Remove documentation, tests, and all docstrings/comments

AutoMxL/Explore/Explore.py

@@ -1,77 +1,25 @@
-""" 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')
 
     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:
@@ -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) + ')')