, verbose=False):
-    """Identify variables types and gives global information about the dataset
-
-    - Variables type :
-        - date
-        - identifier
-        - verbatim
-        - boolean
-        - categorical
-        - numerical
-    - variables containing NA values
-    - low variance and unique values variables
-
-    See get_features_type function doc for type identification heuristics
-
-    Parameters
-    ----------
-    df : DataFrame
-        input dataset
-    verbose : boolean (Default False)
-        Get logging information
-
-    Returns
-    -------
-    dict
-        {x : variables names list }
-
-        - date : date features
-        - identifier : identifier features
-        - verbatim : verbatim features
-        - boolean : boolean features
-        - categorical : categorical features
-        - numerical : numerical features
-        - categorical : categorical features
-        - date : date features
-        - NA : features which contains NA values
-        - low_variance : list of the features with low variance
-    """
-    # dataset dimensions
-    if verbose:
-        color_print("Dimensions :")
-        print("  > row number :", df.shape[0], "\n  > col number :", df.shape[1])
-
-    #########################
-    # Low variance features
-    #########################
-    if verbose:
-        color_print('Low variance features')
-
-    l_low_var = \
-        low_variance_features(df, var_list=df._get_numeric_data().columns.tolist(), threshold=0, rescale=True,
-                              verbose=verbose).index.tolist()
-
-    # categorical features with unique values
-    l_unique = [col for col in df.columns.tolist() if df[col].dtype == 'object' and df[col].nunique(dropna=True) == 1]
-
-    l_low_var = l_low_var + l_unique
-
-    df_valid = df.drop(l_low_var, axis=1).copy()
-
-    #################
-    # features type #
-    #################
-    d_features = get_features_type(df_valid, l_var=None, th=0.95)
-
-    if verbose:
-        color_print("Features type identification : ")
-        list(map(lambda typ:
-                 print("  > " + typ + " : " + str(len(d_features[typ])) + ' (' + str(
-                     round(len(d_features[typ]) / df_valid.shape[1] * 100)) + '%)'),
-                 d_features.keys()))
-
-    ######################
-    # NA values analysis
-    ######################
-    df_col = pd.DataFrame(df_valid.columns.values, columns=['variables'])
-    df_col['Nbr NA'] = df_valid.isna().sum().tolist()
-    df_col['Taux NA'] = df_col['Nbr NA'] / df_valid.shape[0]
-    # features containing NA values
-    NA_columns = df_col.loc[df_col['Nbr NA'] > 0].sort_values('Nbr NA', ascending=False).variables.tolist()
-    col_des = df_col['Taux NA'].describe()
-
-    if verbose:
-        color_print(str(len(NA_columns)) + " features containing NA")
-        print('  > Taux NA moyen : ' + str(round(col_des['mean'] * 100, 2)) + '%',
-              '\n  >           min : ' + str(round(col_des['min'] * 100, 2)) + '%',
-              '\n  >           max : ' + str(round(col_des['max'] * 100, 2)) + '%')
-
-    # store into DataFrame
-    d_features['NA'] = NA_columns
-    d_features['low_variance'] = l_low_var
-
-    return d_features

-
-
-"""
--------------------------------------------------------------------------------------------------------------------------
-"""
-
-
-
[docs]def get_features_type(df, l_var=None, th=0.95):
-    """ Get all features per type :
-
-    - date : try to apply to_datetime
-    - identifier :
-        - #(unique values)/#(total values) > threshold (default 0.95)
-        - AND length is the same for all values (for non NA)
-    - verbatim :
-        - #(unique values)/#(total values) >= threshold (default 0.95)
-        - AND length is NOT the same for all values (for non NA)
-    - boolean : #(distinct values) = 2
-    - categorical :
-        - not a date
-        - #(unique values)/#(total values) < threshold (default 0.95)
-        - AND #(uniques values)>2
-        - AND for num values #(unique values)<30
-    - numerical : others
-
-    Parameters
-    ----------
-    df : DataFrame
-        input dataset
-    l_var : list (Default  : None)
-        variable names
-    th : float (Default : 0.95)
-        threshold used to identify identifiers/verbatims variables
-
-    Returns
-    -------
-    dict
-        { type : variables name list}
-    """
-    d_output = {}
-
-    if l_var is None:
-        df_local = df.copy()
-    else:
-        df_local = df[l_var].copy()
-
-    l_col = df_local.columns.tolist()
-
-    for typ in ['date', 'identifier', 'verbatim', 'boolean', 'categorical']:
-        d_output[typ] = features_from_type(df_local, typ, l_var=l_col, th=th)
-        l_col = [x for x in l_col if (x not in d_output[typ])]
-
-    d_output['numerical'] = l_col
-
-    return d_output

-
-
-"""
--------------------------------------------------------------------------------------------------------------------------
-"""
-
-
-
[docs]def low_variance_features(df, var_list=None, threshold=0, rescale=True, verbose=False):
-    """Identify numerical features with low variance : (< threshold).
-    Possible to rescale feature before computing.
-
-    Parameters
-    ----------
-     df : DataFrame
-        input DataFrame
-     var_list : list (default : None)
-        names of the variables to check variance
-        if None : all the numerical features
-     threshold : float (default : 0)
-        variance threshold
-     rescale : bool (default : true)
-        enable  MinMaxScaler before computing variance
-    verbose : boolean (Default False)
-        Get logging information
-
-    Returns
-    -------
-    list
-       Names of the variables with low variance
-    """
-    # if var_list = None, get all num features
-    # else, remove features from var_list whose type is not num
-    l_num = df._get_numeric_data().columns.tolist()
-
-    if var_list is None:
-        var_list = l_num
-    else:
-        var_list = [col for col in var_list if col in l_num]
-
-    df_bis = df.copy()
-
-    if rescale:
-        scler = MinMaxScaler()
-        df_bis[var_list] = scler.fit_transform(df_bis[var_list].astype('float64'))
-
-    selected_var = df_bis[var_list].var().loc[df_bis.var() <= threshold]
-
-    if verbose:
-        # print('features : ',list(var_list))
-        if rescale:
-            print('  **MinMaxScaler [0,1]')
-        print('  ', str(len(selected_var)) + ' feature(s) with  variance <= threshold (' + str(threshold) + ')')
-
-    return selected_var.sort_values(ascending=True)

-
- -
- -
-
- - -
- -
-

- - © Copyright 2020, Maxence LABESSE - -

-
- - - - Built with Sphinx using a - - theme - - provided by Read the Docs. - -
- -
-
- -
- -
- - - - - - - - - - - \ No newline at end of file diff --git a/docs/_build/html/_modules/AutoMxL/Explore/Features_Type.html b/docs/_build/html/_modules/AutoMxL/Explore/Features_Type.html deleted file mode 100644 index 97b1b80..0000000 --- a/docs/_build/html/_modules/AutoMxL/Explore/Features_Type.html +++ /dev/null @@ -1,496 +0,0 @@ - - - - - - - - - - AutoMxL.Explore.Features_Type — AutoMxL 1.0.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
- - - -
- - - - - -
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- - - - - - - - - - - - - - - - - -
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    • - -
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    • - - -
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-
- -

Source code for AutoMxL.Explore.Features_Type

-"""Variables type identification function
-
-- features_from_type (func): get all features for a selected type
-- is_date (func): test if a variable is a date
-- is_identifier (func): test if a variable is an identifier
-- is_verbatim (func): test if a variable is a verbatim
-- is_boolean (func): test if a variable is a boolean
-- is_categorical (func): test if a variable is a categorical one (with more than 2 categories)
-"""
-import pandas as pd
-from time import time
-from AutoMxL.Utils.Decorators import timer
-
-
-
[docs]def features_from_type(df, typ, l_var=None, th=0.95):
-    """Get features of a selected type :
-
-    - date : try to apply to_datetime
-    - identifier :
-        - #(unique values)/#(total values) > threshold (default 0.95)
-        - AND length is the same for all values (for non NA)
-    - verbatim :
-        - #(unique values)/#(total values) >= threshold (default 0.95)
-        - AND length is NOT the same for all values (for non NA)
-    - boolean : #(distinct values) = 2
-    - categorical :
-        - not a date
-        - #(unique values)/#(total values) < threshold (default 0.95)
-        - AND #(uniques values)>2
-        - AND for num values #(unique values)<30
-
-    Parameters
-    ----------
-    df : DataFrame
-        input dataset
-    typ : string
-        selected type to get features:
-
-        - 'date'
-        - 'identifier'
-        - 'verbatim'
-        - 'boolean'
-        - categorical
-        
-    l_var : list (Default : None)
-        variables names. If None, all dataset columns
-    th : float (Default : 0.95)
-        threshold used to identify identifiers/verbatims variables
-
-    Returns
-    -------
-    list
-        identified variables names
-    """
-    assert typ in ['date', 'identifier', 'verbatim', 'boolean', 'categorical'], 'Invalid type'
-
-    if l_var is None:
-        df_local = df.copy()
-    else:
-        df_local = df[l_var].copy()
-
-    if typ == 'date':
-        l_var = [col for col in df_local.columns if is_date(df_local, col)]
-    elif typ == 'identifier':
-        l_var = [col for col in df_local.columns if is_identifier(df_local, col, th)]
-    elif typ == 'verbatim':
-        l_var = [col for col in df_local.columns if is_verbatim(df_local, col, th)]
-    elif typ == 'boolean':
-        l_var = [col for col in df_local.columns if is_boolean(df_local, col)]
-    elif typ == 'categorical':
-        l_var = [col for col in df_local.columns if is_categorical(df_local, col, th)]
-
-    return l_var

-
-
-"""
--------------------------------------------------------------------------------------------------------------------------
-"""
-
-
-
[docs]def is_date(df, col):
-    """Test if a variable is a date.
-
-    Method : try to apply to_datetime
-
-    Parameters
-    ----------
-    df : DataFrame
-        input dataset
-    col : string
-        variable name
-
-    Returns
-    -------
-    res : boolean
-        test result
-    """
-    sample_size = 10
-    full_col = df[col].loc[~df[col].isna()]
-
-    smpl_size = min(sample_size, len(full_col))
-    smpl = full_col.sample(smpl_size).copy()
-    # if col is numerical/object type, try apply to_datetime
-    if df[col].dtype != 'datetime64[ns]':
-        try:
-            if smpl.dtype == 'object':
-                smpl = pd.to_datetime(smpl, errors='raise')
-            else:
-                smpl = pd.to_datetime(smpl.astype('Int32').astype(str), errors='raise')
-        except ValueError:
-            pass
-        except OverflowError:
-            pass
-        except TypeError:
-            pass
-    # if col is datetime type, res = True
-    return smpl.dtype == 'datetime64[ns]'

-
-
-"""
--------------------------------------------------------------------------------------------------------------------------
-"""
-
-
-
[docs]def is_identifier(df, col, th=0.95):
-    """Test if a variable is an identifier.
-
-    - #(unique values)/#(total values) > threshold (default 0.95)
-    - AND length is the same for all values (for non NA)
-    - AND not date
-
-    Parameters
-    ----------
-    df : DataFrame
-        input dataset
-    col : string
-        variable name
-    th : float (Default : 0.95)
-        threshold rate
-
-    Returns
-    -------
-    res : boolean
-        test result
-    """
-    full_col = df[col].loc[~df[col].isna()]
-
-    # test if #(v unique values)/#(v,total,values) >= threshold (default 0.95)
-    if full_col.nunique() / full_col.count() >= th:
-        if df[col].dtype != 'object':
-            try:
-                full_col = full_col.astype('Int32').astype(str)
-            except ValueError:
-                return False
-            except OverflowError:
-                return False
-            except TypeError:
-                return False
-
-        # test if all (non NA) values have the same length
-        if full_col.apply(lambda x: len(x)).nunique() == 1:
-            if not is_date(df, col):
-                return True
-            else:
-                return False
-        else:
-            return False
-    else:
-        return False

-
-
-"""
--------------------------------------------------------------------------------------------------------------------------
-"""
-
-
-
[docs]def is_verbatim(df, col, th=0.95):
-    """Test if a variable is a verbatim.
-
-    - #(unique values)/#(total values) >= threshold (default 0.95)
-    - AND length is NOT the same for all values (for non NA)
-
-    Parameters
-    ----------
-    df : DataFrame
-        input dataset
-    col : string
-        variable name
-    th : float (Default : 0.95)
-        threshold rate
-
-    Returns
-    -------
-    res : boolean
-        test result
-    """
-    # get variable serie with non NA values
-    if df[col].dtype == 'object':
-        full_col = df[col].loc[~df[col].isna()]
-    else:
-        return False
-
-    # test if #(v unique values)/#(v,total,values) > threshold (default 0.95)
-    if full_col.nunique() / full_col.count() >= th:
-        # test if all (non NA) values have the same length
-        if full_col.apply(lambda x: len(x)).nunique() > 1:
-            return True
-        else:
-            return False
-    else:
-        return False

-
-
-"""
--------------------------------------------------------------------------------------------------------------------------
-"""
-
-
-
[docs]def is_boolean(df, col):
-    """Test if a variable is a boolean.
-
-    - #(distinct values) = 2
-
-    Parameters
-    ----------
-    df : DataFrame
-        input dataset
-    col : string
-        variable name
-
-    Returns
-    -------
-    res : boolean
-        test result
-    """
-    full_col = df[col].loc[~df[col].isna()]
-    # get variable serie with non NA values
-
-    if full_col.nunique() == 2:
-        if len(full_col) > 2:
-            return True
-        else:
-            return False
-
-    else:
-        return False

-
-
-"""
--------------------------------------------------------------------------------------------------------------------------
-"""
-
-
-
[docs]def is_categorical(df, col, th=0.95):
-    """Test if a variable is a categorical one (with more than 2 categories).
-
-    - not a date
-    - #(unique values)/#(total values) < threshold (default 0.95
-    - AND #(uniques values)>2
-    - AND for num values #(unique values)<30
-
-    Parameters
-    ----------
-    df : DataFrame
-        input dataset
-    col : string
-        variable name
-    th : float (Default : 0.95)
-        threshold
-
-    Returns
-    -------
-    res : boolean
-        test result
-    """
-    # get variable serie with non NA values
-    full_col = df[col].loc[~df[col].isna()]
-    if full_col.nunique() > 2:
-        if (full_col.nunique() / full_col.count()) < th:
-            if df[col].dtype == 'object':
-                return True
-            else:
-                if full_col.nunique() < 5:
-                    return True
-                else:
-                    return False
-        else:
-            return False
-    else:
-        return False

-
- -
- -
-
- - -
- -
-

- - © Copyright 2020, Maxence LABESSE - -

-
- - - - Built with Sphinx using a - - theme - - provided by Read the Docs. - -
- -
-
- -
- -
- - - - - - - - - - - \ No newline at end of file diff --git a/docs/_build/html/_modules/AutoMxL/Explore/Get_Info.html b/docs/_build/html/_modules/AutoMxL/Explore/Get_Info.html deleted file mode 100644 index e7964e5..0000000 --- a/docs/_build/html/_modules/AutoMxL/Explore/Get_Info.html +++ /dev/null @@ -1,399 +0,0 @@ - - - - - - - - - - - MLBG59.Explore.Get_Info — MLBG59 1.0.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
- - - -
- - - - - -
- -
- - - - - - - - - - - - - - - - - -
- - - - -
-
-
-
- -

Source code for MLBG59.Explore.Get_Info

-""" Global dataset information functions :
-
- - recap : get global information about the dataset (NA, features type, low variance features, ...)
- - is_date : test if a variable is as date
- - get_all_dates : identify date features
- - low variance features : identify features with low variance
-"""
-import pandas as pd
-from sklearn.preprocessing import MinMaxScaler
-from MLBG59.Utils.Display import *
-from MLBG59.Utils.Utils import get_type_features
-
-
-
[docs]def recap(df, verbose=False):
-    """Get global information about the dataset
-
-    - Variables type (num, cat, date)
-    - NA values
-    - low variance variables
-        
-    Parameters
-    ----------
-    df : DataFrame
-        input dataset
-    verbose : boolean (Default False)
-        Get logging information
-            
-    Returns
-    -------
-    dict
-        {x : list of variables names}
-
-        - x = numerical : numerical features
-        - x = categorical : categorical features
-        - x = date : date features
-        - x = NA : features which contains NA values
-        - x = low_variance : list of the features with low variance
-    """
-    # dataset dimensions
-    if verbose:
-        color_print("Dimensions : ")
-        print("  > row number : ", df.shape[0], "\n  > col number : ", df.shape[1])
-
-    #################
-    # features type #
-    #################
-    # numerical
-    num_columns = df._get_numeric_data().columns.tolist()
-    # date
-    date_columns = get_all_dates(df)
-    # categorical
-    cat_columns = [x for x in df.columns if (x not in num_columns) and (x not in date_columns)]
-
-    if verbose:
-        color_print("Features type identification : ")
-        print("  > cat : " + str(len(cat_columns)) + ' (' + str(round(len(cat_columns) / df.shape[1] * 100)) + '%)',
-              '\n  > num : ' + str(len(num_columns)) + ' (' + str(round(len(num_columns) / df.shape[1] * 100)) + '%)',
-              '\n  > dates: ' + str(len(date_columns)) + ' (' + str(
-                  round(len(date_columns) / df.shape[1] * 100)) + ' %)')
-
-    ######################
-    # NA values analysis
-    ######################
-    df_col = pd.DataFrame(df.columns.values, columns=['variables'])
-    df_col['Nbr NA'] = df.isna().sum().tolist()
-    df_col['Taux NA'] = df_col['Nbr NA'] / df.shape[0]
-    # features containing NA values
-    NA_columns = df_col.loc[df_col['Nbr NA'] > 0].sort_values('Nbr NA', ascending=False).variables.tolist()
-    col_des = df_col['Taux NA'].describe()
-
-    if verbose:
-        color_print(str(len(NA_columns)) + " features containing NA")
-        print('  > Taux NA moyen : ' + str(round(col_des['mean'] * 100, 2)) + '%',
-              '\n  >           min : ' + str(round(col_des['min'] * 100, 2)) + '%',
-              '\n  >           max : ' + str(round(col_des['max'] * 100, 2)) + '%')
-
-    #########################
-    # Low variance features
-    #########################
-    if verbose:
-        color_print('Low variance features')
-    low_var_columns = \
-        low_variance_features(df, var_list=num_columns, threshold=0, rescale=True, verbose=verbose).index.tolist()
-
-    # store into DataFrame
-    d_features = {'numerical': num_columns,
-                  'date': date_columns,
-                  'categorical': cat_columns,
-                  'NA': NA_columns,
-                  'low_variance': low_var_columns}
-
-    return d_features

-
-
-"""
--------------------------------------------------------------------------------------------------------------------------
-"""
-
-
-
[docs]def is_date(df, col):
-    """Test if a variable is as date.
-
-    Method : try to apply to_datetime
-
-    Parameters
-    ----------
-    df : DataFrame
-        input dataset
-    col : string
-        variable name
-
-    Returns
-    -------
-    res : boolean
-        test result
-    """
-    # if col is datetime type, res = True
-    if df[col].dtype == 'datetime64[ns]':
-        return True
-
-    # if col is object type, try apply to_datetime
-    elif df[col].dtype == 'object':
-        try:
-            df_smpl = df.sample(100).copy()
-            pd.to_datetime(df_smpl[col])
-            return True
-        except ValueError:
-            return False
-        except OverflowError:
-            return False

-
-
-"""
--------------------------------------------------------------------------------------------------------------------------
-"""
-
-
-
[docs]def get_all_dates(df):
-    """Identify dates variables.
-
-    Method : try to apply to_datetime
-    
-    Parameters
-    ----------
-    df : DataFrame
-        input DataFrame
-
-    Returns
-    -------
-    list
-        features identified as date
-    """
-    date_list = list()
-
-    for col in df.columns:
-        # if col is recognized as date
-        if is_date(df, col): date_list.append(col)
-
-    return date_list

-
-
-"""
--------------------------------------------------------------------------------------------------------------------------
-"""
-
-
-
[docs]def low_variance_features(df, var_list=None, threshold=0, rescale=True, verbose=1):
-    """Identify  features with low variance (< threshold).
-    Possible to rescale feature before computing.
-
-    Parameters
-    ----------
-     df : DataFrame
-        input DataFrame
-     var_list : list (default : None)
-        names of the variables to test variance
-     threshold : float (default : 0)
-        variance threshold
-     rescale : bool (default : true)
-        enable  MinMaxScaler before computing variance
-
-    Returns
-    -------
-    list
-       Names of the variables with low variance
-    """
-    # if var_list = None, get all numerical features
-    # else, exclude features from var_list whose type is not numerical
-    var_list = get_type_features(df, 'num', var_list)
-
-    df_bis = df.copy()
-
-    if rescale:
-        scler = MinMaxScaler()
-        df_bis[var_list] = scler.fit_transform(df_bis[var_list].astype('float64'))
-
-    selected_var = df_bis[var_list].var().loc[df_bis.var() <= threshold]
-
-    if verbose > 0:
-        # print('features : ',list(var_list))
-        if rescale: print('  **MinMaxScaler [0,1]')
-        print('  ', str(len(selected_var)) + ' feature(s) with  variance <= threshold (' + str(threshold) + ')')
-
-    return selected_var.sort_values(ascending=True)

-
- -
- -
-
- - -
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-

- © Copyright 2020, Maxence LABESSE - -

-
- Built with Sphinx using a theme provided by Read the Docs. - -
- -
-
- -
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- - - - - - - - - - - - \ No newline at end of file diff --git a/docs/_build/html/_modules/AutoMxL/Explore/Get_Infos.html b/docs/_build/html/_modules/AutoMxL/Explore/Get_Infos.html deleted file mode 100644 index 4495d65..0000000 --- a/docs/_build/html/_modules/AutoMxL/Explore/Get_Infos.html +++ /dev/null @@ -1,397 +0,0 @@ - - - - - - - - - - - MLBG59.Explore.Get_Infos — MLBG59 1.0.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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Source code for MLBG59.Explore.Get_Infos

-""" Dataset Features analysis :
-
- - recap : get and store informations related to the dataset (NA, features type, low variance features, ...)
- - is_date : Test if a variable can be considered as date
- - get_all_dates : identify all dates features
- - low variance features : identify all features with low variance (<threshold)
-"""
-import pandas as pd
-from sklearn.preprocessing import MinMaxScaler
-from MLBG59.Utils.Display import *
-from MLBG59.Utils.Utils import get_type_features
-
-
-
[docs]def recap(df, verbose=False):
-    """get and store global informations about the dataset :
-
-    - Variables type (num, cat, date)
-    - NA values
-    - low variance variables
-        
-    Parameters
-    ----------
-    df : DataFrame
-        input dataset
-    target : string (Default : None)
-        target name
-    verbose : boolean (Default False)
-        Get logging information
-            
-    Returns
-    -------
-    dict
-        {x : list of variables names}
-
-        - x = numerical : numerical features
-        - x = categorical : categorical features
-        - x = date : date features
-        - x = NA : features which contains NA values
-        - x = low_variance : list of the features with low variance
-    """
-    # dataset dimensions
-    if verbose:
-        color_print("Dimensions : ")
-        print("  > row number : ", df.shape[0], "\n  > col number : ", df.shape[1])
-
-    #################
-    # features type #
-    #################
-    # numerical
-    num_columns = df._get_numeric_data().columns.tolist()
-    # date
-    date_columns = get_all_dates(df)
-    # categorical
-    cat_columns = [x for x in df.columns if (x not in num_columns) and (x not in date_columns)]
-
-    if verbose:
-        color_print("Features type identification : ")
-        print("  > cat : " + str(len(cat_columns)) + ' (' + str(round(len(cat_columns) / df.shape[1] * 100)) + '%)',
-              '\n  > num : ' + str(len(num_columns)) + ' (' + str(round(len(num_columns) / df.shape[1] * 100)) + '%)',
-              '\n  > dates: ' + str(len(date_columns)) + ' (' + str(
-                  round(len(date_columns) / df.shape[1] * 100)) + ' %)')
-
-    ######################
-    # NA values analysis
-    ######################
-    df_col = pd.DataFrame(df.columns.values, columns=['variables'])
-    df_col['Nbr NA'] = df.isna().sum().tolist()
-    df_col['Taux NA'] = df_col['Nbr NA'] / df.shape[0]
-    # features containing NA values
-    NA_columns = df_col.loc[df_col['Nbr NA'] > 0].sort_values('Nbr NA', ascending=False).variables.tolist()
-    col_des = df_col['Taux NA'].describe()
-
-    if verbose:
-        color_print(str(len(NA_columns)) + " features containing NA")
-        print('  > Taux NA moyen : ' + str(round(col_des['mean'] * 100, 2)) + '%',
-              '\n  >           min : ' + str(round(col_des['min'] * 100, 2)) + '%',
-              '\n  >           max : ' + str(round(col_des['max'] * 100, 2)) + '%')
-
-    #########################
-    # Low variance features
-    #########################
-    if verbose:
-        color_print('Low variance features')
-    low_var_columns = \
-        low_variance_features(df, var_list=num_columns, threshold=0, rescale=True, verbose=verbose).index.tolist()
-
-    # store into DataFrame
-    d_features = {'numerical': num_columns,
-                  'date': date_columns,
-                  'categorical': cat_columns,
-                  'NA': NA_columns,
-                  'low_variance': low_var_columns}
-
-    return d_features

-
-
-"""
--------------------------------------------------------------------------------------------------------------------------
-"""
-
-
-
[docs]def is_date(df, col):
-    """Test if a DataFrame feature can be considered as a date (using to_datetime)
-
-    Parameters
-    ----------
-     df : DataFrame
-        input dataset
-     col : string
-        feature name
-
-    Returns
-    -------
-    res : boolean
-        Test result
-    """
-    # if col is datetime type, res = True
-    if df[col].dtype == 'datetime64[ns]':
-        return True
-
-    # if col is object type, try apply to_datetime
-    elif df[col].dtype == 'object':
-        try:
-            df_smpl = df.sample(100).copy()
-            pd.to_datetime(df_smpl[col])
-            return True
-        except ValueError:
-            return False
-        except OverflowError:
-            return False

-
-
-"""
--------------------------------------------------------------------------------------------------------------------------
-"""
-
-
-
[docs]def get_all_dates(df):
-    """Identify all date features of a DataFrame
-    
-    Parameters
-    ----------
-     df : DataFrame
-        input DataFrame
-
-    Returns
-    -------
-     list
-        list of features identified as date
-    """
-    date_list = list()
-
-    for col in df.columns:
-        # if col is recognized as date
-        if is_date(df, col): date_list.append(col)
-
-    return date_list

-
-
-"""
--------------------------------------------------------------------------------------------------------------------------
-"""
-
-
-
[docs]def low_variance_features(df, var_list=None, threshold=0, rescale=True, verbose=1):
-    """Identify  features with low variance (<= threshold).
-    Possible to rescale feature before computing.
-
-    Parameters
-    ----------
-     df : DataFrame
-        input DataFrame
-     var_list : list (default : None)
-        names of the variables to test variance
-     threshold : float (default : 0)
-        variance threshold
-     rescale : bool (default : true)
-        enable  MinMaxScaler before computing variance
-
-    Returns
-    -------
-    list
-       Names of the variables with low variance
-    """
-    # if var_list = None, get all numerical features
-    # else, exclude features from var_list whose type is not numerical
-    var_list = get_type_features(df, 'num', var_list)
-
-    df_bis = df.copy()
-
-    if rescale:
-        scler = MinMaxScaler()
-        df_bis[var_list] = scler.fit_transform(df_bis[var_list].astype('float64'))
-
-    selected_var = df_bis[var_list].var().loc[df_bis.var() <= threshold]
-
-    if verbose > 0:
-        # print('features : ',list(var_list))
-        if rescale: print('  **MinMaxScaler [0,1]')
-        print('  ', str(len(selected_var)) + ' feature(s) with  variance <= threshold (' + str(threshold) + ')')
-
-    return selected_var.sort_values(ascending=True)

-
- -
- -
-
- - -
- -
-

- © Copyright 2020, Maxence LABESSE - -

-
- Built with Sphinx using a theme provided by Read the Docs. - -
- -
-
- -
- -
- - - - - - - - - - - - \ No newline at end of file diff --git a/docs/_build/html/_modules/AutoMxL/Explore/Get_Outliers.html b/docs/_build/html/_modules/AutoMxL/Explore/Get_Outliers.html deleted file mode 100644 index 64fd686..0000000 --- a/docs/_build/html/_modules/AutoMxL/Explore/Get_Outliers.html +++ /dev/null @@ -1,318 +0,0 @@ - - - - - - - - - - - MLBG59.Explore.Get_Outliers — MLBG59 1.0.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
- - - -
- - - - - -
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- - - - - - - - - - - - - - - - - -
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    • - -
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    • - - -
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-
- -

Source code for MLBG59.Explore.Get_Outliers

-""" Outliers detection functions :
-
- - get_cat_outliers : identify categorical features containing outliers
- - get_num_outliers : identify numerical features containing outliers
-"""
-import pandas as pd
-import numpy as np
-from MLBG59.Utils.Display import *
-from MLBG59.Utils.Utils import get_type_features
-
-
-
[docs]def get_cat_outliers(df, var_list=None, threshold=0.05, verbose=False):
-    """Outliers detection for selected/all categorical features.
-
-    Method : Modalities with frequency <x% (Default 5%)
-
-    Parameters
-    ----------
-     df : DataFrame
-        Input dataset
-     var_list : list (Default : None)
-        Names of the features
-        If None, all the categorical features
-     threshold : float (Default : 0.05)
-        Minimum modality frequency
-     verbose : boolean (Default False)
-        Get logging information
-
-    Returns
-    -------
-    dict
-        {variable : list of categories considered as outliers}
-    """
-    # if var_list = None, get all categorical features
-    # else, exclude features from var_list whose type is not categorical
-    var_list = get_type_features(df, 'cat', var_list)
-
-    df_local = df[var_list].copy()
-
-    if verbose:
-        color_print('cat features outliers identification (frequency<' + str(threshold) + ')')
-        print('  > features : ', var_list, )
-
-    # initialize output dict
-    outlier_dict = {}
-
-    # value count (frequency as number and percent for each modality) for features in var_list
-    for col in df_local.columns:
-        # percent
-        freq_perc = pd.value_counts(df[col], dropna=False) / len(df[col])
-
-        # if feature contain modalities with frequency < trehshold, store in outlier_dict
-        if len(freq_perc.loc[freq_perc < threshold]) > 0:
-            outlier_dict[col] = freq_perc.loc[freq_perc < threshold].index.tolist()
-
-    if verbose:
-        print("  > containing outliers", list(outlier_dict.keys()))
-
-    return outlier_dict

-
-
-"""
--------------------------------------------------------------------------------------------------------------------------
-"""
-
-
-
[docs]def get_num_outliers(df, var_list=None, xstd=3, verbose=False):
-    """Outliers detection for selected/all numerical features.
-
-    Method : x outlier <=> abs(x - mean) > xstd * var
-    
-    Parameters
-    ----------
-     df : DataFrame
-        Input dataset
-     var_list : list (Default : None)
-        Names of the features
-        If None, all the num features
-     xstd : int (Default : 3)
-        Variance gap coef
-     verbose : boolean (Default False)
-        Get logging information
-            
-    Returns
-    -------
-    dict
-        {variable : [lower_limit, upper_limit]}
-    """
-    # if var_list = None, get all num features
-    # else, exclude features from var_list whose type is not num
-    var_list = get_type_features(df, 'num', var_list)
-
-    df_bis = df[var_list].copy()
-
-    if verbose:
-        color_print('num features outliers identification ( x: |x - mean| > ' + str(xstd) + ' * var)')
-        print('  > features : ', var_list, )
-
-    # initialize output dict
-    outlier_dict = {}
-
-    # compute features upper and lower limit (abs(x - mean) > xstd * var (x=3 by default))
-    data_std = np.std(df_bis)
-    data_mean = np.mean(df_bis)
-    anomaly_cut_off = data_std * xstd
-    lower_limit = data_mean - anomaly_cut_off
-    upper_limit = data_mean + anomaly_cut_off
-
-    df_outliers = pd.DataFrame()
-
-    # mask (1 if outlier, else 0)
-    for col in df_bis.columns:
-        df_outliers[col] = np.where((df_bis[col] < lower_limit[col]) | (df_bis[col] > upper_limit[col]), 1, 0)
-
-    # for features containing outliers
-    for col in df_outliers.sum().loc[df_outliers.sum() > 0].index.tolist():
-        # store features and outliers index in outlier_dict
-        outlier_dict[col] = [lower_limit[col], upper_limit[col]]
-
-    if verbose:
-        print("  > containing outliers", list(outlier_dict.keys()))
-
-    return outlier_dict

-
- -
- -
-
- - -
- -
-

- © Copyright 2020, Maxence LABESSE - -

-
- Built with Sphinx using a theme provided by Read the Docs. - -
- -
-
- -
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- - - - - - - - - - - - \ No newline at end of file diff --git a/docs/_build/html/_modules/AutoMxL/Load/Load.html b/docs/_build/html/_modules/AutoMxL/Load/Load.html deleted file mode 100644 index ce44af3..0000000 --- a/docs/_build/html/_modules/AutoMxL/Load/Load.html +++ /dev/null @@ -1,326 +0,0 @@ - - - - - - - - - - - MLBG59.Load.Load — MLBG59 1.0.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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Source code for MLBG59.Load.Load

-""" Data importation :
-
- - get_delimiter : identify csv file delimiter
- - load data
-"""
-import pandas as pd
-
-
-
[docs]def get_delimiter(csvfile):
-    """Identify the delimiter of a .csv file
-
-    Parameters
-    ----------
-    csvfile : string
-        Path and name of the file (Ex : "data/file.csv")
-
-    Returns
-    -------
-    string
-        Identified delimiter
-
-    """
-    # csv file reading
-    with open(csvfile, 'r') as myCsvfile:
-        # Reads one entire line from the file
-        header = myCsvfile.readline()
-
-        # Returns the lowest index of the substring if it is found in given string. (-1 = not found)
-        if header.find(";") != -1:
-            delimiter = ";"
-        elif header.find(",") != -1:
-            delimiter = ","
-
-    return delimiter

-
-
-"""
--------------------------------------------------------------------------------------------------------------------------
-"""
-
-
-
[docs]def load_data(file, index_col=None, verbose=1):
-    """Import dataset as a DataFrame
-    accept .csv, .xlsx, .xls files
-
-    Parameters
-    ----------
-    file : string
-        Path and name of the file (Ex : "data/file.csv")
-        If file is .csv, automatically identify delimiter
-    index_col : int, str, sequence of int / str, or False, default None
-        Column(s) to use as the row labels of the DataFrame, either given as string name or column index.
-        If a sequence of int / str is given, a MultiIndex is used.
-    verbose : int (0/1) (Default : 1)
-        Get more operations information
-
-    Returns
-    -------
-    DataFrame :
-        dataset imported as DataFrame
-
-    """
-    # CSV
-    if file.endswith('.csv') or file.endswith('.txt'):
-        # Find file delimiter
-        file_sep = get_delimiter(file)
-        # import 
-        df = pd.read_csv(file, encoding="iso-8859-1", sep=file_sep, index_col=index_col)
-
-    # Excel
-    elif (file.endswith('.xlsx')) or (file.endswith('.xsl')):
-        df = pd.read_excel(file)
-
-    # JSON
-    elif file.endswith('.json'):
-        # to-do
-        pass
-
-    else:
-        df = None
-
-    if verbose == 1:
-        if df is not None:
-            print('-> File ' + file + ' successfully imported as DataFrame')
-            print('-> DataFrame size  : ', df.shape)
-        else:
-            print("File couldn't be imported")
-
-    return df

-
-
-"""
--------------------------------------------------------------------------------------------------------------
-"""
-
-
-
[docs]def parse_target(df, target, modalite):
-    """Transform target to boolean (1/0), choosing the reference modality
-
-    Parameters
-    ----------
-    df : dataframe
-        input dataset
-    target : string
-        target feature
-    modalite : string
-         modality name
-
-    Returns
-    -------
-    dataframe
-        modified dataframe
-    string
-        new target name
-    """
-    # Si la variable cible est numérique, transformation en string (nécessaire pour dichotomiser)
-    if target in df._get_numeric_data().columns:
-        df[target] = df[target].apply(str)
-
-    # Dichotomisation
-    target_dummies = pd.get_dummies(df[target])
-    # Choix de la nouvelle variable cible et renommage
-    target_dummies[target + '_' + modalite] = target_dummies[modalite]
-
-    # Intégration de la nouvelle variable cible dans le dataset
-    df_bis = pd.concat((df, target_dummies[target + '_' + modalite]), axis=1)
-
-    # suppresion de l'ancienne variable cible
-    del df_bis[target]
-
-    return df_bis, target + '_' + modalite

-
- -
- -
-
- - -
- -
-

- © Copyright 2020, Maxence LABESSE - -

-
- Built with Sphinx using a theme provided by Read the Docs. - -
- -
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- - - - - - - - - - - - \ No newline at end of file diff --git a/docs/_build/html/_modules/AutoMxL/Modelisation/Bagging.html b/docs/_build/html/_modules/AutoMxL/Modelisation/Bagging.html deleted file mode 100644 index a879c7f..0000000 --- a/docs/_build/html/_modules/AutoMxL/Modelisation/Bagging.html +++ /dev/null @@ -1,437 +0,0 @@ - - - - - - - - - - AutoMxL.Modelisation.Bagging — AutoMxL 1.0.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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Source code for AutoMxL.Modelisation.Bagging

-""" Bagging algorithm class. Methods :
-
-- Bagging (class) : generate new training more balanced and train model for each
-- Bagging_sample (func) : generate bagging sample
-
-"""
-from sklearn.ensemble import RandomForestClassifier
-from AutoMxL.Modelisation.Utils import *
-import pandas as pd
-
-"""
-Default bagging parameters
-"""
-default_bagging_param = {'n_sample': 5,
-                         'pos_sample_size': 1.0,
-                         'replace': False}
-
-
-
[docs]class Bagging(object):
-    """Meta-algo designed to improve the stability and accuracy of ML classif/regression algos
-    or to face an "imbalanced target distribution" issue.
-    
-    Bagging generates m new training sets more balanced. Then, a model is fitted on each
-    sample and outputs are combined by averaging (for regression) or voting (for classification).
-
-    Available classifiers : Random Forest and XGBOOST
-    
-    Parameters
-    ----------
-    clf : Model fitted on samples (Default  : RandomForestClassifier(n_estimators=100, max_leaf_nodes=100)
-        Model fitted on the samples
-    n_sample : int (Default : 5)
-        number a samples
-    pos_sample_size : int/float (Default : 1.0)
-        Number/rate of target=1 observations in each sample (filled with 3 times more target=0 )
-
-        - if int : number of target=1
-        - if float : rate of total target=1
-
-    replace : Boolean (Default : False)
-        Enable sampling with replacement
-
-    list_model : list (Default : None)
-        Fitted models (created with fit method)
-    """
-
-    def __init__(self,
-                 clf=RandomForestClassifier(n_estimators=100, max_leaf_nodes=100),
-                 n_sample=5,
-                 pos_sample_size=1.0,
-                 replace=True):
-
-        self.classifier = clf
-        self.niter = n_sample
-        self.pos_sample_size = pos_sample_size
-        self.replace = replace
-        self.list_model = list()
-        self.is_fitted = False
-
-    """
-    -------------------------------------------------------------------------------------------------------------
-    """
-
-
[docs]    def get_params(self):
-        """Get bagging object parameters
-
-        Returns
-        -------
-        dict
-            {param : value}
-        """
-        return {'classifier': self.classifier,
-                'niter': self.niter,
-                'pos_sample_size': self.pos_sample_size,
-                'replace': self.replace,
-                'list_model': self.list_model}

-
-    """
-    -------------------------------------------------------------------------------------------------------------
-    """
-
-
[docs]    def fit(self, df_train, target):
-        """Create bagging samples from a DataFrame and fit the model (self.clf) on each sample
-        
-        Parameters
-        ----------
-        df_train : DataFrame
-            Training dataset
-        target : String
-            Target name
-
-        Returns
-        -------
-         self.list_model : list
-            Fitted models
-        """
-        # list_model init
-        self.list_model = [None] * self.niter
-
-        # get number of target=1 in bagging samples
-        if isinstance(self.pos_sample_size, int):
-            N = self.pos_sample_size
-        else:
-            N = int(self.pos_sample_size * df_train.loc[df_train[target] == 1].shape[0])
-
-        for i in range(self.niter):
-            # Sample creation
-            df_train_bag = create_sample(df_train, target, N, replace=self.replace)
-
-            # X_train / y_train
-            X_train_bag = df_train_bag.copy()
-            y_train_bag = X_train_bag[target]
-            del X_train_bag[target]
-
-            # Create and store model
-            self.list_model[i] = self.classifier
-
-            # fit model for each sample
-            self.list_model[i].fit(X_train_bag, y_train_bag)
-
-            self.is_fitted = True
-
-        return self

-
-    """
-    -------------------------------------------------------------------------------------------------------------
-    """
-
-
[docs]    def predict(self, df):
-        """Apply models fitted on sample to a  dataset.
-        Combine models by averaging the outputs (for regression) or voting (for classification)
-
-        Parameters
-        ----------
-        df : DataFrame
-            Dataset to apply the model
-
-        Returns
-        -------
-        numpy.ndarray (float)
-            Averaged classification probabilities
-        numpy.ndarray (int)
-            Predictions for each observation
-        """
-        assert self.is_fitted, "Fit first !"
-        # Init probs storage matrix
-        mat_prob = np.zeros((self.niter, df.shape[0]))
-
-        # for each fitted models
-        for j in range(self.niter):
-            # apply the model on test set
-            y_prob_rf = self.list_model[j].predict_proba(df)
-            # probabilities storage in matrix
-            mat_prob[j] = y_prob_rf[:, 1]
-
-        # probas averaging
-        list_prob_pred = mat_prob.sum(axis=0) / self.niter
-        # voting
-        list_pred = [round(elem, 0) for elem in list_prob_pred]
-
-        return list_prob_pred, list_pred

-
-    """
-    -------------------------------------------------------------------------------------------------------------
-    """
-
-
[docs]    def bag_feature_importance(self, X):
-        """Get features importance of the model by averaging importance of models fitted on the samples
-        
-        Parameters
-        ----------
-        X : DataFrame
-            Input Dataset
-            
-        Returns
-        -------
-        dict
-            {feature : importance}
-            
-        """
-        # Init importance storage matrix
-        mat_feat_imp = np.zeros((self.niter, len(X.columns)))
-
-        #  for each fitted models
-        for i in range(self.niter):
-            # importances storage in matrix
-            mat_feat_imp[i] = self.list_model[i].feature_importances_
-
-        # Averaging importances
-        list_feat_imp_moy = mat_feat_imp.sum(axis=0) / self.niter
-
-        features_dict = dict(zip(X.columns, list_feat_imp_moy))
-
-        return features_dict

-
-
-"""
--------------------------------------------------------------------------------------------------------------
-"""
-
-
-
[docs]def create_sample(df, target, pos_target_nb, replace=False):
-    """Generate a DataFrame sample with selected number of target=1
-        
-    Parameters
-    ----------
-    df : DataFrame
-        Input dataset
-    target : String
-        Target name
-    pos_target_nb : int
-        Number of target=1 observations in the sample
-    replace : Boolean (défaut : False)
-        If True, create samples with replacement
-            
-    Returns
-    -------
-    DataFrame
-        sample dataset
-    """
-    # split target = 1 / 0
-    df_pos = df.loc[(df[target] == 1)]
-    df_neg = df.loc[(df[target] == 0)]
-
-    n_size = min(3 * pos_target_nb, df_neg.shape[0])
-
-    # sample creation
-    df_bag = pd.concat(
-        (df_pos.sample(n=pos_target_nb, replace=replace), df_neg.sample(n=n_size, replace=replace)), axis=0)
-
-    return df_bag

-
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- - © Copyright 2020, Maxence LABESSE - -

-
- - - - Built with Sphinx using a - - theme - - provided by Read the Docs. - -
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- - - - - - - - - - - \ No newline at end of file diff --git a/docs/_build/html/_modules/AutoMxL/Modelisation/Classifiers.html b/docs/_build/html/_modules/AutoMxL/Modelisation/Classifiers.html deleted file mode 100644 index 6e7cd7b..0000000 --- a/docs/_build/html/_modules/AutoMxL/Modelisation/Classifiers.html +++ /dev/null @@ -1,233 +0,0 @@ - - - - - - - - - - - MLBG59.Modelisation.Classifiers — MLBG59 1.0.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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Source code for MLBG59.Modelisation.Classifiers

-""" Classifiers utils functions :
-
-- features_importance_select : select top features according to model importance
-"""
-
-
[docs]def features_importance_select(eval_dict, treshold):
-    """Get most important features according to a threshold
-
-    Parameters
-    ----------
-    eval_dict : dict
-        Model evaluation dict
-    threshold : int/float
-
-        - if int : number of top important features to get
-        - if float : cumulative importance rate of top features
-
-    Returns
-    -------
-     list
-        most important features
-    """
-    rf_top_feat = eval_dict['feature_importances']
-    rf_top_feat['Features'] = rf_top_feat.index
-    rf_top_feat = rf_top_feat.reset_index(drop=True)
-    rf_top_feat = rf_top_feat.sort_values('importance', ascending=False)
-
-    if isinstance(treshold, int):
-        n_feat_list = rf_top_feat['Features'].tolist()[0:treshold]
-
-    elif isinstance(treshold, float):
-        rf_top_feat['cum_importance'] = rf_top_feat['importance'].cumsum()
-        val_ref = rf_top_feat['cum_importance'].loc[rf_top_feat['cum_importance'] >= treshold].min()
-        n_feat_list = rf_top_feat['Features'].loc[rf_top_feat['cum_importance'] <= val_ref].tolist()
-    else:
-        print("il faut que le seuil soit un entier ou un décimal !")
-
-    return n_feat_list

-
- -
- -
-
- - -
- -
-

- © Copyright 2020, Maxence LABESSE - -

-
- Built with Sphinx using a theme provided by Read the Docs. - -
- -
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- -
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- - - - - - - - - - - - \ No newline at end of file diff --git a/docs/_build/html/_modules/AutoMxL/Modelisation/HyperOpt.html b/docs/_build/html/_modules/AutoMxL/Modelisation/HyperOpt.html deleted file mode 100644 index 39bda7d..0000000 --- a/docs/_build/html/_modules/AutoMxL/Modelisation/HyperOpt.html +++ /dev/null @@ -1,568 +0,0 @@ - - - - - - - - - - AutoMxL.Modelisation.HyperOpt — AutoMxL 1.0.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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Source code for AutoMxL.Modelisation.HyperOpt

-""" Hyperopt class :
-Model hyper-optimisation with random search
-
-- Hyperopt (class) : Model hyper-optimisation with random search
-
-"""
-import xgboost
-import random
-import itertools as it
-# import datetime
-from AutoMxL.Modelisation.Bagging import *
-from AutoMxL.Modelisation.Utils import *
-from AutoMxL.Utils.Display import color_print
-from datetime import datetime
-from AutoMxL.param_config import default_bagging_param, default_RF_grid_param, default_XGB_grid_param
-
-
-
[docs]class HyperOpt(object):
-    """Model hyper-optimisation with random search :
-
-    - From a hyper-parameters grid, creates random HPs combinations
-    - train a model for each combination
-    - apply the model
-    
-    Parameters
-    ----------
-    classifier : string (Default : 'RF')
-        classifier for modelisation
-    grid_param : dict (Default : Default_RF_grid_param)
-        HP grid
-    n_param_comb : int (Default : 10)
-        number of HP combinations
-    bagging : Boolean (Default = False)
-        use bagging method
-    bagging_param : n-uple
-        bagging parameters (Default : default_bagging_param (Bagging module))
-    train_model_dict (created with fit method) : dict
-        {model_index : {'HP', 'probas', 'model', 'features_importance', 'train_metrics'}
-    bagging_object : Bagging
-        bagging object
-    comb_seed : int
-        seed for randomized HP combinations
-    """
-
-    def __init__(self,
-                 classifier='RF',
-                 grid_param=None,
-                 n_param_comb=10,
-                 bagging=False,
-                 bagging_param=default_bagging_param,
-                 comb_seed=None):
-
-        # parameters
-        if grid_param is None:
-            if classifier == 'RF':
-                self.grid_param = default_RF_grid_param
-            elif classifier == 'XGBOOST':
-                self.grid_param = default_XGB_grid_param
-        else:
-            self.grid_param = grid_param
-        self.classifier = classifier
-        self.n_param_comb = n_param_comb
-        self.bagging = bagging
-        self.bagging_param = bagging_param
-        self.comb_seed = comb_seed
-        # attributes
-        self.d_train_model = {}
-        self.d_bagging = {}
-        self.is_fitted = False
-
-    """
-    -------------------------------------------------------------------------------------------------------------
-    """
-
-
[docs]    def get_params(self):
-        """Return Hyperopt object parameters
-
-        Returns
-        -------
-        dict
-            {param : value}
-        """
-        return {'classifier': self.classifier,
-                'grid_param': self.grid_param,
-                'n_param_comb': self.n_param_comb,
-                'top_bagging': self.bagging,
-                'bagging_param': self.bagging_param,
-                'comb_seed': self.comb_seed}

-
-    """
-    -------------------------------------------------------------------------------------------------------------
-    """
-
-
[docs]    def fit(self, df_train, target, verbose=False):
-        """Fit a model for each HP combination
-        
-        Parameters
-        ----------
-        df_train : DataFrame
-            Training dataset
-        target : string
-            Target name
-        verbose : boolean (Default False)
-            Get logging information
-            
-        Returns
-        -------
-        self.train_model_dict (created with fit method) : dict
-            {model_index : {'HP', 'probas', 'model', 'features_importance', 'train_metrics'}
-        """
-        # X / y
-        y_train = df_train[target]
-        X_train = df_train.drop(target, axis=1)
-
-        # Sort HPs grid dict by param name (a->z)
-        grid_names = sorted(self.grid_param)
-        # random sampling : 'n_param_comb' HPS combinations
-        # list(it.product(*(self.grid_param[Name] for Name in grid_names))) create all the possible combinations
-        if self.comb_seed is not None:
-            random.seed(self.comb_seed)
-
-        sample_combinations = random.sample(list(it.product(*(self.grid_param[Name] for Name in grid_names))),
-                                            k=self.n_param_comb)
-
-        if verbose :
-            print('\033[34m' + 'Random search:', self.n_param_comb, 'HP combs', '\033[0m')
-            print('\033[34m' + 'Model : ', self.classifier, '\033[0m')
-
-        # for each HP combination :
-        for model_idx in range(len(sample_combinations)):
-            t_ini_model = datetime.now()
-
-            # Model params in dict
-            HP_dict = dict(zip(grid_names, sample_combinations[model_idx]))
-
-            # instantiate model
-            if self.classifier == 'RF':  # Classifier Random Forest
-                clf = RandomForestClassifier(**HP_dict)
-            # elif self.classifier == 'XGBOOST':
-            else:
-                clf = xgboost.XGBClassifier(**HP_dict)
-
-            # disabling bagging
-            if not self.bagging:
-
-                # model training
-                clf_fit = clf.fit(X_train, y_train)
-                # features importance
-                features_dict = dict(zip(X_train.columns, clf.feature_importances_))
-                # outputs
-                y_proba = clf_fit.predict_proba(X_train)[:, 1]
-                y_pred = clf_fit.predict(X_train)
-
-            # enabling bagging
-            else:
-                # init bagging object with default params
-                bag = Bagging(clf, **self.bagging_param)
-                # model training
-                bag.fit(df_train, target)
-                clf_fit = bag.list_model
-                # features importance
-                features_dict = bag.bag_feature_importance(X_train)
-                # classification probas
-                y_proba, y_pred = bag.predict(df_train.drop(target, axis=1))
-
-                self.d_bagging[model_idx] = bag
-
-            # Model evaluation
-            eval_dict = classifier_evaluate(y_train, y_pred, y_proba, verbose=0)
-
-            # store
-            train_model = {'HP': HP_dict,
-                           'model': clf_fit,
-                           'features_importance': features_dict,
-                           'train_output': {'y_proba': y_proba, 'y_pred': y_pred},
-                           'train_metrics': eval_dict}
-
-            # store model results for each combination
-            self.d_train_model[model_idx] = train_model
-
-            # Fitted !
-            self.is_fitted = True
-
-            if verbose:
-                t_fin_model = datetime.now()
-                print(str(model_idx + 1) + '/' + str(len(sample_combinations)) +
-                      ' >> {} Sec.'.format((t_fin_model - t_ini_model).total_seconds()))
-
-        return self

-
-    """
-    -------------------------------------------------------------------------------------------------------------
-    """
-
-
[docs]    def predict(self, df, target, delta_auc, verbose=False):
-        """Apply the models
-        
-        Parameters
-        ----------
-        df : DataFrame
-            Dataset to apply the models
-        target : string
-            Target name
-        delta_auc_th : float
-            Threshold for valid models : abs(auc(train) - auc(test))
-        verbose : boolean (Default False)
-            Get logging information
-
-        Returns
-        -------
-        dict
-            {model_index : {'HP', 'probas', 'model', 'features_importance', 'train_metrics', 'metrics', 'output'}
-        """
-        assert self.is_fitted, 'fit first'
-
-        d_apply_model = self.d_train_model
-
-        # X / y
-        y = df[target]
-        X = df.drop(target, axis=1)
-
-        # For each HPs combination
-        for key, value in self.d_train_model.items():
-            t_ini_model = datetime.now()
-
-            modl = value['model']
-
-            # Without bagging
-            if not self.bagging:
-
-                # classification probas
-                y_proba = modl.predict_proba(X)[:, 1]
-
-                # classification votes
-                y_pred = modl.predict(X)
-
-            # With bagging
-            elif self.bagging:
-
-                # classification probs and votes
-                y_proba, y_pred = self.d_bagging[key].predict(X)
-
-            # store outputs
-            d_output = {'y_proba': y_proba,
-                        'y_pred': y_pred}
-
-            # compute model metrics
-            eval_dict = classifier_evaluate(y, y_pred, y_proba, verbose=0)
-            eval_dict['delta_auc'] = abs(self.d_train_model[key]['train_metrics']['Roc_auc'] - eval_dict["Roc_auc"])
-
-            # store 
-            d_apply_model[key]['outputs'] = d_output
-            d_apply_model[key]['metrics'] = eval_dict
-
-            # print metrics
-            if verbose:
-                print(value['HP'])
-                if eval_dict['delta_auc'] <= delta_auc:
-                    c_code = 32
-                else:
-                    c_code = 31
-
-                color_print(
-                    ' > AUC test: ' + str(round(eval_dict["Roc_auc"], 3)) + ' train: ' + str(
-                        round(self.d_train_model[key]['train_metrics']['Roc_auc'], 3)) +
-                    ' / F1: ' + str(round(eval_dict['F1'], 3)) +
-                    ' / prec: ' + str(round(eval_dict['Precision'], 3)) +
-                    ' / recall: ' + str(round(eval_dict['Recall'], 3)), color_code=c_code)
-
-                t_fin_model = datetime.now()
-                print('{} Sec.'.format((t_fin_model - t_ini_model).total_seconds()))
-
-        return d_apply_model

-
-    """
-    -------------------------------------------------------------------------------------------------------------
-    """
-
-
[docs]    def get_best_model(self, d_model_info, metric='F1', delta_auc_th=0.03, verbose=False):
-        """Identify valid models according to delta auc (test/train).
-        Get the best model in respect of a selected metric among valid model
-
-        Parameters
-        ----------
-        d_model_info : dict
-            {model_index : {'HP', 'probas', 'model', 'features_importance', 'train_metrics', 'metrics', 'output'}
-        metric : string (default = F1-score)
-            Metric used to get the best model
-        delta_auc_th : float
-            Threshold for valid models : abs(auc(train) - auc(test))
-        verbose : boolean (Default False)
-            Get logging information
-
-        Returns
-        -------
-        int
-            Best model index
-        list
-            Valid model indexes
-        """
-        # select valid models (abs(auc_train - auc_test)<0.03)
-        valid_model = {}
-        for key, param in d_model_info.items():
-            if param['metrics']['delta_auc'] <= delta_auc_th:
-                valid_model[key] = param
-
-        # Best model according to selected metric
-        if len(valid_model.keys()) > 0:
-            best_model_idx = max(valid_model, key=lambda x: valid_model[x].get('metrics').get(metric))
-            if verbose:
-                print(' >', len(valid_model.keys()), ' valid models |auc(train)-auc(test)|<=' + str(delta_auc_th))
-                print(' > best model : ' + str(best_model_idx))
-        else:
-            best_model_idx = None
-            print('0 valid model')
-
-        return best_model_idx, list(valid_model.keys())

-
-    """
-    ---------------------------------------------------------------------------------------------------------------
-    """
-
-
[docs]    def model_res_to_df(self, d_model_infos, sort_metric='F1'):
-        """Store models summary in DataFrame
-
-        Parameters
-        ----------
-        d_model_info : dict
-            {model_index : {'HP', 'probas', 'model', 'features_importance', 'train_metrics', 'metrics', 'output'}
-        sort_metric : string (default = 'F1')
-            metric to sort models (descendant)
-
-        Returns
-        -------
-        DataFrame
-            model infos and metrics
-        """
-        # dataFrame columns names
-        model_col = ['model_index']
-        HP_col = list(self.d_train_model[0]['HP'].keys())
-        bagging_col = ['bagging']
-        metrics_col = ['Accuracy', 'Roc_auc', 'F1', 'Logloss', 'Precision', 'Recall', 'delta_auc']
-        feat_imp_col = ['TOP_feat1', 'TOP_feat2', 'TOP_feat3', 'TOP_feat4', 'TOP_feat5']
-
-        df_local = pd.DataFrame(columns=model_col + HP_col + bagging_col + metrics_col + feat_imp_col)
-
-        # store informations in df
-        for key, value in self.d_train_model.items():
-            dict_tmp = {'model_index': key}
-            dict_tmp.update(value['HP'].copy())
-            dict_tmp.update({x: d_model_infos[key]['metrics'][x] for x in metrics_col})
-
-            dict_tmp.update({'bagging': self.bagging})
-            df_tmp = pd.DataFrame.from_dict(self.d_train_model[key]['features_importance'],
-                                            orient='index').reset_index().rename(
-                columns={'index': 'feat', 0: 'importance'}).sort_values(by='importance', ascending=False).head(5)
-            serie_tmp = df_tmp['feat'] + ' ' + round(df_tmp['importance'], 5).astype(str)
-            dict_tmp.update(dict(zip(feat_imp_col, serie_tmp.tolist())))
-
-            df_local = df_local.append(dict_tmp, ignore_index=True)
-
-        return df_local.loc[df_local['delta_auc'] <= 0.03].sort_values(by=sort_metric, ascending=False)

-
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- -
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- - © Copyright 2020, Maxence LABESSE - -

-
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Source code for AutoMxL.Preprocessing.Categorical

-""" Categorical features processing
-
- - CategoricalEncoder (class) : Encode categorical features
- - dummy_all_var (func) : get one hot encoded vector for each category of a categorical features list
- - get_embedded_cat (func) : get embedding representation with NN
- - mca (func) : to do
-
-"""
-import pandas as pd
-from AutoMxL.Preprocessing.Deep_Encoder import *
-from sklearn.preprocessing import LabelEncoder
-from AutoMxL.param_config import batch_size, n_epoch, learning_rate
-from AutoMxL.Explore.Features_Type import is_categorical, is_boolean
-
-
-
[docs]class CategoricalEncoder(object):
-    """Encode categorical features
-
-    Available encoding methods :
-
-    - one hot encoding
-    - deep_encoder : Build and train a Neural Network for the creation of embeddings for categorical variables.
-    (https://www.fast.ai/2018/04/29/categorical-embeddings/)
-
-    Default NN model parameters are stored in param_config.py file
-
-    Parameters
-    ----------
-    method : string (Default : deep_encoder)
-        method used to get categorical encoding
-        Available methods : "one_hot", "deep_encoder"
-    """
-
-    def __init__(self,
-                 method='deep_encoder'
-                 ):
-
-        assert method in ['deep_encoder', 'one_hot'], 'invalid method : select deep_encoder / one_hot'
-
-        self.method = method
-        self.is_fitted = False
-        self.l_var2encode = []
-        self.l_var_other = []
-        self.target = None
-        self.d_embeddings = {}
-        self.d_int_encoders = {}
-        self.d_metrics = {}
-
-    """
-    ----------------------------------------------------------------------------------------------
-    """
-
-
[docs]    def fit(self, df, l_var=None, target=None, verbose=False):
-        """ Fit encoder on dataset following method
-
-        Parameters
-        ----------
-        df : DataFrame
-            input dataset
-        l_var : list (Default None)
-            names of the variables to encode.
-            If None, all the categorical and boolean features
-        target : string (Default None)
-            name of the target for deep_encoder method
-        verbose : boolean (Default False)
-            Get logging information
-        """
-        if self.method == 'deep_encoder':
-            assert target is not None, 'fill target parameter to use deep encoder'
-
-        # get categorical and boolean features (see Features_Type module doc)
-        l_cat = [col for col in df.columns.tolist() if
-                 (is_categorical(df, col) or is_boolean(df, col)) and col != target]
-
-        # list of features to encode
-        if l_var is None:
-            self.l_var2encode = l_cat
-        else:
-            self.l_var2encode = [col for col in l_var if col in l_cat]
-
-        df_local = df.copy()
-
-        # store target
-        self.target = target
-
-        if verbose:
-            print(" **method : " + self.method)
-            if (self.method == 'deep_encoder') and (len(self.l_var2encode) > 20):
-                color_print('  might take a little while, make coffee', 32)
-            print("  >", len(self.l_var2encode), "features to encode")
-            if len(self.l_var2encode) > 0:
-                print(" ", self.l_var2encode)
-
-        if len(self.l_var2encode) > 0:
-            # deep learning embedded representation method
-            if self.method == 'deep_encoder':
-                self.d_int_encoders, self.d_embeddings, self.d_metrics = \
-                    get_embedded_cat(df_local, self.l_var2encode, target, batch_size, n_epoch, learning_rate,
-                                     verbose=False)
-
-        # Fitted !
-        self.is_fitted = True
-
-        # verbose
-        if verbose:
-            if (self.method == "deep_encoder") and len(self.l_var2encode) > 0:
-                print("  NN Loss:", round(self.d_metrics['loss'], 4), "/ Accuracy:",
-                      round(self.d_metrics['accuracy'], 4))
-                print("  Epoch:", n_epoch, "/ batch:", batch_size, "/ l_rate:", learning_rate)

-
-    """
-    ----------------------------------------------------------------------------------------------
-    """
-
-
[docs]    def transform(self, df, verbose=False):
-        """ transform dataset categorical features using the encoder.
-        Can be done only if encoder has been fitted
-
-        Parameters
-        ----------
-        df : DataFrame
-            dataset to transform
-        verbose : boolean (Default False)
-            Get logging information
-
-        Returns
-        -------
-        DataFrame : modified dataset
-        """
-        assert self.is_fitted, 'fit the encoding first using .fit method'
-
-        df_local = df.copy()
-
-        # if list of features to encode is not empty
-        if len(self.l_var2encode) > 0:
-            # one hot encoding method
-            if self.method == 'one_hot':
-
-                return dummy_all_var(df_local, var_list=self.l_var2encode, prefix_list=None, keep=False,
-                                     verbose=verbose)
-
-            # Deep learning embedding method
-            elif self.method == 'deep_encoder':
-                # features not to encode
-                self.l_var_other = [col for col in df_local.columns.tolist() if col not in self.l_var2encode]
-
-                # transform data with int encoder
-                for col in self.l_var2encode:
-                    df_local[col] = self.d_int_encoders[col].fit_transform(df_local[col].astype('str'))
-
-                # get embedding
-                if verbose:
-                    print(' Deep Encoder Embedding dim:')
-
-                df_embedded = df_local[self.l_var2encode].copy()
-
-                for col, d_level in self.d_embeddings.items():
-                    for i in range(len(d_level[0])):
-                        # replace int values with new embedding
-                        df_embedded[col + '_' + str(i)] = df_embedded[col].replace(
-                            {k: v[i] for k, v in d_level.items()})
-
-                    # drop raw feature
-                    df_embedded = df_embedded.drop(col, axis=1)
-
-                    # verbose
-                    if verbose:
-                        print("  > " + col + ":", len(d_level[0]))
-
-                return pd.concat([df[self.l_var_other], df_embedded], axis=1)
-
-        # if no feature to encode
-        else:
-            print(" No variable to encode")
-
-            return df_local

-
-    """
-    ----------------------------------------------------------------------------------------------
-    """
-
-
[docs]    def fit_transform(self, df, l_var=None, target=None, verbose=False):
-        """fit and transform dataset categorical features
-
-        Parameters
-        ----------
-        df : DataFrame
-            input dataset
-        l_var : list (Default None)
-            names of the variables to encode.
-            If None, all the categorical and boolean features
-        target : string (Default None)
-            name of the target for deep_encoder method
-        verbose : boolean (Default False)
-            Get logging information
-
-        Returns
-        -------
-        DataFrame : modified dataset
-        """
-        df_local = df.copy()
-        # fit
-        self.fit(df_local, l_var, target, verbose)
-        df_local = self.transform(df_local, verbose)
-
-        return df_local

-
-
-"""
-----------------------------------------------------------------------------------------------
-"""
-
-
-
[docs]def dummy_all_var(df, var_list=None, prefix_list=None, keep=False, verbose=False):
-    """Get one hot encoded vector for selected/all categorical features
-
-    Parameters
-    ----------
-     df : DatraFrame
-        Input dataset
-     var_list : list (Default : None)
-        Names of the features to dummify
-        If None, all the num features
-     prefix_list : list (default : None)
-        Prefix to add before new features name (prefix+'_'+cat).
-        If None, prefix=variable name
-     keep : boolean (Default = False)
-        If True, delete the original feature
-     verbose : boolean (Default False)
-        Get logging information
-
-    Returns
-    -------
-    DataFrame
-          Modified dataset
-    """
-    df_local = df.copy()
-
-    for col in var_list:
-        # if prefix_list == None, add column name as prefix, else add prefix_list
-        if prefix_list is None:
-            pref = col
-        else:
-            pref = prefix_list[var_list.index(col)]
-
-        # dummify
-        df_cat = pd.get_dummies(df_local[col], prefix=pref, drop_first=True)
-        # concat source DataFrame and new features
-        df_local = pd.concat((df_local, df_cat), axis=1)
-
-        # if keep = False, remove original features
-        if not keep:
-            df_local = df_local.drop(col, axis=1)
-        if verbose:
-            print('  > ' + col + ' ->', df_cat.columns.tolist())
-
-    return df_local

-
-
-"""
-----------------------------------------------------------------------------------------------
-"""
-
-
-
[docs]def get_embedded_cat(df, var_list, target, batchsize, n_epochs, lr, verbose=False):
-    """Get embedded representation for categorical features using NN encoder
-
-    Parameters
-    ----------
-    df : DataFrame
-        input Dataset
-    var_list : list of strings
-        features names
-    target : string
-        target name
-    batchsize : int
-        batch size for encoder training
-    n_epochs : int
-        number of epoch for encoder training
-    lr : float
-        encoder learning rate
-    verbose : boolean (Default False)
-        Get logging information
-
-    Returns
-    -------
-        DataFrame : modified dataset
-    """
-    ######################
-    # Get list to encode #
-    ######################
-    df_local = df[var_list + [target]].copy()
-
-    ############################
-    # Categories to int labels #
-    ############################
-    d_int_encoders = {}
-    for cat_col in var_list:
-        d_int_encoders[cat_col] = LabelEncoder()
-        df_local[cat_col] = d_int_encoders[cat_col].fit_transform(df_local[cat_col].astype('str'))
-
-    ###################
-    # Get layer sizes #
-    ###################
-    d_exp = {col: np.exp(-df_local[col].nunique() * 0.05) for col in var_list}
-    d_tmp = {col: np.int(5 * (1 - exp) + 1) for col, exp in d_exp.items()}
-
-    sum_ = sum([1. * np.log(k) for k in d_tmp.values()])
-
-    A, B = 10, 5
-    nlayer1 = min(1000, int(A * (len(d_tmp) ** 0.5) * sum_ + 1))
-    nlayer2 = int(nlayer1 / B) + 2
-
-    emb_dims = [(df_local[col].nunique(), d_tmp[col]) for col in var_list]
-
-    #####################
-    # Train the encoder #
-    #####################
-    # Create Torch_Dataset
-    df_to_encoder = Torch_Dataset(data=df_local, cat_cols=var_list, output_col=target)
-
-    model = Deep_Cat_Encoder(emb_dims, layer_sizes=[nlayer1, nlayer2], output_size=1)
-
-    fit_model, loss, accuracy = train_deep_encoder(df_to_encoder, model=model, optimizer='Adam', criterion='MSE',
-                                                   lr=lr, n_epochs=n_epochs, batchsize=batchsize,
-                                                   verbose=verbose)
-
-    d_metrics = {'loss': loss, 'accuracy': accuracy}
-
-    ############################################
-    # Store embedding and get output DataFrame #
-    ############################################
-    i = 0
-    d_embeddings = {}
-    for param in fit_model.emb_layers.parameters():
-        d_embeddings[var_list[i]] = dict(zip(list(range(len(param.data[:, 0]))), param.data.tolist()))
-        i += 1
-
-    return d_int_encoders, d_embeddings, d_metrics

-
- -
- -
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- - -
- -
-

- - © Copyright 2020, Maxence LABESSE - -

-
- - - - Built with Sphinx using a - - theme - - provided by Read the Docs. - -
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- - - - - - - - - - - \ No newline at end of file diff --git a/docs/_build/html/_modules/AutoMxL/Preprocessing/Categorical_Data.html b/docs/_build/html/_modules/AutoMxL/Preprocessing/Categorical_Data.html deleted file mode 100644 index 9126580..0000000 --- a/docs/_build/html/_modules/AutoMxL/Preprocessing/Categorical_Data.html +++ /dev/null @@ -1,255 +0,0 @@ - - - - - - - - - - - MLBG59.Preprocessing.Categorical_Data — MLBG59 1.0.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
- - - -
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    • - - -
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Source code for MLBG59.Preprocessing.Categorical_Data

-""" Categorical features processing
-
- - dummy_all_var : get one hot encoded vector for each category of a categorical features list
- - label encoding : coming soon
-"""
-import pandas as pd
-from MLBG59.Utils.Utils import get_type_features
-
-
-
[docs]def dummy_all_var(df, var_list=None, prefix_list=None, keep=False, verbose=1):
-    """Get one hot encoded vector for selected/all categorical features
-    
-    Parameters
-    ----------
-     df : DatraFrame
-        Input dataset
-     var_list : list (Default : None)
-        Names of the features to dummify
-        If None, all the num features
-     prefix_list : list (default : None)
-        Prefix to add before new features name (prefix+'_'+cat).
-        It None, prefix=variable name
-     Keep : boolean (Default = False)
-        If True, delete the original feature
-     verbose : boolean (Default False)
-        Get logging information
-        
-    Returns
-    -------
-    DataFrame
-          Modified dataset
-    """
-    # if var_list = None, get all categorical features
-    # else, exclude features from var_list whose type is not categorical
-    var_list = get_type_features(df, 'cat', var_list)
-
-    df_local = df.copy()
-
-    if verbose:
-        print('  ** method : one hot encoding')
-
-    for col in var_list:
-        # if prefix_list == None, add column name as prefix, else add prefix_list
-        if prefix_list == None:
-            pref = col
-        else:
-            pref = prefix_list[var_list.index(col)]
-
-        # dummify
-        df_cat = pd.get_dummies(df_local[col], prefix=pref)
-        # concat source DataFrame and new features
-        df_local = pd.concat((df_local, df_cat), axis=1)
-
-        # if keep = False, delete original features
-        if keep == False:
-            df_local = df_local.drop(col, axis=1)
-        if verbose:
-            print('  > ' + col + ' ->', df_cat.columns.tolist())
-
-    return df_local

-
- -
- -
-
- - -
- -
-

- © Copyright 2020, Maxence LABESSE - -

-
- Built with Sphinx using a theme provided by Read the Docs. - -
- -
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- -
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- - - - - - - - - - - - \ No newline at end of file diff --git a/docs/_build/html/_modules/AutoMxL/Preprocessing/Date.html b/docs/_build/html/_modules/AutoMxL/Preprocessing/Date.html deleted file mode 100644 index a17a7e3..0000000 --- a/docs/_build/html/_modules/AutoMxL/Preprocessing/Date.html +++ /dev/null @@ -1,462 +0,0 @@ - - - - - - - - - - AutoMxL.Preprocessing.Date — AutoMxL 1.0.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    • - - -
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- -

Source code for AutoMxL.Preprocessing.Date

-""" Date Features processing functions:
-
- - DateEncoder (class) : encode date features
- - all_to_date (func): detect dates from num/cat features and transform them to datetime format.
- - date_to_anc (func): transform datetime features to timedelta according to a ref date
-"""
-import pandas as pd
-from datetime import datetime
-from AutoMxL.Explore.Features_Type import features_from_type
-
-
-
[docs]class DateEncoder(object):
-    """Encode categorical features
-
-    Available methods :
-
-    - timedelta : compute time between date feature and parameter date_ref
-
-    Parameters
-    ----------
-    method : string (Default : timedelta)
-        method used to encode dates
-        Available methods : "timedelta"
-    date_ref : string '%d/%m/%y' (Default : None)
-        Date to compute timedelta.
-        If None, today date
-    """
-
-    def __init__(self,
-                 method='timedelta',
-                 date_ref=None,):
-
-        assert method in ['timedelta'], "invalid method : select timedelta"
-
-        self.method = method
-        self.is_fitted = False
-        self.l_var2encode = []
-        # if date_ref not filled, set to today's date
-        if date_ref is None:
-            self.date_ref = datetime.now()
-        else:
-            self.date_ref = date_ref
-
-    """
-    ----------------------------------------------------------------------------------------------
-    """
-
-
[docs]    def fit(self, df, l_var=None, verbose=False):
-        """fit encoder
-
-        Parameters
-        ----------
-        df : DataFrame
-            input dataset
-        l_var : list
-            features to encode.
-            If None, contains all features identified as dates (see Features_Type module)
-        verbose : boolean (Default False)
-            Get logging information
-        """
-        # get date features
-        l_date_var = features_from_type(df, typ='date', l_var=None)
-
-        # list of features to encode (in l_var and l_date_var)
-        if l_var is None:
-            self.l_var2encode = l_date_var
-        else:
-            self.l_var2encode = [col for col in l_var if col in l_date_var]
-
-        # Fitted !!!!
-        self.is_fitted = True
-
-        # verbose
-        if verbose:
-            if self.method == 'timedelta':
-                print(" **method " + self.method + " / date ref : ", self.date_ref)
-
-            print("  >", len(self.l_var2encode), "features to transform")
-            if len(self.l_var2encode) > 0:
-                print(" ", self.l_var2encode)

-
-    """
-    ----------------------------------------------------------------------------------------------
-    """
-
-
[docs]    def transform(self, df, verbose=False):
-        """ transform dataset date features using the encoder.
-        Can be done only if encoder has been fitted
-
-        Parameters
-        ----------
-        df : DataFrame
-            dataset to transform
-        verbose : boolean (Default False)
-            Get logging information
-        """
-        assert self.is_fitted, 'fit the encoding first using .fit method'
-
-        df_local = df.copy()
-
-        # if list of features to encode not empty
-        if len(self.l_var2encode) > 0:
-            # transform features to datetime
-            df_local = all_to_date(df_local, l_var=self.l_var2encode, verbose=verbose)
-
-            # method timedelta
-            if self.method == 'timedelta':
-                df_local, _ = date_to_anc(df_local, l_var=self.l_var2encode, date_ref=self.date_ref, verbose=verbose)
-
-        # if no features to transform
-        elif verbose:
-            print("  > No date to transform")
-
-        return df_local

-
-    """
-    ----------------------------------------------------------------------------------------------
-    """
-
-
[docs]    def fit_transform(self, df, l_var=None, verbose=False):
-        """fit and transform dataset with encoder
-
-        Parameters
-        ----------
-        df : DataFrame
-            input dataset
-        l_var : list
-            features to encode.
-            If None, all features identified as dates (see Features_Type module)
-        verbose : boolean (Default False)
-            Get logging information
-        """
-        df_local = df.copy()
-        # fit
-        self.fit(df_local, l_var=l_var, verbose=verbose)
-        # transform
-        df_local = self.transform(df_local, verbose=verbose)
-
-        return df_local

-
-
-"""
-----------------------------------------------------------------------------------------------
-"""
-
-
-
[docs]def all_to_date(df, l_var=None, verbose=False):
-    """Detect dates from selected/all features and transform them to datetime format.
-    
-    Parameters
-    ----------
-    df : DataFrame
-        Input dataset
-    l_var : list (Default : None)
-        Names of the features
-        If None, all the features
-    verbose : boolean (Default False)
-        Get logging information
-        
-    Return
-    -------
-    DataFrame
-        Modified dataset
-    """
-    # if var_list = None, get all df features
-    # else, exclude features if not in df
-    if l_var is None:
-        l_var = df.columns.tolist()
-    else:
-        l_var = [col for col in l_var if col in df.columns.tolist()]
-
-    df_local = df.copy()
-
-    if verbose:
-        print('  > features : ', l_var)
-        print('  > features conversion to date using "try .to_datetime')
-
-    # for each feature in var_list, try to convert to datetime
-    for col in l_var:
-        try:
-            if df_local[col].dtype == 'object':
-                df_local[col] = pd.to_datetime(df_local[col], errors='raise')
-            else:
-                df_smpl = df.loc[~df[col].isna()].copy()
-                df_smpl[col] = pd.to_datetime(df_smpl[col].astype('Int32').astype(str), errors='raise')
-                df_local[col] = pd.to_datetime(df_local[col].astype('Int32').astype(str), errors='coerce')
-        except ValueError:
-            pass
-        except OverflowError:
-            pass
-        except TypeError:
-            pass
-
-    return df_local

-
-
-"""
--------------------------------------------------------------------------------------------------------------------------
-"""
-
-
-
[docs]def date_to_anc(df, l_var=None, date_ref=None, verbose=False):
-    """Transform selected/all datetime features to timedelta according to a ref date
-    
-    Parameters
-    ----------
-    df : DataFrame
-        Input dataset
-    l_var : list (Default : None)
-        List of the features to analyze.
-        If None, contains all the datetime features
-    date_ref : string '%d/%m/%y' (Default : None)
-        Date to compute timedelta.
-        If None, today date
-    verbose : boolean (Default False)
-        Get logging information
-
-    Returns
-    -------
-    DataFrame
-        Modified dataset
-        
-    list
-        New timedelta features names
-    """
-    # if date_ref is None, use today date
-    if date_ref is None:
-        date_ref = datetime.now()
-    else:
-        if isinstance(date_ref, datetime):
-            pass
-        else:
-            date_ref = datetime.strptime(date_ref, '%d/%m/%Y')
-
-    # if var_list = None, get all datetime features
-    # else, exclude features from var_list whose type is not datetime
-    l_date = df.dtypes[df.dtypes == 'datetime64[ns]'].index.tolist()
-    if l_var is None:
-        l_var = l_date
-    else:
-        l_var = [col for col in l_var if col in l_date]
-
-    df_local = df.copy()
-
-    # new variables names
-    l_new_var_names = ['anc_' + col for col in l_var]
-    # compute time delta for selected dates variables
-    df_local = df_local.apply(lambda x: (date_ref - x).dt.days / 365 if x.name in l_var else x)
-    # rename columns
-    df_local = df_local.rename(columns=dict(zip(l_var, l_new_var_names)))
-
-    if verbose:
-        print('  ** Reference date for timelapse computing : ', date_ref)
-        list(map(lambda x, y: print("  >", x + ' -> ' + y), l_var, l_new_var_names))
-
-    return df_local, l_new_var_names

-
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- - © Copyright 2020, Maxence LABESSE - -

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- - - - - - - - - - - \ No newline at end of file diff --git a/docs/_build/html/_modules/AutoMxL/Preprocessing/Date_Data.html b/docs/_build/html/_modules/AutoMxL/Preprocessing/Date_Data.html deleted file mode 100644 index 9206231..0000000 --- a/docs/_build/html/_modules/AutoMxL/Preprocessing/Date_Data.html +++ /dev/null @@ -1,303 +0,0 @@ - - - - - - - - - - - MLBG59.Preprocessing.Date_Data — MLBG59 1.0.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    • - -
  • MLBG59.Preprocessing.Date_Data
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Source code for MLBG59.Preprocessing.Date_Data

-""" Date Features processing functions:
-
- - all_to_date : detect dates from num/cat features and transform them to datetime format.
- - date_to_anc : Transform datetime features to timedelta according to a ref date
-"""
-import pandas as pd
-from datetime import datetime
-from MLBG59.Utils.Utils import get_type_features
-
-
-
[docs]def all_to_date(df, var_list=None, verbose=1):
-    """Detect dates from selected/all features and transform them to datetime format.
-    
-    Parameters
-    ----------
-    df : DataFrame
-        Input dataset
-    var_list : list (Default : None)
-        Names of the features
-        If None, all the features
-    verbose : boolean (Default False)
-        Get logging information
-        
-    Return
-    -------
-    DataFrame
-        Modified dataset
-    """
-    # if var_list = None, get all df features
-    # else, exclude features if not in df
-    var_list = get_type_features(df, 'all', var_list)
-    df_local = df.copy()
-
-    if verbose:
-        print('  > features : ', var_list)
-        print('  > features conversion to date using "try .to_datetime')
-
-    # for each feature in var_list, try to convert to datetime
-    for col in var_list:
-        try:
-            if df_local[col].dtype == 'object':
-                df_local[col] = pd.to_datetime(df_local[col], errors='coerce')
-            else:
-                df_local[col] = pd.to_datetime(df_local[col].astype('Int32').astype(str), errors='coerce')
-        except:
-            pass
-
-    return df_local

-
-
-"""
--------------------------------------------------------------------------------------------------------------------------
-"""
-
-
-
[docs]def date_to_anc(df, var_list=None, date_ref=None, verbose=1):
-    """Transform selected/all datetime features to timedelta according to a ref date
-    
-    Parameters
-    ----------
-    df : DataFrame
-        Input dataset
-    var_list : list (Default : None)
-        List of the features to analyze.
-        If None, contains all the datetime features
-    date_ref : string '%d/%m/%y' (Default : None)
-        Date to compute timedelta.
-        If None, today date
-    verbose : boolean (Default False)
-        Get logging information
-
-    Returns
-    -------
-    DataFrame
-        Modified dataset
-        
-    list
-        New timedelta features names
-    """
-    # if date_ref is None, use today date
-    if date_ref is None:
-        date_ref = datetime.now()
-    else:
-        date_ref = datetime.strptime(date_ref, '%d/%m/%Y')
-
-    # if var_list = None, get all datetime features
-    # else, exclude features from var_list whose type is not datetime
-    var_list = get_type_features(df, 'date', var_list)
-
-    df_local = df.copy()
-
-    if verbose > 0:
-        print('  ** Reference date for timelapse computing : ', date_ref)
-
-    # initialisation
-    new_var_list = []
-
-    for col in var_list:
-        # new feature name
-        var_name = 'anc_' + col
-        df_local[var_name] = (date_ref - df_local[col]).dt.days / 365
-        del df_local[col]
-        new_var_list.append(var_name)
-
-        if verbose > 0:
-            print("  >", col + ' -> ' + var_name)
-
-    return df_local, new_var_list

-
- -
- -
-
- - -
- -
-

- © Copyright 2020, Maxence LABESSE - -

-
- Built with Sphinx using a theme provided by Read the Docs. - -
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- - - - - - - - - - - - \ No newline at end of file diff --git a/docs/_build/html/_modules/AutoMxL/Preprocessing/Missing_Values.html b/docs/_build/html/_modules/AutoMxL/Preprocessing/Missing_Values.html deleted file mode 100644 index 76e9432..0000000 --- a/docs/_build/html/_modules/AutoMxL/Preprocessing/Missing_Values.html +++ /dev/null @@ -1,487 +0,0 @@ - - - - - - - - - - AutoMxL.Preprocessing.Missing_Values — AutoMxL 1.0.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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Source code for AutoMxL.Preprocessing.Missing_Values

-""" Missing values handling functions :
-
- - NAEncoder (class): encoder that replaces missing values
- - fill_numerical (func): replace missing values for numerical features
- - fill_categorical (func): replace missing values for categorical features
- - get_NA_features (func): get features containing NA values
-"""
-import pandas as pd
-import numpy as np
-
-
-
[docs]class NAEncoder(object):
-    """ Missing values filling
-
-    Available methods to replace missing values
-
-    - num : metdian/mean/zero
-    - cat : 'NR'
-
-    Parameters
-    ----------
-    replace_num_with: string
-        method used to replace numerical missing values
-    replace_cat_with: string
-        method used to replace categorical missing values
-    """
-
-    def __init__(self,
-                 replace_num_with='median',
-                 replace_cat_with='NR',
-                 track_num_NA=True
-                 ):
-
-        assert replace_num_with in ['median', 'mean', 'zero'], 'invalid method, select median/mean/zero'
-        assert replace_cat_with in ['NR'], 'invalid method, select NR'
-        self.replace_num_with = replace_num_with
-        self.replace_cat_with = replace_cat_with
-        self.track_num_NA = track_num_NA
-        self.l_var_cat = []
-        self.l_var_num = []
-        self.is_fitted = False
-
-    """
-    ----------------------------------------------------------------------------------------------
-    """
-
-
[docs]    def fit(self, df, l_var, verbose=False):
-        """fit encoder
-
-        Parameters
-        ----------
-        df : DataFrame
-            input dataset
-        l_var : list
-            features to encode.
-            If None, all features
-        verbose : boolean (Default False)
-            Get logging information
-        """
-        # get num and categorical columns
-        l_num = [col for col in df.columns.tolist() if df[col].dtype != 'object']
-        l_str = [col for col in df.columns.tolist() if df[col].dtype == 'object']
-
-        # get list of valid features (containing NA)
-        if l_var is None:
-            self.l_var_cat = [col for col in l_str if df[col].isna().sum() > 0]
-            self.l_var_num = [col for col in l_num if df[col].isna().sum() > 0]
-        else:
-            self.l_var_cat = [col for col in l_var if col in l_str and df[col].isna().sum() > 0]
-            self.l_var_num = [col for col in l_var if col in l_num and df[col].isna().sum() > 0]
-
-        # Fitted !
-        self.is_fitted = True
-
-        # verbose
-        if verbose:
-            print(" **method cat:", self.replace_cat_with, " / num:", self.replace_num_with)
-            print("  >", len(self.l_var_cat) + len(self.l_var_num), "features to fill")
-            if len(self.l_var_cat) > 0:
-                print("  - cat", self.l_var_cat)
-            if len(self.l_var_num) > 0:
-                print("  - num", self.l_var_num)

-
-    """
-    ----------------------------------------------------------------------------------------------
-    """
-
-
[docs]    def transform(self, df, verbose=False):
-        """ transform dataset categorical features using the encoder.
-        Can be done only if encoder has been fitted
-
-        Parameters
-        ----------
-        df : DataFrame
-            dataset to transform
-        verbose : boolean (Default False)
-            Get logging information
-        """
-        assert self.is_fitted, 'fit the encoding first using .fit method'
-
-        df_local = df.copy()
-
-        # categorical features filling
-        if len(self.l_var_cat) > 0:
-            df_local = fill_categorical(df_local, l_var=self.l_var_cat, method=self.replace_cat_with,
-                                        verbose=verbose)
-
-        # numerical features filling
-        if len(self.l_var_num) > 0:
-            df_local = fill_numerical(df_local, l_var=self.l_var_num, method=self.replace_num_with,
-                                      track_num_NA=self.track_num_NA, verbose=verbose)
-
-        # if no feature to fill
-        if len(self.l_var_cat) + len(self.l_var_num) == 0 and verbose:
-            print("  > no transformation to apply")
-
-        return df_local

-
-    """
-    ----------------------------------------------------------------------------------------------
-    """
-
-
[docs]    def fit_transform(self, df, l_var=None, verbose=False):
-        """fit and transform dataset with encoder
-
-        Parameters
-        ----------
-        df : DataFrame
-            input dataset
-        l_var : list
-            features to encode.
-            If None, all features identified as dates (see Features_Type module)
-        verbose : boolean (Default False)
-            Get logging information
-        """
-        df_local = df.copy()
-        # fit
-        self.fit(df_local, l_var=l_var, verbose=verbose)
-        # transform
-        df_local = self.transform(df_local, verbose=verbose)
-
-        return df_local

-
-    """
-    ----------------------------------------------------------------------------------------------
-    """

-
-
-
[docs]def fill_numerical(df, l_var=None, method='median', track_num_NA=True, verbose=False):
-    """Fill missing values for selected/all numerical features.
-    top_var_NA parameter allows to create a variable to keep track of missing values.
-
-    Available methods : replace with zero, median or mean (Default = median)
-
-    Parameters
-    ----------
-    df : DataFrame
-        Input dataset
-    l_var : list (Default : None)
-        names of the features to fill.
-        If None, all the numerical features
-    method : string (Default : 'median')
-        Method used to fill the NA values :
-
-        - zero : replace with zero
-        - median : replace with median
-        - mean : replace with mean
-
-    track_num_NA : boolean (Defaut : True)
-        If True, create a boolean column to keep track of missing values
-    verbose : boolean (Default False)
-        Get logging information
-
-    Returns
-    -------
-    DataFrame
-        Modified dataset
-    """
-    assert method in ['zero', 'median', 'mean'], method + ' invalid method : choose zero, median or mean'
-
-    # if var_list = None, get all num features
-    # else, remove features from var_list whose type is not num
-    l_num = df._get_numeric_data().columns.tolist()
-
-    if l_var is None:
-        l_var = l_num
-    else:
-        l_var = [col for col in l_var if col in l_num]
-
-    df_local = df.copy()
-
-    # values to fill NA
-    if method == 'median':
-        fill_value = df_local[l_var].mean()
-    elif method == 'mean':
-        fill_value = df_local[l_var].mean()
-    elif method == 'zero':
-        fill_value = pd.Series([0] * len(l_var), index=l_var)
-
-    for var in l_var:
-        if track_num_NA:
-            # keep track of NA values in Top_var_NA
-            df_local['top_NA_' + var] = df_local.apply(lambda x: 1 if np.isnan(x[var]) else 0, axis=1)
-        # fill NA
-        df_local[var] = df_local[var].fillna(fill_value[var])
-
-    if verbose:
-        print('  > method: ' + method)
-        print('  > filled features:', df[l_var].isna().sum().loc[df[l_var].isna().sum() > 0].index.tolist())
-
-    return df_local

-
-
-"""
--------------------------------------------------------------------------------------------------------------------------
-"""
-
-
-
[docs]def fill_categorical(df, l_var=None, method='NR', verbose=False):
-    """Fill missing values for selected/all categorical features.
-
-    Parameters
-    ----------
-    df : DataFrame
-        Input dataset
-    l_var : list (Default : None)
-        list of the features to fill.
-        If None, contains all the categorical features
-    method : string (Default : 'NR')
-        Method used to fill the NA values :
-
-        - NR : replace NA with 'NR'
-
-    verbose : boolean (Default False)
-        Get logging information
-
-    Returns
-    -------
-    DataFrame
-        Modified dataset
-    """
-    assert method in ['NR'], method + ' invalid method : choose NR '
-
-    # if var_list = None, get all categorical features
-    # else, remove features from var_list whose type is not categorical
-    l_cat = [col for col in df.columns.tolist() if df[col].dtype == 'object']
-
-    if l_var is None:
-        l_var = l_cat
-    else:
-        l_var = [col for col in l_var if col in l_cat]
-
-    df_local = df.copy()
-
-    # values to fill NA
-    if method in ['NR']:
-        fill_value = 'NR'
-
-    for var in l_var:
-        df_local[var] = df_local[var].fillna(fill_value)
-
-    if verbose:
-        print('  > method: ' + method)
-        print('  > filled features:', df[l_var].isna().sum().loc[df[l_var].isna().sum() > 0].index.tolist())
-
-    return df_local

-
-
-
[docs]def get_NA_features(df):
-    """identify features containing NA values
-
-    Parameters
-    ----------
-    df : DataFrame
-        input dataset
-
-    Returns
-    -------
-    list : features containing missing values
-    """
-    return df.isna().sum()[df.isna().sum() > 0].index.tolist()

-
- -
- -
-
- - -
- -
-

- - © Copyright 2020, Maxence LABESSE - -

-
- - - - Built with Sphinx using a - - theme - - provided by Read the Docs. - -
- -
-
- -
- -
- - - - - - - - - - - \ No newline at end of file diff --git a/docs/_build/html/_modules/AutoMxL/Preprocessing/Outliers.html b/docs/_build/html/_modules/AutoMxL/Preprocessing/Outliers.html deleted file mode 100644 index 9bb9ec3..0000000 --- a/docs/_build/html/_modules/AutoMxL/Preprocessing/Outliers.html +++ /dev/null @@ -1,557 +0,0 @@ - - - - - - - - - - AutoMxL.Preprocessing.Outliers — AutoMxL 1.0.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
- - - -
- - - - - -
- -
- - - - - - - - - - - - - - - - - -
- -
    - -
  • »
    • - -
  • Module code »
    • - -
  • AutoMxL.Preprocessing.Outliers
    • - - -
  • - -
    • - -
- - -
-
-
-
- -

Source code for AutoMxL.Preprocessing.Outliers

-""" Outliers handling functions
-
- - OutliersEncoding (class) : identify and replace outliers
- - get_cat_outliers (funct): identify categorical features containing outliers
- - get_num_outliers (func): identify numerical features containing outliers
- - replace_category (func): replace categories of a categorical variable
- - replace_extreme_values (func): replace extreme values (oh!)
-"""
-import pandas as pd
-import numpy as np
-from AutoMxL.Utils.Display import *
-
-
-
[docs]class OutliersEncoder(object):
-    """Identify et replace outliers for categorical dang numerical features
-
-    - num : x outlier <=> abs(x - mean) > xstd * var
-    - cat : x outlier category <=> with frequency <x% (Default 5%)
-
-    Parameters
-    ----------
-    cat_threshold : float (default 0.02)
-        Minimum modality frequency
-    num_xstd : int (Default : 3)
-        Variance gap coef
-
-    """
-
-    def __init__(self,
-                 cat_threshold=0.02,
-                 num_xstd=4
-                 ):
-
-        self.cat_threshold = cat_threshold,
-        self.num_xstd = num_xstd
-        self.is_fitted = False
-        self.l_var_num = []
-        self.l_var_cat = []
-        self.d_num_outliers = {}
-        self.d_cat_outliers = {}
-
-    """
-    ----------------------------------------------------------------------------------------------
-    """
-
-
[docs]    def fit(self, df, l_var, verbose=False):
-        """Fit encoder
-
-        Parameters
-        ----------
-        df : DataFrame
-            input dataset
-        l_var : list
-            features to encode.
-            If None, all features
-        verbose : boolean (Default False)
-            Get logging information
-        """
-        # get num and cat features
-        l_num = [col for col in df.columns.tolist() if df[col].dtype != 'object']
-        l_str = [col for col in df.columns.tolist() if df[col].dtype == 'object']
-
-        # get valid values (not boolean)
-        if l_var is None:
-            self.l_var_cat = [col for col in l_str if df[col].nunique() > 2]
-            self.l_var_num = [col for col in l_num if df[col].nunique() > 2]
-        else:
-            self.l_var_cat = [col for col in l_var if col in l_str and df[col].nunique() > 2]
-            self.l_var_num = [col for col in l_var if col in l_num and df[col].nunique() > 2]
-
-
-        # cat outliers
-        if len(self.l_var_cat) > 0:
-            self.d_cat_outliers = get_cat_outliers(df, l_var=self.l_var_cat, threshold=self.cat_threshold,
-                                                   verbose=False)
-
-        # num outliers
-        if len(self.l_var_num) > 0:
-            self.d_num_outliers = get_num_outliers(df, l_var=self.l_var_num, xstd=self.num_xstd, verbose=False)
-
-        # Fitted !
-        self.is_fitted = True
-
-        # verbose
-        if verbose:
-            print(" **method cat: frequency<" + str(self.cat_threshold)
-                  + " / num:( x: |x - mean| > " + str(self.num_xstd) + "* var)")
-            print("  >", len(self.d_cat_outliers.keys()) + len(self.d_num_outliers.keys()), "features with outliers")
-            if len(self.d_cat_outliers.keys()) > 0:
-                print("  - cat", list(self.d_cat_outliers.keys()))
-            if len(self.d_num_outliers.keys()) > 0:
-                print("  - num", list(self.d_num_outliers.keys()))

-
-    """
-    ----------------------------------------------------------------------------------------------
-    """
-
-
[docs]    def transform(self, df, verbose=False):
-        """Transform dataset  features using the encoder.
-        Can be done only if encoder has been fitted
-
-        Parameters
-        ----------
-        df : DataFrame
-            dataset to transform
-        verbose : boolean (Default False)
-            Get logging information
-        """
-        assert self.is_fitted, 'fit the encoding first using .fit method'
-        df_local = df.copy()
-
-        # cat features
-        if len(list(self.d_cat_outliers.keys())) > 0:
-            if verbose:
-                print(" - cat aggregated values:")
-            for col in self.d_cat_outliers.keys():
-                df_local = replace_category(df_local, col, self.d_cat_outliers[col], replace_with='outliers',
-                                            verbose=verbose)
-
-        # num features
-        if len(list(self.d_num_outliers.keys())) > 0:
-            if verbose:
-                print(" - num values replaces:")
-            for col in self.d_num_outliers.keys():
-                df_local = replace_extreme_values(df_local, col, self.d_num_outliers[col][0],
-                                                  self.d_num_outliers[col][1], verbose=verbose)
-
-        # if no features with outliers
-        if len(list(self.d_cat_outliers.keys())) + len(list(self.d_num_outliers.keys())) == 0:
-            print("  > no outlier to replace")
-
-        return df_local

-
-    """
-    ----------------------------------------------------------------------------------------------
-    """
-
-
[docs]    def fit_transform(self, df, l_var=None, verbose=False):
-        """Fit and transform dataset with encoder
-
-        Parameters
-        ----------
-        df : DataFrame
-            input dataset
-        l_var : list
-            features to encode.
-            If None, all features identified as dates (see Features_Type module)
-        verbose : boolean (Default False)
-            Get logging information
-        """
-        df_local = df.copy()
-        # fit
-        self.fit(df_local, l_var=l_var, verbose=False)
-        # transform
-        df_local = self.transform(df_local, verbose=verbose)
-
-        return df_local

-
-
-"""
-----------------------------------------------------------------------------------------------
-"""
-
-
-
[docs]def get_cat_outliers(df, l_var=None, threshold=0.05, verbose=False):
-    """Outliers detection for selected/all categorical features.
-
-    Method : Modalities with frequency <x% (Default 5%)
-
-    Parameters
-    ----------
-     df : DataFrame
-        Input dataset
-     l_var : list (Default : None)
-        Names of the features
-        If None, all the categorical features
-     threshold : float (Default : 0.05)
-        Minimum modality frequency
-     verbose : boolean (Default False)
-        Get logging information
-
-    Returns
-    -------
-    dict
-        {variable : list of categories considered as outliers}
-    """
-    # if var_list = None, get all categorical features
-    # else, remove features from var_list whose type is not categorical
-    l_cat = [col for col in df.columns.tolist() if df[col].dtype == 'object']
-
-    if l_var is None:
-        l_var = l_cat
-    else:
-        l_var = [col for col in l_var if col in l_cat]
-
-    df_local = df[l_var].copy()
-
-    # dict containing value_counts for each variable
-    d_freq = {col: pd.value_counts(df[col], dropna=False, normalize=True) for col in l_var}
-
-    # if features contain at least 1 outlier category (frequency <threshold)
-    # store outliers categories in dict
-    d_outliers = {k: v[v < threshold].index.tolist()
-                  for k, v in d_freq.items()
-                  if len(v[v < threshold]) > 1}
-
-    if verbose:
-        color_print('cat features outliers identification (frequency<' + str(threshold) + ')')
-        print('  > features : ', df_local.columns, )
-        print("  > containing outliers", list(d_outliers.keys()))
-
-    return d_outliers

-
-
-"""
--------------------------------------------------------------------------------------------------------------------------
-"""
-
-
-
[docs]def get_num_outliers(df, l_var=None, xstd=3, verbose=False):
-    """Outliers detection for selected/all numerical features.
-
-    Method : x outlier <=> abs(x - mean) > xstd * var
-
-    Parameters
-    ----------
-     df : DataFrame
-        Input dataset
-     l_var : list (Default : None)
-        Names of the features
-        If None, all the num features
-     xstd : int (Default : 3)
-        Variance gap coef
-     verbose : boolean (Default False)
-        Get logging information
-
-    Returns
-    -------
-    dict
-        {variable : [lower_limit, upper_limit]}
-    """
-    # if var_list = None, get all num features
-    # else, remove features from var_list whose type is not num
-    l_num = df._get_numeric_data().columns.tolist()
-
-    if l_var is None:
-        l_var = l_num
-    else:
-        l_var = [col for col in l_var if col in l_num]
-
-    df_local = df[l_var].copy()
-
-    # compute features upper and lower limit (abs(x - mean) > xstd * var (x=3 by default))
-    data_std = np.std(df_local)
-    data_mean = np.mean(df_local)
-    anomaly_cut_off = data_std * xstd
-    lower_limit = data_mean - anomaly_cut_off
-    upper_limit = data_mean + anomaly_cut_off
-    data_min = np.min(df_local)
-    data_max = np.max(df_local)
-
-    # store variables and lower/upper limits
-    d_outliers = {col: [lower_limit[col], upper_limit[col]]
-                  for col in df_local.columns.tolist()
-                  if (data_min[col] < lower_limit[col] or data_max[col] > upper_limit[col])}
-
-    if verbose:
-        color_print('num features outliers identification ( x: |x - mean| > ' + str(xstd) + ' * var)')
-        print('  > features : ', l_var)
-        print("  > containing outliers", list(d_outliers.keys()))
-
-    return d_outliers

-
-
-"""
--------------------------------------------------------------------------------------------------------------------------
-"""
-
-
-
[docs]def replace_category(df, var, categories, replace_with='outliers', verbose=False):
-    """Replace categories of a categorical variable
-    
-    Parameters
-    ----------
-    df : DataFrame
-        Input dataset
-    var : string
-        variable to modify
-    categories : list(string)
-        categories to replace
-    replace_with : string (Default : 'outliers')
-        word to replace categories with
-    verbose : boolean (Default False)
-        Get logging information
-        
-    Returns
-    -------
-    DataFrame
-        Modified dataset
-    """
-    df_local = df.copy()
-
-    # replace categories
-    df_local.loc[df_local[var].isin(categories), var] = replace_with
-
-    if verbose:
-        print('  > ' + var + ' ', categories)
-
-    return df_local

-
-
-"""
--------------------------------------------------------------------------------------------------------------------------
-"""
-
-
-
[docs]def replace_extreme_values(df, var, lower_th=None, upper_th=None, verbose=False):
-    """Replace extrem values : > upper threshold or < lower threshold
-    
-    Parameters
-    ----------
-    df : DataFrame
-        Input dataset
-    var : string
-        variable to modify
-    lower_th : int/float (Default=None)
-        lower threshold
-    upper_th : int/float (Default=None)
-        upper threshold
-    verbose : boolean (Default False)
-        Get logging information
-
-    Returns
-    -------
-    DataFrame
-        Modified dataset
-    """
-    assert (lower_th is not None or upper_th is not None), 'specify at least one limit value'
-    df_local = df.copy()
-
-    # replace values with upper_limit and lower_limit
-    if upper_th is not None:
-        df_local.loc[df_local[var] > upper_th, var] = upper_th
-    if lower_th is not None:
-        df_local.loc[df_local[var] < lower_th, var] = lower_th
-
-    if verbose:
-        print('  > ' + var + ' < ' + str(round(lower_th, 4)) + ' or > ' + str(
-            round(upper_th, 4)))
-
-    return df_local

-
- -
- -
-
- - -
- -
-

- - © Copyright 2020, Maxence LABESSE - -

-
- - - - Built with Sphinx using a - - theme - - provided by Read the Docs. - -
- -
-
- -
- -
- - - - - - - - - - - \ No newline at end of file diff --git a/docs/_build/html/_modules/AutoMxL/Preprocessing/Process_Outliers.html b/docs/_build/html/_modules/AutoMxL/Preprocessing/Process_Outliers.html deleted file mode 100644 index c56b436..0000000 --- a/docs/_build/html/_modules/AutoMxL/Preprocessing/Process_Outliers.html +++ /dev/null @@ -1,273 +0,0 @@ - - - - - - - - - - - MLBG59.Preprocessing.Process_Outliers — MLBG59 1.0.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
- - - -
- - - - - -
- -
- - - - - - - - - - - - - - - - - -
- -
    - -
  • Docs »
    • - -
  • Module code »
    • - -
  • MLBG59.Preprocessing.Process_Outliers
    • - - -
  • - -
    • - -
- - -
-
-
-
- -

Source code for MLBG59.Preprocessing.Process_Outliers

-""" Outliers handling functions
-
- - replace_category : replace categories of a categorical variable
- - replace_extreme_values : replace extreme values (oh!)
-"""
-
-
-
[docs]def replace_category(df, var, categories, replace_with='outliers', verbose=False):
-    """Replace categories of a categorical variable
-    
-    Parameters
-    ----------
-    df : DataFrame
-        Input dataset
-    var : string
-        variable to modify
-    categories : list(string)
-        categories to replace
-    replace_with : string (Default : 'outliers')
-        word to replace categories with
-    verbose : boolean (Default False)
-        Get logging information
-        
-    Returns
-    -------
-    DataFrame
-        Modified dataset
-    """
-    df_local = df.copy()
-
-    # replace categories
-    df_local.loc[df_local[var].isin(categories), var] = replace_with
-
-    if verbose:
-        print('  > ' + var + ' ' + replace_with + ' ->', categories, )
-
-    return df_local

-
-
-"""
--------------------------------------------------------------------------------------------------------------------------
-"""
-
-
-
[docs]def replace_extreme_values(df, var, lower_th=None, upper_th=None, verbose=False):
-    """Replace extrem values : > upper threshold or < lower threshold
-    
-    Parameters
-    ----------
-    df : DataFrame
-        Input dataset
-    var : string
-        variable to modify
-    lower_th : int/float (Default=None)
-        lower threshold
-    upper_th : int/float (Default=None)
-        upper threshold
-    verbose : boolean (Default False)
-        Get logging information
-
-    Returns
-    -------
-    DataFrame
-        Modified dataset
-    """
-    df_local = df.copy()
-
-    # replace values with upper_limit and lower_limit
-    if upper_th is not None:
-        df_local.loc[df_local[var] > upper_th, var] = upper_th
-    if lower_th is not None:
-        df_local.loc[df_local[var] < lower_th, var] = lower_th
-
-    if verbose:
-        print('  > Values replaced for variable ' + var + ' : <' + str(round(lower_th, 4)) + ' or >' + str(
-            round(upper_th, 4)))
-
-    return df_local

-
- -
- -
-
- - -
- -
-

- © Copyright 2020, Maxence LABESSE - -

-
- Built with Sphinx using a theme provided by Read the Docs. - -
- -
-
- -
- -
- - - - - - - - - - - - \ No newline at end of file diff --git a/docs/_build/html/_modules/AutoMxL/Select_Features/Select_Features.html b/docs/_build/html/_modules/AutoMxL/Select_Features/Select_Features.html deleted file mode 100644 index 607351a..0000000 --- a/docs/_build/html/_modules/AutoMxL/Select_Features/Select_Features.html +++ /dev/null @@ -1,441 +0,0 @@ - - - - - - - - - - AutoMxL.Select_Features.Select_Features — AutoMxL 1.0.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
- - - -
- - - - - -
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    • - - -
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- - -
-
-
-
- -

Source code for AutoMxL.Select_Features.Select_Features

-""" Features selection
-
-- select_features (func) : features selection following method
-
-"""
-from sklearn.decomposition import PCA
-from sklearn.preprocessing import StandardScaler
-import pandas as pd
-import numpy as np
-
-
-
[docs]class FeatSelector(object):
-    """features selection following  method
-
-    - pca : use pca to reduce dataset dimensions
-    - no_rescale_pca : use pca without rescaling data
-
-    Parameters
-    ----------
-    method : string (Default pca)
-        method use to select features
-    """
-
-    def __init__(self,
-                 method='pca'
-                 ):
-        assert method in ['pca', 'no_rescale_pca'], 'invalid method : select pca / no_rescale_pca'
-
-        self.method = method
-        self.is_fitted = False
-        self.l_select_var = []
-        self.selector = None
-        self.scaler = None
-
-    """
-    ----------------------------------------------------------------------------------------------
-    """
-
-
[docs]    def fit(self, df, l_var=None, verbose=False):
-        """fit selector
-
-        Parameters
-        ----------
-        df : DataFrame
-            input dataset
-        l_var : list
-            features to encode.
-            If None, all features identified as numerical
-        verbose : boolean (Default False)
-            Get logging information
-        """
-        # get categorical and boolean features (see Features_Type module doc)
-        l_num = [col for col in df.columns.tolist() if df[col].dtype != 'object']
-
-        # list of features to encode
-        if l_var is None:
-            self.l_select_var = l_num
-        else:
-            self.l_select_var = [col for col in l_var if col in l_num]
-
-        if len(self.l_select_var) > 1:
-            # PCA method
-            if self.method in ['pca', 'no_rescale_pca']:
-
-                if self.method == 'pca':
-                    scaler = StandardScaler()
-                    df_local = scaler.fit_transform(df[self.l_select_var])
-                    self.scaler = scaler
-                else:
-                    df_local = df[self.l_select_var].copy()
-
-            # init pca object
-            pca = PCA()
-
-            # fit and transform with pca
-            pca.fit(df_local)
-            self.selector = pca
-
-            # Fitted !
-            self.is_fitted = True
-
-            # verbose
-            if verbose:
-                print(" **method : " + self.method)
-                print("  >", len(self.l_select_var), "features to encode")
-
-        else:
-            print('not enough features !')

-
-    """
-    ----------------------------------------------------------------------------------------------
-    """
-
-
[docs]    def transform(self, df, verbose=False):
-        """ apply features selection on a dataset
-
-        Parameters
-        ----------
-        df : DataFrame
-            dataset to transform
-        verbose : boolean (Default False)
-            Get logging information
-
-        Returns
-        -------
-        DataFrame : modified dataset
-        """
-        assert self.is_fitted, 'fit the encoding first using .fit method'
-
-        l_var_other = [col for col in df.columns.tolist() if col not in self.l_select_var]
-        df_local = df[self.l_select_var].copy()
-
-        # pca methods
-        if self.method in ['pca', 'no_rescale_pca']:
-            if self.scaler is not None:
-                df_local = self.scaler.transform(df_local)
-
-            pca = self.selector
-            df_local = pd.DataFrame(pca.transform(df_local))
-            df_local = df_local.rename(
-                columns=dict(zip(df_local.columns.tolist(), ['Dim' + str(v) for v in df_local.columns.tolist()])))
-
-        # find argmin to get 90% of variance
-        n_dim = np.argwhere(np.cumsum(pca.explained_variance_ratio_) > 0.95)[0][0]
-
-        # concat with other dataset features
-        if len(l_var_other) > 0:
-            df_reduced = pd.concat((df[l_var_other].reset_index(drop=True), df_local.iloc[:, :n_dim + 1]), axis=1)
-        else:
-            df_reduced = df_local.iloc[:, :n_dim + 1]
-
-        # verbose
-        if verbose:
-            print("Numerical Dimensions reduction : " + str(len(self.l_select_var)) + " - > " + str(n_dim + 1))
-            print("explained inertia : " + str(round(np.cumsum(pca.explained_variance_ratio_)[n_dim], 4)))
-        return df_reduced

-
-    """
-    ----------------------------------------------------------------------------------------------
-    """
-
-
[docs]    def fit_transform(self, df, l_var, verbose=False):
-        """ fit and apply features selection
-
-        Parameters
-        ----------
-        df : DataFrame
-            input dataset
-        l_var : list
-            features to encode.
-            If None, all features identified as dates (see Features_Type module)
-        verbose : boolean (Default False)
-            Get logging information
-
-        Returns
-        -------
-        DataFrame : modified dataset
-        """
-        df_local = df.copy()
-        self.fit(df_local, l_var=l_var, verbose=verbose)
-        df_reduced = self.transform(df_local, verbose=verbose)
-
-        return df_reduced

-
-
-"""
-----------------------------------------------------------------------------------------------
-"""
-
-
-
[docs]def select_features(df, target, method='pca', verbose=False):
-    """features selection following  method
-
-    - pca : use pca to reduce dataset dimensions
-    - no_rescale_pca : use pca without rescaling data
-
-    Parameters
-    ----------
-    df : DataFrame
-        input dataset containing features
-    target : string
-        target name
-    method : string (Default pca)
-        method use to select features
-    verbose : boolean (Default False)
-        Get logging information
-
-    Returns
-    -------
-    DataFrame
-        modified dataset
-    """
-    # assert valid method
-    assert method in ['pca', 'no_rescale_pca'], method + " invalid method : select pca, no_rescale_pca"
-
-    # get numerical features (except target) and others
-    l_num = [col for col in df._get_numeric_data().columns.tolist() if col != target]
-    l_other = [col for col in df.columns.tolist() if col not in l_num]
-
-    # prepare dataset to apply PCA
-    df_num = df[l_num].copy()
-
-    # PCA method
-    if method in ['pca', 'no_rescale_pca']:
-
-        if method == 'pca':
-            scaler = StandardScaler()
-            X = scaler.fit_transform(df_num)
-        else:
-            X = df_num.copy()
-
-        # init pca object
-        pca = PCA()
-
-        # fit and transform with pca
-        X_transform = pd.DataFrame(pca.fit_transform(X))
-        X_transform = X_transform.rename(
-            columns=dict(zip(X_transform.columns.tolist(), ['Dim' + str(v) for v in X_transform.columns.tolist()])))
-
-        # find argmin to get 90% of variance
-        n_dim = np.argwhere(np.cumsum(pca.explained_variance_ratio_) > 0.95)[0][0]
-
-    # concat with other dataset features
-    if len(l_other) > 0:
-
-        df_pca = pd.concat((df[l_other].reset_index(drop=True), X_transform.iloc[:, :n_dim + 1]), axis=1)
-    else:
-        df_pca = X_transform.iloc[:, :n_dim + 1]
-
-    # verbose
-    if verbose:
-        print("Numerical Dimensions reduction : " + str(len(l_num)) + " - > " + str(n_dim + 1))
-        print("explained inertia : " + str(round(np.cumsum(pca.explained_variance_ratio_)[n_dim], 4)))
-
-    return df_pca

-
- -
- -
-
- - -
- -
-

- - © Copyright 2020, Maxence LABESSE - -

-
- - - - Built with Sphinx using a - - theme - - provided by Read the Docs. - -
- -
-
- -
- -
- - - - - - - - - - - \ No newline at end of file diff --git a/docs/_build/html/_modules/AutoMxL/Start/Encode_Target.html b/docs/_build/html/_modules/AutoMxL/Start/Encode_Target.html deleted file mode 100644 index dc736f5..0000000 --- a/docs/_build/html/_modules/AutoMxL/Start/Encode_Target.html +++ /dev/null @@ -1,310 +0,0 @@ - - - - - - - - - - AutoMxL.Start.Encode_Target — AutoMxL 1.0.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
- - - -
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-
-
-
- -

Source code for AutoMxL.Start.Encode_Target

-"""Target encoding functions :
-
-- category_to_target : create a target variable (1/0) from a selected category
-- range_to_target : create a target variable (1/0) from a selected range
-"""
-import pandas as pd
-import numpy as np
-
-
-
[docs]def category_to_target(df, var, cat):
-    """Create a target variable (1/0) from a selected category
-
-    Parameters
-    ----------
-    df : DataFrame
-        input dataset
-    var : string
-        variable containing the target category
-    cat : string
-         target category
-
-    Returns
-    -------
-    DataFrame : modified dataset
-    string : new target name (var+'_'+cat)
-    """
-    df_local = df.copy()
-
-    # transform variable to string if numerical
-    if var in df._get_numeric_data().columns:
-        df_local[var] = df_local[var].apply(str)
-        cat = str(cat)
-
-    # one hot encoding
-    target_dummies = pd.get_dummies(df_local[var])
-    # select cat feature
-    target_dummies[var + '_' + cat] = target_dummies[cat]
-
-    # add encoded cat feature to dataset
-    df_local = pd.concat((df_local, target_dummies[var + '_' + cat]), axis=1)
-
-    # remove var
-    del df_local[var]
-
-    return df_local, var + '_' + cat

-
-
-"""
------------------------------------------------------------------------------------------------------
-"""
-
-
-
[docs]def range_to_target(df, var, min=None, max=None, verbose=False):
-    """Create a target variable (1/0) from a selected range
-
-    Parameters
-    ----------
-    df : DataFrame
-        input dataset
-    var : string
-        variable containing the target range
-    min : float
-        lower limit.
-        If None, no min
-    max : float
-        upper limit.
-        If None, no max
-    verbose : boolean (Default False)
-        Get logging information
-
-    Returns
-    -------
-    DataFrame : modified dataset
-    string : new target name (var+'_'+lower+'_'+upper)
-    """
-    assert min is not None or max is not None, 'fill at least one limit parameter (lower,upper)'
-
-    df_local = df.copy()
-
-    # transform variable to numeric if string
-    if var not in df_local._get_numeric_data().columns:
-        df_local[var] = pd.to_numeric(df_local[var], errors='coerce')
-
-    # handle None limits : replace by infinity
-    if min is None:
-        min = -float("inf")
-    if max is None:
-        max = float("inf")
-
-    # define target name, using lower and upper values
-    target_name = var + '_' + str(min) + '_' + str(max)
-
-    # encode target
-    df_local[target_name] = np.where((df_local[var] >= min) & (df_local[var] <= max), 1, 0)
-
-    if verbose:
-        print("Created target : ", target_name)
-        print(df_local[target_name].value_counts().rename_axis('values').to_frame('counts'))
-
-    # remove var
-    del df_local[var]
-
-    return df_local, target_name

-
-
- -
- -
-
- - -
- -
-

- - © Copyright 2020, Maxence LABESSE - -

-
- - - - Built with Sphinx using a - - theme - - provided by Read the Docs. - -
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-
- -
- -
- - - - - - - - - - - \ No newline at end of file diff --git a/docs/_build/html/_modules/AutoMxL/Start/Load.html b/docs/_build/html/_modules/AutoMxL/Start/Load.html deleted file mode 100644 index 8bdf7e0..0000000 --- a/docs/_build/html/_modules/AutoMxL/Start/Load.html +++ /dev/null @@ -1,298 +0,0 @@ - - - - - - - - - - AutoMxL.Start.Load — AutoMxL 1.0.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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Source code for AutoMxL.Start.Load

-"""Data_handling import functions :
-
-- get_delimiter : identify delimiter for a .csv/.txt file
-- load_data : import dataset file into dataframe
-"""
-import pandas as pd
-
-
-
[docs]def get_delimiter(file):
-    """Identify the delimiter for a csv/txt file
-
-    Parameters
-    ----------
-    file : string
-        Path and name of the file (Ex : "data/file.csv")
-
-    Returns
-    -------
-    string
-        identified delimiter
-    """
-    if file.endswith('.csv') or file.endswith('.txt'):
-        # file reading
-        with open(file, 'r') as myCsvfile:
-            # Reads one entire line from the file
-            header = myCsvfile.readline()
-
-            # Returns the lowest index of the substring if it is found in given string. (-1 = not found)
-            if header.find(";") != -1:
-                delimiter = ";"
-            elif header.find(",") != -1:
-                delimiter = ","
-
-        return delimiter
-
-    else:
-        print('Please use a .csv or .txt file')

-
-
-"""
--------------------------------------------------------------------------------------------------------------------------
-"""
-
-
-
[docs]def import_data(file, index_col=None, verbose=False):
-    """Import dataset as a DataFrame (identify delimiter for txt and csv files)
-
-    Available files : .txt, .csv, .xlsx, .xls files
-
-    Parameters
-    ----------
-    file : string
-        Path and name of the file (Ex : "data/file.csv")
-        If file is .csv, automatically identify delimiter
-    index_col : int, str, sequence of int / str, or False (Default None)
-        Column(s) to use as the row labels of the DataFrame, either given as string name or column index.
-        If a sequence of int / str is given, a MultiIndex is used.
-    verbose : boolean (Default False)
-        Get logging information
-
-    Returns
-    -------
-    DataFrame
-        imported dataset
-    """
-    # CSV
-    if file.endswith('.csv') or file.endswith('.txt'):
-        # Find file delimiter
-        file_sep = get_delimiter(file)
-        # import 
-        df = pd.read_csv(file, encoding="iso-8859-1", sep=file_sep, index_col=index_col)
-
-    # Excel
-    elif (file.endswith('.xlsx')) or (file.endswith('.xsl')):
-        df = pd.read_excel(file)
-
-    # JSON
-    elif file.endswith('.json'):
-        # to-do
-        pass
-
-    else:
-        df = None
-
-    if verbose:
-        if df is not None:
-            print('-> File ' + file + ' successfully imported as DataFrame')
-            print('-> DataFrame size  : ', df.shape)
-        else:
-            print("File couldn't be imported")
-
-    return df

-
- -
- -
-
- - -
- -
-

- - © Copyright 2020, Maxence LABESSE - -

-
- - - - Built with Sphinx using a - - theme - - provided by Read the Docs. - -
- -
-
- -
- -
- - - - - - - - - - - \ No newline at end of file diff --git a/docs/_build/html/_modules/AutoMxL/Utils/Decorators.html b/docs/_build/html/_modules/AutoMxL/Utils/Decorators.html deleted file mode 100644 index a91b723..0000000 --- a/docs/_build/html/_modules/AutoMxL/Utils/Decorators.html +++ /dev/null @@ -1,221 +0,0 @@ - - - - - - - - - - - MLBG59.Utils.Decorators — MLBG59 1.0.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
- - - -
- - - - - -
- -
- - - - - - - - - - - - - - - - - -
- - - - -
-
-
-
- -

Source code for MLBG59.Utils.Decorators

-from time import time
-
-# Timer
-def timer(func):
-    """Function decorator to get the execution time
-
-    Parameters
-    ----------
-    func : function
-        input function
-
-    Returns
-    -------
-    function
-        wrapped function
-    """
-    def f(*args, **kwargs):
-        before = time()
-        rv = func(*args, **kwargs)
-        after = time()
-        print('\t\t>>>',func.__name__,'execution time:', round(after - before, 4),'secs. <<<')
-        return rv
-    f.__name__ = func.__name__
-    f.__doc__ = func.__doc__
-
-    return f
-
- -
- -
-
- - -
- -
-

- © Copyright 2020, Maxence LABESSE - -

-
- Built with Sphinx using a theme provided by Read the Docs. - -
- -
-
- -
- -
- - - - - - - - - - - - \ No newline at end of file diff --git a/docs/_build/html/_modules/AutoMxL/__main__.html b/docs/_build/html/_modules/AutoMxL/__main__.html deleted file mode 100644 index 5e726a3..0000000 --- a/docs/_build/html/_modules/AutoMxL/__main__.html +++ /dev/null @@ -1,818 +0,0 @@ - - - - - - - - - - AutoMxL.__main__ — AutoMxL 1.0.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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Source code for AutoMxL.__main__

-from AutoMxL.Utils.Display import print_title1
-from AutoMxL.Utils.Decorators import timer
-from AutoMxL.Explore.Explore import explore
-from AutoMxL.Preprocessing.Date import DateEncoder
-from AutoMxL.Preprocessing.Missing_Values import NAEncoder
-from AutoMxL.Preprocessing.Outliers import OutliersEncoder
-from AutoMxL.Preprocessing.Categorical import CategoricalEncoder
-from AutoMxL.Modelisation.HyperOpt import *
-from AutoMxL.Select_Features.Select_Features import FeatSelector
-from time import time
-
-
-
[docs]class AML(pd.DataFrame):
-    """Covers the complete pipeline of a classification project from a raw dataset to a deployable model.
-
-    AML is built as a class inherited from pandas DataFrame. Each Machine Learning step corresponds to method that
-    can be called with default or filled parameters.
-
-    - explore: explore dataset and identify features types
-    - preprocess: clean and prepare data (optional : outliers processing).
-    - select_features: features selection (optional)
-    - model_train_predict : split AML in train/test sets to fits/apply models with random search.
-      Returns the list of the valid models (without overfitting) and the best one.
-
-    deployment methods:
-
-    - preprocess_apply : apply fitted preprocessing transformation to a new dataset
-    - select_features_apply : idem
-    - model_apply : apply fitted models to a new dataset
-
-
-    Notes :
-
-    - A method requires that the former one has been applied (actuel step is given by "step" attribute)
-    - Target has to be binary and encoded as int (1/0) (see MLGB59.Start.Encode_Target module if you need help)
-    - don't call your target "target" please :>
-
-    Parameters
-    ----------
-    _obj : DataFrame
-        Source Dataset
-    target : string
-        target name
-  """
-
-    def __init__(self, *args, target=None, **kwargs):
-        super(AML, self).__init__(*args, **kwargs)
-        assert target != 'target', 'target name cannot be "target"'
-        # parameters
-        self.target = target
-        # attributes
-        self.step = 'None'
-        self.d_features = None
-        self.d_preprocess = None
-        self.features_selector = None
-        self.d_hyperopt = None
-        self.is_fitted_preprocessing = False
-        self.is_fitted_selector = False
-        self.is_fitted_model = False
-
-    """
-    --------------------------------------------------------------------------------------------------------------------
-    """
-
-    def __repr__(self):
-        return 'AutoMxL instance'
-
-    """
-    --------------------------------------------------------------------------------------------------------------------
-    """
-
-    def duplicate(self):
-        res = AML(self)
-        res.__dict__.update(self.__dict__)
-        return res
-
-    """
-    --------------------------------------------------------------------------------------------------------------------
-    """
-
-
[docs]    def explore(self, verbose=False):
-        """data exploration and features type identification
-
-        Note :  if you disagree with automated identification, you can directly modify d_features attribute
-
-        Create self.d_features : dict {x : list of variables names}
-            - date: date features
-            - identifier: identifier features
-            - verbatim: verbatim features
-            - boolean: boolean features
-            - categorical: categorical features
-            - numerical: numerical features
-            - NA: features which contains NA values
-            - low_variance: list of the features with low variance and unique values
-
-        Parameters
-        ----------
-        verbose : boolean (Default False)
-            Get logging information
-        """
-        if verbose:
-            start_time = time()
-            print_title1('Explore')
-
-        df_local = self.copy()
-        if self.target is not None:
-            df_local = df_local.drop(self.target, axis=1)
-
-        # call std_audit_dataset function
-        self.d_features = explore(
-            df_local, verbose=verbose)
-
-        self.step = 'explore'
-
-        # created attributes display
-        if verbose:
-            color_print("\nCreated attributes :  d_features (dict) ")
-            print("Keys :")
-            print("  -> date")
-            print("  -> identifier")
-            print("  -> verbatim")
-            print("  -> boolean")
-            print("  -> categorical")
-            print("  -> numerical")
-            print("  -> date")
-            print("  -> NA")
-            print("  -> low_variance")
-            print('\n\t\t>>>', 'explore execution time:', round(time() - start_time, 4), 'secs. <<<')

-
-    """
-    --------------------------------------------------------------------------------------------------------------------
-    """
-
-
[docs]    def preprocess(self, date_ref=None, process_outliers=False,
-                   cat_method='deep_encoder', verbose=False):
-        """Prepare the data before feeding it to the model :
-
-            - remove low variance features
-            - remove identifiers and verbatims features
-            - transform date features to timedelta
-            - fill missing values
-            - process categorical and boolean data (one-hot-encoding or Pytorch NN encoder)
-            - replace outliers (optional)
-
-            create self.d_preprocess : dict {step : transformation}
-                - remove: list of the features to remove
-                - date: fitted DateEncoder object
-                - NA: fitted NAEncoder object
-                - categorical: fitted CategoricalEncoder object
-                - outlier: fitted OutlierEncoder object
-
-        Parameters
-        ----------
-        date_ref : string '%d/%m/%y' (Default : None)
-            ref date to compute date features timedelta.
-            If None, today date
-        process_outliers : boolean (Default : False)
-            Enable outliers replacement
-        cat_method : string (Default : 'deep_encoder')
-            Categorical features encoding method
-        verbose : boolean (Default False)
-            Get logging information
-
-        """
-        # check pipe step
-        assert self.step in ['explore'], 'apply explore method first'
-        assert not self.is_fitted_preprocessing, 'preprocessing encoders already fitted'
-
-        ###############################
-        # Fit and apply preprocessing #
-        ###############################
-        if verbose:
-            start_time = time()
-            print_title1('Fit and apply preprocessing')
-
-        target = self.target
-        df_local = self.copy()
-
-        # Features Removing 'zero variance / verbatims / identifiers)
-        if verbose:
-            color_print("Features removing (zero variance / verbatims / identifiers)")
-
-        l_remove = self.d_features['low_variance'] + self.d_features['verbatim'] + self.d_features['identifier']
-        if len(l_remove) > 0:
-            df_local = df_local.drop(l_remove, axis=1)
-
-        if verbose:
-            print("  >", len(l_remove), "features to remove")
-            if len(l_remove) > 0:
-                print(" ", l_remove)
-
-        # Transform date -> time between date and date_ref
-        if verbose:
-            color_print("Transform date")
-
-        date_encoder = DateEncoder(method='timedelta', date_ref=date_ref)
-        date_encoder.fit(self, l_var=self.d_features['date'], verbose=False)
-        df_local = date_encoder.transform(df_local, verbose=verbose)
-
-        # Missing Values
-        if verbose:
-            color_print('Missing values')
-
-        NA_encoder = NAEncoder()
-        NA_encoder.fit(df_local, l_var=None, verbose=False)
-        df_local = NA_encoder.transform(df_local, verbose=verbose)
-
-        # replace outliers
-        if process_outliers:
-            if verbose:
-                color_print('Outliers')
-            out_encoder = OutliersEncoder()
-            out_encoder.fit(df_local, l_var=None, verbose=False)
-            df_local = out_encoder.transform(df_local, verbose=verbose)
-        else:
-            out_encoder = None
-
-        # categorical processing
-        if verbose:
-            color_print('Encode Categorical and boolean')
-
-        cat_col = self.d_features['categorical'] + self.d_features['boolean']
-        # apply one-hot encoding if target not filled in class parameters
-        if self.target is None:
-            cat_method = 'one_hot'
-            color_print('No target -> one_hot encoding !', 31)
-
-        # get embedding
-        cat_encoder = CategoricalEncoder(method=cat_method)
-        cat_encoder.fit(self, l_var=cat_col, target=self.target, verbose=verbose)
-        df_local = cat_encoder.transform(df_local, verbose=verbose)
-
-        # store preprocessing params
-        self.d_preprocess = {'remove': l_remove, 'date': date_encoder, 'NA': NA_encoder, 'categorical': cat_encoder}
-        if out_encoder is not None:
-            self.d_preprocess['outlier'] = out_encoder
-
-        if verbose:
-            color_print("\nCreated attributes :  d_preprocess (dict) ")
-            print("Keys :")
-            print("  -> remove")
-            print("  -> date")
-            print("  -> NA")
-            print("  -> categorical")
-            print("  -> outlier (optional)")
-
-        # is_fitted
-        self.is_fitted_preprocessing = True
-
-        # update self
-        self.__dict__.update(df_local.__dict__)
-        self.target = target
-        self.step = 'preprocess'
-
-        if verbose:
-            color_print("New DataFrame size ")
-            print("  > row number : ", self.shape[0], "\n  > col number : ", self.shape[1])
-            print('\n\t\t>>>', 'proprocess execution time:', round(time() - start_time, 4), 'secs. <<<')

-
-    """
-    --------------------------------------------------------------------------------------------------------------------
-    """
-
-
[docs]    def preprocess_apply(self, df, verbose=False):
-        """Apply preprocessing.
-
-        Requires preprocess method to have been applied (so that all encoder are fitted).
-
-        Parameters
-        ----------
-        df : DataFrame
-            dataset to apply preprocessing on
-        verbose : boolean (Default False)
-            Get logging information
-
-        Returns
-        -------
-        DataFrame : Preprocessed dataset
-        """
-        if verbose:
-            start_time = time()
-            print_title1('Apply Preprocessing')
-
-        # check pipe step and is_fitted
-        assert self.is_fitted_preprocessing, "fit first (please)"
-
-        #
-        df_local = df.copy()
-
-        # Remove features with zero variance / verbatims and identifiers
-        if verbose:
-            color_print("Remove features (zero variance, verbatims and identifiers")
-
-        if len(self.d_preprocess['remove']) > 0:
-            df_local = df_local.drop(self.d_preprocess['remove'], axis=1)
-            if verbose:
-                print("  >", len(self.d_preprocess['remove']), 'removed features')
-        else:
-            if verbose:
-                print("  > No features to remove")
-
-        # Transform date -> time between date and date_ref
-        if verbose:
-            color_print("Transform date")
-        df_local = self.d_preprocess['date'].transform(df_local, verbose=verbose)
-
-        # Missing Values
-        if verbose:
-            color_print('Missing values')
-        df_local = self.d_preprocess['NA'].transform(df_local, verbose=verbose)
-
-        # replace outliers
-        if 'outlier' in list(self.d_preprocess.keys()):
-            if verbose:
-                color_print('Outliers')
-            df_local = self.d_preprocess['outlier'].transform(df_local, verbose=verbose)
-
-        # categorical processing
-        if verbose:
-            color_print('Encode categorical and boolean')
-            print('\n\t\t>>>', 'preprocess_apply execution time:', round(time() - start_time, 4), 'secs. <<<')
-        df_local = self.d_preprocess['categorical'].transform(df_local, verbose=verbose)
-
-        return df_local

-
-    """
-    --------------------------------------------------------------------------------------------------------------------
-    """
-
-
[docs]    def select_features(self, method='pca', verbose=False):
-        """ fit and apply features selection (optional)
-
-        Parameters
-        ----------
-        method : string (Default pca)
-            method use to select features
-        verbose : boolean (Default False)
-            Get logging information
-
-        """
-        assert self.step in ['preprocess'], 'apply preprocess method'
-
-        target = self.target
-
-        if verbose:
-            start_time = time()
-            print_title1('Features Selection')
-
-        df_local = self.copy()
-
-        l_select_var = [col for col in df_local.columns.tolist() if col != self.target]
-
-        # df_local = select_features(df=df_local, target=self.target, method=method, verbose=verbose)
-
-        features_selector = FeatSelector(method=method)
-        features_selector.fit(df_local, l_var=l_select_var, verbose=verbose)
-        df_local = features_selector.transform(df_local, verbose=verbose)
-
-        self.__dict__.update(df_local.__dict__)
-        self.target = target
-        self.features_selector = features_selector
-        self.is_fitted_selector = True
-        self.step = 'features_selection'
-
-        if verbose :
-            print('\n\t\t>>>', 'select_features execution time:', round(time() - start_time, 4), 'secs. <<<')

-
-    """
-        --------------------------------------------------------------------------------------------------------------------
-    """
-
-
[docs]    def select_features_apply(self, df, verbose=False):
-        """Apply features selection.
-
-         Requires Select_Features method to have been applied
-
-         Parameters
-         ----------
-         df : DataFrame
-             dataset to apply selection on
-         verbose : boolean (Default False)
-             Get logging information
-
-         Returns
-         -------
-         DataFrame : reduced dataset
-         """
-        # check pipe step and is_fitted
-        assert self.is_fitted_selector, "fit first (please)"
-
-        if verbose:
-            start_time = time()
-            print_title1('Apply select_features')
-
-        df_local = df.copy()
-
-        df_local = self.features_selector.transform(df_local, verbose=verbose)
-
-        if verbose:
-            print('\n\t\t>>>', 'select_features_apply execution time:', round(time() - start_time, 4), 'secs. <<<')
-
-        return df_local

-
-    """
-        --------------------------------------------------------------------------------------------------------------------
-    """
-
-
[docs]    def model_train_test(self, clf='XGBOOST', grid_param=None, metric='F1', delta_auc=0.03, top_bagging=False, n_comb=10,
-                         comb_seed=None,
-                         verbose=False):
-        """train and test models with random search
-
-        - creates models with random hyper-parameters combinations from HP grid
-        - splits (random 80/20) train/test sets to fit/apply models
-        - identifies valid models (auc(train)-auc(test)<0.03
-        - gets the best model in respect of a selected metric among valid model
-
-
-        Notes :
-
-        - Available classifiers : Random Forest, XGBOOST
-        - can enable bagging algo with top_bagging parameter
-
-        Parameters
-        ----------
-        clf : string (Default : 'XGBOOST')
-            classifier used for modelisation
-        grid_param : dict
-            random search grid {Hyperparameter name : values list}
-        metric : string (Default : 'F1')
-            objective metric
-        top_bagging : boolean (Default : False)
-            enable Bagging
-        n_comb : int (Default : 10)
-            HP combination number
-        comb_seed : int (Default : None)
-            random combination seed
-        verbose : boolean (Default False)
-            Get logging information
-
-        Returns
-        -------
-        dict
-            {model_index : {'HP', 'probas', 'model', 'features_importance', 'train_metrics', 'metrics', 'output'}
-        list
-            valid models indexes
-        int
-            best model index
-        DataFrame
-            models summary
-        """
-        assert self.step in ['preprocess', 'features_selection'], 'apply preprocess method'
-
-        if verbose:
-            start_time = time()
-            print_title1('Train predict')
-
-        # Train/Test split
-        df_train, df_test = train_test(self, 0.2)
-
-        # Create Hyperopt object
-        hyperopt = HyperOpt(classifier=clf, grid_param=grid_param, n_param_comb=n_comb,
-                            bagging=top_bagging, comb_seed=comb_seed)
-
-        # fit model on train set
-        if verbose:
-            color_print('training models')
-
-        hyperopt.fit(df_train, self.target, verbose=verbose)
-
-        # Apply model on test set
-        if verbose:
-            color_print('\napplying models')
-
-        d_fitted_models = hyperopt.predict(df_test, self.target, delta_auc=delta_auc, verbose=verbose)
-
-        # model selection
-        if verbose:
-            color_print('\nbest model selection')
-        best_model_idx, l_valid_models = hyperopt.get_best_model(d_fitted_models, metric=metric, delta_auc_th=delta_auc,
-                                                                 verbose=False)
-
-        df_model_res = hyperopt.model_res_to_df(d_fitted_models, sort_metric=metric)
-
-        if best_model_idx is not None:
-            print_title1('best model : ' + str(best_model_idx))
-            print(metric + ' : ' + str(round(d_fitted_models[best_model_idx]['metrics'][metric], 4)))
-            print('AUC : ' + str(round(d_fitted_models[best_model_idx]['metrics']['Roc_auc'], 4)))
-            if round(d_fitted_models[best_model_idx]['metrics'][metric], 4) == 1.0:
-                color_print("C'était pas qu'un physique finalement hein ?", 32)
-            print('\n\t\t>>>', 'model_train_test execution time:', round(time() - start_time, 4), 'secs. <<<')
-
-        self.d_hyperopt = hyperopt
-        self.is_fitted_model = True
-
-        return d_fitted_models, l_valid_models, best_model_idx, df_model_res

-
-    """
-    ------------------------------------------------------------------------------------------------------------------------
-    """
-
-
[docs]    def model_train(self, clf='XGBOOST', grid_param=None, top_bagging=False, n_comb=10, comb_seed=None, verbose=False):
-        """train models with random search
-
-        - creates models with random hyper-parameters combinations from HP grid
-        - fits models on self
-
-        Notes :
-
-        - Available classifiers : Random Forest, XGBOOST
-        - can enable bagging algo with top_bagging parameter
-
-        Parameters
-        ----------
-        clf : string (Default : 'XGBOOST')
-            classifier used for modelisation
-        grid_param : dict
-            random search grid {Hyperparameter name : values list}
-        top_bagging : boolean (Default : False)
-            enable Bagging
-        n_comb : int (Default : 10)
-            HP combination number
-        comb_seed : int (Default : None)
-            random combination seed
-        verbose : boolean (Default False)
-            Get logging information
-
-        """
-        assert self.step in ['preprocess', 'features_selection'], 'apply preprocess method'
-
-        df_train = self.copy()
-        target = self.target
-
-
-        if verbose:
-            start_time = time()
-            print_title1('Train Models')
-
-        # instantiate Hyperopt object
-        hyperopt = HyperOpt(classifier=clf, grid_param=grid_param, n_param_comb=n_comb,
-                            bagging=top_bagging, comb_seed=comb_seed)
-
-        # fit model on train set
-        if verbose:
-            color_print('training models')
-
-        # fit hyperopt on self
-        hyperopt.fit(df_train, self.target, verbose=verbose)
-
-        self.d_hyperopt = hyperopt
-        self.is_fitted_model = True
-        self.target = target
-        self.step = 'train_model'
-
-        if verbose:
-            print('\n\t\t>>>', 'model_train execution time:', round(time() - start_time, 4), 'secs. <<<')

-
-    """
-    ------------------------------------------------------------------------------------------------------------------------
-    """
-
-
[docs]    def model_predict(self, df, metric='F1', delta_auc=0.03, verbose=False):
-        """apply fitted models on a dataset
-
-        - identifies valid models (auc(train)-auc(test)<0.03
-        - gets the best model in respect of a selected metric among valid model
-
-        Parameters
-        ----------
-        metric : string (Default : 'F1')
-            objective metric
-        verbose : boolean (Default False)
-            Get logging information
-
-        Returns
-        -------
-        dict
-            {model_index : {'HP', 'probas', 'model', 'features_importance', 'train_metrics', 'metrics', 'output'}
-        list
-            valid models indexes
-        int
-            best model index
-        DataFrame
-            models summary
-        """
-        assert self.is_fitted_model, "model is not fitted yet, apply model_train_predict or model_train methods"
-
-        if verbose:
-            start_time = time()
-            color_print('\napplying models')
-
-        # apply models on dataset
-        d_fitted_models = self.d_hyperopt.predict(df, self.target, delta_auc=delta_auc, verbose=verbose)
-
-        # model selection
-        if verbose:
-            color_print('\nbest model selection')
-        best_model_idx, l_valid_models = self.d_hyperopt.get_best_model(d_fitted_models, metric=metric,
-                                                                        delta_auc_th=delta_auc,
-                                                                        verbose=False)
-        # store model results
-        df_model_res = self.d_hyperopt.model_res_to_df(d_fitted_models, sort_metric=metric)
-
-        if best_model_idx is not None:
-            print_title1('best model : ' + str(best_model_idx))
-            print(metric + ' : ' + str(round(d_fitted_models[best_model_idx]['metrics'][metric], 4)))
-            print('AUC : ' + str(round(d_fitted_models[best_model_idx]['metrics']['Roc_auc'], 4)))
-            if round(d_fitted_models[best_model_idx]['metrics'][metric], 4) == 1.0:
-                color_print("C'était pas qu'un physique finalement hein ?", 32)
-            print('\n\t\t>>>', 'model_predict execution time:', round(time() - start_time, 4), 'secs. <<<')
-
-        return d_fitted_models, l_valid_models, best_model_idx, df_model_res

-
- -
- -
-
- - -
- -
-

- - © Copyright 2020, Maxence LABESSE - -

-
- - - - Built with Sphinx using a - - theme - - provided by Read the Docs. - -
- -
-
- -
- -
- - - - - - - - - - - \ No newline at end of file diff --git a/docs/_build/html/_modules/Load/Load.html b/docs/_build/html/_modules/Load/Load.html deleted file mode 100644 index b8640c1..0000000 --- a/docs/_build/html/_modules/Load/Load.html +++ /dev/null @@ -1,276 +0,0 @@ - - - - - - - - - - - Load.Load — MLBG59 1.0.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
- - - -
- - - - - -
- -
- - - - - - - - - - - - - - - - - -
- - - - -
-
-
-
- -

Source code for Load.Load

-""" contains objets related to data importation :
-- get_delimiter : identify csv file delimiter
-- load data
-
-"""
-import pandas as pd
-
-
-
[docs]def get_delimiter(csvfile):
-    """
-    Identify the delimiter of a .csv file
-
-    Parameters
-    ----------
-    csvfile : string
-        path and name of the file (Ex : "data/file.csv")
-
-    Returns
-    -------
-    string
-        identified delimiter
-    """
-    # csv file reading
-    with open(csvfile, 'r') as myCsvfile:
-        # Reads one entire line from the file
-        header = myCsvfile.readline()
-        
-        # Returns the lowest index of the substring if it is found in given string. (-1 = not found)
-        if header.find(";") != -1:
-            delimiter = ";"
-        elif header.find(",") != -1:
-            delimiter = ","
-
-    return delimiter

-
-
-"""
--------------------------------------------------------------------------------------------------------------------------
-"""
-
-
-
[docs]def import_data(file, index_col=None, verbose=1):
-    """
-    Import dataset as a DataFrame
-    accept .csv, .xlsx, .xls files
-
-    Parameters
-    ----------
-    file : string
-        path and name of the file (Ex : "data/file.csv")
-        if file is .csv, automatically identify delimiter
-    index_col : int, str, sequence of int / str, or False, default None
-        Column(s) to use as the row labels of the DataFrame, either given as string name or column index.
-        If a sequence of int / str is given, a MultiIndex is used.
-    verbose : int (0/1) (Default : 1)
-        get more operations information
-
-    Returns
-    -------
-    DataFrame :
-        dataset imported as dataset
-    """
-    # CSV
-    if file.endswith('.csv'):
-        # Find separator
-        file_sep = get_delimiter(file)
-        # import 
-        df = pd.read_csv(file, encoding="iso-8859-1", sep=file_sep, index_col=index_col)
-    
-    # Excel
-    elif (file.endswith('.xlsx')) or (file.endswith('.xsl')):
-        df = pd.read_excel(file)
-
-    # JSON
-    elif file.endswith('.json'):
-        pass
-
-    else:
-        df = None
-
-    if verbose==1:
-        if df is not None:
-            print('-> Fichier '+file+' importé avec succès')
-            print('-> Taille du dataframe créé : ', df.shape)
-        else:
-            print("Le Fichier n'a pas pu être importé (dommage)")
-
-    return df

-
- -
- -
-
- - -
- -
-

- © Copyright 2020, Maxence LABESSE - -

-
- Built with Sphinx using a theme provided by Read the Docs. - -
- -
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- - - - - - - - - - - - \ No newline at end of file diff --git a/docs/_build/html/_modules/index.html b/docs/_build/html/_modules/index.html deleted file mode 100644 index efa42ab..0000000 --- a/docs/_build/html/_modules/index.html +++ /dev/null @@ -1,216 +0,0 @@ - - - - - - - - - - Overview: module code — AutoMxL 1.0.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
- - - -
- - - - - -
- -
- - - - - - - - - - - - - - - - - -
- -
    - -
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    • - -
  • Overview: module code
    • - - -
  • - -
    • - -
- - -
-
-
- - -
-
- - -
- -
-

- - © Copyright 2020, Maxence LABESSE - -

-
- - - - Built with Sphinx using a - - theme - - provided by Read the Docs. - -
- -
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- -
- -
- - - - - - - - - - - \ No newline at end of file diff --git a/docs/_build/html/_sources/AutoML.rst.txt b/docs/_build/html/_sources/AutoML.rst.txt deleted file mode 100644 index de1a640..0000000 --- a/docs/_build/html/_sources/AutoML.rst.txt +++ /dev/null @@ -1,4 +0,0 @@ -AML class -============ -.. autoclass:: AutoMxL.__main__.AML - :members: diff --git a/docs/_build/html/_sources/Features.rst.txt b/docs/_build/html/_sources/Features.rst.txt deleted file mode 100644 index 31384ba..0000000 --- a/docs/_build/html/_sources/Features.rst.txt +++ /dev/null @@ -1,64 +0,0 @@ -Start -===== -Load ----- -.. automodule:: AutoMxL.Start.Load - :members: - -Encode_Target -------------- -.. automodule:: AutoMxL.Start.Encode_Target - :members: - -Explore -======= -Explore --------- -.. automodule:: AutoMxL.Explore.Explore - :members: - -Features_Type ------------------- -.. automodule:: AutoMxL.Explore.Features_Type - :members: - - -Preprocessing -============= -Missing_Values --------------- -.. automodule:: AutoMxL.Preprocessing.Missing_Values - :members: - -Categorical Data ----------------- -.. automodule:: AutoMxL.Preprocessing.Categorical - :members: - -Date Data --------------------------------------- -.. automodule:: AutoMxL.Preprocessing.Date - :members: - -Process Outliers ------------------ -.. automodule:: AutoMxL.Preprocessing.Outliers - :members: - -Features Selection -================== -.. automodule:: AutoMxL.Select_Features.Select_Features - :members: - - -Modelisation -============ -Bagging -------- -.. automodule:: AutoMxL.Modelisation.Bagging - :members: - -Hyperoptimisation ------------------ -.. automodule:: AutoMxL.Modelisation.HyperOpt - :members: diff --git a/docs/_build/html/_sources/docstring_test.rst.txt b/docs/_build/html/_sources/docstring_test.rst.txt deleted file mode 100644 index a822dcd..0000000 --- a/docs/_build/html/_sources/docstring_test.rst.txt +++ /dev/null @@ -1,6 +0,0 @@ -Test -==== -Features_type -------------- -.. automodule:: dev.Features_type - :members: diff --git a/docs/_build/html/_sources/index.rst.txt b/docs/_build/html/_sources/index.rst.txt deleted file mode 100644 index 312cba2..0000000 --- a/docs/_build/html/_sources/index.rst.txt +++ /dev/null @@ -1,22 +0,0 @@ -.. AutoMxL documentation master file, created by - sphinx-quickstart on Mon Feb 10 14:44:06 2020. - You can adapt this file completely to your liking, but it should at least - contain the root `toctree` directive. - -Welcome to AutoMxL's documentation! -================================== - - -.. toctree:: - :maxdepth: 3 - :caption: AutoML class - :hidden: - - autoML - -.. toctree:: - :maxdepth: 3 - :caption: Features - :hidden: - - features diff --git a/docs/_build/html/_static/ajax-loader.gif b/docs/_build/html/_static/ajax-loader.gif deleted file mode 100644 index 61faf8c..0000000 Binary files a/docs/_build/html/_static/ajax-loader.gif and /dev/null differ diff --git a/docs/_build/html/_static/basic.css b/docs/_build/html/_static/basic.css deleted file mode 100644 index 2e3cf32..0000000 --- a/docs/_build/html/_static/basic.css +++ /dev/null @@ -1,855 +0,0 @@ -/* - * basic.css - * ~~~~~~~~~ - * - * Sphinx stylesheet -- basic theme. - * - * :copyright: Copyright 2007-2020 by the Sphinx team, see AUTHORS. - * :license: BSD, see LICENSE for details. - * - */ - -/* -- main layout ----------------------------------------------------------- */ - -div.clearer { - clear: both; -} - -div.section::after { - display: block; - content: ''; - clear: left; -} - -/* -- relbar ---------------------------------------------------------------- */ - -div.related { - width: 100%; - font-size: 90%; -} - -div.related h3 { - display: none; -} - -div.related ul { - margin: 0; - padding: 0 0 0 10px; - list-style: none; -} - -div.related li { - display: inline; -} - -div.related li.right { - float: right; - margin-right: 5px; -} - -/* -- sidebar --------------------------------------------------------------- */ - -div.sphinxsidebarwrapper { - padding: 10px 5px 0 10px; -} - -div.sphinxsidebar { - float: left; - width: 230px; - margin-left: -100%; - font-size: 90%; - word-wrap: break-word; - overflow-wrap : break-word; -} - -div.sphinxsidebar ul { - list-style: none; -} - -div.sphinxsidebar ul 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-.align-left { - text-align: left; -} - -.align-center { - text-align: center; -} - -.align-default { - text-align: center; -} - -.align-right { - text-align: right; -} - -/* -- sidebars -------------------------------------------------------------- */ - -div.sidebar { - margin: 0 0 0.5em 1em; - border: 1px solid #ddb; - padding: 7px; - background-color: #ffe; - width: 40%; - float: right; - clear: right; - overflow-x: auto; -} - -p.sidebar-title { - font-weight: bold; -} - -div.admonition, div.topic, blockquote { - clear: left; -} - -/* -- topics ---------------------------------------------------------------- */ - -div.topic { - border: 1px solid #ccc; - padding: 7px; - margin: 10px 0 10px 0; -} - -p.topic-title { - font-size: 1.1em; - font-weight: bold; - margin-top: 10px; -} - -/* -- admonitions ----------------------------------------------------------- */ - -div.admonition { - margin-top: 10px; - margin-bottom: 10px; - padding: 7px; -} - -div.admonition dt { - font-weight: bold; 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- margin: 0; -} - -.field-name { - -moz-hyphens: manual; - -ms-hyphens: manual; - -webkit-hyphens: manual; - hyphens: manual; -} - -/* -- hlist styles ---------------------------------------------------------- */ - -table.hlist { - margin: 1em 0; -} - -table.hlist td { - vertical-align: top; -} - - -/* -- other body styles ----------------------------------------------------- */ - -ol.arabic { - list-style: decimal; -} - -ol.loweralpha { - list-style: lower-alpha; -} - -ol.upperalpha { - list-style: upper-alpha; -} - -ol.lowerroman { - list-style: lower-roman; -} - -ol.upperroman { - list-style: upper-roman; -} - -:not(li) > ol > li:first-child > :first-child, -:not(li) > ul > li:first-child > :first-child { - margin-top: 0px; -} - -:not(li) > ol > li:last-child > :last-child, -:not(li) > ul > li:last-child > :last-child { - margin-bottom: 0px; -} - -ol.simple ol p, -ol.simple ul p, -ul.simple ol p, -ul.simple ul p { - margin-top: 0; -} - -ol.simple > li:not(:first-child) > p, 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All rights reserved. - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - diff --git a/docs/_build/html/_static/fonts/fontawesome-webfont.ttf b/docs/_build/html/_static/fonts/fontawesome-webfont.ttf deleted file mode 100644 index 35acda2..0000000 Binary files a/docs/_build/html/_static/fonts/fontawesome-webfont.ttf and /dev/null differ diff --git a/docs/_build/html/_static/fonts/fontawesome-webfont.woff b/docs/_build/html/_static/fonts/fontawesome-webfont.woff deleted file mode 100644 index 400014a..0000000 Binary files a/docs/_build/html/_static/fonts/fontawesome-webfont.woff and /dev/null differ diff --git a/docs/_build/html/_static/fonts/fontawesome-webfont.woff2 b/docs/_build/html/_static/fonts/fontawesome-webfont.woff2 deleted file mode 100644 index 4d13fc6..0000000 Binary files a/docs/_build/html/_static/fonts/fontawesome-webfont.woff2 and /dev/null differ diff --git a/docs/_build/html/_static/fonts/lato-bold-italic.woff b/docs/_build/html/_static/fonts/lato-bold-italic.woff deleted file mode 100644 index 88ad05b..0000000 Binary files a/docs/_build/html/_static/fonts/lato-bold-italic.woff and /dev/null differ diff --git a/docs/_build/html/_static/fonts/lato-bold-italic.woff2 b/docs/_build/html/_static/fonts/lato-bold-italic.woff2 deleted file mode 100644 index c4e3d80..0000000 Binary files a/docs/_build/html/_static/fonts/lato-bold-italic.woff2 and /dev/null differ diff --git a/docs/_build/html/_static/fonts/lato-bold.woff b/docs/_build/html/_static/fonts/lato-bold.woff deleted file mode 100644 index c6dff51..0000000 Binary files a/docs/_build/html/_static/fonts/lato-bold.woff and /dev/null differ diff --git a/docs/_build/html/_static/fonts/lato-bold.woff2 b/docs/_build/html/_static/fonts/lato-bold.woff2 deleted file mode 100644 index bb19504..0000000 Binary files a/docs/_build/html/_static/fonts/lato-bold.woff2 and /dev/null differ diff --git a/docs/_build/html/_static/fonts/lato-normal-italic.woff b/docs/_build/html/_static/fonts/lato-normal-italic.woff deleted file mode 100644 index 76114bc..0000000 Binary files a/docs/_build/html/_static/fonts/lato-normal-italic.woff and /dev/null differ diff --git a/docs/_build/html/_static/fonts/lato-normal-italic.woff2 b/docs/_build/html/_static/fonts/lato-normal-italic.woff2 deleted file mode 100644 index 3404f37..0000000 Binary files a/docs/_build/html/_static/fonts/lato-normal-italic.woff2 and /dev/null differ diff --git a/docs/_build/html/_static/fonts/lato-normal.woff b/docs/_build/html/_static/fonts/lato-normal.woff deleted file mode 100644 index ae1307f..0000000 Binary files a/docs/_build/html/_static/fonts/lato-normal.woff and /dev/null differ diff --git a/docs/_build/html/_static/fonts/lato-normal.woff2 b/docs/_build/html/_static/fonts/lato-normal.woff2 deleted file mode 100644 index 3bf9843..0000000 Binary files a/docs/_build/html/_static/fonts/lato-normal.woff2 and /dev/null differ diff --git a/docs/_build/html/_static/jquery-3.2.1.js b/docs/_build/html/_static/jquery-3.2.1.js deleted file mode 100644 index d2d8ca4..0000000 --- a/docs/_build/html/_static/jquery-3.2.1.js +++ /dev/null @@ -1,10253 +0,0 @@ -/*! - * jQuery JavaScript Library v3.2.1 - * https://jquery.com/ - * - * Includes Sizzle.js - * https://sizzlejs.com/ - * - * Copyright JS Foundation and other contributors - * Released under the MIT license - * https://jquery.org/license - * - * Date: 2017-03-20T18:59Z - */ -( function( global, factory ) { - - "use strict"; - - if ( typeof module === "object" && typeof module.exports === "object" ) { - - // For CommonJS and CommonJS-like environments where a proper `window` - // is present, execute the factory and get jQuery. - // For environments that do not have a `window` with a `document` - // (such as Node.js), expose a factory as module.exports. - // This accentuates the need for the creation of a real `window`. - // e.g. var jQuery = require("jquery")(window); - // See ticket #14549 for more info. - module.exports = global.document ? - factory( global, true ) : - function( w ) { - if ( !w.document ) { - throw new Error( "jQuery requires a window with a document" ); - } - return factory( w ); - }; - } else { - factory( global ); - } - -// Pass this if window is not defined yet -} )( typeof window !== "undefined" ? window : this, function( window, noGlobal ) { - -// Edge <= 12 - 13+, Firefox <=18 - 45+, IE 10 - 11, Safari 5.1 - 9+, iOS 6 - 9.1 -// throw exceptions when non-strict code (e.g., ASP.NET 4.5) accesses strict mode -// arguments.callee.caller (trac-13335). But as of jQuery 3.0 (2016), strict mode should be common -// enough that all such attempts are guarded in a try block. -"use strict"; - -var arr = []; - -var document = window.document; - -var getProto = Object.getPrototypeOf; - -var slice = arr.slice; - -var concat = arr.concat; - -var push = arr.push; - -var indexOf = arr.indexOf; - -var class2type = {}; - -var toString = class2type.toString; - -var hasOwn = class2type.hasOwnProperty; - -var fnToString = hasOwn.toString; - -var ObjectFunctionString = fnToString.call( Object ); - -var support = {}; - - - - function DOMEval( code, doc ) { - doc = doc || document; - - var script = doc.createElement( "script" ); - - script.text = code; - doc.head.appendChild( script ).parentNode.removeChild( script ); - } -/* global Symbol */ -// Defining this global in .eslintrc.json would create a danger of using the global -// unguarded in another place, it seems safer to define global only for this module - - - -var - version = "3.2.1", - - // Define a local copy of jQuery - jQuery = function( selector, context ) { - - // The jQuery object is actually just the init constructor 'enhanced' - // Need init if jQuery is called (just allow error to be thrown if not included) - return new jQuery.fn.init( selector, context ); - }, - - // Support: Android <=4.0 only - // Make sure we trim BOM and NBSP - rtrim = /^[\s\uFEFF\xA0]+|[\s\uFEFF\xA0]+$/g, - - // Matches dashed string for camelizing - rmsPrefix = /^-ms-/, - rdashAlpha = /-([a-z])/g, - - // Used by jQuery.camelCase as callback to replace() - fcamelCase = function( all, letter ) { - return letter.toUpperCase(); - }; - -jQuery.fn = jQuery.prototype = { - - // The current version of jQuery being used - jquery: version, - - constructor: jQuery, - - // The default length of a jQuery object is 0 - length: 0, - - toArray: function() { - return slice.call( this ); - }, - - // Get the Nth element in the matched element set OR - // Get the whole matched element set as a clean array - get: function( num ) { - - // Return all the elements in a clean array - if ( num == null ) { - return slice.call( this ); - } - - // Return just the one element from the set - return num < 0 ? this[ num + this.length ] : this[ num ]; - }, - - // Take an array of elements and push it onto the stack - // (returning the new matched element set) - pushStack: function( elems ) { - - // Build a new jQuery matched element set - var ret = jQuery.merge( this.constructor(), elems ); - - // Add the old object onto the stack (as a reference) - ret.prevObject = this; - - // Return the newly-formed element set - return ret; - }, - - // Execute a callback for every element in the matched set. - each: function( callback ) { - return jQuery.each( this, callback ); - }, - - map: function( callback ) { - return this.pushStack( jQuery.map( this, function( elem, i ) { - return callback.call( elem, i, elem ); - } ) ); - }, - - slice: function() { - return this.pushStack( slice.apply( this, arguments ) ); - }, - - first: function() { - return this.eq( 0 ); - }, - - last: function() { - return this.eq( -1 ); - }, - - eq: function( i ) { - var len = this.length, - j = +i + ( i < 0 ? len : 0 ); - return this.pushStack( j >= 0 && j < len ? [ this[ j ] ] : [] ); - }, - - end: function() { - return this.prevObject || this.constructor(); - }, - - // For internal use only. - // Behaves like an Array's method, not like a jQuery method. - push: push, - sort: arr.sort, - splice: arr.splice -}; - -jQuery.extend = jQuery.fn.extend = function() { - var options, name, src, copy, copyIsArray, clone, - target = arguments[ 0 ] || {}, - i = 1, - length = arguments.length, - deep = false; - - // Handle a deep copy situation - if ( typeof target === "boolean" ) { - deep = target; - - // Skip the boolean and the target - target = arguments[ i ] || {}; - i++; - } - - // Handle case when target is a string or something (possible in deep copy) - if ( typeof target !== "object" && !jQuery.isFunction( target ) ) { - target = {}; - } - - // Extend jQuery itself if only one argument is passed - if ( i === length ) { - target = this; - i--; - } - - for ( ; i < length; i++ ) { - - // Only deal with non-null/undefined values - if ( ( options = arguments[ i ] ) != null ) { - - // Extend the base object - for ( name in options ) { - src = target[ name ]; - copy = options[ name ]; - - // Prevent never-ending loop - if ( target === copy ) { - continue; - } - - // Recurse if we're merging plain objects or arrays - if ( deep && copy && ( jQuery.isPlainObject( copy ) || - ( copyIsArray = Array.isArray( copy ) ) ) ) { - - if ( copyIsArray ) { - copyIsArray = false; - clone = src && Array.isArray( src ) ? src : []; - - } else { - clone = src && jQuery.isPlainObject( src ) ? src : {}; - } - - // Never move original objects, clone them - target[ name ] = jQuery.extend( deep, clone, copy ); - - // Don't bring in undefined values - } else if ( copy !== undefined ) { - target[ name ] = copy; - } - } - } - } - - // Return the modified object - return target; -}; - -jQuery.extend( { - - // Unique for each copy of jQuery on the page - expando: "jQuery" + ( version + Math.random() ).replace( /\D/g, "" ), - - // Assume jQuery is ready without the ready module - isReady: true, - - error: function( msg ) { - throw new Error( msg ); - }, - - noop: function() {}, - - isFunction: function( obj ) { - return jQuery.type( obj ) === "function"; - }, - - isWindow: function( obj ) { - return obj != null && obj === obj.window; - }, - - isNumeric: function( obj ) { - - // As of jQuery 3.0, isNumeric is limited to - // strings and numbers (primitives or objects) - // that can be coerced to finite numbers (gh-2662) - var type = jQuery.type( obj ); - return ( type === "number" || type === "string" ) && - - // parseFloat NaNs numeric-cast false positives ("") - // ...but misinterprets leading-number strings, particularly hex literals ("0x...") - // subtraction forces infinities to NaN - !isNaN( obj - parseFloat( obj ) ); - }, - - isPlainObject: function( obj ) { - var proto, Ctor; - - // Detect obvious negatives - // Use toString instead of jQuery.type to catch host objects - if ( !obj || toString.call( obj ) !== "[object Object]" ) { - return false; - } - - proto = getProto( obj ); - - // Objects with no prototype (e.g., `Object.create( null )`) are plain - if ( !proto ) { - return true; - } - - // Objects with prototype are plain iff they were constructed by a global Object function - Ctor = hasOwn.call( proto, "constructor" ) && proto.constructor; - return typeof Ctor === "function" && fnToString.call( Ctor ) === ObjectFunctionString; - }, - - isEmptyObject: function( obj ) { - - /* eslint-disable no-unused-vars */ - // See https://github.com/eslint/eslint/issues/6125 - var name; - - for ( name in obj ) { - return false; - } - return true; - }, - - type: function( obj ) { - if ( obj == null ) { - return obj + ""; - } - - // Support: Android <=2.3 only (functionish RegExp) - return typeof obj === "object" || typeof obj === "function" ? - class2type[ toString.call( obj ) ] || "object" : - typeof obj; - }, - - // Evaluates a script in a global context - globalEval: function( code ) { - DOMEval( code ); - }, - - // Convert dashed to camelCase; used by the css and data modules - // Support: IE <=9 - 11, Edge 12 - 13 - // Microsoft forgot to hump their vendor prefix (#9572) - camelCase: function( string ) { - return string.replace( rmsPrefix, "ms-" ).replace( rdashAlpha, fcamelCase ); - }, - - each: function( obj, callback ) { - var length, i = 0; - - if ( isArrayLike( obj ) ) { - length = obj.length; - for ( ; i < length; i++ ) { - if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) { - break; - } - } - } else { - for ( i in obj ) { - if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) { - break; - } - } - } - - return obj; - }, - - // Support: Android <=4.0 only - trim: function( text ) { - return text == null ? - "" : - ( text + "" ).replace( rtrim, "" ); - }, - - // results is for internal usage only - makeArray: function( arr, results ) { - var ret = results || []; - - if ( arr != null ) { - if ( isArrayLike( Object( arr ) ) ) { - jQuery.merge( ret, - typeof arr === "string" ? - [ arr ] : arr - ); - } else { - push.call( ret, arr ); - } - } - - return ret; - }, - - inArray: function( elem, arr, i ) { - return arr == null ? -1 : indexOf.call( arr, elem, i ); - }, - - // Support: Android <=4.0 only, PhantomJS 1 only - // push.apply(_, arraylike) throws on ancient WebKit - merge: function( first, second ) { - var len = +second.length, - j = 0, - i = first.length; - - for ( ; j < len; j++ ) { - first[ i++ ] = second[ j ]; - } - - first.length = i; - - return first; - }, - - grep: function( elems, callback, invert ) { - var callbackInverse, - matches = [], - i = 0, - length = elems.length, - callbackExpect = !invert; - - // Go through the array, only saving the items - // that pass the validator function - for ( ; i < length; i++ ) { - callbackInverse = !callback( elems[ i ], i ); - if ( callbackInverse !== callbackExpect ) { - matches.push( elems[ i ] ); - } - } - - return matches; - }, - - // arg is for internal usage only - map: function( elems, callback, arg ) { - var length, value, - i = 0, - ret = []; - - // Go through the array, translating each of the items to their new values - if ( isArrayLike( elems ) ) { - length = elems.length; - for ( ; i < length; i++ ) { - value = callback( elems[ i ], i, arg ); - - if ( value != null ) { - ret.push( value ); - } - } - - // Go through every key on the object, - } else { - for ( i in elems ) { - value = callback( elems[ i ], i, arg ); - - if ( value != null ) { - ret.push( value ); - } - } - } - - // Flatten any nested arrays - return concat.apply( [], ret ); - }, - - // A global GUID counter for objects - guid: 1, - - // Bind a function to a context, optionally partially applying any - // arguments. - proxy: function( fn, context ) { - var tmp, args, proxy; - - if ( typeof context === "string" ) { - tmp = fn[ context ]; - context = fn; - fn = tmp; - } - - // Quick check to determine if target is callable, in the spec - // this throws a TypeError, but we will just return undefined. - if ( !jQuery.isFunction( fn ) ) { - return undefined; - } - - // Simulated bind - args = slice.call( arguments, 2 ); - proxy = function() { - return fn.apply( context || this, args.concat( slice.call( arguments ) ) ); - }; - - // Set the guid of unique handler to the same of original handler, so it can be removed - proxy.guid = fn.guid = fn.guid || jQuery.guid++; - - return proxy; - }, - - now: Date.now, - - // jQuery.support is not used in Core but other projects attach their - // properties to it so it needs to exist. - support: support -} ); - -if ( typeof Symbol === "function" ) { - jQuery.fn[ Symbol.iterator ] = arr[ Symbol.iterator ]; -} - -// Populate the class2type map -jQuery.each( "Boolean Number String Function Array Date RegExp Object Error Symbol".split( " " ), -function( i, name ) { - class2type[ "[object " + name + "]" ] = name.toLowerCase(); -} ); - -function isArrayLike( obj ) { - - // Support: real iOS 8.2 only (not reproducible in simulator) - // `in` check used to prevent JIT error (gh-2145) - // hasOwn isn't used here due to false negatives - // regarding Nodelist length in IE - var length = !!obj && "length" in obj && obj.length, - type = jQuery.type( obj ); - - if ( type === "function" || jQuery.isWindow( obj ) ) { - return false; - } - - return type === "array" || length === 0 || - typeof length === "number" && length > 0 && ( length - 1 ) in obj; -} -var Sizzle = -/*! - * Sizzle CSS Selector Engine v2.3.3 - * https://sizzlejs.com/ - * - * Copyright jQuery Foundation and other contributors - * Released under the MIT license - * http://jquery.org/license - * - * Date: 2016-08-08 - */ -(function( window ) { - -var i, - support, - Expr, - getText, - isXML, - tokenize, - compile, - select, - outermostContext, - sortInput, - hasDuplicate, - - // Local document vars - setDocument, - document, - docElem, - documentIsHTML, - rbuggyQSA, - rbuggyMatches, - matches, - contains, - - // Instance-specific data - expando = "sizzle" + 1 * new Date(), - preferredDoc = window.document, - dirruns = 0, - done = 0, - classCache = createCache(), - tokenCache = createCache(), - compilerCache = createCache(), - sortOrder = function( a, b ) { - if ( a === b ) { - hasDuplicate = true; - } - return 0; - }, - - // Instance methods - hasOwn = ({}).hasOwnProperty, - arr = [], - pop = arr.pop, - push_native = arr.push, - push = arr.push, - slice = arr.slice, - // Use a stripped-down indexOf as it's faster than native - // https://jsperf.com/thor-indexof-vs-for/5 - indexOf = function( list, elem ) { - var i = 0, - len = list.length; - for ( ; i < len; i++ ) { - if ( list[i] === elem ) { - return i; - } - } - return -1; - }, - - booleans = "checked|selected|async|autofocus|autoplay|controls|defer|disabled|hidden|ismap|loop|multiple|open|readonly|required|scoped", - - // Regular expressions - - // http://www.w3.org/TR/css3-selectors/#whitespace - whitespace = "[\\x20\\t\\r\\n\\f]", - - // http://www.w3.org/TR/CSS21/syndata.html#value-def-identifier - identifier = "(?:\\\\.|[\\w-]|[^\0-\\xa0])+", - - // Attribute selectors: http://www.w3.org/TR/selectors/#attribute-selectors - attributes = "\\[" + whitespace + "*(" + identifier + ")(?:" + whitespace + - // Operator (capture 2) - "*([*^$|!~]?=)" + whitespace + - // "Attribute values must be CSS identifiers [capture 5] or strings [capture 3 or capture 4]" - "*(?:'((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\"|(" + identifier + "))|)" + whitespace + - "*\\]", - - pseudos = ":(" + identifier + ")(?:\\((" + - // To reduce the number of selectors needing tokenize in the preFilter, prefer arguments: - // 1. quoted (capture 3; capture 4 or capture 5) - "('((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\")|" + - // 2. simple (capture 6) - "((?:\\\\.|[^\\\\()[\\]]|" + attributes + ")*)|" + - // 3. anything else (capture 2) - ".*" + - ")\\)|)", - - // Leading and non-escaped trailing whitespace, capturing some non-whitespace characters preceding the latter - rwhitespace = new RegExp( whitespace + "+", "g" ), - rtrim = new RegExp( "^" + whitespace + "+|((?:^|[^\\\\])(?:\\\\.)*)" + whitespace + "+$", "g" ), - - rcomma = new RegExp( "^" + whitespace + "*," + whitespace + "*" ), - rcombinators = new RegExp( "^" + whitespace + "*([>+~]|" + whitespace + ")" + whitespace + "*" ), - - rattributeQuotes = new RegExp( "=" + whitespace + "*([^\\]'\"]*?)" + whitespace + "*\\]", "g" ), - - rpseudo = new RegExp( pseudos ), - ridentifier = new RegExp( "^" + identifier + "$" ), - - matchExpr = { - "ID": new RegExp( "^#(" + identifier + ")" ), - "CLASS": new RegExp( "^\\.(" + identifier + ")" ), - "TAG": new RegExp( "^(" + identifier + "|[*])" ), - "ATTR": new RegExp( "^" + attributes ), - "PSEUDO": new RegExp( "^" + pseudos ), - "CHILD": new RegExp( "^:(only|first|last|nth|nth-last)-(child|of-type)(?:\\(" + whitespace + - "*(even|odd|(([+-]|)(\\d*)n|)" + whitespace + "*(?:([+-]|)" + whitespace + - "*(\\d+)|))" + whitespace + "*\\)|)", "i" ), - "bool": new RegExp( "^(?:" + booleans + ")$", "i" ), - // For use in libraries implementing .is() - // We use this for POS matching in `select` - "needsContext": new RegExp( "^" + whitespace + "*[>+~]|:(even|odd|eq|gt|lt|nth|first|last)(?:\\(" + - whitespace + "*((?:-\\d)?\\d*)" + whitespace + "*\\)|)(?=[^-]|$)", "i" ) - }, - - rinputs = /^(?:input|select|textarea|button)$/i, - rheader = /^h\d$/i, - - rnative = /^[^{]+\{\s*\[native \w/, - - // Easily-parseable/retrievable ID or TAG or CLASS selectors - rquickExpr = /^(?:#([\w-]+)|(\w+)|\.([\w-]+))$/, - - rsibling = /[+~]/, - - // CSS escapes - // http://www.w3.org/TR/CSS21/syndata.html#escaped-characters - runescape = new RegExp( "\\\\([\\da-f]{1,6}" + whitespace + "?|(" + whitespace + ")|.)", "ig" ), - funescape = function( _, escaped, escapedWhitespace ) { - var high = "0x" + escaped - 0x10000; - // NaN means non-codepoint - // Support: Firefox<24 - // Workaround erroneous numeric interpretation of +"0x" - return high !== high || escapedWhitespace ? - escaped : - high < 0 ? - // BMP codepoint - String.fromCharCode( high + 0x10000 ) : - // Supplemental Plane codepoint (surrogate pair) - String.fromCharCode( high >> 10 | 0xD800, high & 0x3FF | 0xDC00 ); - }, - - // CSS string/identifier serialization - // https://drafts.csswg.org/cssom/#common-serializing-idioms - rcssescape = /([\0-\x1f\x7f]|^-?\d)|^-$|[^\0-\x1f\x7f-\uFFFF\w-]/g, - fcssescape = function( ch, asCodePoint ) { - if ( asCodePoint ) { - - // U+0000 NULL becomes U+FFFD REPLACEMENT CHARACTER - if ( ch === "\0" ) { - return "\uFFFD"; - } - - // Control characters and (dependent upon position) numbers get escaped as code points - return ch.slice( 0, -1 ) + "\\" + ch.charCodeAt( ch.length - 1 ).toString( 16 ) + " "; - } - - // Other potentially-special ASCII characters get backslash-escaped - return "\\" + ch; - }, - - // Used for iframes - // See setDocument() - // Removing the function wrapper causes a "Permission Denied" - // error in IE - unloadHandler = function() { - setDocument(); - }, - - disabledAncestor = addCombinator( - function( elem ) { - return elem.disabled === true && ("form" in elem || "label" in elem); - }, - { dir: "parentNode", next: "legend" } - ); - -// Optimize for push.apply( _, NodeList ) -try { - push.apply( - (arr = slice.call( preferredDoc.childNodes )), - preferredDoc.childNodes - ); - // Support: Android<4.0 - // Detect silently failing push.apply - arr[ preferredDoc.childNodes.length ].nodeType; -} catch ( e ) { - push = { apply: arr.length ? - - // Leverage slice if possible - function( target, els ) { - push_native.apply( target, slice.call(els) ); - } : - - // Support: IE<9 - // Otherwise append directly - function( target, els ) { - var j = target.length, - i = 0; - // Can't trust NodeList.length - while ( (target[j++] = els[i++]) ) {} - target.length = j - 1; - } - }; -} - -function Sizzle( selector, context, results, seed ) { - var m, i, elem, nid, match, groups, newSelector, - newContext = context && context.ownerDocument, - - // nodeType defaults to 9, since context defaults to document - nodeType = context ? context.nodeType : 9; - - results = results || []; - - // Return early from calls with invalid selector or context - if ( typeof selector !== "string" || !selector || - nodeType !== 1 && nodeType !== 9 && nodeType !== 11 ) { - - return results; - } - - // Try to shortcut find operations (as opposed to filters) in HTML documents - if ( !seed ) { - - if ( ( context ? context.ownerDocument || context : preferredDoc ) !== document ) { - setDocument( context ); - } - context = context || document; - - if ( documentIsHTML ) { - - // If the selector is sufficiently simple, try using a "get*By*" DOM method - // (excepting DocumentFragment context, where the methods don't exist) - if ( nodeType !== 11 && (match = rquickExpr.exec( selector )) ) { - - // ID selector - if ( (m = match[1]) ) { - - // Document context - if ( nodeType === 9 ) { - if ( (elem = context.getElementById( m )) ) { - - // Support: IE, Opera, Webkit - // TODO: identify versions - // getElementById can match elements by name instead of ID - if ( elem.id === m ) { - results.push( elem ); - return results; - } - } else { - return results; - } - - // Element context - } else { - - // Support: IE, Opera, Webkit - // TODO: identify versions - // getElementById can match elements by name instead of ID - if ( newContext && (elem = newContext.getElementById( m )) && - contains( context, elem ) && - elem.id === m ) { - - results.push( elem ); - return results; - } - } - - // Type selector - } else if ( match[2] ) { - push.apply( results, context.getElementsByTagName( selector ) ); - return results; - - // Class selector - } else if ( (m = match[3]) && support.getElementsByClassName && - context.getElementsByClassName ) { - - push.apply( results, context.getElementsByClassName( m ) ); - return results; - } - } - - // Take advantage of querySelectorAll - if ( support.qsa && - !compilerCache[ selector + " " ] && - (!rbuggyQSA || !rbuggyQSA.test( selector )) ) { - - if ( nodeType !== 1 ) { - newContext = context; - newSelector = selector; - - // qSA looks outside Element context, which is not what we want - // Thanks to Andrew Dupont for this workaround technique - // Support: IE <=8 - // Exclude object elements - } else if ( context.nodeName.toLowerCase() !== "object" ) { - - // Capture the context ID, setting it first if necessary - if ( (nid = context.getAttribute( "id" )) ) { - nid = nid.replace( rcssescape, fcssescape ); - } else { - context.setAttribute( "id", (nid = expando) ); - } - - // Prefix every selector in the list - groups = tokenize( selector ); - i = groups.length; - while ( i-- ) { - groups[i] = "#" + nid + " " + toSelector( groups[i] ); - } - newSelector = groups.join( "," ); - - // Expand context for sibling selectors - newContext = rsibling.test( selector ) && testContext( context.parentNode ) || - context; - } - - if ( newSelector ) { - try { - push.apply( results, - newContext.querySelectorAll( newSelector ) - ); - return results; - } catch ( qsaError ) { - } finally { - if ( nid === expando ) { - context.removeAttribute( "id" ); - } - } - } - } - } - } - - // All others - return select( selector.replace( rtrim, "$1" ), context, results, seed ); -} - -/** - * Create key-value caches of limited size - * @returns {function(string, object)} Returns the Object data after storing it on itself with - * property name the (space-suffixed) string and (if the cache is larger than Expr.cacheLength) - * deleting the oldest entry - */ -function createCache() { - var keys = []; - - function cache( key, value ) { - // Use (key + " ") to avoid collision with native prototype properties (see Issue #157) - if ( keys.push( key + " " ) > Expr.cacheLength ) { - // Only keep the most recent entries - delete cache[ keys.shift() ]; - } - return (cache[ key + " " ] = value); - } - return cache; -} - -/** - * Mark a function for special use by Sizzle - * @param {Function} fn The function to mark - */ -function markFunction( fn ) { - fn[ expando ] = true; - return fn; -} - -/** - * Support testing using an element - * @param {Function} fn Passed the created element and returns a boolean result - */ -function assert( fn ) { - var el = document.createElement("fieldset"); - - try { - return !!fn( el ); - } catch (e) { - return false; - } finally { - // Remove from its parent by default - if ( el.parentNode ) { - el.parentNode.removeChild( el ); - } - // release memory in IE - el = null; - } -} - -/** - * Adds the same handler for all of the specified attrs - * @param {String} attrs Pipe-separated list of attributes - * @param {Function} handler The method that will be applied - */ -function addHandle( attrs, handler ) { - var arr = attrs.split("|"), - i = arr.length; - - while ( i-- ) { - Expr.attrHandle[ arr[i] ] = handler; - } -} - -/** - * Checks document order of two siblings - * @param {Element} a - * @param {Element} b - * @returns {Number} Returns less than 0 if a precedes b, greater than 0 if a follows b - */ -function siblingCheck( a, b ) { - var cur = b && a, - diff = cur && a.nodeType === 1 && b.nodeType === 1 && - a.sourceIndex - b.sourceIndex; - - // Use IE sourceIndex if available on both nodes - if ( diff ) { - return diff; - } - - // Check if b follows a - if ( cur ) { - while ( (cur = cur.nextSibling) ) { - if ( cur === b ) { - return -1; - } - } - } - - return a ? 1 : -1; -} - -/** - * Returns a function to use in pseudos for input types - * @param {String} type - */ -function createInputPseudo( type ) { - return function( elem ) { - var name = elem.nodeName.toLowerCase(); - return name === "input" && elem.type === type; - }; -} - -/** - * Returns a function to use in pseudos for buttons - * @param {String} type - */ -function createButtonPseudo( type ) { - return function( elem ) { - var name = elem.nodeName.toLowerCase(); - return (name === "input" || name === "button") && elem.type === type; - }; -} - -/** - * Returns a function to use in pseudos for :enabled/:disabled - * @param {Boolean} disabled true for :disabled; false for :enabled - */ -function createDisabledPseudo( disabled ) { - - // Known :disabled false positives: fieldset[disabled] > legend:nth-of-type(n+2) :can-disable - return function( elem ) { - - // Only certain elements can match :enabled or :disabled - // https://html.spec.whatwg.org/multipage/scripting.html#selector-enabled - // https://html.spec.whatwg.org/multipage/scripting.html#selector-disabled - if ( "form" in elem ) { - - // Check for inherited disabledness on relevant non-disabled elements: - // * listed form-associated elements in a disabled fieldset - // https://html.spec.whatwg.org/multipage/forms.html#category-listed - // https://html.spec.whatwg.org/multipage/forms.html#concept-fe-disabled - // * option elements in a disabled optgroup - // https://html.spec.whatwg.org/multipage/forms.html#concept-option-disabled - // All such elements have a "form" property. - if ( elem.parentNode && elem.disabled === false ) { - - // Option elements defer to a parent optgroup if present - if ( "label" in elem ) { - if ( "label" in elem.parentNode ) { - return elem.parentNode.disabled === disabled; - } else { - return elem.disabled === disabled; - } - } - - // Support: IE 6 - 11 - // Use the isDisabled shortcut property to check for disabled fieldset ancestors - return elem.isDisabled === disabled || - - // Where there is no isDisabled, check manually - /* jshint -W018 */ - elem.isDisabled !== !disabled && - disabledAncestor( elem ) === disabled; - } - - return elem.disabled === disabled; - - // Try to winnow out elements that can't be disabled before trusting the disabled property. - // Some victims get caught in our net (label, legend, menu, track), but it shouldn't - // even exist on them, let alone have a boolean value. - } else if ( "label" in elem ) { - return elem.disabled === disabled; - } - - // Remaining elements are neither :enabled nor :disabled - return false; - }; -} - -/** - * Returns a function to use in pseudos for positionals - * @param {Function} fn - */ -function createPositionalPseudo( fn ) { - return markFunction(function( argument ) { - argument = +argument; - return markFunction(function( seed, matches ) { - var j, - matchIndexes = fn( [], seed.length, argument ), - i = matchIndexes.length; - - // Match elements found at the specified indexes - while ( i-- ) { - if ( seed[ (j = matchIndexes[i]) ] ) { - seed[j] = !(matches[j] = seed[j]); - } - } - }); - }); -} - -/** - * Checks a node for validity as a Sizzle context - * @param {Element|Object=} context - * @returns {Element|Object|Boolean} The input node if acceptable, otherwise a falsy value - */ -function testContext( context ) { - return context && typeof context.getElementsByTagName !== "undefined" && context; -} - -// Expose support vars for convenience -support = Sizzle.support = {}; - -/** - * Detects XML nodes - * @param {Element|Object} elem An element or a document - * @returns {Boolean} True iff elem is a non-HTML XML node - */ -isXML = Sizzle.isXML = function( elem ) { - // documentElement is verified for cases where it doesn't yet exist - // (such as loading iframes in IE - #4833) - var documentElement = elem && (elem.ownerDocument || elem).documentElement; - return documentElement ? documentElement.nodeName !== "HTML" : false; -}; - -/** - * Sets document-related variables once based on the current document - * @param {Element|Object} [doc] An element or document object to use to set the document - * @returns {Object} Returns the current document - */ -setDocument = Sizzle.setDocument = function( node ) { - var hasCompare, subWindow, - doc = node ? node.ownerDocument || node : preferredDoc; - - // Return early if doc is invalid or already selected - if ( doc === document || doc.nodeType !== 9 || !doc.documentElement ) { - return document; - } - - // Update global variables - document = doc; - docElem = document.documentElement; - documentIsHTML = !isXML( document ); - - // Support: IE 9-11, Edge - // Accessing iframe documents after unload throws "permission denied" errors (jQuery #13936) - if ( preferredDoc !== document && - (subWindow = document.defaultView) && subWindow.top !== subWindow ) { - - // Support: IE 11, Edge - if ( subWindow.addEventListener ) { - subWindow.addEventListener( "unload", unloadHandler, false ); - - // Support: IE 9 - 10 only - } else if ( subWindow.attachEvent ) { - subWindow.attachEvent( "onunload", unloadHandler ); - } - } - - /* Attributes - ---------------------------------------------------------------------- */ - - // Support: IE<8 - // Verify that getAttribute really returns attributes and not properties - // (excepting IE8 booleans) - support.attributes = assert(function( el ) { - el.className = "i"; - return !el.getAttribute("className"); - }); - - /* getElement(s)By* - ---------------------------------------------------------------------- */ - - // Check if getElementsByTagName("*") returns only elements - support.getElementsByTagName = assert(function( el ) { - el.appendChild( document.createComment("") ); - return !el.getElementsByTagName("*").length; - }); - - // Support: IE<9 - support.getElementsByClassName = rnative.test( document.getElementsByClassName ); - - // Support: IE<10 - // Check if getElementById returns elements by name - // The broken getElementById methods don't pick up programmatically-set names, - // so use a roundabout getElementsByName test - support.getById = assert(function( el ) { - docElem.appendChild( el ).id = expando; - return !document.getElementsByName || !document.getElementsByName( expando ).length; - }); - - // ID filter and find - if ( support.getById ) { - Expr.filter["ID"] = function( id ) { - var attrId = id.replace( runescape, funescape ); - return function( elem ) { - return elem.getAttribute("id") === attrId; - }; - }; - Expr.find["ID"] = function( id, context ) { - if ( typeof context.getElementById !== "undefined" && documentIsHTML ) { - var elem = context.getElementById( id ); - return elem ? [ elem ] : []; - } - }; - } else { - Expr.filter["ID"] = function( id ) { - var attrId = id.replace( runescape, funescape ); - return function( elem ) { - var node = typeof elem.getAttributeNode !== "undefined" && - elem.getAttributeNode("id"); - return node && node.value === attrId; - }; - }; - - // Support: IE 6 - 7 only - // getElementById is not reliable as a find shortcut - Expr.find["ID"] = function( id, context ) { - if ( typeof context.getElementById !== "undefined" && documentIsHTML ) { - var node, i, elems, - elem = context.getElementById( id ); - - if ( elem ) { - - // Verify the id attribute - node = elem.getAttributeNode("id"); - if ( node && node.value === id ) { - return [ elem ]; - } - - // Fall back on getElementsByName - elems = context.getElementsByName( id ); - i = 0; - while ( (elem = elems[i++]) ) { - node = elem.getAttributeNode("id"); - if ( node && node.value === id ) { - return [ elem ]; - } - } - } - - return []; - } - }; - } - - // Tag - Expr.find["TAG"] = support.getElementsByTagName ? - function( tag, context ) { - if ( typeof context.getElementsByTagName !== "undefined" ) { - return context.getElementsByTagName( tag ); - - // DocumentFragment nodes don't have gEBTN - } else if ( support.qsa ) { - return context.querySelectorAll( tag ); - } - } : - - function( tag, context ) { - var elem, - tmp = [], - i = 0, - // By happy coincidence, a (broken) gEBTN appears on DocumentFragment nodes too - results = context.getElementsByTagName( tag ); - - // Filter out possible comments - if ( tag === "*" ) { - while ( (elem = results[i++]) ) { - if ( elem.nodeType === 1 ) { - tmp.push( elem ); - } - } - - return tmp; - } - return results; - }; - - // Class - Expr.find["CLASS"] = support.getElementsByClassName && function( className, context ) { - if ( typeof context.getElementsByClassName !== "undefined" && documentIsHTML ) { - return context.getElementsByClassName( className ); - } - }; - - /* QSA/matchesSelector - ---------------------------------------------------------------------- */ - - // QSA and matchesSelector support - - // matchesSelector(:active) reports false when true (IE9/Opera 11.5) - rbuggyMatches = []; - - // qSa(:focus) reports false when true (Chrome 21) - // We allow this because of a bug in IE8/9 that throws an error - // whenever `document.activeElement` is accessed on an iframe - // So, we allow :focus to pass through QSA all the time to avoid the IE error - // See https://bugs.jquery.com/ticket/13378 - rbuggyQSA = []; - - if ( (support.qsa = rnative.test( document.querySelectorAll )) ) { - // Build QSA regex - // Regex strategy adopted from Diego Perini - assert(function( el ) { - // Select is set to empty string on purpose - // This is to test IE's treatment of not explicitly - // setting a boolean content attribute, - // since its presence should be enough - // https://bugs.jquery.com/ticket/12359 - docElem.appendChild( el ).innerHTML = "" + - ""; - - // Support: IE8, Opera 11-12.16 - // Nothing should be selected when empty strings follow ^= or $= or *= - // The test attribute must be unknown in Opera but "safe" for WinRT - // https://msdn.microsoft.com/en-us/library/ie/hh465388.aspx#attribute_section - if ( el.querySelectorAll("[msallowcapture^='']").length ) { - rbuggyQSA.push( "[*^$]=" + whitespace + "*(?:''|\"\")" ); - } - - // Support: IE8 - // Boolean attributes and "value" are not treated correctly - if ( !el.querySelectorAll("[selected]").length ) { - rbuggyQSA.push( "\\[" + whitespace + "*(?:value|" + booleans + ")" ); - } - - // Support: Chrome<29, Android<4.4, Safari<7.0+, iOS<7.0+, PhantomJS<1.9.8+ - if ( !el.querySelectorAll( "[id~=" + expando + "-]" ).length ) { - rbuggyQSA.push("~="); - } - - // Webkit/Opera - :checked should return selected option elements - // http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked - // IE8 throws error here and will not see later tests - if ( !el.querySelectorAll(":checked").length ) { - rbuggyQSA.push(":checked"); - } - - // Support: Safari 8+, iOS 8+ - // https://bugs.webkit.org/show_bug.cgi?id=136851 - // In-page `selector#id sibling-combinator selector` fails - if ( !el.querySelectorAll( "a#" + expando + "+*" ).length ) { - rbuggyQSA.push(".#.+[+~]"); - } - }); - - assert(function( el ) { - el.innerHTML = "" + - ""; - - // Support: Windows 8 Native Apps - // The type and name attributes are restricted during .innerHTML assignment - var input = document.createElement("input"); - input.setAttribute( "type", "hidden" ); - el.appendChild( input ).setAttribute( "name", "D" ); - - // Support: IE8 - // Enforce case-sensitivity of name attribute - if ( el.querySelectorAll("[name=d]").length ) { - rbuggyQSA.push( "name" + whitespace + "*[*^$|!~]?=" ); - } - - // FF 3.5 - :enabled/:disabled and hidden elements (hidden elements are still enabled) - // IE8 throws error here and will not see later tests - if ( el.querySelectorAll(":enabled").length !== 2 ) { - rbuggyQSA.push( ":enabled", ":disabled" ); - } - - // Support: IE9-11+ - // IE's :disabled selector does not pick up the children of disabled fieldsets - docElem.appendChild( el ).disabled = true; - if ( el.querySelectorAll(":disabled").length !== 2 ) { - rbuggyQSA.push( ":enabled", ":disabled" ); - } - - // Opera 10-11 does not throw on post-comma invalid pseudos - el.querySelectorAll("*,:x"); - rbuggyQSA.push(",.*:"); - }); - } - - if ( (support.matchesSelector = rnative.test( (matches = docElem.matches || - docElem.webkitMatchesSelector || - docElem.mozMatchesSelector || - docElem.oMatchesSelector || - docElem.msMatchesSelector) )) ) { - - assert(function( el ) { - // Check to see if it's possible to do matchesSelector - // on a disconnected node (IE 9) - support.disconnectedMatch = matches.call( el, "*" ); - - // This should fail with an exception - // Gecko does not error, returns false instead - matches.call( el, "[s!='']:x" ); - rbuggyMatches.push( "!=", pseudos ); - }); - } - - rbuggyQSA = rbuggyQSA.length && new RegExp( rbuggyQSA.join("|") ); - rbuggyMatches = rbuggyMatches.length && new RegExp( rbuggyMatches.join("|") ); - - /* Contains - ---------------------------------------------------------------------- */ - hasCompare = rnative.test( docElem.compareDocumentPosition ); - - // Element contains another - // Purposefully self-exclusive - // As in, an element does not contain itself - contains = hasCompare || rnative.test( docElem.contains ) ? - function( a, b ) { - var adown = a.nodeType === 9 ? a.documentElement : a, - bup = b && b.parentNode; - return a === bup || !!( bup && bup.nodeType === 1 && ( - adown.contains ? - adown.contains( bup ) : - a.compareDocumentPosition && a.compareDocumentPosition( bup ) & 16 - )); - } : - function( a, b ) { - if ( b ) { - while ( (b = b.parentNode) ) { - if ( b === a ) { - return true; - } - } - } - return false; - }; - - /* Sorting - ---------------------------------------------------------------------- */ - - // Document order sorting - sortOrder = hasCompare ? - function( a, b ) { - - // Flag for duplicate removal - if ( a === b ) { - hasDuplicate = true; - return 0; - } - - // Sort on method existence if only one input has compareDocumentPosition - var compare = !a.compareDocumentPosition - !b.compareDocumentPosition; - if ( compare ) { - return compare; - } - - // Calculate position if both inputs belong to the same document - compare = ( a.ownerDocument || a ) === ( b.ownerDocument || b ) ? - a.compareDocumentPosition( b ) : - - // Otherwise we know they are disconnected - 1; - - // Disconnected nodes - if ( compare & 1 || - (!support.sortDetached && b.compareDocumentPosition( a ) === compare) ) { - - // Choose the first element that is related to our preferred document - if ( a === document || a.ownerDocument === preferredDoc && contains(preferredDoc, a) ) { - return -1; - } - if ( b === document || b.ownerDocument === preferredDoc && contains(preferredDoc, b) ) { - return 1; - } - - // Maintain original order - return sortInput ? - ( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) : - 0; - } - - return compare & 4 ? -1 : 1; - } : - function( a, b ) { - // Exit early if the nodes are identical - if ( a === b ) { - hasDuplicate = true; - return 0; - } - - var cur, - i = 0, - aup = a.parentNode, - bup = b.parentNode, - ap = [ a ], - bp = [ b ]; - - // Parentless nodes are either documents or disconnected - if ( !aup || !bup ) { - return a === document ? -1 : - b === document ? 1 : - aup ? -1 : - bup ? 1 : - sortInput ? - ( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) : - 0; - - // If the nodes are siblings, we can do a quick check - } else if ( aup === bup ) { - return siblingCheck( a, b ); - } - - // Otherwise we need full lists of their ancestors for comparison - cur = a; - while ( (cur = cur.parentNode) ) { - ap.unshift( cur ); - } - cur = b; - while ( (cur = cur.parentNode) ) { - bp.unshift( cur ); - } - - // Walk down the tree looking for a discrepancy - while ( ap[i] === bp[i] ) { - i++; - } - - return i ? - // Do a sibling check if the nodes have a common ancestor - siblingCheck( ap[i], bp[i] ) : - - // Otherwise nodes in our document sort first - ap[i] === preferredDoc ? -1 : - bp[i] === preferredDoc ? 1 : - 0; - }; - - return document; -}; - -Sizzle.matches = function( expr, elements ) { - return Sizzle( expr, null, null, elements ); -}; - -Sizzle.matchesSelector = function( elem, expr ) { - // Set document vars if needed - if ( ( elem.ownerDocument || elem ) !== document ) { - setDocument( elem ); - } - - // Make sure that attribute selectors are quoted - expr = expr.replace( rattributeQuotes, "='$1']" ); - - if ( support.matchesSelector && documentIsHTML && - !compilerCache[ expr + " " ] && - ( !rbuggyMatches || !rbuggyMatches.test( expr ) ) && - ( !rbuggyQSA || !rbuggyQSA.test( expr ) ) ) { - - try { - var ret = matches.call( elem, expr ); - - // IE 9's matchesSelector returns false on disconnected nodes - if ( ret || support.disconnectedMatch || - // As well, disconnected nodes are said to be in a document - // fragment in IE 9 - elem.document && elem.document.nodeType !== 11 ) { - return ret; - } - } catch (e) {} - } - - return Sizzle( expr, document, null, [ elem ] ).length > 0; -}; - -Sizzle.contains = function( context, elem ) { - // Set document vars if needed - if ( ( context.ownerDocument || context ) !== document ) { - setDocument( context ); - } - return contains( context, elem ); -}; - -Sizzle.attr = function( elem, name ) { - // Set document vars if needed - if ( ( elem.ownerDocument || elem ) !== document ) { - setDocument( elem ); - } - - var fn = Expr.attrHandle[ name.toLowerCase() ], - // Don't get fooled by Object.prototype properties (jQuery #13807) - val = fn && hasOwn.call( Expr.attrHandle, name.toLowerCase() ) ? - fn( elem, name, !documentIsHTML ) : - undefined; - - return val !== undefined ? - val : - support.attributes || !documentIsHTML ? - elem.getAttribute( name ) : - (val = elem.getAttributeNode(name)) && val.specified ? - val.value : - null; -}; - -Sizzle.escape = function( sel ) { - return (sel + "").replace( rcssescape, fcssescape ); -}; - -Sizzle.error = function( msg ) { - throw new Error( "Syntax error, unrecognized expression: " + msg ); -}; - -/** - * Document sorting and removing duplicates - * @param {ArrayLike} results - */ -Sizzle.uniqueSort = function( results ) { - var elem, - duplicates = [], - j = 0, - i = 0; - - // Unless we *know* we can detect duplicates, assume their presence - hasDuplicate = !support.detectDuplicates; - sortInput = !support.sortStable && results.slice( 0 ); - results.sort( sortOrder ); - - if ( hasDuplicate ) { - while ( (elem = results[i++]) ) { - if ( elem === results[ i ] ) { - j = duplicates.push( i ); - } - } - while ( j-- ) { - results.splice( duplicates[ j ], 1 ); - } - } - - // Clear input after sorting to release objects - // See https://github.com/jquery/sizzle/pull/225 - sortInput = null; - - return results; -}; - -/** - * Utility function for retrieving the text value of an array of DOM nodes - * @param {Array|Element} elem - */ -getText = Sizzle.getText = function( elem ) { - var node, - ret = "", - i = 0, - nodeType = elem.nodeType; - - if ( !nodeType ) { - // If no nodeType, this is expected to be an array - while ( (node = elem[i++]) ) { - // Do not traverse comment nodes - ret += getText( node ); - } - } else if ( nodeType === 1 || nodeType === 9 || nodeType === 11 ) { - // Use textContent for elements - // innerText usage removed for consistency of new lines (jQuery #11153) - if ( typeof elem.textContent === "string" ) { - return elem.textContent; - } else { - // Traverse its children - for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) { - ret += getText( elem ); - } - } - } else if ( nodeType === 3 || nodeType === 4 ) { - return elem.nodeValue; - } - // Do not include comment or processing instruction nodes - - return ret; -}; - -Expr = Sizzle.selectors = { - - // Can be adjusted by the user - cacheLength: 50, - - createPseudo: markFunction, - - match: matchExpr, - - attrHandle: {}, - - find: {}, - - relative: { - ">": { dir: "parentNode", first: true }, - " ": { dir: "parentNode" }, - "+": { dir: "previousSibling", first: true }, - "~": { dir: "previousSibling" } - }, - - preFilter: { - "ATTR": function( match ) { - match[1] = match[1].replace( runescape, funescape ); - - // Move the given value to match[3] whether quoted or unquoted - match[3] = ( match[3] || match[4] || match[5] || "" ).replace( runescape, funescape ); - - if ( match[2] === "~=" ) { - match[3] = " " + match[3] + " "; - } - - return match.slice( 0, 4 ); - }, - - "CHILD": function( match ) { - /* matches from matchExpr["CHILD"] - 1 type (only|nth|...) - 2 what (child|of-type) - 3 argument (even|odd|\d*|\d*n([+-]\d+)?|...) - 4 xn-component of xn+y argument ([+-]?\d*n|) - 5 sign of xn-component - 6 x of xn-component - 7 sign of y-component - 8 y of y-component - */ - match[1] = match[1].toLowerCase(); - - if ( match[1].slice( 0, 3 ) === "nth" ) { - // nth-* requires argument - if ( !match[3] ) { - Sizzle.error( match[0] ); - } - - // numeric x and y parameters for Expr.filter.CHILD - // remember that false/true cast respectively to 0/1 - match[4] = +( match[4] ? match[5] + (match[6] || 1) : 2 * ( match[3] === "even" || match[3] === "odd" ) ); - match[5] = +( ( match[7] + match[8] ) || match[3] === "odd" ); - - // other types prohibit arguments - } else if ( match[3] ) { - Sizzle.error( match[0] ); - } - - return match; - }, - - "PSEUDO": function( match ) { - var excess, - unquoted = !match[6] && match[2]; - - if ( matchExpr["CHILD"].test( match[0] ) ) { - return null; - } - - // Accept quoted arguments as-is - if ( match[3] ) { - match[2] = match[4] || match[5] || ""; - - // Strip excess characters from unquoted arguments - } else if ( unquoted && rpseudo.test( unquoted ) && - // Get excess from tokenize (recursively) - (excess = tokenize( unquoted, true )) && - // advance to the next closing parenthesis - (excess = unquoted.indexOf( ")", unquoted.length - excess ) - unquoted.length) ) { - - // excess is a negative index - match[0] = match[0].slice( 0, excess ); - match[2] = unquoted.slice( 0, excess ); - } - - // Return only captures needed by the pseudo filter method (type and argument) - return match.slice( 0, 3 ); - } - }, - - filter: { - - "TAG": function( nodeNameSelector ) { - var nodeName = nodeNameSelector.replace( runescape, funescape ).toLowerCase(); - return nodeNameSelector === "*" ? - function() { return true; } : - function( elem ) { - return elem.nodeName && elem.nodeName.toLowerCase() === nodeName; - }; - }, - - "CLASS": function( className ) { - var pattern = classCache[ className + " " ]; - - return pattern || - (pattern = new RegExp( "(^|" + whitespace + ")" + className + "(" + whitespace + "|$)" )) && - classCache( className, function( elem ) { - return pattern.test( typeof elem.className === "string" && elem.className || typeof elem.getAttribute !== "undefined" && elem.getAttribute("class") || "" ); - }); - }, - - "ATTR": function( name, operator, check ) { - return function( elem ) { - var result = Sizzle.attr( elem, name ); - - if ( result == null ) { - return operator === "!="; - } - if ( !operator ) { - return true; - } - - result += ""; - - return operator === "=" ? result === check : - operator === "!=" ? result !== check : - operator === "^=" ? check && result.indexOf( check ) === 0 : - operator === "*=" ? check && result.indexOf( check ) > -1 : - operator === "$=" ? check && result.slice( -check.length ) === check : - operator === "~=" ? ( " " + result.replace( rwhitespace, " " ) + " " ).indexOf( check ) > -1 : - operator === "|=" ? result === check || result.slice( 0, check.length + 1 ) === check + "-" : - false; - }; - }, - - "CHILD": function( type, what, argument, first, last ) { - var simple = type.slice( 0, 3 ) !== "nth", - forward = type.slice( -4 ) !== "last", - ofType = what === "of-type"; - - return first === 1 && last === 0 ? - - // Shortcut for :nth-*(n) - function( elem ) { - return !!elem.parentNode; - } : - - function( elem, context, xml ) { - var cache, uniqueCache, outerCache, node, nodeIndex, start, - dir = simple !== forward ? "nextSibling" : "previousSibling", - parent = elem.parentNode, - name = ofType && elem.nodeName.toLowerCase(), - useCache = !xml && !ofType, - diff = false; - - if ( parent ) { - - // :(first|last|only)-(child|of-type) - if ( simple ) { - while ( dir ) { - node = elem; - while ( (node = node[ dir ]) ) { - if ( ofType ? - node.nodeName.toLowerCase() === name : - node.nodeType === 1 ) { - - return false; - } - } - // Reverse direction for :only-* (if we haven't yet done so) - start = dir = type === "only" && !start && "nextSibling"; - } - return true; - } - - start = [ forward ? parent.firstChild : parent.lastChild ]; - - // non-xml :nth-child(...) stores cache data on `parent` - if ( forward && useCache ) { - - // Seek `elem` from a previously-cached index - - // ...in a gzip-friendly way - node = parent; - outerCache = node[ expando ] || (node[ expando ] = {}); - - // Support: IE <9 only - // Defend against cloned attroperties (jQuery gh-1709) - uniqueCache = outerCache[ node.uniqueID ] || - (outerCache[ node.uniqueID ] = {}); - - cache = uniqueCache[ type ] || []; - nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ]; - diff = nodeIndex && cache[ 2 ]; - node = nodeIndex && parent.childNodes[ nodeIndex ]; - - while ( (node = ++nodeIndex && node && node[ dir ] || - - // Fallback to seeking `elem` from the start - (diff = nodeIndex = 0) || start.pop()) ) { - - // When found, cache indexes on `parent` and break - if ( node.nodeType === 1 && ++diff && node === elem ) { - uniqueCache[ type ] = [ dirruns, nodeIndex, diff ]; - break; - } - } - - } else { - // Use previously-cached element index if available - if ( useCache ) { - // ...in a gzip-friendly way - node = elem; - outerCache = node[ expando ] || (node[ expando ] = {}); - - // Support: IE <9 only - // Defend against cloned attroperties (jQuery gh-1709) - uniqueCache = outerCache[ node.uniqueID ] || - (outerCache[ node.uniqueID ] = {}); - - cache = uniqueCache[ type ] || []; - nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ]; - diff = nodeIndex; - } - - // xml :nth-child(...) - // or :nth-last-child(...) or :nth(-last)?-of-type(...) - if ( diff === false ) { - // Use the same loop as above to seek `elem` from the start - while ( (node = ++nodeIndex && node && node[ dir ] || - (diff = nodeIndex = 0) || start.pop()) ) { - - if ( ( ofType ? - node.nodeName.toLowerCase() === name : - node.nodeType === 1 ) && - ++diff ) { - - // Cache the index of each encountered element - if ( useCache ) { - outerCache = node[ expando ] || (node[ expando ] = {}); - - // Support: IE <9 only - // Defend against cloned attroperties (jQuery gh-1709) - uniqueCache = outerCache[ node.uniqueID ] || - (outerCache[ node.uniqueID ] = {}); - - uniqueCache[ type ] = [ dirruns, diff ]; - } - - if ( node === elem ) { - break; - } - } - } - } - } - - // Incorporate the offset, then check against cycle size - diff -= last; - return diff === first || ( diff % first === 0 && diff / first >= 0 ); - } - }; - }, - - "PSEUDO": function( pseudo, argument ) { - // pseudo-class names are case-insensitive - // http://www.w3.org/TR/selectors/#pseudo-classes - // Prioritize by case sensitivity in case custom pseudos are added with uppercase letters - // Remember that setFilters inherits from pseudos - var args, - fn = Expr.pseudos[ pseudo ] || Expr.setFilters[ pseudo.toLowerCase() ] || - Sizzle.error( "unsupported pseudo: " + pseudo ); - - // The user may use createPseudo to indicate that - // arguments are needed to create the filter function - // just as Sizzle does - if ( fn[ expando ] ) { - return fn( argument ); - } - - // But maintain support for old signatures - if ( fn.length > 1 ) { - args = [ pseudo, pseudo, "", argument ]; - return Expr.setFilters.hasOwnProperty( pseudo.toLowerCase() ) ? - markFunction(function( seed, matches ) { - var idx, - matched = fn( seed, argument ), - i = matched.length; - while ( i-- ) { - idx = indexOf( seed, matched[i] ); - seed[ idx ] = !( matches[ idx ] = matched[i] ); - } - }) : - function( elem ) { - return fn( elem, 0, args ); - }; - } - - return fn; - } - }, - - pseudos: { - // Potentially complex pseudos - "not": markFunction(function( selector ) { - // Trim the selector passed to compile - // to avoid treating leading and trailing - // spaces as combinators - var input = [], - results = [], - matcher = compile( selector.replace( rtrim, "$1" ) ); - - return matcher[ expando ] ? - markFunction(function( seed, matches, context, xml ) { - var elem, - unmatched = matcher( seed, null, xml, [] ), - i = seed.length; - - // Match elements unmatched by `matcher` - while ( i-- ) { - if ( (elem = unmatched[i]) ) { - seed[i] = !(matches[i] = elem); - } - } - }) : - function( elem, context, xml ) { - input[0] = elem; - matcher( input, null, xml, results ); - // Don't keep the element (issue #299) - input[0] = null; - return !results.pop(); - }; - }), - - "has": markFunction(function( selector ) { - return function( elem ) { - return Sizzle( selector, elem ).length > 0; - }; - }), - - "contains": markFunction(function( text ) { - text = text.replace( runescape, funescape ); - return function( elem ) { - return ( elem.textContent || elem.innerText || getText( elem ) ).indexOf( text ) > -1; - }; - }), - - // "Whether an element is represented by a :lang() selector - // is based solely on the element's language value - // being equal to the identifier C, - // or beginning with the identifier C immediately followed by "-". - // The matching of C against the element's language value is performed case-insensitively. - // The identifier C does not have to be a valid language name." - // http://www.w3.org/TR/selectors/#lang-pseudo - "lang": markFunction( function( lang ) { - // lang value must be a valid identifier - if ( !ridentifier.test(lang || "") ) { - Sizzle.error( "unsupported lang: " + lang ); - } - lang = lang.replace( runescape, funescape ).toLowerCase(); - return function( elem ) { - var elemLang; - do { - if ( (elemLang = documentIsHTML ? - elem.lang : - elem.getAttribute("xml:lang") || elem.getAttribute("lang")) ) { - - elemLang = elemLang.toLowerCase(); - return elemLang === lang || elemLang.indexOf( lang + "-" ) === 0; - } - } while ( (elem = elem.parentNode) && elem.nodeType === 1 ); - return false; - }; - }), - - // Miscellaneous - "target": function( elem ) { - var hash = window.location && window.location.hash; - return hash && hash.slice( 1 ) === elem.id; - }, - - "root": function( elem ) { - return elem === docElem; - }, - - "focus": function( elem ) { - return elem === document.activeElement && (!document.hasFocus || document.hasFocus()) && !!(elem.type || elem.href || ~elem.tabIndex); - }, - - // Boolean properties - "enabled": createDisabledPseudo( false ), - "disabled": createDisabledPseudo( true ), - - "checked": function( elem ) { - // In CSS3, :checked should return both checked and selected elements - // http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked - var nodeName = elem.nodeName.toLowerCase(); - return (nodeName === "input" && !!elem.checked) || (nodeName === "option" && !!elem.selected); - }, - - "selected": function( elem ) { - // Accessing this property makes selected-by-default - // options in Safari work properly - if ( elem.parentNode ) { - elem.parentNode.selectedIndex; - } - - return elem.selected === true; - }, - - // Contents - "empty": function( elem ) { - // http://www.w3.org/TR/selectors/#empty-pseudo - // :empty is negated by element (1) or content nodes (text: 3; cdata: 4; entity ref: 5), - // but not by others (comment: 8; processing instruction: 7; etc.) - // nodeType < 6 works because attributes (2) do not appear as children - for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) { - if ( elem.nodeType < 6 ) { - return false; - } - } - return true; - }, - - "parent": function( elem ) { - return !Expr.pseudos["empty"]( elem ); - }, - - // Element/input types - "header": function( elem ) { - return rheader.test( elem.nodeName ); - }, - - "input": function( elem ) { - return rinputs.test( elem.nodeName ); - }, - - "button": function( elem ) { - var name = elem.nodeName.toLowerCase(); - return name === "input" && elem.type === "button" || name === "button"; - }, - - "text": function( elem ) { - var attr; - return elem.nodeName.toLowerCase() === "input" && - elem.type === "text" && - - // Support: IE<8 - // New HTML5 attribute values (e.g., "search") appear with elem.type === "text" - ( (attr = elem.getAttribute("type")) == null || attr.toLowerCase() === "text" ); - }, - - // Position-in-collection - "first": createPositionalPseudo(function() { - return [ 0 ]; - }), - - "last": createPositionalPseudo(function( matchIndexes, length ) { - return [ length - 1 ]; - }), - - "eq": createPositionalPseudo(function( matchIndexes, length, argument ) { - return [ argument < 0 ? argument + length : argument ]; - }), - - "even": createPositionalPseudo(function( matchIndexes, length ) { - var i = 0; - for ( ; i < length; i += 2 ) { - matchIndexes.push( i ); - } - return matchIndexes; - }), - - "odd": createPositionalPseudo(function( matchIndexes, length ) { - var i = 1; - for ( ; i < length; i += 2 ) { - matchIndexes.push( i ); - } - return matchIndexes; - }), - - "lt": createPositionalPseudo(function( matchIndexes, length, argument ) { - var i = argument < 0 ? argument + length : argument; - for ( ; --i >= 0; ) { - matchIndexes.push( i ); - } - return matchIndexes; - }), - - "gt": createPositionalPseudo(function( matchIndexes, length, argument ) { - var i = argument < 0 ? argument + length : argument; - for ( ; ++i < length; ) { - matchIndexes.push( i ); - } - return matchIndexes; - }) - } -}; - -Expr.pseudos["nth"] = Expr.pseudos["eq"]; - -// Add button/input type pseudos -for ( i in { radio: true, checkbox: true, file: true, password: true, image: true } ) { - Expr.pseudos[ i ] = createInputPseudo( i ); -} -for ( i in { submit: true, reset: true } ) { - Expr.pseudos[ i ] = createButtonPseudo( i ); -} - -// Easy API for creating new setFilters -function setFilters() {} -setFilters.prototype = Expr.filters = Expr.pseudos; -Expr.setFilters = new setFilters(); - -tokenize = Sizzle.tokenize = function( selector, parseOnly ) { - var matched, match, tokens, type, - soFar, groups, preFilters, - cached = tokenCache[ selector + " " ]; - - if ( cached ) { - return parseOnly ? 0 : cached.slice( 0 ); - } - - soFar = selector; - groups = []; - preFilters = Expr.preFilter; - - while ( soFar ) { - - // Comma and first run - if ( !matched || (match = rcomma.exec( soFar )) ) { - if ( match ) { - // Don't consume trailing commas as valid - soFar = soFar.slice( match[0].length ) || soFar; - } - groups.push( (tokens = []) ); - } - - matched = false; - - // Combinators - if ( (match = rcombinators.exec( soFar )) ) { - matched = match.shift(); - tokens.push({ - value: matched, - // Cast descendant combinators to space - type: match[0].replace( rtrim, " " ) - }); - soFar = soFar.slice( matched.length ); - } - - // Filters - for ( type in Expr.filter ) { - if ( (match = matchExpr[ type ].exec( soFar )) && (!preFilters[ type ] || - (match = preFilters[ type ]( match ))) ) { - matched = match.shift(); - tokens.push({ - value: matched, - type: type, - matches: match - }); - soFar = soFar.slice( matched.length ); - } - } - - if ( !matched ) { - break; - } - } - - // Return the length of the invalid excess - // if we're just parsing - // Otherwise, throw an error or return tokens - return parseOnly ? - soFar.length : - soFar ? - Sizzle.error( selector ) : - // Cache the tokens - tokenCache( selector, groups ).slice( 0 ); -}; - -function toSelector( tokens ) { - var i = 0, - len = tokens.length, - selector = ""; - for ( ; i < len; i++ ) { - selector += tokens[i].value; - } - return selector; -} - -function addCombinator( matcher, combinator, base ) { - var dir = combinator.dir, - skip = combinator.next, - key = skip || dir, - checkNonElements = base && key === "parentNode", - doneName = done++; - - return combinator.first ? - // Check against closest ancestor/preceding element - function( elem, context, xml ) { - while ( (elem = elem[ dir ]) ) { - if ( elem.nodeType === 1 || checkNonElements ) { - return matcher( elem, context, xml ); - } - } - return false; - } : - - // Check against all ancestor/preceding elements - function( elem, context, xml ) { - var oldCache, uniqueCache, outerCache, - newCache = [ dirruns, doneName ]; - - // We can't set arbitrary data on XML nodes, so they don't benefit from combinator caching - if ( xml ) { - while ( (elem = elem[ dir ]) ) { - if ( elem.nodeType === 1 || checkNonElements ) { - if ( matcher( elem, context, xml ) ) { - return true; - } - } - } - } else { - while ( (elem = elem[ dir ]) ) { - if ( elem.nodeType === 1 || checkNonElements ) { - outerCache = elem[ expando ] || (elem[ expando ] = {}); - - // Support: IE <9 only - // Defend against cloned attroperties (jQuery gh-1709) - uniqueCache = outerCache[ elem.uniqueID ] || (outerCache[ elem.uniqueID ] = {}); - - if ( skip && skip === elem.nodeName.toLowerCase() ) { - elem = elem[ dir ] || elem; - } else if ( (oldCache = uniqueCache[ key ]) && - oldCache[ 0 ] === dirruns && oldCache[ 1 ] === doneName ) { - - // Assign to newCache so results back-propagate to previous elements - return (newCache[ 2 ] = oldCache[ 2 ]); - } else { - // Reuse newcache so results back-propagate to previous elements - uniqueCache[ key ] = newCache; - - // A match means we're done; a fail means we have to keep checking - if ( (newCache[ 2 ] = matcher( elem, context, xml )) ) { - return true; - } - } - } - } - } - return false; - }; -} - -function elementMatcher( matchers ) { - return matchers.length > 1 ? - function( elem, context, xml ) { - var i = matchers.length; - while ( i-- ) { - if ( !matchers[i]( elem, context, xml ) ) { - return false; - } - } - return true; - } : - matchers[0]; -} - -function multipleContexts( selector, contexts, results ) { - var i = 0, - len = contexts.length; - for ( ; i < len; i++ ) { - Sizzle( selector, contexts[i], results ); - } - return results; -} - -function condense( unmatched, map, filter, context, xml ) { - var elem, - newUnmatched = [], - i = 0, - len = unmatched.length, - mapped = map != null; - - for ( ; i < len; i++ ) { - if ( (elem = unmatched[i]) ) { - if ( !filter || filter( elem, context, xml ) ) { - newUnmatched.push( elem ); - if ( mapped ) { - map.push( i ); - } - } - } - } - - return newUnmatched; -} - -function setMatcher( preFilter, selector, matcher, postFilter, postFinder, postSelector ) { - if ( postFilter && !postFilter[ expando ] ) { - postFilter = setMatcher( postFilter ); - } - if ( postFinder && !postFinder[ expando ] ) { - postFinder = setMatcher( postFinder, postSelector ); - } - return markFunction(function( seed, results, context, xml ) { - var temp, i, elem, - preMap = [], - postMap = [], - preexisting = results.length, - - // Get initial elements from seed or context - elems = seed || multipleContexts( selector || "*", context.nodeType ? [ context ] : context, [] ), - - // Prefilter to get matcher input, preserving a map for seed-results synchronization - matcherIn = preFilter && ( seed || !selector ) ? - condense( elems, preMap, preFilter, context, xml ) : - elems, - - matcherOut = matcher ? - // If we have a postFinder, or filtered seed, or non-seed postFilter or preexisting results, - postFinder || ( seed ? preFilter : preexisting || postFilter ) ? - - // ...intermediate processing is necessary - [] : - - // ...otherwise use results directly - results : - matcherIn; - - // Find primary matches - if ( matcher ) { - matcher( matcherIn, matcherOut, context, xml ); - } - - // Apply postFilter - if ( postFilter ) { - temp = condense( matcherOut, postMap ); - postFilter( temp, [], context, xml ); - - // Un-match failing elements by moving them back to matcherIn - i = temp.length; - while ( i-- ) { - if ( (elem = temp[i]) ) { - matcherOut[ postMap[i] ] = !(matcherIn[ postMap[i] ] = elem); - } - } - } - - if ( seed ) { - if ( postFinder || preFilter ) { - if ( postFinder ) { - // Get the final matcherOut by condensing this intermediate into postFinder contexts - temp = []; - i = matcherOut.length; - while ( i-- ) { - if ( (elem = matcherOut[i]) ) { - // Restore matcherIn since elem is not yet a final match - temp.push( (matcherIn[i] = elem) ); - } - } - postFinder( null, (matcherOut = []), temp, xml ); - } - - // Move matched elements from seed to results to keep them synchronized - i = matcherOut.length; - while ( i-- ) { - if ( (elem = matcherOut[i]) && - (temp = postFinder ? indexOf( seed, elem ) : preMap[i]) > -1 ) { - - seed[temp] = !(results[temp] = elem); - } - } - } - - // Add elements to results, through postFinder if defined - } else { - matcherOut = condense( - matcherOut === results ? - matcherOut.splice( preexisting, matcherOut.length ) : - matcherOut - ); - if ( postFinder ) { - postFinder( null, results, matcherOut, xml ); - } else { - push.apply( results, matcherOut ); - } - } - }); -} - -function matcherFromTokens( tokens ) { - var checkContext, matcher, j, - len = tokens.length, - leadingRelative = Expr.relative[ tokens[0].type ], - implicitRelative = leadingRelative || Expr.relative[" "], - i = leadingRelative ? 1 : 0, - - // The foundational matcher ensures that elements are reachable from top-level context(s) - matchContext = addCombinator( function( elem ) { - return elem === checkContext; - }, implicitRelative, true ), - matchAnyContext = addCombinator( function( elem ) { - return indexOf( checkContext, elem ) > -1; - }, implicitRelative, true ), - matchers = [ function( elem, context, xml ) { - var ret = ( !leadingRelative && ( xml || context !== outermostContext ) ) || ( - (checkContext = context).nodeType ? - matchContext( elem, context, xml ) : - matchAnyContext( elem, context, xml ) ); - // Avoid hanging onto element (issue #299) - checkContext = null; - return ret; - } ]; - - for ( ; i < len; i++ ) { - if ( (matcher = Expr.relative[ tokens[i].type ]) ) { - matchers = [ addCombinator(elementMatcher( matchers ), matcher) ]; - } else { - matcher = Expr.filter[ tokens[i].type ].apply( null, tokens[i].matches ); - - // Return special upon seeing a positional matcher - if ( matcher[ expando ] ) { - // Find the next relative operator (if any) for proper handling - j = ++i; - for ( ; j < len; j++ ) { - if ( Expr.relative[ tokens[j].type ] ) { - break; - } - } - return setMatcher( - i > 1 && elementMatcher( matchers ), - i > 1 && toSelector( - // If the preceding token was a descendant combinator, insert an implicit any-element `*` - tokens.slice( 0, i - 1 ).concat({ value: tokens[ i - 2 ].type === " " ? "*" : "" }) - ).replace( rtrim, "$1" ), - matcher, - i < j && matcherFromTokens( tokens.slice( i, j ) ), - j < len && matcherFromTokens( (tokens = tokens.slice( j )) ), - j < len && toSelector( tokens ) - ); - } - matchers.push( matcher ); - } - } - - return elementMatcher( matchers ); -} - -function matcherFromGroupMatchers( elementMatchers, setMatchers ) { - var bySet = setMatchers.length > 0, - byElement = elementMatchers.length > 0, - superMatcher = function( seed, context, xml, results, outermost ) { - var elem, j, matcher, - matchedCount = 0, - i = "0", - unmatched = seed && [], - setMatched = [], - contextBackup = outermostContext, - // We must always have either seed elements or outermost context - elems = seed || byElement && Expr.find["TAG"]( "*", outermost ), - // Use integer dirruns iff this is the outermost matcher - dirrunsUnique = (dirruns += contextBackup == null ? 1 : Math.random() || 0.1), - len = elems.length; - - if ( outermost ) { - outermostContext = context === document || context || outermost; - } - - // Add elements passing elementMatchers directly to results - // Support: IE<9, Safari - // Tolerate NodeList properties (IE: "length"; Safari: ) matching elements by id - for ( ; i !== len && (elem = elems[i]) != null; i++ ) { - if ( byElement && elem ) { - j = 0; - if ( !context && elem.ownerDocument !== document ) { - setDocument( elem ); - xml = !documentIsHTML; - } - while ( (matcher = elementMatchers[j++]) ) { - if ( matcher( elem, context || document, xml) ) { - results.push( elem ); - break; - } - } - if ( outermost ) { - dirruns = dirrunsUnique; - } - } - - // Track unmatched elements for set filters - if ( bySet ) { - // They will have gone through all possible matchers - if ( (elem = !matcher && elem) ) { - matchedCount--; - } - - // Lengthen the array for every element, matched or not - if ( seed ) { - unmatched.push( elem ); - } - } - } - - // `i` is now the count of elements visited above, and adding it to `matchedCount` - // makes the latter nonnegative. - matchedCount += i; - - // Apply set filters to unmatched elements - // NOTE: This can be skipped if there are no unmatched elements (i.e., `matchedCount` - // equals `i`), unless we didn't visit _any_ elements in the above loop because we have - // no element matchers and no seed. - // Incrementing an initially-string "0" `i` allows `i` to remain a string only in that - // case, which will result in a "00" `matchedCount` that differs from `i` but is also - // numerically zero. - if ( bySet && i !== matchedCount ) { - j = 0; - while ( (matcher = setMatchers[j++]) ) { - matcher( unmatched, setMatched, context, xml ); - } - - if ( seed ) { - // Reintegrate element matches to eliminate the need for sorting - if ( matchedCount > 0 ) { - while ( i-- ) { - if ( !(unmatched[i] || setMatched[i]) ) { - setMatched[i] = pop.call( results ); - } - } - } - - // Discard index placeholder values to get only actual matches - setMatched = condense( setMatched ); - } - - // Add matches to results - push.apply( results, setMatched ); - - // Seedless set matches succeeding multiple successful matchers stipulate sorting - if ( outermost && !seed && setMatched.length > 0 && - ( matchedCount + setMatchers.length ) > 1 ) { - - Sizzle.uniqueSort( results ); - } - } - - // Override manipulation of globals by nested matchers - if ( outermost ) { - dirruns = dirrunsUnique; - outermostContext = contextBackup; - } - - return unmatched; - }; - - return bySet ? - markFunction( superMatcher ) : - superMatcher; -} - -compile = Sizzle.compile = function( selector, match /* Internal Use Only */ ) { - var i, - setMatchers = [], - elementMatchers = [], - cached = compilerCache[ selector + " " ]; - - if ( !cached ) { - // Generate a function of recursive functions that can be used to check each element - if ( !match ) { - match = tokenize( selector ); - } - i = match.length; - while ( i-- ) { - cached = matcherFromTokens( match[i] ); - if ( cached[ expando ] ) { - setMatchers.push( cached ); - } else { - elementMatchers.push( cached ); - } - } - - // Cache the compiled function - cached = compilerCache( selector, matcherFromGroupMatchers( elementMatchers, setMatchers ) ); - - // Save selector and tokenization - cached.selector = selector; - } - return cached; -}; - -/** - * A low-level selection function that works with Sizzle's compiled - * selector functions - * @param {String|Function} selector A selector or a pre-compiled - * selector function built with Sizzle.compile - * @param {Element} context - * @param {Array} [results] - * @param {Array} [seed] A set of elements to match against - */ -select = Sizzle.select = function( selector, context, results, seed ) { - var i, tokens, token, type, find, - compiled = typeof selector === "function" && selector, - match = !seed && tokenize( (selector = compiled.selector || selector) ); - - results = results || []; - - // Try to minimize operations if there is only one selector in the list and no seed - // (the latter of which guarantees us context) - if ( match.length === 1 ) { - - // Reduce context if the leading compound selector is an ID - tokens = match[0] = match[0].slice( 0 ); - if ( tokens.length > 2 && (token = tokens[0]).type === "ID" && - context.nodeType === 9 && documentIsHTML && Expr.relative[ tokens[1].type ] ) { - - context = ( Expr.find["ID"]( token.matches[0].replace(runescape, funescape), context ) || [] )[0]; - if ( !context ) { - return results; - - // Precompiled matchers will still verify ancestry, so step up a level - } else if ( compiled ) { - context = context.parentNode; - } - - selector = selector.slice( tokens.shift().value.length ); - } - - // Fetch a seed set for right-to-left matching - i = matchExpr["needsContext"].test( selector ) ? 0 : tokens.length; - while ( i-- ) { - token = tokens[i]; - - // Abort if we hit a combinator - if ( Expr.relative[ (type = token.type) ] ) { - break; - } - if ( (find = Expr.find[ type ]) ) { - // Search, expanding context for leading sibling combinators - if ( (seed = find( - token.matches[0].replace( runescape, funescape ), - rsibling.test( tokens[0].type ) && testContext( context.parentNode ) || context - )) ) { - - // If seed is empty or no tokens remain, we can return early - tokens.splice( i, 1 ); - selector = seed.length && toSelector( tokens ); - if ( !selector ) { - push.apply( results, seed ); - return results; - } - - break; - } - } - } - } - - // Compile and execute a filtering function if one is not provided - // Provide `match` to avoid retokenization if we modified the selector above - ( compiled || compile( selector, match ) )( - seed, - context, - !documentIsHTML, - results, - !context || rsibling.test( selector ) && testContext( context.parentNode ) || context - ); - return results; -}; - -// One-time assignments - -// Sort stability -support.sortStable = expando.split("").sort( sortOrder ).join("") === expando; - -// Support: Chrome 14-35+ -// Always assume duplicates if they aren't passed to the comparison function -support.detectDuplicates = !!hasDuplicate; - -// Initialize against the default document -setDocument(); - -// Support: Webkit<537.32 - Safari 6.0.3/Chrome 25 (fixed in Chrome 27) -// Detached nodes confoundingly follow *each other* -support.sortDetached = assert(function( el ) { - // Should return 1, but returns 4 (following) - return el.compareDocumentPosition( document.createElement("fieldset") ) & 1; -}); - -// Support: IE<8 -// Prevent attribute/property "interpolation" -// https://msdn.microsoft.com/en-us/library/ms536429%28VS.85%29.aspx -if ( !assert(function( el ) { - el.innerHTML = ""; - return el.firstChild.getAttribute("href") === "#" ; -}) ) { - addHandle( "type|href|height|width", function( elem, name, isXML ) { - if ( !isXML ) { - return elem.getAttribute( name, name.toLowerCase() === "type" ? 1 : 2 ); - } - }); -} - -// Support: IE<9 -// Use defaultValue in place of getAttribute("value") -if ( !support.attributes || !assert(function( el ) { - el.innerHTML = ""; - el.firstChild.setAttribute( "value", "" ); - return el.firstChild.getAttribute( "value" ) === ""; -}) ) { - addHandle( "value", function( elem, name, isXML ) { - if ( !isXML && elem.nodeName.toLowerCase() === "input" ) { - return elem.defaultValue; - } - }); -} - -// Support: IE<9 -// Use getAttributeNode to fetch booleans when getAttribute lies -if ( !assert(function( el ) { - return el.getAttribute("disabled") == null; -}) ) { - addHandle( booleans, function( elem, name, isXML ) { - var val; - if ( !isXML ) { - return elem[ name ] === true ? name.toLowerCase() : - (val = elem.getAttributeNode( name )) && val.specified ? - val.value : - null; - } - }); -} - -return Sizzle; - -})( window ); - - - -jQuery.find = Sizzle; -jQuery.expr = Sizzle.selectors; - -// Deprecated -jQuery.expr[ ":" ] = jQuery.expr.pseudos; -jQuery.uniqueSort = jQuery.unique = Sizzle.uniqueSort; -jQuery.text = Sizzle.getText; -jQuery.isXMLDoc = Sizzle.isXML; -jQuery.contains = Sizzle.contains; -jQuery.escapeSelector = Sizzle.escape; - - - - -var dir = function( elem, dir, until ) { - var matched = [], - truncate = until !== undefined; - - while ( ( elem = elem[ dir ] ) && elem.nodeType !== 9 ) { - if ( elem.nodeType === 1 ) { - if ( truncate && jQuery( elem ).is( until ) ) { - break; - } - matched.push( elem ); - } - } - return matched; -}; - - -var siblings = function( n, elem ) { - var matched = []; - - for ( ; n; n = n.nextSibling ) { - if ( n.nodeType === 1 && n !== elem ) { - matched.push( n ); - } - } - - return matched; -}; - - -var rneedsContext = jQuery.expr.match.needsContext; - - - -function nodeName( elem, name ) { - - return elem.nodeName && elem.nodeName.toLowerCase() === name.toLowerCase(); - -}; -var rsingleTag = ( /^<([a-z][^\/\0>:\x20\t\r\n\f]*)[\x20\t\r\n\f]*\/?>(?:<\/\1>|)$/i ); - - - -var risSimple = /^.[^:#\[\.,]*$/; - -// Implement the identical functionality for filter and not -function winnow( elements, qualifier, not ) { - if ( jQuery.isFunction( qualifier ) ) { - return jQuery.grep( elements, function( elem, i ) { - return !!qualifier.call( elem, i, elem ) !== not; - } ); - } - - // Single element - if ( qualifier.nodeType ) { - return jQuery.grep( elements, function( elem ) { - return ( elem === qualifier ) !== not; - } ); - } - - // Arraylike of elements (jQuery, arguments, Array) - if ( typeof qualifier !== "string" ) { - return jQuery.grep( elements, function( elem ) { - return ( indexOf.call( qualifier, elem ) > -1 ) !== not; - } ); - } - - // Simple selector that can be filtered directly, removing non-Elements - if ( risSimple.test( qualifier ) ) { - return jQuery.filter( qualifier, elements, not ); - } - - // Complex selector, compare the two sets, removing non-Elements - qualifier = jQuery.filter( qualifier, elements ); - return jQuery.grep( elements, function( elem ) { - return ( indexOf.call( qualifier, elem ) > -1 ) !== not && elem.nodeType === 1; - } ); -} - -jQuery.filter = function( expr, elems, not ) { - var elem = elems[ 0 ]; - - if ( not ) { - expr = ":not(" + expr + ")"; - } - - if ( elems.length === 1 && elem.nodeType === 1 ) { - return jQuery.find.matchesSelector( elem, expr ) ? [ elem ] : []; - } - - return jQuery.find.matches( expr, jQuery.grep( elems, function( elem ) { - return elem.nodeType === 1; - } ) ); -}; - -jQuery.fn.extend( { - find: function( selector ) { - var i, ret, - len = this.length, - self = this; - - if ( typeof selector !== "string" ) { - return this.pushStack( jQuery( selector ).filter( function() { - for ( i = 0; i < len; i++ ) { - if ( jQuery.contains( self[ i ], this ) ) { - return true; - } - } - } ) ); - } - - ret = this.pushStack( [] ); - - for ( i = 0; i < len; i++ ) { - jQuery.find( selector, self[ i ], ret ); - } - - return len > 1 ? jQuery.uniqueSort( ret ) : ret; - }, - filter: function( selector ) { - return this.pushStack( winnow( this, selector || [], false ) ); - }, - not: function( selector ) { - return this.pushStack( winnow( this, selector || [], true ) ); - }, - is: function( selector ) { - return !!winnow( - this, - - // If this is a positional/relative selector, check membership in the returned set - // so $("p:first").is("p:last") won't return true for a doc with two "p". - typeof selector === "string" && rneedsContext.test( selector ) ? - jQuery( selector ) : - selector || [], - false - ).length; - } -} ); - - -// Initialize a jQuery object - - -// A central reference to the root jQuery(document) -var rootjQuery, - - // A simple way to check for HTML strings - // Prioritize #id over to avoid XSS via location.hash (#9521) - // Strict HTML recognition (#11290: must start with <) - // Shortcut simple #id case for speed - rquickExpr = /^(?:\s*(<[\w\W]+>)[^>]*|#([\w-]+))$/, - - init = jQuery.fn.init = function( selector, context, root ) { - var match, elem; - - // HANDLE: $(""), $(null), $(undefined), $(false) - if ( !selector ) { - return this; - } - - // Method init() accepts an alternate rootjQuery - // so migrate can support jQuery.sub (gh-2101) - root = root || rootjQuery; - - // Handle HTML strings - if ( typeof selector === "string" ) { - if ( selector[ 0 ] === "<" && - selector[ selector.length - 1 ] === ">" && - selector.length >= 3 ) { - - // Assume that strings that start and end with <> are HTML and skip the regex check - match = [ null, selector, null ]; - - } else { - match = rquickExpr.exec( selector ); - } - - // Match html or make sure no context is specified for #id - if ( match && ( match[ 1 ] || !context ) ) { - - // HANDLE: $(html) -> $(array) - if ( match[ 1 ] ) { - context = context instanceof jQuery ? context[ 0 ] : context; - - // Option to run scripts is true for back-compat - // Intentionally let the error be thrown if parseHTML is not present - jQuery.merge( this, jQuery.parseHTML( - match[ 1 ], - context && context.nodeType ? context.ownerDocument || context : document, - true - ) ); - - // HANDLE: $(html, props) - if ( rsingleTag.test( match[ 1 ] ) && jQuery.isPlainObject( context ) ) { - for ( match in context ) { - - // Properties of context are called as methods if possible - if ( jQuery.isFunction( this[ match ] ) ) { - this[ match ]( context[ match ] ); - - // ...and otherwise set as attributes - } else { - this.attr( match, context[ match ] ); - } - } - } - - return this; - - // HANDLE: $(#id) - } else { - elem = document.getElementById( match[ 2 ] ); - - if ( elem ) { - - // Inject the element directly into the jQuery object - this[ 0 ] = elem; - this.length = 1; - } - return this; - } - - // HANDLE: $(expr, $(...)) - } else if ( !context || context.jquery ) { - return ( context || root ).find( selector ); - - // HANDLE: $(expr, context) - // (which is just equivalent to: $(context).find(expr) - } else { - return this.constructor( context ).find( selector ); - } - - // HANDLE: $(DOMElement) - } else if ( selector.nodeType ) { - this[ 0 ] = selector; - this.length = 1; - return this; - - // HANDLE: $(function) - // Shortcut for document ready - } else if ( jQuery.isFunction( selector ) ) { - return root.ready !== undefined ? - root.ready( selector ) : - - // Execute immediately if ready is not present - selector( jQuery ); - } - - return jQuery.makeArray( selector, this ); - }; - -// Give the init function the jQuery prototype for later instantiation -init.prototype = jQuery.fn; - -// Initialize central reference -rootjQuery = jQuery( document ); - - -var rparentsprev = /^(?:parents|prev(?:Until|All))/, - - // Methods guaranteed to produce a unique set when starting from a unique set - guaranteedUnique = { - children: true, - contents: true, - next: true, - prev: true - }; - -jQuery.fn.extend( { - has: function( target ) { - var targets = jQuery( target, this ), - l = targets.length; - - return this.filter( function() { - var i = 0; - for ( ; i < l; i++ ) { - if ( jQuery.contains( this, targets[ i ] ) ) { - return true; - } - } - } ); - }, - - closest: function( selectors, context ) { - var cur, - i = 0, - l = this.length, - matched = [], - targets = typeof selectors !== "string" && jQuery( selectors ); - - // Positional selectors never match, since there's no _selection_ context - if ( !rneedsContext.test( selectors ) ) { - for ( ; i < l; i++ ) { - for ( cur = this[ i ]; cur && cur !== context; cur = cur.parentNode ) { - - // Always skip document fragments - if ( cur.nodeType < 11 && ( targets ? - targets.index( cur ) > -1 : - - // Don't pass non-elements to Sizzle - cur.nodeType === 1 && - jQuery.find.matchesSelector( cur, selectors ) ) ) { - - matched.push( cur ); - break; - } - } - } - } - - return this.pushStack( matched.length > 1 ? jQuery.uniqueSort( matched ) : matched ); - }, - - // Determine the position of an element within the set - index: function( elem ) { - - // No argument, return index in parent - if ( !elem ) { - return ( this[ 0 ] && this[ 0 ].parentNode ) ? this.first().prevAll().length : -1; - } - - // Index in selector - if ( typeof elem === "string" ) { - return indexOf.call( jQuery( elem ), this[ 0 ] ); - } - - // Locate the position of the desired element - return indexOf.call( this, - - // If it receives a jQuery object, the first element is used - elem.jquery ? elem[ 0 ] : elem - ); - }, - - add: function( selector, context ) { - return this.pushStack( - jQuery.uniqueSort( - jQuery.merge( this.get(), jQuery( selector, context ) ) - ) - ); - }, - - addBack: function( selector ) { - return this.add( selector == null ? - this.prevObject : this.prevObject.filter( selector ) - ); - } -} ); - -function sibling( cur, dir ) { - while ( ( cur = cur[ dir ] ) && cur.nodeType !== 1 ) {} - return cur; -} - -jQuery.each( { - parent: function( elem ) { - var parent = elem.parentNode; - return parent && parent.nodeType !== 11 ? parent : null; - }, - parents: function( elem ) { - return dir( elem, "parentNode" ); - }, - parentsUntil: function( elem, i, until ) { - return dir( elem, "parentNode", until ); - }, - next: function( elem ) { - return sibling( elem, "nextSibling" ); - }, - prev: function( elem ) { - return sibling( elem, "previousSibling" ); - }, - nextAll: function( elem ) { - return dir( elem, "nextSibling" ); - }, - prevAll: function( elem ) { - return dir( elem, "previousSibling" ); - }, - nextUntil: function( elem, i, until ) { - return dir( elem, "nextSibling", until ); - }, - prevUntil: function( elem, i, until ) { - return dir( elem, "previousSibling", until ); - }, - siblings: function( elem ) { - return siblings( ( elem.parentNode || {} ).firstChild, elem ); - }, - children: function( elem ) { - return siblings( elem.firstChild ); - }, - contents: function( elem ) { - if ( nodeName( elem, "iframe" ) ) { - return elem.contentDocument; - } - - // Support: IE 9 - 11 only, iOS 7 only, Android Browser <=4.3 only - // Treat the template element as a regular one in browsers that - // don't support it. - if ( nodeName( elem, "template" ) ) { - elem = elem.content || elem; - } - - return jQuery.merge( [], elem.childNodes ); - } -}, function( name, fn ) { - jQuery.fn[ name ] = function( until, selector ) { - var matched = jQuery.map( this, fn, until ); - - if ( name.slice( -5 ) !== "Until" ) { - selector = until; - } - - if ( selector && typeof selector === "string" ) { - matched = jQuery.filter( selector, matched ); - } - - if ( this.length > 1 ) { - - // Remove duplicates - if ( !guaranteedUnique[ name ] ) { - jQuery.uniqueSort( matched ); - } - - // Reverse order for parents* and prev-derivatives - if ( rparentsprev.test( name ) ) { - matched.reverse(); - } - } - - return this.pushStack( matched ); - }; -} ); -var rnothtmlwhite = ( /[^\x20\t\r\n\f]+/g ); - - - -// Convert String-formatted options into Object-formatted ones -function createOptions( options ) { - var object = {}; - jQuery.each( options.match( rnothtmlwhite ) || [], function( _, flag ) { - object[ flag ] = true; - } ); - return object; -} - -/* - * Create a callback list using the following parameters: - * - * options: an optional list of space-separated options that will change how - * the callback list behaves or a more traditional option object - * - * By default a callback list will act like an event callback list and can be - * "fired" multiple times. - * - * Possible options: - * - * once: will ensure the callback list can only be fired once (like a Deferred) - * - * memory: will keep track of previous values and will call any callback added - * after the list has been fired right away with the latest "memorized" - * values (like a Deferred) - * - * unique: will ensure a callback can only be added once (no duplicate in the list) - * - * stopOnFalse: interrupt callings when a callback returns false - * - */ -jQuery.Callbacks = function( options ) { - - // Convert options from String-formatted to Object-formatted if needed - // (we check in cache first) - options = typeof options === "string" ? - createOptions( options ) : - jQuery.extend( {}, options ); - - var // Flag to know if list is currently firing - firing, - - // Last fire value for non-forgettable lists - memory, - - // Flag to know if list was already fired - fired, - - // Flag to prevent firing - locked, - - // Actual callback list - list = [], - - // Queue of execution data for repeatable lists - queue = [], - - // Index of currently firing callback (modified by add/remove as needed) - firingIndex = -1, - - // Fire callbacks - fire = function() { - - // Enforce single-firing - locked = locked || options.once; - - // Execute callbacks for all pending executions, - // respecting firingIndex overrides and runtime changes - fired = firing = true; - for ( ; queue.length; firingIndex = -1 ) { - memory = queue.shift(); - while ( ++firingIndex < list.length ) { - - // Run callback and check for early termination - if ( list[ firingIndex ].apply( memory[ 0 ], memory[ 1 ] ) === false && - options.stopOnFalse ) { - - // Jump to end and forget the data so .add doesn't re-fire - firingIndex = list.length; - memory = false; - } - } - } - - // Forget the data if we're done with it - if ( !options.memory ) { - memory = false; - } - - firing = false; - - // Clean up if we're done firing for good - if ( locked ) { - - // Keep an empty list if we have data for future add calls - if ( memory ) { - list = []; - - // Otherwise, this object is spent - } else { - list = ""; - } - } - }, - - // Actual Callbacks object - self = { - - // Add a callback or a collection of callbacks to the list - add: function() { - if ( list ) { - - // If we have memory from a past run, we should fire after adding - if ( memory && !firing ) { - firingIndex = list.length - 1; - queue.push( memory ); - } - - ( function add( args ) { - jQuery.each( args, function( _, arg ) { - if ( jQuery.isFunction( arg ) ) { - if ( !options.unique || !self.has( arg ) ) { - list.push( arg ); - } - } else if ( arg && arg.length && jQuery.type( arg ) !== "string" ) { - - // Inspect recursively - add( arg ); - } - } ); - } )( arguments ); - - if ( memory && !firing ) { - fire(); - } - } - return this; - }, - - // Remove a callback from the list - remove: function() { - jQuery.each( arguments, function( _, arg ) { - var index; - while ( ( index = jQuery.inArray( arg, list, index ) ) > -1 ) { - list.splice( index, 1 ); - - // Handle firing indexes - if ( index <= firingIndex ) { - firingIndex--; - } - } - } ); - return this; - }, - - // Check if a given callback is in the list. - // If no argument is given, return whether or not list has callbacks attached. - has: function( fn ) { - return fn ? - jQuery.inArray( fn, list ) > -1 : - list.length > 0; - }, - - // Remove all callbacks from the list - empty: function() { - if ( list ) { - list = []; - } - return this; - }, - - // Disable .fire and .add - // Abort any current/pending executions - // Clear all callbacks and values - disable: function() { - locked = queue = []; - list = memory = ""; - return this; - }, - disabled: function() { - return !list; - }, - - // Disable .fire - // Also disable .add unless we have memory (since it would have no effect) - // Abort any pending executions - lock: function() { - locked = queue = []; - if ( !memory && !firing ) { - list = memory = ""; - } - return this; - }, - locked: function() { - return !!locked; - }, - - // Call all callbacks with the given context and arguments - fireWith: function( context, args ) { - if ( !locked ) { - args = args || []; - args = [ context, args.slice ? args.slice() : args ]; - queue.push( args ); - if ( !firing ) { - fire(); - } - } - return this; - }, - - // Call all the callbacks with the given arguments - fire: function() { - self.fireWith( this, arguments ); - return this; - }, - - // To know if the callbacks have already been called at least once - fired: function() { - return !!fired; - } - }; - - return self; -}; - - -function Identity( v ) { - return v; -} -function Thrower( ex ) { - throw ex; -} - -function adoptValue( value, resolve, reject, noValue ) { - var method; - - try { - - // Check for promise aspect first to privilege synchronous behavior - if ( value && jQuery.isFunction( ( method = value.promise ) ) ) { - method.call( value ).done( resolve ).fail( reject ); - - // Other thenables - } else if ( value && jQuery.isFunction( ( method = value.then ) ) ) { - method.call( value, resolve, reject ); - - // Other non-thenables - } else { - - // Control `resolve` arguments by letting Array#slice cast boolean `noValue` to integer: - // * false: [ value ].slice( 0 ) => resolve( value ) - // * true: [ value ].slice( 1 ) => resolve() - resolve.apply( undefined, [ value ].slice( noValue ) ); - } - - // For Promises/A+, convert exceptions into rejections - // Since jQuery.when doesn't unwrap thenables, we can skip the extra checks appearing in - // Deferred#then to conditionally suppress rejection. - } catch ( value ) { - - // Support: Android 4.0 only - // Strict mode functions invoked without .call/.apply get global-object context - reject.apply( undefined, [ value ] ); - } -} - -jQuery.extend( { - - Deferred: function( func ) { - var tuples = [ - - // action, add listener, callbacks, - // ... .then handlers, argument index, [final state] - [ "notify", "progress", jQuery.Callbacks( "memory" ), - jQuery.Callbacks( "memory" ), 2 ], - [ "resolve", "done", jQuery.Callbacks( "once memory" ), - jQuery.Callbacks( "once memory" ), 0, "resolved" ], - [ "reject", "fail", jQuery.Callbacks( "once memory" ), - jQuery.Callbacks( "once memory" ), 1, "rejected" ] - ], - state = "pending", - promise = { - state: function() { - return state; - }, - always: function() { - deferred.done( arguments ).fail( arguments ); - return this; - }, - "catch": function( fn ) { - return promise.then( null, fn ); - }, - - // Keep pipe for back-compat - pipe: function( /* fnDone, fnFail, fnProgress */ ) { - var fns = arguments; - - return jQuery.Deferred( function( newDefer ) { - jQuery.each( tuples, function( i, tuple ) { - - // Map tuples (progress, done, fail) to arguments (done, fail, progress) - var fn = jQuery.isFunction( fns[ tuple[ 4 ] ] ) && fns[ tuple[ 4 ] ]; - - // deferred.progress(function() { bind to newDefer or newDefer.notify }) - // deferred.done(function() { bind to newDefer or newDefer.resolve }) - // deferred.fail(function() { bind to newDefer or newDefer.reject }) - deferred[ tuple[ 1 ] ]( function() { - var returned = fn && fn.apply( this, arguments ); - if ( returned && jQuery.isFunction( returned.promise ) ) { - returned.promise() - .progress( newDefer.notify ) - .done( newDefer.resolve ) - .fail( newDefer.reject ); - } else { - newDefer[ tuple[ 0 ] + "With" ]( - this, - fn ? [ returned ] : arguments - ); - } - } ); - } ); - fns = null; - } ).promise(); - }, - then: function( onFulfilled, onRejected, onProgress ) { - var maxDepth = 0; - function resolve( depth, deferred, handler, special ) { - return function() { - var that = this, - args = arguments, - mightThrow = function() { - var returned, then; - - // Support: Promises/A+ section 2.3.3.3.3 - // https://promisesaplus.com/#point-59 - // Ignore double-resolution attempts - if ( depth < maxDepth ) { - return; - } - - returned = handler.apply( that, args ); - - // Support: Promises/A+ section 2.3.1 - // https://promisesaplus.com/#point-48 - if ( returned === deferred.promise() ) { - throw new TypeError( "Thenable self-resolution" ); - } - - // Support: Promises/A+ sections 2.3.3.1, 3.5 - // https://promisesaplus.com/#point-54 - // https://promisesaplus.com/#point-75 - // Retrieve `then` only once - then = returned && - - // Support: Promises/A+ section 2.3.4 - // https://promisesaplus.com/#point-64 - // Only check objects and functions for thenability - ( typeof returned === "object" || - typeof returned === "function" ) && - returned.then; - - // Handle a returned thenable - if ( jQuery.isFunction( then ) ) { - - // Special processors (notify) just wait for resolution - if ( special ) { - then.call( - returned, - resolve( maxDepth, deferred, Identity, special ), - resolve( maxDepth, deferred, Thrower, special ) - ); - - // Normal processors (resolve) also hook into progress - } else { - - // ...and disregard older resolution values - maxDepth++; - - then.call( - returned, - resolve( maxDepth, deferred, Identity, special ), - resolve( maxDepth, deferred, Thrower, special ), - resolve( maxDepth, deferred, Identity, - deferred.notifyWith ) - ); - } - - // Handle all other returned values - } else { - - // Only substitute handlers pass on context - // and multiple values (non-spec behavior) - if ( handler !== Identity ) { - that = undefined; - args = [ returned ]; - } - - // Process the value(s) - // Default process is resolve - ( special || deferred.resolveWith )( that, args ); - } - }, - - // Only normal processors (resolve) catch and reject exceptions - process = special ? - mightThrow : - function() { - try { - mightThrow(); - } catch ( e ) { - - if ( jQuery.Deferred.exceptionHook ) { - jQuery.Deferred.exceptionHook( e, - process.stackTrace ); - } - - // Support: Promises/A+ section 2.3.3.3.4.1 - // https://promisesaplus.com/#point-61 - // Ignore post-resolution exceptions - if ( depth + 1 >= maxDepth ) { - - // Only substitute handlers pass on context - // and multiple values (non-spec behavior) - if ( handler !== Thrower ) { - that = undefined; - args = [ e ]; - } - - deferred.rejectWith( that, args ); - } - } - }; - - // Support: Promises/A+ section 2.3.3.3.1 - // https://promisesaplus.com/#point-57 - // Re-resolve promises immediately to dodge false rejection from - // subsequent errors - if ( depth ) { - process(); - } else { - - // Call an optional hook to record the stack, in case of exception - // since it's otherwise lost when execution goes async - if ( jQuery.Deferred.getStackHook ) { - process.stackTrace = jQuery.Deferred.getStackHook(); - } - window.setTimeout( process ); - } - }; - } - - return jQuery.Deferred( function( newDefer ) { - - // progress_handlers.add( ... ) - tuples[ 0 ][ 3 ].add( - resolve( - 0, - newDefer, - jQuery.isFunction( onProgress ) ? - onProgress : - Identity, - newDefer.notifyWith - ) - ); - - // fulfilled_handlers.add( ... ) - tuples[ 1 ][ 3 ].add( - resolve( - 0, - newDefer, - jQuery.isFunction( onFulfilled ) ? - onFulfilled : - Identity - ) - ); - - // rejected_handlers.add( ... ) - tuples[ 2 ][ 3 ].add( - resolve( - 0, - newDefer, - jQuery.isFunction( onRejected ) ? - onRejected : - Thrower - ) - ); - } ).promise(); - }, - - // Get a promise for this deferred - // If obj is provided, the promise aspect is added to the object - promise: function( obj ) { - return obj != null ? jQuery.extend( obj, promise ) : promise; - } - }, - deferred = {}; - - // Add list-specific methods - jQuery.each( tuples, function( i, tuple ) { - var list = tuple[ 2 ], - stateString = tuple[ 5 ]; - - // promise.progress = list.add - // promise.done = list.add - // promise.fail = list.add - promise[ tuple[ 1 ] ] = list.add; - - // Handle state - if ( stateString ) { - list.add( - function() { - - // state = "resolved" (i.e., fulfilled) - // state = "rejected" - state = stateString; - }, - - // rejected_callbacks.disable - // fulfilled_callbacks.disable - tuples[ 3 - i ][ 2 ].disable, - - // progress_callbacks.lock - tuples[ 0 ][ 2 ].lock - ); - } - - // progress_handlers.fire - // fulfilled_handlers.fire - // rejected_handlers.fire - list.add( tuple[ 3 ].fire ); - - // deferred.notify = function() { deferred.notifyWith(...) } - // deferred.resolve = function() { deferred.resolveWith(...) } - // deferred.reject = function() { deferred.rejectWith(...) } - deferred[ tuple[ 0 ] ] = function() { - deferred[ tuple[ 0 ] + "With" ]( this === deferred ? undefined : this, arguments ); - return this; - }; - - // deferred.notifyWith = list.fireWith - // deferred.resolveWith = list.fireWith - // deferred.rejectWith = list.fireWith - deferred[ tuple[ 0 ] + "With" ] = list.fireWith; - } ); - - // Make the deferred a promise - promise.promise( deferred ); - - // Call given func if any - if ( func ) { - func.call( deferred, deferred ); - } - - // All done! - return deferred; - }, - - // Deferred helper - when: function( singleValue ) { - var - - // count of uncompleted subordinates - remaining = arguments.length, - - // count of unprocessed arguments - i = remaining, - - // subordinate fulfillment data - resolveContexts = Array( i ), - resolveValues = slice.call( arguments ), - - // the master Deferred - master = jQuery.Deferred(), - - // subordinate callback factory - updateFunc = function( i ) { - return function( value ) { - resolveContexts[ i ] = this; - resolveValues[ i ] = arguments.length > 1 ? slice.call( arguments ) : value; - if ( !( --remaining ) ) { - master.resolveWith( resolveContexts, resolveValues ); - } - }; - }; - - // Single- and empty arguments are adopted like Promise.resolve - if ( remaining <= 1 ) { - adoptValue( singleValue, master.done( updateFunc( i ) ).resolve, master.reject, - !remaining ); - - // Use .then() to unwrap secondary thenables (cf. gh-3000) - if ( master.state() === "pending" || - jQuery.isFunction( resolveValues[ i ] && resolveValues[ i ].then ) ) { - - return master.then(); - } - } - - // Multiple arguments are aggregated like Promise.all array elements - while ( i-- ) { - adoptValue( resolveValues[ i ], updateFunc( i ), master.reject ); - } - - return master.promise(); - } -} ); - - -// These usually indicate a programmer mistake during development, -// warn about them ASAP rather than swallowing them by default. -var rerrorNames = /^(Eval|Internal|Range|Reference|Syntax|Type|URI)Error$/; - -jQuery.Deferred.exceptionHook = function( error, stack ) { - - // Support: IE 8 - 9 only - // Console exists when dev tools are open, which can happen at any time - if ( window.console && window.console.warn && error && rerrorNames.test( error.name ) ) { - window.console.warn( "jQuery.Deferred exception: " + error.message, error.stack, stack ); - } -}; - - - - -jQuery.readyException = function( error ) { - window.setTimeout( function() { - throw error; - } ); -}; - - - - -// The deferred used on DOM ready -var readyList = jQuery.Deferred(); - -jQuery.fn.ready = function( fn ) { - - readyList - .then( fn ) - - // Wrap jQuery.readyException in a function so that the lookup - // happens at the time of error handling instead of callback - // registration. - .catch( function( error ) { - jQuery.readyException( error ); - } ); - - return this; -}; - -jQuery.extend( { - - // Is the DOM ready to be used? Set to true once it occurs. - isReady: false, - - // A counter to track how many items to wait for before - // the ready event fires. See #6781 - readyWait: 1, - - // Handle when the DOM is ready - ready: function( wait ) { - - // Abort if there are pending holds or we're already ready - if ( wait === true ? --jQuery.readyWait : jQuery.isReady ) { - return; - } - - // Remember that the DOM is ready - jQuery.isReady = true; - - // If a normal DOM Ready event fired, decrement, and wait if need be - if ( wait !== true && --jQuery.readyWait > 0 ) { - return; - } - - // If there are functions bound, to execute - readyList.resolveWith( document, [ jQuery ] ); - } -} ); - -jQuery.ready.then = readyList.then; - -// The ready event handler and self cleanup method -function completed() { - document.removeEventListener( "DOMContentLoaded", completed ); - window.removeEventListener( "load", completed ); - jQuery.ready(); -} - -// Catch cases where $(document).ready() is called -// after the browser event has already occurred. -// Support: IE <=9 - 10 only -// Older IE sometimes signals "interactive" too soon -if ( document.readyState === "complete" || - ( document.readyState !== "loading" && !document.documentElement.doScroll ) ) { - - // Handle it asynchronously to allow scripts the opportunity to delay ready - window.setTimeout( jQuery.ready ); - -} else { - - // Use the handy event callback - document.addEventListener( "DOMContentLoaded", completed ); - - // A fallback to window.onload, that will always work - window.addEventListener( "load", completed ); -} - - - - -// Multifunctional method to get and set values of a collection -// The value/s can optionally be executed if it's a function -var access = function( elems, fn, key, value, chainable, emptyGet, raw ) { - var i = 0, - len = elems.length, - bulk = key == null; - - // Sets many values - if ( jQuery.type( key ) === "object" ) { - chainable = true; - for ( i in key ) { - access( elems, fn, i, key[ i ], true, emptyGet, raw ); - } - - // Sets one value - } else if ( value !== undefined ) { - chainable = true; - - if ( !jQuery.isFunction( value ) ) { - raw = true; - } - - if ( bulk ) { - - // Bulk operations run against the entire set - if ( raw ) { - fn.call( elems, value ); - fn = null; - - // ...except when executing function values - } else { - bulk = fn; - fn = function( elem, key, value ) { - return bulk.call( jQuery( elem ), value ); - }; - } - } - - if ( fn ) { - for ( ; i < len; i++ ) { - fn( - elems[ i ], key, raw ? - value : - value.call( elems[ i ], i, fn( elems[ i ], key ) ) - ); - } - } - } - - if ( chainable ) { - return elems; - } - - // Gets - if ( bulk ) { - return fn.call( elems ); - } - - return len ? fn( elems[ 0 ], key ) : emptyGet; -}; -var acceptData = function( owner ) { - - // Accepts only: - // - Node - // - Node.ELEMENT_NODE - // - Node.DOCUMENT_NODE - // - Object - // - Any - return owner.nodeType === 1 || owner.nodeType === 9 || !( +owner.nodeType ); -}; - - - - -function Data() { - this.expando = jQuery.expando + Data.uid++; -} - -Data.uid = 1; - -Data.prototype = { - - cache: function( owner ) { - - // Check if the owner object already has a cache - var value = owner[ this.expando ]; - - // If not, create one - if ( !value ) { - value = {}; - - // We can accept data for non-element nodes in modern browsers, - // but we should not, see #8335. - // Always return an empty object. - if ( acceptData( owner ) ) { - - // If it is a node unlikely to be stringify-ed or looped over - // use plain assignment - if ( owner.nodeType ) { - owner[ this.expando ] = value; - - // Otherwise secure it in a non-enumerable property - // configurable must be true to allow the property to be - // deleted when data is removed - } else { - Object.defineProperty( owner, this.expando, { - value: value, - configurable: true - } ); - } - } - } - - return value; - }, - set: function( owner, data, value ) { - var prop, - cache = this.cache( owner ); - - // Handle: [ owner, key, value ] args - // Always use camelCase key (gh-2257) - if ( typeof data === "string" ) { - cache[ jQuery.camelCase( data ) ] = value; - - // Handle: [ owner, { properties } ] args - } else { - - // Copy the properties one-by-one to the cache object - for ( prop in data ) { - cache[ jQuery.camelCase( prop ) ] = data[ prop ]; - } - } - return cache; - }, - get: function( owner, key ) { - return key === undefined ? - this.cache( owner ) : - - // Always use camelCase key (gh-2257) - owner[ this.expando ] && owner[ this.expando ][ jQuery.camelCase( key ) ]; - }, - access: function( owner, key, value ) { - - // In cases where either: - // - // 1. No key was specified - // 2. A string key was specified, but no value provided - // - // Take the "read" path and allow the get method to determine - // which value to return, respectively either: - // - // 1. The entire cache object - // 2. The data stored at the key - // - if ( key === undefined || - ( ( key && typeof key === "string" ) && value === undefined ) ) { - - return this.get( owner, key ); - } - - // When the key is not a string, or both a key and value - // are specified, set or extend (existing objects) with either: - // - // 1. An object of properties - // 2. A key and value - // - this.set( owner, key, value ); - - // Since the "set" path can have two possible entry points - // return the expected data based on which path was taken[*] - return value !== undefined ? value : key; - }, - remove: function( owner, key ) { - var i, - cache = owner[ this.expando ]; - - if ( cache === undefined ) { - return; - } - - if ( key !== undefined ) { - - // Support array or space separated string of keys - if ( Array.isArray( key ) ) { - - // If key is an array of keys... - // We always set camelCase keys, so remove that. - key = key.map( jQuery.camelCase ); - } else { - key = jQuery.camelCase( key ); - - // If a key with the spaces exists, use it. - // Otherwise, create an array by matching non-whitespace - key = key in cache ? - [ key ] : - ( key.match( rnothtmlwhite ) || [] ); - } - - i = key.length; - - while ( i-- ) { - delete cache[ key[ i ] ]; - } - } - - // Remove the expando if there's no more data - if ( key === undefined || jQuery.isEmptyObject( cache ) ) { - - // Support: Chrome <=35 - 45 - // Webkit & Blink performance suffers when deleting properties - // from DOM nodes, so set to undefined instead - // https://bugs.chromium.org/p/chromium/issues/detail?id=378607 (bug restricted) - if ( owner.nodeType ) { - owner[ this.expando ] = undefined; - } else { - delete owner[ this.expando ]; - } - } - }, - hasData: function( owner ) { - var cache = owner[ this.expando ]; - return cache !== undefined && !jQuery.isEmptyObject( cache ); - } -}; -var dataPriv = new Data(); - -var dataUser = new Data(); - - - -// Implementation Summary -// -// 1. Enforce API surface and semantic compatibility with 1.9.x branch -// 2. Improve the module's maintainability by reducing the storage -// paths to a single mechanism. -// 3. Use the same single mechanism to support "private" and "user" data. -// 4. _Never_ expose "private" data to user code (TODO: Drop _data, _removeData) -// 5. Avoid exposing implementation details on user objects (eg. expando properties) -// 6. Provide a clear path for implementation upgrade to WeakMap in 2014 - -var rbrace = /^(?:\{[\w\W]*\}|\[[\w\W]*\])$/, - rmultiDash = /[A-Z]/g; - -function getData( data ) { - if ( data === "true" ) { - return true; - } - - if ( data === "false" ) { - return false; - } - - if ( data === "null" ) { - return null; - } - - // Only convert to a number if it doesn't change the string - if ( data === +data + "" ) { - return +data; - } - - if ( rbrace.test( data ) ) { - return JSON.parse( data ); - } - - return data; -} - -function dataAttr( elem, key, data ) { - var name; - - // If nothing was found internally, try to fetch any - // data from the HTML5 data-* attribute - if ( data === undefined && elem.nodeType === 1 ) { - name = "data-" + key.replace( rmultiDash, "-$&" ).toLowerCase(); - data = elem.getAttribute( name ); - - if ( typeof data === "string" ) { - try { - data = getData( data ); - } catch ( e ) {} - - // Make sure we set the data so it isn't changed later - dataUser.set( elem, key, data ); - } else { - data = undefined; - } - } - return data; -} - -jQuery.extend( { - hasData: function( elem ) { - return dataUser.hasData( elem ) || dataPriv.hasData( elem ); - }, - - data: function( elem, name, data ) { - return dataUser.access( elem, name, data ); - }, - - removeData: function( elem, name ) { - dataUser.remove( elem, name ); - }, - - // TODO: Now that all calls to _data and _removeData have been replaced - // with direct calls to dataPriv methods, these can be deprecated. - _data: function( elem, name, data ) { - return dataPriv.access( elem, name, data ); - }, - - _removeData: function( elem, name ) { - dataPriv.remove( elem, name ); - } -} ); - -jQuery.fn.extend( { - data: function( key, value ) { - var i, name, data, - elem = this[ 0 ], - attrs = elem && elem.attributes; - - // Gets all values - if ( key === undefined ) { - if ( this.length ) { - data = dataUser.get( elem ); - - if ( elem.nodeType === 1 && !dataPriv.get( elem, "hasDataAttrs" ) ) { - i = attrs.length; - while ( i-- ) { - - // Support: IE 11 only - // The attrs elements can be null (#14894) - if ( attrs[ i ] ) { - name = attrs[ i ].name; - if ( name.indexOf( "data-" ) === 0 ) { - name = jQuery.camelCase( name.slice( 5 ) ); - dataAttr( elem, name, data[ name ] ); - } - } - } - dataPriv.set( elem, "hasDataAttrs", true ); - } - } - - return data; - } - - // Sets multiple values - if ( typeof key === "object" ) { - return this.each( function() { - dataUser.set( this, key ); - } ); - } - - return access( this, function( value ) { - var data; - - // The calling jQuery object (element matches) is not empty - // (and therefore has an element appears at this[ 0 ]) and the - // `value` parameter was not undefined. An empty jQuery object - // will result in `undefined` for elem = this[ 0 ] which will - // throw an exception if an attempt to read a data cache is made. - if ( elem && value === undefined ) { - - // Attempt to get data from the cache - // The key will always be camelCased in Data - data = dataUser.get( elem, key ); - if ( data !== undefined ) { - return data; - } - - // Attempt to "discover" the data in - // HTML5 custom data-* attrs - data = dataAttr( elem, key ); - if ( data !== undefined ) { - return data; - } - - // We tried really hard, but the data doesn't exist. - return; - } - - // Set the data... - this.each( function() { - - // We always store the camelCased key - dataUser.set( this, key, value ); - } ); - }, null, value, arguments.length > 1, null, true ); - }, - - removeData: function( key ) { - return this.each( function() { - dataUser.remove( this, key ); - } ); - } -} ); - - -jQuery.extend( { - queue: function( elem, type, data ) { - var queue; - - if ( elem ) { - type = ( type || "fx" ) + "queue"; - queue = dataPriv.get( elem, type ); - - // Speed up dequeue by getting out quickly if this is just a lookup - if ( data ) { - if ( !queue || Array.isArray( data ) ) { - queue = dataPriv.access( elem, type, jQuery.makeArray( data ) ); - } else { - queue.push( data ); - } - } - return queue || []; - } - }, - - dequeue: function( elem, type ) { - type = type || "fx"; - - var queue = jQuery.queue( elem, type ), - startLength = queue.length, - fn = queue.shift(), - hooks = jQuery._queueHooks( elem, type ), - next = function() { - jQuery.dequeue( elem, type ); - }; - - // If the fx queue is dequeued, always remove the progress sentinel - if ( fn === "inprogress" ) { - fn = queue.shift(); - startLength--; - } - - if ( fn ) { - - // Add a progress sentinel to prevent the fx queue from being - // automatically dequeued - if ( type === "fx" ) { - queue.unshift( "inprogress" ); - } - - // Clear up the last queue stop function - delete hooks.stop; - fn.call( elem, next, hooks ); - } - - if ( !startLength && hooks ) { - hooks.empty.fire(); - } - }, - - // Not public - generate a queueHooks object, or return the current one - _queueHooks: function( elem, type ) { - var key = type + "queueHooks"; - return dataPriv.get( elem, key ) || dataPriv.access( elem, key, { - empty: jQuery.Callbacks( "once memory" ).add( function() { - dataPriv.remove( elem, [ type + "queue", key ] ); - } ) - } ); - } -} ); - -jQuery.fn.extend( { - queue: function( type, data ) { - var setter = 2; - - if ( typeof type !== "string" ) { - data = type; - type = "fx"; - setter--; - } - - if ( arguments.length < setter ) { - return jQuery.queue( this[ 0 ], type ); - } - - return data === undefined ? - this : - this.each( function() { - var queue = jQuery.queue( this, type, data ); - - // Ensure a hooks for this queue - jQuery._queueHooks( this, type ); - - if ( type === "fx" && queue[ 0 ] !== "inprogress" ) { - jQuery.dequeue( this, type ); - } - } ); - }, - dequeue: function( type ) { - return this.each( function() { - jQuery.dequeue( this, type ); - } ); - }, - clearQueue: function( type ) { - return this.queue( type || "fx", [] ); - }, - - // Get a promise resolved when queues of a certain type - // are emptied (fx is the type by default) - promise: function( type, obj ) { - var tmp, - count = 1, - defer = jQuery.Deferred(), - elements = this, - i = this.length, - resolve = function() { - if ( !( --count ) ) { - defer.resolveWith( elements, [ elements ] ); - } - }; - - if ( typeof type !== "string" ) { - obj = type; - type = undefined; - } - type = type || "fx"; - - while ( i-- ) { - tmp = dataPriv.get( elements[ i ], type + "queueHooks" ); - if ( tmp && tmp.empty ) { - count++; - tmp.empty.add( resolve ); - } - } - resolve(); - return defer.promise( obj ); - } -} ); -var pnum = ( /[+-]?(?:\d*\.|)\d+(?:[eE][+-]?\d+|)/ ).source; - -var rcssNum = new RegExp( "^(?:([+-])=|)(" + pnum + ")([a-z%]*)$", "i" ); - - -var cssExpand = [ "Top", "Right", "Bottom", "Left" ]; - -var isHiddenWithinTree = function( elem, el ) { - - // isHiddenWithinTree might be called from jQuery#filter function; - // in that case, element will be second argument - elem = el || elem; - - // Inline style trumps all - return elem.style.display === "none" || - elem.style.display === "" && - - // Otherwise, check computed style - // Support: Firefox <=43 - 45 - // Disconnected elements can have computed display: none, so first confirm that elem is - // in the document. - jQuery.contains( elem.ownerDocument, elem ) && - - jQuery.css( elem, "display" ) === "none"; - }; - -var swap = function( elem, options, callback, args ) { - var ret, name, - old = {}; - - // Remember the old values, and insert the new ones - for ( name in options ) { - old[ name ] = elem.style[ name ]; - elem.style[ name ] = options[ name ]; - } - - ret = callback.apply( elem, args || [] ); - - // Revert the old values - for ( name in options ) { - elem.style[ name ] = old[ name ]; - } - - return ret; -}; - - - - -function adjustCSS( elem, prop, valueParts, tween ) { - var adjusted, - scale = 1, - maxIterations = 20, - currentValue = tween ? - function() { - return tween.cur(); - } : - function() { - return jQuery.css( elem, prop, "" ); - }, - initial = currentValue(), - unit = valueParts && valueParts[ 3 ] || ( jQuery.cssNumber[ prop ] ? "" : "px" ), - - // Starting value computation is required for potential unit mismatches - initialInUnit = ( jQuery.cssNumber[ prop ] || unit !== "px" && +initial ) && - rcssNum.exec( jQuery.css( elem, prop ) ); - - if ( initialInUnit && initialInUnit[ 3 ] !== unit ) { - - // Trust units reported by jQuery.css - unit = unit || initialInUnit[ 3 ]; - - // Make sure we update the tween properties later on - valueParts = valueParts || []; - - // Iteratively approximate from a nonzero starting point - initialInUnit = +initial || 1; - - do { - - // If previous iteration zeroed out, double until we get *something*. - // Use string for doubling so we don't accidentally see scale as unchanged below - scale = scale || ".5"; - - // Adjust and apply - initialInUnit = initialInUnit / scale; - jQuery.style( elem, prop, initialInUnit + unit ); - - // Update scale, tolerating zero or NaN from tween.cur() - // Break the loop if scale is unchanged or perfect, or if we've just had enough. - } while ( - scale !== ( scale = currentValue() / initial ) && scale !== 1 && --maxIterations - ); - } - - if ( valueParts ) { - initialInUnit = +initialInUnit || +initial || 0; - - // Apply relative offset (+=/-=) if specified - adjusted = valueParts[ 1 ] ? - initialInUnit + ( valueParts[ 1 ] + 1 ) * valueParts[ 2 ] : - +valueParts[ 2 ]; - if ( tween ) { - tween.unit = unit; - tween.start = initialInUnit; - tween.end = adjusted; - } - } - return adjusted; -} - - -var defaultDisplayMap = {}; - -function getDefaultDisplay( elem ) { - var temp, - doc = elem.ownerDocument, - nodeName = elem.nodeName, - display = defaultDisplayMap[ nodeName ]; - - if ( display ) { - return display; - } - - temp = doc.body.appendChild( doc.createElement( nodeName ) ); - display = jQuery.css( temp, "display" ); - - temp.parentNode.removeChild( temp ); - - if ( display === "none" ) { - display = "block"; - } - defaultDisplayMap[ nodeName ] = display; - - return display; -} - -function showHide( elements, show ) { - var display, elem, - values = [], - index = 0, - length = elements.length; - - // Determine new display value for elements that need to change - for ( ; index < length; index++ ) { - elem = elements[ index ]; - if ( !elem.style ) { - continue; - } - - display = elem.style.display; - if ( show ) { - - // Since we force visibility upon cascade-hidden elements, an immediate (and slow) - // check is required in this first loop unless we have a nonempty display value (either - // inline or about-to-be-restored) - if ( display === "none" ) { - values[ index ] = dataPriv.get( elem, "display" ) || null; - if ( !values[ index ] ) { - elem.style.display = ""; - } - } - if ( elem.style.display === "" && isHiddenWithinTree( elem ) ) { - values[ index ] = getDefaultDisplay( elem ); - } - } else { - if ( display !== "none" ) { - values[ index ] = "none"; - - // Remember what we're overwriting - dataPriv.set( elem, "display", display ); - } - } - } - - // Set the display of the elements in a second loop to avoid constant reflow - for ( index = 0; index < length; index++ ) { - if ( values[ index ] != null ) { - elements[ index ].style.display = values[ index ]; - } - } - - return elements; -} - -jQuery.fn.extend( { - show: function() { - return showHide( this, true ); - }, - hide: function() { - return showHide( this ); - }, - toggle: function( state ) { - if ( typeof state === "boolean" ) { - return state ? this.show() : this.hide(); - } - - return this.each( function() { - if ( isHiddenWithinTree( this ) ) { - jQuery( this ).show(); - } else { - jQuery( this ).hide(); - } - } ); - } -} ); -var rcheckableType = ( /^(?:checkbox|radio)$/i ); - -var rtagName = ( /<([a-z][^\/\0>\x20\t\r\n\f]+)/i ); - -var rscriptType = ( /^$|\/(?:java|ecma)script/i ); - - - -// We have to close these tags to support XHTML (#13200) -var wrapMap = { - - // Support: IE <=9 only - option: [ 1, "" ], - - // XHTML parsers do not magically insert elements in the - // same way that tag soup parsers do. So we cannot shorten - // this by omitting or other required elements. - thead: [ 1, "", "
" ], - col: [ 2, "", "
" ], - tr: [ 2, "", "
" ], - td: [ 3, "", "
" ], - - _default: [ 0, "", "" ] -}; - -// Support: IE <=9 only -wrapMap.optgroup = wrapMap.option; - -wrapMap.tbody = wrapMap.tfoot = wrapMap.colgroup = wrapMap.caption = wrapMap.thead; -wrapMap.th = wrapMap.td; - - -function getAll( context, tag ) { - - // Support: IE <=9 - 11 only - // Use typeof to avoid zero-argument method invocation on host objects (#15151) - var ret; - - if ( typeof context.getElementsByTagName !== "undefined" ) { - ret = context.getElementsByTagName( tag || "*" ); - - } else if ( typeof context.querySelectorAll !== "undefined" ) { - ret = context.querySelectorAll( tag || "*" ); - - } else { - ret = []; - } - - if ( tag === undefined || tag && nodeName( context, tag ) ) { - return jQuery.merge( [ context ], ret ); - } - - return ret; -} - - -// Mark scripts as having already been evaluated -function setGlobalEval( elems, refElements ) { - var i = 0, - l = elems.length; - - for ( ; i < l; i++ ) { - dataPriv.set( - elems[ i ], - "globalEval", - !refElements || dataPriv.get( refElements[ i ], "globalEval" ) - ); - } -} - - -var rhtml = /<|&#?\w+;/; - -function buildFragment( elems, context, scripts, selection, ignored ) { - var elem, tmp, tag, wrap, contains, j, - fragment = context.createDocumentFragment(), - nodes = [], - i = 0, - l = elems.length; - - for ( ; i < l; i++ ) { - elem = elems[ i ]; - - if ( elem || elem === 0 ) { - - // Add nodes directly - if ( jQuery.type( elem ) === "object" ) { - - // Support: Android <=4.0 only, PhantomJS 1 only - // push.apply(_, arraylike) throws on ancient WebKit - jQuery.merge( nodes, elem.nodeType ? [ elem ] : elem ); - - // Convert non-html into a text node - } else if ( !rhtml.test( elem ) ) { - nodes.push( context.createTextNode( elem ) ); - - // Convert html into DOM nodes - } else { - tmp = tmp || fragment.appendChild( context.createElement( "div" ) ); - - // Deserialize a standard representation - tag = ( rtagName.exec( elem ) || [ "", "" ] )[ 1 ].toLowerCase(); - wrap = wrapMap[ tag ] || wrapMap._default; - tmp.innerHTML = wrap[ 1 ] + jQuery.htmlPrefilter( elem ) + wrap[ 2 ]; - - // Descend through wrappers to the right content - j = wrap[ 0 ]; - while ( j-- ) { - tmp = tmp.lastChild; - } - - // Support: Android <=4.0 only, PhantomJS 1 only - // push.apply(_, arraylike) throws on ancient WebKit - jQuery.merge( nodes, tmp.childNodes ); - - // Remember the top-level container - tmp = fragment.firstChild; - - // Ensure the created nodes are orphaned (#12392) - tmp.textContent = ""; - } - } - } - - // Remove wrapper from fragment - fragment.textContent = ""; - - i = 0; - while ( ( elem = nodes[ i++ ] ) ) { - - // Skip elements already in the context collection (trac-4087) - if ( selection && jQuery.inArray( elem, selection ) > -1 ) { - if ( ignored ) { - ignored.push( elem ); - } - continue; - } - - contains = jQuery.contains( elem.ownerDocument, elem ); - - // Append to fragment - tmp = getAll( fragment.appendChild( elem ), "script" ); - - // Preserve script evaluation history - if ( contains ) { - setGlobalEval( tmp ); - } - - // Capture executables - if ( scripts ) { - j = 0; - while ( ( elem = tmp[ j++ ] ) ) { - if ( rscriptType.test( elem.type || "" ) ) { - scripts.push( elem ); - } - } - } - } - - return fragment; -} - - -( function() { - var fragment = document.createDocumentFragment(), - div = fragment.appendChild( document.createElement( "div" ) ), - input = document.createElement( "input" ); - - // Support: Android 4.0 - 4.3 only - // Check state lost if the name is set (#11217) - // Support: Windows Web Apps (WWA) - // `name` and `type` must use .setAttribute for WWA (#14901) - input.setAttribute( "type", "radio" ); - input.setAttribute( "checked", "checked" ); - input.setAttribute( "name", "t" ); - - div.appendChild( input ); - - // Support: Android <=4.1 only - // Older WebKit doesn't clone checked state correctly in fragments - support.checkClone = div.cloneNode( true ).cloneNode( true ).lastChild.checked; - - // Support: IE <=11 only - // Make sure textarea (and checkbox) defaultValue is properly cloned - div.innerHTML = ""; - support.noCloneChecked = !!div.cloneNode( true ).lastChild.defaultValue; -} )(); -var documentElement = document.documentElement; - - - -var - rkeyEvent = /^key/, - rmouseEvent = /^(?:mouse|pointer|contextmenu|drag|drop)|click/, - rtypenamespace = /^([^.]*)(?:\.(.+)|)/; - -function returnTrue() { - return true; -} - -function returnFalse() { - return false; -} - -// Support: IE <=9 only -// See #13393 for more info -function safeActiveElement() { - try { - return document.activeElement; - } catch ( err ) { } -} - -function on( elem, types, selector, data, fn, one ) { - var origFn, type; - - // Types can be a map of types/handlers - if ( typeof types === "object" ) { - - // ( types-Object, selector, data ) - if ( typeof selector !== "string" ) { - - // ( types-Object, data ) - data = data || selector; - selector = undefined; - } - for ( type in types ) { - on( elem, type, selector, data, types[ type ], one ); - } - return elem; - } - - if ( data == null && fn == null ) { - - // ( types, fn ) - fn = selector; - data = selector = undefined; - } else if ( fn == null ) { - if ( typeof selector === "string" ) { - - // ( types, selector, fn ) - fn = data; - data = undefined; - } else { - - // ( types, data, fn ) - fn = data; - data = selector; - selector = undefined; - } - } - if ( fn === false ) { - fn = returnFalse; - } else if ( !fn ) { - return elem; - } - - if ( one === 1 ) { - origFn = fn; - fn = function( event ) { - - // Can use an empty set, since event contains the info - jQuery().off( event ); - return origFn.apply( this, arguments ); - }; - - // Use same guid so caller can remove using origFn - fn.guid = origFn.guid || ( origFn.guid = jQuery.guid++ ); - } - return elem.each( function() { - jQuery.event.add( this, types, fn, data, selector ); - } ); -} - -/* - * Helper functions for managing events -- not part of the public interface. - * Props to Dean Edwards' addEvent library for many of the ideas. - */ -jQuery.event = { - - global: {}, - - add: function( elem, types, handler, data, selector ) { - - var handleObjIn, eventHandle, tmp, - events, t, handleObj, - special, handlers, type, namespaces, origType, - elemData = dataPriv.get( elem ); - - // Don't attach events to noData or text/comment nodes (but allow plain objects) - if ( !elemData ) { - return; - } - - // Caller can pass in an object of custom data in lieu of the handler - if ( handler.handler ) { - handleObjIn = handler; - handler = handleObjIn.handler; - selector = handleObjIn.selector; - } - - // Ensure that invalid selectors throw exceptions at attach time - // Evaluate against documentElement in case elem is a non-element node (e.g., document) - if ( selector ) { - jQuery.find.matchesSelector( documentElement, selector ); - } - - // Make sure that the handler has a unique ID, used to find/remove it later - if ( !handler.guid ) { - handler.guid = jQuery.guid++; - } - - // Init the element's event structure and main handler, if this is the first - if ( !( events = elemData.events ) ) { - events = elemData.events = {}; - } - if ( !( eventHandle = elemData.handle ) ) { - eventHandle = elemData.handle = function( e ) { - - // Discard the second event of a jQuery.event.trigger() and - // when an event is called after a page has unloaded - return typeof jQuery !== "undefined" && jQuery.event.triggered !== e.type ? - jQuery.event.dispatch.apply( elem, arguments ) : undefined; - }; - } - - // Handle multiple events separated by a space - types = ( types || "" ).match( rnothtmlwhite ) || [ "" ]; - t = types.length; - while ( t-- ) { - tmp = rtypenamespace.exec( types[ t ] ) || []; - type = origType = tmp[ 1 ]; - namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort(); - - // There *must* be a type, no attaching namespace-only handlers - if ( !type ) { - continue; - } - - // If event changes its type, use the special event handlers for the changed type - special = jQuery.event.special[ type ] || {}; - - // If selector defined, determine special event api type, otherwise given type - type = ( selector ? special.delegateType : special.bindType ) || type; - - // Update special based on newly reset type - special = jQuery.event.special[ type ] || {}; - - // handleObj is passed to all event handlers - handleObj = jQuery.extend( { - type: type, - origType: origType, - data: data, - handler: handler, - guid: handler.guid, - selector: selector, - needsContext: selector && jQuery.expr.match.needsContext.test( selector ), - namespace: namespaces.join( "." ) - }, handleObjIn ); - - // Init the event handler queue if we're the first - if ( !( handlers = events[ type ] ) ) { - handlers = events[ type ] = []; - handlers.delegateCount = 0; - - // Only use addEventListener if the special events handler returns false - if ( !special.setup || - special.setup.call( elem, data, namespaces, eventHandle ) === false ) { - - if ( elem.addEventListener ) { - elem.addEventListener( type, eventHandle ); - } - } - } - - if ( special.add ) { - special.add.call( elem, handleObj ); - - if ( !handleObj.handler.guid ) { - handleObj.handler.guid = handler.guid; - } - } - - // Add to the element's handler list, delegates in front - if ( selector ) { - handlers.splice( handlers.delegateCount++, 0, handleObj ); - } else { - handlers.push( handleObj ); - } - - // Keep track of which events have ever been used, for event optimization - jQuery.event.global[ type ] = true; - } - - }, - - // Detach an event or set of events from an element - remove: function( elem, types, handler, selector, mappedTypes ) { - - var j, origCount, tmp, - events, t, handleObj, - special, handlers, type, namespaces, origType, - elemData = dataPriv.hasData( elem ) && dataPriv.get( elem ); - - if ( !elemData || !( events = elemData.events ) ) { - return; - } - - // Once for each type.namespace in types; type may be omitted - types = ( types || "" ).match( rnothtmlwhite ) || [ "" ]; - t = types.length; - while ( t-- ) { - tmp = rtypenamespace.exec( types[ t ] ) || []; - type = origType = tmp[ 1 ]; - namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort(); - - // Unbind all events (on this namespace, if provided) for the element - if ( !type ) { - for ( type in events ) { - jQuery.event.remove( elem, type + types[ t ], handler, selector, true ); - } - continue; - } - - special = jQuery.event.special[ type ] || {}; - type = ( selector ? special.delegateType : special.bindType ) || type; - handlers = events[ type ] || []; - tmp = tmp[ 2 ] && - new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" ); - - // Remove matching events - origCount = j = handlers.length; - while ( j-- ) { - handleObj = handlers[ j ]; - - if ( ( mappedTypes || origType === handleObj.origType ) && - ( !handler || handler.guid === handleObj.guid ) && - ( !tmp || tmp.test( handleObj.namespace ) ) && - ( !selector || selector === handleObj.selector || - selector === "**" && handleObj.selector ) ) { - handlers.splice( j, 1 ); - - if ( handleObj.selector ) { - handlers.delegateCount--; - } - if ( special.remove ) { - special.remove.call( elem, handleObj ); - } - } - } - - // Remove generic event handler if we removed something and no more handlers exist - // (avoids potential for endless recursion during removal of special event handlers) - if ( origCount && !handlers.length ) { - if ( !special.teardown || - special.teardown.call( elem, namespaces, elemData.handle ) === false ) { - - jQuery.removeEvent( elem, type, elemData.handle ); - } - - delete events[ type ]; - } - } - - // Remove data and the expando if it's no longer used - if ( jQuery.isEmptyObject( events ) ) { - dataPriv.remove( elem, "handle events" ); - } - }, - - dispatch: function( nativeEvent ) { - - // Make a writable jQuery.Event from the native event object - var event = jQuery.event.fix( nativeEvent ); - - var i, j, ret, matched, handleObj, handlerQueue, - args = new Array( arguments.length ), - handlers = ( dataPriv.get( this, "events" ) || {} )[ event.type ] || [], - special = jQuery.event.special[ event.type ] || {}; - - // Use the fix-ed jQuery.Event rather than the (read-only) native event - args[ 0 ] = event; - - for ( i = 1; i < arguments.length; i++ ) { - args[ i ] = arguments[ i ]; - } - - event.delegateTarget = this; - - // Call the preDispatch hook for the mapped type, and let it bail if desired - if ( special.preDispatch && special.preDispatch.call( this, event ) === false ) { - return; - } - - // Determine handlers - handlerQueue = jQuery.event.handlers.call( this, event, handlers ); - - // Run delegates first; they may want to stop propagation beneath us - i = 0; - while ( ( matched = handlerQueue[ i++ ] ) && !event.isPropagationStopped() ) { - event.currentTarget = matched.elem; - - j = 0; - while ( ( handleObj = matched.handlers[ j++ ] ) && - !event.isImmediatePropagationStopped() ) { - - // Triggered event must either 1) have no namespace, or 2) have namespace(s) - // a subset or equal to those in the bound event (both can have no namespace). - if ( !event.rnamespace || event.rnamespace.test( handleObj.namespace ) ) { - - event.handleObj = handleObj; - event.data = handleObj.data; - - ret = ( ( jQuery.event.special[ handleObj.origType ] || {} ).handle || - handleObj.handler ).apply( matched.elem, args ); - - if ( ret !== undefined ) { - if ( ( event.result = ret ) === false ) { - event.preventDefault(); - event.stopPropagation(); - } - } - } - } - } - - // Call the postDispatch hook for the mapped type - if ( special.postDispatch ) { - special.postDispatch.call( this, event ); - } - - return event.result; - }, - - handlers: function( event, handlers ) { - var i, handleObj, sel, matchedHandlers, matchedSelectors, - handlerQueue = [], - delegateCount = handlers.delegateCount, - cur = event.target; - - // Find delegate handlers - if ( delegateCount && - - // Support: IE <=9 - // Black-hole SVG instance trees (trac-13180) - cur.nodeType && - - // Support: Firefox <=42 - // Suppress spec-violating clicks indicating a non-primary pointer button (trac-3861) - // https://www.w3.org/TR/DOM-Level-3-Events/#event-type-click - // Support: IE 11 only - // ...but not arrow key "clicks" of radio inputs, which can have `button` -1 (gh-2343) - !( event.type === "click" && event.button >= 1 ) ) { - - for ( ; cur !== this; cur = cur.parentNode || this ) { - - // Don't check non-elements (#13208) - // Don't process clicks on disabled elements (#6911, #8165, #11382, #11764) - if ( cur.nodeType === 1 && !( event.type === "click" && cur.disabled === true ) ) { - matchedHandlers = []; - matchedSelectors = {}; - for ( i = 0; i < delegateCount; i++ ) { - handleObj = handlers[ i ]; - - // Don't conflict with Object.prototype properties (#13203) - sel = handleObj.selector + " "; - - if ( matchedSelectors[ sel ] === undefined ) { - matchedSelectors[ sel ] = handleObj.needsContext ? - jQuery( sel, this ).index( cur ) > -1 : - jQuery.find( sel, this, null, [ cur ] ).length; - } - if ( matchedSelectors[ sel ] ) { - matchedHandlers.push( handleObj ); - } - } - if ( matchedHandlers.length ) { - handlerQueue.push( { elem: cur, handlers: matchedHandlers } ); - } - } - } - } - - // Add the remaining (directly-bound) handlers - cur = this; - if ( delegateCount < handlers.length ) { - handlerQueue.push( { elem: cur, handlers: handlers.slice( delegateCount ) } ); - } - - return handlerQueue; - }, - - addProp: function( name, hook ) { - Object.defineProperty( jQuery.Event.prototype, name, { - enumerable: true, - configurable: true, - - get: jQuery.isFunction( hook ) ? - function() { - if ( this.originalEvent ) { - return hook( this.originalEvent ); - } - } : - function() { - if ( this.originalEvent ) { - return this.originalEvent[ name ]; - } - }, - - set: function( value ) { - Object.defineProperty( this, name, { - enumerable: true, - configurable: true, - writable: true, - value: value - } ); - } - } ); - }, - - fix: function( originalEvent ) { - return originalEvent[ jQuery.expando ] ? - originalEvent : - new jQuery.Event( originalEvent ); - }, - - special: { - load: { - - // Prevent triggered image.load events from bubbling to window.load - noBubble: true - }, - focus: { - - // Fire native event if possible so blur/focus sequence is correct - trigger: function() { - if ( this !== safeActiveElement() && this.focus ) { - this.focus(); - return false; - } - }, - delegateType: "focusin" - }, - blur: { - trigger: function() { - if ( this === safeActiveElement() && this.blur ) { - this.blur(); - return false; - } - }, - delegateType: "focusout" - }, - click: { - - // For checkbox, fire native event so checked state will be right - trigger: function() { - if ( this.type === "checkbox" && this.click && nodeName( this, "input" ) ) { - this.click(); - return false; - } - }, - - // For cross-browser consistency, don't fire native .click() on links - _default: function( event ) { - return nodeName( event.target, "a" ); - } - }, - - beforeunload: { - postDispatch: function( event ) { - - // Support: Firefox 20+ - // Firefox doesn't alert if the returnValue field is not set. - if ( event.result !== undefined && event.originalEvent ) { - event.originalEvent.returnValue = event.result; - } - } - } - } -}; - -jQuery.removeEvent = function( elem, type, handle ) { - - // This "if" is needed for plain objects - if ( elem.removeEventListener ) { - elem.removeEventListener( type, handle ); - } -}; - -jQuery.Event = function( src, props ) { - - // Allow instantiation without the 'new' keyword - if ( !( this instanceof jQuery.Event ) ) { - return new jQuery.Event( src, props ); - } - - // Event object - if ( src && src.type ) { - this.originalEvent = src; - this.type = src.type; - - // Events bubbling up the document may have been marked as prevented - // by a handler lower down the tree; reflect the correct value. - this.isDefaultPrevented = src.defaultPrevented || - src.defaultPrevented === undefined && - - // Support: Android <=2.3 only - src.returnValue === false ? - returnTrue : - returnFalse; - - // Create target properties - // Support: Safari <=6 - 7 only - // Target should not be a text node (#504, #13143) - this.target = ( src.target && src.target.nodeType === 3 ) ? - src.target.parentNode : - src.target; - - this.currentTarget = src.currentTarget; - this.relatedTarget = src.relatedTarget; - - // Event type - } else { - this.type = src; - } - - // Put explicitly provided properties onto the event object - if ( props ) { - jQuery.extend( this, props ); - } - - // Create a timestamp if incoming event doesn't have one - this.timeStamp = src && src.timeStamp || jQuery.now(); - - // Mark it as fixed - this[ jQuery.expando ] = true; -}; - -// jQuery.Event is based on DOM3 Events as specified by the ECMAScript Language Binding -// https://www.w3.org/TR/2003/WD-DOM-Level-3-Events-20030331/ecma-script-binding.html -jQuery.Event.prototype = { - constructor: jQuery.Event, - isDefaultPrevented: returnFalse, - isPropagationStopped: returnFalse, - isImmediatePropagationStopped: returnFalse, - isSimulated: false, - - preventDefault: function() { - var e = this.originalEvent; - - this.isDefaultPrevented = returnTrue; - - if ( e && !this.isSimulated ) { - e.preventDefault(); - } - }, - stopPropagation: function() { - var e = this.originalEvent; - - this.isPropagationStopped = returnTrue; - - if ( e && !this.isSimulated ) { - e.stopPropagation(); - } - }, - stopImmediatePropagation: function() { - var e = this.originalEvent; - - this.isImmediatePropagationStopped = returnTrue; - - if ( e && !this.isSimulated ) { - e.stopImmediatePropagation(); - } - - this.stopPropagation(); - } -}; - -// Includes all common event props including KeyEvent and MouseEvent specific props -jQuery.each( { - altKey: true, - bubbles: true, - cancelable: true, - changedTouches: true, - ctrlKey: true, - detail: true, - eventPhase: true, - metaKey: true, - pageX: true, - pageY: true, - shiftKey: true, - view: true, - "char": true, - charCode: true, - key: true, - keyCode: true, - button: true, - buttons: true, - clientX: true, - clientY: true, - offsetX: true, - offsetY: true, - pointerId: true, - pointerType: true, - screenX: true, - screenY: true, - targetTouches: true, - toElement: true, - touches: true, - - which: function( event ) { - var button = event.button; - - // Add which for key events - if ( event.which == null && rkeyEvent.test( event.type ) ) { - return event.charCode != null ? event.charCode : event.keyCode; - } - - // Add which for click: 1 === left; 2 === middle; 3 === right - if ( !event.which && button !== undefined && rmouseEvent.test( event.type ) ) { - if ( button & 1 ) { - return 1; - } - - if ( button & 2 ) { - return 3; - } - - if ( button & 4 ) { - return 2; - } - - return 0; - } - - return event.which; - } -}, jQuery.event.addProp ); - -// Create mouseenter/leave events using mouseover/out and event-time checks -// so that event delegation works in jQuery. -// Do the same for pointerenter/pointerleave and pointerover/pointerout -// -// Support: Safari 7 only -// Safari sends mouseenter too often; see: -// https://bugs.chromium.org/p/chromium/issues/detail?id=470258 -// for the description of the bug (it existed in older Chrome versions as well). -jQuery.each( { - mouseenter: "mouseover", - mouseleave: "mouseout", - pointerenter: "pointerover", - pointerleave: "pointerout" -}, function( orig, fix ) { - jQuery.event.special[ orig ] = { - delegateType: fix, - bindType: fix, - - handle: function( event ) { - var ret, - target = this, - related = event.relatedTarget, - handleObj = event.handleObj; - - // For mouseenter/leave call the handler if related is outside the target. - // NB: No relatedTarget if the mouse left/entered the browser window - if ( !related || ( related !== target && !jQuery.contains( target, related ) ) ) { - event.type = handleObj.origType; - ret = handleObj.handler.apply( this, arguments ); - event.type = fix; - } - return ret; - } - }; -} ); - -jQuery.fn.extend( { - - on: function( types, selector, data, fn ) { - return on( this, types, selector, data, fn ); - }, - one: function( types, selector, data, fn ) { - return on( this, types, selector, data, fn, 1 ); - }, - off: function( types, selector, fn ) { - var handleObj, type; - if ( types && types.preventDefault && types.handleObj ) { - - // ( event ) dispatched jQuery.Event - handleObj = types.handleObj; - jQuery( types.delegateTarget ).off( - handleObj.namespace ? - handleObj.origType + "." + handleObj.namespace : - handleObj.origType, - handleObj.selector, - handleObj.handler - ); - return this; - } - if ( typeof types === "object" ) { - - // ( types-object [, selector] ) - for ( type in types ) { - this.off( type, selector, types[ type ] ); - } - return this; - } - if ( selector === false || typeof selector === "function" ) { - - // ( types [, fn] ) - fn = selector; - selector = undefined; - } - if ( fn === false ) { - fn = returnFalse; - } - return this.each( function() { - jQuery.event.remove( this, types, fn, selector ); - } ); - } -} ); - - -var - - /* eslint-disable max-len */ - - // See https://github.com/eslint/eslint/issues/3229 - rxhtmlTag = /<(?!area|br|col|embed|hr|img|input|link|meta|param)(([a-z][^\/\0>\x20\t\r\n\f]*)[^>]*)\/>/gi, - - /* eslint-enable */ - - // Support: IE <=10 - 11, Edge 12 - 13 - // In IE/Edge using regex groups here causes severe slowdowns. - // See https://connect.microsoft.com/IE/feedback/details/1736512/ - rnoInnerhtml = /\s*$/g; - -// Prefer a tbody over its parent table for containing new rows -function manipulationTarget( elem, content ) { - if ( nodeName( elem, "table" ) && - nodeName( content.nodeType !== 11 ? content : content.firstChild, "tr" ) ) { - - return jQuery( ">tbody", elem )[ 0 ] || elem; - } - - return elem; -} - -// Replace/restore the type attribute of script elements for safe DOM manipulation -function disableScript( elem ) { - elem.type = ( elem.getAttribute( "type" ) !== null ) + "/" + elem.type; - return elem; -} -function restoreScript( elem ) { - var match = rscriptTypeMasked.exec( elem.type ); - - if ( match ) { - elem.type = match[ 1 ]; - } else { - elem.removeAttribute( "type" ); - } - - return elem; -} - -function cloneCopyEvent( src, dest ) { - var i, l, type, pdataOld, pdataCur, udataOld, udataCur, events; - - if ( dest.nodeType !== 1 ) { - return; - } - - // 1. Copy private data: events, handlers, etc. - if ( dataPriv.hasData( src ) ) { - pdataOld = dataPriv.access( src ); - pdataCur = dataPriv.set( dest, pdataOld ); - events = pdataOld.events; - - if ( events ) { - delete pdataCur.handle; - pdataCur.events = {}; - - for ( type in events ) { - for ( i = 0, l = events[ type ].length; i < l; i++ ) { - jQuery.event.add( dest, type, events[ type ][ i ] ); - } - } - } - } - - // 2. Copy user data - if ( dataUser.hasData( src ) ) { - udataOld = dataUser.access( src ); - udataCur = jQuery.extend( {}, udataOld ); - - dataUser.set( dest, udataCur ); - } -} - -// Fix IE bugs, see support tests -function fixInput( src, dest ) { - var nodeName = dest.nodeName.toLowerCase(); - - // Fails to persist the checked state of a cloned checkbox or radio button. - if ( nodeName === "input" && rcheckableType.test( src.type ) ) { - dest.checked = src.checked; - - // Fails to return the selected option to the default selected state when cloning options - } else if ( nodeName === "input" || nodeName === "textarea" ) { - dest.defaultValue = src.defaultValue; - } -} - -function domManip( collection, args, callback, ignored ) { - - // Flatten any nested arrays - args = concat.apply( [], args ); - - var fragment, first, scripts, hasScripts, node, doc, - i = 0, - l = collection.length, - iNoClone = l - 1, - value = args[ 0 ], - isFunction = jQuery.isFunction( value ); - - // We can't cloneNode fragments that contain checked, in WebKit - if ( isFunction || - ( l > 1 && typeof value === "string" && - !support.checkClone && rchecked.test( value ) ) ) { - return collection.each( function( index ) { - var self = collection.eq( index ); - if ( isFunction ) { - args[ 0 ] = value.call( this, index, self.html() ); - } - domManip( self, args, callback, ignored ); - } ); - } - - if ( l ) { - fragment = buildFragment( args, collection[ 0 ].ownerDocument, false, collection, ignored ); - first = fragment.firstChild; - - if ( fragment.childNodes.length === 1 ) { - fragment = first; - } - - // Require either new content or an interest in ignored elements to invoke the callback - if ( first || ignored ) { - scripts = jQuery.map( getAll( fragment, "script" ), disableScript ); - hasScripts = scripts.length; - - // Use the original fragment for the last item - // instead of the first because it can end up - // being emptied incorrectly in certain situations (#8070). - for ( ; i < l; i++ ) { - node = fragment; - - if ( i !== iNoClone ) { - node = jQuery.clone( node, true, true ); - - // Keep references to cloned scripts for later restoration - if ( hasScripts ) { - - // Support: Android <=4.0 only, PhantomJS 1 only - // push.apply(_, arraylike) throws on ancient WebKit - jQuery.merge( scripts, getAll( node, "script" ) ); - } - } - - callback.call( collection[ i ], node, i ); - } - - if ( hasScripts ) { - doc = scripts[ scripts.length - 1 ].ownerDocument; - - // Reenable scripts - jQuery.map( scripts, restoreScript ); - - // Evaluate executable scripts on first document insertion - for ( i = 0; i < hasScripts; i++ ) { - node = scripts[ i ]; - if ( rscriptType.test( node.type || "" ) && - !dataPriv.access( node, "globalEval" ) && - jQuery.contains( doc, node ) ) { - - if ( node.src ) { - - // Optional AJAX dependency, but won't run scripts if not present - if ( jQuery._evalUrl ) { - jQuery._evalUrl( node.src ); - } - } else { - DOMEval( node.textContent.replace( rcleanScript, "" ), doc ); - } - } - } - } - } - } - - return collection; -} - -function remove( elem, selector, keepData ) { - var node, - nodes = selector ? jQuery.filter( selector, elem ) : elem, - i = 0; - - for ( ; ( node = nodes[ i ] ) != null; i++ ) { - if ( !keepData && node.nodeType === 1 ) { - jQuery.cleanData( getAll( node ) ); - } - - if ( node.parentNode ) { - if ( keepData && jQuery.contains( node.ownerDocument, node ) ) { - setGlobalEval( getAll( node, "script" ) ); - } - node.parentNode.removeChild( node ); - } - } - - return elem; -} - -jQuery.extend( { - htmlPrefilter: function( html ) { - return html.replace( rxhtmlTag, "<$1>" ); - }, - - clone: function( elem, dataAndEvents, deepDataAndEvents ) { - var i, l, srcElements, destElements, - clone = elem.cloneNode( true ), - inPage = jQuery.contains( elem.ownerDocument, elem ); - - // Fix IE cloning issues - if ( !support.noCloneChecked && ( elem.nodeType === 1 || elem.nodeType === 11 ) && - !jQuery.isXMLDoc( elem ) ) { - - // We eschew Sizzle here for performance reasons: https://jsperf.com/getall-vs-sizzle/2 - destElements = getAll( clone ); - srcElements = getAll( elem ); - - for ( i = 0, l = srcElements.length; i < l; i++ ) { - fixInput( srcElements[ i ], destElements[ i ] ); - } - } - - // Copy the events from the original to the clone - if ( dataAndEvents ) { - if ( deepDataAndEvents ) { - srcElements = srcElements || getAll( elem ); - destElements = destElements || getAll( clone ); - - for ( i = 0, l = srcElements.length; i < l; i++ ) { - cloneCopyEvent( srcElements[ i ], destElements[ i ] ); - } - } else { - cloneCopyEvent( elem, clone ); - } - } - - // Preserve script evaluation history - destElements = getAll( clone, "script" ); - if ( destElements.length > 0 ) { - setGlobalEval( destElements, !inPage && getAll( elem, "script" ) ); - } - - // Return the cloned set - return clone; - }, - - cleanData: function( elems ) { - var data, elem, type, - special = jQuery.event.special, - i = 0; - - for ( ; ( elem = elems[ i ] ) !== undefined; i++ ) { - if ( acceptData( elem ) ) { - if ( ( data = elem[ dataPriv.expando ] ) ) { - if ( data.events ) { - for ( type in data.events ) { - if ( special[ type ] ) { - jQuery.event.remove( elem, type ); - - // This is a shortcut to avoid jQuery.event.remove's overhead - } else { - jQuery.removeEvent( elem, type, data.handle ); - } - } - } - - // Support: Chrome <=35 - 45+ - // Assign undefined instead of using delete, see Data#remove - elem[ dataPriv.expando ] = undefined; - } - if ( elem[ dataUser.expando ] ) { - - // Support: Chrome <=35 - 45+ - // Assign undefined instead of using delete, see Data#remove - elem[ dataUser.expando ] = undefined; - } - } - } - } -} ); - -jQuery.fn.extend( { - detach: function( selector ) { - return remove( this, selector, true ); - }, - - remove: function( selector ) { - return remove( this, selector ); - }, - - text: function( value ) { - return access( this, function( value ) { - return value === undefined ? - jQuery.text( this ) : - this.empty().each( function() { - if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) { - this.textContent = value; - } - } ); - }, null, value, arguments.length ); - }, - - append: function() { - return domManip( this, arguments, function( elem ) { - if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) { - var target = manipulationTarget( this, elem ); - target.appendChild( elem ); - } - } ); - }, - - prepend: function() { - return domManip( this, arguments, function( elem ) { - if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) { - var target = manipulationTarget( this, elem ); - target.insertBefore( elem, target.firstChild ); - } - } ); - }, - - before: function() { - return domManip( this, arguments, function( elem ) { - if ( this.parentNode ) { - this.parentNode.insertBefore( elem, this ); - } - } ); - }, - - after: function() { - return domManip( this, arguments, function( elem ) { - if ( this.parentNode ) { - this.parentNode.insertBefore( elem, this.nextSibling ); - } - } ); - }, - - empty: function() { - var elem, - i = 0; - - for ( ; ( elem = this[ i ] ) != null; i++ ) { - if ( elem.nodeType === 1 ) { - - // Prevent memory leaks - jQuery.cleanData( getAll( elem, false ) ); - - // Remove any remaining nodes - elem.textContent = ""; - } - } - - return this; - }, - - clone: function( dataAndEvents, deepDataAndEvents ) { - dataAndEvents = dataAndEvents == null ? false : dataAndEvents; - deepDataAndEvents = deepDataAndEvents == null ? dataAndEvents : deepDataAndEvents; - - return this.map( function() { - return jQuery.clone( this, dataAndEvents, deepDataAndEvents ); - } ); - }, - - html: function( value ) { - return access( this, function( value ) { - var elem = this[ 0 ] || {}, - i = 0, - l = this.length; - - if ( value === undefined && elem.nodeType === 1 ) { - return elem.innerHTML; - } - - // See if we can take a shortcut and just use innerHTML - if ( typeof value === "string" && !rnoInnerhtml.test( value ) && - !wrapMap[ ( rtagName.exec( value ) || [ "", "" ] )[ 1 ].toLowerCase() ] ) { - - value = jQuery.htmlPrefilter( value ); - - try { - for ( ; i < l; i++ ) { - elem = this[ i ] || {}; - - // Remove element nodes and prevent memory leaks - if ( elem.nodeType === 1 ) { - jQuery.cleanData( getAll( elem, false ) ); - elem.innerHTML = value; - } - } - - elem = 0; - - // If using innerHTML throws an exception, use the fallback method - } catch ( e ) {} - } - - if ( elem ) { - this.empty().append( value ); - } - }, null, value, arguments.length ); - }, - - replaceWith: function() { - var ignored = []; - - // Make the changes, replacing each non-ignored context element with the new content - return domManip( this, arguments, function( elem ) { - var parent = this.parentNode; - - if ( jQuery.inArray( this, ignored ) < 0 ) { - jQuery.cleanData( getAll( this ) ); - if ( parent ) { - parent.replaceChild( elem, this ); - } - } - - // Force callback invocation - }, ignored ); - } -} ); - -jQuery.each( { - appendTo: "append", - prependTo: "prepend", - insertBefore: "before", - insertAfter: "after", - replaceAll: "replaceWith" -}, function( name, original ) { - jQuery.fn[ name ] = function( selector ) { - var elems, - ret = [], - insert = jQuery( selector ), - last = insert.length - 1, - i = 0; - - for ( ; i <= last; i++ ) { - elems = i === last ? this : this.clone( true ); - jQuery( insert[ i ] )[ original ]( elems ); - - // Support: Android <=4.0 only, PhantomJS 1 only - // .get() because push.apply(_, arraylike) throws on ancient WebKit - push.apply( ret, elems.get() ); - } - - return this.pushStack( ret ); - }; -} ); -var rmargin = ( /^margin/ ); - -var rnumnonpx = new RegExp( "^(" + pnum + ")(?!px)[a-z%]+$", "i" ); - -var getStyles = function( elem ) { - - // Support: IE <=11 only, Firefox <=30 (#15098, #14150) - // IE throws on elements created in popups - // FF meanwhile throws on frame elements through "defaultView.getComputedStyle" - var view = elem.ownerDocument.defaultView; - - if ( !view || !view.opener ) { - view = window; - } - - return view.getComputedStyle( elem ); - }; - - - -( function() { - - // Executing both pixelPosition & boxSizingReliable tests require only one layout - // so they're executed at the same time to save the second computation. - function computeStyleTests() { - - // This is a singleton, we need to execute it only once - if ( !div ) { - return; - } - - div.style.cssText = - "box-sizing:border-box;" + - "position:relative;display:block;" + - "margin:auto;border:1px;padding:1px;" + - "top:1%;width:50%"; - div.innerHTML = ""; - documentElement.appendChild( container ); - - var divStyle = window.getComputedStyle( div ); - pixelPositionVal = divStyle.top !== "1%"; - - // Support: Android 4.0 - 4.3 only, Firefox <=3 - 44 - reliableMarginLeftVal = divStyle.marginLeft === "2px"; - boxSizingReliableVal = divStyle.width === "4px"; - - // Support: Android 4.0 - 4.3 only - // Some styles come back with percentage values, even though they shouldn't - div.style.marginRight = "50%"; - pixelMarginRightVal = divStyle.marginRight === "4px"; - - documentElement.removeChild( container ); - - // Nullify the div so it wouldn't be stored in the memory and - // it will also be a sign that checks already performed - div = null; - } - - var pixelPositionVal, boxSizingReliableVal, pixelMarginRightVal, reliableMarginLeftVal, - container = document.createElement( "div" ), - div = document.createElement( "div" ); - - // Finish early in limited (non-browser) environments - if ( !div.style ) { - return; - } - - // Support: IE <=9 - 11 only - // Style of cloned element affects source element cloned (#8908) - div.style.backgroundClip = "content-box"; - div.cloneNode( true ).style.backgroundClip = ""; - support.clearCloneStyle = div.style.backgroundClip === "content-box"; - - container.style.cssText = "border:0;width:8px;height:0;top:0;left:-9999px;" + - "padding:0;margin-top:1px;position:absolute"; - container.appendChild( div ); - - jQuery.extend( support, { - pixelPosition: function() { - computeStyleTests(); - return pixelPositionVal; - }, - boxSizingReliable: function() { - computeStyleTests(); - return boxSizingReliableVal; - }, - pixelMarginRight: function() { - computeStyleTests(); - return pixelMarginRightVal; - }, - reliableMarginLeft: function() { - computeStyleTests(); - return reliableMarginLeftVal; - } - } ); -} )(); - - -function curCSS( elem, name, computed ) { - var width, minWidth, maxWidth, ret, - - // Support: Firefox 51+ - // Retrieving style before computed somehow - // fixes an issue with getting wrong values - // on detached elements - style = elem.style; - - computed = computed || getStyles( elem ); - - // getPropertyValue is needed for: - // .css('filter') (IE 9 only, #12537) - // .css('--customProperty) (#3144) - if ( computed ) { - ret = computed.getPropertyValue( name ) || computed[ name ]; - - if ( ret === "" && !jQuery.contains( elem.ownerDocument, elem ) ) { - ret = jQuery.style( elem, name ); - } - - // A tribute to the "awesome hack by Dean Edwards" - // Android Browser returns percentage for some values, - // but width seems to be reliably pixels. - // This is against the CSSOM draft spec: - // https://drafts.csswg.org/cssom/#resolved-values - if ( !support.pixelMarginRight() && rnumnonpx.test( ret ) && rmargin.test( name ) ) { - - // Remember the original values - width = style.width; - minWidth = style.minWidth; - maxWidth = style.maxWidth; - - // Put in the new values to get a computed value out - style.minWidth = style.maxWidth = style.width = ret; - ret = computed.width; - - // Revert the changed values - style.width = width; - style.minWidth = minWidth; - style.maxWidth = maxWidth; - } - } - - return ret !== undefined ? - - // Support: IE <=9 - 11 only - // IE returns zIndex value as an integer. - ret + "" : - ret; -} - - -function addGetHookIf( conditionFn, hookFn ) { - - // Define the hook, we'll check on the first run if it's really needed. - return { - get: function() { - if ( conditionFn() ) { - - // Hook not needed (or it's not possible to use it due - // to missing dependency), remove it. - delete this.get; - return; - } - - // Hook needed; redefine it so that the support test is not executed again. - return ( this.get = hookFn ).apply( this, arguments ); - } - }; -} - - -var - - // Swappable if display is none or starts with table - // except "table", "table-cell", or "table-caption" - // See here for display values: https://developer.mozilla.org/en-US/docs/CSS/display - rdisplayswap = /^(none|table(?!-c[ea]).+)/, - rcustomProp = /^--/, - cssShow = { position: "absolute", visibility: "hidden", display: "block" }, - cssNormalTransform = { - letterSpacing: "0", - fontWeight: "400" - }, - - cssPrefixes = [ "Webkit", "Moz", "ms" ], - emptyStyle = document.createElement( "div" ).style; - -// Return a css property mapped to a potentially vendor prefixed property -function vendorPropName( name ) { - - // Shortcut for names that are not vendor prefixed - if ( name in emptyStyle ) { - return name; - } - - // Check for vendor prefixed names - var capName = name[ 0 ].toUpperCase() + name.slice( 1 ), - i = cssPrefixes.length; - - while ( i-- ) { - name = cssPrefixes[ i ] + capName; - if ( name in emptyStyle ) { - return name; - } - } -} - -// Return a property mapped along what jQuery.cssProps suggests or to -// a vendor prefixed property. -function finalPropName( name ) { - var ret = jQuery.cssProps[ name ]; - if ( !ret ) { - ret = jQuery.cssProps[ name ] = vendorPropName( name ) || name; - } - return ret; -} - -function setPositiveNumber( elem, value, subtract ) { - - // Any relative (+/-) values have already been - // normalized at this point - var matches = rcssNum.exec( value ); - return matches ? - - // Guard against undefined "subtract", e.g., when used as in cssHooks - Math.max( 0, matches[ 2 ] - ( subtract || 0 ) ) + ( matches[ 3 ] || "px" ) : - value; -} - -function augmentWidthOrHeight( elem, name, extra, isBorderBox, styles ) { - var i, - val = 0; - - // If we already have the right measurement, avoid augmentation - if ( extra === ( isBorderBox ? "border" : "content" ) ) { - i = 4; - - // Otherwise initialize for horizontal or vertical properties - } else { - i = name === "width" ? 1 : 0; - } - - for ( ; i < 4; i += 2 ) { - - // Both box models exclude margin, so add it if we want it - if ( extra === "margin" ) { - val += jQuery.css( elem, extra + cssExpand[ i ], true, styles ); - } - - if ( isBorderBox ) { - - // border-box includes padding, so remove it if we want content - if ( extra === "content" ) { - val -= jQuery.css( elem, "padding" + cssExpand[ i ], true, styles ); - } - - // At this point, extra isn't border nor margin, so remove border - if ( extra !== "margin" ) { - val -= jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles ); - } - } else { - - // At this point, extra isn't content, so add padding - val += jQuery.css( elem, "padding" + cssExpand[ i ], true, styles ); - - // At this point, extra isn't content nor padding, so add border - if ( extra !== "padding" ) { - val += jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles ); - } - } - } - - return val; -} - -function getWidthOrHeight( elem, name, extra ) { - - // Start with computed style - var valueIsBorderBox, - styles = getStyles( elem ), - val = curCSS( elem, name, styles ), - isBorderBox = jQuery.css( elem, "boxSizing", false, styles ) === "border-box"; - - // Computed unit is not pixels. Stop here and return. - if ( rnumnonpx.test( val ) ) { - return val; - } - - // Check for style in case a browser which returns unreliable values - // for getComputedStyle silently falls back to the reliable elem.style - valueIsBorderBox = isBorderBox && - ( support.boxSizingReliable() || val === elem.style[ name ] ); - - // Fall back to offsetWidth/Height when value is "auto" - // This happens for inline elements with no explicit setting (gh-3571) - if ( val === "auto" ) { - val = elem[ "offset" + name[ 0 ].toUpperCase() + name.slice( 1 ) ]; - } - - // Normalize "", auto, and prepare for extra - val = parseFloat( val ) || 0; - - // Use the active box-sizing model to add/subtract irrelevant styles - return ( val + - augmentWidthOrHeight( - elem, - name, - extra || ( isBorderBox ? "border" : "content" ), - valueIsBorderBox, - styles - ) - ) + "px"; -} - -jQuery.extend( { - - // Add in style property hooks for overriding the default - // behavior of getting and setting a style property - cssHooks: { - opacity: { - get: function( elem, computed ) { - if ( computed ) { - - // We should always get a number back from opacity - var ret = curCSS( elem, "opacity" ); - return ret === "" ? "1" : ret; - } - } - } - }, - - // Don't automatically add "px" to these possibly-unitless properties - cssNumber: { - "animationIterationCount": true, - "columnCount": true, - "fillOpacity": true, - "flexGrow": true, - "flexShrink": true, - "fontWeight": true, - "lineHeight": true, - "opacity": true, - "order": true, - "orphans": true, - "widows": true, - "zIndex": true, - "zoom": true - }, - - // Add in properties whose names you wish to fix before - // setting or getting the value - cssProps: { - "float": "cssFloat" - }, - - // Get and set the style property on a DOM Node - style: function( elem, name, value, extra ) { - - // Don't set styles on text and comment nodes - if ( !elem || elem.nodeType === 3 || elem.nodeType === 8 || !elem.style ) { - return; - } - - // Make sure that we're working with the right name - var ret, type, hooks, - origName = jQuery.camelCase( name ), - isCustomProp = rcustomProp.test( name ), - style = elem.style; - - // Make sure that we're working with the right name. We don't - // want to query the value if it is a CSS custom property - // since they are user-defined. - if ( !isCustomProp ) { - name = finalPropName( origName ); - } - - // Gets hook for the prefixed version, then unprefixed version - hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ]; - - // Check if we're setting a value - if ( value !== undefined ) { - type = typeof value; - - // Convert "+=" or "-=" to relative numbers (#7345) - if ( type === "string" && ( ret = rcssNum.exec( value ) ) && ret[ 1 ] ) { - value = adjustCSS( elem, name, ret ); - - // Fixes bug #9237 - type = "number"; - } - - // Make sure that null and NaN values aren't set (#7116) - if ( value == null || value !== value ) { - return; - } - - // If a number was passed in, add the unit (except for certain CSS properties) - if ( type === "number" ) { - value += ret && ret[ 3 ] || ( jQuery.cssNumber[ origName ] ? "" : "px" ); - } - - // background-* props affect original clone's values - if ( !support.clearCloneStyle && value === "" && name.indexOf( "background" ) === 0 ) { - style[ name ] = "inherit"; - } - - // If a hook was provided, use that value, otherwise just set the specified value - if ( !hooks || !( "set" in hooks ) || - ( value = hooks.set( elem, value, extra ) ) !== undefined ) { - - if ( isCustomProp ) { - style.setProperty( name, value ); - } else { - style[ name ] = value; - } - } - - } else { - - // If a hook was provided get the non-computed value from there - if ( hooks && "get" in hooks && - ( ret = hooks.get( elem, false, extra ) ) !== undefined ) { - - return ret; - } - - // Otherwise just get the value from the style object - return style[ name ]; - } - }, - - css: function( elem, name, extra, styles ) { - var val, num, hooks, - origName = jQuery.camelCase( name ), - isCustomProp = rcustomProp.test( name ); - - // Make sure that we're working with the right name. We don't - // want to modify the value if it is a CSS custom property - // since they are user-defined. - if ( !isCustomProp ) { - name = finalPropName( origName ); - } - - // Try prefixed name followed by the unprefixed name - hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ]; - - // If a hook was provided get the computed value from there - if ( hooks && "get" in hooks ) { - val = hooks.get( elem, true, extra ); - } - - // Otherwise, if a way to get the computed value exists, use that - if ( val === undefined ) { - val = curCSS( elem, name, styles ); - } - - // Convert "normal" to computed value - if ( val === "normal" && name in cssNormalTransform ) { - val = cssNormalTransform[ name ]; - } - - // Make numeric if forced or a qualifier was provided and val looks numeric - if ( extra === "" || extra ) { - num = parseFloat( val ); - return extra === true || isFinite( num ) ? num || 0 : val; - } - - return val; - } -} ); - -jQuery.each( [ "height", "width" ], function( i, name ) { - jQuery.cssHooks[ name ] = { - get: function( elem, computed, extra ) { - if ( computed ) { - - // Certain elements can have dimension info if we invisibly show them - // but it must have a current display style that would benefit - return rdisplayswap.test( jQuery.css( elem, "display" ) ) && - - // Support: Safari 8+ - // Table columns in Safari have non-zero offsetWidth & zero - // getBoundingClientRect().width unless display is changed. - // Support: IE <=11 only - // Running getBoundingClientRect on a disconnected node - // in IE throws an error. - ( !elem.getClientRects().length || !elem.getBoundingClientRect().width ) ? - swap( elem, cssShow, function() { - return getWidthOrHeight( elem, name, extra ); - } ) : - getWidthOrHeight( elem, name, extra ); - } - }, - - set: function( elem, value, extra ) { - var matches, - styles = extra && getStyles( elem ), - subtract = extra && augmentWidthOrHeight( - elem, - name, - extra, - jQuery.css( elem, "boxSizing", false, styles ) === "border-box", - styles - ); - - // Convert to pixels if value adjustment is needed - if ( subtract && ( matches = rcssNum.exec( value ) ) && - ( matches[ 3 ] || "px" ) !== "px" ) { - - elem.style[ name ] = value; - value = jQuery.css( elem, name ); - } - - return setPositiveNumber( elem, value, subtract ); - } - }; -} ); - -jQuery.cssHooks.marginLeft = addGetHookIf( support.reliableMarginLeft, - function( elem, computed ) { - if ( computed ) { - return ( parseFloat( curCSS( elem, "marginLeft" ) ) || - elem.getBoundingClientRect().left - - swap( elem, { marginLeft: 0 }, function() { - return elem.getBoundingClientRect().left; - } ) - ) + "px"; - } - } -); - -// These hooks are used by animate to expand properties -jQuery.each( { - margin: "", - padding: "", - border: "Width" -}, function( prefix, suffix ) { - jQuery.cssHooks[ prefix + suffix ] = { - expand: function( value ) { - var i = 0, - expanded = {}, - - // Assumes a single number if not a string - parts = typeof value === "string" ? value.split( " " ) : [ value ]; - - for ( ; i < 4; i++ ) { - expanded[ prefix + cssExpand[ i ] + suffix ] = - parts[ i ] || parts[ i - 2 ] || parts[ 0 ]; - } - - return expanded; - } - }; - - if ( !rmargin.test( prefix ) ) { - jQuery.cssHooks[ prefix + suffix ].set = setPositiveNumber; - } -} ); - -jQuery.fn.extend( { - css: function( name, value ) { - return access( this, function( elem, name, value ) { - var styles, len, - map = {}, - i = 0; - - if ( Array.isArray( name ) ) { - styles = getStyles( elem ); - len = name.length; - - for ( ; i < len; i++ ) { - map[ name[ i ] ] = jQuery.css( elem, name[ i ], false, styles ); - } - - return map; - } - - return value !== undefined ? - jQuery.style( elem, name, value ) : - jQuery.css( elem, name ); - }, name, value, arguments.length > 1 ); - } -} ); - - -function Tween( elem, options, prop, end, easing ) { - return new Tween.prototype.init( elem, options, prop, end, easing ); -} -jQuery.Tween = Tween; - -Tween.prototype = { - constructor: Tween, - init: function( elem, options, prop, end, easing, unit ) { - this.elem = elem; - this.prop = prop; - this.easing = easing || jQuery.easing._default; - this.options = options; - this.start = this.now = this.cur(); - this.end = end; - this.unit = unit || ( jQuery.cssNumber[ prop ] ? "" : "px" ); - }, - cur: function() { - var hooks = Tween.propHooks[ this.prop ]; - - return hooks && hooks.get ? - hooks.get( this ) : - Tween.propHooks._default.get( this ); - }, - run: function( percent ) { - var eased, - hooks = Tween.propHooks[ this.prop ]; - - if ( this.options.duration ) { - this.pos = eased = jQuery.easing[ this.easing ]( - percent, this.options.duration * percent, 0, 1, this.options.duration - ); - } else { - this.pos = eased = percent; - } - this.now = ( this.end - this.start ) * eased + this.start; - - if ( this.options.step ) { - this.options.step.call( this.elem, this.now, this ); - } - - if ( hooks && hooks.set ) { - hooks.set( this ); - } else { - Tween.propHooks._default.set( this ); - } - return this; - } -}; - -Tween.prototype.init.prototype = Tween.prototype; - -Tween.propHooks = { - _default: { - get: function( tween ) { - var result; - - // Use a property on the element directly when it is not a DOM element, - // or when there is no matching style property that exists. - if ( tween.elem.nodeType !== 1 || - tween.elem[ tween.prop ] != null && tween.elem.style[ tween.prop ] == null ) { - return tween.elem[ tween.prop ]; - } - - // Passing an empty string as a 3rd parameter to .css will automatically - // attempt a parseFloat and fallback to a string if the parse fails. - // Simple values such as "10px" are parsed to Float; - // complex values such as "rotate(1rad)" are returned as-is. - result = jQuery.css( tween.elem, tween.prop, "" ); - - // Empty strings, null, undefined and "auto" are converted to 0. - return !result || result === "auto" ? 0 : result; - }, - set: function( tween ) { - - // Use step hook for back compat. - // Use cssHook if its there. - // Use .style if available and use plain properties where available. - if ( jQuery.fx.step[ tween.prop ] ) { - jQuery.fx.step[ tween.prop ]( tween ); - } else if ( tween.elem.nodeType === 1 && - ( tween.elem.style[ jQuery.cssProps[ tween.prop ] ] != null || - jQuery.cssHooks[ tween.prop ] ) ) { - jQuery.style( tween.elem, tween.prop, tween.now + tween.unit ); - } else { - tween.elem[ tween.prop ] = tween.now; - } - } - } -}; - -// Support: IE <=9 only -// Panic based approach to setting things on disconnected nodes -Tween.propHooks.scrollTop = Tween.propHooks.scrollLeft = { - set: function( tween ) { - if ( tween.elem.nodeType && tween.elem.parentNode ) { - tween.elem[ tween.prop ] = tween.now; - } - } -}; - -jQuery.easing = { - linear: function( p ) { - return p; - }, - swing: function( p ) { - return 0.5 - Math.cos( p * Math.PI ) / 2; - }, - _default: "swing" -}; - -jQuery.fx = Tween.prototype.init; - -// Back compat <1.8 extension point -jQuery.fx.step = {}; - - - - -var - fxNow, inProgress, - rfxtypes = /^(?:toggle|show|hide)$/, - rrun = /queueHooks$/; - -function schedule() { - if ( inProgress ) { - if ( document.hidden === false && window.requestAnimationFrame ) { - window.requestAnimationFrame( schedule ); - } else { - window.setTimeout( schedule, jQuery.fx.interval ); - } - - jQuery.fx.tick(); - } -} - -// Animations created synchronously will run synchronously -function createFxNow() { - window.setTimeout( function() { - fxNow = undefined; - } ); - return ( fxNow = jQuery.now() ); -} - -// Generate parameters to create a standard animation -function genFx( type, includeWidth ) { - var which, - i = 0, - attrs = { height: type }; - - // If we include width, step value is 1 to do all cssExpand values, - // otherwise step value is 2 to skip over Left and Right - includeWidth = includeWidth ? 1 : 0; - for ( ; i < 4; i += 2 - includeWidth ) { - which = cssExpand[ i ]; - attrs[ "margin" + which ] = attrs[ "padding" + which ] = type; - } - - if ( includeWidth ) { - attrs.opacity = attrs.width = type; - } - - return attrs; -} - -function createTween( value, prop, animation ) { - var tween, - collection = ( Animation.tweeners[ prop ] || [] ).concat( Animation.tweeners[ "*" ] ), - index = 0, - length = collection.length; - for ( ; index < length; index++ ) { - if ( ( tween = collection[ index ].call( animation, prop, value ) ) ) { - - // We're done with this property - return tween; - } - } -} - -function defaultPrefilter( elem, props, opts ) { - var prop, value, toggle, hooks, oldfire, propTween, restoreDisplay, display, - isBox = "width" in props || "height" in props, - anim = this, - orig = {}, - style = elem.style, - hidden = elem.nodeType && isHiddenWithinTree( elem ), - dataShow = dataPriv.get( elem, "fxshow" ); - - // Queue-skipping animations hijack the fx hooks - if ( !opts.queue ) { - hooks = jQuery._queueHooks( elem, "fx" ); - if ( hooks.unqueued == null ) { - hooks.unqueued = 0; - oldfire = hooks.empty.fire; - hooks.empty.fire = function() { - if ( !hooks.unqueued ) { - oldfire(); - } - }; - } - hooks.unqueued++; - - anim.always( function() { - - // Ensure the complete handler is called before this completes - anim.always( function() { - hooks.unqueued--; - if ( !jQuery.queue( elem, "fx" ).length ) { - hooks.empty.fire(); - } - } ); - } ); - } - - // Detect show/hide animations - for ( prop in props ) { - value = props[ prop ]; - if ( rfxtypes.test( value ) ) { - delete props[ prop ]; - toggle = toggle || value === "toggle"; - if ( value === ( hidden ? "hide" : "show" ) ) { - - // Pretend to be hidden if this is a "show" and - // there is still data from a stopped show/hide - if ( value === "show" && dataShow && dataShow[ prop ] !== undefined ) { - hidden = true; - - // Ignore all other no-op show/hide data - } else { - continue; - } - } - orig[ prop ] = dataShow && dataShow[ prop ] || jQuery.style( elem, prop ); - } - } - - // Bail out if this is a no-op like .hide().hide() - propTween = !jQuery.isEmptyObject( props ); - if ( !propTween && jQuery.isEmptyObject( orig ) ) { - return; - } - - // Restrict "overflow" and "display" styles during box animations - if ( isBox && elem.nodeType === 1 ) { - - // Support: IE <=9 - 11, Edge 12 - 13 - // Record all 3 overflow attributes because IE does not infer the shorthand - // from identically-valued overflowX and overflowY - opts.overflow = [ style.overflow, style.overflowX, style.overflowY ]; - - // Identify a display type, preferring old show/hide data over the CSS cascade - restoreDisplay = dataShow && dataShow.display; - if ( restoreDisplay == null ) { - restoreDisplay = dataPriv.get( elem, "display" ); - } - display = jQuery.css( elem, "display" ); - if ( display === "none" ) { - if ( restoreDisplay ) { - display = restoreDisplay; - } else { - - // Get nonempty value(s) by temporarily forcing visibility - showHide( [ elem ], true ); - restoreDisplay = elem.style.display || restoreDisplay; - display = jQuery.css( elem, "display" ); - showHide( [ elem ] ); - } - } - - // Animate inline elements as inline-block - if ( display === "inline" || display === "inline-block" && restoreDisplay != null ) { - if ( jQuery.css( elem, "float" ) === "none" ) { - - // Restore the original display value at the end of pure show/hide animations - if ( !propTween ) { - anim.done( function() { - style.display = restoreDisplay; - } ); - if ( restoreDisplay == null ) { - display = style.display; - restoreDisplay = display === "none" ? "" : display; - } - } - style.display = "inline-block"; - } - } - } - - if ( opts.overflow ) { - style.overflow = "hidden"; - anim.always( function() { - style.overflow = opts.overflow[ 0 ]; - style.overflowX = opts.overflow[ 1 ]; - style.overflowY = opts.overflow[ 2 ]; - } ); - } - - // Implement show/hide animations - propTween = false; - for ( prop in orig ) { - - // General show/hide setup for this element animation - if ( !propTween ) { - if ( dataShow ) { - if ( "hidden" in dataShow ) { - hidden = dataShow.hidden; - } - } else { - dataShow = dataPriv.access( elem, "fxshow", { display: restoreDisplay } ); - } - - // Store hidden/visible for toggle so `.stop().toggle()` "reverses" - if ( toggle ) { - dataShow.hidden = !hidden; - } - - // Show elements before animating them - if ( hidden ) { - showHide( [ elem ], true ); - } - - /* eslint-disable no-loop-func */ - - anim.done( function() { - - /* eslint-enable no-loop-func */ - - // The final step of a "hide" animation is actually hiding the element - if ( !hidden ) { - showHide( [ elem ] ); - } - dataPriv.remove( elem, "fxshow" ); - for ( prop in orig ) { - jQuery.style( elem, prop, orig[ prop ] ); - } - } ); - } - - // Per-property setup - propTween = createTween( hidden ? dataShow[ prop ] : 0, prop, anim ); - if ( !( prop in dataShow ) ) { - dataShow[ prop ] = propTween.start; - if ( hidden ) { - propTween.end = propTween.start; - propTween.start = 0; - } - } - } -} - -function propFilter( props, specialEasing ) { - var index, name, easing, value, hooks; - - // camelCase, specialEasing and expand cssHook pass - for ( index in props ) { - name = jQuery.camelCase( index ); - easing = specialEasing[ name ]; - value = props[ index ]; - if ( Array.isArray( value ) ) { - easing = value[ 1 ]; - value = props[ index ] = value[ 0 ]; - } - - if ( index !== name ) { - props[ name ] = value; - delete props[ index ]; - } - - hooks = jQuery.cssHooks[ name ]; - if ( hooks && "expand" in hooks ) { - value = hooks.expand( value ); - delete props[ name ]; - - // Not quite $.extend, this won't overwrite existing keys. - // Reusing 'index' because we have the correct "name" - for ( index in value ) { - if ( !( index in props ) ) { - props[ index ] = value[ index ]; - specialEasing[ index ] = easing; - } - } - } else { - specialEasing[ name ] = easing; - } - } -} - -function Animation( elem, properties, options ) { - var result, - stopped, - index = 0, - length = Animation.prefilters.length, - deferred = jQuery.Deferred().always( function() { - - // Don't match elem in the :animated selector - delete tick.elem; - } ), - tick = function() { - if ( stopped ) { - return false; - } - var currentTime = fxNow || createFxNow(), - remaining = Math.max( 0, animation.startTime + animation.duration - currentTime ), - - // Support: Android 2.3 only - // Archaic crash bug won't allow us to use `1 - ( 0.5 || 0 )` (#12497) - temp = remaining / animation.duration || 0, - percent = 1 - temp, - index = 0, - length = animation.tweens.length; - - for ( ; index < length; index++ ) { - animation.tweens[ index ].run( percent ); - } - - deferred.notifyWith( elem, [ animation, percent, remaining ] ); - - // If there's more to do, yield - if ( percent < 1 && length ) { - return remaining; - } - - // If this was an empty animation, synthesize a final progress notification - if ( !length ) { - deferred.notifyWith( elem, [ animation, 1, 0 ] ); - } - - // Resolve the animation and report its conclusion - deferred.resolveWith( elem, [ animation ] ); - return false; - }, - animation = deferred.promise( { - elem: elem, - props: jQuery.extend( {}, properties ), - opts: jQuery.extend( true, { - specialEasing: {}, - easing: jQuery.easing._default - }, options ), - originalProperties: properties, - originalOptions: options, - startTime: fxNow || createFxNow(), - duration: options.duration, - tweens: [], - createTween: function( prop, end ) { - var tween = jQuery.Tween( elem, animation.opts, prop, end, - animation.opts.specialEasing[ prop ] || animation.opts.easing ); - animation.tweens.push( tween ); - return tween; - }, - stop: function( gotoEnd ) { - var index = 0, - - // If we are going to the end, we want to run all the tweens - // otherwise we skip this part - length = gotoEnd ? animation.tweens.length : 0; - if ( stopped ) { - return this; - } - stopped = true; - for ( ; index < length; index++ ) { - animation.tweens[ index ].run( 1 ); - } - - // Resolve when we played the last frame; otherwise, reject - if ( gotoEnd ) { - deferred.notifyWith( elem, [ animation, 1, 0 ] ); - deferred.resolveWith( elem, [ animation, gotoEnd ] ); - } else { - deferred.rejectWith( elem, [ animation, gotoEnd ] ); - } - return this; - } - } ), - props = animation.props; - - propFilter( props, animation.opts.specialEasing ); - - for ( ; index < length; index++ ) { - result = Animation.prefilters[ index ].call( animation, elem, props, animation.opts ); - if ( result ) { - if ( jQuery.isFunction( result.stop ) ) { - jQuery._queueHooks( animation.elem, animation.opts.queue ).stop = - jQuery.proxy( result.stop, result ); - } - return result; - } - } - - jQuery.map( props, createTween, animation ); - - if ( jQuery.isFunction( animation.opts.start ) ) { - animation.opts.start.call( elem, animation ); - } - - // Attach callbacks from options - animation - .progress( animation.opts.progress ) - .done( animation.opts.done, animation.opts.complete ) - .fail( animation.opts.fail ) - .always( animation.opts.always ); - - jQuery.fx.timer( - jQuery.extend( tick, { - elem: elem, - anim: animation, - queue: animation.opts.queue - } ) - ); - - return animation; -} - -jQuery.Animation = jQuery.extend( Animation, { - - tweeners: { - "*": [ function( prop, value ) { - var tween = this.createTween( prop, value ); - adjustCSS( tween.elem, prop, rcssNum.exec( value ), tween ); - return tween; - } ] - }, - - tweener: function( props, callback ) { - if ( jQuery.isFunction( props ) ) { - callback = props; - props = [ "*" ]; - } else { - props = props.match( rnothtmlwhite ); - } - - var prop, - index = 0, - length = props.length; - - for ( ; index < length; index++ ) { - prop = props[ index ]; - Animation.tweeners[ prop ] = Animation.tweeners[ prop ] || []; - Animation.tweeners[ prop ].unshift( callback ); - } - }, - - prefilters: [ defaultPrefilter ], - - prefilter: function( callback, prepend ) { - if ( prepend ) { - Animation.prefilters.unshift( callback ); - } else { - Animation.prefilters.push( callback ); - } - } -} ); - -jQuery.speed = function( speed, easing, fn ) { - var opt = speed && typeof speed === "object" ? jQuery.extend( {}, speed ) : { - complete: fn || !fn && easing || - jQuery.isFunction( speed ) && speed, - duration: speed, - easing: fn && easing || easing && !jQuery.isFunction( easing ) && easing - }; - - // Go to the end state if fx are off - if ( jQuery.fx.off ) { - opt.duration = 0; - - } else { - if ( typeof opt.duration !== "number" ) { - if ( opt.duration in jQuery.fx.speeds ) { - opt.duration = jQuery.fx.speeds[ opt.duration ]; - - } else { - opt.duration = jQuery.fx.speeds._default; - } - } - } - - // Normalize opt.queue - true/undefined/null -> "fx" - if ( opt.queue == null || opt.queue === true ) { - opt.queue = "fx"; - } - - // Queueing - opt.old = opt.complete; - - opt.complete = function() { - if ( jQuery.isFunction( opt.old ) ) { - opt.old.call( this ); - } - - if ( opt.queue ) { - jQuery.dequeue( this, opt.queue ); - } - }; - - return opt; -}; - -jQuery.fn.extend( { - fadeTo: function( speed, to, easing, callback ) { - - // Show any hidden elements after setting opacity to 0 - return this.filter( isHiddenWithinTree ).css( "opacity", 0 ).show() - - // Animate to the value specified - .end().animate( { opacity: to }, speed, easing, callback ); - }, - animate: function( prop, speed, easing, callback ) { - var empty = jQuery.isEmptyObject( prop ), - optall = jQuery.speed( speed, easing, callback ), - doAnimation = function() { - - // Operate on a copy of prop so per-property easing won't be lost - var anim = Animation( this, jQuery.extend( {}, prop ), optall ); - - // Empty animations, or finishing resolves immediately - if ( empty || dataPriv.get( this, "finish" ) ) { - anim.stop( true ); - } - }; - doAnimation.finish = doAnimation; - - return empty || optall.queue === false ? - this.each( doAnimation ) : - this.queue( optall.queue, doAnimation ); - }, - stop: function( type, clearQueue, gotoEnd ) { - var stopQueue = function( hooks ) { - var stop = hooks.stop; - delete hooks.stop; - stop( gotoEnd ); - }; - - if ( typeof type !== "string" ) { - gotoEnd = clearQueue; - clearQueue = type; - type = undefined; - } - if ( clearQueue && type !== false ) { - this.queue( type || "fx", [] ); - } - - return this.each( function() { - var dequeue = true, - index = type != null && type + "queueHooks", - timers = jQuery.timers, - data = dataPriv.get( this ); - - if ( index ) { - if ( data[ index ] && data[ index ].stop ) { - stopQueue( data[ index ] ); - } - } else { - for ( index in data ) { - if ( data[ index ] && data[ index ].stop && rrun.test( index ) ) { - stopQueue( data[ index ] ); - } - } - } - - for ( index = timers.length; index--; ) { - if ( timers[ index ].elem === this && - ( type == null || timers[ index ].queue === type ) ) { - - timers[ index ].anim.stop( gotoEnd ); - dequeue = false; - timers.splice( index, 1 ); - } - } - - // Start the next in the queue if the last step wasn't forced. - // Timers currently will call their complete callbacks, which - // will dequeue but only if they were gotoEnd. - if ( dequeue || !gotoEnd ) { - jQuery.dequeue( this, type ); - } - } ); - }, - finish: function( type ) { - if ( type !== false ) { - type = type || "fx"; - } - return this.each( function() { - var index, - data = dataPriv.get( this ), - queue = data[ type + "queue" ], - hooks = data[ type + "queueHooks" ], - timers = jQuery.timers, - length = queue ? queue.length : 0; - - // Enable finishing flag on private data - data.finish = true; - - // Empty the queue first - jQuery.queue( this, type, [] ); - - if ( hooks && hooks.stop ) { - hooks.stop.call( this, true ); - } - - // Look for any active animations, and finish them - for ( index = timers.length; index--; ) { - if ( timers[ index ].elem === this && timers[ index ].queue === type ) { - timers[ index ].anim.stop( true ); - timers.splice( index, 1 ); - } - } - - // Look for any animations in the old queue and finish them - for ( index = 0; index < length; index++ ) { - if ( queue[ index ] && queue[ index ].finish ) { - queue[ index ].finish.call( this ); - } - } - - // Turn off finishing flag - delete data.finish; - } ); - } -} ); - -jQuery.each( [ "toggle", "show", "hide" ], function( i, name ) { - var cssFn = jQuery.fn[ name ]; - jQuery.fn[ name ] = function( speed, easing, callback ) { - return speed == null || typeof speed === "boolean" ? - cssFn.apply( this, arguments ) : - this.animate( genFx( name, true ), speed, easing, callback ); - }; -} ); - -// Generate shortcuts for custom animations -jQuery.each( { - slideDown: genFx( "show" ), - slideUp: genFx( "hide" ), - slideToggle: genFx( "toggle" ), - fadeIn: { opacity: "show" }, - fadeOut: { opacity: "hide" }, - fadeToggle: { opacity: "toggle" } -}, function( name, props ) { - jQuery.fn[ name ] = function( speed, easing, callback ) { - return this.animate( props, speed, easing, callback ); - }; -} ); - -jQuery.timers = []; -jQuery.fx.tick = function() { - var timer, - i = 0, - timers = jQuery.timers; - - fxNow = jQuery.now(); - - for ( ; i < timers.length; i++ ) { - timer = timers[ i ]; - - // Run the timer and safely remove it when done (allowing for external removal) - if ( !timer() && timers[ i ] === timer ) { - timers.splice( i--, 1 ); - } - } - - if ( !timers.length ) { - jQuery.fx.stop(); - } - fxNow = undefined; -}; - -jQuery.fx.timer = function( timer ) { - jQuery.timers.push( timer ); - jQuery.fx.start(); -}; - -jQuery.fx.interval = 13; -jQuery.fx.start = function() { - if ( inProgress ) { - return; - } - - inProgress = true; - schedule(); -}; - -jQuery.fx.stop = function() { - inProgress = null; -}; - -jQuery.fx.speeds = { - slow: 600, - fast: 200, - - // Default speed - _default: 400 -}; - - -// Based off of the plugin by Clint Helfers, with permission. -// https://web.archive.org/web/20100324014747/http://blindsignals.com/index.php/2009/07/jquery-delay/ -jQuery.fn.delay = function( time, type ) { - time = jQuery.fx ? jQuery.fx.speeds[ time ] || time : time; - type = type || "fx"; - - return this.queue( type, function( next, hooks ) { - var timeout = window.setTimeout( next, time ); - hooks.stop = function() { - window.clearTimeout( timeout ); - }; - } ); -}; - - -( function() { - var input = document.createElement( "input" ), - select = document.createElement( "select" ), - opt = select.appendChild( document.createElement( "option" ) ); - - input.type = "checkbox"; - - // Support: Android <=4.3 only - // Default value for a checkbox should be "on" - support.checkOn = input.value !== ""; - - // Support: IE <=11 only - // Must access selectedIndex to make default options select - support.optSelected = opt.selected; - - // Support: IE <=11 only - // An input loses its value after becoming a radio - input = document.createElement( "input" ); - input.value = "t"; - input.type = "radio"; - support.radioValue = input.value === "t"; -} )(); - - -var boolHook, - attrHandle = jQuery.expr.attrHandle; - -jQuery.fn.extend( { - attr: function( name, value ) { - return access( this, jQuery.attr, name, value, arguments.length > 1 ); - }, - - removeAttr: function( name ) { - return this.each( function() { - jQuery.removeAttr( this, name ); - } ); - } -} ); - -jQuery.extend( { - attr: function( elem, name, value ) { - var ret, hooks, - nType = elem.nodeType; - - // Don't get/set attributes on text, comment and attribute nodes - if ( nType === 3 || nType === 8 || nType === 2 ) { - return; - } - - // Fallback to prop when attributes are not supported - if ( typeof elem.getAttribute === "undefined" ) { - return jQuery.prop( elem, name, value ); - } - - // Attribute hooks are determined by the lowercase version - // Grab necessary hook if one is defined - if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) { - hooks = jQuery.attrHooks[ name.toLowerCase() ] || - ( jQuery.expr.match.bool.test( name ) ? boolHook : undefined ); - } - - if ( value !== undefined ) { - if ( value === null ) { - jQuery.removeAttr( elem, name ); - return; - } - - if ( hooks && "set" in hooks && - ( ret = hooks.set( elem, value, name ) ) !== undefined ) { - return ret; - } - - elem.setAttribute( name, value + "" ); - return value; - } - - if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) { - return ret; - } - - ret = jQuery.find.attr( elem, name ); - - // Non-existent attributes return null, we normalize to undefined - return ret == null ? undefined : ret; - }, - - attrHooks: { - type: { - set: function( elem, value ) { - if ( !support.radioValue && value === "radio" && - nodeName( elem, "input" ) ) { - var val = elem.value; - elem.setAttribute( "type", value ); - if ( val ) { - elem.value = val; - } - return value; - } - } - } - }, - - removeAttr: function( elem, value ) { - var name, - i = 0, - - // Attribute names can contain non-HTML whitespace characters - // https://html.spec.whatwg.org/multipage/syntax.html#attributes-2 - attrNames = value && value.match( rnothtmlwhite ); - - if ( attrNames && elem.nodeType === 1 ) { - while ( ( name = attrNames[ i++ ] ) ) { - elem.removeAttribute( name ); - } - } - } -} ); - -// Hooks for boolean attributes -boolHook = { - set: function( elem, value, name ) { - if ( value === false ) { - - // Remove boolean attributes when set to false - jQuery.removeAttr( elem, name ); - } else { - elem.setAttribute( name, name ); - } - return name; - } -}; - -jQuery.each( jQuery.expr.match.bool.source.match( /\w+/g ), function( i, name ) { - var getter = attrHandle[ name ] || jQuery.find.attr; - - attrHandle[ name ] = function( elem, name, isXML ) { - var ret, handle, - lowercaseName = name.toLowerCase(); - - if ( !isXML ) { - - // Avoid an infinite loop by temporarily removing this function from the getter - handle = attrHandle[ lowercaseName ]; - attrHandle[ lowercaseName ] = ret; - ret = getter( elem, name, isXML ) != null ? - lowercaseName : - null; - attrHandle[ lowercaseName ] = handle; - } - return ret; - }; -} ); - - - - -var rfocusable = /^(?:input|select|textarea|button)$/i, - rclickable = /^(?:a|area)$/i; - -jQuery.fn.extend( { - prop: function( name, value ) { - return access( this, jQuery.prop, name, value, arguments.length > 1 ); - }, - - removeProp: function( name ) { - return this.each( function() { - delete this[ jQuery.propFix[ name ] || name ]; - } ); - } -} ); - -jQuery.extend( { - prop: function( elem, name, value ) { - var ret, hooks, - nType = elem.nodeType; - - // Don't get/set properties on text, comment and attribute nodes - if ( nType === 3 || nType === 8 || nType === 2 ) { - return; - } - - if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) { - - // Fix name and attach hooks - name = jQuery.propFix[ name ] || name; - hooks = jQuery.propHooks[ name ]; - } - - if ( value !== undefined ) { - if ( hooks && "set" in hooks && - ( ret = hooks.set( elem, value, name ) ) !== undefined ) { - return ret; - } - - return ( elem[ name ] = value ); - } - - if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) { - return ret; - } - - return elem[ name ]; - }, - - propHooks: { - tabIndex: { - get: function( elem ) { - - // Support: IE <=9 - 11 only - // elem.tabIndex doesn't always return the - // correct value when it hasn't been explicitly set - // https://web.archive.org/web/20141116233347/http://fluidproject.org/blog/2008/01/09/getting-setting-and-removing-tabindex-values-with-javascript/ - // Use proper attribute retrieval(#12072) - var tabindex = jQuery.find.attr( elem, "tabindex" ); - - if ( tabindex ) { - return parseInt( tabindex, 10 ); - } - - if ( - rfocusable.test( elem.nodeName ) || - rclickable.test( elem.nodeName ) && - elem.href - ) { - return 0; - } - - return -1; - } - } - }, - - propFix: { - "for": "htmlFor", - "class": "className" - } -} ); - -// Support: IE <=11 only -// Accessing the selectedIndex property -// forces the browser to respect setting selected -// on the option -// The getter ensures a default option is selected -// when in an optgroup -// eslint rule "no-unused-expressions" is disabled for this code -// since it considers such accessions noop -if ( !support.optSelected ) { - jQuery.propHooks.selected = { - get: function( elem ) { - - /* eslint no-unused-expressions: "off" */ - - var parent = elem.parentNode; - if ( parent && parent.parentNode ) { - parent.parentNode.selectedIndex; - } - return null; - }, - set: function( elem ) { - - /* eslint no-unused-expressions: "off" */ - - var parent = elem.parentNode; - if ( parent ) { - parent.selectedIndex; - - if ( parent.parentNode ) { - parent.parentNode.selectedIndex; - } - } - } - }; -} - -jQuery.each( [ - "tabIndex", - "readOnly", - "maxLength", - "cellSpacing", - "cellPadding", - "rowSpan", - "colSpan", - "useMap", - "frameBorder", - "contentEditable" -], function() { - jQuery.propFix[ this.toLowerCase() ] = this; -} ); - - - - - // Strip and collapse whitespace according to HTML spec - // https://html.spec.whatwg.org/multipage/infrastructure.html#strip-and-collapse-whitespace - function stripAndCollapse( value ) { - var tokens = value.match( rnothtmlwhite ) || []; - return tokens.join( " " ); - } - - -function getClass( elem ) { - return elem.getAttribute && elem.getAttribute( "class" ) || ""; -} - -jQuery.fn.extend( { - addClass: function( value ) { - var classes, elem, cur, curValue, clazz, j, finalValue, - i = 0; - - if ( jQuery.isFunction( value ) ) { - return this.each( function( j ) { - jQuery( this ).addClass( value.call( this, j, getClass( this ) ) ); - } ); - } - - if ( typeof value === "string" && value ) { - classes = value.match( rnothtmlwhite ) || []; - - while ( ( elem = this[ i++ ] ) ) { - curValue = getClass( elem ); - cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " ); - - if ( cur ) { - j = 0; - while ( ( clazz = classes[ j++ ] ) ) { - if ( cur.indexOf( " " + clazz + " " ) < 0 ) { - cur += clazz + " "; - } - } - - // Only assign if different to avoid unneeded rendering. - finalValue = stripAndCollapse( cur ); - if ( curValue !== finalValue ) { - elem.setAttribute( "class", finalValue ); - } - } - } - } - - return this; - }, - - removeClass: function( value ) { - var classes, elem, cur, curValue, clazz, j, finalValue, - i = 0; - - if ( jQuery.isFunction( value ) ) { - return this.each( function( j ) { - jQuery( this ).removeClass( value.call( this, j, getClass( this ) ) ); - } ); - } - - if ( !arguments.length ) { - return this.attr( "class", "" ); - } - - if ( typeof value === "string" && value ) { - classes = value.match( rnothtmlwhite ) || []; - - while ( ( elem = this[ i++ ] ) ) { - curValue = getClass( elem ); - - // This expression is here for better compressibility (see addClass) - cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " ); - - if ( cur ) { - j = 0; - while ( ( clazz = classes[ j++ ] ) ) { - - // Remove *all* instances - while ( cur.indexOf( " " + clazz + " " ) > -1 ) { - cur = cur.replace( " " + clazz + " ", " " ); - } - } - - // Only assign if different to avoid unneeded rendering. - finalValue = stripAndCollapse( cur ); - if ( curValue !== finalValue ) { - elem.setAttribute( "class", finalValue ); - } - } - } - } - - return this; - }, - - toggleClass: function( value, stateVal ) { - var type = typeof value; - - if ( typeof stateVal === "boolean" && type === "string" ) { - return stateVal ? this.addClass( value ) : this.removeClass( value ); - } - - if ( jQuery.isFunction( value ) ) { - return this.each( function( i ) { - jQuery( this ).toggleClass( - value.call( this, i, getClass( this ), stateVal ), - stateVal - ); - } ); - } - - return this.each( function() { - var className, i, self, classNames; - - if ( type === "string" ) { - - // Toggle individual class names - i = 0; - self = jQuery( this ); - classNames = value.match( rnothtmlwhite ) || []; - - while ( ( className = classNames[ i++ ] ) ) { - - // Check each className given, space separated list - if ( self.hasClass( className ) ) { - self.removeClass( className ); - } else { - self.addClass( className ); - } - } - - // Toggle whole class name - } else if ( value === undefined || type === "boolean" ) { - className = getClass( this ); - if ( className ) { - - // Store className if set - dataPriv.set( this, "__className__", className ); - } - - // If the element has a class name or if we're passed `false`, - // then remove the whole classname (if there was one, the above saved it). - // Otherwise bring back whatever was previously saved (if anything), - // falling back to the empty string if nothing was stored. - if ( this.setAttribute ) { - this.setAttribute( "class", - className || value === false ? - "" : - dataPriv.get( this, "__className__" ) || "" - ); - } - } - } ); - }, - - hasClass: function( selector ) { - var className, elem, - i = 0; - - className = " " + selector + " "; - while ( ( elem = this[ i++ ] ) ) { - if ( elem.nodeType === 1 && - ( " " + stripAndCollapse( getClass( elem ) ) + " " ).indexOf( className ) > -1 ) { - return true; - } - } - - return false; - } -} ); - - - - -var rreturn = /\r/g; - -jQuery.fn.extend( { - val: function( value ) { - var hooks, ret, isFunction, - elem = this[ 0 ]; - - if ( !arguments.length ) { - if ( elem ) { - hooks = jQuery.valHooks[ elem.type ] || - jQuery.valHooks[ elem.nodeName.toLowerCase() ]; - - if ( hooks && - "get" in hooks && - ( ret = hooks.get( elem, "value" ) ) !== undefined - ) { - return ret; - } - - ret = elem.value; - - // Handle most common string cases - if ( typeof ret === "string" ) { - return ret.replace( rreturn, "" ); - } - - // Handle cases where value is null/undef or number - return ret == null ? "" : ret; - } - - return; - } - - isFunction = jQuery.isFunction( value ); - - return this.each( function( i ) { - var val; - - if ( this.nodeType !== 1 ) { - return; - } - - if ( isFunction ) { - val = value.call( this, i, jQuery( this ).val() ); - } else { - val = value; - } - - // Treat null/undefined as ""; convert numbers to string - if ( val == null ) { - val = ""; - - } else if ( typeof val === "number" ) { - val += ""; - - } else if ( Array.isArray( val ) ) { - val = jQuery.map( val, function( value ) { - return value == null ? "" : value + ""; - } ); - } - - hooks = jQuery.valHooks[ this.type ] || jQuery.valHooks[ this.nodeName.toLowerCase() ]; - - // If set returns undefined, fall back to normal setting - if ( !hooks || !( "set" in hooks ) || hooks.set( this, val, "value" ) === undefined ) { - this.value = val; - } - } ); - } -} ); - -jQuery.extend( { - valHooks: { - option: { - get: function( elem ) { - - var val = jQuery.find.attr( elem, "value" ); - return val != null ? - val : - - // Support: IE <=10 - 11 only - // option.text throws exceptions (#14686, #14858) - // Strip and collapse whitespace - // https://html.spec.whatwg.org/#strip-and-collapse-whitespace - stripAndCollapse( jQuery.text( elem ) ); - } - }, - select: { - get: function( elem ) { - var value, option, i, - options = elem.options, - index = elem.selectedIndex, - one = elem.type === "select-one", - values = one ? null : [], - max = one ? index + 1 : options.length; - - if ( index < 0 ) { - i = max; - - } else { - i = one ? index : 0; - } - - // Loop through all the selected options - for ( ; i < max; i++ ) { - option = options[ i ]; - - // Support: IE <=9 only - // IE8-9 doesn't update selected after form reset (#2551) - if ( ( option.selected || i === index ) && - - // Don't return options that are disabled or in a disabled optgroup - !option.disabled && - ( !option.parentNode.disabled || - !nodeName( option.parentNode, "optgroup" ) ) ) { - - // Get the specific value for the option - value = jQuery( option ).val(); - - // We don't need an array for one selects - if ( one ) { - return value; - } - - // Multi-Selects return an array - values.push( value ); - } - } - - return values; - }, - - set: function( elem, value ) { - var optionSet, option, - options = elem.options, - values = jQuery.makeArray( value ), - i = options.length; - - while ( i-- ) { - option = options[ i ]; - - /* eslint-disable no-cond-assign */ - - if ( option.selected = - jQuery.inArray( jQuery.valHooks.option.get( option ), values ) > -1 - ) { - optionSet = true; - } - - /* eslint-enable no-cond-assign */ - } - - // Force browsers to behave consistently when non-matching value is set - if ( !optionSet ) { - elem.selectedIndex = -1; - } - return values; - } - } - } -} ); - -// Radios and checkboxes getter/setter -jQuery.each( [ "radio", "checkbox" ], function() { - jQuery.valHooks[ this ] = { - set: function( elem, value ) { - if ( Array.isArray( value ) ) { - return ( elem.checked = jQuery.inArray( jQuery( elem ).val(), value ) > -1 ); - } - } - }; - if ( !support.checkOn ) { - jQuery.valHooks[ this ].get = function( elem ) { - return elem.getAttribute( "value" ) === null ? "on" : elem.value; - }; - } -} ); - - - - -// Return jQuery for attributes-only inclusion - - -var rfocusMorph = /^(?:focusinfocus|focusoutblur)$/; - -jQuery.extend( jQuery.event, { - - trigger: function( event, data, elem, onlyHandlers ) { - - var i, cur, tmp, bubbleType, ontype, handle, special, - eventPath = [ elem || document ], - type = hasOwn.call( event, "type" ) ? event.type : event, - namespaces = hasOwn.call( event, "namespace" ) ? event.namespace.split( "." ) : []; - - cur = tmp = elem = elem || document; - - // Don't do events on text and comment nodes - if ( elem.nodeType === 3 || elem.nodeType === 8 ) { - return; - } - - // focus/blur morphs to focusin/out; ensure we're not firing them right now - if ( rfocusMorph.test( type + jQuery.event.triggered ) ) { - return; - } - - if ( type.indexOf( "." ) > -1 ) { - - // Namespaced trigger; create a regexp to match event type in handle() - namespaces = type.split( "." ); - type = namespaces.shift(); - namespaces.sort(); - } - ontype = type.indexOf( ":" ) < 0 && "on" + type; - - // Caller can pass in a jQuery.Event object, Object, or just an event type string - event = event[ jQuery.expando ] ? - event : - new jQuery.Event( type, typeof event === "object" && event ); - - // Trigger bitmask: & 1 for native handlers; & 2 for jQuery (always true) - event.isTrigger = onlyHandlers ? 2 : 3; - event.namespace = namespaces.join( "." ); - event.rnamespace = event.namespace ? - new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" ) : - null; - - // Clean up the event in case it is being reused - event.result = undefined; - if ( !event.target ) { - event.target = elem; - } - - // Clone any incoming data and prepend the event, creating the handler arg list - data = data == null ? - [ event ] : - jQuery.makeArray( data, [ event ] ); - - // Allow special events to draw outside the lines - special = jQuery.event.special[ type ] || {}; - if ( !onlyHandlers && special.trigger && special.trigger.apply( elem, data ) === false ) { - return; - } - - // Determine event propagation path in advance, per W3C events spec (#9951) - // Bubble up to document, then to window; watch for a global ownerDocument var (#9724) - if ( !onlyHandlers && !special.noBubble && !jQuery.isWindow( elem ) ) { - - bubbleType = special.delegateType || type; - if ( !rfocusMorph.test( bubbleType + type ) ) { - cur = cur.parentNode; - } - for ( ; cur; cur = cur.parentNode ) { - eventPath.push( cur ); - tmp = cur; - } - - // Only add window if we got to document (e.g., not plain obj or detached DOM) - if ( tmp === ( elem.ownerDocument || document ) ) { - eventPath.push( tmp.defaultView || tmp.parentWindow || window ); - } - } - - // Fire handlers on the event path - i = 0; - while ( ( cur = eventPath[ i++ ] ) && !event.isPropagationStopped() ) { - - event.type = i > 1 ? - bubbleType : - special.bindType || type; - - // jQuery handler - handle = ( dataPriv.get( cur, "events" ) || {} )[ event.type ] && - dataPriv.get( cur, "handle" ); - if ( handle ) { - handle.apply( cur, data ); - } - - // Native handler - handle = ontype && cur[ ontype ]; - if ( handle && handle.apply && acceptData( cur ) ) { - event.result = handle.apply( cur, data ); - if ( event.result === false ) { - event.preventDefault(); - } - } - } - event.type = type; - - // If nobody prevented the default action, do it now - if ( !onlyHandlers && !event.isDefaultPrevented() ) { - - if ( ( !special._default || - special._default.apply( eventPath.pop(), data ) === false ) && - acceptData( elem ) ) { - - // Call a native DOM method on the target with the same name as the event. - // Don't do default actions on window, that's where global variables be (#6170) - if ( ontype && jQuery.isFunction( elem[ type ] ) && !jQuery.isWindow( elem ) ) { - - // Don't re-trigger an onFOO event when we call its FOO() method - tmp = elem[ ontype ]; - - if ( tmp ) { - elem[ ontype ] = null; - } - - // Prevent re-triggering of the same event, since we already bubbled it above - jQuery.event.triggered = type; - elem[ type ](); - jQuery.event.triggered = undefined; - - if ( tmp ) { - elem[ ontype ] = tmp; - } - } - } - } - - return event.result; - }, - - // Piggyback on a donor event to simulate a different one - // Used only for `focus(in | out)` events - simulate: function( type, elem, event ) { - var e = jQuery.extend( - new jQuery.Event(), - event, - { - type: type, - isSimulated: true - } - ); - - jQuery.event.trigger( e, null, elem ); - } - -} ); - -jQuery.fn.extend( { - - trigger: function( type, data ) { - return this.each( function() { - jQuery.event.trigger( type, data, this ); - } ); - }, - triggerHandler: function( type, data ) { - var elem = this[ 0 ]; - if ( elem ) { - return jQuery.event.trigger( type, data, elem, true ); - } - } -} ); - - -jQuery.each( ( "blur focus focusin focusout resize scroll click dblclick " + - "mousedown mouseup mousemove mouseover mouseout mouseenter mouseleave " + - "change select submit keydown keypress keyup contextmenu" ).split( " " ), - function( i, name ) { - - // Handle event binding - jQuery.fn[ name ] = function( data, fn ) { - return arguments.length > 0 ? - this.on( name, null, data, fn ) : - this.trigger( name ); - }; -} ); - -jQuery.fn.extend( { - hover: function( fnOver, fnOut ) { - return this.mouseenter( fnOver ).mouseleave( fnOut || fnOver ); - } -} ); - - - - -support.focusin = "onfocusin" in window; - - -// Support: Firefox <=44 -// Firefox doesn't have focus(in | out) events -// Related ticket - https://bugzilla.mozilla.org/show_bug.cgi?id=687787 -// -// Support: Chrome <=48 - 49, Safari <=9.0 - 9.1 -// focus(in | out) events fire after focus & blur events, -// which is spec violation - http://www.w3.org/TR/DOM-Level-3-Events/#events-focusevent-event-order -// Related ticket - https://bugs.chromium.org/p/chromium/issues/detail?id=449857 -if ( !support.focusin ) { - jQuery.each( { focus: "focusin", blur: "focusout" }, function( orig, fix ) { - - // Attach a single capturing handler on the document while someone wants focusin/focusout - var handler = function( event ) { - jQuery.event.simulate( fix, event.target, jQuery.event.fix( event ) ); - }; - - jQuery.event.special[ fix ] = { - setup: function() { - var doc = this.ownerDocument || this, - attaches = dataPriv.access( doc, fix ); - - if ( !attaches ) { - doc.addEventListener( orig, handler, true ); - } - dataPriv.access( doc, fix, ( attaches || 0 ) + 1 ); - }, - teardown: function() { - var doc = this.ownerDocument || this, - attaches = dataPriv.access( doc, fix ) - 1; - - if ( !attaches ) { - doc.removeEventListener( orig, handler, true ); - dataPriv.remove( doc, fix ); - - } else { - dataPriv.access( doc, fix, attaches ); - } - } - }; - } ); -} -var location = window.location; - -var nonce = jQuery.now(); - -var rquery = ( /\?/ ); - - - -// Cross-browser xml parsing -jQuery.parseXML = function( data ) { - var xml; - if ( !data || typeof data !== "string" ) { - return null; - } - - // Support: IE 9 - 11 only - // IE throws on parseFromString with invalid input. - try { - xml = ( new window.DOMParser() ).parseFromString( data, "text/xml" ); - } catch ( e ) { - xml = undefined; - } - - if ( !xml || xml.getElementsByTagName( "parsererror" ).length ) { - jQuery.error( "Invalid XML: " + data ); - } - return xml; -}; - - -var - rbracket = /\[\]$/, - rCRLF = /\r?\n/g, - rsubmitterTypes = /^(?:submit|button|image|reset|file)$/i, - rsubmittable = /^(?:input|select|textarea|keygen)/i; - -function buildParams( prefix, obj, traditional, add ) { - var name; - - if ( Array.isArray( obj ) ) { - - // Serialize array item. - jQuery.each( obj, function( i, v ) { - if ( traditional || rbracket.test( prefix ) ) { - - // Treat each array item as a scalar. - add( prefix, v ); - - } else { - - // Item is non-scalar (array or object), encode its numeric index. - buildParams( - prefix + "[" + ( typeof v === "object" && v != null ? i : "" ) + "]", - v, - traditional, - add - ); - } - } ); - - } else if ( !traditional && jQuery.type( obj ) === "object" ) { - - // Serialize object item. - for ( name in obj ) { - buildParams( prefix + "[" + name + "]", obj[ name ], traditional, add ); - } - - } else { - - // Serialize scalar item. - add( prefix, obj ); - } -} - -// Serialize an array of form elements or a set of -// key/values into a query string -jQuery.param = function( a, traditional ) { - var prefix, - s = [], - add = function( key, valueOrFunction ) { - - // If value is a function, invoke it and use its return value - var value = jQuery.isFunction( valueOrFunction ) ? - valueOrFunction() : - valueOrFunction; - - s[ s.length ] = encodeURIComponent( key ) + "=" + - encodeURIComponent( value == null ? "" : value ); - }; - - // If an array was passed in, assume that it is an array of form elements. - if ( Array.isArray( a ) || ( a.jquery && !jQuery.isPlainObject( a ) ) ) { - - // Serialize the form elements - jQuery.each( a, function() { - add( this.name, this.value ); - } ); - - } else { - - // If traditional, encode the "old" way (the way 1.3.2 or older - // did it), otherwise encode params recursively. - for ( prefix in a ) { - buildParams( prefix, a[ prefix ], traditional, add ); - } - } - - // Return the resulting serialization - return s.join( "&" ); -}; - -jQuery.fn.extend( { - serialize: function() { - return jQuery.param( this.serializeArray() ); - }, - serializeArray: function() { - return this.map( function() { - - // Can add propHook for "elements" to filter or add form elements - var elements = jQuery.prop( this, "elements" ); - return elements ? jQuery.makeArray( elements ) : this; - } ) - .filter( function() { - var type = this.type; - - // Use .is( ":disabled" ) so that fieldset[disabled] works - return this.name && !jQuery( this ).is( ":disabled" ) && - rsubmittable.test( this.nodeName ) && !rsubmitterTypes.test( type ) && - ( this.checked || !rcheckableType.test( type ) ); - } ) - .map( function( i, elem ) { - var val = jQuery( this ).val(); - - if ( val == null ) { - return null; - } - - if ( Array.isArray( val ) ) { - return jQuery.map( val, function( val ) { - return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) }; - } ); - } - - return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) }; - } ).get(); - } -} ); - - -var - r20 = /%20/g, - rhash = /#.*$/, - rantiCache = /([?&])_=[^&]*/, - rheaders = /^(.*?):[ \t]*([^\r\n]*)$/mg, - - // #7653, #8125, #8152: local protocol detection - rlocalProtocol = /^(?:about|app|app-storage|.+-extension|file|res|widget):$/, - rnoContent = /^(?:GET|HEAD)$/, - rprotocol = /^\/\//, - - /* Prefilters - * 1) They are useful to introduce custom dataTypes (see ajax/jsonp.js for an example) - * 2) These are called: - * - BEFORE asking for a transport - * - AFTER param serialization (s.data is a string if s.processData is true) - * 3) key is the dataType - * 4) the catchall symbol "*" can be used - * 5) execution will start with transport dataType and THEN continue down to "*" if needed - */ - prefilters = {}, - - /* Transports bindings - * 1) key is the dataType - * 2) the catchall symbol "*" can be used - * 3) selection will start with transport dataType and THEN go to "*" if needed - */ - transports = {}, - - // Avoid comment-prolog char sequence (#10098); must appease lint and evade compression - allTypes = "*/".concat( "*" ), - - // Anchor tag for parsing the document origin - originAnchor = document.createElement( "a" ); - originAnchor.href = location.href; - -// Base "constructor" for jQuery.ajaxPrefilter and jQuery.ajaxTransport -function addToPrefiltersOrTransports( structure ) { - - // dataTypeExpression is optional and defaults to "*" - return function( dataTypeExpression, func ) { - - if ( typeof dataTypeExpression !== "string" ) { - func = dataTypeExpression; - dataTypeExpression = "*"; - } - - var dataType, - i = 0, - dataTypes = dataTypeExpression.toLowerCase().match( rnothtmlwhite ) || []; - - if ( jQuery.isFunction( func ) ) { - - // For each dataType in the dataTypeExpression - while ( ( dataType = dataTypes[ i++ ] ) ) { - - // Prepend if requested - if ( dataType[ 0 ] === "+" ) { - dataType = dataType.slice( 1 ) || "*"; - ( structure[ dataType ] = structure[ dataType ] || [] ).unshift( func ); - - // Otherwise append - } else { - ( structure[ dataType ] = structure[ dataType ] || [] ).push( func ); - } - } - } - }; -} - -// Base inspection function for prefilters and transports -function inspectPrefiltersOrTransports( structure, options, originalOptions, jqXHR ) { - - var inspected = {}, - seekingTransport = ( structure === transports ); - - function inspect( dataType ) { - var selected; - inspected[ dataType ] = true; - jQuery.each( structure[ dataType ] || [], function( _, prefilterOrFactory ) { - var dataTypeOrTransport = prefilterOrFactory( options, originalOptions, jqXHR ); - if ( typeof dataTypeOrTransport === "string" && - !seekingTransport && !inspected[ dataTypeOrTransport ] ) { - - options.dataTypes.unshift( dataTypeOrTransport ); - inspect( dataTypeOrTransport ); - return false; - } else if ( seekingTransport ) { - return !( selected = dataTypeOrTransport ); - } - } ); - return selected; - } - - return inspect( options.dataTypes[ 0 ] ) || !inspected[ "*" ] && inspect( "*" ); -} - -// A special extend for ajax options -// that takes "flat" options (not to be deep extended) -// Fixes #9887 -function ajaxExtend( target, src ) { - var key, deep, - flatOptions = jQuery.ajaxSettings.flatOptions || {}; - - for ( key in src ) { - if ( src[ key ] !== undefined ) { - ( flatOptions[ key ] ? target : ( deep || ( deep = {} ) ) )[ key ] = src[ key ]; - } - } - if ( deep ) { - jQuery.extend( true, target, deep ); - } - - return target; -} - -/* Handles responses to an ajax request: - * - finds the right dataType (mediates between content-type and expected dataType) - * - returns the corresponding response - */ -function ajaxHandleResponses( s, jqXHR, responses ) { - - var ct, type, finalDataType, firstDataType, - contents = s.contents, - dataTypes = s.dataTypes; - - // Remove auto dataType and get content-type in the process - while ( dataTypes[ 0 ] === "*" ) { - dataTypes.shift(); - if ( ct === undefined ) { - ct = s.mimeType || jqXHR.getResponseHeader( "Content-Type" ); - } - } - - // Check if we're dealing with a known content-type - if ( ct ) { - for ( type in contents ) { - if ( contents[ type ] && contents[ type ].test( ct ) ) { - dataTypes.unshift( type ); - break; - } - } - } - - // Check to see if we have a response for the expected dataType - if ( dataTypes[ 0 ] in responses ) { - finalDataType = dataTypes[ 0 ]; - } else { - - // Try convertible dataTypes - for ( type in responses ) { - if ( !dataTypes[ 0 ] || s.converters[ type + " " + dataTypes[ 0 ] ] ) { - finalDataType = type; - break; - } - if ( !firstDataType ) { - firstDataType = type; - } - } - - // Or just use first one - finalDataType = finalDataType || firstDataType; - } - - // If we found a dataType - // We add the dataType to the list if needed - // and return the corresponding response - if ( finalDataType ) { - if ( finalDataType !== dataTypes[ 0 ] ) { - dataTypes.unshift( finalDataType ); - } - return responses[ finalDataType ]; - } -} - -/* Chain conversions given the request and the original response - * Also sets the responseXXX fields on the jqXHR instance - */ -function ajaxConvert( s, response, jqXHR, isSuccess ) { - var conv2, current, conv, tmp, prev, - converters = {}, - - // Work with a copy of dataTypes in case we need to modify it for conversion - dataTypes = s.dataTypes.slice(); - - // Create converters map with lowercased keys - if ( dataTypes[ 1 ] ) { - for ( conv in s.converters ) { - converters[ conv.toLowerCase() ] = s.converters[ conv ]; - } - } - - current = dataTypes.shift(); - - // Convert to each sequential dataType - while ( current ) { - - if ( s.responseFields[ current ] ) { - jqXHR[ s.responseFields[ current ] ] = response; - } - - // Apply the dataFilter if provided - if ( !prev && isSuccess && s.dataFilter ) { - response = s.dataFilter( response, s.dataType ); - } - - prev = current; - current = dataTypes.shift(); - - if ( current ) { - - // There's only work to do if current dataType is non-auto - if ( current === "*" ) { - - current = prev; - - // Convert response if prev dataType is non-auto and differs from current - } else if ( prev !== "*" && prev !== current ) { - - // Seek a direct converter - conv = converters[ prev + " " + current ] || converters[ "* " + current ]; - - // If none found, seek a pair - if ( !conv ) { - for ( conv2 in converters ) { - - // If conv2 outputs current - tmp = conv2.split( " " ); - if ( tmp[ 1 ] === current ) { - - // If prev can be converted to accepted input - conv = converters[ prev + " " + tmp[ 0 ] ] || - converters[ "* " + tmp[ 0 ] ]; - if ( conv ) { - - // Condense equivalence converters - if ( conv === true ) { - conv = converters[ conv2 ]; - - // Otherwise, insert the intermediate dataType - } else if ( converters[ conv2 ] !== true ) { - current = tmp[ 0 ]; - dataTypes.unshift( tmp[ 1 ] ); - } - break; - } - } - } - } - - // Apply converter (if not an equivalence) - if ( conv !== true ) { - - // Unless errors are allowed to bubble, catch and return them - if ( conv && s.throws ) { - response = conv( response ); - } else { - try { - response = conv( response ); - } catch ( e ) { - return { - state: "parsererror", - error: conv ? e : "No conversion from " + prev + " to " + current - }; - } - } - } - } - } - } - - return { state: "success", data: response }; -} - -jQuery.extend( { - - // Counter for holding the number of active queries - active: 0, - - // Last-Modified header cache for next request - lastModified: {}, - etag: {}, - - ajaxSettings: { - url: location.href, - type: "GET", - isLocal: rlocalProtocol.test( location.protocol ), - global: true, - processData: true, - async: true, - contentType: "application/x-www-form-urlencoded; charset=UTF-8", - - /* - timeout: 0, - data: null, - dataType: null, - username: null, - password: null, - cache: null, - throws: false, - traditional: false, - headers: {}, - */ - - accepts: { - "*": allTypes, - text: "text/plain", - html: "text/html", - xml: "application/xml, text/xml", - json: "application/json, text/javascript" - }, - - contents: { - xml: /\bxml\b/, - html: /\bhtml/, - json: /\bjson\b/ - }, - - responseFields: { - xml: "responseXML", - text: "responseText", - json: "responseJSON" - }, - - // Data converters - // Keys separate source (or catchall "*") and destination types with a single space - converters: { - - // Convert anything to text - "* text": String, - - // Text to html (true = no transformation) - "text html": true, - - // Evaluate text as a json expression - "text json": JSON.parse, - - // Parse text as xml - "text xml": jQuery.parseXML - }, - - // For options that shouldn't be deep extended: - // you can add your own custom options here if - // and when you create one that shouldn't be - // deep extended (see ajaxExtend) - flatOptions: { - url: true, - context: true - } - }, - - // Creates a full fledged settings object into target - // with both ajaxSettings and settings fields. - // If target is omitted, writes into ajaxSettings. - ajaxSetup: function( target, settings ) { - return settings ? - - // Building a settings object - ajaxExtend( ajaxExtend( target, jQuery.ajaxSettings ), settings ) : - - // Extending ajaxSettings - ajaxExtend( jQuery.ajaxSettings, target ); - }, - - ajaxPrefilter: addToPrefiltersOrTransports( prefilters ), - ajaxTransport: addToPrefiltersOrTransports( transports ), - - // Main method - ajax: function( url, options ) { - - // If url is an object, simulate pre-1.5 signature - if ( typeof url === "object" ) { - options = url; - url = undefined; - } - - // Force options to be an object - options = options || {}; - - var transport, - - // URL without anti-cache param - cacheURL, - - // Response headers - responseHeadersString, - responseHeaders, - - // timeout handle - timeoutTimer, - - // Url cleanup var - urlAnchor, - - // Request state (becomes false upon send and true upon completion) - completed, - - // To know if global events are to be dispatched - fireGlobals, - - // Loop variable - i, - - // uncached part of the url - uncached, - - // Create the final options object - s = jQuery.ajaxSetup( {}, options ), - - // Callbacks context - callbackContext = s.context || s, - - // Context for global events is callbackContext if it is a DOM node or jQuery collection - globalEventContext = s.context && - ( callbackContext.nodeType || callbackContext.jquery ) ? - jQuery( callbackContext ) : - jQuery.event, - - // Deferreds - deferred = jQuery.Deferred(), - completeDeferred = jQuery.Callbacks( "once memory" ), - - // Status-dependent callbacks - statusCode = s.statusCode || {}, - - // Headers (they are sent all at once) - requestHeaders = {}, - requestHeadersNames = {}, - - // Default abort message - strAbort = "canceled", - - // Fake xhr - jqXHR = { - readyState: 0, - - // Builds headers hashtable if needed - getResponseHeader: function( key ) { - var match; - if ( completed ) { - if ( !responseHeaders ) { - responseHeaders = {}; - while ( ( match = rheaders.exec( responseHeadersString ) ) ) { - responseHeaders[ match[ 1 ].toLowerCase() ] = match[ 2 ]; - } - } - match = responseHeaders[ key.toLowerCase() ]; - } - return match == null ? null : match; - }, - - // Raw string - getAllResponseHeaders: function() { - return completed ? responseHeadersString : null; - }, - - // Caches the header - setRequestHeader: function( name, value ) { - if ( completed == null ) { - name = requestHeadersNames[ name.toLowerCase() ] = - requestHeadersNames[ name.toLowerCase() ] || name; - requestHeaders[ name ] = value; - } - return this; - }, - - // Overrides response content-type header - overrideMimeType: function( type ) { - if ( completed == null ) { - s.mimeType = type; - } - return this; - }, - - // Status-dependent callbacks - statusCode: function( map ) { - var code; - if ( map ) { - if ( completed ) { - - // Execute the appropriate callbacks - jqXHR.always( map[ jqXHR.status ] ); - } else { - - // Lazy-add the new callbacks in a way that preserves old ones - for ( code in map ) { - statusCode[ code ] = [ statusCode[ code ], map[ code ] ]; - } - } - } - return this; - }, - - // Cancel the request - abort: function( statusText ) { - var finalText = statusText || strAbort; - if ( transport ) { - transport.abort( finalText ); - } - done( 0, finalText ); - return this; - } - }; - - // Attach deferreds - deferred.promise( jqXHR ); - - // Add protocol if not provided (prefilters might expect it) - // Handle falsy url in the settings object (#10093: consistency with old signature) - // We also use the url parameter if available - s.url = ( ( url || s.url || location.href ) + "" ) - .replace( rprotocol, location.protocol + "//" ); - - // Alias method option to type as per ticket #12004 - s.type = options.method || options.type || s.method || s.type; - - // Extract dataTypes list - s.dataTypes = ( s.dataType || "*" ).toLowerCase().match( rnothtmlwhite ) || [ "" ]; - - // A cross-domain request is in order when the origin doesn't match the current origin. - if ( s.crossDomain == null ) { - urlAnchor = document.createElement( "a" ); - - // Support: IE <=8 - 11, Edge 12 - 13 - // IE throws exception on accessing the href property if url is malformed, - // e.g. http://example.com:80x/ - try { - urlAnchor.href = s.url; - - // Support: IE <=8 - 11 only - // Anchor's host property isn't correctly set when s.url is relative - urlAnchor.href = urlAnchor.href; - s.crossDomain = originAnchor.protocol + "//" + originAnchor.host !== - urlAnchor.protocol + "//" + urlAnchor.host; - } catch ( e ) { - - // If there is an error parsing the URL, assume it is crossDomain, - // it can be rejected by the transport if it is invalid - s.crossDomain = true; - } - } - - // Convert data if not already a string - if ( s.data && s.processData && typeof s.data !== "string" ) { - s.data = jQuery.param( s.data, s.traditional ); - } - - // Apply prefilters - inspectPrefiltersOrTransports( prefilters, s, options, jqXHR ); - - // If request was aborted inside a prefilter, stop there - if ( completed ) { - return jqXHR; - } - - // We can fire global events as of now if asked to - // Don't fire events if jQuery.event is undefined in an AMD-usage scenario (#15118) - fireGlobals = jQuery.event && s.global; - - // Watch for a new set of requests - if ( fireGlobals && jQuery.active++ === 0 ) { - jQuery.event.trigger( "ajaxStart" ); - } - - // Uppercase the type - s.type = s.type.toUpperCase(); - - // Determine if request has content - s.hasContent = !rnoContent.test( s.type ); - - // Save the URL in case we're toying with the If-Modified-Since - // and/or If-None-Match header later on - // Remove hash to simplify url manipulation - cacheURL = s.url.replace( rhash, "" ); - - // More options handling for requests with no content - if ( !s.hasContent ) { - - // Remember the hash so we can put it back - uncached = s.url.slice( cacheURL.length ); - - // If data is available, append data to url - if ( s.data ) { - cacheURL += ( rquery.test( cacheURL ) ? "&" : "?" ) + s.data; - - // #9682: remove data so that it's not used in an eventual retry - delete s.data; - } - - // Add or update anti-cache param if needed - if ( s.cache === false ) { - cacheURL = cacheURL.replace( rantiCache, "$1" ); - uncached = ( rquery.test( cacheURL ) ? "&" : "?" ) + "_=" + ( nonce++ ) + uncached; - } - - // Put hash and anti-cache on the URL that will be requested (gh-1732) - s.url = cacheURL + uncached; - - // Change '%20' to '+' if this is encoded form body content (gh-2658) - } else if ( s.data && s.processData && - ( s.contentType || "" ).indexOf( "application/x-www-form-urlencoded" ) === 0 ) { - s.data = s.data.replace( r20, "+" ); - } - - // Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode. - if ( s.ifModified ) { - if ( jQuery.lastModified[ cacheURL ] ) { - jqXHR.setRequestHeader( "If-Modified-Since", jQuery.lastModified[ cacheURL ] ); - } - if ( jQuery.etag[ cacheURL ] ) { - jqXHR.setRequestHeader( "If-None-Match", jQuery.etag[ cacheURL ] ); - } - } - - // Set the correct header, if data is being sent - if ( s.data && s.hasContent && s.contentType !== false || options.contentType ) { - jqXHR.setRequestHeader( "Content-Type", s.contentType ); - } - - // Set the Accepts header for the server, depending on the dataType - jqXHR.setRequestHeader( - "Accept", - s.dataTypes[ 0 ] && s.accepts[ s.dataTypes[ 0 ] ] ? - s.accepts[ s.dataTypes[ 0 ] ] + - ( s.dataTypes[ 0 ] !== "*" ? ", " + allTypes + "; q=0.01" : "" ) : - s.accepts[ "*" ] - ); - - // Check for headers option - for ( i in s.headers ) { - jqXHR.setRequestHeader( i, s.headers[ i ] ); - } - - // Allow custom headers/mimetypes and early abort - if ( s.beforeSend && - ( s.beforeSend.call( callbackContext, jqXHR, s ) === false || completed ) ) { - - // Abort if not done already and return - return jqXHR.abort(); - } - - // Aborting is no longer a cancellation - strAbort = "abort"; - - // Install callbacks on deferreds - completeDeferred.add( s.complete ); - jqXHR.done( s.success ); - jqXHR.fail( s.error ); - - // Get transport - transport = inspectPrefiltersOrTransports( transports, s, options, jqXHR ); - - // If no transport, we auto-abort - if ( !transport ) { - done( -1, "No Transport" ); - } else { - jqXHR.readyState = 1; - - // Send global event - if ( fireGlobals ) { - globalEventContext.trigger( "ajaxSend", [ jqXHR, s ] ); - } - - // If request was aborted inside ajaxSend, stop there - if ( completed ) { - return jqXHR; - } - - // Timeout - if ( s.async && s.timeout > 0 ) { - timeoutTimer = window.setTimeout( function() { - jqXHR.abort( "timeout" ); - }, s.timeout ); - } - - try { - completed = false; - transport.send( requestHeaders, done ); - } catch ( e ) { - - // Rethrow post-completion exceptions - if ( completed ) { - throw e; - } - - // Propagate others as results - done( -1, e ); - } - } - - // Callback for when everything is done - function done( status, nativeStatusText, responses, headers ) { - var isSuccess, success, error, response, modified, - statusText = nativeStatusText; - - // Ignore repeat invocations - if ( completed ) { - return; - } - - completed = true; - - // Clear timeout if it exists - if ( timeoutTimer ) { - window.clearTimeout( timeoutTimer ); - } - - // Dereference transport for early garbage collection - // (no matter how long the jqXHR object will be used) - transport = undefined; - - // Cache response headers - responseHeadersString = headers || ""; - - // Set readyState - jqXHR.readyState = status > 0 ? 4 : 0; - - // Determine if successful - isSuccess = status >= 200 && status < 300 || status === 304; - - // Get response data - if ( responses ) { - response = ajaxHandleResponses( s, jqXHR, responses ); - } - - // Convert no matter what (that way responseXXX fields are always set) - response = ajaxConvert( s, response, jqXHR, isSuccess ); - - // If successful, handle type chaining - if ( isSuccess ) { - - // Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode. - if ( s.ifModified ) { - modified = jqXHR.getResponseHeader( "Last-Modified" ); - if ( modified ) { - jQuery.lastModified[ cacheURL ] = modified; - } - modified = jqXHR.getResponseHeader( "etag" ); - if ( modified ) { - jQuery.etag[ cacheURL ] = modified; - } - } - - // if no content - if ( status === 204 || s.type === "HEAD" ) { - statusText = "nocontent"; - - // if not modified - } else if ( status === 304 ) { - statusText = "notmodified"; - - // If we have data, let's convert it - } else { - statusText = response.state; - success = response.data; - error = response.error; - isSuccess = !error; - } - } else { - - // Extract error from statusText and normalize for non-aborts - error = statusText; - if ( status || !statusText ) { - statusText = "error"; - if ( status < 0 ) { - status = 0; - } - } - } - - // Set data for the fake xhr object - jqXHR.status = status; - jqXHR.statusText = ( nativeStatusText || statusText ) + ""; - - // Success/Error - if ( isSuccess ) { - deferred.resolveWith( callbackContext, [ success, statusText, jqXHR ] ); - } else { - deferred.rejectWith( callbackContext, [ jqXHR, statusText, error ] ); - } - - // Status-dependent callbacks - jqXHR.statusCode( statusCode ); - statusCode = undefined; - - if ( fireGlobals ) { - globalEventContext.trigger( isSuccess ? "ajaxSuccess" : "ajaxError", - [ jqXHR, s, isSuccess ? success : error ] ); - } - - // Complete - completeDeferred.fireWith( callbackContext, [ jqXHR, statusText ] ); - - if ( fireGlobals ) { - globalEventContext.trigger( "ajaxComplete", [ jqXHR, s ] ); - - // Handle the global AJAX counter - if ( !( --jQuery.active ) ) { - jQuery.event.trigger( "ajaxStop" ); - } - } - } - - return jqXHR; - }, - - getJSON: function( url, data, callback ) { - return jQuery.get( url, data, callback, "json" ); - }, - - getScript: function( url, callback ) { - return jQuery.get( url, undefined, callback, "script" ); - } -} ); - -jQuery.each( [ "get", "post" ], function( i, method ) { - jQuery[ method ] = function( url, data, callback, type ) { - - // Shift arguments if data argument was omitted - if ( jQuery.isFunction( data ) ) { - type = type || callback; - callback = data; - data = undefined; - } - - // The url can be an options object (which then must have .url) - return jQuery.ajax( jQuery.extend( { - url: url, - type: method, - dataType: type, - data: data, - success: callback - }, jQuery.isPlainObject( url ) && url ) ); - }; -} ); - - -jQuery._evalUrl = function( url ) { - return jQuery.ajax( { - url: url, - - // Make this explicit, since user can override this through ajaxSetup (#11264) - type: "GET", - dataType: "script", - cache: true, - async: false, - global: false, - "throws": true - } ); -}; - - -jQuery.fn.extend( { - wrapAll: function( html ) { - var wrap; - - if ( this[ 0 ] ) { - if ( jQuery.isFunction( html ) ) { - html = html.call( this[ 0 ] ); - } - - // The elements to wrap the target around - wrap = jQuery( html, this[ 0 ].ownerDocument ).eq( 0 ).clone( true ); - - if ( this[ 0 ].parentNode ) { - wrap.insertBefore( this[ 0 ] ); - } - - wrap.map( function() { - var elem = this; - - while ( elem.firstElementChild ) { - elem = elem.firstElementChild; - } - - return elem; - } ).append( this ); - } - - return this; - }, - - wrapInner: function( html ) { - if ( jQuery.isFunction( html ) ) { - return this.each( function( i ) { - jQuery( this ).wrapInner( html.call( this, i ) ); - } ); - } - - return this.each( function() { - var self = jQuery( this ), - contents = self.contents(); - - if ( contents.length ) { - contents.wrapAll( html ); - - } else { - self.append( html ); - } - } ); - }, - - wrap: function( html ) { - var isFunction = jQuery.isFunction( html ); - - return this.each( function( i ) { - jQuery( this ).wrapAll( isFunction ? html.call( this, i ) : html ); - } ); - }, - - unwrap: function( selector ) { - this.parent( selector ).not( "body" ).each( function() { - jQuery( this ).replaceWith( this.childNodes ); - } ); - return this; - } -} ); - - -jQuery.expr.pseudos.hidden = function( elem ) { - return !jQuery.expr.pseudos.visible( elem ); -}; -jQuery.expr.pseudos.visible = function( elem ) { - return !!( elem.offsetWidth || elem.offsetHeight || elem.getClientRects().length ); -}; - - - - -jQuery.ajaxSettings.xhr = function() { - try { - return new window.XMLHttpRequest(); - } catch ( e ) {} -}; - -var xhrSuccessStatus = { - - // File protocol always yields status code 0, assume 200 - 0: 200, - - // Support: IE <=9 only - // #1450: sometimes IE returns 1223 when it should be 204 - 1223: 204 - }, - xhrSupported = jQuery.ajaxSettings.xhr(); - -support.cors = !!xhrSupported && ( "withCredentials" in xhrSupported ); -support.ajax = xhrSupported = !!xhrSupported; - -jQuery.ajaxTransport( function( options ) { - var callback, errorCallback; - - // Cross domain only allowed if supported through XMLHttpRequest - if ( support.cors || xhrSupported && !options.crossDomain ) { - return { - send: function( headers, complete ) { - var i, - xhr = options.xhr(); - - xhr.open( - options.type, - options.url, - options.async, - options.username, - options.password - ); - - // Apply custom fields if provided - if ( options.xhrFields ) { - for ( i in options.xhrFields ) { - xhr[ i ] = options.xhrFields[ i ]; - } - } - - // Override mime type if needed - if ( options.mimeType && xhr.overrideMimeType ) { - xhr.overrideMimeType( options.mimeType ); - } - - // X-Requested-With header - // For cross-domain requests, seeing as conditions for a preflight are - // akin to a jigsaw puzzle, we simply never set it to be sure. - // (it can always be set on a per-request basis or even using ajaxSetup) - // For same-domain requests, won't change header if already provided. - if ( !options.crossDomain && !headers[ "X-Requested-With" ] ) { - headers[ "X-Requested-With" ] = "XMLHttpRequest"; - } - - // Set headers - for ( i in headers ) { - xhr.setRequestHeader( i, headers[ i ] ); - } - - // Callback - callback = function( type ) { - return function() { - if ( callback ) { - callback = errorCallback = xhr.onload = - xhr.onerror = xhr.onabort = xhr.onreadystatechange = null; - - if ( type === "abort" ) { - xhr.abort(); - } else if ( type === "error" ) { - - // Support: IE <=9 only - // On a manual native abort, IE9 throws - // errors on any property access that is not readyState - if ( typeof xhr.status !== "number" ) { - complete( 0, "error" ); - } else { - complete( - - // File: protocol always yields status 0; see #8605, #14207 - xhr.status, - xhr.statusText - ); - } - } else { - complete( - xhrSuccessStatus[ xhr.status ] || xhr.status, - xhr.statusText, - - // Support: IE <=9 only - // IE9 has no XHR2 but throws on binary (trac-11426) - // For XHR2 non-text, let the caller handle it (gh-2498) - ( xhr.responseType || "text" ) !== "text" || - typeof xhr.responseText !== "string" ? - { binary: xhr.response } : - { text: xhr.responseText }, - xhr.getAllResponseHeaders() - ); - } - } - }; - }; - - // Listen to events - xhr.onload = callback(); - errorCallback = xhr.onerror = callback( "error" ); - - // Support: IE 9 only - // Use onreadystatechange to replace onabort - // to handle uncaught aborts - if ( xhr.onabort !== undefined ) { - xhr.onabort = errorCallback; - } else { - xhr.onreadystatechange = function() { - - // Check readyState before timeout as it changes - if ( xhr.readyState === 4 ) { - - // Allow onerror to be called first, - // but that will not handle a native abort - // Also, save errorCallback to a variable - // as xhr.onerror cannot be accessed - window.setTimeout( function() { - if ( callback ) { - errorCallback(); - } - } ); - } - }; - } - - // Create the abort callback - callback = callback( "abort" ); - - try { - - // Do send the request (this may raise an exception) - xhr.send( options.hasContent && options.data || null ); - } catch ( e ) { - - // #14683: Only rethrow if this hasn't been notified as an error yet - if ( callback ) { - throw e; - } - } - }, - - abort: function() { - if ( callback ) { - callback(); - } - } - }; - } -} ); - - - - -// Prevent auto-execution of scripts when no explicit dataType was provided (See gh-2432) -jQuery.ajaxPrefilter( function( s ) { - if ( s.crossDomain ) { - s.contents.script = false; - } -} ); - -// Install script dataType -jQuery.ajaxSetup( { - accepts: { - script: "text/javascript, application/javascript, " + - "application/ecmascript, application/x-ecmascript" - }, - contents: { - script: /\b(?:java|ecma)script\b/ - }, - converters: { - "text script": function( text ) { - jQuery.globalEval( text ); - return text; - } - } -} ); - -// Handle cache's special case and crossDomain -jQuery.ajaxPrefilter( "script", function( s ) { - if ( s.cache === undefined ) { - s.cache = false; - } - if ( s.crossDomain ) { - s.type = "GET"; - } -} ); - -// Bind script tag hack transport -jQuery.ajaxTransport( "script", function( s ) { - - // This transport only deals with cross domain requests - if ( s.crossDomain ) { - var script, callback; - return { - send: function( _, complete ) { - script = jQuery( " - - - - - - - - - - - - - - - - - -
- - - -
- - - - - -
- -
- - - - - - - - - - - - - - - - - -
- - - - -
-
-
-
- -
-

AML class

-
-
-class AutoMxL.__main__.AML(*args: Any, **kwargs: Any)[source]
-

Covers the complete pipeline of a classification project from a raw dataset to a deployable model.

-

AML is built as a class inherited from pandas DataFrame. Each Machine Learning step corresponds to method that -can be called with default or filled parameters.

-
    -

  • explore: explore dataset and identify features types

    • -

  • preprocess: clean and prepare data (optional : outliers processing).

    • -

  • select_features: features selection (optional)

    • -

  • model_train_predict : split AML in train/test sets to fits/apply models with random search. -Returns the list of the valid models (without overfitting) and the best one.

    • -
-

deployment methods:

-
    -

  • preprocess_apply : apply fitted preprocessing transformation to a new dataset

    • -

  • select_features_apply : idem

    • -

  • model_apply : apply fitted models to a new dataset

    • -
-

Notes :

-
    -

  • A method requires that the former one has been applied (actuel step is given by “step” attribute)

    • -

  • Target has to be binary and encoded as int (1/0) (see MLGB59.Start.Encode_Target module if you need help)

    • -

  • don’t call your target “target” please :>

    • -
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-
Parameters
-
    -

  • _obj (DataFrame) – Source Dataset

    • -

  • target (string) – target name

    • -
-
-
-
-
-explore(verbose=False)[source]
-

data exploration and features type identification

-

Note : if you disagree with automated identification, you can directly modify d_features attribute

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-
Create self.d_featuresdict {xlist of variables names}
    -

  • date: date features

    • -

  • identifier: identifier features

    • -

  • verbatim: verbatim features

    • -

  • boolean: boolean features

    • -

  • categorical: categorical features

    • -

  • numerical: numerical features

    • -

  • NA: features which contains NA values

    • -

  • low_variance: list of the features with low variance and unique values

    • -
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-
-
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Parameters
-

verbose (boolean (Default False)) – Get logging information

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-
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- -
-
-model_predict(df, metric='F1', delta_auc=0.03, verbose=False)[source]
-

apply fitted models on a dataset

-
    -

  • identifies valid models (auc(train)-auc(test)<0.03

    • -

  • gets the best model in respect of a selected metric among valid model

    • -
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Parameters
-
    -

  • metric (string (Default : 'F1')) – objective metric

    • -

  • verbose (boolean (Default False)) – Get logging information

    • -
-
-
Returns
-

    -

  • dict – {model_index : {‘HP’, ‘probas’, ‘model’, ‘features_importance’, ‘train_metrics’, ‘metrics’, ‘output’}

    • -

  • list – valid models indexes

    • -

  • int – best model index

    • -

  • DataFrame – models summary

    • -
-

-
-
-
- -
-
-model_train(clf='XGBOOST', grid_param=None, top_bagging=False, n_comb=10, comb_seed=None, verbose=False)[source]
-

train models with random search

-
    -

  • creates models with random hyper-parameters combinations from HP grid

    • -

  • fits models on self

    • -
-

Notes :

-
    -

  • Available classifiers : Random Forest, XGBOOST

    • -

  • can enable bagging algo with top_bagging parameter

    • -
-
-
Parameters
-
    -

  • clf (string (Default : 'XGBOOST')) – classifier used for modelisation

    • -

  • grid_param (dict) – random search grid {Hyperparameter name : values list}

    • -

  • top_bagging (boolean (Default : False)) – enable Bagging

    • -

  • n_comb (int (Default : 10)) – HP combination number

    • -

  • comb_seed (int (Default : None)) – random combination seed

    • -

  • verbose (boolean (Default False)) – Get logging information

    • -
-
-
-
- -
-
-model_train_test(clf='XGBOOST', grid_param=None, metric='F1', delta_auc=0.03, top_bagging=False, n_comb=10, comb_seed=None, verbose=False)[source]
-

train and test models with random search

-
    -

  • creates models with random hyper-parameters combinations from HP grid

    • -

  • splits (random 80/20) train/test sets to fit/apply models

    • -

  • identifies valid models (auc(train)-auc(test)<0.03

    • -

  • gets the best model in respect of a selected metric among valid model

    • -
-

Notes :

-
    -

  • Available classifiers : Random Forest, XGBOOST

    • -

  • can enable bagging algo with top_bagging parameter

    • -
-
-
Parameters
-
    -

  • clf (string (Default : 'XGBOOST')) – classifier used for modelisation

    • -

  • grid_param (dict) – random search grid {Hyperparameter name : values list}

    • -

  • metric (string (Default : 'F1')) – objective metric

    • -

  • top_bagging (boolean (Default : False)) – enable Bagging

    • -

  • n_comb (int (Default : 10)) – HP combination number

    • -

  • comb_seed (int (Default : None)) – random combination seed

    • -

  • verbose (boolean (Default False)) – Get logging information

    • -
-
-
Returns
-

    -

  • dict – {model_index : {‘HP’, ‘probas’, ‘model’, ‘features_importance’, ‘train_metrics’, ‘metrics’, ‘output’}

    • -

  • list – valid models indexes

    • -

  • int – best model index

    • -

  • DataFrame – models summary

    • -
-

-
-
-
- -
-
-preprocess(date_ref=None, process_outliers=False, cat_method='deep_encoder', verbose=False)[source]
-

Prepare the data before feeding it to the model :

-
-
    -

  • remove low variance features

    • -

  • remove identifiers and verbatims features

    • -

  • transform date features to timedelta

    • -

  • fill missing values

    • -

  • process categorical and boolean data (one-hot-encoding or Pytorch NN encoder)

    • -

  • replace outliers (optional)

    • -
-
-
create self.d_preprocessdict {steptransformation}
    -

  • remove: list of the features to remove

    • -

  • date: fitted DateEncoder object

    • -

  • NA: fitted NAEncoder object

    • -

  • categorical: fitted CategoricalEncoder object

    • -

  • outlier: fitted OutlierEncoder object

    • -
-
-
-
-
-
Parameters
-
    -

  • date_ref (string '%d/%m/%y' (Default : None)) – ref date to compute date features timedelta. -If None, today date

    • -

  • process_outliers (boolean (Default : False)) – Enable outliers replacement

    • -

  • cat_method (string (Default : 'deep_encoder')) – Categorical features encoding method

    • -

  • verbose (boolean (Default False)) – Get logging information

    • -
-
-
-
- -
-
-preprocess_apply(df, verbose=False)[source]
-

Apply preprocessing.

-

Requires preprocess method to have been applied (so that all encoder are fitted).

-
-
Parameters
-
    -

  • df (DataFrame) – dataset to apply preprocessing on

    • -

  • verbose (boolean (Default False)) – Get logging information

    • -
-
-
Returns
-

DataFrame

-
-
Return type
-

Preprocessed dataset

-
-
-
- -
-
-select_features(method='pca', verbose=False)[source]
-

fit and apply features selection (optional)

-
-
Parameters
-
    -

  • method (string (Default pca)) – method use to select features

    • -

  • verbose (boolean (Default False)) – Get logging information

    • -
-
-
-
- -
-
-select_features_apply(df, verbose=False)[source]
-

Apply features selection.

-

Requires Select_Features method to have been applied

-
-
Parameters
-
    -

  • df (DataFrame) – dataset to apply selection on

    • -

  • verbose (boolean (Default False)) – Get logging information

    • -
-
-
Returns
-

DataFrame

-
-
Return type
-

reduced dataset

-
-
-
- -
- -
- - -
- -
- - -
-
- -
- -
- - - - - - - - - - - \ No newline at end of file diff --git a/docs/_build/html/docstring_test.html b/docs/_build/html/docstring_test.html deleted file mode 100644 index 71556fa..0000000 --- a/docs/_build/html/docstring_test.html +++ /dev/null @@ -1,398 +0,0 @@ - - - - - - - - - - - Test — MLBG59 1.0.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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Test

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Features_type

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Variables type identification function

-
    -
  • is_date : test if a variable is a date
    • -
  • is_identifier : test if a variable is an identifier
    • -
  • is_verbatim : test if a variable is a verbatim
    • -
  • is_boolean : test if a variable is a boolean
    • -
  • is_categorical : test if a variable is a categorical one (with more than 2 categories)
    • -
  • features_from_type : get all
    • -
-
-
-dev.Features_type.features_from_type(df, type, var_list=None, th=0.95)[source]
-

Get features of a selected type :

-
    -
  • date : try to apply to_datetime
    • -
  • -
    identifier :
    -
      -
    • #(unique values)/#(total values) > threshold (default 0.95)
      • -
    • AND length is the same for all values (for non NA)
      • -
    -
    -
    -
    • -
  • -
    verbatim :
    -
      -
    • #(unique values)/#(total values) >= threshold (default 0.95)
      • -
    • AND length is NOT the same for all values (for non NA)
      • -
    -
    -
    -
    • -
  • boolean : #(distinct values) = 2
    • -
  • categorical : #(unique values)/#(total values) < threshold (default 0.95)
    • -
- --- - - - - - - - -
Parameters:
    -
  • df (DataFrame) – input dataset
    • -
  • var_list (list) – variables names
    • -
  • type – selected type to get features
    • -
  • th (float (Default : 0.90)) – threshold used to identify identifiers/verbatims/categorcial variables
    • -
-
Returns:

identified variables names

-
Return type:

list

-
-
- -
-
-dev.Features_type.is_boolean(df, col)[source]
-

Test if a variable is a boolean.

-
    -
  • #(distinct values) = 2
    • -
- --- - - - - - - - -
Parameters:
    -
  • df (DataFrame) – input dataset
    • -
  • col (string) – variable name
    • -
-
Returns:

res – test result

-
Return type:

boolean

-
-
- -
-
-dev.Features_type.is_categorical(df, col, th=0.95)[source]
-

Test if a variable is a categorical one (with more than 2 categories).

-
    -
  • #(unique values)/#(total values) < threshold (default 0.95)
    • -
- --- - - - - - - - -
Parameters:
    -
  • df (DataFrame) – input dataset
    • -
  • col (string) – variable name
    • -
  • th (float (Default : 0.95)) – threshold rate
    • -
-
Returns:

res – test result

-
Return type:

boolean

-
-
- -
-
-dev.Features_type.is_date(df, col)[source]
-

Test if a variable is a date.

-

Method : try to apply to_datetime

- --- - - - - - - - -
Parameters:
    -
  • df (DataFrame) – input dataset
    • -
  • col (string) – variable name
    • -
-
Returns:

res – test result

-
Return type:

boolean

-
-
- -
-
-dev.Features_type.is_identifier(df, col, th=0.95)[source]
-

Test if a variable is an identifier.

-
    -
  • #(unique values)/#(total values) > threshold (default 0.95)
    • -
  • AND length is the same for all values (for non NA)
    • -
  • AND not date
    • -
- --- - - - - - - - -
Parameters:
    -
  • df (DataFrame) – input dataset
    • -
  • col (string) – variable name
    • -
  • th (float (Default : 0.95)) – threshold rate
    • -
-
Returns:

res – test result

-
Return type:

boolean

-
-
- -
-
-dev.Features_type.is_verbatim(df, col, th=0.95)[source]
-

Test if a variable is a verbatim.

-
    -
  • #(unique values)/#(total values) >= threshold (default 0.95)
    • -
  • AND length is NOT the same for all values (for non NA)
    • -
- --- - - - - - - - -
Parameters:
    -
  • df (DataFrame) – input dataset
    • -
  • col (string) – variable name
    • -
  • th (float (Default : 0.95)) – threshold rate
    • -
-
Returns:

res – test result

-
Return type:

boolean

-
-
- -
-
- - -
- -
-
- - -
- -
-

- © Copyright 2020, Maxence LABESSE - -

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A

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B

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C

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D

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E

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F

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G

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H

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I

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L

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M

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N

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O

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P

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- - - - Built with Sphinx using a - - theme - - provided by Read the Docs. - -
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Welcome to AutoMxL’s documentation!

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Python Module Index

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- a
- AutoMxL -
    - AutoMxL.Explore.Explore -
    - AutoMxL.Explore.Features_Type -
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    - AutoMxL.Preprocessing.Categorical -
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class","Start","Welcome to AutoMxL\u2019s documentation!"],titleterms:{"class":0,aml:0,automxl:2,bag:1,categor:1,data:1,date:1,document:2,encode_target:1,explor:1,featur:1,features_typ:1,hyperoptimis:1,load:1,missing_valu:1,modelis:1,outlier:1,preprocess:1,process:1,select:1,start:1,welcom:2}}) \ No newline at end of file diff --git a/docs/autoML.rst b/docs/autoML.rst deleted file mode 100644 index de1a640..0000000 --- a/docs/autoML.rst +++ /dev/null @@ -1,4 +0,0 @@ -AML class -============ -.. autoclass:: AutoMxL.__main__.AML - :members: diff --git a/docs/conf.py b/docs/conf.py deleted file mode 100644 index d340c4f..0000000 --- a/docs/conf.py +++ /dev/null @@ -1,179 +0,0 @@ -# -*- coding: utf-8 -*- -# -# Configuration file for the Sphinx documentation builder. -# -# This file does only contain a selection of the most common options. For a -# full list see the documentation: -# http://www.sphinx-doc.org/en/master/config - -# -- Path setup -------------------------------------------------------------- - -# If extensions (or modules to document with autodoc) are in another -# directory, add these directories to sys.path here. If the directory is -autodoc_mock_imports = ['pandas', 'sklearn', 'xgboost', 'numpy', 'prince', 'torch', 'matplotlib'] -# relative to the documentation root, use os.path.abspath to make it -# absolute, like shown here. -import sys -import os - -# Get the project root dir, which is the parent dir of this -cwd = os.getcwd() -project_root = os.path.dirname(cwd) - -# Insert the project root dir as the first element in the PYTHONPATH. -# This lets us ensure that the source package is imported, and that its -# version is used. - -print("project_root : ", project_root) -sys.path.insert(0, project_root) - -# -- Project information ----------------------------------------------------- - -project = 'AutoMxL' -copyright = '2020, Maxence LABESSE' -author = 'Maxence LABESSE' - -# The short X.Y version -version = '' -# The full version, including alpha/beta/rc tags -release = '1.0.0' - -# -- General configuration --------------------------------------------------- - -# If your documentation needs a minimal Sphinx version, state it here. -# -# needs_sphinx = '1.0' - -# Add any Sphinx extension module names here, as strings. They can be -# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom -# ones. -extensions = ['sphinx.ext.autodoc', 'sphinx.ext.viewcode', 'sphinx.ext.napoleon'] -napoleon_numpy_docstring = True - -# Add any paths that contain templates here, relative to this directory. -templates_path = ['_templates'] - -# The suffix(es) of source filenames. -# You can specify multiple suffix as a list of string: -# -# source_suffix = ['.rst', '.md'] -source_suffix = '.rst' - -# The master toctree document. -master_doc = 'index' - -# The language for content autogenerated by Sphinx. Refer to documentation -# for a list of supported languages. -# -# This is also used if you do content translation via gettext catalogs. -# Usually you set "language" from the command line for these cases. -language = None - -# List of patterns, relative to source directory, that match files and -# directories to ignore when looking for source files. -# This pattern also affects html_static_path and html_extra_path. -exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store'] - -# The name of the Pygments (syntax highlighting) style to use. -pygments_style = None - -# -- Options for HTML output ------------------------------------------------- - -# The theme to use for HTML and HTML Help pages. See the documentation for -# a list of builtin themes. -# -html_theme = 'sphinx_rtd_theme' - -# Theme options are theme-specific and customize the look and feel of a theme -# further. For a list of options available for each theme, see the -# documentation. -# -# html_theme_options = {} - -# Add any paths that contain custom static files (such as style sheets) here, -# relative to this directory. They are copied after the builtin static files, -# so a file named "default.css" will overwrite the builtin "default.css". -#html_static_path = ['_static'] - -# Custom sidebar templates, must be a dictionary that maps document names -# to template names. -# -# The default sidebars (for documents that don't match any pattern) are -# defined by theme itself. Builtin themes are using these templates by -# default: ``['localtoc.html', 'relations.html', 'sourcelink.html', -# 'searchbox.html']``. -# -# html_sidebars = {} - - -# -- Options for HTMLHelp output --------------------------------------------- - -# Output file base name for HTML help builder. -htmlhelp_basename = 'MLBG59doc' - -# -- Options for LaTeX output ------------------------------------------------ - -latex_elements = { - # The paper size ('letterpaper' or 'a4paper'). - # - # 'papersize': 'letterpaper', - - # The font size ('10pt', '11pt' or '12pt'). - # - # 'pointsize': '10pt', - - # Additional stuff for the LaTeX preamble. - # - # 'preamble': '', - - # Latex figure (float) alignment - # - # 'figure_align': 'htbp', -} - -# Grouping the document tree into LaTeX files. List of tuples -# (source start file, target name, title, -# author, documentclass [howto, manual, or own class]). -latex_documents = [ - (master_doc, 'AutoMxL.tex', 'AutoMxL Documentation', - 'Maxence LABESSE', 'manual'), -] - -# -- Options for manual page output ------------------------------------------ - -# One entry per manual page. List of tuples -# (source start file, name, description, authors, manual section). -man_pages = [ - (master_doc, 'mlbg59', 'AutoMxL Documentation', - [author], 1) -] - -# -- Options for Texinfo output ---------------------------------------------- - -# Grouping the document tree into Texinfo files. List of tuples -# (source start file, target name, title, author, -# dir menu entry, description, category) -texinfo_documents = [ - (master_doc, 'AutoMxL', 'AutoMxL Documentation', - author, 'AutoMxL', 'One line description of project.', - 'Miscellaneous'), -] - -# -- Options for Epub output ------------------------------------------------- - -# Bibliographic Dublin Core info. -epub_title = project - -# The unique identifier of the text. This can be a ISBN number -# or the project homepage. -# -# epub_identifier = '' - -# A unique identification for the text. -# -# epub_uid = '' - -# A list of files that should not be packed into the epub file. -epub_exclude_files = ['search.html'] - -# -- Extension configuration ------------------------------------------------- diff --git a/docs/features.rst b/docs/features.rst deleted file mode 100644 index 31384ba..0000000 --- a/docs/features.rst +++ /dev/null @@ -1,64 +0,0 @@ -Start -===== -Load ----- -.. automodule:: AutoMxL.Start.Load - :members: - -Encode_Target -------------- -.. automodule:: AutoMxL.Start.Encode_Target - :members: - -Explore -======= -Explore --------- -.. automodule:: AutoMxL.Explore.Explore - :members: - -Features_Type ------------------- -.. automodule:: AutoMxL.Explore.Features_Type - :members: - - -Preprocessing -============= -Missing_Values --------------- -.. automodule:: AutoMxL.Preprocessing.Missing_Values - :members: - -Categorical Data ----------------- -.. automodule:: AutoMxL.Preprocessing.Categorical - :members: - -Date Data --------------------------------------- -.. automodule:: AutoMxL.Preprocessing.Date - :members: - -Process Outliers ------------------ -.. automodule:: AutoMxL.Preprocessing.Outliers - :members: - -Features Selection -================== -.. automodule:: AutoMxL.Select_Features.Select_Features - :members: - - -Modelisation -============ -Bagging -------- -.. automodule:: AutoMxL.Modelisation.Bagging - :members: - -Hyperoptimisation ------------------ -.. automodule:: AutoMxL.Modelisation.HyperOpt - :members: diff --git a/docs/image.jpg b/docs/image.jpg deleted file mode 100644 index 2808338..0000000 Binary files a/docs/image.jpg and /dev/null differ diff --git a/docs/index.rst b/docs/index.rst deleted file mode 100644 index 312cba2..0000000 --- a/docs/index.rst +++ /dev/null @@ -1,22 +0,0 @@ -.. AutoMxL documentation master file, created by - sphinx-quickstart on Mon Feb 10 14:44:06 2020. - You can adapt this file completely to your liking, but it should at least - contain the root `toctree` directive. - -Welcome to AutoMxL's documentation! -================================== - - -.. toctree:: - :maxdepth: 3 - :caption: AutoML class - :hidden: - - autoML - -.. toctree:: - :maxdepth: 3 - :caption: Features - :hidden: - - features diff --git a/docs/make.bat b/docs/make.bat deleted file mode 100644 index 7893348..0000000 --- a/docs/make.bat +++ /dev/null @@ -1,35 +0,0 @@ -@ECHO OFF - -pushd %~dp0 - -REM Command file for Sphinx documentation - -if "%SPHINXBUILD%" == "" ( - set SPHINXBUILD=sphinx-build -) -set SOURCEDIR=. -set BUILDDIR=_build - -if "%1" == "" goto help - -%SPHINXBUILD% >NUL 2>NUL -if errorlevel 9009 ( - echo. - echo.The 'sphinx-build' command was not found. Make sure you have Sphinx - echo.installed, then set the SPHINXBUILD environment variable to point - echo.to the full path of the 'sphinx-build' executable. Alternatively you - echo.may add the Sphinx directory to PATH. - echo. - echo.If you don't have Sphinx installed, grab it from - echo.http://sphinx-doc.org/ - exit /b 1 -) - -%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% -goto end - -:help -%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% - -:end -popd diff --git a/tests/AutoML_Test.py b/tests/AutoML_Test.py deleted file mode 100644 index b047eed..0000000 --- a/tests/AutoML_Test.py +++ /dev/null @@ -1,170 +0,0 @@ -from AutoMxL.__main__ import AML -import unittest -import pandas as pd -from AutoMxL.Preprocessing.Categorical import CategoricalEncoder -from AutoMxL.Preprocessing.Date import DateEncoder -from AutoMxL.Preprocessing.Missing_Values import NAEncoder -from AutoMxL.Select_Features.Select_Features import FeatSelector -import numpy as np - -# import numpy as np - -# Defaults HP grid for RF -default_RF_grid_param = { - 'n_estimators': np.random.uniform(low=20, high=100, size=20).astype(int), - 'max_features': ['auto', 'log2'], - 'max_depth': np.random.uniform(low=2, high=10, size=20).astype(int), - 'min_samples_split': [5, 10, 15]} - -df_test = pd.read_csv('tests/df_test.csv') -# init -auto_df_init = AML(df_test, target='y_yes') -# explore -auto_df_explore = auto_df_init.duplicate() -auto_df_explore.explore() -# preprocess -auto_df_preprocess = auto_df_explore.duplicate() -auto_df_preprocess.preprocess(date_ref=None, process_outliers=False, cat_method='one_hot') -# select_features -auto_df_select = auto_df_preprocess.duplicate() -auto_df_select.select_features(method='pca') -# model_train_test -auto_df_model = auto_df_select.duplicate() -d_res_model, l_valid, best_model_idx, df_res = auto_df_model.model_train_test(grid_param=default_RF_grid_param, - n_comb=5, comb_seed=2, verbose=True) - -# apply -df_prep = auto_df_select.preprocess_apply(df_test) -df_sel = auto_df_select.select_features_apply(df_prep) - - -class TestInit(unittest.TestCase): - """ init method""" - - # Test autoML object instantiation from DataFrame - def test_df_is_not_none(self): - self.assertIsNotNone(auto_df_init) - - # Test target Attribute - def test_target_is_not_none(self): - self.assertIsNotNone(auto_df_init.target) - - -""" -------------------------------------------------------------------------------------------------------------------------- -""" - -# Instantiate autoML object from df_test and target -auto_df = AML(df_test.copy(), target='y_yes') -# Explore -auto_df.explore(verbose=False) - - -class TestExplore(unittest.TestCase): - """ explore method """ - - def test_d_features(self): - # numerical features - self.assertEqual(auto_df_explore.d_features['numerical'], ['age', 'euribor3m']) - # boolean features - self.assertEqual(auto_df_explore.d_features['boolean'], []) - # categorical features - self.assertEqual(auto_df_explore.d_features['categorical'], ['job', 'education']) - # date features - self.assertEqual(auto_df_explore.d_features['date'], ['date_1', 'date_2']) - # features containing NA values - self.assertEqual(auto_df_explore.d_features['NA'], ['job', 'age', 'date_1']) - # null variance features - self.assertEqual(auto_df_explore.d_features['low_variance'], ['null_var']) - - # unchange dataset - self.assertEqual(df_test.columns.tolist(), auto_df_explore.columns.tolist()) - self.assertTrue(auto_df_explore.isnull().sum().max() > 0) - - -""" -------------------------------------------------------------------------------------------------------------------------- -""" - - -class TestPreprocess(unittest.TestCase): - """ preprocess method """ - - def test_encoders(self): - self.assertEqual(auto_df_preprocess.d_preprocess['remove'], ['null_var']) - self.assertTrue(isinstance(auto_df_preprocess.d_preprocess['date'], DateEncoder)) - self.assertTrue(isinstance(auto_df_preprocess.d_preprocess['NA'], NAEncoder)) - self.assertTrue(isinstance(auto_df_preprocess.d_preprocess['categorical'], CategoricalEncoder)) - self.assertNotIn('outlier', auto_df_preprocess.d_preprocess.keys()) - - def test_preprocessing(self): - self.assertTrue(auto_df_preprocess.isnull().sum().max() == 0) - self.assertEqual(auto_df_preprocess.columns.tolist(), auto_df_preprocess._get_numeric_data().columns.tolist()) - self.assertTrue(auto_df_preprocess.is_fitted_preprocessing) - - -""" -------------------------------------------------------------------------------------------------------------------------- -""" - - -class TestPreprocessApply(unittest.TestCase): - """ preproces method """ - - def test_preprocessing_apply(self): - self.assertTrue(auto_df_preprocess.isnull().sum().max() == 0) - self.assertEqual(auto_df_preprocess.columns.tolist(), auto_df_preprocess._get_numeric_data().columns.tolist()) - self.assertEqual((df_prep == auto_df_preprocess).sum().min(), 41188) - - -""" -------------------------------------------------------------------------------------------------------------------------- -""" - - -class TestSelectFeatures(unittest.TestCase): - """ select_features method """ - - def test_select_features(self): - # - self.assertLess(auto_df_select.shape[1], auto_df_preprocess.shape[1]) - self.assertFalse(auto_df_preprocess.is_fitted_selector) - self.assertTrue(auto_df_select.is_fitted_selector) - self.assertTrue(isinstance(auto_df_select.features_selector, FeatSelector)) - - -""" -------------------------------------------------------------------------------------------------------------------------- -""" - - -class TestSelectFeaturesApply(unittest.TestCase): - """ select_features method """ - - def test_select_features_apply(self): - # - self.assertLess(df_sel.shape[1], df_prep.shape[1]) - self.assertEqual((df_sel == auto_df_select).sum().min(), 41188) - - -""" -------------------------------------------------------------------------------------------------------------------------- -""" - - -class TestModelTrainTest(unittest.TestCase): - """ method """ - - def test_comb_samples(self): - # tests HP comb values - self.assertEqual(auto_df_model.hyperopt.d_train_model[0]['HP']['min_samples_split'], 15) - - def test_best_model(self): - if best_model_idx is not None: - # test best model is valid (delta_auc) - self.assertTrue(d_res_model[best_model_idx]['metrics']['delta_auc'] < 0.03) - # best model have the max F1 score among valid models - for i in range(5): - if d_res_model[i]['metrics']['delta_auc'] < 0.03: - self.assertTrue( - d_res_model[best_model_idx]['metrics']['F1'] >= d_res_model[i]['metrics']['F1']) diff --git a/tests/Explore_Test.py b/tests/Explore_Test.py deleted file mode 100644 index 4672bc6..0000000 --- a/tests/Explore_Test.py +++ /dev/null @@ -1,108 +0,0 @@ -from AutoMxL.Explore.Explore import * -import unittest -import pandas as pd - -# test configt -df_test = pd.read_csv('tests/df_test.csv') -df_test_bis = pd.read_csv('tests/df_test_bis.csv') - - -class TestExplore(unittest.TestCase): - """ - Explore module - """ - - def test_explore(self): - """ explore function""" - d_features = explore(df_test, verbose=False) - - # numerical features - self.assertEqual(d_features['numerical'], ['age', 'euribor3m']) - # boolean features - self.assertEqual(d_features['boolean'], ['y_yes']) - # categorical features - self.assertEqual(d_features['categorical'], ['job', 'education']) - # date features - self.assertEqual(d_features['date'], ['date_1', 'date_2']) - # features containing NA values - self.assertEqual(d_features['NA'], ['job', 'age', 'date_1']) - # null variance features - self.assertEqual(d_features['low_variance'], ['null_var']) - - def test_get_features_type(self): - """ get_features_type function """ - l_feat = df_test_bis.columns.tolist() - l_feat.remove('Date_nai') - l_feat.remove('Is_man_cat') - d_features = get_features_type(df_test_bis, l_var=l_feat) - - # date features - self.assertEqual(d_features['date'], ['American_date_nai']) - # identifier features - self.assertEqual(d_features['identifier'], ['Unnamed: 0', 'Id_cat', 'Id_num']) - # verbatim features - self.assertEqual(d_features['verbatim'], ['Name', 'Verb']) - # boolean features - self.assertEqual(d_features['boolean'], ['Sexe', 'Is_man_num']) - # categorical features - self.assertEqual(d_features['categorical'], ['Age', 'Height', 'Eyes', 'Hair']) - # numerical features - self.assertEqual(d_features['numerical'], []) - - def test_low_variance(self): - self.assertEqual(low_variance_features(df_test, verbose=False).index.tolist(), ['null_var']) - - -""" ---------------------------------------------------------------------------------------------------- -""" - - -class TestFeaturesType(unittest.TestCase): - """ - Features_Type module - """ - - def test_is_date(self): - """ is_date function""" - self.assertTrue(is_date(df_test_bis, 'Date_nai')) - self.assertTrue(is_date(df_test_bis, 'American_date_nai')) - self.assertFalse(is_date(df_test_bis, 'Sexe')) - - def test_is_id(self): - """ is_identifier function""" - self.assertFalse(is_identifier(df_test_bis, 'Name')) - self.assertTrue(is_identifier(df_test_bis, 'Id_cat')) - self.assertTrue(is_identifier(df_test_bis, 'Id_num')) - - def test_is_verb(self): - """ is_verbatim function""" - self.assertTrue(is_verbatim(df_test_bis, 'Verb')) - self.assertFalse(is_verbatim(df_test_bis, 'Hair')) - - def test_is_bool(self): - """ is_boolean function""" - self.assertTrue(is_boolean(df_test_bis, 'Is_man_cat')) - self.assertTrue(is_boolean(df_test_bis, 'Is_man_num')) - - def test_is_cat(self): - """ is_categorical function""" - self.assertTrue(is_categorical(df_test_bis, 'Eyes')) - self.assertTrue(is_categorical(df_test_bis, 'Hair')) - self.assertFalse(is_categorical(df_test_bis, 'Is_man_num')) - - def test_features_per_type(self): - """ features_per_type function""" - # date - self.assertEqual(features_from_type(df_test_bis, typ='date'), ['Date_nai', 'American_date_nai']) - # identifier - self.assertEqual(features_from_type(df_test_bis, l_var=['Id_cat', 'Id_num', 'American_date_nai', 'Verb'], - typ='identifier'), ['Id_cat', 'Id_num']) - # verbatim - self.assertEqual(features_from_type(df_test_bis, typ='verbatim'), ['Name', 'Verb']) - # boolean - self.assertEqual(features_from_type(df_test_bis, typ='boolean'), ['Sexe', 'Is_man_cat', 'Is_man_num']) - # cateogircal - self.assertEqual( - features_from_type(df_test_bis, l_var=['Eyes', 'Hair', 'American_date_nai'], typ='categorical'), - ['Eyes', 'Hair']) diff --git a/tests/Modelisation_Test.py b/tests/Modelisation_Test.py deleted file mode 100644 index 5989f59..0000000 --- a/tests/Modelisation_Test.py +++ /dev/null @@ -1,108 +0,0 @@ -from AutoMxL.Modelisation.Bagging import * -from AutoMxL.Modelisation.HyperOpt import HyperOpt -import unittest -import pandas as pd -import sklearn - -df_iris_binary = pd.read_csv('tests/iris_binary.csv') -df_train, df_test = train_test(df_iris_binary, 0.2) -df_test = df_test.drop('Setosa', axis=1) - - -class TestBagging(unittest.TestCase): - """ - Bagging class - """ - bagging = Bagging(clf=RandomForestClassifier(n_estimators=100, max_leaf_nodes=100), - n_sample=3, - pos_sample_size=1.0, - replace=True) - - # - def test_init(self): - """ init method """ - self.assertIsNotNone(self.bagging) - self.assertEqual(type(self.bagging.classifier), sklearn.ensemble.forest.RandomForestClassifier) - self.assertEqual(self.bagging.niter, 3) - self.assertEqual(self.bagging.pos_sample_size, 1.0) - self.assertEqual(self.bagging.replace, True) - - # - def test_fit(self): - """ fit method""" - self.bagging.fit(df_train, 'Setosa') - for i in range(self.bagging.niter): - self.assertEqual(type(self.bagging.list_model[i]), sklearn.ensemble.forest.RandomForestClassifier) - self.assertTrue(self.bagging.is_fitted) - - def test_predict(self): - """ test method """ - self.bagging.fit(df_train, 'Setosa') - res_bagging = self.bagging.predict(df_test) - # le test nul :> - self.assertEqual(len(res_bagging[0]), df_test.shape[0]) - self.assertEqual(len(res_bagging[1]), df_test.shape[0]) - - -""" ----------------------------------------------------------------------------------------------------------------- -""" - -default_XGB_grid_param = { - 'n_estimators': np.random.uniform(low=100, high=300, size=20).astype(int), - 'max_features': ['auto', 'log2'], - 'max_depth': np.random.uniform(low=3, high=10, size=20).astype(int), - 'min_samples_split': [5, 10, 15], - 'min_samples_leaf': [1, 2, 4, 8], - 'learning_rate': [0.0001, 0.0003, 0.0006, 0.0009, 0.001, 0.003, 0.006, 0.009, 0.01, 0.03, 0.06, 0.09, 0.1, 0.3, - 0.6], - 'scale_pos_weight': [2, 3, 4, 5, 6, 7]} - - -class TestHyperOpt(unittest.TestCase): - """ - HyperOpt class - """ - hyperopt = HyperOpt(classifier='XGBOOST', grid_param=default_XGB_grid_param, n_param_comb=2, bagging=False, - comb_seed=1) - - hyperopt.fit(df_iris_binary, target='Setosa') - - def test_init(self): - """ init method """ - self.assertEqual(self.hyperopt.classifier, 'XGBOOST') - self.assertEqual(self.hyperopt.grid_param, default_XGB_grid_param) - self.assertEqual(self.hyperopt.n_param_comb, 2) - self.assertEqual(self.hyperopt.bagging, False) - self.assertEqual(self.hyperopt.comb_seed, 1) - - def test_fit(self): - """ fit method """ - self.hyperopt.fit(df_iris_binary, target='Setosa') - # - self.assertEqual(len(self.hyperopt.d_train_model.keys()), 2) - - def test_predict(self): - """ predict method""" - d_apply_model = self.hyperopt.predict(df_iris_binary, target='Setosa', delta_auc=0.03) - self.assertEqual(len(d_apply_model.keys()), 2) - - def test_get_best_model(self): - """ get_best_model method """ - d_apply_model = self.hyperopt.predict(df_iris_binary, target='Setosa', delta_auc=0.03) - best_idx, l_valid = self.hyperopt.get_best_model(d_apply_model, metric='F1', delta_auc_th=0.03) - self.assertIn(best_idx, range(2)) - self.assertLessEqual(len(l_valid), 2) - - # test best model is valid (delta_auc) - self.assertTrue(d_apply_model[best_idx]['evaluation']['delta_auc'] < 0.03) - # best model have the max F1 score among valid models - for i in range(2): - if d_apply_model[i]['evaluation']['delta_auc'] < 0.03: - self.assertTrue(d_apply_model[best_idx]['evaluation']['F1'] >= d_apply_model[i]['evaluation']['F1']) - - def test_model_res_to_df(self): - """ model_res_to_df """ - d_apply_model = self.hyperopt.predict(df_iris_binary, target='Setosa', delta_auc=0.03) - df_res = self.hyperopt.model_res_to_df(d_apply_model, sort_metric='F1') - self.assertEqual(df_res.shape[0], 2) diff --git a/tests/Preprocessing_Test.py b/tests/Preprocessing_Test.py deleted file mode 100644 index a0f1fbf..0000000 --- a/tests/Preprocessing_Test.py +++ /dev/null @@ -1,207 +0,0 @@ -from AutoMxL.Preprocessing.Categorical import * -from AutoMxL.Preprocessing.Date import * -from AutoMxL.Preprocessing.Outliers import * -from AutoMxL.Preprocessing.Missing_Values import * -import unittest -import pandas as pd -import math - -# test config -df = pd.read_csv('tests/df_test_bis.csv') - - -class TestMissingValues(unittest.TestCase): - """ - Test Missing_Values module - """ - - def test_fill_numerical(self): - """ fill_numerical function""" - df_fill_all_num = fill_numerical(df, ['Age', 'Height'], method='zero', track_num_NA=True, verbose=False) - self.assertIn('top_NA_Height', df_fill_all_num.columns.tolist()) - self.assertIn('top_NA_Age', df_fill_all_num.columns.tolist()) - self.assertEqual(df_fill_all_num.iloc[0]['Height'], 0) - self.assertEqual(df_fill_all_num.iloc[1]['Age'], 0) - - def test_fill_categorical(self): - """ fill_categorical function""" - df_fill_all_cat = fill_categorical(df, l_var=['Name', 'Sexe'], method='NR', verbose=False) - self.assertEqual(df_fill_all_cat.iloc[3]['Name'], 'NR') - self.assertEqual(df_fill_all_cat.iloc[3]['Sexe'], 'NR') - - def test_NAEncoder(self): - """ NAEncoder class""" - NA_encoder1 = NAEncoder(replace_num_with='median', replace_cat_with='NR', track_num_NA=True) - NA_encoder1.fit(df, l_var=['Name', 'Age']) - df_NA1 = NA_encoder1.transform(df) - # - NA_encoder2 = NAEncoder(replace_num_with='zero', replace_cat_with='NR', track_num_NA=False) - df_NA2 = NA_encoder2.fit_transform(df) - - # created features - self.assertIn("top_NA_Age", df_NA1.columns.tolist()) - self.assertNotIn('top_NA_Height', df_NA1.columns.tolist()) - self.assertNotIn("top_NA_Age", df_NA2.columns.tolist()) - # raw features contain NA - self.assertEqual(get_NA_features(df), - ['Name', 'Id_cat', 'Id_num', 'Verb', 'Age', 'Height', 'Sexe', 'Date_nai', 'American_date_nai']) - # filled features - self.assertEqual(get_NA_features(df_NA1), - ['Id_cat', 'Id_num', 'Verb', 'Height', 'Sexe', 'Date_nai', 'American_date_nai']) - self.assertEqual(get_NA_features(df_NA2), []) - # modified values - self.assertTrue(math.isnan(df['Name'][3])) - self.assertTrue(math.isnan(df['Age'][1])) - self.assertEqual(df_NA1['Name'][3], 'NR') - self.assertEqual(df_NA1['Age'][1], 25.5) - self.assertEqual(df_NA2['Name'][3], 'NR') - self.assertEqual(df_NA2['Age'][1], 0) - - -""" ------------------------------------------------------------------------------------------------- -""" - -df_to_date = all_to_date(df, ['Date_nai', 'American_date_nai'], verbose=False) -df_to_anc, new_var_list = date_to_anc(df_to_date, l_var=['American_date_nai', 'Date_nai'], date_ref='27/10/2010') - - -class TestDate(unittest.TestCase): - """ - Test Date Module - """ - - def test_all_to_date(self): - """ all_to_date function """ - self.assertEqual(np.dtype(df_to_date['American_date_nai']), 'datetime64[ns]') - self.assertEqual(np.dtype(df_to_date['Date_nai']), 'datetime64[ns]') - self.assertEqual(np.dtype(df_to_date['American_date_nai']), 'datetime64[ns]') - - def test_date_to_anc(self): - """ date_to_anc function""" - self.assertIn('anc_American_date_nai', df_to_anc.columns) - self.assertIn('anc_Date_nai', df_to_anc.columns) - self.assertNotIn('Date_nai', df_to_anc.columns) - self.assertNotIn('American_Date_nai', df_to_anc.columns) - self.assertEqual(df_to_anc['anc_Date_nai'][0], 0.0) - self.assertIn('anc_American_date_nai', new_var_list) - - def test_DateEncoder(self): - """ DateEncoder class""" - Date_encoder1 = DateEncoder(method='timedelta', date_ref='27/10/2010') - Date_encoder1.fit(df, l_var=['American_date_nai', 'Age']) - df_date1 = Date_encoder1.transform(df) - # - date_encoder2 = DateEncoder(method='timedelta', date_ref='27/10/2011') - df_date2 = date_encoder2.fit_transform(df) - - # created/removed features - self.assertIn('anc_American_date_nai', df_date1.columns.tolist()) - self.assertIn('Date_nai', df_date1.columns.tolist()) - self.assertNotIn('anc_Date_nai', df_date1.columns.tolist()) - self.assertIn('Age', df_date1.columns.tolist()) - self.assertNotIn('American_date_nai', df_date2.columns.tolist()) - self.assertNotIn('Date_nai', df_date2.columns.tolist()) - # features formats - self.assertEqual(df_date1['anc_American_date_nai'].dtype, 'float64') - self.assertEqual(df_date2['anc_Date_nai'].dtype, 'float64') - # features values - self.assertEqual(df_date1['anc_American_date_nai'][0], 0.0) - self.assertEqual(df_date2['anc_Date_nai'][0], 1.0) - self.assertEqual(df_date2['anc_American_date_nai'][0], 1.0) - - -""" ------------------------------------------------------------------------------------------------- -""" - - -class TestCategorical(unittest.TestCase): - """ - Test Categorical module - """ - - def test_dummy_all_var(self): - """ dummy_all_var func """ - df_dummy = dummy_all_var(df, var_list=['Eyes', 'Sexe'], prefix_list=None, keep=False, verbose=False) - df_dummy_pref = dummy_all_var(df, var_list=['Eyes', 'Sexe'], prefix_list=['Ey', 'Sx'], keep=True, - verbose=False) - # created/removed features - self.assertIn('Eyes_blue', df_dummy.columns) - self.assertIn('Eyes_red', df_dummy.columns) - self.assertNotIn('Eyes', df_dummy.columns) - self.assertNotIn('Sexe', df_dummy.columns) - self.assertIn('Ey_blue', df_dummy_pref.columns) - self.assertIn('Sx_M', df_dummy_pref.columns) - self.assertIn('Eyes', df_dummy_pref.columns) - self.assertIn('Sexe', df_dummy_pref.columns) - # features values - self.assertEqual(df_dummy['Eyes_blue'].tolist(), [1, 0, 0, 1, 0, 1]) - self.assertEqual(df_dummy['Sexe_M'].tolist(), [1, 1, 1, 0, 0, 1]) - - def test_CategoricalEncoder(self): - """ CategoricalEncoder """ - df_pred = pd.read_csv('tests/df_test.Csv') - df_pred['job'] = df_pred['job'].fillna('NR') - - cat_encoder1 = CategoricalEncoder(method='deep_encoder') - df_cat1 = cat_encoder1.fit_transform(df_pred, target='y_yes', l_var=['job', 'education'], verbose=False) - print('\n\n') - # - cat_encoder2 = CategoricalEncoder(method='one_hot') - cat_encoder2.fit(df, l_var=['Name', 'Eyes'], verbose=False) - print('\n\n') - df_cat2 = cat_encoder2.transform(df) - - # features created/removed - self.assertIn('job_0', df_cat1.columns.tolist()) - self.assertIn('education_0', df_cat1.columns.tolist()) - self.assertNotIn('job', df_cat1.columns.tolist()) - self.assertIn('Eyes_blue', df_cat2.columns.tolist()) - self.assertNotIn('Eyes', df_cat2.columns.tolist()) - - # features embedding - self.assertEqual(list(cat_encoder1.d_embeddings.keys()), ['job', 'education']) - # features values - self.assertEqual(df_cat2['Eyes_green'].tolist(), [0, 0, 0, 0, 1, 0]) - - -""" ------------------------------------------------------------------------------------------------- -""" - - -class TestOutliers(unittest.TestCase): - - def test_replace_category(self): - """ replace_category function """ - df_process_cat = replace_category(df, 'Hair', ['blond'], verbose=False) - df_process_cat = replace_category(df_process_cat, 'Name', ['Tom', 'Nick'], verbose=False) - - # features values - self.assertEqual(df_process_cat['Name'].tolist(), - ['outliers', 'outliers', 'Krish', np.nan, 'John', 'Jack']) - self.assertEqual(df_process_cat['Hair'].tolist(), - ['brown', 'brown', 'dark', 'outliers', 'outliers', 'outliers']) - - def test_extrem_values(self): - """ extreme_values function """ - df_outlier_proc = replace_extreme_values(df, 'Height', 175, 185) - self.assertEqual(df_outlier_proc['Height'].tolist()[1:], [175.0, 180.0, 185.0, 185.0, 185.0]) - - def test_OutlierEncode(self): - """ OutlierEncodeR class """ - out_encoder1 = OutliersEncoder(cat_threshold=0.25, num_xstd=1) - out_encoder1.fit(df, l_var=['Height', 'Sexe', 'Hair', 'Age'], verbose=False) - df_out1 = out_encoder1.transform(df, verbose=False) - out_encoder2 = OutliersEncoder(cat_threshold=0.2, num_xstd=1) - df_out2 = out_encoder2.fit_transform(df, verbose=False) - - # cat outliers - self.assertEqual(list(df_out1['Hair']), ['brown', 'brown', 'dark', 'blond', 'blond', 'blond']) - self.assertEqual(list(df_out1['Sexe']), ['M', 'M', 'M', np.nan, 'F', 'M']) - self.assertEqual(list(df_out2['Name']), ['outliers'] * 6) - # num outliers - self.assertEqual(list(df_out1['Height'].round(4))[1:], [175.2177, 180.0, 188.7823, 185.0, 185.0]) - self.assertEqual(list(df_out2['Unnamed: 0'].round(4)), [0.7922, 1.0, 2.0, 3.0, 4.0, 4.2078]) - print('out2') diff --git a/tests/Select_Features_Test.py b/tests/Select_Features_Test.py deleted file mode 100644 index ce08d45..0000000 --- a/tests/Select_Features_Test.py +++ /dev/null @@ -1,45 +0,0 @@ -import unittest -import pandas as pd -from AutoMxL.Select_Features.Select_Features import FeatSelector - -df = pd.read_csv('tests/df_test.csv') -df['age'] = df['age'].fillna(df['age'].median()) - - -class TestFeatSelector(unittest.TestCase): - """ - FeatSelector class - """ - sel1 = FeatSelector(method='pca') - sel2 = FeatSelector() - - def test_init(self): - """ init method """ - self.assertEqual(self.sel1.method, 'pca') - - def test_fit(self): - """ fit method """ - self.sel1.fit(df, l_var=['age', 'euribor3m']) - self.assertEqual(self.sel1.l_select_var, ['age', 'euribor3m']) - self.assertIsNotNone(self.sel1.selector) - self.assertIsNotNone(self.sel1.scaler) - self.assertTrue(self.sel1.is_fitted) - self.assertEqual(df.columns.tolist(), - ['age', 'job', 'education', 'euribor3m', 'date_1', 'date_2', "null_var", 'y_yes']) - - def test_transform(self): - """ transform method""" - self.sel1.fit(df, l_var=['age', 'euribor3m']) - df_sel1 = self.sel1.transform(df) - self.assertEqual(df_sel1.columns.tolist(), ['job', 'education', 'date_1', 'date_2', 'null_var', 'y_yes', 'Dim0', - 'Dim1']) - - def test_fit_transform(self): - df_sel2 = self.sel2.fit_transform(df, l_var=None) - self.assertEqual(self.sel2.l_select_var, ['age', 'euribor3m', 'null_var', 'y_yes']) - self.assertIsNotNone(self.sel2.selector) - self.assertIsNotNone(self.sel2.scaler) - self.assertTrue(self.sel2.is_fitted) - self.assertEqual(df.columns.tolist(), - ['age', 'job', 'education', 'euribor3m', 'date_1', 'date_2', "null_var", 'y_yes']) - self.assertEqual(df_sel2.columns.tolist(), ['job', 'education', 'date_1', 'date_2', 'Dim0', 'Dim1', 'Dim2']) diff --git a/tests/Start_Test.py b/tests/Start_Test.py deleted file mode 100644 index 5faffe6..0000000 --- a/tests/Start_Test.py +++ /dev/null @@ -1,67 +0,0 @@ -import unittest -import pandas as pd -from AutoMxL.Start.Load import get_delimiter, import_data -from AutoMxL.Start.Encode_Target import category_to_target, range_to_target - -# test config -file = 'tests/df_test.csv' -var = 'job' -cat = 'admin.' -df_test = pd.read_csv('tests/df_test.csv') -df_test_bis = pd.read_csv('tests/df_test_bis.csv') -raw_target = 'Height' - - -class TestLoad(unittest.TestCase): - """ - Encode module - """ - - def test_get_delimiter(self): - """test get_delimiter function""" - # identify delimiter - self.assertEqual(get_delimiter(file), ',') - - def test_import_data(self): - """test import_data function""" - # DataFrame created - self.assertEqual(type(import_data(file=file, verbose=False)), pd.DataFrame) - - -""" ------------------------------------------------------------------------------------------------- -""" - - -class TestEncodeTarget(unittest.TestCase): - """ - Encode_Target module - """ - - def test_cat_to_target(self): - """cat_to_target function""" - df_test_cat_target, new_var = category_to_target(df_test, var, cat) - # test new target name - self.assertEqual(new_var, var + '_' + cat) - # new target in new dataset - self.assertIn(new_var, df_test_cat_target.columns.tolist()) - # old target removed from new dataset - self.assertNotIn(var, df_test_cat_target.columns.tolist()) - # volumetry test - self.assertEqual(df_test[var].value_counts()[cat], df_test_cat_target[new_var].sum()) - - def test_range_to_target(self): - """range_to_target function""" - df_range_target, new_var = range_to_target(df_test_bis, var=raw_target, min=180, max=185, verbose=False) - # new target in new dataset - self.assertIn(new_var, df_range_target.columns.tolist()) - # old target removed from new dataset - self.assertNotIn(raw_target, df_range_target.columns.tolist()) - # lower and upper filled - self.assertEqual(df_range_target[new_var].tolist(), [0, 0, 1, 0, 1, 1]) - # only lower filled - df_range_target, new_var = range_to_target(df_test_bis, var=raw_target, min=180, verbose=False) - self.assertEqual(df_range_target[new_var].tolist(), [0, 0, 1, 1, 1, 1]) - # only upper filled - df_range_target, new_var = range_to_target(df_test_bis, var=raw_target, max=185, verbose=False) - self.assertEqual(df_range_target[new_var].tolist(), [0, 1, 1, 0, 1, 1]) diff --git a/tests/df_test.csv b/tests/df_test.csv deleted file mode 100644 index 9ac7228..0000000 --- a/tests/df_test.csv +++ /dev/null @@ -1,41189 +0,0 @@ -age,job,education,euribor3m,date_1,date_2,null_var,y_yes -,,basic.4y,4.857,24/02/2016,23/03/2012,1.0,0 -57.0,services,high.school,4.857,25/02/2014,03/10/2000,1.0,0 -37.0,services,high.school,4.857,05/03/1987,01/12/2006,1.0,0 -40.0,admin.,basic.6y,4.857,10/03/2006,05/11/1994,1.0,0 -56.0,services,high.school,4.857,09/04/1993,03/04/2018,1.0,0 -45.0,services,basic.9y,4.857,,14/03/2006,1.0,0 -59.0,admin.,professional.course,4.857,29/06/2018,01/12/1994,1.0,0 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