Ad Demand Forecast Avito.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CatBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd\n",
    "import os\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn import feature_selection\n",
    "from catboost import CatBoostRegressor\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import preprocessing\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pre-processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>item_id</th>\n",
       "      <th>user_id</th>\n",
       "      <th>region</th>\n",
       "      <th>city</th>\n",
       "      <th>parent_category_name</th>\n",
       "      <th>category_name</th>\n",
       "      <th>param_1</th>\n",
       "      <th>param_2</th>\n",
       "      <th>param_3</th>\n",
       "      <th>title</th>\n",
       "      <th>description</th>\n",
       "      <th>price</th>\n",
       "      <th>item_seq_number</th>\n",
       "      <th>activation_date</th>\n",
       "      <th>user_type</th>\n",
       "      <th>image</th>\n",
       "      <th>image_top_1</th>\n",
       "      <th>deal_probability</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>b912c3c6a6ad</td>\n",
       "      <td>e00f8ff2eaf9</td>\n",
       "      <td>Свердловская область</td>\n",
       "      <td>Екатеринбург</td>\n",
       "      <td>Личные вещи</td>\n",
       "      <td>Товары для детей и игрушки</td>\n",
       "      <td>Постельные принадлежности</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Кокоби(кокон для сна)</td>\n",
       "      <td>Кокон для сна малыша,пользовались меньше месяц...</td>\n",
       "      <td>400.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-03-28</td>\n",
       "      <td>Private</td>\n",
       "      <td>d10c7e016e03247a3bf2d13348fe959fe6f436c1caf64c...</td>\n",
       "      <td>1008.0</td>\n",
       "      <td>0.12789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2dac0150717d</td>\n",
       "      <td>39aeb48f0017</td>\n",
       "      <td>Самарская область</td>\n",
       "      <td>Самара</td>\n",
       "      <td>Для дома и дачи</td>\n",
       "      <td>Мебель и интерьер</td>\n",
       "      <td>Другое</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Стойка для Одежды</td>\n",
       "      <td>Стойка для одежды, под вешалки. С бутика.</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>19</td>\n",
       "      <td>2017-03-26</td>\n",
       "      <td>Private</td>\n",
       "      <td>79c9392cc51a9c81c6eb91eceb8e552171db39d7142700...</td>\n",
       "      <td>692.0</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ba83aefab5dc</td>\n",
       "      <td>91e2f88dd6e3</td>\n",
       "      <td>Ростовская область</td>\n",
       "      <td>Ростов-на-Дону</td>\n",
       "      <td>Бытовая электроника</td>\n",
       "      <td>Аудио и видео</td>\n",
       "      <td>Видео, DVD и Blu-ray плееры</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Philips bluray</td>\n",
       "      <td>В хорошем состоянии, домашний кинотеатр с blu ...</td>\n",
       "      <td>4000.0</td>\n",
       "      <td>9</td>\n",
       "      <td>2017-03-20</td>\n",
       "      <td>Private</td>\n",
       "      <td>b7f250ee3f39e1fedd77c141f273703f4a9be59db4b48a...</td>\n",
       "      <td>3032.0</td>\n",
       "      <td>0.43177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>02996f1dd2ea</td>\n",
       "      <td>bf5cccea572d</td>\n",
       "      <td>Татарстан</td>\n",
       "      <td>Набережные Челны</td>\n",
       "      <td>Личные вещи</td>\n",
       "      <td>Товары для детей и игрушки</td>\n",
       "      <td>Автомобильные кресла</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Автокресло</td>\n",
       "      <td>Продам кресло от0-25кг</td>\n",
       "      <td>2200.0</td>\n",
       "      <td>286</td>\n",
       "      <td>2017-03-25</td>\n",
       "      <td>Company</td>\n",
       "      <td>e6ef97e0725637ea84e3d203e82dadb43ed3cc0a1c8413...</td>\n",
       "      <td>796.0</td>\n",
       "      <td>0.80323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7c90be56d2ab</td>\n",
       "      <td>ef50846afc0b</td>\n",
       "      <td>Волгоградская область</td>\n",
       "      <td>Волгоград</td>\n",
       "      <td>Транспорт</td>\n",
       "      <td>Автомобили</td>\n",
       "      <td>С пробегом</td>\n",
       "      <td>ВАЗ (LADA)</td>\n",
       "      <td>2110</td>\n",
       "      <td>ВАЗ 2110, 2003</td>\n",
       "      <td>Все вопросы по телефону.</td>\n",
       "      <td>40000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2017-03-16</td>\n",
       "      <td>Private</td>\n",
       "      <td>54a687a3a0fc1d68aed99bdaaf551c5c70b761b16fd0a2...</td>\n",
       "      <td>2264.0</td>\n",
       "      <td>0.20797</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        item_id       user_id                 region              city  \\\n",
       "0  b912c3c6a6ad  e00f8ff2eaf9   Свердловская область      Екатеринбург   \n",
       "1  2dac0150717d  39aeb48f0017      Самарская область            Самара   \n",
       "2  ba83aefab5dc  91e2f88dd6e3     Ростовская область    Ростов-на-Дону   \n",
       "3  02996f1dd2ea  bf5cccea572d              Татарстан  Набережные Челны   \n",
       "4  7c90be56d2ab  ef50846afc0b  Волгоградская область         Волгоград   \n",
       "\n",
       "  parent_category_name               category_name  \\\n",
       "0          Личные вещи  Товары для детей и игрушки   \n",
       "1      Для дома и дачи           Мебель и интерьер   \n",
       "2  Бытовая электроника               Аудио и видео   \n",
       "3          Личные вещи  Товары для детей и игрушки   \n",
       "4            Транспорт                  Автомобили   \n",
       "\n",
       "                       param_1     param_2 param_3                  title  \\\n",
       "0    Постельные принадлежности         NaN     NaN  Кокоби(кокон для сна)   \n",
       "1                       Другое         NaN     NaN      Стойка для Одежды   \n",
       "2  Видео, DVD и Blu-ray плееры         NaN     NaN         Philips bluray   \n",
       "3         Автомобильные кресла         NaN     NaN             Автокресло   \n",
       "4                   С пробегом  ВАЗ (LADA)    2110         ВАЗ 2110, 2003   \n",
       "\n",
       "                                         description    price  \\\n",
       "0  Кокон для сна малыша,пользовались меньше месяц...    400.0   \n",
       "1          Стойка для одежды, под вешалки. С бутика.   3000.0   \n",
       "2  В хорошем состоянии, домашний кинотеатр с blu ...   4000.0   \n",
       "3                             Продам кресло от0-25кг   2200.0   \n",
       "4                           Все вопросы по телефону.  40000.0   \n",
       "\n",
       "   item_seq_number activation_date user_type  \\\n",
       "0                2      2017-03-28   Private   \n",
       "1               19      2017-03-26   Private   \n",
       "2                9      2017-03-20   Private   \n",
       "3              286      2017-03-25   Company   \n",
       "4                3      2017-03-16   Private   \n",
       "\n",
       "                                               image  image_top_1  \\\n",
       "0  d10c7e016e03247a3bf2d13348fe959fe6f436c1caf64c...       1008.0   \n",
       "1  79c9392cc51a9c81c6eb91eceb8e552171db39d7142700...        692.0   \n",
       "2  b7f250ee3f39e1fedd77c141f273703f4a9be59db4b48a...       3032.0   \n",
       "3  e6ef97e0725637ea84e3d203e82dadb43ed3cc0a1c8413...        796.0   \n",
       "4  54a687a3a0fc1d68aed99bdaaf551c5c70b761b16fd0a2...       2264.0   \n",
       "\n",
       "   deal_probability  \n",
       "0           0.12789  \n",
       "1           0.00000  \n",
       "2           0.43177  \n",
       "3           0.80323  \n",
       "4           0.20797  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('train_avito.csv', parse_dates = ['activation_date'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count       1418062.00\n",
       "mean         316708.09\n",
       "std        66891542.10\n",
       "min               0.00\n",
       "25%             500.00\n",
       "50%            1300.00\n",
       "75%            7000.00\n",
       "max     79501011850.00\n",
       "Name: price, dtype: float64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('display.float_format', '{:.2f}'.format)\n",
    "df.price.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2c3879fada0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "pd.set_option('display.float_format', '{:.2f}'.format)\n",
    "plt.figure(figsize = (10, 4))\n",
    "n, bins, patches = plt.hist(df['deal_probability'], 100, facecolor='blue', alpha=0.75)\n",
    "plt.xlabel('Deal probability')\n",
    "plt.xlim(0, 1)\n",
    "plt.title('Histogram of deal probability')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2c3879fab70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10, 4))\n",
    "plt.scatter(range(df.shape[0]), np.sort(df['deal_probability'].values))\n",
    "plt.xlabel('index')\n",
    "plt.ylabel('deal probability')\n",
    "plt.title(\"Deal Probability Distribution\")\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Almost one million Ads has 0 probaility, which means it did not sell anything, and few ads have a probability of 1 which means it did sell something. The other ads have a probability in between 0 and 1. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "param_1         61576\n",
       "param_2        654542\n",
       "param_3        862565\n",
       "description    116276\n",
       "image          112588\n",
       "image_top_1    112588\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "null_value_stats = df.isnull().sum()\n",
    "null_value_stats[null_value_stats != 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fill missing features with -999, by filling missing values out of their distributions, the model would be able to easily distinguish between them and take it into account."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.fillna(-999, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create date time features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['year'] = df['activation_date'].dt.year\n",
    "df['day_of_year'] = df['activation_date'].dt.dayofyear\n",
    "df['weekday'] = df['activation_date'].dt.weekday\n",
    "df['week_of_year'] = df['activation_date'].dt.week\n",
    "df['day_of_month'] = df['activation_date'].dt.day\n",
    "df['quarter'] = df['activation_date'].dt.quarter\n",
    "\n",
    "df.drop('activation_date', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['item_id', 'user_id', 'region', 'city', 'parent_category_name',\n",
       "       'category_name', 'param_1', 'param_2', 'param_3', 'title',\n",
       "       'description', 'price', 'item_seq_number', 'user_type', 'image',\n",
       "       'image_top_1', 'deal_probability', 'year', 'day_of_year', 'weekday',\n",
       "       'week_of_year', 'day_of_month', 'quarter'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "our features are of differnt types - some of them are numeric, some are categorical, and some are text such as title and description, and we could treat these text features just as categorical features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "categorical = ['item_id', 'user_id', 'region', 'city', 'parent_category_name', 'category_name', 'param_1', 'param_2', 'param_3', 'title', 'description', 'item_seq_number', 'user_type', 'image', 'image_top_1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "# lbl = preprocessing.LabelEncoder()\n",
    "# for col in categorical: \n",
    "#    df[col] = lbl.fit_transform(df[col].astype(str))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "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>item_id</th>\n",
       "      <th>user_id</th>\n",
       "      <th>region</th>\n",
       "      <th>city</th>\n",
       "      <th>parent_category_name</th>\n",
       "      <th>category_name</th>\n",
       "      <th>param_1</th>\n",
       "      <th>param_2</th>\n",
       "      <th>param_3</th>\n",
       "      <th>title</th>\n",
       "      <th>...</th>\n",
       "      <th>user_type</th>\n",
       "      <th>image</th>\n",
       "      <th>image_top_1</th>\n",
       "      <th>deal_probability</th>\n",
       "      <th>year</th>\n",
       "      <th>day_of_year</th>\n",
       "      <th>weekday</th>\n",
       "      <th>week_of_year</th>\n",
       "      <th>day_of_month</th>\n",
       "      <th>quarter</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1087111</td>\n",
       "      <td>675853</td>\n",
       "      <td>19</td>\n",
       "      <td>460</td>\n",
       "      <td>4</td>\n",
       "      <td>42</td>\n",
       "      <td>249</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>298998</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1135701</td>\n",
       "      <td>13</td>\n",
       "      <td>0.12789</td>\n",
       "      <td>2017</td>\n",
       "      <td>87</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>267623</td>\n",
       "      <td>173962</td>\n",
       "      <td>17</td>\n",
       "      <td>1300</td>\n",
       "      <td>2</td>\n",
       "      <td>22</td>\n",
       "      <td>122</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>676606</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>661422</td>\n",
       "      <td>2722</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>2017</td>\n",
       "      <td>85</td>\n",
       "      <td>6</td>\n",
       "      <td>12</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1095557</td>\n",
       "      <td>440069</td>\n",
       "      <td>16</td>\n",
       "      <td>1276</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>84</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>79843</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999242</td>\n",
       "      <td>2260</td>\n",
       "      <td>0.43177</td>\n",
       "      <td>2017</td>\n",
       "      <td>79</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   item_id  user_id  region  city  parent_category_name  category_name  \\\n",
       "0  1087111   675853      19   460                     4             42   \n",
       "1   267623   173962      17  1300                     2             22   \n",
       "2  1095557   440069      16  1276                     0              2   \n",
       "\n",
       "   param_1  param_2  param_3   title   ...     user_type    image  \\\n",
       "0      249        0        0  298998   ...             1  1135701   \n",
       "1      122        0        0  676606   ...             1   661422   \n",
       "2       84        0        0   79843   ...             1   999242   \n",
       "\n",
       "   image_top_1  deal_probability  year  day_of_year  weekday  week_of_year  \\\n",
       "0           13           0.12789  2017           87        1            13   \n",
       "1         2722           0.00000  2017           85        6            12   \n",
       "2         2260           0.43177  2017           79        0            12   \n",
       "\n",
       "   day_of_month  quarter  \n",
       "0            28        1  \n",
       "1            26        1  \n",
       "2            20        1  \n",
       "\n",
       "[3 rows x 23 columns]"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop('deal_probability', axis=1)\n",
    "y = df.deal_probability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Prepare Categorical features indices\n",
    "def column_index(df, query_cols):\n",
    "    cols = df.columns.values\n",
    "    sidx = np.argsort(cols)\n",
    "    return sidx[np.searchsorted(cols, query_cols, sorter=sidx)]\n",
    "\n",
    "\n",
    "categorical_features_indices = column_index(X, categorical)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 12, 13, 14, 15], dtype=int64)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical_features_indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_valid, y_train, y_valid = train_test_split(\n",
    "    X, y, test_size=0.25, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
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I=this.container.selectAll(\"rect.scrollbar-vertical\").data(O?[0]:[]);I.exit().on(\".drag\",null).remove(),I.enter().append(\"rect\").classed(\"scrollbar-vertical\",!0).call(o.fill,n.barColor),O?(this.vbar=I.attr({rx:n.barRadius,ry:n.barRadius,x:E,y:N,width:D,height:P}),this._vbarYMin=N+P/2,this._vbarTranslateMax=M-P):(delete this.vbar,delete this._vbarYMin,delete this._vbarTranslateMax);var R=this.id,F=u-.5,j=O?f+D+.5:f+.5,B=d-.5,q=A?h+L+.5:h+.5,H=l._topdefs.selectAll(\"#\"+R).data(A||O?[0]:[]);if(H.exit().remove(),H.enter().append(\"clipPath\").attr(\"id\",R).append(\"rect\"),A||O?(this._clipRect=H.select(\"rect\").attr({x:Math.floor(F),y:Math.floor(B),width:Math.ceil(j)-Math.floor(F),height:Math.ceil(q)-Math.floor(B)}),this.container.call(i.setClipUrl,R),this.bg.attr({x:p,y:v,width:g,height:m})):(this.bg.attr({width:0,height:0}),this.container.on(\"wheel\",null).on(\".drag\",null).call(i.setClipUrl,null),delete this._clipRect),A||O){var V=a.behavior.drag().on(\"dragstart\",function(){a.event.sourceEvent.preventDefault()}).on(\"drag\",this._onBoxDrag.bind(this));this.container.on(\"wheel\",null).on(\"wheel\",this._onBoxWheel.bind(this)).on(\".drag\",null).call(V);var U=a.behavior.drag().on(\"dragstart\",function(){a.event.sourceEvent.preventDefault(),a.event.sourceEvent.stopPropagation()}).on(\"drag\",this._onBarDrag.bind(this));A&&this.hbar.on(\".drag\",null).call(U),O&&this.vbar.on(\".drag\",null).call(U)}this.setTranslate(e,r)},n.prototype.disable=function(){(this.hbar||this.vbar)&&(this.bg.attr({width:0,height:0}),this.container.on(\"wheel\",null).on(\".drag\",null).call(i.setClipUrl,null),delete this._clipRect),this.hbar&&(this.hbar.on(\".drag\",null),this.hbar.remove(),delete this.hbar,delete this._hbarXMin,delete this._hbarTranslateMax),this.vbar&&(this.vbar.on(\".drag\",null),this.vbar.remove(),delete this.vbar,delete this._vbarYMin,delete this._vbarTranslateMax)},n.prototype._onBoxDrag=function(){var 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r=this.position.w-this._box.w,n=this.position.h-this._box.h;if(t=l.constrain(t||0,0,r),e=l.constrain(e||0,0,n),this.translateX=t,this.translateY=e,this.container.call(i.setTranslate,this._box.l-this.position.l-t,this._box.t-this.position.t-e),this._clipRect&&this._clipRect.attr({x:Math.floor(this.position.l+t-.5),y:Math.floor(this.position.t+e-.5)}),this.hbar){var a=t/r;this.hbar.call(i.setTranslate,t+a*this._hbarTranslateMax,e)}if(this.vbar){var o=e/n;this.vbar.call(i.setTranslate,t,e+o*this._vbarTranslateMax)}}},{\"../../lib\":136,\"../color\":25,\"../drawing\":49,d3:7}],121:[function(t,e,r){\"use strict\";e.exports={SHOW_PLACEHOLDER:100,HIDE_PLACEHOLDER:1e3,DBLCLICKDELAY:300}},{}],122:[function(t,e,r){\"use strict\";e.exports={BADNUM:void 0,FP_SAFE:Number.MAX_VALUE/1e4,ONEAVGYEAR:315576e5,ONEAVGMONTH:26298e5,ONEDAY:864e5,ONEHOUR:36e5,ONEMIN:6e4,ONESEC:1e3,EPOCHJD:2440587.5,ALMOST_EQUAL:1-1e-6}},{}],123:[function(t,e,r){\"use 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b,_=[f.margin.left+x,f.margin.top+x];if(p){b=[0,n.max(i.util.sumArrays(i.util.arrayLast(h).r[0],i.util.arrayLast(m)))]}else b=n.extent(i.util.flattenArray(h.map(function(t,e){return t.r})));f.radialAxis.domain!=i.DATAEXTENT&&(b[0]=0),a=n.scale.linear().domain(f.radialAxis.domain!=i.DATAEXTENT&&f.radialAxis.domain?f.radialAxis.domain:b).range([0,x]),u.layout.radialAxis.domain=a.domain();var w,k=i.util.flattenArray(h.map(function(t,e){return t.t})),M=\"string\"==typeof k[0];M&&(k=i.util.deduplicate(k),w=k.slice(),k=n.range(k.length),h=h.map(function(t,e){var r=t;return t.t=[k],p&&(r.yStack=t.yStack),r}));var A=h.filter(function(t,e){return\"LinePlot\"===t.geometry||\"DotPlot\"===t.geometry}).length===h.length,T=null===f.needsEndSpacing?M||!A:f.needsEndSpacing,L=f.angularAxis.domain&&f.angularAxis.domain!=i.DATAEXTENT&&!M&&f.angularAxis.domain[0]>=0,C=L?f.angularAxis.domain:n.extent(k),S=Math.abs(k[1]-k[0]);A&&!M&&(S=0);var z=C.slice();T&&M&&(z[1]+=S);var O=f.angularAxis.ticksCount||4;O>8&&(O=O/(O/8)+O%8),f.angularAxis.ticksStep&&(O=(z[1]-z[0])/O);var D=f.angularAxis.ticksStep||(z[1]-z[0])/(O*(f.minorTicks+1));w&&(D=Math.max(Math.round(D),1)),z[2]||(z[2]=D);var P=n.range.apply(this,z);if(P=P.map(function(t,e){return parseFloat(t.toPrecision(12))}),l=n.scale.linear().domain(z.slice(0,2)).range(\"clockwise\"===f.direction?[0,360]:[360,0]),u.layout.angularAxis.domain=l.domain(),u.layout.angularAxis.endPadding=T?S:0,void 0===(e=n.select(this).select(\"svg.chart-root\"))||e.empty()){var E=(new DOMParser).parseFromString(\"<svg xmlns='http://www.w3.org/2000/svg' class='chart-root'>' + '<g class='outer-group'>' + '<g class='chart-group'>' + '<circle class='background-circle'></circle>' + '<g class='geometry-group'></g>' + '<g class='radial axis-group'>' + '<circle class='outside-circle'></circle>' + '</g>' + '<g class='angular axis-group'></g>' + '<g class='guides-group'><line></line><circle r='0'></circle></g>' + '</g>' + '<g 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r.symbol=\"DotPlot\"===t.geometry?t.dotType||\"circle\":\"LinePlot\"!=t.geometry?\"square\":\"line\",r.visibleInLegend=void 0===t.visibleInLegend||t.visibleInLegend,r.color=\"LinePlot\"===t.geometry?t.strokeColor:t.color,r});i.Legend().config({data:h.map(function(t,e){return t.name||\"Element\"+e}),legendConfig:o({},i.Legend.defaultConfig().legendConfig,{container:I,elements:B,reverseOrder:f.legend.reverseOrder})})();var q=I.node().getBBox();x=Math.min(f.width-q.width-f.margin.left-f.margin.right,f.height-f.margin.top-f.margin.bottom)/2,x=Math.max(10,x),_=[f.margin.left+x,f.margin.top+x],a.range([0,x]),u.layout.radialAxis.domain=a.domain(),I.attr(\"transform\",\"translate(\"+[_[0]+x,_[1]-x]+\")\")}else I=e.select(\".legend-group\").style({display:\"none\"});e.attr({width:f.width,height:f.height}).style({opacity:f.opacity}),R.attr(\"transform\",\"translate(\"+_+\")\").style({cursor:\"crosshair\"});var H=[(f.width-(f.margin.left+f.margin.right+2*x+(q?q.width:0)))/2,(f.height-(f.margin.top+f.margin.bottom+2*x))/2];if(H[0]=Math.max(0,H[0]),H[1]=Math.max(0,H[1]),e.select(\".outer-group\").attr(\"transform\",\"translate(\"+H+\")\"),f.title){var V=e.select(\"g.title-group text\").style(j).text(f.title),U=V.node().getBBox();V.attr({x:_[0]-U.width/2,y:_[1]-x-20})}var X=e.select(\".radial.axis-group\");if(f.radialAxis.gridLinesVisible){var G=X.selectAll(\"circle.grid-circle\").data(a.ticks(5));G.enter().append(\"circle\").attr({class:\"grid-circle\"}).style(F),G.attr(\"r\",a),G.exit().remove()}X.select(\"circle.outside-circle\").attr({r:x}).style(F);var Y=e.select(\"circle.background-circle\").attr({r:x}).style({fill:f.backgroundColor,stroke:f.stroke});if(f.radialAxis.visible){var Z=n.svg.axis().scale(a).ticks(5).tickSize(5);X.call(Z).attr({transform:\"rotate(\"+f.radialAxis.orientation+\")\"}),X.selectAll(\".domain\").style(F),X.selectAll(\"g>text\").text(function(t,e){return this.textContent+f.radialAxis.ticksSuffix}).style(j).style({\"text-anchor\":\"start\"}).attr({x:0,y:0,dx:0,dy:0,transform:function(t,e){return\"horizontal\"===f.radialAxis.tickOrientation?\"rotate(\"+-f.radialAxis.orientation+\") translate(\"+[0,j[\"font-size\"]]+\")\":\"translate(\"+[0,j[\"font-size\"]]+\")\"}}),X.selectAll(\"g>line\").style({stroke:\"black\"})}var W=e.select(\".angular.axis-group\").selectAll(\"g.angular-tick\").data(P),$=W.enter().append(\"g\").classed(\"angular-tick\",!0);W.attr({transform:function(t,e){return\"rotate(\"+s(t,e)+\")\"}}).style({display:f.angularAxis.visible?\"block\":\"none\"}),W.exit().remove(),$.append(\"line\").classed(\"grid-line\",!0).classed(\"major\",function(t,e){return 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e%(f.minorTicks+1)!=0?\"\":f.angularAxis.rewriteTicks(this.textContent,e)});var J=n.max(R.selectAll(\".angular-tick text\")[0].map(function(t,e){return t.getCTM().e+t.getBBox().width}));I.attr({transform:\"translate(\"+[x+J,f.margin.top]+\")\"});var K=e.select(\"g.geometry-group\").selectAll(\"g\").size()>0,tt=e.select(\"g.geometry-group\").selectAll(\"g.geometry\").data(h);if(tt.enter().append(\"g\").attr({class:function(t,e){return\"geometry geometry\"+e}}),tt.exit().remove(),h[0]||K){var et=[];h.forEach(function(t,e){var r={};r.radialScale=a,r.angularScale=l,r.container=tt.filter(function(t,r){return r==e}),r.geometry=t.geometry,r.orientation=f.orientation,r.direction=f.direction,r.index=e,et.push({data:t,geometryConfig:r})});var rt=n.nest().key(function(t,e){return void 0!==t.data.groupId||\"unstacked\"}).entries(et),nt=[];rt.forEach(function(t,e){\"unstacked\"===t.key?nt=nt.concat(t.values.map(function(t,e){return[t]})):nt.push(t.values)}),nt.forEach(function(t,e){var r;r=Array.isArray(t)?t[0].geometryConfig.geometry:t.geometryConfig.geometry;var n=t.map(function(t,e){return o(i[r].defaultConfig(),t)});i[r]().config(n)()})}var at,ot,it=e.select(\".guides-group\"),lt=e.select(\".tooltips-group\"),st=i.tooltipPanel().config({container:lt,fontSize:8})(),ct=i.tooltipPanel().config({container:lt,fontSize:8})(),ut=i.tooltipPanel().config({container:lt,hasTick:!0})();if(!M){var ft=it.select(\"line\").attr({x1:0,y1:0,y2:0}).style({stroke:\"grey\",\"pointer-events\":\"none\"});R.on(\"mousemove.angular-guide\",function(t,e){var r=i.util.getMousePos(Y).angle;ft.attr({x2:-x,transform:\"rotate(\"+r+\")\"}).style({opacity:.5});var n=(r+180+360-f.orientation)%360;at=l.invert(n);var a=i.util.convertToCartesian(x+12,r+180);st.text(i.util.round(at)).move([a[0]+_[0],a[1]+_[1]])}).on(\"mouseout.angular-guide\",function(t,e){it.select(\"line\").style({opacity:0})})}var dt=it.select(\"circle\").style({stroke:\"grey\",fill:\"none\"});R.on(\"mousemove.radial-guide\",function(t,e){var r=i.util.getMousePos(Y).radius;dt.attr({r:r}).style({opacity:.5}),ot=a.invert(i.util.getMousePos(Y).radius);var n=i.util.convertToCartesian(r,f.radialAxis.orientation);ct.text(i.util.round(ot)).move([n[0]+_[0],n[1]+_[1]])}).on(\"mouseout.radial-guide\",function(t,e){dt.style({opacity:0}),ut.hide(),st.hide(),ct.hide()}),e.selectAll(\".geometry-group .mark\").on(\"mouseover.tooltip\",function(t,r){var a=n.select(this),o=a.style(\"fill\"),l=\"black\",s=a.style(\"opacity\")||1;if(a.attr({\"data-opacity\":s}),\"none\"!=o){a.attr({\"data-fill\":o}),l=n.hsl(o).darker().toString(),a.style({fill:l,opacity:1});var c={t:i.util.round(t[0]),r:i.util.round(t[1])};M&&(c.t=w[t[0]]);var u=\"t: \"+c.t+\", r: \"+c.r,f=this.getBoundingClientRect(),d=e.node().getBoundingClientRect(),h=[f.left+f.width/2-H[0]-d.left,f.top+f.height/2-H[1]-d.top];ut.config({color:l}).text(u),ut.move(h)}else 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l},d.svg=function(){return e},n.rebind(d,f,\"on\"),d},i.Axis.defaultConfig=function(t,e){return{data:[{t:[1,2,3,4],r:[10,11,12,13],name:\"Line1\",geometry:\"LinePlot\",color:null,strokeDash:\"solid\",strokeColor:null,strokeSize:\"1\",visibleInLegend:!0,opacity:1}],layout:{defaultColorRange:n.scale.category10().range(),title:null,height:450,width:500,margin:{top:40,right:40,bottom:40,left:40},font:{size:12,color:\"gray\",outlineColor:\"white\",family:\"Tahoma, 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n=e*Math.PI/180;return[e,t(n)]})},i.util.dataFromEquation=function(t,e,r){var a=e||6,o=[],i=[];n.range(0,360+a,a).forEach(function(e,r){var n=e*Math.PI/180,a=t(n);o.push(e),i.push(a)});var l={t:o,r:i};return r&&(l.name=r),l},i.util.ensureArray=function(t,e){if(void 0===t)return null;var r=[].concat(t);return n.range(e).map(function(t,e){return r[e]||r[0]})},i.util.fillArrays=function(t,e,r){return e.forEach(function(e,n){t[e]=i.util.ensureArray(t[e],r)}),t},i.util.cloneJson=function(t){return JSON.parse(JSON.stringify(t))},i.util.validateKeys=function(t,e){\"string\"==typeof e&&(e=e.split(\".\"));var r=e.shift();return t[r]&&(!e.length||objHasKeys(t[r],e))},i.util.sumArrays=function(t,e){return n.zip(t,e).map(function(t,e){return n.sum(t)})},i.util.arrayLast=function(t){return t[t.length-1]},i.util.arrayEqual=function(t,e){for(var r=Math.max(t.length,e.length,1);r-- >=0&&t[r]===e[r];);return r===-2},i.util.flattenArray=function(t){for(var e=[];!i.util.arrayEqual(e,t);)e=t,t=[].concat.apply([],t);return t},i.util.deduplicate=function(t){return t.filter(function(t,e,r){return r.indexOf(t)==e})},i.util.convertToCartesian=function(t,e){var r=e*Math.PI/180;return[t*Math.cos(r),t*Math.sin(r)]},i.util.round=function(t,e){var r=e||2,n=Math.pow(10,r);return Math.round(t*n)/n},i.util.getMousePos=function(t){var e=n.mouse(t.node()),r=e[0],a=e[1],o={};return o.x=r,o.y=a,o.pos=e,o.angle=180*(Math.atan2(a,r)+Math.PI)/Math.PI,o.radius=Math.sqrt(r*r+a*a),o},i.util.duplicatesCount=function(t){for(var e,r={},n={},a=0,o=t.length;a<o;a++)e=t[a],e in r?(r[e]++,n[e]=r[e]):r[e]=1;return n},i.util.duplicates=function(t){return Object.keys(i.util.duplicatesCount(t))},i.util.translator=function(t,e,r,n){if(n){var a=r.slice();r=e,e=a}var o=e.reduce(function(t,e){if(void 0!==t)return t[e]},t);void 0!==o&&(e.reduce(function(t,r,n){if(void 0!==t)return n===e.length-1&&delete t[r],t[r]},t),r.reduce(function(t,e,n){return void 0===t[e]&&(t[e]={}),n===r.length-1&&(t[e]=o),t[e]},t))},i.PolyChart=function(){function t(){var t=e[0].geometryConfig,r=t.container;\"string\"==typeof r&&(r=n.select(r)),r.datum(e).each(function(e,r){function o(e,r){return{r:t.radialScale(e[1]),t:(t.angularScale(e[0])+t.orientation)*Math.PI/180}}function i(t){return{x:t.r*Math.cos(t.t),y:t.r*Math.sin(t.t)}}var l=!!e[0].data.yStack,s=e.map(function(t,e){return l?n.zip(t.data.t[0],t.data.r[0],t.data.yStack[0]):n.zip(t.data.t[0],t.data.r[0])}),c=t.angularScale,u=t.radialScale.domain()[0],f={};f.bar=function(r,a,o){var i=e[o].data,l=t.radialScale(r[1])-t.radialScale(0),s=t.radialScale(r[2]||0),u=i.barWidth;n.select(this).attr({class:\"mark bar\",d:\"M\"+[[l+s,-u/2],[l+s,u/2],[s,u/2],[s,-u/2]].join(\"L\")+\"Z\",transform:function(e,r){return\"rotate(\"+(t.orientation+c(e[0]))+\")\"}})},f.dot=function(t,r,a){var l=t[2]?[t[0],t[1]+t[2]]:t,s=n.svg.symbol().size(e[a].data.dotSize).type(e[a].data.dotType)(t,r);n.select(this).attr({class:\"mark 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n={};n.attributes=t(\"./attributes\"),n.supplyDefaults=t(\"./defaults\"),n.supplyLayoutDefaults=t(\"./layout_defaults\"),n.layoutAttributes=t(\"./layout_attributes\"),n.calc=t(\"./calc\"),n.plot=t(\"./plot\"),n.style=t(\"./style\"),n.styleOne=t(\"./style_one\"),n.moduleType=\"trace\",n.name=\"pie\",n.basePlotModule=t(\"./base_plot\"),n.categories=[\"pie\",\"showLegend\"],n.meta={},e.exports=n},{\"./attributes\":228,\"./base_plot\":229,\"./calc\":230,\"./defaults\":231,\"./layout_attributes\":234,\"./layout_defaults\":235,\"./plot\":236,\"./style\":237,\"./style_one\":238}],234:[function(t,e,r){\"use strict\";e.exports={hiddenlabels:{valType:\"data_array\"}}},{}],235:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),a=t(\"./layout_attributes\");e.exports=function(t,e){!function(r,o){n.coerce(t,e,a,r,o)}(\"hiddenlabels\")}},{\"../../lib\":136,\"./layout_attributes\":234}],236:[function(t,e,r){\"use strict\";function n(t,e,r){var n=Math.sqrt(t.width*t.width+t.height*t.height),o=t.width/t.height,i=Math.PI*Math.min(e.v/r.vTotal,.5),l=1-r.trace.hole,s=a(e,r),c={scale:s*r.r*2/n,rCenter:1-s,rotate:0};if(c.scale>=1)return c;var u=o+1/(2*Math.tan(i)),f=r.r*Math.min(1/(Math.sqrt(u*u+.5)+u),l/(Math.sqrt(o*o+l/2)+o)),d={scale:2*f/t.height,rCenter:Math.cos(f/r.r)-f*o/r.r,rotate:(180/Math.PI*e.midangle+720)%180-90},h=1/o,p=h+1/(2*Math.tan(i)),g=r.r*Math.min(1/(Math.sqrt(p*p+.5)+p),l/(Math.sqrt(h*h+l/2)+h)),v={scale:2*g/t.width,rCenter:Math.cos(g/r.r)-g/o/r.r,rotate:(180/Math.PI*e.midangle+810)%180-90},m=v.scale>d.scale?v:d;return c.scale<1&&m.scale>c.scale?m:c}function a(t,e){if(t.v===e.vTotal&&!e.trace.hole)return 1;var r=Math.PI*Math.min(t.v/e.vTotal,.5);return Math.min(1/(1+1/Math.sin(r)),(1-e.trace.hole)/2)}function o(t,e){var r=e.pxmid[0],n=e.pxmid[1],a=t.width/2,o=t.height/2;return r<0&&(a*=-1),n<0&&(o*=-1),{scale:1,rCenter:1,rotate:0,x:a+Math.abs(o)*(a>0?1:-1)/2,y:o/(1+r*r/(n*n)),outside:!0}}function i(t,e){function r(t,e){return t.pxmid[1]-e.pxmid[1]}function n(t,e){return e.pxmid[1]-t.pxmid[1]}var a,o,i,l,s,c,u,f,d,h,p,g,v;for(o=0;o<2;o++)for(i=o?r:n,s=o?Math.max:Math.min,u=o?1:-1,a=0;a<2;a++){for(l=a?Math.max:Math.min,c=a?1:-1,f=t[o][a],f.sort(i),d=t[1-o][a],h=d.concat(f),g=[],p=0;p<f.length;p++)void 0!==f[p].yLabelMid&&g.push(f[p]);for(v=!1,p=0;o&&p<d.length;p++)if(void 0!==d[p].yLabelMid){v=d[p];break}for(p=0;p<g.length;p++){var m=p&&g[p-1];v&&!p&&(m=v),function(t,r){r||(r={});var n,a,i,f,d,p,g=r.labelExtraY+(o?r.yLabelMax:r.yLabelMin),v=o?t.yLabelMin:t.yLabelMax,m=o?t.yLabelMax:t.yLabelMin,y=t.cyFinal+s(t.px0[1],t.px1[1]),x=g-v;if(x*u>0&&(t.labelExtraY=x),Array.isArray(e.pull))for(a=0;a<h.length;a++)(i=h[a])===t||(e.pull[t.i]||0)>=e.pull[i.i]||((t.pxmid[1]-i.pxmid[1])*u>0?(f=i.cyFinal+s(i.px0[1],i.px1[1]),(x=f-v-t.labelExtraY)*u>0&&(t.labelExtraY+=x)):(m+t.labelExtraY-y)*u>0&&(n=3*c*Math.abs(a-h.indexOf(t)),d=i.cxFinal+l(i.px0[0],i.px1[0]),(p=d+n-(t.cxFinal+t.pxmid[0])-t.labelExtraX)*c>0&&(t.labelExtraX+=p)))}(g[p],m)}}}function l(t,e){var r,n,a,o,i,l,s,u,f,d,h=[];for(a=0;a<t.length;a++){if(i=t[a][0],l=i.trace,r=e.w*(l.domain.x[1]-l.domain.x[0]),n=e.h*(l.domain.y[1]-l.domain.y[0]),s=l.tiltaxis*Math.PI/180,u=l.pull,Array.isArray(u))for(u=0,o=0;o<l.pull.length;o++)l.pull[o]>u&&(u=l.pull[o]);i.r=Math.min(r/c(l.tilt,Math.sin(s),l.depth),n/c(l.tilt,Math.cos(s),l.depth))/(2+2*u),i.cx=e.l+e.w*(l.domain.x[1]+l.domain.x[0])/2,i.cy=e.t+e.h*(2-l.domain.y[1]-l.domain.y[0])/2,\n",
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n=Math.sin(t*Math.PI/180);return Math.max(.01,r*n*Math.abs(e)+2*Math.sqrt(1-n*n*e*e))}var u=t(\"d3\"),f=t(\"../../components/fx\"),d=t(\"../../components/color\"),h=t(\"../../components/drawing\"),p=t(\"../../lib/svg_text_utils\"),g=t(\"./helpers\");e.exports=function(t,e){var r=t._fullLayout;l(e,r._size);var c=r._pielayer.selectAll(\"g.trace\").data(e);c.enter().append(\"g\").attr({\"stroke-linejoin\":\"round\",class:\"trace\"}),c.exit().remove(),c.order(),c.each(function(e){var l=u.select(this),c=e[0],v=c.trace,m=(v.depth||0)*c.r*Math.sin(0)/2,y=v.tiltaxis||0,x=y*Math.PI/180,b=[m*Math.sin(x),m*Math.cos(x)],_=c.r*Math.cos(0),w=l.selectAll(\"g.part\").data(v.tilt?[\"top\",\"sides\"]:[\"top\"]);w.enter().append(\"g\").attr(\"class\",function(t){return t+\" part\"}),w.exit().remove(),w.order(),s(e),l.selectAll(\".top\").each(function(){var l=u.select(this).selectAll(\"g.slice\").data(e);l.enter().append(\"g\").classed(\"slice\",!0),l.exit().remove();var s=[[[],[]],[[],[]]],m=!1;l.each(function(e){function i(n){n.originalEvent=u.event;var o=t._fullLayout,i=t._fullData[v.index],l=i.hoverinfo;if(\"all\"===l&&(l=\"label+text+value+percent+name\"),t._dragging||o.hovermode===!1||\"none\"===l||\"skip\"===l||!l)return void f.hover(t,n,\"pie\");var s=a(e,c),d=w+e.pxmid[0]*(1-s),h=k+e.pxmid[1]*(1-s),p=r.separators,m=[];l.indexOf(\"label\")!==-1&&m.push(e.label),l.indexOf(\"text\")!==-1&&(i.hovertext?m.push(Array.isArray(i.hovertext)?i.hovertext[e.i]:i.hovertext):i.text&&i.text[e.i]&&m.push(i.text[e.i])),l.indexOf(\"value\")!==-1&&m.push(g.formatPieValue(e.v,p)),l.indexOf(\"percent\")!==-1&&m.push(g.formatPiePercent(e.v/c.vTotal,p));var y=i.hoverlabel;f.loneHover({x0:d-s*c.r,x1:d+s*c.r,y:h,text:m.join(\"<br>\"),name:l.indexOf(\"name\")!==-1?i.name:void 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w=c.cx+b[0],k=c.cy+b[1],M=u.select(this),A=M.selectAll(\"path.surface\").data([e]),T=!1;if(A.enter().append(\"path\").classed(\"surface\",!0).style({\"pointer-events\":\"all\"}),M.select(\"path.textline\").remove(),M.on(\"mouseover\",i).on(\"mouseout\",l).on(\"click\",d),v.pull){var L=+(Array.isArray(v.pull)?v.pull[e.i]:v.pull)||0;L>0&&(w+=L*e.pxmid[0],k+=L*e.pxmid[1])}e.cxFinal=w,e.cyFinal=k;var C=v.hole;if(e.v===c.vTotal){var S=\"M\"+(w+e.px0[0])+\",\"+(k+e.px0[1])+x(e.px0,e.pxmid,!0,1)+x(e.pxmid,e.px0,!0,1)+\"Z\";C?A.attr(\"d\",\"M\"+(w+C*e.px0[0])+\",\"+(k+C*e.px0[1])+x(e.px0,e.pxmid,!1,C)+x(e.pxmid,e.px0,!1,C)+\"Z\"+S):A.attr(\"d\",S)}else{var z=x(e.px0,e.px1,!0,1);if(C){var O=1-C;A.attr(\"d\",\"M\"+(w+C*e.px1[0])+\",\"+(k+C*e.px1[1])+x(e.px1,e.px0,!1,C)+\"l\"+O*e.px0[0]+\",\"+O*e.px0[1]+z+\"Z\")}else A.attr(\"d\",\"M\"+w+\",\"+k+\"l\"+e.px0[0]+\",\"+e.px0[1]+z+\"Z\")}var D=Array.isArray(v.textposition)?v.textposition[e.i]:v.textposition,P=M.selectAll(\"g.slicetext\").data(e.text&&\"none\"!==D?[0]:[]);P.enter().append(\"g\").classed(\"slicetext\",!0),P.exit().remove(),P.each(function(){var t=u.select(this).selectAll(\"text\").data([0]);t.enter().append(\"text\").attr(\"data-notex\",1),t.exit().remove(),t.text(e.text).attr({class:\"slicetext\",transform:\"\",\"data-bb\":\"\",\"text-anchor\":\"middle\",x:0,y:0}).call(h.font,\"outside\"===D?v.outsidetextfont:v.insidetextfont).call(p.convertToTspans),t.selectAll(\"tspan.line\").attr({x:0,y:0});var r,a=h.bBox(t.node());\"outside\"===D?r=o(a,e):(r=n(a,e,c),\"auto\"===D&&r.scale<1&&(t.call(h.font,v.outsidetextfont),v.outsidetextfont.family===v.insidetextfont.family&&v.outsidetextfont.size===v.insidetextfont.size||(t.attr({\"data-bb\":\"\"}),a=h.bBox(t.node())),r=o(a,e)));var i=w+e.pxmid[0]*r.rCenter+(r.x||0),l=k+e.pxmid[1]*r.rCenter+(r.y||0);r.outside&&(e.yLabelMin=l-a.height/2,e.yLabelMid=l,e.yLabelMax=l+a.height/2,e.labelExtraX=0,e.labelExtraY=0,m=!0),t.attr(\"transform\",\"translate(\"+i+\",\"+l+\")\"+(r.scale<1?\"scale(\"+r.scale+\")\":\"\")+(r.rotate?\"rotate(\"+r.rotate+\")\":\"\")+\"translate(\"+-(a.left+a.right)/2+\",\"+-(a.top+a.bottom)/2+\")\")})}),m&&i(s,v),l.each(function(t){if(t.labelExtraX||t.labelExtraY){var e=u.select(this),r=e.select(\"g.slicetext text\");r.attr(\"transform\",\"translate(\"+t.labelExtraX+\",\"+t.labelExtraY+\")\"+r.attr(\"transform\"));var n=t.cxFinal+t.pxmid[0],a=t.cyFinal+t.pxmid[1],o=\"M\"+n+\",\"+a,i=(t.yLabelMax-t.yLabelMin)*(t.pxmid[0]<0?-1:1)/4;if(t.labelExtraX){var l=t.labelExtraX*t.pxmid[1]/t.pxmid[0],s=t.yLabelMid+t.labelExtraY-(t.cyFinal+t.pxmid[1]);Math.abs(l)>Math.abs(s)?o+=\"l\"+s*t.pxmid[0]/t.pxmid[1]+\",\"+s+\"H\"+(n+t.labelExtraX+i):o+=\"l\"+t.labelExtraX+\",\"+l+\"v\"+(s-l)+\"h\"+i}else o+=\"V\"+(t.yLabelMid+t.labelExtraY)+\"h\"+i;e.append(\"path\").classed(\"textline\",!0).call(d.stroke,v.outsidetextfont.color).attr({\"stroke-width\":Math.min(2,v.outsidetextfont.size/8),d:o,fill:\"none\"})}})})}),setTimeout(function(){c.selectAll(\"tspan\").each(function(){var t=u.select(this);t.attr(\"dy\")&&t.attr(\"dy\",t.attr(\"dy\"))})},0)}},{\"../../components/color\":25,\"../../components/drawing\":49,\"../../components/fx\":66,\"../../lib/svg_text_utils\":153,\"./helpers\":232,d3:7}],237:[function(t,e,r){\"use strict\";var n=t(\"d3\"),a=t(\"./style_one\");e.exports=function(t){t._fullLayout._pielayer.selectAll(\".trace\").each(function(t){var e=t[0],r=e.trace,o=n.select(this);o.style({opacity:r.opacity}),o.selectAll(\".top path.surface\").each(function(t){n.select(this).call(a,t,r)})})}},{\"./style_one\":238,d3:7}],238:[function(t,e,r){\"use strict\";var n=t(\"../../components/color\");e.exports=function(t,e,r){var a=r.marker.line.color;Array.isArray(a)&&(a=a[e.i]||n.defaultLine);var o=r.marker.line.width||0;Array.isArray(o)&&(o=o[e.i]||0),t.style({\"stroke-width\":o}).call(n.fill,e.color).call(n.stroke,a)}},{\"../../components/color\":25}],239:[function(t,e,r){\"use strict\";var n=t(\"../../lib\");e.exports=function(t,e){for(var r=0;r<t.length;r++)t[r].i=r;n.mergeArray(e.text,t,\"tx\"),n.mergeArray(e.hovertext,t,\"htx\"),n.mergeArray(e.customdata,t,\"data\"),n.mergeArray(e.textposition,t,\"tp\"),e.textfont&&(n.mergeArray(e.textfont.size,t,\"ts\"),n.mergeArray(e.textfont.color,t,\"tc\"),n.mergeArray(e.textfont.family,t,\"tf\"));var a=e.marker;if(a){n.mergeArray(a.size,t,\"ms\"),n.mergeArray(a.opacity,t,\"mo\"),n.mergeArray(a.symbol,t,\"mx\"),n.mergeArray(a.color,t,\"mc\");var o=a.line;a.line&&(n.mergeArray(o.color,t,\"mlc\"),n.mergeArray(o.width,t,\"mlw\"));var i=a.gradient;i&&\"none\"!==i.type&&(n.mergeArray(i.type,t,\"mgt\"),n.mergeArray(i.color,t,\"mgc\"))}}},{\"../../lib\":136}],240:[function(t,e,r){\"use strict\";var n=t(\"../../components/colorscale/color_attributes\"),a=t(\"../../components/errorbars/attributes\"),o=t(\"../../components/colorbar/attributes\"),i=t(\"../../components/drawing/attributes\").dash,l=t(\"../../components/drawing\"),s=(t(\"./constants\"),t(\"../../lib/extend\").extendFlat);e.exports={x:{valType:\"data_array\"},x0:{valType:\"any\",dflt:0},dx:{valType:\"number\",dflt:1},y:{valType:\"data_array\"},y0:{valType:\"any\",dflt:0},customdata:{valType:\"data_array\"},dy:{valType:\"number\",dflt:1},ids:{valType:\"data_array\"},text:{valType:\"string\",dflt:\"\",arrayOk:!0},hovertext:{valType:\"string\",dflt:\"\",arrayOk:!0},mode:{valType:\"flaglist\",flags:[\"lines\",\"markers\",\"text\"],extras:[\"none\"]},hoveron:{valType:\"flaglist\",flags:[\"points\",\"fills\"]},line:{color:{valType:\"color\"},width:{valType:\"number\",min:0,dflt:2},shape:{valType:\"enumerated\",values:[\"linear\",\"spline\",\"hv\",\"vh\",\"hvh\",\"vhv\"],dflt:\"linear\"},smoothing:{valType:\"number\",min:0,max:1.3,dflt:1},dash:i,simplify:{valType:\"boolean\",dflt:!0}},connectgaps:{valType:\"boolean\",dflt:!1},fill:{valType:\"enumerated\",values:[\"none\",\"tozeroy\",\"tozerox\",\"tonexty\",\"tonextx\",\"toself\",\"tonext\"],dflt:\"none\"},fillcolor:{valType:\"color\"},marker:s({},{symbol:{valType:\"enumerated\",values:l.symbolList,dflt:\"circle\",arrayOk:!0},opacity:{valType:\"number\",min:0,max:1,arrayOk:!0},size:{valType:\"number\",min:0,dflt:6,arrayOk:!0},maxdisplayed:{valType:\"number\",min:0,dflt:0},sizeref:{valType:\"number\",dflt:1},sizemin:{valType:\"number\",min:0,dflt:0},sizemode:{valType:\"enumerated\",values:[\"diameter\",\"area\"],dflt:\"diameter\"},showscale:{valType:\"boolean\",dflt:!1},colorbar:o,line:s({},{width:{valType:\"number\",min:0,arrayOk:!0}},n(\"marker.line\")),gradient:{type:{valType:\"enumerated\",values:[\"radial\",\"horizontal\",\"vertical\",\"none\"],arrayOk:!0,dflt:\"none\"},color:{valType:\"color\",arrayOk:!0}}},n(\"marker\")),textposition:{valType:\"enumerated\",values:[\"top left\",\"top center\",\"top right\",\"middle left\",\"middle center\",\"middle right\",\"bottom left\",\"bottom center\",\"bottom right\"],dflt:\"middle center\",arrayOk:!0},textfont:{family:{valType:\"string\",noBlank:!0,strict:!0,arrayOk:!0},size:{valType:\"number\",min:1,arrayOk:!0},color:{valType:\"color\",arrayOk:!0}},r:{valType:\"data_array\"},t:{valType:\"data_array\"},error_y:a,error_x:a}},{\"../../components/colorbar/attributes\":26,\"../../components/colorscale/color_attributes\":32,\"../../components/drawing\":49,\"../../components/drawing/attributes\":48,\"../../components/errorbars/attributes\":51,\"../../lib/extend\":132,\"./constants\":245}],241:[function(t,e,r){\"use strict\";var n=t(\"fast-isnumeric\"),a=t(\"../../plots/cartesian/axes\"),o=t(\"../../constants/numerical\").BADNUM,i=t(\"./subtypes\"),l=t(\"./colorscale_calc\"),s=t(\"./arrays_to_calcdata\");e.exports=function(t,e){var r,c,u,f=a.getFromId(t,e.xaxis||\"x\"),d=a.getFromId(t,e.yaxis||\"y\"),h=f.makeCalcdata(e,\"x\"),p=d.makeCalcdata(e,\"y\"),g=Math.min(h.length,p.length);f._minDtick=0,d._minDtick=0,h.length>g&&h.splice(g,h.length-g),p.length>g&&p.splice(g,p.length-g);var v={padded:!0},m={padded:!0};if(i.hasMarkers(e)){if(r=e.marker,c=r.size,Array.isArray(c)){var y={type:\"linear\"};a.setConvert(y),c=y.makeCalcdata(e.marker,\"size\"),c.length>g&&c.splice(g,c.length-g)}var x,b=1.6*(e.marker.sizeref||1);x=\"area\"===e.marker.sizemode?function(t){return Math.max(Math.sqrt((t||0)/b),3)}:function(t){return 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o,i=a(\"x\"),l=a(\"y\");if(n.getComponentMethod(\"calendars\",\"handleTraceDefaults\")(t,e,[\"x\",\"y\"],r),i)l?(o=Math.min(i.length,l.length),o<i.length&&(e.x=i.slice(0,o)),o<l.length&&(e.y=l.slice(0,o))):(o=i.length,a(\"y0\"),a(\"dy\"));else{if(!l)return 0;o=e.y.length,a(\"x0\"),a(\"dx\")}return o}},{\"../../registry\":206}]},{},[5])(5)});window.define = window.__define;window.require = window.__require;window.__define = undefined; window.__require = undefined;/* global Highcharts, Plotly*/\n",
       "/* eslint-disable */\n",
       "function throttle(fn, timeout, invokeAsap, ctx) {\n",
       "    var typeofInvokeAsap = typeof invokeAsap;\n",
       "    if(typeofInvokeAsap === 'undefined') {\n",
       "        invokeAsap = true;\n",
       "    } else if(arguments.length === 3 && typeofInvokeAsap !== 'boolean') {\n",
       "        ctx = invokeAsap;\n",
       "        invokeAsap = true;\n",
       "    }\n",
       "\n",
       "    var timer, args, needInvoke,\n",
       "        wrapper = function() {\n",
       "            if(needInvoke) {\n",
       "                fn.apply(ctx, args);\n",
       "                needInvoke = false;\n",
       "                timer = setTimeout(wrapper, timeout);\n",
       "            } else {\n",
       "                timer = null;\n",
       "            }\n",
       "        };\n",
       "\n",
       "    return function() {\n",
       "        args = arguments;\n",
       "        ctx || (ctx = this);\n",
       "        needInvoke = true;\n",
       "\n",
       "        if(!timer) {\n",
       "            invokeAsap?\n",
       "                wrapper() :\n",
       "                timer = setTimeout(wrapper, timeout);\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "/* eslint-enable */\n",
       "function CatboostIpython() {\n",
       "\n",
       "}\n",
       "\n",
       "CatboostIpython.prototype.init = function() {\n",
       "    this.charts = {};\n",
       "    /*\n",
       "        {\n",
       "            \"rmse_1\": {\n",
       "                ...\n",
       "            }\n",
       "        }\n",
       "    */\n",
       "    this.traces = {};\n",
       "    /*\n",
       "        {\n",
       "            \"rmse_1\": {\n",
       "                name: \"rmse\",\n",
       "                id: \"rmse_1\",\n",
       "                parent: \"div\",\n",
       "                traces: [\n",
       "                    {\n",
       "                        name: \"current;learn;0;;;\",\n",
       "                        x: [],\n",
       "                        y: []\n",
       "                    },\n",
       "                    {\n",
       "                        name: \"current;learn;0;smoothed;;\",\n",
       "                        x: [],\n",
       "                        y: []\n",
       "                    },\n",
       "                    {\n",
       "                        name: \"current;learn;1;;;\",\n",
       "                        x: [],\n",
       "                        y: []\n",
       "                    },\n",
       "                    {\n",
       "                        name: \"current;learn;1;smoothed;;\",\n",
       "                        x: [],\n",
       "                        y: []\n",
       "                    },\n",
       "                    {\n",
       "                        name: \"current;test;0;;;\",\n",
       "                        x: [],\n",
       "                        y: []\n",
       "                    },\n",
       "                    {\n",
       "                        name: \"current;test;0;smoothed;;\",\n",
       "                        x: [],\n",
       "                        y: []\n",
       "                    },\n",
       "                    {\n",
       "                        name: \"current;test;0;;best_point;\",\n",
       "                        x: [],\n",
       "                        y: []\n",
       "                    },\n",
       "                    {\n",
       "                        name: \"current;test;0;;;best_value\",\n",
       "                        x: [],\n",
       "                        y: []\n",
       "                    }\n",
       "                ]\n",
       "            }\n",
       "        }\n",
       "    */\n",
       "\n",
       "    this.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",
       "                }\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",
       "\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",
       "\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",
       "\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",
       "    }\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",
       "\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",
       "\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": "b54008d687ac404ab8eb210ef5b7e8f3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/html": [
       "<p>Failed to display Jupyter Widget of type <code>MetricVisualizer</code>.</p>\n",
       "<p>\n",
       "  If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
       "  that the widgets JavaScript is still loading. If this message persists, it\n",
       "  likely means that the widgets JavaScript library is either not installed or\n",
       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
       "  Widgets Documentation</a> for setup instructions.\n",
       "</p>\n",
       "<p>\n",
       "  If you're reading this message in another frontend (for example, a static\n",
       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
       "  it may mean that your frontend doesn't currently support widgets.\n",
       "</p>\n"
      ],
      "text/plain": [
       "MetricVisualizer(layout=Layout(align_self='stretch', height='500px'))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0:\tlearn: 0.2858681\ttest: 0.2853937\tbest: 0.2853937 (0)\ttotal: 3.29s\tremaining: 2m 41s\n",
      "1:\tlearn: 0.2780544\ttest: 0.2775383\tbest: 0.2775383 (1)\ttotal: 5.7s\tremaining: 2m 16s\n",
      "2:\tlearn: 0.2714314\ttest: 0.2709142\tbest: 0.2709142 (2)\ttotal: 8.59s\tremaining: 2m 14s\n",
      "3:\tlearn: 0.2659673\ttest: 0.2654333\tbest: 0.2654333 (3)\ttotal: 11.5s\tremaining: 2m 12s\n",
      "4:\tlearn: 0.2611966\ttest: 0.2606643\tbest: 0.2606643 (4)\ttotal: 14.1s\tremaining: 2m 7s\n",
      "5:\tlearn: 0.2574038\ttest: 0.2568564\tbest: 0.2568564 (5)\ttotal: 17.3s\tremaining: 2m 7s\n",
      "6:\tlearn: 0.2541006\ttest: 0.2535551\tbest: 0.2535551 (6)\ttotal: 18.1s\tremaining: 1m 50s\n",
      "7:\tlearn: 0.2513629\ttest: 0.2508195\tbest: 0.2508195 (7)\ttotal: 19.7s\tremaining: 1m 43s\n",
      "8:\tlearn: 0.2490407\ttest: 0.2484664\tbest: 0.2484664 (8)\ttotal: 22.5s\tremaining: 1m 42s\n",
      "9:\tlearn: 0.2470872\ttest: 0.2465007\tbest: 0.2465007 (9)\ttotal: 23.3s\tremaining: 1m 33s\n",
      "10:\tlearn: 0.2454546\ttest: 0.2448721\tbest: 0.2448721 (10)\ttotal: 24.8s\tremaining: 1m 28s\n",
      "11:\tlearn: 0.2441198\ttest: 0.2435209\tbest: 0.2435209 (11)\ttotal: 25.6s\tremaining: 1m 21s\n",
      "12:\tlearn: 0.2429737\ttest: 0.2423656\tbest: 0.2423656 (12)\ttotal: 27.9s\tremaining: 1m 19s\n",
      "13:\tlearn: 0.2420226\ttest: 0.2414019\tbest: 0.2414019 (13)\ttotal: 28.6s\tremaining: 1m 13s\n",
      "14:\tlearn: 0.2410954\ttest: 0.2404255\tbest: 0.2404255 (14)\ttotal: 31.3s\tremaining: 1m 13s\n",
      "15:\tlearn: 0.2403304\ttest: 0.2396413\tbest: 0.2396413 (15)\ttotal: 33.3s\tremaining: 1m 10s\n",
      "16:\tlearn: 0.2397302\ttest: 0.2390261\tbest: 0.2390261 (16)\ttotal: 34.9s\tremaining: 1m 7s\n",
      "17:\tlearn: 0.2389787\ttest: 0.2382486\tbest: 0.2382486 (17)\ttotal: 38.5s\tremaining: 1m 8s\n",
      "18:\tlearn: 0.2383857\ttest: 0.2376287\tbest: 0.2376287 (18)\ttotal: 41.3s\tremaining: 1m 7s\n",
      "19:\tlearn: 0.2378960\ttest: 0.2371198\tbest: 0.2371198 (19)\ttotal: 44s\tremaining: 1m 5s\n",
      "20:\tlearn: 0.2374480\ttest: 0.2366388\tbest: 0.2366388 (20)\ttotal: 46.8s\tremaining: 1m 4s\n",
      "21:\tlearn: 0.2370570\ttest: 0.2361977\tbest: 0.2361977 (21)\ttotal: 47.6s\tremaining: 1m\n",
      "22:\tlearn: 0.2367166\ttest: 0.2358385\tbest: 0.2358385 (22)\ttotal: 49.3s\tremaining: 57.8s\n",
      "23:\tlearn: 0.2364344\ttest: 0.2355218\tbest: 0.2355218 (23)\ttotal: 50.8s\tremaining: 55s\n",
      "24:\tlearn: 0.2362035\ttest: 0.2352596\tbest: 0.2352596 (24)\ttotal: 52.5s\tremaining: 52.5s\n",
      "25:\tlearn: 0.2359974\ttest: 0.2350346\tbest: 0.2350346 (25)\ttotal: 55.6s\tremaining: 51.4s\n",
      "26:\tlearn: 0.2356303\ttest: 0.2345912\tbest: 0.2345912 (26)\ttotal: 58.3s\tremaining: 49.7s\n",
      "27:\tlearn: 0.2353429\ttest: 0.2342654\tbest: 0.2342654 (27)\ttotal: 1m\tremaining: 47.8s\n",
      "28:\tlearn: 0.2351054\ttest: 0.2339800\tbest: 0.2339800 (28)\ttotal: 1m 3s\tremaining: 46s\n",
      "29:\tlearn: 0.2349222\ttest: 0.2337437\tbest: 0.2337437 (29)\ttotal: 1m 6s\tremaining: 44.4s\n",
      "30:\tlearn: 0.2347419\ttest: 0.2335068\tbest: 0.2335068 (30)\ttotal: 1m 9s\tremaining: 42.7s\n",
      "31:\tlearn: 0.2346012\ttest: 0.2333401\tbest: 0.2333401 (31)\ttotal: 1m 12s\tremaining: 41s\n",
      "32:\tlearn: 0.2344969\ttest: 0.2332046\tbest: 0.2332046 (32)\ttotal: 1m 13s\tremaining: 38s\n",
      "33:\tlearn: 0.2343604\ttest: 0.2330100\tbest: 0.2330100 (33)\ttotal: 1m 15s\tremaining: 35.5s\n",
      "34:\tlearn: 0.2342449\ttest: 0.2328760\tbest: 0.2328760 (34)\ttotal: 1m 16s\tremaining: 32.9s\n",
      "35:\tlearn: 0.2341073\ttest: 0.2327096\tbest: 0.2327096 (35)\ttotal: 1m 18s\tremaining: 30.4s\n",
      "36:\tlearn: 0.2340149\ttest: 0.2326001\tbest: 0.2326001 (36)\ttotal: 1m 21s\tremaining: 28.5s\n",
      "37:\tlearn: 0.2338990\ttest: 0.2324869\tbest: 0.2324869 (37)\ttotal: 1m 23s\tremaining: 26.3s\n",
      "38:\tlearn: 0.2338050\ttest: 0.2323665\tbest: 0.2323665 (38)\ttotal: 1m 26s\tremaining: 24.5s\n",
      "39:\tlearn: 0.2336183\ttest: 0.2321560\tbest: 0.2321560 (39)\ttotal: 1m 29s\tremaining: 22.5s\n",
      "40:\tlearn: 0.2335277\ttest: 0.2320529\tbest: 0.2320529 (40)\ttotal: 1m 30s\tremaining: 19.9s\n",
      "41:\tlearn: 0.2334501\ttest: 0.2319679\tbest: 0.2319679 (41)\ttotal: 1m 33s\tremaining: 17.8s\n",
      "42:\tlearn: 0.2333718\ttest: 0.2318715\tbest: 0.2318715 (42)\ttotal: 1m 36s\tremaining: 15.7s\n",
      "43:\tlearn: 0.2332779\ttest: 0.2317737\tbest: 0.2317737 (43)\ttotal: 1m 38s\tremaining: 13.4s\n",
      "44:\tlearn: 0.2332028\ttest: 0.2316887\tbest: 0.2316887 (44)\ttotal: 1m 38s\tremaining: 11s\n",
      "45:\tlearn: 0.2331450\ttest: 0.2316158\tbest: 0.2316158 (45)\ttotal: 1m 41s\tremaining: 8.79s\n",
      "46:\tlearn: 0.2330551\ttest: 0.2315177\tbest: 0.2315177 (46)\ttotal: 1m 44s\tremaining: 6.67s\n",
      "47:\tlearn: 0.2329861\ttest: 0.2314222\tbest: 0.2314222 (47)\ttotal: 1m 46s\tremaining: 4.44s\n",
      "48:\tlearn: 0.2329219\ttest: 0.2313491\tbest: 0.2313491 (48)\ttotal: 1m 49s\tremaining: 2.23s\n",
      "49:\tlearn: 0.2328528\ttest: 0.2312739\tbest: 0.2312739 (49)\ttotal: 1m 52s\tremaining: 0us\n",
      "\n",
      "bestTest = 0.2312738635\n",
      "bestIteration = 49\n",
      "\n"
     ]
    }
   ],
   "source": [
    "model=CatBoostRegressor(iterations=50, depth=3, learning_rate=0.1, loss_function='RMSE')\n",
    "model.fit(X_train, y_train,cat_features=categorical_features_indices,eval_set=(X_valid, y_valid),plot=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df['price'] = np.log(df['price'] + 0.001)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CatBoost model training"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Iterations is maximum number of trees that can be built when solving machine learning problems.\n",
    "\n",
    "* learning_rate is used for reducing the gradient step.\n",
    "\n",
    "* Depth is the depth of the tree. Any integer up to  16 when using CPU. \n",
    "\n",
    "* We calculate RMSE as metric.\n",
    "\n",
    "* bagging_temperature defines the settings of the Bayesian bootstrap, the higher the value the more aggressive the bagging is. We do not want it high.\n",
    "\n",
    "* We will use the overfitting detector, so, if overfitting occurs, CatBoost can stop the training earlier than the training parameters dictate. And the type of the overfitting detector is \"Iter\". \n",
    "\n",
    "* metric_period is the frequency of iterations to calculate the values of objectives and metrics.\n",
    "\n",
    "* od_wait, Consider the model overfitted and stop training after the specified number of iterations (100) since the iteration with the optimal metric value.\n",
    "\n",
    "* eval_set is the validation dataset for overfitting detector, best iteration selection and monitoring metrics' changes.\n",
    "\n",
    "* use_bset_model = True if a validation set is input (the eval_set parameter is defined) and at least one of the label values of objects in this set differs from the others."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning: Overfitting detector is active, thus evaluation metric is calculated on every iteration. 'metric_period' is ignored for evaluation metric.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0:\tlearn: 0.2939829\ttest: 0.2934699\tbest: 0.2934699 (0)\ttotal: 35.6s\tremaining: 6h 55m 4s\n",
      "75:\tlearn: 0.2470605\ttest: 0.2465756\tbest: 0.2465756 (75)\ttotal: 53m 22s\tremaining: 7h 18m 11s\n",
      "150:\tlearn: 0.2335739\ttest: 0.2334741\tbest: 0.2334741 (150)\ttotal: 1h 46m 25s\tremaining: 6h 26m 57s\n",
      "225:\tlearn: 0.2284714\ttest: 0.2285134\tbest: 0.2285134 (225)\ttotal: 2h 37m 43s\tremaining: 5h 30m 48s\n",
      "300:\tlearn: 0.2262009\ttest: 0.2265418\tbest: 0.2265418 (300)\ttotal: 3h 21m 44s\tremaining: 4h 27m 25s\n",
      "375:\tlearn: 0.2238186\ttest: 0.2253192\tbest: 0.2253192 (375)\ttotal: 4h 7m 2s\tremaining: 3h 32m 52s\n",
      "450:\tlearn: 0.2223908\ttest: 0.2245667\tbest: 0.2245667 (450)\ttotal: 4h 53m 35s\tremaining: 2h 42m 5s\n",
      "525:\tlearn: 0.2213835\ttest: 0.2240747\tbest: 0.2240747 (525)\ttotal: 5h 42m 2s\tremaining: 1h 53m 8s\n",
      "600:\tlearn: 0.2204280\ttest: 0.2237288\tbest: 0.2237288 (600)\ttotal: 6h 29m 49s\tremaining: 1h 4m 12s\n",
      "675:\tlearn: 0.2194643\ttest: 0.2234397\tbest: 0.2234397 (675)\ttotal: 7h 16m 55s\tremaining: 15m 30s\n",
      "699:\tlearn: 0.2191635\ttest: 0.2233636\tbest: 0.2233636 (699)\ttotal: 7h 32m 50s\tremaining: 0us\n",
      "\n",
      "bestTest = 0.2233636469\n",
      "bestIteration = 699\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<catboost.core.CatBoostRegressor at 0x2c3886ce1d0>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model =  CatBoostRegressor(iterations=700,\n",
    "                             learning_rate=0.01,\n",
    "                             depth=16,\n",
    "                             eval_metric='RMSE',\n",
    "                             random_seed = 42,\n",
    "                             bagging_temperature = 0.2,\n",
    "                             od_type='Iter',\n",
    "                             metric_period = 75,\n",
    "                             od_wait=100)\n",
    "model.fit(X_train, y_train,\n",
    "                 eval_set=(X_valid, y_valid),\n",
    "                 cat_features=categorical_features_indices,\n",
    "                 use_best_model=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2c3886ce908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fea_imp = pd.DataFrame({'imp': model.feature_importances_, 'col': X.columns})\n",
    "fea_imp = fea_imp.sort_values(['imp', 'col'], ascending=[True, False]).iloc[-30:]\n",
    "fea_imp.plot(kind='barh', x='col', y='imp', figsize=(10, 7), legend=None)\n",
    "plt.title('CatBoost - Feature Importance')\n",
    "plt.ylabel('Features')\n",
    "plt.xlabel('Importance');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>col</th>\n",
       "      <th>imp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>year</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>quarter</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>item_id</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>image</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>week_of_year</td>\n",
       "      <td>0.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>weekday</td>\n",
       "      <td>1.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>day_of_month</td>\n",
       "      <td>1.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>description</td>\n",
       "      <td>1.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>day_of_year</td>\n",
       "      <td>1.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>param_3</td>\n",
       "      <td>2.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>item_seq_number</td>\n",
       "      <td>3.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>region</td>\n",
       "      <td>3.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>param_2</td>\n",
       "      <td>4.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>title</td>\n",
       "      <td>5.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>user_type</td>\n",
       "      <td>5.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>city</td>\n",
       "      <td>6.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>category_name</td>\n",
       "      <td>6.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>user_id</td>\n",
       "      <td>6.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>parent_category_name</td>\n",
       "      <td>7.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>image_top_1</td>\n",
       "      <td>11.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>param_1</td>\n",
       "      <td>13.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>price</td>\n",
       "      <td>18.23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     col   imp\n",
       "16                  year  0.00\n",
       "21               quarter  0.00\n",
       "0                item_id  0.00\n",
       "14                 image  0.00\n",
       "19          week_of_year  0.41\n",
       "18               weekday  1.20\n",
       "20          day_of_month  1.20\n",
       "10           description  1.37\n",
       "17           day_of_year  1.93\n",
       "8                param_3  2.49\n",
       "12       item_seq_number  3.28\n",
       "2                 region  3.92\n",
       "7                param_2  4.74\n",
       "9                  title  5.59\n",
       "13             user_type  5.71\n",
       "3                   city  6.00\n",
       "5          category_name  6.08\n",
       "1                user_id  6.16\n",
       "4   parent_category_name  7.52\n",
       "15           image_top_1 11.12\n",
       "6                param_1 13.06\n",
       "11                 price 18.23"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fea_imp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model evaluation:\n",
      "{'bagging_temperature': 0.2, 'eval_metric': 'RMSE', 'metric_period': 75, 'random_seed': 42, 'od_type': 'Iter', 'od_wait': 100, 'loss_function': 'RMSE', 'depth': 16, 'learning_rate': 0.01, 'iterations': 700}\n",
      "RMSE: 0.22336364763\n"
     ]
    }
   ],
   "source": [
    "# model evaluation\n",
    "from sklearn.metrics import mean_squared_error\n",
    "print('Model evaluation:')\n",
    "print(model.get_params())\n",
    "print('RMSE:', np.sqrt(mean_squared_error(y_valid, model.predict(X_valid))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## LightGBM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "import gc\n",
    "import random\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn import feature_selection\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import preprocessing\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\n",
    "from sklearn.pipeline import FeatureUnion\n",
    "from scipy.sparse import hstack, csr_matrix\n",
    "import lightgbm as lgb\n",
    "import matplotlib.pyplot as plt\n",
    "import string\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import lightgbm as lgb\n",
    "\n",
    "df = pd.read_csv('train_avito.csv', parse_dates = ['activation_date'])\n",
    "df['year'] = df['activation_date'].dt.year\n",
    "df['day_of_year'] = df['activation_date'].dt.dayofyear\n",
    "df['weekday'] = df['activation_date'].dt.weekday\n",
    "df['week_of_year'] = df['activation_date'].dt.week\n",
    "df['day_of_month'] = df['activation_date'].dt.day\n",
    "df['quarter'] = df['activation_date'].dt.quarter\n",
    "\n",
    "df.drop('activation_date', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop('image', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df['deal_probability'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>item_id</th>\n",
       "      <th>user_id</th>\n",
       "      <th>region</th>\n",
       "      <th>city</th>\n",
       "      <th>parent_category_name</th>\n",
       "      <th>category_name</th>\n",
       "      <th>param_1</th>\n",
       "      <th>param_2</th>\n",
       "      <th>param_3</th>\n",
       "      <th>title</th>\n",
       "      <th>...</th>\n",
       "      <th>item_seq_number</th>\n",
       "      <th>user_type</th>\n",
       "      <th>image_top_1</th>\n",
       "      <th>deal_probability</th>\n",
       "      <th>year</th>\n",
       "      <th>day_of_year</th>\n",
       "      <th>weekday</th>\n",
       "      <th>week_of_year</th>\n",
       "      <th>day_of_month</th>\n",
       "      <th>quarter</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>b912c3c6a6ad</td>\n",
       "      <td>e00f8ff2eaf9</td>\n",
       "      <td>Свердловская область</td>\n",
       "      <td>Екатеринбург</td>\n",
       "      <td>Личные вещи</td>\n",
       "      <td>Товары для детей и игрушки</td>\n",
       "      <td>Постельные принадлежности</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Кокоби(кокон для сна)</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>Private</td>\n",
       "      <td>1008.0</td>\n",
       "      <td>0.12789</td>\n",
       "      <td>2017</td>\n",
       "      <td>87</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2dac0150717d</td>\n",
       "      <td>39aeb48f0017</td>\n",
       "      <td>Самарская область</td>\n",
       "      <td>Самара</td>\n",
       "      <td>Для дома и дачи</td>\n",
       "      <td>Мебель и интерьер</td>\n",
       "      <td>Другое</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Стойка для Одежды</td>\n",
       "      <td>...</td>\n",
       "      <td>19</td>\n",
       "      <td>Private</td>\n",
       "      <td>692.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>2017</td>\n",
       "      <td>85</td>\n",
       "      <td>6</td>\n",
       "      <td>12</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ba83aefab5dc</td>\n",
       "      <td>91e2f88dd6e3</td>\n",
       "      <td>Ростовская область</td>\n",
       "      <td>Ростов-на-Дону</td>\n",
       "      <td>Бытовая электроника</td>\n",
       "      <td>Аудио и видео</td>\n",
       "      <td>Видео, DVD и Blu-ray плееры</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Philips bluray</td>\n",
       "      <td>...</td>\n",
       "      <td>9</td>\n",
       "      <td>Private</td>\n",
       "      <td>3032.0</td>\n",
       "      <td>0.43177</td>\n",
       "      <td>2017</td>\n",
       "      <td>79</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        item_id       user_id                region            city  \\\n",
       "0  b912c3c6a6ad  e00f8ff2eaf9  Свердловская область    Екатеринбург   \n",
       "1  2dac0150717d  39aeb48f0017     Самарская область          Самара   \n",
       "2  ba83aefab5dc  91e2f88dd6e3    Ростовская область  Ростов-на-Дону   \n",
       "\n",
       "  parent_category_name               category_name  \\\n",
       "0          Личные вещи  Товары для детей и игрушки   \n",
       "1      Для дома и дачи           Мебель и интерьер   \n",
       "2  Бытовая электроника               Аудио и видео   \n",
       "\n",
       "                       param_1 param_2 param_3                  title  \\\n",
       "0    Постельные принадлежности     NaN     NaN  Кокоби(кокон для сна)   \n",
       "1                       Другое     NaN     NaN      Стойка для Одежды   \n",
       "2  Видео, DVD и Blu-ray плееры     NaN     NaN         Philips bluray   \n",
       "\n",
       "    ...    item_seq_number  user_type  image_top_1 deal_probability  year  \\\n",
       "0   ...                  2    Private       1008.0          0.12789  2017   \n",
       "1   ...                 19    Private        692.0          0.00000  2017   \n",
       "2   ...                  9    Private       3032.0          0.43177  2017   \n",
       "\n",
       "   day_of_year  weekday  week_of_year  day_of_month  quarter  \n",
       "0           87        1            13            28        1  \n",
       "1           85        6            12            26        1  \n",
       "2           79        0            12            20        1  \n",
       "\n",
       "[3 rows x 22 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "categorical = [\"user_id\", \"region\",\"city\", \"parent_category_name\", \"category_name\", \"user_type\", \"image_top_1\", \"param_1\", \"param_2\", \"param_3\"]\n",
    "# Fill NA values for image_top_1 with -1\n",
    "df[\"image_top_1\"].fillna(-1, inplace=True)\n",
    "\n",
    "label_encoder = preprocessing.LabelEncoder()\n",
    "for col in categorical:\n",
    "    df[col].fillna(\"unknown\")\n",
    "    df[col] = label_encoder.fit_transform(df[col].astype(str))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"price\"].fillna(df.price.median(), inplace=True)\n",
    "df[\"price\"] = np.log1p(df[\"price\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['item_id', 'user_id', 'region', 'city', 'parent_category_name',\n",
       "       'category_name', 'param_1', 'param_2', 'param_3', 'title',\n",
       "       'description', 'price', 'item_seq_number', 'user_type', 'image_top_1',\n",
       "       'deal_probability', 'year', 'day_of_year', 'weekday', 'week_of_year',\n",
       "       'day_of_month', 'quarter'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['item_id', 'title', 'description'], axis=1, inplace=True)\n",
    "X = df.loc[:, df.columns != 'deal_probability']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of featues:  18\n"
     ]
    }
   ],
   "source": [
    "feature_names = X.columns.tolist()\n",
    "print(\"Number of featues: \", len(feature_names))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### lightGBM model training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "params = {\n",
    "    'objective' : 'regression',\n",
    "    'metric' : 'rmse',\n",
    "    'num_leaves' : 200,\n",
    "    'max_depth': 15,\n",
    "    'learning_rate' : 0.01,\n",
    "    'feature_fraction' : 0.6,\n",
    "    'verbosity' : -1\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "44"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train, X_valid, y_train, y_valid = train_test_split(\n",
    "    X, y, test_size=0.25, random_state=42)\n",
    "    \n",
    "# LightGBM dataset formatting \n",
    "lgtrain = lgb.Dataset(X_train, y_train,\n",
    "                feature_name=feature_names,\n",
    "                categorical_feature = categorical)\n",
    "lgvalid = lgb.Dataset(X_valid, y_valid,\n",
    "                feature_name=feature_names,\n",
    "                categorical_feature = categorical)\n",
    "del X, X_train\n",
    "gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\SusanLi\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\lightgbm\\basic.py:1040: UserWarning: Using categorical_feature in Dataset.\n",
      "  warnings.warn('Using categorical_feature in Dataset.')\n",
      "C:\\Users\\SusanLi\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\lightgbm\\basic.py:685: UserWarning: categorical_feature in param dict is overridden.\n",
      "  warnings.warn('categorical_feature in param dict is overridden.')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training until validation scores don't improve for 500 rounds.\n",
      "[500]\ttrain's rmse: 0.219561\tvalid's rmse: 0.224314\n",
      "[1000]\ttrain's rmse: 0.216259\tvalid's rmse: 0.223433\n",
      "[1500]\ttrain's rmse: 0.214056\tvalid's rmse: 0.223106\n",
      "[2000]\ttrain's rmse: 0.212158\tvalid's rmse: 0.22292\n",
      "[2500]\ttrain's rmse: 0.210293\tvalid's rmse: 0.22278\n",
      "[3000]\ttrain's rmse: 0.208684\tvalid's rmse: 0.222705\n",
      "[3500]\ttrain's rmse: 0.207132\tvalid's rmse: 0.222644\n",
      "[4000]\ttrain's rmse: 0.205718\tvalid's rmse: 0.222613\n",
      "[4500]\ttrain's rmse: 0.204391\tvalid's rmse: 0.222597\n",
      "Early stopping, best iteration is:\n",
      "[4345]\ttrain's rmse: 0.204802\tvalid's rmse: 0.222595\n",
      "RMSE of the validation set: 0.222595150292\n"
     ]
    }
   ],
   "source": [
    "lgb_clf = lgb.train(\n",
    "    params,\n",
    "    lgtrain,\n",
    "    num_iterations=20000,\n",
    "    valid_sets=[lgtrain, lgvalid],\n",
    "    valid_names=[\"train\", \"valid\"],\n",
    "    early_stopping_rounds=500,\n",
    "    verbose_eval=500\n",
    ")\n",
    "print(\"RMSE of the validation set:\", np.sqrt(mean_squared_error(y_valid, lgb_clf.predict(X_valid))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x173cde1bd30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10, 7))\n",
    "lgb.plot_importance(lgb_clf, max_num_features=30, ax=ax)\n",
    "plt.title(\"LightGBM - Feature Importance\");"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}