Proba 1 in Heart diseases-XGBC.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "37333473",
   "metadata": {},
   "source": [
    "## Part 4 – Prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08845d6b",
   "metadata": {},
   "source": [
    "## Initial"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0fb78984",
   "metadata": {},
   "source": [
    "### Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "0b653b73",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import joblib\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from matplotlib.colors import LinearSegmentedColormap\n",
    "import plotly.graph_objects as go\n",
    "import plotly.io as pio\n",
    "\n",
    "import os\n",
    "import json\n",
    "import warnings\n",
    "from datetime import date, time, datetime\n",
    "from tqdm.notebook import tqdm\n",
    "\n",
    "from sklearn.compose import ColumnTransformer, make_column_transformer, make_column_selector\n",
    "from sklearn.preprocessing import StandardScaler, OrdinalEncoder, OneHotEncoder\n",
    "\n",
    "from sklearn.model_selection import train_test_split, cross_val_score, StratifiedKFold\n",
    "from imblearn.pipeline import Pipeline, make_pipeline\n",
    "\n",
    "from xgboost import XGBClassifier\n",
    "\n",
    "from sklearn.metrics import roc_auc_score, matthews_corrcoef, cohen_kappa_score, accuracy_score\n",
    "from sklearn.inspection import permutation_importance\n",
    "\n",
    "import optuna\n",
    "from optuna.integration import OptunaSearchCV\n",
    "from optuna.distributions import FloatDistribution, IntDistribution, CategoricalDistribution"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9c8a3b4",
   "metadata": {},
   "source": [
    "### Constants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "f2a23ec8",
   "metadata": {},
   "outputs": [],
   "source": [
    "#PATH_LOCAL = 'datasets/'                                           # local path to data\n",
    "#PATH_REMOTE = '/kaggle/input/yap15-heart-diseases-predictions/'    # remote path to data\n",
    "\n",
    "CR = '\\n'                                                          # new line\n",
    "RANDOM_STATE = RS = 66                                             # random_state\n",
    "N_CV = 5                                                           # num of cross-val batches\n",
    "\n",
    "VALID_FRAC = 0.1                          # размер валидационной выборки, выделяемой из обцчающей\n",
    "N_TRIALS = 1000                           # количество попыток для Optuna\n",
    "TIMEOUT = 300                             # максимальный порог времени для Optuna\n",
    "SCORING = 'roc_auc'                       # основная метрика"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d00e7ec",
   "metadata": {},
   "source": [
    "### Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "d6db7057",
   "metadata": {},
   "outputs": [],
   "source": [
    "# def custom_read_csv(file_name, separator=','):\n",
    "#     \"\"\"\n",
    "#     чтение датасета в формате CSV:\n",
    "#       сначала из локального хранилища;\n",
    "#       при неудаче — из удаленного хранилища Kaggle.\n",
    "#     \"\"\"\n",
    "\n",
    "#     path_local = f'{PATH_LOCAL}{file_name}'\n",
    "#     path_remote = f'{PATH_REMOTE}{file_name}'\n",
    "    \n",
    "#     if os.path.exists(path_local):\n",
    "#         return pd.read_csv(path_local, sep=separator)\n",
    "\n",
    "#     elif os.path.exists(path_remote):\n",
    "#         return pd.read_csv(path_remote, sep=separator)\n",
    "\n",
    "#     else:\n",
    "#         print(f'File \"{file_name}\" not found at the specified path ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "fc559ccc",
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_model_metrics(models, X_train, Y_train, X_valid, Y_valid, cv=N_CV, scoring_list=['f1']):\n",
    "    '''\n",
    "    Принимает датафрейм со списком моделей и их характеристиками.\n",
    "    Добавляет в принятый датафрейм несколько метрик.\n",
    "    \n",
    "    :models: датафрейм с информацией об обученных моделях\n",
    "    :X_train: обучающая выборка (признаки)\n",
    "    :Y_train: обучающая выборка (целевая переменная)\n",
    "    :X_test: тестовая выборка (признаки)\n",
    "    :Y_test: тестовая выборка (целевая переменная)\n",
    "    :scoring: метрики для оценки\n",
    "    '''\n",
    "\n",
    "    for scoring in scoring_list:\n",
    "    \n",
    "        # результаты моделей на обучающей выборке (усреднение на кроссвалидации)\n",
    "        models[scoring + '_train'] = models.model.apply(\n",
    "                              lambda model: cross_val_score(model, X_train, Y_train, scoring=scoring, cv=cv, n_jobs=-1).mean())\n",
    "\n",
    "        # результаты моделей на тестовой выборке (усреднение на кроссвалидации)\n",
    "        models[scoring + '_valid'] = models.model.apply(\n",
    "                              lambda model: cross_val_score(model, X_valid, Y_valid, scoring=scoring, cv=cv, n_jobs=-1).mean())\n",
    "    \n",
    "    # ROC_AUC, коэффициент корреляции Мэтьюса, коэффициент каппа Коэна\n",
    "    models['ROC_AUC'] = models.model.apply(lambda model: roc_auc_score(Y_valid, model.predict_proba(X_valid)[:,1]))\n",
    "    models['matthews_coeff'] = models.model.apply(lambda model: matthews_corrcoef(Y_valid, model.predict(X_valid)))\n",
    "    models['cohen_kappa'] = models.model.apply(lambda model: cohen_kappa_score(Y_valid, model.predict(X_valid)))\n",
    "    \n",
    "    # оптимальные гиперпараметры\n",
    "    models['best_params'] = models.study.apply(lambda model: model.best_params)\n",
    "    \n",
    "    return models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "a84fdd9d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_Optuna(study, plot_kind='plot_slice', model_name=''):\n",
    "    '''\n",
    "    Дополнительная настройка оригинальных графиков Optuna.\n",
    "    Например, на графике `plot_slice` изначально цвет точек зависел от номера итерации.\n",
    "    Теперь они все одинакового цвета и полупрозрачные, лучше видны скопления точек.\n",
    "    \n",
    "    :study: обученный объект класса OptunaSearchCV\n",
    "    :plot_kind: тип графика Optuna\n",
    "    :model_name: название модели\n",
    "    '''\n",
    "    \n",
    "    if plot_kind == 'plot_slice':\n",
    "        fig = optuna.visualization.plot_slice(study)\n",
    "        fig.update_traces(\n",
    "                          marker_color='Darkgrey',\n",
    "                          marker_size=3,\n",
    "                          marker_opacity=0.2,\n",
    "                          marker_line_width=1,\n",
    "                          marker_line_color='Black',\n",
    "                         )\n",
    "    \n",
    "    elif plot_kind == 'plot_param_importances':\n",
    "        fig = optuna.visualization.plot_param_importances(study)\n",
    "        \n",
    "    elif plot_kind == 'plot_optimization_history':\n",
    "        fig = optuna.visualization.plot_optimization_history(study)\n",
    "        fig.update_traces(\n",
    "                          marker_size=5,\n",
    "                          marker_opacity=0.3,\n",
    "                          marker_line_width=1,\n",
    "                          marker_line_color='Black',\n",
    "                         )\n",
    "\n",
    "    fig.update_layout(\n",
    "                      title_text=model_name,\n",
    "                      title_x=0,\n",
    "                      font_size=10,\n",
    "                     )    \n",
    "    fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "7b5449e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_feature_importances(chart_title, feature_names, feature_importances):\n",
    "    \"\"\"\n",
    "    Выводит график важности признаков, использованных моделью.\n",
    "    \n",
    "    :chart_title: заголовок графика\n",
    "    :feature_names: названия признаков\n",
    "    :feature_importances: важность признаков\n",
    "    \"\"\"\n",
    "    \n",
    "    fig, ax = plt.subplots(figsize=(15,5), dpi=PLOT_DPI)\n",
    "\n",
    "    df = pd.DataFrame({'features': feature_names,\n",
    "                       'importances': feature_importances.importances_mean,\n",
    "                       'std_err': feature_importances.importances_std,\n",
    "                      }).sort_values('importances', ascending=False)\n",
    "    \n",
    "    sns.barplot(\n",
    "                x=df.importances,\n",
    "                y=df.features,\n",
    "                xerr=df.std_err,\n",
    "                color='skyblue',\n",
    "               )\n",
    "    \n",
    "    ax.set_title(f'{chart_title}')\n",
    "    ax.set_xlim(-0.02,)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5c7e318",
   "metadata": {},
   "source": [
    "### Settings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "5e30b600",
   "metadata": {},
   "outputs": [],
   "source": [
    "# text styles\n",
    "class f:\n",
    "    BOLD = \"\\033[1m\"\n",
    "    ITALIC = \"\\033[3m\"\n",
    "    END = \"\\033[0m\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "8c88bf36",
   "metadata": {},
   "outputs": [],
   "source": [
    "# defaults for charts\n",
    "\n",
    "# Matplotlib, Seaborn\n",
    "PLOT_DPI = 150  # dpi for charts rendering \n",
    "sns.set_style('whitegrid', {'axes.facecolor': '0.98', 'grid.color': '0.9', 'axes.edgecolor': '1.0'})\n",
    "plt.rc(\n",
    "       'axes',\n",
    "       labelweight='bold',\n",
    "       titlesize=14,\n",
    "       titlepad=10,\n",
    "      )\n",
    "\n",
    "# Plotly Graph_Objects\n",
    "pio.templates['my_theme'] = go.layout.Template(\n",
    "                                               layout_autosize=True,\n",
    "                                               # width=900,\n",
    "                                               layout_height=200,\n",
    "                                               layout_legend_orientation=\"h\",\n",
    "                                               layout_margin=dict(t=40, b=40),         # (l=0, r=0, b=0, t=0, pad=0)\n",
    "                                               layout_template='seaborn',\n",
    "                                              )\n",
    "pio.templates.default = 'my_theme'\n",
    "\n",
    "# colors and maps\n",
    "SYMMETRIC_CMAP = LinearSegmentedColormap.from_list('', ['steelblue', 'aliceblue', 'steelblue'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "0b4f2f3c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pandas defaults\n",
    "pd.options.display.max_colwidth = 100\n",
    "pd.options.display.max_rows = 500\n",
    "pd.options.display.max_columns = 100\n",
    "pd.options.display.float_format = '{:.3f}'.format\n",
    "pd.options.display.colheader_justify = 'left'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "d69ef16b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# оформление Optuna\n",
    "optuna.logging.set_verbosity(optuna.logging.WARNING)  # отключение вывода логов при работе optuna"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "e05120f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# others\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1147981",
   "metadata": {},
   "source": [
    "## Read Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "6b4b5711",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_train = pd.read_csv('C:\\\\Users\\\\Admin\\\\Desktop\\\\DS studies\\\\Data\\\\Heart diseases\\\\PROJECT\\\\For work\\\\FE_train.csv')  # обучающая выборка после этапа EDA\n",
    "data_test = pd.read_csv('C:\\\\Users\\\\Admin\\\\Desktop\\\\DS studies\\\\Data\\\\Heart diseases\\\\PROJECT\\\\For work\\\\FE_test.csv')    # тестовая выборка после этапа EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "d47538a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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: left;\">\n",
       "      <th></th>\n",
       "      <th>cardio</th>\n",
       "      <th>age</th>\n",
       "      <th>cholesterol</th>\n",
       "      <th>gluc</th>\n",
       "      <th>gender_aphi_bined_aplo_bined_TRG_mean</th>\n",
       "      <th>gender_age_bined_weight_bined_TRG_mean</th>\n",
       "      <th>gender_cholesterol_gluc_bmi_TRG_mean</th>\n",
       "      <th>gender_smoke_alco_active_TRG_mean</th>\n",
       "      <th>gender_height_TRG_mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.263</td>\n",
       "      <td>0.373</td>\n",
       "      <td>0.303</td>\n",
       "      <td>0.496</td>\n",
       "      <td>0.502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>55</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.821</td>\n",
       "      <td>0.631</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.485</td>\n",
       "      <td>0.513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>52</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.529</td>\n",
       "      <td>0.462</td>\n",
       "      <td>0.667</td>\n",
       "      <td>0.523</td>\n",
       "      <td>0.466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>48</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.871</td>\n",
       "      <td>0.489</td>\n",
       "      <td>0.562</td>\n",
       "      <td>0.496</td>\n",
       "      <td>0.501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>48</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.194</td>\n",
       "      <td>0.264</td>\n",
       "      <td>0.299</td>\n",
       "      <td>0.523</td>\n",
       "      <td>0.513</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   cardio  age  cholesterol  gluc  gender_aphi_bined_aplo_bined_TRG_mean  \\\n",
       "0  0       50   1            1    0.263                                    \n",
       "1  1       55   3            1    0.821                                    \n",
       "2  1       52   3            1    0.529                                    \n",
       "3  1       48   1            1    0.871                                    \n",
       "4  0       48   1            1    0.194                                    \n",
       "\n",
       "   gender_age_bined_weight_bined_TRG_mean  \\\n",
       "0 0.373                                     \n",
       "1 0.631                                     \n",
       "2 0.462                                     \n",
       "3 0.489                                     \n",
       "4 0.264                                     \n",
       "\n",
       "   gender_cholesterol_gluc_bmi_TRG_mean  gender_smoke_alco_active_TRG_mean  \\\n",
       "0 0.303                                 0.496                                \n",
       "1 1.000                                 0.485                                \n",
       "2 0.667                                 0.523                                \n",
       "3 0.562                                 0.496                                \n",
       "4 0.299                                 0.523                                \n",
       "\n",
       "   gender_height_TRG_mean  \n",
       "0 0.502                    \n",
       "1 0.513                    \n",
       "2 0.466                    \n",
       "3 0.501                    \n",
       "4 0.513                    "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "084fce17",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                                        30000\n",
       "age                                       30000\n",
       "cholesterol                               30000\n",
       "gluc                                      30000\n",
       "gender_aphi_bined_aplo_bined_TRG_mean     30000\n",
       "gender_age_bined_weight_bined_TRG_mean    30000\n",
       "gender_cholesterol_gluc_bmi_TRG_mean      30000\n",
       "gender_smoke_alco_active_TRG_mean         30000\n",
       "gender_height_TRG_mean                    30000\n",
       "dtype: int64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_test[data_test.notna()].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f55bd75d",
   "metadata": {},
   "source": [
    "## Данные для моделей"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ddf2653a",
   "metadata": {},
   "source": [
    "### Выделение признаков и целевой переменной"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "fda8f925",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((68753, 8), (68753,))"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = data_train.drop('cardio', axis=1)\n",
    "Y = data_train.cardio\n",
    "\n",
    "X.shape, Y.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dedc669c",
   "metadata": {},
   "source": [
    "### Разделение на обучающую и валидационную выборки\n",
    "\n",
    "Валидационная выборка – часть, отрезанная от train. Нужна для локальной проверки модели."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "aa1027db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((61877, 8), (61877,), (6876, 8), (6876,))"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train, X_valid, Y_train, Y_valid = train_test_split(X, Y, test_size=VALID_FRAC, stratify=Y, random_state=RS)\n",
    "\n",
    "X_train.shape, Y_train.shape, X_valid.shape, Y_valid.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ec71aec",
   "metadata": {},
   "source": [
    "## Модель"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ab37103",
   "metadata": {},
   "source": [
    "### Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db8dc98b",
   "metadata": {},
   "source": [
    "#### Селекторы числовых и категориальных признаков"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "ea389671",
   "metadata": {},
   "outputs": [],
   "source": [
    "selector_num = make_column_selector(dtype_include=np.number)\n",
    "selector_cat = make_column_selector(dtype_exclude=np.number)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eda5ba11",
   "metadata": {},
   "source": [
    "#### Предбработка числовых признаков"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "fc20f374",
   "metadata": {},
   "outputs": [],
   "source": [
    "num_preprocessor = make_pipeline(\n",
    "                                 StandardScaler(),\n",
    "#                                IterativeImputer(initial_strategy='mean', random_state=RS),  # в данных нет пропусков\n",
    "                                )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0570c50",
   "metadata": {},
   "source": [
    "#### Предбработка категориальных признаков"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc3c7b96",
   "metadata": {},
   "source": [
    "Раздельно для линейных моделей и моделей на базе деревьев."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "9578d2b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# for linear models\n",
    "cat_preprocessor_linr = OneHotEncoder(sparse=False, drop='first', handle_unknown='ignore')\n",
    "\n",
    "# for tree models\n",
    "cat_preprocessor_tree = OrdinalEncoder(handle_unknown='use_encoded_value', unknown_value=999)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d8f47c8",
   "metadata": {},
   "source": [
    "#### Объединение предобработки"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "0876c65f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# for linear models\n",
    "preprocessing_linr = make_column_transformer(\n",
    "                                             (num_preprocessor, selector_num),\n",
    "                                             (cat_preprocessor_linr, selector_cat),\n",
    "                                             remainder='passthrough'\n",
    "                                            )\n",
    "# for tree models\n",
    "preprocessing_tree = make_column_transformer(\n",
    "                                             (num_preprocessor, selector_num),\n",
    "                                             (cat_preprocessor_tree, selector_cat),\n",
    "                                             remainder='passthrough'\n",
    "                                            )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2cc5d012",
   "metadata": {},
   "source": [
    "#### Pipelines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "50c5edde",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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: left;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>short_name</th>\n",
       "      <th>model</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>XGBClassifier</td>\n",
       "      <td>XGBC</td>\n",
       "      <td>(ColumnTransformer(remainder='passthrough',\\n                  transformers=[('pipeline',\\n     ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  name           short_name  \\\n",
       "0  XGBClassifier  XGBC        \n",
       "\n",
       "  model                                                                                                 \n",
       "0  (ColumnTransformer(remainder='passthrough',\\n                  transformers=[('pipeline',\\n     ...  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipelines_list = [\n",
    "                  Pipeline([('PT', preprocessing_tree),\n",
    "                            ('XGBC', XGBClassifier(objective='binary:logistic', eval_metric='auc', random_state=RS))\n",
    "                           ])\n",
    "                 ]\n",
    "\n",
    "names_list = ['XGBClassifier']\n",
    "short_names_list = ['XGBC']\n",
    "\n",
    "models = pd.DataFrame(\n",
    "                      data={'name': names_list,\n",
    "                            'short_name': short_names_list,\n",
    "                            'model': pipelines_list,\n",
    "                           },\n",
    "                     )\n",
    "models"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56322b8e",
   "metadata": {},
   "source": [
    "### Подбор гиперпараметров"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ede8a3e4",
   "metadata": {},
   "source": [
    "#### Objective functions для Optuna"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "e5f4e22b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def objective_XGBC(trial):\n",
    "\n",
    "    params = {\n",
    "              'XGBC__max_depth': trial.suggest_int('XGBC__max_depth', 1, 20),\n",
    "              'XGBC__n_estimators': trial.suggest_int('XGBC__n_estimators', 10, 200, log=True),\n",
    "              'XGBC__subsample': trial.suggest_float('XGBC__subsample', 0.3, 1.0),\n",
    "#               'XGBC__colsample_bytree': trial.suggest_float('XGBC__colsample_bytree', 0.3, 1.0),\n",
    "              'XGBC__scale_pos_weight': trial.suggest_float('XGBC__scale_pos_weight', 0.4, 0.6),\n",
    "              'XGBC__learning_rate': trial.suggest_float('XGBC__learning_rate', 0.01, 0.99),\n",
    "              'XGBC__gamma': trial.suggest_float('XGBC__gamma', 0.1, 10, log=True),\n",
    "              'XGBC__min_child_weight': trial.suggest_float('XGBC__min_child_weight', 0.1, 10, log=True),\n",
    "              'XGBC__lambda': trial.suggest_float('XGBC__lambda', 0.1, 10, log=True),\n",
    "#               'XGBC__max_leaves': trial.suggest_int('XGBC__max_leaves', 2, 200, log=True),\n",
    "#               'XGBC__max_bin': trial.suggest_int('XGBC__max_bin', 10, 1000, log=True),\n",
    "             }\n",
    "    model.set_params(**params)\n",
    "    cv_SKF = StratifiedKFold(n_splits=N_CV)\n",
    "    \n",
    "    return cross_val_score(model, X_train, Y_train, scoring=SCORING, cv=cv_SKF, n_jobs=-1).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3c26160",
   "metadata": {},
   "source": [
    "#### Вызов Optuna"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "dcff5041",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ab183e1079ee4988bd076fd5849a58a6",
       "version_major": 2,
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      },
      "text/plain": [
       "  0%|          | 0/1000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\u001b[1mXGBClassifier\u001b[0m\n",
      "\n",
      "Количество попыток: 179\n",
      "Лучший результат: \u001b[1m0.8757\u001b[0m\n",
      "\n",
      "Комбинация гиперпараметров:\n",
      "{\n",
      " \"XGBC__gamma\": 8.743417721091333,\n",
      " \"XGBC__lambda\": 0.2684956565838877,\n",
      " \"XGBC__learning_rate\": 0.15943136127528545,\n",
      " \"XGBC__max_depth\": 14,\n",
      " \"XGBC__min_child_weight\": 0.780312977508543,\n",
      " \"XGBC__n_estimators\": 38,\n",
      " \"XGBC__scale_pos_weight\": 0.5208166272698982,\n",
      " \"XGBC__subsample\": 0.9101978401379107\n",
      "} \n",
      "\n"
     ]
    }
   ],
   "source": [
    "for item in range(models.shape[0]):\n",
    "    \n",
    "    model = models.loc[item,'model']\n",
    "    \n",
    "    # создание объекта optuna.study\n",
    "    study = optuna.create_study(\n",
    "                                study_name=models.loc[item,'name'],\n",
    "                                direction=\"maximize\",\n",
    "                                sampler=optuna.samplers.TPESampler(seed=RS)\n",
    "                               )\n",
    "\n",
    "    # оптимизация (подбор гиперпараметров)\n",
    "    if models.loc[item,'short_name'] == 'XGBC':\n",
    "        study.optimize(objective_XGBC, n_trials=N_TRIALS, timeout=TIMEOUT, show_progress_bar=True, n_jobs=-1)\n",
    "\n",
    "    # извлечение и обучение лучшей модели (здесь можно сделать обучение на полном наборе данных X, Y)\n",
    "    model.set_params(**study.best_params).fit(X_train, Y_train)\n",
    "\n",
    "    # сохранение результатов в таблице моделей\n",
    "    models.loc[item,'model'] = model\n",
    "    models.loc[item,'study'] = study\n",
    "    \n",
    "    print(f'{CR}{f.BOLD}{study.study_name}{f.END}{CR}')\n",
    "    print(f'Количество попыток: {len(study.trials)}')\n",
    "    print(f'Лучший результат: {f.BOLD}{study.best_value:0.4f}{f.END}{CR}')\n",
    "    print('Комбинация гиперпараметров:')\n",
    "    print(json.dumps(study.best_params, indent=1, sort_keys=True), f'{CR}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92e7e571",
   "metadata": {},
   "source": [
    "### Визуализация результатов подбора гиперпараметров\n",
    "\n",
    "Помогает cкорректировать процесс подбора."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "54f201e2",
   "metadata": {},
   "outputs": [
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   "source": [
    "for item in range(models.shape[0]):\n",
    "    plot_Optuna(models.loc[item,'study'], 'plot_slice', models.loc[item, 'name'])\n",
    "#     plot_Optuna(models.loc[item,'study'], 'plot_param_importances', models.loc[item, 'name'])     # может работать долго !\n",
    "#     plot_Optuna(models.loc[item,'study'], 'plot_optimization_history', models.loc[item, 'name'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46a25b89",
   "metadata": {},
   "source": [
    "## Results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06bad396",
   "metadata": {},
   "source": [
    "### Выбор лучшей модели\n",
    "\n",
    "XGBClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "889118d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# выбор модели из таблицы\n",
    "\n",
    "BEST_MODEL_NAME = 'XGBClassifier'\n",
    "model = models.model.values[models.name == BEST_MODEL_NAME][0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5608cc2b",
   "metadata": {},
   "source": [
    "### Важность признаков"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "f93da5c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2250x750 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2250x750 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# permutation importance for train\n",
    "plot_feature_importances(\n",
    "                         'Permutation importance for train',\n",
    "                         # извлечение списка признаков\n",
    "                         model.feature_names_in_,\n",
    "                         # вычисление важности признаков\n",
    "                         permutation_importance(model,\n",
    "                                                X_train, Y_train,\n",
    "                                                scoring=SCORING, n_repeats=5, n_jobs=-1, random_state=RS)\n",
    "                        )\n",
    "\n",
    "# permutation importance for valid\n",
    "plot_feature_importances(\n",
    "                         'Permutation importance for valid',\n",
    "                         # извлечение списка признаков\n",
    "                         model.feature_names_in_,\n",
    "                         # вычисление важности признаков\n",
    "                         permutation_importance(model,\n",
    "                                                X_valid, Y_valid,\n",
    "                                                scoring=SCORING, n_repeats=5, n_jobs=-1, random_state=RS)\n",
    "                        )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5936e9ee",
   "metadata": {},
   "source": [
    "### Additional metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "0d888084",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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: left;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>roc_auc_train</th>\n",
       "      <th>roc_auc_valid</th>\n",
       "      <th>f1_train</th>\n",
       "      <th>f1_valid</th>\n",
       "      <th>accuracy_train</th>\n",
       "      <th>accuracy_valid</th>\n",
       "      <th>ROC_AUC</th>\n",
       "      <th>matthews_coeff</th>\n",
       "      <th>cohen_kappa</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>XGBClassifier</td>\n",
       "      <td>0.876</td>\n",
       "      <td>0.869</td>\n",
       "      <td>0.730</td>\n",
       "      <td>0.725</td>\n",
       "      <td>0.773</td>\n",
       "      <td>0.767</td>\n",
       "      <td>0.874</td>\n",
       "      <td>0.567</td>\n",
       "      <td>0.545</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  name            roc_auc_train  roc_auc_valid  f1_train  f1_valid  \\\n",
       "0  XGBClassifier 0.876          0.869          0.730     0.725       \n",
       "\n",
       "   accuracy_train  accuracy_valid  ROC_AUC  matthews_coeff  cohen_kappa  \n",
       "0 0.773           0.767           0.874    0.567           0.545         "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# дополнительные метрики моделей\n",
    "models = add_model_metrics(models, X_train, Y_train, X_valid, Y_valid, cv=5, scoring_list=['roc_auc','f1','accuracy'])\n",
    "\n",
    "models.drop(['short_name','model','study','best_params'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fcfab7d8",
   "metadata": {},
   "source": [
    "### Save models parameters and metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "b291599f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# # запись в файл\n",
    "# models.to_excel('C:\\\\Users\\\\Admin\\\\Desktop\\\\DS studies\\\\Data\\\\Heart diseases\\\\PROJECT\\\\For work\\\\models{datetime.now().strftime(\"date_%Y-%m-%d time_%H-%M\")}score_{study.best_value:0.4f}.xlsx',sheet_name='best params', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "d739244c",
   "metadata": {},
   "outputs": [],
   "source": [
    "models.to_excel('C:\\\\Users\\\\Admin\\\\Desktop\\\\DS studies\\\\Data\\\\Heart diseases\\\\PROJECT\\\\FINAL\\\\models.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "d93d03f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "models.to_csv('C:\\\\Users\\\\Admin\\\\Desktop\\\\DS studies\\\\Data\\\\Heart diseases\\\\PROJECT\\\\FINAL\\\\models.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "2c3fd2ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['model_dump.mdl']"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joblib.dump(models, 'model_dump.mdl', compress=0, protocol=None, cache_size=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39530712",
   "metadata": {},
   "source": [
    "## Подготовка прогноза для участия в соревновании"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "4dc83d45",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_pred = data_test[['id']].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "a60f7d67",
   "metadata": {},
   "outputs": [],
   "source": [
    "# обучение модели на полном наборе данных и прогноз модели (вероятность)\n",
    "data_pred['cardio'] = model.fit(X,Y).predict_proba(data_test)[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "1af558f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2250x450 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15,3), dpi=PLOT_DPI)\n",
    "sns.histplot(x=data_pred.cardio, bins=40);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "51c1a78d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0003699999999999999, 0.289]    15000\n",
       "(0.289, 0.999]                    15000\n",
       "Name: cardio, dtype: int64"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.qcut(data_pred.cardio, q=2).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "ff1046f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# запись в файл\n",
    "data_pred[['id','cardio']].to_csv(\n",
    "    'C:\\\\Users\\\\Admin\\\\Desktop\\\\DS studies\\\\Data\\\\Heart diseases\\\\PROJECT\\\\FINAL\\\\Result.csv',\n",
    "                      index=False,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "5d9de939",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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: left;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>cardio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>0.316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6</td>\n",
       "      <td>0.358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7</td>\n",
       "      <td>0.277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10</td>\n",
       "      <td>0.001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11</td>\n",
       "      <td>0.115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29995</th>\n",
       "      <td>99984</td>\n",
       "      <td>0.897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29996</th>\n",
       "      <td>99987</td>\n",
       "      <td>0.075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29997</th>\n",
       "      <td>99989</td>\n",
       "      <td>0.284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29998</th>\n",
       "      <td>99994</td>\n",
       "      <td>0.303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29999</th>\n",
       "      <td>99997</td>\n",
       "      <td>0.001</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>30000 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       id     cardio\n",
       "0          5 0.316  \n",
       "1          6 0.358  \n",
       "2          7 0.277  \n",
       "3         10 0.001  \n",
       "4         11 0.115  \n",
       "...      ...     ...\n",
       "29995  99984 0.897  \n",
       "29996  99987 0.075  \n",
       "29997  99989 0.284  \n",
       "29998  99994 0.303  \n",
       "29999  99997 0.001  \n",
       "\n",
       "[30000 rows x 2 columns]"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a0f2ac51",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}