notebooks/10_Transfer_Learn_MXNet.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tutorial. How to do the following. \n",
    "- Sklearn Data Transformations\n",
    "- MXNet Dataset and Dataloaders\n",
    "- MXNet Custom Networks\n",
    "- Training Base Model\n",
    "- Extracting Embeddings from the Network\n",
    "- Transfer Learning on a Different Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# basic utils for data preparation, plotting\n",
    "import os, sys, time, math, ast, re, string, random\n",
    "from collections import defaultdict\n",
    "import datetime\n",
    "import joblib, boto3\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import boto3\n",
    "import gzip\n",
    "import tarfile\n",
    "from io import StringIO\n",
    "\n",
    "# mxnet/gluon/gluonnlp \n",
    "import mxnet as mx\n",
    "from mxnet import gluon, autograd\n",
    "import mxnet.ndarray as nd\n",
    "import gluonnlp as nlp\n",
    "\n",
    "# sklearn ML stuff\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.preprocessing import normalize\n",
    "\n",
    "# For creating sklearn transformers\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.base import BaseEstimator, TransformerMixin\n",
    "\n",
    "# For t-SNE\n",
    "from sklearn.manifold import TSNE\n",
    "from sklearn.neighbors import NearestNeighbors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading and Splitting Data "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data sources: \n",
    "1. Hotel reviews data - https://www.kaggle.com/harmanpreet93/hotelreviews\n",
    "2. IMDB reviews data - https://www.kaggle.com/jcblaise/imdb-sentiments#train.csv\n",
    "\n",
    "We will train the neural net on IMDB reviews data and use the learnt embeddings from different layers of neural net as features for classifying Hotel reviews. We will then calculate how much lift it gives to the baseline Hotel model which doesn't use these embeddings. \n",
    "\n",
    "For IMDB, 0 means positive label.     \n",
    "For Hotel, happy means positive label"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "imdb = pd.read_csv(\"data/imdb.csv\")\n",
    "hotel = pd.read_csv(\"data/hotel-reviews.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((25000, 2), (38932, 5))"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imdb.shape, hotel.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "      <th>sentiment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>For a movie that gets no respect there sure ar...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bizarre horror movie filled with famous faces ...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A solid, if unremarkable film. Matthau, as Ein...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                text  sentiment\n",
       "0  For a movie that gets no respect there sure ar...          0\n",
       "1  Bizarre horror movie filled with famous faces ...          0\n",
       "2  A solid, if unremarkable film. Matthau, as Ein...          0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imdb.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 0 is positive for imdb, making it same for hotel\n",
    "hotel[\"Is_Response\"] = pd.Series(hotel[\"Is_Response\"]!=\"happy\").astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>User_ID</th>\n",
       "      <th>Description</th>\n",
       "      <th>Browser_Used</th>\n",
       "      <th>Device_Used</th>\n",
       "      <th>Is_Response</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>id10326</td>\n",
       "      <td>The room was kind of clean but had a VERY stro...</td>\n",
       "      <td>Edge</td>\n",
       "      <td>Mobile</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>id10327</td>\n",
       "      <td>I stayed at the Crown Plaza April -- - April -...</td>\n",
       "      <td>Internet Explorer</td>\n",
       "      <td>Mobile</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>id10328</td>\n",
       "      <td>I booked this hotel through Hotwire at the low...</td>\n",
       "      <td>Mozilla</td>\n",
       "      <td>Tablet</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   User_ID                                        Description  \\\n",
       "0  id10326  The room was kind of clean but had a VERY stro...   \n",
       "1  id10327  I stayed at the Crown Plaza April -- - April -...   \n",
       "2  id10328  I booked this hotel through Hotwire at the low...   \n",
       "\n",
       "        Browser_Used Device_Used  Is_Response  \n",
       "0               Edge      Mobile            1  \n",
       "1  Internet Explorer      Mobile            1  \n",
       "2            Mozilla      Tablet            1  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hotel.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ratio of positive to negative labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "68.12133977190999"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(hotel.Is_Response==0)/hotel.shape[0]*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50.0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(imdb.sentiment==0)/imdb.shape[0]*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pd.set_option('display.max_colwidth', -1)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train valid test split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "imdb_X_train, imdb_X_test, imdb_y_train, imdb_y_test = train_test_split(imdb[\"text\"],\n",
    "                                                                    imdb[\"sentiment\"],\n",
    "                                                                    test_size=0.20,\n",
    "                                                                    random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 20% for train for validation\n",
    "imdb_X_train, imdb_X_valid, imdb_y_train, imdb_y_valid = train_test_split(imdb_X_train,\n",
    "                                                                    imdb_y_train,\n",
    "                                                                    test_size=0.20,\n",
    "                                                                    random_state=1)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "assert imdb_X_train.shape[0] + imdb_X_valid.shape[0] + imdb_X_test.shape[0] == imdb.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(16000,)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imdb_X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "hotel_X_train, hotel_X_test, hotel_y_train, hotel_y_test = train_test_split(hotel[\"Description\"],\n",
    "                                                                    hotel[\"Is_Response\"],\n",
    "                                                                    test_size=0.20,\n",
    "                                                                    random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "hotel_X_train, hotel_X_valid, hotel_y_train, hotel_y_valid = train_test_split(hotel_X_train,\n",
    "                                                                    hotel_y_train,\n",
    "                                                                    test_size=0.20,\n",
    "                                                                    random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "assert hotel_X_train.shape[0] + hotel_X_valid.shape[0] + hotel_X_test.shape[0] == hotel.shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sklearn Data Transformations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Tokenize(BaseEstimator, TransformerMixin):\n",
    "    \"\"\"\n",
    "    Takes in pandas series and applies tokenization on each row based on given split pattern. \n",
    "    \"\"\"\n",
    "    def __init__(self, split_pat=f\"([{string.punctuation}])\"):\n",
    "        self.split_pat = split_pat  # re pattern used to split string to tokens. default splits over any string punctuation\n",
    "    \n",
    "    def tokenize(self, s):\n",
    "        \"\"\" Tokenize string \"\"\"\n",
    "        re_tok = re.compile(self.split_pat)\n",
    "        return re_tok.sub(r' \\1 ', s).split()  # substitute all delimiters specified in pattern with space and then splits over space\n",
    "    \n",
    "    def fit(self, X, y=None):  # no need to learn anything from training data for tokenization. so returning self\n",
    "        return self\n",
    "\n",
    "    def transform(self, X, y=None):\n",
    "        return X.apply(self.tokenize)\n",
    "    \n",
    "    \n",
    "class Tok2Idx(BaseEstimator, TransformerMixin):\n",
    "    \"\"\"\n",
    "    Creates integer index from tokenizes columns. \n",
    "    Creates a dictionary of all unique tokens and corresponding integer index\n",
    "    Transform maps any token unseen in the training data to <unk> token \n",
    "    Need pandas series as input. \n",
    "    \"\"\"\n",
    "            \n",
    "    def map_tok_idx(self, char):\n",
    "        try:\n",
    "            return self.tok2idx[char]\n",
    "        except KeyError:\n",
    "            return self.tok2idx[\"<unk>\"]\n",
    "\n",
    "    def fit(self, X, y=None):\n",
    "        \"\"\" To be called for training data. Creates a token to integer map \"\"\"\n",
    "        self.uniq_set = list(set([y for x in list(X.values) for y in x]))\n",
    "        self.uniq_set.append(\"<unk>\")\n",
    "        self.tok2idx = {j:i for i,j in enumerate(self.uniq_set)}\n",
    "        self.idx2tok = {i:j for i,j in enumerate(self.uniq_set)}\n",
    "        return self\n",
    "\n",
    "    def transform(self, X, y=None):\n",
    "        return X.map(lambda x: [self.map_tok_idx(c) for c in x])\n",
    "    \n",
    "    \n",
    "class LowerCaser(BaseEstimator, TransformerMixin):\n",
    "    \"\"\"\n",
    "    Lower case all string values\n",
    "    Need pandas series as input. \n",
    "    \"\"\"\n",
    "    def fit(self, X, y=None):\n",
    "        return self\n",
    "\n",
    "    def transform(self, X, y=None):\n",
    "        return X.str.lower()\n",
    "    \n",
    "    \n",
    "class NullImputer(SimpleImputer):\n",
    "    \"\"\"\n",
    "    SimpleImputer works with 2D array. For the purpose of this analysis, we are working with pd Series. \n",
    "    Modifying it a bit\n",
    "    \"\"\"\n",
    "    def __init__(self, missing_values=np.nan, strategy='mean', fill_value=None, **kw):\n",
    "        super(NullImputer, self).__init__(\n",
    "            missing_values=missing_values,\n",
    "            strategy=strategy,\n",
    "            fill_value=fill_value\n",
    "        )\n",
    "\n",
    "    def fit(self, X, y=None):\n",
    "        super(NullImputer, self).fit(pd.DataFrame(X), y)\n",
    "        return self\n",
    "\n",
    "    def transform(self, X):\n",
    "        result = super(NullImputer, self).transform(pd.DataFrame(X))\n",
    "        return pd.DataFrame(result)[0]  # converting 2D array back to series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "transformer = Pipeline(steps=[\n",
    "    (\"null_impute\", NullImputer(strategy=\"constant\", fill_value=\"null\")),\n",
    "    (\"lower_case\", LowerCaser()),\n",
    "    (\"tokenize\", Tokenize()),\n",
    "    (\"token2index\", Tok2Idx())\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "imdb_X_train_transformed = transformer.fit_transform(imdb_X_train)\n",
    "imdb_X_valid_transformed = transformer.transform(imdb_X_valid)\n",
    "imdb_X_test_transformed = transformer.transform(imdb_X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    [25405, 909, 37236, 52086, 42321, 40316]\n",
       "1                  [25405, 909, 23001, 42321]\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's see how it's working\n",
    "transformer.transform(pd.Series([\"This is just an example.\",\n",
    "                                \"This is another example\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# total tokens\n",
    "tt = transformer.named_steps['token2index']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "63166"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# unknown token\n",
    "tt.tok2idx['<unk>']  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## MXNet Dataset and Dataloaders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_dataset(review_array, labels_array, max_seq_len=20):\n",
    "\n",
    "    dataset = gluon.data.SimpleDataset(list(zip(review_array,\n",
    "                                                labels_array))).\\\n",
    "        transform(lambda review, label: (\n",
    "            nlp.data.ClipSequence(max_seq_len)(review), label))\n",
    "    return dataset\n",
    "\n",
    "\n",
    "def get_dataloader(dataset,\n",
    "                   dataset_type=\"train\",  # valid/test\n",
    "                   batch_size=256,\n",
    "                   bucket_num=5,\n",
    "                   shuffle=True,  # true for training\n",
    "                   num_workers=1):\n",
    "    \n",
    "    # Batchify function appends the length of each sequence to feed as addtional input \n",
    "    combined_batchify_fn = nlp.data.batchify.Tuple(\n",
    "        nlp.data.batchify.Pad(axis=0, ret_length=True),\n",
    "        nlp.data.batchify.Stack(dtype='float32'))  # stack input samples\n",
    "  \n",
    "    if dataset_type == \"train\":\n",
    "        data_lengths = dataset.transform(\n",
    "            lambda review, label: float(len(review)), lazy=False)\n",
    "    \n",
    "        # We need to shuffle for training data.\n",
    "        # It's more efficient to shuffle training data such that sequences with similar length come together\n",
    "        # This is achieved using FixedBucketSampler\n",
    "        batch_sampler = nlp.data.sampler.FixedBucketSampler(\n",
    "            data_lengths,\n",
    "            batch_size=batch_size,\n",
    "            num_buckets=bucket_num,\n",
    "            shuffle=shuffle)\n",
    "\n",
    "        dataloader = gluon.data.DataLoader(\n",
    "            dataset=dataset,\n",
    "            batch_sampler=batch_sampler,\n",
    "            batchify_fn=combined_batchify_fn,\n",
    "            num_workers=num_workers,\n",
    "        )\n",
    "    # No bucketing or shuffling is performed for valid and test\n",
    "    elif dataset_type in [\"valid\", \"test\"]:\n",
    "        batch_sampler = None\n",
    "        dataloader = gluon.data.DataLoader(\n",
    "            dataset=dataset,\n",
    "            shuffle=shuffle,\n",
    "            batch_size=batch_size,\n",
    "            batchify_fn=combined_batchify_fn,\n",
    "            num_workers=num_workers)\n",
    "    else:\n",
    "        raise Exception(\"Pass dataset type from train, dev, valid or test\")\n",
    "\n",
    "    return dataloader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "555.0"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 90th percentile of review length. We use this to clip the LSTM sequence to max of this length\n",
    "clip_val = np.percentile(imdb_X_train_transformed.apply(lambda x: len(x)).values, 90)\n",
    "clip_val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "max_len=int(clip_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "imdb_train_dataset = get_dataset(imdb_X_train_transformed.values, \n",
    "                                  imdb_y_train.values,\n",
    "                                  max_len)\n",
    "imdb_valid_dataset = get_dataset(imdb_X_valid_transformed.values, \n",
    "                                  imdb_y_valid.values,\n",
    "                                  max_len)\n",
    "imdb_test_dataset = get_dataset(imdb_X_test_transformed.values, \n",
    "                                  imdb_y_test.values,\n",
    "                                  max_len)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16000"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(imdb_train_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sequence:  [17276, 4747, 31258, 52086, 12922]\n",
      "Sentiment:  0\n"
     ]
    }
   ],
   "source": [
    "print(\"Sequence: \", imdb_train_dataset[0][0][:5])\n",
    "print(\"Sentiment: \", imdb_train_dataset[0][1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16000"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(imdb_train_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "imdb_train_dataloader = get_dataloader(imdb_train_dataset,\n",
    "                                       batch_size=64,\n",
    "                                       bucket_num=5,\n",
    "                                       shuffle=True,\n",
    "                                       num_workers=0,\n",
    "                                       dataset_type=\"train\",)\n",
    "\n",
    "imdb_valid_dataloader = get_dataloader(imdb_valid_dataset,\n",
    "                                       batch_size=64,\n",
    "                                       bucket_num=5,\n",
    "                                       shuffle=False,\n",
    "                                       num_workers=0,\n",
    "                                       dataset_type=\"valid\")\n",
    "\n",
    "imdb_test_dataloader = get_dataloader(imdb_test_dataset,\n",
    "                                      batch_size=64,\n",
    "                                      bucket_num=5,\n",
    "                                      shuffle=False,\n",
    "                                      num_workers=0,\n",
    "                                      dataset_type=\"test\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((\n",
       "  [[31876. 12660.  3825. ...     0.     0.     0.]\n",
       "   [55342. 17276. 32304. ...     0.     0.     0.]\n",
       "   [62462. 18475. 10281. ...     0.     0.     0.]\n",
       "   ...\n",
       "   [10259. 52466. 40934. ...     0.     0.     0.]\n",
       "   [18894. 40934. 33774. ...     0.     0.     0.]\n",
       "   [25405. 52875. 18894. ...     0.     0.     0.]]\n",
       "  <NDArray 64x555 @cpu_shared(0)>,\n",
       "  \n",
       "  [256 268  71  89 364 142 144 152 555 189  51 125 555 261 139 109 501 555\n",
       "   149 313 240 321 189 479 351 225 555 265 172 161 163 197 555 142 325 192\n",
       "   179 176 555 318 204  58 163 555 223 143  42 217 186 555 151 183  72  94\n",
       "   106 135 273 161 280 152 555  72 169 151]\n",
       "  <NDArray 64 @cpu_shared(0)>),\n",
       " \n",
       " [0. 1. 1. 1. 0. 1. 1. 0. 1. 1. 0. 0. 0. 1. 1. 0. 1. 0. 1. 1. 1. 1. 1. 0.\n",
       "  0. 0. 0. 0. 0. 0. 0. 1. 0. 1. 1. 1. 1. 1. 0. 1. 0. 1. 1. 1. 1. 0. 0. 1.\n",
       "  1. 1. 1. 0. 0. 1. 1. 1. 1. 1. 0. 0. 0. 0. 0. 0.]\n",
       " <NDArray 64 @cpu_shared(0)>)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "next(iter(imdb_valid_dataloader))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Defining MXNet Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "class CustomSeqNet(gluon.nn.HybridBlock):\n",
    "    \"\"\"\n",
    "    Custom defined network for sequence data that is used to predict a binary label.   \n",
    "    \n",
    "    \"\"\"\n",
    "    def __init__(self, input_output_embed_map, dense_sizes=[100], dropouts=[0.2], activation=\"relu\"):\n",
    "        \"\"\"\n",
    "        input_output_embed_map: {\"token_embed\": (max_tok_idx, tok_embed_dim), \"hidden_embed\": (,hidden_embed_dim))}\n",
    "        \"\"\"\n",
    "        self.dense_sizes = dense_sizes  # list of output dimension of dense layers\n",
    "        self.dropouts = dropouts  # list of dropout for each dense layer\n",
    "\n",
    "        super(CustomSeqNet, self).__init__(prefix='CustomSeqNet_')\n",
    "\n",
    "        with self.name_scope():  # name space object to manage parameter names\n",
    "            # 1. Embedding layer\n",
    "            self.embed = gluon.nn.Embedding(\n",
    "                input_dim=input_output_embed_map[\"token_embed\"][0],\n",
    "                output_dim=input_output_embed_map[\"token_embed\"][1],\n",
    "                prefix='token_embed_'\n",
    "            )  # output = (bs, sequence_len, input_size) = (N,T,C)\n",
    "            \n",
    "            # 2. GRU layer\n",
    "            self.rnn = gluon.rnn.GRU(\n",
    "                hidden_size=input_output_embed_map[\"hidden_embed\"][1],\n",
    "                bidirectional=True, \n",
    "                layout='NTC', # batch size, sequence length and feature dimensions respectively\n",
    "                prefix='review_gru_'\n",
    "            ) \n",
    "\n",
    "            # 3. Dense layers\n",
    "            # need to specify in_units in Dense for some initialization issues\n",
    "            for i, sz in enumerate(self.dense_sizes):\n",
    "                setattr(self, \"dense_{}\".format(i), gluon.nn.Dense(sz))\n",
    "                setattr(self, \"bn_dense_{}\".format(\n",
    "                    i), gluon.nn.BatchNorm(axis=1))\n",
    "                setattr(self, \"activation_dense_{}\".format(\n",
    "                    i), gluon.nn.Activation(activation))\n",
    "                setattr(self, \"drop_dense_{}\".format(i),\n",
    "                        gluon.nn.Dropout(self.dropouts[i]))\n",
    "            # 4. Output layer\n",
    "            self.output = gluon.nn.Dense(1, prefix=\"output_\")\n",
    "\n",
    "    def hybrid_forward(self, F, review, review_len):\n",
    "        embed = self.embed(review) # 1\n",
    "        rnn_all = self.rnn(embed) # 2\n",
    "        # Extract last output in sequence\n",
    "        net = F.SequenceLast(rnn_all, sequence_length=F.cast(\n",
    "            review_len, 'float32'), use_sequence_length=True, axis=1)  # use review_len to remove the paddings\n",
    "        for i, sz in enumerate(self.dense_sizes): # 3\n",
    "            net = getattr(self, \"dense_{}\".format(i))(net)  # MLP\n",
    "            net = getattr(self, \"bn_dense_{}\".format(i))(net)  # BN\n",
    "            net = getattr(self, \"activation_dense_{}\".format(i))(net)  # relu\n",
    "            net = getattr(self, \"drop_dense_{}\".format(i))(net)  # dropouts\n",
    "        net = self.output(net) # 4\n",
    "        return net"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "ctx = [mx.gpu(0)]  # use GPU"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "63167"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_idx = max(tt.tok2idx.values())+1  # starts from 0\n",
    "max_idx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "tok_embed_dim = 64\n",
    "review_embed_dim = 50  # embedding size of hidden state in GRU\n",
    "\n",
    "input_output_embed_map = {\"token_embed\": (max_idx, tok_embed_dim),\n",
    "                         \"hidden_embed\": (None, review_embed_dim)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'token_embed': (63167, 64), 'hidden_embed': (None, 50)}"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input_output_embed_map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "dropouts = [0.2, 0.2, 0.2]\n",
    "dense_sizes=[100, 100, 10]\n",
    "activation=\"relu\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "net1 = CustomSeqNet(input_output_embed_map, dense_sizes, dropouts, activation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "net1.initialize(mx.init.Xavier(), ctx=ctx, force_reinit=True)  # initialize network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "# net1.hybridize()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "# net1.forward(next(iter(imdb_train_dataloader))[0][0].as_in_context(ctx[0]), \n",
    "#              next(iter(imdb_train_dataloader))[0][1].as_in_context(ctx[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training Base Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate_network(network, loss, data_iterator, ctx, calculate_metrics=True):\n",
    "    loss_acc = 0.\n",
    "    y_true = np.array([])\n",
    "    y_hat = np.array([])\n",
    "    for idx, ((data, length), label) in enumerate(data_iterator):\n",
    "        X_ = gluon.utils.split_and_load(data, ctx, even_split=False)  # splits data to go to each gpu\n",
    "        X_l_ = gluon.utils.split_and_load(length, ctx, even_split=False)\n",
    "        y_ = gluon.utils.split_and_load(label, ctx, even_split=False)\n",
    "        y_true = np.concatenate([y_true] + [y_i.asnumpy() for y_i in y_])  # get all in one big list\n",
    "        preds = [network(x_, x_l_) for x_, x_l_ in zip(X_, X_l_)]\n",
    "        y_hat = np.concatenate([y_hat] + [p.asnumpy().flatten() for p in preds]) # get all in one big list\n",
    "        if calculate_metrics:\n",
    "            losses = [loss(p, y) for p, y in zip(preds, y_)]  \n",
    "            loss_acc += sum(losses).mean() / len(ctx)\n",
    "    if calculate_metrics: \n",
    "        auc = roc_auc_score(y_true, y_hat)\n",
    "    else:\n",
    "        return y_true, y_hat\n",
    "    return loss_acc.asnumpy()[0]/float(idx+1), auc, y_true, y_hat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train(network, \n",
    "          train_data, \n",
    "          holdout_data, \n",
    "          loss,\n",
    "          epochs, \n",
    "          ctx, \n",
    "          lr=1e-2,\n",
    "          wd=1e-5,\n",
    "          optimizer='adam'):\n",
    "    \n",
    "    # 1. Define optimizer\n",
    "    trainer = gluon.Trainer(network.collect_params(), optimizer,\n",
    "                            {'learning_rate': lr, 'wd': wd})\n",
    "    \n",
    "    # Hybridize network for faster computations. (Symbolic)\n",
    "    network.hybridize()\n",
    "    \n",
    "    # Print loss values before training starts\n",
    "    valid_loss, valid_auc, _, _ = evaluate_network(network, loss, holdout_data, ctx)\n",
    "    train_loss, train_auc, _, _ = evaluate_network(network, loss, train_data, ctx)\n",
    "\n",
    "    print(\"Start \\n Training BCE {:.4f}, Train AUC {:.4f}, Valid AUC {:.4f}\".format(train_loss,\n",
    "                                                                                    train_auc,\n",
    "                                                                                    valid_auc))\n",
    "    \n",
    "    # 3. Train the network\n",
    "    for e in range(epochs):\n",
    "        for idx, ((data, length), label) in enumerate(train_data):  # For each mini batch\n",
    "            X_ = gluon.utils.split_and_load(data, ctx, even_split=False)  # splits data to go to each gpu\n",
    "            X_l_ = gluon.utils.split_and_load(length, ctx, even_split=False)\n",
    "            y_ = gluon.utils.split_and_load(label, ctx, even_split=False)\n",
    "            \n",
    "            # Forward pass to be done in .record() mode. \n",
    "            # By default, record mode takes it to training mode and helps with layers like dropout which\n",
    "            # require different treatment for predict model\n",
    "            with autograd.record():\n",
    "                preds = [network(x_, x_l_) for x_, x_l_ in zip(X_, X_l_)]  # forward pass\n",
    "                losses = [loss(p, y) for p, y in zip(preds, y_)]  # loss calculation\n",
    "\n",
    "            [k.backward() for k in losses]  # gradient calculation using chain rule\n",
    "            trainer.step(data.shape[0])  # performs one step of gradient descent. input parameter is # rows in mini batch\n",
    "\n",
    "        valid_loss, valid_auc, _, _ = evaluate_network(network, loss, holdout_data, ctx)\n",
    "        train_loss, train_auc, _, _ = evaluate_network(network, loss, train_data, ctx)\n",
    "\n",
    "        print(\"Epoch [{}], Training BCE {:.4f}, Train AUC {:.4f}, Valid AUC {:.4f}\".format(e+1, \n",
    "                                                                                           train_loss,\n",
    "                                                                                           train_auc,\n",
    "                                                                                           valid_auc))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the defined function to train the network "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "optimizer = 'adam'\n",
    "wd = 3e-4\n",
    "lr=1e-2\n",
    "ctx = [mx.gpu(0)]\n",
    "loss = gluon.loss.SigmoidBCELoss()  # define the loss fn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1764"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "epochs=7\n",
    "total_iterations = len(imdb_train_dataloader)*epochs\n",
    "total_iterations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Start \n",
      " Training BCE 0.6931, Train AUC 0.5059, Valid AUC 0.5028\n",
      "Epoch [1], Training BCE 1.6850, Train AUC 0.9445, Valid AUC 0.9217\n",
      "Epoch [2], Training BCE 0.2190, Train AUC 0.9731, Valid AUC 0.9498\n",
      "Epoch [3], Training BCE 0.2420, Train AUC 0.9755, Valid AUC 0.9490\n",
      "Epoch [4], Training BCE 0.2663, Train AUC 0.9790, Valid AUC 0.9483\n",
      "Epoch [5], Training BCE 0.1998, Train AUC 0.9865, Valid AUC 0.9507\n",
      "CPU times: user 1min 58s, sys: 21 s, total: 2min 19s\n",
      "Wall time: 2min 1s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "net1.initialize(mx.init.Xavier(), ctx=ctx, force_reinit=True)\n",
    "\n",
    "train(\n",
    "    net1, \n",
    "    imdb_train_dataloader, \n",
    "    imdb_valid_dataloader,\n",
    "    loss,\n",
    "    epochs=5, \n",
    "    lr=lr,\n",
    "    wd=wd, \n",
    "    optimizer=optimizer, \n",
    "    ctx=ctx)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Train some more with reduced learning rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Start \n",
      " Training BCE 0.2007, Train AUC 0.9865, Valid AUC 0.9507\n",
      "Epoch [1], Training BCE 0.0978, Train AUC 0.9940, Valid AUC 0.9563\n",
      "CPU times: user 29 s, sys: 4.5 s, total: 33.5 s\n",
      "Wall time: 30.1 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "train(\n",
    "    net1, \n",
    "    imdb_train_dataloader, \n",
    "    imdb_valid_dataloader,\n",
    "    loss,\n",
    "    epochs=1, \n",
    "    lr=lr/10,\n",
    "    wd=wd, \n",
    "    optimizer=optimizer, \n",
    "    ctx=ctx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9562818647077602"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "_, _, y_valid, hat_valid = evaluate_network(net1, \n",
    "                                            loss,\n",
    "                                            imdb_valid_dataloader,\n",
    "                                            ctx)\n",
    "roc_auc_score(y_valid, hat_valid) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9518646542216113"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "_, _, y_test, hat_test = evaluate_network(net1, \n",
    "                                          loss,\n",
    "                                          imdb_test_dataloader,\n",
    "                                          ctx)\n",
    "roc_auc_score(y_test, hat_test) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We get >95% auc on the test test. So the model has learnt well. Let's see how the score distribution looks like"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sigmoid(x, derivative=False):\n",
    "    sigm = 1. / (1. + np.exp(-x))\n",
    "    if derivative:\n",
    "        return sigm * (1. - sigm)\n",
    "    return sigm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "sigmoid_v = np.vectorize(sigmoid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f04372a8390>"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "h=plt.hist(sigmoid_v(hat_valid[y_valid==0]), color='g')\n",
    "h=plt.hist(sigmoid_v(hat_valid[y_valid==1]), color='r')\n",
    "plt.xlabel(\"Prediction score bw o and 1\")\n",
    "plt.ylabel(\"# observations\")\n",
    "plt.legend([\"Positive\", 'Negative'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "# saving the network\n",
    "net1.export(os.path.join(\"models\", \"imdb_v2\"), epoch=0) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TSNE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Token embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(63167, 64)"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tok_embed = net1.embed.weight.list_data()[0].asnumpy()  # extract weights of embedding layer from network\n",
    "tok_embed.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "few_words = ['great', 'excellent', 'best', 'perfect', 'wonderful', 'well',\n",
    "       'fun', 'love', 'amazing', 'also', 'enjoyed', 'favorite', 'it',\n",
    "       'and', 'loved', 'highly', 'bit', 'job', 'today', 'beautiful',\n",
    "       'you', 'definitely', 'superb', 'brilliant', 'world', 'liked',\n",
    "       'still', 'enjoy', 'life', 'very', 'especially', 'see', 'fantastic',\n",
    "       'both', 'shows', 'good', 'may', 'terrific', 'heart', 'classic',\n",
    "       'will', 'enjoyable', 'beautifully', 'always', 'true', 'perfectly',\n",
    "       'surprised', 'think', 'outstanding', 'most',\n",
    "             \n",
    "       'bad', 'worst', 'awful', 'waste', 'boring', 'poor', 'terrible',\n",
    "       'no', 'nothing', 'poorly', 'dull', 'horrible', 'script', 'stupid',\n",
    "       'worse', 'even', 'minutes', 'instead', 'fails', 'unfortunately',\n",
    "       'just', 'annoying', 'ridiculous', 'plot', 'money', 'supposed',\n",
    "       'avoid', 'mess', 'disappointing', 'disappointment', 'lame', 'crap',\n",
    "       'predictable', 'any', 'pointless', 'weak', 'badly', 'not', 'only',\n",
    "       'unless', 'looks', 'why', 'wasted', 'save', 'oh', 'attempt',\n",
    "       'problem', 'acting', 'lacks', 'seems']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(few_words)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting TSNE of 100 words "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100, 64)"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tok_trans = transformer.named_steps['token2index']\n",
    "tok_embed_sub = tok_embed[[tok_trans.tok2idx[i] for i in few_words]]\n",
    "tok_embed_sub.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "tsne = TSNE(perplexity=40, n_iter=1000,)\n",
    "Y_char = tsne.fit_transform(tok_embed_sub)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10,10))\n",
    "ax.scatter(x=Y_char[:,0], y=Y_char[:,1], s=4)\n",
    "ax.grid()\n",
    "\n",
    "for i in range(Y_char.shape[0]):\n",
    "    txt = few_words[i]\n",
    "    ax.annotate(txt, (Y_char[i,0], Y_char[i,1]), fontsize=10)    \n",
    "_ = ax.set_title('t-SNE of Word Tokens')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Transfer Learning on a Different Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_base_model(model_path, epoch, ctx, layer_name=None, n_inputs=2):\n",
    "    \"\"\" Loads the model from given model path \n",
    "        and returns a subnetwork that gives output from layer_name \n",
    "    \"\"\"\n",
    "    xfer_net = gluon.nn.SymbolBlock.imports(\n",
    "        model_path + \"-symbol.json\",\n",
    "        ['data%i' % i for i in range(n_inputs)],\n",
    "        model_path + \"-%.4d.params\" % epoch,\n",
    "        ctx=ctx,\n",
    "    )\n",
    "    inputs = [mx.sym.var(('data%i')% i) for i in range(n_inputs)]\n",
    "    output = xfer_net(*inputs)\n",
    "    print(output.get_internals())\n",
    "    outputs = output.get_internals()[layer_name]\n",
    "    return gluon.SymbolBlock(outputs, inputs, params=xfer_net.collect_params())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_embeddings(layer_name, ctx, X, y, max_seq_len):\n",
    "    \n",
    "    net = load_base_model(pretrained_model_path, 0, ctx, layer_name=layer_name, n_inputs=2)\n",
    "    loss = loss = gluon.loss.SigmoidBCELoss()  \n",
    "    \n",
    "    dataset = get_dataset(X, y, max_seq_len)\n",
    "    dataloader = get_dataloader(dataset, \n",
    "                                dataset_type='valid', \n",
    "                                batch_size=256, \n",
    "                                shuffle=False)\n",
    "    y, yhat = evaluate_network(net, loss, dataloader, ctx, calculate_metrics=False)\n",
    "    yhat = yhat.reshape(y.shape[0], int(yhat.shape[0]/y.shape[0]))\n",
    "    return y, yhat"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Weights as it is"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "hotel_X_transformed = transformer.transform(hotel_X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    [28865, 43121, 40934, 34486, 24519, 40316, 254...\n",
       "1    [15347, 14751, 13343, 36401, 37175, 35196, 511...\n",
       "2    [17276, 35406, 25308, 35131, 18693, 56304, 351...\n",
       "3    [38162, 25922, 32604, 17276, 25308, 61502, 279...\n",
       "4    [25405, 63166, 18894, 37236, 60787, 32604, 489...\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hotel_X_transformed.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "pretrained_model_path = 'models/imdb_v2'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<Symbol group [data0, CustomSeqNet_token_embed_weight, CustomSeqNet_token_embed_fwd, CustomSeqNet_review_gru_swapaxes0, CustomSeqNet_review_gru_l0_i2h_weight, CustomSeqNet_review_gru_reshape0, CustomSeqNet_review_gru_l0_h2h_weight, CustomSeqNet_review_gru_reshape1, CustomSeqNet_review_gru_r0_i2h_weight, CustomSeqNet_review_gru_reshape2, CustomSeqNet_review_gru_r0_h2h_weight, CustomSeqNet_review_gru_reshape3, CustomSeqNet_review_gru_l0_i2h_bias, CustomSeqNet_review_gru_reshape4, CustomSeqNet_review_gru_l0_h2h_bias, CustomSeqNet_review_gru_reshape5, CustomSeqNet_review_gru_r0_i2h_bias, CustomSeqNet_review_gru_reshape6, CustomSeqNet_review_gru_r0_h2h_bias, CustomSeqNet_review_gru_reshape7, CustomSeqNet_review_gru__rnn_param_concat0, CustomSeqNet_CustomSeqNet_review_gru_h0_0, CustomSeqNet_review_gru_rnn0, CustomSeqNet_review_gru_rnn0, CustomSeqNet_review_gru_swapaxes1, data1, CustomSeqNet_cast0, CustomSeqNet_sequencelast0, CustomSeqNet_dense0_weight, CustomSeqNet_dense0_bias, CustomSeqNet_dense0_fwd, CustomSeqNet_batchnorm0_gamma, CustomSeqNet_batchnorm0_beta, CustomSeqNet_batchnorm0_running_mean, CustomSeqNet_batchnorm0_running_var, CustomSeqNet_batchnorm0_fwd, CustomSeqNet_relu0_fwd, CustomSeqNet_dropout0_fwd, CustomSeqNet_dense1_weight, CustomSeqNet_dense1_bias, CustomSeqNet_dense1_fwd, CustomSeqNet_batchnorm1_gamma, CustomSeqNet_batchnorm1_beta, CustomSeqNet_batchnorm1_running_mean, CustomSeqNet_batchnorm1_running_var, CustomSeqNet_batchnorm1_fwd, CustomSeqNet_relu1_fwd, CustomSeqNet_dropout1_fwd, CustomSeqNet_dense2_weight, CustomSeqNet_dense2_bias, CustomSeqNet_dense2_fwd, CustomSeqNet_batchnorm2_gamma, CustomSeqNet_batchnorm2_beta, CustomSeqNet_batchnorm2_running_mean, CustomSeqNet_batchnorm2_running_var, CustomSeqNet_batchnorm2_fwd, CustomSeqNet_relu2_fwd, CustomSeqNet_dropout2_fwd, CustomSeqNet_output_weight, CustomSeqNet_output_bias, CustomSeqNet_output_fwd]>\n"
     ]
    }
   ],
   "source": [
    "hotel_y, hotel_yhat = get_embeddings(\"CustomSeqNet_output_fwd_output\",  # name of final layer\n",
    "                                     ctx,\n",
    "                                     hotel_X_transformed.values, \n",
    "                                     hotel_y_test,\n",
    "                                     max_len)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8572269034747125"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_auc_score(hotel_y, hotel_yhat)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So without even training any on the hotel reviews dataset, we able to infer and get an AUC of 85% using the model trained on movie reviews dataset. Pretty good!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Weights as feature extractor\n",
    "Using Catboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "# catboost\n",
    "from catboost import CatBoostClassifier, Pool"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ec2-user/anaconda3/envs/mxnet_p36/lib/python3.6/site-packages/mxnet/gluon/block.py:1159: UserWarning: Cannot decide type for the following arguments. Consider providing them as input:\n",
      "\tdata0: None\n",
      "  input_sym_arg_type = in_param.infer_type()[0]\n"
     ]
    }
   ],
   "source": [
    "y, X = get_embeddings(\"CustomSeqNet_sequencelast0_output\", \n",
    "                      ctx,\n",
    "                     transformer.transform(hotel_X_train).values, \n",
    "                     hotel_y_train,\n",
    "                     max_len)\n",
    "train_dataset = Pool(X,\n",
    "                    y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ec2-user/anaconda3/envs/mxnet_p36/lib/python3.6/site-packages/mxnet/gluon/block.py:1159: UserWarning: Cannot decide type for the following arguments. Consider providing them as input:\n",
      "\tdata0: None\n",
      "  input_sym_arg_type = in_param.infer_type()[0]\n"
     ]
    }
   ],
   "source": [
    "y, X = get_embeddings(\"CustomSeqNet_sequencelast0_output\", \n",
    "                      ctx,\n",
    "                    transformer.transform(hotel_X_valid).values, \n",
    "                    hotel_y_valid,\n",
    "                    max_len)\n",
    "valid_dataset = Pool(X,\n",
    "                    y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ec2-user/anaconda3/envs/mxnet_p36/lib/python3.6/site-packages/mxnet/gluon/block.py:1159: UserWarning: Cannot decide type for the following arguments. Consider providing them as input:\n",
      "\tdata0: None\n",
      "  input_sym_arg_type = in_param.infer_type()[0]\n"
     ]
    }
   ],
   "source": [
    "y, X = get_embeddings(\"CustomSeqNet_sequencelast0_output\", \n",
    "                      ctx,\n",
    "                      transformer.transform(hotel_X_test).values, \n",
    "                      hotel_y_test,\n",
    "                      max_len)\n",
    "test_dataset = Pool(X,\n",
    "                    y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
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I=this.container.selectAll(\"rect.scrollbar-vertical\").data(O?[0]:[]);I.exit().on(\".drag\",null).remove(),I.enter().append(\"rect\").classed(\"scrollbar-vertical\",!0).call(o.fill,n.barColor),O?(this.vbar=I.attr({rx:n.barRadius,ry:n.barRadius,x:E,y:N,width:D,height:P}),this._vbarYMin=N+P/2,this._vbarTranslateMax=M-P):(delete this.vbar,delete this._vbarYMin,delete this._vbarTranslateMax);var R=this.id,F=u-.5,j=O?f+D+.5:f+.5,B=d-.5,q=A?h+L+.5:h+.5,H=l._topdefs.selectAll(\"#\"+R).data(A||O?[0]:[]);if(H.exit().remove(),H.enter().append(\"clipPath\").attr(\"id\",R).append(\"rect\"),A||O?(this._clipRect=H.select(\"rect\").attr({x:Math.floor(F),y:Math.floor(B),width:Math.ceil(j)-Math.floor(F),height:Math.ceil(q)-Math.floor(B)}),this.container.call(i.setClipUrl,R),this.bg.attr({x:p,y:v,width:g,height:m})):(this.bg.attr({width:0,height:0}),this.container.on(\"wheel\",null).on(\".drag\",null).call(i.setClipUrl,null),delete this._clipRect),A||O){var V=a.behavior.drag().on(\"dragstart\",function(){a.event.sourceEvent.preventDefault()}).on(\"drag\",this._onBoxDrag.bind(this));this.container.on(\"wheel\",null).on(\"wheel\",this._onBoxWheel.bind(this)).on(\".drag\",null).call(V);var U=a.behavior.drag().on(\"dragstart\",function(){a.event.sourceEvent.preventDefault(),a.event.sourceEvent.stopPropagation()}).on(\"drag\",this._onBarDrag.bind(this));A&&this.hbar.on(\".drag\",null).call(U),O&&this.vbar.on(\".drag\",null).call(U)}this.setTranslate(e,r)},n.prototype.disable=function(){(this.hbar||this.vbar)&&(this.bg.attr({width:0,height:0}),this.container.on(\"wheel\",null).on(\".drag\",null).call(i.setClipUrl,null),delete this._clipRect),this.hbar&&(this.hbar.on(\".drag\",null),this.hbar.remove(),delete this.hbar,delete this._hbarXMin,delete this._hbarTranslateMax),this.vbar&&(this.vbar.on(\".drag\",null),this.vbar.remove(),delete this.vbar,delete this._vbarYMin,delete this._vbarTranslateMax)},n.prototype._onBoxDrag=function(){var 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r=this.position.w-this._box.w,n=this.position.h-this._box.h;if(t=l.constrain(t||0,0,r),e=l.constrain(e||0,0,n),this.translateX=t,this.translateY=e,this.container.call(i.setTranslate,this._box.l-this.position.l-t,this._box.t-this.position.t-e),this._clipRect&&this._clipRect.attr({x:Math.floor(this.position.l+t-.5),y:Math.floor(this.position.t+e-.5)}),this.hbar){var a=t/r;this.hbar.call(i.setTranslate,t+a*this._hbarTranslateMax,e)}if(this.vbar){var o=e/n;this.vbar.call(i.setTranslate,t,e+o*this._vbarTranslateMax)}}},{\"../../lib\":136,\"../color\":25,\"../drawing\":49,d3:7}],121:[function(t,e,r){\"use strict\";e.exports={SHOW_PLACEHOLDER:100,HIDE_PLACEHOLDER:1e3,DBLCLICKDELAY:300}},{}],122:[function(t,e,r){\"use strict\";e.exports={BADNUM:void 0,FP_SAFE:Number.MAX_VALUE/1e4,ONEAVGYEAR:315576e5,ONEAVGMONTH:26298e5,ONEDAY:864e5,ONEHOUR:36e5,ONEMIN:6e4,ONESEC:1e3,EPOCHJD:2440587.5,ALMOST_EQUAL:1-1e-6}},{}],123:[function(t,e,r){\"use 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b,_=[f.margin.left+x,f.margin.top+x];if(p){b=[0,n.max(i.util.sumArrays(i.util.arrayLast(h).r[0],i.util.arrayLast(m)))]}else b=n.extent(i.util.flattenArray(h.map(function(t,e){return t.r})));f.radialAxis.domain!=i.DATAEXTENT&&(b[0]=0),a=n.scale.linear().domain(f.radialAxis.domain!=i.DATAEXTENT&&f.radialAxis.domain?f.radialAxis.domain:b).range([0,x]),u.layout.radialAxis.domain=a.domain();var w,k=i.util.flattenArray(h.map(function(t,e){return t.t})),M=\"string\"==typeof k[0];M&&(k=i.util.deduplicate(k),w=k.slice(),k=n.range(k.length),h=h.map(function(t,e){var r=t;return t.t=[k],p&&(r.yStack=t.yStack),r}));var A=h.filter(function(t,e){return\"LinePlot\"===t.geometry||\"DotPlot\"===t.geometry}).length===h.length,T=null===f.needsEndSpacing?M||!A:f.needsEndSpacing,L=f.angularAxis.domain&&f.angularAxis.domain!=i.DATAEXTENT&&!M&&f.angularAxis.domain[0]>=0,C=L?f.angularAxis.domain:n.extent(k),S=Math.abs(k[1]-k[0]);A&&!M&&(S=0);var z=C.slice();T&&M&&(z[1]+=S);var O=f.angularAxis.ticksCount||4;O>8&&(O=O/(O/8)+O%8),f.angularAxis.ticksStep&&(O=(z[1]-z[0])/O);var D=f.angularAxis.ticksStep||(z[1]-z[0])/(O*(f.minorTicks+1));w&&(D=Math.max(Math.round(D),1)),z[2]||(z[2]=D);var P=n.range.apply(this,z);if(P=P.map(function(t,e){return parseFloat(t.toPrecision(12))}),l=n.scale.linear().domain(z.slice(0,2)).range(\"clockwise\"===f.direction?[0,360]:[360,0]),u.layout.angularAxis.domain=l.domain(),u.layout.angularAxis.endPadding=T?S:0,void 0===(e=n.select(this).select(\"svg.chart-root\"))||e.empty()){var E=(new DOMParser).parseFromString(\"<svg xmlns='http://www.w3.org/2000/svg' class='chart-root'>' + '<g class='outer-group'>' + '<g class='chart-group'>' + '<circle class='background-circle'></circle>' + '<g class='geometry-group'></g>' + '<g class='radial axis-group'>' + '<circle class='outside-circle'></circle>' + '</g>' + '<g class='angular axis-group'></g>' + '<g class='guides-group'><line></line><circle r='0'></circle></g>' + '</g>' + '<g 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r.symbol=\"DotPlot\"===t.geometry?t.dotType||\"circle\":\"LinePlot\"!=t.geometry?\"square\":\"line\",r.visibleInLegend=void 0===t.visibleInLegend||t.visibleInLegend,r.color=\"LinePlot\"===t.geometry?t.strokeColor:t.color,r});i.Legend().config({data:h.map(function(t,e){return t.name||\"Element\"+e}),legendConfig:o({},i.Legend.defaultConfig().legendConfig,{container:I,elements:B,reverseOrder:f.legend.reverseOrder})})();var q=I.node().getBBox();x=Math.min(f.width-q.width-f.margin.left-f.margin.right,f.height-f.margin.top-f.margin.bottom)/2,x=Math.max(10,x),_=[f.margin.left+x,f.margin.top+x],a.range([0,x]),u.layout.radialAxis.domain=a.domain(),I.attr(\"transform\",\"translate(\"+[_[0]+x,_[1]-x]+\")\")}else I=e.select(\".legend-group\").style({display:\"none\"});e.attr({width:f.width,height:f.height}).style({opacity:f.opacity}),R.attr(\"transform\",\"translate(\"+_+\")\").style({cursor:\"crosshair\"});var H=[(f.width-(f.margin.left+f.margin.right+2*x+(q?q.width:0)))/2,(f.height-(f.margin.top+f.margin.bottom+2*x))/2];if(H[0]=Math.max(0,H[0]),H[1]=Math.max(0,H[1]),e.select(\".outer-group\").attr(\"transform\",\"translate(\"+H+\")\"),f.title){var V=e.select(\"g.title-group text\").style(j).text(f.title),U=V.node().getBBox();V.attr({x:_[0]-U.width/2,y:_[1]-x-20})}var X=e.select(\".radial.axis-group\");if(f.radialAxis.gridLinesVisible){var G=X.selectAll(\"circle.grid-circle\").data(a.ticks(5));G.enter().append(\"circle\").attr({class:\"grid-circle\"}).style(F),G.attr(\"r\",a),G.exit().remove()}X.select(\"circle.outside-circle\").attr({r:x}).style(F);var Y=e.select(\"circle.background-circle\").attr({r:x}).style({fill:f.backgroundColor,stroke:f.stroke});if(f.radialAxis.visible){var Z=n.svg.axis().scale(a).ticks(5).tickSize(5);X.call(Z).attr({transform:\"rotate(\"+f.radialAxis.orientation+\")\"}),X.selectAll(\".domain\").style(F),X.selectAll(\"g>text\").text(function(t,e){return this.textContent+f.radialAxis.ticksSuffix}).style(j).style({\"text-anchor\":\"start\"}).attr({x:0,y:0,dx:0,dy:0,transform:function(t,e){return\"horizontal\"===f.radialAxis.tickOrientation?\"rotate(\"+-f.radialAxis.orientation+\") translate(\"+[0,j[\"font-size\"]]+\")\":\"translate(\"+[0,j[\"font-size\"]]+\")\"}}),X.selectAll(\"g>line\").style({stroke:\"black\"})}var W=e.select(\".angular.axis-group\").selectAll(\"g.angular-tick\").data(P),$=W.enter().append(\"g\").classed(\"angular-tick\",!0);W.attr({transform:function(t,e){return\"rotate(\"+s(t,e)+\")\"}}).style({display:f.angularAxis.visible?\"block\":\"none\"}),W.exit().remove(),$.append(\"line\").classed(\"grid-line\",!0).classed(\"major\",function(t,e){return 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e%(f.minorTicks+1)!=0?\"\":f.angularAxis.rewriteTicks(this.textContent,e)});var J=n.max(R.selectAll(\".angular-tick text\")[0].map(function(t,e){return t.getCTM().e+t.getBBox().width}));I.attr({transform:\"translate(\"+[x+J,f.margin.top]+\")\"});var K=e.select(\"g.geometry-group\").selectAll(\"g\").size()>0,tt=e.select(\"g.geometry-group\").selectAll(\"g.geometry\").data(h);if(tt.enter().append(\"g\").attr({class:function(t,e){return\"geometry geometry\"+e}}),tt.exit().remove(),h[0]||K){var et=[];h.forEach(function(t,e){var r={};r.radialScale=a,r.angularScale=l,r.container=tt.filter(function(t,r){return r==e}),r.geometry=t.geometry,r.orientation=f.orientation,r.direction=f.direction,r.index=e,et.push({data:t,geometryConfig:r})});var rt=n.nest().key(function(t,e){return void 0!==t.data.groupId||\"unstacked\"}).entries(et),nt=[];rt.forEach(function(t,e){\"unstacked\"===t.key?nt=nt.concat(t.values.map(function(t,e){return[t]})):nt.push(t.values)}),nt.forEach(function(t,e){var r;r=Array.isArray(t)?t[0].geometryConfig.geometry:t.geometryConfig.geometry;var n=t.map(function(t,e){return o(i[r].defaultConfig(),t)});i[r]().config(n)()})}var at,ot,it=e.select(\".guides-group\"),lt=e.select(\".tooltips-group\"),st=i.tooltipPanel().config({container:lt,fontSize:8})(),ct=i.tooltipPanel().config({container:lt,fontSize:8})(),ut=i.tooltipPanel().config({container:lt,hasTick:!0})();if(!M){var ft=it.select(\"line\").attr({x1:0,y1:0,y2:0}).style({stroke:\"grey\",\"pointer-events\":\"none\"});R.on(\"mousemove.angular-guide\",function(t,e){var r=i.util.getMousePos(Y).angle;ft.attr({x2:-x,transform:\"rotate(\"+r+\")\"}).style({opacity:.5});var n=(r+180+360-f.orientation)%360;at=l.invert(n);var a=i.util.convertToCartesian(x+12,r+180);st.text(i.util.round(at)).move([a[0]+_[0],a[1]+_[1]])}).on(\"mouseout.angular-guide\",function(t,e){it.select(\"line\").style({opacity:0})})}var dt=it.select(\"circle\").style({stroke:\"grey\",fill:\"none\"});R.on(\"mousemove.radial-guide\",function(t,e){var r=i.util.getMousePos(Y).radius;dt.attr({r:r}).style({opacity:.5}),ot=a.invert(i.util.getMousePos(Y).radius);var n=i.util.convertToCartesian(r,f.radialAxis.orientation);ct.text(i.util.round(ot)).move([n[0]+_[0],n[1]+_[1]])}).on(\"mouseout.radial-guide\",function(t,e){dt.style({opacity:0}),ut.hide(),st.hide(),ct.hide()}),e.selectAll(\".geometry-group .mark\").on(\"mouseover.tooltip\",function(t,r){var a=n.select(this),o=a.style(\"fill\"),l=\"black\",s=a.style(\"opacity\")||1;if(a.attr({\"data-opacity\":s}),\"none\"!=o){a.attr({\"data-fill\":o}),l=n.hsl(o).darker().toString(),a.style({fill:l,opacity:1});var c={t:i.util.round(t[0]),r:i.util.round(t[1])};M&&(c.t=w[t[0]]);var u=\"t: \"+c.t+\", r: \"+c.r,f=this.getBoundingClientRect(),d=e.node().getBoundingClientRect(),h=[f.left+f.width/2-H[0]-d.left,f.top+f.height/2-H[1]-d.top];ut.config({color:l}).text(u),ut.move(h)}else 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l},d.svg=function(){return e},n.rebind(d,f,\"on\"),d},i.Axis.defaultConfig=function(t,e){return{data:[{t:[1,2,3,4],r:[10,11,12,13],name:\"Line1\",geometry:\"LinePlot\",color:null,strokeDash:\"solid\",strokeColor:null,strokeSize:\"1\",visibleInLegend:!0,opacity:1}],layout:{defaultColorRange:n.scale.category10().range(),title:null,height:450,width:500,margin:{top:40,right:40,bottom:40,left:40},font:{size:12,color:\"gray\",outlineColor:\"white\",family:\"Tahoma, 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n=e*Math.PI/180;return[e,t(n)]})},i.util.dataFromEquation=function(t,e,r){var a=e||6,o=[],i=[];n.range(0,360+a,a).forEach(function(e,r){var n=e*Math.PI/180,a=t(n);o.push(e),i.push(a)});var l={t:o,r:i};return r&&(l.name=r),l},i.util.ensureArray=function(t,e){if(void 0===t)return null;var r=[].concat(t);return n.range(e).map(function(t,e){return r[e]||r[0]})},i.util.fillArrays=function(t,e,r){return e.forEach(function(e,n){t[e]=i.util.ensureArray(t[e],r)}),t},i.util.cloneJson=function(t){return JSON.parse(JSON.stringify(t))},i.util.validateKeys=function(t,e){\"string\"==typeof e&&(e=e.split(\".\"));var r=e.shift();return t[r]&&(!e.length||objHasKeys(t[r],e))},i.util.sumArrays=function(t,e){return n.zip(t,e).map(function(t,e){return n.sum(t)})},i.util.arrayLast=function(t){return t[t.length-1]},i.util.arrayEqual=function(t,e){for(var r=Math.max(t.length,e.length,1);r-- >=0&&t[r]===e[r];);return r===-2},i.util.flattenArray=function(t){for(var e=[];!i.util.arrayEqual(e,t);)e=t,t=[].concat.apply([],t);return t},i.util.deduplicate=function(t){return t.filter(function(t,e,r){return r.indexOf(t)==e})},i.util.convertToCartesian=function(t,e){var r=e*Math.PI/180;return[t*Math.cos(r),t*Math.sin(r)]},i.util.round=function(t,e){var r=e||2,n=Math.pow(10,r);return Math.round(t*n)/n},i.util.getMousePos=function(t){var e=n.mouse(t.node()),r=e[0],a=e[1],o={};return o.x=r,o.y=a,o.pos=e,o.angle=180*(Math.atan2(a,r)+Math.PI)/Math.PI,o.radius=Math.sqrt(r*r+a*a),o},i.util.duplicatesCount=function(t){for(var e,r={},n={},a=0,o=t.length;a<o;a++)e=t[a],e in r?(r[e]++,n[e]=r[e]):r[e]=1;return n},i.util.duplicates=function(t){return Object.keys(i.util.duplicatesCount(t))},i.util.translator=function(t,e,r,n){if(n){var a=r.slice();r=e,e=a}var o=e.reduce(function(t,e){if(void 0!==t)return t[e]},t);void 0!==o&&(e.reduce(function(t,r,n){if(void 0!==t)return n===e.length-1&&delete t[r],t[r]},t),r.reduce(function(t,e,n){return void 0===t[e]&&(t[e]={}),n===r.length-1&&(t[e]=o),t[e]},t))},i.PolyChart=function(){function t(){var t=e[0].geometryConfig,r=t.container;\"string\"==typeof r&&(r=n.select(r)),r.datum(e).each(function(e,r){function o(e,r){return{r:t.radialScale(e[1]),t:(t.angularScale(e[0])+t.orientation)*Math.PI/180}}function i(t){return{x:t.r*Math.cos(t.t),y:t.r*Math.sin(t.t)}}var l=!!e[0].data.yStack,s=e.map(function(t,e){return l?n.zip(t.data.t[0],t.data.r[0],t.data.yStack[0]):n.zip(t.data.t[0],t.data.r[0])}),c=t.angularScale,u=t.radialScale.domain()[0],f={};f.bar=function(r,a,o){var i=e[o].data,l=t.radialScale(r[1])-t.radialScale(0),s=t.radialScale(r[2]||0),u=i.barWidth;n.select(this).attr({class:\"mark bar\",d:\"M\"+[[l+s,-u/2],[l+s,u/2],[s,u/2],[s,-u/2]].join(\"L\")+\"Z\",transform:function(e,r){return\"rotate(\"+(t.orientation+c(e[0]))+\")\"}})},f.dot=function(t,r,a){var l=t[2]?[t[0],t[1]+t[2]]:t,s=n.svg.symbol().size(e[a].data.dotSize).type(e[a].data.dotType)(t,r);n.select(this).attr({class:\"mark 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n={};n.attributes=t(\"./attributes\"),n.supplyDefaults=t(\"./defaults\"),n.supplyLayoutDefaults=t(\"./layout_defaults\"),n.layoutAttributes=t(\"./layout_attributes\"),n.calc=t(\"./calc\"),n.plot=t(\"./plot\"),n.style=t(\"./style\"),n.styleOne=t(\"./style_one\"),n.moduleType=\"trace\",n.name=\"pie\",n.basePlotModule=t(\"./base_plot\"),n.categories=[\"pie\",\"showLegend\"],n.meta={},e.exports=n},{\"./attributes\":228,\"./base_plot\":229,\"./calc\":230,\"./defaults\":231,\"./layout_attributes\":234,\"./layout_defaults\":235,\"./plot\":236,\"./style\":237,\"./style_one\":238}],234:[function(t,e,r){\"use strict\";e.exports={hiddenlabels:{valType:\"data_array\"}}},{}],235:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),a=t(\"./layout_attributes\");e.exports=function(t,e){!function(r,o){n.coerce(t,e,a,r,o)}(\"hiddenlabels\")}},{\"../../lib\":136,\"./layout_attributes\":234}],236:[function(t,e,r){\"use strict\";function n(t,e,r){var n=Math.sqrt(t.width*t.width+t.height*t.height),o=t.width/t.height,i=Math.PI*Math.min(e.v/r.vTotal,.5),l=1-r.trace.hole,s=a(e,r),c={scale:s*r.r*2/n,rCenter:1-s,rotate:0};if(c.scale>=1)return c;var u=o+1/(2*Math.tan(i)),f=r.r*Math.min(1/(Math.sqrt(u*u+.5)+u),l/(Math.sqrt(o*o+l/2)+o)),d={scale:2*f/t.height,rCenter:Math.cos(f/r.r)-f*o/r.r,rotate:(180/Math.PI*e.midangle+720)%180-90},h=1/o,p=h+1/(2*Math.tan(i)),g=r.r*Math.min(1/(Math.sqrt(p*p+.5)+p),l/(Math.sqrt(h*h+l/2)+h)),v={scale:2*g/t.width,rCenter:Math.cos(g/r.r)-g/o/r.r,rotate:(180/Math.PI*e.midangle+810)%180-90},m=v.scale>d.scale?v:d;return c.scale<1&&m.scale>c.scale?m:c}function a(t,e){if(t.v===e.vTotal&&!e.trace.hole)return 1;var r=Math.PI*Math.min(t.v/e.vTotal,.5);return Math.min(1/(1+1/Math.sin(r)),(1-e.trace.hole)/2)}function o(t,e){var r=e.pxmid[0],n=e.pxmid[1],a=t.width/2,o=t.height/2;return r<0&&(a*=-1),n<0&&(o*=-1),{scale:1,rCenter:1,rotate:0,x:a+Math.abs(o)*(a>0?1:-1)/2,y:o/(1+r*r/(n*n)),outside:!0}}function i(t,e){function r(t,e){return t.pxmid[1]-e.pxmid[1]}function n(t,e){return e.pxmid[1]-t.pxmid[1]}var a,o,i,l,s,c,u,f,d,h,p,g,v;for(o=0;o<2;o++)for(i=o?r:n,s=o?Math.max:Math.min,u=o?1:-1,a=0;a<2;a++){for(l=a?Math.max:Math.min,c=a?1:-1,f=t[o][a],f.sort(i),d=t[1-o][a],h=d.concat(f),g=[],p=0;p<f.length;p++)void 0!==f[p].yLabelMid&&g.push(f[p]);for(v=!1,p=0;o&&p<d.length;p++)if(void 0!==d[p].yLabelMid){v=d[p];break}for(p=0;p<g.length;p++){var m=p&&g[p-1];v&&!p&&(m=v),function(t,r){r||(r={});var n,a,i,f,d,p,g=r.labelExtraY+(o?r.yLabelMax:r.yLabelMin),v=o?t.yLabelMin:t.yLabelMax,m=o?t.yLabelMax:t.yLabelMin,y=t.cyFinal+s(t.px0[1],t.px1[1]),x=g-v;if(x*u>0&&(t.labelExtraY=x),Array.isArray(e.pull))for(a=0;a<h.length;a++)(i=h[a])===t||(e.pull[t.i]||0)>=e.pull[i.i]||((t.pxmid[1]-i.pxmid[1])*u>0?(f=i.cyFinal+s(i.px0[1],i.px1[1]),(x=f-v-t.labelExtraY)*u>0&&(t.labelExtraY+=x)):(m+t.labelExtraY-y)*u>0&&(n=3*c*Math.abs(a-h.indexOf(t)),d=i.cxFinal+l(i.px0[0],i.px1[0]),(p=d+n-(t.cxFinal+t.pxmid[0])-t.labelExtraX)*c>0&&(t.labelExtraX+=p)))}(g[p],m)}}}function l(t,e){var r,n,a,o,i,l,s,u,f,d,h=[];for(a=0;a<t.length;a++){if(i=t[a][0],l=i.trace,r=e.w*(l.domain.x[1]-l.domain.x[0]),n=e.h*(l.domain.y[1]-l.domain.y[0]),s=l.tiltaxis*Math.PI/180,u=l.pull,Array.isArray(u))for(u=0,o=0;o<l.pull.length;o++)l.pull[o]>u&&(u=l.pull[o]);i.r=Math.min(r/c(l.tilt,Math.sin(s),l.depth),n/c(l.tilt,Math.cos(s),l.depth))/(2+2*u),i.cx=e.l+e.w*(l.domain.x[1]+l.domain.x[0])/2,i.cy=e.t+e.h*(2-l.domain.y[1]-l.domain.y[0])/2,\n",
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n=Math.sin(t*Math.PI/180);return Math.max(.01,r*n*Math.abs(e)+2*Math.sqrt(1-n*n*e*e))}var u=t(\"d3\"),f=t(\"../../components/fx\"),d=t(\"../../components/color\"),h=t(\"../../components/drawing\"),p=t(\"../../lib/svg_text_utils\"),g=t(\"./helpers\");e.exports=function(t,e){var r=t._fullLayout;l(e,r._size);var c=r._pielayer.selectAll(\"g.trace\").data(e);c.enter().append(\"g\").attr({\"stroke-linejoin\":\"round\",class:\"trace\"}),c.exit().remove(),c.order(),c.each(function(e){var l=u.select(this),c=e[0],v=c.trace,m=(v.depth||0)*c.r*Math.sin(0)/2,y=v.tiltaxis||0,x=y*Math.PI/180,b=[m*Math.sin(x),m*Math.cos(x)],_=c.r*Math.cos(0),w=l.selectAll(\"g.part\").data(v.tilt?[\"top\",\"sides\"]:[\"top\"]);w.enter().append(\"g\").attr(\"class\",function(t){return t+\" part\"}),w.exit().remove(),w.order(),s(e),l.selectAll(\".top\").each(function(){var l=u.select(this).selectAll(\"g.slice\").data(e);l.enter().append(\"g\").classed(\"slice\",!0),l.exit().remove();var s=[[[],[]],[[],[]]],m=!1;l.each(function(e){function i(n){n.originalEvent=u.event;var o=t._fullLayout,i=t._fullData[v.index],l=i.hoverinfo;if(\"all\"===l&&(l=\"label+text+value+percent+name\"),t._dragging||o.hovermode===!1||\"none\"===l||\"skip\"===l||!l)return void f.hover(t,n,\"pie\");var s=a(e,c),d=w+e.pxmid[0]*(1-s),h=k+e.pxmid[1]*(1-s),p=r.separators,m=[];l.indexOf(\"label\")!==-1&&m.push(e.label),l.indexOf(\"text\")!==-1&&(i.hovertext?m.push(Array.isArray(i.hovertext)?i.hovertext[e.i]:i.hovertext):i.text&&i.text[e.i]&&m.push(i.text[e.i])),l.indexOf(\"value\")!==-1&&m.push(g.formatPieValue(e.v,p)),l.indexOf(\"percent\")!==-1&&m.push(g.formatPiePercent(e.v/c.vTotal,p));var y=i.hoverlabel;f.loneHover({x0:d-s*c.r,x1:d+s*c.r,y:h,text:m.join(\"<br>\"),name:l.indexOf(\"name\")!==-1?i.name:void 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w=c.cx+b[0],k=c.cy+b[1],M=u.select(this),A=M.selectAll(\"path.surface\").data([e]),T=!1;if(A.enter().append(\"path\").classed(\"surface\",!0).style({\"pointer-events\":\"all\"}),M.select(\"path.textline\").remove(),M.on(\"mouseover\",i).on(\"mouseout\",l).on(\"click\",d),v.pull){var L=+(Array.isArray(v.pull)?v.pull[e.i]:v.pull)||0;L>0&&(w+=L*e.pxmid[0],k+=L*e.pxmid[1])}e.cxFinal=w,e.cyFinal=k;var C=v.hole;if(e.v===c.vTotal){var S=\"M\"+(w+e.px0[0])+\",\"+(k+e.px0[1])+x(e.px0,e.pxmid,!0,1)+x(e.pxmid,e.px0,!0,1)+\"Z\";C?A.attr(\"d\",\"M\"+(w+C*e.px0[0])+\",\"+(k+C*e.px0[1])+x(e.px0,e.pxmid,!1,C)+x(e.pxmid,e.px0,!1,C)+\"Z\"+S):A.attr(\"d\",S)}else{var z=x(e.px0,e.px1,!0,1);if(C){var O=1-C;A.attr(\"d\",\"M\"+(w+C*e.px1[0])+\",\"+(k+C*e.px1[1])+x(e.px1,e.px0,!1,C)+\"l\"+O*e.px0[0]+\",\"+O*e.px0[1]+z+\"Z\")}else A.attr(\"d\",\"M\"+w+\",\"+k+\"l\"+e.px0[0]+\",\"+e.px0[1]+z+\"Z\")}var D=Array.isArray(v.textposition)?v.textposition[e.i]:v.textposition,P=M.selectAll(\"g.slicetext\").data(e.text&&\"none\"!==D?[0]:[]);P.enter().append(\"g\").classed(\"slicetext\",!0),P.exit().remove(),P.each(function(){var t=u.select(this).selectAll(\"text\").data([0]);t.enter().append(\"text\").attr(\"data-notex\",1),t.exit().remove(),t.text(e.text).attr({class:\"slicetext\",transform:\"\",\"data-bb\":\"\",\"text-anchor\":\"middle\",x:0,y:0}).call(h.font,\"outside\"===D?v.outsidetextfont:v.insidetextfont).call(p.convertToTspans),t.selectAll(\"tspan.line\").attr({x:0,y:0});var r,a=h.bBox(t.node());\"outside\"===D?r=o(a,e):(r=n(a,e,c),\"auto\"===D&&r.scale<1&&(t.call(h.font,v.outsidetextfont),v.outsidetextfont.family===v.insidetextfont.family&&v.outsidetextfont.size===v.insidetextfont.size||(t.attr({\"data-bb\":\"\"}),a=h.bBox(t.node())),r=o(a,e)));var i=w+e.pxmid[0]*r.rCenter+(r.x||0),l=k+e.pxmid[1]*r.rCenter+(r.y||0);r.outside&&(e.yLabelMin=l-a.height/2,e.yLabelMid=l,e.yLabelMax=l+a.height/2,e.labelExtraX=0,e.labelExtraY=0,m=!0),t.attr(\"transform\",\"translate(\"+i+\",\"+l+\")\"+(r.scale<1?\"scale(\"+r.scale+\")\":\"\")+(r.rotate?\"rotate(\"+r.rotate+\")\":\"\")+\"translate(\"+-(a.left+a.right)/2+\",\"+-(a.top+a.bottom)/2+\")\")})}),m&&i(s,v),l.each(function(t){if(t.labelExtraX||t.labelExtraY){var e=u.select(this),r=e.select(\"g.slicetext text\");r.attr(\"transform\",\"translate(\"+t.labelExtraX+\",\"+t.labelExtraY+\")\"+r.attr(\"transform\"));var n=t.cxFinal+t.pxmid[0],a=t.cyFinal+t.pxmid[1],o=\"M\"+n+\",\"+a,i=(t.yLabelMax-t.yLabelMin)*(t.pxmid[0]<0?-1:1)/4;if(t.labelExtraX){var l=t.labelExtraX*t.pxmid[1]/t.pxmid[0],s=t.yLabelMid+t.labelExtraY-(t.cyFinal+t.pxmid[1]);Math.abs(l)>Math.abs(s)?o+=\"l\"+s*t.pxmid[0]/t.pxmid[1]+\",\"+s+\"H\"+(n+t.labelExtraX+i):o+=\"l\"+t.labelExtraX+\",\"+l+\"v\"+(s-l)+\"h\"+i}else o+=\"V\"+(t.yLabelMid+t.labelExtraY)+\"h\"+i;e.append(\"path\").classed(\"textline\",!0).call(d.stroke,v.outsidetextfont.color).attr({\"stroke-width\":Math.min(2,v.outsidetextfont.size/8),d:o,fill:\"none\"})}})})}),setTimeout(function(){c.selectAll(\"tspan\").each(function(){var t=u.select(this);t.attr(\"dy\")&&t.attr(\"dy\",t.attr(\"dy\"))})},0)}},{\"../../components/color\":25,\"../../components/drawing\":49,\"../../components/fx\":66,\"../../lib/svg_text_utils\":153,\"./helpers\":232,d3:7}],237:[function(t,e,r){\"use strict\";var n=t(\"d3\"),a=t(\"./style_one\");e.exports=function(t){t._fullLayout._pielayer.selectAll(\".trace\").each(function(t){var e=t[0],r=e.trace,o=n.select(this);o.style({opacity:r.opacity}),o.selectAll(\".top path.surface\").each(function(t){n.select(this).call(a,t,r)})})}},{\"./style_one\":238,d3:7}],238:[function(t,e,r){\"use strict\";var n=t(\"../../components/color\");e.exports=function(t,e,r){var a=r.marker.line.color;Array.isArray(a)&&(a=a[e.i]||n.defaultLine);var o=r.marker.line.width||0;Array.isArray(o)&&(o=o[e.i]||0),t.style({\"stroke-width\":o}).call(n.fill,e.color).call(n.stroke,a)}},{\"../../components/color\":25}],239:[function(t,e,r){\"use strict\";var n=t(\"../../lib\");e.exports=function(t,e){for(var r=0;r<t.length;r++)t[r].i=r;n.mergeArray(e.text,t,\"tx\"),n.mergeArray(e.hovertext,t,\"htx\"),n.mergeArray(e.customdata,t,\"data\"),n.mergeArray(e.textposition,t,\"tp\"),e.textfont&&(n.mergeArray(e.textfont.size,t,\"ts\"),n.mergeArray(e.textfont.color,t,\"tc\"),n.mergeArray(e.textfont.family,t,\"tf\"));var a=e.marker;if(a){n.mergeArray(a.size,t,\"ms\"),n.mergeArray(a.opacity,t,\"mo\"),n.mergeArray(a.symbol,t,\"mx\"),n.mergeArray(a.color,t,\"mc\");var o=a.line;a.line&&(n.mergeArray(o.color,t,\"mlc\"),n.mergeArray(o.width,t,\"mlw\"));var i=a.gradient;i&&\"none\"!==i.type&&(n.mergeArray(i.type,t,\"mgt\"),n.mergeArray(i.color,t,\"mgc\"))}}},{\"../../lib\":136}],240:[function(t,e,r){\"use strict\";var n=t(\"../../components/colorscale/color_attributes\"),a=t(\"../../components/errorbars/attributes\"),o=t(\"../../components/colorbar/attributes\"),i=t(\"../../components/drawing/attributes\").dash,l=t(\"../../components/drawing\"),s=(t(\"./constants\"),t(\"../../lib/extend\").extendFlat);e.exports={x:{valType:\"data_array\"},x0:{valType:\"any\",dflt:0},dx:{valType:\"number\",dflt:1},y:{valType:\"data_array\"},y0:{valType:\"any\",dflt:0},customdata:{valType:\"data_array\"},dy:{valType:\"number\",dflt:1},ids:{valType:\"data_array\"},text:{valType:\"string\",dflt:\"\",arrayOk:!0},hovertext:{valType:\"string\",dflt:\"\",arrayOk:!0},mode:{valType:\"flaglist\",flags:[\"lines\",\"markers\",\"text\"],extras:[\"none\"]},hoveron:{valType:\"flaglist\",flags:[\"points\",\"fills\"]},line:{color:{valType:\"color\"},width:{valType:\"number\",min:0,dflt:2},shape:{valType:\"enumerated\",values:[\"linear\",\"spline\",\"hv\",\"vh\",\"hvh\",\"vhv\"],dflt:\"linear\"},smoothing:{valType:\"number\",min:0,max:1.3,dflt:1},dash:i,simplify:{valType:\"boolean\",dflt:!0}},connectgaps:{valType:\"boolean\",dflt:!1},fill:{valType:\"enumerated\",values:[\"none\",\"tozeroy\",\"tozerox\",\"tonexty\",\"tonextx\",\"toself\",\"tonext\"],dflt:\"none\"},fillcolor:{valType:\"color\"},marker:s({},{symbol:{valType:\"enumerated\",values:l.symbolList,dflt:\"circle\",arrayOk:!0},opacity:{valType:\"number\",min:0,max:1,arrayOk:!0},size:{valType:\"number\",min:0,dflt:6,arrayOk:!0},maxdisplayed:{valType:\"number\",min:0,dflt:0},sizeref:{valType:\"number\",dflt:1},sizemin:{valType:\"number\",min:0,dflt:0},sizemode:{valType:\"enumerated\",values:[\"diameter\",\"area\"],dflt:\"diameter\"},showscale:{valType:\"boolean\",dflt:!1},colorbar:o,line:s({},{width:{valType:\"number\",min:0,arrayOk:!0}},n(\"marker.line\")),gradient:{type:{valType:\"enumerated\",values:[\"radial\",\"horizontal\",\"vertical\",\"none\"],arrayOk:!0,dflt:\"none\"},color:{valType:\"color\",arrayOk:!0}}},n(\"marker\")),textposition:{valType:\"enumerated\",values:[\"top left\",\"top center\",\"top right\",\"middle left\",\"middle center\",\"middle right\",\"bottom left\",\"bottom center\",\"bottom right\"],dflt:\"middle center\",arrayOk:!0},textfont:{family:{valType:\"string\",noBlank:!0,strict:!0,arrayOk:!0},size:{valType:\"number\",min:1,arrayOk:!0},color:{valType:\"color\",arrayOk:!0}},r:{valType:\"data_array\"},t:{valType:\"data_array\"},error_y:a,error_x:a}},{\"../../components/colorbar/attributes\":26,\"../../components/colorscale/color_attributes\":32,\"../../components/drawing\":49,\"../../components/drawing/attributes\":48,\"../../components/errorbars/attributes\":51,\"../../lib/extend\":132,\"./constants\":245}],241:[function(t,e,r){\"use strict\";var n=t(\"fast-isnumeric\"),a=t(\"../../plots/cartesian/axes\"),o=t(\"../../constants/numerical\").BADNUM,i=t(\"./subtypes\"),l=t(\"./colorscale_calc\"),s=t(\"./arrays_to_calcdata\");e.exports=function(t,e){var r,c,u,f=a.getFromId(t,e.xaxis||\"x\"),d=a.getFromId(t,e.yaxis||\"y\"),h=f.makeCalcdata(e,\"x\"),p=d.makeCalcdata(e,\"y\"),g=Math.min(h.length,p.length);f._minDtick=0,d._minDtick=0,h.length>g&&h.splice(g,h.length-g),p.length>g&&p.splice(g,p.length-g);var v={padded:!0},m={padded:!0};if(i.hasMarkers(e)){if(r=e.marker,c=r.size,Array.isArray(c)){var y={type:\"linear\"};a.setConvert(y),c=y.makeCalcdata(e.marker,\"size\"),c.length>g&&c.splice(g,c.length-g)}var x,b=1.6*(e.marker.sizeref||1);x=\"area\"===e.marker.sizemode?function(t){return Math.max(Math.sqrt((t||0)/b),3)}:function(t){return 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o,i=a(\"x\"),l=a(\"y\");if(n.getComponentMethod(\"calendars\",\"handleTraceDefaults\")(t,e,[\"x\",\"y\"],r),i)l?(o=Math.min(i.length,l.length),o<i.length&&(e.x=i.slice(0,o)),o<l.length&&(e.y=l.slice(0,o))):(o=i.length,a(\"y0\"),a(\"dy\"));else{if(!l)return 0;o=e.y.length,a(\"x0\"),a(\"dx\")}return o}},{\"../../registry\":206}]},{},[5])(5)});window.define = window.__define;window.require = window.__require;window.__define = undefined; window.__require = undefined;/* global Highcharts, Plotly*/\n",
       "/* eslint-disable */\n",
       "function throttle(fn, timeout, invokeAsap, ctx) {\n",
       "    var typeofInvokeAsap = typeof invokeAsap;\n",
       "    if(typeofInvokeAsap === 'undefined') {\n",
       "        invokeAsap = true;\n",
       "    } else if(arguments.length === 3 && typeofInvokeAsap !== 'boolean') {\n",
       "        ctx = invokeAsap;\n",
       "        invokeAsap = true;\n",
       "    }\n",
       "\n",
       "    var timer, args, needInvoke,\n",
       "        wrapper = function() {\n",
       "            if(needInvoke) {\n",
       "                fn.apply(ctx, args);\n",
       "                needInvoke = false;\n",
       "                timer = setTimeout(wrapper, timeout);\n",
       "            } else {\n",
       "                timer = null;\n",
       "            }\n",
       "        };\n",
       "\n",
       "    return function() {\n",
       "        args = arguments;\n",
       "        ctx || (ctx = this);\n",
       "        needInvoke = true;\n",
       "\n",
       "        if(!timer) {\n",
       "            invokeAsap?\n",
       "                wrapper() :\n",
       "                timer = setTimeout(wrapper, timeout);\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "/* eslint-enable */\n",
       "function CatboostIpython() {\n",
       "\n",
       "}\n",
       "\n",
       "CatboostIpython.prototype.init = function() {\n",
       "    this.charts = {};\n",
       "    /*\n",
       "        {\n",
       "            \"rmse_1\": {\n",
       "                ...\n",
       "            }\n",
       "        }\n",
       "    */\n",
       "    this.traces = {};\n",
       "    /*\n",
       "        {\n",
       "            \"rmse_1\": {\n",
       "                name: \"rmse\",\n",
       "                id: \"rmse_1\",\n",
       "                parent: \"div\",\n",
       "                traces: [\n",
       "                    {\n",
       "                        name: \"current;learn;0;;;\",\n",
       "                        x: [],\n",
       "                        y: []\n",
       "                    },\n",
       "                    {\n",
       "                        name: \"current;learn;0;smoothed;;\",\n",
       "                        x: [],\n",
       "                        y: []\n",
       "                    },\n",
       "                    {\n",
       "                        name: \"current;learn;1;;;\",\n",
       "                        x: [],\n",
       "                        y: []\n",
       "                    },\n",
       "                    {\n",
       "                        name: \"current;learn;1;smoothed;;\",\n",
       "                        x: [],\n",
       "                        y: []\n",
       "                    },\n",
       "                    {\n",
       "                        name: \"current;test;0;;;\",\n",
       "                        x: [],\n",
       "                        y: []\n",
       "                    },\n",
       "                    {\n",
       "                        name: \"current;test;0;smoothed;;\",\n",
       "                        x: [],\n",
       "                        y: []\n",
       "                    },\n",
       "                    {\n",
       "                        name: \"current;test;0;;best_point;\",\n",
       "                        x: [],\n",
       "                        y: []\n",
       "                    },\n",
       "                    {\n",
       "                        name: \"current;test;0;;;best_value\",\n",
       "                        x: [],\n",
       "                        y: []\n",
       "                    }\n",
       "                ]\n",
       "            }\n",
       "        }\n",
       "    */\n",
       "\n",
       "    this.hovertextParameters = [];\n",
       "    this.chartsToRedraw = {};\n",
       "    this.lastIndexes = {};\n",
       "    this.smoothness = -1;\n",
       "    this.layoutDisabled = {\n",
       "        series: {},\n",
       "        traces: {}\n",
       "    };\n",
       "    this.clickMode = false;\n",
       "    this.logarithmMode = 'linear';\n",
       "    this.lastSmooth = 0;\n",
       "    this.layout = null;\n",
       "    this.activeTab = '';\n",
       "    this.meta = {};\n",
       "    this.timeLeft = {};\n",
       "\n",
       "    this.hasCVMode = false;\n",
       "    this.stddevEnabled = false;\n",
       "\n",
       "    this.colors = [\n",
       "        '#68E256',\n",
       "        '#56AEE2',\n",
       "        '#CF56E2',\n",
       "        '#E28956',\n",
       "        '#56E289',\n",
       "        '#5668E2',\n",
       "        '#E256AE',\n",
       "        '#E2CF56',\n",
       "        '#56E2CF',\n",
       "        '#8A56E2',\n",
       "        '#E25668',\n",
       "        '#AEE256'\n",
       "    ];\n",
       "    this.colorsByPath = {};\n",
       "    this.colorIndex = 0;\n",
       "    this.lossFuncs = {};\n",
       "\n",
       "    this.isCVinited = false;\n",
       "};\n",
       "\n",
       "/* eslint-disable */\n",
       "CatboostIpython.prototype.loadStyles = function(path, fn, scope) {\n",
       "    $('link[catboost=\"1\"]').remove();\n",
       "\n",
       "    var head = document.getElementsByTagName('head')[0], // reference to document.head for appending/ removing link nodes\n",
       "        link = document.createElement('link');           // create the link node\n",
       "    link.setAttribute('href', path);\n",
       "    link.setAttribute('rel', 'stylesheet');\n",
       "    link.setAttribute('type', 'text/css');\n",
       "    link.setAttribute('catboost', '1');\n",
       "\n",
       "    var sheet, cssRules;\n",
       "    // get the correct properties to check for depending on the browser\n",
       "    if ('sheet' in link) {\n",
       "        sheet = 'sheet'; cssRules = 'cssRules';\n",
       "    } else {\n",
       "        sheet = 'styleSheet'; cssRules = 'rules';\n",
       "    }\n",
       "\n",
       "    var interval_id = setInterval(function() {                     // start checking whether the style sheet has successfully loaded\n",
       "        try {\n",
       "            if (link[sheet] && link[sheet][cssRules].length) { // SUCCESS! our style sheet has loaded\n",
       "                clearInterval(interval_id);                      // clear the counters\n",
       "                clearTimeout(timeout_id);\n",
       "                fn.call( scope || window, true, link);           // fire the callback with success == true\n",
       "            }\n",
       "        } catch(e) {} finally {}\n",
       "    }, 50 ),                                                   // how often to check if the stylesheet is loaded\n",
       "    timeout_id = setTimeout( function() {       // start counting down till fail\n",
       "        clearInterval( interval_id );             // clear the counters\n",
       "        clearTimeout( timeout_id );\n",
       "        head.removeChild( link );                // since the style sheet didn't load, remove the link node from the DOM\n",
       "        fn.call( scope || window, false, link ); // fire the callback with success == false\n",
       "    }, 15000 );                                 // how long to wait before failing\n",
       "\n",
       "    head.appendChild( link );  // insert the link node into the DOM and start loading the style sheet\n",
       "\n",
       "    return link; // return the link node;\n",
       "};\n",
       "/* eslint-enable */\n",
       "\n",
       "CatboostIpython.prototype.resizeCharts = function() {\n",
       "    // width fix for development\n",
       "    $('.catboost-graph__charts', this.layout).css({width: $('.catboost-graph').width()});\n",
       "\n",
       "    this.plotly.Plots.resize(this.traces[this.activeTab].parent);\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.addMeta = function(path, meta) {\n",
       "    this.meta[path] = meta;\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.addLayout = function(parent) {\n",
       "    if (this.layout) {\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var cvAreaControls = '';\n",
       "\n",
       "    if (this.hasCVMode) {\n",
       "        cvAreaControls =    '<div>' +\n",
       "                                '<input type=\"checkbox\" class=\"catboost-panel__control_checkbox\" id=\"catboost-control2-cvstddev' + this.index + '\"' + (this.stddevEnabled ? ' checked=\"checked\"' : '') + '></input>' +\n",
       "                                '<label for=\"catboost-control2-cvstddev' + this.index + '\" class=\"catboost-panel__controls2_label catboost-panel__controls2_label-long\">Standard Deviation</label>' +\n",
       "                            '</div>';\n",
       "    }\n",
       "\n",
       "    this.layout = $('<div class=\"catboost\">' +\n",
       "                        '<div class=\"catboost-panel\">' +\n",
       "                            '<div class=\"catboost-panel__controls\">' +\n",
       "                                '<input type=\"checkbox\" class=\"catboost-panel__controls_checkbox\" id=\"catboost-control-learn' + this.index + '\" ' + (!this.layoutDisabled.learn ? ' checked=\"checked\"' : '') + '></input>' +\n",
       "                                '<label for=\"catboost-control-learn' + this.index + '\" class=\"catboost-panel__controls_label\"><div class=\"catboost-panel__serie_learn_pic\" style=\"border-color:#999\"></div>Learn</label>' +\n",
       "                                '<input type=\"checkbox\" class=\"catboost-panel__controls_checkbox\" id=\"catboost-control-test' + this.index + '\" ' + (!this.layoutDisabled.test ? ' checked=\"checked\"' : '') + '></input>' +\n",
       "                                '<label for=\"catboost-control-test' + this.index + '\" class=\"catboost-panel__controls_label\"><div class=\"catboost-panel__serie_test_pic\" style=\"border-color:#999\"></div>Eval</label>' +\n",
       "                            '</div>' +\n",
       "                            '<div class=\"catboost-panel__series ' + (this.layoutDisabled.learn ? ' catboost-panel__series_learn_disabled' : '') + '\">' +\n",
       "                            '</div>' +\n",
       "                            '<div class=\"catboost-panel__controls2\">' +\n",
       "                                '<input type=\"checkbox\" class=\"catboost-panel__control_checkbox\" id=\"catboost-control2-clickmode' + this.index + '\"></input>' +\n",
       "                                '<label for=\"catboost-control2-clickmode' + this.index + '\" class=\"catboost-panel__controls2_label\">Click Mode</label>' +\n",
       "                                '<input type=\"checkbox\" class=\"catboost-panel__control_checkbox\" id=\"catboost-control2-log' + this.index + '\"></input>' +\n",
       "                                '<label for=\"catboost-control2-log' + this.index + '\" class=\"catboost-panel__controls2_label\">Logarithm</label>' +\n",
       "                                '<div>' +\n",
       "                                    '<input type=\"checkbox\" class=\"catboost-panel__control_checkbox\" id=\"catboost-control2-smooth' + this.index + '\"></input>' +\n",
       "                                    '<label for=\"catboost-control2-smooth' + this.index + '\" class=\"catboost-panel__controls2_label\">Smooth</label>' +\n",
       "                                    '<input id=\"catboost-control2-slider' + this.index + '\" disabled=\"disabled\" class=\"catboost-panel__control_slider\" type =\"range\" value=\"0\" min=\"0\" max=\"1\" step =\"0.01\" for=\"rangeInputValue\" name=\"rangeInput\"/>' +\n",
       "                                    '<input id=\"catboost-control2-slidervalue' + this.index + '\" disabled=\"disabled\" class=\"catboost-panel__control_slidervalue\" value=\"0\" min=\"0\" max=\"1\" for=\"rangeInput\" name=\"rangeInputValue\"/>' +\n",
       "                                '</div>' +\n",
       "                                cvAreaControls +\n",
       "                            '</div>' +\n",
       "                        '</div>' +\n",
       "                        '<div class=\"catboost-graph\">' +\n",
       "                            '<div class=\"catboost-graph__tabs\"></div>' +\n",
       "                            '<div class=\"catboost-graph__charts\"></div>' +\n",
       "                        '</div>' +\n",
       "                    '</div>');\n",
       "    $(parent).append(this.layout);\n",
       "\n",
       "    this.addTabEvents();\n",
       "    this.addControlEvents();\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.addTabEvents = function() {\n",
       "    var self = this;\n",
       "\n",
       "    $('.catboost-graph__tabs', this.layout).click(function(e) {\n",
       "        if (!$(e.target).is('.catboost-graph__tab:not(.catboost-graph__tab_active)')) {\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var id = $(e.target).attr('tabid');\n",
       "\n",
       "        self.activeTab = id;\n",
       "\n",
       "        $('.catboost-graph__tab_active', self.layout).removeClass('catboost-graph__tab_active');\n",
       "        $('.catboost-graph__chart_active', self.layout).removeClass('catboost-graph__chart_active');\n",
       "\n",
       "        $('.catboost-graph__tab[tabid=\"' + id + '\"]', self.layout).addClass('catboost-graph__tab_active');\n",
       "        $('.catboost-graph__chart[tabid=\"' + id + '\"]', self.layout).addClass('catboost-graph__chart_active');\n",
       "\n",
       "        self.cleanSeries();\n",
       "\n",
       "        self.redrawActiveChart();\n",
       "        self.resizeCharts();\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.addControlEvents = function() {\n",
       "    var self = this;\n",
       "\n",
       "    $('#catboost-control-learn' + this.index, this.layout).click(function() {\n",
       "        self.layoutDisabled.learn = !$(this)[0].checked;\n",
       "\n",
       "        $('.catboost-panel__series', self.layout).toggleClass('catboost-panel__series_learn_disabled', self.layoutDisabled.learn);\n",
       "\n",
       "        self.redrawActiveChart();\n",
       "    });\n",
       "\n",
       "    $('#catboost-control-test' + this.index, this.layout).click(function() {\n",
       "        self.layoutDisabled.test = !$(this)[0].checked;\n",
       "\n",
       "        $('.catboost-panel__series', self.layout).toggleClass('catboost-panel__series_test_disabled', self.layoutDisabled.test);\n",
       "\n",
       "        self.redrawActiveChart();\n",
       "    });\n",
       "\n",
       "    $('#catboost-control2-clickmode' + this.index, this.layout).click(function() {\n",
       "        self.clickMode = $(this)[0].checked;\n",
       "    });\n",
       "\n",
       "    $('#catboost-control2-log' + this.index, this.layout).click(function() {\n",
       "        self.logarithmMode = $(this)[0].checked ? 'log' : 'linear';\n",
       "\n",
       "        self.forEveryLayout(function(layout) {\n",
       "            layout.yaxis = {type: self.logarithmMode};\n",
       "        });\n",
       "\n",
       "        self.redrawActiveChart();\n",
       "    });\n",
       "\n",
       "    var slider = $('#catboost-control2-slider' + this.index),\n",
       "        sliderValue = $('#catboost-control2-slidervalue' + this.index);\n",
       "\n",
       "    $('#catboost-control2-smooth' + this.index, this.layout).click(function() {\n",
       "        var enabled = $(this)[0].checked;\n",
       "\n",
       "        self.setSmoothness(enabled ? self.lastSmooth : -1);\n",
       "\n",
       "        slider.prop('disabled', !enabled);\n",
       "        sliderValue.prop('disabled', !enabled);\n",
       "\n",
       "        self.redrawActiveChart();\n",
       "    });\n",
       "\n",
       "    $('#catboost-control2-cvstddev' + this.index, this.layout).click(function() {\n",
       "        var enabled = $(this)[0].checked;\n",
       "\n",
       "        self.setStddev(enabled);\n",
       "\n",
       "        self.redrawActiveChart();\n",
       "    });\n",
       "\n",
       "    slider.on('input change', function() {\n",
       "        var smooth = Number($(this).val());\n",
       "\n",
       "        sliderValue.val(isNaN(smooth) ? 0 : smooth);\n",
       "\n",
       "        self.setSmoothness(smooth);\n",
       "        self.lastSmooth = smooth;\n",
       "\n",
       "        self.redrawActiveChart();\n",
       "    });\n",
       "\n",
       "    sliderValue.on('input change', function() {\n",
       "        var smooth = Number($(this).val());\n",
       "\n",
       "        slider.val(isNaN(smooth) ? 0 : smooth);\n",
       "\n",
       "        self.setSmoothness(smooth);\n",
       "        self.lastSmooth = smooth;\n",
       "\n",
       "        self.redrawActiveChart();\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.setTraceVisibility = function(trace, visibility) {\n",
       "    if (trace) {\n",
       "        trace.visible = visibility;\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.updateTracesVisibility = function() {\n",
       "    var tracesHash = this.groupTraces(),\n",
       "        traces,\n",
       "        smoothDisabled = this.getSmoothness() === -1,\n",
       "        self = this;\n",
       "\n",
       "    for (var train in tracesHash) {\n",
       "        if (tracesHash.hasOwnProperty(train)) {\n",
       "            traces = tracesHash[train].traces;\n",
       "\n",
       "            if (this.layoutDisabled.traces[train]) {\n",
       "                traces.forEach(function(trace) {\n",
       "                    self.setTraceVisibility(trace, false);\n",
       "                });\n",
       "            } else {\n",
       "                traces.forEach(function(trace) {\n",
       "                    self.setTraceVisibility(trace, true);\n",
       "                });\n",
       "\n",
       "                if (this.hasCVMode) {\n",
       "                    if (this.stddevEnabled) {\n",
       "                        self.filterTracesOne(traces, {type: 'learn'}).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, false);\n",
       "                        });\n",
       "                        self.filterTracesOne(traces, {type: 'test'}).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, false);\n",
       "                        });\n",
       "\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'learn', cv_avg: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, true);\n",
       "                        });\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'test', cv_avg: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, true);\n",
       "                        });\n",
       "\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'learn', cv_avg: true, smoothed: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, true);\n",
       "                        });\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'test', cv_avg: true, smoothed: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, true);\n",
       "                        });\n",
       "\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'test', cv_avg: true, best_point: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, true);\n",
       "                        });\n",
       "\n",
       "                        self.filterTracesOne(traces, {cv_stddev_first: true}).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, true);\n",
       "                        });\n",
       "                        self.filterTracesOne(traces, {cv_stddev_last: true}).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, true);\n",
       "                        });\n",
       "                    } else {\n",
       "                        self.filterTracesOne(traces, {cv_stddev_first: true}).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, false);\n",
       "                        });\n",
       "                        self.filterTracesOne(traces, {cv_stddev_last: true}).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, false);\n",
       "                        });\n",
       "\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'learn', cv_avg: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, false);\n",
       "                        });\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'test', cv_avg: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, false);\n",
       "                        });\n",
       "\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'learn', cv_avg: true, smoothed: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, false);\n",
       "                        });\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'test', cv_avg: true, smoothed: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, false);\n",
       "                        });\n",
       "\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'test', cv_avg: true, best_point: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, false);\n",
       "                        });\n",
       "                    }\n",
       "                }\n",
       "\n",
       "                if (smoothDisabled) {\n",
       "                    self.filterTracesOne(traces, {smoothed: true}).forEach(function(trace) {\n",
       "                        self.setTraceVisibility(trace, false);\n",
       "                    });\n",
       "                }\n",
       "\n",
       "                if (this.layoutDisabled['learn']) {\n",
       "                    self.filterTracesOne(traces, {type: 'learn'}).forEach(function(trace) {\n",
       "                        self.setTraceVisibility(trace, false);\n",
       "                    });\n",
       "                }\n",
       "\n",
       "                if (this.layoutDisabled['test']) {\n",
       "                    self.filterTracesOne(traces, {type: 'test'}).forEach(function(trace) {\n",
       "                        self.setTraceVisibility(trace, false);\n",
       "                    });\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.getSmoothness = function() {\n",
       "    return this.smoothness && this.smoothness > -1 ? this.smoothness : -1;\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.setSmoothness = function(weight) {\n",
       "    if (weight < 0 && weight !== -1 || weight > 1) {\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    this.smoothness = weight;\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.setStddev = function(enabled) {\n",
       "    this.stddevEnabled = enabled;\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.redrawActiveChart = function() {\n",
       "    this.chartsToRedraw[this.activeTab] = true;\n",
       "\n",
       "    this.redrawAll();\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.redraw = function() {\n",
       "    if (this.chartsToRedraw[this.activeTab]) {\n",
       "        this.chartsToRedraw[this.activeTab] = false;\n",
       "\n",
       "        this.updateTracesVisibility();\n",
       "        this.updateTracesCV();\n",
       "        this.updateTracesBest();\n",
       "        this.updateTracesValues();\n",
       "        this.updateTracesSmoothness();\n",
       "\n",
       "        this.plotly.redraw(this.traces[this.activeTab].parent);\n",
       "    }\n",
       "\n",
       "    this.drawTraces();\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.addRedrawFunc = function() {\n",
       "    this.redrawFunc = throttle(this.redraw, 400, false, this);\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.redrawAll = function() {\n",
       "    if (!this.redrawFunc) {\n",
       "        this.addRedrawFunc();\n",
       "    }\n",
       "\n",
       "    this.redrawFunc();\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.addPoints = function(parent, data) {\n",
       "    var self = this;\n",
       "\n",
       "    data.chunks.forEach(function(item) {\n",
       "        if (typeof item.remaining_time !== 'undefined' && typeof item.passed_time !== 'undefined') {\n",
       "            if (!self.timeLeft[data.path]) {\n",
       "                self.timeLeft[data.path] = [];\n",
       "            }\n",
       "\n",
       "            self.timeLeft[data.path][item.iteration] = [item.remaining_time, item.passed_time];\n",
       "        }\n",
       "\n",
       "        ['test', 'learn'].forEach(function(type) {\n",
       "            var sets = self.meta[data.path][type + '_sets'],\n",
       "                metrics = self.meta[data.path][type + '_metrics'];\n",
       "\n",
       "            for (var i = 0; i < metrics.length; i++) {\n",
       "                var nameOfMetric = metrics[i].name,\n",
       "                    cvAdded = false;\n",
       "                    hovertextParametersAdded = false;\n",
       "\n",
       "                self.lossFuncs[nameOfMetric] = metrics[i].best_value;\n",
       "\n",
       "                for (var j = 0; j < sets.length; j++) {\n",
       "                    var nameOfSet = sets[j],\n",
       "                        params = {\n",
       "                            chartName: nameOfMetric,\n",
       "                            index: i,\n",
       "                            train: data.train,\n",
       "                            type: type,\n",
       "                            path: data.path,\n",
       "                            indexOfSet: j,\n",
       "                            nameOfSet: nameOfSet\n",
       "                        },\n",
       "                        key = self.getKey(params),\n",
       "                        launchMode = self.getLaunchMode(data.path);\n",
       "\n",
       "                    if (!self.activeTab) {\n",
       "                        self.activeTab = key.chartId;\n",
       "                    }\n",
       "\n",
       "                    if (launchMode === 'CV' ) {\n",
       "                        // we need to set launch mode before first getTrace call\n",
       "                        self.hasCVMode = true;\n",
       "\n",
       "                        if (!self.isCVinited) {\n",
       "                            // and we don't need to reset setting for next iterations\n",
       "                            self.layoutDisabled.learn = true;\n",
       "                            self.setStddev(true);\n",
       "\n",
       "                            self.isCVinited = true;\n",
       "                        }\n",
       "                    }\n",
       "\n",
       "                    var valuesOfSet = item[nameOfSet],\n",
       "                        pointValue = valuesOfSet[i],\n",
       "                        pointIndex = item.iteration,\n",
       "                        // traces\n",
       "                        trace = self.getTrace(parent, params),\n",
       "                        smoothedTrace = self.getTrace(parent, $.extend({smoothed: true}, params)),\n",
       "                        bestValueTrace = null;\n",
       "\n",
       "                    if (type === 'test') {\n",
       "                        if (launchMode !== 'CV') {\n",
       "                            self.getTrace(parent, $.extend({best_point: true}, params));\n",
       "                        }\n",
       "\n",
       "                        if (typeof self.lossFuncs[nameOfMetric] === 'number') {\n",
       "                            bestValueTrace = self.getTrace(parent, $.extend({best_value: true}, params));\n",
       "                        }\n",
       "                    }\n",
       "\n",
       "                    if (pointValue !== 'inf' && pointValue !== 'nan') {\n",
       "                        trace.x[pointIndex] = pointIndex;\n",
       "                        trace.y[pointIndex] = valuesOfSet[i];\n",
       "                        trace.hovertext[pointIndex] = nameOfSet + ': ' + valuesOfSet[i].toPrecision(7);\n",
       "                        if (item.hasOwnProperty('parameters')) {\n",
       "                            self.hovertextParameters[pointIndex] = '';\n",
       "                            for (var parameter in item.parameters[0]) {\n",
       "                                if (item.parameters[0].hasOwnProperty(parameter)) {\n",
       "                                    valueOfParameter = item.parameters[0][parameter];\n",
       "                                    self.hovertextParameters[pointIndex] += '<br>' + parameter + ' : ' + valueOfParameter;\n",
       "                                }\n",
       "                            }\n",
       "                        }\n",
       "                        if (!hovertextParametersAdded && type === 'test') {\n",
       "                            hovertextParametersAdded = true;\n",
       "                            trace.hovertext[pointIndex] += self.hovertextParameters[pointIndex];\n",
       "                        }\n",
       "                        smoothedTrace.x[pointIndex] = pointIndex;\n",
       "                    }\n",
       "\n",
       "                    if (bestValueTrace) {\n",
       "                        bestValueTrace.x[pointIndex] = pointIndex;\n",
       "                        bestValueTrace.y[pointIndex] = self.lossFuncs[nameOfMetric];\n",
       "                    }\n",
       "\n",
       "                    if (launchMode === 'CV' && !cvAdded) {\n",
       "                        cvAdded = true;\n",
       "\n",
       "                        self.getTrace(parent, $.extend({cv_stddev_first: true}, params));\n",
       "                        self.getTrace(parent, $.extend({cv_stddev_last: true}, params));\n",
       "\n",
       "                        self.getTrace(parent, $.extend({cv_stddev_first: true, smoothed: true}, params));\n",
       "                        self.getTrace(parent, $.extend({cv_stddev_last: true, smoothed: true}, params));\n",
       "\n",
       "                        self.getTrace(parent, $.extend({cv_avg: true}, params));\n",
       "                        self.getTrace(parent, $.extend({cv_avg: true, smoothed: true}, params));\n",
       "\n",
       "                        if (type === 'test') {\n",
       "                            self.getTrace(parent, $.extend({cv_avg: true, best_point: true}, params));\n",
       "                        }\n",
       "                    }\n",
       "                }\n",
       "\n",
       "                self.chartsToRedraw[key.chartId] = true;\n",
       "\n",
       "                self.redrawAll();\n",
       "            }\n",
       "        });\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.getLaunchMode = function(path) {\n",
       "    return this.meta[path].launch_mode;\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.getChartNode = function(params, active) {\n",
       "    var node = $('<div class=\"catboost-graph__chart\" tabid=\"' + params.id + '\"></div>');\n",
       "\n",
       "    if (active) {\n",
       "        node.addClass('catboost-graph__chart_active');\n",
       "    }\n",
       "\n",
       "    return node;\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.getChartTab = function(params, active) {\n",
       "    var node = $('<div class=\"catboost-graph__tab\" tabid=\"' + params.id + '\">' + params.name + '</div>');\n",
       "\n",
       "    if (active) {\n",
       "        node.addClass('catboost-graph__tab_active');\n",
       "    }\n",
       "\n",
       "    return node;\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.forEveryChart = function(callback) {\n",
       "    for (var name in this.traces) {\n",
       "        if (this.traces.hasOwnProperty(name)) {\n",
       "            callback(this.traces[name]);\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.forEveryLayout = function(callback) {\n",
       "    this.forEveryChart(function(chart) {\n",
       "        callback(chart.layout);\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.getChart = function(parent, params) {\n",
       "    var id = params.id,\n",
       "        self = this;\n",
       "\n",
       "    if (this.charts[id]) {\n",
       "        return this.charts[id];\n",
       "    }\n",
       "\n",
       "    this.addLayout(parent);\n",
       "\n",
       "    var active = this.activeTab === params.id,\n",
       "        chartNode = this.getChartNode(params, active),\n",
       "        chartTab = this.getChartTab(params, active);\n",
       "\n",
       "    $('.catboost-graph__charts', this.layout).append(chartNode);\n",
       "    $('.catboost-graph__tabs', this.layout).append(chartTab);\n",
       "\n",
       "    this.traces[id] = {\n",
       "        id: params.id,\n",
       "        name: params.name,\n",
       "        parent: chartNode[0],\n",
       "        traces: [],\n",
       "        layout: {\n",
       "            xaxis: {\n",
       "                range: [0, Number(this.meta[params.path].iteration_count)],\n",
       "                type: 'linear',\n",
       "                tickmode: 'auto',\n",
       "                showspikes: true,\n",
       "                spikethickness: 1,\n",
       "                spikedash: 'longdashdot',\n",
       "                spikemode: 'across',\n",
       "                zeroline: false,\n",
       "                showgrid: false\n",
       "            },\n",
       "            yaxis: {\n",
       "                zeroline: false\n",
       "                //showgrid: false\n",
       "                //hoverformat : '.7f'\n",
       "            },\n",
       "            separators: '. ',\n",
       "            //hovermode: 'x',\n",
       "            margin: {l: 38, r: 0, t: 35, b: 30},\n",
       "            autosize: true,\n",
       "            showlegend: false\n",
       "        },\n",
       "        options: {\n",
       "            scrollZoom: false,\n",
       "            modeBarButtonsToRemove: ['toggleSpikelines'],\n",
       "            displaylogo: false\n",
       "        }\n",
       "    };\n",
       "\n",
       "    this.charts[id] = this.plotly.plot(chartNode[0], this.traces[id].traces, this.traces[id].layout, this.traces[id].options);\n",
       "\n",
       "    chartNode[0].on('plotly_hover', function(e) {\n",
       "        self.updateTracesValues(e.points[0].x);\n",
       "    });\n",
       "\n",
       "    chartNode[0].on('plotly_click', function(e) {\n",
       "        self.updateTracesValues(e.points[0].x, true);\n",
       "    });\n",
       "\n",
       "    return this.charts[id];\n",
       "};\n",
       "\n",
       "\n",
       "CatboostIpython.prototype.getTrace = function(parent, params) {\n",
       "    var key = this.getKey(params),\n",
       "        chartSeries = [];\n",
       "\n",
       "    if (this.traces[key.chartId]) {\n",
       "        chartSeries = this.traces[key.chartId].traces.filter(function(trace) {\n",
       "            return trace.name === key.traceName;\n",
       "        });\n",
       "    }\n",
       "\n",
       "    if (chartSeries.length) {\n",
       "        return chartSeries[0];\n",
       "    } else {\n",
       "        this.getChart(parent, {id: key.chartId, name: params.chartName, path: params.path});\n",
       "\n",
       "        var plotParams = {\n",
       "                color: this.getNextColor(params.path, params.smoothed ? 0.2 : 1),\n",
       "                fillsmoothcolor: this.getNextColor(params.path, 0.1),\n",
       "                fillcolor: this.getNextColor(params.path, 0.4),\n",
       "                hoverinfo: params.cv_avg ? 'skip' : 'text+x',\n",
       "                width: params.cv_avg ? 2 : 1,\n",
       "                dash: params.type === 'test' ? 'solid' : 'dot'\n",
       "            },\n",
       "            trace = {\n",
       "                name: key.traceName,\n",
       "                _params: params,\n",
       "                x: [],\n",
       "                y: [],\n",
       "                hovertext: [],\n",
       "                hoverinfo: plotParams.hoverinfo,\n",
       "                line: {\n",
       "                    width: plotParams.width,\n",
       "                    dash: plotParams.dash,\n",
       "                    color: plotParams.color\n",
       "                },\n",
       "                mode: 'lines',\n",
       "                hoveron: 'points',\n",
       "                connectgaps: true\n",
       "            };\n",
       "\n",
       "        if (params.best_point) {\n",
       "            trace = {\n",
       "                name: key.traceName,\n",
       "                _params: params,\n",
       "                x: [],\n",
       "                y: [],\n",
       "                marker: {\n",
       "                    width: 2,\n",
       "                    color: plotParams.color\n",
       "                },\n",
       "                hovertext: [],\n",
       "                hoverinfo: 'text',\n",
       "                mode: 'markers',\n",
       "                type: 'scatter'\n",
       "            };\n",
       "        }\n",
       "\n",
       "        if (params.best_value) {\n",
       "            trace = {\n",
       "                name: key.traceName,\n",
       "                _params: params,\n",
       "                x: [],\n",
       "                y: [],\n",
       "                line: {\n",
       "                    width: 1,\n",
       "                    dash: 'dash',\n",
       "                    color: '#CCCCCC'\n",
       "                },\n",
       "                mode: 'lines',\n",
       "                connectgaps: true,\n",
       "                hoverinfo: 'skip'\n",
       "            };\n",
       "        }\n",
       "\n",
       "        if (params.cv_stddev_last) {\n",
       "            trace.fill = 'tonexty';\n",
       "        }\n",
       "\n",
       "        trace._params.plotParams = plotParams;\n",
       "\n",
       "        this.traces[key.chartId].traces.push(trace);\n",
       "\n",
       "        return trace;\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.getKey = function(params) {\n",
       "    var traceName = [\n",
       "        params.train,\n",
       "        params.type,\n",
       "        params.indexOfSet,\n",
       "        (params.smoothed ? 'smoothed' : ''),\n",
       "        (params.best_point ? 'best_pount' : ''),\n",
       "        (params.best_value ? 'best_value' : ''),\n",
       "        (params.cv_avg ? 'cv_avg' : ''),\n",
       "        (params.cv_stddev_first ? 'cv_stddev_first' : ''),\n",
       "        (params.cv_stddev_last ? 'cv_stddev_last' : '')\n",
       "    ].join(';');\n",
       "\n",
       "    return {\n",
       "        chartId: params.chartName,\n",
       "        traceName: traceName,\n",
       "        colorId: params.train\n",
       "    };\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.filterTracesEvery = function(traces, filter) {\n",
       "    traces = traces || this.traces[this.activeTab].traces;\n",
       "\n",
       "    return traces.filter(function(trace) {\n",
       "        for (var prop in filter) {\n",
       "            if (filter.hasOwnProperty(prop)) {\n",
       "                if (filter[prop] !== trace._params[prop]) {\n",
       "                    return false;\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "\n",
       "        return true;\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.filterTracesOne = function(traces, filter) {\n",
       "    traces = traces || this.traces[this.activeTab].traces;\n",
       "\n",
       "    return traces.filter(function(trace) {\n",
       "        for (var prop in filter) {\n",
       "            if (filter.hasOwnProperty(prop)) {\n",
       "                if (filter[prop] === trace._params[prop]) {\n",
       "                    return true;\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "\n",
       "        return false;\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.cleanSeries = function() {\n",
       "    $('.catboost-panel__series', this.layout).html('');\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.groupTraces = function() {\n",
       "    var traces = this.traces[this.activeTab].traces,\n",
       "        index = 0,\n",
       "        tracesHash = {};\n",
       "\n",
       "    traces.map(function(trace) {\n",
       "        var train = trace._params.train;\n",
       "\n",
       "        if (!tracesHash[train]) {\n",
       "            tracesHash[train] = {\n",
       "                index: index,\n",
       "                traces: [],\n",
       "                info: {\n",
       "                    path: trace._params.path,\n",
       "                    color: trace._params.plotParams.color\n",
       "                }\n",
       "            };\n",
       "\n",
       "            index++;\n",
       "        }\n",
       "\n",
       "        tracesHash[train].traces.push(trace);\n",
       "    });\n",
       "\n",
       "    return tracesHash;\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.drawTraces = function() {\n",
       "    if ($('.catboost-panel__series .catboost-panel__serie', this.layout).length) {\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var html = '',\n",
       "        tracesHash = this.groupTraces();\n",
       "\n",
       "    for (var train in tracesHash) {\n",
       "        if (tracesHash.hasOwnProperty(train)) {\n",
       "            html += this.drawTrace(train, tracesHash[train]);\n",
       "        }\n",
       "    }\n",
       "\n",
       "    $('.catboost-panel__series', this.layout).html(html);\n",
       "\n",
       "    this.updateTracesValues();\n",
       "\n",
       "    this.addTracesEvents();\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.getTraceDefParams = function(params) {\n",
       "    var defParams =  {\n",
       "        smoothed: undefined,\n",
       "        best_point: undefined,\n",
       "        best_value: undefined,\n",
       "        cv_avg: undefined,\n",
       "        cv_stddev_first: undefined,\n",
       "        cv_stddev_last: undefined\n",
       "    };\n",
       "\n",
       "    if (params) {\n",
       "        return $.extend(defParams, params);\n",
       "    } else {\n",
       "        return defParams;\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.drawTrace = function(train, hash) {\n",
       "    var info = hash.info,\n",
       "        id = 'catboost-serie-' + this.index + '-' + hash.index,\n",
       "        traces = {\n",
       "            learn: this.filterTracesEvery(hash.traces, this.getTraceDefParams({type: 'learn'})),\n",
       "            test: this.filterTracesEvery(hash.traces, this.getTraceDefParams({type: 'test'}))\n",
       "        },\n",
       "        items = {\n",
       "            learn: {\n",
       "                middle: '',\n",
       "                bottom: ''\n",
       "            },\n",
       "            test: {\n",
       "                middle: '',\n",
       "                bottom: ''\n",
       "            }\n",
       "        },\n",
       "        tracesNames = '';\n",
       "\n",
       "    ['learn', 'test'].forEach(function(type) {\n",
       "        traces[type].forEach(function(trace) {\n",
       "            items[type].middle += '<div class=\"catboost-panel__serie_' + type + '_pic\" style=\"border-color:' + info.color + '\"></div>' +\n",
       "                                  '<div data-index=\"' + trace._params.indexOfSet + '\" class=\"catboost-panel__serie_' + type + '_value\"></div>';\n",
       "\n",
       "            items[type].bottom += '<div class=\"catboost-panel__serie_' + type + '_pic\" style=\"border-color:transparent\"></div>' +\n",
       "                                  '<div data-index=\"' + trace._params.indexOfSet + '\" class=\"catboost-panel__serie_best_' + type + '_value\"></div>';\n",
       "\n",
       "            tracesNames += '<div class=\"catboost-panel__serie_' + type + '_pic\" style=\"border-color:' + info.color + '\"></div>' +\n",
       "                           '<div class=\"catboost-panel__serie_' + type + '_name\">' + trace._params.nameOfSet + '</div>';\n",
       "        });\n",
       "    });\n",
       "\n",
       "    var timeSpendHtml = '<div class=\"catboost-panel__serie_time\">' +\n",
       "                             '<div class=\"catboost-panel__serie_time_spend\" title=\"Time spend\"></div>' +\n",
       "                        '</div>';\n",
       "\n",
       "    var html = '<div id=\"' + id + '\" class=\"catboost-panel__serie\" style=\"color:' + info.color + '\">' +\n",
       "                    '<div class=\"catboost-panel__serie_top\">' +\n",
       "                        '<input type=\"checkbox\" data-seriename=\"' + train + '\" class=\"catboost-panel__serie_checkbox\" id=\"' + id + '-box\" ' + (!this.layoutDisabled.series[train] ? 'checked=\"checked\"' : '') + '></input>' +\n",
       "                        '<label title=' + this.meta[info.path].name + ' for=\"' + id + '-box\" class=\"catboost-panel__serie_label\">' + train + '<div class=\"catboost-panel__serie_time_left\" title=\"Estimate time\"></div></label>' +\n",
       "                        (this.getLaunchMode(info.path) !== 'Eval' ? timeSpendHtml : '') +\n",
       "                    '</div>' +\n",
       "                    '<div class=\"catboost-panel__serie_hint catboost-panel__serie__learn_hint\">curr</div>' +\n",
       "                    '<div class=\"catboost-panel__serie_hint catboost-panel__serie__test_hint\">best</div>' +\n",
       "                    '<div class=\"catboost-panel__serie_iteration\" title=\"curr iteration\"></div>' +\n",
       "                    '<div class=\"catboost-panel__serie_best_iteration\" title=\"best ' + (this.hasCVMode ? 'avg ' : '') + 'iteration\"></div>' +\n",
       "                    '<div class=\"catboost-panel__serie_scroll\">' +\n",
       "                        '<div class=\"catboost-panel__serie_names\">' +\n",
       "                            tracesNames +\n",
       "                        '</div>' +\n",
       "                        '<div class=\"catboost-panel__serie_middle\">' +\n",
       "                            items.learn.middle +\n",
       "                            items.test.middle +\n",
       "                        '</div>' +\n",
       "                        '<div class=\"catboost-panel__serie_bottom\">' +\n",
       "                            items.learn.bottom +\n",
       "                            items.test.bottom +\n",
       "                        '</div>' +\n",
       "                    '</div>' +\n",
       "                '</div>';\n",
       "\n",
       "    return html;\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.updateTracesValues = function(iteration, click) {\n",
       "    var tracesHash = this.groupTraces();\n",
       "\n",
       "    for (var train in tracesHash) {\n",
       "        if (tracesHash.hasOwnProperty(train) && !this.layoutDisabled.traces[train]) {\n",
       "            this.updateTraceValues(train, tracesHash[train], iteration, click);\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.updateTracesBest = function() {\n",
       "    var tracesHash = this.groupTraces();\n",
       "\n",
       "    for (var train in tracesHash) {\n",
       "        if (tracesHash.hasOwnProperty(train) && !this.layoutDisabled.traces[train]) {\n",
       "            this.updateTraceBest(train, tracesHash[train]);\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.getBestValue = function(data) {\n",
       "    if (!data.length) {\n",
       "        return {\n",
       "            best: undefined,\n",
       "            index: -1\n",
       "        };\n",
       "    }\n",
       "\n",
       "    var best = data[0],\n",
       "        index = 0,\n",
       "        func = this.lossFuncs[this.traces[this.activeTab].name],\n",
       "        bestDiff = typeof func === 'number' ? Math.abs(data[0] - func) : 0;\n",
       "\n",
       "    for (var i = 1, l = data.length; i < l; i++) {\n",
       "        if (func === 'Min' && data[i] < best) {\n",
       "            best = data[i];\n",
       "            index = i;\n",
       "        }\n",
       "\n",
       "        if (func === 'Max' && data[i] > best) {\n",
       "            best = data[i];\n",
       "            index = i;\n",
       "        }\n",
       "\n",
       "        if (typeof func === 'number' && Math.abs(data[i] - func) < bestDiff) {\n",
       "            best = data[i];\n",
       "            bestDiff = Math.abs(data[i] - func);\n",
       "            index = i;\n",
       "        }\n",
       "    }\n",
       "\n",
       "    return {\n",
       "        best: best,\n",
       "        index: index,\n",
       "        func: func\n",
       "    };\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.updateTracesCV = function() {\n",
       "    this.updateTracesCVAvg();\n",
       "\n",
       "    if (this.hasCVMode && this.stddevEnabled) {\n",
       "        this.updateTracesCVStdDev();\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.updateTracesCVAvg = function() {\n",
       "    var tracesHash = this.groupTraces(),\n",
       "        avgTraces = this.filterTracesEvery(tracesHash.traces, this.getTraceDefParams({\n",
       "            cv_avg: true\n",
       "        })),\n",
       "        self = this;\n",
       "\n",
       "    avgTraces.forEach(function(trace) {\n",
       "        var origTraces = self.filterTracesEvery(tracesHash.traces, self.getTraceDefParams({\n",
       "                train: trace._params.train,\n",
       "                type: trace._params.type,\n",
       "                smoothed: trace._params.smoothed\n",
       "            }));\n",
       "\n",
       "        if (origTraces.length) {\n",
       "            self.cvAvgFunc(origTraces, trace);\n",
       "        }\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.cvAvgFunc = function(origTraces, avgTrace) {\n",
       "    var maxCount = origTraces.length,\n",
       "        maxLength = -1,\n",
       "        count,\n",
       "        sum;\n",
       "\n",
       "    origTraces.forEach(function(origTrace) {\n",
       "        if (origTrace.y.length > maxLength) {\n",
       "            maxLength = origTrace.y.length;\n",
       "        }\n",
       "    });\n",
       "\n",
       "    for (var i = 0; i < maxLength; i++) {\n",
       "        sum = 0;\n",
       "        count = 0;\n",
       "\n",
       "        for (var j = 0; j < maxCount; j++) {\n",
       "            if (typeof origTraces[j].y[i] !== 'undefined') {\n",
       "                sum += origTraces[j].y[i];\n",
       "                count++;\n",
       "            }\n",
       "        }\n",
       "\n",
       "        if (count > 0) {\n",
       "            avgTrace.x[i] = i;\n",
       "            avgTrace.y[i] = sum / count;\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.updateTracesCVStdDev = function() {\n",
       "    var tracesHash = this.groupTraces(),\n",
       "        firstTraces = this.filterTracesOne(tracesHash.traces, {cv_stddev_first: true}),\n",
       "        self = this;\n",
       "\n",
       "    firstTraces.forEach(function(trace) {\n",
       "        var origTraces = self.filterTracesEvery(tracesHash.traces, self.getTraceDefParams({\n",
       "                train: trace._params.train,\n",
       "                type: trace._params.type,\n",
       "                smoothed: trace._params.smoothed\n",
       "            })),\n",
       "            lastTraces = self.filterTracesEvery(tracesHash.traces, self.getTraceDefParams({\n",
       "                train: trace._params.train,\n",
       "                type: trace._params.type,\n",
       "                smoothed: trace._params.smoothed,\n",
       "                cv_stddev_last: true\n",
       "            }));\n",
       "\n",
       "        if (origTraces.length && lastTraces.length === 1) {\n",
       "            self.cvStdDevFunc(origTraces, trace, lastTraces[0]);\n",
       "        }\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.cvStdDevFunc = function(origTraces, firstTrace, lastTrace) {\n",
       "    var maxCount = origTraces.length,\n",
       "        maxLength = -1,\n",
       "        count,\n",
       "        sum,\n",
       "        i, j;\n",
       "\n",
       "    origTraces.forEach(function(origTrace) {\n",
       "        if (origTrace.y.length > maxLength) {\n",
       "            maxLength = origTrace.y.length;\n",
       "        }\n",
       "    });\n",
       "\n",
       "    for (i = 0; i < maxLength; i++) {\n",
       "        sum = 0;\n",
       "        count = 0;\n",
       "\n",
       "        for (j = 0; j < maxCount; j++) {\n",
       "            if (typeof origTraces[j].y[i] !== 'undefined') {\n",
       "                sum += origTraces[j].y[i];\n",
       "                count++;\n",
       "            }\n",
       "        }\n",
       "\n",
       "        if (count <= 0) {\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        var std = 0,\n",
       "            avg = sum / count;\n",
       "\n",
       "        for (j = 0; j < maxCount; j++) {\n",
       "            if (typeof origTraces[j].y[i] !== 'undefined') {\n",
       "                std += Math.pow(origTraces[j].y[i] - avg, 2);\n",
       "            }\n",
       "        }\n",
       "\n",
       "        std /= (count - 1);\n",
       "        std = Math.pow(std, 0.5);\n",
       "\n",
       "        firstTrace.x[i] = i;\n",
       "        firstTrace.y[i] = avg - std;\n",
       "        firstTrace.hovertext[i] = firstTrace._params.type + ' std: ' + avg.toFixed(7) + '-' + std.toFixed(7);\n",
       "        lastTrace.x[i] = i;\n",
       "        lastTrace.y[i] = avg + std;\n",
       "        lastTrace.hovertext[i] = lastTrace._params.type + ' std: ' + avg.toFixed(7) + '+' + std.toFixed(7);\n",
       "        if (this.hovertextParameters.length > i) {\n",
       "            firstTrace.hovertext[i] += this.hovertextParameters[i];\n",
       "            lastTrace.hovertext[i] += this.hovertextParameters[i];\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.updateTracesSmoothness = function() {\n",
       "    var tracesHash = this.groupTraces(),\n",
       "        smoothedTraces = this.filterTracesOne(tracesHash.traces, {smoothed: true}),\n",
       "        enabled = this.getSmoothness() > -1,\n",
       "        self = this;\n",
       "\n",
       "    smoothedTraces.forEach(function(trace) {\n",
       "        var origTraces = self.filterTracesEvery(tracesHash.traces, self.getTraceDefParams({\n",
       "                train: trace._params.train,\n",
       "                type: trace._params.type,\n",
       "                indexOfSet: trace._params.indexOfSet,\n",
       "                cv_avg: trace._params.cv_avg,\n",
       "                cv_stddev_first: trace._params.cv_stddev_first,\n",
       "                cv_stddev_last: trace._params.cv_stddev_last\n",
       "            })),\n",
       "            colorFlag = false;\n",
       "\n",
       "        if (origTraces.length === 1) {\n",
       "            origTraces = origTraces[0];\n",
       "\n",
       "            if (origTraces.visible) {\n",
       "                if (enabled) {\n",
       "                    self.smoothFunc(origTraces, trace);\n",
       "                    colorFlag = true;\n",
       "                }\n",
       "\n",
       "                self.highlightSmoothedTrace(origTraces, trace, colorFlag);\n",
       "            }\n",
       "        }\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.highlightSmoothedTrace = function(trace, smoothedTrace, flag) {\n",
       "    if (flag) {\n",
       "        smoothedTrace.line.color = trace._params.plotParams.color;\n",
       "        trace.line.color = smoothedTrace._params.plotParams.color;\n",
       "        trace.hoverinfo = 'skip';\n",
       "\n",
       "        if (trace._params.cv_stddev_last) {\n",
       "            trace.fillcolor = trace._params.plotParams.fillsmoothcolor;\n",
       "        }\n",
       "    } else {\n",
       "        trace.line.color = trace._params.plotParams.color;\n",
       "        trace.hoverinfo = trace._params.plotParams.hoverinfo;\n",
       "\n",
       "        if (trace._params.cv_stddev_last) {\n",
       "            trace.fillcolor = trace._params.plotParams.fillcolor;\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.smoothFunc = function(origTrace, smoothedTrace) {\n",
       "    var data = origTrace.y,\n",
       "        smoothedPoints = this.smooth(data, this.getSmoothness()),\n",
       "        smoothedIndex = 0,\n",
       "        self = this;\n",
       "\n",
       "    if (smoothedPoints.length) {\n",
       "        data.forEach(function (d, index) {\n",
       "            if (!smoothedTrace.x[index]) {\n",
       "                smoothedTrace.x[index] = index;\n",
       "            }\n",
       "\n",
       "            var nameOfSet = smoothedTrace._params.nameOfSet;\n",
       "\n",
       "            if (smoothedTrace._params.cv_stddev_first || smoothedTrace._params.cv_stddev_last) {\n",
       "                nameOfSet = smoothedTrace._params.type + ' std';\n",
       "            }\n",
       "\n",
       "            smoothedTrace.y[index] = smoothedPoints[smoothedIndex];\n",
       "            smoothedTrace.hovertext[index] = nameOfSet + '`: ' + smoothedPoints[smoothedIndex].toPrecision(7);\n",
       "            if (self.hovertextParameters.length > index) {\n",
       "                smoothedTrace.hovertext[index] += self.hovertextParameters[index];\n",
       "            }\n",
       "            smoothedIndex++;\n",
       "        });\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.formatItemValue = function(value, index, suffix) {\n",
       "    if (typeof value === 'undefined') {\n",
       "        return '';\n",
       "    }\n",
       "\n",
       "    suffix = suffix || '';\n",
       "\n",
       "    return '<span title=\"' + suffix + 'value ' + value + '\">' + value + '</span>';\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.updateTraceBest = function(train, hash) {\n",
       "    var traces = this.filterTracesOne(hash.traces, {best_point: true}),\n",
       "        self = this;\n",
       "\n",
       "    traces.forEach(function(trace) {\n",
       "        var testTrace = self.filterTracesEvery(hash.traces, self.getTraceDefParams({\n",
       "                train: trace._params.train,\n",
       "                type: 'test',\n",
       "                indexOfSet: trace._params.indexOfSet\n",
       "            }));\n",
       "\n",
       "        if (self.hasCVMode) {\n",
       "            testTrace = self.filterTracesEvery(hash.traces, self.getTraceDefParams({\n",
       "                train: trace._params.train,\n",
       "                type: 'test',\n",
       "                cv_avg: true\n",
       "            }));\n",
       "        }\n",
       "\n",
       "        var bestValue = self.getBestValue(testTrace.length === 1 ? testTrace[0].y : []);\n",
       "\n",
       "        if (bestValue.index !== -1) {\n",
       "            trace.x[0] = bestValue.index;\n",
       "            trace.y[0] = bestValue.best;\n",
       "            trace.hovertext[0] = bestValue.func + ' (' + (self.hasCVMode ? 'avg' : trace._params.nameOfSet) + '): ' + bestValue.index + ' ' + bestValue.best;\n",
       "        }\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.updateTraceValues = function(name, hash, iteration, click) {\n",
       "    var id = 'catboost-serie-' + this.index + '-' + hash.index,\n",
       "        traces = {\n",
       "            learn: this.filterTracesEvery(hash.traces, this.getTraceDefParams({type: 'learn'})),\n",
       "            test: this.filterTracesEvery(hash.traces, this.getTraceDefParams({type: 'test'}))\n",
       "        },\n",
       "        path = hash.info.path,\n",
       "        self = this;\n",
       "\n",
       "    ['learn', 'test'].forEach(function(type) {\n",
       "        traces[type].forEach(function(trace) {\n",
       "            var data = trace.y || [],\n",
       "                index = typeof iteration !== 'undefined' && iteration < data.length - 1 ? iteration : data.length - 1,\n",
       "                value = data.length ? data[index] : undefined,\n",
       "                testTrace = self.filterTracesEvery(hash.traces, self.getTraceDefParams({\n",
       "                    type: 'test',\n",
       "                    indexOfSet: trace._params.indexOfSet\n",
       "                })),\n",
       "                bestValue = self.getBestValue(testTrace.length === 1 ? testTrace[0].y : []),\n",
       "                timeLeft = '',\n",
       "                timeSpend = '';\n",
       "\n",
       "            if (click || !self.clickMode) {\n",
       "                $('#' + id + ' .catboost-panel__serie_' + type + '_value[data-index=' + trace._params.indexOfSet + ']', self.layout)\n",
       "                    .html(self.formatItemValue(value, index, type + ' '));\n",
       "                $('#' + id + ' .catboost-panel__serie_iteration', self.layout).html(index);\n",
       "\n",
       "                if (self.timeLeft[path] && self.timeLeft[path][data.length - 1]) {\n",
       "                    timeLeft = self.timeLeft[path][data.length - 1][0];\n",
       "                }\n",
       "                $('#' + id + ' .catboost-panel__serie_time_left', self.layout).html(timeLeft ? ('~' + self.convertTime(timeLeft)) : '');\n",
       "\n",
       "                if (self.timeLeft[path] && self.timeLeft[path][index]) {\n",
       "                    timeSpend = self.timeLeft[path][index][1];\n",
       "                }\n",
       "\n",
       "                $('#' + id + ' .catboost-panel__serie_time_spend', self.layout).html(self.convertTime(timeSpend));\n",
       "                $('#' + id + ' .catboost-panel__serie_best_iteration', self.layout).html(bestValue.index > -1 ? bestValue.index : '');\n",
       "\n",
       "                $('#' + id + ' .catboost-panel__serie_best_test_value[data-index=' + trace._params.indexOfSet + ']', self.layout)\n",
       "                    .html(self.formatItemValue(bestValue.best, bestValue.index, 'best ' + trace._params.nameOfSet + ' '));\n",
       "            }\n",
       "        });\n",
       "    });\n",
       "\n",
       "    if (this.hasCVMode) {\n",
       "        var testTrace = this.filterTracesEvery(hash.traces, this.getTraceDefParams({\n",
       "                type: 'test',\n",
       "                cv_avg: true\n",
       "            })),\n",
       "            bestValue = this.getBestValue(testTrace.length === 1 ? testTrace[0].y : []);\n",
       "\n",
       "            $('#' + id + ' .catboost-panel__serie_best_iteration', this.layout).html(bestValue.index > -1 ? bestValue.index : '');\n",
       "    }\n",
       "\n",
       "    if (click) {\n",
       "        this.clickMode = true;\n",
       "\n",
       "        $('#catboost-control2-clickmode' + this.index, this.layout)[0].checked = true;\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.addTracesEvents = function() {\n",
       "    var self = this;\n",
       "\n",
       "    $('.catboost-panel__serie_checkbox', this.layout).click(function() {\n",
       "        var name = $(this).data('seriename');\n",
       "\n",
       "        self.layoutDisabled.traces[name] = !$(this)[0].checked;\n",
       "\n",
       "        self.redrawActiveChart();\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.getNextColor = function(path, opacity) {\n",
       "    var color;\n",
       "\n",
       "    if (this.colorsByPath[path]) {\n",
       "        color = this.colorsByPath[path];\n",
       "    } else {\n",
       "        color = this.colors[this.colorIndex];\n",
       "        this.colorsByPath[path] = color;\n",
       "\n",
       "        this.colorIndex++;\n",
       "\n",
       "        if (this.colorIndex > this.colors.length - 1) {\n",
       "            this.colorIndex = 0;\n",
       "        }\n",
       "    }\n",
       "\n",
       "    return this.hexToRgba(color, opacity);\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.hexToRgba = function(value, opacity) {\n",
       "    if (value.length < 6) {\n",
       "        var pattern = /^#?([a-f\\d])([a-f\\d])([a-f\\d])/i;\n",
       "        value = value.replace(pattern, function(m, r, g, b) {\n",
       "            return '#' + r + r + g + g + b + b;\n",
       "        });\n",
       "    }\n",
       "\n",
       "    var result = /^#?([a-f\\d]{2})([a-f\\d]{2})([a-f\\d]{2})/i.exec(value);\n",
       "    var rgb = {\n",
       "        r: parseInt(result[1], 16),\n",
       "        g: parseInt(result[2], 16),\n",
       "        b: parseInt(result[3], 16)\n",
       "    };\n",
       "\n",
       "    return 'rgba(' + rgb.r + ',' + rgb.g + ',' + rgb.b + ',' + opacity + ')';\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.convertTime = function(time) {\n",
       "    if (!time) {\n",
       "        return '0s';\n",
       "    }\n",
       "\n",
       "    time = Math.floor(time * 1000);\n",
       "\n",
       "    var millis = time % 1000;\n",
       "    time = parseInt(time / 1000, 10);\n",
       "    var seconds = time % 60;\n",
       "    time = parseInt(time / 60, 10);\n",
       "    var minutes = time % 60;\n",
       "    time = parseInt(time / 60, 10);\n",
       "    var hours = time % 24;\n",
       "    var out = \"\";\n",
       "    if (hours && hours > 0) {\n",
       "        out += hours + 'h ';\n",
       "        seconds = 0;\n",
       "        millis = 0;\n",
       "    }\n",
       "    if (minutes && minutes > 0) {\n",
       "        out += minutes + 'm ';\n",
       "        millis = 0;\n",
       "    }\n",
       "    if (seconds && seconds > 0) {\n",
       "        out += seconds + 's ';\n",
       "    }\n",
       "    if (millis && millis > 0) {\n",
       "        out += millis + 'ms';\n",
       "    }\n",
       "\n",
       "    return out.trim();\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.mean = function(values, valueof) {\n",
       "    var n = values.length,\n",
       "        m = n,\n",
       "        i = -1,\n",
       "        value,\n",
       "        sum = 0,\n",
       "        number = function(x) {\n",
       "            return x === null ? NaN : +x;\n",
       "        };\n",
       "\n",
       "    if (valueof === null) {\n",
       "        while (++i < n) {\n",
       "            if (!isNaN(value = number(values[i]))) {\n",
       "                sum += value;\n",
       "            } else {\n",
       "                --m;\n",
       "            }\n",
       "        }\n",
       "    } else {\n",
       "        while (++i < n) {\n",
       "            if (!isNaN(value = number(valueof(values[i], i, values)))) {\n",
       "                sum += value;\n",
       "            } else {\n",
       "                --m;\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "\n",
       "    if (m) {\n",
       "        return sum / m;\n",
       "    }\n",
       "};\n",
       "\n",
       "// from TensorBoard\n",
       "CatboostIpython.prototype.smooth = function(data, weight) {\n",
       "    // When increasing the smoothing window, it smoothes a lot with the first\n",
       "    // few points and then starts to gradually smooth slower, so using an\n",
       "    // exponential function makes the slider more consistent. 1000^x has a\n",
       "    // range of [1, 1000], so subtracting 1 and dividing by 999 results in a\n",
       "    // range of [0, 1], which can be used as the percentage of the data, so\n",
       "    // that the kernel size can be specified as a percentage instead of a\n",
       "    // hardcoded number, what would be bad with multiple series.\n",
       "    var factor = (Math.pow(1000, weight) - 1) / 999,\n",
       "        kernelRadius = Math.floor(data.length * factor / 2),\n",
       "        res = [],\n",
       "        self = this;\n",
       "\n",
       "    data.forEach(function (d, i) {\n",
       "        var actualKernelRadius = Math.min(kernelRadius, i, data.length - i - 1);\n",
       "        var start = i - actualKernelRadius;\n",
       "        var end = i + actualKernelRadius + 1;\n",
       "        var point = d;\n",
       "        // Only smooth finite numbers.\n",
       "        if (!isFinite(point)) {\n",
       "            res.push(point);\n",
       "        } else {\n",
       "            res.push(self.mean(data.slice(start, end).filter(function(d) {\n",
       "                return isFinite(d);\n",
       "            }), null));\n",
       "        }\n",
       "    });\n",
       "\n",
       "    return res;\n",
       "};\n",
       "var debug = false;\n",
       "\n",
       "if (debug) {\n",
       "    require.config({\n",
       "        shim:{\n",
       "            \"custom/CatboostIpythonPlotly\":{\n",
       "                deps:[\"custom/plotly-basic.min\"]\n",
       "            }\n",
       "        }\n",
       "    })\n",
       "\n",
       "    require.undef('catboost_module');\n",
       "    require.undef('custom/CatboostIpythonPlotly');\n",
       "}\n",
       "\n",
       "var moduleBase = '@jupyter-widgets/base';\n",
       "var modules = [moduleBase];\n",
       "\n",
       "if (debug) {\n",
       "    modules.push('custom/CatboostIpythonPlotly');\n",
       "}\n",
       "\n",
       "define('catboost_module', modules, function(widgets) {\n",
       "    var getInstance = function(el) {\n",
       "            var id = $(el).attr('catboost-id');\n",
       "\n",
       "            if (!id) {\n",
       "                return null;\n",
       "            }\n",
       "\n",
       "            id = id.replace('catboost_', '');\n",
       "\n",
       "            if (!window.catboostIpythonInstances[id]) {\n",
       "                return null;\n",
       "            }\n",
       "\n",
       "            return window.catboostIpythonInstances[id];\n",
       "        },\n",
       "        addInstance = function(el) {\n",
       "            $(el).attr('catboost-id', 'catboost_' + window.catboostIpythonIndex);\n",
       "\n",
       "            var catboostIpython = new CatboostIpython();\n",
       "            catboostIpython.index = window.catboostIpythonIndex;\n",
       "            catboostIpython.plotly = window.Plotly;\n",
       "            if (debug) {\n",
       "                catboostIpython.loadStyles('/custom/CatboostIpython.css', function(){catboostIpython.resizeCharts();})\n",
       "            }\n",
       "\n",
       "            window.catboostIpythonInstances[window.catboostIpythonIndex] = catboostIpython;\n",
       "\n",
       "            window.catboostIpythonIndex++;\n",
       "\n",
       "            return catboostIpython;\n",
       "        };\n",
       "\n",
       "    var CatboostIpythonWidget = widgets.DOMWidgetView.extend({\n",
       "\n",
       "        initialize: function() {\n",
       "            CatboostIpythonWidget.__super__.initialize.apply(this, arguments);\n",
       "\n",
       "            if (!window.catboostIpythonInstances) {\n",
       "                window.catboostIpythonInstances = {};\n",
       "            }\n",
       "\n",
       "            if (typeof window.catboostIpythonIndex === 'undefined') {\n",
       "                window.catboostIpythonIndex = 0;\n",
       "            }\n",
       "\n",
       "            var catboostIpythonInstance = getInstance(this.el);\n",
       "\n",
       "            if (!catboostIpythonInstance) {\n",
       "                catboostIpythonInstance = addInstance(this.el);\n",
       "            }\n",
       "\n",
       "            catboostIpythonInstance.init();\n",
       "        },\n",
       "\n",
       "        render: function() {\n",
       "            this.value_changed();\n",
       "            this.model.on('change:value', this.value_changed, this);\n",
       "        },\n",
       "\n",
       "        update: function() {\n",
       "            this.value_changed();\n",
       "        },\n",
       "\n",
       "        value_changed: function() {\n",
       "            this.el.style['width'] = this.model.get('width');\n",
       "            this.el.style['height'] = this.model.get('height');\n",
       "            this.displayed.then(_.bind(this.render_charts, this));\n",
       "        },\n",
       "\n",
       "        process_all: function(parent, params) {\n",
       "            var data = params.data;\n",
       "\n",
       "            for (var path in data) {\n",
       "                if (data.hasOwnProperty(path)) {\n",
       "                    this.process_row(parent, data[path])\n",
       "                }\n",
       "            }\n",
       "        },\n",
       "\n",
       "        process_row: function(parent, data) {\n",
       "            var catboostIpython = getInstance(parent),\n",
       "                path = data.path,\n",
       "                content = data.content,\n",
       "                items = content.data.iterations,\n",
       "                firstIndex = 0,\n",
       "                chunks = [];\n",
       "\n",
       "            if (!items || !items.length) {\n",
       "                return;\n",
       "            }\n",
       "\n",
       "            if (!catboostIpython.lastIndex) {\n",
       "                catboostIpython.lastIndex = {}\n",
       "            }\n",
       "\n",
       "            if (catboostIpython.lastIndex[path]) {\n",
       "                firstIndex = catboostIpython.lastIndex[path] + 1;\n",
       "            }\n",
       "\n",
       "            catboostIpython.lastIndex[path] = items.length - 1;\n",
       "\n",
       "            for (var i = firstIndex; i < items.length; i++) {\n",
       "                chunks.push(items[i]);\n",
       "            }\n",
       "\n",
       "            catboostIpython.addMeta(data.path, content.data.meta);\n",
       "\n",
       "            catboostIpython.addPoints(parent, {\n",
       "                chunks: chunks,\n",
       "                train: data.name,\n",
       "                path: data.path\n",
       "            });\n",
       "        },\n",
       "\n",
       "        render_charts: function () {\n",
       "            this.process_all(this.el, {\n",
       "                data: this.model.get('data')\n",
       "            });\n",
       "\n",
       "            return this;\n",
       "        }\n",
       "    });\n",
       "\n",
       "    return {\n",
       "        CatboostIpythonWidgetView: CatboostIpythonWidget\n",
       "    };\n",
       "});\n",
       "\n",
       "            </script>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "099fc96efe35452cb4e58964fd677667",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "MetricVisualizer(layout=Layout(align_self='stretch', height='500px'))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Count of trees in model = 115\n"
     ]
    }
   ],
   "source": [
    "# Initialize CatBoostClassifier\n",
    "model = CatBoostClassifier(random_state=10, \n",
    "                           early_stopping_rounds=50, \n",
    "                           task_type='GPU',\n",
    "                           devices=\"0\"\n",
    "                          )\n",
    "\n",
    "model.fit(train_dataset,\n",
    "          use_best_model=True,\n",
    "          eval_set=valid_dataset, \n",
    "          verbose=False,\n",
    "          plot=True)\n",
    "print(\"Count of trees in model = {}\".format(model.tree_count_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8622846987738766"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_auc_score(test_dataset.get_label(), \n",
    "              model.predict_proba(test_dataset)[:,1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nice, we get a little bit better than just using the scores from pre-trained model. (86% vs 85% AUC). \n",
    "Below are the results on Hotel reviews data (33% happy) if we use different layers as feature extractor. We can see that the inner layers (which have more generic representations perform better as features for catboost model)\n",
    "- sequencelast0 = 86.09\n",
    "- dense0 = 85.87\n",
    "- dense1 = 85.32\n",
    "- dense2 = 85.05\n",
    "- output = 85.01\n",
    "- generic - 85.01 (directly using scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f040d73a438>]"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(['generic', 'output', 'dense2', 'dense1', 'dense0', 'sequencelast0'],\n",
    "        [85.01, 85.01, 85.05, 85.32, 85.87, 86.09])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Weights as initializations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method Block.summary of CustomSeqNet(\n",
       "  (embed): Embedding(63167 -> 64, float32)\n",
       "  (rnn): GRU(64 -> 50, NTC, bidirectional)\n",
       "  (dense_0): Dense(100 -> 100, linear)\n",
       "  (bn_dense_0): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=100)\n",
       "  (activation_dense_0): Activation(relu)\n",
       "  (drop_dense_0): Dropout(p = 0.2, axes=())\n",
       "  (dense_1): Dense(100 -> 100, linear)\n",
       "  (bn_dense_1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=100)\n",
       "  (activation_dense_1): Activation(relu)\n",
       "  (drop_dense_1): Dropout(p = 0.2, axes=())\n",
       "  (dense_2): Dense(100 -> 10, linear)\n",
       "  (bn_dense_2): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=10)\n",
       "  (activation_dense_2): Activation(relu)\n",
       "  (drop_dense_2): Dropout(p = 0.2, axes=())\n",
       "  (output): Dense(10 -> 1, linear)\n",
       ")>"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "net1.summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CustomSeqNet_ (\n",
       "  Parameter CustomSeqNet_token_embed_weight (shape=(63167, 64), dtype=float32)\n",
       "  Parameter CustomSeqNet_review_gru_l0_i2h_weight (shape=(150, 64), dtype=float32)\n",
       "  Parameter CustomSeqNet_review_gru_l0_h2h_weight (shape=(150, 50), dtype=float32)\n",
       "  Parameter CustomSeqNet_review_gru_l0_i2h_bias (shape=(150,), dtype=float32)\n",
       "  Parameter CustomSeqNet_review_gru_l0_h2h_bias (shape=(150,), dtype=float32)\n",
       "  Parameter CustomSeqNet_review_gru_r0_i2h_weight (shape=(150, 64), dtype=float32)\n",
       "  Parameter CustomSeqNet_review_gru_r0_h2h_weight (shape=(150, 50), dtype=float32)\n",
       "  Parameter CustomSeqNet_review_gru_r0_i2h_bias (shape=(150,), dtype=float32)\n",
       "  Parameter CustomSeqNet_review_gru_r0_h2h_bias (shape=(150,), dtype=float32)\n",
       "  Parameter CustomSeqNet_dense0_weight (shape=(100, 100), dtype=float32)\n",
       "  Parameter CustomSeqNet_dense0_bias (shape=(100,), dtype=float32)\n",
       "  Parameter CustomSeqNet_batchnorm0_gamma (shape=(100,), dtype=<class 'numpy.float32'>)\n",
       "  Parameter CustomSeqNet_batchnorm0_beta (shape=(100,), dtype=<class 'numpy.float32'>)\n",
       "  Parameter CustomSeqNet_batchnorm0_running_mean (shape=(100,), dtype=<class 'numpy.float32'>)\n",
       "  Parameter CustomSeqNet_batchnorm0_running_var (shape=(100,), dtype=<class 'numpy.float32'>)\n",
       "  Parameter CustomSeqNet_dense1_weight (shape=(100, 100), dtype=float32)\n",
       "  Parameter CustomSeqNet_dense1_bias (shape=(100,), dtype=float32)\n",
       "  Parameter CustomSeqNet_batchnorm1_gamma (shape=(100,), dtype=<class 'numpy.float32'>)\n",
       "  Parameter CustomSeqNet_batchnorm1_beta (shape=(100,), dtype=<class 'numpy.float32'>)\n",
       "  Parameter CustomSeqNet_batchnorm1_running_mean (shape=(100,), dtype=<class 'numpy.float32'>)\n",
       "  Parameter CustomSeqNet_batchnorm1_running_var (shape=(100,), dtype=<class 'numpy.float32'>)\n",
       "  Parameter CustomSeqNet_dense2_weight (shape=(10, 100), dtype=float32)\n",
       "  Parameter CustomSeqNet_dense2_bias (shape=(10,), dtype=float32)\n",
       "  Parameter CustomSeqNet_batchnorm2_gamma (shape=(10,), dtype=<class 'numpy.float32'>)\n",
       "  Parameter CustomSeqNet_batchnorm2_beta (shape=(10,), dtype=<class 'numpy.float32'>)\n",
       "  Parameter CustomSeqNet_batchnorm2_running_mean (shape=(10,), dtype=<class 'numpy.float32'>)\n",
       "  Parameter CustomSeqNet_batchnorm2_running_var (shape=(10,), dtype=<class 'numpy.float32'>)\n",
       "  Parameter CustomSeqNet_output_weight (shape=(1, 10), dtype=float32)\n",
       "  Parameter CustomSeqNet_output_bias (shape=(1,), dtype=float32)\n",
       ")"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "net1.collect_params()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Check results\n",
    "# print(trainer.network.collect_params('.*email_alias'))  # index\n",
    "# print(list(net1.collect_params('.*review|.*emb').values())[0].data())\n",
    "# print(list(net1.collect_params('.*dense').values())[0].data())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Freeze layers except last dense layer\n",
    "for param in net1.collect_params('.*review|.*emb').values():\n",
    "    param.grad_req = 'null'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = get_dataset(transformer.transform(hotel_X_train), \n",
    "                      hotel_y_train,\n",
    "                      max_len)\n",
    "hotel_train_dataloader = get_dataloader(dataset, \n",
    "                            batch_size=64, \n",
    "                            bucket_num=5,\n",
    "                            shuffle=False, \n",
    "                            num_workers=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = get_dataset(transformer.transform(hotel_X_valid), \n",
    "                      hotel_y_valid,\n",
    "                      max_len)\n",
    "hotel_valid_dataloader = get_dataloader(dataset, \n",
    "                            batch_size=64, \n",
    "                            bucket_num=5,\n",
    "                            shuffle=False, \n",
    "                            num_workers=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = get_dataset(transformer.transform(hotel_X_test), \n",
    "                      hotel_y_test,\n",
    "                      max_len)\n",
    "hotel_test_dataloader = get_dataloader(dataset, \n",
    "                            batch_size=64, \n",
    "                            bucket_num=5,\n",
    "                            shuffle=False, \n",
    "                            num_workers=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Start \n",
      " Training BCE 0.5487, Train AUC 0.8593, Valid AUC 0.8510\n",
      "Epoch [1], Training BCE 0.5384, Train AUC 0.8627, Valid AUC 0.8550\n",
      "Epoch [2], Training BCE 0.4720, Train AUC 0.8632, Valid AUC 0.8560\n",
      "Epoch [3], Training BCE 0.5595, Train AUC 0.8642, Valid AUC 0.8558\n",
      "CPU times: user 53.7 s, sys: 5.26 s, total: 59 s\n",
      "Wall time: 49.8 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "train(\n",
    "    net1, \n",
    "    hotel_train_dataloader,\n",
    "    hotel_valid_dataloader,\n",
    "    loss,\n",
    "    epochs=3,\n",
    "    lr=1e-2, \n",
    "    wd=wd,\n",
    "    optimizer=optimizer,\n",
    "    ctx=ctx\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The AUC in the very first epoch is high, i.e. 84.6%. Learning the final layers first, which require more learning as final layers are more specific to data at hand (as compared to the inner layers). After learning on outer dense layers we can learn the overall network including inner layers to achieve better performance. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Unfreeze\n",
    "for param in net1.collect_params('.*review|.*emb').values():\n",
    "    param.grad_req = 'write'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Start \n",
      " Training BCE 0.5595, Train AUC 0.8642, Valid AUC 0.8558\n",
      "Epoch [1], Training BCE 0.3158, Train AUC 0.9563, Valid AUC 0.9266\n",
      "Epoch [2], Training BCE 0.2471, Train AUC 0.9663, Valid AUC 0.9302\n",
      "Epoch [3], Training BCE 0.2529, Train AUC 0.9723, Valid AUC 0.9292\n",
      "CPU times: user 1min 28s, sys: 14.6 s, total: 1min 43s\n",
      "Wall time: 1min 29s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "train(\n",
    "    net1, \n",
    "    hotel_train_dataloader,\n",
    "    hotel_valid_dataloader,\n",
    "    loss,\n",
    "    epochs=3,\n",
    "    lr=3e-3, \n",
    "    wd=wd,\n",
    "    optimizer=optimizer,\n",
    "    ctx=ctx\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "_, auc_test, _, _ = evaluate_network(net1, loss, hotel_test_dataloader, ctx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9396677948001941"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "auc_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "AUC reached 94% on test data which is pretty great. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## End"
   ]
  }
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