{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Ak Bars Online (mobile) churn rate prediction\n", "\n", "### Источник данных\n", "\n", "Для составления исходного dataFrame (df_main) используются сессионные данные, доступные в приложении ApMetrica, а также данные о платежах и переводах, отправляемых разработчиками в AppMetrica.\n", "\n", "Количество пользователей считается по уникальному идентификатору устройства, который присваивается AppMetrica (параметр DeviceID)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Импорт библиотек + вспомогательные функции для работы с данными Clickhouse" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "HOST = 'http://localhost:8123'\n", "import requests\n", "import pandas as pd\n", "import numpy as np\n", "import seaborn as sns\n", "try:\n", " from StringIO import StringIO\n", "except ImportError:\n", " from io import StringIO\n", "import seaborn as sns\n", "import matplotlib\n", "import matplotlib.pyplot as plt\n", "import datetime\n", "%matplotlib inline\n", "\n", "# убираем scientific notation\n", "pd.options.display.float_format = '{:.2f}'.format\n", "\n", "# отключим предупреждения\n", "import warnings\n", "warnings.filterwarnings('ignore')\n", "\n", "import itertools\n", "from pylab import rcParams\n", "rcParams['figure.figsize'] = 12, 8\n", "\n", "# возвращаем результат из DataBase\n", "def get_clickhouse_data(query, host = HOST, connection_timeout = 1500):\n", " r = requests.post(host, params = {'query': query}, timeout = connection_timeout)\n", " if r.status_code == 200:\n", " return r.text\n", " else:\n", " raise ValueError(r.text)\n", " \n", "# преобразуем полученные данные в pandas DataFrame \n", "def get_clickhouse_df(query, host = HOST, connection_timeout = 1500):\n", " data = get_clickhouse_data(query, host, connection_timeout) \n", " df = pd.read_csv(StringIO(data), sep = '\\t')\n", " return df\n", "\n", "# константы для работы с временными диапазонами\n", "\n", "# вчерашняя дата\n", "yesterday = [(datetime.date.today() - datetime.timedelta(days=2)).strftime('%Y-%m-%d'),\n", " (datetime.date.today() - datetime.timedelta(days=2)).strftime('%Y-%m-%d')]\n", "# последние 7 дней (без учета текущей даты)\n", "week_dates = [(datetime.date.today() - datetime.timedelta(days=7)).strftime('%Y-%m-%d'), \\\n", "(datetime.date.today() - datetime.timedelta(days=1)).strftime('%Y-%m-%d')]\n", "# текущий месяц (без учета текущей даты)\n", "month_dates = [datetime.date.today().strftime('%Y-%m-') + '01', \\\n", "(datetime.date.today() - datetime.timedelta(days=1)).strftime('%Y-%m-%d')]\n", "\n", "# выставляем даты для отчета\n", "query_dates = ['2018-06-01', '2018-07-31'];\n", "\n", "# выставляем даты для целевого признака по оттоку\n", "churn_query_dates = ['2018-08-01', '2018-08-31'];\n", "\n", "# формат даты\n", "fmt = '%Y-%m-%d'\n", "\n", "# функция для расчета признаков, связанных с сессиями пользователей (например, avgdaylag (средний кол-во дней между сессиями))\n", "def func(a):\n", " i1, i2 = itertools.tee(iter(a))\n", " next(i2)\n", " res = [(int(round((datetime.strptime(y, fmt) - datetime.strptime(x, fmt)).total_seconds() / 60))) / 1440\n", " for x, y in zip(i1, i2)] # or just zip in Python 3\n", " return round(np.mean(res),2)\n", "\n", "from datetime import datetime\n", "\n", "# здесь выставляем первое число месяца, на который прогнозируем отток\n", "\n", "reportDate = datetime(2018, 8, 1)\n", "\n", "sns.set(rc={'axes.facecolor':'grey', 'figure.facecolor':'grey'})\n", "sns.set(palette='Blues')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## ВСЕ СЕССИИ" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Получим данные по сессиям за период. В нашем случае за два месяца (июнь и июль 2018)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 12.7 s, sys: 7.6 s, total: 20.3 s\n", "Wall time: 26.7 s\n" ] } ], "source": [ "%%time\n", "\n", "q = '''\n", "SELECT\n", " DeviceID,\n", " AppVersionName,\n", " OSName,\n", " City,\n", " SessionStartDate,\n", " SessionStartDateTime,\n", " SessionStartTimestamp\n", "FROM\n", " mobile.sessions_starts_all\n", "WHERE\n", " SessionStartDate BETWEEN ''' + ''' \\'''' + \\\n", " query_dates[0] + '''\\' AND ''' + '''\\'''' + \\\n", " query_dates[1] + '''\\' ''''FORMAT TabSeparatedWithNames'''\n", "\n", "sessions_2months = get_clickhouse_df(q)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Добавим данные за май и апрель 2018" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "sessions_may = pd.read_csv('../dmitriilin/Downloads/sessions_starts_may.csv', sep=',')\n", "sessions_april = pd.read_csv('../dmitriilin/Downloads/sessions_starts_april.csv', sep=',')\n", "sessions_may['SessionStartDate'] = sessions_may['SessionStartDateTime'].apply(lambda x: x.split(' ')[0])" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true }, "outputs": [], "source": [ "frames = [sessions_2months, sessions_may, sessions_april]\n", "sessions = pd.concat(frames)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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AppVersionNameCityDeviceIDOSNameSessionStartDateSessionStartDateTimeSessionStartTimestampUnnamed: 0
03.17.2Kazan7543012021622200292ios2018-06-012018-06-01 11:57:291527854249nan
13.17.1Samara16883537386825900026android2018-06-012018-06-01 11:18:101527851890nan
23.17.1Samara16883537386825900026android2018-06-012018-06-01 07:11:191527837079nan
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" ], "text/plain": [ " AppVersionName City DeviceID OSName SessionStartDate \\\n", "0 3.17.2 Kazan 7543012021622200292 ios 2018-06-01 \n", "1 3.17.1 Samara 16883537386825900026 android 2018-06-01 \n", "2 3.17.1 Samara 16883537386825900026 android 2018-06-01 \n", "3 3.18.0 Kazan 170802125429386870 android 2018-06-01 \n", "4 3.18.0 Kazan 170802125429386870 android 2018-06-01 \n", "\n", " SessionStartDateTime SessionStartTimestamp Unnamed: 0 \n", "0 2018-06-01 11:57:29 1527854249 nan \n", "1 2018-06-01 11:18:10 1527851890 nan \n", "2 2018-06-01 07:11:19 1527837079 nan \n", "3 2018-06-01 08:03:44 1527840224 nan \n", "4 2018-06-01 11:31:19 1527852679 nan " ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sessions.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Создадим общий dataframe для пользователей, куда будем добавлять агрегированные данные. Посчитаем кол-во сессий на DeviceID, используя SessionStartTimestamp в качестве ID сессии" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 1.45 s, sys: 610 ms, total: 2.06 s\n", "Wall time: 2.17 s\n" ] } ], "source": [ "%%time\n", "\n", "df_main = sessions.groupby('DeviceID')[['SessionStartTimestamp']]\\\n", " .count().reset_index().sort_values(by='SessionStartTimestamp', ascending=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Запишем дни сессий в порядке возрастания массивом в колонку SessionStartDate" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df_main['SessionStartDate'] = [sorted(list(set(sessions['SessionStartDate'].loc[sessions['DeviceID'] == x['DeviceID']]))) \n", " for _, x in df_main.iterrows()]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Посчитаем среднюю продолжительность перерыва между днями, в течение которых были сессии" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df_main['sessions_avgdaylag'] = df_main.apply(lambda x: func(x['SessionStartDate']), axis=1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Если у пользователя был один активный день, то avgdaylag будет равен кол-ву дней с этого дня и до даты отчета" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": true }, "outputs": [], "source": [ "tempSer = df_main[df_main['sessions_avgdaylag'].isnull()]['SessionStartDate'].apply(\n", " lambda x: round((int(round((reportDate - datetime.strptime(x[0], fmt)).total_seconds() / 60))) / 1440))" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df_main['sessions_avgdaylag'] = df_main['sessions_avgdaylag'].fillna(tempSer)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Посчитаем кол-во дней, прошешдишх с последней сессии пользователя от даты формирования отчета" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": true }, "outputs": [], "source": [ "temp = sessions.groupby('DeviceID')[['SessionStartDate']].max().reset_index()\n", "temp['sessions_daysSinceLastSession'] = temp['SessionStartDate'].apply(\n", " lambda x: round((int(round((reportDate - datetime.strptime(x, fmt)).total_seconds() / 60))) / 1440))\n", "df_main = df_main.join(temp.drop(['SessionStartDate'], axis=1).set_index('DeviceID'), on='DeviceID')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Посчитаем количество дней, когда пользователь был активен в приложении" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df_main['sessions_totaldaysactive'] = df_main['SessionStartDate'].apply(lambda x: len(x))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Удалим массив уникальных дат с сессиями и переименуем столбец SessionStartTimestamp" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df_main = df_main.drop(['SessionStartDate'], axis=1).rename(index=str, columns={\n", " \"SessionStartTimestamp\": \"sessions_totalnumber\"\n", "})" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "## ВСЕ СОБЫТИЯ" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Создадим временный dataframe для всех событий" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": true, "scrolled": true }, "outputs": [], "source": [ "q = '''\n", "SELECT\n", " AppVersionName,\n", " OSName,\n", " EventDate,\n", " ReceiveDate,\n", " ReceiveTimestamp,\n", " EventTimestamp,\n", " EventDateTime,\n", " EventName,\n", " DeviceID,\n", " CAST(visitParamExtractRaw(replaceAll(EventParameters, '\"\"', '\"'), 'crmId') AS VARCHAR) AS crmId,\n", " CAST(visitParamExtractRaw(replaceAll(EventParameters, '\"\"', '\"'), 'status') AS VARCHAR) AS authStatus,\n", " CAST(visitParamExtractRaw(replaceAll(EventParameters, '\"\"', '\"'), 'gender') AS VARCHAR) AS gender,\n", " CAST(visitParamExtractRaw(replaceAll(EventParameters, '\"\"', '\"'), 'тип операции') AS VARCHAR) AS operationType,\n", " CAST(visitParamExtractRaw(replaceAll(EventParameters, '\"\"', '\"'), 'сумма операции') AS VARCHAR) AS operationAmount,\n", " CAST(visitParamExtractRaw(replaceAll(EventParameters, '\"\"', '\"'), 'источник') AS VARCHAR) AS sender,\n", " CAST(visitParamExtractRaw(replaceAll(EventParameters, '\"\"', '\"'), 'валюта отправителя') AS VARCHAR) AS senderCurrency,\n", " CAST(visitParamExtractRaw(replaceAll(EventParameters, '\"\"', '\"'), 'получатель') AS VARCHAR) AS recipient,\n", " CAST(visitParamExtractRaw(replaceAll(EventParameters, '\"\"', '\"'), 'валюта получателя') AS VARCHAR) AS recipientCurrency,\n", " CAST(visitParamExtractRaw(replaceAll(EventParameters, '\"\"', '\"'), 'размер комиссии') AS VARCHAR) AS operationFee,\n", " CAST(visitParamExtractRaw(replaceAll(EventParameters, '\"\"', '\"'), 'способ операции') AS VARCHAR) AS operationMethod,\n", " CAST(visitParamExtractRaw(replaceAll(EventParameters, '\"\"', '\"'), 'срок') AS VARCHAR) AS depositTerm,\n", " CAST(visitParamExtractRaw(replaceAll(EventParameters, '\"\"', '\"'), 'валюта') AS VARCHAR) AS depositCurrency,\n", " CAST(visitParamExtractRaw(replaceAll(EventParameters, '\"\"', '\"'), 'ставка') AS VARCHAR) AS depositRate,\n", " CAST(visitParamExtractRaw(replaceAll(EventParameters, '\"\"', '\"'), 'название') AS VARCHAR) AS depositName,\n", " CAST(visitParamExtractRaw(replaceAll(EventParameters, '\"\"', '\"'), 'тип чата') AS VARCHAR) AS chatType,\n", " CAST(visitParamExtractRaw(replaceAll(EventParameters, '\"\"', '\"'), 'операция') AS VARCHAR) AS operation\n", "FROM\n", " mobile.events_all\n", "WHERE\n", " match(EventName,'продажи|переводы|Переводы|^платежи$')\n", " AND EventDate BETWEEN ''' + ''' \\'''' + \\\n", " query_dates[0] + '''\\' AND ''' + '''\\'''' + \\\n", " query_dates[1] + '''\\' ''''FORMAT TabSeparatedWithNames'''\n", "\n", "events = get_clickhouse_df(q)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## ПЛАТЕЖИ" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Создадим отдельный dataframe для платежей без добавления платежи, где сумма операция неизвестна (недоступна). Используем сумму операции, так как, если она пустая, то все остальные поля платежа тоже пустые." ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": true }, "outputs": [], "source": [ "payments_2months = events[(events['EventName'] == 'платежи')]" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": true }, "outputs": [], "source": [ "payments_2months = payments_2months.dropna(subset = ['operationAmount'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Добавим данные за май и апрель 2018" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": true }, "outputs": [], "source": [ "payments_may = pd.read_csv('../dmitriilin/Downloads/payments_may.csv', sep=',')" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": true }, "outputs": [], "source": [ "payments_april = pd.read_csv('../dmitriilin/Downloads/payments_april.csv', sep=',')" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": true }, "outputs": [], "source": [ "frames = [payments_2months, payments_may, payments_april]\n", "payments = pd.concat(frames)" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": true }, "outputs": [], "source": [ "payments.drop(['Unnamed: 0', 'chatType', 'depositCurrency', 'depositName', 'depositRate', 'depositTerm'],\n", " axis=1,\n", " inplace=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Рассчитаем агрегаты для платежей" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": true }, "outputs": [], "source": [ "f = {\n", " 'operationAmount':['count','sum', 'median', 'min', 'max'], #количество, сумма, медиана, минимум и максимум\n", " 'sender': ['nunique'], #кол-во уникальных источников платежей\n", " 'recipient': ['nunique'], #кол-во получателей платежей\n", " 'operationMethod': ['nunique'] #кол-во способов платежей\n", "}\n", "\n", "# расчитаем агрегаты по каждому DeviceID\n", "\n", "payments_agg = payments.groupby(['DeviceID']).agg(f)\n", "payments_agg.columns = payments_agg.columns.map('_'.join)\n", "payments_agg = payments_agg.reset_index().rename(columns={\n", " 'operationAmount_count': 'payments_operationAmount_count',\n", " 'operationAmount_sum': 'payments_operationAmount_sum',\n", " 'operationAmount_median': 'payments_operationAmount_median',\n", " 'operationAmount_min': 'payments_operationAmount_min',\n", " 'operationAmount_max': 'payments_operationAmount_max',\n", " 'sender_nunique': 'payments_sender_nunique',\n", " 'recipient_nunique': 'payments_recipient_nunique',\n", " 'operationMethod_nunique': 'payments_operationMethod_nunique'\n", "})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Запишем дни платежей в порядке возрастания массивом в колонку payments_date" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": true }, "outputs": [], "source": [ "payments_agg['payments_date'] = [sorted(list(set(payments['EventDate'].loc[payments['DeviceID'] == x['DeviceID']]))) \n", " for _, x in payments_agg.iterrows()]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Посчитаем среднюю продолжительность перерыва между днями, в течение которых были платежи.\n", "Посчитаем количество дней, когда пользователь совершал платежи в приложении." ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": true }, "outputs": [], "source": [ "payments_agg['payments_avgdaylag'] = payments_agg.apply(lambda x: func(x['payments_date']), axis=1)\n", "payments_agg['payments_daysactive'] = payments_agg['payments_date'].apply(lambda x: len(x))\n", "\n", "payments_agg = payments_agg.drop(['payments_date'], axis=1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Запишем данные о платежах в основой dataFrame" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df_main = df_main.join(payments_agg.set_index('DeviceID'), on='DeviceID')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Посчитаем кол-во дней, прошешдишх с последнего платежаа пользователя от даты формирования отчета" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": true }, "outputs": [], "source": [ "temp = payments.groupby('DeviceID')[['EventDate']].max().reset_index()\n", "temp['payments_daysSinceLastPayment'] = temp['EventDate'].apply(\n", " lambda x: round((int(round((reportDate - datetime.strptime(x, fmt)).total_seconds() / 60))) / 1440))\n", "df_main = df_main.join(temp.drop(['EventDate'], axis=1).set_index('DeviceID'), on='DeviceID')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Заполним отсутствующие данные для тех, кто ни разу не совершал платеж в выбранный период. Для признаков avgdaylag & daysSinceLastPayemnt запишем 122 дней (общее количество дней в периоде)." ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": true }, "outputs": [], "source": [ "fillna0 = [\n", " 'payments_operationAmount_count',\n", " 'payments_operationAmount_sum',\n", " 'payments_operationAmount_median',\n", " 'payments_operationAmount_min',\n", " 'payments_operationAmount_max',\n", " 'payments_sender_nunique',\n", " 'payments_recipient_nunique',\n", " 'payments_operationMethod_nunique',\n", " 'payments_daysactive'\n", "]\n", "\n", "fillna122 = ['payments_avgdaylag', 'payments_daysSinceLastPayment']\n", "\n", "for i in fillna0:\n", " df_main[i].fillna(0, inplace=True)\n", "\n", "for i in fillna122:\n", " df_main[i].fillna(122, inplace=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## ПЕРЕВОДЫ" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Для переводов посчитаем те же агрегаты, что и для платежей" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": true }, "outputs": [], "source": [ "fundTransfers_2months = events[(events['EventName'] == 'переводы')]\n", "fundTransfers_2months = fundTransfers_2months.dropna(subset = ['sender'])" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": true }, "outputs": [], "source": [ "fundTransfers_may = pd.read_csv('../dmitriilin/Downloads/transfers_may.csv', sep=',')" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": true }, "outputs": [], "source": [ "fundTransfers_april = pd.read_csv('../dmitriilin/Downloads/transfers_april.csv', sep=',')" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": true }, "outputs": [], "source": [ "frames = [fundTransfers_2months, fundTransfers_may, fundTransfers_april]\n", "fundTransfers = pd.concat(frames)" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": true }, "outputs": [], "source": [ "f = {\n", " 'operationAmount':['count','sum', 'median', 'min', 'max'], #количество и сумма операций\n", " 'operationFee': ['sum'], #сумма комиссий\n", " 'sender': ['nunique'], #кол-во источников перевода\n", " 'recipient': ['nunique'], #кол-во получателей перевода\n", " 'operationMethod': ['nunique'], #кол-во способов перевода\n", " 'senderCurrency': ['nunique'], #кол-во валют источника перевода\n", " 'recipientCurrency': ['nunique'] #кол-во валют получателя перевода\n", "}\n", "\n", "fundTransfers_agg = fundTransfers.groupby(['DeviceID']).agg(f)\n", "fundTransfers_agg.columns = fundTransfers_agg.columns.map('_'.join)\n", "fundTransfers_agg = fundTransfers_agg.reset_index().rename(columns={\n", " 'operationAmount_count': 'transfers_operationAmount_count',\n", " 'operationAmount_sum': 'transfers_operationAmount_sum',\n", " 'operationAmount_median': 'transfers_operationAmount_median',\n", " 'operationAmount_min': 'transfers_operationAmount_min',\n", " 'operationAmount_max': 'transfers_operationAmount_max',\n", " 'operationFee_sum': 'transfers_operationFee_sum',\n", " 'sender_nunique': 'transfers_sender_nunique',\n", " 'recipient_nunique': 'transfers_recipient_nunique',\n", " 'operationMethod_nunique': 'transfers_operationMethod_nunique',\n", " 'senderCurrency_nunique': 'transfers_senderCurrency_nunique',\n", " 'recipientCurrency_nunique': 'transfers_recipientCurrency_nunique'\n", "})" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": true }, "outputs": [], "source": [ "fundTransfers_agg['transfers_date'] = [sorted(list(set(fundTransfers['EventDate'].loc[fundTransfers['DeviceID'] == x['DeviceID']]))) \n", " for _, x in fundTransfers_agg.iterrows()]\n", "\n", "fundTransfers_agg['transfers_avgdaylag'] = fundTransfers_agg.apply(lambda x: func(x['transfers_date']), axis=1)\n", "fundTransfers_agg['transfers_daysactive'] = fundTransfers_agg['transfers_date'].apply(lambda x: len(x))\n", "\n", "fundTransfers_agg = fundTransfers_agg.drop(['transfers_date'], axis=1)" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df_main = df_main.join(fundTransfers_agg.set_index('DeviceID'), on='DeviceID')" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": true }, "outputs": [], "source": [ "temp = fundTransfers.groupby('DeviceID')[['EventDate']].max().reset_index()\n", "temp['transfers_daysSinceLastTransfer'] = temp['EventDate'].apply(\n", " lambda x: round((int(round((reportDate - datetime.strptime(x, fmt)).total_seconds() / 60))) / 1440))\n", "df_main = df_main.join(temp.drop(['EventDate'], axis=1).set_index('DeviceID'), on='DeviceID')" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": true }, "outputs": [], "source": [ "fillna0 = [\n", " 'transfers_operationAmount_count',\n", " 'transfers_operationAmount_sum',\n", " 'transfers_operationAmount_median',\n", " 'transfers_operationAmount_min',\n", " 'transfers_operationAmount_max',\n", " 'transfers_operationFee_sum',\n", " 'transfers_sender_nunique',\n", " 'transfers_recipient_nunique',\n", " 'transfers_operationMethod_nunique',\n", " 'transfers_senderCurrency_nunique',\n", " 'transfers_recipientCurrency_nunique',\n", "]\n", "\n", "fillna122 = ['transfers_avgdaylag', 'transfers_daysactive', 'transfers_daysSinceLastTransfer']\n", "\n", "for i in fillna0:\n", " df_main[i].fillna(0, inplace=True)\n", "\n", "for i in fillna122:\n", " df_main[i].fillna(122, inplace=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## СЧИТАЕМ CHURN И ПРОСТАВЛЯЕМ TARGET FEATURE" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "collapsed": true }, "outputs": [], "source": [ "q = '''\n", "SELECT\n", " DeviceID,\n", " AppVersionName,\n", " OSName,\n", " ReceiveDate,\n", " ReceiveTimestamp,\n", " SessionStartDate,\n", " SessionStartDateTime,\n", " SessionStartTimestamp\n", "FROM\n", " mobile.sessions_starts_all\n", "WHERE\n", " SessionStartDate BETWEEN ''' + ''' \\'''' + \\\n", " churn_query_dates[0] + '''\\' AND ''' + '''\\'''' + \\\n", " churn_query_dates[1] + '''\\' ''''FORMAT TabSeparatedWithNames'''\n", "\n", "sessions = get_clickhouse_df(q)\n", "\n", "\n", "# посчитаем кол-во сессий на DeviceID, используя SessionStartTimestamp в качестве ID сессии\n", "\n", "df_sessions_churn = sessions.groupby('DeviceID')[['SessionStartTimestamp']]\\\n", " .count().reset_index().sort_values(by='SessionStartTimestamp', ascending=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Посмотрим, какие DeviceID, из тех, что присутствуют в общем dataFrame за период, вернулись в прогнозируем периоде. Если DeviceID найден, ставим \"0\" (неотток), в противном случае - \"1\" (отток)" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df_main['churned'] = df_main['DeviceID'].isin(df_sessions_churn['DeviceID']).apply(lambda x: 0 if x is True else 1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Как видим, 4-месячный отток составляет порядка 30% процентов." ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 0.67\n", "1 0.33\n", "Name: churned, dtype: float64" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_main['churned'].value_counts(normalize=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Посмотрим, как признаки из разных категория коррелируют с целевым" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "data": { "image/png": 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l8uTJLF++nOnTp+Pn54fZbGbkyJEsWrSI9evXU6FCBRo1amTZ7/nz56lVqxYGgwFnZ2dq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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.heatmap(df_main[[f for f in df_main.columns if 'sessions' in f or 'churned' in f]].corr(), annot=True);\n", "# sns.heatmap(df_main[[f for f in df_main.columns if 'payments' in f or 'churned' in f]].corr(), annot=True);\n", "# sns.heatmap(df_main[[f for f in df_main.columns if 'transfers' in f or 'churned' in f]].corr(), annot=True);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Наконец, посмотрим на получившийся dataFrame" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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5 rows × 31 columns

\n", "
" ], "text/plain": [ " DeviceID sessions_totalnumber sessions_avgdaylag \\\n", "107876 10313336111367727706 1 53.00 \n", "68108 6514306888048145041 1 114.00 \n", "38900 3725470542799209790 1 59.00 \n", "107882 10313953945953922630 1 122.00 \n", "45952 4396644337809361834 1 10.00 \n", "\n", " sessions_daysSinceLastSession sessions_totaldaysactive \\\n", "107876 53 1 \n", "68108 114 1 \n", "38900 59 1 \n", "107882 122 1 \n", "45952 10 1 \n", "\n", " payments_operationAmount_count payments_operationAmount_sum \\\n", "107876 0.00 0.00 \n", "68108 0.00 0.00 \n", "38900 0.00 0.00 \n", "107882 0.00 0.00 \n", "45952 0.00 0.00 \n", "\n", " payments_operationAmount_median payments_operationAmount_min \\\n", "107876 0.00 0.00 \n", "68108 0.00 0.00 \n", "38900 0.00 0.00 \n", "107882 0.00 0.00 \n", "45952 0.00 0.00 \n", "\n", " payments_operationAmount_max ... transfers_operationFee_sum \\\n", "107876 0.00 ... 0.00 \n", "68108 0.00 ... 0.00 \n", "38900 0.00 ... 0.00 \n", "107882 0.00 ... 0.00 \n", "45952 0.00 ... 0.00 \n", "\n", " transfers_sender_nunique transfers_recipient_nunique \\\n", "107876 0.00 0.00 \n", "68108 0.00 0.00 \n", "38900 0.00 0.00 \n", "107882 0.00 0.00 \n", "45952 0.00 0.00 \n", "\n", " transfers_operationMethod_nunique transfers_senderCurrency_nunique \\\n", "107876 0.00 0.00 \n", "68108 0.00 0.00 \n", "38900 0.00 0.00 \n", "107882 0.00 0.00 \n", "45952 0.00 0.00 \n", "\n", " transfers_recipientCurrency_nunique transfers_avgdaylag \\\n", "107876 0.00 122.00 \n", "68108 0.00 122.00 \n", "38900 0.00 122.00 \n", "107882 0.00 122.00 \n", "45952 0.00 122.00 \n", "\n", " transfers_daysactive transfers_daysSinceLastTransfer churned \n", "107876 122.00 122.00 1 \n", "68108 122.00 122.00 1 \n", "38900 122.00 122.00 0 \n", "107882 122.00 122.00 1 \n", "45952 122.00 122.00 1 \n", "\n", "[5 rows x 31 columns]" ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_main.tail()" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['DeviceID', 'sessions_totalnumber', 'sessions_avgdaylag',\n", " 'sessions_daysSinceLastSession', 'sessions_totaldaysactive',\n", " 'payments_operationAmount_count', 'payments_operationAmount_sum',\n", " 'payments_operationAmount_median', 'payments_operationAmount_min',\n", " 'payments_operationAmount_max', 'payments_sender_nunique',\n", " 'payments_recipient_nunique', 'payments_operationMethod_nunique',\n", " 'payments_avgdaylag', 'payments_daysactive',\n", " 'payments_daysSinceLastPayment', 'transfers_operationAmount_count',\n", " 'transfers_operationAmount_sum', 'transfers_operationAmount_median',\n", " 'transfers_operationAmount_min', 'transfers_operationAmount_max',\n", " 'transfers_operationFee_sum', 'transfers_sender_nunique',\n", " 'transfers_recipient_nunique', 'transfers_operationMethod_nunique',\n", " 'transfers_senderCurrency_nunique',\n", " 'transfers_recipientCurrency_nunique', 'transfers_avgdaylag',\n", " 'transfers_daysactive', 'transfers_daysSinceLastTransfer', 'churned'],\n", " dtype='object')" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_main.columns" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(193054, 31)" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_main.shape" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Index: 193054 entries, 185272 to 45952\n", "Data columns (total 31 columns):\n", "DeviceID 193054 non-null uint64\n", "sessions_totalnumber 193054 non-null int64\n", "sessions_avgdaylag 193054 non-null float64\n", "sessions_daysSinceLastSession 193054 non-null int64\n", "sessions_totaldaysactive 193054 non-null int64\n", "payments_operationAmount_count 193054 non-null float64\n", "payments_operationAmount_sum 193054 non-null float64\n", "payments_operationAmount_median 193054 non-null float64\n", "payments_operationAmount_min 193054 non-null float64\n", "payments_operationAmount_max 193054 non-null float64\n", "payments_sender_nunique 193054 non-null float64\n", "payments_recipient_nunique 193054 non-null float64\n", "payments_operationMethod_nunique 193054 non-null float64\n", "payments_avgdaylag 193054 non-null float64\n", "payments_daysactive 193054 non-null float64\n", "payments_daysSinceLastPayment 193054 non-null float64\n", "transfers_operationAmount_count 193054 non-null float64\n", "transfers_operationAmount_sum 193054 non-null float64\n", "transfers_operationAmount_median 193054 non-null float64\n", "transfers_operationAmount_min 193054 non-null float64\n", "transfers_operationAmount_max 193054 non-null float64\n", "transfers_operationFee_sum 193054 non-null float64\n", "transfers_sender_nunique 193054 non-null float64\n", "transfers_recipient_nunique 193054 non-null float64\n", "transfers_operationMethod_nunique 193054 non-null float64\n", "transfers_senderCurrency_nunique 193054 non-null float64\n", "transfers_recipientCurrency_nunique 193054 non-null float64\n", "transfers_avgdaylag 193054 non-null float64\n", "transfers_daysactive 193054 non-null float64\n", "transfers_daysSinceLastTransfer 193054 non-null float64\n", "churned 193054 non-null int64\n", "dtypes: float64(26), int64(4), uint64(1)\n", "memory usage: 47.1+ MB\n" ] } ], "source": [ "df_main.info()" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "__________________" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## FEATURE ENGINEERING" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Добавим признаки" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Добавим несколько новых признаков, основывающихся, прежде всего, на сессионых данных пользователей." ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# общее количество сессий за 3 месяца меньше 2\n", "\n", "df_main['sessions_totalnumber_less_2'] = df_main['sessions_totalnumber'].apply(lambda x: 1 if x < 2 else 0)" ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# общее количество сессий за 3 месяца меньше 6\n", "\n", "df_main['sessions_totalnumber_less_6'] = df_main['sessions_totalnumber'].apply(lambda x: 1 if x < 6 else 0)" ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# общее количество активных дней за 1 месяц меньше 2\n", "\n", "df_main['sessions_totaldaysactive_less_2'] = df_main['sessions_totaldaysactive'].apply(lambda x: 1 if x < 2 else 0)" ] }, { "cell_type": "code", "execution_count": 54, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df_main['sessions_avgdaylag_greater_20'] = df_main['sessions_avgdaylag'].apply(lambda x: 1 if x > 20.5 else 0)\n", "df_main['sessions_avgdaylag_greater_10'] = df_main['sessions_avgdaylag'].apply(lambda x: 1 if x > 10 else 0)\n", "df_main['sessions_avgdaylag_greater_15'] = df_main['sessions_avgdaylag'].apply(lambda x: 1 if x > 15 else 0)\n", "df_main['sessions_totaldaysactive_equalsless_2'] = df_main['sessions_totaldaysactive'].apply(lambda x: 1 if x <= 2 else 0)\n", "df_main['sessions_daysSinceLastSession_equalsgreater_19'] = df_main['sessions_daysSinceLastSession'].apply(lambda x: 1 if x >= 19 else 0)\n", "df_main['sessions_totalnumber_equalsless_5'] = df_main['sessions_totalnumber'].apply(lambda x: 1 if x <= 5 else 0)" ] }, { "cell_type": "code", "execution_count": 55, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def label_race(row):\n", " if (row['sessions_avgdaylag_greater_10'] == 1 and row['sessions_totaldaysactive_equalsless_2'] == 1 and\n", " row['sessions_daysSinceLastSession_equalsgreater_19'] == 1 and row['sessions_totalnumber_equalsless_5'] == 1):\n", " return 1\n", " else:\n", " return 0" ] }, { "cell_type": "code", "execution_count": 56, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df_main['sessions_isPassive'] = df_main.apply(lambda row: label_race (row),axis=1)" ] }, { "cell_type": "code", "execution_count": 57, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df_main['payments_isPassive'] = df_main['payments_operationAmount_count'].apply(lambda x: 1 if x == 0 else 0)\n", "df_main['transfers_isPassive'] = df_main['transfers_operationAmount_count'].apply(lambda x: 1 if x == 0 else 0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Построим кор. матрицу для \"сессионных\" признаков" ] }, { "cell_type": "code", "execution_count": 58, "metadata": {}, "outputs": [ { "data": { "image/png": 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GDpnCtyunoFarCXnwCL+B+itZf1r+E8bN8qGjS3c69XTFqagj9ZvWpn7T9Cnx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g7HcVGyKQ/v3yNMOv5C/AuRkZG5o+3Vq1fHy8uL2rVro1KpCAgIICcnJ98+d3eaKleu7HBf7dr2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YQQQogSyoS5yP7/v6hfvz7ff/89AMeOHaNBgwa53s/MzMTX15e33nqLkSNH5iwPCAjgq6++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\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sk6E5OTmZzZs3U1paStu2bc3vu7m5kZiYKLDMOqPRyJUrV2jTpg3p6ekMHDgQd3d3CgoK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\nDP4AAAAAADgYgz8AAAAAAA72v7zasp6vCaNrAAAAAElFTkSuQmCC\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize=(15,10))\n", "# sns.heatmap(df_main.corr());\n", "sns.heatmap(df_main[[f for f in df_main.columns if 'sessions' in f or 'churned' in f]].corr(), annot=True);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "_______" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## ОБУЧАЕМ МОДЕЛЬ (-И)" ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from sklearn.model_selection import train_test_split, GridSearchCV, StratifiedKFold, cross_val_score\n", "from sklearn.metrics import accuracy_score\n", "from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor\n", "from sklearn.neighbors import KNeighborsClassifier\n", "from sklearn.linear_model import LogisticRegression\n", "from sklearn.metrics import confusion_matrix\n", "from sklearn.metrics import precision_recall_curve, classification_report\n", "from sklearn.metrics import f1_score\n", "from collections import Counter" ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "collapsed": true }, "outputs": [], "source": [ "y = df_main['churned']\n", "X = df_main.drop('churned', axis=1)\n", "\n", "X_train, X_valid, y_train, y_valid = train_test_split(X, y, test_size=.33, random_state=42)\n", "\n", "# df_small = X_valid" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "__________________" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## RANDOM FOREST" ] }, { "cell_type": "code", "execution_count": 147, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from sklearn.ensemble import RandomForestClassifier" ] }, { "cell_type": "code", "execution_count": 148, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.8655079818761076\n" ] } ], "source": [ "forest = RandomForestClassifier(n_estimators=100, n_jobs=-1, random_state=42, max_depth=6, max_features=10)\n", "print(np.mean(cross_val_score(forest, X_train, y_train, cv=10)))" ] }, { "cell_type": "code", "execution_count": 149, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n", " max_depth=6, max_features=10, max_leaf_nodes=None,\n", " min_impurity_decrease=0.0, min_impurity_split=None,\n", " min_samples_leaf=1, min_samples_split=2,\n", " min_weight_fraction_leaf=0.0, n_estimators=100, n_jobs=-1,\n", " oob_score=False, random_state=42, verbose=0, warm_start=False)" ] }, "execution_count": 149, "metadata": {}, "output_type": "execute_result" } ], "source": [ "forest.fit(X_train, y_train)" ] }, { "cell_type": "code", "execution_count": 150, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " precision recall f1-score support\n", "\n", "non-churned 0.90 0.90 0.90 42202\n", " churned 0.80 0.80 0.80 21506\n", "\n", "avg / total 0.86 0.86 0.86 63708\n", "\n" ] } ], "source": [ "print(classification_report(y_valid, forest.predict(X_valid), target_names=['non-churned', 'churned']))" ] }, { "cell_type": "code", "execution_count": 203, "metadata": { "collapsed": true }, "outputs": [], "source": [ "forest_params = {\n", " 'max_depth': list(range(5,40,10)),\n", " 'max_features': list(range(5,41,10)) + [40]\n", "}\n", "\n", "forest_grid = GridSearchCV(forest, forest_params, cv=10, n_jobs=-1, verbose=True, scoring='f1') #try randomsearchgrid" ] }, { "cell_type": "code", "execution_count": 204, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Fitting 10 folds for each of 20 candidates, totalling 200 fits\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[Parallel(n_jobs=-1)]: Done 42 tasks | elapsed: 4.0min\n", "[Parallel(n_jobs=-1)]: Done 192 tasks | elapsed: 44.3min\n", "[Parallel(n_jobs=-1)]: Done 200 out of 200 | elapsed: 47.5min finished\n" ] }, { "data": { "text/plain": [ "GridSearchCV(cv=10, error_score='raise',\n", " estimator=RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n", " max_depth=6, max_features=10, max_leaf_nodes=None,\n", " min_impurity_decrease=0.0, min_impurity_split=None,\n", " min_samples_leaf=1, min_samples_split=2,\n", " min_weight_fraction_leaf=0.0, n_estimators=100, n_jobs=-1,\n", " oob_score=False, random_state=42, verbose=0, warm_start=False),\n", " fit_params={}, iid=True, n_jobs=-1,\n", " param_grid={'max_depth': [5, 15, 25, 35], 'max_features': [5, 15, 25, 35, 40]},\n", " pre_dispatch='2*n_jobs', refit=True, scoring='f1', verbose=True)" ] }, "execution_count": 204, "metadata": {}, "output_type": "execute_result" } ], "source": [ "forest_grid.fit(X_train, y_train)" ] }, { "cell_type": "code", "execution_count": 205, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "({'max_depth': 5, 'max_features': 25}, 0.7977511867871151)" ] }, "execution_count": 205, "metadata": {}, "output_type": "execute_result" } ], "source": [ "forest_grid.best_params_, forest_grid.best_score_" ] }, { "cell_type": "code", "execution_count": 206, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " precision recall f1-score support\n", "\n", "non-churned 0.89 0.92 0.90 42202\n", " churned 0.83 0.77 0.80 21506\n", "\n", "avg / total 0.87 0.87 0.87 63708\n", "\n" ] } ], "source": [ "print(classification_report(y_valid, forest_grid.predict(X_valid), target_names=['non-churned', 'churned']))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## XGBOOST" ] }, { "cell_type": "code", "execution_count": 132, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from IPython.display import display\n", "\n", "import re\n", "import pandas as pd\n", "import numpy as np\n", "import xgboost as xgb\n", "\n", "from sklearn import preprocessing\n", "from sklearn import cross_validation\n", "from sklearn.model_selection import KFold\n", "from sklearn.feature_selection import RFECV\n", "from sklearn.grid_search import GridSearchCV" ] }, { "cell_type": "code", "execution_count": 133, "metadata": { "collapsed": true }, "outputs": [], "source": [ "X = np.array(df_main.drop('churned', 1))\n", "training_features = np.array(df_main.drop('churned', 1).columns)\n", "#X = preprocessing.scale(X) --- not needed for XGboost?\n", "y = np.array(df_main['churned'])" ] }, { "cell_type": "code", "execution_count": 134, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "[Parallel(n_jobs=-1)]: Done 5 out of 5 | elapsed: 1.3min finished\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[0.01780643 0.01222984 0.02732627 0.15069699 0.50539171]\n", "F1: 0.143 stdev: 0.19\n" ] } ], "source": [ "xgb = xgb.XGBClassifier()\n", "scores = cross_validation.cross_val_score(xgb, X, y, cv=5, n_jobs=-1, verbose=True, scoring='f1')\n", "xgb.fit(X_train, y_train)\n", "print(scores)\n", "print('F1: %.3f stdev: %.2f' % (np.mean(np.abs(scores)), np.std(scores)))" ] }, { "cell_type": "code", "execution_count": 135, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " precision recall f1-score support\n", "\n", "Non-churned 0.89 0.92 0.90 42202\n", " Churned 0.83 0.77 0.80 21506\n", "\n", "avg / total 0.87 0.87 0.87 63708\n", "\n" ] } ], "source": [ "report = classification_report(y_valid, xgb.predict(X_valid), target_names=['Non-churned', 'Churned'])\n", "print(report)" ] }, { "cell_type": "code", "execution_count": 199, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Fitting 5 folds for each of 56 candidates, totalling 280 fits\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[Parallel(n_jobs=-1)]: Done 42 tasks | elapsed: 5.5min\n", "[Parallel(n_jobs=-1)]: Done 192 tasks | elapsed: 21.7min\n", "[Parallel(n_jobs=-1)]: Done 280 out of 280 | elapsed: 31.4min finished\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "{'learning_rate': 0.1, 'reg_lambda': 1.0}\n", "0.7995659727017969\n" ] } ], "source": [ "xgbgridparams = {'learning_rate':[0, 0.001, 0.002, 0.004, 0.006, 0.008, 0.010, 0.1], \n", " 'reg_lambda':[0, 0.1, 0.2, 0.4, 0.6, 0.8, 1.0]}\n", "\n", "xgb_grid = GridSearchCV(\n", " estimator=xgb,\n", " param_grid=xgbgridparams,\n", " scoring='f1',\n", " n_jobs=-1,\n", " refit=True,\n", " verbose=True,\n", " cv=5)\n", "xgb_grid.fit(X_train,y_train)\n", "\n", "print(xgb_grid.best_params_)\n", "print(xgb_grid.best_score_)" ] }, { "cell_type": "code", "execution_count": 200, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " precision recall f1-score support\n", "\n", "Non-churned 0.89 0.92 0.90 42202\n", " Churned 0.82 0.77 0.80 21506\n", "\n", "avg / total 0.87 0.87 0.87 63708\n", "\n" ] } ], "source": [ "report = classification_report(y_valid, xgb_grid.predict(X_valid), target_names=['Non-churned', 'Churned'])\n", "print(report)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## GradientBoosting (LIGHT)" ] }, { "cell_type": "code", "execution_count": 54, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import lightgbm as lgb\n", "\n", "# Set params\n", "# Scores ~0.784 (without tuning and early stopping)\n", "params = {\n", " 'boosting_type': 'gbdt',\n", " 'max_depth' : -1,\n", " 'objective': 'binary',\n", " 'nthread': 3, # Updated from nthread\n", " 'num_leaves': 34,\n", " 'learning_rate': 0.1,\n", " 'max_bin': 1024,\n", " 'subsample_for_bin': 200,\n", " 'subsample': 1,\n", " 'subsample_freq': 1,\n", " 'colsample_bytree': 0.8,\n", " 'reg_alpha': 5,\n", " 'reg_lambda': 10,\n", " 'min_split_gain': 0.5,\n", " 'min_child_weight': 1,\n", " 'min_child_samples': 5,\n", " 'scale_pos_weight': 1,\n", " 'num_class' : 1,\n", " 'metric' : 'binary_logloss',\n", " 'num_iterations' : 200\n", "}\n", "\n", "gridParams = {\n", " 'learning_rate': [0.1],\n", " 'n_estimators': [40],\n", " 'num_leaves': [31],\n", " 'boosting_type' : ['gbdt'],\n", " 'objective' : ['binary'],\n", " 'random_state' : [42], # Updated from 'seed'\n", " 'colsample_bytree' : [0.65, 0.66],\n", " 'subsample' : [0.7,0.75],\n", " 'reg_alpha' : [1.2, 1.4],\n", " 'reg_lambda' : [1.2, 1.4]\n", "}\n", "\n", "lgbm = lgb.LGBMClassifier(\n", " boosting_type= params['boosting_type'],\n", " objective = params['objective'],\n", " n_jobs = -1, # Updated from 'nthread'\n", " silent = True,\n", " max_depth = params['max_depth'],\n", " max_bin = params['max_bin'],\n", " subsample_for_bin = params['subsample_for_bin'],\n", " subsample = params['subsample'],\n", " subsample_freq = params['subsample_freq'],\n", " min_split_gain = params['min_split_gain'],\n", " min_child_weight = params['min_child_weight'],\n", " min_child_samples = params['min_child_samples'],\n", " scale_pos_weight = params['scale_pos_weight'],\n", " learning_rate = params['learning_rate'],\n", " num_iterations=params['num_iterations'],\n", " num_leaves=params['num_leaves']\n", ")" ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "LGBMClassifier(boosting_type='gbdt', class_weight=None, colsample_bytree=1.0,\n", " learning_rate=0.1, max_bin=1024, max_depth=-1, min_child_samples=5,\n", " min_child_weight=1, min_split_gain=0.5, n_estimators=100,\n", " n_jobs=-1, num_iterations=200, num_leaves=34, objective='binary',\n", " random_state=None, reg_alpha=0.0, reg_lambda=0.0,\n", " scale_pos_weight=1, silent=True, subsample=1,\n", " subsample_for_bin=200, subsample_freq=1)" ] }, "execution_count": 55, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lgbm.fit(X_train, y_train)" ] }, { "cell_type": "code", "execution_count": 56, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " precision recall f1-score support\n", "\n", "Non-churned 0.89 0.91 0.90 42202\n", " Churned 0.82 0.78 0.80 21506\n", "\n", "avg / total 0.87 0.87 0.87 63708\n", "\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/dmitriilin/anaconda3/lib/python3.6/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n", " if diff:\n" ] } ], "source": [ "# F1 score для litegb - параметры НЕоптимизированы\n", "\n", "report = classification_report(y_valid, lgbm.predict(X_valid), target_names=['Non-churned', 'Churned'])\n", "print(report)" ] }, { "cell_type": "code", "execution_count": 190, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Fitting 5 folds for each of 16 candidates, totalling 80 fits\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[Parallel(n_jobs=-1)]: Done 42 tasks | elapsed: 3.2min\n", "[Parallel(n_jobs=-1)]: Done 80 out of 80 | elapsed: 6.0min finished\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 18.5 s, sys: 1.84 s, total: 20.3 s\n", "Wall time: 6min 1s\n" ] } ], "source": [ "%%time\n", "lgbm_grid = GridSearchCV(lgbm, gridParams,\n", " verbose=True,\n", " cv=5,\n", " n_jobs=-1, scoring='f1')\n", "lgbm_grid.fit(X_train, y_train)" ] }, { "cell_type": "code", "execution_count": 191, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "({'boosting_type': 'gbdt',\n", " 'colsample_bytree': 0.65,\n", " 'learning_rate': 0.1,\n", " 'n_estimators': 40,\n", " 'num_leaves': 31,\n", " 'objective': 'binary',\n", " 'random_state': 42,\n", " 'reg_alpha': 1.2,\n", " 'reg_lambda': 1.2,\n", " 'subsample': 0.75},\n", " 0.8005829365766747)" ] }, "execution_count": 191, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lgbm_grid.best_params_, lgbm_grid.best_score_" ] }, { "cell_type": "code", "execution_count": 192, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " precision recall f1-score support\n", "\n", "Non-churned 0.89 0.91 0.90 42202\n", " Churned 0.82 0.78 0.80 21506\n", "\n", "avg / total 0.87 0.87 0.87 63708\n", "\n" ] } ], "source": [ "# F1 score для litegb - параметры НЕоптимизированы\n", "\n", "report = classification_report(y_valid, lgbm.predict(X_valid), target_names=['Non-churned', 'Churned'])\n", "print(report)" ] }, { "cell_type": "code", "execution_count": 193, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " precision recall f1-score support\n", "\n", "Non-churned 0.89 0.91 0.90 42202\n", " Churned 0.82 0.78 0.80 21506\n", "\n", "avg / total 0.87 0.87 0.87 63708\n", "\n" ] } ], "source": [ "# F1 score для litegb - параметры оптимизированы\n", "\n", "report = classification_report(y_valid, lgbm_grid.predict(X_valid), target_names=['Non-churned', 'Churned'])\n", "print(report)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## CATBOOST" ] }, { "cell_type": "code", "execution_count": 219, "metadata": { "_cell_guid": "24967b57-732b-7180-bfd5-005beff75974", "_uuid": "d3b5087c7fab5542cac722ad7ebc52e0244d0559", "collapsed": true }, "outputs": [], "source": [ "from catboost import CatBoostClassifier, cv, Pool" ] }, { "cell_type": "code", "execution_count": 220, "metadata": { "collapsed": true }, "outputs": [], "source": [ "model = CatBoostClassifier(\n", " custom_loss=['F1'],\n", " random_seed=42,\n", " logging_level='Silent'\n", ")" ] }, { "cell_type": "code", "execution_count": 221, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# np.where(X.dtypes != np.float)[0]" ] }, { "cell_type": "code", "execution_count": 222, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Learning rate set to 0.130013\n" ] } ], "source": [ "model.fit(\n", " X_train, y_train,\n", " eval_set=(X_valid, y_valid),\n", " plot=False\n", ");" ] }, { "cell_type": "code", "execution_count": 223, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Learning rate set to 0.066243\n" ] } ], "source": [ "cv_data = cv(\n", " Pool(X, y),\n", " model.get_params(),\n", " plot=False\n", ")" ] }, { "cell_type": "code", "execution_count": 224, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Best validation F1 score: 0.80±0.00 on step 190\n" ] } ], "source": [ "print('Best validation F1 score: {:.2f}±{:.2f} on step {}'.format(\n", " np.max(cv_data['test-F1-mean']),\n", " cv_data['test-F1-std'][np.argmax(cv_data['test-F1-mean'])],\n", " np.argmax(cv_data['test-F1-mean'])\n", "))" ] }, { "cell_type": "code", "execution_count": 225, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Precise validation F1 score: 0.8021206213855446\n" ] } ], "source": [ "print('Precise validation F1 score: {}'.format(np.max(cv_data['test-F1-mean'])))" ] }, { "cell_type": "code", "execution_count": 226, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Learning rate set to 0.123018\n", "Random seed assigned for this model: 155474316006478\n" ] } ], "source": [ "model_without_seed = CatBoostClassifier(iterations=500, logging_level='Silent')\n", "model_without_seed.fit(X_train, y_train)\n", "\n", "print('Random seed assigned for this model: {}'.format(model_without_seed.random_seed_))" ] }, { "cell_type": "code", "execution_count": 227, "metadata": { "collapsed": true }, "outputs": [], "source": [ "params = {\n", " 'iterations': 200,\n", " 'learning_rate': 0.1,\n", " 'eval_metric': 'F1',\n", " 'random_seed': 42,\n", " 'logging_level': 'Silent'\n", "}\n", "train_pool = Pool(X_train, y_train)\n", "validate_pool = Pool(X_valid, y_valid)" ] }, { "cell_type": "code", "execution_count": 228, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Best model validation F1: 0.8028\n" ] } ], "source": [ "cb = CatBoostClassifier(**params)\n", "cb.fit(train_pool, eval_set=validate_pool)\n", "\n", "# best_model_params = params.copy()\n", "# best_model_params.update({\n", "# 'use_best_model': True\n", "# })\n", "# best_model = CatBoostClassifier(**best_model_params)\n", "# best_model.fit(train_pool, eval_set=validate_pool);\n", "\n", "print('Best model validation F1: {:.4}'.format(\n", "# accuracy_score(y_valid, best_model.predict(X_valid))\n", " f1_score(y_valid, cb.predict(X_valid))\n", "))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Пробуем Early Stopping" ] }, { "cell_type": "code", "execution_count": 229, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 30.1 s, sys: 1.02 s, total: 31.1 s\n", "Wall time: 9.57 s\n" ] } ], "source": [ "%%time\n", "params.update({\n", " 'od_type': 'Iter',\n", " 'od_wait': 50\n", "})\n", "cb_early_stopping = CatBoostClassifier(**params)\n", "cb_early_stopping.fit(train_pool, eval_set=validate_pool);" ] }, { "cell_type": "code", "execution_count": 230, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Early-stopped best model tree count: 73\n", "Early-stopped best model validation F1: 0.8028\n" ] } ], "source": [ "print('Early-stopped best model tree count: {}'.format(cb.tree_count_))\n", "print('Early-stopped best model validation F1: {:.4}'.format(\n", " f1_score(y_valid, cb_early_stopping.predict(X_valid))\n", "))" ] }, { "cell_type": "code", "execution_count": 231, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "({'eval_metric': 'F1',\n", " 'iterations': 200,\n", " 'learning_rate': 0.1,\n", " 'logging_level': 'Silent',\n", " 'loss_function': 'Logloss',\n", " 'od_type': 'Iter',\n", " 'od_wait': 50,\n", " 'random_seed': 42},\n", " {'eval_metric': 'F1',\n", " 'iterations': 200,\n", " 'learning_rate': 0.1,\n", " 'logging_level': 'Silent',\n", " 'loss_function': 'Logloss',\n", " 'random_seed': 42})" ] }, "execution_count": 231, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cb_early_stopping.get_params(), cb.get_params()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Пробуем оптимизировать параметры" ] }, { "cell_type": "code", "execution_count": 232, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import hyperopt\n", "\n", "def hyperopt_objective(params):\n", " model = CatBoostClassifier(\n", " l2_leaf_reg=int(params['l2_leaf_reg']),\n", " learning_rate=params['learning_rate'],\n", " iterations=100, #make 50\n", " eval_metric='F1',\n", " random_seed=42,\n", " logging_level='Silent'\n", " )\n", " \n", " cv_data = cv(\n", " Pool(X, y),\n", " model.get_params()\n", " )\n", " best_accuracy = np.max(cv_data['test-F1-mean'])\n", " \n", " return 1 - best_accuracy # as hyperopt minimises" ] }, { "cell_type": "code", "execution_count": 233, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'l2_leaf_reg': 3.0, 'learning_rate': 0.3320996532481362}\n" ] } ], "source": [ "from numpy.random import RandomState\n", "\n", "params_space = {\n", " 'l2_leaf_reg': hyperopt.hp.qloguniform('l2_leaf_reg', 0, 2, 1),\n", " 'learning_rate': hyperopt.hp.uniform('learning_rate', 1e-3, 5e-1)\n", "}\n", "\n", "trials = hyperopt.Trials()\n", "\n", "best = hyperopt.fmin(\n", " hyperopt_objective,\n", " space=params_space,\n", " algo=hyperopt.tpe.suggest,\n", " max_evals=50,\n", " trials=trials,\n", " rstate=RandomState(123)\n", ")\n", "\n", "print(best)" ] }, { "cell_type": "code", "execution_count": 234, "metadata": { "collapsed": true }, "outputs": [], "source": [ "model = CatBoostClassifier(\n", " l2_leaf_reg=int(best['l2_leaf_reg']),\n", " learning_rate=best['learning_rate'],\n", " iterations=100,\n", " eval_metric='F1',\n", " random_seed=42,\n", " logging_level='Silent',\n", " use_best_model=True\n", ")\n", "cv_data = cv(Pool(X, y), model.get_params())" ] }, { "cell_type": "code", "execution_count": 235, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Precise validation F1 score: 0.8021127156643941\n" ] } ], "source": [ "print('Precise validation F1 score: {}'.format(np.max(cv_data['test-F1-mean'])))\n" ] }, { "cell_type": "code", "execution_count": 236, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " precision recall f1-score support\n", "\n", "Non-churned 0.89 0.91 0.90 42202\n", " Churned 0.82 0.79 0.80 21506\n", "\n", "avg / total 0.87 0.87 0.87 63708\n", "\n" ] } ], "source": [ "# F1 score для CatBoost - параметры НЕоптимизированы\n", "\n", "report = classification_report(y_valid, cb.predict(X_valid), target_names=['Non-churned', 'Churned'])\n", "print(report)" ] }, { "cell_type": "code", "execution_count": 237, "metadata": { "collapsed": true }, "outputs": [], "source": [ "model.fit(train_pool, eval_set=validate_pool);" ] }, { "cell_type": "code", "execution_count": 238, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " precision recall f1-score support\n", "\n", "Non-churned 0.89 0.91 0.90 42202\n", " Churned 0.82 0.79 0.80 21506\n", "\n", "avg / total 0.87 0.87 0.87 63708\n", "\n" ] } ], "source": [ "# F1 score для CatBoost - параметры оптимизированы\n", "\n", "report = classification_report(y_valid, model.predict(X_valid), target_names=['Non-churned', 'Churned'])\n", "print(report)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### CONFUSION MATRIX ДЛЯ LGBM" ] }, { "cell_type": "code", "execution_count": 239, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Confusion matrix, without normalization\n", "[[38560 3642]\n", " [ 4688 16818]]\n" ] }, { "data": { "image/png": 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odWvTISj5rn8bJbcHxxT3MFCILh+MKu4hoAi4OknphPVlyp+L/zvFE088oalT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\nuv/++/XEE0/o4sWL2rt3r0JDQ2kUgTuolbsLAJq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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Воспользуемся функцией построения матрицы ошибок из документации sklearn\n", "\n", "def plot_confusion_matrix(cm, classes,\n", " normalize=False,\n", " title='Confusion matrix',\n", " cmap=plt.cm.Blues):\n", " \"\"\"\n", " This function prints and plots the confusion matrix.\n", " Normalization can be applied by setting `normalize=True`.\n", " \"\"\"\n", " plt.imshow(cm, interpolation='nearest', cmap=cmap)\n", " plt.title(title)\n", " plt.colorbar()\n", " tick_marks = np.arange(len(classes))\n", " plt.xticks(tick_marks, classes, rotation=45)\n", " plt.yticks(tick_marks, classes)\n", "\n", " if normalize:\n", " cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n", " print(\"Normalized confusion matrix\")\n", " else:\n", " print('Confusion matrix, without normalization')\n", "\n", " print(cm)\n", "\n", " thresh = cm.max() / 2.\n", " for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n", " plt.text(j, i, cm[i, j],\n", " horizontalalignment=\"center\",\n", " color=\"white\" if cm[i, j] > thresh else \"black\")\n", "\n", " plt.tight_layout()\n", " plt.ylabel('True label')\n", " plt.xlabel('Predicted label')\n", "\n", "font = {'size' : 18}\n", "\n", "plt.rc('font', **font)\n", "\n", "cnf_matrix = confusion_matrix(y_valid, lgbm.predict(X_valid))\n", "plt.figure(figsize=(10, 8))\n", "plot_confusion_matrix(cnf_matrix, classes=['Non-churned', 'Churned'],\n", " title='Confusion matrix')\n", "# plt.savefig(\"conf_matrix.png\")\n", "plt.show()" ] } ], "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": "3.6.3" } }, "nbformat": 4, "nbformat_minor": 2 }