magic-notebook-to-reach-80-roc-auc-score.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# magic-notebook-to-reach-80-roc-auc-score:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
    "# For example, here's several helpful packages to load\n",
    "\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "import lightgbm as lgb\n",
    "\n",
    "from sklearn.metrics import mean_squared_error, roc_auc_score, roc_curve\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "import random\n",
    "import os\n",
    "# from hyperopt import STATUS_OK, Trials, fmin, hp, tpe, pyll\n",
    "import time\n",
    "import colorama  # https://pypi.python.org/pypi/colorama\n",
    "import pickle\n",
    "from collections import Counter\n",
    "from sklearn.model_selection import KFold, StratifiedKFold\n",
    "\n",
    "from catboost import CatBoostClassifier, Pool\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# Input data files are available in the read-only \"../input/\" directory\n",
    "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
    "\n",
    "import os\n",
    "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
    "    for filename in filenames:\n",
    "        print(os.path.join(dirname, filename))\n",
    "\n",
    "# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n",
    "# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "SEED = 2021\n",
    "\n",
    "random.seed(SEED)\n",
    "np.random.seed(SEED)\n",
    "os.environ['PYTHONHASHSEED']=str(SEED)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df = pd.read_csv('train_Df64byy.csv')\n",
    "test_df = pd.read_csv('test_YCcRUnU.csv')\n",
    "sub_df = pd.read_csv('sample_submission_QrCyCoT.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>City_Code</th>\n",
       "      <th>Region_Code</th>\n",
       "      <th>Accomodation_Type</th>\n",
       "      <th>Reco_Insurance_Type</th>\n",
       "      <th>Upper_Age</th>\n",
       "      <th>Lower_Age</th>\n",
       "      <th>Is_Spouse</th>\n",
       "      <th>Health Indicator</th>\n",
       "      <th>Holding_Policy_Duration</th>\n",
       "      <th>Holding_Policy_Type</th>\n",
       "      <th>Reco_Policy_Cat</th>\n",
       "      <th>Reco_Policy_Premium</th>\n",
       "      <th>Response</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>C3</td>\n",
       "      <td>3213</td>\n",
       "      <td>Rented</td>\n",
       "      <td>Individual</td>\n",
       "      <td>36</td>\n",
       "      <td>36</td>\n",
       "      <td>No</td>\n",
       "      <td>X1</td>\n",
       "      <td>14+</td>\n",
       "      <td>3.0</td>\n",
       "      <td>22</td>\n",
       "      <td>11628.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>C5</td>\n",
       "      <td>1117</td>\n",
       "      <td>Owned</td>\n",
       "      <td>Joint</td>\n",
       "      <td>75</td>\n",
       "      <td>22</td>\n",
       "      <td>No</td>\n",
       "      <td>X2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22</td>\n",
       "      <td>30510.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>C5</td>\n",
       "      <td>3732</td>\n",
       "      <td>Owned</td>\n",
       "      <td>Individual</td>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>19</td>\n",
       "      <td>7450.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>C24</td>\n",
       "      <td>4378</td>\n",
       "      <td>Owned</td>\n",
       "      <td>Joint</td>\n",
       "      <td>52</td>\n",
       "      <td>48</td>\n",
       "      <td>No</td>\n",
       "      <td>X1</td>\n",
       "      <td>14+</td>\n",
       "      <td>3.0</td>\n",
       "      <td>19</td>\n",
       "      <td>17780.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>C8</td>\n",
       "      <td>2190</td>\n",
       "      <td>Rented</td>\n",
       "      <td>Individual</td>\n",
       "      <td>44</td>\n",
       "      <td>44</td>\n",
       "      <td>No</td>\n",
       "      <td>X2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>16</td>\n",
       "      <td>10404.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ID City_Code  Region_Code Accomodation_Type Reco_Insurance_Type  Upper_Age  \\\n",
       "0   1        C3         3213            Rented          Individual         36   \n",
       "1   2        C5         1117             Owned               Joint         75   \n",
       "2   3        C5         3732             Owned          Individual         32   \n",
       "3   4       C24         4378             Owned               Joint         52   \n",
       "4   5        C8         2190            Rented          Individual         44   \n",
       "\n",
       "   Lower_Age Is_Spouse Health Indicator Holding_Policy_Duration  \\\n",
       "0         36        No               X1                     14+   \n",
       "1         22        No               X2                     NaN   \n",
       "2         32        No              NaN                     1.0   \n",
       "3         48        No               X1                     14+   \n",
       "4         44        No               X2                     3.0   \n",
       "\n",
       "   Holding_Policy_Type  Reco_Policy_Cat  Reco_Policy_Premium  Response  \n",
       "0                  3.0               22              11628.0         0  \n",
       "1                  NaN               22              30510.0         0  \n",
       "2                  1.0               19               7450.0         1  \n",
       "3                  3.0               19              17780.0         0  \n",
       "4                  1.0               16              10404.0         0  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((50882, 14), (21805, 13))"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.shape, test_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 50882 entries, 0 to 50881\n",
      "Data columns (total 14 columns):\n",
      " #   Column                   Non-Null Count  Dtype  \n",
      "---  ------                   --------------  -----  \n",
      " 0   ID                       50882 non-null  int64  \n",
      " 1   City_Code                50882 non-null  object \n",
      " 2   Region_Code              50882 non-null  int64  \n",
      " 3   Accomodation_Type        50882 non-null  object \n",
      " 4   Reco_Insurance_Type      50882 non-null  object \n",
      " 5   Upper_Age                50882 non-null  int64  \n",
      " 6   Lower_Age                50882 non-null  int64  \n",
      " 7   Is_Spouse                50882 non-null  object \n",
      " 8   Health Indicator         39191 non-null  object \n",
      " 9   Holding_Policy_Duration  30631 non-null  object \n",
      " 10  Holding_Policy_Type      30631 non-null  float64\n",
      " 11  Reco_Policy_Cat          50882 non-null  int64  \n",
      " 12  Reco_Policy_Premium      50882 non-null  float64\n",
      " 13  Response                 50882 non-null  int64  \n",
      "dtypes: float64(2), int64(6), object(6)\n",
      "memory usage: 5.4+ MB\n"
     ]
    }
   ],
   "source": [
    "train_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.760053\n",
       "1    0.239947\n",
       "Name: Response, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.Response.value_counts() / len(train_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 21805 entries, 0 to 21804\n",
      "Data columns (total 13 columns):\n",
      " #   Column                   Non-Null Count  Dtype  \n",
      "---  ------                   --------------  -----  \n",
      " 0   ID                       21805 non-null  int64  \n",
      " 1   City_Code                21805 non-null  object \n",
      " 2   Region_Code              21805 non-null  int64  \n",
      " 3   Accomodation_Type        21805 non-null  object \n",
      " 4   Reco_Insurance_Type      21805 non-null  object \n",
      " 5   Upper_Age                21805 non-null  int64  \n",
      " 6   Lower_Age                21805 non-null  int64  \n",
      " 7   Is_Spouse                21805 non-null  object \n",
      " 8   Health Indicator         16778 non-null  object \n",
      " 9   Holding_Policy_Duration  13202 non-null  object \n",
      " 10  Holding_Policy_Type      13202 non-null  float64\n",
      " 11  Reco_Policy_Cat          21805 non-null  int64  \n",
      " 12  Reco_Policy_Premium      21805 non-null  float64\n",
      "dtypes: float64(2), int64(5), object(6)\n",
      "memory usage: 2.2+ MB\n"
     ]
    }
   ],
   "source": [
    "test_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "City_Code 36\n",
      "Accomodation_Type 2\n",
      "Reco_Insurance_Type 2\n",
      "Is_Spouse 2\n",
      "Health Indicator 9\n",
      "Holding_Policy_Duration 15\n"
     ]
    }
   ],
   "source": [
    "for col in train_df.select_dtypes('object').columns:\n",
    "    print(col, train_df[col].nunique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID                         0.000000\n",
       "City_Code                  0.000000\n",
       "Region_Code                0.000000\n",
       "Accomodation_Type          0.000000\n",
       "Reco_Insurance_Type        0.000000\n",
       "Upper_Age                  0.000000\n",
       "Lower_Age                  0.000000\n",
       "Is_Spouse                  0.000000\n",
       "Health Indicator           0.229767\n",
       "Holding_Policy_Duration    0.397999\n",
       "Holding_Policy_Type        0.397999\n",
       "Reco_Policy_Cat            0.000000\n",
       "Reco_Policy_Premium        0.000000\n",
       "Response                   0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.isnull().sum() / len(train_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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",
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       "    }\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>Holding_Policy_Type</th>\n",
       "      <th>Holding_Policy_Duration</th>\n",
       "      <th>Health Indicator</th>\n",
       "      <th>Response</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>X2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>X2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>X2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>X3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50863</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>X3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50864</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>X4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50865</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50867</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>X2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50877</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>X3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20251 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Holding_Policy_Type Holding_Policy_Duration Health Indicator  Response\n",
       "1                      NaN                     NaN               X2         0\n",
       "6                      NaN                     NaN              NaN         0\n",
       "10                     NaN                     NaN               X2         1\n",
       "13                     NaN                     NaN               X2         0\n",
       "15                     NaN                     NaN               X3         0\n",
       "...                    ...                     ...              ...       ...\n",
       "50863                  NaN                     NaN               X3         1\n",
       "50864                  NaN                     NaN               X4         0\n",
       "50865                  NaN                     NaN              NaN         0\n",
       "50867                  NaN                     NaN               X2         0\n",
       "50877                  NaN                     NaN               X3         0\n",
       "\n",
       "[20251 rows x 4 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mask = train_df.Holding_Policy_Type.isnull()\n",
    "train_df[mask][['Holding_Policy_Type', 'Holding_Policy_Duration', 'Health Indicator','Response']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Imputing Health Indicator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['X1', 'X2', nan, 'X4', 'X3', 'X6', 'X5', 'X8', 'X7', 'X9'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df['Health Indicator'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Health Indicator', ylabel='count'>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(train_df['Health Indicator'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X1\n"
     ]
    }
   ],
   "source": [
    "most_freq = train_df['Health Indicator'].mode()[0]\n",
    "print(most_freq)\n",
    "\n",
    "train_df['Health Indicator'] = train_df['Health Indicator'].fillna(most_freq)\n",
    "test_df['Health Indicator'] = test_df['Health Indicator'].fillna(most_freq)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Health Indicator', ylabel='count'>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(train_df['Health Indicator'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Imputing : Holding_Policy_Duration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['14+', nan, '1.0', '3.0', '5.0', '9.0', '14.0', '7.0', '2.0',\n",
       "       '11.0', '10.0', '8.0', '6.0', '4.0', '13.0', '12.0'], dtype=object)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df['Holding_Policy_Duration'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Holding_Policy_Duration', ylabel='count'>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(train_df['Holding_Policy_Duration'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0\n"
     ]
    }
   ],
   "source": [
    "# most_freq = train_df['Holding_Policy_Duration'].mode()[0]\n",
    "most_freq = str(0.0)\n",
    "print(most_freq)\n",
    "\n",
    "train_df['Holding_Policy_Duration'] = train_df['Holding_Policy_Duration'].fillna(most_freq)\n",
    "test_df['Holding_Policy_Duration'] = test_df['Holding_Policy_Duration'].fillna(most_freq)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Holding_Policy_Duration', ylabel='count'>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ESfuk3s2MVNbMzFqoLT0SSbtS3G11ein8r5ImAQE8VFkXEeskXUdxEX0bcFZEPJvqORtYAowA5kXEulbtg5mZFdqSSCLiD8Are8VO7KP8RcBFVeKLgcUNb6CZmdWt3XdtmZnZEOdEYmZmWZxIzMwsixOJmZllcSIxM7MsTiRmZpbFicTMzLI4kZiZWRYnEjMzy+JEYmZmWZxIzMwsixOJmZllcSIxM7MsTiRmZpbFicTMzLI4kZiZWRYnEjMzy9K2RCLpIUlrJK2S1JVie0paKml9+jkqxSXpUkndklZLOqRUz8xUfr2kme3aHzOz4ardPZJ3RsSkiOhMz2cDyyJiIrAsPQc4GpiYHrOAOVAkHuAC4HDgMOCCSvIxM7PWaHci6W0aMD8tzweml+JXReF2YA9JY4CjgKURsTUiHgeWAlNb3GYzs2GtnYkkgFskrZA0K8VGR8SmtPwoMDotjwUeKW27IcVqxV9A0ixJXZK6enp6GrkPZmbD3sg2vvbbImKjpFcBSyX9urwyIkJSNOKFImIuMBegs7OzIXWamVmhbT2SiNiYfm4BbqS4xrE5nbIi/dySim8Expc2H5diteJmZtYibUkkknaVtFtlGTgSWAssAip3Xs0EbkrLi4CT0t1bk4En0imwJcCRkkali+xHppiZmbVIu05tjQZulFRpw/cj4ieSlgPXSToVeBj4YCq/GDgG6AaeBk4BiIitkr4ALE/lLoyIra3bDTMza0siiYgHgDdWiT8GTKkSD+CsGnXNA+Y1uo3bi+Xffl92HW86/UcNaImZWXXb2+2/ZmY2xLTzrq226pnzb9l1dPzjRxrQErMXeu/1V2fX8ePjP9yAlpjVxz0SMzPL4kRiZmZZnEjMzCyLE4mZmWVxIjEzsyxOJGZmlmXY3v47nN007+is7ad99OYGtcTMXgrcIzEzsyxOJGZmlsWJxMzMsjiRmJlZFicSMzPL4kRiZmZZnEjMzCxLyxOJpPGSbpV0j6R1kj6e4p+TtFHSqvQ4prTNpyR1S7pP0lGl+NQU65Y0u9X7YmZm7flC4jbgvIhYmeZtXyFpaVr31Yj4UrmwpAOAGcCBwN7ATyW9Lq3+BvAeYAOwXNKiiLinJXthZsPWum9tzq7jwDNGN6Al24eWJ5KI2ARsSstPSboXGNvHJtOABRHxDPCgpG7gsLSuO03bi6QFqawTiZlZC7X1GomkCcDBwB0pdLak1ZLmSRqVYmOBR0qbbUixWvFqrzNLUpekrp6enkbugpnZsNe2RCLp5cBC4NyIeBKYA+wHTKLosXy5Ua8VEXMjojMiOjs6OhpVrZmZ0aZBGyXtQJFEro6IGwAiYnNp/XeAH6enG4Hxpc3HpRh9xK3Fvv29o/ov1IfTT1zSoJaYWau1464tAVcA90bEV0rxMaVixwFr0/IiYIaknSTtA0wE7gSWAxMl7SNpR4oL8otasQ9mZva8dvRI3gqcCKyRtCrFPg2cIGkSEMBDwOkAEbFO0nUUF9G3AWdFxLMAks4GlgAjgHkRsa51u2FmZtCeu7Z+CajKqsV9bHMRcFGV+OK+tjMzs+bzN9vNzCyLZ0g0s5e0m6/9bXYdR39orwa05KXLPRIzM8viHolZhr+74bLsOv79/ec0oCVm7eMeiZmZZXEiMTOzLD61ZWbbjUtu3JRdx/nHjem/kDWUeyRmZpbFicTMzLL41JZtt86/fmrW9pcc/5MGtcTM+uIeiZmZZXEiMTOzLD61ZWaDcvzCldl1XP/3hzSgJdZu7pGYmVkW90jMhoFp19+cXcdNxx/dgJbYS5ETiQ0bx/zwvOw6Fk//cgNaYvZij37519l1vPq817/g+ZbLbs2u81XnvLPfMj61ZWZmWYZ8IpE0VdJ9krolzW53e8zMhpshnUgkjQC+ARwNHEAx7/sB7W2VmdnwMqQTCXAY0B0RD0TEn4EFwLQ2t8nMbFhRRLS7DYMm6XhgakT8Q3p+InB4RJzdq9wsYFZ6uj9wXx3V7wXkz9HpOl2n62xknUOhjS/VOv9HRHRUWzEs7tqKiLnA3IFsI6krIjob2Q7X6Tpd5/ZVn+tsTJ1D/dTWRmB86fm4FDMzsxYZ6olkOTBR0j6SdgRmAIva3CYzs2FlSJ/aiohtks4GlgAjgHkRsa5B1Q/oVJjrdJ2usyV1DoU2Drs6h/TFdjMza7+hfmrLzMzazInEzMyyDMtEImmepC2S1lZZd56kkLRXRv19DtsiaSdJ16b1d0iaMNj2pvWSdGmqb7Wkfid5kLSzpDsl3S1pnaTP57YzbfOQpDWSVknqalBbPy5pbWrnuYOpM+czlzRT0vr0mFmjzP5pnyuPJ3u3dSD7Lmm8pFsl3ZP2++OD2e9a+y7pA6nev0qqeftnrWO5Rp17Slqa3qelkkbVqLOe9/N/pfatlXSNpJ17rR/MsbmHpOsl/VrSvZLe3Gv9gI/NtN0ISXdJ+nGVdf22s8Z7+YXUhlWSbpG0d43Xrvpe1qjzi2nfV0u6UdIeNeoc+LBTETHsHsA7gEOAtb3i4yku3D8M7FVluyuBI/qpewRwP7AvsCNwN3BArzJnAt9KyzOAawfT3tL6Y4CbAQGTgTvqeA8EvDwt7wDcAUzOaWcq91C1926wbQUOAtYCL6O4OeSnwGsHWmfGZ74n8ED6OSotj6rjGHiU4gtcg9p3YAxwSFreDfhNleOorvqq7TvwtxRfzv0Z0DnQY7lGnf8KzE7Ls4FLBvN+AmOBB4Fd0vPrgJMbcGzOB/4hLe8I7JH7e5S2+wTwfeDHVdb1284a7+XupeWPVeqo972sUeeRwMi0fEmNz6ffv1/VHsOyRxIRtwFbq6z6KvBPQM4dCPUM2zKN4qAGuB6YIkmDaG+5vquicDuwh6QxfTUylf19erpDevTe7wG1s04DbevfUvxCPx0R24CfA+8faJ0Zn/lRwNKI2BoRjwNLgan97OMU4P6IeHig7Sy1d1NErEzLTwH3UvyBHXB91fY9Iu6NiP5GeKh5LNd4P8vHy3xgepU6630/RwK7SBpJ8U/Ef/fxWv0em5JeQfHH9YrU/j9HxO+q1Dmg3yNJ44C/Ay6vUaTfdtb4fJ4sPd2V6sdnzfeyRp23pN8hgNspvnfX26CGnRqWiaQaSdOAjRFxd2ZVY4FHSs838OI/AM+VSR/sE8Arm/yaL5K65KuALRQH5B0NaGcAt0haoWJomty2rgXeLumVkl5G8V/j+F5lBrv/9Xzmg6l7BnBNg+oinQ45mKLXmF3fAAy0/tERsSktPwqMHkydEbER+BLwX8Am4ImIuKVWPXUem/sAPcB302moyyXtOtC2VfF/Kf4R+WuN9YP+XZd0kaRHgA8Dn+2r7gG0t+KjFL2vhtTpRAKkP1CfpsqHJemodJ5yFXAscHl63vuXesiJiGcjYhLFfyaHSTqoAdW+LSIOoRiR+SxJ78hs470U3fBbgJ8Aq4BncxvZ12eeWe+OFMfJDxpU38uBhcC5vf5L3a5FcZ5kUD37dG1lGsUf/72BXSV9JLNJIylO9cyJiIOBP1Ccfhs0Se8FtkTEisy2VRURn4mI8cDVwNn9la+XpM8A21K9DeFEUtiP4qC9W9JDFH9YV0p6dUQsiYhJ6Q/uIopzrJMi4vAaddUzbMtzZVLX/RXAYxntzxoqJnXxb+XFpxgG3M703yQRsQW4kaKrnNXWiLgiIg6NiHcAj1NcL8iqkz4+88y6jwZWRsTmKusGVJekHSiSyNURcUNufYMw0Po3V04FpZ9bBlnnu4EHI6InIv4C3AC8pVY9dR6bG4ANpV739RSJZaBtK3srcGw6fhYA75L0b5ntrOZq4O+rxAf8+Us6GXgv8OGU7LPrBCcSACJiTUS8KiImRMQEioPukIh4dBDV1TNsyyKgcofF8cB/1PhQ67UIOCnddTKZ4lTApr42kNRRuWtD0i7Ae4Dec30OqJ2SdpW0W2WZ4uJe77ukBtPWV6Wfr6G4PvL93DoH8JkvAY6UNCr9p3xkitVyAtVPaw2onek8+hXAvRHxldz6BmmgQxCVj5eZwE1VytTzfv4XMFnSy9L7MIXiGlGt1+r32Eyf6yOS9k+hKcA9Veqs+/2MiE9FxLh0/MxIbejdcxrU77qkiaWn03jx7yYM8NiUNJXiNNyxEfF0jWKDG3Yq6rgr4aX2oPhF3wT8heIPyKm91j/EIO/aiufv/vgNxd0Pn0mxC9MHCLAzxamPbuBOYN+Bthc4AzgjrRfFBF/3A2uocRdOrzrfANwFrKb4Y//ZBrRzX4q7PO4G1pX2Pbetv6D4pb8bmDKYOgfymQOdwOWldR9N70E3cEof7dyV4r/NV5Rig9p34G0Up4ZWU5zOW5WOqwHXV+P4OS4tPwNsBpaksnsDi/s6lvuo85XAMmA9xd11ew72/QQ+T/HHcy3wPWAnMo7NtM0koCu9pz+kuNMp69gs1X0E6a6tgbazxnu5MO37auBHwNiBvJc16uymuP5ROZ4qd5PV9Zn39fAQKWZmlsWntszMLIsTiZmZZXEiMTOzLE4kZmaWxYnEzMyyOJGYmVkWJxLbrkn6fa/nJ0v6ej/bfE7S/64Sn6A0rLakTkmXNritV0p6MA2hs1K9himvUv736efekq5vYDsqQ/mvUTEM/b+o1zDsmfVPl3RA6fmFkt7dqPpt6HEisWEpIroi4mNNqPqTUQynMxv4dp1t+e+IOL7B7XhnRPxPiiFq9q23LRWSRvSxejrwXCKJiM9GxE8H00h7aXAisSEr9TD+Q8VEPcvSECq9yxyqYvKuu4GzSvEjlCYiSj2YeZJ+JukBSR8rlftnFZP8/FLFBEsv6unUcBvw2lTHJ1RM0LRW1SfmKveURkj6Uiq7WtI5kt4l6Yel8u+RdGM9jYhiqoAzgOkqJp56br9TXV9XMf5SpSdziaSVwAcknSZpeXr/FqYhS95CMSjlF1PPa7/UEzs+1TFFxei6a9J7ulOp7s+nntoaSa+v8320IcCJxLZ3u6g08yDF8BMVlwHzI+INFAPbVTtV9V3gnIh4Yz+v83qK+R0OAy6QtIOkN1EMlvdGisEYa84kWMX7gDWSDgVOAQ6nmCzpNEkH97HdLGACMKm0X7cCr5fUkcqcAsyrtyFRjBr8IDCxv7LAYxFxSEQsAG6IiDel9+5eimFlfkUx9tInoxi89P7Khun02ZXAh1JvaCTwj6W6fxvFyNBzgHoTsg0BTiS2vftj+oM1KZ0yKg/7/maeH8DxexTjUz1HxaCUe0QxyU+lTC3/HhHPRMRvKUatHU0xuutNEfGnKCaX+lEd7f1iSnizKMY3ehtwY0T8IfUObgDe3sf27wa+HWkCoigmLYrU9o+kfXoz1eeS6Eu9E5JdW1o+SNIvJK2hmBPjwH623Z9i1N7K6MzzKSaTqqiMYLyCIlnaS8TIdjfAbDvxTGn5WQb/u/HJiHjuwrmkKVmtet53KRLZn4AfxPMz3fVLxYjMEygG4juQF/4D2fsi/B9Ky1cC0yPi7nT664iBNrqXynuc8/7adsg9EhvKfkUxzDUU/zH/orwyinlWfifpbaUyA/H/gPdJ2lnFBFPvHUQbf0FxfeJlKobWP653O3tZCpyuYu4KJO0JxQV5iulm/w9FUqlLavc3gR9GMR3rw8ABknZKvZu+Et1uwCYV86KU37un0rre7gMmSHpten4ixdTI9hLn/wpsKDuHYurUT1JMo3pKlTKnAPMkBcUsi3WLiOWSFlEM5b2ZYmjxJwZYx0pJV1IMIQ7FEOB39bHJ5cDrgNWS/gJ8B6jc7nw10BHFrJH9uVWSKP5ZvBH4QmrPI5Kuoxii/EGKqQRq+WeK6X170s9K8lgAfCfdlPDc3WYR8SdJpwA/SIlwOfCtOtpqQ5yHkTfrg6SXR8TvVUzNexswKyJWtqktXwfuiogr2vH6ZrW4R2LWt7npy3c7U9wh1q4ksoLi+sV57Xh9s764R2I2QJK+QXFHV9nXIqLuaxcNascdFDMHlp0YEWta2Q4zJxIzM8viu7bMzCyLE4mZmWVxIjEzsyxOJGZmluX/A9AfbR2wssNtAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(train_df['Holding_Policy_Duration'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Imputing : Holding_Policy_Type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 3., nan,  1.,  4.,  2.])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df['Holding_Policy_Type'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Holding_Policy_Type', ylabel='count'>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(train_df['Holding_Policy_Type'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "no_policies\n"
     ]
    }
   ],
   "source": [
    "# most_freq = train_df['Holding_Policy_Type'].mode()[0]\n",
    "most_freq = 'no_policies'\n",
    "print(most_freq)\n",
    "\n",
    "train_df['Holding_Policy_Type'] = train_df['Holding_Policy_Type'].fillna(most_freq)\n",
    "test_df['Holding_Policy_Type'] = test_df['Holding_Policy_Type'].fillna(most_freq)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Holding_Policy_Type', ylabel='count'>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(train_df['Holding_Policy_Type'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ID', 'City_Code', 'Region_Code', 'Accomodation_Type',\n",
       "       'Reco_Insurance_Type', 'Upper_Age', 'Lower_Age', 'Is_Spouse',\n",
       "       'Health Indicator', 'Holding_Policy_Duration', 'Holding_Policy_Type',\n",
       "       'Reco_Policy_Cat', 'Reco_Policy_Premium', 'Response'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df['Holding_Policy_Type'] = train_df['Holding_Policy_Type'].astype(str)\n",
    "test_df['Holding_Policy_Type'] = test_df['Holding_Policy_Type'].astype(str)\n",
    "\n",
    "train_df['Reco_Policy_Cat'] = train_df['Reco_Policy_Cat'].astype(str)\n",
    "test_df['Reco_Policy_Cat'] = test_df['Reco_Policy_Cat'].astype(str)\n",
    "\n",
    "train_df['Region_Code'] = train_df['Region_Code'].astype(str)\n",
    "test_df['Region_Code'] = test_df['Region_Code'].astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols_to_remove = ['ID']\n",
    "target = 'Response'\n",
    "\n",
    "_X = train_df.drop(cols_to_remove + [target], axis=1)\n",
    "y = train_df[target]\n",
    "_XTEST = test_df.drop(cols_to_remove , axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((50882, 12), (21805, 12))"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "_X.shape, _XTEST.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(72687, 12)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_all = pd.concat([_X, _XTEST]).reset_index(drop=True)\n",
    "X_all.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Label Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "City_Code\n",
      "Region_Code\n",
      "Accomodation_Type\n",
      "Reco_Insurance_Type\n",
      "Is_Spouse\n",
      "Health Indicator\n",
      "Holding_Policy_Duration\n",
      "Holding_Policy_Type\n",
      "Reco_Policy_Cat\n"
     ]
    }
   ],
   "source": [
    "cat_columns = []\n",
    "for col in X_all.select_dtypes('object').columns:\n",
    "    print(col)\n",
    "    cat_columns.append(col)\n",
    "    le = LabelEncoder()\n",
    "    X_all[col] = le.fit_transform(X_all[col])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(72687, 12)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_all.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((50882, 12), (21805, 12))"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = X_all[:len(y)]\n",
    "XTEST = X_all[len(y):]\n",
    "X.shape, XTEST.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['City_Code', 'Region_Code', 'Accomodation_Type', 'Reco_Insurance_Type',\n",
       "       'Upper_Age', 'Lower_Age', 'Is_Spouse', 'Health Indicator',\n",
       "       'Holding_Policy_Duration', 'Holding_Policy_Type', 'Reco_Policy_Cat',\n",
       "       'Reco_Policy_Premium'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "NUM_OF_BOOST_ROUND = 10000\n",
    "EARLY_STOPPING = 300"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1, 2, 3, 6, 7, 8, 9, 10]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_features_index = [i for i,col in enumerate(X.columns) if col in cat_columns]\n",
    "cat_features_index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['City_Code', 'Region_Code', 'Accomodation_Type', 'Reco_Insurance_Type',\n",
       "       'Upper_Age', 'Lower_Age', 'Is_Spouse', 'Health Indicator',\n",
       "       'Holding_Policy_Duration', 'Holding_Policy_Type', 'Reco_Policy_Cat',\n",
       "       'Reco_Policy_Premium'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train & Validation data split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train , X_valid, y_train, y_valid = train_test_split(X,y, \n",
    "                                                       test_size=0.2, \n",
    "                                                       random_state=SEED, \n",
    "                                                       stratify=y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LGBMClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "params = {\n",
    "    'cat_features' : cat_features_index,\n",
    "    'metric': 'auc',\n",
    "    'seed': SEED,\n",
    "    'n_estimators': NUM_OF_BOOST_ROUND\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = lgb.LGBMClassifier(**params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning] Unknown parameter: cat_features\n",
      "Training until validation scores don't improve for 100 rounds\n",
      "Early stopping, best iteration is:\n",
      "[94]\tvalid_0's auc: 0.672559\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LGBMClassifier(cat_features=[0, 1, 2, 3, 6, 7, 8, 9, 10], metric='auc',\n",
       "               n_estimators=10000, seed=2021)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(X_train, y_train, eval_set=(X_valid, y_valid), early_stopping_rounds=100, verbose=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "ypred_lgb = clf.predict_proba(X_valid)[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6725591313826609"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_auc_score(y_valid, ypred_lgb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "ytest = clf.predict_proba(XTEST)[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "sub_df.Response = ytest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Response', ylabel='Density'>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(sub_df.Response)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "sub_df.to_csv('lgb_submission.csv', index=False)"
   ]
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Magic Word: 🤐\n",
    "Just Change the algorithm to CatBoostClassifier, you will observe a dramatic change in roc_auc_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CatBoostClassifier 🔥"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "params = {\n",
    "    'cat_features': cat_features_index,\n",
    "    'eval_metric': 'AUC',\n",
    "    'random_seed': SEED,\n",
    "    'n_estimators': NUM_OF_BOOST_ROUND,\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
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I=this.container.selectAll(\"rect.scrollbar-vertical\").data(O?[0]:[]);I.exit().on(\".drag\",null).remove(),I.enter().append(\"rect\").classed(\"scrollbar-vertical\",!0).call(o.fill,n.barColor),O?(this.vbar=I.attr({rx:n.barRadius,ry:n.barRadius,x:E,y:N,width:D,height:P}),this._vbarYMin=N+P/2,this._vbarTranslateMax=M-P):(delete this.vbar,delete this._vbarYMin,delete this._vbarTranslateMax);var R=this.id,F=u-.5,j=O?f+D+.5:f+.5,B=d-.5,q=A?h+L+.5:h+.5,H=l._topdefs.selectAll(\"#\"+R).data(A||O?[0]:[]);if(H.exit().remove(),H.enter().append(\"clipPath\").attr(\"id\",R).append(\"rect\"),A||O?(this._clipRect=H.select(\"rect\").attr({x:Math.floor(F),y:Math.floor(B),width:Math.ceil(j)-Math.floor(F),height:Math.ceil(q)-Math.floor(B)}),this.container.call(i.setClipUrl,R),this.bg.attr({x:p,y:v,width:g,height:m})):(this.bg.attr({width:0,height:0}),this.container.on(\"wheel\",null).on(\".drag\",null).call(i.setClipUrl,null),delete this._clipRect),A||O){var V=a.behavior.drag().on(\"dragstart\",function(){a.event.sourceEvent.preventDefault()}).on(\"drag\",this._onBoxDrag.bind(this));this.container.on(\"wheel\",null).on(\"wheel\",this._onBoxWheel.bind(this)).on(\".drag\",null).call(V);var U=a.behavior.drag().on(\"dragstart\",function(){a.event.sourceEvent.preventDefault(),a.event.sourceEvent.stopPropagation()}).on(\"drag\",this._onBarDrag.bind(this));A&&this.hbar.on(\".drag\",null).call(U),O&&this.vbar.on(\".drag\",null).call(U)}this.setTranslate(e,r)},n.prototype.disable=function(){(this.hbar||this.vbar)&&(this.bg.attr({width:0,height:0}),this.container.on(\"wheel\",null).on(\".drag\",null).call(i.setClipUrl,null),delete this._clipRect),this.hbar&&(this.hbar.on(\".drag\",null),this.hbar.remove(),delete this.hbar,delete this._hbarXMin,delete this._hbarTranslateMax),this.vbar&&(this.vbar.on(\".drag\",null),this.vbar.remove(),delete this.vbar,delete this._vbarYMin,delete this._vbarTranslateMax)},n.prototype._onBoxDrag=function(){var 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r=this.position.w-this._box.w,n=this.position.h-this._box.h;if(t=l.constrain(t||0,0,r),e=l.constrain(e||0,0,n),this.translateX=t,this.translateY=e,this.container.call(i.setTranslate,this._box.l-this.position.l-t,this._box.t-this.position.t-e),this._clipRect&&this._clipRect.attr({x:Math.floor(this.position.l+t-.5),y:Math.floor(this.position.t+e-.5)}),this.hbar){var a=t/r;this.hbar.call(i.setTranslate,t+a*this._hbarTranslateMax,e)}if(this.vbar){var o=e/n;this.vbar.call(i.setTranslate,t,e+o*this._vbarTranslateMax)}}},{\"../../lib\":136,\"../color\":25,\"../drawing\":49,d3:7}],121:[function(t,e,r){\"use strict\";e.exports={SHOW_PLACEHOLDER:100,HIDE_PLACEHOLDER:1e3,DBLCLICKDELAY:300}},{}],122:[function(t,e,r){\"use strict\";e.exports={BADNUM:void 0,FP_SAFE:Number.MAX_VALUE/1e4,ONEAVGYEAR:315576e5,ONEAVGMONTH:26298e5,ONEDAY:864e5,ONEHOUR:36e5,ONEMIN:6e4,ONESEC:1e3,EPOCHJD:2440587.5,ALMOST_EQUAL:1-1e-6}},{}],123:[function(t,e,r){\"use 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b,_=[f.margin.left+x,f.margin.top+x];if(p){b=[0,n.max(i.util.sumArrays(i.util.arrayLast(h).r[0],i.util.arrayLast(m)))]}else b=n.extent(i.util.flattenArray(h.map(function(t,e){return t.r})));f.radialAxis.domain!=i.DATAEXTENT&&(b[0]=0),a=n.scale.linear().domain(f.radialAxis.domain!=i.DATAEXTENT&&f.radialAxis.domain?f.radialAxis.domain:b).range([0,x]),u.layout.radialAxis.domain=a.domain();var w,k=i.util.flattenArray(h.map(function(t,e){return t.t})),M=\"string\"==typeof k[0];M&&(k=i.util.deduplicate(k),w=k.slice(),k=n.range(k.length),h=h.map(function(t,e){var r=t;return t.t=[k],p&&(r.yStack=t.yStack),r}));var A=h.filter(function(t,e){return\"LinePlot\"===t.geometry||\"DotPlot\"===t.geometry}).length===h.length,T=null===f.needsEndSpacing?M||!A:f.needsEndSpacing,L=f.angularAxis.domain&&f.angularAxis.domain!=i.DATAEXTENT&&!M&&f.angularAxis.domain[0]>=0,C=L?f.angularAxis.domain:n.extent(k),S=Math.abs(k[1]-k[0]);A&&!M&&(S=0);var z=C.slice();T&&M&&(z[1]+=S);var O=f.angularAxis.ticksCount||4;O>8&&(O=O/(O/8)+O%8),f.angularAxis.ticksStep&&(O=(z[1]-z[0])/O);var D=f.angularAxis.ticksStep||(z[1]-z[0])/(O*(f.minorTicks+1));w&&(D=Math.max(Math.round(D),1)),z[2]||(z[2]=D);var P=n.range.apply(this,z);if(P=P.map(function(t,e){return parseFloat(t.toPrecision(12))}),l=n.scale.linear().domain(z.slice(0,2)).range(\"clockwise\"===f.direction?[0,360]:[360,0]),u.layout.angularAxis.domain=l.domain(),u.layout.angularAxis.endPadding=T?S:0,void 0===(e=n.select(this).select(\"svg.chart-root\"))||e.empty()){var E=(new DOMParser).parseFromString(\"<svg xmlns='http://www.w3.org/2000/svg' class='chart-root'>' + '<g class='outer-group'>' + '<g class='chart-group'>' + '<circle class='background-circle'></circle>' + '<g class='geometry-group'></g>' + '<g class='radial axis-group'>' + '<circle class='outside-circle'></circle>' + '</g>' + '<g class='angular axis-group'></g>' + '<g class='guides-group'><line></line><circle r='0'></circle></g>' + '</g>' + '<g 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r.symbol=\"DotPlot\"===t.geometry?t.dotType||\"circle\":\"LinePlot\"!=t.geometry?\"square\":\"line\",r.visibleInLegend=void 0===t.visibleInLegend||t.visibleInLegend,r.color=\"LinePlot\"===t.geometry?t.strokeColor:t.color,r});i.Legend().config({data:h.map(function(t,e){return t.name||\"Element\"+e}),legendConfig:o({},i.Legend.defaultConfig().legendConfig,{container:I,elements:B,reverseOrder:f.legend.reverseOrder})})();var q=I.node().getBBox();x=Math.min(f.width-q.width-f.margin.left-f.margin.right,f.height-f.margin.top-f.margin.bottom)/2,x=Math.max(10,x),_=[f.margin.left+x,f.margin.top+x],a.range([0,x]),u.layout.radialAxis.domain=a.domain(),I.attr(\"transform\",\"translate(\"+[_[0]+x,_[1]-x]+\")\")}else I=e.select(\".legend-group\").style({display:\"none\"});e.attr({width:f.width,height:f.height}).style({opacity:f.opacity}),R.attr(\"transform\",\"translate(\"+_+\")\").style({cursor:\"crosshair\"});var H=[(f.width-(f.margin.left+f.margin.right+2*x+(q?q.width:0)))/2,(f.height-(f.margin.top+f.margin.bottom+2*x))/2];if(H[0]=Math.max(0,H[0]),H[1]=Math.max(0,H[1]),e.select(\".outer-group\").attr(\"transform\",\"translate(\"+H+\")\"),f.title){var V=e.select(\"g.title-group text\").style(j).text(f.title),U=V.node().getBBox();V.attr({x:_[0]-U.width/2,y:_[1]-x-20})}var X=e.select(\".radial.axis-group\");if(f.radialAxis.gridLinesVisible){var G=X.selectAll(\"circle.grid-circle\").data(a.ticks(5));G.enter().append(\"circle\").attr({class:\"grid-circle\"}).style(F),G.attr(\"r\",a),G.exit().remove()}X.select(\"circle.outside-circle\").attr({r:x}).style(F);var Y=e.select(\"circle.background-circle\").attr({r:x}).style({fill:f.backgroundColor,stroke:f.stroke});if(f.radialAxis.visible){var Z=n.svg.axis().scale(a).ticks(5).tickSize(5);X.call(Z).attr({transform:\"rotate(\"+f.radialAxis.orientation+\")\"}),X.selectAll(\".domain\").style(F),X.selectAll(\"g>text\").text(function(t,e){return this.textContent+f.radialAxis.ticksSuffix}).style(j).style({\"text-anchor\":\"start\"}).attr({x:0,y:0,dx:0,dy:0,transform:function(t,e){return\"horizontal\"===f.radialAxis.tickOrientation?\"rotate(\"+-f.radialAxis.orientation+\") translate(\"+[0,j[\"font-size\"]]+\")\":\"translate(\"+[0,j[\"font-size\"]]+\")\"}}),X.selectAll(\"g>line\").style({stroke:\"black\"})}var W=e.select(\".angular.axis-group\").selectAll(\"g.angular-tick\").data(P),$=W.enter().append(\"g\").classed(\"angular-tick\",!0);W.attr({transform:function(t,e){return\"rotate(\"+s(t,e)+\")\"}}).style({display:f.angularAxis.visible?\"block\":\"none\"}),W.exit().remove(),$.append(\"line\").classed(\"grid-line\",!0).classed(\"major\",function(t,e){return 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e%(f.minorTicks+1)!=0?\"\":f.angularAxis.rewriteTicks(this.textContent,e)});var J=n.max(R.selectAll(\".angular-tick text\")[0].map(function(t,e){return t.getCTM().e+t.getBBox().width}));I.attr({transform:\"translate(\"+[x+J,f.margin.top]+\")\"});var K=e.select(\"g.geometry-group\").selectAll(\"g\").size()>0,tt=e.select(\"g.geometry-group\").selectAll(\"g.geometry\").data(h);if(tt.enter().append(\"g\").attr({class:function(t,e){return\"geometry geometry\"+e}}),tt.exit().remove(),h[0]||K){var et=[];h.forEach(function(t,e){var r={};r.radialScale=a,r.angularScale=l,r.container=tt.filter(function(t,r){return r==e}),r.geometry=t.geometry,r.orientation=f.orientation,r.direction=f.direction,r.index=e,et.push({data:t,geometryConfig:r})});var rt=n.nest().key(function(t,e){return void 0!==t.data.groupId||\"unstacked\"}).entries(et),nt=[];rt.forEach(function(t,e){\"unstacked\"===t.key?nt=nt.concat(t.values.map(function(t,e){return[t]})):nt.push(t.values)}),nt.forEach(function(t,e){var r;r=Array.isArray(t)?t[0].geometryConfig.geometry:t.geometryConfig.geometry;var n=t.map(function(t,e){return o(i[r].defaultConfig(),t)});i[r]().config(n)()})}var at,ot,it=e.select(\".guides-group\"),lt=e.select(\".tooltips-group\"),st=i.tooltipPanel().config({container:lt,fontSize:8})(),ct=i.tooltipPanel().config({container:lt,fontSize:8})(),ut=i.tooltipPanel().config({container:lt,hasTick:!0})();if(!M){var ft=it.select(\"line\").attr({x1:0,y1:0,y2:0}).style({stroke:\"grey\",\"pointer-events\":\"none\"});R.on(\"mousemove.angular-guide\",function(t,e){var r=i.util.getMousePos(Y).angle;ft.attr({x2:-x,transform:\"rotate(\"+r+\")\"}).style({opacity:.5});var n=(r+180+360-f.orientation)%360;at=l.invert(n);var a=i.util.convertToCartesian(x+12,r+180);st.text(i.util.round(at)).move([a[0]+_[0],a[1]+_[1]])}).on(\"mouseout.angular-guide\",function(t,e){it.select(\"line\").style({opacity:0})})}var dt=it.select(\"circle\").style({stroke:\"grey\",fill:\"none\"});R.on(\"mousemove.radial-guide\",function(t,e){var r=i.util.getMousePos(Y).radius;dt.attr({r:r}).style({opacity:.5}),ot=a.invert(i.util.getMousePos(Y).radius);var n=i.util.convertToCartesian(r,f.radialAxis.orientation);ct.text(i.util.round(ot)).move([n[0]+_[0],n[1]+_[1]])}).on(\"mouseout.radial-guide\",function(t,e){dt.style({opacity:0}),ut.hide(),st.hide(),ct.hide()}),e.selectAll(\".geometry-group .mark\").on(\"mouseover.tooltip\",function(t,r){var a=n.select(this),o=a.style(\"fill\"),l=\"black\",s=a.style(\"opacity\")||1;if(a.attr({\"data-opacity\":s}),\"none\"!=o){a.attr({\"data-fill\":o}),l=n.hsl(o).darker().toString(),a.style({fill:l,opacity:1});var c={t:i.util.round(t[0]),r:i.util.round(t[1])};M&&(c.t=w[t[0]]);var u=\"t: \"+c.t+\", r: \"+c.r,f=this.getBoundingClientRect(),d=e.node().getBoundingClientRect(),h=[f.left+f.width/2-H[0]-d.left,f.top+f.height/2-H[1]-d.top];ut.config({color:l}).text(u),ut.move(h)}else 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l},d.svg=function(){return e},n.rebind(d,f,\"on\"),d},i.Axis.defaultConfig=function(t,e){return{data:[{t:[1,2,3,4],r:[10,11,12,13],name:\"Line1\",geometry:\"LinePlot\",color:null,strokeDash:\"solid\",strokeColor:null,strokeSize:\"1\",visibleInLegend:!0,opacity:1}],layout:{defaultColorRange:n.scale.category10().range(),title:null,height:450,width:500,margin:{top:40,right:40,bottom:40,left:40},font:{size:12,color:\"gray\",outlineColor:\"white\",family:\"Tahoma, 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n=e*Math.PI/180;return[e,t(n)]})},i.util.dataFromEquation=function(t,e,r){var a=e||6,o=[],i=[];n.range(0,360+a,a).forEach(function(e,r){var n=e*Math.PI/180,a=t(n);o.push(e),i.push(a)});var l={t:o,r:i};return r&&(l.name=r),l},i.util.ensureArray=function(t,e){if(void 0===t)return null;var r=[].concat(t);return n.range(e).map(function(t,e){return r[e]||r[0]})},i.util.fillArrays=function(t,e,r){return e.forEach(function(e,n){t[e]=i.util.ensureArray(t[e],r)}),t},i.util.cloneJson=function(t){return JSON.parse(JSON.stringify(t))},i.util.validateKeys=function(t,e){\"string\"==typeof e&&(e=e.split(\".\"));var r=e.shift();return t[r]&&(!e.length||objHasKeys(t[r],e))},i.util.sumArrays=function(t,e){return n.zip(t,e).map(function(t,e){return n.sum(t)})},i.util.arrayLast=function(t){return t[t.length-1]},i.util.arrayEqual=function(t,e){for(var r=Math.max(t.length,e.length,1);r-- >=0&&t[r]===e[r];);return r===-2},i.util.flattenArray=function(t){for(var e=[];!i.util.arrayEqual(e,t);)e=t,t=[].concat.apply([],t);return t},i.util.deduplicate=function(t){return t.filter(function(t,e,r){return r.indexOf(t)==e})},i.util.convertToCartesian=function(t,e){var r=e*Math.PI/180;return[t*Math.cos(r),t*Math.sin(r)]},i.util.round=function(t,e){var r=e||2,n=Math.pow(10,r);return Math.round(t*n)/n},i.util.getMousePos=function(t){var e=n.mouse(t.node()),r=e[0],a=e[1],o={};return o.x=r,o.y=a,o.pos=e,o.angle=180*(Math.atan2(a,r)+Math.PI)/Math.PI,o.radius=Math.sqrt(r*r+a*a),o},i.util.duplicatesCount=function(t){for(var e,r={},n={},a=0,o=t.length;a<o;a++)e=t[a],e in r?(r[e]++,n[e]=r[e]):r[e]=1;return n},i.util.duplicates=function(t){return Object.keys(i.util.duplicatesCount(t))},i.util.translator=function(t,e,r,n){if(n){var a=r.slice();r=e,e=a}var o=e.reduce(function(t,e){if(void 0!==t)return t[e]},t);void 0!==o&&(e.reduce(function(t,r,n){if(void 0!==t)return n===e.length-1&&delete t[r],t[r]},t),r.reduce(function(t,e,n){return void 0===t[e]&&(t[e]={}),n===r.length-1&&(t[e]=o),t[e]},t))},i.PolyChart=function(){function t(){var t=e[0].geometryConfig,r=t.container;\"string\"==typeof r&&(r=n.select(r)),r.datum(e).each(function(e,r){function o(e,r){return{r:t.radialScale(e[1]),t:(t.angularScale(e[0])+t.orientation)*Math.PI/180}}function i(t){return{x:t.r*Math.cos(t.t),y:t.r*Math.sin(t.t)}}var l=!!e[0].data.yStack,s=e.map(function(t,e){return l?n.zip(t.data.t[0],t.data.r[0],t.data.yStack[0]):n.zip(t.data.t[0],t.data.r[0])}),c=t.angularScale,u=t.radialScale.domain()[0],f={};f.bar=function(r,a,o){var i=e[o].data,l=t.radialScale(r[1])-t.radialScale(0),s=t.radialScale(r[2]||0),u=i.barWidth;n.select(this).attr({class:\"mark bar\",d:\"M\"+[[l+s,-u/2],[l+s,u/2],[s,u/2],[s,-u/2]].join(\"L\")+\"Z\",transform:function(e,r){return\"rotate(\"+(t.orientation+c(e[0]))+\")\"}})},f.dot=function(t,r,a){var l=t[2]?[t[0],t[1]+t[2]]:t,s=n.svg.symbol().size(e[a].data.dotSize).type(e[a].data.dotType)(t,r);n.select(this).attr({class:\"mark 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n={};n.attributes=t(\"./attributes\"),n.supplyDefaults=t(\"./defaults\"),n.supplyLayoutDefaults=t(\"./layout_defaults\"),n.layoutAttributes=t(\"./layout_attributes\"),n.calc=t(\"./calc\"),n.plot=t(\"./plot\"),n.style=t(\"./style\"),n.styleOne=t(\"./style_one\"),n.moduleType=\"trace\",n.name=\"pie\",n.basePlotModule=t(\"./base_plot\"),n.categories=[\"pie\",\"showLegend\"],n.meta={},e.exports=n},{\"./attributes\":228,\"./base_plot\":229,\"./calc\":230,\"./defaults\":231,\"./layout_attributes\":234,\"./layout_defaults\":235,\"./plot\":236,\"./style\":237,\"./style_one\":238}],234:[function(t,e,r){\"use strict\";e.exports={hiddenlabels:{valType:\"data_array\"}}},{}],235:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),a=t(\"./layout_attributes\");e.exports=function(t,e){!function(r,o){n.coerce(t,e,a,r,o)}(\"hiddenlabels\")}},{\"../../lib\":136,\"./layout_attributes\":234}],236:[function(t,e,r){\"use strict\";function n(t,e,r){var n=Math.sqrt(t.width*t.width+t.height*t.height),o=t.width/t.height,i=Math.PI*Math.min(e.v/r.vTotal,.5),l=1-r.trace.hole,s=a(e,r),c={scale:s*r.r*2/n,rCenter:1-s,rotate:0};if(c.scale>=1)return c;var u=o+1/(2*Math.tan(i)),f=r.r*Math.min(1/(Math.sqrt(u*u+.5)+u),l/(Math.sqrt(o*o+l/2)+o)),d={scale:2*f/t.height,rCenter:Math.cos(f/r.r)-f*o/r.r,rotate:(180/Math.PI*e.midangle+720)%180-90},h=1/o,p=h+1/(2*Math.tan(i)),g=r.r*Math.min(1/(Math.sqrt(p*p+.5)+p),l/(Math.sqrt(h*h+l/2)+h)),v={scale:2*g/t.width,rCenter:Math.cos(g/r.r)-g/o/r.r,rotate:(180/Math.PI*e.midangle+810)%180-90},m=v.scale>d.scale?v:d;return c.scale<1&&m.scale>c.scale?m:c}function a(t,e){if(t.v===e.vTotal&&!e.trace.hole)return 1;var r=Math.PI*Math.min(t.v/e.vTotal,.5);return Math.min(1/(1+1/Math.sin(r)),(1-e.trace.hole)/2)}function o(t,e){var r=e.pxmid[0],n=e.pxmid[1],a=t.width/2,o=t.height/2;return r<0&&(a*=-1),n<0&&(o*=-1),{scale:1,rCenter:1,rotate:0,x:a+Math.abs(o)*(a>0?1:-1)/2,y:o/(1+r*r/(n*n)),outside:!0}}function i(t,e){function r(t,e){return t.pxmid[1]-e.pxmid[1]}function n(t,e){return e.pxmid[1]-t.pxmid[1]}var a,o,i,l,s,c,u,f,d,h,p,g,v;for(o=0;o<2;o++)for(i=o?r:n,s=o?Math.max:Math.min,u=o?1:-1,a=0;a<2;a++){for(l=a?Math.max:Math.min,c=a?1:-1,f=t[o][a],f.sort(i),d=t[1-o][a],h=d.concat(f),g=[],p=0;p<f.length;p++)void 0!==f[p].yLabelMid&&g.push(f[p]);for(v=!1,p=0;o&&p<d.length;p++)if(void 0!==d[p].yLabelMid){v=d[p];break}for(p=0;p<g.length;p++){var m=p&&g[p-1];v&&!p&&(m=v),function(t,r){r||(r={});var n,a,i,f,d,p,g=r.labelExtraY+(o?r.yLabelMax:r.yLabelMin),v=o?t.yLabelMin:t.yLabelMax,m=o?t.yLabelMax:t.yLabelMin,y=t.cyFinal+s(t.px0[1],t.px1[1]),x=g-v;if(x*u>0&&(t.labelExtraY=x),Array.isArray(e.pull))for(a=0;a<h.length;a++)(i=h[a])===t||(e.pull[t.i]||0)>=e.pull[i.i]||((t.pxmid[1]-i.pxmid[1])*u>0?(f=i.cyFinal+s(i.px0[1],i.px1[1]),(x=f-v-t.labelExtraY)*u>0&&(t.labelExtraY+=x)):(m+t.labelExtraY-y)*u>0&&(n=3*c*Math.abs(a-h.indexOf(t)),d=i.cxFinal+l(i.px0[0],i.px1[0]),(p=d+n-(t.cxFinal+t.pxmid[0])-t.labelExtraX)*c>0&&(t.labelExtraX+=p)))}(g[p],m)}}}function l(t,e){var r,n,a,o,i,l,s,u,f,d,h=[];for(a=0;a<t.length;a++){if(i=t[a][0],l=i.trace,r=e.w*(l.domain.x[1]-l.domain.x[0]),n=e.h*(l.domain.y[1]-l.domain.y[0]),s=l.tiltaxis*Math.PI/180,u=l.pull,Array.isArray(u))for(u=0,o=0;o<l.pull.length;o++)l.pull[o]>u&&(u=l.pull[o]);i.r=Math.min(r/c(l.tilt,Math.sin(s),l.depth),n/c(l.tilt,Math.cos(s),l.depth))/(2+2*u),i.cx=e.l+e.w*(l.domain.x[1]+l.domain.x[0])/2,i.cy=e.t+e.h*(2-l.domain.y[1]-l.domain.y[0])/2,\n",
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n=Math.sin(t*Math.PI/180);return Math.max(.01,r*n*Math.abs(e)+2*Math.sqrt(1-n*n*e*e))}var u=t(\"d3\"),f=t(\"../../components/fx\"),d=t(\"../../components/color\"),h=t(\"../../components/drawing\"),p=t(\"../../lib/svg_text_utils\"),g=t(\"./helpers\");e.exports=function(t,e){var r=t._fullLayout;l(e,r._size);var c=r._pielayer.selectAll(\"g.trace\").data(e);c.enter().append(\"g\").attr({\"stroke-linejoin\":\"round\",class:\"trace\"}),c.exit().remove(),c.order(),c.each(function(e){var l=u.select(this),c=e[0],v=c.trace,m=(v.depth||0)*c.r*Math.sin(0)/2,y=v.tiltaxis||0,x=y*Math.PI/180,b=[m*Math.sin(x),m*Math.cos(x)],_=c.r*Math.cos(0),w=l.selectAll(\"g.part\").data(v.tilt?[\"top\",\"sides\"]:[\"top\"]);w.enter().append(\"g\").attr(\"class\",function(t){return t+\" part\"}),w.exit().remove(),w.order(),s(e),l.selectAll(\".top\").each(function(){var l=u.select(this).selectAll(\"g.slice\").data(e);l.enter().append(\"g\").classed(\"slice\",!0),l.exit().remove();var s=[[[],[]],[[],[]]],m=!1;l.each(function(e){function i(n){n.originalEvent=u.event;var o=t._fullLayout,i=t._fullData[v.index],l=i.hoverinfo;if(\"all\"===l&&(l=\"label+text+value+percent+name\"),t._dragging||o.hovermode===!1||\"none\"===l||\"skip\"===l||!l)return void f.hover(t,n,\"pie\");var s=a(e,c),d=w+e.pxmid[0]*(1-s),h=k+e.pxmid[1]*(1-s),p=r.separators,m=[];l.indexOf(\"label\")!==-1&&m.push(e.label),l.indexOf(\"text\")!==-1&&(i.hovertext?m.push(Array.isArray(i.hovertext)?i.hovertext[e.i]:i.hovertext):i.text&&i.text[e.i]&&m.push(i.text[e.i])),l.indexOf(\"value\")!==-1&&m.push(g.formatPieValue(e.v,p)),l.indexOf(\"percent\")!==-1&&m.push(g.formatPiePercent(e.v/c.vTotal,p));var y=i.hoverlabel;f.loneHover({x0:d-s*c.r,x1:d+s*c.r,y:h,text:m.join(\"<br>\"),name:l.indexOf(\"name\")!==-1?i.name:void 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w=c.cx+b[0],k=c.cy+b[1],M=u.select(this),A=M.selectAll(\"path.surface\").data([e]),T=!1;if(A.enter().append(\"path\").classed(\"surface\",!0).style({\"pointer-events\":\"all\"}),M.select(\"path.textline\").remove(),M.on(\"mouseover\",i).on(\"mouseout\",l).on(\"click\",d),v.pull){var L=+(Array.isArray(v.pull)?v.pull[e.i]:v.pull)||0;L>0&&(w+=L*e.pxmid[0],k+=L*e.pxmid[1])}e.cxFinal=w,e.cyFinal=k;var C=v.hole;if(e.v===c.vTotal){var S=\"M\"+(w+e.px0[0])+\",\"+(k+e.px0[1])+x(e.px0,e.pxmid,!0,1)+x(e.pxmid,e.px0,!0,1)+\"Z\";C?A.attr(\"d\",\"M\"+(w+C*e.px0[0])+\",\"+(k+C*e.px0[1])+x(e.px0,e.pxmid,!1,C)+x(e.pxmid,e.px0,!1,C)+\"Z\"+S):A.attr(\"d\",S)}else{var z=x(e.px0,e.px1,!0,1);if(C){var O=1-C;A.attr(\"d\",\"M\"+(w+C*e.px1[0])+\",\"+(k+C*e.px1[1])+x(e.px1,e.px0,!1,C)+\"l\"+O*e.px0[0]+\",\"+O*e.px0[1]+z+\"Z\")}else A.attr(\"d\",\"M\"+w+\",\"+k+\"l\"+e.px0[0]+\",\"+e.px0[1]+z+\"Z\")}var D=Array.isArray(v.textposition)?v.textposition[e.i]:v.textposition,P=M.selectAll(\"g.slicetext\").data(e.text&&\"none\"!==D?[0]:[]);P.enter().append(\"g\").classed(\"slicetext\",!0),P.exit().remove(),P.each(function(){var t=u.select(this).selectAll(\"text\").data([0]);t.enter().append(\"text\").attr(\"data-notex\",1),t.exit().remove(),t.text(e.text).attr({class:\"slicetext\",transform:\"\",\"data-bb\":\"\",\"text-anchor\":\"middle\",x:0,y:0}).call(h.font,\"outside\"===D?v.outsidetextfont:v.insidetextfont).call(p.convertToTspans),t.selectAll(\"tspan.line\").attr({x:0,y:0});var r,a=h.bBox(t.node());\"outside\"===D?r=o(a,e):(r=n(a,e,c),\"auto\"===D&&r.scale<1&&(t.call(h.font,v.outsidetextfont),v.outsidetextfont.family===v.insidetextfont.family&&v.outsidetextfont.size===v.insidetextfont.size||(t.attr({\"data-bb\":\"\"}),a=h.bBox(t.node())),r=o(a,e)));var i=w+e.pxmid[0]*r.rCenter+(r.x||0),l=k+e.pxmid[1]*r.rCenter+(r.y||0);r.outside&&(e.yLabelMin=l-a.height/2,e.yLabelMid=l,e.yLabelMax=l+a.height/2,e.labelExtraX=0,e.labelExtraY=0,m=!0),t.attr(\"transform\",\"translate(\"+i+\",\"+l+\")\"+(r.scale<1?\"scale(\"+r.scale+\")\":\"\")+(r.rotate?\"rotate(\"+r.rotate+\")\":\"\")+\"translate(\"+-(a.left+a.right)/2+\",\"+-(a.top+a.bottom)/2+\")\")})}),m&&i(s,v),l.each(function(t){if(t.labelExtraX||t.labelExtraY){var e=u.select(this),r=e.select(\"g.slicetext text\");r.attr(\"transform\",\"translate(\"+t.labelExtraX+\",\"+t.labelExtraY+\")\"+r.attr(\"transform\"));var n=t.cxFinal+t.pxmid[0],a=t.cyFinal+t.pxmid[1],o=\"M\"+n+\",\"+a,i=(t.yLabelMax-t.yLabelMin)*(t.pxmid[0]<0?-1:1)/4;if(t.labelExtraX){var l=t.labelExtraX*t.pxmid[1]/t.pxmid[0],s=t.yLabelMid+t.labelExtraY-(t.cyFinal+t.pxmid[1]);Math.abs(l)>Math.abs(s)?o+=\"l\"+s*t.pxmid[0]/t.pxmid[1]+\",\"+s+\"H\"+(n+t.labelExtraX+i):o+=\"l\"+t.labelExtraX+\",\"+l+\"v\"+(s-l)+\"h\"+i}else o+=\"V\"+(t.yLabelMid+t.labelExtraY)+\"h\"+i;e.append(\"path\").classed(\"textline\",!0).call(d.stroke,v.outsidetextfont.color).attr({\"stroke-width\":Math.min(2,v.outsidetextfont.size/8),d:o,fill:\"none\"})}})})}),setTimeout(function(){c.selectAll(\"tspan\").each(function(){var t=u.select(this);t.attr(\"dy\")&&t.attr(\"dy\",t.attr(\"dy\"))})},0)}},{\"../../components/color\":25,\"../../components/drawing\":49,\"../../components/fx\":66,\"../../lib/svg_text_utils\":153,\"./helpers\":232,d3:7}],237:[function(t,e,r){\"use strict\";var n=t(\"d3\"),a=t(\"./style_one\");e.exports=function(t){t._fullLayout._pielayer.selectAll(\".trace\").each(function(t){var e=t[0],r=e.trace,o=n.select(this);o.style({opacity:r.opacity}),o.selectAll(\".top path.surface\").each(function(t){n.select(this).call(a,t,r)})})}},{\"./style_one\":238,d3:7}],238:[function(t,e,r){\"use strict\";var n=t(\"../../components/color\");e.exports=function(t,e,r){var a=r.marker.line.color;Array.isArray(a)&&(a=a[e.i]||n.defaultLine);var o=r.marker.line.width||0;Array.isArray(o)&&(o=o[e.i]||0),t.style({\"stroke-width\":o}).call(n.fill,e.color).call(n.stroke,a)}},{\"../../components/color\":25}],239:[function(t,e,r){\"use strict\";var n=t(\"../../lib\");e.exports=function(t,e){for(var r=0;r<t.length;r++)t[r].i=r;n.mergeArray(e.text,t,\"tx\"),n.mergeArray(e.hovertext,t,\"htx\"),n.mergeArray(e.customdata,t,\"data\"),n.mergeArray(e.textposition,t,\"tp\"),e.textfont&&(n.mergeArray(e.textfont.size,t,\"ts\"),n.mergeArray(e.textfont.color,t,\"tc\"),n.mergeArray(e.textfont.family,t,\"tf\"));var a=e.marker;if(a){n.mergeArray(a.size,t,\"ms\"),n.mergeArray(a.opacity,t,\"mo\"),n.mergeArray(a.symbol,t,\"mx\"),n.mergeArray(a.color,t,\"mc\");var o=a.line;a.line&&(n.mergeArray(o.color,t,\"mlc\"),n.mergeArray(o.width,t,\"mlw\"));var i=a.gradient;i&&\"none\"!==i.type&&(n.mergeArray(i.type,t,\"mgt\"),n.mergeArray(i.color,t,\"mgc\"))}}},{\"../../lib\":136}],240:[function(t,e,r){\"use strict\";var n=t(\"../../components/colorscale/color_attributes\"),a=t(\"../../components/errorbars/attributes\"),o=t(\"../../components/colorbar/attributes\"),i=t(\"../../components/drawing/attributes\").dash,l=t(\"../../components/drawing\"),s=(t(\"./constants\"),t(\"../../lib/extend\").extendFlat);e.exports={x:{valType:\"data_array\"},x0:{valType:\"any\",dflt:0},dx:{valType:\"number\",dflt:1},y:{valType:\"data_array\"},y0:{valType:\"any\",dflt:0},customdata:{valType:\"data_array\"},dy:{valType:\"number\",dflt:1},ids:{valType:\"data_array\"},text:{valType:\"string\",dflt:\"\",arrayOk:!0},hovertext:{valType:\"string\",dflt:\"\",arrayOk:!0},mode:{valType:\"flaglist\",flags:[\"lines\",\"markers\",\"text\"],extras:[\"none\"]},hoveron:{valType:\"flaglist\",flags:[\"points\",\"fills\"]},line:{color:{valType:\"color\"},width:{valType:\"number\",min:0,dflt:2},shape:{valType:\"enumerated\",values:[\"linear\",\"spline\",\"hv\",\"vh\",\"hvh\",\"vhv\"],dflt:\"linear\"},smoothing:{valType:\"number\",min:0,max:1.3,dflt:1},dash:i,simplify:{valType:\"boolean\",dflt:!0}},connectgaps:{valType:\"boolean\",dflt:!1},fill:{valType:\"enumerated\",values:[\"none\",\"tozeroy\",\"tozerox\",\"tonexty\",\"tonextx\",\"toself\",\"tonext\"],dflt:\"none\"},fillcolor:{valType:\"color\"},marker:s({},{symbol:{valType:\"enumerated\",values:l.symbolList,dflt:\"circle\",arrayOk:!0},opacity:{valType:\"number\",min:0,max:1,arrayOk:!0},size:{valType:\"number\",min:0,dflt:6,arrayOk:!0},maxdisplayed:{valType:\"number\",min:0,dflt:0},sizeref:{valType:\"number\",dflt:1},sizemin:{valType:\"number\",min:0,dflt:0},sizemode:{valType:\"enumerated\",values:[\"diameter\",\"area\"],dflt:\"diameter\"},showscale:{valType:\"boolean\",dflt:!1},colorbar:o,line:s({},{width:{valType:\"number\",min:0,arrayOk:!0}},n(\"marker.line\")),gradient:{type:{valType:\"enumerated\",values:[\"radial\",\"horizontal\",\"vertical\",\"none\"],arrayOk:!0,dflt:\"none\"},color:{valType:\"color\",arrayOk:!0}}},n(\"marker\")),textposition:{valType:\"enumerated\",values:[\"top left\",\"top center\",\"top right\",\"middle left\",\"middle center\",\"middle right\",\"bottom left\",\"bottom center\",\"bottom right\"],dflt:\"middle center\",arrayOk:!0},textfont:{family:{valType:\"string\",noBlank:!0,strict:!0,arrayOk:!0},size:{valType:\"number\",min:1,arrayOk:!0},color:{valType:\"color\",arrayOk:!0}},r:{valType:\"data_array\"},t:{valType:\"data_array\"},error_y:a,error_x:a}},{\"../../components/colorbar/attributes\":26,\"../../components/colorscale/color_attributes\":32,\"../../components/drawing\":49,\"../../components/drawing/attributes\":48,\"../../components/errorbars/attributes\":51,\"../../lib/extend\":132,\"./constants\":245}],241:[function(t,e,r){\"use strict\";var n=t(\"fast-isnumeric\"),a=t(\"../../plots/cartesian/axes\"),o=t(\"../../constants/numerical\").BADNUM,i=t(\"./subtypes\"),l=t(\"./colorscale_calc\"),s=t(\"./arrays_to_calcdata\");e.exports=function(t,e){var r,c,u,f=a.getFromId(t,e.xaxis||\"x\"),d=a.getFromId(t,e.yaxis||\"y\"),h=f.makeCalcdata(e,\"x\"),p=d.makeCalcdata(e,\"y\"),g=Math.min(h.length,p.length);f._minDtick=0,d._minDtick=0,h.length>g&&h.splice(g,h.length-g),p.length>g&&p.splice(g,p.length-g);var v={padded:!0},m={padded:!0};if(i.hasMarkers(e)){if(r=e.marker,c=r.size,Array.isArray(c)){var y={type:\"linear\"};a.setConvert(y),c=y.makeCalcdata(e.marker,\"size\"),c.length>g&&c.splice(g,c.length-g)}var x,b=1.6*(e.marker.sizeref||1);x=\"area\"===e.marker.sizemode?function(t){return Math.max(Math.sqrt((t||0)/b),3)}:function(t){return 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o,i=a(\"x\"),l=a(\"y\");if(n.getComponentMethod(\"calendars\",\"handleTraceDefaults\")(t,e,[\"x\",\"y\"],r),i)l?(o=Math.min(i.length,l.length),o<i.length&&(e.x=i.slice(0,o)),o<l.length&&(e.y=l.slice(0,o))):(o=i.length,a(\"y0\"),a(\"dy\"));else{if(!l)return 0;o=e.y.length,a(\"x0\"),a(\"dx\")}return o}},{\"../../registry\":206}]},{},[5])(5)});window.define = window.__define;window.require = window.__require;window.__define = undefined; window.__require = undefined;/* global Highcharts, Plotly*/\n",
       "/* eslint-disable */\n",
       "function throttle(fn, timeout, invokeAsap, ctx) {\n",
       "    var typeofInvokeAsap = typeof invokeAsap;\n",
       "    if(typeofInvokeAsap === 'undefined') {\n",
       "        invokeAsap = true;\n",
       "    } else if(arguments.length === 3 && typeofInvokeAsap !== 'boolean') {\n",
       "        ctx = invokeAsap;\n",
       "        invokeAsap = true;\n",
       "    }\n",
       "\n",
       "    var timer, args, needInvoke,\n",
       "        wrapper = function() {\n",
       "            if(needInvoke) {\n",
       "                fn.apply(ctx, args);\n",
       "                needInvoke = false;\n",
       "                timer = setTimeout(wrapper, timeout);\n",
       "            } else {\n",
       "                timer = null;\n",
       "            }\n",
       "        };\n",
       "\n",
       "    return function() {\n",
       "        args = arguments;\n",
       "        ctx || (ctx = this);\n",
       "        needInvoke = true;\n",
       "\n",
       "        if(!timer) {\n",
       "            invokeAsap?\n",
       "                wrapper() :\n",
       "                timer = setTimeout(wrapper, timeout);\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "/* eslint-enable */\n",
       "function CatboostIpython() {\n",
       "\n",
       "}\n",
       "\n",
       "CatboostIpython.prototype.init = function() {\n",
       "    this.charts = {};\n",
       "    /*\n",
       "        {\n",
       "            \"rmse_1\": {\n",
       "                ...\n",
       "            }\n",
       "        }\n",
       "    */\n",
       "    this.traces = {};\n",
       "    /*\n",
       "        {\n",
       "            \"rmse_1\": {\n",
       "                name: \"rmse\",\n",
       "                id: \"rmse_1\",\n",
       "                parent: \"div\",\n",
       "                traces: [\n",
       "                    {\n",
       "                        name: \"current;learn;0;;;\",\n",
       "                        x: [],\n",
       "                        y: []\n",
       "                    },\n",
       "                    {\n",
       "                        name: \"current;learn;0;smoothed;;\",\n",
       "                        x: [],\n",
       "                        y: []\n",
       "                    },\n",
       "                    {\n",
       "                        name: \"current;learn;1;;;\",\n",
       "                        x: [],\n",
       "                        y: []\n",
       "                    },\n",
       "                    {\n",
       "                        name: \"current;learn;1;smoothed;;\",\n",
       "                        x: [],\n",
       "                        y: []\n",
       "                    },\n",
       "                    {\n",
       "                        name: \"current;test;0;;;\",\n",
       "                        x: [],\n",
       "                        y: []\n",
       "                    },\n",
       "                    {\n",
       "                        name: \"current;test;0;smoothed;;\",\n",
       "                        x: [],\n",
       "                        y: []\n",
       "                    },\n",
       "                    {\n",
       "                        name: \"current;test;0;;best_point;\",\n",
       "                        x: [],\n",
       "                        y: []\n",
       "                    },\n",
       "                    {\n",
       "                        name: \"current;test;0;;;best_value\",\n",
       "                        x: [],\n",
       "                        y: []\n",
       "                    }\n",
       "                ]\n",
       "            }\n",
       "        }\n",
       "    */\n",
       "\n",
       "    this.hovertextParameters = [];\n",
       "    this.chartsToRedraw = {};\n",
       "    this.lastIndexes = {};\n",
       "    this.smoothness = -1;\n",
       "    this.layoutDisabled = {\n",
       "        series: {},\n",
       "        traces: {}\n",
       "    };\n",
       "    this.clickMode = false;\n",
       "    this.logarithmMode = 'linear';\n",
       "    this.lastSmooth = 0;\n",
       "    this.layout = null;\n",
       "    this.activeTab = '';\n",
       "    this.meta = {};\n",
       "    this.timeLeft = {};\n",
       "\n",
       "    this.hasCVMode = false;\n",
       "    this.stddevEnabled = false;\n",
       "\n",
       "    this.colors = [\n",
       "        '#68E256',\n",
       "        '#56AEE2',\n",
       "        '#CF56E2',\n",
       "        '#E28956',\n",
       "        '#56E289',\n",
       "        '#5668E2',\n",
       "        '#E256AE',\n",
       "        '#E2CF56',\n",
       "        '#56E2CF',\n",
       "        '#8A56E2',\n",
       "        '#E25668',\n",
       "        '#AEE256'\n",
       "    ];\n",
       "    this.colorsByPath = {};\n",
       "    this.colorIndex = 0;\n",
       "    this.lossFuncs = {};\n",
       "\n",
       "    this.isCVinited = false;\n",
       "};\n",
       "\n",
       "/* eslint-disable */\n",
       "CatboostIpython.prototype.loadStyles = function(path, fn, scope) {\n",
       "    $('link[catboost=\"1\"]').remove();\n",
       "\n",
       "    var head = document.getElementsByTagName('head')[0], // reference to document.head for appending/ removing link nodes\n",
       "        link = document.createElement('link');           // create the link node\n",
       "    link.setAttribute('href', path);\n",
       "    link.setAttribute('rel', 'stylesheet');\n",
       "    link.setAttribute('type', 'text/css');\n",
       "    link.setAttribute('catboost', '1');\n",
       "\n",
       "    var sheet, cssRules;\n",
       "    // get the correct properties to check for depending on the browser\n",
       "    if ('sheet' in link) {\n",
       "        sheet = 'sheet'; cssRules = 'cssRules';\n",
       "    } else {\n",
       "        sheet = 'styleSheet'; cssRules = 'rules';\n",
       "    }\n",
       "\n",
       "    var interval_id = setInterval(function() {                     // start checking whether the style sheet has successfully loaded\n",
       "        try {\n",
       "            if (link[sheet] && link[sheet][cssRules].length) { // SUCCESS! our style sheet has loaded\n",
       "                clearInterval(interval_id);                      // clear the counters\n",
       "                clearTimeout(timeout_id);\n",
       "                fn.call( scope || window, true, link);           // fire the callback with success == true\n",
       "            }\n",
       "        } catch(e) {} finally {}\n",
       "    }, 50 ),                                                   // how often to check if the stylesheet is loaded\n",
       "    timeout_id = setTimeout( function() {       // start counting down till fail\n",
       "        clearInterval( interval_id );             // clear the counters\n",
       "        clearTimeout( timeout_id );\n",
       "        head.removeChild( link );                // since the style sheet didn't load, remove the link node from the DOM\n",
       "        fn.call( scope || window, false, link ); // fire the callback with success == false\n",
       "    }, 15000 );                                 // how long to wait before failing\n",
       "\n",
       "    head.appendChild( link );  // insert the link node into the DOM and start loading the style sheet\n",
       "\n",
       "    return link; // return the link node;\n",
       "};\n",
       "/* eslint-enable */\n",
       "\n",
       "CatboostIpython.prototype.resizeCharts = function() {\n",
       "    // width fix for development\n",
       "    $('.catboost-graph__charts', this.layout).css({width: $('.catboost-graph').width()});\n",
       "\n",
       "    this.plotly.Plots.resize(this.traces[this.activeTab].parent);\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.addMeta = function(path, meta) {\n",
       "    this.meta[path] = meta;\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.addLayout = function(parent) {\n",
       "    if (this.layout) {\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var cvAreaControls = '';\n",
       "\n",
       "    if (this.hasCVMode) {\n",
       "        cvAreaControls =    '<div>' +\n",
       "                                '<input type=\"checkbox\" class=\"catboost-panel__control_checkbox\" id=\"catboost-control2-cvstddev' + this.index + '\"' + (this.stddevEnabled ? ' checked=\"checked\"' : '') + '></input>' +\n",
       "                                '<label for=\"catboost-control2-cvstddev' + this.index + '\" class=\"catboost-panel__controls2_label catboost-panel__controls2_label-long\">Standard Deviation</label>' +\n",
       "                            '</div>';\n",
       "    }\n",
       "\n",
       "    this.layout = $('<div class=\"catboost\">' +\n",
       "                        '<div class=\"catboost-panel\">' +\n",
       "                            '<div class=\"catboost-panel__controls\">' +\n",
       "                                '<input type=\"checkbox\" class=\"catboost-panel__controls_checkbox\" id=\"catboost-control-learn' + this.index + '\" ' + (!this.layoutDisabled.learn ? ' checked=\"checked\"' : '') + '></input>' +\n",
       "                                '<label for=\"catboost-control-learn' + this.index + '\" class=\"catboost-panel__controls_label\"><div class=\"catboost-panel__serie_learn_pic\" style=\"border-color:#999\"></div>Learn</label>' +\n",
       "                                '<input type=\"checkbox\" class=\"catboost-panel__controls_checkbox\" id=\"catboost-control-test' + this.index + '\" ' + (!this.layoutDisabled.test ? ' checked=\"checked\"' : '') + '></input>' +\n",
       "                                '<label for=\"catboost-control-test' + this.index + '\" class=\"catboost-panel__controls_label\"><div class=\"catboost-panel__serie_test_pic\" style=\"border-color:#999\"></div>Eval</label>' +\n",
       "                            '</div>' +\n",
       "                            '<div class=\"catboost-panel__series ' + (this.layoutDisabled.learn ? ' catboost-panel__series_learn_disabled' : '') + '\">' +\n",
       "                            '</div>' +\n",
       "                            '<div class=\"catboost-panel__controls2\">' +\n",
       "                                '<input type=\"checkbox\" class=\"catboost-panel__control_checkbox\" id=\"catboost-control2-clickmode' + this.index + '\"></input>' +\n",
       "                                '<label for=\"catboost-control2-clickmode' + this.index + '\" class=\"catboost-panel__controls2_label\">Click Mode</label>' +\n",
       "                                '<input type=\"checkbox\" class=\"catboost-panel__control_checkbox\" id=\"catboost-control2-log' + this.index + '\"></input>' +\n",
       "                                '<label for=\"catboost-control2-log' + this.index + '\" class=\"catboost-panel__controls2_label\">Logarithm</label>' +\n",
       "                                '<div>' +\n",
       "                                    '<input type=\"checkbox\" class=\"catboost-panel__control_checkbox\" id=\"catboost-control2-smooth' + this.index + '\"></input>' +\n",
       "                                    '<label for=\"catboost-control2-smooth' + this.index + '\" class=\"catboost-panel__controls2_label\">Smooth</label>' +\n",
       "                                    '<input id=\"catboost-control2-slider' + this.index + '\" disabled=\"disabled\" class=\"catboost-panel__control_slider\" type =\"range\" value=\"0\" min=\"0\" max=\"1\" step =\"0.01\" for=\"rangeInputValue\" name=\"rangeInput\"/>' +\n",
       "                                    '<input id=\"catboost-control2-slidervalue' + this.index + '\" disabled=\"disabled\" class=\"catboost-panel__control_slidervalue\" value=\"0\" min=\"0\" max=\"1\" for=\"rangeInput\" name=\"rangeInputValue\"/>' +\n",
       "                                '</div>' +\n",
       "                                cvAreaControls +\n",
       "                            '</div>' +\n",
       "                        '</div>' +\n",
       "                        '<div class=\"catboost-graph\">' +\n",
       "                            '<div class=\"catboost-graph__tabs\"></div>' +\n",
       "                            '<div class=\"catboost-graph__charts\"></div>' +\n",
       "                        '</div>' +\n",
       "                    '</div>');\n",
       "    $(parent).append(this.layout);\n",
       "\n",
       "    this.addTabEvents();\n",
       "    this.addControlEvents();\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.addTabEvents = function() {\n",
       "    var self = this;\n",
       "\n",
       "    $('.catboost-graph__tabs', this.layout).click(function(e) {\n",
       "        if (!$(e.target).is('.catboost-graph__tab:not(.catboost-graph__tab_active)')) {\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var id = $(e.target).attr('tabid');\n",
       "\n",
       "        self.activeTab = id;\n",
       "\n",
       "        $('.catboost-graph__tab_active', self.layout).removeClass('catboost-graph__tab_active');\n",
       "        $('.catboost-graph__chart_active', self.layout).removeClass('catboost-graph__chart_active');\n",
       "\n",
       "        $('.catboost-graph__tab[tabid=\"' + id + '\"]', self.layout).addClass('catboost-graph__tab_active');\n",
       "        $('.catboost-graph__chart[tabid=\"' + id + '\"]', self.layout).addClass('catboost-graph__chart_active');\n",
       "\n",
       "        self.cleanSeries();\n",
       "\n",
       "        self.redrawActiveChart();\n",
       "        self.resizeCharts();\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.addControlEvents = function() {\n",
       "    var self = this;\n",
       "\n",
       "    $('#catboost-control-learn' + this.index, this.layout).click(function() {\n",
       "        self.layoutDisabled.learn = !$(this)[0].checked;\n",
       "\n",
       "        $('.catboost-panel__series', self.layout).toggleClass('catboost-panel__series_learn_disabled', self.layoutDisabled.learn);\n",
       "\n",
       "        self.redrawActiveChart();\n",
       "    });\n",
       "\n",
       "    $('#catboost-control-test' + this.index, this.layout).click(function() {\n",
       "        self.layoutDisabled.test = !$(this)[0].checked;\n",
       "\n",
       "        $('.catboost-panel__series', self.layout).toggleClass('catboost-panel__series_test_disabled', self.layoutDisabled.test);\n",
       "\n",
       "        self.redrawActiveChart();\n",
       "    });\n",
       "\n",
       "    $('#catboost-control2-clickmode' + this.index, this.layout).click(function() {\n",
       "        self.clickMode = $(this)[0].checked;\n",
       "    });\n",
       "\n",
       "    $('#catboost-control2-log' + this.index, this.layout).click(function() {\n",
       "        self.logarithmMode = $(this)[0].checked ? 'log' : 'linear';\n",
       "\n",
       "        self.forEveryLayout(function(layout) {\n",
       "            layout.yaxis = {type: self.logarithmMode};\n",
       "        });\n",
       "\n",
       "        self.redrawActiveChart();\n",
       "    });\n",
       "\n",
       "    var slider = $('#catboost-control2-slider' + this.index),\n",
       "        sliderValue = $('#catboost-control2-slidervalue' + this.index);\n",
       "\n",
       "    $('#catboost-control2-smooth' + this.index, this.layout).click(function() {\n",
       "        var enabled = $(this)[0].checked;\n",
       "\n",
       "        self.setSmoothness(enabled ? self.lastSmooth : -1);\n",
       "\n",
       "        slider.prop('disabled', !enabled);\n",
       "        sliderValue.prop('disabled', !enabled);\n",
       "\n",
       "        self.redrawActiveChart();\n",
       "    });\n",
       "\n",
       "    $('#catboost-control2-cvstddev' + this.index, this.layout).click(function() {\n",
       "        var enabled = $(this)[0].checked;\n",
       "\n",
       "        self.setStddev(enabled);\n",
       "\n",
       "        self.redrawActiveChart();\n",
       "    });\n",
       "\n",
       "    slider.on('input change', function() {\n",
       "        var smooth = Number($(this).val());\n",
       "\n",
       "        sliderValue.val(isNaN(smooth) ? 0 : smooth);\n",
       "\n",
       "        self.setSmoothness(smooth);\n",
       "        self.lastSmooth = smooth;\n",
       "\n",
       "        self.redrawActiveChart();\n",
       "    });\n",
       "\n",
       "    sliderValue.on('input change', function() {\n",
       "        var smooth = Number($(this).val());\n",
       "\n",
       "        slider.val(isNaN(smooth) ? 0 : smooth);\n",
       "\n",
       "        self.setSmoothness(smooth);\n",
       "        self.lastSmooth = smooth;\n",
       "\n",
       "        self.redrawActiveChart();\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.setTraceVisibility = function(trace, visibility) {\n",
       "    if (trace) {\n",
       "        trace.visible = visibility;\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.updateTracesVisibility = function() {\n",
       "    var tracesHash = this.groupTraces(),\n",
       "        traces,\n",
       "        smoothDisabled = this.getSmoothness() === -1,\n",
       "        self = this;\n",
       "\n",
       "    for (var train in tracesHash) {\n",
       "        if (tracesHash.hasOwnProperty(train)) {\n",
       "            traces = tracesHash[train].traces;\n",
       "\n",
       "            if (this.layoutDisabled.traces[train]) {\n",
       "                traces.forEach(function(trace) {\n",
       "                    self.setTraceVisibility(trace, false);\n",
       "                });\n",
       "            } else {\n",
       "                traces.forEach(function(trace) {\n",
       "                    self.setTraceVisibility(trace, true);\n",
       "                });\n",
       "\n",
       "                if (this.hasCVMode) {\n",
       "                    if (this.stddevEnabled) {\n",
       "                        self.filterTracesOne(traces, {type: 'learn'}).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, false);\n",
       "                        });\n",
       "                        self.filterTracesOne(traces, {type: 'test'}).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, false);\n",
       "                        });\n",
       "\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'learn', cv_avg: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, true);\n",
       "                        });\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'test', cv_avg: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, true);\n",
       "                        });\n",
       "\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'learn', cv_avg: true, smoothed: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, true);\n",
       "                        });\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'test', cv_avg: true, smoothed: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, true);\n",
       "                        });\n",
       "\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'test', cv_avg: true, best_point: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, true);\n",
       "                        });\n",
       "\n",
       "                        self.filterTracesOne(traces, {cv_stddev_first: true}).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, true);\n",
       "                        });\n",
       "                        self.filterTracesOne(traces, {cv_stddev_last: true}).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, true);\n",
       "                        });\n",
       "                    } else {\n",
       "                        self.filterTracesOne(traces, {cv_stddev_first: true}).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, false);\n",
       "                        });\n",
       "                        self.filterTracesOne(traces, {cv_stddev_last: true}).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, false);\n",
       "                        });\n",
       "\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'learn', cv_avg: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, false);\n",
       "                        });\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'test', cv_avg: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, false);\n",
       "                        });\n",
       "\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'learn', cv_avg: true, smoothed: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, false);\n",
       "                        });\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'test', cv_avg: true, smoothed: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, false);\n",
       "                        });\n",
       "\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'test', cv_avg: true, best_point: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, false);\n",
       "                        });\n",
       "                    }\n",
       "                }\n",
       "\n",
       "                if (smoothDisabled) {\n",
       "                    self.filterTracesOne(traces, {smoothed: true}).forEach(function(trace) {\n",
       "                        self.setTraceVisibility(trace, false);\n",
       "                    });\n",
       "                }\n",
       "\n",
       "                if (this.layoutDisabled['learn']) {\n",
       "                    self.filterTracesOne(traces, {type: 'learn'}).forEach(function(trace) {\n",
       "                        self.setTraceVisibility(trace, false);\n",
       "                    });\n",
       "                }\n",
       "\n",
       "                if (this.layoutDisabled['test']) {\n",
       "                    self.filterTracesOne(traces, {type: 'test'}).forEach(function(trace) {\n",
       "                        self.setTraceVisibility(trace, false);\n",
       "                    });\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.getSmoothness = function() {\n",
       "    return this.smoothness && this.smoothness > -1 ? this.smoothness : -1;\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.setSmoothness = function(weight) {\n",
       "    if (weight < 0 && weight !== -1 || weight > 1) {\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    this.smoothness = weight;\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.setStddev = function(enabled) {\n",
       "    this.stddevEnabled = enabled;\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.redrawActiveChart = function() {\n",
       "    this.chartsToRedraw[this.activeTab] = true;\n",
       "\n",
       "    this.redrawAll();\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.redraw = function() {\n",
       "    if (this.chartsToRedraw[this.activeTab]) {\n",
       "        this.chartsToRedraw[this.activeTab] = false;\n",
       "\n",
       "        this.updateTracesVisibility();\n",
       "        this.updateTracesCV();\n",
       "        this.updateTracesBest();\n",
       "        this.updateTracesValues();\n",
       "        this.updateTracesSmoothness();\n",
       "\n",
       "        this.plotly.redraw(this.traces[this.activeTab].parent);\n",
       "    }\n",
       "\n",
       "    this.drawTraces();\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.addRedrawFunc = function() {\n",
       "    this.redrawFunc = throttle(this.redraw, 400, false, this);\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.redrawAll = function() {\n",
       "    if (!this.redrawFunc) {\n",
       "        this.addRedrawFunc();\n",
       "    }\n",
       "\n",
       "    this.redrawFunc();\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.addPoints = function(parent, data) {\n",
       "    var self = this;\n",
       "\n",
       "    data.chunks.forEach(function(item) {\n",
       "        if (typeof item.remaining_time !== 'undefined' && typeof item.passed_time !== 'undefined') {\n",
       "            if (!self.timeLeft[data.path]) {\n",
       "                self.timeLeft[data.path] = [];\n",
       "            }\n",
       "\n",
       "            self.timeLeft[data.path][item.iteration] = [item.remaining_time, item.passed_time];\n",
       "        }\n",
       "\n",
       "        ['test', 'learn'].forEach(function(type) {\n",
       "            var sets = self.meta[data.path][type + '_sets'],\n",
       "                metrics = self.meta[data.path][type + '_metrics'];\n",
       "\n",
       "            for (var i = 0; i < metrics.length; i++) {\n",
       "                var nameOfMetric = metrics[i].name,\n",
       "                    cvAdded = false;\n",
       "                    hovertextParametersAdded = false;\n",
       "\n",
       "                self.lossFuncs[nameOfMetric] = metrics[i].best_value;\n",
       "\n",
       "                for (var j = 0; j < sets.length; j++) {\n",
       "                    var nameOfSet = sets[j],\n",
       "                        params = {\n",
       "                            chartName: nameOfMetric,\n",
       "                            index: i,\n",
       "                            train: data.train,\n",
       "                            type: type,\n",
       "                            path: data.path,\n",
       "                            indexOfSet: j,\n",
       "                            nameOfSet: nameOfSet\n",
       "                        },\n",
       "                        key = self.getKey(params),\n",
       "                        launchMode = self.getLaunchMode(data.path);\n",
       "\n",
       "                    if (!self.activeTab) {\n",
       "                        self.activeTab = key.chartId;\n",
       "                    }\n",
       "\n",
       "                    if (launchMode === 'CV' ) {\n",
       "                        // we need to set launch mode before first getTrace call\n",
       "                        self.hasCVMode = true;\n",
       "\n",
       "                        if (!self.isCVinited) {\n",
       "                            // and we don't need to reset setting for next iterations\n",
       "                            self.layoutDisabled.learn = true;\n",
       "                            self.setStddev(true);\n",
       "\n",
       "                            self.isCVinited = true;\n",
       "                        }\n",
       "                    }\n",
       "\n",
       "                    var valuesOfSet = item[nameOfSet],\n",
       "                        pointValue = valuesOfSet[i],\n",
       "                        pointIndex = item.iteration,\n",
       "                        // traces\n",
       "                        trace = self.getTrace(parent, params),\n",
       "                        smoothedTrace = self.getTrace(parent, $.extend({smoothed: true}, params)),\n",
       "                        bestValueTrace = null;\n",
       "\n",
       "                    if (type === 'test') {\n",
       "                        if (launchMode !== 'CV') {\n",
       "                            self.getTrace(parent, $.extend({best_point: true}, params));\n",
       "                        }\n",
       "\n",
       "                        if (typeof self.lossFuncs[nameOfMetric] === 'number') {\n",
       "                            bestValueTrace = self.getTrace(parent, $.extend({best_value: true}, params));\n",
       "                        }\n",
       "                    }\n",
       "\n",
       "                    if (pointValue !== 'inf' && pointValue !== 'nan') {\n",
       "                        trace.x[pointIndex] = pointIndex;\n",
       "                        trace.y[pointIndex] = valuesOfSet[i];\n",
       "                        trace.hovertext[pointIndex] = nameOfSet + ': ' + valuesOfSet[i].toPrecision(7);\n",
       "                        if (item.hasOwnProperty('parameters')) {\n",
       "                            self.hovertextParameters[pointIndex] = '';\n",
       "                            for (var parameter in item.parameters[0]) {\n",
       "                                if (item.parameters[0].hasOwnProperty(parameter)) {\n",
       "                                    valueOfParameter = item.parameters[0][parameter];\n",
       "                                    self.hovertextParameters[pointIndex] += '<br>' + parameter + ' : ' + valueOfParameter;\n",
       "                                }\n",
       "                            }\n",
       "                        }\n",
       "                        if (!hovertextParametersAdded && type === 'test') {\n",
       "                            hovertextParametersAdded = true;\n",
       "                            trace.hovertext[pointIndex] += self.hovertextParameters[pointIndex];\n",
       "                        }\n",
       "                        smoothedTrace.x[pointIndex] = pointIndex;\n",
       "                    }\n",
       "\n",
       "                    if (bestValueTrace) {\n",
       "                        bestValueTrace.x[pointIndex] = pointIndex;\n",
       "                        bestValueTrace.y[pointIndex] = self.lossFuncs[nameOfMetric];\n",
       "                    }\n",
       "\n",
       "                    if (launchMode === 'CV' && !cvAdded) {\n",
       "                        cvAdded = true;\n",
       "\n",
       "                        self.getTrace(parent, $.extend({cv_stddev_first: true}, params));\n",
       "                        self.getTrace(parent, $.extend({cv_stddev_last: true}, params));\n",
       "\n",
       "                        self.getTrace(parent, $.extend({cv_stddev_first: true, smoothed: true}, params));\n",
       "                        self.getTrace(parent, $.extend({cv_stddev_last: true, smoothed: true}, params));\n",
       "\n",
       "                        self.getTrace(parent, $.extend({cv_avg: true}, params));\n",
       "                        self.getTrace(parent, $.extend({cv_avg: true, smoothed: true}, params));\n",
       "\n",
       "                        if (type === 'test') {\n",
       "                            self.getTrace(parent, $.extend({cv_avg: true, best_point: true}, params));\n",
       "                        }\n",
       "                    }\n",
       "                }\n",
       "\n",
       "                self.chartsToRedraw[key.chartId] = true;\n",
       "\n",
       "                self.redrawAll();\n",
       "            }\n",
       "        });\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.getLaunchMode = function(path) {\n",
       "    return this.meta[path].launch_mode;\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.getChartNode = function(params, active) {\n",
       "    var node = $('<div class=\"catboost-graph__chart\" tabid=\"' + params.id + '\"></div>');\n",
       "\n",
       "    if (active) {\n",
       "        node.addClass('catboost-graph__chart_active');\n",
       "    }\n",
       "\n",
       "    return node;\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.getChartTab = function(params, active) {\n",
       "    var node = $('<div class=\"catboost-graph__tab\" tabid=\"' + params.id + '\">' + params.name + '</div>');\n",
       "\n",
       "    if (active) {\n",
       "        node.addClass('catboost-graph__tab_active');\n",
       "    }\n",
       "\n",
       "    return node;\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.forEveryChart = function(callback) {\n",
       "    for (var name in this.traces) {\n",
       "        if (this.traces.hasOwnProperty(name)) {\n",
       "            callback(this.traces[name]);\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.forEveryLayout = function(callback) {\n",
       "    this.forEveryChart(function(chart) {\n",
       "        callback(chart.layout);\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.getChart = function(parent, params) {\n",
       "    var id = params.id,\n",
       "        self = this;\n",
       "\n",
       "    if (this.charts[id]) {\n",
       "        return this.charts[id];\n",
       "    }\n",
       "\n",
       "    this.addLayout(parent);\n",
       "\n",
       "    var active = this.activeTab === params.id,\n",
       "        chartNode = this.getChartNode(params, active),\n",
       "        chartTab = this.getChartTab(params, active);\n",
       "\n",
       "    $('.catboost-graph__charts', this.layout).append(chartNode);\n",
       "    $('.catboost-graph__tabs', this.layout).append(chartTab);\n",
       "\n",
       "    this.traces[id] = {\n",
       "        id: params.id,\n",
       "        name: params.name,\n",
       "        parent: chartNode[0],\n",
       "        traces: [],\n",
       "        layout: {\n",
       "            xaxis: {\n",
       "                range: [0, Number(this.meta[params.path].iteration_count)],\n",
       "                type: 'linear',\n",
       "                tickmode: 'auto',\n",
       "                showspikes: true,\n",
       "                spikethickness: 1,\n",
       "                spikedash: 'longdashdot',\n",
       "                spikemode: 'across',\n",
       "                zeroline: false,\n",
       "                showgrid: false\n",
       "            },\n",
       "            yaxis: {\n",
       "                zeroline: false\n",
       "                //showgrid: false\n",
       "                //hoverformat : '.7f'\n",
       "            },\n",
       "            separators: '. ',\n",
       "            //hovermode: 'x',\n",
       "            margin: {l: 38, r: 0, t: 35, b: 30},\n",
       "            autosize: true,\n",
       "            showlegend: false\n",
       "        },\n",
       "        options: {\n",
       "            scrollZoom: false,\n",
       "            modeBarButtonsToRemove: ['toggleSpikelines'],\n",
       "            displaylogo: false\n",
       "        }\n",
       "    };\n",
       "\n",
       "    this.charts[id] = this.plotly.plot(chartNode[0], this.traces[id].traces, this.traces[id].layout, this.traces[id].options);\n",
       "\n",
       "    chartNode[0].on('plotly_hover', function(e) {\n",
       "        self.updateTracesValues(e.points[0].x);\n",
       "    });\n",
       "\n",
       "    chartNode[0].on('plotly_click', function(e) {\n",
       "        self.updateTracesValues(e.points[0].x, true);\n",
       "    });\n",
       "\n",
       "    return this.charts[id];\n",
       "};\n",
       "\n",
       "\n",
       "CatboostIpython.prototype.getTrace = function(parent, params) {\n",
       "    var key = this.getKey(params),\n",
       "        chartSeries = [];\n",
       "\n",
       "    if (this.traces[key.chartId]) {\n",
       "        chartSeries = this.traces[key.chartId].traces.filter(function(trace) {\n",
       "            return trace.name === key.traceName;\n",
       "        });\n",
       "    }\n",
       "\n",
       "    if (chartSeries.length) {\n",
       "        return chartSeries[0];\n",
       "    } else {\n",
       "        this.getChart(parent, {id: key.chartId, name: params.chartName, path: params.path});\n",
       "\n",
       "        var plotParams = {\n",
       "                color: this.getNextColor(params.path, params.smoothed ? 0.2 : 1),\n",
       "                fillsmoothcolor: this.getNextColor(params.path, 0.1),\n",
       "                fillcolor: this.getNextColor(params.path, 0.4),\n",
       "                hoverinfo: params.cv_avg ? 'skip' : 'text+x',\n",
       "                width: params.cv_avg ? 2 : 1,\n",
       "                dash: params.type === 'test' ? 'solid' : 'dot'\n",
       "            },\n",
       "            trace = {\n",
       "                name: key.traceName,\n",
       "                _params: params,\n",
       "                x: [],\n",
       "                y: [],\n",
       "                hovertext: [],\n",
       "                hoverinfo: plotParams.hoverinfo,\n",
       "                line: {\n",
       "                    width: plotParams.width,\n",
       "                    dash: plotParams.dash,\n",
       "                    color: plotParams.color\n",
       "                },\n",
       "                mode: 'lines',\n",
       "                hoveron: 'points',\n",
       "                connectgaps: true\n",
       "            };\n",
       "\n",
       "        if (params.best_point) {\n",
       "            trace = {\n",
       "                name: key.traceName,\n",
       "                _params: params,\n",
       "                x: [],\n",
       "                y: [],\n",
       "                marker: {\n",
       "                    width: 2,\n",
       "                    color: plotParams.color\n",
       "                },\n",
       "                hovertext: [],\n",
       "                hoverinfo: 'text',\n",
       "                mode: 'markers',\n",
       "                type: 'scatter'\n",
       "            };\n",
       "        }\n",
       "\n",
       "        if (params.best_value) {\n",
       "            trace = {\n",
       "                name: key.traceName,\n",
       "                _params: params,\n",
       "                x: [],\n",
       "                y: [],\n",
       "                line: {\n",
       "                    width: 1,\n",
       "                    dash: 'dash',\n",
       "                    color: '#CCCCCC'\n",
       "                },\n",
       "                mode: 'lines',\n",
       "                connectgaps: true,\n",
       "                hoverinfo: 'skip'\n",
       "            };\n",
       "        }\n",
       "\n",
       "        if (params.cv_stddev_last) {\n",
       "            trace.fill = 'tonexty';\n",
       "        }\n",
       "\n",
       "        trace._params.plotParams = plotParams;\n",
       "\n",
       "        this.traces[key.chartId].traces.push(trace);\n",
       "\n",
       "        return trace;\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.getKey = function(params) {\n",
       "    var traceName = [\n",
       "        params.train,\n",
       "        params.type,\n",
       "        params.indexOfSet,\n",
       "        (params.smoothed ? 'smoothed' : ''),\n",
       "        (params.best_point ? 'best_pount' : ''),\n",
       "        (params.best_value ? 'best_value' : ''),\n",
       "        (params.cv_avg ? 'cv_avg' : ''),\n",
       "        (params.cv_stddev_first ? 'cv_stddev_first' : ''),\n",
       "        (params.cv_stddev_last ? 'cv_stddev_last' : '')\n",
       "    ].join(';');\n",
       "\n",
       "    return {\n",
       "        chartId: params.chartName,\n",
       "        traceName: traceName,\n",
       "        colorId: params.train\n",
       "    };\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.filterTracesEvery = function(traces, filter) {\n",
       "    traces = traces || this.traces[this.activeTab].traces;\n",
       "\n",
       "    return traces.filter(function(trace) {\n",
       "        for (var prop in filter) {\n",
       "            if (filter.hasOwnProperty(prop)) {\n",
       "                if (filter[prop] !== trace._params[prop]) {\n",
       "                    return false;\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "\n",
       "        return true;\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.filterTracesOne = function(traces, filter) {\n",
       "    traces = traces || this.traces[this.activeTab].traces;\n",
       "\n",
       "    return traces.filter(function(trace) {\n",
       "        for (var prop in filter) {\n",
       "            if (filter.hasOwnProperty(prop)) {\n",
       "                if (filter[prop] === trace._params[prop]) {\n",
       "                    return true;\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "\n",
       "        return false;\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.cleanSeries = function() {\n",
       "    $('.catboost-panel__series', this.layout).html('');\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.groupTraces = function() {\n",
       "    var traces = this.traces[this.activeTab].traces,\n",
       "        index = 0,\n",
       "        tracesHash = {};\n",
       "\n",
       "    traces.map(function(trace) {\n",
       "        var train = trace._params.train;\n",
       "\n",
       "        if (!tracesHash[train]) {\n",
       "            tracesHash[train] = {\n",
       "                index: index,\n",
       "                traces: [],\n",
       "                info: {\n",
       "                    path: trace._params.path,\n",
       "                    color: trace._params.plotParams.color\n",
       "                }\n",
       "            };\n",
       "\n",
       "            index++;\n",
       "        }\n",
       "\n",
       "        tracesHash[train].traces.push(trace);\n",
       "    });\n",
       "\n",
       "    return tracesHash;\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.drawTraces = function() {\n",
       "    if ($('.catboost-panel__series .catboost-panel__serie', this.layout).length) {\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var html = '',\n",
       "        tracesHash = this.groupTraces();\n",
       "\n",
       "    for (var train in tracesHash) {\n",
       "        if (tracesHash.hasOwnProperty(train)) {\n",
       "            html += this.drawTrace(train, tracesHash[train]);\n",
       "        }\n",
       "    }\n",
       "\n",
       "    $('.catboost-panel__series', this.layout).html(html);\n",
       "\n",
       "    this.updateTracesValues();\n",
       "\n",
       "    this.addTracesEvents();\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.getTraceDefParams = function(params) {\n",
       "    var defParams =  {\n",
       "        smoothed: undefined,\n",
       "        best_point: undefined,\n",
       "        best_value: undefined,\n",
       "        cv_avg: undefined,\n",
       "        cv_stddev_first: undefined,\n",
       "        cv_stddev_last: undefined\n",
       "    };\n",
       "\n",
       "    if (params) {\n",
       "        return $.extend(defParams, params);\n",
       "    } else {\n",
       "        return defParams;\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.drawTrace = function(train, hash) {\n",
       "    var info = hash.info,\n",
       "        id = 'catboost-serie-' + this.index + '-' + hash.index,\n",
       "        traces = {\n",
       "            learn: this.filterTracesEvery(hash.traces, this.getTraceDefParams({type: 'learn'})),\n",
       "            test: this.filterTracesEvery(hash.traces, this.getTraceDefParams({type: 'test'}))\n",
       "        },\n",
       "        items = {\n",
       "            learn: {\n",
       "                middle: '',\n",
       "                bottom: ''\n",
       "            },\n",
       "            test: {\n",
       "                middle: '',\n",
       "                bottom: ''\n",
       "            }\n",
       "        },\n",
       "        tracesNames = '';\n",
       "\n",
       "    ['learn', 'test'].forEach(function(type) {\n",
       "        traces[type].forEach(function(trace) {\n",
       "            items[type].middle += '<div class=\"catboost-panel__serie_' + type + '_pic\" style=\"border-color:' + info.color + '\"></div>' +\n",
       "                                  '<div data-index=\"' + trace._params.indexOfSet + '\" class=\"catboost-panel__serie_' + type + '_value\"></div>';\n",
       "\n",
       "            items[type].bottom += '<div class=\"catboost-panel__serie_' + type + '_pic\" style=\"border-color:transparent\"></div>' +\n",
       "                                  '<div data-index=\"' + trace._params.indexOfSet + '\" class=\"catboost-panel__serie_best_' + type + '_value\"></div>';\n",
       "\n",
       "            tracesNames += '<div class=\"catboost-panel__serie_' + type + '_pic\" style=\"border-color:' + info.color + '\"></div>' +\n",
       "                           '<div class=\"catboost-panel__serie_' + type + '_name\">' + trace._params.nameOfSet + '</div>';\n",
       "        });\n",
       "    });\n",
       "\n",
       "    var timeSpendHtml = '<div class=\"catboost-panel__serie_time\">' +\n",
       "                             '<div class=\"catboost-panel__serie_time_spend\" title=\"Time spend\"></div>' +\n",
       "                        '</div>';\n",
       "\n",
       "    var html = '<div id=\"' + id + '\" class=\"catboost-panel__serie\" style=\"color:' + info.color + '\">' +\n",
       "                    '<div class=\"catboost-panel__serie_top\">' +\n",
       "                        '<input type=\"checkbox\" data-seriename=\"' + train + '\" class=\"catboost-panel__serie_checkbox\" id=\"' + id + '-box\" ' + (!this.layoutDisabled.series[train] ? 'checked=\"checked\"' : '') + '></input>' +\n",
       "                        '<label title=' + this.meta[info.path].name + ' for=\"' + id + '-box\" class=\"catboost-panel__serie_label\">' + train + '<div class=\"catboost-panel__serie_time_left\" title=\"Estimate time\"></div></label>' +\n",
       "                        (this.getLaunchMode(info.path) !== 'Eval' ? timeSpendHtml : '') +\n",
       "                    '</div>' +\n",
       "                    '<div class=\"catboost-panel__serie_hint catboost-panel__serie__learn_hint\">curr</div>' +\n",
       "                    '<div class=\"catboost-panel__serie_hint catboost-panel__serie__test_hint\">best</div>' +\n",
       "                    '<div class=\"catboost-panel__serie_iteration\" title=\"curr iteration\"></div>' +\n",
       "                    '<div class=\"catboost-panel__serie_best_iteration\" title=\"best ' + (this.hasCVMode ? 'avg ' : '') + 'iteration\"></div>' +\n",
       "                    '<div class=\"catboost-panel__serie_scroll\">' +\n",
       "                        '<div class=\"catboost-panel__serie_names\">' +\n",
       "                            tracesNames +\n",
       "                        '</div>' +\n",
       "                        '<div class=\"catboost-panel__serie_middle\">' +\n",
       "                            items.learn.middle +\n",
       "                            items.test.middle +\n",
       "                        '</div>' +\n",
       "                        '<div class=\"catboost-panel__serie_bottom\">' +\n",
       "                            items.learn.bottom +\n",
       "                            items.test.bottom +\n",
       "                        '</div>' +\n",
       "                    '</div>' +\n",
       "                '</div>';\n",
       "\n",
       "    return html;\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.updateTracesValues = function(iteration, click) {\n",
       "    var tracesHash = this.groupTraces();\n",
       "\n",
       "    for (var train in tracesHash) {\n",
       "        if (tracesHash.hasOwnProperty(train) && !this.layoutDisabled.traces[train]) {\n",
       "            this.updateTraceValues(train, tracesHash[train], iteration, click);\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.updateTracesBest = function() {\n",
       "    var tracesHash = this.groupTraces();\n",
       "\n",
       "    for (var train in tracesHash) {\n",
       "        if (tracesHash.hasOwnProperty(train) && !this.layoutDisabled.traces[train]) {\n",
       "            this.updateTraceBest(train, tracesHash[train]);\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.getBestValue = function(data) {\n",
       "    if (!data.length) {\n",
       "        return {\n",
       "            best: undefined,\n",
       "            index: -1\n",
       "        };\n",
       "    }\n",
       "\n",
       "    var best = data[0],\n",
       "        index = 0,\n",
       "        func = this.lossFuncs[this.traces[this.activeTab].name],\n",
       "        bestDiff = typeof func === 'number' ? Math.abs(data[0] - func) : 0;\n",
       "\n",
       "    for (var i = 1, l = data.length; i < l; i++) {\n",
       "        if (func === 'Min' && data[i] < best) {\n",
       "            best = data[i];\n",
       "            index = i;\n",
       "        }\n",
       "\n",
       "        if (func === 'Max' && data[i] > best) {\n",
       "            best = data[i];\n",
       "            index = i;\n",
       "        }\n",
       "\n",
       "        if (typeof func === 'number' && Math.abs(data[i] - func) < bestDiff) {\n",
       "            best = data[i];\n",
       "            bestDiff = Math.abs(data[i] - func);\n",
       "            index = i;\n",
       "        }\n",
       "    }\n",
       "\n",
       "    return {\n",
       "        best: best,\n",
       "        index: index,\n",
       "        func: func\n",
       "    };\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.updateTracesCV = function() {\n",
       "    this.updateTracesCVAvg();\n",
       "\n",
       "    if (this.hasCVMode && this.stddevEnabled) {\n",
       "        this.updateTracesCVStdDev();\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.updateTracesCVAvg = function() {\n",
       "    var tracesHash = this.groupTraces(),\n",
       "        avgTraces = this.filterTracesEvery(tracesHash.traces, this.getTraceDefParams({\n",
       "            cv_avg: true\n",
       "        })),\n",
       "        self = this;\n",
       "\n",
       "    avgTraces.forEach(function(trace) {\n",
       "        var origTraces = self.filterTracesEvery(tracesHash.traces, self.getTraceDefParams({\n",
       "                train: trace._params.train,\n",
       "                type: trace._params.type,\n",
       "                smoothed: trace._params.smoothed\n",
       "            }));\n",
       "\n",
       "        if (origTraces.length) {\n",
       "            self.cvAvgFunc(origTraces, trace);\n",
       "        }\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.cvAvgFunc = function(origTraces, avgTrace) {\n",
       "    var maxCount = origTraces.length,\n",
       "        maxLength = -1,\n",
       "        count,\n",
       "        sum;\n",
       "\n",
       "    origTraces.forEach(function(origTrace) {\n",
       "        if (origTrace.y.length > maxLength) {\n",
       "            maxLength = origTrace.y.length;\n",
       "        }\n",
       "    });\n",
       "\n",
       "    for (var i = 0; i < maxLength; i++) {\n",
       "        sum = 0;\n",
       "        count = 0;\n",
       "\n",
       "        for (var j = 0; j < maxCount; j++) {\n",
       "            if (typeof origTraces[j].y[i] !== 'undefined') {\n",
       "                sum += origTraces[j].y[i];\n",
       "                count++;\n",
       "            }\n",
       "        }\n",
       "\n",
       "        if (count > 0) {\n",
       "            avgTrace.x[i] = i;\n",
       "            avgTrace.y[i] = sum / count;\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.updateTracesCVStdDev = function() {\n",
       "    var tracesHash = this.groupTraces(),\n",
       "        firstTraces = this.filterTracesOne(tracesHash.traces, {cv_stddev_first: true}),\n",
       "        self = this;\n",
       "\n",
       "    firstTraces.forEach(function(trace) {\n",
       "        var origTraces = self.filterTracesEvery(tracesHash.traces, self.getTraceDefParams({\n",
       "                train: trace._params.train,\n",
       "                type: trace._params.type,\n",
       "                smoothed: trace._params.smoothed\n",
       "            })),\n",
       "            lastTraces = self.filterTracesEvery(tracesHash.traces, self.getTraceDefParams({\n",
       "                train: trace._params.train,\n",
       "                type: trace._params.type,\n",
       "                smoothed: trace._params.smoothed,\n",
       "                cv_stddev_last: true\n",
       "            }));\n",
       "\n",
       "        if (origTraces.length && lastTraces.length === 1) {\n",
       "            self.cvStdDevFunc(origTraces, trace, lastTraces[0]);\n",
       "        }\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.cvStdDevFunc = function(origTraces, firstTrace, lastTrace) {\n",
       "    var maxCount = origTraces.length,\n",
       "        maxLength = -1,\n",
       "        count,\n",
       "        sum,\n",
       "        i, j;\n",
       "\n",
       "    origTraces.forEach(function(origTrace) {\n",
       "        if (origTrace.y.length > maxLength) {\n",
       "            maxLength = origTrace.y.length;\n",
       "        }\n",
       "    });\n",
       "\n",
       "    for (i = 0; i < maxLength; i++) {\n",
       "        sum = 0;\n",
       "        count = 0;\n",
       "\n",
       "        for (j = 0; j < maxCount; j++) {\n",
       "            if (typeof origTraces[j].y[i] !== 'undefined') {\n",
       "                sum += origTraces[j].y[i];\n",
       "                count++;\n",
       "            }\n",
       "        }\n",
       "\n",
       "        if (count <= 0) {\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        var std = 0,\n",
       "            avg = sum / count;\n",
       "\n",
       "        for (j = 0; j < maxCount; j++) {\n",
       "            if (typeof origTraces[j].y[i] !== 'undefined') {\n",
       "                std += Math.pow(origTraces[j].y[i] - avg, 2);\n",
       "            }\n",
       "        }\n",
       "\n",
       "        std /= (count - 1);\n",
       "        std = Math.pow(std, 0.5);\n",
       "\n",
       "        firstTrace.x[i] = i;\n",
       "        firstTrace.y[i] = avg - std;\n",
       "        firstTrace.hovertext[i] = firstTrace._params.type + ' std: ' + avg.toFixed(7) + '-' + std.toFixed(7);\n",
       "        lastTrace.x[i] = i;\n",
       "        lastTrace.y[i] = avg + std;\n",
       "        lastTrace.hovertext[i] = lastTrace._params.type + ' std: ' + avg.toFixed(7) + '+' + std.toFixed(7);\n",
       "        if (this.hovertextParameters.length > i) {\n",
       "            firstTrace.hovertext[i] += this.hovertextParameters[i];\n",
       "            lastTrace.hovertext[i] += this.hovertextParameters[i];\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.updateTracesSmoothness = function() {\n",
       "    var tracesHash = this.groupTraces(),\n",
       "        smoothedTraces = this.filterTracesOne(tracesHash.traces, {smoothed: true}),\n",
       "        enabled = this.getSmoothness() > -1,\n",
       "        self = this;\n",
       "\n",
       "    smoothedTraces.forEach(function(trace) {\n",
       "        var origTraces = self.filterTracesEvery(tracesHash.traces, self.getTraceDefParams({\n",
       "                train: trace._params.train,\n",
       "                type: trace._params.type,\n",
       "                indexOfSet: trace._params.indexOfSet,\n",
       "                cv_avg: trace._params.cv_avg,\n",
       "                cv_stddev_first: trace._params.cv_stddev_first,\n",
       "                cv_stddev_last: trace._params.cv_stddev_last\n",
       "            })),\n",
       "            colorFlag = false;\n",
       "\n",
       "        if (origTraces.length === 1) {\n",
       "            origTraces = origTraces[0];\n",
       "\n",
       "            if (origTraces.visible) {\n",
       "                if (enabled) {\n",
       "                    self.smoothFunc(origTraces, trace);\n",
       "                    colorFlag = true;\n",
       "                }\n",
       "\n",
       "                self.highlightSmoothedTrace(origTraces, trace, colorFlag);\n",
       "            }\n",
       "        }\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.highlightSmoothedTrace = function(trace, smoothedTrace, flag) {\n",
       "    if (flag) {\n",
       "        smoothedTrace.line.color = trace._params.plotParams.color;\n",
       "        trace.line.color = smoothedTrace._params.plotParams.color;\n",
       "        trace.hoverinfo = 'skip';\n",
       "\n",
       "        if (trace._params.cv_stddev_last) {\n",
       "            trace.fillcolor = trace._params.plotParams.fillsmoothcolor;\n",
       "        }\n",
       "    } else {\n",
       "        trace.line.color = trace._params.plotParams.color;\n",
       "        trace.hoverinfo = trace._params.plotParams.hoverinfo;\n",
       "\n",
       "        if (trace._params.cv_stddev_last) {\n",
       "            trace.fillcolor = trace._params.plotParams.fillcolor;\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.smoothFunc = function(origTrace, smoothedTrace) {\n",
       "    var data = origTrace.y,\n",
       "        smoothedPoints = this.smooth(data, this.getSmoothness()),\n",
       "        smoothedIndex = 0,\n",
       "        self = this;\n",
       "\n",
       "    if (smoothedPoints.length) {\n",
       "        data.forEach(function (d, index) {\n",
       "            if (!smoothedTrace.x[index]) {\n",
       "                smoothedTrace.x[index] = index;\n",
       "            }\n",
       "\n",
       "            var nameOfSet = smoothedTrace._params.nameOfSet;\n",
       "\n",
       "            if (smoothedTrace._params.cv_stddev_first || smoothedTrace._params.cv_stddev_last) {\n",
       "                nameOfSet = smoothedTrace._params.type + ' std';\n",
       "            }\n",
       "\n",
       "            smoothedTrace.y[index] = smoothedPoints[smoothedIndex];\n",
       "            smoothedTrace.hovertext[index] = nameOfSet + '`: ' + smoothedPoints[smoothedIndex].toPrecision(7);\n",
       "            if (self.hovertextParameters.length > index) {\n",
       "                smoothedTrace.hovertext[index] += self.hovertextParameters[index];\n",
       "            }\n",
       "            smoothedIndex++;\n",
       "        });\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.formatItemValue = function(value, index, suffix) {\n",
       "    if (typeof value === 'undefined') {\n",
       "        return '';\n",
       "    }\n",
       "\n",
       "    suffix = suffix || '';\n",
       "\n",
       "    return '<span title=\"' + suffix + 'value ' + value + '\">' + value + '</span>';\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.updateTraceBest = function(train, hash) {\n",
       "    var traces = this.filterTracesOne(hash.traces, {best_point: true}),\n",
       "        self = this;\n",
       "\n",
       "    traces.forEach(function(trace) {\n",
       "        var testTrace = self.filterTracesEvery(hash.traces, self.getTraceDefParams({\n",
       "                train: trace._params.train,\n",
       "                type: 'test',\n",
       "                indexOfSet: trace._params.indexOfSet\n",
       "            }));\n",
       "\n",
       "        if (self.hasCVMode) {\n",
       "            testTrace = self.filterTracesEvery(hash.traces, self.getTraceDefParams({\n",
       "                train: trace._params.train,\n",
       "                type: 'test',\n",
       "                cv_avg: true\n",
       "            }));\n",
       "        }\n",
       "\n",
       "        var bestValue = self.getBestValue(testTrace.length === 1 ? testTrace[0].y : []);\n",
       "\n",
       "        if (bestValue.index !== -1) {\n",
       "            trace.x[0] = bestValue.index;\n",
       "            trace.y[0] = bestValue.best;\n",
       "            trace.hovertext[0] = bestValue.func + ' (' + (self.hasCVMode ? 'avg' : trace._params.nameOfSet) + '): ' + bestValue.index + ' ' + bestValue.best;\n",
       "        }\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.updateTraceValues = function(name, hash, iteration, click) {\n",
       "    var id = 'catboost-serie-' + this.index + '-' + hash.index,\n",
       "        traces = {\n",
       "            learn: this.filterTracesEvery(hash.traces, this.getTraceDefParams({type: 'learn'})),\n",
       "            test: this.filterTracesEvery(hash.traces, this.getTraceDefParams({type: 'test'}))\n",
       "        },\n",
       "        path = hash.info.path,\n",
       "        self = this;\n",
       "\n",
       "    ['learn', 'test'].forEach(function(type) {\n",
       "        traces[type].forEach(function(trace) {\n",
       "            var data = trace.y || [],\n",
       "                index = typeof iteration !== 'undefined' && iteration < data.length - 1 ? iteration : data.length - 1,\n",
       "                value = data.length ? data[index] : undefined,\n",
       "                testTrace = self.filterTracesEvery(hash.traces, self.getTraceDefParams({\n",
       "                    type: 'test',\n",
       "                    indexOfSet: trace._params.indexOfSet\n",
       "                })),\n",
       "                bestValue = self.getBestValue(testTrace.length === 1 ? testTrace[0].y : []),\n",
       "                timeLeft = '',\n",
       "                timeSpend = '';\n",
       "\n",
       "            if (click || !self.clickMode) {\n",
       "                $('#' + id + ' .catboost-panel__serie_' + type + '_value[data-index=' + trace._params.indexOfSet + ']', self.layout)\n",
       "                    .html(self.formatItemValue(value, index, type + ' '));\n",
       "                $('#' + id + ' .catboost-panel__serie_iteration', self.layout).html(index);\n",
       "\n",
       "                if (self.timeLeft[path] && self.timeLeft[path][data.length - 1]) {\n",
       "                    timeLeft = self.timeLeft[path][data.length - 1][0];\n",
       "                }\n",
       "                $('#' + id + ' .catboost-panel__serie_time_left', self.layout).html(timeLeft ? ('~' + self.convertTime(timeLeft)) : '');\n",
       "\n",
       "                if (self.timeLeft[path] && self.timeLeft[path][index]) {\n",
       "                    timeSpend = self.timeLeft[path][index][1];\n",
       "                }\n",
       "\n",
       "                $('#' + id + ' .catboost-panel__serie_time_spend', self.layout).html(self.convertTime(timeSpend));\n",
       "                $('#' + id + ' .catboost-panel__serie_best_iteration', self.layout).html(bestValue.index > -1 ? bestValue.index : '');\n",
       "\n",
       "                $('#' + id + ' .catboost-panel__serie_best_test_value[data-index=' + trace._params.indexOfSet + ']', self.layout)\n",
       "                    .html(self.formatItemValue(bestValue.best, bestValue.index, 'best ' + trace._params.nameOfSet + ' '));\n",
       "            }\n",
       "        });\n",
       "    });\n",
       "\n",
       "    if (this.hasCVMode) {\n",
       "        var testTrace = this.filterTracesEvery(hash.traces, this.getTraceDefParams({\n",
       "                type: 'test',\n",
       "                cv_avg: true\n",
       "            })),\n",
       "            bestValue = this.getBestValue(testTrace.length === 1 ? testTrace[0].y : []);\n",
       "\n",
       "            $('#' + id + ' .catboost-panel__serie_best_iteration', this.layout).html(bestValue.index > -1 ? bestValue.index : '');\n",
       "    }\n",
       "\n",
       "    if (click) {\n",
       "        this.clickMode = true;\n",
       "\n",
       "        $('#catboost-control2-clickmode' + this.index, this.layout)[0].checked = true;\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.addTracesEvents = function() {\n",
       "    var self = this;\n",
       "\n",
       "    $('.catboost-panel__serie_checkbox', this.layout).click(function() {\n",
       "        var name = $(this).data('seriename');\n",
       "\n",
       "        self.layoutDisabled.traces[name] = !$(this)[0].checked;\n",
       "\n",
       "        self.redrawActiveChart();\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.getNextColor = function(path, opacity) {\n",
       "    var color;\n",
       "\n",
       "    if (this.colorsByPath[path]) {\n",
       "        color = this.colorsByPath[path];\n",
       "    } else {\n",
       "        color = this.colors[this.colorIndex];\n",
       "        this.colorsByPath[path] = color;\n",
       "\n",
       "        this.colorIndex++;\n",
       "\n",
       "        if (this.colorIndex > this.colors.length - 1) {\n",
       "            this.colorIndex = 0;\n",
       "        }\n",
       "    }\n",
       "\n",
       "    return this.hexToRgba(color, opacity);\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.hexToRgba = function(value, opacity) {\n",
       "    if (value.length < 6) {\n",
       "        var pattern = /^#?([a-f\\d])([a-f\\d])([a-f\\d])/i;\n",
       "        value = value.replace(pattern, function(m, r, g, b) {\n",
       "            return '#' + r + r + g + g + b + b;\n",
       "        });\n",
       "    }\n",
       "\n",
       "    var result = /^#?([a-f\\d]{2})([a-f\\d]{2})([a-f\\d]{2})/i.exec(value);\n",
       "    var rgb = {\n",
       "        r: parseInt(result[1], 16),\n",
       "        g: parseInt(result[2], 16),\n",
       "        b: parseInt(result[3], 16)\n",
       "    };\n",
       "\n",
       "    return 'rgba(' + rgb.r + ',' + rgb.g + ',' + rgb.b + ',' + opacity + ')';\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.convertTime = function(time) {\n",
       "    if (!time) {\n",
       "        return '0s';\n",
       "    }\n",
       "\n",
       "    time = Math.floor(time * 1000);\n",
       "\n",
       "    var millis = time % 1000;\n",
       "    time = parseInt(time / 1000, 10);\n",
       "    var seconds = time % 60;\n",
       "    time = parseInt(time / 60, 10);\n",
       "    var minutes = time % 60;\n",
       "    time = parseInt(time / 60, 10);\n",
       "    var hours = time % 24;\n",
       "    var out = \"\";\n",
       "    if (hours && hours > 0) {\n",
       "        out += hours + 'h ';\n",
       "        seconds = 0;\n",
       "        millis = 0;\n",
       "    }\n",
       "    if (minutes && minutes > 0) {\n",
       "        out += minutes + 'm ';\n",
       "        millis = 0;\n",
       "    }\n",
       "    if (seconds && seconds > 0) {\n",
       "        out += seconds + 's ';\n",
       "    }\n",
       "    if (millis && millis > 0) {\n",
       "        out += millis + 'ms';\n",
       "    }\n",
       "\n",
       "    return out.trim();\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.mean = function(values, valueof) {\n",
       "    var n = values.length,\n",
       "        m = n,\n",
       "        i = -1,\n",
       "        value,\n",
       "        sum = 0,\n",
       "        number = function(x) {\n",
       "            return x === null ? NaN : +x;\n",
       "        };\n",
       "\n",
       "    if (valueof === null) {\n",
       "        while (++i < n) {\n",
       "            if (!isNaN(value = number(values[i]))) {\n",
       "                sum += value;\n",
       "            } else {\n",
       "                --m;\n",
       "            }\n",
       "        }\n",
       "    } else {\n",
       "        while (++i < n) {\n",
       "            if (!isNaN(value = number(valueof(values[i], i, values)))) {\n",
       "                sum += value;\n",
       "            } else {\n",
       "                --m;\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "\n",
       "    if (m) {\n",
       "        return sum / m;\n",
       "    }\n",
       "};\n",
       "\n",
       "// from TensorBoard\n",
       "CatboostIpython.prototype.smooth = function(data, weight) {\n",
       "    // When increasing the smoothing window, it smoothes a lot with the first\n",
       "    // few points and then starts to gradually smooth slower, so using an\n",
       "    // exponential function makes the slider more consistent. 1000^x has a\n",
       "    // range of [1, 1000], so subtracting 1 and dividing by 999 results in a\n",
       "    // range of [0, 1], which can be used as the percentage of the data, so\n",
       "    // that the kernel size can be specified as a percentage instead of a\n",
       "    // hardcoded number, what would be bad with multiple series.\n",
       "    var factor = (Math.pow(1000, weight) - 1) / 999,\n",
       "        kernelRadius = Math.floor(data.length * factor / 2),\n",
       "        res = [],\n",
       "        self = this;\n",
       "\n",
       "    data.forEach(function (d, i) {\n",
       "        var actualKernelRadius = Math.min(kernelRadius, i, data.length - i - 1);\n",
       "        var start = i - actualKernelRadius;\n",
       "        var end = i + actualKernelRadius + 1;\n",
       "        var point = d;\n",
       "        // Only smooth finite numbers.\n",
       "        if (!isFinite(point)) {\n",
       "            res.push(point);\n",
       "        } else {\n",
       "            res.push(self.mean(data.slice(start, end).filter(function(d) {\n",
       "                return isFinite(d);\n",
       "            }), null));\n",
       "        }\n",
       "    });\n",
       "\n",
       "    return res;\n",
       "};\n",
       "var debug = false;\n",
       "\n",
       "if (debug) {\n",
       "    require.config({\n",
       "        shim:{\n",
       "            \"custom/CatboostIpythonPlotly\":{\n",
       "                deps:[\"custom/plotly-basic.min\"]\n",
       "            }\n",
       "        }\n",
       "    })\n",
       "\n",
       "    require.undef('catboost_module');\n",
       "    require.undef('custom/CatboostIpythonPlotly');\n",
       "}\n",
       "\n",
       "var moduleBase = '@jupyter-widgets/base';\n",
       "var modules = [moduleBase];\n",
       "\n",
       "if (debug) {\n",
       "    modules.push('custom/CatboostIpythonPlotly');\n",
       "}\n",
       "\n",
       "define('catboost_module', modules, function(widgets) {\n",
       "    var getInstance = function(el) {\n",
       "            var id = $(el).attr('catboost-id');\n",
       "\n",
       "            if (!id) {\n",
       "                return null;\n",
       "            }\n",
       "\n",
       "            id = id.replace('catboost_', '');\n",
       "\n",
       "            if (!window.catboostIpythonInstances[id]) {\n",
       "                return null;\n",
       "            }\n",
       "\n",
       "            return window.catboostIpythonInstances[id];\n",
       "        },\n",
       "        addInstance = function(el) {\n",
       "            $(el).attr('catboost-id', 'catboost_' + window.catboostIpythonIndex);\n",
       "\n",
       "            var catboostIpython = new CatboostIpython();\n",
       "            catboostIpython.index = window.catboostIpythonIndex;\n",
       "            catboostIpython.plotly = window.Plotly;\n",
       "            if (debug) {\n",
       "                catboostIpython.loadStyles('/custom/CatboostIpython.css', function(){catboostIpython.resizeCharts();})\n",
       "            }\n",
       "\n",
       "            window.catboostIpythonInstances[window.catboostIpythonIndex] = catboostIpython;\n",
       "\n",
       "            window.catboostIpythonIndex++;\n",
       "\n",
       "            return catboostIpython;\n",
       "        };\n",
       "\n",
       "    var CatboostIpythonWidget = widgets.DOMWidgetView.extend({\n",
       "\n",
       "        initialize: function() {\n",
       "            CatboostIpythonWidget.__super__.initialize.apply(this, arguments);\n",
       "\n",
       "            if (!window.catboostIpythonInstances) {\n",
       "                window.catboostIpythonInstances = {};\n",
       "            }\n",
       "\n",
       "            if (typeof window.catboostIpythonIndex === 'undefined') {\n",
       "                window.catboostIpythonIndex = 0;\n",
       "            }\n",
       "\n",
       "            var catboostIpythonInstance = getInstance(this.el);\n",
       "\n",
       "            if (!catboostIpythonInstance) {\n",
       "                catboostIpythonInstance = addInstance(this.el);\n",
       "            }\n",
       "\n",
       "            catboostIpythonInstance.init();\n",
       "        },\n",
       "\n",
       "        render: function() {\n",
       "            this.value_changed();\n",
       "            this.model.on('change:value', this.value_changed, this);\n",
       "        },\n",
       "\n",
       "        update: function() {\n",
       "            this.value_changed();\n",
       "        },\n",
       "\n",
       "        value_changed: function() {\n",
       "            this.el.style['width'] = this.model.get('width');\n",
       "            this.el.style['height'] = this.model.get('height');\n",
       "            this.displayed.then(_.bind(this.render_charts, this));\n",
       "        },\n",
       "\n",
       "        process_all: function(parent, params) {\n",
       "            var data = params.data;\n",
       "\n",
       "            for (var path in data) {\n",
       "                if (data.hasOwnProperty(path)) {\n",
       "                    this.process_row(parent, data[path])\n",
       "                }\n",
       "            }\n",
       "        },\n",
       "\n",
       "        process_row: function(parent, data) {\n",
       "            var catboostIpython = getInstance(parent),\n",
       "                path = data.path,\n",
       "                content = data.content,\n",
       "                items = content.data.iterations,\n",
       "                firstIndex = 0,\n",
       "                chunks = [];\n",
       "\n",
       "            if (!items || !items.length) {\n",
       "                return;\n",
       "            }\n",
       "\n",
       "            if (!catboostIpython.lastIndex) {\n",
       "                catboostIpython.lastIndex = {}\n",
       "            }\n",
       "\n",
       "            if (catboostIpython.lastIndex[path]) {\n",
       "                firstIndex = catboostIpython.lastIndex[path] + 1;\n",
       "            }\n",
       "\n",
       "            catboostIpython.lastIndex[path] = items.length - 1;\n",
       "\n",
       "            for (var i = firstIndex; i < items.length; i++) {\n",
       "                chunks.push(items[i]);\n",
       "            }\n",
       "\n",
       "            catboostIpython.addMeta(data.path, content.data.meta);\n",
       "\n",
       "            catboostIpython.addPoints(parent, {\n",
       "                chunks: chunks,\n",
       "                train: data.name,\n",
       "                path: data.path\n",
       "            });\n",
       "        },\n",
       "\n",
       "        render_charts: function () {\n",
       "            this.process_all(this.el, {\n",
       "                data: this.model.get('data')\n",
       "            });\n",
       "\n",
       "            return this;\n",
       "        }\n",
       "    });\n",
       "\n",
       "    return {\n",
       "        CatboostIpythonWidgetView: CatboostIpythonWidget\n",
       "    };\n",
       "});\n",
       "\n",
       "            </script>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bfa96d8bba5c4d8392f8870f3ccf4a2c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "MetricVisualizer(layout=Layout(align_self='stretch', height='500px'))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bst = CatBoostClassifier(**params, early_stopping_rounds=EARLY_STOPPING)\n",
    "_ = bst.fit(X_train, y_train, eval_set=(X_valid,y_valid), plot=True, verbose=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "ypred_cat = bst.predict_proba(X_valid)[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8083042553630788"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_auc_score(y_valid, ypred_cat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "f_importance_df = pd.DataFrame(bst.get_feature_importance(), columns=['importance'], index=X_valid.columns)\n",
    "f_importance_df = f_importance_df.sort_values(by='importance', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "sns.barplot(x=f_importance_df.importance[:500], y=f_importance_df.index[:500]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "ypred_test = bst.predict_proba(XTEST)[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Density'>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(ypred_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "sub_df['Response'] = ypred_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "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: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Response</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>50883</td>\n",
       "      <td>0.072036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50884</td>\n",
       "      <td>0.096185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>50885</td>\n",
       "      <td>0.460200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>50886</td>\n",
       "      <td>0.083263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>50887</td>\n",
       "      <td>0.058881</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      ID  Response\n",
       "0  50883  0.072036\n",
       "1  50884  0.096185\n",
       "2  50885  0.460200\n",
       "3  50886  0.083263\n",
       "4  50887  0.058881"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sub_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "sub_df.to_csv('catboost_submission.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_roc(y_trues, y_preds, labels, x_max=1.0):\n",
    "    fig, ax = plt.subplots()\n",
    "    for i, y_pred in enumerate(y_preds):\n",
    "        y_true = y_trues[i]\n",
    "        fpr, tpr, thresholds = roc_curve(y_true, y_pred)\n",
    "        auc = roc_auc_score(y_true, y_pred)\n",
    "        ax.plot(fpr, tpr, label='%s; AUC=%.3f' % (labels[i], auc), marker='o', markersize=1)\n",
    "\n",
    "    ax.legend()\n",
    "    ax.grid()\n",
    "    ax.plot(np.linspace(0, 1, 20), np.linspace(0, 1, 20), linestyle='--')\n",
    "    ax.set_title('ROC curve')\n",
    "    ax.set_xlabel('False Positive Rate')\n",
    "    ax.set_xlim([-0.01, x_max])\n",
    "    _ = ax.set_ylabel('True Positive Rate')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ROC Comparison:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_roc(\n",
    "    [y_valid]*2,\n",
    "    [ypred_lgb, ypred_cat],\n",
    "    ['LGBM', 'CATBOOST']\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}