diff --git a/dacon/lstm_base.ipynb b/dacon/lstm_base.ipynb new file mode 100644 index 0000000..08cd2bc --- /dev/null +++ b/dacon/lstm_base.ipynb @@ -0,0 +1,1158 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import random\n", + "import os\n", + "\n", + "from tqdm import tqdm\n", + "from statsmodels.tsa.arima.model import ARIMA\n", + "\n", + "import warnings\n", + "warnings.filterwarnings(\"ignore\")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "def seed_everything(seed):\n", + " random.seed(seed)\n", + " os.environ['PYTHONHASHSEED'] = str(seed)\n", + " np.random.seed(seed)\n", + "\n", + "seed_everything(42) # Seed 고정" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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일자종목코드종목명거래량시가고가저가종가
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" + ], + "text/plain": [ + " 일자 종목코드 종목명 거래량 시가 고가 저가 종가\n", + "0 20210601 A060310 3S 166690 2890 2970 2885 2920\n", + "1 20210601 A095570 AJ네트웍스 63836 5860 5940 5750 5780\n", + "2 20210601 A006840 AK홀딩스 103691 35500 35600 34150 34400\n", + "3 20210601 A054620 APS 462544 14600 14950 13800 14950\n", + "4 20210601 A265520 AP시스템 131987 29150 29150 28800 29050" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "train = pd.read_csv('./train.csv')\n", + "train.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 0%| | 0/2000 [00:00.predict_function at 0x0000023EBD534EE0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "1/1 [==============================] - 1s 993ms/step\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 0%| | 1/2000 [00:08<4:42:21, 8.48s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1/1 [==============================] - 0s 460ms/step\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 0%| | 2/2000 [00:16<4:32:19, 8.18s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1/1 [==============================] - 0s 480ms/step\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 0%| | 3/2000 [00:24<4:26:21, 8.00s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1/1 [==============================] - 0s 478ms/step\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 0%| | 4/2000 [00:33<4:47:30, 8.64s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1/1 [==============================] - 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predictions[0]) / predictions[0]\n", + "\n", + " # 결과 저장\n", + " results_df = results_df.append({'종목코드': code, 'final_return': final_return}, ignore_index=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "results_df" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " 종목코드 final_return 순위\n", + "5 A211270 [-0.06308102] 1\n", + "1 A095570 [-0.04776682] 2\n", + "8 A126600 [-0.038834322] 3\n", + "7 A282330 [-0.01267713] 4\n", + "6 A027410 [-0.009586] 5\n", + "2 A006840 [0.016609492] 6\n", + "9 A138930 [0.01706481] 7\n", + "3 A054620 [0.01961614] 8\n", + "4 A265520 [0.05338428] 9\n", + "0 A060310 [0.073663026] 10" + ] + }, + "execution_count": 123, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "results_df['순위'] = results_df['final_return'].rank(method='first').astype('int') # 각 순위를 중복없이 생성\n", + "results_df.sort_values('순위')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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종목코드final_return순위
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" + ], + "text/plain": [ + " 종목코드 final_return 순위\n", + "0 A060310 [-8.4089585e-08] 1\n", + "1 A095570 [0.0] 2\n", + "2 A006840 [8.503587e-08] 9\n", + "3 A054620 [0.0] 3\n", + "4 A265520 [0.0] 4\n", + "5 A211270 [0.0] 5\n", + "6 A027410 [0.0] 6\n", + "7 A282330 [0.0] 7\n", + "8 A126600 [0.0] 8\n", + "9 A138930 [8.691532e-08] 10" + ] + }, + "execution_count": 118, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sample_submission = pd.read_csv('./sample_submission.csv')\n", + "sample_submission" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "baseline_submission = sample_submission[['종목코드']].merge(results_df[['종목코드', '순위']], on='종목코드', how='left')\n", + "baseline_submission" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "baseline_submission.to_csv('baseline_submission.csv', index=False)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/robo/finance_predict.ipynb b/robo/finance_predict.ipynb index cc06f36..7e58067 100644 --- a/robo/finance_predict.ipynb +++ b/robo/finance_predict.ipynb @@ -332,28 +332,6 @@ "df_list[0].head()" ] }, - { - "cell_type": "code", - "execution_count": 127, - "metadata": {}, - "outputs": [ - { - "ename": "AttributeError", - "evalue": "'Series' object has no attribute 'to_datetime'", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[127], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m a \u001b[39m=\u001b[39m df_list[\u001b[39m0\u001b[39;49m][\u001b[39m'\u001b[39;49m\u001b[39mdate\u001b[39;49m\u001b[39m'\u001b[39;49m]\u001b[39m.\u001b[39;49mto_datetime()\n\u001b[0;32m 2\u001b[0m a\n", - "File \u001b[1;32mc:\\Users\\chocs\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\pandas\\core\\generic.py:5902\u001b[0m, in \u001b[0;36mNDFrame.__getattr__\u001b[1;34m(self, name)\u001b[0m\n\u001b[0;32m 5895\u001b[0m \u001b[39mif\u001b[39;00m (\n\u001b[0;32m 5896\u001b[0m name \u001b[39mnot\u001b[39;00m \u001b[39min\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_internal_names_set\n\u001b[0;32m 5897\u001b[0m \u001b[39mand\u001b[39;00m name \u001b[39mnot\u001b[39;00m \u001b[39min\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_metadata\n\u001b[0;32m 5898\u001b[0m \u001b[39mand\u001b[39;00m name \u001b[39mnot\u001b[39;00m \u001b[39min\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_accessors\n\u001b[0;32m 5899\u001b[0m \u001b[39mand\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_info_axis\u001b[39m.\u001b[39m_can_hold_identifiers_and_holds_name(name)\n\u001b[0;32m 5900\u001b[0m ):\n\u001b[0;32m 5901\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m[name]\n\u001b[1;32m-> 5902\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mobject\u001b[39;49m\u001b[39m.\u001b[39;49m\u001b[39m__getattribute__\u001b[39;49m(\u001b[39mself\u001b[39;49m, name)\n", - "\u001b[1;31mAttributeError\u001b[0m: 'Series' object has no attribute 'to_datetime'" - ] - } - ], - "source": [ - "knn 분류 선이 그어지면 " - ] - }, { "cell_type": "code", "execution_count": 79,