{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 导入第三方包" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "ExecuteTime": { "end_time": "2021-03-15T00:52:16.629173Z", "start_time": "2021-03-15T00:52:16.621194Z" } }, "outputs": [], "source": [ "import os\n", "import gc\n", "import math\n", "\n", "import pandas as pd\n", "import numpy as np\n", "\n", "import lightgbm as lgb\n", "import xgboost as xgb\n", "from catboost import CatBoostRegressor\n", "from sklearn.linear_model import SGDRegressor, LinearRegression, Ridge\n", "from sklearn.preprocessing import MinMaxScaler\n", "\n", "\n", "from sklearn.model_selection import StratifiedKFold, KFold\n", "from sklearn.metrics import log_loss\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.preprocessing import OneHotEncoder\n", "\n", "from tqdm import tqdm\n", "import matplotlib.pyplot as plt\n", "import time\n", "import warnings\n", "warnings.filterwarnings('ignore')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 读取数据" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "ExecuteTime": { "end_time": "2021-03-15T00:52:22.085956Z", "start_time": "2021-03-15T00:52:19.571864Z" } }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
idheartbeat_signalslabel
000.9912297987616655,0.9435330436439665,0.764677...0.0
110.9714822034884503,0.9289687459588268,0.572932...0.0
221.0,0.9591487564065292,0.7013782792997189,0.23...2.0
330.9757952826275774,0.9340884687738161,0.659636...0.0
440.0,0.055816398940721094,0.26129357194994196,0...2.0
\n", "
" ], "text/plain": [ " id heartbeat_signals label\n", "0 0 0.9912297987616655,0.9435330436439665,0.764677... 0.0\n", "1 1 0.9714822034884503,0.9289687459588268,0.572932... 0.0\n", "2 2 1.0,0.9591487564065292,0.7013782792997189,0.23... 2.0\n", "3 3 0.9757952826275774,0.9340884687738161,0.659636... 0.0\n", "4 4 0.0,0.055816398940721094,0.26129357194994196,0... 2.0" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train = pd.read_csv('train.csv')\n", "test=pd.read_csv('testA.csv')\n", "train.head()" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "ExecuteTime": { "end_time": "2021-03-15T00:52:41.773931Z", "start_time": "2021-03-15T00:52:41.760966Z" } }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
idheartbeat_signals
01000000.9915713654170097,1.0,0.6318163407681274,0.13...
11000010.6075533139615096,0.5417083883163654,0.340694...
21000020.9752726292239277,0.6710965234906665,0.686758...
31000030.9956348033996116,0.9170249621481004,0.521096...
41000041.0,0.8879490481178918,0.745564725322326,0.531...
\n", "
" ], "text/plain": [ " id heartbeat_signals\n", "0 100000 0.9915713654170097,1.0,0.6318163407681274,0.13...\n", "1 100001 0.6075533139615096,0.5417083883163654,0.340694...\n", "2 100002 0.9752726292239277,0.6710965234906665,0.686758...\n", "3 100003 0.9956348033996116,0.9170249621481004,0.521096...\n", "4 100004 1.0,0.8879490481178918,0.745564725322326,0.531..." ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "test.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 数据预处理" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "ExecuteTime": { "end_time": "2021-03-15T00:53:20.837171Z", "start_time": "2021-03-15T00:53:20.824203Z" } }, "outputs": [], "source": [ "def reduce_mem_usage(df):\n", " start_mem = df.memory_usage().sum() / 1024**2 \n", " print('Memory usage of dataframe is {:.2f} MB'.format(start_mem))\n", " \n", " for col in df.columns:\n", " col_type = df[col].dtype\n", " \n", " if col_type != object:\n", " c_min = df[col].min()\n", " c_max = df[col].max()\n", " if str(col_type)[:3] == 'int':\n", " if c_min > np.iinfo(np.int8).min and c_max < np.iinfo(np.int8).max:\n", " df[col] = df[col].astype(np.int8)\n", " elif c_min > np.iinfo(np.int16).min and c_max < np.iinfo(np.int16).max:\n", " df[col] = df[col].astype(np.int16)\n", " elif c_min > np.iinfo(np.int32).min and c_max < np.iinfo(np.int32).max:\n", " df[col] = df[col].astype(np.int32)\n", " elif c_min > np.iinfo(np.int64).min and c_max < np.iinfo(np.int64).max:\n", " df[col] = df[col].astype(np.int64) \n", " else:\n", " if c_min > np.finfo(np.float16).min and c_max < np.finfo(np.float16).max:\n", " df[col] = df[col].astype(np.float16)\n", " elif c_min > np.finfo(np.float32).min and c_max < np.finfo(np.float32).max:\n", " df[col] = df[col].astype(np.float32)\n", " else:\n", " df[col] = df[col].astype(np.float64)\n", " else:\n", " df[col] = df[col].astype('category')\n", "\n", " end_mem = df.memory_usage().sum() / 1024**2 \n", " print('Memory usage after optimization is: {:.2f} MB'.format(end_mem))\n", " print('Decreased by {:.1f}%'.format(100 * (start_mem - end_mem) / start_mem))\n", " \n", " return df" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "ExecuteTime": { "end_time": "2021-03-15T00:54:18.244721Z", "start_time": "2021-03-15T00:53:59.807775Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Memory usage of dataframe is 157.93 MB\n", "Memory usage after optimization is: 39.67 MB\n", "Decreased by 74.9%\n", "Memory usage of dataframe is 31.43 MB\n", "Memory usage after optimization is: 7.90 MB\n", "Decreased by 74.9%\n" ] } ], "source": [ "# 简单预处理\n", "train_list = []\n", "\n", "for items in train.values:\n", " train_list.append([items[0]] + [float(i) for i in items[1].split(',')] + [items[2]])\n", "\n", "train = pd.DataFrame(np.array(train_list))\n", "train.columns = ['id'] + ['s_'+str(i) for i in range(len(train_list[0])-2)] + ['label']\n", "train = reduce_mem_usage(train)\n", "\n", "test_list=[]\n", "for items in test.values:\n", " test_list.append([items[0]] + [float(i) for i in items[1].split(',')])\n", "\n", "test = pd.DataFrame(np.array(test_list))\n", "test.columns = ['id'] + ['s_'+str(i) for i in range(len(test_list[0])-1)]\n", "test = reduce_mem_usage(test)\n" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "ExecuteTime": { "end_time": "2021-03-15T00:54:57.351196Z", "start_time": "2021-03-15T00:54:57.321310Z" } }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
ids_0s_1s_2s_3s_4s_5s_6s_7s_8...s_196s_197s_198s_199s_200s_201s_202s_203s_204label
00.00.9912110.9433590.7646480.6186520.3796390.1907960.0402220.0260010.031708...0.00.00.00.00.00.00.00.00.00.0
11.00.9716800.9291990.5727540.1784670.1229860.1323240.0944210.0896000.030487...0.00.00.00.00.00.00.00.00.00.0
22.01.0000000.9589840.7011720.2318120.0000000.0806880.1284180.1875000.280762...0.00.00.00.00.00.00.00.00.02.0
33.00.9755860.9340820.6596680.2498780.2370610.2814940.2498780.2498780.241455...0.00.00.00.00.00.00.00.00.00.0
44.00.0000000.0558170.2612300.3598630.4331050.4536130.4990230.5429690.616699...0.00.00.00.00.00.00.00.00.02.0
\n", "

5 rows × 207 columns

\n", "
" ], "text/plain": [ " id s_0 s_1 s_2 s_3 s_4 s_5 s_6 \\\n", "0 0.0 0.991211 0.943359 0.764648 0.618652 0.379639 0.190796 0.040222 \n", "1 1.0 0.971680 0.929199 0.572754 0.178467 0.122986 0.132324 0.094421 \n", "2 2.0 1.000000 0.958984 0.701172 0.231812 0.000000 0.080688 0.128418 \n", "3 3.0 0.975586 0.934082 0.659668 0.249878 0.237061 0.281494 0.249878 \n", "4 4.0 0.000000 0.055817 0.261230 0.359863 0.433105 0.453613 0.499023 \n", "\n", " s_7 s_8 ... s_196 s_197 s_198 s_199 s_200 s_201 s_202 \\\n", "0 0.026001 0.031708 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", "1 0.089600 0.030487 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", "2 0.187500 0.280762 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", "3 0.249878 0.241455 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", "4 0.542969 0.616699 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", "\n", " s_203 s_204 label \n", "0 0.0 0.0 0.0 \n", "1 0.0 0.0 0.0 \n", "2 0.0 0.0 2.0 \n", "3 0.0 0.0 0.0 \n", "4 0.0 0.0 2.0 \n", "\n", "[5 rows x 207 columns]" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train.head()" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "ExecuteTime": { "end_time": "2021-03-15T00:55:36.644040Z", "start_time": "2021-03-15T00:55:36.619678Z" } }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
ids_0s_1s_2s_3s_4s_5s_6s_7s_8...s_195s_196s_197s_198s_199s_200s_201s_202s_203s_204
0100000.00.9916991.0000000.6318360.1362300.0414120.1027220.1208500.1234130.107910...0.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.00000
1100001.00.6074220.5415040.3405760.0000000.0906980.1649170.1950680.1688230.198853...0.3898930.3869630.3671880.3640140.3605960.3571780.3505860.3505860.3505860.36377
2100002.00.9750980.6708980.6865230.7084960.7187500.7167970.7207030.7016600.596680...0.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.00000
3100003.00.9956050.9169920.5209960.0000000.2218020.4040530.4904790.5273440.518066...0.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.00000
4100004.01.0000000.8881840.7456050.5317380.3803710.2246090.0911250.0576480.003914...0.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.00000
\n", "

5 rows × 206 columns

\n", "
" ], "text/plain": [ " id s_0 s_1 s_2 s_3 s_4 s_5 \\\n", "0 100000.0 0.991699 1.000000 0.631836 0.136230 0.041412 0.102722 \n", "1 100001.0 0.607422 0.541504 0.340576 0.000000 0.090698 0.164917 \n", "2 100002.0 0.975098 0.670898 0.686523 0.708496 0.718750 0.716797 \n", "3 100003.0 0.995605 0.916992 0.520996 0.000000 0.221802 0.404053 \n", "4 100004.0 1.000000 0.888184 0.745605 0.531738 0.380371 0.224609 \n", "\n", " s_6 s_7 s_8 ... s_195 s_196 s_197 s_198 \\\n", "0 0.120850 0.123413 0.107910 ... 0.000000 0.000000 0.000000 0.000000 \n", "1 0.195068 0.168823 0.198853 ... 0.389893 0.386963 0.367188 0.364014 \n", "2 0.720703 0.701660 0.596680 ... 0.000000 0.000000 0.000000 0.000000 \n", "3 0.490479 0.527344 0.518066 ... 0.000000 0.000000 0.000000 0.000000 \n", "4 0.091125 0.057648 0.003914 ... 0.000000 0.000000 0.000000 0.000000 \n", "\n", " s_199 s_200 s_201 s_202 s_203 s_204 \n", "0 0.000000 0.000000 0.000000 0.000000 0.000000 0.00000 \n", "1 0.360596 0.357178 0.350586 0.350586 0.350586 0.36377 \n", "2 0.000000 0.000000 0.000000 0.000000 0.000000 0.00000 \n", "3 0.000000 0.000000 0.000000 0.000000 0.000000 0.00000 \n", "4 0.000000 0.000000 0.000000 0.000000 0.000000 0.00000 \n", "\n", "[5 rows x 206 columns]" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "test.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 训练数据/测试数据准备" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "ExecuteTime": { "end_time": "2021-03-15T00:56:15.971953Z", "start_time": "2021-03-15T00:56:15.876344Z" } }, "outputs": [], "source": [ "x_train = train.drop(['id','label'], axis=1)\n", "y_train = train['label']\n", "x_test=test.drop(['id'], axis=1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 模型训练" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "ExecuteTime": { "end_time": "2021-03-15T00:57:01.757175Z", "start_time": "2021-03-15T00:57:01.750341Z" } }, "outputs": [], "source": [ "def abs_sum(y_pre,y_tru):\n", " y_pre=np.array(y_pre)\n", " y_tru=np.array(y_tru)\n", " loss=sum(sum(abs(y_pre-y_tru)))\n", " return loss\n", " " ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "ExecuteTime": { "end_time": "2021-03-15T00:57:42.940805Z", "start_time": "2021-03-15T00:57:42.928082Z" } }, "outputs": [], "source": [ "def cv_model(clf, train_x, train_y, test_x, clf_name):\n", " folds = 5\n", " seed = 2021\n", " kf = KFold(n_splits=folds, shuffle=True, random_state=seed)\n", " test = np.zeros((test_x.shape[0],4))\n", "\n", " cv_scores = []\n", " onehot_encoder = OneHotEncoder(sparse=False)\n", " for i, (train_index, valid_index) in enumerate(kf.split(train_x, train_y)):\n", " print('************************************ {} ************************************'.format(str(i+1)))\n", " trn_x, trn_y, val_x, val_y = train_x.iloc[train_index], train_y[train_index], train_x.iloc[valid_index], train_y[valid_index]\n", " \n", " if clf_name == \"lgb\":\n", " train_matrix = clf.Dataset(trn_x, label=trn_y)\n", " valid_matrix = clf.Dataset(val_x, label=val_y)\n", "\n", " params = {\n", " 'boosting_type': 'gbdt',\n", " 'objective': 'multiclass',\n", " 'num_class': 4,\n", " 'num_leaves': 2 ** 5,\n", " 'feature_fraction': 0.8,\n", " 'bagging_fraction': 0.8,\n", " 'bagging_freq': 4,\n", " 'learning_rate': 0.1,\n", " 'seed': seed,\n", " 'nthread': 28,\n", " 'n_jobs':24,\n", " 'verbose': -1,\n", " }\n", "\n", " model = clf.train(params, \n", " train_set=train_matrix, \n", " valid_sets=valid_matrix, \n", " num_boost_round=2000, \n", " verbose_eval=100, \n", " early_stopping_rounds=200)\n", " val_pred = model.predict(val_x, num_iteration=model.best_iteration)\n", " test_pred = model.predict(test_x, num_iteration=model.best_iteration) \n", " \n", " val_y=np.array(val_y).reshape(-1, 1)\n", " val_y = onehot_encoder.fit_transform(val_y)\n", " print('预测的概率矩阵为：')\n", " print(test_pred)\n", " test += test_pred\n", " score=abs_sum(val_y, val_pred)\n", " cv_scores.append(score)\n", " print(cv_scores)\n", " print(\"%s_scotrainre_list:\" % clf_name, cv_scores)\n", " print(\"%s_score_mean:\" % clf_name, np.mean(cv_scores))\n", " print(\"%s_score_std:\" % clf_name, np.std(cv_scores))\n", " test=test/kf.n_splits\n", "\n", " return test" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "ExecuteTime": { "end_time": "2021-03-15T00:58:22.378103Z", "start_time": "2021-03-15T00:58:22.373222Z" } }, "outputs": [], "source": [ "def lgb_model(x_train, y_train, x_test):\n", " lgb_test = cv_model(lgb, x_train, y_train, x_test, \"lgb\")\n", " return lgb_test" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "start_time": "2021-03-15T00:53:32.384Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "************************************ 1 ************************************\n", "Training until validation scores don't improve for 200 rounds\n", "[100]\tvalid_0's multi_logloss: 0.0525735\n", "[200]\tvalid_0's multi_logloss: 0.0422444\n", "[300]\tvalid_0's multi_logloss: 0.0407076\n", "[400]\tvalid_0's multi_logloss: 0.0420398\n", "Early stopping, best iteration is:\n", "[289]\tvalid_0's multi_logloss: 0.0405457\n", "预测的概率矩阵为：\n", "[[9.99969791e-01 2.85197261e-05 1.00341946e-06 6.85357631e-07]\n", " [7.93287264e-05 7.69060914e-04 9.99151590e-01 2.00810971e-08]\n", " [5.75356884e-07 5.04051497e-08 3.15322414e-07 9.99999059e-01]\n", " ...\n", " [6.79267940e-02 4.30206297e-04 9.31640185e-01 2.81516302e-06]\n", " [9.99960477e-01 3.94098074e-05 8.34030725e-08 2.94638661e-08]\n", " [9.88705846e-01 2.14081630e-03 6.67418381e-03 2.47915423e-03]]\n", "[607.0736049372186]\n", "************************************ 2 ************************************\n", "[LightGBM] [Warning] num_threads is set with nthread=28, will be overridden by n_jobs=24. Current value: num_threads=24\n", "Training until validation scores don't improve for 200 rounds\n", "[100]\tvalid_0's multi_logloss: 0.0566626\n", "[200]\tvalid_0's multi_logloss: 0.0450852\n" ] } ], "source": [ "lgb_test = lgb_model(x_train, y_train, x_test)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "start_time": "2021-03-15T00:53:33.065Z" } }, "outputs": [], "source": [ "lgb_test" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "start_time": "2021-03-15T00:53:33.810Z" } }, "outputs": [], "source": [ "temp=pd.DataFrame(lgb_test)\n", "temp" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "start_time": "2021-03-15T00:53:34.680Z" } }, "outputs": [], "source": [ "result=pd.read_csv('sample_submit.csv')\n", "result['label_0']=temp[0]\n", "result['label_1']=temp[1]\n", "result['label_2']=temp[2]\n", "result['label_3']=temp[3]\n", "result.to_csv('submit.csv',index=False)" ] } ], "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": "3.8.3" }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": true, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": true, "toc_window_display": true }, "varInspector": { "cols": { "lenName": 16, "lenType": 16, "lenVar": 40 }, "kernels_config": { "python": { "delete_cmd_postfix": "", "delete_cmd_prefix": "del ", "library": "var_list.py", "varRefreshCmd": "print(var_dic_list())" }, "r": { "delete_cmd_postfix": ") ", "delete_cmd_prefix": "rm(", "library": "var_list.r", "varRefreshCmd": "cat(var_dic_list()) " } }, "types_to_exclude": [ "module", "function", "builtin_function_or_method", "instance", "_Feature" ], "window_display": false } }, "nbformat": 4, "nbformat_minor": 4 }