Lead Scoring Classification Model with 92% Accuracy using RandomForest.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import Visualizer\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from lightgbm import LGBMClassifier\n",
    "from catboost import CatBoostClassifier\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, ExtraTreesClassifier, BaggingClassifier, StackingClassifier, GradientBoostingClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.linear_model import LogisticRegression, SGDClassifier\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.naive_bayes import BernoulliNB, GaussianNB\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.model_selection import GridSearchCV, RandomizedSearchCV, cross_val_score\n",
    "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix, roc_curve, roc_auc_score, recall_score, precision_score, f1_score\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv(\"train.csv\")\n",
    "test = pd.read_csv(\"test.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(6461, 90)\n",
      "(2769, 90)\n"
     ]
    }
   ],
   "source": [
    "print(train.shape)\n",
    "print(test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.drop(\"Unnamed: 0\", axis = 1, inplace = True)\n",
    "test.drop(\"Unnamed: 0\", axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.sample(frac = 0.1).reset_index().drop(\"index\", axis = 1, inplace = True)\n",
    "test.sample(frac = 0.1).reset_index().drop(\"index\", axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = train.drop(\"Converted\", axis = 1)\n",
    "y_train = train.iloc[:, -1]\n",
    "x_test = test.drop(\"Converted\", axis = 1)\n",
    "y_test = test.iloc[:, -1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_columns', 500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Tags_Will revert after reading the email</th>\n",
       "      <th>Total Time Spent on Website</th>\n",
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       "      <th>Tags_Interested  in full time MBA</th>\n",
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       "      <th>Lead Profile_Student of SomeSchool</th>\n",
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       "      <th>Tags_switched off</th>\n",
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       "      <th>Specialization_Retail Management</th>\n",
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       "      <th>Lead Source_Facebook</th>\n",
       "      <th>Tags_Lost to EINS</th>\n",
       "      <th>Specialization_E-Business</th>\n",
       "      <th>Last Notable Activity_Unreachable</th>\n",
       "      <th>Lead Source_Olark Chat</th>\n",
       "      <th>Lead Origin_Quick Add Form</th>\n",
       "      <th>Specialization_International Business</th>\n",
       "      <th>Tags_Want to take admission but has financial problems</th>\n",
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       "      <th>Specialization_Supply Chain Management</th>\n",
       "      <th>How did you hear about X Education_Word Of Mouth</th>\n",
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       "      <th>How did you hear about X Education_Online Search</th>\n",
       "      <th>Tags_opp hangup</th>\n",
       "      <th>Tags_Diploma holder (Not Eligible)</th>\n",
       "      <th>Last Notable Activity_Page Visited on Website</th>\n",
       "      <th>Specialization_Services Excellence</th>\n",
       "      <th>How did you hear about X Education_SMS</th>\n",
       "      <th>City_Other Cities</th>\n",
       "      <th>Last Activity_Page Visited on Website</th>\n",
       "      <th>Specialization_Operations Management</th>\n",
       "      <th>How did you hear about X Education_Unspecified</th>\n",
       "      <th>Last Notable Activity_Email Link Clicked</th>\n",
       "      <th>What is your current occupation_Housewife</th>\n",
       "      <th>City_Tier II Cities</th>\n",
       "      <th>Lead Source_Others</th>\n",
       "      <th>Last Notable Activity_Email Bounced</th>\n",
       "      <th>Last Activity_Visited Booth in Tradeshow</th>\n",
       "      <th>City_Other Cities of Maharashtra</th>\n",
       "      <th>Specialization_Travel and Tourism</th>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Tags_Will revert after reading the email  Total Time Spent on Website  \\\n",
       "0                                         0                            0   \n",
       "1                                         0                         1440   \n",
       "2                                         0                          169   \n",
       "3                                         1                          734   \n",
       "4                                         0                            0   \n",
       "\n",
       "   Lead Profile_Potential Lead  Last Notable Activity_SMS Sent  \\\n",
       "0                            0                               0   \n",
       "1                            0                               0   \n",
       "2                            0                               0   \n",
       "3                            0                               1   \n",
       "4                            0                               0   \n",
       "\n",
       "   Last Activity_SMS Sent  \\\n",
       "0                       0   \n",
       "1                       0   \n",
       "2                       0   \n",
       "3                       1   \n",
       "4                       0   \n",
       "\n",
       "   What is your current occupation_Working Professional  \\\n",
       "0                                                  0      \n",
       "1                                                  0      \n",
       "2                                                  0      \n",
       "3                                                  0      \n",
       "4                                                  0      \n",
       "\n",
       "   Lead Origin_Lead Add Form  Lead Profile_Unspecified  Lead Source_Reference  \\\n",
       "0                          0                         1                      0   \n",
       "1                          0                         1                      0   \n",
       "2                          0                         1                      0   \n",
       "3                          0                         1                      0   \n",
       "4                          0                         1                      0   \n",
       "\n",
       "   What is your current occupation_Unemployed  \\\n",
       "0                                           1   \n",
       "1                                           1   \n",
       "2                                           1   \n",
       "3                                           1   \n",
       "4                                           1   \n",
       "\n",
       "   Tags_Interested in other courses  Tags_Ringing  Tags_Closed by Horizzon  \\\n",
       "0                                 1             0                        0   \n",
       "1                                 0             0                        0   \n",
       "2                                 0             0                        0   \n",
       "3                                 0             0                        0   \n",
       "4                                 0             0                        0   \n",
       "\n",
       "   Last Notable Activity_Modified  TotalVisits  \\\n",
       "0                               1          0.0   \n",
       "1                               1          3.0   \n",
       "2                               1          2.0   \n",
       "3                               0          4.0   \n",
       "4                               0          0.0   \n",
       "\n",
       "   Last Activity_Olark Chat Conversation  Specialization_Unspecified  \\\n",
       "0                                      1                           1   \n",
       "1                                      1                           0   \n",
       "2                                      0                           0   \n",
       "3                                      0                           0   \n",
       "4                                      1                           1   \n",
       "\n",
       "   Tags_Interested  in full time MBA  Lead Source_Welingak Website  \\\n",
       "0                                  0                             0   \n",
       "1                                  0                             0   \n",
       "2                                  0                             0   \n",
       "3                                  0                             0   \n",
       "4                                  0                             0   \n",
       "\n",
       "   Tags_number not provided  Last Activity_Had a Phone Conversation  \\\n",
       "0                         0                                       0   \n",
       "1                         0                                       0   \n",
       "2                         0                                       0   \n",
       "3                         0                                       0   \n",
       "4                         0                                       0   \n",
       "\n",
       "   Page Views Per Visit  Tags_Graduation in progress  \\\n",
       "0                   0.0                            0   \n",
       "1                   1.5                            0   \n",
       "2                   2.0                            0   \n",
       "3                   4.0                            0   \n",
       "4                   0.0                            0   \n",
       "\n",
       "   Lead Profile_Student of SomeSchool  Tags_Not doing further education  \\\n",
       "0                                   0                                 0   \n",
       "1                                   0                                 0   \n",
       "2                                   0                                 0   \n",
       "3                                   0                                 0   \n",
       "4                                   0                                 0   \n",
       "\n",
       "   Lead Source_Social Media  Do Not Email_Yes  \\\n",
       "0                         0                 0   \n",
       "1                         0                 0   \n",
       "2                         0                 0   \n",
       "3                         0                 0   \n",
       "4                         0                 0   \n",
       "\n",
       "   Tags_In confusion whether part time or DLP  Last Activity_Email Bounced  \\\n",
       "0                                           0                            0   \n",
       "1                                           0                            0   \n",
       "2                                           0                            0   \n",
       "3                                           0                            0   \n",
       "4                                           0                            0   \n",
       "\n",
       "   Tags_Lateral student  What is your current occupation_Other  \\\n",
       "0                     0                                      0   \n",
       "1                     0                                      0   \n",
       "2                     0                                      0   \n",
       "3                     0                                      0   \n",
       "4                     0                                      0   \n",
       "\n",
       "   Lead Source_youtubechannel  Lead Source_Direct Traffic  \\\n",
       "0                           0                           0   \n",
       "1                           0                           0   \n",
       "2                           0                           0   \n",
       "3                           0                           0   \n",
       "4                           0                           0   \n",
       "\n",
       "   Specialization_Business Administration  \\\n",
       "0                                       0   \n",
       "1                                       0   \n",
       "2                                       0   \n",
       "3                                       0   \n",
       "4                                       0   \n",
       "\n",
       "   Last Activity_Resubscribed to emails  Last Activity_Converted to Lead  \\\n",
       "0                                     0                                0   \n",
       "1                                     0                                0   \n",
       "2                                     0                                0   \n",
       "3                                     0                                0   \n",
       "4                                     0                                0   \n",
       "\n",
       "   Last Notable Activity_Email Received  Tags_switched off  \\\n",
       "0                                     0                  0   \n",
       "1                                     0                  0   \n",
       "2                                     0                  1   \n",
       "3                                     0                  0   \n",
       "4                                     0                  0   \n",
       "\n",
       "   City_Other Metro Cities  Specialization_Retail Management  \\\n",
       "0                        0                                 0   \n",
       "1                        0                                 0   \n",
       "2                        0                                 0   \n",
       "3                        0                                 0   \n",
       "4                        0                                 0   \n",
       "\n",
       "   Tags_wrong number given  Specialization_Media and Advertising  \\\n",
       "0                        0                                     0   \n",
       "1                        0                                     0   \n",
       "2                        0                                     0   \n",
       "3                        0                                     0   \n",
       "4                        0                                     0   \n",
       "\n",
       "   Last Activity_Email Opened  Last Notable Activity_Email Opened  \\\n",
       "0                           0                                   0   \n",
       "1                           0                                   0   \n",
       "2                           1                                   0   \n",
       "3                           0                                   0   \n",
       "4                           0                                   0   \n",
       "\n",
       "   Lead Source_Facebook  Tags_Lost to EINS  Specialization_E-Business  \\\n",
       "0                     0                  0                          0   \n",
       "1                     0                  0                          0   \n",
       "2                     0                  0                          0   \n",
       "3                     0                  0                          0   \n",
       "4                     0                  0                          0   \n",
       "\n",
       "   Last Notable Activity_Unreachable  Lead Source_Olark Chat  \\\n",
       "0                                  0                       1   \n",
       "1                                  0                       1   \n",
       "2                                  0                       0   \n",
       "3                                  0                       0   \n",
       "4                                  0                       1   \n",
       "\n",
       "   Lead Origin_Quick Add Form  Specialization_International Business  \\\n",
       "0                           0                                      0   \n",
       "1                           0                                      0   \n",
       "2                           0                                      0   \n",
       "3                           0                                      0   \n",
       "4                           0                                      0   \n",
       "\n",
       "   Tags_Want to take admission but has financial problems  \\\n",
       "0                                                  0        \n",
       "1                                                  0        \n",
       "2                                                  0        \n",
       "3                                                  0        \n",
       "4                                                  0        \n",
       "\n",
       "   Tags_Recognition issue (DEC approval)  \\\n",
       "0                                      0   \n",
       "1                                      0   \n",
       "2                                      0   \n",
       "3                                      0   \n",
       "4                                      1   \n",
       "\n",
       "   Specialization_Supply Chain Management  \\\n",
       "0                                       0   \n",
       "1                                       0   \n",
       "2                                       0   \n",
       "3                                       0   \n",
       "4                                       0   \n",
       "\n",
       "   How did you hear about X Education_Word Of Mouth  \\\n",
       "0                                                 0   \n",
       "1                                                 0   \n",
       "2                                                 0   \n",
       "3                                                 0   \n",
       "4                                                 0   \n",
       "\n",
       "   Last Activity_Unsubscribed  Tags_Interested in Next batch  \\\n",
       "0                           0                              0   \n",
       "1                           0                              0   \n",
       "2                           0                              0   \n",
       "3                           0                              0   \n",
       "4                           0                              0   \n",
       "\n",
       "   Lead Source_welearnblog_Home  How did you hear about X Education_Email  \\\n",
       "0                             0                                         0   \n",
       "1                             0                                         0   \n",
       "2                             0                                         0   \n",
       "3                             0                                         0   \n",
       "4                             0                                         0   \n",
       "\n",
       "   What is your current occupation_Student  Lead Profile_Lateral Student  \\\n",
       "0                                        0                             0   \n",
       "1                                        0                             0   \n",
       "2                                        0                             0   \n",
       "3                                        0                             0   \n",
       "4                                        0                             0   \n",
       "\n",
       "   How did you hear about X Education_Online Search  Tags_opp hangup  \\\n",
       "0                                                 0                0   \n",
       "1                                                 1                0   \n",
       "2                                                 1                0   \n",
       "3                                                 0                0   \n",
       "4                                                 0                0   \n",
       "\n",
       "   Tags_Diploma holder (Not Eligible)  \\\n",
       "0                                   0   \n",
       "1                                   0   \n",
       "2                                   0   \n",
       "3                                   0   \n",
       "4                                   0   \n",
       "\n",
       "   Last Notable Activity_Page Visited on Website  \\\n",
       "0                                              0   \n",
       "1                                              0   \n",
       "2                                              0   \n",
       "3                                              0   \n",
       "4                                              0   \n",
       "\n",
       "   Specialization_Services Excellence  How did you hear about X Education_SMS  \\\n",
       "0                                   0                                       0   \n",
       "1                                   0                                       0   \n",
       "2                                   0                                       0   \n",
       "3                                   0                                       0   \n",
       "4                                   0                                       0   \n",
       "\n",
       "   City_Other Cities  Last Activity_Page Visited on Website  \\\n",
       "0                  0                                      0   \n",
       "1                  1                                      0   \n",
       "2                  0                                      0   \n",
       "3                  0                                      0   \n",
       "4                  0                                      0   \n",
       "\n",
       "   Specialization_Operations Management  \\\n",
       "0                                     0   \n",
       "1                                     0   \n",
       "2                                     0   \n",
       "3                                     1   \n",
       "4                                     0   \n",
       "\n",
       "   How did you hear about X Education_Unspecified  \\\n",
       "0                                               1   \n",
       "1                                               0   \n",
       "2                                               0   \n",
       "3                                               0   \n",
       "4                                               1   \n",
       "\n",
       "   Last Notable Activity_Email Link Clicked  \\\n",
       "0                                         0   \n",
       "1                                         0   \n",
       "2                                         0   \n",
       "3                                         0   \n",
       "4                                         0   \n",
       "\n",
       "   What is your current occupation_Housewife  City_Tier II Cities  \\\n",
       "0                                          0                    0   \n",
       "1                                          0                    0   \n",
       "2                                          0                    0   \n",
       "3                                          0                    0   \n",
       "4                                          0                    0   \n",
       "\n",
       "   Lead Source_Others  Last Notable Activity_Email Bounced  \\\n",
       "0                   0                                    0   \n",
       "1                   0                                    0   \n",
       "2                   0                                    0   \n",
       "3                   0                                    0   \n",
       "4                   0                                    0   \n",
       "\n",
       "   Last Activity_Visited Booth in Tradeshow  City_Other Cities of Maharashtra  \\\n",
       "0                                         0                                 0   \n",
       "1                                         0                                 0   \n",
       "2                                         0                                 0   \n",
       "3                                         0                                 0   \n",
       "4                                         0                                 0   \n",
       "\n",
       "   Specialization_Travel and Tourism  City_Unspecified  \\\n",
       "0                                  0                 1   \n",
       "1                                  0                 0   \n",
       "2                                  0                 0   \n",
       "3                                  0                 0   \n",
       "4                                  0                 1   \n",
       "\n",
       "   Tags_University not recognized  Last Activity_Email Link Clicked  \\\n",
       "0                               0                                 0   \n",
       "1                               0                                 0   \n",
       "2                               0                                 0   \n",
       "3                               0                                 0   \n",
       "4                               0                                 0   \n",
       "\n",
       "   Lead Origin_Landing Page Submission  \\\n",
       "0                                    0   \n",
       "1                                    0   \n",
       "2                                    1   \n",
       "3                                    0   \n",
       "4                                    0   \n",
       "\n",
       "   Last Activity_Form Submitted on Website  \\\n",
       "0                                        0   \n",
       "1                                        0   \n",
       "2                                        0   \n",
       "3                                        0   \n",
       "4                                        0   \n",
       "\n",
       "   Specialization_Marketing Management  \\\n",
       "0                                    0   \n",
       "1                                    1   \n",
       "2                                    0   \n",
       "3                                    0   \n",
       "4                                    0   \n",
       "\n",
       "   Last Notable Activity_Olark Chat Conversation  \\\n",
       "0                                              0   \n",
       "1                                              0   \n",
       "2                                              0   \n",
       "3                                              0   \n",
       "4                                              1   \n",
       "\n",
       "   Last Activity_Email Received  Lead Source_Live Chat  \n",
       "0                             0                      0  \n",
       "1                             0                      0  \n",
       "2                             0                      0  \n",
       "3                             0                      0  \n",
       "4                             0                      0  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "ct = ColumnTransformer([('se', StandardScaler(), ['Total Time Spent on Website', 'Page Views Per Visit', 'TotalVisits'])], remainder='passthrough')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "random_forest_pipeline = Pipeline([('transformer', ct), ('RandomForest', RandomForestClassifier(random_state = 42))])\n",
    "adaboost_pipeline = Pipeline([('transformer', ct), ('Adaboost', AdaBoostClassifier(random_state = 42))])\n",
    "ExtraTree_pipeline = Pipeline([('transformer', ct), ('ExtraTreeClassifier', ExtraTreesClassifier(random_state = 42))])\n",
    "BaggingClassifier_pipeline = Pipeline([('transformer', ct), ('BaggingClassifier', BaggingClassifier(base_estimator = DecisionTreeClassifier(), random_state = 42))])\n",
    "GradientBoost_pipeline = Pipeline([('transformer', ct), ('GradientBoosting', GradientBoostingClassifier(random_state = 42))])\n",
    "dtree_pipeline = Pipeline([('transformer', ct), ('DecisionTree', DecisionTreeClassifier(random_state = 42))])\n",
    "knn_pipeline = Pipeline([('transformer', ct), ('KNN', KNeighborsClassifier())])\n",
    "lr_pipeline = Pipeline([('transformer', ct), ('LogisticRegression', LogisticRegression(random_state = 42))])\n",
    "sgd_pipeline = Pipeline([('transformer', ct), ('StochasticGradient', SGDClassifier(random_state = 42))])\n",
    "mlp_pipeline = Pipeline([('transformer', ct), ('MLPClassifier', MLPClassifier(random_state = 42))])\n",
    "naive_pipeline = Pipeline([('transformer', ct), ('NaiveBayes', GaussianNB())])\n",
    "svc_pipeline = Pipeline([('transformer', ct), ('SVM', SVC(random_state = 42))])\n",
    "lightgbm_pipeline = Pipeline([('transformer', ct), ('lightgbm', LGBMClassifier(random_state = 42))])\n",
    "catboost_pipeline = Pipeline([('transformer', ct), ('CatBoost', CatBoostClassifier(random_state = 42, silent = True))])\n",
    "xgboost_pipeline = Pipeline([('transformer', ct), ('XGBoost', XGBClassifier(random_state = 42))])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipeline_list = [random_forest_pipeline, adaboost_pipeline, ExtraTree_pipeline, BaggingClassifier_pipeline, GradientBoost_pipeline,\n",
    "                dtree_pipeline, knn_pipeline, lr_pipeline, sgd_pipeline, mlp_pipeline, naive_pipeline, svc_pipeline,\n",
    "                lightgbm_pipeline, catboost_pipeline, xgboost_pipeline]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipe_dict = {0: \"RandomForest\", 1: \"Adaboost\", 2: \"ExtraTree\", 3: \"BaggingClassifier\", 4: \"GradientBoosting\", 5: \"DecisionTree\",\n",
    "            6: \"KNN\", 7: \"Logistic\", 8: \"SGD Classifier\", 9: \"MLPClassifier\", 10: \"NaiveBayes\",\n",
    "            11: \"SVM\", 12: \"LightGBM\", 13: \"Catboost\", 14: \"XGBoost\"}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RandomForest : 0.9114682674501511\n",
      "Adaboost : 0.9054311157473647\n",
      "ExtraTree : 0.904040558711079\n",
      "BaggingClassifier : 0.9037302434192581\n",
      "GradientBoosting : 0.9100762748766634\n",
      "DecisionTree : 0.8775752341121921\n",
      "KNN : 0.8854682961608951\n",
      "Logistic : 0.9034192103588363\n",
      "SGD Classifier : 0.8970727003890306\n",
      "MLPClassifier : 0.9015635392691204\n",
      "NaiveBayes : 0.8593082624736219\n",
      "SVM : 0.909456601317823\n",
      "LightGBM : 0.913480172838679\n",
      "Catboost : 0.9128612170484398\n",
      "XGBoost : 0.9075985376661035\n"
     ]
    }
   ],
   "source": [
    "for idx, pipe in enumerate(pipeline_list):\n",
    "    score = cross_val_score(pipe, x_train, y_train, cv = 10, scoring = 'accuracy')\n",
    "    print(pipe_dict[idx], \":\", score.mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the above results, we will be choosing the **RandomForest Classifier, GradientBoosting, LightGBM & Catboost** on which we are going to test the other metrics to see in depth performance of these 4 models based on several different metrics to choose the best model for our analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate_model(model, x_train, y_train, x_test, y_test):\n",
    "    model = model.fit(x_train, y_train)\n",
    "    predict_train_y = model.predict(x_train)\n",
    "    predict_test_y = model.predict(x_test)\n",
    "    \n",
    "    print(\"**Accuracy Score**\")\n",
    "    train_accuracy = accuracy_score(y_train, predict_train_y)\n",
    "    test_accuracy = accuracy_score(y_test, predict_test_y)\n",
    "    print(\"Train Accuracy is: %s\"%(train_accuracy))\n",
    "    print(\"\\nTest Accuracy is: %s\"%(test_accuracy))\n",
    "    print(\"---------------------------------------------------------\")\n",
    "    \n",
    "    print(\"\\n**Accuracy Error**\")\n",
    "    train_error = (1-train_accuracy)\n",
    "    test_error = (1-test_accuracy)\n",
    "    print(\"Train Error: %s\"%(train_error))\n",
    "    print(\"\\nTest Error: %s\"%(test_error))\n",
    "    print(\"---------------------------------------------------------\")\n",
    "    \n",
    "    print(\"\\n**Classification Report**\")\n",
    "    train_cf_report = pd.DataFrame(classification_report(y_train, predict_train_y, output_dict = True))\n",
    "    test_cf_report = pd.DataFrame(classification_report(y_test, predict_test_y, output_dict = True))\n",
    "    print(\"Train Classification Report:\")\n",
    "    print(train_cf_report)\n",
    "    print(\"\\n Test Classification Report:\")\n",
    "    print(test_cf_report)\n",
    "    print(\"---------------------------------------------------------\")\n",
    "    \n",
    "    print(\"\\n**Confusion Matrix**\")\n",
    "    train_conf = confusion_matrix(y_train, predict_train_y)\n",
    "    test_conf = confusion_matrix(y_test, predict_test_y)\n",
    "    print(\"Train Confusion Matrix Report:\")\n",
    "    print((train_conf))\n",
    "    print(\"\\n Test Confusion Matrix Report:\")\n",
    "    print((test_conf))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### RANDOM FOREST CLASSIFIER"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "rforest = RandomForestClassifier(random_state= 42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "**Accuracy Score**\n",
      "Train Accuracy is: 0.9846772945364495\n",
      "\n",
      "Test Accuracy is: 0.9169375225713254\n",
      "---------------------------------------------------------\n",
      "\n",
      "**Accuracy Error**\n",
      "Train Error: 0.015322705463550523\n",
      "\n",
      "Test Error: 0.08306247742867456\n",
      "---------------------------------------------------------\n",
      "\n",
      "**Classification Report**\n",
      "Train Classification Report:\n",
      "                     0            1  accuracy    macro avg  weighted avg\n",
      "precision     0.979597     0.993038  0.984677     0.986318      0.984813\n",
      "recall        0.995701     0.967292  0.984677     0.981496      0.984677\n",
      "f1-score      0.987583     0.979996  0.984677     0.983790      0.984639\n",
      "support    3954.000000  2507.000000  0.984677  6461.000000   6461.000000\n",
      "\n",
      " Test Classification Report:\n",
      "                     0            1  accuracy    macro avg  weighted avg\n",
      "precision     0.918448     0.914228  0.916938     0.916338      0.916846\n",
      "recall        0.950524     0.862036  0.916938     0.906280      0.916938\n",
      "f1-score      0.934211     0.887365  0.916938     0.910788      0.916430\n",
      "support    1718.000000  1051.000000  0.916938  2769.000000   2769.000000\n",
      "---------------------------------------------------------\n",
      "\n",
      "**Confusion Matrix**\n",
      "Train Confusion Matrix Report:\n",
      "[[3937   17]\n",
      " [  82 2425]]\n",
      "\n",
      " Test Confusion Matrix Report:\n",
      "[[1633   85]\n",
      " [ 145  906]]\n"
     ]
    }
   ],
   "source": [
    "evaluate_model(rforest, x_train, y_train, x_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### GRADIENT BOOSTING CLASSIFIER"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "GradientBoost = GradientBoostingClassifier(random_state = 42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "**Accuracy Score**\n",
      "Train Accuracy is: 0.9173502553784244\n",
      "\n",
      "Test Accuracy is: 0.9165763813651138\n",
      "---------------------------------------------------------\n",
      "\n",
      "**Accuracy Error**\n",
      "Train Error: 0.0826497446215756\n",
      "\n",
      "Test Error: 0.08342361863488623\n",
      "---------------------------------------------------------\n",
      "\n",
      "**Classification Report**\n",
      "Train Classification Report:\n",
      "                     0            1  accuracy    macro avg  weighted avg\n",
      "precision     0.907143     0.936311   0.91735     0.921727      0.918461\n",
      "recall        0.963581     0.844436   0.91735     0.904008      0.917350\n",
      "f1-score      0.934511     0.888003   0.91735     0.911257      0.916465\n",
      "support    3954.000000  2507.000000   0.91735  6461.000000   6461.000000\n",
      "\n",
      " Test Classification Report:\n",
      "                     0            1  accuracy    macro avg  weighted avg\n",
      "precision     0.913745     0.921811  0.916576     0.917778      0.916806\n",
      "recall        0.955763     0.852521  0.916576     0.904142      0.916576\n",
      "f1-score      0.934282     0.885813  0.916576     0.910047      0.915885\n",
      "support    1718.000000  1051.000000  0.916576  2769.000000   2769.000000\n",
      "---------------------------------------------------------\n",
      "\n",
      "**Confusion Matrix**\n",
      "Train Confusion Matrix Report:\n",
      "[[3810  144]\n",
      " [ 390 2117]]\n",
      "\n",
      " Test Confusion Matrix Report:\n",
      "[[1642   76]\n",
      " [ 155  896]]\n"
     ]
    }
   ],
   "source": [
    "evaluate_model(GradientBoost, x_train, y_train, x_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LIGHTGBM CLASSIFIER"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "lgbm = LGBMClassifier(random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "**Accuracy Score**\n",
      "Train Accuracy is: 0.9458288190682557\n",
      "\n",
      "Test Accuracy is: 0.9154929577464789\n",
      "---------------------------------------------------------\n",
      "\n",
      "**Accuracy Error**\n",
      "Train Error: 0.054171180931744334\n",
      "\n",
      "Test Error: 0.08450704225352113\n",
      "---------------------------------------------------------\n",
      "\n",
      "**Classification Report**\n",
      "Train Classification Report:\n",
      "                     0            1  accuracy    macro avg  weighted avg\n",
      "precision     0.941234     0.953723  0.945829     0.947479      0.946080\n",
      "recall        0.972180     0.904268  0.945829     0.938224      0.945829\n",
      "f1-score      0.956457     0.928337  0.945829     0.942397      0.945546\n",
      "support    3954.000000  2507.000000  0.945829  6461.000000   6461.000000\n",
      "\n",
      " Test Classification Report:\n",
      "                     0            1  accuracy    macro avg  weighted avg\n",
      "precision     0.921112     0.905660  0.915493     0.913386      0.915247\n",
      "recall        0.944703     0.867745  0.915493     0.906224      0.915493\n",
      "f1-score      0.932759     0.886297  0.915493     0.909528      0.915124\n",
      "support    1718.000000  1051.000000  0.915493  2769.000000   2769.000000\n",
      "---------------------------------------------------------\n",
      "\n",
      "**Confusion Matrix**\n",
      "Train Confusion Matrix Report:\n",
      "[[3844  110]\n",
      " [ 240 2267]]\n",
      "\n",
      " Test Confusion Matrix Report:\n",
      "[[1623   95]\n",
      " [ 139  912]]\n"
     ]
    }
   ],
   "source": [
    "evaluate_model(lgbm, x_train, y_train, x_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CATBOOST CLASSIFIER"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "catboost_classif = CatBoostClassifier(random_state=42, silent = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "**Accuracy Score**\n",
      "Train Accuracy is: 0.9405664757777434\n",
      "\n",
      "Test Accuracy is: 0.92018779342723\n",
      "---------------------------------------------------------\n",
      "\n",
      "**Accuracy Error**\n",
      "Train Error: 0.05943352422225656\n",
      "\n",
      "Test Error: 0.07981220657277\n",
      "---------------------------------------------------------\n",
      "\n",
      "**Classification Report**\n",
      "Train Classification Report:\n",
      "                     0            1  accuracy    macro avg  weighted avg\n",
      "precision     0.934307     0.951510  0.940566     0.942908      0.940982\n",
      "recall        0.971168     0.892302  0.940566     0.931735      0.940566\n",
      "f1-score      0.952381     0.920955  0.940566     0.936668      0.940187\n",
      "support    3954.000000  2507.000000  0.940566  6461.000000   6461.000000\n",
      "\n",
      " Test Classification Report:\n",
      "                     0            1  accuracy    macro avg  weighted avg\n",
      "precision     0.920742     0.919192  0.920188     0.919967      0.920154\n",
      "recall        0.953434     0.865842  0.920188     0.909638      0.920188\n",
      "f1-score      0.936803     0.891720  0.920188     0.914261      0.919691\n",
      "support    1718.000000  1051.000000  0.920188  2769.000000   2769.000000\n",
      "---------------------------------------------------------\n",
      "\n",
      "**Confusion Matrix**\n",
      "Train Confusion Matrix Report:\n",
      "[[3840  114]\n",
      " [ 270 2237]]\n",
      "\n",
      " Test Confusion Matrix Report:\n",
      "[[1638   80]\n",
      " [ 141  910]]\n"
     ]
    }
   ],
   "source": [
    "evaluate_model(catboost_classif, x_train, y_train, x_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Models Evaluation & Performance Benchmarking\n",
    "<img src = \"https://storage.googleapis.com/kaggle-media/launches/model-evaluation-workshop/model-evaluation-spot.png\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### A) Model Accuracy:\n",
    "\n",
    "**1) Random Forest:** When it comes to train accuracy, Random Forest have the accuracy of 98.4677% while test accuracy has been declined to 91.693% which is significant drop.\n",
    "\n",
    "**2) Gradient Boosting:** For train dataset, we have a accuracy score of 91.7350% while for test dataset, we have a accuracy score of 91.657% which is pretty good as there is no much accuracy drop as compared to Random Forest.\n",
    "\n",
    "**3) LightGBM:** The LightGBM algorithm gives us a train accuracy of 94.582% while test accuracy of 91.549%.\n",
    "\n",
    "**4) CatBoost:** Under Catboost, we have a train accuracy of 94.05% while test accuracy of 92.018%. In Catboost algorithm, we have the highest test accuracy as compared to Random Forest, Gradient Boosting, LightGBM.\n",
    "\n",
    "#### B) Model Precision:\n",
    "**1) Random Forest:** When it comes to train precision for our class labels, we have a precision score of 97.95% for class label \"0\" and 99.30% for class label \"1\" while on test dataset this has been reduced. On testing dataset, precision score for class label \"0\" is coming out to be 91.84% while for class label \"1\" it is coming out to be 91.42%.\n",
    "\n",
    "This indicating that our model requires parameters needs to be change as the score has come down significantly on the testing dataset.\n",
    "\n",
    "**2) Gradient Boosting:** On our training data for class label \"0\" we have a precision score of 90.71% while for class label \"1\" we have a precision score of 93.63%,\n",
    "\n",
    "On testing dataset for our class label \"0\" this has been increased from 90.71% to 91.37% while for class label \"1\" this is slightly down i.e; 92.18% but still it is pretty good as compared to Random Forest.\n",
    "\n",
    "**3) Light GBM:** When it comes to Light GBM, our training precision score for class label \"0\" is coming out to be 94.12% while for class label \"1\" it is coming 95.37%.\n",
    "\n",
    "As far as the testing dataset concern, the precision score of class label \"0\" is coming out to be 92.11% while for class label \"1\" it is coming out to be 90.56%.\n",
    "\n",
    "**4) CatBoost:** Under CatBoost, for class label \"0\" under training dataset our precision score is coming out to be 93.43% while for class label \"1\" it is coming out to be 95.15%.\n",
    "\n",
    "For testing dataset, the precision score class label \"0\" it is slightly down from 93.43% to 92.07% while for class label \"1\" it is coming out to be 91.91%.\n",
    "\n",
    "#### C) F1-Score:\n",
    "\n",
    "**1) Random Forest:** If we take a look at the F1-Score for Random Forest Classifier on training dataset, it is coming out to be 98.75% for class label \"0\" while 97.99% for class label \"1\".\n",
    "\n",
    "On testing dataset, our F1-score has come down from 98.75% to 93.42% for class label \"0\" while for class label \"1\" it is coming out to be 88.73% which is again huge drop.\n",
    "\n",
    "**2) Gradient Boosting:** On training dataset for class label \"0\" our F1-score is coming out to be 93.45% while for class label \"1\" it is coming as 88.80%. For testing dataset, the F1-score for class label \"0\" has been reduced to 93.42% while for class label \"1\" it is 88.58%.\n",
    "\n",
    "**3) LightGBM:** On training dataset for class label \"0\" our F1-score is coming out to be 95.64% while for class label \"1\" it is coming as 92.83%. For testing dataset, the F1-score for class label \"0\" has been reduced to 93.27% while for class label \"1\" it is 88.62%.\n",
    "\n",
    "**4) CatBoost:** On training dataset for class label \"0\" our F1-score is coming out to be 95.23% while for class label \"1\" it is coming as 92.09%. For testing dataset, the F1-score for class label \"0\" has been increased to 93.68% while for class label \"1\" it is 89.17%.\n",
    "\n",
    "Also when it comes to confusion matrix, we are looking to increase our TP (True Positive) & TN (True Negative) as well as aiming to reduce the FN (False Negative). So for further analysis, we are taking random forest classifier and catboost classifier on which we're going to perform the hyper parameter tuning. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Forest Hyperparameter Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_pipeline = Pipeline([('transformer', ct), ('classifier', RandomForestClassifier(random_state=42))])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf_params = [{\n",
    "                'classifier': [RandomForestClassifier()],\n",
    "                'classifier__n_estimators': np.arange(100,2000, 200),\n",
    "                'classifier__max_depth': [None, 10, 20, 30, 50, 70, 80, 100],\n",
    "                'classifier__min_samples_split': [2, 3, 5, 7, 10],\n",
    "                'classifier__min_samples_leaf': [1,2,3,4,5,],\n",
    "                'classifier__max_features': ['auto', 'sqrt', 'log2'],\n",
    "                'classifier__bootstrap': [True, False]\n",
    "               }]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {},
   "outputs": [],
   "source": [
    "random_search = RandomizedSearchCV(estimator = new_pipeline, param_distributions = rf_param_grid, scoring = 'accuracy', n_jobs = -1, cv = 10, random_state = 42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_rf_model = random_search.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'classifier__n_estimators': 300,\n",
       " 'classifier__min_samples_split': 10,\n",
       " 'classifier__min_samples_leaf': 2,\n",
       " 'classifier__max_features': 'auto',\n",
       " 'classifier__max_depth': None,\n",
       " 'classifier__bootstrap': False,\n",
       " 'classifier': RandomForestClassifier(bootstrap=False, min_samples_leaf=2,\n",
       "                        min_samples_split=10, n_estimators=300)}"
      ]
     },
     "execution_count": 276,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_rf_model.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Score: 0.9137902488742997\n"
     ]
    }
   ],
   "source": [
    "print(\"Best Score: %s\" %(best_rf_model.best_score_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('transformer',\n",
       "                 ColumnTransformer(remainder='passthrough',\n",
       "                                   transformers=[('se', StandardScaler(),\n",
       "                                                  ['Total Time Spent on '\n",
       "                                                   'Website',\n",
       "                                                   'Page Views Per Visit',\n",
       "                                                   'TotalVisits'])])),\n",
       "                ('classifier',\n",
       "                 RandomForestClassifier(bootstrap=False, min_samples_leaf=2,\n",
       "                                        min_samples_split=10,\n",
       "                                        n_estimators=300))])"
      ]
     },
     "execution_count": 278,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_rf_model.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf_classif_pipeline = Pipeline([('transformer', ct), ('RandomForest', RandomForestClassifier(n_estimators = 300, min_samples_split = 10, min_samples_leaf = 2, max_features = 'auto', bootstrap = False, max_depth = None, random_state = 42))])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('transformer',\n",
       "                 ColumnTransformer(remainder='passthrough',\n",
       "                                   transformers=[('se', StandardScaler(),\n",
       "                                                  ['Total Time Spent on '\n",
       "                                                   'Website',\n",
       "                                                   'Page Views Per Visit',\n",
       "                                                   'TotalVisits'])])),\n",
       "                ('RandomForest',\n",
       "                 RandomForestClassifier(bootstrap=False, min_samples_leaf=2,\n",
       "                                        min_samples_split=10, n_estimators=300,\n",
       "                                        random_state=42))])"
      ]
     },
     "execution_count": 286,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_classif_pipeline.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_prediction = rf_classif_pipeline.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9209100758396533"
      ]
     },
     "execution_count": 288,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test, test_prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "metadata": {},
   "outputs": [],
   "source": [
    "def check_metric(y_test, y_predict):\n",
    "    \n",
    "    print(\"**Accuracy Score**\")\n",
    "    test_accuracy = accuracy_score(y_test, y_predict)\n",
    "    print(\"\\nTest Accuracy is: %s\"%(test_accuracy))\n",
    "    print(\"---------------------------------------------------------\")\n",
    "    \n",
    "    print(\"\\n**Accuracy Error**\")\n",
    "    test_error = (1-test_accuracy)\n",
    "    print(\"\\nTest Error: %s\"%(test_error))\n",
    "    print(\"---------------------------------------------------------\")\n",
    "    \n",
    "    print(\"\\n**Classification Report**\")\n",
    "    test_cf_report = pd.DataFrame(classification_report(y_test, y_predict, output_dict = True))\n",
    "    print(\"\\n Test Classification Report:\")\n",
    "    print(test_cf_report)\n",
    "    print(\"---------------------------------------------------------\")\n",
    "    \n",
    "    print(\"\\n**Confusion Matrix**\")\n",
    "    test_conf = confusion_matrix(y_test, y_predict)\n",
    "    print(\"\\n Test Confusion Matrix Report:\")\n",
    "    print((test_conf))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "**Accuracy Score**\n",
      "\n",
      "Test Accuracy is: 0.9209100758396533\n",
      "---------------------------------------------------------\n",
      "\n",
      "**Accuracy Error**\n",
      "\n",
      "Test Error: 0.07908992416034666\n",
      "---------------------------------------------------------\n",
      "\n",
      "**Classification Report**\n",
      "\n",
      " Test Classification Report:\n",
      "                     0            1  accuracy    macro avg  weighted avg\n",
      "precision     0.919418     0.923625   0.92091     0.921522      0.921015\n",
      "recall        0.956345     0.862988   0.92091     0.909666      0.920910\n",
      "f1-score      0.937518     0.892277   0.92091     0.914898      0.920346\n",
      "support    1718.000000  1051.000000   0.92091  2769.000000   2769.000000\n",
      "---------------------------------------------------------\n",
      "\n",
      "**Confusion Matrix**\n",
      "\n",
      " Test Confusion Matrix Report:\n",
      "[[1643   75]\n",
      " [ 144  907]]\n"
     ]
    }
   ],
   "source": [
    "check_metric(y_test, test_prediction)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cat Boost Hyperparameter Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "metadata": {},
   "outputs": [],
   "source": [
    "cb_new_pipeline = Pipeline([('transformer', ct), ('classifier', CatBoostClassifier(random_state=42, task_type = 'CPU', silent = True, eval_metric = 'accuracy'))])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "metadata": {},
   "outputs": [],
   "source": [
    "catboost_params = [{\n",
    "    'classifier': [CatBoostClassifier()],\n",
    "    'classifier__iterations': [10],\n",
    "    'classifier__learning_rate': [0.0001, 0.001, 0.003, 0.005, 0.01, 0.05, 0.1, 0.5, 1.0],\n",
    "    'classifier__depth': [2,4,6,8,10,12],\n",
    "    'classifier__l2_leaf_reg': [2,3,5,7,9,11,12,15,18,20,25,27],\n",
    "    'classifier__random_strength': [1],\n",
    "    'classifier__border_count': [50, 100, 150, 200, 254],\n",
    "}]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "metadata": {},
   "outputs": [],
   "source": [
    "cb_random_search = RandomizedSearchCV(estimator = cb_new_pipeline, param_distributions = catboost_params, scoring = 'accuracy', n_jobs = -1, cv = 10, random_state = 42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0:\tlearn: 0.3594392\ttotal: 88.2ms\tremaining: 794ms\n",
      "1:\tlearn: 0.2809763\ttotal: 176ms\tremaining: 702ms\n",
      "2:\tlearn: 0.2448799\ttotal: 267ms\tremaining: 624ms\n",
      "3:\tlearn: 0.2251484\ttotal: 397ms\tremaining: 596ms\n",
      "4:\tlearn: 0.2182179\ttotal: 492ms\tremaining: 492ms\n",
      "5:\tlearn: 0.2100850\ttotal: 582ms\tremaining: 388ms\n",
      "6:\tlearn: 0.2055419\ttotal: 699ms\tremaining: 300ms\n",
      "7:\tlearn: 0.2002186\ttotal: 788ms\tremaining: 197ms\n",
      "8:\tlearn: 0.1971786\ttotal: 880ms\tremaining: 97.8ms\n",
      "9:\tlearn: 0.1958321\ttotal: 996ms\tremaining: 0us\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=10,\n",
       "                   estimator=Pipeline(steps=[('transformer',\n",
       "                                              ColumnTransformer(remainder='passthrough',\n",
       "                                                                transformers=[('se',\n",
       "                                                                               StandardScaler(),\n",
       "                                                                               ['Total '\n",
       "                                                                                'Time '\n",
       "                                                                                'Spent '\n",
       "                                                                                'on '\n",
       "                                                                                'Website',\n",
       "                                                                                'Page '\n",
       "                                                                                'Views '\n",
       "                                                                                'Per '\n",
       "                                                                                'Visit',\n",
       "                                                                                'TotalVisits'])])),\n",
       "                                             ('classifier',\n",
       "                                              <catboost.core.CatBoostClassifier object at 0x000002B44B3741D0>)]),\n",
       "                   n_jobs=-1,\n",
       "                   param_distributions=[{'classifier': [<...object at 0x000002B44B338668>],\n",
       "                                         'classifier__border_count': [50, 100,\n",
       "                                                                      150, 200,\n",
       "                                                                      254],\n",
       "                                         'classifier__depth': [2, 4, 6, 8, 10,\n",
       "                                                               12],\n",
       "                                         'classifier__iterations': [10],\n",
       "                                         'classifier__l2_leaf_reg': [2, 3, 5, 7,\n",
       "                                                                     9, 11, 12,\n",
       "                                                                     15, 18, 20,\n",
       "                                                                     25, 27],\n",
       "                                         'classifier__learning_rate': [0.0001,\n",
       "                                                                       0.001,\n",
       "                                                                       0.003,\n",
       "                                                                       0.005,\n",
       "                                                                       0.01,\n",
       "                                                                       0.05,\n",
       "                                                                       0.1, 0.5,\n",
       "                                                                       1.0],\n",
       "                                         'classifier__random_strength': [1]}],\n",
       "                   random_state=42, scoring='accuracy')"
      ]
     },
     "execution_count": 308,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cb_random_search.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'classifier__random_strength': 1,\n",
       " 'classifier__learning_rate': 0.5,\n",
       " 'classifier__l2_leaf_reg': 7,\n",
       " 'classifier__iterations': 10,\n",
       " 'classifier__depth': 10,\n",
       " 'classifier__border_count': 50,\n",
       " 'classifier': <catboost.core.CatBoostClassifier at 0x2b44b338668>}"
      ]
     },
     "execution_count": 309,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cb_random_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9125525765500214"
      ]
     },
     "execution_count": 310,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cb_random_search.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "metadata": {},
   "outputs": [],
   "source": [
    "catboost_model = CatBoostClassifier(random_strength=1, learning_rate=0.5, l2_leaf_reg=7, iterations=10, depth=10, border_count=50, \n",
    "                                    silent = True, eval_metric='Accuracy', task_type='CPU')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
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o=t(\"d3\"),i=t(\"../../lib\"),l=t(\"../dragelement\"),s=t(\"./helpers\"),c=t(\"./layout_attributes\");e.exports={moduleType:\"component\",name:\"fx\",constants:t(\"./constants\"),schema:{layout:c},attributes:t(\"./attributes\"),layoutAttributes:c,supplyLayoutGlobalDefaults:t(\"./layout_global_defaults\"),supplyDefaults:t(\"./defaults\"),supplyLayoutDefaults:t(\"./layout_defaults\"),calc:t(\"./calc\"),getDistanceFunction:s.getDistanceFunction,getClosest:s.getClosest,inbox:s.inbox,castHoverOption:a,hover:t(\"./hover\").hover,unhover:l.unhover,loneHover:t(\"./hover\").loneHover,loneUnhover:n,click:t(\"./click\")}},{\"../../lib\":136,\"../dragelement\":46,\"./attributes\":58,\"./calc\":59,\"./click\":60,\"./constants\":61,\"./defaults\":62,\"./helpers\":63,\"./hover\":64,\"./layout_attributes\":67,\"./layout_defaults\":68,\"./layout_global_defaults\":69,d3:7}],67:[function(t,e,r){\"use strict\";var 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I=this.container.selectAll(\"rect.scrollbar-vertical\").data(O?[0]:[]);I.exit().on(\".drag\",null).remove(),I.enter().append(\"rect\").classed(\"scrollbar-vertical\",!0).call(o.fill,n.barColor),O?(this.vbar=I.attr({rx:n.barRadius,ry:n.barRadius,x:E,y:N,width:D,height:P}),this._vbarYMin=N+P/2,this._vbarTranslateMax=M-P):(delete this.vbar,delete this._vbarYMin,delete this._vbarTranslateMax);var R=this.id,F=u-.5,j=O?f+D+.5:f+.5,B=d-.5,q=A?h+L+.5:h+.5,H=l._topdefs.selectAll(\"#\"+R).data(A||O?[0]:[]);if(H.exit().remove(),H.enter().append(\"clipPath\").attr(\"id\",R).append(\"rect\"),A||O?(this._clipRect=H.select(\"rect\").attr({x:Math.floor(F),y:Math.floor(B),width:Math.ceil(j)-Math.floor(F),height:Math.ceil(q)-Math.floor(B)}),this.container.call(i.setClipUrl,R),this.bg.attr({x:p,y:v,width:g,height:m})):(this.bg.attr({width:0,height:0}),this.container.on(\"wheel\",null).on(\".drag\",null).call(i.setClipUrl,null),delete this._clipRect),A||O){var 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r=this.position.w-this._box.w,n=this.position.h-this._box.h;if(t=l.constrain(t||0,0,r),e=l.constrain(e||0,0,n),this.translateX=t,this.translateY=e,this.container.call(i.setTranslate,this._box.l-this.position.l-t,this._box.t-this.position.t-e),this._clipRect&&this._clipRect.attr({x:Math.floor(this.position.l+t-.5),y:Math.floor(this.position.t+e-.5)}),this.hbar){var a=t/r;this.hbar.call(i.setTranslate,t+a*this._hbarTranslateMax,e)}if(this.vbar){var o=e/n;this.vbar.call(i.setTranslate,t,e+o*this._vbarTranslateMax)}}},{\"../../lib\":136,\"../color\":25,\"../drawing\":49,d3:7}],121:[function(t,e,r){\"use strict\";e.exports={SHOW_PLACEHOLDER:100,HIDE_PLACEHOLDER:1e3,DBLCLICKDELAY:300}},{}],122:[function(t,e,r){\"use strict\";e.exports={BADNUM:void 0,FP_SAFE:Number.MAX_VALUE/1e4,ONEAVGYEAR:315576e5,ONEAVGMONTH:26298e5,ONEDAY:864e5,ONEHOUR:36e5,ONEMIN:6e4,ONESEC:1e3,EPOCHJD:2440587.5,ALMOST_EQUAL:1-1e-6}},{}],123:[function(t,e,r){\"use 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b,_=[f.margin.left+x,f.margin.top+x];if(p){b=[0,n.max(i.util.sumArrays(i.util.arrayLast(h).r[0],i.util.arrayLast(m)))]}else b=n.extent(i.util.flattenArray(h.map(function(t,e){return t.r})));f.radialAxis.domain!=i.DATAEXTENT&&(b[0]=0),a=n.scale.linear().domain(f.radialAxis.domain!=i.DATAEXTENT&&f.radialAxis.domain?f.radialAxis.domain:b).range([0,x]),u.layout.radialAxis.domain=a.domain();var w,k=i.util.flattenArray(h.map(function(t,e){return t.t})),M=\"string\"==typeof k[0];M&&(k=i.util.deduplicate(k),w=k.slice(),k=n.range(k.length),h=h.map(function(t,e){var r=t;return t.t=[k],p&&(r.yStack=t.yStack),r}));var A=h.filter(function(t,e){return\"LinePlot\"===t.geometry||\"DotPlot\"===t.geometry}).length===h.length,T=null===f.needsEndSpacing?M||!A:f.needsEndSpacing,L=f.angularAxis.domain&&f.angularAxis.domain!=i.DATAEXTENT&&!M&&f.angularAxis.domain[0]>=0,C=L?f.angularAxis.domain:n.extent(k),S=Math.abs(k[1]-k[0]);A&&!M&&(S=0);var z=C.slice();T&&M&&(z[1]+=S);var O=f.angularAxis.ticksCount||4;O>8&&(O=O/(O/8)+O%8),f.angularAxis.ticksStep&&(O=(z[1]-z[0])/O);var D=f.angularAxis.ticksStep||(z[1]-z[0])/(O*(f.minorTicks+1));w&&(D=Math.max(Math.round(D),1)),z[2]||(z[2]=D);var P=n.range.apply(this,z);if(P=P.map(function(t,e){return parseFloat(t.toPrecision(12))}),l=n.scale.linear().domain(z.slice(0,2)).range(\"clockwise\"===f.direction?[0,360]:[360,0]),u.layout.angularAxis.domain=l.domain(),u.layout.angularAxis.endPadding=T?S:0,void 0===(e=n.select(this).select(\"svg.chart-root\"))||e.empty()){var E=(new DOMParser).parseFromString(\"<svg xmlns='http://www.w3.org/2000/svg' class='chart-root'>' + '<g class='outer-group'>' + '<g class='chart-group'>' + '<circle class='background-circle'></circle>' + '<g class='geometry-group'></g>' + '<g class='radial axis-group'>' + '<circle class='outside-circle'></circle>' + '</g>' + '<g class='angular axis-group'></g>' + '<g class='guides-group'><line></line><circle r='0'></circle></g>' + '</g>' + '<g 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r.symbol=\"DotPlot\"===t.geometry?t.dotType||\"circle\":\"LinePlot\"!=t.geometry?\"square\":\"line\",r.visibleInLegend=void 0===t.visibleInLegend||t.visibleInLegend,r.color=\"LinePlot\"===t.geometry?t.strokeColor:t.color,r});i.Legend().config({data:h.map(function(t,e){return t.name||\"Element\"+e}),legendConfig:o({},i.Legend.defaultConfig().legendConfig,{container:I,elements:B,reverseOrder:f.legend.reverseOrder})})();var q=I.node().getBBox();x=Math.min(f.width-q.width-f.margin.left-f.margin.right,f.height-f.margin.top-f.margin.bottom)/2,x=Math.max(10,x),_=[f.margin.left+x,f.margin.top+x],a.range([0,x]),u.layout.radialAxis.domain=a.domain(),I.attr(\"transform\",\"translate(\"+[_[0]+x,_[1]-x]+\")\")}else I=e.select(\".legend-group\").style({display:\"none\"});e.attr({width:f.width,height:f.height}).style({opacity:f.opacity}),R.attr(\"transform\",\"translate(\"+_+\")\").style({cursor:\"crosshair\"});var H=[(f.width-(f.margin.left+f.margin.right+2*x+(q?q.width:0)))/2,(f.height-(f.margin.top+f.margin.bottom+2*x))/2];if(H[0]=Math.max(0,H[0]),H[1]=Math.max(0,H[1]),e.select(\".outer-group\").attr(\"transform\",\"translate(\"+H+\")\"),f.title){var V=e.select(\"g.title-group text\").style(j).text(f.title),U=V.node().getBBox();V.attr({x:_[0]-U.width/2,y:_[1]-x-20})}var X=e.select(\".radial.axis-group\");if(f.radialAxis.gridLinesVisible){var G=X.selectAll(\"circle.grid-circle\").data(a.ticks(5));G.enter().append(\"circle\").attr({class:\"grid-circle\"}).style(F),G.attr(\"r\",a),G.exit().remove()}X.select(\"circle.outside-circle\").attr({r:x}).style(F);var Y=e.select(\"circle.background-circle\").attr({r:x}).style({fill:f.backgroundColor,stroke:f.stroke});if(f.radialAxis.visible){var Z=n.svg.axis().scale(a).ticks(5).tickSize(5);X.call(Z).attr({transform:\"rotate(\"+f.radialAxis.orientation+\")\"}),X.selectAll(\".domain\").style(F),X.selectAll(\"g>text\").text(function(t,e){return this.textContent+f.radialAxis.ticksSuffix}).style(j).style({\"text-anchor\":\"start\"}).attr({x:0,y:0,dx:0,dy:0,transform:function(t,e){return\"horizontal\"===f.radialAxis.tickOrientation?\"rotate(\"+-f.radialAxis.orientation+\") translate(\"+[0,j[\"font-size\"]]+\")\":\"translate(\"+[0,j[\"font-size\"]]+\")\"}}),X.selectAll(\"g>line\").style({stroke:\"black\"})}var W=e.select(\".angular.axis-group\").selectAll(\"g.angular-tick\").data(P),$=W.enter().append(\"g\").classed(\"angular-tick\",!0);W.attr({transform:function(t,e){return\"rotate(\"+s(t,e)+\")\"}}).style({display:f.angularAxis.visible?\"block\":\"none\"}),W.exit().remove(),$.append(\"line\").classed(\"grid-line\",!0).classed(\"major\",function(t,e){return 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e%(f.minorTicks+1)!=0?\"\":f.angularAxis.rewriteTicks(this.textContent,e)});var J=n.max(R.selectAll(\".angular-tick text\")[0].map(function(t,e){return t.getCTM().e+t.getBBox().width}));I.attr({transform:\"translate(\"+[x+J,f.margin.top]+\")\"});var K=e.select(\"g.geometry-group\").selectAll(\"g\").size()>0,tt=e.select(\"g.geometry-group\").selectAll(\"g.geometry\").data(h);if(tt.enter().append(\"g\").attr({class:function(t,e){return\"geometry geometry\"+e}}),tt.exit().remove(),h[0]||K){var et=[];h.forEach(function(t,e){var r={};r.radialScale=a,r.angularScale=l,r.container=tt.filter(function(t,r){return r==e}),r.geometry=t.geometry,r.orientation=f.orientation,r.direction=f.direction,r.index=e,et.push({data:t,geometryConfig:r})});var rt=n.nest().key(function(t,e){return void 0!==t.data.groupId||\"unstacked\"}).entries(et),nt=[];rt.forEach(function(t,e){\"unstacked\"===t.key?nt=nt.concat(t.values.map(function(t,e){return[t]})):nt.push(t.values)}),nt.forEach(function(t,e){var r;r=Array.isArray(t)?t[0].geometryConfig.geometry:t.geometryConfig.geometry;var n=t.map(function(t,e){return o(i[r].defaultConfig(),t)});i[r]().config(n)()})}var at,ot,it=e.select(\".guides-group\"),lt=e.select(\".tooltips-group\"),st=i.tooltipPanel().config({container:lt,fontSize:8})(),ct=i.tooltipPanel().config({container:lt,fontSize:8})(),ut=i.tooltipPanel().config({container:lt,hasTick:!0})();if(!M){var ft=it.select(\"line\").attr({x1:0,y1:0,y2:0}).style({stroke:\"grey\",\"pointer-events\":\"none\"});R.on(\"mousemove.angular-guide\",function(t,e){var r=i.util.getMousePos(Y).angle;ft.attr({x2:-x,transform:\"rotate(\"+r+\")\"}).style({opacity:.5});var n=(r+180+360-f.orientation)%360;at=l.invert(n);var a=i.util.convertToCartesian(x+12,r+180);st.text(i.util.round(at)).move([a[0]+_[0],a[1]+_[1]])}).on(\"mouseout.angular-guide\",function(t,e){it.select(\"line\").style({opacity:0})})}var dt=it.select(\"circle\").style({stroke:\"grey\",fill:\"none\"});R.on(\"mousemove.radial-guide\",function(t,e){var r=i.util.getMousePos(Y).radius;dt.attr({r:r}).style({opacity:.5}),ot=a.invert(i.util.getMousePos(Y).radius);var n=i.util.convertToCartesian(r,f.radialAxis.orientation);ct.text(i.util.round(ot)).move([n[0]+_[0],n[1]+_[1]])}).on(\"mouseout.radial-guide\",function(t,e){dt.style({opacity:0}),ut.hide(),st.hide(),ct.hide()}),e.selectAll(\".geometry-group .mark\").on(\"mouseover.tooltip\",function(t,r){var a=n.select(this),o=a.style(\"fill\"),l=\"black\",s=a.style(\"opacity\")||1;if(a.attr({\"data-opacity\":s}),\"none\"!=o){a.attr({\"data-fill\":o}),l=n.hsl(o).darker().toString(),a.style({fill:l,opacity:1});var c={t:i.util.round(t[0]),r:i.util.round(t[1])};M&&(c.t=w[t[0]]);var u=\"t: \"+c.t+\", r: \"+c.r,f=this.getBoundingClientRect(),d=e.node().getBoundingClientRect(),h=[f.left+f.width/2-H[0]-d.left,f.top+f.height/2-H[1]-d.top];ut.config({color:l}).text(u),ut.move(h)}else 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l},d.svg=function(){return e},n.rebind(d,f,\"on\"),d},i.Axis.defaultConfig=function(t,e){return{data:[{t:[1,2,3,4],r:[10,11,12,13],name:\"Line1\",geometry:\"LinePlot\",color:null,strokeDash:\"solid\",strokeColor:null,strokeSize:\"1\",visibleInLegend:!0,opacity:1}],layout:{defaultColorRange:n.scale.category10().range(),title:null,height:450,width:500,margin:{top:40,right:40,bottom:40,left:40},font:{size:12,color:\"gray\",outlineColor:\"white\",family:\"Tahoma, 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n=e*Math.PI/180;return[e,t(n)]})},i.util.dataFromEquation=function(t,e,r){var a=e||6,o=[],i=[];n.range(0,360+a,a).forEach(function(e,r){var n=e*Math.PI/180,a=t(n);o.push(e),i.push(a)});var l={t:o,r:i};return r&&(l.name=r),l},i.util.ensureArray=function(t,e){if(void 0===t)return null;var r=[].concat(t);return n.range(e).map(function(t,e){return r[e]||r[0]})},i.util.fillArrays=function(t,e,r){return e.forEach(function(e,n){t[e]=i.util.ensureArray(t[e],r)}),t},i.util.cloneJson=function(t){return JSON.parse(JSON.stringify(t))},i.util.validateKeys=function(t,e){\"string\"==typeof e&&(e=e.split(\".\"));var r=e.shift();return t[r]&&(!e.length||objHasKeys(t[r],e))},i.util.sumArrays=function(t,e){return n.zip(t,e).map(function(t,e){return n.sum(t)})},i.util.arrayLast=function(t){return t[t.length-1]},i.util.arrayEqual=function(t,e){for(var r=Math.max(t.length,e.length,1);r-- >=0&&t[r]===e[r];);return r===-2},i.util.flattenArray=function(t){for(var e=[];!i.util.arrayEqual(e,t);)e=t,t=[].concat.apply([],t);return t},i.util.deduplicate=function(t){return t.filter(function(t,e,r){return r.indexOf(t)==e})},i.util.convertToCartesian=function(t,e){var r=e*Math.PI/180;return[t*Math.cos(r),t*Math.sin(r)]},i.util.round=function(t,e){var r=e||2,n=Math.pow(10,r);return Math.round(t*n)/n},i.util.getMousePos=function(t){var e=n.mouse(t.node()),r=e[0],a=e[1],o={};return o.x=r,o.y=a,o.pos=e,o.angle=180*(Math.atan2(a,r)+Math.PI)/Math.PI,o.radius=Math.sqrt(r*r+a*a),o},i.util.duplicatesCount=function(t){for(var e,r={},n={},a=0,o=t.length;a<o;a++)e=t[a],e in r?(r[e]++,n[e]=r[e]):r[e]=1;return n},i.util.duplicates=function(t){return Object.keys(i.util.duplicatesCount(t))},i.util.translator=function(t,e,r,n){if(n){var a=r.slice();r=e,e=a}var o=e.reduce(function(t,e){if(void 0!==t)return t[e]},t);void 0!==o&&(e.reduce(function(t,r,n){if(void 0!==t)return n===e.length-1&&delete t[r],t[r]},t),r.reduce(function(t,e,n){return void 0===t[e]&&(t[e]={}),n===r.length-1&&(t[e]=o),t[e]},t))},i.PolyChart=function(){function t(){var t=e[0].geometryConfig,r=t.container;\"string\"==typeof r&&(r=n.select(r)),r.datum(e).each(function(e,r){function o(e,r){return{r:t.radialScale(e[1]),t:(t.angularScale(e[0])+t.orientation)*Math.PI/180}}function i(t){return{x:t.r*Math.cos(t.t),y:t.r*Math.sin(t.t)}}var l=!!e[0].data.yStack,s=e.map(function(t,e){return l?n.zip(t.data.t[0],t.data.r[0],t.data.yStack[0]):n.zip(t.data.t[0],t.data.r[0])}),c=t.angularScale,u=t.radialScale.domain()[0],f={};f.bar=function(r,a,o){var i=e[o].data,l=t.radialScale(r[1])-t.radialScale(0),s=t.radialScale(r[2]||0),u=i.barWidth;n.select(this).attr({class:\"mark bar\",d:\"M\"+[[l+s,-u/2],[l+s,u/2],[s,u/2],[s,-u/2]].join(\"L\")+\"Z\",transform:function(e,r){return\"rotate(\"+(t.orientation+c(e[0]))+\")\"}})},f.dot=function(t,r,a){var l=t[2]?[t[0],t[1]+t[2]]:t,s=n.svg.symbol().size(e[a].data.dotSize).type(e[a].data.dotType)(t,r);n.select(this).attr({class:\"mark 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n={};n.attributes=t(\"./attributes\"),n.supplyDefaults=t(\"./defaults\"),n.supplyLayoutDefaults=t(\"./layout_defaults\"),n.layoutAttributes=t(\"./layout_attributes\"),n.calc=t(\"./calc\"),n.plot=t(\"./plot\"),n.style=t(\"./style\"),n.styleOne=t(\"./style_one\"),n.moduleType=\"trace\",n.name=\"pie\",n.basePlotModule=t(\"./base_plot\"),n.categories=[\"pie\",\"showLegend\"],n.meta={},e.exports=n},{\"./attributes\":228,\"./base_plot\":229,\"./calc\":230,\"./defaults\":231,\"./layout_attributes\":234,\"./layout_defaults\":235,\"./plot\":236,\"./style\":237,\"./style_one\":238}],234:[function(t,e,r){\"use strict\";e.exports={hiddenlabels:{valType:\"data_array\"}}},{}],235:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),a=t(\"./layout_attributes\");e.exports=function(t,e){!function(r,o){n.coerce(t,e,a,r,o)}(\"hiddenlabels\")}},{\"../../lib\":136,\"./layout_attributes\":234}],236:[function(t,e,r){\"use strict\";function n(t,e,r){var n=Math.sqrt(t.width*t.width+t.height*t.height),o=t.width/t.height,i=Math.PI*Math.min(e.v/r.vTotal,.5),l=1-r.trace.hole,s=a(e,r),c={scale:s*r.r*2/n,rCenter:1-s,rotate:0};if(c.scale>=1)return c;var u=o+1/(2*Math.tan(i)),f=r.r*Math.min(1/(Math.sqrt(u*u+.5)+u),l/(Math.sqrt(o*o+l/2)+o)),d={scale:2*f/t.height,rCenter:Math.cos(f/r.r)-f*o/r.r,rotate:(180/Math.PI*e.midangle+720)%180-90},h=1/o,p=h+1/(2*Math.tan(i)),g=r.r*Math.min(1/(Math.sqrt(p*p+.5)+p),l/(Math.sqrt(h*h+l/2)+h)),v={scale:2*g/t.width,rCenter:Math.cos(g/r.r)-g/o/r.r,rotate:(180/Math.PI*e.midangle+810)%180-90},m=v.scale>d.scale?v:d;return c.scale<1&&m.scale>c.scale?m:c}function a(t,e){if(t.v===e.vTotal&&!e.trace.hole)return 1;var r=Math.PI*Math.min(t.v/e.vTotal,.5);return Math.min(1/(1+1/Math.sin(r)),(1-e.trace.hole)/2)}function o(t,e){var r=e.pxmid[0],n=e.pxmid[1],a=t.width/2,o=t.height/2;return r<0&&(a*=-1),n<0&&(o*=-1),{scale:1,rCenter:1,rotate:0,x:a+Math.abs(o)*(a>0?1:-1)/2,y:o/(1+r*r/(n*n)),outside:!0}}function i(t,e){function r(t,e){return t.pxmid[1]-e.pxmid[1]}function n(t,e){return e.pxmid[1]-t.pxmid[1]}var a,o,i,l,s,c,u,f,d,h,p,g,v;for(o=0;o<2;o++)for(i=o?r:n,s=o?Math.max:Math.min,u=o?1:-1,a=0;a<2;a++){for(l=a?Math.max:Math.min,c=a?1:-1,f=t[o][a],f.sort(i),d=t[1-o][a],h=d.concat(f),g=[],p=0;p<f.length;p++)void 0!==f[p].yLabelMid&&g.push(f[p]);for(v=!1,p=0;o&&p<d.length;p++)if(void 0!==d[p].yLabelMid){v=d[p];break}for(p=0;p<g.length;p++){var m=p&&g[p-1];v&&!p&&(m=v),function(t,r){r||(r={});var n,a,i,f,d,p,g=r.labelExtraY+(o?r.yLabelMax:r.yLabelMin),v=o?t.yLabelMin:t.yLabelMax,m=o?t.yLabelMax:t.yLabelMin,y=t.cyFinal+s(t.px0[1],t.px1[1]),x=g-v;if(x*u>0&&(t.labelExtraY=x),Array.isArray(e.pull))for(a=0;a<h.length;a++)(i=h[a])===t||(e.pull[t.i]||0)>=e.pull[i.i]||((t.pxmid[1]-i.pxmid[1])*u>0?(f=i.cyFinal+s(i.px0[1],i.px1[1]),(x=f-v-t.labelExtraY)*u>0&&(t.labelExtraY+=x)):(m+t.labelExtraY-y)*u>0&&(n=3*c*Math.abs(a-h.indexOf(t)),d=i.cxFinal+l(i.px0[0],i.px1[0]),(p=d+n-(t.cxFinal+t.pxmid[0])-t.labelExtraX)*c>0&&(t.labelExtraX+=p)))}(g[p],m)}}}function l(t,e){var r,n,a,o,i,l,s,u,f,d,h=[];for(a=0;a<t.length;a++){if(i=t[a][0],l=i.trace,r=e.w*(l.domain.x[1]-l.domain.x[0]),n=e.h*(l.domain.y[1]-l.domain.y[0]),s=l.tiltaxis*Math.PI/180,u=l.pull,Array.isArray(u))for(u=0,o=0;o<l.pull.length;o++)l.pull[o]>u&&(u=l.pull[o]);i.r=Math.min(r/c(l.tilt,Math.sin(s),l.depth),n/c(l.tilt,Math.cos(s),l.depth))/(2+2*u),i.cx=e.l+e.w*(l.domain.x[1]+l.domain.x[0])/2,i.cy=e.t+e.h*(2-l.domain.y[1]-l.domain.y[0])/2,\n",
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n=Math.sin(t*Math.PI/180);return Math.max(.01,r*n*Math.abs(e)+2*Math.sqrt(1-n*n*e*e))}var u=t(\"d3\"),f=t(\"../../components/fx\"),d=t(\"../../components/color\"),h=t(\"../../components/drawing\"),p=t(\"../../lib/svg_text_utils\"),g=t(\"./helpers\");e.exports=function(t,e){var r=t._fullLayout;l(e,r._size);var c=r._pielayer.selectAll(\"g.trace\").data(e);c.enter().append(\"g\").attr({\"stroke-linejoin\":\"round\",class:\"trace\"}),c.exit().remove(),c.order(),c.each(function(e){var l=u.select(this),c=e[0],v=c.trace,m=(v.depth||0)*c.r*Math.sin(0)/2,y=v.tiltaxis||0,x=y*Math.PI/180,b=[m*Math.sin(x),m*Math.cos(x)],_=c.r*Math.cos(0),w=l.selectAll(\"g.part\").data(v.tilt?[\"top\",\"sides\"]:[\"top\"]);w.enter().append(\"g\").attr(\"class\",function(t){return t+\" part\"}),w.exit().remove(),w.order(),s(e),l.selectAll(\".top\").each(function(){var l=u.select(this).selectAll(\"g.slice\").data(e);l.enter().append(\"g\").classed(\"slice\",!0),l.exit().remove();var s=[[[],[]],[[],[]]],m=!1;l.each(function(e){function i(n){n.originalEvent=u.event;var o=t._fullLayout,i=t._fullData[v.index],l=i.hoverinfo;if(\"all\"===l&&(l=\"label+text+value+percent+name\"),t._dragging||o.hovermode===!1||\"none\"===l||\"skip\"===l||!l)return void f.hover(t,n,\"pie\");var s=a(e,c),d=w+e.pxmid[0]*(1-s),h=k+e.pxmid[1]*(1-s),p=r.separators,m=[];l.indexOf(\"label\")!==-1&&m.push(e.label),l.indexOf(\"text\")!==-1&&(i.hovertext?m.push(Array.isArray(i.hovertext)?i.hovertext[e.i]:i.hovertext):i.text&&i.text[e.i]&&m.push(i.text[e.i])),l.indexOf(\"value\")!==-1&&m.push(g.formatPieValue(e.v,p)),l.indexOf(\"percent\")!==-1&&m.push(g.formatPiePercent(e.v/c.vTotal,p));var y=i.hoverlabel;f.loneHover({x0:d-s*c.r,x1:d+s*c.r,y:h,text:m.join(\"<br>\"),name:l.indexOf(\"name\")!==-1?i.name:void 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w=c.cx+b[0],k=c.cy+b[1],M=u.select(this),A=M.selectAll(\"path.surface\").data([e]),T=!1;if(A.enter().append(\"path\").classed(\"surface\",!0).style({\"pointer-events\":\"all\"}),M.select(\"path.textline\").remove(),M.on(\"mouseover\",i).on(\"mouseout\",l).on(\"click\",d),v.pull){var L=+(Array.isArray(v.pull)?v.pull[e.i]:v.pull)||0;L>0&&(w+=L*e.pxmid[0],k+=L*e.pxmid[1])}e.cxFinal=w,e.cyFinal=k;var C=v.hole;if(e.v===c.vTotal){var S=\"M\"+(w+e.px0[0])+\",\"+(k+e.px0[1])+x(e.px0,e.pxmid,!0,1)+x(e.pxmid,e.px0,!0,1)+\"Z\";C?A.attr(\"d\",\"M\"+(w+C*e.px0[0])+\",\"+(k+C*e.px0[1])+x(e.px0,e.pxmid,!1,C)+x(e.pxmid,e.px0,!1,C)+\"Z\"+S):A.attr(\"d\",S)}else{var z=x(e.px0,e.px1,!0,1);if(C){var O=1-C;A.attr(\"d\",\"M\"+(w+C*e.px1[0])+\",\"+(k+C*e.px1[1])+x(e.px1,e.px0,!1,C)+\"l\"+O*e.px0[0]+\",\"+O*e.px0[1]+z+\"Z\")}else A.attr(\"d\",\"M\"+w+\",\"+k+\"l\"+e.px0[0]+\",\"+e.px0[1]+z+\"Z\")}var D=Array.isArray(v.textposition)?v.textposition[e.i]:v.textposition,P=M.selectAll(\"g.slicetext\").data(e.text&&\"none\"!==D?[0]:[]);P.enter().append(\"g\").classed(\"slicetext\",!0),P.exit().remove(),P.each(function(){var t=u.select(this).selectAll(\"text\").data([0]);t.enter().append(\"text\").attr(\"data-notex\",1),t.exit().remove(),t.text(e.text).attr({class:\"slicetext\",transform:\"\",\"data-bb\":\"\",\"text-anchor\":\"middle\",x:0,y:0}).call(h.font,\"outside\"===D?v.outsidetextfont:v.insidetextfont).call(p.convertToTspans),t.selectAll(\"tspan.line\").attr({x:0,y:0});var r,a=h.bBox(t.node());\"outside\"===D?r=o(a,e):(r=n(a,e,c),\"auto\"===D&&r.scale<1&&(t.call(h.font,v.outsidetextfont),v.outsidetextfont.family===v.insidetextfont.family&&v.outsidetextfont.size===v.insidetextfont.size||(t.attr({\"data-bb\":\"\"}),a=h.bBox(t.node())),r=o(a,e)));var i=w+e.pxmid[0]*r.rCenter+(r.x||0),l=k+e.pxmid[1]*r.rCenter+(r.y||0);r.outside&&(e.yLabelMin=l-a.height/2,e.yLabelMid=l,e.yLabelMax=l+a.height/2,e.labelExtraX=0,e.labelExtraY=0,m=!0),t.attr(\"transform\",\"translate(\"+i+\",\"+l+\")\"+(r.scale<1?\"scale(\"+r.scale+\")\":\"\")+(r.rotate?\"rotate(\"+r.rotate+\")\":\"\")+\"translate(\"+-(a.left+a.right)/2+\",\"+-(a.top+a.bottom)/2+\")\")})}),m&&i(s,v),l.each(function(t){if(t.labelExtraX||t.labelExtraY){var e=u.select(this),r=e.select(\"g.slicetext text\");r.attr(\"transform\",\"translate(\"+t.labelExtraX+\",\"+t.labelExtraY+\")\"+r.attr(\"transform\"));var n=t.cxFinal+t.pxmid[0],a=t.cyFinal+t.pxmid[1],o=\"M\"+n+\",\"+a,i=(t.yLabelMax-t.yLabelMin)*(t.pxmid[0]<0?-1:1)/4;if(t.labelExtraX){var l=t.labelExtraX*t.pxmid[1]/t.pxmid[0],s=t.yLabelMid+t.labelExtraY-(t.cyFinal+t.pxmid[1]);Math.abs(l)>Math.abs(s)?o+=\"l\"+s*t.pxmid[0]/t.pxmid[1]+\",\"+s+\"H\"+(n+t.labelExtraX+i):o+=\"l\"+t.labelExtraX+\",\"+l+\"v\"+(s-l)+\"h\"+i}else o+=\"V\"+(t.yLabelMid+t.labelExtraY)+\"h\"+i;e.append(\"path\").classed(\"textline\",!0).call(d.stroke,v.outsidetextfont.color).attr({\"stroke-width\":Math.min(2,v.outsidetextfont.size/8),d:o,fill:\"none\"})}})})}),setTimeout(function(){c.selectAll(\"tspan\").each(function(){var t=u.select(this);t.attr(\"dy\")&&t.attr(\"dy\",t.attr(\"dy\"))})},0)}},{\"../../components/color\":25,\"../../components/drawing\":49,\"../../components/fx\":66,\"../../lib/svg_text_utils\":153,\"./helpers\":232,d3:7}],237:[function(t,e,r){\"use strict\";var n=t(\"d3\"),a=t(\"./style_one\");e.exports=function(t){t._fullLayout._pielayer.selectAll(\".trace\").each(function(t){var e=t[0],r=e.trace,o=n.select(this);o.style({opacity:r.opacity}),o.selectAll(\".top path.surface\").each(function(t){n.select(this).call(a,t,r)})})}},{\"./style_one\":238,d3:7}],238:[function(t,e,r){\"use strict\";var n=t(\"../../components/color\");e.exports=function(t,e,r){var a=r.marker.line.color;Array.isArray(a)&&(a=a[e.i]||n.defaultLine);var o=r.marker.line.width||0;Array.isArray(o)&&(o=o[e.i]||0),t.style({\"stroke-width\":o}).call(n.fill,e.color).call(n.stroke,a)}},{\"../../components/color\":25}],239:[function(t,e,r){\"use strict\";var n=t(\"../../lib\");e.exports=function(t,e){for(var r=0;r<t.length;r++)t[r].i=r;n.mergeArray(e.text,t,\"tx\"),n.mergeArray(e.hovertext,t,\"htx\"),n.mergeArray(e.customdata,t,\"data\"),n.mergeArray(e.textposition,t,\"tp\"),e.textfont&&(n.mergeArray(e.textfont.size,t,\"ts\"),n.mergeArray(e.textfont.color,t,\"tc\"),n.mergeArray(e.textfont.family,t,\"tf\"));var a=e.marker;if(a){n.mergeArray(a.size,t,\"ms\"),n.mergeArray(a.opacity,t,\"mo\"),n.mergeArray(a.symbol,t,\"mx\"),n.mergeArray(a.color,t,\"mc\");var o=a.line;a.line&&(n.mergeArray(o.color,t,\"mlc\"),n.mergeArray(o.width,t,\"mlw\"));var i=a.gradient;i&&\"none\"!==i.type&&(n.mergeArray(i.type,t,\"mgt\"),n.mergeArray(i.color,t,\"mgc\"))}}},{\"../../lib\":136}],240:[function(t,e,r){\"use strict\";var n=t(\"../../components/colorscale/color_attributes\"),a=t(\"../../components/errorbars/attributes\"),o=t(\"../../components/colorbar/attributes\"),i=t(\"../../components/drawing/attributes\").dash,l=t(\"../../components/drawing\"),s=(t(\"./constants\"),t(\"../../lib/extend\").extendFlat);e.exports={x:{valType:\"data_array\"},x0:{valType:\"any\",dflt:0},dx:{valType:\"number\",dflt:1},y:{valType:\"data_array\"},y0:{valType:\"any\",dflt:0},customdata:{valType:\"data_array\"},dy:{valType:\"number\",dflt:1},ids:{valType:\"data_array\"},text:{valType:\"string\",dflt:\"\",arrayOk:!0},hovertext:{valType:\"string\",dflt:\"\",arrayOk:!0},mode:{valType:\"flaglist\",flags:[\"lines\",\"markers\",\"text\"],extras:[\"none\"]},hoveron:{valType:\"flaglist\",flags:[\"points\",\"fills\"]},line:{color:{valType:\"color\"},width:{valType:\"number\",min:0,dflt:2},shape:{valType:\"enumerated\",values:[\"linear\",\"spline\",\"hv\",\"vh\",\"hvh\",\"vhv\"],dflt:\"linear\"},smoothing:{valType:\"number\",min:0,max:1.3,dflt:1},dash:i,simplify:{valType:\"boolean\",dflt:!0}},connectgaps:{valType:\"boolean\",dflt:!1},fill:{valType:\"enumerated\",values:[\"none\",\"tozeroy\",\"tozerox\",\"tonexty\",\"tonextx\",\"toself\",\"tonext\"],dflt:\"none\"},fillcolor:{valType:\"color\"},marker:s({},{symbol:{valType:\"enumerated\",values:l.symbolList,dflt:\"circle\",arrayOk:!0},opacity:{valType:\"number\",min:0,max:1,arrayOk:!0},size:{valType:\"number\",min:0,dflt:6,arrayOk:!0},maxdisplayed:{valType:\"number\",min:0,dflt:0},sizeref:{valType:\"number\",dflt:1},sizemin:{valType:\"number\",min:0,dflt:0},sizemode:{valType:\"enumerated\",values:[\"diameter\",\"area\"],dflt:\"diameter\"},showscale:{valType:\"boolean\",dflt:!1},colorbar:o,line:s({},{width:{valType:\"number\",min:0,arrayOk:!0}},n(\"marker.line\")),gradient:{type:{valType:\"enumerated\",values:[\"radial\",\"horizontal\",\"vertical\",\"none\"],arrayOk:!0,dflt:\"none\"},color:{valType:\"color\",arrayOk:!0}}},n(\"marker\")),textposition:{valType:\"enumerated\",values:[\"top left\",\"top center\",\"top right\",\"middle left\",\"middle center\",\"middle right\",\"bottom left\",\"bottom center\",\"bottom right\"],dflt:\"middle center\",arrayOk:!0},textfont:{family:{valType:\"string\",noBlank:!0,strict:!0,arrayOk:!0},size:{valType:\"number\",min:1,arrayOk:!0},color:{valType:\"color\",arrayOk:!0}},r:{valType:\"data_array\"},t:{valType:\"data_array\"},error_y:a,error_x:a}},{\"../../components/colorbar/attributes\":26,\"../../components/colorscale/color_attributes\":32,\"../../components/drawing\":49,\"../../components/drawing/attributes\":48,\"../../components/errorbars/attributes\":51,\"../../lib/extend\":132,\"./constants\":245}],241:[function(t,e,r){\"use strict\";var n=t(\"fast-isnumeric\"),a=t(\"../../plots/cartesian/axes\"),o=t(\"../../constants/numerical\").BADNUM,i=t(\"./subtypes\"),l=t(\"./colorscale_calc\"),s=t(\"./arrays_to_calcdata\");e.exports=function(t,e){var r,c,u,f=a.getFromId(t,e.xaxis||\"x\"),d=a.getFromId(t,e.yaxis||\"y\"),h=f.makeCalcdata(e,\"x\"),p=d.makeCalcdata(e,\"y\"),g=Math.min(h.length,p.length);f._minDtick=0,d._minDtick=0,h.length>g&&h.splice(g,h.length-g),p.length>g&&p.splice(g,p.length-g);var v={padded:!0},m={padded:!0};if(i.hasMarkers(e)){if(r=e.marker,c=r.size,Array.isArray(c)){var y={type:\"linear\"};a.setConvert(y),c=y.makeCalcdata(e.marker,\"size\"),c.length>g&&c.splice(g,c.length-g)}var x,b=1.6*(e.marker.sizeref||1);x=\"area\"===e.marker.sizemode?function(t){return Math.max(Math.sqrt((t||0)/b),3)}:function(t){return 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o,i=a(\"x\"),l=a(\"y\");if(n.getComponentMethod(\"calendars\",\"handleTraceDefaults\")(t,e,[\"x\",\"y\"],r),i)l?(o=Math.min(i.length,l.length),o<i.length&&(e.x=i.slice(0,o)),o<l.length&&(e.y=l.slice(0,o))):(o=i.length,a(\"y0\"),a(\"dy\"));else{if(!l)return 0;o=e.y.length,a(\"x0\"),a(\"dx\")}return o}},{\"../../registry\":206}]},{},[5])(5)});window.define = window.__define;window.require = window.__require;window.__define = undefined; window.__require = undefined;/* global Highcharts, Plotly*/\n",
       "/* eslint-disable */\n",
       "function throttle(fn, timeout, invokeAsap, ctx) {\n",
       "    var typeofInvokeAsap = typeof invokeAsap;\n",
       "    if(typeofInvokeAsap === 'undefined') {\n",
       "        invokeAsap = true;\n",
       "    } else if(arguments.length === 3 && typeofInvokeAsap !== 'boolean') {\n",
       "        ctx = invokeAsap;\n",
       "        invokeAsap = true;\n",
       "    }\n",
       "\n",
       "    var timer, args, needInvoke,\n",
       "        wrapper = function() {\n",
       "            if(needInvoke) {\n",
       "                fn.apply(ctx, args);\n",
       "                needInvoke = false;\n",
       "                timer = setTimeout(wrapper, timeout);\n",
       "            } else {\n",
       "                timer = null;\n",
       "            }\n",
       "        };\n",
       "\n",
       "    return function() {\n",
       "        args = arguments;\n",
       "        ctx || (ctx = this);\n",
       "        needInvoke = true;\n",
       "\n",
       "        if(!timer) {\n",
       "            invokeAsap?\n",
       "                wrapper() :\n",
       "                timer = setTimeout(wrapper, timeout);\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "/* eslint-enable */\n",
       "function CatboostIpython() {\n",
       "\n",
       "}\n",
       "\n",
       "CatboostIpython.prototype.init = function() {\n",
       "    this.charts = {};\n",
       "    /*\n",
       "        {\n",
       "            \"rmse_1\": {\n",
       "                ...\n",
       "            }\n",
       "        }\n",
       "    */\n",
       "    this.traces = {};\n",
       "    /*\n",
       "        {\n",
       "            \"rmse_1\": {\n",
       "                name: \"rmse\",\n",
       "                id: \"rmse_1\",\n",
       "                parent: \"div\",\n",
       "                traces: [\n",
       "                    {\n",
       "                        name: \"current;learn;0;;;\",\n",
       "                        x: [],\n",
       "                        y: []\n",
       "                    },\n",
       "                    {\n",
       "                        name: \"current;learn;0;smoothed;;\",\n",
       "                        x: [],\n",
       "                        y: []\n",
       "                    },\n",
       "                    {\n",
       "                        name: \"current;learn;1;;;\",\n",
       "                        x: [],\n",
       "                        y: []\n",
       "                    },\n",
       "                    {\n",
       "                        name: \"current;learn;1;smoothed;;\",\n",
       "                        x: [],\n",
       "                        y: []\n",
       "                    },\n",
       "                    {\n",
       "                        name: \"current;test;0;;;\",\n",
       "                        x: [],\n",
       "                        y: []\n",
       "                    },\n",
       "                    {\n",
       "                        name: \"current;test;0;smoothed;;\",\n",
       "                        x: [],\n",
       "                        y: []\n",
       "                    },\n",
       "                    {\n",
       "                        name: \"current;test;0;;best_point;\",\n",
       "                        x: [],\n",
       "                        y: []\n",
       "                    },\n",
       "                    {\n",
       "                        name: \"current;test;0;;;best_value\",\n",
       "                        x: [],\n",
       "                        y: []\n",
       "                    }\n",
       "                ]\n",
       "            }\n",
       "        }\n",
       "    */\n",
       "\n",
       "    this.hovertextParameters = [];\n",
       "    this.chartsToRedraw = {};\n",
       "    this.lastIndexes = {};\n",
       "    this.smoothness = -1;\n",
       "    this.layoutDisabled = {\n",
       "        series: {},\n",
       "        traces: {}\n",
       "    };\n",
       "    this.clickMode = false;\n",
       "    this.logarithmMode = 'linear';\n",
       "    this.lastSmooth = 0;\n",
       "    this.layout = null;\n",
       "    this.activeTab = '';\n",
       "    this.meta = {};\n",
       "    this.timeLeft = {};\n",
       "\n",
       "    this.hasCVMode = false;\n",
       "    this.stddevEnabled = false;\n",
       "\n",
       "    this.colors = [\n",
       "        '#68E256',\n",
       "        '#56AEE2',\n",
       "        '#CF56E2',\n",
       "        '#E28956',\n",
       "        '#56E289',\n",
       "        '#5668E2',\n",
       "        '#E256AE',\n",
       "        '#E2CF56',\n",
       "        '#56E2CF',\n",
       "        '#8A56E2',\n",
       "        '#E25668',\n",
       "        '#AEE256'\n",
       "    ];\n",
       "    this.colorsByPath = {};\n",
       "    this.colorIndex = 0;\n",
       "    this.lossFuncs = {};\n",
       "\n",
       "    this.isCVinited = false;\n",
       "};\n",
       "\n",
       "/* eslint-disable */\n",
       "CatboostIpython.prototype.loadStyles = function(path, fn, scope) {\n",
       "    $('link[catboost=\"1\"]').remove();\n",
       "\n",
       "    var head = document.getElementsByTagName('head')[0], // reference to document.head for appending/ removing link nodes\n",
       "        link = document.createElement('link');           // create the link node\n",
       "    link.setAttribute('href', path);\n",
       "    link.setAttribute('rel', 'stylesheet');\n",
       "    link.setAttribute('type', 'text/css');\n",
       "    link.setAttribute('catboost', '1');\n",
       "\n",
       "    var sheet, cssRules;\n",
       "    // get the correct properties to check for depending on the browser\n",
       "    if ('sheet' in link) {\n",
       "        sheet = 'sheet'; cssRules = 'cssRules';\n",
       "    } else {\n",
       "        sheet = 'styleSheet'; cssRules = 'rules';\n",
       "    }\n",
       "\n",
       "    var interval_id = setInterval(function() {                     // start checking whether the style sheet has successfully loaded\n",
       "        try {\n",
       "            if (link[sheet] && link[sheet][cssRules].length) { // SUCCESS! our style sheet has loaded\n",
       "                clearInterval(interval_id);                      // clear the counters\n",
       "                clearTimeout(timeout_id);\n",
       "                fn.call( scope || window, true, link);           // fire the callback with success == true\n",
       "            }\n",
       "        } catch(e) {} finally {}\n",
       "    }, 50 ),                                                   // how often to check if the stylesheet is loaded\n",
       "    timeout_id = setTimeout( function() {       // start counting down till fail\n",
       "        clearInterval( interval_id );             // clear the counters\n",
       "        clearTimeout( timeout_id );\n",
       "        head.removeChild( link );                // since the style sheet didn't load, remove the link node from the DOM\n",
       "        fn.call( scope || window, false, link ); // fire the callback with success == false\n",
       "    }, 15000 );                                 // how long to wait before failing\n",
       "\n",
       "    head.appendChild( link );  // insert the link node into the DOM and start loading the style sheet\n",
       "\n",
       "    return link; // return the link node;\n",
       "};\n",
       "/* eslint-enable */\n",
       "\n",
       "CatboostIpython.prototype.resizeCharts = function() {\n",
       "    // width fix for development\n",
       "    $('.catboost-graph__charts', this.layout).css({width: $('.catboost-graph').width()});\n",
       "\n",
       "    this.plotly.Plots.resize(this.traces[this.activeTab].parent);\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.addMeta = function(path, meta) {\n",
       "    this.meta[path] = meta;\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.addLayout = function(parent) {\n",
       "    if (this.layout) {\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var cvAreaControls = '';\n",
       "\n",
       "    if (this.hasCVMode) {\n",
       "        cvAreaControls =    '<div>' +\n",
       "                                '<input type=\"checkbox\" class=\"catboost-panel__control_checkbox\" id=\"catboost-control2-cvstddev' + this.index + '\"' + (this.stddevEnabled ? ' checked=\"checked\"' : '') + '></input>' +\n",
       "                                '<label for=\"catboost-control2-cvstddev' + this.index + '\" class=\"catboost-panel__controls2_label catboost-panel__controls2_label-long\">Standard Deviation</label>' +\n",
       "                            '</div>';\n",
       "    }\n",
       "\n",
       "    this.layout = $('<div class=\"catboost\">' +\n",
       "                        '<div class=\"catboost-panel\">' +\n",
       "                            '<div class=\"catboost-panel__controls\">' +\n",
       "                                '<input type=\"checkbox\" class=\"catboost-panel__controls_checkbox\" id=\"catboost-control-learn' + this.index + '\" ' + (!this.layoutDisabled.learn ? ' checked=\"checked\"' : '') + '></input>' +\n",
       "                                '<label for=\"catboost-control-learn' + this.index + '\" class=\"catboost-panel__controls_label\"><div class=\"catboost-panel__serie_learn_pic\" style=\"border-color:#999\"></div>Learn</label>' +\n",
       "                                '<input type=\"checkbox\" class=\"catboost-panel__controls_checkbox\" id=\"catboost-control-test' + this.index + '\" ' + (!this.layoutDisabled.test ? ' checked=\"checked\"' : '') + '></input>' +\n",
       "                                '<label for=\"catboost-control-test' + this.index + '\" class=\"catboost-panel__controls_label\"><div class=\"catboost-panel__serie_test_pic\" style=\"border-color:#999\"></div>Eval</label>' +\n",
       "                            '</div>' +\n",
       "                            '<div class=\"catboost-panel__series ' + (this.layoutDisabled.learn ? ' catboost-panel__series_learn_disabled' : '') + '\">' +\n",
       "                            '</div>' +\n",
       "                            '<div class=\"catboost-panel__controls2\">' +\n",
       "                                '<input type=\"checkbox\" class=\"catboost-panel__control_checkbox\" id=\"catboost-control2-clickmode' + this.index + '\"></input>' +\n",
       "                                '<label for=\"catboost-control2-clickmode' + this.index + '\" class=\"catboost-panel__controls2_label\">Click Mode</label>' +\n",
       "                                '<input type=\"checkbox\" class=\"catboost-panel__control_checkbox\" id=\"catboost-control2-log' + this.index + '\"></input>' +\n",
       "                                '<label for=\"catboost-control2-log' + this.index + '\" class=\"catboost-panel__controls2_label\">Logarithm</label>' +\n",
       "                                '<div>' +\n",
       "                                    '<input type=\"checkbox\" class=\"catboost-panel__control_checkbox\" id=\"catboost-control2-smooth' + this.index + '\"></input>' +\n",
       "                                    '<label for=\"catboost-control2-smooth' + this.index + '\" class=\"catboost-panel__controls2_label\">Smooth</label>' +\n",
       "                                    '<input id=\"catboost-control2-slider' + this.index + '\" disabled=\"disabled\" class=\"catboost-panel__control_slider\" type =\"range\" value=\"0\" min=\"0\" max=\"1\" step =\"0.01\" for=\"rangeInputValue\" name=\"rangeInput\"/>' +\n",
       "                                    '<input id=\"catboost-control2-slidervalue' + this.index + '\" disabled=\"disabled\" class=\"catboost-panel__control_slidervalue\" value=\"0\" min=\"0\" max=\"1\" for=\"rangeInput\" name=\"rangeInputValue\"/>' +\n",
       "                                '</div>' +\n",
       "                                cvAreaControls +\n",
       "                            '</div>' +\n",
       "                        '</div>' +\n",
       "                        '<div class=\"catboost-graph\">' +\n",
       "                            '<div class=\"catboost-graph__tabs\"></div>' +\n",
       "                            '<div class=\"catboost-graph__charts\"></div>' +\n",
       "                        '</div>' +\n",
       "                    '</div>');\n",
       "    $(parent).append(this.layout);\n",
       "\n",
       "    this.addTabEvents();\n",
       "    this.addControlEvents();\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.addTabEvents = function() {\n",
       "    var self = this;\n",
       "\n",
       "    $('.catboost-graph__tabs', this.layout).click(function(e) {\n",
       "        if (!$(e.target).is('.catboost-graph__tab:not(.catboost-graph__tab_active)')) {\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var id = $(e.target).attr('tabid');\n",
       "\n",
       "        self.activeTab = id;\n",
       "\n",
       "        $('.catboost-graph__tab_active', self.layout).removeClass('catboost-graph__tab_active');\n",
       "        $('.catboost-graph__chart_active', self.layout).removeClass('catboost-graph__chart_active');\n",
       "\n",
       "        $('.catboost-graph__tab[tabid=\"' + id + '\"]', self.layout).addClass('catboost-graph__tab_active');\n",
       "        $('.catboost-graph__chart[tabid=\"' + id + '\"]', self.layout).addClass('catboost-graph__chart_active');\n",
       "\n",
       "        self.cleanSeries();\n",
       "\n",
       "        self.redrawActiveChart();\n",
       "        self.resizeCharts();\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.addControlEvents = function() {\n",
       "    var self = this;\n",
       "\n",
       "    $('#catboost-control-learn' + this.index, this.layout).click(function() {\n",
       "        self.layoutDisabled.learn = !$(this)[0].checked;\n",
       "\n",
       "        $('.catboost-panel__series', self.layout).toggleClass('catboost-panel__series_learn_disabled', self.layoutDisabled.learn);\n",
       "\n",
       "        self.redrawActiveChart();\n",
       "    });\n",
       "\n",
       "    $('#catboost-control-test' + this.index, this.layout).click(function() {\n",
       "        self.layoutDisabled.test = !$(this)[0].checked;\n",
       "\n",
       "        $('.catboost-panel__series', self.layout).toggleClass('catboost-panel__series_test_disabled', self.layoutDisabled.test);\n",
       "\n",
       "        self.redrawActiveChart();\n",
       "    });\n",
       "\n",
       "    $('#catboost-control2-clickmode' + this.index, this.layout).click(function() {\n",
       "        self.clickMode = $(this)[0].checked;\n",
       "    });\n",
       "\n",
       "    $('#catboost-control2-log' + this.index, this.layout).click(function() {\n",
       "        self.logarithmMode = $(this)[0].checked ? 'log' : 'linear';\n",
       "\n",
       "        self.forEveryLayout(function(layout) {\n",
       "            layout.yaxis = {type: self.logarithmMode};\n",
       "        });\n",
       "\n",
       "        self.redrawActiveChart();\n",
       "    });\n",
       "\n",
       "    var slider = $('#catboost-control2-slider' + this.index),\n",
       "        sliderValue = $('#catboost-control2-slidervalue' + this.index);\n",
       "\n",
       "    $('#catboost-control2-smooth' + this.index, this.layout).click(function() {\n",
       "        var enabled = $(this)[0].checked;\n",
       "\n",
       "        self.setSmoothness(enabled ? self.lastSmooth : -1);\n",
       "\n",
       "        slider.prop('disabled', !enabled);\n",
       "        sliderValue.prop('disabled', !enabled);\n",
       "\n",
       "        self.redrawActiveChart();\n",
       "    });\n",
       "\n",
       "    $('#catboost-control2-cvstddev' + this.index, this.layout).click(function() {\n",
       "        var enabled = $(this)[0].checked;\n",
       "\n",
       "        self.setStddev(enabled);\n",
       "\n",
       "        self.redrawActiveChart();\n",
       "    });\n",
       "\n",
       "    slider.on('input change', function() {\n",
       "        var smooth = Number($(this).val());\n",
       "\n",
       "        sliderValue.val(isNaN(smooth) ? 0 : smooth);\n",
       "\n",
       "        self.setSmoothness(smooth);\n",
       "        self.lastSmooth = smooth;\n",
       "\n",
       "        self.redrawActiveChart();\n",
       "    });\n",
       "\n",
       "    sliderValue.on('input change', function() {\n",
       "        var smooth = Number($(this).val());\n",
       "\n",
       "        slider.val(isNaN(smooth) ? 0 : smooth);\n",
       "\n",
       "        self.setSmoothness(smooth);\n",
       "        self.lastSmooth = smooth;\n",
       "\n",
       "        self.redrawActiveChart();\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.setTraceVisibility = function(trace, visibility) {\n",
       "    if (trace) {\n",
       "        trace.visible = visibility;\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.updateTracesVisibility = function() {\n",
       "    var tracesHash = this.groupTraces(),\n",
       "        traces,\n",
       "        smoothDisabled = this.getSmoothness() === -1,\n",
       "        self = this;\n",
       "\n",
       "    for (var train in tracesHash) {\n",
       "        if (tracesHash.hasOwnProperty(train)) {\n",
       "            traces = tracesHash[train].traces;\n",
       "\n",
       "            if (this.layoutDisabled.traces[train]) {\n",
       "                traces.forEach(function(trace) {\n",
       "                    self.setTraceVisibility(trace, false);\n",
       "                });\n",
       "            } else {\n",
       "                traces.forEach(function(trace) {\n",
       "                    self.setTraceVisibility(trace, true);\n",
       "                });\n",
       "\n",
       "                if (this.hasCVMode) {\n",
       "                    if (this.stddevEnabled) {\n",
       "                        self.filterTracesOne(traces, {type: 'learn'}).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, false);\n",
       "                        });\n",
       "                        self.filterTracesOne(traces, {type: 'test'}).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, false);\n",
       "                        });\n",
       "\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'learn', cv_avg: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, true);\n",
       "                        });\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'test', cv_avg: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, true);\n",
       "                        });\n",
       "\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'learn', cv_avg: true, smoothed: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, true);\n",
       "                        });\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'test', cv_avg: true, smoothed: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, true);\n",
       "                        });\n",
       "\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'test', cv_avg: true, best_point: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, true);\n",
       "                        });\n",
       "\n",
       "                        self.filterTracesOne(traces, {cv_stddev_first: true}).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, true);\n",
       "                        });\n",
       "                        self.filterTracesOne(traces, {cv_stddev_last: true}).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, true);\n",
       "                        });\n",
       "                    } else {\n",
       "                        self.filterTracesOne(traces, {cv_stddev_first: true}).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, false);\n",
       "                        });\n",
       "                        self.filterTracesOne(traces, {cv_stddev_last: true}).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, false);\n",
       "                        });\n",
       "\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'learn', cv_avg: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, false);\n",
       "                        });\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'test', cv_avg: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, false);\n",
       "                        });\n",
       "\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'learn', cv_avg: true, smoothed: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, false);\n",
       "                        });\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'test', cv_avg: true, smoothed: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, false);\n",
       "                        });\n",
       "\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'test', cv_avg: true, best_point: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, false);\n",
       "                        });\n",
       "                    }\n",
       "                }\n",
       "\n",
       "                if (smoothDisabled) {\n",
       "                    self.filterTracesOne(traces, {smoothed: true}).forEach(function(trace) {\n",
       "                        self.setTraceVisibility(trace, false);\n",
       "                    });\n",
       "                }\n",
       "\n",
       "                if (this.layoutDisabled['learn']) {\n",
       "                    self.filterTracesOne(traces, {type: 'learn'}).forEach(function(trace) {\n",
       "                        self.setTraceVisibility(trace, false);\n",
       "                    });\n",
       "                }\n",
       "\n",
       "                if (this.layoutDisabled['test']) {\n",
       "                    self.filterTracesOne(traces, {type: 'test'}).forEach(function(trace) {\n",
       "                        self.setTraceVisibility(trace, false);\n",
       "                    });\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.getSmoothness = function() {\n",
       "    return this.smoothness && this.smoothness > -1 ? this.smoothness : -1;\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.setSmoothness = function(weight) {\n",
       "    if (weight < 0 && weight !== -1 || weight > 1) {\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    this.smoothness = weight;\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.setStddev = function(enabled) {\n",
       "    this.stddevEnabled = enabled;\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.redrawActiveChart = function() {\n",
       "    this.chartsToRedraw[this.activeTab] = true;\n",
       "\n",
       "    this.redrawAll();\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.redraw = function() {\n",
       "    if (this.chartsToRedraw[this.activeTab]) {\n",
       "        this.chartsToRedraw[this.activeTab] = false;\n",
       "\n",
       "        this.updateTracesVisibility();\n",
       "        this.updateTracesCV();\n",
       "        this.updateTracesBest();\n",
       "        this.updateTracesValues();\n",
       "        this.updateTracesSmoothness();\n",
       "\n",
       "        this.plotly.redraw(this.traces[this.activeTab].parent);\n",
       "    }\n",
       "\n",
       "    this.drawTraces();\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.addRedrawFunc = function() {\n",
       "    this.redrawFunc = throttle(this.redraw, 400, false, this);\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.redrawAll = function() {\n",
       "    if (!this.redrawFunc) {\n",
       "        this.addRedrawFunc();\n",
       "    }\n",
       "\n",
       "    this.redrawFunc();\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.addPoints = function(parent, data) {\n",
       "    var self = this;\n",
       "\n",
       "    data.chunks.forEach(function(item) {\n",
       "        if (typeof item.remaining_time !== 'undefined' && typeof item.passed_time !== 'undefined') {\n",
       "            if (!self.timeLeft[data.path]) {\n",
       "                self.timeLeft[data.path] = [];\n",
       "            }\n",
       "\n",
       "            self.timeLeft[data.path][item.iteration] = [item.remaining_time, item.passed_time];\n",
       "        }\n",
       "\n",
       "        ['test', 'learn'].forEach(function(type) {\n",
       "            var sets = self.meta[data.path][type + '_sets'],\n",
       "                metrics = self.meta[data.path][type + '_metrics'];\n",
       "\n",
       "            for (var i = 0; i < metrics.length; i++) {\n",
       "                var nameOfMetric = metrics[i].name,\n",
       "                    cvAdded = false;\n",
       "                    hovertextParametersAdded = false;\n",
       "\n",
       "                self.lossFuncs[nameOfMetric] = metrics[i].best_value;\n",
       "\n",
       "                for (var j = 0; j < sets.length; j++) {\n",
       "                    var nameOfSet = sets[j],\n",
       "                        params = {\n",
       "                            chartName: nameOfMetric,\n",
       "                            index: i,\n",
       "                            train: data.train,\n",
       "                            type: type,\n",
       "                            path: data.path,\n",
       "                            indexOfSet: j,\n",
       "                            nameOfSet: nameOfSet\n",
       "                        },\n",
       "                        key = self.getKey(params),\n",
       "                        launchMode = self.getLaunchMode(data.path);\n",
       "\n",
       "                    if (!self.activeTab) {\n",
       "                        self.activeTab = key.chartId;\n",
       "                    }\n",
       "\n",
       "                    if (launchMode === 'CV' ) {\n",
       "                        // we need to set launch mode before first getTrace call\n",
       "                        self.hasCVMode = true;\n",
       "\n",
       "                        if (!self.isCVinited) {\n",
       "                            // and we don't need to reset setting for next iterations\n",
       "                            self.layoutDisabled.learn = true;\n",
       "                            self.setStddev(true);\n",
       "\n",
       "                            self.isCVinited = true;\n",
       "                        }\n",
       "                    }\n",
       "\n",
       "                    var valuesOfSet = item[nameOfSet],\n",
       "                        pointValue = valuesOfSet[i],\n",
       "                        pointIndex = item.iteration,\n",
       "                        // traces\n",
       "                        trace = self.getTrace(parent, params),\n",
       "                        smoothedTrace = self.getTrace(parent, $.extend({smoothed: true}, params)),\n",
       "                        bestValueTrace = null;\n",
       "\n",
       "                    if (type === 'test') {\n",
       "                        if (launchMode !== 'CV') {\n",
       "                            self.getTrace(parent, $.extend({best_point: true}, params));\n",
       "                        }\n",
       "\n",
       "                        if (typeof self.lossFuncs[nameOfMetric] === 'number') {\n",
       "                            bestValueTrace = self.getTrace(parent, $.extend({best_value: true}, params));\n",
       "                        }\n",
       "                    }\n",
       "\n",
       "                    if (pointValue !== 'inf' && pointValue !== 'nan') {\n",
       "                        trace.x[pointIndex] = pointIndex;\n",
       "                        trace.y[pointIndex] = valuesOfSet[i];\n",
       "                        trace.hovertext[pointIndex] = nameOfSet + ': ' + valuesOfSet[i].toPrecision(7);\n",
       "                        if (item.hasOwnProperty('parameters')) {\n",
       "                            self.hovertextParameters[pointIndex] = '';\n",
       "                            for (var parameter in item.parameters[0]) {\n",
       "                                if (item.parameters[0].hasOwnProperty(parameter)) {\n",
       "                                    valueOfParameter = item.parameters[0][parameter];\n",
       "                                    self.hovertextParameters[pointIndex] += '<br>' + parameter + ' : ' + valueOfParameter;\n",
       "                                }\n",
       "                            }\n",
       "                        }\n",
       "                        if (!hovertextParametersAdded && type === 'test') {\n",
       "                            hovertextParametersAdded = true;\n",
       "                            trace.hovertext[pointIndex] += self.hovertextParameters[pointIndex];\n",
       "                        }\n",
       "                        smoothedTrace.x[pointIndex] = pointIndex;\n",
       "                    }\n",
       "\n",
       "                    if (bestValueTrace) {\n",
       "                        bestValueTrace.x[pointIndex] = pointIndex;\n",
       "                        bestValueTrace.y[pointIndex] = self.lossFuncs[nameOfMetric];\n",
       "                    }\n",
       "\n",
       "                    if (launchMode === 'CV' && !cvAdded) {\n",
       "                        cvAdded = true;\n",
       "\n",
       "                        self.getTrace(parent, $.extend({cv_stddev_first: true}, params));\n",
       "                        self.getTrace(parent, $.extend({cv_stddev_last: true}, params));\n",
       "\n",
       "                        self.getTrace(parent, $.extend({cv_stddev_first: true, smoothed: true}, params));\n",
       "                        self.getTrace(parent, $.extend({cv_stddev_last: true, smoothed: true}, params));\n",
       "\n",
       "                        self.getTrace(parent, $.extend({cv_avg: true}, params));\n",
       "                        self.getTrace(parent, $.extend({cv_avg: true, smoothed: true}, params));\n",
       "\n",
       "                        if (type === 'test') {\n",
       "                            self.getTrace(parent, $.extend({cv_avg: true, best_point: true}, params));\n",
       "                        }\n",
       "                    }\n",
       "                }\n",
       "\n",
       "                self.chartsToRedraw[key.chartId] = true;\n",
       "\n",
       "                self.redrawAll();\n",
       "            }\n",
       "        });\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.getLaunchMode = function(path) {\n",
       "    return this.meta[path].launch_mode;\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.getChartNode = function(params, active) {\n",
       "    var node = $('<div class=\"catboost-graph__chart\" tabid=\"' + params.id + '\"></div>');\n",
       "\n",
       "    if (active) {\n",
       "        node.addClass('catboost-graph__chart_active');\n",
       "    }\n",
       "\n",
       "    return node;\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.getChartTab = function(params, active) {\n",
       "    var node = $('<div class=\"catboost-graph__tab\" tabid=\"' + params.id + '\">' + params.name + '</div>');\n",
       "\n",
       "    if (active) {\n",
       "        node.addClass('catboost-graph__tab_active');\n",
       "    }\n",
       "\n",
       "    return node;\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.forEveryChart = function(callback) {\n",
       "    for (var name in this.traces) {\n",
       "        if (this.traces.hasOwnProperty(name)) {\n",
       "            callback(this.traces[name]);\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.forEveryLayout = function(callback) {\n",
       "    this.forEveryChart(function(chart) {\n",
       "        callback(chart.layout);\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.getChart = function(parent, params) {\n",
       "    var id = params.id,\n",
       "        self = this;\n",
       "\n",
       "    if (this.charts[id]) {\n",
       "        return this.charts[id];\n",
       "    }\n",
       "\n",
       "    this.addLayout(parent);\n",
       "\n",
       "    var active = this.activeTab === params.id,\n",
       "        chartNode = this.getChartNode(params, active),\n",
       "        chartTab = this.getChartTab(params, active);\n",
       "\n",
       "    $('.catboost-graph__charts', this.layout).append(chartNode);\n",
       "    $('.catboost-graph__tabs', this.layout).append(chartTab);\n",
       "\n",
       "    this.traces[id] = {\n",
       "        id: params.id,\n",
       "        name: params.name,\n",
       "        parent: chartNode[0],\n",
       "        traces: [],\n",
       "        layout: {\n",
       "            xaxis: {\n",
       "                range: [0, Number(this.meta[params.path].iteration_count)],\n",
       "                type: 'linear',\n",
       "                tickmode: 'auto',\n",
       "                showspikes: true,\n",
       "                spikethickness: 1,\n",
       "                spikedash: 'longdashdot',\n",
       "                spikemode: 'across',\n",
       "                zeroline: false,\n",
       "                showgrid: false\n",
       "            },\n",
       "            yaxis: {\n",
       "                zeroline: false\n",
       "                //showgrid: false\n",
       "                //hoverformat : '.7f'\n",
       "            },\n",
       "            separators: '. ',\n",
       "            //hovermode: 'x',\n",
       "            margin: {l: 38, r: 0, t: 35, b: 30},\n",
       "            autosize: true,\n",
       "            showlegend: false\n",
       "        },\n",
       "        options: {\n",
       "            scrollZoom: false,\n",
       "            modeBarButtonsToRemove: ['toggleSpikelines'],\n",
       "            displaylogo: false\n",
       "        }\n",
       "    };\n",
       "\n",
       "    this.charts[id] = this.plotly.plot(chartNode[0], this.traces[id].traces, this.traces[id].layout, this.traces[id].options);\n",
       "\n",
       "    chartNode[0].on('plotly_hover', function(e) {\n",
       "        self.updateTracesValues(e.points[0].x);\n",
       "    });\n",
       "\n",
       "    chartNode[0].on('plotly_click', function(e) {\n",
       "        self.updateTracesValues(e.points[0].x, true);\n",
       "    });\n",
       "\n",
       "    return this.charts[id];\n",
       "};\n",
       "\n",
       "\n",
       "CatboostIpython.prototype.getTrace = function(parent, params) {\n",
       "    var key = this.getKey(params),\n",
       "        chartSeries = [];\n",
       "\n",
       "    if (this.traces[key.chartId]) {\n",
       "        chartSeries = this.traces[key.chartId].traces.filter(function(trace) {\n",
       "            return trace.name === key.traceName;\n",
       "        });\n",
       "    }\n",
       "\n",
       "    if (chartSeries.length) {\n",
       "        return chartSeries[0];\n",
       "    } else {\n",
       "        this.getChart(parent, {id: key.chartId, name: params.chartName, path: params.path});\n",
       "\n",
       "        var plotParams = {\n",
       "                color: this.getNextColor(params.path, params.smoothed ? 0.2 : 1),\n",
       "                fillsmoothcolor: this.getNextColor(params.path, 0.1),\n",
       "                fillcolor: this.getNextColor(params.path, 0.4),\n",
       "                hoverinfo: params.cv_avg ? 'skip' : 'text+x',\n",
       "                width: params.cv_avg ? 2 : 1,\n",
       "                dash: params.type === 'test' ? 'solid' : 'dot'\n",
       "            },\n",
       "            trace = {\n",
       "                name: key.traceName,\n",
       "                _params: params,\n",
       "                x: [],\n",
       "                y: [],\n",
       "                hovertext: [],\n",
       "                hoverinfo: plotParams.hoverinfo,\n",
       "                line: {\n",
       "                    width: plotParams.width,\n",
       "                    dash: plotParams.dash,\n",
       "                    color: plotParams.color\n",
       "                },\n",
       "                mode: 'lines',\n",
       "                hoveron: 'points',\n",
       "                connectgaps: true\n",
       "            };\n",
       "\n",
       "        if (params.best_point) {\n",
       "            trace = {\n",
       "                name: key.traceName,\n",
       "                _params: params,\n",
       "                x: [],\n",
       "                y: [],\n",
       "                marker: {\n",
       "                    width: 2,\n",
       "                    color: plotParams.color\n",
       "                },\n",
       "                hovertext: [],\n",
       "                hoverinfo: 'text',\n",
       "                mode: 'markers',\n",
       "                type: 'scatter'\n",
       "            };\n",
       "        }\n",
       "\n",
       "        if (params.best_value) {\n",
       "            trace = {\n",
       "                name: key.traceName,\n",
       "                _params: params,\n",
       "                x: [],\n",
       "                y: [],\n",
       "                line: {\n",
       "                    width: 1,\n",
       "                    dash: 'dash',\n",
       "                    color: '#CCCCCC'\n",
       "                },\n",
       "                mode: 'lines',\n",
       "                connectgaps: true,\n",
       "                hoverinfo: 'skip'\n",
       "            };\n",
       "        }\n",
       "\n",
       "        if (params.cv_stddev_last) {\n",
       "            trace.fill = 'tonexty';\n",
       "        }\n",
       "\n",
       "        trace._params.plotParams = plotParams;\n",
       "\n",
       "        this.traces[key.chartId].traces.push(trace);\n",
       "\n",
       "        return trace;\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.getKey = function(params) {\n",
       "    var traceName = [\n",
       "        params.train,\n",
       "        params.type,\n",
       "        params.indexOfSet,\n",
       "        (params.smoothed ? 'smoothed' : ''),\n",
       "        (params.best_point ? 'best_pount' : ''),\n",
       "        (params.best_value ? 'best_value' : ''),\n",
       "        (params.cv_avg ? 'cv_avg' : ''),\n",
       "        (params.cv_stddev_first ? 'cv_stddev_first' : ''),\n",
       "        (params.cv_stddev_last ? 'cv_stddev_last' : '')\n",
       "    ].join(';');\n",
       "\n",
       "    return {\n",
       "        chartId: params.chartName,\n",
       "        traceName: traceName,\n",
       "        colorId: params.train\n",
       "    };\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.filterTracesEvery = function(traces, filter) {\n",
       "    traces = traces || this.traces[this.activeTab].traces;\n",
       "\n",
       "    return traces.filter(function(trace) {\n",
       "        for (var prop in filter) {\n",
       "            if (filter.hasOwnProperty(prop)) {\n",
       "                if (filter[prop] !== trace._params[prop]) {\n",
       "                    return false;\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "\n",
       "        return true;\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.filterTracesOne = function(traces, filter) {\n",
       "    traces = traces || this.traces[this.activeTab].traces;\n",
       "\n",
       "    return traces.filter(function(trace) {\n",
       "        for (var prop in filter) {\n",
       "            if (filter.hasOwnProperty(prop)) {\n",
       "                if (filter[prop] === trace._params[prop]) {\n",
       "                    return true;\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "\n",
       "        return false;\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.cleanSeries = function() {\n",
       "    $('.catboost-panel__series', this.layout).html('');\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.groupTraces = function() {\n",
       "    var traces = this.traces[this.activeTab].traces,\n",
       "        index = 0,\n",
       "        tracesHash = {};\n",
       "\n",
       "    traces.map(function(trace) {\n",
       "        var train = trace._params.train;\n",
       "\n",
       "        if (!tracesHash[train]) {\n",
       "            tracesHash[train] = {\n",
       "                index: index,\n",
       "                traces: [],\n",
       "                info: {\n",
       "                    path: trace._params.path,\n",
       "                    color: trace._params.plotParams.color\n",
       "                }\n",
       "            };\n",
       "\n",
       "            index++;\n",
       "        }\n",
       "\n",
       "        tracesHash[train].traces.push(trace);\n",
       "    });\n",
       "\n",
       "    return tracesHash;\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.drawTraces = function() {\n",
       "    if ($('.catboost-panel__series .catboost-panel__serie', this.layout).length) {\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var html = '',\n",
       "        tracesHash = this.groupTraces();\n",
       "\n",
       "    for (var train in tracesHash) {\n",
       "        if (tracesHash.hasOwnProperty(train)) {\n",
       "            html += this.drawTrace(train, tracesHash[train]);\n",
       "        }\n",
       "    }\n",
       "\n",
       "    $('.catboost-panel__series', this.layout).html(html);\n",
       "\n",
       "    this.updateTracesValues();\n",
       "\n",
       "    this.addTracesEvents();\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.getTraceDefParams = function(params) {\n",
       "    var defParams =  {\n",
       "        smoothed: undefined,\n",
       "        best_point: undefined,\n",
       "        best_value: undefined,\n",
       "        cv_avg: undefined,\n",
       "        cv_stddev_first: undefined,\n",
       "        cv_stddev_last: undefined\n",
       "    };\n",
       "\n",
       "    if (params) {\n",
       "        return $.extend(defParams, params);\n",
       "    } else {\n",
       "        return defParams;\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.drawTrace = function(train, hash) {\n",
       "    var info = hash.info,\n",
       "        id = 'catboost-serie-' + this.index + '-' + hash.index,\n",
       "        traces = {\n",
       "            learn: this.filterTracesEvery(hash.traces, this.getTraceDefParams({type: 'learn'})),\n",
       "            test: this.filterTracesEvery(hash.traces, this.getTraceDefParams({type: 'test'}))\n",
       "        },\n",
       "        items = {\n",
       "            learn: {\n",
       "                middle: '',\n",
       "                bottom: ''\n",
       "            },\n",
       "            test: {\n",
       "                middle: '',\n",
       "                bottom: ''\n",
       "            }\n",
       "        },\n",
       "        tracesNames = '';\n",
       "\n",
       "    ['learn', 'test'].forEach(function(type) {\n",
       "        traces[type].forEach(function(trace) {\n",
       "            items[type].middle += '<div class=\"catboost-panel__serie_' + type + '_pic\" style=\"border-color:' + info.color + '\"></div>' +\n",
       "                                  '<div data-index=\"' + trace._params.indexOfSet + '\" class=\"catboost-panel__serie_' + type + '_value\"></div>';\n",
       "\n",
       "            items[type].bottom += '<div class=\"catboost-panel__serie_' + type + '_pic\" style=\"border-color:transparent\"></div>' +\n",
       "                                  '<div data-index=\"' + trace._params.indexOfSet + '\" class=\"catboost-panel__serie_best_' + type + '_value\"></div>';\n",
       "\n",
       "            tracesNames += '<div class=\"catboost-panel__serie_' + type + '_pic\" style=\"border-color:' + info.color + '\"></div>' +\n",
       "                           '<div class=\"catboost-panel__serie_' + type + '_name\">' + trace._params.nameOfSet + '</div>';\n",
       "        });\n",
       "    });\n",
       "\n",
       "    var timeSpendHtml = '<div class=\"catboost-panel__serie_time\">' +\n",
       "                             '<div class=\"catboost-panel__serie_time_spend\" title=\"Time spend\"></div>' +\n",
       "                        '</div>';\n",
       "\n",
       "    var html = '<div id=\"' + id + '\" class=\"catboost-panel__serie\" style=\"color:' + info.color + '\">' +\n",
       "                    '<div class=\"catboost-panel__serie_top\">' +\n",
       "                        '<input type=\"checkbox\" data-seriename=\"' + train + '\" class=\"catboost-panel__serie_checkbox\" id=\"' + id + '-box\" ' + (!this.layoutDisabled.series[train] ? 'checked=\"checked\"' : '') + '></input>' +\n",
       "                        '<label title=' + this.meta[info.path].name + ' for=\"' + id + '-box\" class=\"catboost-panel__serie_label\">' + train + '<div class=\"catboost-panel__serie_time_left\" title=\"Estimate time\"></div></label>' +\n",
       "                        (this.getLaunchMode(info.path) !== 'Eval' ? timeSpendHtml : '') +\n",
       "                    '</div>' +\n",
       "                    '<div class=\"catboost-panel__serie_hint catboost-panel__serie__learn_hint\">curr</div>' +\n",
       "                    '<div class=\"catboost-panel__serie_hint catboost-panel__serie__test_hint\">best</div>' +\n",
       "                    '<div class=\"catboost-panel__serie_iteration\" title=\"curr iteration\"></div>' +\n",
       "                    '<div class=\"catboost-panel__serie_best_iteration\" title=\"best ' + (this.hasCVMode ? 'avg ' : '') + 'iteration\"></div>' +\n",
       "                    '<div class=\"catboost-panel__serie_scroll\">' +\n",
       "                        '<div class=\"catboost-panel__serie_names\">' +\n",
       "                            tracesNames +\n",
       "                        '</div>' +\n",
       "                        '<div class=\"catboost-panel__serie_middle\">' +\n",
       "                            items.learn.middle +\n",
       "                            items.test.middle +\n",
       "                        '</div>' +\n",
       "                        '<div class=\"catboost-panel__serie_bottom\">' +\n",
       "                            items.learn.bottom +\n",
       "                            items.test.bottom +\n",
       "                        '</div>' +\n",
       "                    '</div>' +\n",
       "                '</div>';\n",
       "\n",
       "    return html;\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.updateTracesValues = function(iteration, click) {\n",
       "    var tracesHash = this.groupTraces();\n",
       "\n",
       "    for (var train in tracesHash) {\n",
       "        if (tracesHash.hasOwnProperty(train) && !this.layoutDisabled.traces[train]) {\n",
       "            this.updateTraceValues(train, tracesHash[train], iteration, click);\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.updateTracesBest = function() {\n",
       "    var tracesHash = this.groupTraces();\n",
       "\n",
       "    for (var train in tracesHash) {\n",
       "        if (tracesHash.hasOwnProperty(train) && !this.layoutDisabled.traces[train]) {\n",
       "            this.updateTraceBest(train, tracesHash[train]);\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.getBestValue = function(data) {\n",
       "    if (!data.length) {\n",
       "        return {\n",
       "            best: undefined,\n",
       "            index: -1\n",
       "        };\n",
       "    }\n",
       "\n",
       "    var best = data[0],\n",
       "        index = 0,\n",
       "        func = this.lossFuncs[this.traces[this.activeTab].name],\n",
       "        bestDiff = typeof func === 'number' ? Math.abs(data[0] - func) : 0;\n",
       "\n",
       "    for (var i = 1, l = data.length; i < l; i++) {\n",
       "        if (func === 'Min' && data[i] < best) {\n",
       "            best = data[i];\n",
       "            index = i;\n",
       "        }\n",
       "\n",
       "        if (func === 'Max' && data[i] > best) {\n",
       "            best = data[i];\n",
       "            index = i;\n",
       "        }\n",
       "\n",
       "        if (typeof func === 'number' && Math.abs(data[i] - func) < bestDiff) {\n",
       "            best = data[i];\n",
       "            bestDiff = Math.abs(data[i] - func);\n",
       "            index = i;\n",
       "        }\n",
       "    }\n",
       "\n",
       "    return {\n",
       "        best: best,\n",
       "        index: index,\n",
       "        func: func\n",
       "    };\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.updateTracesCV = function() {\n",
       "    this.updateTracesCVAvg();\n",
       "\n",
       "    if (this.hasCVMode && this.stddevEnabled) {\n",
       "        this.updateTracesCVStdDev();\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.updateTracesCVAvg = function() {\n",
       "    var tracesHash = this.groupTraces(),\n",
       "        avgTraces = this.filterTracesEvery(tracesHash.traces, this.getTraceDefParams({\n",
       "            cv_avg: true\n",
       "        })),\n",
       "        self = this;\n",
       "\n",
       "    avgTraces.forEach(function(trace) {\n",
       "        var origTraces = self.filterTracesEvery(tracesHash.traces, self.getTraceDefParams({\n",
       "                train: trace._params.train,\n",
       "                type: trace._params.type,\n",
       "                smoothed: trace._params.smoothed\n",
       "            }));\n",
       "\n",
       "        if (origTraces.length) {\n",
       "            self.cvAvgFunc(origTraces, trace);\n",
       "        }\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.cvAvgFunc = function(origTraces, avgTrace) {\n",
       "    var maxCount = origTraces.length,\n",
       "        maxLength = -1,\n",
       "        count,\n",
       "        sum;\n",
       "\n",
       "    origTraces.forEach(function(origTrace) {\n",
       "        if (origTrace.y.length > maxLength) {\n",
       "            maxLength = origTrace.y.length;\n",
       "        }\n",
       "    });\n",
       "\n",
       "    for (var i = 0; i < maxLength; i++) {\n",
       "        sum = 0;\n",
       "        count = 0;\n",
       "\n",
       "        for (var j = 0; j < maxCount; j++) {\n",
       "            if (typeof origTraces[j].y[i] !== 'undefined') {\n",
       "                sum += origTraces[j].y[i];\n",
       "                count++;\n",
       "            }\n",
       "        }\n",
       "\n",
       "        if (count > 0) {\n",
       "            avgTrace.x[i] = i;\n",
       "            avgTrace.y[i] = sum / count;\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.updateTracesCVStdDev = function() {\n",
       "    var tracesHash = this.groupTraces(),\n",
       "        firstTraces = this.filterTracesOne(tracesHash.traces, {cv_stddev_first: true}),\n",
       "        self = this;\n",
       "\n",
       "    firstTraces.forEach(function(trace) {\n",
       "        var origTraces = self.filterTracesEvery(tracesHash.traces, self.getTraceDefParams({\n",
       "                train: trace._params.train,\n",
       "                type: trace._params.type,\n",
       "                smoothed: trace._params.smoothed\n",
       "            })),\n",
       "            lastTraces = self.filterTracesEvery(tracesHash.traces, self.getTraceDefParams({\n",
       "                train: trace._params.train,\n",
       "                type: trace._params.type,\n",
       "                smoothed: trace._params.smoothed,\n",
       "                cv_stddev_last: true\n",
       "            }));\n",
       "\n",
       "        if (origTraces.length && lastTraces.length === 1) {\n",
       "            self.cvStdDevFunc(origTraces, trace, lastTraces[0]);\n",
       "        }\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.cvStdDevFunc = function(origTraces, firstTrace, lastTrace) {\n",
       "    var maxCount = origTraces.length,\n",
       "        maxLength = -1,\n",
       "        count,\n",
       "        sum,\n",
       "        i, j;\n",
       "\n",
       "    origTraces.forEach(function(origTrace) {\n",
       "        if (origTrace.y.length > maxLength) {\n",
       "            maxLength = origTrace.y.length;\n",
       "        }\n",
       "    });\n",
       "\n",
       "    for (i = 0; i < maxLength; i++) {\n",
       "        sum = 0;\n",
       "        count = 0;\n",
       "\n",
       "        for (j = 0; j < maxCount; j++) {\n",
       "            if (typeof origTraces[j].y[i] !== 'undefined') {\n",
       "                sum += origTraces[j].y[i];\n",
       "                count++;\n",
       "            }\n",
       "        }\n",
       "\n",
       "        if (count <= 0) {\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        var std = 0,\n",
       "            avg = sum / count;\n",
       "\n",
       "        for (j = 0; j < maxCount; j++) {\n",
       "            if (typeof origTraces[j].y[i] !== 'undefined') {\n",
       "                std += Math.pow(origTraces[j].y[i] - avg, 2);\n",
       "            }\n",
       "        }\n",
       "\n",
       "        std /= (count - 1);\n",
       "        std = Math.pow(std, 0.5);\n",
       "\n",
       "        firstTrace.x[i] = i;\n",
       "        firstTrace.y[i] = avg - std;\n",
       "        firstTrace.hovertext[i] = firstTrace._params.type + ' std: ' + avg.toFixed(7) + '-' + std.toFixed(7);\n",
       "        lastTrace.x[i] = i;\n",
       "        lastTrace.y[i] = avg + std;\n",
       "        lastTrace.hovertext[i] = lastTrace._params.type + ' std: ' + avg.toFixed(7) + '+' + std.toFixed(7);\n",
       "        if (this.hovertextParameters.length > i) {\n",
       "            firstTrace.hovertext[i] += this.hovertextParameters[i];\n",
       "            lastTrace.hovertext[i] += this.hovertextParameters[i];\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.updateTracesSmoothness = function() {\n",
       "    var tracesHash = this.groupTraces(),\n",
       "        smoothedTraces = this.filterTracesOne(tracesHash.traces, {smoothed: true}),\n",
       "        enabled = this.getSmoothness() > -1,\n",
       "        self = this;\n",
       "\n",
       "    smoothedTraces.forEach(function(trace) {\n",
       "        var origTraces = self.filterTracesEvery(tracesHash.traces, self.getTraceDefParams({\n",
       "                train: trace._params.train,\n",
       "                type: trace._params.type,\n",
       "                indexOfSet: trace._params.indexOfSet,\n",
       "                cv_avg: trace._params.cv_avg,\n",
       "                cv_stddev_first: trace._params.cv_stddev_first,\n",
       "                cv_stddev_last: trace._params.cv_stddev_last\n",
       "            })),\n",
       "            colorFlag = false;\n",
       "\n",
       "        if (origTraces.length === 1) {\n",
       "            origTraces = origTraces[0];\n",
       "\n",
       "            if (origTraces.visible) {\n",
       "                if (enabled) {\n",
       "                    self.smoothFunc(origTraces, trace);\n",
       "                    colorFlag = true;\n",
       "                }\n",
       "\n",
       "                self.highlightSmoothedTrace(origTraces, trace, colorFlag);\n",
       "            }\n",
       "        }\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.highlightSmoothedTrace = function(trace, smoothedTrace, flag) {\n",
       "    if (flag) {\n",
       "        smoothedTrace.line.color = trace._params.plotParams.color;\n",
       "        trace.line.color = smoothedTrace._params.plotParams.color;\n",
       "        trace.hoverinfo = 'skip';\n",
       "\n",
       "        if (trace._params.cv_stddev_last) {\n",
       "            trace.fillcolor = trace._params.plotParams.fillsmoothcolor;\n",
       "        }\n",
       "    } else {\n",
       "        trace.line.color = trace._params.plotParams.color;\n",
       "        trace.hoverinfo = trace._params.plotParams.hoverinfo;\n",
       "\n",
       "        if (trace._params.cv_stddev_last) {\n",
       "            trace.fillcolor = trace._params.plotParams.fillcolor;\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.smoothFunc = function(origTrace, smoothedTrace) {\n",
       "    var data = origTrace.y,\n",
       "        smoothedPoints = this.smooth(data, this.getSmoothness()),\n",
       "        smoothedIndex = 0,\n",
       "        self = this;\n",
       "\n",
       "    if (smoothedPoints.length) {\n",
       "        data.forEach(function (d, index) {\n",
       "            if (!smoothedTrace.x[index]) {\n",
       "                smoothedTrace.x[index] = index;\n",
       "            }\n",
       "\n",
       "            var nameOfSet = smoothedTrace._params.nameOfSet;\n",
       "\n",
       "            if (smoothedTrace._params.cv_stddev_first || smoothedTrace._params.cv_stddev_last) {\n",
       "                nameOfSet = smoothedTrace._params.type + ' std';\n",
       "            }\n",
       "\n",
       "            smoothedTrace.y[index] = smoothedPoints[smoothedIndex];\n",
       "            smoothedTrace.hovertext[index] = nameOfSet + '`: ' + smoothedPoints[smoothedIndex].toPrecision(7);\n",
       "            if (self.hovertextParameters.length > index) {\n",
       "                smoothedTrace.hovertext[index] += self.hovertextParameters[index];\n",
       "            }\n",
       "            smoothedIndex++;\n",
       "        });\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.formatItemValue = function(value, index, suffix) {\n",
       "    if (typeof value === 'undefined') {\n",
       "        return '';\n",
       "    }\n",
       "\n",
       "    suffix = suffix || '';\n",
       "\n",
       "    return '<span title=\"' + suffix + 'value ' + value + '\">' + value + '</span>';\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.updateTraceBest = function(train, hash) {\n",
       "    var traces = this.filterTracesOne(hash.traces, {best_point: true}),\n",
       "        self = this;\n",
       "\n",
       "    traces.forEach(function(trace) {\n",
       "        var testTrace = self.filterTracesEvery(hash.traces, self.getTraceDefParams({\n",
       "                train: trace._params.train,\n",
       "                type: 'test',\n",
       "                indexOfSet: trace._params.indexOfSet\n",
       "            }));\n",
       "\n",
       "        if (self.hasCVMode) {\n",
       "            testTrace = self.filterTracesEvery(hash.traces, self.getTraceDefParams({\n",
       "                train: trace._params.train,\n",
       "                type: 'test',\n",
       "                cv_avg: true\n",
       "            }));\n",
       "        }\n",
       "\n",
       "        var bestValue = self.getBestValue(testTrace.length === 1 ? testTrace[0].y : []);\n",
       "\n",
       "        if (bestValue.index !== -1) {\n",
       "            trace.x[0] = bestValue.index;\n",
       "            trace.y[0] = bestValue.best;\n",
       "            trace.hovertext[0] = bestValue.func + ' (' + (self.hasCVMode ? 'avg' : trace._params.nameOfSet) + '): ' + bestValue.index + ' ' + bestValue.best;\n",
       "        }\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.updateTraceValues = function(name, hash, iteration, click) {\n",
       "    var id = 'catboost-serie-' + this.index + '-' + hash.index,\n",
       "        traces = {\n",
       "            learn: this.filterTracesEvery(hash.traces, this.getTraceDefParams({type: 'learn'})),\n",
       "            test: this.filterTracesEvery(hash.traces, this.getTraceDefParams({type: 'test'}))\n",
       "        },\n",
       "        path = hash.info.path,\n",
       "        self = this;\n",
       "\n",
       "    ['learn', 'test'].forEach(function(type) {\n",
       "        traces[type].forEach(function(trace) {\n",
       "            var data = trace.y || [],\n",
       "                index = typeof iteration !== 'undefined' && iteration < data.length - 1 ? iteration : data.length - 1,\n",
       "                value = data.length ? data[index] : undefined,\n",
       "                testTrace = self.filterTracesEvery(hash.traces, self.getTraceDefParams({\n",
       "                    type: 'test',\n",
       "                    indexOfSet: trace._params.indexOfSet\n",
       "                })),\n",
       "                bestValue = self.getBestValue(testTrace.length === 1 ? testTrace[0].y : []),\n",
       "                timeLeft = '',\n",
       "                timeSpend = '';\n",
       "\n",
       "            if (click || !self.clickMode) {\n",
       "                $('#' + id + ' .catboost-panel__serie_' + type + '_value[data-index=' + trace._params.indexOfSet + ']', self.layout)\n",
       "                    .html(self.formatItemValue(value, index, type + ' '));\n",
       "                $('#' + id + ' .catboost-panel__serie_iteration', self.layout).html(index);\n",
       "\n",
       "                if (self.timeLeft[path] && self.timeLeft[path][data.length - 1]) {\n",
       "                    timeLeft = self.timeLeft[path][data.length - 1][0];\n",
       "                }\n",
       "                $('#' + id + ' .catboost-panel__serie_time_left', self.layout).html(timeLeft ? ('~' + self.convertTime(timeLeft)) : '');\n",
       "\n",
       "                if (self.timeLeft[path] && self.timeLeft[path][index]) {\n",
       "                    timeSpend = self.timeLeft[path][index][1];\n",
       "                }\n",
       "\n",
       "                $('#' + id + ' .catboost-panel__serie_time_spend', self.layout).html(self.convertTime(timeSpend));\n",
       "                $('#' + id + ' .catboost-panel__serie_best_iteration', self.layout).html(bestValue.index > -1 ? bestValue.index : '');\n",
       "\n",
       "                $('#' + id + ' .catboost-panel__serie_best_test_value[data-index=' + trace._params.indexOfSet + ']', self.layout)\n",
       "                    .html(self.formatItemValue(bestValue.best, bestValue.index, 'best ' + trace._params.nameOfSet + ' '));\n",
       "            }\n",
       "        });\n",
       "    });\n",
       "\n",
       "    if (this.hasCVMode) {\n",
       "        var testTrace = this.filterTracesEvery(hash.traces, this.getTraceDefParams({\n",
       "                type: 'test',\n",
       "                cv_avg: true\n",
       "            })),\n",
       "            bestValue = this.getBestValue(testTrace.length === 1 ? testTrace[0].y : []);\n",
       "\n",
       "            $('#' + id + ' .catboost-panel__serie_best_iteration', this.layout).html(bestValue.index > -1 ? bestValue.index : '');\n",
       "    }\n",
       "\n",
       "    if (click) {\n",
       "        this.clickMode = true;\n",
       "\n",
       "        $('#catboost-control2-clickmode' + this.index, this.layout)[0].checked = true;\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.addTracesEvents = function() {\n",
       "    var self = this;\n",
       "\n",
       "    $('.catboost-panel__serie_checkbox', this.layout).click(function() {\n",
       "        var name = $(this).data('seriename');\n",
       "\n",
       "        self.layoutDisabled.traces[name] = !$(this)[0].checked;\n",
       "\n",
       "        self.redrawActiveChart();\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.getNextColor = function(path, opacity) {\n",
       "    var color;\n",
       "\n",
       "    if (this.colorsByPath[path]) {\n",
       "        color = this.colorsByPath[path];\n",
       "    } else {\n",
       "        color = this.colors[this.colorIndex];\n",
       "        this.colorsByPath[path] = color;\n",
       "\n",
       "        this.colorIndex++;\n",
       "\n",
       "        if (this.colorIndex > this.colors.length - 1) {\n",
       "            this.colorIndex = 0;\n",
       "        }\n",
       "    }\n",
       "\n",
       "    return this.hexToRgba(color, opacity);\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.hexToRgba = function(value, opacity) {\n",
       "    if (value.length < 6) {\n",
       "        var pattern = /^#?([a-f\\d])([a-f\\d])([a-f\\d])/i;\n",
       "        value = value.replace(pattern, function(m, r, g, b) {\n",
       "            return '#' + r + r + g + g + b + b;\n",
       "        });\n",
       "    }\n",
       "\n",
       "    var result = /^#?([a-f\\d]{2})([a-f\\d]{2})([a-f\\d]{2})/i.exec(value);\n",
       "    var rgb = {\n",
       "        r: parseInt(result[1], 16),\n",
       "        g: parseInt(result[2], 16),\n",
       "        b: parseInt(result[3], 16)\n",
       "    };\n",
       "\n",
       "    return 'rgba(' + rgb.r + ',' + rgb.g + ',' + rgb.b + ',' + opacity + ')';\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.convertTime = function(time) {\n",
       "    if (!time) {\n",
       "        return '0s';\n",
       "    }\n",
       "\n",
       "    time = Math.floor(time * 1000);\n",
       "\n",
       "    var millis = time % 1000;\n",
       "    time = parseInt(time / 1000, 10);\n",
       "    var seconds = time % 60;\n",
       "    time = parseInt(time / 60, 10);\n",
       "    var minutes = time % 60;\n",
       "    time = parseInt(time / 60, 10);\n",
       "    var hours = time % 24;\n",
       "    var out = \"\";\n",
       "    if (hours && hours > 0) {\n",
       "        out += hours + 'h ';\n",
       "        seconds = 0;\n",
       "        millis = 0;\n",
       "    }\n",
       "    if (minutes && minutes > 0) {\n",
       "        out += minutes + 'm ';\n",
       "        millis = 0;\n",
       "    }\n",
       "    if (seconds && seconds > 0) {\n",
       "        out += seconds + 's ';\n",
       "    }\n",
       "    if (millis && millis > 0) {\n",
       "        out += millis + 'ms';\n",
       "    }\n",
       "\n",
       "    return out.trim();\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.mean = function(values, valueof) {\n",
       "    var n = values.length,\n",
       "        m = n,\n",
       "        i = -1,\n",
       "        value,\n",
       "        sum = 0,\n",
       "        number = function(x) {\n",
       "            return x === null ? NaN : +x;\n",
       "        };\n",
       "\n",
       "    if (valueof === null) {\n",
       "        while (++i < n) {\n",
       "            if (!isNaN(value = number(values[i]))) {\n",
       "                sum += value;\n",
       "            } else {\n",
       "                --m;\n",
       "            }\n",
       "        }\n",
       "    } else {\n",
       "        while (++i < n) {\n",
       "            if (!isNaN(value = number(valueof(values[i], i, values)))) {\n",
       "                sum += value;\n",
       "            } else {\n",
       "                --m;\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "\n",
       "    if (m) {\n",
       "        return sum / m;\n",
       "    }\n",
       "};\n",
       "\n",
       "// from TensorBoard\n",
       "CatboostIpython.prototype.smooth = function(data, weight) {\n",
       "    // When increasing the smoothing window, it smoothes a lot with the first\n",
       "    // few points and then starts to gradually smooth slower, so using an\n",
       "    // exponential function makes the slider more consistent. 1000^x has a\n",
       "    // range of [1, 1000], so subtracting 1 and dividing by 999 results in a\n",
       "    // range of [0, 1], which can be used as the percentage of the data, so\n",
       "    // that the kernel size can be specified as a percentage instead of a\n",
       "    // hardcoded number, what would be bad with multiple series.\n",
       "    var factor = (Math.pow(1000, weight) - 1) / 999,\n",
       "        kernelRadius = Math.floor(data.length * factor / 2),\n",
       "        res = [],\n",
       "        self = this;\n",
       "\n",
       "    data.forEach(function (d, i) {\n",
       "        var actualKernelRadius = Math.min(kernelRadius, i, data.length - i - 1);\n",
       "        var start = i - actualKernelRadius;\n",
       "        var end = i + actualKernelRadius + 1;\n",
       "        var point = d;\n",
       "        // Only smooth finite numbers.\n",
       "        if (!isFinite(point)) {\n",
       "            res.push(point);\n",
       "        } else {\n",
       "            res.push(self.mean(data.slice(start, end).filter(function(d) {\n",
       "                return isFinite(d);\n",
       "            }), null));\n",
       "        }\n",
       "    });\n",
       "\n",
       "    return res;\n",
       "};\n",
       "var debug = false;\n",
       "\n",
       "if (debug) {\n",
       "    require.config({\n",
       "        shim:{\n",
       "            \"custom/CatboostIpythonPlotly\":{\n",
       "                deps:[\"custom/plotly-basic.min\"]\n",
       "            }\n",
       "        }\n",
       "    })\n",
       "\n",
       "    require.undef('catboost_module');\n",
       "    require.undef('custom/CatboostIpythonPlotly');\n",
       "}\n",
       "\n",
       "var moduleBase = '@jupyter-widgets/base';\n",
       "var modules = [moduleBase];\n",
       "\n",
       "if (debug) {\n",
       "    modules.push('custom/CatboostIpythonPlotly');\n",
       "}\n",
       "\n",
       "define('catboost_module', modules, function(widgets) {\n",
       "    var getInstance = function(el) {\n",
       "            var id = $(el).attr('catboost-id');\n",
       "\n",
       "            if (!id) {\n",
       "                return null;\n",
       "            }\n",
       "\n",
       "            id = id.replace('catboost_', '');\n",
       "\n",
       "            if (!window.catboostIpythonInstances[id]) {\n",
       "                return null;\n",
       "            }\n",
       "\n",
       "            return window.catboostIpythonInstances[id];\n",
       "        },\n",
       "        addInstance = function(el) {\n",
       "            $(el).attr('catboost-id', 'catboost_' + window.catboostIpythonIndex);\n",
       "\n",
       "            var catboostIpython = new CatboostIpython();\n",
       "            catboostIpython.index = window.catboostIpythonIndex;\n",
       "            catboostIpython.plotly = window.Plotly;\n",
       "            if (debug) {\n",
       "                catboostIpython.loadStyles('/custom/CatboostIpython.css', function(){catboostIpython.resizeCharts();})\n",
       "            }\n",
       "\n",
       "            window.catboostIpythonInstances[window.catboostIpythonIndex] = catboostIpython;\n",
       "\n",
       "            window.catboostIpythonIndex++;\n",
       "\n",
       "            return catboostIpython;\n",
       "        };\n",
       "\n",
       "    var CatboostIpythonWidget = widgets.DOMWidgetView.extend({\n",
       "\n",
       "        initialize: function() {\n",
       "            CatboostIpythonWidget.__super__.initialize.apply(this, arguments);\n",
       "\n",
       "            if (!window.catboostIpythonInstances) {\n",
       "                window.catboostIpythonInstances = {};\n",
       "            }\n",
       "\n",
       "            if (typeof window.catboostIpythonIndex === 'undefined') {\n",
       "                window.catboostIpythonIndex = 0;\n",
       "            }\n",
       "\n",
       "            var catboostIpythonInstance = getInstance(this.el);\n",
       "\n",
       "            if (!catboostIpythonInstance) {\n",
       "                catboostIpythonInstance = addInstance(this.el);\n",
       "            }\n",
       "\n",
       "            catboostIpythonInstance.init();\n",
       "        },\n",
       "\n",
       "        render: function() {\n",
       "            this.value_changed();\n",
       "            this.model.on('change:value', this.value_changed, this);\n",
       "        },\n",
       "\n",
       "        update: function() {\n",
       "            this.value_changed();\n",
       "        },\n",
       "\n",
       "        value_changed: function() {\n",
       "            this.el.style['width'] = this.model.get('width');\n",
       "            this.el.style['height'] = this.model.get('height');\n",
       "            this.displayed.then(_.bind(this.render_charts, this));\n",
       "        },\n",
       "\n",
       "        process_all: function(parent, params) {\n",
       "            var data = params.data;\n",
       "\n",
       "            for (var path in data) {\n",
       "                if (data.hasOwnProperty(path)) {\n",
       "                    this.process_row(parent, data[path])\n",
       "                }\n",
       "            }\n",
       "        },\n",
       "\n",
       "        process_row: function(parent, data) {\n",
       "            var catboostIpython = getInstance(parent),\n",
       "                path = data.path,\n",
       "                content = data.content,\n",
       "                items = content.data.iterations,\n",
       "                firstIndex = 0,\n",
       "                chunks = [];\n",
       "\n",
       "            if (!items || !items.length) {\n",
       "                return;\n",
       "            }\n",
       "\n",
       "            if (!catboostIpython.lastIndex) {\n",
       "                catboostIpython.lastIndex = {}\n",
       "            }\n",
       "\n",
       "            if (catboostIpython.lastIndex[path]) {\n",
       "                firstIndex = catboostIpython.lastIndex[path] + 1;\n",
       "            }\n",
       "\n",
       "            catboostIpython.lastIndex[path] = items.length - 1;\n",
       "\n",
       "            for (var i = firstIndex; i < items.length; i++) {\n",
       "                chunks.push(items[i]);\n",
       "            }\n",
       "\n",
       "            catboostIpython.addMeta(data.path, content.data.meta);\n",
       "\n",
       "            catboostIpython.addPoints(parent, {\n",
       "                chunks: chunks,\n",
       "                train: data.name,\n",
       "                path: data.path\n",
       "            });\n",
       "        },\n",
       "\n",
       "        render_charts: function () {\n",
       "            this.process_all(this.el, {\n",
       "                data: this.model.get('data')\n",
       "            });\n",
       "\n",
       "            return this;\n",
       "        }\n",
       "    });\n",
       "\n",
       "    return {\n",
       "        CatboostIpythonWidgetView: CatboostIpythonWidget\n",
       "    };\n",
       "});\n",
       "\n",
       "            </script>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "dd48870aa9f044b39305707a4915f920",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "MetricVisualizer(layout=Layout(align_self='stretch', height='500px'))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<catboost.core.CatBoostClassifier at 0x2b44b37d0b8>"
      ]
     },
     "execution_count": 312,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "catboost_model.fit(x_train, y_train, silent = True, plot = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test_predict = catboost_model.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "**Accuracy Score**\n",
      "\n",
      "Test Accuracy is: 0.9176598049837487\n",
      "---------------------------------------------------------\n",
      "\n",
      "**Accuracy Error**\n",
      "\n",
      "Test Error: 0.08234019501625134\n",
      "---------------------------------------------------------\n",
      "\n",
      "**Classification Report**\n",
      "\n",
      " Test Classification Report:\n",
      "                     0            1  accuracy    macro avg  weighted avg\n",
      "precision     0.916667     0.919470   0.91766     0.918068      0.917731\n",
      "recall        0.954016     0.858230   0.91766     0.906123      0.917660\n",
      "f1-score      0.934969     0.887795   0.91766     0.911382      0.917064\n",
      "support    1718.000000  1051.000000   0.91766  2769.000000   2769.000000\n",
      "---------------------------------------------------------\n",
      "\n",
      "**Confusion Matrix**\n",
      "\n",
      " Test Confusion Matrix Report:\n",
      "[[1639   79]\n",
      " [ 149  902]]\n"
     ]
    }
   ],
   "source": [
    "check_metric(y_test, y_test_predict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Final Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "metadata": {},
   "outputs": [],
   "source": [
    "random_forest = Pipeline([('transformer', ct), ('RandomForest', RandomForestClassifier(n_estimators = 300, min_samples_split = 10, min_samples_leaf = 2, max_features = 'auto', bootstrap = False, max_depth = None, random_state = 42))])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('transformer',\n",
       "                 ColumnTransformer(remainder='passthrough',\n",
       "                                   transformers=[('se', StandardScaler(),\n",
       "                                                  ['Total Time Spent on '\n",
       "                                                   'Website',\n",
       "                                                   'Page Views Per Visit',\n",
       "                                                   'TotalVisits'])])),\n",
       "                ('RandomForest',\n",
       "                 RandomForestClassifier(bootstrap=False, min_samples_leaf=2,\n",
       "                                        min_samples_split=10, n_estimators=300,\n",
       "                                        random_state=42))])"
      ]
     },
     "execution_count": 320,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_forest.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_predict = random_forest.predict(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test_predict = random_forest.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 324,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Accuracy: 0.9436619718309859\n",
      "Test Accuracy: 0.9209100758396533\n"
     ]
    }
   ],
   "source": [
    "print(\"Train Accuracy: %s\"%(accuracy_score(y_train, y_train_predict)))\n",
    "print(\"Test Accuracy: %s\"%(accuracy_score(y_test, y_test_predict)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train.loc[:, \"Actual Class\"] = y_train\n",
    "x_train.loc[:, \"Predicted Class\"] = y_train_predict\n",
    "x_test.loc[:, \"Actual Class\"] = y_test\n",
    "x_test.loc[:, \"Predicted Class\"] = y_test_predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 351,
   "metadata": {},
   "outputs": [],
   "source": [
    "predicted_df = x_train.append(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 354,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2b4479b6550>"
      ]
     },
     "execution_count": 354,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14,6))\n",
    "plt.subplot(121)\n",
    "plt.title(\"Actual Class Label\")\n",
    "sns.countplot(predicted_df[\"Actual Class\"])\n",
    "plt.subplot(122)\n",
    "plt.title(\"Predicted Class Label\")\n",
    "sns.countplot(predicted_df[\"Predicted Class\"])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}