├── .gitignore ├── README.md ├── data └── .gitkeep ├── images ├── ch2_plot2-1.html ├── ch2_plot2-2.html ├── ch2_plot3.html ├── ch2_plot4.html ├── ch2_plot5.html ├── ch3_plot0.html ├── ch3_plot1.html ├── ch3_plot2.html ├── ch3_plot3.html ├── ch3_plot4.html ├── ch3_plot5.html ├── ch4_plot1.html ├── ch4_plot2.html ├── ch4_plot3.html ├── ch5_plot1.html ├── ch5_plot2.html ├── ch5_plot2_2.html ├── ch5_plot2_3.html ├── ch5_plot3.html └── ch5_plot4.html ├── notebook ├── ch1_bias.ipynb ├── ch2_regression.ipynb ├── ch2_voucher.ipynb ├── ch3_lalonde.ipynb ├── ch3_pscore.ipynb ├── ch4_did.ipynb └── ch5_rdd.ipynb ├── poetry.lock └── pyproject.toml /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | wheels/ 23 | pip-wheel-metadata/ 24 | share/python-wheels/ 25 | *.egg-info/ 26 | .installed.cfg 27 | *.egg 28 | MANIFEST 29 | 30 | # PyInstaller 31 | # Usually these files are written by a python script from a template 32 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 33 | *.manifest 34 | *.spec 35 | 36 | # Installer logs 37 | pip-log.txt 38 | pip-delete-this-directory.txt 39 | 40 | # Unit test / coverage reports 41 | htmlcov/ 42 | .tox/ 43 | .nox/ 44 | .coverage 45 | .coverage.* 46 | .cache 47 | nosetests.xml 48 | coverage.xml 49 | *.cover 50 | *.py,cover 51 | .hypothesis/ 52 | .pytest_cache/ 53 | 54 | # Translations 55 | *.mo 56 | *.pot 57 | 58 | # Django stuff: 59 | *.log 60 | local_settings.py 61 | db.sqlite3 62 | db.sqlite3-journal 63 | 64 | # Flask stuff: 65 | instance/ 66 | .webassets-cache 67 | 68 | # Scrapy stuff: 69 | .scrapy 70 | 71 | # Sphinx documentation 72 | docs/_build/ 73 | 74 | # PyBuilder 75 | target/ 76 | 77 | # Jupyter Notebook 78 | .ipynb_checkpoints 79 | 80 | # IPython 81 | profile_default/ 82 | ipython_config.py 83 | 84 | # pyenv 85 | .python-version 86 | 87 | # pipenv 88 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. 89 | # However, in case of collaboration, if having platform-specific dependencies or dependencies 90 | # having no cross-platform support, pipenv may install dependencies that don't work, or not 91 | # install all needed dependencies. 92 | #Pipfile.lock 93 | 94 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow 95 | __pypackages__/ 96 | 97 | # Celery stuff 98 | celerybeat-schedule 99 | celerybeat.pid 100 | 101 | # SageMath parsed files 102 | *.sage.py 103 | 104 | # Environments 105 | .env 106 | .venv 107 | env/ 108 | venv/ 109 | ENV/ 110 | env.bak/ 111 | venv.bak/ 112 | 113 | # Spyder project settings 114 | .spyderproject 115 | .spyproject 116 | 117 | # Rope project settings 118 | .ropeproject 119 | 120 | # mkdocs documentation 121 | /site 122 | 123 | # mypy 124 | .mypy_cache/ 125 | .dmypy.json 126 | dmypy.json 127 | 128 | # Pyre type checker 129 | .pyre/ 130 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # cibook-python 2 | A Python implementation of the R code in https://github.com/ghmagazine/cibook 3 | ## environment 4 | ``` 5 | # Python versions 6 | please see the pyproject.toml 7 | # R version 8 | R version 3.6.2 (2019-12-12) 9 | ``` 10 | 11 | ## Show images on GitHub Pages 12 | https://nekoumei.github.io/cibook-python/images/ch2_plot2-1.html 13 | https://nekoumei.github.io/cibook-python/images/ch2_plot2-2.html 14 | https://nekoumei.github.io/cibook-python/images/ch2_plot3.html 15 | https://nekoumei.github.io/cibook-python/images/ch2_plot4.html 16 | https://nekoumei.github.io/cibook-python/images/ch2_plot5.html 17 | https://nekoumei.github.io/cibook-python/images/ch3_plot0.html 18 | https://nekoumei.github.io/cibook-python/images/ch3_plot1.html 19 | https://nekoumei.github.io/cibook-python/images/ch3_plot2.html 20 | https://nekoumei.github.io/cibook-python/images/ch3_plot3.html 21 | https://nekoumei.github.io/cibook-python/images/ch3_plot4.html 22 | https://nekoumei.github.io/cibook-python/images/ch3_plot5.html 23 | https://nekoumei.github.io/cibook-python/images/ch4_plot1.html 24 | https://nekoumei.github.io/cibook-python/images/ch4_plot2.html 25 | https://nekoumei.github.io/cibook-python/images/ch4_plot3.html 26 | https://nekoumei.github.io/cibook-python/images/ch5_plot1.html 27 | https://nekoumei.github.io/cibook-python/images/ch5_plot2.html 28 | https://nekoumei.github.io/cibook-python/images/ch5_plot2_2.html 29 | https://nekoumei.github.io/cibook-python/images/ch5_plot2_3.html 30 | https://nekoumei.github.io/cibook-python/images/ch5_plot3.html 31 | https://nekoumei.github.io/cibook-python/images/ch5_plot4.html 32 | -------------------------------------------------------------------------------- /data/.gitkeep: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/nekoumei/cibook-python/f426865ddb660498c5538114b65c1f986b7f36b9/data/.gitkeep -------------------------------------------------------------------------------- /notebook/ch1_bias.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": { 7 | "ExecuteTime": { 8 | "end_time": "2021-09-11T07:54:11.047039Z", 9 | "start_time": "2021-09-11T07:54:10.642731Z" 10 | } 11 | }, 12 | "outputs": [], 13 | "source": [ 14 | "import pandas as pd\n", 15 | "from scipy import stats\n", 16 | "import joblib" 17 | ] 18 | }, 19 | { 20 | "cell_type": "markdown", 21 | "metadata": {}, 22 | "source": [ 23 | "## データの読み込み" 24 | ] 25 | }, 26 | { 27 | "cell_type": "code", 28 | "execution_count": 2, 29 | "metadata": { 30 | "ExecuteTime": { 31 | "end_time": "2021-09-11T07:54:32.709618Z", 32 | "start_time": "2021-09-11T07:54:11.048355Z" 33 | } 34 | }, 35 | "outputs": [], 36 | "source": [ 37 | "mail_df = pd.read_csv('http://www.minethatdata.com/Kevin_Hillstrom_MineThatData_E-MailAnalytics_DataMiningChallenge_2008.03.20.csv')" 38 | ] 39 | }, 40 | { 41 | "cell_type": "markdown", 42 | "metadata": {}, 43 | "source": [ 44 | "## (4) データの準備\n", 45 | "### 女性向けメールが配信されたデータを削除したデータを作成" 46 | ] 47 | }, 48 | { 49 | "cell_type": "code", 50 | "execution_count": 3, 51 | "metadata": { 52 | "ExecuteTime": { 53 | "end_time": "2021-09-11T07:54:32.727795Z", 54 | "start_time": "2021-09-11T07:54:32.710623Z" 55 | } 56 | }, 57 | "outputs": [], 58 | "source": [ 59 | "male_df = mail_df[mail_df.segment != 'Womens E-Mail'].copy() # 女性向けメールが配信されたデータを削除\n", 60 | "male_df['treatment'] = male_df.segment.apply(lambda x: 1 if x == 'Mens E-Mail' else 0) #介入を表すtreatment変数を追加" 61 | ] 62 | }, 63 | { 64 | "cell_type": "markdown", 65 | "metadata": {}, 66 | "source": [ 67 | "## (5) 集計による比較\n", 68 | "### group_byとsummairseを使って集計" 69 | ] 70 | }, 71 | { 72 | "cell_type": "code", 73 | "execution_count": 4, 74 | "metadata": { 75 | "ExecuteTime": { 76 | "end_time": "2021-09-11T07:54:32.743595Z", 77 | "start_time": "2021-09-11T07:54:32.728793Z" 78 | } 79 | }, 80 | "outputs": [ 81 | { 82 | "data": { 83 | "text/html": [ 84 | "
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conversion_ratespend_meancount
treatment
00.0057260.65278921306
10.0125311.42261721307
\n", 128 | "
" 129 | ], 130 | "text/plain": [ 131 | " conversion_rate spend_mean count\n", 132 | "treatment \n", 133 | "0 0.005726 0.652789 21306\n", 134 | "1 0.012531 1.422617 21307" 135 | ] 136 | }, 137 | "execution_count": 4, 138 | "metadata": {}, 139 | "output_type": "execute_result" 140 | } 141 | ], 142 | "source": [ 143 | "male_df.groupby('treatment').agg( # データのグループ化\n", 144 | " conversion_rate=('conversion', 'mean'), # グループごとのconversionの平均\n", 145 | " spend_mean=('spend', 'mean'), # グループごとのspendの平均\n", 146 | " count=('treatment', 'count') # グループごとのデータ数\n", 147 | ")" 148 | ] 149 | }, 150 | { 151 | "cell_type": "markdown", 152 | "metadata": {}, 153 | "source": [ 154 | "## (6) t検定を行う" 155 | ] 156 | }, 157 | { 158 | "cell_type": "code", 159 | "execution_count": 5, 160 | "metadata": { 161 | "ExecuteTime": { 162 | "end_time": "2021-09-11T07:54:32.752325Z", 163 | "start_time": "2021-09-11T07:54:32.744829Z" 164 | } 165 | }, 166 | "outputs": [ 167 | { 168 | "data": { 169 | "text/plain": [ 170 | "Ttest_indResult(statistic=5.300090294465472, pvalue=1.163200872605869e-07)" 171 | ] 172 | }, 173 | "execution_count": 5, 174 | "metadata": {}, 175 | "output_type": "execute_result" 176 | } 177 | ], 178 | "source": [ 179 | "### (a)男性向けメールが配信されたグループの購買データを得る\n", 180 | "mens_mail = male_df[male_df.treatment==1].spend.values\n", 181 | "\n", 182 | "### (b)メールが配信されなかったグループの購買データを得る\n", 183 | "no_mail = male_df[male_df.treatment==0].spend.values\n", 184 | "\n", 185 | "### (a)(b)の平均の差に対して有意差検定を実行する\n", 186 | "stats.ttest_ind(mens_mail, no_mail)" 187 | ] 188 | }, 189 | { 190 | "cell_type": "markdown", 191 | "metadata": {}, 192 | "source": [ 193 | "## (7) セレクションバイアスのあるデータの作成\n", 194 | "### バイアスのあるデータの作成" 195 | ] 196 | }, 197 | { 198 | "cell_type": "code", 199 | "execution_count": 6, 200 | "metadata": { 201 | "ExecuteTime": { 202 | "end_time": "2021-09-11T07:54:32.771912Z", 203 | "start_time": "2021-09-11T07:54:32.753141Z" 204 | } 205 | }, 206 | "outputs": [], 207 | "source": [ 208 | "sample_rules = (male_df.history > 300) | (male_df.recency < 6) | (male_df.channel=='Multichannel')\n", 209 | "biased_df = pd.concat([\n", 210 | " male_df[(sample_rules) & (male_df.treatment == 0)].sample(frac=0.5, random_state=1),\n", 211 | " male_df[(sample_rules) & (male_df.treatment == 1)],\n", 212 | " male_df[(~sample_rules) & (male_df.treatment == 0)],\n", 213 | " male_df[(~sample_rules) & (male_df.treatment == 1)].sample(frac=0.5, random_state=1)\n", 214 | "], axis=0, ignore_index=True)" 215 | ] 216 | }, 217 | { 218 | "cell_type": "markdown", 219 | "metadata": {}, 220 | "source": [ 221 | "## (8) セレクションバイアスのあるデータで平均を比較\n", 222 | "### groupbyを使って集計(Biased)" 223 | ] 224 | }, 225 | { 226 | "cell_type": "code", 227 | "execution_count": 7, 228 | "metadata": { 229 | "ExecuteTime": { 230 | "end_time": "2021-09-11T07:54:32.784250Z", 231 | "start_time": "2021-09-11T07:54:32.773100Z" 232 | } 233 | }, 234 | "outputs": [ 235 | { 236 | "data": { 237 | "text/html": [ 238 | "
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conversion_ratespend_meancount
treatment
00.0045400.55795414757
10.0135721.54170417168
\n", 282 | "
" 283 | ], 284 | "text/plain": [ 285 | " conversion_rate spend_mean count\n", 286 | "treatment \n", 287 | "0 0.004540 0.557954 14757\n", 288 | "1 0.013572 1.541704 17168" 289 | ] 290 | }, 291 | "execution_count": 7, 292 | "metadata": {}, 293 | "output_type": "execute_result" 294 | } 295 | ], 296 | "source": [ 297 | "biased_df.groupby('treatment').agg( # データのグループ化\n", 298 | " conversion_rate=('conversion', 'mean'), # グループごとのconversionの平均\n", 299 | " spend_mean=('spend', 'mean'), # グループごとのspendの平均\n", 300 | " count=('treatment', 'count') # グループごとのデータ数\n", 301 | ")\n", 302 | "# 乱数でbiased_dfのデータを作成しているので数値は書籍とは異なる。（結論は変わらない）" 303 | ] 304 | }, 305 | { 306 | "cell_type": "markdown", 307 | "metadata": {}, 308 | "source": [ 309 | "## (9) scipy.statsのttest_indを使ってt検定を行う(Biased)" 310 | ] 311 | }, 312 | { 313 | "cell_type": "code", 314 | "execution_count": 8, 315 | "metadata": { 316 | "ExecuteTime": { 317 | "end_time": "2021-09-11T07:54:32.792946Z", 318 | "start_time": "2021-09-11T07:54:32.785176Z" 319 | } 320 | }, 321 | "outputs": [ 322 | { 323 | "data": { 324 | "text/plain": [ 325 | "Ttest_indResult(statistic=5.595867225527975, pvalue=2.21319841336543e-08)" 326 | ] 327 | }, 328 | "execution_count": 8, 329 | "metadata": {}, 330 | "output_type": "execute_result" 331 | } 332 | ], 333 | "source": [ 334 | "## (a)男性向けメールが配信されたグループの購買データを得る\n", 335 | "mens_mail_biased = biased_df[biased_df.treatment == 1].spend.values\n", 336 | "\n", 337 | "## (b)メールが配信されなかったグループの購買データを得る\n", 338 | "no_mail_biased = biased_df[biased_df.treatment == 0].spend.values\n", 339 | "\n", 340 | "## (a)(b)の平均の差に対して有意差検定を実行\n", 341 | "stats.ttest_ind(mens_mail_biased, no_mail_biased)" 342 | ] 343 | }, 344 | { 345 | "cell_type": "code", 346 | "execution_count": 9, 347 | "metadata": { 348 | "ExecuteTime": { 349 | "end_time": "2021-09-11T07:54:32.810370Z", 350 | "start_time": "2021-09-11T07:54:32.794029Z" 351 | } 352 | }, 353 | "outputs": [ 354 | { 355 | "data": { 356 | "text/plain": [ 357 | "['../data/biased_df.joblib']" 358 | ] 359 | }, 360 | "execution_count": 9, 361 | "metadata": {}, 362 | "output_type": "execute_result" 363 | } 364 | ], 365 | "source": [ 366 | "#ch2で利用するmale_df, biased_dfを保存する\n", 367 | "joblib.dump(male_df, '../data/male_df.joblib')\n", 368 | "joblib.dump(biased_df, '../data/biased_df.joblib')" 369 | ] 370 | } 371 | ], 372 | "metadata": { 373 | "kernelspec": { 374 | "display_name": "Python 3 (ipykernel)", 375 | "language": "python", 376 | "name": "python3" 377 | }, 378 | "language_info": { 379 | "codemirror_mode": { 380 | "name": "ipython", 381 | "version": 3 382 | }, 383 | "file_extension": ".py", 384 | "mimetype": "text/x-python", 385 | "name": "python", 386 | "nbconvert_exporter": "python", 387 | "pygments_lexer": "ipython3", 388 | "version": "3.7.4" 389 | } 390 | }, 391 | "nbformat": 4, 392 | "nbformat_minor": 2 393 | } 394 | -------------------------------------------------------------------------------- /notebook/ch2_regression.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": { 7 | "ExecuteTime": { 8 | "end_time": "2021-09-11T07:56:40.768047Z", 9 | "start_time": "2021-09-11T07:56:40.241769Z" 10 | } 11 | }, 12 | "outputs": [], 13 | "source": [ 14 | "import pandas as pd\n", 15 | "import statsmodels.api as sm\n", 16 | "import joblib\n", 17 | "import os\n", 18 | "\n", 19 | "import warnings\n", 20 | "warnings.filterwarnings('ignore')" 21 | ] 22 | }, 23 | { 24 | "cell_type": "markdown", 25 | "metadata": {}, 26 | "source": [ 27 | "## セレクションバイアスのあるデータの作成" 28 | ] 29 | }, 30 | { 31 | "cell_type": "code", 32 | "execution_count": 2, 33 | "metadata": { 34 | "ExecuteTime": { 35 | "end_time": "2021-09-11T07:56:40.782505Z", 36 | "start_time": "2021-09-11T07:56:40.769286Z" 37 | } 38 | }, 39 | "outputs": [], 40 | "source": [ 41 | "dumped_male_df_path = '../data/male_df.joblib'\n", 42 | "dumped_biased_df_path = '../data/biased_df.joblib'\n", 43 | "\n", 44 | "if os.path.exists(dumped_male_df_path):\n", 45 | " male_df = joblib.load(dumped_male_df_path)\n", 46 | " biased_df = joblib.load(dumped_biased_df_path)\n", 47 | "else:\n", 48 | " # セレクションバイアスのあるデータの作成\n", 49 | " mail_df = pd.read_csv('http://www.minethatdata.com/Kevin_Hillstrom_MineThatData_E-MailAnalytics_DataMiningChallenge_2008.03.20.csv')\n", 50 | " ### 女性向けメールが配信されたデータを削除したデータを作成\n", 51 | " male_df = mail_df[mail_df.segment != 'Womens E-Mail'].copy() # 女性向けメールが配信されたデータを削除\n", 52 | " male_df['treatment'] = male_df.segment.apply(lambda x: 1 if x == 'Mens E-Mail' else 0) #介入を表すtreatment変数を追加\n", 53 | " ## バイアスのあるデータの作成\n", 54 | " sample_rules = (male_df.history > 300) | (male_df.recency < 6) | (male_df.channel=='Multichannel')\n", 55 | " biased_df = pd.concat([\n", 56 | " male_df[(sample_rules) & (male_df.treatment == 0)].sample(frac=0.5, random_state=1),\n", 57 | " male_df[(sample_rules) & (male_df.treatment == 1)],\n", 58 | " male_df[(~sample_rules) & (male_df.treatment == 0)],\n", 59 | " male_df[(~sample_rules) & (male_df.treatment == 1)].sample(frac=0.5, random_state=1)\n", 60 | " ], axis=0, ignore_index=True)" 61 | ] 62 | }, 63 | { 64 | "cell_type": "markdown", 65 | "metadata": {}, 66 | "source": [ 67 | "## (6) バイアスのあるデータでの回帰分析" 68 | ] 69 | }, 70 | { 71 | "cell_type": "code", 72 | "execution_count": 3, 73 | "metadata": { 74 | "ExecuteTime": { 75 | "end_time": "2021-09-11T07:56:40.792254Z", 76 | "start_time": "2021-09-11T07:56:40.783479Z" 77 | } 78 | }, 79 | "outputs": [], 80 | "source": [ 81 | "## 回帰分析の実行\n", 82 | "y = biased_df.spend\n", 83 | "X = biased_df[['treatment', 'history']]\n", 84 | "X = sm.add_constant(X) # statsmodelsではβ0を明示的に入れてあげる必要がある\n", 85 | "model = sm.OLS(y, X)\n", 86 | "results = model.fit()" 87 | ] 88 | }, 89 | { 90 | "cell_type": "code", 91 | "execution_count": 4, 92 | "metadata": { 93 | "ExecuteTime": { 94 | "end_time": "2021-09-11T07:56:40.825251Z", 95 | "start_time": "2021-09-11T07:56:40.796412Z" 96 | } 97 | }, 98 | "outputs": [ 99 | { 100 | "data": { 101 | "text/html": [ 102 | "\n", 103 | "\n", 104 | "\n", 105 | " \n", 106 | "\n", 107 | "\n", 108 | " \n", 109 | "\n", 110 | "\n", 111 | " \n", 112 | "\n", 113 | "\n", 114 | " \n", 115 | "\n", 116 | "\n", 117 | " \n", 118 | "\n", 119 | "\n", 120 | " \n", 121 | "\n", 122 | "\n", 123 | " \n", 124 | "\n", 125 | "\n", 126 | " \n", 127 | "\n", 128 | "\n", 129 | " \n", 130 | "\n", 131 | "
OLS Regression Results
Dep. Variable: spend R-squared: 0.001
Model: OLS Adj. R-squared: 0.001
Method: Least Squares F-statistic: 20.45
Date: Sat, 11 Sep 2021 Prob (F-statistic): 1.32e-09
Time: 16:56:40 Log-Likelihood: -1.3312e+05
No. Observations: 31925 AIC: 2.663e+05
Df Residuals: 31922 BIC: 2.663e+05
Df Model: 2
Covariance Type: nonrobust
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coef std err t P>|t| [0.025 0.975]
const 0.3413 0.147 2.327 0.020 0.054 0.629
treatment 0.9088 0.177 5.122 0.000 0.561 1.257
history 0.0011 0.000 3.096 0.002 0.000 0.002
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Omnibus: 70760.532 Durbin-Watson: 2.002
Prob(Omnibus): 0.000 Jarque-Bera (JB): 352134568.791
Skew: 20.807 Prob(JB): 0.00
Kurtosis: 515.825 Cond. No. 833.


Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified." 160 | ], 161 | "text/plain": [ 162 | "\n", 163 | "\"\"\"\n", 164 | " OLS Regression Results \n", 165 | "==============================================================================\n", 166 | "Dep. Variable: spend R-squared: 0.001\n", 167 | "Model: OLS Adj. R-squared: 0.001\n", 168 | "Method: Least Squares F-statistic: 20.45\n", 169 | "Date: Sat, 11 Sep 2021 Prob (F-statistic): 1.32e-09\n", 170 | "Time: 16:56:40 Log-Likelihood: -1.3312e+05\n", 171 | "No. Observations: 31925 AIC: 2.663e+05\n", 172 | "Df Residuals: 31922 BIC: 2.663e+05\n", 173 | "Df Model: 2 \n", 174 | "Covariance Type: nonrobust \n", 175 | "==============================================================================\n", 176 | " coef std err t P>|t| [0.025 0.975]\n", 177 | "------------------------------------------------------------------------------\n", 178 | "const 0.3413 0.147 2.327 0.020 0.054 0.629\n", 179 | "treatment 0.9088 0.177 5.122 0.000 0.561 1.257\n", 180 | "history 0.0011 0.000 3.096 0.002 0.000 0.002\n", 181 | "==============================================================================\n", 182 | "Omnibus: 70760.532 Durbin-Watson: 2.002\n", 183 | "Prob(Omnibus): 0.000 Jarque-Bera (JB): 352134568.791\n", 184 | "Skew: 20.807 Prob(JB): 0.00\n", 185 | "Kurtosis: 515.825 Cond. No. 833.\n", 186 | "==============================================================================\n", 187 | "\n", 188 | "Notes:\n", 189 | "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", 190 | "\"\"\"" 191 | ] 192 | }, 193 | "execution_count": 4, 194 | "metadata": {}, 195 | "output_type": "execute_result" 196 | } 197 | ], 198 | "source": [ 199 | "## 分析結果のレポート\n", 200 | "summary = results.summary()\n", 201 | "summary" 202 | ] 203 | }, 204 | { 205 | "cell_type": "code", 206 | "execution_count": 5, 207 | "metadata": { 208 | "ExecuteTime": { 209 | "end_time": "2021-09-11T07:56:40.836611Z", 210 | "start_time": "2021-09-11T07:56:40.828565Z" 211 | } 212 | }, 213 | "outputs": [ 214 | { 215 | "data": { 216 | "text/html": [ 217 | "\n", 218 | "\n", 219 | " \n", 220 | "\n", 221 | "\n", 222 | " \n", 223 | "\n", 224 | "\n", 225 | " \n", 226 | "\n", 227 | "\n", 228 | " \n", 229 | "\n", 230 | "
coef std err t P>|t| [0.025 0.975]
const 0.3413 0.147 2.327 0.020 0.054 0.629
treatment 0.9088 0.177 5.122 0.000 0.561 1.257
history 0.0011 0.000 3.096 0.002 0.000 0.002
" 231 | ], 232 | "text/plain": [ 233 | "" 234 | ] 235 | }, 236 | "execution_count": 5, 237 | "metadata": {}, 238 | "output_type": "execute_result" 239 | } 240 | ], 241 | "source": [ 242 | "## 推定されたパラメーターの取り出し\n", 243 | "biased_reg_coef = summary.tables[1]\n", 244 | "biased_reg_coef" 245 | ] 246 | }, 247 | { 248 | "cell_type": "markdown", 249 | "metadata": {}, 250 | "source": [ 251 | "## (7) RCTデータでの回帰分析とバイアスのあるデータでの回帰分析の比較" 252 | ] 253 | }, 254 | { 255 | "cell_type": "code", 256 | "execution_count": 6, 257 | "metadata": { 258 | "ExecuteTime": { 259 | "end_time": "2021-09-11T07:56:40.862393Z", 260 | "start_time": "2021-09-11T07:56:40.840092Z" 261 | } 262 | }, 263 | "outputs": [ 264 | { 265 | "data": { 266 | "text/html": [ 267 | "\n", 268 | "\n", 269 | " \n", 270 | "\n", 271 | "\n", 272 | " \n", 273 | "\n", 274 | "\n", 275 | " \n", 276 | "\n", 277 | "
coef std err t P>|t| [0.025 0.975]
const 0.6528 0.103 6.356 0.000 0.451 0.854
treatment 0.7698 0.145 5.300 0.000 0.485 1.055
" 278 | ], 279 | "text/plain": [ 280 | "" 281 | ] 282 | }, 283 | "execution_count": 6, 284 | "metadata": {}, 285 | "output_type": "execute_result" 286 | } 287 | ], 288 | "source": [ 289 | "## RCTデータでの単回帰\n", 290 | "y = male_df.spend\n", 291 | "X = male_df[['treatment']]\n", 292 | "X = sm.add_constant(X)\n", 293 | "results = sm.OLS(y, X).fit()\n", 294 | "rct_reg_coef = results.summary().tables[1]\n", 295 | "rct_reg_coef" 296 | ] 297 | }, 298 | { 299 | "cell_type": "code", 300 | "execution_count": 7, 301 | "metadata": { 302 | "ExecuteTime": { 303 | "end_time": "2021-09-11T07:56:40.885424Z", 304 | "start_time": "2021-09-11T07:56:40.865902Z" 305 | } 306 | }, 307 | "outputs": [ 308 | { 309 | "data": { 310 | "text/html": [ 311 | "\n", 312 | "\n", 313 | " \n", 314 | "\n", 315 | "\n", 316 | " \n", 317 | "\n", 318 | "\n", 319 | " \n", 320 | "\n", 321 | "
coef std err t P>|t| [0.025 0.975]
const 0.5580 0.129 4.328 0.000 0.305 0.811
treatment 0.9837 0.176 5.596 0.000 0.639 1.328
" 322 | ], 323 | "text/plain": [ 324 | "" 325 | ] 326 | }, 327 | "execution_count": 7, 328 | "metadata": {}, 329 | "output_type": "execute_result" 330 | } 331 | ], 332 | "source": [ 333 | "## バイアスのあるデータでの単回帰\n", 334 | "y = biased_df.spend\n", 335 | "X = biased_df[['treatment']]\n", 336 | "X = sm.add_constant(X)\n", 337 | "results = sm.OLS(y, X).fit()\n", 338 | "nonrct_reg_coef = results.summary().tables[1]\n", 339 | "nonrct_reg_coef" 340 | ] 341 | }, 342 | { 343 | "cell_type": "code", 344 | "execution_count": 8, 345 | "metadata": { 346 | "ExecuteTime": { 347 | "end_time": "2021-09-11T07:56:40.925121Z", 348 | "start_time": "2021-09-11T07:56:40.889005Z" 349 | }, 350 | "scrolled": true 351 | }, 352 | "outputs": [ 353 | { 354 | "data": { 355 | "text/html": [ 356 | "\n", 357 | "\n", 358 | " \n", 359 | "\n", 360 | "\n", 361 | " \n", 362 | "\n", 363 | "\n", 364 | " \n", 365 | "\n", 366 | "\n", 367 | " \n", 368 | "\n", 369 | "\n", 370 | " \n", 371 | "\n", 372 | "\n", 373 | " \n", 374 | "\n", 375 | "\n", 376 | " \n", 377 | "\n", 378 | "
coef std err t P>|t| [0.025 0.975]
const 0.4761 0.386 1.233 0.218 -0.281 1.233
treatment 0.8617 0.181 4.750 0.000 0.506 1.217
recency -0.0361 0.026 -1.372 0.170 -0.088 0.015
history 0.0010 0.000 2.655 0.008 0.000 0.002
channel_Phone -0.0079 0.310 -0.025 0.980 -0.616 0.600
channel_Web 0.2540 0.310 0.820 0.412 -0.353 0.861
" 379 | ], 380 | "text/plain": [ 381 | "" 382 | ] 383 | }, 384 | "execution_count": 8, 385 | "metadata": {}, 386 | "output_type": "execute_result" 387 | } 388 | ], 389 | "source": [ 390 | "## バイアスのあるデータでの重回帰\n", 391 | "y = biased_df.spend\n", 392 | "# R lmではカテゴリ変数は自動的にダミー変数化されているのでそれを再現\n", 393 | "X = pd.get_dummies(biased_df[['treatment', 'recency', 'channel', 'history']], columns=['channel'], drop_first=True)\n", 394 | "X = sm.add_constant(X)\n", 395 | "results = sm.OLS(y, X).fit()\n", 396 | "nonrct_mreg_coef = results.summary().tables[1]\n", 397 | "nonrct_mreg_coef" 398 | ] 399 | }, 400 | { 401 | "cell_type": "markdown", 402 | "metadata": {}, 403 | "source": [ 404 | "## (8) OVBの確認" 405 | ] 406 | }, 407 | { 408 | "cell_type": "code", 409 | "execution_count": 9, 410 | "metadata": { 411 | "ExecuteTime": { 412 | "end_time": "2021-09-11T07:56:40.976432Z", 413 | "start_time": "2021-09-11T07:56:40.930135Z" 414 | } 415 | }, 416 | "outputs": [], 417 | "source": [ 418 | "## (a) history抜きの回帰分析とパラメーターの取り出し\n", 419 | "y = biased_df.spend\n", 420 | "X = pd.get_dummies(biased_df[['treatment', 'recency', 'channel']], columns=['channel'], drop_first=True)\n", 421 | "X = sm.add_constant(X)\n", 422 | "results = sm.OLS(y, X).fit()\n", 423 | "short_coef = results.summary().tables[1]\n", 424 | "short_coef_df = pd.read_html(short_coef.as_html(), header=0, index_col=0)[0] #SimpleTableは扱いなれてないのでpandas DataFrameにする\n", 425 | "\n", 426 | "## aの結果から介入効果に関するパラメーターのみを取り出す\n", 427 | "alpha_1 = results.params['treatment'] # summaryのデータは小数点が四捨五入されているため、正確な値をとってくる\n" 428 | ] 429 | }, 430 | { 431 | "cell_type": "code", 432 | "execution_count": 10, 433 | "metadata": { 434 | "ExecuteTime": { 435 | "end_time": "2021-09-11T07:56:41.018240Z", 436 | "start_time": "2021-09-11T07:56:40.982827Z" 437 | } 438 | }, 439 | "outputs": [], 440 | "source": [ 441 | "## (b) historyを追加した回帰分析とパラメーターの取り出し\n", 442 | "y = biased_df.spend\n", 443 | "X = pd.get_dummies(biased_df[['treatment', 'recency', 'channel', 'history']], columns=['channel'], drop_first=True)\n", 444 | "X = sm.add_constant(X)\n", 445 | "results = sm.OLS(y, X).fit()\n", 446 | "long_coef = results.summary().tables[1]\n", 447 | "long_coef_df = pd.read_html(long_coef.as_html(), header=0, index_col=0)[0] #SimpleTableは扱いなれてないのでpandas DataFrameにする\n", 448 | "\n", 449 | "## bの結果から介入とhistoryに関するパラメーターを取り出す\n", 450 | "beta_1 = results.params['treatment']\n", 451 | "beta_2 = results.params['history']" 452 | ] 453 | }, 454 | { 455 | "cell_type": "code", 456 | "execution_count": 11, 457 | "metadata": { 458 | "ExecuteTime": { 459 | "end_time": "2021-09-11T07:56:41.055593Z", 460 | "start_time": "2021-09-11T07:56:41.023618Z" 461 | } 462 | }, 463 | "outputs": [], 464 | "source": [ 465 | "## (c) 脱落した変数と介入変数での回帰分析\n", 466 | "y = biased_df.history\n", 467 | "X = pd.get_dummies(biased_df[['treatment', 'recency', 'channel']], columns=['channel'], drop_first=True)\n", 468 | "X = sm.add_constant(X)\n", 469 | "results = sm.OLS(y, X).fit()\n", 470 | "omitted_coef = results.summary().tables[1]\n", 471 | "omitted_coef_df = pd.read_html(omitted_coef.as_html(), header=0, index_col=0)[0] #SimpleTableは扱いなれてないのでpandas DataFrameにする\n", 472 | "gamma_1 = results.params['treatment']" 473 | ] 474 | }, 475 | { 476 | "cell_type": "code", 477 | "execution_count": 12, 478 | "metadata": { 479 | "ExecuteTime": { 480 | "end_time": "2021-09-11T07:56:41.067240Z", 481 | "start_time": "2021-09-11T07:56:41.061048Z" 482 | } 483 | }, 484 | "outputs": [ 485 | { 486 | "name": "stdout", 487 | "output_type": "stream", 488 | "text": [ 489 | "0.028816423676830048\n", 490 | "0.028816423676825798\n" 491 | ] 492 | } 493 | ], 494 | "source": [ 495 | "## OVBの確認\n", 496 | "print(beta_2 * gamma_1)\n", 497 | "print(alpha_1 - beta_1)" 498 | ] 499 | }, 500 | { 501 | "cell_type": "markdown", 502 | "metadata": {}, 503 | "source": [ 504 | "## (10) 入れてはいけない変数を入れてみる" 505 | ] 506 | }, 507 | { 508 | "cell_type": "code", 509 | "execution_count": 13, 510 | "metadata": { 511 | "ExecuteTime": { 512 | "end_time": "2021-09-11T07:56:41.104470Z", 513 | "start_time": "2021-09-11T07:56:41.072126Z" 514 | } 515 | }, 516 | "outputs": [ 517 | { 518 | "data": { 519 | "text/html": [ 520 | "\n", 521 | "\n", 522 | " \n", 523 | "\n", 524 | "\n", 525 | " \n", 526 | "\n", 527 | "\n", 528 | " \n", 529 | "\n", 530 | "\n", 531 | " \n", 532 | "\n", 533 | "\n", 534 | " \n", 535 | "\n", 536 | "\n", 537 | " \n", 538 | "\n", 539 | "\n", 540 | " \n", 541 | "\n", 542 | "
coef std err t P>|t| [0.025 0.975]
const 0.7153 0.011 63.968 0.000 0.693 0.737
visit 0.1509 0.008 19.820 0.000 0.136 0.166
recency -0.0282 0.001 -35.621 0.000 -0.030 -0.027
history 0.0001 1.17e-05 9.705 0.000 9.06e-05 0.000
channel_Phone -0.0708 0.009 -7.453 0.000 -0.089 -0.052
channel_Web -0.0771 0.009 -8.131 0.000 -0.096 -0.059
" 543 | ], 544 | "text/plain": [ 545 | "" 546 | ] 547 | }, 548 | "execution_count": 13, 549 | "metadata": {}, 550 | "output_type": "execute_result" 551 | } 552 | ], 553 | "source": [ 554 | "## visitとtreatmentの相関\n", 555 | "y = biased_df.treatment\n", 556 | "X = pd.get_dummies(biased_df[['visit', 'channel', 'recency', 'history']], columns=['channel'], drop_first=True)\n", 557 | "X = sm.add_constant(X)\n", 558 | "results = sm.OLS(y, X).fit()\n", 559 | "results.summary().tables[1]" 560 | ] 561 | }, 562 | { 563 | "cell_type": "code", 564 | "execution_count": 14, 565 | "metadata": { 566 | "ExecuteTime": { 567 | "end_time": "2021-09-11T07:56:41.147546Z", 568 | "start_time": "2021-09-11T07:56:41.109560Z" 569 | } 570 | }, 571 | "outputs": [ 572 | { 573 | "data": { 574 | "text/html": [ 575 | "\n", 576 | "\n", 577 | " \n", 578 | "\n", 579 | "\n", 580 | " \n", 581 | "\n", 582 | "\n", 583 | " \n", 584 | "\n", 585 | "\n", 586 | " \n", 587 | "\n", 588 | "\n", 589 | " \n", 590 | "\n", 591 | "\n", 592 | " \n", 593 | "\n", 594 | "\n", 595 | " \n", 596 | "\n", 597 | "\n", 598 | " \n", 599 | "\n", 600 | "
coef std err t P>|t| [0.025 0.975]
const -0.4057 0.382 -1.062 0.288 -1.155 0.343
treatment 0.2784 0.180 1.546 0.122 -0.075 0.631
recency 0.0090 0.026 0.346 0.729 -0.042 0.060
history 0.0005 0.000 1.316 0.188 -0.000 0.001
visit 7.2368 0.246 29.368 0.000 6.754 7.720
channel_Phone 0.0978 0.306 0.320 0.749 -0.502 0.697
channel_Web 0.1160 0.306 0.380 0.704 -0.483 0.715
" 601 | ], 602 | "text/plain": [ 603 | "" 604 | ] 605 | }, 606 | "execution_count": 14, 607 | "metadata": {}, 608 | "output_type": "execute_result" 609 | } 610 | ], 611 | "source": [ 612 | "# visitを入れた回帰分析を実行\n", 613 | "y = biased_df.spend\n", 614 | "X = pd.get_dummies(biased_df[['treatment', 'channel', 'recency', 'history', 'visit']], columns=['channel'], drop_first=True)\n", 615 | "X = sm.add_constant(X)\n", 616 | "results = sm.OLS(y, X).fit()\n", 617 | "results.summary().tables[1]" 618 | ] 619 | } 620 | ], 621 | "metadata": { 622 | "kernelspec": { 623 | "display_name": "Python 3 (ipykernel)", 624 | "language": "python", 625 | "name": "python3" 626 | }, 627 | "language_info": { 628 | "codemirror_mode": { 629 | "name": "ipython", 630 | "version": 3 631 | }, 632 | "file_extension": ".py", 633 | "mimetype": "text/x-python", 634 | "name": "python", 635 | "nbconvert_exporter": "python", 636 | "pygments_lexer": "ipython3", 637 | "version": "3.7.4" 638 | } 639 | }, 640 | "nbformat": 4, 641 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structures." 216 | category = "main" 217 | optional = false 218 | python-versions = "*" 219 | 220 | [package.dependencies] 221 | six = ">=1.12.0" 222 | 223 | [[package]] 224 | name = "entrypoints" 225 | version = "0.3" 226 | description = "Discover and load entry points from installed packages." 227 | category = "main" 228 | optional = false 229 | python-versions = ">=2.7" 230 | 231 | [[package]] 232 | name = "flatbuffers" 233 | version = "1.12" 234 | description = "The FlatBuffers serialization format for Python" 235 | category = "main" 236 | optional = false 237 | python-versions = "*" 238 | 239 | [[package]] 240 | name = "gast" 241 | version = "0.4.0" 242 | description = "Python AST that abstracts the underlying Python version" 243 | category = "main" 244 | optional = false 245 | python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*" 246 | 247 | [[package]] 248 | name = "google-auth" 249 | version = "1.35.0" 250 | description = "Google Authentication Library" 251 | category = "main" 252 | optional = false 253 | python-versions = ">=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*" 254 | 255 | [package.dependencies] 256 | cachetools = ">=2.0.0,<5.0" 257 | pyasn1-modules = ">=0.2.1" 258 | rsa = {version = ">=3.1.4,<5", markers = "python_version >= \"3.6\""} 259 | six = ">=1.9.0" 260 | 261 | [package.extras] 262 | aiohttp = ["requests (>=2.20.0,<3.0.0dev)", "aiohttp (>=3.6.2,<4.0.0dev)"] 263 | pyopenssl = ["pyopenssl (>=20.0.0)"] 264 | reauth = ["pyu2f (>=0.1.5)"] 265 | 266 | [[package]] 267 | name = "google-auth-oauthlib" 268 | version = "0.4.6" 269 | description = "Google Authentication Library" 270 | category = "main" 271 | optional = false 272 | python-versions = ">=3.6" 273 | 274 | [package.dependencies] 275 | google-auth = ">=1.0.0" 276 | requests-oauthlib = ">=0.7.0" 277 | 278 | [package.extras] 279 | tool = ["click (>=6.0.0)"] 280 | 281 | [[package]] 282 | name = "google-pasta" 283 | version = "0.2.0" 284 | description = "pasta is an AST-based Python refactoring library" 285 | category = "main" 286 | optional = false 287 | python-versions = "*" 288 | 289 | [package.dependencies] 290 | six = "*" 291 | 292 | [[package]] 293 | name = "grpcio" 294 | version = "1.40.0" 295 | description = "HTTP/2-based RPC framework" 296 | category = "main" 297 | optional = false 298 | python-versions = "*" 299 | 300 | [package.dependencies] 301 | six = ">=1.5.2" 302 | 303 | [package.extras] 304 | protobuf = ["grpcio-tools (>=1.40.0)"] 305 | 306 | [[package]] 307 | name = "h5py" 308 | version = "3.1.0" 309 | description = "Read and write HDF5 files from Python" 310 | category = "main" 311 | optional = false 312 | python-versions = ">=3.6" 313 | 314 | [package.dependencies] 315 | cached-property = {version = "*", markers = "python_version < \"3.8\""} 316 | numpy = [ 317 | {version = ">=1.14.5", markers = "python_version == \"3.7\""}, 318 | {version = ">=1.17.5", markers = "python_version == \"3.8\""}, 319 | {version = ">=1.19.3", markers = "python_version >= \"3.9\""}, 320 | ] 321 | 322 | [[package]] 323 | name = "idna" 324 | version = "3.2" 325 | description = "Internationalized Domain Names in Applications (IDNA)" 326 | category = "main" 327 | optional = false 328 | python-versions = ">=3.5" 329 | 330 | [[package]] 331 | name = "importlib-metadata" 332 | version = "4.8.1" 333 | description = "Read metadata from Python packages" 334 | category = "main" 335 | optional = false 336 | python-versions = ">=3.6" 337 | 338 | [package.dependencies] 339 | typing-extensions = {version = ">=3.6.4", markers = "python_version < \"3.8\""} 340 | zipp = ">=0.5" 341 | 342 | [package.extras] 343 | docs = ["sphinx", "jaraco.packaging (>=8.2)", "rst.linker (>=1.9)"] 344 | perf = ["ipython"] 345 | testing = ["pytest (>=4.6)", "pytest-checkdocs (>=2.4)", "pytest-flake8", "pytest-cov", "pytest-enabler (>=1.0.1)", "packaging", "pep517", "pyfakefs", "flufl.flake8", "pytest-perf (>=0.9.2)", "pytest-black (>=0.3.7)", "pytest-mypy", "importlib-resources (>=1.3)"] 346 | 347 | [[package]] 348 | name = "ipykernel" 349 | version = "6.4.1" 350 | description = "IPython Kernel for Jupyter" 351 | category = "main" 352 | optional = false 353 | python-versions = ">=3.7" 354 | 355 | [package.dependencies] 356 | appnope = {version = "*", markers = "platform_system == \"Darwin\""} 357 | argcomplete = {version = ">=1.12.3", markers = "python_version < \"3.8.0\""} 358 | debugpy = ">=1.0.0,<2.0" 359 | importlib-metadata = {version = "<5", markers = "python_version < \"3.8.0\""} 360 | ipython = ">=7.23.1,<8.0" 361 | ipython-genutils = "*" 362 | jupyter-client = "<8.0" 363 | matplotlib-inline = ">=0.1.0,<0.2.0" 364 | tornado = ">=4.2,<7.0" 365 | traitlets = ">=4.1.0,<6.0" 366 | 367 | [package.extras] 368 | test = ["pytest (!=5.3.4)", "pytest-cov", "flaky", "nose", "ipyparallel"] 369 | 370 | [[package]] 371 | name = "ipython" 372 | version = "7.27.0" 373 | description = "IPython: Productive Interactive Computing" 374 | category = "main" 375 | optional = false 376 | python-versions = ">=3.7" 377 | 378 | [package.dependencies] 379 | appnope = {version = "*", markers = "sys_platform == \"darwin\""} 380 | backcall = "*" 381 | colorama = {version = "*", markers = "sys_platform == \"win32\""} 382 | decorator = "*" 383 | jedi = ">=0.16" 384 | matplotlib-inline = "*" 385 | pexpect = {version = ">4.3", markers = "sys_platform != \"win32\""} 386 | pickleshare = "*" 387 | prompt-toolkit = ">=2.0.0,<3.0.0 || >3.0.0,<3.0.1 || >3.0.1,<3.1.0" 388 | pygments = "*" 389 | traitlets = ">=4.2" 390 | 391 | [package.extras] 392 | all = ["Sphinx (>=1.3)", "ipykernel", "ipyparallel", "ipywidgets", "nbconvert", "nbformat", "nose (>=0.10.1)", "notebook", "numpy (>=1.17)", "pygments", "qtconsole", "requests", "testpath"] 393 | doc = ["Sphinx (>=1.3)"] 394 | kernel = ["ipykernel"] 395 | nbconvert = ["nbconvert"] 396 | nbformat = ["nbformat"] 397 | notebook = ["notebook", "ipywidgets"] 398 | parallel = ["ipyparallel"] 399 | qtconsole = ["qtconsole"] 400 | test = ["nose (>=0.10.1)", "requests", "testpath", "pygments", "nbformat", "ipykernel", "numpy (>=1.17)"] 401 | 402 | [[package]] 403 | name = "ipython-genutils" 404 | version = "0.2.0" 405 | description = "Vestigial utilities from IPython" 406 | category = "main" 407 | optional = false 408 | python-versions = "*" 409 | 410 | [[package]] 411 | name = "ipywidgets" 412 | version = "7.6.4" 413 | description = "IPython HTML widgets for Jupyter" 414 | category = "main" 415 | optional = false 416 | python-versions = "*" 417 | 418 | [package.dependencies] 419 | ipykernel = ">=4.5.1" 420 | ipython = {version = ">=4.0.0", markers = "python_version >= \"3.3\""} 421 | ipython-genutils = ">=0.2.0,<0.3.0" 422 | jupyterlab-widgets = {version = ">=1.0.0", markers = "python_version >= \"3.6\""} 423 | nbformat = ">=4.2.0" 424 | traitlets = ">=4.3.1" 425 | widgetsnbextension = ">=3.5.0,<3.6.0" 426 | 427 | [package.extras] 428 | test = ["pytest (>=3.6.0)", "pytest-cov", "mock"] 429 | 430 | [[package]] 431 | name = "jedi" 432 | version = "0.18.0" 433 | description = "An autocompletion tool for Python that can be used for text editors." 434 | category = "main" 435 | optional = false 436 | python-versions = ">=3.6" 437 | 438 | [package.dependencies] 439 | parso = ">=0.8.0,<0.9.0" 440 | 441 | [package.extras] 442 | qa = ["flake8 (==3.8.3)", "mypy (==0.782)"] 443 | testing = ["Django (<3.1)", "colorama", "docopt", "pytest (<6.0.0)"] 444 | 445 | [[package]] 446 | name = "jinja2" 447 | version = "3.0.1" 448 | description = "A very fast and expressive template engine." 449 | category = "main" 450 | optional = false 451 | python-versions = ">=3.6" 452 | 453 | [package.dependencies] 454 | MarkupSafe = ">=2.0" 455 | 456 | [package.extras] 457 | i18n = ["Babel (>=2.7)"] 458 | 459 | [[package]] 460 | name = "joblib" 461 | version = "1.0.1" 462 | description = "Lightweight pipelining with Python functions" 463 | category = "main" 464 | optional = false 465 | python-versions = ">=3.6" 466 | 467 | [[package]] 468 | name = "jsonschema" 469 | version = "3.2.0" 470 | description = "An implementation of JSON Schema validation for Python" 471 | category = "main" 472 | optional = false 473 | python-versions = "*" 474 | 475 | [package.dependencies] 476 | attrs = ">=17.4.0" 477 | importlib-metadata = {version = "*", markers = "python_version < \"3.8\""} 478 | pyrsistent = ">=0.14.0" 479 | six = ">=1.11.0" 480 | 481 | [package.extras] 482 | format = ["idna", "jsonpointer (>1.13)", "rfc3987", "strict-rfc3339", "webcolors"] 483 | format_nongpl = ["idna", "jsonpointer (>1.13)", "webcolors", "rfc3986-validator (>0.1.0)", "rfc3339-validator"] 484 | 485 | [[package]] 486 | name = "jupyter" 487 | version = "1.0.0" 488 | description = "Jupyter metapackage. Install all the Jupyter components in one go." 489 | category = "main" 490 | optional = false 491 | python-versions = "*" 492 | 493 | [package.dependencies] 494 | ipykernel = "*" 495 | ipywidgets = "*" 496 | jupyter-console = "*" 497 | nbconvert = "*" 498 | notebook = "*" 499 | qtconsole = "*" 500 | 501 | [[package]] 502 | name = "jupyter-client" 503 | version = "7.0.2" 504 | description = "Jupyter protocol implementation and client libraries" 505 | category = "main" 506 | optional = false 507 | python-versions = ">=3.6.1" 508 | 509 | [package.dependencies] 510 | entrypoints = "*" 511 | jupyter-core = ">=4.6.0" 512 | nest-asyncio = ">=1.5" 513 | python-dateutil = ">=2.1" 514 | pyzmq = ">=13" 515 | tornado = ">=4.1" 516 | traitlets = "*" 517 | 518 | [package.extras] 519 | doc = ["myst-parser", "sphinx (>=1.3.6)", "sphinx-rtd-theme", "sphinxcontrib-github-alt"] 520 | test = ["codecov", "coverage", "ipykernel", "ipython", "mock", "mypy", "pre-commit", "pytest", "pytest-asyncio", "pytest-cov", "pytest-timeout", "jedi (<0.18)"] 521 | 522 | [[package]] 523 | name = "jupyter-console" 524 | version = "6.4.0" 525 | description = "Jupyter terminal console" 526 | category = "main" 527 | optional = false 528 | python-versions = ">=3.6" 529 | 530 | [package.dependencies] 531 | ipykernel = "*" 532 | ipython = "*" 533 | jupyter-client = "*" 534 | prompt-toolkit = ">=2.0.0,<3.0.0 || >3.0.0,<3.0.1 || >3.0.1,<3.1.0" 535 | pygments = "*" 536 | 537 | [package.extras] 538 | test = ["pexpect"] 539 | 540 | [[package]] 541 | name = "jupyter-core" 542 | version = "4.7.1" 543 | description = "Jupyter core package. A base package on which Jupyter projects rely." 544 | category = "main" 545 | optional = false 546 | python-versions = ">=3.6" 547 | 548 | [package.dependencies] 549 | pywin32 = {version = ">=1.0", markers = "sys_platform == \"win32\""} 550 | traitlets = "*" 551 | 552 | [[package]] 553 | name = "jupyterlab-pygments" 554 | version = "0.1.2" 555 | description = "Pygments theme using JupyterLab CSS variables" 556 | category = "main" 557 | optional = false 558 | python-versions = "*" 559 | 560 | [package.dependencies] 561 | pygments = ">=2.4.1,<3" 562 | 563 | [[package]] 564 | name = "jupyterlab-widgets" 565 | version = "1.0.1" 566 | description = "A JupyterLab extension." 567 | category = "main" 568 | optional = false 569 | python-versions = ">=3.6" 570 | 571 | [[package]] 572 | name = "keras" 573 | version = "2.6.0" 574 | description = "TensorFlow Keras." 575 | category = "main" 576 | optional = false 577 | python-versions = "*" 578 | 579 | [[package]] 580 | name = "keras-preprocessing" 581 | version = "1.1.2" 582 | description = "Easy data preprocessing and data augmentation for deep learning models" 583 | category = "main" 584 | optional = false 585 | python-versions = "*" 586 | 587 | [package.dependencies] 588 | numpy = ">=1.9.1" 589 | six = ">=1.9.0" 590 | 591 | [package.extras] 592 | image = ["scipy (>=0.14)", "Pillow (>=5.2.0)"] 593 | pep8 = ["flake8"] 594 | tests = ["pandas", "pillow", "tensorflow", "keras", "pytest", "pytest-xdist", "pytest-cov"] 595 | 596 | [[package]] 597 | name = "kiwisolver" 598 | version = "1.3.2" 599 | description = "A fast implementation of the Cassowary constraint solver" 600 | category = "main" 601 | optional = false 602 | python-versions = ">=3.7" 603 | 604 | [[package]] 605 | name = "lxml" 606 | version = "4.6.3" 607 | description = "Powerful and Pythonic XML processing library combining libxml2/libxslt with the ElementTree API." 608 | category = "main" 609 | optional = false 610 | python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, != 3.4.*" 611 | 612 | [package.extras] 613 | cssselect = ["cssselect (>=0.7)"] 614 | html5 = ["html5lib"] 615 | htmlsoup = ["beautifulsoup4"] 616 | source = ["Cython (>=0.29.7)"] 617 | 618 | [[package]] 619 | name = "markdown" 620 | version = "3.3.4" 621 | description = "Python implementation of Markdown." 622 | category = "main" 623 | optional = false 624 | python-versions = ">=3.6" 625 | 626 | [package.dependencies] 627 | importlib-metadata = {version = "*", markers = "python_version < \"3.8\""} 628 | 629 | [package.extras] 630 | testing = ["coverage", "pyyaml"] 631 | 632 | [[package]] 633 | name = "markupsafe" 634 | version = "2.0.1" 635 | description = "Safely add untrusted strings to HTML/XML markup." 636 | category = "main" 637 | optional = false 638 | python-versions = ">=3.6" 639 | 640 | [[package]] 641 | name = "matplotlib" 642 | version = "3.4.3" 643 | description = "Python plotting package" 644 | category = "main" 645 | optional = false 646 | python-versions = ">=3.7" 647 | 648 | [package.dependencies] 649 | cycler = ">=0.10" 650 | kiwisolver = ">=1.0.1" 651 | numpy = ">=1.16" 652 | pillow = ">=6.2.0" 653 | pyparsing = ">=2.2.1" 654 | python-dateutil = ">=2.7" 655 | 656 | [[package]] 657 | name = "matplotlib-inline" 658 | version = "0.1.3" 659 | description = "Inline Matplotlib backend for Jupyter" 660 | category = "main" 661 | optional = false 662 | python-versions = ">=3.5" 663 | 664 | [package.dependencies] 665 | traitlets = "*" 666 | 667 | [[package]] 668 | name = "mistune" 669 | version = "0.8.4" 670 | description = "The fastest markdown parser in pure Python" 671 | category = "main" 672 | optional = false 673 | python-versions = "*" 674 | 675 | [[package]] 676 | name = "nbclient" 677 | version = "0.5.4" 678 | description = "A client library for executing notebooks. Formerly nbconvert's ExecutePreprocessor." 679 | category = "main" 680 | optional = false 681 | python-versions = ">=3.6.1" 682 | 683 | [package.dependencies] 684 | jupyter-client = ">=6.1.5" 685 | nbformat = ">=5.0" 686 | nest-asyncio = "*" 687 | traitlets = ">=4.2" 688 | 689 | [package.extras] 690 | dev = ["codecov", "coverage", "ipython", "ipykernel", "ipywidgets", "pytest (>=4.1)", "pytest-cov (>=2.6.1)", "check-manifest", "flake8", "mypy", "tox", "bumpversion", "xmltodict", "pip (>=18.1)", "wheel (>=0.31.0)", "setuptools (>=38.6.0)", "twine (>=1.11.0)", "black"] 691 | sphinx = ["Sphinx (>=1.7)", "sphinx-book-theme", "mock", "moto", "myst-parser"] 692 | test = ["codecov", "coverage", "ipython", "ipykernel", "ipywidgets", "pytest (>=4.1)", "pytest-cov (>=2.6.1)", "check-manifest", "flake8", "mypy", "tox", "bumpversion", "xmltodict", "pip (>=18.1)", "wheel (>=0.31.0)", "setuptools (>=38.6.0)", "twine (>=1.11.0)", "black"] 693 | 694 | [[package]] 695 | name = "nbconvert" 696 | version = "6.1.0" 697 | description = "Converting Jupyter Notebooks" 698 | category = "main" 699 | optional = false 700 | python-versions = ">=3.7" 701 | 702 | [package.dependencies] 703 | bleach = "*" 704 | defusedxml = "*" 705 | entrypoints = ">=0.2.2" 706 | jinja2 = ">=2.4" 707 | jupyter-core = "*" 708 | jupyterlab-pygments = "*" 709 | mistune = ">=0.8.1,<2" 710 | nbclient = ">=0.5.0,<0.6.0" 711 | nbformat = ">=4.4" 712 | pandocfilters = ">=1.4.1" 713 | pygments = ">=2.4.1" 714 | testpath = "*" 715 | traitlets = ">=5.0" 716 | 717 | [package.extras] 718 | all = ["pytest", "pytest-cov", "pytest-dependency", "ipykernel", "ipywidgets (>=7)", "pyppeteer (==0.2.2)", "tornado (>=4.0)", "sphinx (>=1.5.1)", "sphinx-rtd-theme", "nbsphinx (>=0.2.12)", "ipython"] 719 | docs = ["sphinx (>=1.5.1)", "sphinx-rtd-theme", "nbsphinx (>=0.2.12)", "ipython"] 720 | serve = ["tornado (>=4.0)"] 721 | test = ["pytest", "pytest-cov", "pytest-dependency", "ipykernel", "ipywidgets (>=7)", "pyppeteer (==0.2.2)"] 722 | webpdf = ["pyppeteer (==0.2.2)"] 723 | 724 | [[package]] 725 | name = "nbformat" 726 | version = "5.1.3" 727 | description = "The Jupyter Notebook format" 728 | category = "main" 729 | optional = false 730 | python-versions = ">=3.5" 731 | 732 | [package.dependencies] 733 | ipython-genutils = "*" 734 | jsonschema = ">=2.4,<2.5.0 || >2.5.0" 735 | jupyter-core = "*" 736 | traitlets = ">=4.1" 737 | 738 | [package.extras] 739 | fast = ["fastjsonschema"] 740 | test = ["check-manifest", "fastjsonschema", "testpath", "pytest", "pytest-cov"] 741 | 742 | [[package]] 743 | name = "nest-asyncio" 744 | version = "1.5.1" 745 | description = "Patch asyncio to allow nested event loops" 746 | category = "main" 747 | optional = false 748 | python-versions = ">=3.5" 749 | 750 | [[package]] 751 | name = "notebook" 752 | version = "6.4.3" 753 | description = "A web-based notebook environment for interactive computing" 754 | category = "main" 755 | optional = false 756 | python-versions = ">=3.6" 757 | 758 | [package.dependencies] 759 | argon2-cffi = "*" 760 | ipykernel = "*" 761 | ipython-genutils = "*" 762 | jinja2 = "*" 763 | jupyter-client = ">=5.3.4" 764 | jupyter-core = ">=4.6.1" 765 | nbconvert = "*" 766 | nbformat = "*" 767 | prometheus-client = "*" 768 | pyzmq = ">=17" 769 | Send2Trash = ">=1.5.0" 770 | terminado = ">=0.8.3" 771 | tornado = ">=6.1" 772 | traitlets = ">=4.2.1" 773 | 774 | [package.extras] 775 | docs = ["sphinx", "nbsphinx", "sphinxcontrib-github-alt", "sphinx-rtd-theme", "myst-parser"] 776 | json-logging = ["json-logging"] 777 | test = ["pytest", "coverage", "requests", "nbval", "selenium", "pytest-cov", "requests-unixsocket"] 778 | 779 | [[package]] 780 | name = "numpy" 781 | version = "1.19.3" 782 | description = "NumPy is the fundamental package for array computing with Python." 783 | category = "main" 784 | optional = false 785 | python-versions = ">=3.6" 786 | 787 | [[package]] 788 | name = "oauthlib" 789 | version = "3.1.1" 790 | description = "A generic, spec-compliant, thorough implementation of the OAuth request-signing logic" 791 | category = "main" 792 | optional = false 793 | python-versions = ">=3.6" 794 | 795 | [package.extras] 796 | rsa = ["cryptography (>=3.0.0,<4)"] 797 | signals = ["blinker (>=1.4.0)"] 798 | signedtoken = ["cryptography (>=3.0.0,<4)", "pyjwt (>=2.0.0,<3)"] 799 | 800 | [[package]] 801 | name = "opt-einsum" 802 | version = "3.3.0" 803 | description = "Optimizing numpys einsum function" 804 | category = "main" 805 | optional = false 806 | python-versions = ">=3.5" 807 | 808 | [package.dependencies] 809 | numpy = ">=1.7" 810 | 811 | [package.extras] 812 | docs = ["sphinx (==1.2.3)", "sphinxcontrib-napoleon", "sphinx-rtd-theme", "numpydoc"] 813 | tests = ["pytest", "pytest-cov", "pytest-pep8"] 814 | 815 | [[package]] 816 | name = "packaging" 817 | version = "21.0" 818 | description = "Core utilities for Python packages" 819 | category = "main" 820 | optional = false 821 | python-versions = ">=3.6" 822 | 823 | [package.dependencies] 824 | pyparsing = ">=2.0.2" 825 | 826 | [[package]] 827 | name = "pandas" 828 | version = "1.3.2" 829 | description = "Powerful data structures for data analysis, time series, and statistics" 830 | category = "main" 831 | optional = false 832 | python-versions = ">=3.7.1" 833 | 834 | [package.dependencies] 835 | numpy = ">=1.17.3" 836 | python-dateutil = ">=2.7.3" 837 | pytz = ">=2017.3" 838 | 839 | [package.extras] 840 | test = ["hypothesis (>=3.58)", "pytest (>=6.0)", "pytest-xdist"] 841 | 842 | [[package]] 843 | name = "pandocfilters" 844 | version = "1.4.3" 845 | description = "Utilities for writing pandoc filters in python" 846 | category = "main" 847 | optional = false 848 | python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*" 849 | 850 | [[package]] 851 | name = "parso" 852 | version = "0.8.2" 853 | description = "A Python Parser" 854 | category = "main" 855 | optional = false 856 | python-versions = ">=3.6" 857 | 858 | [package.extras] 859 | qa = ["flake8 (==3.8.3)", "mypy (==0.782)"] 860 | testing = ["docopt", "pytest (<6.0.0)"] 861 | 862 | [[package]] 863 | name = "patsy" 864 | version = "0.5.1" 865 | description = "A Python package for describing statistical models and for building design matrices." 866 | category = "main" 867 | optional = false 868 | python-versions = "*" 869 | 870 | [package.dependencies] 871 | numpy = ">=1.4" 872 | six = "*" 873 | 874 | [[package]] 875 | name = "pexpect" 876 | version = "4.8.0" 877 | description = "Pexpect allows easy control of interactive console applications." 878 | category = "main" 879 | optional = false 880 | python-versions = "*" 881 | 882 | [package.dependencies] 883 | ptyprocess = ">=0.5" 884 | 885 | [[package]] 886 | name = "pickleshare" 887 | version = "0.7.5" 888 | description = "Tiny 'shelve'-like database with concurrency support" 889 | category = "main" 890 | optional = false 891 | python-versions = "*" 892 | 893 | [[package]] 894 | name = "pillow" 895 | version = "8.3.2" 896 | description = "Python Imaging Library (Fork)" 897 | category = "main" 898 | optional = false 899 | python-versions = ">=3.6" 900 | 901 | [[package]] 902 | name = "plotly" 903 | version = "5.3.1" 904 | description = "An open-source, interactive data visualization library for Python" 905 | category = "main" 906 | optional = false 907 | python-versions = ">=3.6" 908 | 909 | [package.dependencies] 910 | six = "*" 911 | tenacity = ">=6.2.0" 912 | 913 | [[package]] 914 | name = "prometheus-client" 915 | version = "0.11.0" 916 | description = "Python client for the Prometheus monitoring system." 917 | category = "main" 918 | optional = false 919 | python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*" 920 | 921 | [package.extras] 922 | twisted = ["twisted"] 923 | 924 | [[package]] 925 | name = "prompt-toolkit" 926 | version = "3.0.20" 927 | description = "Library for building powerful interactive command lines in Python" 928 | category = "main" 929 | optional = false 930 | python-versions = ">=3.6.2" 931 | 932 | [package.dependencies] 933 | wcwidth = "*" 934 | 935 | [[package]] 936 | name = "protobuf" 937 | version = "3.17.3" 938 | description = "Protocol Buffers" 939 | category = "main" 940 | optional = false 941 | python-versions = "*" 942 | 943 | [package.dependencies] 944 | six = ">=1.9" 945 | 946 | [[package]] 947 | name = "ptyprocess" 948 | version = "0.7.0" 949 | description = "Run a subprocess in a pseudo terminal" 950 | category = "main" 951 | optional = false 952 | python-versions = "*" 953 | 954 | [[package]] 955 | name = "py" 956 | version = "1.10.0" 957 | description = "library with cross-python path, ini-parsing, io, code, log facilities" 958 | category = "main" 959 | optional = false 960 | python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*" 961 | 962 | [[package]] 963 | name = "pyasn1" 964 | version = "0.4.8" 965 | description = "ASN.1 types and codecs" 966 | category = "main" 967 | optional = false 968 | python-versions = "*" 969 | 970 | [[package]] 971 | name = "pyasn1-modules" 972 | version = "0.2.8" 973 | description = "A collection of ASN.1-based protocols modules." 974 | category = "main" 975 | optional = false 976 | python-versions = "*" 977 | 978 | [package.dependencies] 979 | pyasn1 = ">=0.4.6,<0.5.0" 980 | 981 | [[package]] 982 | name = "pycparser" 983 | version = "2.20" 984 | description = "C parser in Python" 985 | category = "main" 986 | optional = false 987 | python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*" 988 | 989 | [[package]] 990 | name = "pygments" 991 | version = "2.10.0" 992 | description = "Pygments is a syntax highlighting package written in Python." 993 | category = "main" 994 | optional = false 995 | python-versions = ">=3.5" 996 | 997 | [[package]] 998 | name = "pyparsing" 999 | version = "2.4.7" 1000 | description = "Python parsing module" 1001 | category = "main" 1002 | optional = false 1003 | python-versions = ">=2.6, !=3.0.*, !=3.1.*, !=3.2.*" 1004 | 1005 | 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description = "Python interface to the R language (embedded R)" 1140 | category = "main" 1141 | optional = false 1142 | python-versions = "*" 1143 | 1144 | [package.dependencies] 1145 | cffi = ">=1.10.0" 1146 | jinja2 = "*" 1147 | pytz = "*" 1148 | tzlocal = "*" 1149 | 1150 | [package.extras] 1151 | all = ["numpy", "pandas", "pytest"] 1152 | numpy = ["pandas"] 1153 | pandas = ["numpy", "pandas"] 1154 | test = ["pytest"] 1155 | 1156 | [[package]] 1157 | name = "rsa" 1158 | version = "4.7.2" 1159 | description = "Pure-Python RSA implementation" 1160 | category = "main" 1161 | optional = false 1162 | python-versions = ">=3.5, <4" 1163 | 1164 | [package.dependencies] 1165 | pyasn1 = ">=0.1.3" 1166 | 1167 | [[package]] 1168 | name = "scikit-learn" 1169 | version = "0.24.2" 1170 | description = "A set of python modules for machine learning and data mining" 1171 | category = "main" 1172 | optional = false 1173 | python-versions = ">=3.6" 1174 | 1175 | [package.dependencies] 1176 | joblib = 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1195 | [package.dependencies] 1196 | numpy = ">=1.16.5,<1.23.0" 1197 | 1198 | [[package]] 1199 | name = "send2trash" 1200 | version = "1.8.0" 1201 | description = "Send file to trash natively under Mac OS X, Windows and Linux." 1202 | category = "main" 1203 | optional = false 1204 | python-versions = "*" 1205 | 1206 | [package.extras] 1207 | nativelib = ["pyobjc-framework-cocoa", "pywin32"] 1208 | objc = ["pyobjc-framework-cocoa"] 1209 | win32 = ["pywin32"] 1210 | 1211 | [[package]] 1212 | name = "six" 1213 | version = "1.15.0" 1214 | description = "Python 2 and 3 compatibility utilities" 1215 | category = "main" 1216 | optional = false 1217 | python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*" 1218 | 1219 | [[package]] 1220 | name = "statsmodels" 1221 | version = "0.12.2" 1222 | description = "Statistical computations and models for Python" 1223 | category = "main" 1224 | optional = false 1225 | python-versions = ">=3.6" 1226 | 1227 | [package.dependencies] 1228 | numpy = ">=1.15" 1229 | pandas = ">=0.21" 1230 | patsy = ">=0.5" 1231 | scipy = ">=1.1" 1232 | 1233 | [package.extras] 1234 | build = ["cython (>=0.29)"] 1235 | develop = ["cython (>=0.29)"] 1236 | docs = ["sphinx", "nbconvert", "jupyter-client", "ipykernel", "matplotlib", "nbformat", "numpydoc", "pandas-datareader"] 1237 | 1238 | [[package]] 1239 | name = "tenacity" 1240 | version = "8.0.1" 1241 | description = "Retry code until it succeeds" 1242 | category = "main" 1243 | optional = false 1244 | python-versions = ">=3.6" 1245 | 1246 | [package.extras] 1247 | doc = ["reno", "sphinx", "tornado (>=4.5)"] 1248 | 1249 | [[package]] 1250 | name = "tensorboard" 1251 | version = "2.6.0" 1252 | description = "TensorBoard lets you watch Tensors Flow" 1253 | category = "main" 1254 | optional = false 1255 | python-versions = ">=3.6" 1256 | 1257 | [package.dependencies] 1258 | absl-py = ">=0.4" 1259 | google-auth = ">=1.6.3,<2" 1260 | google-auth-oauthlib = ">=0.4.1,<0.5" 1261 | grpcio = ">=1.24.3" 1262 | 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1398 | version = "2.2.0" 1399 | description = "threadpoolctl" 1400 | category = "main" 1401 | optional = false 1402 | python-versions = ">=3.6" 1403 | 1404 | [[package]] 1405 | name = "tornado" 1406 | version = "6.1" 1407 | description = "Tornado is a Python web framework and asynchronous networking library, originally developed at FriendFeed." 1408 | category = "main" 1409 | optional = false 1410 | python-versions = ">= 3.5" 1411 | 1412 | [[package]] 1413 | name = "tqdm" 1414 | version = "4.62.2" 1415 | description = "Fast, Extensible Progress Meter" 1416 | category = "main" 1417 | optional = false 1418 | python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,>=2.7" 1419 | 1420 | [package.dependencies] 1421 | colorama = {version = "*", markers = "platform_system == \"Windows\""} 1422 | 1423 | [package.extras] 1424 | dev = ["py-make (>=0.1.0)", "twine", "wheel"] 1425 | notebook = ["ipywidgets (>=6)"] 1426 | telegram = ["requests"] 1427 | 1428 | [[package]] 1429 | name = "traitlets" 1430 | 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