cataboost_final.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Janatahack: Cross-sell Prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Problem Statement"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Your client is an Insurance company that has provided Health Insurance to its customers now they need your help in building a model to predict whether the policyholders (customers) from past year will also be interested in Vehicle Insurance provided by the company.\n",
    "\n",
    "An insurance policy is an arrangement by which a company undertakes to provide a guarantee of compensation for specified loss, damage, illness, or death in return for the payment of a specified premium. A premium is a sum of money that the customer needs to pay regularly to an insurance company for this guarantee.\n",
    "\n",
    "For example, you may pay a premium of Rs. 5000 each year for a health insurance cover of Rs. 200,000/- so that if, God forbid, you fall ill and need to be hospitalised in that year, the insurance provider company will bear the cost of hospitalisation etc. for upto Rs. 200,000. Now if you are wondering how can company bear such high hospitalisation cost when it charges a premium of only Rs. 5000/-, that is where the concept of probabilities comes in picture. For example, like you, there may be 100 customers who would be paying a premium of Rs. 5000 every year, but only a few of them (say 2-3) would get hospitalised that year and not everyone. This way everyone shares the risk of everyone else.\n",
    "\n",
    "Just like medical insurance, there is vehicle insurance where every year customer needs to pay a premium of certain amount to insurance provider company so that in case of unfortunate accident by the vehicle, the insurance provider company will provide a compensation (called ‘sum assured’) to the customer.\n",
    "\n",
    "Building a model to predict whether a customer would be interested in Vehicle Insurance is extremely helpful for the company because it can then accordingly plan its communication strategy to reach out to those customers and optimise its business model and revenue. \n",
    "\n",
    "Now, in order to predict, whether the customer would be interested in Vehicle insurance, you have information about demographics (gender, age, region code type), Vehicles (Vehicle Age, Damage), Policy (Premium, sourcing channel) etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy  as np\n",
    "import math\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from scipy import stats\n",
    "from sklearn import preprocessing\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.model_selection import train_test_split,KFold,cross_val_score\n",
    "from sklearn.metrics import confusion_matrix,roc_auc_score\n",
    "from pandas_profiling import ProfileReport\n",
    "from imblearn.over_sampling import SMOTE,SMOTENC,ADASYN\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "# Comment this if the data visualisations doesn't work on your side\n",
    "%matplotlib inline\n",
    "\n",
    "plt.style.use('bmh')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Loading Train and Test Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "    }\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>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Driving_License</th>\n",
       "      <th>Region_Code</th>\n",
       "      <th>Previously_Insured</th>\n",
       "      <th>Vehicle_Age</th>\n",
       "      <th>Vehicle_Damage</th>\n",
       "      <th>Annual_Premium</th>\n",
       "      <th>Policy_Sales_Channel</th>\n",
       "      <th>Vintage</th>\n",
       "      <th>Response</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "      <td>44</td>\n",
       "      <td>1</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>&gt; 2 Years</td>\n",
       "      <td>Yes</td>\n",
       "      <td>40454.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>217</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Male</td>\n",
       "      <td>76</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1-2 Year</td>\n",
       "      <td>No</td>\n",
       "      <td>33536.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>183</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Male</td>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>&gt; 2 Years</td>\n",
       "      <td>Yes</td>\n",
       "      <td>38294.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1</td>\n",
       "      <td>&lt; 1 Year</td>\n",
       "      <td>No</td>\n",
       "      <td>28619.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>203</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Female</td>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>41.0</td>\n",
       "      <td>1</td>\n",
       "      <td>&lt; 1 Year</td>\n",
       "      <td>No</td>\n",
       "      <td>27496.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  Gender  Age  Driving_License  Region_Code  Previously_Insured  \\\n",
       "0   1    Male   44                1         28.0                   0   \n",
       "1   2    Male   76                1          3.0                   0   \n",
       "2   3    Male   47                1         28.0                   0   \n",
       "3   4    Male   21                1         11.0                   1   \n",
       "4   5  Female   29                1         41.0                   1   \n",
       "\n",
       "  Vehicle_Age Vehicle_Damage  Annual_Premium  Policy_Sales_Channel  Vintage  \\\n",
       "0   > 2 Years            Yes         40454.0                  26.0      217   \n",
       "1    1-2 Year             No         33536.0                  26.0      183   \n",
       "2   > 2 Years            Yes         38294.0                  26.0       27   \n",
       "3    < 1 Year             No         28619.0                 152.0      203   \n",
       "4    < 1 Year             No         27496.0                 152.0       39   \n",
       "\n",
       "   Response  \n",
       "0         1  \n",
       "1         0  \n",
       "2         1  \n",
       "3         0  \n",
       "4         0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train = pd.read_csv('train.csv')\n",
    "df_test = pd.read_csv('test.csv')\n",
    "df_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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       "model_id": "b33da09965a14659bf8d267c07a868d0",
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       "HBox(children=(FloatProgress(value=0.0, description='Summarize dataset', max=26.0, style=ProgressStyle(descrip…"
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     "output_type": "display_data"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
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     "data": {
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       "model_id": "6ef765e53ef3475a80d8baeed71c918c",
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       "HBox(children=(FloatProgress(value=0.0, description='Generate report structure', max=1.0, style=ProgressStyle(…"
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
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       "VBox(children=(Tab(children=(Tab(children=(GridBox(children=(VBox(children=(GridspecLayout(children=(HTML(valu…"
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     "metadata": {},
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    }
   ],
   "source": [
    "profile = ProfileReport(df_train,title='Profile Report',explorative=True)\n",
    "profile.to_widgets()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Analysis:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1) Firstly Data with 12 variables and has 0 missing values.\n",
    "\n",
    "2) Region_code feature have 2021 zero values which need to be checked if '0' is a region code or else its corrupted data.\n",
    "\n",
    "3) Target Variable (Response) is highly imbalanced which need to be handled using by smote analysis or \n",
    "   other types analysis to balance the highly biased target variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 381109 entries, 0 to 381108\n",
      "Data columns (total 12 columns):\n",
      " #   Column                Non-Null Count   Dtype  \n",
      "---  ------                --------------   -----  \n",
      " 0   id                    381109 non-null  int64  \n",
      " 1   Gender                381109 non-null  int64  \n",
      " 2   Age                   381109 non-null  int64  \n",
      " 3   Driving_License       381109 non-null  int64  \n",
      " 4   Region_Code           381109 non-null  int64  \n",
      " 5   Previously_Insured    381109 non-null  int64  \n",
      " 6   Vehicle_Age           381109 non-null  int64  \n",
      " 7   Vehicle_Damage        381109 non-null  int64  \n",
      " 8   Annual_Premium        381109 non-null  float64\n",
      " 9   Policy_Sales_Channel  381109 non-null  int64  \n",
      " 10  Vintage               381109 non-null  int64  \n",
      " 11  Response              381109 non-null  int64  \n",
      "dtypes: float64(1), int64(11)\n",
      "memory usage: 34.9 MB\n"
     ]
    }
   ],
   "source": [
    "df_train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 127037 entries, 0 to 127036\n",
      "Data columns (total 11 columns):\n",
      " #   Column                Non-Null Count   Dtype  \n",
      "---  ------                --------------   -----  \n",
      " 0   id                    127037 non-null  int64  \n",
      " 1   Gender                127037 non-null  object \n",
      " 2   Age                   127037 non-null  int64  \n",
      " 3   Driving_License       127037 non-null  int64  \n",
      " 4   Region_Code           127037 non-null  float64\n",
      " 5   Previously_Insured    127037 non-null  int64  \n",
      " 6   Vehicle_Age           127037 non-null  object \n",
      " 7   Vehicle_Damage        127037 non-null  object \n",
      " 8   Annual_Premium        127037 non-null  float64\n",
      " 9   Policy_Sales_Channel  127037 non-null  float64\n",
      " 10  Vintage               127037 non-null  int64  \n",
      "dtypes: float64(3), int64(5), object(3)\n",
      "memory usage: 10.7+ MB\n"
     ]
    }
   ],
   "source": [
    "df_test.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pre-Processing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Encoding Categorical features using Label Encoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def label_encoder(data):\n",
    "    le = preprocessing.LabelEncoder()\n",
    "    data = le.fit_transform(data)\n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Previously_Insured</th>\n",
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       "      <th>Vehicle_Damage</th>\n",
       "      <th>Annual_Premium</th>\n",
       "      <th>Policy_Sales_Channel</th>\n",
       "      <th>Vintage</th>\n",
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       "      <td>1</td>\n",
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       "      <td>26.0</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
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       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>28619.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>203</td>\n",
       "      <td>0</td>\n",
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       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>41.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>27496.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   id  Gender  Age  Driving_License  Region_Code  Previously_Insured  \\\n",
       "0   1       1   44                1         28.0                   0   \n",
       "1   2       1   76                1          3.0                   0   \n",
       "2   3       1   47                1         28.0                   0   \n",
       "3   4       1   21                1         11.0                   1   \n",
       "4   5       0   29                1         41.0                   1   \n",
       "\n",
       "   Vehicle_Age  Vehicle_Damage  Annual_Premium  Policy_Sales_Channel  Vintage  \\\n",
       "0            2               1         40454.0                  26.0      217   \n",
       "1            0               0         33536.0                  26.0      183   \n",
       "2            2               1         38294.0                  26.0       27   \n",
       "3            1               0         28619.0                 152.0      203   \n",
       "4            1               0         27496.0                 152.0       39   \n",
       "\n",
       "   Response  \n",
       "0         1  \n",
       "1         0  \n",
       "2         1  \n",
       "3         0  \n",
       "4         0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train['Gender'] = label_encoder(df_train['Gender'])\n",
    "df_train['Vehicle_Age'] = label_encoder(df_train['Vehicle_Age'])\n",
    "df_train['Vehicle_Damage'] = label_encoder(df_train['Vehicle_Damage'])\n",
    "df_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "      <td>217</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>76</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>33536</td>\n",
       "      <td>26</td>\n",
       "      <td>183</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>38294</td>\n",
       "      <td>26</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>28619</td>\n",
       "      <td>152</td>\n",
       "      <td>203</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>27496</td>\n",
       "      <td>152</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  Gender  Age  Driving_License  Region_Code  Previously_Insured  \\\n",
       "0   1       1   44                1           28                   0   \n",
       "1   2       1   76                1            3                   0   \n",
       "2   3       1   47                1           28                   0   \n",
       "3   4       1   21                1           11                   1   \n",
       "4   5       0   29                1           41                   1   \n",
       "\n",
       "   Vehicle_Age  Vehicle_Damage  Annual_Premium  Policy_Sales_Channel  Vintage  \\\n",
       "0            2               1           40454                    26      217   \n",
       "1            0               0           33536                    26      183   \n",
       "2            2               1           38294                    26       27   \n",
       "3            1               0           28619                   152      203   \n",
       "4            1               0           27496                   152       39   \n",
       "\n",
       "   Response  \n",
       "0         1  \n",
       "1         0  \n",
       "2         1  \n",
       "3         0  \n",
       "4         0  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train['Policy_Sales_Channel']  = df_train['Policy_Sales_Channel'].astype(int)\n",
    "df_train['Region_Code']  = df_train['Region_Code'].astype(int)\n",
    "df_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Splitting up Train and Test Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Size of X_train  (285831, 10)\n",
      "Size of X_cv  (95278, 10)\n",
      "Size of y_train  (285831,)\n",
      "Size of y_cv  (95278,)\n"
     ]
    }
   ],
   "source": [
    "category_col=['Gender','Driving_License', 'Region_Code', 'Previously_Insured','Vehicle_Damage',\n",
    "         'Vehicle_Age','Vintage','Policy_Sales_Channel']\n",
    "\n",
    "X = df_train.copy()\n",
    "y = X['Response']\n",
    "X.drop(['id','Response'],axis=1,inplace=True)\n",
    "X_train, X_cv, y_train, y_cv = train_test_split(X, y, test_size=0.25,shuffle=True,random_state=99,stratify=y)\n",
    "print(\"Size of X_train \", X_train.shape)\n",
    "print(\"Size of X_cv \", X_cv.shape)\n",
    "print(\"Size of y_train \", y_train.shape)\n",
    "print(\"Size of y_cv \", y_cv.shape)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
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o=t(\"d3\"),i=t(\"../../lib\"),l=t(\"../dragelement\"),s=t(\"./helpers\"),c=t(\"./layout_attributes\");e.exports={moduleType:\"component\",name:\"fx\",constants:t(\"./constants\"),schema:{layout:c},attributes:t(\"./attributes\"),layoutAttributes:c,supplyLayoutGlobalDefaults:t(\"./layout_global_defaults\"),supplyDefaults:t(\"./defaults\"),supplyLayoutDefaults:t(\"./layout_defaults\"),calc:t(\"./calc\"),getDistanceFunction:s.getDistanceFunction,getClosest:s.getClosest,inbox:s.inbox,castHoverOption:a,hover:t(\"./hover\").hover,unhover:l.unhover,loneHover:t(\"./hover\").loneHover,loneUnhover:n,click:t(\"./click\")}},{\"../../lib\":136,\"../dragelement\":46,\"./attributes\":58,\"./calc\":59,\"./click\":60,\"./constants\":61,\"./defaults\":62,\"./helpers\":63,\"./hover\":64,\"./layout_attributes\":67,\"./layout_defaults\":68,\"./layout_global_defaults\":69,d3:7}],67:[function(t,e,r){\"use strict\";var 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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 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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": "6068413a41974be9a6e13f989d318c26",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "MetricVisualizer(layout=Layout(align_self='stretch', height='500px'))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0:\ttest: 0.8344978\tbest: 0.8344978 (0)\ttotal: 462ms\tremaining: 7m 41s\n",
      "200:\ttest: 0.8578852\tbest: 0.8578918 (199)\ttotal: 1m 5s\tremaining: 4m 21s\n",
      "400:\ttest: 0.8577709\tbest: 0.8579234 (233)\ttotal: 2m 13s\tremaining: 3m 19s\n",
      "Stopped by overfitting detector  (300 iterations wait)\n",
      "\n",
      "bestTest = 0.8579233567\n",
      "bestIteration = 233\n",
      "\n",
      "Shrink model to first 234 iterations.\n"
     ]
    }
   ],
   "source": [
    "from catboost import CatBoostClassifier\n",
    "params = {'iterations':1000,\n",
    "        'learning_rate':0.1,\n",
    "        'cat_features': category_col,\n",
    "        'depth':7,\n",
    "        'eval_metric':'AUC',\n",
    "        'loss_function':'Logloss',\n",
    "        'verbose':200,\n",
    "        'od_type':\"Iter\", # overfit detector\n",
    "        'od_wait':300, # most recent best iteration to wait before stopping\n",
    "        'random_seed': 99,\n",
    "        'l2_leaf_reg' : 11\n",
    "          }\n",
    "\n",
    "cat_model = CatBoostClassifier(**params)\n",
    "cat_model.fit(X_train, y_train,   \n",
    "          eval_set=(X_cv, y_cv), \n",
    "          use_best_model=True, # True if we don't want to save trees created after iteration with the best validation score\n",
    "          plot=True  \n",
    "         );"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.281292\n",
      "         Iterations 11\n",
      "                           Logit Regression Results                           \n",
      "==============================================================================\n",
      "Dep. Variable:               Response   No. Observations:               381109\n",
      "Model:                          Logit   Df Residuals:                   381099\n",
      "Method:                           MLE   Df Model:                            9\n",
      "Date:                Sun, 20 Sep 2020   Pseudo R-squ.:                  0.2438\n",
      "Time:                        23:14:38   Log-Likelihood:            -1.0720e+05\n",
      "converged:                       True   LL-Null:                   -1.4177e+05\n",
      "Covariance Type:            nonrobust   LLR p-value:                     0.000\n",
      "========================================================================================\n",
      "                           coef    std err          z      P>|z|      [0.025      0.975]\n",
      "----------------------------------------------------------------------------------------\n",
      "Gender                   0.1068      0.011      9.742      0.000       0.085       0.128\n",
      "Age                     -0.0103      0.000    -24.818      0.000      -0.011      -0.010\n",
      "Driving_License         -2.0039      0.038    -52.743      0.000      -2.078      -1.929\n",
      "Region_Code             -0.0018      0.000     -4.191      0.000      -0.003      -0.001\n",
      "Previously_Insured      -4.2964      0.082    -52.362      0.000      -4.457      -4.136\n",
      "Vehicle_Age             -0.2107      0.009    -22.961      0.000      -0.229      -0.193\n",
      "Vehicle_Damage           1.8311      0.029     63.584      0.000       1.775       1.888\n",
      "Annual_Premium        9.959e-07   2.94e-07      3.382      0.001    4.19e-07    1.57e-06\n",
      "Policy_Sales_Channel    -0.0043      0.000    -40.673      0.000      -0.004      -0.004\n",
      "Vintage                 -0.0002   6.36e-05     -3.457      0.001      -0.000   -9.52e-05\n",
      "========================================================================================\n"
     ]
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "logit_model=sm.Logit(y,X)\n",
    "result=logit_model.fit()\n",
    "print(result.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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": [
    "from sklearn.metrics import roc_curve\n",
    "logit_roc_auc = roc_auc_score(y_cv, cat_model.predict(X_cv))\n",
    "fpr, tpr, thresholds = roc_curve(y_cv, cat_model.predict_proba(X_cv)[:,1])\n",
    "plt.figure()\n",
    "plt.plot(fpr, tpr, label='Cataboost (area = %0.2f)' % logit_roc_auc)\n",
    "plt.plot([0, 1], [0, 1],'r--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Receiver operating characteristic')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.savefig('Log_ROC')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prediction on Test Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Driving_License</th>\n",
       "      <th>Region_Code</th>\n",
       "      <th>Previously_Insured</th>\n",
       "      <th>Vehicle_Age</th>\n",
       "      <th>Vehicle_Damage</th>\n",
       "      <th>Annual_Premium</th>\n",
       "      <th>Policy_Sales_Channel</th>\n",
       "      <th>Vintage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>35786</td>\n",
       "      <td>152</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>33762</td>\n",
       "      <td>7</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>40050</td>\n",
       "      <td>124</td>\n",
       "      <td>199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>37356</td>\n",
       "      <td>152</td>\n",
       "      <td>187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>59097</td>\n",
       "      <td>152</td>\n",
       "      <td>297</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Gender  Age  Driving_License  Region_Code  Previously_Insured  Vehicle_Age  \\\n",
       "0       1   25                1           11                   1            1   \n",
       "1       1   40                1           28                   0            0   \n",
       "2       1   47                1           28                   0            0   \n",
       "3       1   24                1           27                   1            1   \n",
       "4       1   27                1           28                   1            1   \n",
       "\n",
       "   Vehicle_Damage  Annual_Premium  Policy_Sales_Channel  Vintage  \n",
       "0               0           35786                   152       53  \n",
       "1               1           33762                     7      111  \n",
       "2               1           40050                   124      199  \n",
       "3               1           37356                   152      187  \n",
       "4               0           59097                   152      297  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test = df_test.copy()\n",
    "\n",
    "# Encoding category using Label Encoder\n",
    "X_test['Gender'] = label_encoder(X_test['Gender'])\n",
    "X_test['Vehicle_Damage'] = label_encoder(X_test['Vehicle_Damage'])\n",
    "X_test['Vehicle_Age'] = label_encoder(X_test['Vehicle_Age'])\n",
    "X_test.drop(['id'],axis=1, inplace=True)\n",
    "X_test['Policy_Sales_Channel']  = X_test['Policy_Sales_Channel'].astype(int)\n",
    "X_test['Region_Code']  = X_test['Region_Code'].astype(int)\n",
    "X_test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[9.99698071e-01, 3.01929219e-04],\n",
       "       [6.69023347e-01, 3.30976653e-01],\n",
       "       [6.92378410e-01, 3.07621590e-01],\n",
       "       ...,\n",
       "       [9.99715236e-01, 2.84763973e-04],\n",
       "       [9.99931075e-01, 6.89253760e-05],\n",
       "       [9.98814378e-01, 1.18562226e-03]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "response = cat_model.predict_proba(X_test)\n",
    "response"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Submission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "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>381110</td>\n",
       "      <td>0.000302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>381111</td>\n",
       "      <td>0.330977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>381112</td>\n",
       "      <td>0.307622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>381113</td>\n",
       "      <td>0.007543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>381114</td>\n",
       "      <td>0.000360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>381115</td>\n",
       "      <td>0.000264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>381116</td>\n",
       "      <td>0.000416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>381117</td>\n",
       "      <td>0.000272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>381118</td>\n",
       "      <td>0.343936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>381119</td>\n",
       "      <td>0.001072</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       id  Response\n",
       "0  381110  0.000302\n",
       "1  381111  0.330977\n",
       "2  381112  0.307622\n",
       "3  381113  0.007543\n",
       "4  381114  0.000360\n",
       "5  381115  0.000264\n",
       "6  381116  0.000416\n",
       "7  381117  0.000272\n",
       "8  381118  0.343936\n",
       "9  381119  0.001072"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submission = pd.DataFrame(df_test['id'],columns=['id',])\n",
    "submission['Response'] = response[:, 1]\n",
    "submission.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission.to_csv('cross_sell_cat_96.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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