{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Пример использование подходов из sklift.models в sklearn.pipeline\n", "\n", "
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\n", " SCIKIT-UPLIFT REPO | \n", " SCIKIT-UPLIFT DOCS | \n", " USER GUIDE\n", "
\n", " ENGLISH VERSION\n", "\n", "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "В данном ноутбуке рассмотрим простой пример применения одного из подходов прогнозирования uplift в [sklearn.pipeline](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.pipeline).\n", "\n", "Данные для примера взяты из [MineThatData E-Mail Analytics And Data Mining Challenge dataset by Kevin Hillstrom](https://blog.minethatdata.com/2008/03/minethatdata-e-mail-analytics-and-data.html). Этот набор данных содержит 64 000 клиентов, которые в последний раз совершали покупки в течение двенадцати месяцев. Среди клиентов была проведена рекламная кампания с помощью email рассылки:\n", "\n", "* 1/3 клиентов были выбраны случайным образом для получения электронного письма, рекламирующего мужскую продукцию;\n", "* 1/3 клиентов были выбраны случайным образом для получения электронного письма, рекламирующего женскую продукцию;\n", "* С оставшейся 1/3 коммуникацию не проводили.\n", "\n", "Для каждого клиента из выборки замерили факт перехода по ссылке в письме, факт совершения покупки и сумму трат за две недели, следущими после получения письма.\n", "\n", "Полное описание датасета можной найти по [ссылке](https://blog.minethatdata.com/2008/03/minethatdata-e-mail-analytics-and-data.html).\n", "\n", "Установим необходимые библиотеки:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "ExecuteTime": { "end_time": "2021-02-07T01:01:58.302718Z", "start_time": "2021-02-07T01:01:58.298524Z" } }, "outputs": [], "source": [ "# pip install scikit-uplift xgboost==1.0.2 category_encoders==2.1.0 -U" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Для простоты примера оставим только два сегмента пользователей:\n", "* тем, кому рассылалась по электронной почте рекламная кампания с участием женских товаров;\n", "* тем, кому не рассылалась рекламная кампания.\n", "\n", "В качестве целевой переменной будем использовать переменную `visit`." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "ExecuteTime": { "end_time": "2021-02-07T01:01:59.884250Z", "start_time": "2021-02-07T01:01:58.315398Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Размер датасета до обработки: (64000, 8)\n", "Размер датасета после обработки: (42693, 8)\n" ] }, { "data": { "text/html": [ "
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recencyhistory_segmenthistorymenswomenszip_codenewbiechannel
0102) $100 - $200142.4410Surburban0Phone
163) $200 - $350329.0811Rural1Web
272) $100 - $200180.6501Surburban1Web
421) $0 - $10045.3410Urban0Web
562) $100 - $200134.8301Surburban0Phone
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" ], "text/plain": [ " recency history_segment history mens womens zip_code newbie channel\n", "0 10 2) $100 - $200 142.44 1 0 Surburban 0 Phone\n", "1 6 3) $200 - $350 329.08 1 1 Rural 1 Web\n", "2 7 2) $100 - $200 180.65 0 1 Surburban 1 Web\n", "4 2 1) $0 - $100 45.34 1 0 Urban 0 Web\n", "5 6 2) $100 - $200 134.83 0 1 Surburban 0 Phone" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "from sklift.datasets import fetch_hillstrom\n", "\n", "\n", "%matplotlib inline\n", "\n", "bunch = fetch_hillstrom(target_col='visit')\n", "\n", "dataset, target, treatment = bunch['data'], bunch['target'], bunch['treatment']\n", "\n", "print(f'Размер датасета до обработки: {dataset.shape}')\n", "\n", "# Selecting two segments\n", "dataset = dataset[treatment!='Mens E-Mail']\n", "target = target[treatment!='Mens E-Mail']\n", "treatment = treatment[treatment!='Mens E-Mail'].map({\n", " 'Womens E-Mail': 1,\n", " 'No E-Mail': 0\n", "})\n", "\n", "print(f'Размер датасета после обработки: {dataset.shape}')\n", "dataset.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Разобъем все данные на обучающую и валидационную выборку:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "ExecuteTime": { "end_time": "2021-02-07T01:01:59.976727Z", "start_time": "2021-02-07T01:01:59.889576Z" } }, "outputs": [], "source": [ "from sklearn.model_selection import train_test_split\n", "\n", "\n", "X_tr, X_val, y_tr, y_val, treat_tr, treat_val = train_test_split(\n", " dataset, target, treatment, test_size=0.5, random_state=42\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Select categorical features:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "ExecuteTime": { "end_time": "2021-02-07T01:02:00.003357Z", "start_time": "2021-02-07T01:01:59.983254Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['history_segment', 'zip_code', 'channel']\n" ] } ], "source": [ "cat_cols = X_tr.select_dtypes(include='object').columns.tolist()\n", "print(cat_cols)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Создадим нужные объекты и объединим их в pipieline." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "ExecuteTime": { "end_time": "2021-02-07T01:02:00.079199Z", "start_time": "2021-02-07T01:02:00.009314Z" } }, "outputs": [], "source": [ "from sklearn.pipeline import Pipeline\n", "from category_encoders import CatBoostEncoder\n", "from sklift.models import ClassTransformation\n", "from xgboost import XGBClassifier\n", "\n", "\n", "encoder = CatBoostEncoder(cols=cat_cols)\n", "estimator = XGBClassifier(max_depth=2, random_state=42)\n", "ct = ClassTransformation(estimator=estimator)\n", "\n", "my_pipeline = Pipeline([\n", " ('encoder', encoder),\n", " ('model', ct)\n", "])" ] }, { "cell_type": "markdown", "metadata": { "ExecuteTime": { "end_time": "2020-04-26T18:02:52.236917Z", "start_time": "2020-04-26T18:02:52.110138Z" } }, "source": [ "Обучать pipeline будем как обычно, но колонку treatment добавим как параметр шага model: `model__treatment`." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "ExecuteTime": { "end_time": "2021-02-07T01:02:01.332880Z", "start_time": "2021-02-07T01:02:00.085047Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/Maksim/Library/Python/3.6/lib/python/site-packages/sklearn/pipeline.py:354: UserWarning: It is recommended to use this approach on treatment balanced data. Current sample size is unbalanced.\n", " self._final_estimator.fit(Xt, y, **fit_params)\n" ] } ], "source": [ "my_pipeline = my_pipeline.fit(\n", " X=X_tr,\n", " y=y_tr,\n", " model__treatment=treat_tr\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Предскажем uplift и посчитаем uplift@30%" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "ExecuteTime": { "end_time": "2021-02-07T01:02:01.476617Z", "start_time": "2021-02-07T01:02:01.335371Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "uplift@30%: 0.0661\n" ] } ], "source": [ "from sklift.metrics import uplift_at_k\n", "\n", "\n", "uplift_predictions = my_pipeline.predict(X_val)\n", "\n", "uplift_30 = uplift_at_k(y_val, uplift_predictions, treat_val, strategy='overall')\n", "print(f'uplift@30%: {uplift_30:.4f}')" ] } ], "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": "3.6.1" }, "pycharm": { "stem_cell": { "cell_type": "raw", "source": [], "metadata": { "collapsed": false } } } }, "nbformat": 4, "nbformat_minor": 2 }