Решение ML задачи (DLS).ipynb

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style=\"align: center;\"><img align=center src=\"https://s8.hostingkartinok.com/uploads/images/2018/08/308b49fcfbc619d629fe4604bceb67ac.jpg\" width=500 height=450/></p>\n","\n","<h3 style=\"text-align: center;\"><b>Школа глубокого обучения ФПМИ МФТИ</b></h3>\n","\n","<h3 style=\"text-align: center;\"><b>Домашнее задание. Продвинутый поток. Весна 2021</b></h3>\n","\n","Это домашнее задание будет посвящено полноценному решению задачи машинного обучения."]},{"cell_type":"markdown","metadata":{"id":"zlB-owfaEEYs"},"source":["Есть две части этого домашнего задания: \n","* Сделать полноценный отчет о вашей работе: как вы обработали данные, какие модели попробовали и какие результаты получились (максимум 10 баллов). За каждую выполненную часть будет начислено определенное количество баллов.\n","* Лучшее решение отправить в соревнование на [kaggle](https://www.kaggle.com/c/advanced-dls-spring-2021/) (максимум 5 баллов). За прохождение определенного порогов будут начисляться баллы.\n","\n","\n","**Обе части будут проверяться в формате peer-review. Т.е. вашу посылку на степик будут проверять несколько других студентов и аггрегация их оценок будет выставлена. В то же время вам тоже нужно будет проверить несколько других учеников.**\n","\n","**Пожалуйста, делайте свою работу чистой и понятной, чтобы облегчить проверку. Если у вас будут проблемы с решением или хочется совета, то пишите в наш чат в телеграме или в лс @runfme. Если вы захотите проаппелировать оценку, то пипшите в лс @runfme.**\n","\n","**Во всех пунктах указания это минимальный набор вещей, которые стоит сделать. Если вы можете сделать какой-то шаг лучше или добавить что-то свое - дерзайте!**"]},{"cell_type":"markdown","metadata":{"id":"Tu_JvqcBEN8Y"},"source":["# Как проверять?\n","\n","Ставьте полный балл, если выполнены все рекомендации или сделано что-то более интересное и сложное. За каждый отсустствующий пункт из рекомендация снижайте 1 балл."]},{"cell_type":"markdown","metadata":{"id":"ninJ63mJEEYt"},"source":["# Метрика\n","\n","Перед решением любой задачи важно понимать, как будет оцениваться ваше решение. В данном случае мы используем стандартную для задачи классификации метрику ROC-AUC. Ее можно вычислить используя только предсказанные вероятности и истинные классы без конкретного порога классификации + она раотает даже если классы в данных сильно несбалансированны (примеров одного класса в десятки раз больше примеров длугого). Именно поэтому она очень удобна для соревнований.\n","\n","Посчитать ее легко:\n"]},{"cell_type":"code","metadata":{"id":"SQIrka7yEEYu","outputId":"f0f8ee04-3041-41f9-e8c8-bf34c3b08659","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1669794800423,"user_tz":-180,"elapsed":1269,"user":{"displayName":"Alex Sudakov","userId":"15553705242036807956"}}},"source":["from sklearn.metrics import roc_auc_score\n","\n","y_true = [\n","    0,\n","    1,\n","    1,\n","    0,\n","    1\n","]\n","\n","y_predictions = [\n","    0.1,\n","    0.9,\n","    0.4,\n","    0.6,\n","    0.61\n","]\n","\n","roc_auc_score(y_true, y_predictions)"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.8333333333333333"]},"metadata":{},"execution_count":2}]},{"cell_type":"markdown","metadata":{"id":"IrDNNkNTEEYz"},"source":["# Первая часть. Исследование"]},{"cell_type":"code","metadata":{"id":"lzLqEeZKEEYz"},"source":["import pandas as pd\n","import numpy as np\n","from matplotlib import pyplot as plt"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"LOlxdURSEEY3"},"source":["## Загрузка данных (2 балла)\n","\n","1) Посмотрите на случайные строчки. \n","\n","2) Посмотрите, есть ли в датасете незаполненные значения (nan'ы) с помощью data.isna() или data.info() и, если нужно, замените их на что-то. Будет хорошо, если вы построите табличку с количеством nan в каждой колонке."]},{"cell_type":"code","metadata":{"id":"pw-Brue9EEY3"},"source":["data = pd.read_csv('./train.csv')"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"KgnkkF5bEEY9"},"source":["# Для вашего удобства списки с именами разных колонок\n","\n","# Числовые признаки\n","num_cols = [\n","    'ClientPeriod',\n","    'MonthlySpending',\n","    'TotalSpent'\n","]\n","\n","# Категориальные признаки\n","cat_cols = [\n","    'Sex',\n","    'IsSeniorCitizen',\n","    'HasPartner',\n","    'HasChild',\n","    'HasPhoneService',\n","    'HasMultiplePhoneNumbers',\n","    'HasInternetService',\n","    'HasOnlineSecurityService',\n","    'HasOnlineBackup',\n","    'HasDeviceProtection',\n","    'HasTechSupportAccess',\n","    'HasOnlineTV',\n","    'HasMovieSubscription',\n","    'HasContractPhone',\n","    'IsBillingPaperless',\n","    'PaymentMethod'\n","]\n","\n","\n","\n","feature_cols = num_cols + cat_cols\n","target_col = 'Churn'"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"tHC6gWabEEZA"},"source":["# YOUR CODE\n","cat_indices=[]\n","for i in cat_cols:\n","  cat_indices.append(data.columns.get_loc(i))\n","\n","#data = data.replace('No internet service', np.nan)\n","#data = data.replace(' ', np.nan)\n","data = data.replace(' ', 0)\n","#data = data.dropna()"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"dK370bPCEEZD"},"source":["## Анализ данных (3 балла)\n","\n","1) Для численных призанков постройте гистограмму (*plt.hist(...)*) или boxplot (*plt.boxplot(...)*). Для категориальных посчитайте количество каждого значения для каждого признака. Для каждой колонки надо сделать *data.value_counts()* и построить bar диаграммы *plt.bar(...)* или круговые диаграммы *plt.pie(...)* (хорошо, елси вы сможете это сделать на одном гарфике с помощью *plt.subplots(...)*). \n","\n","2) Посмотрите на распределение целевой переменной и скажите, являются ли классы несбалансированными.\n","\n","3) (Если будет желание) Поиграйте с разными библиотеками для визуализации - *sns*, *pandas_visual_analysis*, etc.\n","\n","Второй пункт очень важен, потому что существуют задачи классификации с несбалансированными классами. Например, это может значить, что в датасете намного больше примеров 0 класса. В таких случаях нужно 1) не использовать accuracy как метрику 2) использовать методы борьбы с imbalanced dataset (обычно если датасет сильно несбалансирован, т.е. класса 1 в 20 раз меньше класса 0)."]},{"cell_type":"code","metadata":{"id":"NZkbgFJZEEZE","executionInfo":{"status":"ok","timestamp":1669794840356,"user_tz":-180,"elapsed":394,"user":{"displayName":"Alex Sudakov","userId":"15553705242036807956"}},"outputId":"1e116e93-3144-41c8-c35b-a2d4f178bbef","colab":{"base_uri":"https://localhost:8080/","height":265}},"source":["# YOUR CODE\n","#plt.hist(num_cols)\n","#fig, ax = plt.subplots(1, 2, figsize=(18,18))\n","#ax = ax.ravel()\n","\n","\n","#Как их строить по отдельности?\n","\n","for i in num_cols:\n","  plt.hist(i)\n"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":["for i in cat_cols:\n","  h = data.value_counts(i)\n","  plt.bar(i, height = h)"],"metadata":{"id":"5YEYCprl8QcU","executionInfo":{"status":"ok","timestamp":1669794842259,"user_tz":-180,"elapsed":671,"user":{"displayName":"Alex Sudakov","userId":"15553705242036807956"}},"outputId":"7c81e470-d89f-4804-b2b2-d2e2e0d90fd2","colab":{"base_uri":"https://localhost:8080/","height":265}},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"zg60u3QDEEZH"},"source":["(Дополнительно) Если вы нашли какие-то ошибки в данных или выбросы, то можете их убрать. Тут можно поэксперементировать с обработкой данных как угодно, но не за баллы."]},{"cell_type":"code","metadata":{"id":"fwfksF1gEEZI"},"source":["# YOUR CODE\n","# data['IsSeniorCitizen'].replase('1', 'Yes')\n","\n","#data['IsSeniorCitizen']=data['IsSeniorCitizen'].map({1 : 'Yes', 0 : 'No'})\n","#MonthlySpending\tTotalSpent\n","#data['TotalSpent'] = data['TotalSpent'].replace(r'\\s+','',regex=True)\n","#data['TotalSpent'] = data['TotalSpent'].str.astype(float)\n","\n","data['TotalSpent']=pd.to_numeric(data['TotalSpent'])\n","data['TotalSpent']=pd.to_numeric(data['MonthlySpending'])\n","# data['TotalSpent'] = data['TotalSpent'].apply(pd.to_numeric)\n","# data['TotalSpent'] = data['MonthlySpending'].apply(pd.to_numeric)\n","\n","#np.round(data['TotalSpent'], 1)\n","#print(type(data.at['0', 'TotalSpent']))\n","#data.at[0, 'TotalSpent'] = round(data.at[0, 'TotalSpent'], 1)\n","\n","# i=0\n","# while True:\n","#   try:\n","#     data.at[i, 'TotalSpent'] = round(data.at[i, 'TotalSpent'], 1)\n","#     data.at[i, 'MonthlySpending'] = round(data.at[i, 'MonthlySpending'], 1)    \n","#     i += 1\n","#     #print(i)\n","#   except Exception as e:\n","#     print(i)\n","#     print(e)\n","#     break"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"DviiJd8REEZK"},"source":["## Применение линейных моделей (3 балла)\n","\n","1) Обработайте данные для того, чтобы к ним можно было применить LogisticRegression. Т.е. отнормируйте числовые признаки, а категориальные закодируйте с помощью one-hot-encoding'а. \n","\n","2) С помощью кроссвалидации или разделения на train/valid выборку протестируйте разные значения гиперпараметра C и выберите лучший (можно тестировать С=100, 10, 1, 0.1, 0.01, 0.001) по метрике ROC-AUC. \n","\n","Если вы разделяете на train/valid, то используйте LogisticRegressionCV. Он сам при вызове .fit() подберет параметр С. (не забудьте передать scroing='roc_auc', чтобы при кроссвалидации сравнивались значения этой метрики, и refit=True, чтобы при потом модель обучилась на всем датасете с лучшим параметром C). \n","\n","\n","(более сложный вариант) Если вы будете использовать кроссвалидацию, то преобразования данных и LogisticRegression нужно соединить в один Pipeline с помощью make_pipeline, как это делалось во втором семинаре. Потом pipeline надо передать в GridSearchCV. Для one-hot-encoding'a можно испльзовать комбинацию LabelEncoder + OneHotEncoder (сначала превращаем строчки в числа, а потом числа првращаем в one-hot вектора.)"]},{"cell_type":"code","metadata":{"id":"xHCLDmwqEEZL"},"source":["from sklearn.linear_model import LogisticRegression, LogisticRegressionCV\n","from sklearn.model_selection import train_test_split, GridSearchCV\n","from sklearn.preprocessing import StandardScaler, RobustScaler, LabelEncoder, OneHotEncoder\n","from sklearn.pipeline import make_pipeline"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"P2Yv3uYtEEZO","executionInfo":{"status":"ok","timestamp":1669801465119,"user_tz":-180,"elapsed":5,"user":{"displayName":"Alex Sudakov","userId":"15553705242036807956"}},"outputId":"39fc02f4-ff8f-448f-b8e4-3f0cd1952279","colab":{"base_uri":"https://localhost:8080/","height":339}},"source":["num_data = data[num_cols]\n","\n","cat_data = data[cat_cols]\n","# cat_data.replase('1', 'Yes')\n","# cat_data.replase('0', 'No')\n","cat_data.head()\n"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["      Sex  IsSeniorCitizen HasPartner HasChild HasPhoneService  \\\n","0    Male                0        Yes      Yes             Yes   \n","1    Male                0        Yes       No             Yes   \n","2    Male                0         No       No             Yes   \n","3  Female                1        Yes       No             Yes   \n","4  Female                0        Yes      Yes             Yes   \n","\n","  HasMultiplePhoneNumbers HasInternetService HasOnlineSecurityService  \\\n","0                      No                 No      No internet service   \n","1                     Yes                 No      No internet service   \n","2                      No        Fiber optic                       No   \n","3                     Yes        Fiber optic                       No   \n","4                     Yes        Fiber optic                      Yes   \n","\n","       HasOnlineBackup  HasDeviceProtection HasTechSupportAccess  \\\n","0  No internet service  No internet service  No internet service   \n","1  No internet service  No internet service  No internet service   \n","2                   No                   No                  Yes   \n","3                   No                  Yes                   No   \n","4                  Yes                  Yes                  Yes   \n","\n","           HasOnlineTV HasMovieSubscription HasContractPhone  \\\n","0  No internet service  No internet service         One year   \n","1  No internet service  No internet service         Two year   \n","2                   No                   No   Month-to-month   \n","3                   No                   No   Month-to-month   \n","4                  Yes                  Yes         Two year   \n","\n","  IsBillingPaperless            PaymentMethod  \n","0                 No             Mailed check  \n","1                 No  Credit card (automatic)  \n","2                Yes         Electronic check  \n","3                 No             Mailed check  \n","4                 No  Credit card (automatic)  "],"text/html":["\n","  <div id=\"df-6c30fbeb-eed8-4e2b-85b9-5720818889e4\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Sex</th>\n","      <th>IsSeniorCitizen</th>\n","      <th>HasPartner</th>\n","      <th>HasChild</th>\n","      <th>HasPhoneService</th>\n","      <th>HasMultiplePhoneNumbers</th>\n","      <th>HasInternetService</th>\n","      <th>HasOnlineSecurityService</th>\n","      <th>HasOnlineBackup</th>\n","      <th>HasDeviceProtection</th>\n","      <th>HasTechSupportAccess</th>\n","      <th>HasOnlineTV</th>\n","      <th>HasMovieSubscription</th>\n","      <th>HasContractPhone</th>\n","      <th>IsBillingPaperless</th>\n","      <th>PaymentMethod</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Male</td>\n","      <td>0</td>\n","      <td>Yes</td>\n","      <td>Yes</td>\n","      <td>Yes</td>\n","      <td>No</td>\n","      <td>No</td>\n","      <td>No internet service</td>\n","      <td>No internet service</td>\n","      <td>No internet service</td>\n","      <td>No internet service</td>\n","      <td>No internet service</td>\n","      <td>No internet service</td>\n","      <td>One year</td>\n","      <td>No</td>\n","      <td>Mailed check</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Male</td>\n","      <td>0</td>\n","      <td>Yes</td>\n","      <td>No</td>\n","      <td>Yes</td>\n","      <td>Yes</td>\n","      <td>No</td>\n","      <td>No internet service</td>\n","      <td>No internet service</td>\n","      <td>No internet service</td>\n","      <td>No internet service</td>\n","      <td>No internet service</td>\n","      <td>No internet service</td>\n","      <td>Two year</td>\n","      <td>No</td>\n","      <td>Credit card (automatic)</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>Male</td>\n","      <td>0</td>\n","      <td>No</td>\n","      <td>No</td>\n","      <td>Yes</td>\n","      <td>No</td>\n","      <td>Fiber optic</td>\n","      <td>No</td>\n","      <td>No</td>\n","      <td>No</td>\n","      <td>Yes</td>\n","      <td>No</td>\n","      <td>No</td>\n","      <td>Month-to-month</td>\n","      <td>Yes</td>\n","      <td>Electronic check</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>Female</td>\n","      <td>1</td>\n","      <td>Yes</td>\n","      <td>No</td>\n","      <td>Yes</td>\n","      <td>Yes</td>\n","      <td>Fiber optic</td>\n","      <td>No</td>\n","      <td>No</td>\n","      <td>Yes</td>\n","      <td>No</td>\n","      <td>No</td>\n","      <td>No</td>\n","      <td>Month-to-month</td>\n","      <td>No</td>\n","      <td>Mailed check</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>Female</td>\n","      <td>0</td>\n","      <td>Yes</td>\n","      <td>Yes</td>\n","      <td>Yes</td>\n","      <td>Yes</td>\n","      <td>Fiber optic</td>\n","      <td>Yes</td>\n","      <td>Yes</td>\n","      <td>Yes</td>\n","      <td>Yes</td>\n","      <td>Yes</td>\n","      <td>Yes</td>\n","      <td>Two year</td>\n","      <td>No</td>\n","      <td>Credit card (automatic)</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6c30fbeb-eed8-4e2b-85b9-5720818889e4')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","          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      No             Yes                     Yes   \n","4        Yes      Yes             Yes                     Yes   \n","\n","  HasInternetService HasOnlineSecurityService      HasOnlineBackup  \\\n","0                 No      No internet service  No internet service   \n","1                 No      No internet service  No internet service   \n","2        Fiber optic                       No                   No   \n","3        Fiber optic                       No                   No   \n","4        Fiber optic                      Yes                  Yes   \n","\n","   HasDeviceProtection HasTechSupportAccess          HasOnlineTV  \\\n","0  No internet service  No internet service  No internet service   \n","1  No internet service  No internet service  No internet service   \n","2                   No                  Yes                   No   \n","3                  Yes                   No                   No   \n","4                  Yes                  Yes                  Yes   \n","\n","  HasMovieSubscription HasContractPhone IsBillingPaperless  \\\n","0  No internet service         One year                 No   \n","1  No internet service         Two year                 No   \n","2                   No   Month-to-month                Yes   \n","3                   No   Month-to-month                 No   \n","4                  Yes         Two year                 No   \n","\n","             PaymentMethod  \n","0             Mailed check  \n","1  Credit card (automatic)  \n","2         Electronic check  \n","3             Mailed check  \n","4  Credit card (automatic)  "],"text/html":["\n","  <div id=\"df-1ec291a2-6dfc-40f0-82b4-3c86853b756b\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: 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Sudakov","userId":"15553705242036807956"}},"outputId":"6f104211-4c0a-4b19-8952-b12a3acc661b","colab":{"base_uri":"https://localhost:8080/"}},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["((5282, 45), (5282, 19))"]},"metadata":{},"execution_count":163}]},{"cell_type":"code","source":["X_train, X_test, y_train, y_test = train_test_split(X.values, y.values, \n","                                                    train_size=0.8,\n","                                                    random_state=42)\n"],"metadata":{"id":"yjuwqxAzB5kc"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["from sklearn.model_selection import GridSearchCV\n","from sklearn.model_selection import RandomizedSearchCV"],"metadata":{"id":"8yJ1lp9wmVPX"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["def search_and_draw(X, y, model, param_name, grid, param_scale='ordinary', draw=True):\n","    parameters = {param_name: grid}\n","    \n","    CV_model = GridSearchCV(estimator=model, \n","                            param_grid=parameters,\n","                            cv=5, \n","                            scoring='f1',\n","                            n_jobs=-1, \n","                            verbose=10)\n","    CV_model.fit(X, y)\n","    means = CV_model.cv_results_['mean_test_score']\n","    error = CV_model.cv_results_['std_test_score']\n","    \n","    if draw:\n","        plt.figure(figsize=(15,8))\n","        plt.title('choose ' + param_name)\n","\n","\n","        if (param_scale == 'log'):\n","            plt.xscale('log')\n","\n","        plt.plot(grid, means, label='mean values of score', color='red', lw=3)\n","\n","        plt.fill_between(grid, means - 2 * error, means + 2 * error, \n","                         color='green', label='filled area between errors', alpha=0.5)\n","        legend_box = plt.legend(framealpha=1).get_frame()\n","        legend_box.set_facecolor(\"white\")\n","        legend_box.set_edgecolor(\"black\")\n","        plt.xlabel('parameter')\n","        plt.ylabel('roc_auc')\n","        plt.show()\n","        \n","    return means, error"],"metadata":{"id":"NArq7mepmeuC"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["from sklearn.neighbors import KNeighborsClassifier\n","from sklearn.tree import DecisionTreeClassifier"],"metadata":{"id":"klzhJ7LGmhgn"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["models = [KNeighborsClassifier(), DecisionTreeClassifier()]\n","param_names = ['n_neighbors', 'max_depth']\n","grids = [np.array(np.linspace(4, 30, 8), dtype='int'), np.arange(1, 30)]\n","param_scales = ['log', 'ordinary']"],"metadata":{"id":"jU6Ta9qvmlSx"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["for model, param_name, grid, param_scale in zip(models, \n","                                                param_names, \n","                                                grids, \n","                                                param_scales):\n","    search_and_draw(X_train, y_train, model, param_name, grid, param_scale)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"DJUVlWiZmnyx","executionInfo":{"status":"ok","timestamp":1669801585451,"user_tz":-180,"elapsed":7543,"user":{"displayName":"Alex Sudakov","userId":"15553705242036807956"}},"outputId":"d0ecc347-8a53-4485-f083-2429f52a9a7e"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Fitting 5 folds for each of 8 candidates, totalling 40 fits\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 1080x576 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}},{"output_type":"stream","name":"stdout","text":["Fitting 5 folds for each of 29 candidates, totalling 145 fits\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 1080x576 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":["from sklearn.ensemble import RandomForestClassifier\n","from sklearn.model_selection import cross_val_score"],"metadata":{"id":"3LFSE-ucmp5y"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["from sklearn.model_selection import KFold\n","from sklearn.metrics import roc_auc_score\n","from tqdm.notebook import tqdm"],"metadata":{"id":"GbFjWYaNoVaW"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["max_trees = 100\n","\n","values = np.arange(max_trees) + 1\n","\n","kf = KFold(n_splits=5, shuffle=True, random_state=1234)\n","\n","global_scores = []\n","\n","for train_indices, val_indices in tqdm(kf.split(X_train), total=5):\n","    scores = []\n","    \n","    X_train_kf = X_train[train_indices]\n","    y_train_kf = y_train[train_indices]\n","    \n","    X_val_kf = X_train[val_indices]\n","    y_val_kf = y_train[val_indices]\n","    \n","    forest = RandomForestClassifier(n_estimators=max_trees)\n","    forest.fit(X_train_kf, y_train_kf)\n","    trees = forest.estimators_\n","    \n","    for number_of_trees in tqdm(values, leave=False):\n","        thinned_forest = RandomForestClassifier(n_estimators=number_of_trees)\n","        \n","        thinned_forest.n_classes_ = 2\n","        thinned_forest.estimators_ = trees[:number_of_trees]\n","\n","        scores.append(roc_auc_score(y_val_kf, thinned_forest.predict_proba(X_val_kf)[:, 1]))\n","    \n","    scores = np.array(scores)\n","    \n","    global_scores.append(scores)\n","\n","global_scores = np.stack(global_scores, axis=0)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":49,"referenced_widgets":["4fb29d1dbf4e44ccb189c3e585b76fa8","d633bdc166cf455582e332024db18888","5bdb7b5fb5024f0eafc7f3d4ae9375cd","b7cef828b8c645eebfd441024fdfcc22","750d679ee97b481fa5662ef3b0322b64","4520ef6b14ff4366b6058716488c49fa","a4f03c0654684e29bb40676f73b2ba29","4224eb80f2694e0daac5617cf8b5bf73","e337a5e189b242739b8db40d90e5f7db","af19769e3e124e239a581d019a34cdc1","7343bf7b60ac4c609c88b7a9c6426ae3","93c57e1d83be45fcac802a333d2cb476","e816ae9cdb03487592cb8f2f5480cb70","c12f0261ea664b2e9fdf1f597b72fff5","af9a7c3258ca4d6d85e96052f2ce7cda","3b3ee3198812436989b87298b9d27292","1dbb2834b31041e8add0127af2b6ba4f","68d2c5d27b2e4615bc4d4a9e2880184f","720b970cae624f5d94e3640ec571c51f","4b3326dc9ef14cda9b96a899ec7113a7","df73611dab8a4103b5bc062d9c28ee97","d44f9f57cc6f4df9a973a407a690d642","e931f140f95e4bef9ec9cead1c840546","5d60be83475c4a23a958e1e361e83b69","10a226a520cf495b8c68072b87608636","da48a072a08a4c2592fbdf3973f76802","c9fa66c6a0764973a35166def2dd9b44","43cd60b3145248239a0ac443d815f62d","379013fa72674dc9818d541d1c68c495","94152053f666423194cee402f0f45019","978753e352f34791a9872f971c2fec1e","d1749ede48644e9ca15d4c1ae681ac14","c87aa64e257c4663960008e8deee1b09","51704c516c124c6da5749e78531902bb","300d5b03db3243c0a3da645b57b20450","c53f1e1aabf84b8baf068e65be64c4a2","6e7a0aadcd564c6890ce1c19263831bb","d8831515a0dc4049a3cfeb0b2ef3a083","ac58a6a46b064559808478d9db116790","99c2412919394217b40b3d94992da682","ff933dce0f9541adb02369fb5bb312ca","6766b80d01dc456da5f40b564957eecc","ad14a5dbf787425ebb7819c38a447efa","de473a0f36bd4d438277265a793e2d12","e2b8f8b479d94f9ba611bdc95e95dba3","057a33fb19b949cd8daa24e71af03087","c401b94151f24c2db90c7b7125664008","f1e3835efd2d4b2b949fd001c5e1071e","78d037874c754fd09a33d64f5f29f9eb","2628126c47854700b0e34d1c0f908747","df2feadc89674e9b89b4c86a499e5e0f","bb4825ea3735453ebd3fb85dbeaf0d7e","49b04ae881eb46e1934c490016ded5b3","24ce3f2801ba49b9b716c293489d1099","a755b322284a467fab254da3177377c7","e22bee158c814f3684f97c3ca553eb95","173bc07905c24cbea1ef2d099505ed3e","843ff48b95c6464ca98040ea698a6510","4484ab88d7604caf8a3e65e9b4b0bad4","fffc845263e846208714e7a4d67edd46","501328fe8e6949359f142243b66afa8f","d77aa9b957114c7296ce0d7355bc82ed","32fd773960af4de8958ab3083755d18e","1abbc82b65244e3b85a495dc4b9251b0","52d1835bf5364b7da0e0282ede455cdc","4d5d0444640743afb7148db682d9a0cd"]},"id":"8cAUuZwIoZa2","executionInfo":{"status":"ok","timestamp":1669801608672,"user_tz":-180,"elapsed":16610,"user":{"displayName":"Alex Sudakov","userId":"15553705242036807956"}},"outputId":"ad7eb3ba-4856-453d-982e-3752fb3c4445"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/5 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"4fb29d1dbf4e44ccb189c3e585b76fa8"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/100 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"93c57e1d83be45fcac802a333d2cb476"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/100 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"e931f140f95e4bef9ec9cead1c840546"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/100 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"51704c516c124c6da5749e78531902bb"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/100 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"e2b8f8b479d94f9ba611bdc95e95dba3"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/100 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"e22bee158c814f3684f97c3ca553eb95"}},"metadata":{}}]},{"cell_type":"code","source":["mean_cross_val_score = global_scores.mean(axis=0)\n","std_cross_val_score = global_scores.std(axis=0)\n","\n","plt.figure(figsize=(15,8))\n","plt.title('Quality of random forest')\n","\n","plt.plot(values, mean_cross_val_score, label='mean values', color='red', lw=3)\n","plt.fill_between(values, \n","                 mean_cross_val_score - 2 * std_cross_val_score, \n","                 mean_cross_val_score + 2 * std_cross_val_score, \n","                 color='green', \n","                 label='filled area between errors',\n","                 alpha=0.5)\n","legend_box = plt.legend(framealpha=1).get_frame()\n","legend_box.set_facecolor(\"white\")\n","legend_box.set_edgecolor(\"black\")\n","plt.xlabel('number of trees')\n","plt.ylabel('roc-auc')\n","\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":513},"id":"0JWjBVFJoaQM","executionInfo":{"status":"ok","timestamp":1669801610763,"user_tz":-180,"elapsed":359,"user":{"displayName":"Alex Sudakov","userId":"15553705242036807956"}},"outputId":"b4e5474c-11c2-4f4c-9cd8-f947f9aacd04"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1080x576 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":["from sklearn.preprocessing import StandardScaler\n","\n","scaler = StandardScaler()\n","\n","X_train_scaled = scaler.fit_transform(X_train)\n","X_test_scaled = scaler.transform(X_test)"],"metadata":{"id":"zhFagDWv8gwa"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["search_and_draw(X_train_scaled, y_train, KNeighborsClassifier(), 'n_neighbors', \n","                np.array(np.linspace(4, 30, 8), dtype='int'), 'log');"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":535},"id":"jEjdd8wR8lHC","executionInfo":{"status":"ok","timestamp":1669801620744,"user_tz":-180,"elapsed":3173,"user":{"displayName":"Alex Sudakov","userId":"15553705242036807956"}},"outputId":"a79208e3-2ed6-420f-c29e-85e08dc737de"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Fitting 5 folds for each of 8 candidates, totalling 40 fits\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 1080x576 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":["model = RandomForestClassifier(n_estimators=50, n_jobs=-1)\n","\n","model.fit(X_train, y_train)\n","y_train_predicted = model.predict_proba(X_train)[:, 1]\n","y_test_predicted = model.predict_proba(X_test)[:, 1]"],"metadata":{"id":"JCgwdaAG8m4M"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["from sklearn.metrics import roc_auc_score, roc_curve"],"metadata":{"id":"8S3Av1Go8vpz"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["train_auc = roc_auc_score(y_train, y_train_predicted)\n","test_auc = roc_auc_score(y_test, y_test_predicted)\n","\n","plt.figure(figsize=(20,10))\n","plt.plot(*roc_curve(y_train, y_train_predicted)[:2], label='train AUC={:.4f}'.format(train_auc))\n","plt.plot(*roc_curve(y_test, y_test_predicted)[:2], label='test AUC={:.4f}'.format(test_auc))\n","legend_box = plt.legend(fontsize='large', framealpha=1).get_frame()\n","legend_box.set_facecolor(\"white\")\n","legend_box.set_edgecolor(\"black\")\n","plt.plot(np.linspace(0,1,100), np.linspace(0,1,100))\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":592},"id":"gAuX_zkg8xKt","executionInfo":{"status":"ok","timestamp":1669801623482,"user_tz":-180,"elapsed":589,"user":{"displayName":"Alex Sudakov","userId":"15553705242036807956"}},"outputId":"6e58ff35-1d46-4f67-f087-52fcba08914d"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1440x720 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":["from sklearn.model_selection import cross_val_predict"],"metadata":{"id":"rBGnR25j8-X-"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["def compute_meta_feature(model, X_train, X_test, y_train, cv):\n","    try:\n","        train_answers = cross_val_predict(model, X_train, y_train, cv=cv, method='predict_proba')[:, 1]\n","        model.fit(X_train, y_train)\n","        return train_answers, model.predict_proba(X_test)[:, 1]\n","    \n","    except Exception:\n","        train_answers = cross_val_predict(model, X_train, y_train, cv=cv, method='predict')[:, 1]\n","        model.fit(X_train, y_train)\n","        return train_answers, model.predict(X_test)[:, 1]"],"metadata":{"id":"3V_2_bN69AHz"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["from sklearn.ensemble import RandomForestClassifier\n","from sklearn.linear_model import LogisticRegression\n","from sklearn.neighbors import KNeighborsClassifier"],"metadata":{"id":"zNJhQwqE9B9L"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["models = []\n","models.append(KNeighborsClassifier(n_jobs=-1, n_neighbors=30))\n","models.append(LogisticRegression())\n","models.append(RandomForestClassifier(max_depth=3, n_estimators=50, n_jobs=-1))\n","models.append(RandomForestClassifier(max_depth=7, n_estimators=50, n_jobs=-1))\n","models.append(DecisionTreeClassifier(max_depth=8))"],"metadata":{"id":"GSGJ37cp9DpN"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["meta_features_train = np.zeros((X_train.shape[0], 0))\n","meta_features_test = np.zeros((X_test.shape[0], 0))"],"metadata":{"id":"T8Q8gOZQ9GtE"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["for model in tqdm(models):\n","    train, test = compute_meta_feature(model, X_train, X_test, y_train, 5)\n","    meta_features_train = np.append(meta_features_train, train.reshape((train.size, 1)), axis=1)\n","    meta_features_test = np.append(meta_features_test, test.reshape((test.size, 1)), axis=1)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":924,"referenced_widgets":["00ec334c83b64761b722fba8c7af0870","b5d059e789bf4488941a0ee22bbfc66e","9f94c124d3f44257b31d1e207f9888f7","1aa40d9570984e7a9c78ae414bb7272d","054b3ec189904821aca9679a1ae77bee","f6c5a900af3840a3a0171bdf0d9b6dcc","e16d3cd1ae474a21ba76008967204a7a","8a47c8ec04fe4132b988397a420fe900","23f5b6a726454be385a72ab2a8807e58","73eabb7d771f4cd3a4fe68f9a4179ae7","bfc17e39da96471496fec2366ea02c73"]},"id":"LgTqfoww9IPN","executionInfo":{"status":"ok","timestamp":1669801646308,"user_tz":-180,"elapsed":6238,"user":{"displayName":"Alex Sudakov","userId":"15553705242036807956"}},"outputId":"900767c3-cbfe-4bc9-ab6f-7fce3e6c6ee1"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/5 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"00ec334c83b64761b722fba8c7af0870"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:818: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG,\n","/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:818: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG,\n","/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:818: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG,\n","/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:818: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG,\n","/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:818: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG,\n","/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:818: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG,\n"]}]},{"cell_type":"code","source":["stacking_model = LogisticRegression()\n","stacking_model.fit(meta_features_train, y_train)\n","\n","y_train_predicted = stacking_model.predict_proba(meta_features_train)[:, 1]\n","y_test_predicted = stacking_model.predict_proba(meta_features_test)[:, 1]"],"metadata":{"id":"tX4yDjkB9J6Y"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["train_auc = roc_auc_score(y_train, y_train_predicted)\n","test_auc = roc_auc_score(y_test, y_test_predicted)\n","\n","plt.figure(figsize=(10,7))\n","plt.plot(*roc_curve(y_train, y_train_predicted)[:2], label='train AUC={:.4f}'.format(train_auc))\n","plt.plot(*roc_curve(y_test, y_test_predicted)[:2], label='test AUC={:.4f}'.format(test_auc))\n","legend_box = plt.legend(fontsize='large', framealpha=1).get_frame()\n","legend_box.set_facecolor(\"white\")\n","legend_box.set_edgecolor(\"black\")\n","plt.plot(np.linspace(0,1,100), np.linspace(0,1,100))\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":428},"id":"GchZv4lQ9Ltj","executionInfo":{"status":"ok","timestamp":1669801650682,"user_tz":-180,"elapsed":5,"user":{"displayName":"Alex Sudakov","userId":"15553705242036807956"}},"outputId":"42321ba9-286c-445b-edda-d7bbad44a76f"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 720x504 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"uVahy6JKEEZQ"},"source":["Выпишите какое лучшее качество и с какими параметрами вам удалось получить"]},{"cell_type":"markdown","metadata":{"id":"36729TOQEEZR"},"source":[]},{"cell_type":"markdown","metadata":{"id":"BlTeVy7fEEZR"},"source":["## Применение градиентного бустинга (2 балла)\n","\n","Если вы хотите получить баллы за точный ответ, то стоит попробовать градиентный бустинг. Часто градиентный бустинг с дефолтными параметрами даст вам 80% результата за 0% усилий.\n","\n","Мы будем использовать catboost, поэтому нам не надо кодировать категориальные признаки. catboost сделает это сам (в .fit() надо передать cat_features=cat_cols). А численные признаки нормировать для моделей, основанных на деревьях не нужно.\n","\n","1) Разделите выборку на train/valid. Протестируйте catboost cо стандартными параметрами.\n","\n","2) Протестируйте разные занчения параметроа количества деревьев и learning_rate'а и выберите лучшую по метрике ROC-AUC комбинацию. \n","\n","(Дополнительно) Есть некоторые сложности с тем, чтобы использовать CatBoostClassifier вместе с GridSearchCV, поэтому мы не просим использовать кроссвалидацию. Но можете попробовать)"]},{"cell_type":"code","metadata":{"id":"Fioxxlp-EEZS","executionInfo":{"status":"ok","timestamp":1669794954332,"user_tz":-180,"elapsed":14246,"user":{"displayName":"Alex Sudakov","userId":"15553705242036807956"}},"outputId":"01c45215-a352-4703-a105-47caa94b84c9","colab":{"base_uri":"https://localhost:8080/"}},"source":["\n","!pip install catboost"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Collecting catboost\n","  Downloading catboost-1.1.1-cp37-none-manylinux1_x86_64.whl (76.6 MB)\n","\u001b[K     |████████████████████████████████| 76.6 MB 1.3 MB/s \n","\u001b[?25hRequirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from catboost) (1.7.3)\n","Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from catboost) (3.2.2)\n","Requirement already satisfied: numpy>=1.16.0 in /usr/local/lib/python3.7/dist-packages (from catboost) (1.21.6)\n","Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from catboost) (1.15.0)\n","Requirement already satisfied: plotly in /usr/local/lib/python3.7/dist-packages (from catboost) (5.5.0)\n","Requirement already satisfied: graphviz in /usr/local/lib/python3.7/dist-packages (from catboost) (0.10.1)\n","Requirement already satisfied: pandas>=0.24.0 in /usr/local/lib/python3.7/dist-packages (from catboost) (1.3.5)\n","Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.24.0->catboost) (2.8.2)\n","Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.24.0->catboost) (2022.6)\n","Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->catboost) (3.0.9)\n","Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->catboost) (1.4.4)\n","Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->catboost) (0.11.0)\n","Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from kiwisolver>=1.0.1->matplotlib->catboost) (4.1.1)\n","Requirement already satisfied: tenacity>=6.2.0 in /usr/local/lib/python3.7/dist-packages (from plotly->catboost) (8.1.0)\n","Installing collected packages: catboost\n","Successfully installed catboost-1.1.1\n"]}]},{"cell_type":"code","source":["import catboost # документация: https://catboost.ai/docs\n","import matplotlib.pyplot as plt\n","from sklearn.metrics import roc_auc_score, roc_curve"],"metadata":{"id":"_LPAR3coDLPN"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# CatBoost умеет работать с категориальными признаками сам\n","X_train_origin, X_test_origin, _, _ = train_test_split(X_origin.values, y.values, \n","                                                       train_size=0.8, \n","                                                       random_state=42)"],"metadata":{"id":"THsdfy2XDWHN"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["X_origin.head()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":339},"id":"otRXCAG5zTC1","executionInfo":{"status":"ok","timestamp":1669801713881,"user_tz":-180,"elapsed":314,"user":{"displayName":"Alex Sudakov","userId":"15553705242036807956"}},"outputId":"c048a9fd-0272-41c3-d6dd-afdd91b9ee41"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["   ClientPeriod  MonthlySpending  TotalSpent     Sex  IsSeniorCitizen  \\\n","0            55            19.50       19.50    Male                0   \n","1            72            25.85       25.85    Male                0   \n","2             1            75.90       75.90    Male                0   \n","3            32            79.30       79.30  Female                1   \n","4            60           115.25      115.25  Female                0   \n","\n","  HasPartner HasChild HasPhoneService HasMultiplePhoneNumbers  \\\n","0        Yes      Yes             Yes                      No   \n","1        Yes       No             Yes                     Yes   \n","2         No       No             Yes                      No   \n","3        Yes       No             Yes                     Yes   \n","4        Yes      Yes             Yes                     Yes   \n","\n","  HasInternetService HasOnlineSecurityService      HasOnlineBackup  \\\n","0                 No      No internet service  No internet service   \n","1                 No      No internet service  No internet service   \n","2        Fiber optic                       No                   No   \n","3        Fiber optic                       No                   No   \n","4        Fiber optic                      Yes                  Yes   \n","\n","   HasDeviceProtection HasTechSupportAccess          HasOnlineTV  \\\n","0  No internet service  No internet service  No internet service   \n","1  No internet service  No internet service  No internet service   \n","2                   No                  Yes                   No   \n","3                  Yes                   No                   No   \n","4                  Yes                  Yes                  Yes   \n","\n","  HasMovieSubscription HasContractPhone IsBillingPaperless  \\\n","0  No internet service         One year                 No   \n","1  No internet service         Two year                 No   \n","2                   No   Month-to-month                Yes   \n","3                   No   Month-to-month                 No   \n","4                  Yes         Two year                 No   \n","\n","             PaymentMethod  \n","0             Mailed check  \n","1  Credit card (automatic)  \n","2         Electronic check  \n","3             Mailed check  \n","4  Credit card (automatic)  "],"text/html":["\n","  <div id=\"df-96d5b2d2-9589-49ae-ba2b-5408b2a20a2c\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>ClientPeriod</th>\n","      <th>MonthlySpending</th>\n","      <th>TotalSpent</th>\n","      <th>Sex</th>\n","      <th>IsSeniorCitizen</th>\n","      <th>HasPartner</th>\n","      <th>HasChild</th>\n","      <th>HasPhoneService</th>\n","      <th>HasMultiplePhoneNumbers</th>\n","      <th>HasInternetService</th>\n","      <th>HasOnlineSecurityService</th>\n","      <th>HasOnlineBackup</th>\n","    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np.linspace(0,1,100))\n","plt.show()"],"metadata":{"id":"5DdVQ6IDDb3v","executionInfo":{"status":"ok","timestamp":1669801720707,"user_tz":-180,"elapsed":350,"user":{"displayName":"Alex Sudakov","userId":"15553705242036807956"}},"outputId":"1b36f43b-6556-4a50-f748-41bf53e6982e","colab":{"base_uri":"https://localhost:8080/","height":428}},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 720x504 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":["!pip install xgboost"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"06JsArv19eGy","executionInfo":{"status":"ok","timestamp":1669785353269,"user_tz":-180,"elapsed":4437,"user":{"displayName":"Alex Sudakov","userId":"15553705242036807956"}},"outputId":"a9e4ff60-d0b5-48fb-d2a1-009ea987aced"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Requirement already satisfied: xgboost in /usr/local/lib/python3.7/dist-packages (0.90)\n","Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from xgboost) (1.7.3)\n","Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from xgboost) (1.21.6)\n"]}]},{"cell_type":"code","source":["import xgboost"],"metadata":{"id":"mbq7bKiW9f5b"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["boosting_model = xgboost.XGBClassifier(n_estimators=500)\n","\n","boosting_model.fit(X_train, y_train)\n","\n","y_train_predicted = boosting_model.predict_proba(X_train)[:, 1]\n","y_test_predicted = boosting_model.predict_proba(X_test)[:, 1]"],"metadata":{"id":"YNMzm7Y19hQE"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["train_auc = roc_auc_score(y_train, y_train_predicted)\n","test_auc = roc_auc_score(y_test, y_test_predicted)\n","\n","plt.figure(figsize=(10,7))\n","plt.plot(*roc_curve(y_train, y_train_predicted)[:2], label='train AUC={:.4f}'.format(train_auc))\n","plt.plot(*roc_curve(y_test, y_test_predicted)[:2], label='test AUC={:.4f}'.format(test_auc))\n","legend_box = plt.legend(fontsize='large', framealpha=1).get_frame()\n","legend_box.set_facecolor(\"white\")\n","legend_box.set_edgecolor(\"black\")\n","plt.plot(np.linspace(0,1,100), np.linspace(0,1,100))\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":428},"id":"KGVYgSbp9i7m","executionInfo":{"status":"ok","timestamp":1669794985649,"user_tz":-180,"elapsed":1118,"user":{"displayName":"Alex Sudakov","userId":"15553705242036807956"}},"outputId":"823021db-551e-45eb-c8ef-e62302032676"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 720x504 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":[],"metadata":{"id":"sRVjP9pY9k5t"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"rf4Kjt96EEZU"},"source":["Выпишите какое лучшее качество и с какими параметрами вам удалось получить"]},{"cell_type":"markdown","metadata":{"id":"d2d9GolXEEZV"},"source":[]},{"cell_type":"markdown","metadata":{"id":"dDMXbvNZEEZV"},"source":["# Предсказания"]},{"cell_type":"code","metadata":{"id":"G_P4wFNaEEZW"},"source":["best_model = boosting_model\n","#best_model = stacking_model"],"execution_count":null,"outputs":[]},{"cell_type":"code","source":["def norm(Data):\n","    num_cols = [\n","    'ClientPeriod',\n","    'MonthlySpending',\n","    'TotalSpent'\n","    ]\n","\n","# Категориальные признаки\n","    cat_cols = [\n","    'Sex',\n","    'IsSeniorCitizen',\n","    'HasPartner',\n","    'HasChild',\n","    'HasPhoneService',\n","    'HasMultiplePhoneNumbers',\n","    'HasInternetService',\n","    'HasOnlineSecurityService',\n","    'HasOnlineBackup',\n","    'HasDeviceProtection',\n","    'HasTechSupportAccess',\n","    'HasOnlineTV',\n","    'HasMovieSubscription',\n","    'HasContractPhone',\n","    'IsBillingPaperless',\n","    'PaymentMethod'\n","    ]\n","\n","\n","\n","    feature_cols = num_cols + cat_cols\n","    cat_indices=[]\n","    for i in cat_cols:\n","        cat_indices.append(Data.columns.get_loc(i))\n","\n","    #Data = Data.replace('No internet service', np.nan)\n","    Data = Data.replace(' ', 0)\n","\n","    Data = Data.dropna()\n","    Data['TotalSpent']=pd.to_numeric(Data['TotalSpent'])\n","    Data['TotalSpent']=pd.to_numeric(Data['MonthlySpending'])\n","    num_Data = Data[num_cols]\n","\n","    cat_Data = Data[cat_cols]\n","# cat_Data.replase('1', 'Yes')\n","# cat_Data.replase('0', 'No')\n","#cat_Data.head()\n","    dummy_features = pd.get_dummies(cat_Data)\n","\n","    X = pd.concat([num_Data, dummy_features], axis=1)\n","    X_origin = Data.iloc[:, :]\n","    return(X_origin)"],"metadata":{"id":"fbdgYgMGmAIs"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["X_test = pd.read_csv('./test.csv')\n","X_Origin=norm(X_test)\n","X_Origin.head()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":357},"id":"pQbPHdNyvepC","executionInfo":{"status":"ok","timestamp":1669801732338,"user_tz":-180,"elapsed":7,"user":{"displayName":"Alex Sudakov","userId":"15553705242036807956"}},"outputId":"cbb8bfe1-047a-4b03-93db-d61037472ffc"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["   ClientPeriod  MonthlySpending  TotalSpent     Sex  IsSeniorCitizen  \\\n","0            42            56.10       56.10    Male                0   \n","1            29            98.50       98.50    Male                1   \n","2             9            76.25       76.25    Male                0   \n","3            67            74.00       74.00  Female                0   \n","4            71            20.10       20.10  Female                0   \n","\n","  HasPartner HasChild HasPhoneService HasMultiplePhoneNumbers  \\\n","0        Yes       No             Yes                     Yes   \n","1        Yes       No             Yes                     Yes   \n","2         No       No             Yes                     Yes   \n","3        Yes      Yes             Yes                     Yes   \n","4        Yes      Yes             Yes                      No   \n","\n","  HasInternetService HasOnlineSecurityService      HasOnlineBackup  \\\n","0                DSL                      Yes                   No   \n","1        Fiber optic                       No                   No   \n","2        Fiber optic                       No                   No   \n","3                DSL                      Yes                  Yes   \n","4                 No      No internet service  No internet service   \n","\n","   HasDeviceProtection HasTechSupportAccess          HasOnlineTV  \\\n","0                   No                   No                   No   \n","1                  Yes                   No                  Yes   \n","2                   No                   No                   No   \n","3                   No                  Yes                  Yes   \n","4  No internet service  No internet service  No internet service   \n","\n","  HasMovieSubscription HasContractPhone IsBillingPaperless  \\\n","0                   No         One year                 No   \n","1                  Yes   Month-to-month                Yes   \n","2                   No   Month-to-month                Yes   \n","3                   No         Two year                 No   \n","4  No internet service         Two year                 No   \n","\n","               PaymentMethod  \n","0    Credit card (automatic)  \n","1           Electronic check  \n","2           Electronic check  \n","3    Credit card (automatic)  \n","4  Bank transfer (automatic)  "],"text/html":["\n","  <div id=\"df-85d07297-130f-4cc6-a623-3bad419ad86c\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>ClientPeriod</th>\n","      <th>MonthlySpending</th>\n","      <th>TotalSpent</th>\n","      <th>Sex</th>\n","      <th>IsSeniorCitizen</th>\n","      <th>HasPartner</th>\n","      <th>HasChild</th>\n","      <th>HasPhoneService</th>\n","      <th>HasMultiplePhoneNumbers</th>\n","      <th>HasInternetService</th>\n","      <th>HasOnlineSecurityService</th>\n","      <th>HasOnlineBackup</th>\n","      <th>HasDeviceProtection</th>\n","      <th>HasTechSupportAccess</th>\n","      <th>HasOnlineTV</th>\n","      <th>HasMovieSubscription</th>\n","      <th>HasContractPhone</th>\n","      <th>IsBillingPaperless</th>\n","      <th>PaymentMethod</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>42</td>\n","      <td>56.10</td>\n","      <td>56.10</td>\n","      <td>Male</td>\n","      <td>0</td>\n","      <td>Yes</td>\n","      <td>No</td>\n","      <td>Yes</td>\n","      <td>Yes</td>\n","      <td>DSL</td>\n","      <td>Yes</td>\n","      <td>No</td>\n","      <td>No</td>\n","      <td>No</td>\n","      <td>No</td>\n","      <td>No</td>\n","      <td>One year</td>\n","      <td>No</td>\n","      <td>Credit card (automatic)</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>29</td>\n","      <td>98.50</td>\n","      <td>98.50</td>\n","      <td>Male</td>\n","      <td>1</td>\n","      <td>Yes</td>\n","      <td>No</td>\n","      <td>Yes</td>\n","      <td>Yes</td>\n","      <td>Fiber optic</td>\n","      <td>No</td>\n","      <td>No</td>\n","      <td>Yes</td>\n","      <td>No</td>\n","      <td>Yes</td>\n","      <td>Yes</td>\n","      <td>Month-to-month</td>\n","      <td>Yes</td>\n","      <td>Electronic check</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>9</td>\n","      <td>76.25</td>\n","      <td>76.25</td>\n","      <td>Male</td>\n","      <td>0</td>\n","      <td>No</td>\n","      <td>No</td>\n","      <td>Yes</td>\n","      <td>Yes</td>\n","      <td>Fiber optic</td>\n","      <td>No</td>\n","      <td>No</td>\n","      <td>No</td>\n","      <td>No</td>\n","      <td>No</td>\n","      <td>No</td>\n","      <td>Month-to-month</td>\n","      <td>Yes</td>\n","      <td>Electronic check</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>67</td>\n","      <td>74.00</td>\n","      <td>74.00</td>\n","      <td>Female</td>\n","      <td>0</td>\n","      <td>Yes</td>\n","      <td>Yes</td>\n","      <td>Yes</td>\n","      <td>Yes</td>\n","      <td>DSL</td>\n","      <td>Yes</td>\n","      <td>Yes</td>\n","      <td>No</td>\n","      <td>Yes</td>\n","      <td>Yes</td>\n","      <td>No</td>\n","      <td>Two year</td>\n","      <td>No</td>\n","      <td>Credit card (automatic)</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>71</td>\n","      <td>20.10</td>\n","      <td>20.10</td>\n","      <td>Female</td>\n","      <td>0</td>\n","      <td>Yes</td>\n","      <td>Yes</td>\n","      <td>Yes</td>\n","      <td>No</td>\n","      <td>No</td>\n","      <td>No internet service</td>\n","      <td>No internet service</td>\n","      <td>No internet service</td>\n","      <td>No internet service</td>\n","      <td>No internet service</td>\n","      <td>No internet service</td>\n","      <td>Two year</td>\n","      <td>No</td>\n","      <td>Bank transfer (automatic)</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-85d07297-130f-4cc6-a623-3bad419ad86c')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","         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Процесс выставления оценок будет проходить при подведении итогового рейтинга. Пожалуйста, укажите во вкладке Team -> Team name свои имя и фамилию в формате Имя_Фамилия (важно, чтобы имя и фамилия совпадали с данными на Stepik).\n","* Обучить модель, получить файл с ответами в формате .csv и сдать его в конкурс. Пробуйте и экспериментируйте. Обратите внимание, что вы можете выполнять до 20 попыток сдачи на kaggle в день.\n","* После окончания соревнования отправить в итоговый ноутбук с решением на степик. \n","* После дедлайна проверьте посылки других участников по критериям. Для этого надо зайти на степик, скачать их ноутбук и проверить скор в соревновании."]}]}