602_Project_IMDB_Movie_Review.ipynb

{
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   "source": [
    "### Welcome to DATA 602: Introduction to Data Analysis and Machine Learning Spring 2019 Term Project\n",
    "#### My experiments with word2vec among other things...\n",
    "\n",
    "We are going to use the following datasets:\n",
    "* [IMDB Movie Review Dataset](https://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz)\n",
    "\n",
    "Below, we install and import required libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Installing collected packages: boto, jmespath, docutils, botocore, s3transfer, boto3, smart-open, gensim, grpcio, mock, absl-py, tensorflow-estimator, werkzeug, markdown, tensorboard, termcolor, astor, gast, keras-applications, keras-preprocessing, tensorflow, wordcloud\n",
      "Successfully installed absl-py-0.7.1 astor-0.7.1 boto-2.49.0 boto3-1.9.143 botocore-1.12.143 docutils-0.14 gast-0.2.2 gensim-3.7.2 grpcio-1.20.1 jmespath-0.9.4 keras-applications-1.0.7 keras-preprocessing-1.0.9 markdown-3.1 mock-3.0.4 s3transfer-0.2.0 smart-open-1.8.3 tensorboard-1.13.1 tensorflow-1.13.1 tensorflow-estimator-1.13.0 termcolor-1.1.0 werkzeug-0.15.2 wordcloud-1.5.0\n",
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      "Successfully installed nltk-3.4.1\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to /home/jovyan/nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n",
      "[nltk_data] Downloading package punkt to /home/jovyan/nltk_data...\n",
      "[nltk_data]   Package punkt is already up-to-date!\n",
      "WARNING: Logging before flag parsing goes to stderr.\n",
      "W0507 14:41:33.977085 140072427317056 __init__.py:56] Some hub symbols are not available because TensorFlow version is less than 1.14\n",
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "!pip install gensim tensorflow wordcloud\n",
    "!pip install -q tensorflow-hub\n",
    "!pip install xgboost\n",
    "!pip install keras\n",
    "!pip install nltk\n",
    "!pip install string\n",
    "!pip install tqdm\n",
    "\n",
    "import gzip\n",
    "import gensim\n",
    "import os\n",
    "import sys\n",
    "import json\n",
    "import shutil\n",
    "import time\n",
    "import re\n",
    "import tarfile\n",
    "import zipfile\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import collections\n",
    "import math\n",
    "import random\n",
    "import datetime as dt\n",
    "\n",
    "import matplotlib.pyplot as plt \n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "from wordcloud import WordCloud, STOPWORDS, ImageColorGenerator\n",
    "\n",
    "import string\n",
    "import nltk\n",
    "nltk.download('stopwords')\n",
    "from nltk.corpus import stopwords\n",
    "nltk.download('punkt')\n",
    "from nltk.tokenize import word_tokenize\n",
    "\n",
    "from gensim.test.utils import get_tmpfile\n",
    "from gensim.models import Word2Vec, FastText\n",
    "\n",
    "from tqdm import tqdm\n",
    "tqdm.pandas(desc=\"progress-bar\")\n",
    "from gensim.models import Doc2Vec\n",
    "from gensim.models.doc2vec import LabeledSentence\n",
    "import multiprocessing\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.svm import SVC\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.manifold import TSNE\n",
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "\n",
    "import tensorflow as tf\n",
    "import tensorflow_hub as hub\n",
    "\n",
    "from keras.preprocessing.text import Tokenizer\n",
    "from keras.preprocessing.sequence import pad_sequences\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Embedding,Dense,Flatten,GlobalMaxPooling1D,LSTM,Dropout, Activation,Bidirectional\n",
    "from keras.layers.convolutional import Conv1D,MaxPooling1D \n",
    "from keras.optimizers import Adam\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import confusion_matrix, classification_report, accuracy_score, roc_curve, precision_recall_curve \n",
    "from sklearn import utils\n",
    "\n",
    "\"\"\"\n",
    "We will ignore FutureWarning and DeprecationWarning\n",
    "\"\"\"\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\", category=FutureWarning)\n",
    "warnings.filterwarnings(\"ignore\", category=DeprecationWarning)\n",
    "\n",
    "\"\"\"\n",
    "We will ignore warnings\n",
    "\"\"\"\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "if not sys.warnoptions:\n",
    "    warnings.simplefilter(\"ignore\")\n",
    "program_start_time=time.time()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Perform some housekeeping tasks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_source_url = 'https://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz'\n",
    "datasets = '/datasets/'\n",
    "data_file_path = os.getcwd()+datasets+'aclImdb_v1.tar.gz'\n",
    "data = 'data_'\n",
    "MOVIE = 'movie'\n",
    "data_folder = os.getcwd()+datasets+data+MOVIE\n",
    "data_json_file = data+MOVIE+'.json'\n",
    "\n",
    "def check_if_file_exists(file):\n",
    "    '''\n",
    "    Checks if 'file' exists\n",
    "    '''\n",
    "    try:\n",
    "        fh = open(file, 'r')\n",
    "        return True\n",
    "    except FileNotFoundError:\n",
    "        print('Please make sure file: ' + file + ' is present before continuing')\n",
    "        return False\n",
    "\n",
    "def check_if_dir_exists(directory):\n",
    "    '''\n",
    "    Checks if 'directory' exists\n",
    "    '''\n",
    "    return(os.path.isdir(directory))\n",
    "\n",
    "def store_json(write_this_data):\n",
    "    '''\n",
    "    Store json if we are processing the first time\n",
    "    '''\n",
    "    open(data_json_file, 'w').write(json.dumps(write_this_data))\n",
    "\n",
    "def return_data_json():\n",
    "    '''\n",
    "    Return data json from data json file\n",
    "    '''\n",
    "    with open(data_json_file, encoding='utf-8') as data_file:\n",
    "        return json.loads(data_file.read())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Download data source**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data file already exists. Not downloading again!\n"
     ]
    }
   ],
   "source": [
    "if not check_if_file_exists(data_file_path):\n",
    "    print('Start of data download')\n",
    "    wget.download(data_source_url, os.getcwd()+datasets)\n",
    "    print('Download complete')\n",
    "else:\n",
    "    print('Data file already exists. Not downloading again!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data foler exists. Won't copy again!\n"
     ]
    }
   ],
   "source": [
    "if not check_if_dir_exists(data_folder):\n",
    "    with tarfile.open(data_file_path) as tar:\n",
    "        tar.extractall(path=data_folder)\n",
    "else:\n",
    "    print('Data foler exists. Won\\'t copy again!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = {}\n",
    "all_reviews = []\n",
    "if not check_if_file_exists(data_json_file):\n",
    "    dirpath = data_folder+\"/aclImdb/train/pos/\"\n",
    "    for root, dirs, files in os.walk(dirpath):\n",
    "        for name in files:\n",
    "            full = os.path.join(root, name)\n",
    "            with open(full, 'r') as f:\n",
    "                string = f.read()\n",
    "                file_content = {\"review\": string,\"rating\": 1}\n",
    "                all_reviews.append(file_content)\n",
    "    dirpath = data_folder+\"/aclImdb/train/neg/\"\n",
    "    for root, dirs, files in os.walk(dirpath):\n",
    "        for name in files:\n",
    "            full = os.path.join(root, name)\n",
    "            with open(full, 'r') as f:\n",
    "                string = f.read()\n",
    "                file_content = {\"review\": string,\"rating\": 0}\n",
    "                all_reviews.append(file_content)\n",
    "    dirpath = data_folder+\"/aclImdb/test/pos/\"\n",
    "    for root, dirs, files in os.walk(dirpath):\n",
    "        for name in files:\n",
    "            full = os.path.join(root, name)\n",
    "            with open(full, 'r') as f:\n",
    "                string = f.read()\n",
    "                file_content = {\"review\": string,\"rating\": 1}\n",
    "                all_reviews.append(file_content)\n",
    "    dirpath = data_folder+\"/aclImdb/test/neg/\"\n",
    "    for root, dirs, files in os.walk(dirpath):\n",
    "        for name in files:\n",
    "            full = os.path.join(root, name)\n",
    "            with open(full, 'r') as f:\n",
    "                string = f.read()\n",
    "                file_content = {\"review\": string,\"rating\": 0}\n",
    "                all_reviews.append(file_content)\n",
    "    data[MOVIE] = all_reviews\n",
    "    store_json(data)\n",
    "pre_loaded_data = return_data_json()\n",
    "len(pre_loaded_data[MOVIE])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Creating review dataframe and Data clean up**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before Cleanup : Shape of the Data Frame : (50000, 2)\n",
      "Remove missing values.\n",
      "Drop columns with duplicate data.\n",
      "After Cleanup : Shape of the Data Frame : (50000, 2)\n",
      "Counting null data per column.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "rating    0\n",
       "review    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_df = pd.DataFrame(pre_loaded_data[MOVIE])\n",
    "print('Before Cleanup : Shape of the Data Frame : {}'.format(movie_df.shape))\n",
    "print('Remove missing values.')\n",
    "movie_df.dropna(inplace=True)\n",
    "movie_df.reset_index(drop=True,inplace=True)\n",
    "print('Drop columns with duplicate data.')\n",
    "movie_df.drop_duplicates()\n",
    "print('After Cleanup : Shape of the Data Frame : {}'.format(movie_df.shape))\n",
    "print('Counting null data per column.')\n",
    "movie_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* **The dataframe contains 50000 rows and 2 columns**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Let us look at the data types of columns**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "rating     int64\n",
       "review    object\n",
       "dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "Ratings\n",
    "\"\"\"\n",
    "movie_df.rating.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Let us explore the data a bit using head(), tail(), info(), describe()**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rating</th>\n",
       "      <th>review</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>This has got to be one of my very favorite Twi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>\"8 SIMPLE RULES... FOR DATING MY TEENAGE DAUGH...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>Famous was \"famous\" for their tension and rele...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>I've noticed that a lot of people who post on ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>Ya. That is what I think. Sure it was still a ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   rating                                             review\n",
       "0       1  This has got to be one of my very favorite Twi...\n",
       "1       1  \"8 SIMPLE RULES... FOR DATING MY TEENAGE DAUGH...\n",
       "2       1  Famous was \"famous\" for their tension and rele...\n",
       "3       1  I've noticed that a lot of people who post on ...\n",
       "4       1  Ya. That is what I think. Sure it was still a ..."
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rating</th>\n",
       "      <th>review</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>49995</th>\n",
       "      <td>0</td>\n",
       "      <td>This is a standard action flick as we have see...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49996</th>\n",
       "      <td>0</td>\n",
       "      <td>... what a porn movie would look like if you t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49997</th>\n",
       "      <td>0</td>\n",
       "      <td>Kudos to Baxley's DP for making this look like...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49998</th>\n",
       "      <td>0</td>\n",
       "      <td>Once a month, I invite a few friends over for ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49999</th>\n",
       "      <td>0</td>\n",
       "      <td>I happened to catch this movie on late night T...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       rating                                             review\n",
       "49995       0  This is a standard action flick as we have see...\n",
       "49996       0  ... what a porn movie would look like if you t...\n",
       "49997       0  Kudos to Baxley's DP for making this look like...\n",
       "49998       0  Once a month, I invite a few friends over for ...\n",
       "49999       0  I happened to catch this movie on late night T..."
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 50000 entries, 0 to 49999\n",
      "Data columns (total 2 columns):\n",
      "rating    50000 non-null int64\n",
      "review    50000 non-null object\n",
      "dtypes: int64(1), object(1)\n",
      "memory usage: 781.3+ KB\n"
     ]
    }
   ],
   "source": [
    "movie_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>50000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.500005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             rating\n",
       "count  50000.000000\n",
       "mean       0.500000\n",
       "std        0.500005\n",
       "min        0.000000\n",
       "25%        0.000000\n",
       "50%        0.500000\n",
       "75%        1.000000\n",
       "max        1.000000"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>review</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>50000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>49582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>Loved today's show!!! It was a variety and not...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   review\n",
       "count                                               50000\n",
       "unique                                              49582\n",
       "top     Loved today's show!!! It was a variety and not...\n",
       "freq                                                    5"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_df.describe(include='object')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rating</th>\n",
       "      <th>review</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>50000.000000</td>\n",
       "      <td>50000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>NaN</td>\n",
       "      <td>49582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Loved today's show!!! It was a variety and not...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.500000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.500005</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.500000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              rating                                             review\n",
       "count   50000.000000                                              50000\n",
       "unique           NaN                                              49582\n",
       "top              NaN  Loved today's show!!! It was a variety and not...\n",
       "freq             NaN                                                  5\n",
       "mean        0.500000                                                NaN\n",
       "std         0.500005                                                NaN\n",
       "min         0.000000                                                NaN\n",
       "25%         0.000000                                                NaN\n",
       "50%         0.500000                                                NaN\n",
       "75%         1.000000                                                NaN\n",
       "max         1.000000                                                NaN"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_df.describe(include='all')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Creating a new column called \"review_length\" which is the length of the review column.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>rating</th>\n",
       "      <th>review</th>\n",
       "      <th>review_length</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>This has got to be one of my very favorite Twi...</td>\n",
       "      <td>665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>\"8 SIMPLE RULES... FOR DATING MY TEENAGE DAUGH...</td>\n",
       "      <td>712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>Famous was \"famous\" for their tension and rele...</td>\n",
       "      <td>908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>I've noticed that a lot of people who post on ...</td>\n",
       "      <td>739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>Ya. That is what I think. Sure it was still a ...</td>\n",
       "      <td>720</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   rating                                             review  review_length\n",
       "0       1  This has got to be one of my very favorite Twi...            665\n",
       "1       1  \"8 SIMPLE RULES... FOR DATING MY TEENAGE DAUGH...            712\n",
       "2       1  Famous was \"famous\" for their tension and rele...            908\n",
       "3       1  I've noticed that a lot of people who post on ...            739\n",
       "4       1  Ya. That is what I think. Sure it was still a ...            720"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_df['review_length'] = movie_df['review'].apply(len)\n",
    "movie_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **Data Visualization and Exploratory Data Analysis**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Using FacetGrid from the seaborn library to create a grid of two histograms of review_length based off of the ratings**\n",
    "\n",
    "[FacetGrid reference](https://seaborn.pydata.org/generated/seaborn.FacetGrid.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set_style('darkgrid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.FacetGrid(movie_df,col='rating',size=5)\n",
    "g.map(plt.hist,'review_length',bins=50)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's try to explain why the x-axis goes all the way to 14000ish, this must mean that there is some really long message!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    50000.000000\n",
       "mean      1309.431020\n",
       "std        989.728014\n",
       "min         32.000000\n",
       "25%        699.000000\n",
       "50%        970.000000\n",
       "75%       1590.250000\n",
       "max      13704.000000\n",
       "Name: review_length, dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_df.review_length.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Max review_length 13704.000000 means review is 13704 character long"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Creating a boxplot of review_length for each rating category.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,8))\n",
    "sns.boxplot(x='rating',y='review_length',data=movie_df,palette='rainbow')\n",
    "plt.title(\"Boxplot of review length for each rating category.\",fontsize=16)\n",
    "plt.xlabel(\"Rating\",fontsize=14)\n",
    "plt.ylabel(\"Review Length\",fontsize=14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Looking at the box and whisker plots for the review_length in words, we can see an exponential distribution. We can observe that the mass of the distribution can possibly be covered with 1400 to 1500 words."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Creating a countplot of the number of occurrences for each type of rating.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,8))\n",
    "sns.countplot(x='rating',data=movie_df,palette='winter')\n",
    "plt.title(\"Number of occurrences for each type of rating\",fontsize=16)\n",
    "plt.xlabel(\"Rating\",fontsize=14)\n",
    "plt.ylabel(\"Number of occurrences\",fontsize=14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Pre-processing of review text**\n",
    "\n",
    "[Regex reference](https://stackoverflow.com/questions/9662346/python-code-to-remove-html-tags-from-a-string/9662410)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def review_preprocess(review):\n",
    "    \"\"\"\n",
    "    Takes in a string of review, then performs the following:\n",
    "    1. Remove HTML tag from review\n",
    "    2. Remove URLs from review\n",
    "    3. Make entire review lowercase\n",
    "    4. Split the review in words\n",
    "    5. Remove all punctuation\n",
    "    6. Remove empty strings from review\n",
    "    7. Remove all stopwords\n",
    "    8. Returns a list of the cleaned review after jioning them back to a sentence\n",
    "    \"\"\"\n",
    "    en_stops = set(stopwords.words('english'))\n",
    "    \n",
    "    \"\"\"\n",
    "    Removing HTML tag from review\n",
    "    \"\"\"\n",
    "    clean = re.compile('<.*?>')\n",
    "    review_without_tag = re.sub(clean, '', review) \n",
    "    \n",
    "    \n",
    "    \"\"\"\n",
    "    Removing URLs\n",
    "    \"\"\"\n",
    "    review_without_tag_and_url = re.sub(r\"http\\S+\", \"\", review_without_tag)\n",
    "    \n",
    "    review_without_tag_and_url = re.sub(r\"www\\S+\", \"\", review_without_tag)\n",
    "    \n",
    "    \"\"\"\n",
    "    Make entire string lowercase\n",
    "    \"\"\"\n",
    "    review_lowercase = review_without_tag_and_url.lower()\n",
    "    \n",
    "    \"\"\"\n",
    "    Split string into words\n",
    "    \"\"\"\n",
    "    list_of_words = word_tokenize(review_lowercase)\n",
    "    \n",
    "    \n",
    "    \"\"\"\n",
    "    Remove punctuation\n",
    "    Checking characters to see if they are in punctuation\n",
    "    \"\"\"\n",
    "\n",
    "    list_of_words_without_punctuation=[''.join(this_char for this_char in this_string if (this_char in string.ascii_lowercase))for this_string in list_of_words]\n",
    "     \n",
    "    \n",
    "    \"\"\"\n",
    "    Remove empty strings\n",
    "    \"\"\"\n",
    "    list_of_words_without_punctuation = list(filter(None, list_of_words_without_punctuation))\n",
    "    \n",
    "    \n",
    "    \"\"\"\n",
    "    Remove any stopwords\n",
    "    \"\"\"\n",
    "  \n",
    "    filtered_word_list = [w for w in list_of_words_without_punctuation if w not in en_stops] \n",
    "    \n",
    "    \"\"\"\n",
    "    Returns a list of the cleaned review after jioning them back to a sentence\n",
    "    \"\"\"\n",
    "    return ' '.join(filtered_word_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "49995    This is a standard action flick as we have see...\n",
       "49996    ... what a porn movie would look like if you t...\n",
       "49997    Kudos to Baxley's DP for making this look like...\n",
       "49998    Once a month, I invite a few friends over for ...\n",
       "49999    I happened to catch this movie on late night T...\n",
       "Name: review, dtype: object"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "Here is the original reviews:\n",
    "\"\"\"\n",
    "movie_df['review'].tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Applying pre-processing to reviews**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Elapsed time for review preprocessing :  2.637855299313863  in minutes\n"
     ]
    }
   ],
   "source": [
    "start_time=time.time()\n",
    "movie_df['review']=movie_df['review'].apply(review_preprocess)\n",
    "print('Elapsed time for review preprocessing : ',((time.time()-start_time)/60),' in minutes')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "49995    standard action flick seen many times much act...\n",
       "49996    porn movie would look like took sex left bad d...\n",
       "49997    kudos baxley dp making look like real movie fi...\n",
       "49998    month invite friends retarded movie night look...\n",
       "49999    happened catch movie late night tv saw opening...\n",
       "Name: review, dtype: object"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "Here is the reviews after preprocessing :\n",
    "\"\"\"\n",
    "movie_df['review'].tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The term frequency distribution of words in the review is obtained using nltk.FreqDist(). This provides us a rough idea of the main topic in the review dataset.\n",
    "\n",
    "[FreqDist reference](http://www.nltk.org/api/nltk.html?highlight=freqdist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of vocabulary : 180605\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['movie',\n",
       " 'film',\n",
       " 'nt',\n",
       " 'one',\n",
       " 'like',\n",
       " 'good',\n",
       " 'would',\n",
       " 'even',\n",
       " 'time',\n",
       " 'really',\n",
       " 'see',\n",
       " 'story',\n",
       " 'much',\n",
       " 'well',\n",
       " 'could',\n",
       " 'get',\n",
       " 'people',\n",
       " 'great',\n",
       " 'bad',\n",
       " 'also',\n",
       " 'first',\n",
       " 'made',\n",
       " 'make',\n",
       " 'way',\n",
       " 'movies',\n",
       " 'think',\n",
       " 'characters',\n",
       " 'character',\n",
       " 'watch',\n",
       " 'films',\n",
       " 'many',\n",
       " 'seen',\n",
       " 'two',\n",
       " 'never',\n",
       " 'love',\n",
       " 'acting',\n",
       " 'plot',\n",
       " 'best',\n",
       " 'show',\n",
       " 'know',\n",
       " 'little',\n",
       " 'life',\n",
       " 'ever',\n",
       " 'better',\n",
       " 'man',\n",
       " 'say',\n",
       " 'still',\n",
       " 'scene',\n",
       " 'end',\n",
       " 'scenes']"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reviews = movie_df['review'].str.cat(sep=' ')\n",
    "\n",
    "\"\"\"\n",
    "function to split review into word\n",
    "\"\"\"\n",
    "tokens = word_tokenize(reviews)\n",
    "\n",
    "vocabulary = set(tokens)\n",
    "print('Number of vocabulary : {}'.format(len(vocabulary)))\n",
    "\n",
    "frequency_distribution = nltk.FreqDist(tokens)\n",
    "sorted(frequency_distribution,key=frequency_distribution.__getitem__, reverse=True)[0:50]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Wordcloud visualization of frequent words\n",
    "\n",
    "[Wordcloud reference](https://www.geeksforgeeks.org/generating-word-cloud-python/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "wordcloud = WordCloud().generate_from_frequencies(frequency_distribution)\n",
    "plt.imshow(wordcloud)\n",
    "plt.axis(\"off\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Train, test split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def split_train_test(x, y):\n",
    "    SEED = 2000\n",
    "    x_train, x_validation_and_test, y_train, y_validation_and_test = train_test_split(x, y, test_size=.2, random_state=SEED)\n",
    "    x_validation, x_test, y_validation, y_test = train_test_split(x_validation_and_test, y_validation_and_test, test_size=.5, random_state=SEED)\n",
    "    return x_train, y_train, x_test, y_test, x_validation, y_validation, pd.concat([x_train,x_validation,x_test])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = movie_df.review\n",
    "y = movie_df.rating\n",
    "x_train, y_train, x_test, y_test, x_validation, y_validation, all_reviews = split_train_test(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### We are done pre-processing! Now on to the data analysis and machine learning tasks...\n",
    "\n",
    "* In the next code cell we reload data if needed or for first time load we simply read data into lists of tokenized items\n",
    "* Then we train the Word2Vec model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<span style=\"display:block;text-align:center\">![hobbits](hobbits.jpg)\n",
    "\n",
    "### Concerning embeddings:\n",
    "* Developed by [Tomas Mikolov in 2013 at Google](https://arxiv.org/pdf/1310.4546.pdf), **Word2Vec** is one of the most popular algorithms to train \"word embeddings\" using a shallow two layer neural networks having one input layer, one hidden layer and one output layer. There are two models for generating word embeddings, i.e. CBOW and Skip-gram.\n",
    "* **Word Embedding** is a language modeling technique that uses vectors with several dimensions to represent words from large amounts of unstructured text data. Word embeddings can be generated using various methods like neural networks, co-occurrence matrix, probabilistic models, etc.\n",
    "* **CBOW (Continuous Bag of Words) model** CBOW model predicts the current word given a context of words. The input layer contains context words and the output layer contains current predicted word. The hidden layer contains the number of dimensions in which we want to represent current word at output layer. The CBOW architecture is shown below ([image credit: Google](https://www.google.com/search?q=cbow&newwindow=1&source=lnms&tbm=isch&sa=X&ved=0ahUKEwibmL_l0YLiAhWoT98KHX8QAisQ_AUIDigB&biw=1855&bih=906))\n",
    "\n",
    "<span style=\"display:block;text-align:center\">![CBOW](CBOW_ORIGINAL.png)\n",
    "\n",
    "* **Skip-gram model** flips CBOW's network architecture and aims to predict context given a word. Given current word, Skip gram predicts the surrounding context words. Input layer for it contains the current word and output layer provides the context words. The hidden layer consists of number of dimensions in which we want to represent current input word. The skip-gram architecture is shown below ([image credit: Google](https://www.google.com/search?q=skip+gram&newwindow=1&source=lnms&tbm=isch&sa=X&ved=0ahUKEwjSirSW0oLiAhUOd98KHbbgA5EQ_AUIDygC&biw=1855&bih=952))\n",
    "\n",
    "<span style=\"display:block;text-align:center\">![SG](skip.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* For our Word2Vec modeling we have used CBOW as it is faster and have better representation for more frequent words.\n",
    "\n",
    "[Word2Vec reference](https://radimrehurek.com/gensim/models/word2vec.html)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Reload previously processed model if exists**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "list_of_tokenized_reviews = []\n",
    "skip_modeling = False\n",
    "\n",
    "filename_to_save_model = MOVIE + \".model\"\n",
    "if check_if_file_exists(filename_to_save_model):\n",
    "    skip_modeling = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "W0507 14:44:51.753911 140072427317056 smart_open_lib.py:385] this function is deprecated, use smart_open.open instead\n"
     ]
    }
   ],
   "source": [
    "if not skip_modeling:\n",
    "    for one_sentence in all_reviews:\n",
    "        list_of_tokenized_reviews.append(gensim.utils.simple_preprocess(one_sentence))\n",
    "    model = Word2Vec(list_of_tokenized_reviews, size=150, window=10, min_count=2, workers=10)\n",
    "    model.save(filename_to_save_model)\n",
    "    model = Word2Vec.load(filename_to_save_model)\n",
    "else:\n",
    "    model = Word2Vec.load(filename_to_save_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Let's look at some output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('horrible', 0.909416675567627),\n",
       " ('awful', 0.8847770690917969),\n",
       " ('horrid', 0.8349412679672241),\n",
       " ('horrendous', 0.8293353319168091),\n",
       " ('atrocious', 0.8127287030220032),\n",
       " ('lousy', 0.7956128120422363),\n",
       " ('dreadful', 0.7910779714584351),\n",
       " ('sucks', 0.7720378041267395),\n",
       " ('pathetic', 0.7427937984466553),\n",
       " ('laughable', 0.742027759552002)]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "look up top 10 words similar to the word 'terrible'.\n",
    "\"\"\"\n",
    "w1 = \"terrible\"\n",
    "model.wv.most_similar(positive=w1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('superb', 0.8720076680183411),\n",
       " ('outstanding', 0.8481285572052002),\n",
       " ('terrific', 0.8366817831993103),\n",
       " ('fantastic', 0.8252926468849182),\n",
       " ('fine', 0.7947401404380798),\n",
       " ('wonderful', 0.7863712906837463),\n",
       " ('exceptional', 0.7859656810760498),\n",
       " ('great', 0.7494642734527588),\n",
       " ('phenomenal', 0.7335102558135986),\n",
       " ('brilliant', 0.7279271483421326)]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "look up top 10 words similar to 'excellent'\n",
    "\"\"\"\n",
    "w1 = [\"excellent\"]\n",
    "model.wv.most_similar (positive=w1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('movies', 0.6096152067184448),\n",
       " ('moviei', 0.5753039717674255),\n",
       " ('film', 0.554874837398529)]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "look up top 3 words similar to 'movie'\n",
    "\"\"\"\n",
    "w1 = [\"movie\"]\n",
    "model.wv.most_similar (positive=w1,topn=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('stupidest', 0.7872093915939331),\n",
       " ('dumbest', 0.7310155630111694),\n",
       " ('lamest', 0.6975992918014526),\n",
       " ('cheesiest', 0.6749032139778137),\n",
       " ('scariest', 0.6707153916358948)]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "look up top 5 words similar to 'worst'\n",
    "\"\"\"\n",
    "w1 = [\"worst\"]\n",
    "model.wv.most_similar (positive=w1,topn=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.013156236"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "similarity between two different words\n",
    "\"\"\"\n",
    "model.wv.similarity(w1=\"great\",w2=\"worse\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "similarity between two identical words\n",
    "\"\"\"\n",
    "model.wv.similarity(w1=\"outstanding\",w2=\"outstanding\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.84812856"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "similarity between two related words\n",
    "\"\"\"\n",
    "model.wv.similarity(w1=\"excellent\",w2=\"outstanding\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'disapointed'"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "Which one is the odd one out in this list?\n",
    "\"\"\"\n",
    "model.wv.doesnt_match([\"best\",\"great\",\"good\",\"disapointed\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'book'"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "Which one is the odd one out in this list?\n",
    "\"\"\"\n",
    "model.wv.doesnt_match([\"movie\",\"film\",\"show\",\"book\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* **Using dimensionality reduction algorithms like PCA and t-SNE to convert multi-dimensional word vectors to two dimensional plots**\n",
    "* The goal is to plot our 150 dimensions vectors into 2 dimensional graphs, and check if we can spot interesting patterns.\n",
    "* Using PCA and t-SNE implementation from scikit-learn for dimension reductions.\n",
    "* In the visualizations we will look at query word (in **blue**), and most similar words (in **green**), and list of words passed in the function (in **red**)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "def word_vectors_plot(model, input_word, word_list):\n",
    "    \"\"\" \n",
    "    Seaborn plot results of query word and most similar words, alongwith other words in corpus\n",
    "    \"\"\"\n",
    "    word_arrays = np.empty((0, 150), dtype='f')\n",
    "    word_tags = [input_word]\n",
    "    color_list  = ['blue']\n",
    "\n",
    "    \"\"\"\n",
    "    Creating Vector of query word\n",
    "    \"\"\"\n",
    "    word_arrays = np.append(word_arrays, model.wv.__getitem__([input_word]), axis=0)\n",
    "    \n",
    "    \"\"\"\n",
    "    Find similar words\n",
    "    \"\"\"\n",
    "    similar_words = model.wv.most_similar([input_word],topn=8)\n",
    "    \n",
    "    \"\"\"\n",
    "    Insert word vector for similar words into array\n",
    "    \"\"\"\n",
    "    for word_score in similar_words:\n",
    "        word_vector = model.wv.__getitem__([word_score[0]])\n",
    "        word_tags.append(word_score[0])\n",
    "        color_list.append('green')\n",
    "        word_arrays = np.append(word_arrays, word_vector, axis=0)\n",
    "    \n",
    "    \"\"\"\n",
    "    Insert word vectors for other words into array\n",
    "    \"\"\"\n",
    "    for word in word_list:\n",
    "        word_vector = model.wv.__getitem__([word])\n",
    "        word_tags.append(word)\n",
    "        color_list.append('red')\n",
    "        word_arrays = np.append(word_arrays, word_vector, axis=0)\n",
    "        \n",
    "    \"\"\"\n",
    "    Dimensionality from 150 to 17 dimensions with PCA\n",
    "    \"\"\"\n",
    "    reduce = PCA(n_components=17).fit_transform(word_arrays)\n",
    "    \n",
    "    \n",
    "    \"\"\"\n",
    "    Finds t-SNE coordinates for 2 dimensions\n",
    "    \"\"\"\n",
    "    np.set_printoptions(suppress=True)\n",
    "    \n",
    "    Y = TSNE(n_components=2, random_state=0, perplexity=15).fit_transform(reduce)\n",
    "    \n",
    "    \n",
    "    \"\"\"\n",
    "    Sets everything up to plot\n",
    "    \"\"\"\n",
    "    df = pd.DataFrame({'x': [x for x in Y[:, 0]],\n",
    "                       'y': [y for y in Y[:, 1]],\n",
    "                       'words': word_tags,\n",
    "                       'color': color_list})\n",
    "    \n",
    "    fig, _ = plt.subplots()\n",
    "    fig.set_size_inches(9, 9)\n",
    "    \n",
    "    \n",
    "    \"\"\"\n",
    "    Original plot\n",
    "    \"\"\"\n",
    "    p1 = sns.regplot(data=df,\n",
    "                     x=\"x\",\n",
    "                     y=\"y\",\n",
    "                     fit_reg=False,\n",
    "                     marker=\"o\",\n",
    "                     scatter_kws={'s': 40,\n",
    "                                  'facecolors': df['color']\n",
    "                                 }\n",
    "                    )\n",
    "    \n",
    "    \n",
    "    \"\"\"\n",
    "    Annotating word in plots\n",
    "    \"\"\"\n",
    "    for line in range(0, df.shape[0]):\n",
    "         p1.text(df[\"x\"][line],\n",
    "                 df['y'][line],\n",
    "                 '  ' + df[\"words\"][line].title(),\n",
    "                 horizontalalignment='left',\n",
    "                 verticalalignment='bottom', size='medium',\n",
    "                 color=df['color'][line],\n",
    "                 weight='normal'\n",
    "                ).set_size(15)\n",
    "\n",
    "    \n",
    "    plt.xlim(Y[:, 0].min()-50, Y[:, 0].max()+50)\n",
    "    plt.ylim(Y[:, 1].min()-50, Y[:, 1].max()+50)\n",
    "            \n",
    "    plt.title('t-SNE viz for input: {}'.format(input_word.title()),fontsize=16)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Eight Most Similar Words Vs. Eight Random Words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "word_vectors_plot(model, 'action', ['good', 'performance', 'cool', 'life', 'issue', 'bore', 'buy', 'love'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Eight Most Similar Words Vs. Ninth To Sixteenth Most Similar Words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Z584OqlQKp8eQF0m/OCb94pj0i2PSL45JvziWX/pFo8n6WZA5ka+TIrPZSmxsslNj8PZ2c3oMeZH0i2PSL45Jvzgm/eKY9Itj+aVfAgI8H1vbMn0mhBBCCIEkRUIIIYQQgCRFQgghhBCAJEVCCCGEEIAkRUIIIYQQgCRFQgghhBCAJEVCCCGEEIAT71NkMBjo3r07aWlpmM1mmjVrxrBhw7hw4QIjRowgLi6OChUqMGPGDLRaLWlpaYwZM4aTJ0/i7e1NdHQ0RYoUcVb4QgghhMhnnDZSpNVqWbFiBdu3b2fr1q0cOnSIEydOMGvWLHr16sXevXvR6/Vs3LgRgA0bNqDX6/nyyy/p1asXs2bNclboQgghhMiHnJYUKRQK3N3dATCZTJhMJhQKBd9//z3NmjUDoH379uzfvx+Ar776ivbt2wPQrFkzvvvuO6zWp/925UIIIYTIG5y6pshsNtOuXTuee+45nnvuOYKDg9Hr9ajV6bN6QUFBxMTEABATE0PBggUBUKvVeHp6cvv2bafFLoQQQoj8xanPPlOpVGzbto34+HgGDx7M2bNnM9RRKBQADkeF7u7LvH0F3t5uuRPsf6RSKZ0eQ14k/eKY9Itj0i+OSb84Jv3imPTLw+WJB8Lq9Xrq1KnDiRMniI+Px2QyoVaruXr1KoGBgUD6qNGVK1cICgrCZDKRkJCAt7d3lu3KA2HzLukXx6RfHJN+cUz6xTHpF8fyS7/kywfC3rp1i/j4eABSU1M5cuQIJUuWpE6dOuzZsweALVu2EBERAUBERARbtmwBYM+ePdStW/ehI0VCCCGEENnltJGia9euMXbsWMxmM1arlebNm9OoUSNKlSrF66+/zgcffED58uXp3LkzAJ06dWL06NFERkbi5eVFdHS0s0IXQgghRD6ksObjS7iMRrPThwrzy3BlbnuW+iUgUG/7f6uLC+biJUjp2YfUXn1AqUT573n8aoYSt2o9bi90fGb65VE87PfFc+gAVH+cIvbLg08wKud7lv4dPQrpF8fyS788zumzPLGmSIj8LnngUAxt2qFISUG7eyeeY0eC1UJqn/7ODk0IIcQdkhQJ8QSYQ0Iw1awNgLF+Q9R//onr8k8kKRJCiDxEnn0mhBOYqlRFdeFfuzJFSgrKQQPxK1kE3yrlcHtvGlgsdnVUp35H/2In/IoXwq94IfR9XkZx515eAJpvDxEQqEfz7SH0fV7Gv1hBfGtWxmXpxxli0G3bjE/DuvgX8ce3anncpk8Gk+ne/nWrCQjUo/71BF5RLfEvWgCfRvVQ/3oCkpLwHHYn1pqV0W3ekKF97e5deEc2xD84AL+KpXCfNAGMRtt+txnT8StXDPVvv+DdIgL/ogXwjghH8/0R+zjXr0H1fAP8yoTgVzoEr/atUJ84/mgdLoQQ2SBJkRBOoLzwL5aAQLsy98kTwcOd+E9WYujUBffZ76HbsfXeMWfP4N26KQqDgYQPF5MwdyGqP0/h1eMFeGBpoMeIoZgqViJu+WrS6oXjOXYk6uM/2vZrDuxH37cXptAqxK1cS0qf/rgtmIvH2FEZYvUcOgBD+07EL/0UrFb0r7yM5+uDMQcVJP6TlRhr1MBzSH+Uly/ZjtFt24y+d3dM1aoTt3IdSaPG4vLpctynvm3XtiIlBc8hA0h5+ZX09nVa9L1ehOR76x5UF/7F8lIP4pesJOGjJVgKFsK7XQuU5/75L10vhBCZkukzIZ4AhcUCJhOK1BS0u3ag27mNlH6D7OoY6z6HasYsjLHJGJ+PQPvVPrS7tmNo1wEA91nvYgkMJG7tJtBqATBXrIjPczXR7ttDWmRzW1uG9p1IHjEmvd169dHt3Y1u1w5M1WumtzVjGmn16pMwf1F6nYjI9PJpb5M8YjSWQoVtbSUPHIqha/f0DasVrxc7k1YvnOTxEwEwVa+Bbsc2tHt2k9r7VbBacZ80AcML3UickX6VqBFAp8Nj7EiSXxuB1dcvvV9SUkic+i7G+g3TX09gEL6Nw9F8/60tpuRRY9F6u2GMTQaLhbSGEahPHMdl43qSR43N+ZsjhBB3yEiREE+Ax5tvEFDIF/8ShfEcNhBDpy4kjR5nVyft+Qi7bVPZsqguX7Zta775mrSWbUCpTJ/mMpkwhxTDEhyC+sTPD7TV+N6GRoO5eMl7IzlmM+pff8HQJsruGENUBxQWC+ofj9qVGxs8b/t/c/ES6WXhDWxlVr0XFj9/lFfTY1Wd+RvVxQsY2rW3xYnJRFp4AxSpqaj/OHXvWI0GY73699ovWy69jftet+qvP1F16ohfhZIEBHkTUMgX9d+nUZ35GyGEyE0yUiTEE5A8+DUM7dpjdXHFXLQYuLpmqGP18rIv0GjBYLBtKm/dxG1eNG7zMt6j6/6pK0dtWbVaFHfaUty8icJozDB9d3db+cAzBS36e21ZNVrHsWq1KFLvtQ/g1a1ThjgBlJcu3mvP0zM9ybuvHQAMqeltJSbg9UIUBAWROHk65iIh4KLD8/WhttcjhBC5RZIiIZ4Ac5EimKpWz1EbVm8fUlu2IfWllzPss9yZjspWO35+WDUalDeu25Urr19Lb8vHJ2dx3jk+YfZcTKGVM+w3hxTNdlvqY0dRXb6E8Ys9GIJCbOWKhPgcxSiEEI5IUiTEUyKtQUPUf/yOqUo1yMkjblQqTJWrotuxNX0N0B26bVuwKpW2Wwf8V+ZSpTEXLITywr+YevTKUVuK1NQ7welsZeqjP6D69zymylVz1LYQQjxIkiIhHsH1RANnbiTh6aKhfAEPlE/w+XvJo8fh3SwC/YudSH2xB1ZfP5RXLqM9eIDUrt3t1uY8TNKY8Xh3aY/nsIGkRnVEfep33N+bSupLvewWWf8nSiVJk6bhObgfyoR40hpHYtVoUZ0/h3b3TuI/+RTcsvekbmONWljcPVAN7I+m/1BUVy7jNvMdzAUL5SxGIYRwQJIiIbLBYrWy4uCf/HT8b0Kv/8NVD1+SixRlaLsaFPHOuD7ocTCXLE3s7v24vzMFz5HDUKSmYgkqSFr9520LoLPL2Kgx8YuX4RY9E69Nn2HxDyB54FCSx4zPlVgNUR2xenri9sFsXNauwqpUYSlaDEPTZvfWDWWDNTCQ+E9W4DV5Il49u2EuXpLEmdG4zp+TK3EKIcT95Nlnj1l+edZMbnva+uXz/13h5Lod/N+xz3C1pN+AcH+Bcqxv0oN3e9bLtRGjp61fnhTpF8ekXxyTfnEsv/TL43z2mVySL0Q2HDz6F73+t9eWEAE0jvkDj4vnOHU1wYmRicctIFDv8Ef9/XcAuL0zGb+KpXJ8HkVMDG4zpqO8eCHHbd3PZfknaL/4PFfbFCK/kukzIbIhPsVIUGrGK54KJtwkLtXk4AiRn9x9oO/9zOXKA5Dasw9prdrm+BzKazG4z3qXtAaNsBQJznF7d7muWIqxajXSmrfMtTaFyK8kKRIiG0oW8uGYb1Eirv9lKzMo1fxaoARt/d2dGJl4Eu5/oO+DLIUKP3xxekqKw3tT/We53Z4QApDpMyGypV14WZbVac/+gLIkqHT87R7A1BqdqFK1FAU8dQ9vQORbD06faQ4eSH8o78ED6Lt3xr9YQTz+L/1xJC6fLscnvBb+IYH4lSuGV1RLVH/9ifLsGXwbhwPg07YZAYF6/Av6PLQ95exZeEc2xK9EYfwqlET/cleU/5y1xeLduinqk7/hunqlbdpPt2Gdbb/LiqXp8RTxx7dGJVwXzHvs/SVEXiYjRUJkQ+kAD4Z2a8DW4EAWX45F76KmYe3StKgkl4Y/C+4+u+5egQJUqiyP8XxtEKndXiJlwBCsLi5oDh3EY+xIkt74P0w1aqJISEBz7AcUCfGYK1Umfv4i9EP6kzBrDqYKFTPci+rB9gC4dImUV/tjKRKMIj4e1+VL8G7TjNvfH8fq4UnC7Lnoe3bDXKYsycNGAGAuXhIA1zmzcZ8xneShwzGGhaP5+Sfcp72N1c2N1F59cq/zhHiKSFIkRDaVCfRgTIeazg5DOIHHm2/g8eYbtm1j7brE7tyb5TGGqI4kv/Gmbdt17vuYQiuTMux1W9n963xM5Sum/7dMOYdTdQ+2B2B5PxrD3auJzGbSGjbCv0IJtHt2Y+j4Qvqz5FzdsPj527WpiIvF/f0ZJI8aS/Lro9NfU8NGKJKScHt/Bqk9X8nZDUKFeErJ9JkQQjxE8uDXuL33a9tPwgcfPvSYtMhmdtumSqGoT/yM+8TxaL4/AmlpjxTDg+0BKL47glfHtviVLUpAQR8CigWhSE5+6MNyNUe/R5GSgqFtlP1De+s3RHX1CsqrVx4pNiHyCxkpEkKIh/gvz6578IG7xohIEqLn47pkEa6LPsTq4Ynhha4kTpicrTt8P9ie8vw5VK1aYqlZm4RZc7EUCAKtBq8u7R/6sNy7D+31DavhcL/y0kUsctdw8QySpEgIIR4HB9NPhm4vYej2Eorr19Ht3IbHW+Ox6PUkj5v4yO1p938JBgNxK9beuxItLQ1FXNxDm7L6+AIQu24TVgcPEzaVKvPweITIhyQpEkKIJ8waEEBq71fR7dyG+s8/0wvvPP5EYUjNVhuK1NT0xd7qex/jui0b0xeF338urebeg3XvMNaug9XFBWVMDIaIyBy8EiHyF0mKhBDPhDSThe/O3ebshevo9e6Elwl8ordTcHtnMorERIx162H180P9ywk0P3xH4qRpAJiDQ7DqdLisW43VzQ20WkxVqmXaXlr9hrhPnpD+UN9uL6E+dRLXRQuweOrt6plLlUHz7SE0B/Zj9fHBXLQYVh9fkkeMwXPcKFTnz2GsEwZWC+q/T6P57gjxy1Y91r4QIq+SpEgIke8lpJp4d+NRAk79Qp3zv3HF048p5Z6jZ1RdahX1eSIxmKrVxHXxAlw2b0CRlIS5cBGSxk4g9ZV+6RXc3EiYNQe392fgvX0LWCzcuHI70/bMoZUxL/4YzeQp6HZtx1SpMvFLP0Xf80W7ekkj38Dz6hX0r/ZEmRBP/IeLMXTuSvLwUZgLFsJ18ULcPpyD1cUVc6lSGNp3epzdIESeJg+EfczyywP4cpv0i2PSL47ltF9WfHMa1/Wr6fvXAe6uzDnj7s+EpgN5v28jXDRZ33Mor5LfF8ekXxzLL/0iD4QVQogc+PHkv7Q7/yP3L1UumXSDUlf/4aQ80FcIcYckRUKIfM9sBbXFnKFcbTFjyb+D5UKIRyRJkRAi36taphBfFK5sV3bFRc8fBUpQIejxDcULIZ4ustBaCJHvdQwryfTzrbjp7kPtK39wxdWL7ZUi6NS0Ku5a+RgUQqSTTwMhRL7n565l0kvP8dUfJfnin6vovdwZEhpMSX93Z4cmhMhDJCkSQjwTPHRq2lYpDFUKOzsUIUQeJWuKhBBCCCGQpEgIIYQQApCkSAghhBACkKRICCGEEAKQpEgIIYQQApCkSAghhBACkKRICCGEEAKQpEgIIYQQApCkSAghhBACkKRICCGEEAKQpEgIIYQQApCkSAghhBACkKRICCGEEAKQpEgIIYQQApCkSAghhBACkKRICCGEEAKQpEgIIYQQApCkSAghhBACkKRICCGEEAKQpEgIIYQQApCkSAghhBACkKRICCGEEAKQpEgIIYQQApCkSAghhBACcGJSdOXKFXr06EGLFi1o1aoVK1asACA2NpbevXvTtGlTevfuTVxcHABWq5WpU6cSGRlJmzZtOHnypLNCF0IIIUQ+5LSkSKVSMXbsWHbv3s369etZs2YNf//9N4sXLyYsLIy9e/cSFhbG4sWLAfjmm284d+4ce/fuZcqUKbz99tvOCl0IIYQQ+ZDTkqLAwEAqVqwIgIeHByVKlCAmJob9+/cTFRUFQFRUFPv27QOwlSsUCqpWrUp8fDzXrl1zVvhCCCGEyGfUzg4A4OLFi5w6dYoqVapw8+ZNAgMDgfTE6datWwDExMQQFBRkOyYoKIiYmBhbXUdUKgXe3m6PN/iHUKmUTo8hL5J+cUz6xTHpF8ekXxyTfnFM+uXhnJ4UJSUlMWzYMMaPH4+Hh0em9axWa4YyhUKRZdtms5XY2OQcx5gT3t5uTo8hL5J+cUz6xTHpF8ekXxyTfnEsv/RLQIDnY2vbqVefGY1Ghg0bRps2bWjatCkAfn6RrQUVAAAgAElEQVR+tmmxa9eu4evrC6SPDF29etV27NWrV7McJRJCCCGEeBROS4qsVitvvvkmJUqUoHfv3rbyiIgItm7dCsDWrVtp3LixXbnVauXEiRN4enpKUiSEEEKIXOO06bOffvqJbdu2UaZMGdq1awfAiBEj6NevH8OHD2fjxo0ULFiQOXPmANCwYUMOHjxIZGQkrq6uTJ8+3VmhCyGEECIfUlgdLdbJJ4xGs9PnT/PLHG5uk35xTPrFMekXx6RfHJN+cSy/9Eu+XVMkhBBCCJFXSFIkhBBCCIEkRUIIIYQQgCRFQgghhBCAJEVCCCGEEIAkRUIIIYQQgCRFQgghhBCAJEVCCCGEEIAkRUIIIYQQgCRFQgghhBCAJEVCCCGEEIAkRUIIIYQQgCRFQgghhBCAJEVCCCGEEIAkRUIIIYQQgCRFQgghhBCAJEVCCCGEEIAkRUIIIYQQgCRF4ikSEKgnIFCPbuP6DPt0n6217c9r3GZMx69cMWeHIYQQ4iEkKRJPFYu7B7otGzOU67ZuwuLu4YSIhBBC5BeSFImnSlqz5mi//gpF7G1bmeL2LbQHD5DWrEXunCQlJXfaEUI803RrVxEQqMdzUF9nh5KB8t/zBATq0e7d7exQ8hRJisRTxVizNpagguh2breV6XZuxxJUEGOt2hnqu095C5+GdfEvVhDfKuXwHNAHRUyMXR3fGpVwnzget9nv4VulHP4lC6Nbuwr/Iv4o4mLt6qr+OEVAoB7NN1/byrS7d+Ed2RD/4AD8KpbCfdIEMBqzfB3K8+fQv9wNvxKF8SteCP1LL6A8e+be/kw+sDyHDsA7suG9epcv4flqT/wqlMA/JBDfWpVxe3dKlucWQjwZLps3AOmfEXnty5alQBC3P9+HsU6Ys0PJUyQpEk8XhQJDuw52U2i6LRsxRHV0XP3GdZJfG0ncmg0kTn0X1flzeHdsDWazXT3d5g1ojnxL4nvvE794OWmt2gCg/Xynfb2tm7D4B2CsVz99e9tm9L27Y6pWnbiV60gaNRaXT5fjPvXtzF+DwYB3x7aoTv9J4vtzSZi7ENW/5/GOagm3bj1Sd3gO6Y/q0kUSZs0lbu0mkl8fjcKQ9khtCCFyn+L6dTSHvyGt/vMokxLRfvmFs0Oyp9Nhqlkbq5e3syPJUyQpEk8dQ/uOaL49hCImBkVMDJojh0nNJClKnLMAQ4fOGJ8LJ61lG+KXfor6rz/RHP0+Q9241Z+R1rwlaa3bYtV7kRbRBJetm+zq6LZtxtA2ClQqsFpxnzQBwwvdSJwRjbFRY1J7v0rS5Om4LvsYxa2bDmNyWbsK5aULxK3dhCGqI2lt2hG3bjPKmzdQfrz4kfpCc/wnkoePIq1la4z16pP6Yg+S3pKRIiGcTbd9MwqzmcR3Z2EuWAiXzRnXQvrWqIT7W2/iOvd9fCuVxq9kEdwnjgerFe2+PfjUr50+kvxyN7slAyQl4TF2JD5h1fEvWgDfmqF4vDECRUK8rYrbjOm2i0/u/mi0anxrVAIcj0bb4vloPr5VyuFXOgTPfr0yjpif/B/eLZvgHxyAT/3aaPftwTuyIZ5DB+RyLz55amcHIMSjMoVWwVyiJLodW1BYrZhLlMQcWhnN0e8y1NXu34vb7Bmo/vwD5X0fGKozf2MMq2fbNtZvCC4udsca2nXAc+gAFDdvYvXzQ/Xbr6jP/E1i9HxbG6qLF0hs1x5MJttxaeENUKSmov7jFMbnwjPEpP75J0yVq2ApVtxWZilUGGPtuqiPfAv9h2W/LyqF4j7tbRS3b2EMb4ClSHC2jxVCPD4umzdiDK2CuXQZDO06pH9Rio/Dqveyq6fbuglTtRokzF2A5pcTuL8zBawWNN8dIemN/0ORmoLHuNG4T51E4qwPAFCkpIDZQtL4iVj9/FFeuojbB7PQ93mZuM+2ApD6Uk/SIprYzqNITsZrwCuYS5TMMm7d9i2YKlQkcfYclJcv4zFxPFbvSSTOiE6vkJyMV5f2WAILEP/RUhSGVNwnjEMZF4u5XPlc7EHnkKRIPJUM7TrgsmUTWK2ZTp2pf/4JfY+uGFq2IXnYCCz+/qBQ4NOiMRhS7epaAgIznqNZSzw1GnS7tpP6cm9ctm3GXLCQbQ5ecTN9JMirWyeH51deuuiwXBVz1eH5LAEBcOVS5i/agfiPl+M+fTIedz6UTBVDSZw0DWOD5x+pHSFE7lFe+Bf1j0dJ+r9JQProtttH89F+vhND1+72lXU64j9ZCSoVxohItF/swnXJIm59/zOWosUAUJ/8Hy7r19iSIqu/P4kzo++1YTJhDimGT5umKC9ewFIkGEuhwlgKFbZV8ezbCzQaEuYvyjp4tZr4FWtBnZ4eqP76A5ctm2xJkcvaVShv3yL2y4NYChYCwFysOD7NI/5jb+UtkhSJp5KhfSfc3p8BQMKcBQ7r6D7ficXPn4SPl4NCAaR/WDl0Z78dDw/SmjRDt20zqS/3vjN11t5W1+rjk37+2XMxhVbOcLg5pKjDU5kLBKH+81SGcuX16+Drm9627s6oVZr9gm27IXTAUrAQCfM+AosF9fEfcZ/5Dl4vd+Xm8ZNYff0cv1YhxGOl25I+7W6I6gCAqVoNTMVL4LJ5Q4akKK1e/fTp+DvMxUugvH3blhDdLVPcvAFpaaDVpp/js7W4fvQh6rNnUCQn2eqqzvydYcTYdW40us93YN63H0uBoCxjTwtvYEuIAMxlyqG4cd12bs2J45gqV7UlRACm6jUdftF7GsmaIvFUMpcpS2qP3qT26I25dBnHlVJTQKOxS3hcNn32SOdJjeqI5shhtHt2ozp/DkP7e6NS5lKlMRcshPLCv5iqVs/wk1lSYqpeE/UvJ1CeP2crU165jObYD1ifS5/SswYEYNVoUJ/+896BiYlofjzqOFClElPN2iSNGosiORnVxQuP9DqFELnHZctGTJWrYtXrUcTFooiLJa1ZSzSHDqK4ft2u7oPTaWi0WB5Y/GzValFYremJCaDdtQP9kP6YatYi/pMV3N69n7jlawBQPDAKrjmwH/fpk0ic/A7WsOceGvuD8Tx4buW1mPRR9wc4KnsayUiReOJSjWY+//UyR38+i6urjrBKwYQW9EThaLQmC3eHkjNjbNgIt0ULcP+/N0hr2gLNsR8c3g07K2mRzbC6uuEx6jXMIcUwVa95b6dSSdKkaXgO7ocyIZ60xpFYNVpU58+h3b2T+E8+BTe3DG2mdu2O27wP8OrWkeQ33sSqVOE+6x0svn5Y+vaztZ3WvBWuiz7EXCQYq5cXrgvnY3VxtbWjiI/Dq0t7Ujt3w1yyFIo0A64L52MOLICpdNlHep1CiNyhOv0X6pO/AeBfOiTDft32LaT26Zejc+h2bMFYo+a9dT6A5sjhDPWU5/5BP+AVDJ26kNqnHy4Zajw6S2ABVH+fzniuGzdyoXXnk6RIPFHJaWbe2XiMQr/9SOQ/PxGvcWVthef5I6IWL9Qp/vAGHkFak2YkTpiM6yeLcF21AmON2sSv/gzfutWz34iLC2nNWuCy6TOSh43IsNsQ1RGrpyduH8zGZe0qrEoVlqLFMDRtZhvmzkCnI3bjNjwmjsdj+BAUVitp9cJJWrYKT19fiE0GIOGdWXiOGobHGyOxenuTPHwUmmM/oPojferNqnPBVL4irh8vRHX5ElZXV4w1aqUvtHR1dXxuIcRjpdv0GVaVivhP12F1tf9S5PHmG7hs2ZjjpEiRkopVq8twXjtJSXj16o65SAgJM7P+AvkojFWro9v0Gcorl21TaOrjP6K8fi3XzuFMkhSJJ2rP/y5T4thBRp76AovZAkD4jb8ZonUlvFxBCnll/l3m+rX4TPcBpPbpT2qf/nZlKUOHkzJ0eJbt3Prpf1m2m7BwCQkLl2S6P61xU9IaN810f/KY8SSPGW9XZilWnPiVa7M8rzUwkPiV6+zKUl/ufW9DpyPx/XlZtiGEyJmr8alcjE0lwENLUd+MI78P0m3dhLFhI9KaNMuwL7XLi7i//SbKC/9iCc44ipRdaQ0b4Tl2JG7RMzFWr4l23160991QFsBj4jhUf/1BwoeLbSNXCk8X1GlWTKFV/vO5U7u9hFv0TPTdXyB51FgUqSm4zZiOxd8fq/LpX5EjSZF4on45eZ5+549z/0SZp8lAvbM/8fPF2lkmRUII8aQYTBY+/vIkp3//hzK3L3JeH4R3iWCGtK6K3kXj8Bj1ieOoz54hfvQ4x2126IT75AnotmwiZdjr/zm21J6voDp/DtePF+KaasDYsBHxH32SfmXtHaozf6MwmdD3f8XuWH1wyEO/CGbJzY24dZvxHPM6+n69MAeHkDRxCu6TJ2D19Pzv7eYRCqvVanV2EI+L0Wgm9s5UhLN4e7s5PYa8ZPLqb+m1+UOqJV3BfGekCGBB+Uh8B/ajefn8cQXDfyW/L45Jvzgm/eJYbvTLym/+wrBtO6/99jlaqxkLClaWDOfvFh0Z1a5aLkX6ZD2u3xfl+XP4hlUnYfZcDN1eyvX2HxQQ8PiSr6d/rEs8VWpWKcm20mFY7hsruqF159sS1akZ7JXFkUII8WQYzRaO/HqePqf2o7WmPxJIiZUXzx7h/JlL3Eg0ODlC53KdMxvdutVovj2Ebv0avF7shMXPn7TWbZ0dWo7J9Jl4oiLLB/JrnXq84e5FvdNHide5s7dcOG2bVMXfQ/fwBoQQ4jFLNVpQpaXhY7QfVdFazQSkxhObanq2P68UCtxnvYcy5gpWrQ5j3TCS3p6G1VPv7MhyTJIi8URpVEpGt6vGXw3L88NvdXFx0TKyTAFCfORqKSFE3uCuU+Hm5cGfHgUolxhjK7+pdeeq3p/Cz/jax5RhI0hxcDVufiBJkXjiVEoFYSX8KO8riZAQIu9RKhREPV+R96+/wMCftlIx7jL/uPuzuGormtQti6tG9fBGxFNJkiIhhBDiAfVK+qPuGsGyIgW5eCuZAA8dkWFlaVzu2b4YJL+TpEgIIYRwoE4xX+oUq+fsMMQTJFefCSGEEEIgSZEQ4immvPAvnoP64lutAv7BAfhWLY/+5a5ovvv2kdrRrVtNQKAeEhPT2/33PAGBerR7d2d5nMsni9KPE0LkCzJ9JoR4Kilib+PdojGWAkEkvfkWlqCCKC/8i+6Lz1EfO4ox7L9Pe1gKBHH7832YS5fJxYiFEHmdJEVCiKeSbsc2lNevcfvAEawBAbZyQ7eXIKc36tfpMNWsncMIhRBPG5k+E0I8lRRxcaDVYvXxcbBTYbep27YZn4Z18S/ij2/V8rhNnwwmU6ZtO5w+MxjwGDsSv1LB+JUJwX3CWBRGY269HCFEHiBJkRDiqWSqXAWFwYDn4L6of/kZLBaH9TQH9qPv2wtTaBXiVq4lpU9/3BbMxWPsqEc6n/vUt3BZvZLkEWNIWLgE1YULuC6cnxsvRQiRR8j0mRDiqWRs8DzJ/QfjungBLls2YfHwxNiwESm9+mBs2MhWz33GNNLq1Sdh/qL04yIi08unvU3yiNFYChV+6LkUt27iumIpSaPHkzJoKABpjZrgE17rMbwyIYSzyEiREOKplTTlHW59d5zEt6ZirBeO9sA+vF6IwmX5J+kVzGbUv/6CoU2U3XGGqA4oLBbUPx7N1nnUp35HkZpKWotW9wqVStKat8r8ICHEU0eSIiHEU81SoiQpg4cR/+l6bv50ElOlyrhPnwRWK4qbN1EYjVgC7O9CfHdbeft2ts6hvJb+/CuLv799O/4BjqoLIZ5SkhQJIfINq58fqd26o4yNRXH9OlY/P6waDcob1+3qKa9fA8DiaJG2A5bAAunH3bhh384D7Qohnm6SFAkhnkqKBxKUu1Rnz2DV6bDq9aBSYapcFd2OrXZ1dNu2YFUqs33Zval8BawuLmh377pXaLGg/WJX5gcJIZ46stBaCJEnxKYY+fVyPCqFgiqF9Xjosv54clm/Bt2mzzC80BVTxVAwGtEeOojrsiWk9uoDLi4AJI0Zj3eX9ngOG0hqVEfUp37H/b2ppL7UK1uLrAGsvn6k9OiF+8zpoFZjKlcO109XoEhKyvHrFkLkHZIUCSGc7ovfLrP9q1+pfukPTCoVqwuV5cUW1Qkv5Z/pMWlNmqL69xwuq1agvHQJVCrMxYqTOH0mqT162eoZGzUmfvEy3KJn4rXpMyz+ASQPHErymPGPFGPSxCkojEbcZr8HSiWGTi+QMmAIHm89WjtCiLxLYbXm9NaveZfRaCY2NtmpMXh7uzk9hrxI+sWxZ7Ff/ryWyOIVX/Le1x/jn5Y+8nLR1Ztxjfoxvk9jCupdnsl+yQ7pF8ekXxzLL/0SEOD52NqWNUVCCKc69NsF2v3vK1tCBFAkJZbIP77l0J8xToxMCPGscWpSNG7cOMLCwmjdurWtLDY2lt69e9O0aVN69+5NXFwcAFarlalTpxIZGUmbNm04efKks8IWQuSixIRkAlITMpT7J8eRlJTihIiEEM8qpyZFHTp0YMmSJXZlixcvJiwsjL179xIWFsbixYsB+Oabbzh37hx79+5lypQpvP32206IWAiR28qUKsSR4Ep2ZVbgSPGqlAkJdHyQEEI8Bk5NimrVqoWXl5dd2f79+4mKSr/7bFRUFPv27bMrVygUVK1alfj4eK5du/bEYxZC5K6GZQL4K7QuS0o35KKrN+fdfJlbsTkJFSpTu6i3s8MTQjxD8tzVZzdv3iQwMP3bYWBgILdu3QIgJiaGoKAgW72goCBiYmJsdYUQTyd3rZrxL9Rhe8kgJv5+AZVSQY1KRXmjeggalSx7FEI8OXkuKcqMo4vkFApFlseoVAq8vd0eV0jZolIpnR5DXiT94tiz2i/e3jC0dWVoXdnh/me1Xx5G+sUx6RfHpF8eLs8lRX5+fly7do3AwECuXbuGr68vkD4ydPXqVVu9q1evPnSUyGy2Ov3yw/xyCWRuk35xTPrFMekXx6RfHJN+cSy/9MszdUl+REQEW7em35J/69atNG7c2K7carVy4sQJPD09ZepMCCGEELnGqSNFI0aM4OjRo9y+fZsGDRowdOhQ+vXrx/Dhw9m4cSMFCxZkzpw5ADRs2JCDBw8SGRmJq6sr06dPd2boQgghhMhn5I7Wj1l+Ga7MbdIvjkm/OCb94pj0i2PSL47ll355pqbPhBBCCCGcQZIiIYQQQggkKRJCCCGEACQpEkIIIYQAJCkSQgghhAAkKRJCCCGEACQpEkIIIYQAJCkSQgghhAAkKRJCCCGEACQpEkIIIYQAJCkSQgghhAAkKRJCCCGEACQpEkIIIYQAJCkSQgghhAAkKRJCCCGEACQpEkIIIYQAJCkSQgghhAAkKRJCCCGEACQpEkIIIYQAJCkSQgghhAAkKRJCCCGEACQpEkIIIYQAJCkSQgghhAAkKRJCCCGEACQpEkIIIYQAJCkSQgghhAAkKRJCCCGEACQpEkIIIYQAJCnK09xmTCcgUJ/lj1dUS2eHmSnl1Su4zZiO8vKlRzswNZWAQD0uq1Y8nsCEEEIIB9TODkBkLvWlnqRFNLFtuy5ZhObwN8QvX20rs3rqnRFatiivXsF91rukRTTBUqiws8MRQgCBCxx/ZhTTF+foS7884WiyZ9vfm0kxpdC1XHe78qitLfF18WNp80+dEte6P1Yz7KuBnO17GQ+Nh1NiELlLkqI8zFKosF0yYdmxDXQ6TDVrOzEqIcTTbmCVobQp2c6uTKd2cVI0D7ft7y3cSr2ZISl6r8H7aJTyZ0zkHpk+yy9MJtymT8a3ann8i/jj07Au2u1b7Kp49uuFd+umaLdvwaduNfyDA/Bq1wLVmdP3Kt2dulr+Ce6TJuBXtih+FUri/uYYMBrt2lOfOI5XVEv8ixbAr2xRPIf0R3HzJgCq03/h0/R5AHxaNiEgUI9/cMC9g69dw2P4YPwqlsI/OACfejVxWfpxhtf0sBiEEI8uRB9CzaDadj+h/pWdHdYjK+tbjhLepZwdhshHJCnKJ9wnT8RtwVxSXulL3Mp1mKpUw+vVnmh37bCrpzp7Bvdpk0h+403iFyxBeeM6Xl06Zkg23ObMRhEXS/xHS0nuPwjXJYtwXb7Etl8ZcxWv9q1RmM3EL1pG4qTpaA/sx6trBzCZMAeHED93IQAJ0fO5/fk+YrftTj84KQl1RCO0+78kacx44tZsJKXvQJRXrzxSDPlNQKAejVadYd2Y+vvvst2G5uABAgL1qE7/leN4lGfPpMf01Zc5bks8PeIMsVRZUY7B+/rZlff4vAt1V1cj2ZhsK9t1dgfNNj5PyKJAyn5SlG47O3Ih4V/b/lM3f+fFnZ0o/nEhin9ciD57XiYmOca2/9tLhwhcoOfAv/vpvqszxRYHUW1lBZb/7xNbnaH7B7Dz7DaOXD5M4AI9gQv0zDg6HUifPnvlix52cR66eJB6y8MIXhRAhWUlGXPwdRKNiRnO+e2lQ/TZ8zLFFhek5qrKLP2f/ZeyY1d/oMfnXQhdXoZii4NotL4eG/9an4OeFU8DGXfMBxTXruG67GOSxrxJyrDXATBGNEF58QLuM98hrVUbW13ljevErd2IqUo1AMwVK+LzXE10mz7D0PXe0LS5VGkS35+X3lajxmiPHEa7awcpfQcC4DovGnRaYtdtBnd3ACxFi+HdrgXaPbtJa9UGc7nyAJjKlrOb8nNdvQLO/E3cwe8xlymbfo76DTO8rofFkB+ZX3+d+MhW9mV3+lGI3GKxWjBZTHZlSoUSpUKJl86b6Ebz6bqzA61KtKVlidasPbWKL8/vYXvUHtw0bgB89udahuzvT/tSHRlR8w2sViuHLx3kZsoNgj1DOBt3htZbmlI1oBofNl6M2WrmvaNT6bHrBfZ0+hqFQmE79+sHhtC5bFf6hPZn19kdjPnmdQp5FKJpsRaMqDmGS4kXiTPE8V6D2QAU8nC8RvHPW3/QdWcHmhRvwtJmn3Ip8RJTv3+b8/HnWN/GfuR8xNdD6VL2RXpU6MWW0xsZ+81IqgZUo3qBmgBcTLhAraC69Kz4CjqVC0evfs9rXw1CqVDSoXTnXHsvRN4iSVE+oD75GwqDAUPbKLtyQ7sOeI4ejiIh3rYg21wk2JYQAZhLlMJcrgKa4z/aJUVpzze2a8tUphy6PZ/btjXHfyKtcVNbQgRgDKuHObAAmh++s0vEHqQ5/A3W2rVtCVFmHhZDvlS0mKwZE4/dm4ff4M3Db9iVdSn7IvMafwRAREgTelTozaiDrxHsGcyEb8cxqOowahesA6QnVVO/f5uWxduwqOkyWxvNi9+7GnbWsXcJdAtkbetNaFVaACr6VeS5tTXZd34PkcWa2+o2LhrJm3Xfsp37fPw5on+aSdNiLSjuVQJvnQ8Wq4WaQVn/25j947sU8Qxmc+etJMQbAPBx8aHv3l4cu/oDtYLq2Oq2L92JETXHAFCvUH32ntvNrrM7bElR+9KdbHWtVithhepxOfEyq35fIUlRPibTZ/mAMuYqAJaAQLvyu9uK2Nh7Zf7+GY63+AegjImxK7N6edlX0mrBYLh3zmsxGc4HYA0IRBl7O+t4b92CoIJZ1slODM8a1Z9/4Nm3V/q6saIF8GlQB5clH4HVmqGu8vIl9N064l+0AL7VK+Ly6XK7/d6tm+LZr5ddWbam3sxm3N6dcm/tWoM66LZsfPS2rVbcomfiW6sy/sEB+FUoiVfXDiiuX89ud4gcGFz1NfZ2+truZ3StcXZ1JtWbhpvGnZabm1DIoxBv1H7Ttu/v26e5mnSFbuVfyvQc31z8mpbF26BUKDFZTJgsJkL0xQj2DOHE9Z/t6rYs3tpuu1WJNvxy/QRmi/mRXtfxa8dpWbwNKqXKVta6RDvUSjU/XPneru7zwfe+dGlUGop7leRy4r3bh8Sm3mb8odFUX1mRQh/5UugjXz79fRlnYv9+pJjE00VGivIBS4EgIH1qzHLfyI3y+jUArN7e98pu3MhwvPLGdYwlSjzaOQMLoLyR8Q+Y4vo1LN4+WR/r64vqgfVD4g6LBUz3TWsoFKBK/4BXXr6EuXQZDJ26YPXwQP3rL7hPn4Ii1UDKkNfsmvF8bRCpXV8kpe9AdDu34TlyGObChTFGROYoPPdpk3Bd9CHJo8dhrFwV3fYt6Pu/QpxKRVrb9tlux2XtKlznfUDShEmYy5ZDcesW2kNfo0hNIWOKJ3JbEc8iVA2snmUdD40HTYs2Y8lvi3ixfA90Kp1t3y3DLQAKuBXI9PhbqTeZ93M0836OzrDv/uQDwN81IMO2yWLiZupNAt0yfvnKzLWkqwQ8UF+lVOGj8yU21f7LmpfW/kuXVqXFYL73pWvYVwP5MeYYI2uOoYxPOTy1niz/3yd8cW5XtuMRTx9JivIBU8VQrDoduu1bSRk63Fau274FU4VKdvcyUl28gPqXn21TaKqzf6P643eSBwx+pHMaa9TEZeN6SE4Gt/Q1Bprvj6C6FoOxThgAVk36kLnigdEdY/2GaCeOR3X6L8ylyzz6C87HVCNeJ2DE67ZtY+26xO7cm/7/jRpjbHTn263VirFOGIqkRFxWLc+QFKU1bU7y2Anpx0U0QXXuH9zfn0lsDpIixc2buC75iORRY0kePupe25cupq9de4SkSH38J9IaR5La+9V7Mbdu+59jE7nvxLXjLD/5CaH+VYj+cSbtS3e2JUG+Ol8Au0XTD/LW+dCyeBteqvByhn2+Ln522zdSrmfYVivV+D1Q72EC3YMytGW2mLltuIW3S9Zf1u6Xakrly/N7eKf+LHpV6mMrt/BxFkeJ/ECSoifMaLbw25V4UtIslCvggZ+7NsdtWgMDSendF/cZ0wAwVayEbttmtIcOErdstV1di38Anv16kzz2/7CqNbi/OwVLkWAMHR5tjjxl8Gu4rFqJV7eOpAwahiL2Nu5T38ZYpRppzVqkn6toUaxaLS5rV2HVatPvsVS5Kikvvoz7p8vx6tSW5JFvYC5REtW5f1BeOE/yuIk57o+nmXnESBGFuf0AACAASURBVOKb31uPZfXwvLczJQW3ObNw2bQB5eVLKO5cMfj/7N13eFPVG8Dxb5I26d4jbemgUGiZFUoB2bNMQUBFBWWoqChLBZUfKKCoCC4ULEOGiItNi0zZskUqS2bZLQVK98r4/VENhAbKaEkp7+d5eJ7ec+89980hTd+cc+65RoWisIdJeW00PK+D+ZyuvA6dcBozqnCo7boJrnfC5uB+FLm5Reeude2O89DXUKRewejucVt16WrUxOm9d9FPGE9+m5jCJF0po/llRa4ul9fWDaBFYCumt51Di18e5c0Ng/i+Q+HdV5Xdw/Bz9Ofnw/OJCWlvsY6mFZpx+MpBans/Yjap2pIVJ+NoFdz22vaJ5dT2jjQNg6lVtuTpc4uNu65PXVacWM6nhk9MZfEnlqEz6Kjv16DY8/+Tr89Db9Sb9Y5l5mew6uSKYl+LeLBJUnQfHU7OYMqS3QReOIFbTibztWE0aRDOUw0q3vMvWtbosRjt7LCf8S3KSynoK4eRPn12kQnP+tBK5LwwAMfxY1GeP0dBnSgyJ31VOF/nDhi0fqQtWo7j+//D5cXnMdrZkd+mHZljPwKbwreV0cmZjE+/wPHzT9Es+hVUKi6dSQFHR3Rr16F/8y0cPxqLIisLfWBQub6r7LYFBaGLtDys4fT+SDS//ET2GyPQ1ayF0dUVTdwyHL76DPLzwe7a4nsGL/PhCKOXN4q8vMLExePOvn3/5+Zz1wqvpbh69baTotzefVBkZWH3wxwcJ36MwcuLnOf7k/3WO5Ic3Qen00+zO2mnWZlCoaCubz0APtoxjovZySx8bDkOtg5MbhnLY0ti+OnwD/QMfxalQsnohmN5Ze0LvLymP93CeqBAweZzm+gW1oNInzq8Ve8dYha05Jn4HjwT0RsPO08uZJ1n45n19Ax/lkYBTUzXXndqDeO3j6WhfyPiTyxn49n1zG3/k2l/ZbcqrDy5ghUn4vB38kfr6IfWsei8xKFRw2n1S2O6L+hGr6p9OJ95nnHb36NFYCuzSdbFcdG48ohPHSbt/gQntTNKhZLJf36Gs8aVzPz0O21u8QCRpOg+yS3Q882inbyx7jsi084CkKHSMCqjN39o3WhUsfg/JlljPiRrzIeWd9rYkP3uaLLfLb6nJa9rd/K6dre8086OlItFf+mzRo0ha9QYszLdI3VJ+2/toZtd6+le5D1tYTKmjw8Z/65jdC8xPEw0y5eQ89LLZkNl6pWW5zcoL6Vw/RRVxaUUjBqNKWkxauxQ5JuvTVXcBPnr567pXa7Nx1D+Ozn6v7lrt1W3SkXOwEHkDByE8uwZ7H79CYePP8AQUIHcXs/fMg5RSGcw8tOuRH7YdYC0HB1VfVzpWy+CalrnYs+dum8yU/dNNitTKVRceCWVHRe2E5vwDd+0moavY+H/ebRffV6u/Rr/2/I2TSs0x98pgO5VnkSjsuOLPyfSf9VzONg4UNe3Hp72hTdzVHIL47fu6/hoxzje2DCIXF0uWkc/mlRoTkVX8zmMn7WYzLSEKcQmfIObxp2Pm04yu5Otb40X+ftSAkPWv8rVvKu8GfU2w6PfLfK6wj0i+LHTQj7ZPY6+K3vhrHbm8crdGf3ouDtu36ltZvLmhsG8vm4A7nYe9Kv5EjkF2Xy3f9od1yUeHJIU3Sc7T1+l2qmDpoQIwFmfx9P717JoVxUaVbz9bzHiIZWTC+pr3fno9WiWLLJ4qGbFcgqat7xuO65wiOrfHkmDvz82e/eYnWO7cf0tL6+rVgOjnR2aZUtMc4oANEsXoasabkq47rRuQ4VAsoe+hd3871H9c/iWMYhrpmw5zIoTG8hXr0bpnM6u1GCOrOjGxx2bEeF788To4qu37umo79eApFeuFil//9EPeP/RD8zKOlV6jE6Vbj4XLMy9ym09l8zP0Z+fOll+LwN42nsyp/38IuVLuhZdoqNpheY8VqMDV69mF9kH0CigicU2uLGuUNdKLOoSV+S465OxnuHPFnn0iHiwSVJ0n2Tk6vBNu1ik3Ccvg4ys4sfKhSho1gL7Gd+iDwrG6OqK/cxpKHSWb1lWr16Jg4cHBdEN0cQtRb1lE2nzfzXtz+vQGbuf5+P43kjyW7ZGvXkj6k0bbnl9o6cnOS+8jMPEjzEqlehq1kazfAnqDb+TNmPOHdXtNPQ1jF7eFNSJwujigu3mDShPn6KgSdO7bZ6HSnJGHmuOHcfosgiVMR8AW/vjpOUsZs4uXz7uVM/KEQrxYJKk6D4J83ZkVnBNnj+2GdV1Nx3v8K5E5ZCb39ZakjKmzb4v13nYGYxG1h6+yOZdR8nIzics2JvODSoT5G5/T/VmfPIZzm8Nxmn4MHBwILfns+S174Tz8KFFj/3iGxymfIXD1K8xuHuQ8ekX5LeOMe3Pb9eBrHdGYTd7JnZzZ5HfsTOZY8bj2vfW33qz3h2NUW2L/cxphcNolSqT/u1MszvPbqduXb362M2bg92cmSjy89GHhJLx5RTy21qetCvMnbychV51EoVSx/XjpDaa0/yTkma9wIR4wCmMRgsrv5UTBQX6m3ah3i9ubg5cvZqN0Wjk87h9aDZv5Jl/NuJSkMNmnyosrP8YI59thNal7D6hujT81y7l0awNh7m4bjO9Dq7DOy+D7Z6h/FqvM28926TYxKg8t8u9kHYxdzg5gzdXxFHgMg2DwWAq1xd4E6x8nVk9iz4252Ei7xfLyku7eHsXP2/ubklP0X2iUCh4vUMtlgd4Mv7P2uQU6IkI8WFEw8oPXUJUniWl5/LXnqPE7vwJu3+fLfXY+QSUu4wsC9XyWocH70nkouyp4uNEBacgjmTXQKlJQKEAo8EW29y2dIsOs3Z4QjywJCm6j2xVSrrVCaRbnUBrhyJKyT8Xs4hMOmJKiP7T8NIJfj4lj7AQJUOpUPB+TF0+/N2GxNREDIrLqAwhdKhaiY7Vi3+EjhDCMkmKhChBznYqUhyLrpx7Se2Ek52tFSIS5ZXWxY65zzdn19EU0nILCPV0LJHFYIV4mMkqaUKUoFp+LpwPqMRO92BTWZ5CxbyIljSpJ8MaomQpFAqq+DhRL8hdEiIhSoD0FAlRgmxUSl5/PIqvMLLs7DF8Mi6zxz+carXDiKmmtXZ4QgghbkGSIiFKWCUvRyb2a8q+c5Fk5Olo6e1IBbd7ux1fCCFE6ZOkSIhSYKtSEhXkZu0whBBC3AGZUySEEEIIgSRFQgghhBCAJEVCCCGEEMADmBRt2rSJmJgY2rRpw7Rp06wdjhBCCCHKiQcqKdLr9YwdO5YZM2YQHx9PXFwcx44ds3ZYQgghhCgHHqikKCEhgeDgYAIDA1Gr1XTs2JF169ZZOywhhBBClAMP1C35ycnJaLXXFsDz9fUlISHhpserVArc3BzuR2i3iEFp9RjKImkXy6RdLJN2sUzaxTJpF8ukXYr3QCVFRqOxSJlCobjp8Xq9katXs0szpGK5uTlYPYaySNrFMmkXy6RdLJN2sUzaxbLy0i7e3s6lVvcDNXym1WpJSkoybScnJ+Pj42PFiIQQQghRXjxQSVHNmjVJTEzkzJkz5OfnEx8fT8uWLa0dlhBCCCHKgQdq+MzGxobRo0fzwgsvoNfr6d69O2Fh8uRxIYQQQty7ByopAmjWrBnNmjWzdhhCCCGEKGceqOEzIYQQQojSIkmREEIIIQSSFAkhhBBCAJIUCSGEEEIAkhQJIYQQQgCSFAkhhBBCAJIUCSGEEEIAkhQJIYQQQgCSFAkhhBBCAJIUCSGEEEIAkhQJIYQQQgCSFAkhhBBCAJIUCSGEEEIAkhQJIYQQQgCSFAkhhBBCAJIUCSGEEEIAkhQJIYQQQgCSFAkhhBBCAJIUCSGEEEIAkhQJIYQQQgCSFAkhhBBCAJIUCSGEEEIAkhQJIYQQQgCSFAkhhBBCAJIUCSGEEEIAkhQJIYQQQgCSFAkhhBBCAJIUCSGEEEIAkhQJIYQQQgCSFAkhhBBCAJIUCSGEEEIAkhQJIYQQQgCSFAkhhBBCAJIUCSGEEEIAt5EUzZs3j7S0tPsRixBCCCGE1RSbFKWkpNCjRw8GDx7Mpk2bMBqN9yMuIYQQQoj7qtikaOjQoaxevZoePXqwePFi2rZty2effcbp06fvR3xCCCGEEPfFbc0pUigUeHt74+XlhUqlIi0tjUGDBjFhwoTSjk8IIYQQ4r6wKe6AuXPnsmTJEtzd3enRowfDhw/H1tYWg8FA27ZtGT58+P2IUwghhBCiVBWbFKWmpjJ58mQCAgLMypVKJbGxsaUWmBBCCCHE/VRsUjR48OCb7qtUqVKJBiOEEEIIYS3FJkVCCPEg8JniYrF82eOraODXsESvtfjoAvL1+TwV/kyJ1nurujstaou/kz/T2s4u8WsKIQpJUiSEKDdeqf06nSt1MSsL94go8essPraQrIKs0kmKblL3pOZfYauyLfHrCSGukaRICFFuBLkEEaWNtnYYpaKqR7i1QxCi3JPHfAghHgr/XDnMi6v6EDknguBpvjT9qT4zEr41W5B245n1+ExxYfv5P+i3sjch07REzavFnAPfmY55Zc0LrDwZz+azG/CZ4oLPFBc+2124PMmqxN/ovuwxIr6rSOj0ANovbMXGM+vN4jibcYZ+K3sT8V1FgmJ9iJ5Xmwk7xxdbd6dFbXlpdR+zuvYl7+OZuB5UmlGBitP9ab+wJZvPbiyN5hPioSA9RUKIcsNgNKAz6EzbChSolCoAzmeeI8y9Cj2qPoWTrRMJKfsYv2Mcufo8XnvE/IaSIesH0jP8WZ6r3pcFR37mrY1DeMSnDrW8Ixke/S7ns86Rq8vhw8aFCUuAUwUATqcn0j6kA69FDkahULD21Cqeinuc+G5rqOtbD4BX176I3qjnsxZf46x25lRaIifSjgPcsu4bHb5yiPYLW1LFvSoTm32Bm8adfSl7OZd5tgRbVIiHiyRFQohyY+SWEYzcMsK0Ha1tQFy31QC0CGpFi6BWABiNRur7NSSrIJN5B2cXSYp6VHmKIXXfBOBR/8asSVxJ/Ill1PKOpKJrKK4aN2yUtkWG6l6s9YrpZ4PRQOOAphy6fJD5h743JUV7L+5hdrsfaBXcFoDGAU1N59yq7ht9uusjPOw9WNp1JXY2dqbXKIS4e5IUCSHKjYGRg+lS+XHTtpOts+nnHF0OX+6ZyMKjv3I+8xwFhgKgsDfJYDSgVFybTdA8sKXpZ7VKTYhrRc5nni/2+ucyzvLhjjFsObeJ5KwkjBQOzemNetMxNbxqMnbbe6TkpNAkoBkBzpZ7goqz5exG+j3S35QQCSHunSRFQohyo4JzBSJ96ljc9/4fI/nln594I2oENb1q4apxJe74Mr7a+xn5+nyz5MJV42Z2rq1STZ4+95bX1hv0PLviSfL0ubwTPYpglxAcbB34cPsYMgsyTMfNjPmeD7eP4X9b3iY9P42aXrUZ0+hDsx6j23E17yp+Tn53dI4Q4tYkKRJCPBSWH1/CS7VeNhsqW3kyvsTqP3b1KAcv7+fXzktpFtjCVJ57QzLl7xTAN62noTfo+fPibj7ZOZ7eK3ry13MHiyRjt+KmceNC5oUSi18IIXefCSEeEjm6XNQqjWlbb9Cz5Niiu6pLrVSTpzNPdnJ1OQBorrtGYtpJ9iTvsliHSqminrY+b0aNIKsgk7MZZ29atyVNKjTn14O/kKfPu6vXIIQoSnqKhBBlToHewJXsAlztbLCzVZVInc0qtGBGwrcEOQfjqnFl5t/T0F031+dOVHYPY+2p1fx2Mh4/Rz/8HP2p6hGB1tGPUVvfYUT0u6Tnp/PJzg/xd7z23MgruZfpFf8UT1TtSSW3yuTqcpjy12S0jn5Udg+7ad2+jtoiMQyv9y7tFrWg65L2DKg1EHc7DxIu7cPb3pue4c/eXSMJ8ZCTniIhRJlhNBpZuO8Mz877nRcXrODpeeuYtu0I+TrDPdf9SbPPqKetz/BNwxi6/nVqeNXitcibP9vxVvrXGEDTwOYM+v0V2i5ozg+H5mJnY8esdvMA6LeyNxN2jueNqBHU09Y3nWdv40BVj3CmJUyh94qnGPz7qzirnfml8xJTD5Olui2p4lGV33tvwEXtypD1r9F3ZS9WnFhOBefAu3pNQghQGK9fuaycKSjQc/VqtlVjcHNzsHoMZZG0i2UPe7ss23+OKTvXUGC/AKVNBga9PcrsdjxRoxMvRVe2dnhlzsP+frkZaRfLyku7eHs7F3/QXbJKT9Fvv/1Gx44dCQ8P5++//zbbFxsbS5s2bYiJiWHz5s2m8k2bNhETE0ObNm2YNm3a/Q5ZCFHKDEYjP+79h3z7xShtCu/WUqpyMDrG8dvBY6TlFFg5QiFEeWeVpKhKlSpMnjyZevXqmZUfO3aM+Ph44uPjmTFjBmPGjEGv16PX6xk7diwzZswgPj6euLg4jh07Zo3QhRClJDtfT2Z+Diqbq2blCmUBqK5wMVMmFAshSpdVJlpXqlTJYvm6devo2LEjarWawMBAgoODSUhIACA4OJjAwMKx8o4dO7Ju3ToqV5budCHKC3tbFY5qe7J1riht0kzlRoMt6D3wdtLc4mwhhLh3ZWqidXJyMlrttbssfH19SU5Ovmm5EKL8UCkVPFm7CrY5XTDoHQEwGjQostvTJjwUN3tbK0cohCjvSq2nqE+fPly6dKlI+ZAhQ2jdurXFcyzN+VYoFBgMRe88USgUxcagUilwc3O4jWhLj0qltHoMZZG0i2UPe7v0aRyG0lbJ/N0hFOSnoTA60b5GJYa2roHNbfzOP2we9vfLzUi7WCbtUrxSS4pmz559x+dotVqSkpJM28nJyfj4+ADctPxW9Hqj1Wfal5fZ/iVN2sUyaRfoGuFPu8q+XM7Ox9XOFieNDTYKxUPfLpbI+8UyaRfLyku7lObdZ2Vq8caWLVvyxhtv0LdvX5KTk0lMTKRWrVoYjUYSExM5c+YMvr6+xMfHM2nSJGuHK4QoJXa2KgJc7a0dhrgNE3aOZ+Luj295zKP+jVnSdUWpXP9o6hEa/Rhl9niVtNw0+q3sx5ZzG7mad5XYNt+x/9LfLDjyM/ueP1wqcYjywSpJ0Zo1axg3bhxXrlxhwIABREREMHPmTMLCwmjfvj0dOnRApVIxevRoVKrC1WxHjx7NCy+8gF6vp3v37oSFhVkjdCGEENfpVe15WgZdmxIx4+9YtpzbxOx2P5jKnNUupXb9QOcgVnRbS1WPcFPZN7u/ZsOZ3/m6VSw+Dj6EulUiWtuArpW7lVoconyQxRtLWXnprixp0i6WSbtYJu1iWVlsl/e2jiTuxFL29N5fIvXpDXoMRgO2qqIT7XN1udjZ2BUpH7zxZY5eOsaK7mtLJIbyoiy+X+5GuVu8UQghxMNp1v4ZNJofRYVvvYj6viax+74x2//S6j50WtSWpccWFR4X68X+SwnM3j8TnykuJKT8RefFMQTF+jDj71iOph7BZ4oLG8+sB6D6rMr8eGA+u5N34jPFhcBYbwDGbXuP2nPCza6Vkp3CkN8HUn1WZQJjvWk0P4rv9k+/Pw0hyqQyNadIiAfV2YwzTNz1Mb+fWcuVnMv4OmppF9KBYVEj8LT3vOP65h6YhZe9Nx1CO932Ofn6fL7YM5H2oZ2o6VXrjq95L9r82oxwjwgmt/oWgNfXvczhK4dY88TG+xqHKNsm7f6Ez3d/yut1htLA71H2JO9i7LbRONo60ava86bjTqQd45OdH/JG1Ag87Dzxd67AvpS/AHhxdR/61XiREdEjcdO4F7nGj50W8NGuMVzISGZisy9QKix/988qyKLLknZk5GcwPPpdQl0rcfzqMc5nniudFy8eCJIUCXGPDl85xONLOuBl78279UcT5BzM0atH+GLPRFafWsnyx1ehdfS7ozq/PzibcI+IO0uKDPlM3P0xQS7B9z0putGwqOHk6nKtGoMoW1Jzr/DlnkkMjx7JoDpDAWgW2IKM/Awm7f7ELCm6nHOZJV1+o4pH1SL1vBo5iOer9zNtH009Yra/lnckHvaeZORmEaWNvmk8Pxycw8m0E2x8arvpOk0qNLun1yiuuZh9kcl7P2dN4krOZZ5FpbChsnsY3cKeoFfEc7hoXO9bLNP2TMPH0Yeu4V2LPVaSIiHugdFo5NW1L+KqcWNF97WmCaWPBjSmbXA7mv/ckOGbhjG3/Y9WjvT+qugaau0QRBmz/cI2cvW5PFapKzqDzlTepEIzvvnrSy5mX8THoXCplWCXEIsJEUCb4JgSiWfLuU3U8Y266XXE3TuaeoRuSzthb2PPi7VeJsKzOvn6fHYl7eDrvZ+z/1ICU1rfv2HKaXumUcOnxm0lRTKnSIh7sO38VvZfSmBo3beK3GHj5+TPC7VeZtXJFZxOPwXAT4d/wGeKC5kFmWbH1v2+Bu9tHQlA1yUd2Jeyl5//mY/PFBd8prjw0+HCO3lWnlxB61+bEjJNS9jMINotaMEf57YAEDrdH4BBv79iOu+/647b9h7NfmpAyDQ/as8J5+U1/UnOTrYYw7f7vqb2nHDCZgbx0uo+pOWZP4vs0OWDdFzUxjQHY+XJordav77uZdr8eu1b93+v++DlA/RY1oWQaVoenV+XuOPLzM4zGo18vGMc1WaFEjo9gMG/v8riowtQj7cxvRbxYLqScxmA6B9q4/+th+lfz7jCO8LOZ541HevtcPN16Lzti1+j7rbiyb2Cr4O2+APFHXt5TX887DxZ9+RmXqz1Co0DmtIyqDUjokey49m/aB3c9qbnGo1Gq/YyS0+REPdg24WtALSv2NHi/vYVO/Hpro/YcWEbQS7Bt1XnJ00/o9/KXgS7hDAsajgAIa6hnEw7Qf9VvXmx1iu813Acefpc9qX8RWpeKgCLusTRbWknhtV9i9b/fpv2dSz80L+Uk8Lgum+gdfDjcu4lpvw1me5LO7Hxqe2olCrTtZcdX0w1z+pMav4l5zPPM3rru7hpxjCh2ecA5OhyeCrucTzsPJnaeia5+hz+t/VtsgsyCfeIKPa1vbKmP72r9WFg5CBm/h3LgDV92eWbgL9TAACxCd/wxZ+TGFL3TeprG7IyMZ6x20bfVruJss3NrnD+zy+dl+CmcSuyP8z9Wo+NgpuvXn47TzO4HR52HiRnJxV/oLgjf5zbwt+X9jG/468Wl2JwVrvQLewJ0/aEneP5bv80Zrf/kdFb3ubg5QN81mIyT1Z9mtTcK3ywfQy/nYwjIz+dml61Gdf4I9p5tzSdP+mPSfx04CeOXD6CnY0d0QHRfB7zOZU9Cp+N2nx2c/Zc2MOeC3uYs28OALO6zKJPZB+L8UtSJMQ9uJB5AVeN203HxwOdCx9ifCHrwm3XWdUjHAdbRzztvczmRGw7vwUnWyfef/QDU1nr64YSIn3qAIUJ1I1zKb5sOcX0s96gJ8o3mtpzw9mZtJ2G/o1M+2yUNsxp/yM2ysKPhiOph1l8dKEpKfrx8Dwu5aSwsvvvpkQm0DmYzotv/s3vegNqD+SZiN4A1PaJpPqsyqxOXEmfGv3RG/R8vfdLnq/ej7ej/wdAi6BWnE4/xbnrehHEg6m+X0PUSjUXs5NpHtiy+BNKWZMKzRi99V2Oph4hzL2KtcMpN7Zd2IqN0obGAbc/PytHl8Pr6wbw2iNDqORWGa2DH3n6PHos60JafhrvNRyHt4M3s/fPpMeyLhwLOYrWqfAL39n0s7xW7zWC3YJJz0vn293f0ui7Rhx57Qiudq5M6TiF7r90J9Q9lFFNRwFQyd3yQ+lBkiIhStWtvvHeqQiP6qTnp/PaugF0D3uSaL8GONo63ta5606tZtLuCfyTepiM/HRT+fGrx8ySosYBTU0JEUAV93Au5aSQr89HrVKzN3kPtb0jTQkRQH2/BnjZe99WHNf/MfSw88TL3psLWYV3+5zLPMvF7GRiQjqYnRMT0oF1p9fcVv3i3hmNRk5czuafi5k4a2yoF+SGna2q+BOL4WXvxZC6bzJ84zBOpp2gvl9D9AYdx64eZVfSTmbEzCmB6G/fMxHPMefAd/RY9hhvRI0g1K0SiWknOZNxinfqS+/k3UrKSsLDzrPI+lF6gx4jhcsiKlCY9VDn6HIY2+gjsx73Hw7O5fCVg2zuuYNQt8Jen6YVWtBwfh0m/TGJT9t+CsDn7T43u0ab0Db4TPRh6T9Lea72c1TzroajrSPeDt40qNCg2PglKRLiHvg5+ZGWd5WM/HSLXcWnM04XHneHd59ZUtk9jLntf+SrvZ/zTHwPbJW2dAjtxAeNJ+Bl73XT8/Ym76H3bz3pULEzg+oMw8veC4VCQfuFrcjTm4/du6jNe7zUKjVGjOQbCpOii9nJFhMg79tMim7sUVOr1OTq8gC4+O8cpxtfi+ctXpsoWTq9gQnr9/PH6SNkK/5GrfDCRVmNMTHRRPje+4J5b9Z7mwCnCkz/+1u+3vsF9jb2VHILo/t1wyn3i6OtI4u7rGDc9tF8tGMsWQVZBDoH8WKtV+57LOWJ0Wi0+GWw0owKZOuygMKhy8P9Ek37FChoFdTG7PhNZ9dTyzuSIJcQs4n5j/o3ZveF3abt7We3M2r9KP688CdXcq6Yyo9cNr8r8XZJUiTEPWjoV9jLsvLkCp6o2rPI/lWJK1CgoIH/owBoVBoACvT5cN0CvVdvmMx8M21C2tEmpB3peWmsObWKUVvf5t3NbzKt7eybnrPiZByedl5MbzvbNB/jzL/J2p3ycfDl2NWiHzYpOSl3Vd+NdQNcyrlkVn75hm1RepYfuMCG05swOC1EpTCgB1LyAhizWsncp5uhtin+3pwxjT5kTKMPb7r/6YhePB3R66b7b/Ze7lOjP31q9C9SHuZehYuvppuV/fD4/CIrN49qOIZRDceYlXk7ePNVy6k3jUXcOT+nwnmLefo80+cdwPLHV6I36vn+4GziT5jfYOGmcUOtUpuVXc69wp7kXfh/61HkGv8Nf51OO03b79sSHRBNbKdY/J39UavUdJzf8a4nTjXwUgAAIABJREFUa0tSJMQ9aOjfiBpetfhszwTaV+yIk/rat+nkrCSmJ0ylXcWOBDoHAeD377DTkdQj1Pcr7Mrdk7zLbEgLwFZpW6QX53ouGle6V3mSP85vZXfSTgDUysIPlRs/DHJ1Odgqbc0mqC488stdvd5InzosPPoL5zPPmYbQdlzYzqUSSIoCnCrg4+DLypPxZs/SWpVYOg8SFUXFHTqOTrMRlcJgKrPRnCM96xj7zkdSL6joYolCXK+hXyN0Bh1bz22i5XW9PzW9awOwOnFlkXMsTZ5317gT6f2IaT7j9bRehYnSymMryS7IZmnPpTiqC6cS6Aw6sx6jOyVJkRDXycrXsePUVdJzCgjzdqSa1vmWd7soFAq+aTWNbks70mFRawZGDibIJZijqYWLNzqrXfik6STT8XV86uLn6M/ILcN5O3okqbmpfL33yyJDb2HuVVh/Zh2/n16Lh50HQS7BLD++lN1JO2kZ1Bqtox8nrh5n+fHFPFn1aaBwKCrIJYRlxxcT7lkNO5WGap41aBbYgtiEKfxvywjahrRnV9IOFhz5+a7a5+nwXny+51OeiX+Ct+q9Q64uh493foCn3Z2v2n0jlVLFwMjBjNn2PzztvYjWNmBV4goOXT4IcNOViUXJycrXobAt+mwsA+lk5xssnCGEuUcDGlPTqzYfbB9DtLaB2RfFO9GkQjM2bP+dAKdAvB3Mh+f/e/ZZTkEOSoXSbB7kLwd+MRtug/+G6W+v50iSIiH+dTg5g9GrdpJmSCDPmIQjtantU5XRbSNvOdE0wrMaa57YxMRdH/PB9ve5knsZXwct7St2LPKYD7VKzex2PzBi0zD6r3qOSm5hTGj2Oa+ufcGszqF13+Js5lleXN2HjPx0vmo5lWqe1Vl1cgWjt77L1bxUfB209KrWhxHRI03nfdr0c97/4388sewx8vR57O71N62DYxjVcCwzE2KZd3AOdbXR/NDhFxrMr3PHbeRg68DPnRbz1sYhDFjdl0CXIMY8Op7P93x6x3VZ8nLtgaTlpTJ7/wy+3fcNMSHtGVz3DUZsGobzXX64itsXVcGXuFNhKB33msqMBjW2hqpE+DpZMTJhLbkFepIy8nC3t8XVvuhDeS35ts1Mui3tRKtfm/BizZcJ96yG3qDnRNpxlh5bhKNt8e+lJ6s+zZwD3/H40g68GjmIYJcQruReYe/FPYR6BzG04VBaVmyJ3qin79K+9H+kPwdSDjDxj4m42Zkv+RDuFc6q46tYdWwVng6eVHSriKeD5S9yCqPRaLytV/kAKijQW/2JwOXlqcQlray1S4HewPM/buQC07HRFN4NZTQqILMjz9fqQe+oivcljrLWLmXB0PWvsfncBnb3KpmnrpcnJf1+OZ+Wy9ClW7loWIlCfQyj3hm7glb0qNaI/g0ql9h1Spv8Hll2J+1iMBr58c9EFiYcRadIBYMLTSsG8Vrj8Nu6GzE5O5mv937B6sTfOJ95DpXChkpulWkb0o4Xar5s+rL43zpF10+8/k96Xhqf7PyQ+BPLScm5iJe9N4/41OXd5iNoFFQ4n3PuvrmM2TiG8xnnqe1bmy/bfclTC56iR7UeTGw7EYATqSd4aflL7Dq/i/S89FuuUyRJUSmTX07Lylq7/HU2jf+tXUSe42yzcr3ODT/DEH54tsV9iaOstcv9dujyQZYeW0g9bX0UCiW/n17DjL9jGd/iI/qFy11BNyqN90tSei4LExLZcy4ZN3sNXauH0STUo8QWTbwfHvbfo5u5k3ZZ8vdZvt21igKHBShV2RgNtpDditZBnXi7Vc1SjvTW/hs+Kw0yfCYEkKvTY6Doh4VCkUdugd4KET2cHGwd2HFhOzP3Tye7IIsKzoGMajCWofWHkZaWY+3wHgpaFzsGNg4Hwq0dirASg9HIz38dId9+GSpV4eeiQlmA0XENW0/V5FJWVbwc1cXU8mCSpEgIINzXGVtDKHl6e5Sqa3989blVqBfoa8XIHi7BLiEs7hpfpPxB6qUQ4kGXrzOQkZeHys58qRCFQg+qy6Rk5pXbpEhu5xACcLO35ZlHqqHJfp6CnEroC7zRZTbAV9mF3lEPzlwKIYS4VxobJe4O9ugLzBdONRpsUei90TprbnLmg096ioT415ORQVTydGXpgTBSsnKoU1FL1xpB+JTjDwAhhLiRQqGgV51wvvjjcfIUC1DZpGLQO6DKbkfryhVxdyifvUQgSZEQJgqFgqggN6KCoqwdihBCWFXbqr7o9C2Z96c/mTmZ2Cjs6VStIs/VC7V2aKVKkqIyyGdK0WdoASx7fBUN/Bry0Y6xzDs4lwN9j93nyIQQQjwMFAoFHav7ExPhR3puAU5qm9t6zMuDTpKiMuqV2q/TuVIXs7JwjwgAnq/en46hj1kjLCGEEA8RG6UCj3I8XHYjSYrKqCCXIKK00Rb3+TsFmJ47JYQQQoiSUf77wsqhj3aMpfqsa3dEbTyzHp8pLmw//wf9VvYmZJqWqHm1mHPguyLn/nFuC48tbkfwNF+qzgzmjQ2DySzIvJ/hCyGEEGWSJEVllMFoQGfQmf7pDcUvIDhk/UBqeddmdvv5NPBryFsbh5CQ8pdp/7bzW3lieRf8HP34LuZ7xjQaz6rEFQxb/1ppvhQhhBDigSDDZ2XUyC0jGLllhGk7WtuAuG6rb3lOjypPMaTumwA86t+YNYkriT+xjFrekQCM3Taahv6NiW07y3SOr4OWnnHdeKveu4S5VymFVyKEEEI8GCQpKqMGRg6mS+XHTdtOtsU/66V5YEvTz2qVmhDXipzPPA9AZkEmfybvZmLzL9EZdKbjGvo3QqlQsi9lryRFQgghHmqSFJVRFZwrEOlT547OcdW4mW3bKtXk6XMBSM29ghEjb2wYxBsbBhU593zmubsPVgghhCgHJCl6SLj9mzC9Ez2KFkGtiuz3c/S/3yEJIYQQZYokRQ8JZ7ULj/jU4XjaMYb6vGXtcIQQQogyR5KiUnb+ag4/bT/GsSupVPb0oENEAFoXO6vEMrrhOJ5c3hWATqFdcLR15GzGGdacWsWohmOo6Fq+l28XQgghbkWSolJ0KDmD0au3c0m/DlTn2HYxgLhDLRjf/lHCfYufOF3SGgU0YUnXFXy66yNeXfsiBqOeCs6BtAxqg5e9V/EVCCGEEOWYwmg0Gq0dRGkpKNBz9Wq2Va5tNBp5ecFWjuXPQKk+aSrX5YZQ1e5FpvZohEKhsEpsZYGbm4PV/m/KMmkXy6RdLJN2sUzaxbLy0i7e3qXXqSCLN5aSS1n5nM+4jI3dKbNylSaRcxmXuZSVb53AhBBCCGGRJEWlRKVUgFEJ3NgbpACjqnC/EEIIIcoMSYpKiYeDmspePhTkVDUr1+WEE+bl/VA9dVgIIYR4EEhSVIqGNa1FgKoniqxO5GfVQJHVCX/VUwxtWsvaoQkhhBDiBnL3WSkKdLdnft82xO0N51RqGsHurjQJ9cRJI80uhBBClDXy17mUOdvZ0D7CF/C1dihCCCGEuAUZPhNCCCGEQJIiIYQQQghAkiIhhBBCCECSIiGEEEIIQJIiIYQQQghAkiIhhBBCCECSIiGEEEIIQJIiIYQQQghAkiIhhBBCCECSIiGEEEIIQJIiIYQQQghAkiIhhBBCCECSIiGEEEIIQJIiIYQQQghAkiIhhBBCCECSIiGEEEIIwEpJ0SeffEK7du3o3LkzAwcOJD093bQvNjaWNm3aEBMTw+bNm03lmzZtIiYmhjZt2jBt2jRrhC2EEEKIcswqSVGjRo2Ii4tj+fLlhISEEBsbC8CxY8eIj48nPj6eGTNmMGbMGPR6PXq9nrFjxzJjxgzi4+OJi4vj2LFj1ghdCCGEEOWUVZKixo0bY2NjA0BkZCRJSUkArFu3jo4dO6JWqwkMDCQ4OJiEhAQSEhIIDg4mMDAQtVpNx44dWbdunTVCF0IIIUQ5ZWPtABYuXEj79u0BSE5Opnbt2qZ9vr6+JCcnA6DVas3KExISiq1bpVLg5uZQwhHfGZVKafUYyiJpF8ukXSyTdrFM2sUyaRfLpF2KV2pJUZ8+fbh06VKR8iFDhtC6dWsApk6dikql4rHHHgPAaDQWOV6hUGAwGCyWF0evN3L1avadhl6i3NwcrB5DWSTtYpm0i2XSLpZJu1gm7WJZeWkXb2/nUqu71JKi2bNn33L/4sWL2bBhA7NnzzYlOFqt1jSUBoU9Rz4+PgA3LRdCCCGEKAlWmVO0adMmpk+fztSpU7G3tzeVt2zZkvj4ePLz8zlz5gyJiYnUqlWLmjVrkpiYyJkzZ8jPzyc+Pp6WLVtaI3QhhBBClFNWmVM0btw48vPz6du3LwC1a9dm7NixhIWF0b59ezp06IBKpWL06NGoVCoARo8ezQsvvIBer6d79+6EhYVZI3QhhBBClFMKo6WJPOVEQYHe6uOn5WUMt6RJu1gm7WKZtItl0i6WSbtYVl7apTTnFMmK1kIIIYQQSFIkhBBCCAFIUiSEEEIIAUhSJIQQQggBSFIkhBBCCAFIUiSEEEIIAUhSJIQQQggBSFIkhCinIudEMHT9a2ZlmQWZ+E11J3pe7SLHd1zUhieWdblf4RH+XQgTdo4vsfp2XNhOq1+aEBjrjc8Ul9s+76fDP+AzxYXMgswSi0WIB5VVVrQWQojSVk9bn11JO8zK/kzejUalITH9JCnZKXg7eAOQr88nIeUvBtUZZo1QS8RbGwfjZe/Nz50Wo1aprR2OEA8k6SkSQpRL9bTRHE09wtXcVFPZ7qSdNPRvRKBzkFnCtC9lL3n6PKK1DawR6l0zGo3k6nIBOJp6hPYVO/JoQGOitNFWjkyIB5MkRUKIcinarwFGjOxO3mkq25W0gyhtNFG+9cySol1JO1EpVNT1jTKVnUpP5LnfniZ0egAVp/vTK/5JTqQdN7uGzxQXpu2bwofbxxDxXUWqzQplxKZh5OnzzI7bdn4rzX9+lMBYb1r/2pSdF8x7sP7z28l42vzajMBYb6rPqsyYP0ZRoC8w7Z+wczzh34Ww/cI22v7aDOcJjiw7vhifKS7ojXpGbhmBzxQXXl/3sim+mX/Hml3jvzqEEEVJUiSEKJdqeNXCwcbBlPwYjUb2JO8iyjeaKG30DUnRDiI8q+OkLnymUp4+j+7LHuNo6j981vwrvmo5ldMZp+i6pAOpuVfMrjN139ckZV3gm9bTeTVyMHMPzGJawlTT/qSsCzwd1x13jTszY+byXLW+vLr2BXJ0OWb1LD22iL4rn+URnzrMbf8Tb9Z7m+8PzuaD7e+bHZejy+H1dQN4ttrzxD0VTzXPGqzothaAV2q/zopuaxkWNbzE2lGIh4nMKRJClEs2ShsifeqwK6mwp+ho6hHS89Op41sXF7UL47a9R74+H7VKze6knXQM7Ww698dD8ziXcYZtz/xJiGtFAOr6RlFvXi3mHpjF4LpvmI4NdA5icqtvAWgZ1JqdSdtZcWIZrz8yBIDYfVPQqDT80PFXHGwdAHCwdeDVtS+a6jAajYz5YxRPVn2aCc0+N5VrVBre3vQGg+sOw8POEyhMisY2+oj2FTsWecBnkEuQDJ0JcQ+kp0gIUW5FaxvwZ/Ie9AY9u5J2UNU9Ame1CzW8agGQkPIXp9ITSc5Oop62vum8vRf3UMu7tikhAvB3CiBa24AdF7aZXaN5YEuz7aru4ZzPPG9WV7PAFqaECKBj6GNm5xy/eoyzmWfoUulxdAad6V/jgKbk6nM5fPmQ6VgFCloFtbmHVhFC3Iz0FAkhyq162mi++HMiBy7/ze7knabEx1ZlSy3vSHYl7TTdgRbtd22SdXJ2Et72PkXq83bw5kzGGbMyV42r2bat0pY8fa5p+2J2MtU8q5sdY29jj6Otk2n7cu5lAJ6O72HxdZzLPGv62U3jJneXCVFKJCkSQpRb9bT1UaBgV9IOdiXt4LV/h7QA07wibwdv/Bz9CXQOMu3zddDyz5VDRepLyU7BXeN+RzH4OPhyKSfFrCxHl0PWdesC/VfnpOZfUfPfXqzrBbkEm35WKBS3fW2NSkP+dRO1Aa7mpd7kaCGEDJ8JIcotNzt3wtyrsPbUao6mHqHedfNtonyj/02WdpoNnQHU8Y1i379Da/+5kHmeXUk7qO/X8I5iiPSpw8Yz68kuuDb3J/7EMrNjKruH4efoz5n000T61Cny77/5RHfKz9Gfo6n/mLYNRgNbzm26q7qEeBhIT5EQolyrp63P/EPf427nTiW3sOvKo0nOTuJidjI9qz5jdk7P8GeZvPcLno7rzojokSgVKibu+ggPe0+eq973jq4/oNarzNo/nV4rnuTl2gNJykriqz8/w97G3nSMUqFkzKMfMnDdS2QUpNMqqA22SjWn0hP57WQcM2O+N5uTdLs6hHZm1v7p1PCuRYhLCPMOziUjP+OO6xHiYSFJkRCizLuQlsuMzYfZdeYCjmpbHqtWmbbhvqiUxQ8lRWsb8MOhudT1rWdW7uuoJdA5iDMZp4v0FGlUGhZ0XsroP95lyPrXMBqNNApozKx283C387ij2P2c/JnfcQHvbh5Ov5W9CXOvyjetp/P8bz3Njusa1h1ntTNf/DmJHw/NQ6lQEewSQtuQmLueQ/Rmvbe5lJPCxzvGYatU07/mS4R7RPDd/ml3VZ8Q5Z3CaDQarR1EaSko0JvdrmoNN94yKwpJu1gm7VLUxYw8hi3fxrn8eJSafzAYHFDnNadNxSa82aJ68RWUY/J+sUzaxbLy0i7e3s6lVrfMKRJClGkL9iVyQbcSG8edKG3SsFFfQO/0CxtO/kPi5Qf/A14IUXZIUiSEKNN2nb2AUnPErEyh0JOr+JvDF+XJ7kKIkiNJkRCiTHO1U2PQOxYpt1F44KBWWSEiIUR5JUmREKJMe6xaGJr8lhgNtqYyXb4fzopq1Atys2JkQojyRu4+E0KUac3DvEhMb8aiBE/ylAewxRV3RVXea1sPe1vpKRJClBxJioQQZZpSoWBY6xrEhPlxKKkhDmoVdSq4YicJkRCihElSJIR4IAS42hPgal/8gUIIcZdkTpEQQgghBJIUCSGEEEIAkhQJIYQQQgCSFAkhhBBCAJIUCSGEEEIAkhQJIYQQQgCSFAkhhBBCAJIUCQHAmYzTvLr2RR6ZW43AWG8i50Tw3IqebDu/1dqhWeQzxYWZf8daOwwhhChXZPFG8dC7mptK+4Wt8HXQMrLBe2gd/TiTfpqViSvYlbSThv6NrB2iEEKI+0CSIvHQW35iKSnZF1n/5B94O3gXFgbA0xG9MBqN1g3uBjm6HOxtZFVnIYQoDTJ8Jh56aXlpqFVq3O3ci+xTKBSmn7su6UC/lb3N9m89txmfKS4cunwQgNPpp/CZ4sLCI7/w6toXqTjdn2qzQvl010dF6j50+SDPxPWg4nR/Kk73p/+q50jKTCpS9++n19J7xVOETPPjnU1vmvbn6wsYuXk4VWYGUXlGIO9sfpN8ff49t4cQQjysJCkSD71a3rXJ0+cxcO2L7Lu4F4PRcM91jtk2CnsbB2bGzKVXRB8m7vqYmX9PM+0/kXacTovbkqfP45tW0/iq5VT+uXKIx3/tWqR3auj616juWYPvO/zEMxHPmcqn7pvM+azzTG09g6F13+L7A7MZv2PsPccuhBAPKxk+Ew+9phWaM6D2QKbtm8LiYwtxsnWmWWAL+lTvT7PAFndVZ1X3cCY1/xKAlkGtuZSTwpd/TqJvjRdQKpRM3PUxPg4+/NhpIWqVGoDqntV59Mco1p5aRZuQdqa6HqvUlbfrjypyDSdbJ2bGzEWpUNIquC35+jy++HMig+sMw93O467iFkKIh5n0FAkBjGv0Edue/ZP3Gn5Ao4DGrD+9lieXd2X2/pl3VV+H0M5m2x1DO5OUdYHzmecA2HR2Ax0qdkapUKIz6NAZdAS5hBDiGsJfKXvNzm0dHGPxGu0qdkSpuPYr3DH0MXJ0ORy+cuiuYhZCiIed9BQJ8a9Q10oMfGQQAx8ZxOWcyzy5vCvjd4zh+er9zOYW3Q4ve2+L28nZSVRwDuRK7mUm7/2cyXs/L3Luf4nTf7wdfG7vGg5ehdfISrJ0uBBCiGJIUiSEBZ72njwd/izvbhlOSk4KPg4+aFQaCgzmE5lTc1Mtnn8pJ8Xitq+DFgA3jTsdKnamV7XnzI5zdrbDtsDRrEyB5YSsyDWyLxVew1F7q5cmhBDiJmT4TDz0LuVcslh+Iu04GpUGF7ULAP5OARxNPWJ2zMaz6y2eu+LEcrPt+BPL8XXQ4u8UAEDTCs04fOUgtb0fIdKnjulfXb8oglyCbyvulSfjzSaFx59Yhr2NPeEeEbd1vhBCCHPSUyTKHYPRSOKVbAxGqOjhgEp566Gvnw/PZ+HRX3iyak+qe9akwFDA5rMbmbV/Bn1q9MfOxg6ADhU78cOhuYza8jatg2PYem4z68+ss1jnP6mHeWPDYDqFPsb2C1v54dBcPmz8iWkO0Fv13iFmQUueie/BMxG98bDz5ELWebYlb6Zb6FM0CmhS7OvMLMik/6rn6F3teQ5fOcyk3Z/Qr+ZLMslaCCHukiRFolw5kJTBhPV/ciU3CRQGXG21vNmsDpEVXG96TuvgtpzOSGTewTmcyzyHSqEixLUi45t8Su9qfUzHtQlpx8j67zFr/wzmHZpLu5AOfNDoY577rWeROkc3HMuaxJX0W9UbO5WGYVHD6V9zgGl/Jbcwfuu+jo92jOONDYPI1eWidfSjdWhrKrqG3tZrfaX265xKP8mANf0xGA08W+05RjZ47/YbSwghhBmFsawt2VuCCgr0XL2abdUY3NwcrB5DWVQa7XIpK58Bv27gis0cbDRnAdDla3Er6MOUbi3wc7Er0etZcjr9FFHzajKvw8+0DWl/x+fL+8UyaRfLpF0sk3axrLy0i7e3c6nVLXOKRLmx/mgyacZtpoQIwEadxFU28tuhc7c4UwhhTd4+LqZ/Xr6ueNSsgvOLfVCeSryt8x0mjMczPMS0rdi4AW8fF1SHDppdw27mtYcou3btgEs/8xXqS4pm6SI0P/1QKnWL0iXDZ6LcOJuWjk55Htsbyo2qZM6lpVklJiHE7cl+5XXyOncBoxHV6VM4TBiP67NPkLphG9jc+k9Vbq/nyY+5s57ZzE8+A9vS+ROoWboYxZXL5PV8tlTqF6VHkiJRboR7e/LbiTCMHDYrt9FVIsLH+yZnlawgl2Auvpp+X64lRHmiDwpCFxUNgK5efYyurrg+8wSq48fQVw23fFJBASiVGPwDMPgH3Nn1blaneKjJ8JkoN5pU8sJXUw9d1iMYDTYYjSoKsmvgbfMoravK2j1CPEiMTv/OGykoMJX9N+RlN3cWHvVq4RXojTLpQpHhs9tx4/CZ6ugRnF/qg0dkBF7Bvrg3icY+9hswXFv2wnbrZrx9XLDduhmX/s/hFeKHR1Qt7L6bbjrG+fWX0cQtRf3HFtOQoMOE8XfXCOK+k54iUW44aWz4tFN9Yre5svtsMkYg0t+LlxtWw83+xkE1IURZojAYQKf7d/gsEYcJ49GFVkIfUc3sOJud21EmniRz9Fiwt8fo4lIi11deOI++Uhh53Z/C6OSEzf4EHCZ8BLm55Ax+w+xYp2Gvk/fUM+T07oNm8QKc334DXeQj6OpEkTVsOMpzZ1GkpZH5ySSAO+7FEtYjSZEoV7QudrwXE0m+zoAR0NhIZ6gQDwKnkSNwGjnCtK33DyBt/gJQqcyOU6ankbpuC0Zf3xK9fkHT5hQ0bV64YTRSUL8hipwc7L+fUyQpynu8B9nDhhee16gJmtW/oYlfjq5OFIaKoRjd3MFgMA0HigeHJEWiXFJLMiTEAyV74GDyujwOgPJSCnbfTcf1mR5c/W0dBj9/03G6WpElnhABkJuLw5eTsFv4S2FPz3XDduh0ZpO985u3urbP1hZ9xUooz8sdruWBVf5yfPHFF3Tu3JkuXbrQr18/kpOTATAajXzwwQe0adOGzp07c+DAAdM5ixcvpm3btrRt25bFixdbI2whhBClRF+hArrIOugi65DfOob0WT+gyMvF/ttvzI4zeFt+QPK9chw3Goepk8np3Ze0+QtIXb2BrGFvFe7MzTU71uhqvhisUa1GkZdXKnGJ+8sqSdELL7zA8uXLWbp0Kc2bN+ebbwrf9Js2bSIxMZHVq1czbtw43n//fQCuXr3K119/zS+//MKvv/7K119/TZrcYi2EEOWXRoM+OATV0X/MyxW3fmzPXV9u2RJy+g8g5/UhFDRrgS6yDqhkMOVhY5WkyMnJyfRzTk4Oin/f5OvWraNr164oFAoiIyNJT0/n4sWLbNmyhUaNGuHm5oarqyuNGjVi8+bN1ghdCCHE/ZCbiyrxJAb/CvflcorcXIxq9bUCvR7NkoV3VZdRbYsiL7f4A0WZY7U0+PPPP2fJkiU4Ozszd+5cAJKTk9Fqr906rdVqSU5OLlLu6+trGnITQghRdiSl55KckYevswbtHTxaR3X6NDa7dwKgvHwZ+1nTUaSnk/ts6aw6faP8Zi2wnzUdfcVQjO7u2H03HUVe/l3Vpa9cBc3KFahXxGHw98eg9cOg9SvhiEVpKLWkqE+fPly6dKlI+ZAhQ2jdujVDhw5l6NChxMbGMm/ePAYNGoSlx7ApFIqblhdHpVLg5uZwdy+ghKhUSqvHUBZJu1gm7WKZtItlZaldcvL1fLn0T/7Zf5JK6Ukcd9FStUZFBnepg71aVez5DlMn4zB1MgBGT0+MNWqgX/EbTs2bmI5R2SjBVlXkNSvtbFEorn3eK5WFgyDOznZw3bEO9mrs/t0uUtc3X8PAV3F+502wt8fQqzfGHt3hlZcLj3FyQOGksVhvkbqGDMJ4+AAuQweiSE1F/79RGEZb/2HNZen9UlZZ/YGw586dY8CAAcTFxTF69Giio6Pp1KkTADExMXz//ffs3LmTnTt3MnbsWIAix92MPBC27JJ2sUzaxTJpF8vKUrtMW3MQTfwTQmZWAAAT+UlEQVQyXjm4GrVRT75CxbcRbcjt1IWX2lQrvoISVJbapSwpL+1S7h4Im5iYaPr5999/JzQ0FICWLVuyZMkSjEYjf/31F87Ozvj4+NC4cWO2bNlCWloaaWlpbNmyhcaNG1sjdCGEEDfIzNOx9+Bp+h9eh9qoB0Bt1NPvn9/Ze/A0mXk6K0f4//buPbjK+s7j+PvkfiUxTA8HaaRAV3AYhF23aAFlDSYBY+RibOuFXdGdiqKIrs5aL3WwispoURmHSSkOVjq6gxSdehlXcYzQ4jLOiKiDFi9UUDhUgYCQe377R+qBQNQ4JZxc3q+ZzCS/c86T7/MdzpPP+f2e50HqnKScU/TAAw/w8ccfE4lEGDRoEPPnzwdg4sSJ1NTUUFpaSnZ2NgsWtN0avbCwkKuvvpqqqioA5syZQ2FhYTJKlyQd4cuGZvIbD5LX0v4cnLyWRvIbD/JlQzN5mV7Jpe4vKf9KFy9e3OF4JBLhjjs6XnetqqpKhCJJUvfRPzeDurwCPssq4MT6Q7dL+SyrgLq8AvrnZnzDq6Xuw9v+SpL+IempKUwZP4J7x/6E9/MGEIC/5EW5d+xPmDJ+BOmp/qlRz+B8piTpHzZl1IlkZZbxwKBidu1vIJqfyeTxp3D2yd9LdmlSpxmKJEn/sEgkQsnwKCXDo7SGQEoX3Xla6krOaUqSjikDkXoqQ5EkSRKGIkmSJMBQJEmSBBiKJEmSAEORJEkSYCiSJEkCDEWSJEmAoUiSJAkwFEmSJAGGIkmSJMBQJEmSBBiKJEmSAEORJEkSYCiSJEkCDEWSJEmAoUiSJAkwFEmSJAGGIkmSJMBQJEmSBBiKJEmSAEORJEkSYCiSJEkCDEWSJEmAoUiSJAkwFEmSJAGGIkmSJMBQJEmSBBiKJEmSAEORJEkSYCiSJEkCDEWSJEmAoUiSJAkwFEmSJAGGIkmSJMBQJEmSBBiKJEmSAEORJEkSYCiSJEkCDEWSJEmAoUiSJAkwFEnqQtFofodfY8fmHvda/vSnVKLRfDZvPnTYi0bzWbYsvUt+3+LFGfzpT6ldsm1JXSMt2QVI6t2uuqqRysqmdmOZmUkq5jh65JF0Lr8cxo9vSXYpkjrJUCSpS510Uiv/+q+tyS5Dkr6Vy2eSkqa2FkaPzmXOnKx24zNnZnPGGbkcPHho7Lnn0igvz+Gkk/IYPjyPiy7KZtu2SOLxzZtTuPjibIYMyWPIkDyuuCKLeDzCd/XCC2mUluZQXJzHyJG5zJ+fSdNhE10LF2YwYkQub7+dwpQpOQwenEdJSQ6vv35oqey003LZvTuF++/PTCwZupQmdX+GIkldqrUVmpvbf7X+feKooAAWLapn5cp0nn++beL6iSfSeOmlVB5+uI6cnLbnrVgRYdasbH7wg1aWLq3joYfqGDaslS++aAs9H30U4bzzcmhogEceqefhh+t5//0UZs7MJoTO1/rMM2nMmpXFP/9zC7/7XR033tjI44+nc9dd7df76uoiXHNNFv/+7408+mgdmZlw2WVZiRC3fHkd/foFLrmkkeefP8Dzzx/g1FNdRpO6O5fPJHWpW2/N4tZb288E/fSnTSxeXA9ASUkLM2c2cuONmRQXt3L77VlcfXUjY8e2JafWVrjttgjnnttEdXV9YhuTJx8KGW0zMoEnnqgjI6NtbOTIFsaNy+Xll1MpLf32QBICzJ+fyU9+0szChQ1/H20hMzNw881ZXHddA0VFbaN1dRHuuquBM89s2240Ws+kSbm8/noqJSUtjBrVSlpaYODA4NKh1IMYiiR1qTlzGpk6tf2J1kVF7adv5s9voKYmjXPPzWHIkFb++78bE4998EEKn30W4b772m/jcK+9lspPf9pESkrbTBTASScFiosDGzd2LhR9+GGE7dtTmDq1PrENgAkTWqivj/Dee6mMG9e2nfT00O4E6uHD24LPZ5+lAM4IST2VoUhSl/r+91sZM+abZ0vy8qCsrJnf/jaDiy9uand12u7dbUtkAwZ8/TrY7t0RFi/OZPHioy9r++yzzp1X9MUXbWcTXHRRToePf/rpoe3k5wdSDjv54KvZqYYGJPVghiJJSbdxYwrLl6czalQLixZlMn16cyIEfTWr9E0nTRcWBs49t4lLLz16NunIWamvc8IJbc974IF6Ro06erbnpJNcBpN6O0ORpKSqr4drrsni7LNbWLq0jrPPzuXGG7N4/PE6AH74w1YGDQr8z/+kU17e8dLUWWe18N57KYwe3Urku19wlvg9Awe2sm1bhJkz//EAlJ7uzJHU0xiKJH2rhuZWXt+6m3e3H2RAQTr/dnJ/+udmdOq1n3ySwhtvtL/QNRKB005rCx733JPJrl0prFp1gJwcWLy4jvPPz+HJJ9P42c+aSUmBBQsC//Ef6cyeDTNmNBGJwNq1acyY0cSYMa3cdFMD5eW5XHxxNhdf3ERRUWDHjgg1NWn87GdNnbqBYkpK27lNc+ZksX9/hEmTmklPh7/+NYUXXkhj2bJDV8N1xj/9Uysvv5xGSUkLubmBH/6wlby8zr9e0vGX1FC0bNkyFi5cyPr16ykqKiKEwN13301NTQ1ZWVnce++9jBw5EoDVq1ezZMkSAK666iqmT5+ezNKlPmPvwSZuWrmFbe/nULftBNIL6/j9ye9zx4yTGD2o4Ftfv2RJBkuWtA9QqamBHTu+5P/+L5Xq6nQeeaQ+sVw2dmwrs2c3cdttWZx11gFOPDFw0UWBlpY6HnwwgyuuyCYnB047rYX+/dteM2xY4IUXDnLPPRn8139lUV8PsVjgzDObGTKk87M+06Y1k5/f9nueeCKblBQYPLiVsrLmxHlDnXXHHQ3cfHMWl1ySzcGDEVavPujdraVuLhLCd7mLx7GzY8cObrvtNj766CNWrVpFUVERNTU1PP744yxdupS33nqLu+++m5UrV7J3714uuOACVq1aRSQSYcaMGfzhD3+goOCbD8hNTS3s3XvwG5/T1QoLc5JeQ3dkXzrWHfvy4Mt/5ZmV2aR99IPEWHP+PgaWb+Z3/zmStNSuv91Zd+xLd2BfOmZfOtZb+vK97+V32baTdvPGe+65h5tuuonIYScArFmzhmnTphGJRBgzZgz79u1j165drFu3jvHjx1NYWEhBQQHjx49n7dq1ySpd6jNCCNS8t5eUTwa1G0/b3499u7J4/28HklSZJB17SVk+W7NmDdFolBEjRrQbj8fjxGKxxM+xWIx4PH7U+IABA4jH49/6e1JTIxQWfoeTALpAampK0mvojuxLx7pbX0IIhEiEtEgqkSNmhFJIIycn47jU29360l3Yl47Zl47Zl2/XZaHosssu4/PPPz9qfN68eVRXV/Poo48e9VhHK3mRSORrx79NS0tI+lRhb5muPNbsS8e6Y1/G/iCP/43uIP3TQ7NFLdkHySvaz4k56cel3u7Yl+7AvnTMvnSst/SlK5fPuiwULV++vMPx999/n+3btzN16lQAdu7cyYwZM1i5ciWxWIydO3cmnrtz506i0SixWIwNGzYkxuPxOGPHju2q0iUd5vIzT+TdTz8g/k49LTtPIOTW0W/Up1w3+ftkpvnfJ0rqPY778tnw4cNZv3594ueSkhKeeuopioqKKCkpYcWKFVRUVPDWW2+Rn59PNBplwoQJ/PrXv6a2thaAdevWccMNNxzv0qU+KdYvi0dmDuelzZ+zaeunxE5IZ8qpQxjaPzfZpUnSMdWt7lM0ceJEampqKC0tJTs7mwULFgBQWFjI1VdfTVVVFQBz5syhsLAwmaVKfUphdjoX/stALvyXZFciSV0naZfkHw9ekt992ZeO2ZeO2ZeO2ZeO2ZeO9Za+9MpL8iVJkroTQ5EkSRKGIkmSJMBQJEmSBBiKJEmSAEORJEkSYCiSJEkCDEWSJEmAoUiSJAkwFEmSJAGGIkmSJMBQJEmSBBiKJEmSAEORJEkSYCiSJEkCDEWSJEmAoUiSJAkwFEmSJAGGIkmSJMBQJEmSBBiKJEmSAEORJEkSYCiSJEkCDEWSJEmAoUiSJAkwFEmSJAEQCSGEZBchSZKUbM4USZIkYSiSJEkCDEWSJEmAoUiSJAkwFEmSJAGGIkmSJMBQdMw8+OCDVFZWMnXqVC6//HLi8TgAIQTuuusuSktLqays5N133028ZvXq1ZSVlVFWVsbq1auTVXqXuu+++5g8eTKVlZXMmTOHffv2JR6rrq6mtLSU8vJy1q5dmxh/7bXXKC8vp7S0lN/85jfJKLvLvfDCC1RUVDBixAjefvvtdo/15b4cqS/u8+F+8Ytf8OMf/5jzzjsvMbZ3715mzZpFWVkZs2bNora2FvjmY01vsmPHDmbOnMmUKVOoqKjgscceA+wLQENDA1VVVZx//vlUVFTw8MMPA7Bt2zYuvPBCysrKmDdvHo2NjQA0NjYyb948SktLufDCC9m+fXsyy+8ego6J/fv3J75/7LHHwu233x5CCOHVV18NV1xxRWhtbQ1vvvlmqKqqCiGEsGfPnlBSUhL27NkT9u7dG0pKSsLevXuTUntXWrt2bWhqagohhLBw4cKwcOHCEEIIW7ZsCZWVlaGhoSF88sknYdKkSaG5uTk0NzeHSZMmhU8++SQ0NDSEysrKsGXLlmTuQpf44IMPwocffhguvfTSsGnTpsR4X+/L4friPh9pw4YN4Z133gkVFRWJsfvuuy9UV1eHEEKorq5OvKe+7ljT28Tj8fDOO++EENqOu2VlZWHLli19vi8hhNDa2hq+/PLLEEIIjY2NoaqqKrz55pth7ty54dlnnw0hhHD77beH3//+9yGEEFasWJH4W/Xss8+G6667LjmFdyPOFB0jeXl5ie/r6uqIRCIArFmzhmnTphGJRBgzZgz79u1j165drFu3jvHjx1NYWEhBQQHjx49vNyvQW0yYMIG0tDQAxowZw86dO4G2vlRUVJCRkUFxcTGDBw9m06ZNbNq0icGDB1NcXExGRgYVFRWsWbMmmbvQJYYNG8bQoUOPGu/rfTlcX9znI/3oRz+ioKCg3dhXxxSAadOm8fLLL7cbP/JY09tEo1FGjhwJtB13hw4dSjwe7/N9AYhEIuTm5gLQ3NxMc3MzkUiE119/nfLycgCmT5+eeB+98sorTJ8+HYDy8nLWr19P6OP3czYUHUOLFi1i4sSJ/PGPf+S6664DIB6PE4vFEs+JxWLE4/GjxgcMGJBYcuutVq1axVlnnQUc3Zev9r8v9uVw9uWQvrjPnfHFF18QjUaBtoCwe/du4OuPNb3Z9u3b2bx5M6NHj7Yvf9fS0sLUqVMZN24c48aNo7i4mH79+iU+nB6+//F4nIEDBwKQlpZGfn4+e/bsSVrt3UFasgvoSS677DI+//zzo8bnzZvHOeecw/XXX8/1119PdXU1K1asYO7cuR2m7kgk8rXjPdG39QVgyZIlpKamcv755wN87f63trZ2ON4TdaYvR+oLfems3vQeOR76Wr8OHDjA3LlzueWWW9rN1B+pr/UlNTWVZ555hn379jFnzhw++uijo57z1f73td50hqHoO1i+fHmnnnfeeedx5ZVXMnfuXGKxWGLJCGDnzp1Eo1FisRgbNmxIjMfjccaOHXusSz4uvq0vq1ev5tVXX2X58uWJN9yRfYnH44lPeV833tN09t/L4fpCXzrrm3rRl/Xv359du3YRjUbZtWsXRUVFwNH9+upY0xs1NTUxd+5cKisrKSsrA+zLkfr168fpp5/Oxo0b2bdvH83NzaSlpbXb/1gsxo4dO4jFYjQ3N7N//34KCwuTXHlyuXx2jGzdujXx/SuvvJI4X6SkpISnn36aEAIbN24kPz+faDTKhAkTWLduHbW1tdTW1rJu3TomTJiQpOq7zmuvvcbSpUtZsmQJ2dnZifGSkhKee+45Ghsb2bZtG1u3buXUU09l1KhRbN26lW3bttHY2Mhzzz1HSUlJEvfg+LIvh/TFfe6Mr44pAE8//TSTJk1qN37ksaa3CSFw6623MnToUGbNmpUY7+t9Adi9e3fiCt/6+nr+/Oc/M2zYME4//XRefPFFoO1D6lfvo5KSksSVzy+++CJnnHFGn58pioS+flbVMXLttdfy8ccfE4lEGDRoEPPnz2fAgAGEELjzzjtZu3Yt2dnZLFiwgFGjRgHw1FNPUV1dDcDs2bO54IILkrkLXaK0tJTGxsbEp4/Ro0dz5513Am1LaqtWrSI1NZVbbrmFiRMnAlBTU8OCBQtoaWnhggsu4Kqrrkpa/V3lpZde4le/+hW7d++mX79+nHLKKSxbtgzo2305Ul/c58PdcMMNbNiwgT179tC/f3+uvfZazjnnHObNm8eOHTsYOHAgDz30EIWFhd94rOlN3njjDS655BJOPvlkUlLaPtffcMMNnHrqqX26LwDvvfceN998My0tLYQQmDx5Mtdccw3btm3j+uuvp7a2llNOOYX777+fjIwMGhoauOmmm9i8eTMFBQUsWrSI4uLiZO9GUhmKJEmScPlMkiQJMBRJkiQBhiJJkiTAUCRJkgQYiiRJkgBDkSRJEmAokiRJAgxFknqwTZs2UVlZSUNDAwcPHqSiooK//OUvyS5LUg/lzRsl9WiLFi2isbGR+vp6YrEYV155ZbJLktRDGYok9WiNjY1UVVWRmZnJk08+SWpqarJLktRDuXwmqUerra3l4MGDHDhwgIaGhmSXI6kHc6ZIUo82e/ZsKioq2L59O3/729/45S9/meySJPVQzhRJ6rGefvpp0tLSqKys5Oc//zlvv/0269evT3ZZknooZ4okSZJwpkiSJAkwFEmSJAGGIkmSJMBQJEmSBBiKJEmSAEORJEkSYCiSJEkCDEWSJEkA/D8RnR2rFJYRKwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 648x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "word_vectors_plot(model, \"excellent\", [t[0] for t in model.wv.most_similar(positive=[\"excellent\"], \n",
    "                                                                             topn=16)][8:])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Eight Most Similar Words Vs. Ninth To Sixteenth Most Similar Words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "word_vectors_plot(model, \"comedy\", [t[0] for t in model.wv.most_similar(positive=[\"comedy\"], \n",
    "                                                                             topn=16)][8:])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Eight Most Similar Words Vs. Ninth To Sixteenth Most Similar Words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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M3vQwBoOB7pV68VS90RxPOUZiRiKvN3sTgFI+pW9U+SIiUkwoTMktw2qzYraaSTensfbgJ3x6cDWDag7Jte/59HPM+XkmT9YdxVP1RgPQMrI1J1KOM/m7V+leqRfl/MsT4B6I1Wa1Lx+KiIhcK4UpuWU8t/0Zntv+DAAGDPSuci+jYsfm2vf3c/tINafSuUI3h/YuFbsz4ovBxKfGE+oVWug1i4hI8acwJbeMobUep0vFbni4eFLWLwpPF888+8ZdOAWQIzCF/bMsmJiRoDAlIiIFQmFKbhkRvhHUCqtzVX1LeIcDcCb1DEEewfb206mnAQjwCCz4AkVE5Lakb/NJsVQ16A68XLxY89dKh/Y1B1ZSIaAiIZ4hALiZXMmwpBdFiSIiUkxoZkpuuDMpGWzcf4rDCQlUDAmmdaUSBHi5Fug9Aj2CGFRzCFN+mIyL0YWY0NqsPfgJm45sYHab9+39KgZU5rND61h38FNK+ZQi3Lsk4d4lC7QWEREp3hSm5IbaH5fCc5/t5LxtGxbjcVyORrHs58a83qEhZYO8CvRez8Q+h8loYv6vc4lPO005//LMbP0u3Sr1tPcZWOMRfjnzM09sGUJCRgIj641hdOyzBVqHiIgUbwabzWYr6iIKS1aWhYSE1KIuwy4gwOumqqew/Xu8NpuNQR9/zYGMObh4HLa3Z6VWpV7gQ7zR+dZ+PMHt9Pt7O40VNN7iTuO9tYSG+hZ1CTloz5TcMCeTMjiVEo/J/bBDu4vnfn6PP0NSelYRVSYiInL9FKbkJmDAUNQliIiIXCeFKblhSvq5U9InFEtGlEO7Oa0yVcNC8PMo2E3oIiIiN4LClNwwBoOBp++qRYj1fmwXWpKVVgkutCXc+B+GNa5R1OWJiIhcF32bT26oKmE+vNOzOZv+qMLh8wlUCgmmZaUw/D01KyUiIrcmhSm54YK93fhP7UggsqhLERERcZqW+UREREScoDAlIiIi4gSFKREREREnKEyJiIiIOEFhSkRERMQJClMiIiIiTlCYEhEREXGCwpSIiIiIExSmRERERJygMCUiIiLiBIUpEREREScoTImIiIg4QWFKRERExAkKUyIiIiJOUJgSERERcYLClIiIiIgTFKZEREREnKAwJSIiIuIEhSkRERERJyhMiYiIiDhBYUpERETECQpTIiIiIk5QmBIRERFxgsKUiIiIiBMUpkREREScoDAlIiIi4gSFKREREREnKEyJiIiIOEFhSkQkF0eTjzBk0yPUXlAN39e9qfXBHTywrg87T3xt77P6wAqW/L6oQO/79fFthM30Y9/ZvQV6XREpPC5FXYCIyM0mIf087Ze3ooRXOM81fJGKYVHsO/knn/29ju9O7aJRqcYArD6wknPpZ+lTtW+B3btmaAzrum8iyr9cgV1TRAqXwpSIyL98cnA18amn2dJ7B6FeoQQEeFEroAH33nE/NputUO5ps9nIsGTg6+ZHvfDYQrmHiBQOLfOJiPxLYkYibiY3Aj0CcxwzGAwADN/8GJ8eXM2OE9sJm+lH2Ew/Ju2aaO8395fZNFhUi4h3QohdGMM7e6Y7XGfSrolUfT+Kb07upO3HzSkzO5Q1f63MdZnParPy9o9vEbswhoh3Qmi4qHaO5cVvTu6k08p2lH+3NOXfLU2LpY1Zc2BlQX4sIpIHzUyJiPxLzdAYMiwZDN30CENqjaCpf6McfZ6qN5rjKcdIzEjk9WZvAlDKpzQAH+6dz9hto3gsZhgtyrTi6+PbePHr58i0ZDKizlP2a6SZ0xi++VGG1X6CCgEVCfcqSVzqqRz3GrttJEt//x9P13+GmiExfHlsC09sGUqQRxBto9qTnJnE/Wt7c3e5e3i63jPYbDb2ndtLYmZiIX1CInI5hSkRkX9pFnEXj8YMZc6emaw8sBxfN1+aRbRgQPWHaF6mBQDl/MsT4B6I1WZ1WJaz2qxM/u5V+lTty/jG2TNVLSJbkZSZyNQf32JQzSF4uHgA2WFqfONXaV+ug/38f4epg4l/Mf/XuUxtOdO+N6t5mRbEXTjFG9+9Rtuo9vyVcICkzERea/oGPm6+9nuKyI2hZT4RkVy83PhVdvb9kRcbvUKzss3ZcmQTvT/pyvxf5+Z73omU45y6cJLOFbo6tHet2IPkzCT2nf3N3mbAQKvINvleb9uxLzEajHQo3wmz1Wx/NY1ozq9nf8FitRDlVw5vVx8e2/gQ6w+tJTEj4foHLiLXTDNTIiJ5KO9fgaG1RxAQMIa/Th6l9yddmfjtS/Sv/qB979S/XZxZCvUMc2gP9cp+fz7jvL0twD0AN5NbvjWcSzuLxWahwnsRed6vlE9pPuq0kje+e41HPu+PFSt3RbRkYtPJ+lagyA2gMCUichWCPYO5t2pfnt0+mvi0eMK8wnLtV8IrHIAzafEO7fGppwEIdL+0qT2vQHa5AI9AXIwufNptA0ZDzsWEEM9QAOqHN2Bpp5WkmdP46thWxn09lsGbHmJ9jy+uboAict0UpkRE/uVM2hlCPENytB9M/At3kzt+bn4AuJlcybCkO/Qp5VOacO+SrPlrFa3KtrW3r/5rBb5uftwRXP2aamlaujkWq4WkzCTuKtPyiv09XTxpF9We38/uZeqPb13TvUTk+ihMiUixl2Wx8uPRRBLTs6gY6k35YO98+y/9fTHL//yI3lX6UD04GvdzRtb/voF5v77HgBoP2TeQVwyozGeH1rHu4KeU8ilFuHdJwr1LMqr+WEZufZxAjyCaR7Rg54mvmf/rXJ5r+KL93KtVMbAS/as/yKMbBjK09hPUCqtNhjmd38//zsGEA0xpMZ2Nf3/G4t8X0r5cByJ8ynDywgkW7J1Hk4hm1/2ZicjVU5gSkWLt4NkLvLD+O86b95PJaTys1WgQUZExraJxNeX+HZzWZdtyJPlvFu79gOMpx3ExmijrV46JTSfTr9oAe7+BNR7hlzM/88SWISRkJDCy3hhGxz5Lv2oDyLRkMHvPTN79eRYlfUrzUuMJPBYz7LrG8Hqzt6gQUJGFez9g0q4J+Lr5UjmoKvdV7QdAOf8KGDAw8ZvxnEmLJ9gzhDZl7+a5huOu634icm0MtsJ6nO9NICvLQkJCalGXYRcQ4HVT1VPYNN7i61YZq8Vq48Gl2zhsfh9Xj0MA2GxGDCldeKhWd/rUibyq69wq4y0oGm/xdquPNzTUt6hLyEGPRhCRYmtfXDJnM/62BykAg8GKxXMrn+77qwgrE5HiRGFKRIqtCxkWrIakHO1GYyoXMi1FUJGIFEcKUyJSbFUO88bVUh6b1d2hPSu9ArVKBRdRVSJS3ChMiUixFejlRvfoqrheuB9zRhmsZn+yLsQQYutG/3pViro8ESkmivzbfBaLhR49elCiRAlmz57N0aNHeeqpp0hMTKRatWpMmjQJNzc3MjMzGT16NL/99hsBAQFMmTKFiIjcnwgsInLRA/XKUT7In+W/VOJcajo1y4bxn1rlKRPoWdSliUgxUeQzUwsWLKBChQr292+88QYDBgxgw4YN+Pn5sWzZMgA+/vhj/Pz82LhxIwMGDOCNN94oqpJF5BZiMBhoWiGY/3ZtxIL7WjCyRXUFKREpUEUapk6dOsXWrVvp2bMnADabjW+++YZ27doB0K1bNzZv3gzAF198Qbdu3QBo164dO3fupBg/1UFERERuEUW6zDdx4kRGjRrFhQsXADh//jx+fn64uGSXFR4eTlxcHABxcXGULFkSABcXF3x9fTl//jxBQUF5Xt9kMhAQ4FXIo7h6JpPxpqqnsGm8xdftNFbQeIs7jVecVWRhasuWLQQFBVGjRg2+/fbbPPtd/EGguc1CXemHhFostpvqwWS3+oPSrpXGW3zdTmMFjbe403hvLTfjQzuLLEz9+OOPfPHFF3z11VdkZGSQkpLChAkTSEpKwmw24+LiwqlTpwgLy/7J7OHh4Zw8eZLw8HDMZjPJyckEBAQUVfkiIiIiQBHumXr66af56quv+OKLL3jrrbdo2LAhb775Jg0aNODzzz8HYOXKlbRsmf1T0lu2bMnKlSsB+Pzzz2nYsOEVZ6ZERERECluRf5vv30aNGsW8efNo06YNCQkJ9OrVC4CePXuSkJBAmzZtmDdvHiNHjiziSkVERET0g45vqFt9nfpaabzF1+00VtB4izuN99ZyM+6ZuulmpkRERERuJQpTIiIiIk5QmBIRERFxgsKUiIiIiBMUpkREREScoDAlIiIi4gSFKREREREnKEyJiIiIOEFhSkRERMQJClMiIiIiTlCYEhEREXGCwpSIiIiIExSmRERERJygMCUiIiLiBIUpEREREScoTImIiIg4QWFKRERExAkKUyIiIiJOUJgSERERcYLClIiIiIgTFKZEREREnKAwJSIiIuIEhSkRERERJyhMiYiIiDhBYUpERETECQpTIiIiIk5QmBIRERFxgsKUiIiIiBMUpkREREScoDAlIiIi4gSFKREREREnKEyJiIiIOEFhSkRERMQJClMiIiIiTnAp6gKkcBw9auDVV93ZudPEmTMGgoNt1KxpYfDgLBo1sgCwerULaWnQp4+5iKsVESlck3ZN5I3vX8u3z52lmrCq67qrut6gDQM4kXKCT7tvKIjy5BanMFUMJSRA+/ZelChh47nnMggPt3H0qIHPPnPhu+9MDmHq3DmDwpSIFHv3V+tPy8jW9vfv/TKb7ce/Yv7di/D19SA5OR1fN78irFBuZQpTxdAnn7gSH29gy5ZUQkNt9vZ77zVjs+VzohNsNsjIAA+Pwrm+iIgzSvmUppRPafv7T/5ajbvJnXrhsQQEeJGQkFqE1cmtTnumiqHERHBzg8DAnMnJYMj+5/DhHnz6qSs7drgQFuZLWJgvkya52fvNnetKgwbeRET4EBvrzTvvuDpcZ9IkN6pW9eabb0y0betFmTI+rFnjQnS0N5Mnu/FvXbp4MmCAkpaI3PwW7V3APctbU2luJFXmlqXHms78cubnK553OOlvHvr8ASq+V4aoOeHc92lPDiUevAEVS1FTmCqGata0kpFhYOhQD/bsMWK15uzz1FMZNGliJjrawrp1F1i37gL3358FwIcfujJ2rAft2pn58MM0OnfO4sUX3Xn7bceQlJZmYPhwD/r2zWLJkjTq1LHwn/9ksXSpq8MM2N9/G/jmGxP33ptVmMMWESkQR1OOcN8d/Xi/3YfMaD2HYI8gOq1ox/HkY3meE58aT8cVbTmadJgpLabxTpv3OZd+lt6fdCXTknkDq5eioGW+YqhZMwuPPprJnDmurFzpio+PjebNzQwYkEXz5tn7pcqVsxEQYMNqhXr1LqUtqxUmT3ajT58sxo/PAKBFCwtJSQamTnVj0KBM+1JeWpqB8ePTad/+0p6r++7L4u233fn6axMdO2a3LVniSkiIjVatLDfmAxARccKY2Oftv7ZYLTSPaEmjxXVYcWAZw2s/kes5M3ZPxWqzsqzzGvzc/QGoFx5LvQ+j+Xj/EvpWe+CG1C5FQzNTxdTLL2ewc+cFXnwxncaNLWzZ4kLv3p7Mn++a73knThg4dcpI586Os0hdu5pJTjawb9+lPzIGg41WrRw3r5cvb6NRIzNLlmTfx2aDjz92pVcvMy6K7iJyC9h79jf6rfsP1eaVp+Q7gZSeHcyR5MMcTDiQ5zlfHdtKy8jWeLl6Y7aaMVvNBLgHUD2kBrvjf7qB1UtR0F9vxVj58jaGDs1i6NAszp410Lu3JxMnutO/f5Z979S/xcVlH7h84/rl78+fv3RiQED23qx/u+++LJ55xoPkZCvbtpk4etSoJT4RuSUkpJ+n9yddKeNbhleavE4pnwg8TO4M2/wo6eb0PM87l3aWpfsXs3T/4hzHAtwDCrNkuQkoTN0mgoNt3HtvFs8+60F8vIGwsNy/1leiRHb7mTOOaSs+Pvv95ZvaDYbcr9G5s5nnnoPlyw1s2OBK3boWqlTJZeOWiMhN5puTOzmdGse67puI9Ctrb0/ISMj3vACPQOqG1891GdBPj1wo9hSmiqEzZwyEhOQMOgcPGnF3t+Hnl33MzQ0yMhxDU6lSNsLDraxZ4+qwx2n1ahd8fW3ccceVQ5GnJ3TrlsWsWa7s3+/CSy9lODkiEZEbI92cBoC7yd3etu3Yl5xOjcsLXFD5AAAgAElEQVT3vKYRzfni8EaqBdfAzZTLlL0UawpTt4AMs5WfjiWQnGGhcqg3ZYO88u2/dKkLy5e70rt3FtWrW8nKgm3bXJg3z5UBA7LsG8grVrTy2WcurFvnQqlSVsLDbYSH2xg1KpORI90JDHSneXMzO3eamD/fleeey7zq50j17ZvFBx+44elpo1s3LfGJSME7ej6NXUfOY7VaqR8ZRFRw/v9tvBqxJRvi6eLJE1uG8mjMUI4mH+HN714nzKtEvucNq/0Eq/5cTo81nXiwxiOU8ArndGocX5/YTrOIu+hUoYvTtcnNS2HqJvdnfArjVhwi8ZgvWUnuuEccolm0F0+1jcLFmPvGp9atLRw5YmThQleOHzdiMkFUlJWJEzPo1+9SsBk4MItffjHyxBMeJCQYGDkyg9GjM+nXL4vMTJg9241333WlZEkbL72UwWOPXX0oqlXLSunSNho2NOOnGW4RKWBLfjzMot27STZ8C1jx/akB3apHMzC2Aoa8NoVehVI+pZnTdj7jd7zAA+v6UCGgEv9tOYPXd72S73klvErwWY8vmPjteJ7bPprkzGRKeIXTsNSd3BFU7brrkVuDwWYrrGdiF72sLMtN9VTba33KbpbFyoC5ezmxsRIuCYEA2AxWjHX3MfReT7rUDC+sUp22f7+Rpk29WbYslWbNbo9HItxOT1G+ncYKGu/N5s/4FJ78ZANp3rMxGrM3hdusbrhfeJhX725PzVLX9n9wN/t4C9qtPt7QUN+iLiEHPRrhJvbLySQSTnjZgxSAwWbEvK8sq78/X4SV5e3cOdi1y8izz7pTvbqNpk1vjyAlIjfO1r9OkWLcZg9SAAZjJheMX7H5z7wfrClSWBSmbmIpGRYsF3JuZDRkuXIh4+b84cQbNrjQqZMXcXEG5s615vkIBhGR65VhNmMjl6eKGzNIy9IeTbnxFKZuYlXDfHAtmYDN5BicLCFnqR3lU0RV5a9PHzNxcSls355KnTpFXY2IFEcNIkvgbY3FZrv0f2s2mwEPS32alIsowsrkdqUwdRML83WnUz1/XBrsxeyfgNUjjayI44TGHuX+RjfvfikRkcJUJyKA2FIxmFL6YE6PwpweiTGlJzGhdWkUFXjlC4gUMH2b7yb3cNMIKoafZeWuQySmmakV5U3v+hWJCPAs6tJERIqEyWjg+TY1+eqv0nz+Rx1sNhstK0bRslIoribNEciNpzB1kzMaDLSsHELLyiFFXYqIyE3DxWSkZeVQWlYOLepSRLTMJyIiIuIMhSkRERERJyhMiYiIiDhBYUpERETECQpTIiIiIk5QmBIRERFxgsKUiIiIiBMUpkREREScoDAlIiIi4gSFKREREREnKEyJiIiIOEFhSkRERMQJClMiIiIiTlCYEhEREXGCwpSIiIiIExSmRERERJygMCUiIiLiBIUpEREREScoTImIiIg4QWFKRERExAkKUyIiIiJOUJgSERERcYLClIiIiIgTFKZEREREnKAwJSIiIuIEhSkRERERJyhMiYiIiDhBYUpERETECQpTIlIshc30I2ymH9+d+tahfd/ZvYTN9OPr49uKqDJH0376b661hM30Y+4vs4ugIhG5VgpTIlKsTfl+clGXkK8ZeYSpdd030alCtyKoSESulcKUiBRbjUs1ZdORDfwSv6fAr22xWsi0ZOZ6LN2c7vT164XHEuYV5vR1RKTwKUyJSLHVoXwnqgRWZcoPb+Tbb+nvi+m4oi2V50ZSaW4k3VZ1YPfpHx36DN/8GG0+bs66g5/S9H+xlJkTyo9x37Pk90WEzfTjx7jv6brqHiJnhzFj91QAXt75Is2XNCRqTkliPqjKYxsfIi41zn7Nuh/W4Fz6Od74/jX7suTFWarLl/le3zWB6vMqYrVZHWra8Pd6wmb6cTDxL3vbwr0f0PR/sUS8E0KdBdWZ9tN/r/8DFJGrojAlIsWWwWDg8bpPs/bgGvaf+z3PfkeTj9C7yr28124B77R+j5I+peiyqj1/Jx76V7/DjN/5AiPqPMXiDsuI9CtrP/boxgdpU/ZuFndcRtuydwNwJi2ex+s+zeIOH/NKk9c4nPQ3PVZ3xGK1ADD/7kX4ufnT944HWNd9E+u6b6JmaEyO+rpV7El82ml2nNju0L76wEpiQmtT3r8CANN/msror56kfbmOLOzwEQNqPMTr376ivVcihcylqAsQESlM3Sr2ZNKuiUz98U1mtn431z4j64+x/9pqs9K8TEt2n/6RZX8sdTh2Lv0cH3deQ3RIzRzXeCT6MQbFDHFom9pypv3XFquFeiViiVlQlV2nvqFRqcZEh8bgYjRR0rsU9cJj8xxD5aAqVAuuwao/V9CkdDMAMiwZfPb3Op6qOxqA5Mwk3vjuNZ6sO4pR9ccCcFeZlqSaU3nr+8kMqP4wJqPpSh+XiFwHzUyJSLFmMpoYUecpVv65jEOJB3Pt88e5/fRffx/V5lUgfFYApd4J4kDCn/yVcMChX0nvUrkGKYDWUe1ytG0+vIF7lremwnsRlHwnkJgFVQFyXPdqdK3YnbUHV2O2mv+59kZSMpPpUjF7k/p3p3aRar5A5wrdMFvN9lfT0s2JTzvNiQvHr/meInJ1FKZEpNjrXeVewr1LMj2X/UMpmcn0/qQrJ1KOM77xRNZ0+5wNPbdSPTiaDEuGQ9/QfDaEh3k6Hvsp7gf6re9DKZ/SzGg1h3XdN7G+x2YAMizXvkG9a8UenE0/y7ZjXwKw+sBy6oXHEuFbBoBz6WcBaLokllLvBNlf3VZ3AOB4isKUSGHRMp+IFHtuJjeG1hrB/+14ng7lOzkc++7ULk5cOM7HnVdTKbCyvT05MynHdQwY8r6JwfHYukOfEuwRwrtt52P459jR5CPXPYYo/3LUCq3N6gMraFCyEZ///RnPNRxnPx7oHgjAons+yjX0VQyodN33FpH8KUyJyG3h/moDmPLDG0z/aapDe/o/s0RuJjd7266T33Ik+TA1Q2td9/3SzWm4Gl3tQQpg+R8f5ejnanTLMQOWl66VevLfHybTJKIZ6ZY0Ol/2HKp64bF4unhyKvUUbaLuvu66ReTaKUyJyE0tPcvC1wfP8d2xE/h7eNKmcmkqhnpf83U8XDx4rNYwXt45zqG9bon6eLv68NTWEQyr9TgnL5xg8nevUtK7lFN1Ny/Tgtk/z+T57c/QNqo93536lmV/LM3Rr1JgZTYd/pyWka3xdvWmYkAlfNx8c71mlwrdeGnH87y04wUalWxMCe9w+zF/9wBG1h/L89uf4VjyERqWbIwNK38lHGD78W180H6xU+MRkbwV2Z6pkydP0q9fP9q3b0+HDh344IMPAEhISGDgwIG0bduWgQMHkpiYCIDNZuOVV16hTZs2dOrUid9++62oSheRGyQlw8zINd8xacdSPjs+jaV/zuCJTzbyyW8nrut6A2s8bF8OuyjMK4y57T4gPjWO/uvvZfaemUxuPoVy/uWdqr112Xa80Gg8n/61hgfW9WHHia9ZdE/OmakXG72Ml6sXfdf2ou2yu9gTvzvPa5b2jaB+eAPiUk/RtVKPHMeH136CN5pPZfORTfRffy+PbnyI5X98RMOSdzo1FhHJn8Fms9mK4sanT58mPj6e6tWrk5KSQo8ePZgxYwYrVqwgICCAQYMGMWfOHBITExk1ahRffvklH374Ie+++y579uxhwoQJfPzxx/neIyvLQkJC6g0a0ZUFBHjdVPUUNo23+LpRY138w2Hm/rwIg/fn9i1JVos3PqnDmdenFUFebvlfoIDcTr+3oPEWd7f6eENDc5+5LUpFNjMVFhZG9erVAfDx8aF8+fLExcWxefNmunbtCkDXrl3ZtGkTgL3dYDBQq1YtkpKSOH36dFGVLyI3wOYDf2Nz/8Fhb7fRdIE046/sPpZYdIWJiFzmpng0wrFjx9i3bx8xMTGcPXuWsLDsb6KEhYVx7tw5AOLi4ggPv7Q/IDw8nLi4uFyvJyLFQ/bm7ZzfoDNgyPHtORGRolLkG9AvXLjAiBEjePbZZ/Hx8cmzX26rkYYr/MfUZDIQEODldI0FxWQy3lT1FDaNt/i6UWPtFF2ZGbtiMbivv7TMZ/bB2xDNXdXCCfC+Mct8t9PvLWi8xd3tNt4boUjDVFZWFiNGjKBTp060bdsWgODgYE6fPk1YWBinT58mKCgIyJ6JOnXqlP3cU6dO2Wew8mKx2G6qdeFbfZ36Wmm8xdeNGmubCqFs2d+c38/7kWbcjdHmi4+1KYMa1cSYZSYhwVzoNcDt9XsLGm9xd6uP92bcM1VkYcpms/Hcc89Rvnx5Bg4caG9v2bIlq1atYtCgQaxatYpWrVrZ2xcuXEiHDh3Ys2cPvr6+VwxTInJr83IzMblTfXYcqsgPx5oQ4OlJy4oliQrW/1WLyM2jyMLUDz/8wOrVq6lcuTJdunQB4KmnnmLQoEE88cQTLFu2jJIlSzJ1avYD9po3b86XX35JmzZt8PT0ZOLEiUVVuojcQK4mI80rBtO8YnBRlyIikqsiezTCjaBHIxQtjbf4up3GChpvcafx3lpuxmW+m+LbfCIiIiK3KoUpEREREScoTImIiIg4QWFKRERExAkKUyIiIiJOUJgSERERcYLClIiIiIgTFKZEREREnKAwJSIiIuIEhSkRERERJyhMiYiIiDhBYUpERETECQpTIiIiIk5QmBIRERFxgsKUiIiIiBMUpkREREScoDAlUoAMp0/j/fwzBNWvSUhECMGVIvHv0x3XLzYVdWkOjEcOExrmh9uG9UVdiojILc+lqAsQKTb27yewdStsXl6kDhmBpUpVDMnJuG3agP8DfTj/2RYsNaKLukoRESlgClMiBcTU/wGsgYEkrN2IzdfP3p7Zrj1pAx7C5u9fhNWJiEhh0TKfSAFw3fk1xh9/4MJz/+cQpC6yVK+BNaKM/b376hUENm9ISEQIQbXuwGvieDCbLx1fsojQMD9cft6Nf9d7CClbgsAWjXH5eTdcuIDviMEEV4ggqF5N3Fd87HAv/6734PdgPzwWzCOobg1CIsPwu68nxpMnrjgOj4UfENg0NruuOtXxnPZf+zFDYgJBMVXxHTrI4Ry/fv8hsGFtSE296s9LRKQ4UZgSKQCuO7ZjM5nIbHbXlftu2YzfIwMwR8eQuOB/pD30KF4z38ZnzMgcfX2HP0ZGt54kvf8h2Gz4PfgAvk8OxRJekqS5C8iqWxffYY9iPHHc4TyX73fhOXc2KS9NJHnKdFz2/oZf/3vzrctz+lR8Rj9JRvuOJC78iLQBD+H9+it4zJ0NgM0/gOQp0/H4eAmG1asBcP/fQtw2fk7y2++Al9dVfloiIsWLlvlECoDx5EkIDQVPzyv29Z40gczGTUmenh1Sslq2yW6f8H+kPjUKa6nS9r6pg4eT0adv9hubDf/7epHZuAmpz44DwFynLu6frMbt8/WkD3z4Uj1n4jm3diPWMpEAWCIiCezUFtcvNtrvdzlDchLeb7xG6pOjSB01Nruuu1piSE3F+63JpA94GEwmslq2Jq3fQDyGDsblf8vxeWEsaUNGYI5tcB2fmohI8aCZKZGCYjBcuY/FgsvPe8jo1NWhOaNrdwxWKy7f73Joz7pspstSrnx2W5Nm9jabnz/W4BCMpxyX8Mw1Y+xBCsDcoCHWkFBcf/wh17JcvtuFIfUCGZ27ZS83/vPKatocY/xph5mvlJcmgLc3Afe0xlqqFBeeee7K4xYRKcY0MyVSAKwlS0J8PKSng4dHnv0MZ89iyMrCGhrmeP4/743nzzu2+13atG5zdcv+5783sru5YUjPcDwvJDRnjaGhGOPicq3LeO4sAEFNY3M/fvz4pXDm44P1ng6YZkwn/b5+4O6e6zkiIrcLhSmRApDVuCmG1yfgtm0rmW3uzrOfLTgYm6srxjPxDu3G+NMAWAMDC6Sef18/+x7xWEuUyL2uf+6buOijHEEPwFKxkv3XLrt/xDj7HbKiY/CaMpn0br2w5XFdEZHbgZb5RApAVsM7sdapi/eE8RhSknMcN+39DePxY2AyYa5ZC/dPVjkcd1+9EpvRiLle7jND18rl5z0Yjx299P7bbzCeiSerTt3c668Xi83TE+OpU5hr1cnxsvn4ZndMT8d32KPY2rYl4ZPPsQYE4jtyRIHULCJyq9LMlEguDp9L5ZsD8WSZLcREhVC9pC/GK+yJsnywAGPrVgS0aU7ao0P/eWhnEm5bNuOx8APOr/8CSkdwYfSzBPynG74jBpPetQcu+/bi/forpN8/wGHzuTOsIaH49+3NhVFjMWSk4/3yi2TVrJXr5nPI/qbehZFj8Xn+GYzHjpDVsDHYrLj8dQDX7dtI+mAxAN6vvozxdByWjZvA04vkabMJ6NwO9yWLLm2UFxG5zShMifzLmp+O8sXmH2mzfweeWel8VKkhYXWiGdKuOiZjPoGqShXOb9qG19tv4jV9KsZTJ7B5emGuXYekWXPtTz/PatGKpDnz8JoyGf/lH2ENCSV18HBSRz9bYGMw14sls9ld+LwwBuPZM2Td2YTkN9/O95y04U9gDQ/Hc/ZMvGZNx+bugaVCBTK69ACyZ7c8Z88gecYcPEuWhIRUzLENSHtsGD7PjyGr2V0FFgZFRG4lBpvNZivqIgpLVpaFhISb50GCAQFeN1U9he1WHO+xhDQmz93EfzfPIDArDYBMg4lxDe+jcf9uNK0QnOe5N8t4/bvegy0oOPvZVIXkZhnrjaLxFm8a760lNNS3qEvIQXumRC6z69A5Wu7faQ9SAG42C533f82uPYeKsDIREblZKUyJXMZsseJqyczR7mY1YzFbiqAiERG52WnPlMhlapcN5N3KDel+7Cc8rNk/K88GfF6+PrWio4q0tquVuGpdUZcgInJbUZgSuUzFEG+qxNZgTOZAOu/7Ek9LFhvL1SOhTiwPV8r5IEwRERGFKZHLGAwGBt5Vhe/Ll+DrX2qQlWWmZrVImlYIwd1Fq+IiIpKTwpTIvxgNBmLLBhJbtmCeRi4iIsWb/ldbRERExAkKUyIiIiJOUJgSERERcYLClIiIiIgTFKZEREREnKAwJSIiIuIEhSkRERERJyhMiYiIiDhBYUpERETECQpTIiIiIk5QmBIRERFxgsKUiIiIiBMUpkREREScoDAlIiIi4gSFKREREREnuBR1ASIiIlK8hYb55dpuiSrHuV17bnA1/5g0CWJj4a67nL6UwpSIiIgUutTBw8no1MWhzebuUUTVkB2mhg1TmBIRkZtX2MxLsxEeJg+CPUOICa3NvXfcT7uo9oV23wW/zSPEM5R7ynd0aK/7YQ06lu/CS40n5Hnu18e30W11B778zzfcEVyt0Gq8HVkiIzHXiy3qMgqF9kyJiEihGRwznHXdN/FRp1U83/D/cDO58sC6Pjz+xZBCu+eHe+ez/tCn13VuzdAY1nXfRJR/uQKuSvLj/r+FhESEYEhMcGg3/b6P0DA/XL/aeqlx9WqoVw88PCA8HEaPhqysS8f/7/8gJAR++gkaNgQvL6hdG7Ztu9QnKgrOnoWXXgKDIfu19Z97zJ0L1auDp2f2dZo3h99+y7d+hSkRESk0kX6R1AuPpWGpO+lRuTdz2s5nSovp/O/3hSz5fVFRl5eDr5sf9cJj8XTxLOpSih2D1Qpms+PLagUgs0MnANzWOYZg91XLsYaEktW4afb71Suge/fsvU5r1sCLL8KcOTB2rOPNUlOhf3949FFYvhzc3aFbt+x2gJUrwd8fHnoIdu7MftWpA199BY89BvffD+vXw/vvw513QmJivmNTmBIRkRvqvjv6UbdEPT74ba5D+zcnd9JlVXvKzilBlblleWrLcFIyk+3Hl/y+iLCZfvwU9wOdVrYjcnYYDRfVZu3BT+x9uq66hz3xP7F0/2LCZvoRNtMvR2h7Z890Yj6oSqW5kQzaMICE9EuzIV8f30bYTD/2nd1bSKO/ffk89wyhpYIcXr6PZ89Q2vz8yWzZGo9Vyx3OcV+9gozOXcFkApsN75degAcegJkzoW1bGDwY3noLZszInmm6KC0N/vtfGDgQ2reHd97JPv7VV9nHa9cGFxeIiMievWrYEPz8YNcuqFkzO5zddRd07gyvvpodqPKhMCUiIjdc84gW7InfTZYle3nm25Pf0HN1J8I8SzC33QJebvIam45sYEQuy4GPbBzI3VEdmHf3Qu4Irs7Dnz/Ar2d+AeD1Zm9RKaAyrSPbsq77JtZ130Trsu3s5675ayXbjn3Jm3dN5YWGL7Hh7895YevzN2bQt7nUoY9zfsNWh9eFUZdmlDK6dMd125cY/glFpl9+xuWvA2R07ZH9/q8DmI4dhd69HWe3WraE9HT49ddLN3N1ddxYXu2f/W/HjuVfZK1a2cuDTz6ZHbwyM69qbNqALiIiN1xJn9KYrWbOZ5wnzCuMV755kfrhDXi33fxLfbxL0WNNJ/ad3euwGfz+Ox5gaO0RALSIbE2T/9Xn7R/fZE7b+VQJqoqXqzfBniHUC8+52dnF6MIH7f+HizH7r78/zv/Oqv0reLnhpMIdsGCJiMBcq06exzPa3YOvqyvua9eQ/sBAPFavwFKyFFkNGgHYQxb33JP7BY4evfRrPz8wXjZf5OaW/c/09PyLbN0a5s2Dt9+GqVPBxyd7yW/yZPD2zvO0K85MLVy4kMQrrBWKiIhcC5vNZv91alYq35/aReeK3TBbzfZXg5KNcDW6sif+J4dz7ynXyf5ro8HI3eU68OPpH6/qvk1KN7MHKYDKgVU5feE0mZarm4GQQuTjQ2brdtn7ori4xNcte3M4YAsMzO43Zw58913OV/sC+oZo//7www8QF5cdoubPh5dfzveUK85MxcfH07NnT6pVq0aPHj1o2rQphn8GJiIicj1OXTiBq9GVQPdAzqTFY7FZeOarp3jmq6dy9D2RctzhfYhXiON7z1BOXzh1Vff1c/N3eO9mcsOGjUxrJm4mt2schRS09K498Bs0ALfP12M6/DcZ3XrYj1kqVsJSshSmv/+GRx5x/mZubvnPVIWGZm9gX7EC9ua/h+6KYerJJ5/kiSeeYPv27axYsYKXX36Z9u3b07NnTyIjI6+5dhERka1Hv6BmaC1cTa74uftjwMCo+mNpXbZtjr7h3iUd3p9JPUOQR/Cl92nxhHmHF3rNks1ms3Eh04K7ixFX09VvvTYdOYLL97scGw0GzHXr299mtmmHzdMLn5GPY4mMwlyn3qW+RiMXXpqA39BBkJSUPRPl5gYHD8KqVbBsWfZjEK5W1aqwdi3cfXf2cl6VKvDGG3DuXPZ+q4uPV/jyS3jttXwvdVV7pgwGA6GhoYSEhGAymUhMTGTEiBHceeedjB49+uoLFxGR297ifR/y4+kfmNbyHQC8Xb2pW6I+BxL+ZGT9MVc8f92hT6gcVAUAq83KZ4fWUifs0l4cV6MrGZYr7I2R6/Ld4fOs+OIXEs6nYHV1IbZGJPc1qYSnq+mK53rNmobXrGkObTaTiTMnz19q8PAgs117PJZ/ROqInLOUGV17QEQJmDgx+7EFJhOULw8dO17aF3W1Jk+GoUOhQ4fsRyZs2QL168OUKbBkCSQnQ9my2c+tevzxfC91xTC1YMECVq1aRWBgID179mT06NG4urpitVpp27atwpSIyG0iLjmD9CwLpQM8cTFe3XaPI0lH+P7ULsxWMycuHOezQ2tZfWAl91Xtx3+q3mfvN+7Ol+m5uhNDDEY6VeiKj6sPx1OOsfHw5zzb4AUqBFSy9124bwGuRjfuCL6DD/d+wKHEg8xu8779eKXAymw5upkvjmwiyCOISL+yDjNZcn1+OZHE4o+/4smdS4lOOk6SiwcLDjTn7YR2jO5aJ98tQPGnk676Psmz3iN51nt5d2jfPv/9Uf/3f9mvf7tsnx4AdevCN9/k7NexY862K7himDp//jzTpk2jdOnSDu1Go5HZs2df8w1FROTWciopnclbf+aPMycxGDLxcQ1myJ01aVI+5IrnztozjVl7puFucifYI4RaYXVYcM+SHD9OpmHJRqzutp5JuyYydNMgrDYLEb5laBHZmlDPMIe+c9q8zwtfj+W1XS9T0rsUc9rOJzo0xn78ybqjOJZyjEc2DCA5M4m3W86iT9W+BfNh3MbW7tjPg9+vpmZS9h42f3M6Q/du4LFSVTh0rirlg/P+tltxZ7DZ/h3Vio+sLAsJCalFXYZdQIDXTVVPYdN4i6/baaxwe483y2Jl0MfbOZSxFBfP3zAYbFiyQvBO78fkDq2oWsL3htW15PdFjPhiMAcfOYGPq0+BXfd2/v29FsNmbWb6J5MJyEpzaJ8a04moxwbSvOKNmf0LDb1xf+aulh7aWcRCw/xyfbl8sxMAr0kTCa4aVaD3NB45TGiYH24b1ufbrzDunRf3JYsIDfODlJQbcj8RuTo/HE0gLn0frl6/YjBk/7+3yfUMSabPWPbzwSKuTm6kED9PDno7zkbagINBpQjzvb2/CamHdt4EUgcPJ6NTF4c2S9U7iqgaEZFLTidnkmo7hOu/2k2upzmSoGcQ3k7a3FmV9//uQOmdiyiRkYzZYGRlmbpYy5ajSljBzRTeihSmbgKWyEjM9XI+qVdEpKiVDvDAiypksdOh3ZpVikrhQTe0lj5V+2rvUxFqXC6IxE7NeDIghBKJ8Zxz9yE8qhRPtq+J8TZ//qSW+W41Fy7gM+ZpAhvVIaRsCYLqRePzzFMYkh2/KeGxaAGBTWMJiQwjuGoU/l3aY/p9n0MfQ1oaPk8/TnCFCIJiquL1+gT7T/C+nMu33xDQqikhZUIJbNHYvgR5kfvSxQR0bEtw5UiCK0Xi360DLrtzPo3YdefX+HfrQEhUSYIrRODf9R5cftmT51A9p08lpEwobp+tA8D0+z78/9ON4MqRhESFE9i4Hh5z51z1Ryci165WaX/K+lb5f/buOzqqon3g+HdLtiXZbHqjE3oJXZASuoKvIILSFRsg/S66/F8AACAASURBVBVRARVF6ZafjSIioKKAIkVA9EVBiiIgKKKiAiIlEFp6tu/9/RFcWAME2IS053NOzsnOztx55gaSZ+fOnYsruwWKJwBFAZe9AhZPV3omVi7q8MRN1rV+PK8Mbk//wd2ZMPg2xvdsQpipbF/iA5mZKhZUHk/uwxq9BarcvTMuV9dqBbeH7AnPooRHoD5xHNP/vYT5oftIX74KyE1agsaNIfvJibiaNEOVmUnA7p2oMnwTrsDJz2K/oxsZC95Dt/UbAl+egbtmLezd7/bpzzz8EXJGPYYnOgbjnDcI6duT8zv2okRHA6A5dhTbvX1xV6qMyulAv+JjLN274Nr7E4TlbqQXsH0rIfd0x9myNRlvzEUJNBGwcwfqk8lQL5F/M708A9Prr5C++COc7TsCEDKwN65q1cmcPR9Fp0dz8E9UWZl52gohCo5GrWJa16bM/jaIHUfb4lFclAuxMKx9/TJ991ZZZgjQkBAhP/tLSTJVDARNfJKgiU96XzubNSdt7ZeXratERJA169WLBS4X7gqVCL2zM+rjx/CUK492zw+4atfFOnqst5rj9rwPhnQ2v5XsyVNzv2/bHt3XG9GtW5Mnmcoe/wz2nvfmHqdla8Ib1cb09myyn3kegJzHL9lkz+PBkdQe7Y97UH+4BEbkxhD44nO46tTLTfguTAc723e67BgDpzyP4Z15pH/4Cc6WrXPjOHcOzd9HSF/8Ee7adXLbt2l72fZCiIJlMQUwoWN9rM46OFwezAatPFZMiEtIMlUM5Awfjb17D+9rJejqt33ql3+Ece5baA8fQpWT7S3XHDqIp1x5XHXrETj5GQKfeQpH1ztxNm562Z1hHW3b+7x21aiB5vjxPPXsXS8+VJSgoNxkae8PF/v943cCpzxPwK7vUZ894y33/Pln7jfZ2Wj37CZrygxvInUlQZMmoF+9kvRlK3E1u8VbroSG4o4vR/C4MVgfGYqjZRuUyMirHksIUbCMAZpr2ulaiLJG1kwVA+5y5XA1aOT9cidUu2Jd3brPMI8YgqtJUzIWLCb1869IX/QhACp77uMTnEntyHxtNgHffUvIXV0Jr1mZoCf+C9nZPsdSQnwf+EmADux2nyJPYBAYjb7tIiJQp+Q+VFSVlUnIvXehTj5B1uSppK75gtQvN+OqU8/7AEl1ehoqRcETnf+zs/RrV+NKbICrUWPfN9Rq0pevwhMVTfCY4YTXTcBy521XXXMlhBBC3AySTJUw+s9W4mzchKyZr+Lo0BlX46YoFkueevY+/UnbuIVzvxwie9ILGJZ9SOArM6+7P3V2Flh9N2hTnT3rTYy0u3aiST5B5uz52Hv1xtW8Ba4GjXwWxHtCLChqtTcBu5r0D5aj3b+P4BGD8yyGd1erTsbCDzj75zHSP1kDdhvmfvdcdtG8EEIIcbNIMlXCqKw2FJ3ep0y/YvkV6ysREdjufxDnLS3Q/HHghvrUr//s4ousLHTffI2rYe7MkerC7JNyyWVE7c7v0Rz9+2KbwEBcjZpgWP5R3mcj/YurVh3SP1qB7ssvCBo35vKVAgJwtk7COnQEmpRTqNLTbmhcQgghREGQNVMFyOZ08/3fqZzLcVI5zERivLnA995wJLUj+KmxmF6dhbNRE3Qbv0S3ZbNPHdOMKajTUnHc2holPBztzz8R8N12sp9+7rr7U4xGAqe9gCo7G09MDMbZb6ByOLEOfhQAZ+OmeAKDCH5sFDkjRqM5mYxp1jTcsXFcOvLsZ54npFc3QvrcjXXgAygmEwG7d+Jq0BBHZ99ndLkaNSFjyXJC+tyNEhRM9vNT0Pyyn6DnJmK/qyfuipVQpaVheuP/cNWphxJ6c/e6EUIIIS4lyVQBOXwum1c/2Untv/ZT4exR1lasx+pqNXm8RyOC9AV3mm33P4jm7yMY58/BaLPjTGpHxtwFhHbp4K3jatgI47zZBK9agSorC3e58mSPG4918LDr7k8xGsl8cx5B48eh+fN33AnVSf/oE+9lPiUqiowFiwl67mlC7u+Lu3JVsma9ivHN13z+cTlbtCT949WYpr+IefgjKAE6XPXqY+9y+adzO1u0JH3hB4Tc1xclKAjroIfxREZhevUl1CknUcwhOFq1JvuZydc9JiGEEKIgyYOOC4BHUXhy0TYe3LCAFuf/AnKfVzS3RgdsvXrzQNsagDxMs7QrS+MtS2MFGW9pJ+MtWeRBx6XUobPZGE+doPmFRApABfQ9/C3f7T9GKc5XhRBCiDJPkqkCYHd5CHJY+ffqKJPLgdPtQVIpIYQQovSSZKoAJEQEciS8HKf0vlOPWyOqUat8WJl/AKQQQghRmskC9AJgCNDQo11dns1+gN4/fUGFnPP8GF6J1Q0781hSraIOTwghhBCFSJKpAtKpTizRoZ34ulYVUtOyqFA+kvGNKhIfYsy/sRBCCCFKLEmmClD9ODP1uzUs6jCEKDMio8ze7xWVCk9UdO4DvJ9+Dk/FSgXaV8D2rVh63MH5b3bgrlX7ho6hX/0pWK3Y+/Qv0NiEEEVLkikhRImW8+hI7Hd2B0VBc/RvTDOnEtL/HlI3fwfa4vUrTr96Jarz5ySZEqKUKV6/aYQQ4jq5K1TA1aQZAK6mt6CEhBDS7x40hw7irlGziKMrRIqS+2Byg6GoIxGizJO7+YQQpYoSdOGuWqfTW6b73wZCenUnvHYVwqvEY+nSnoBNX+Vpq/llP+YB9xKeUJ6ISrFYbmtLwOavr9iXfuUnRMSHY1i0AAB18gmCH76f8NpViKgQRVjT+pimvwBA8Mih6NeuRvftNiKjzERGmTHNnOo9lmHBPEJvaUBEuQjCmiVinPumT1+mmVMJr1kJ7Y7vsHROIqJ8JPo1KwmrVx3TrGl5Ygvp3gXzIJkBE+JmkJkpIUSJpvJ4wOW6cJnvCKaZU3FVqeqzrkl99G8ct91OzrCRoFaj++p/hPTtSdrqDbhuaQ6A5s8/sPynM+6EamTNehVPaBjan/aiST5x2X71S5cQPHYUmS+/7r1sFzxiCCqrlcyXXkcJCUHz9xE0f/4BQPZjT6A+cRxVejpZM14GwBMXD4Dh/UUEjx9HztARONp1QLd9K4GTJoLDgXXUYxfHarViHjmEnBFjcFdNwBMTi713PwzLPiTn8afgwjYs6iN/EbDjWzLeW1rAZ1sIcTmSTAkhSrSgiU8SNPFJ72t3XDzpH34CGo23zPbQkIsNPB6crdqg/f03DB++R9aFZMr00jQUs5m0NRvAmHsXrrNtewD+fSHNsGgBQU8/Seab87D36OUtD9jzAxnz3sVxW+7Du50tW1/stnIVFEsoeDzey5L/xGOaNQ1bn/5kT86dqXK264AqIx3Ta6/kPlPzwqU8ldVK1uRpOLrccXFs/QZgev0VArZvxdmqTW58S5egRETi6NDpus6lEOLGyGU+IUSJljN8NKlfbib1y82kf/gxrtp1COnXC/XJZG8ddfIJgkcMIax+DSJiQ4mMC0O3+Wu0hw566+i2bsHe/W5vInUlxnfmEvTMU2TMW+iTSAG46tYjcMpz6JcuQX382DXFr04+gebUSezd7vIpt9/VE3VmBtrffvGWKSpVngTJXSUBR4uWGJYuuVBJwfDxUmz39Cl2C/CFKK0kmRJClGjucuVwNWiEq0EjHB1vI2PhElR2G8a5b+VW8HgwD+yDdtf35Dw5kfSV60j9cjP2Dp1yF3BfoEo9jyc6Jt/+9GtX465cBWdS2zzvZcxfhCuxIUHPjCe8UR1C27UkYMvmqx5PnXIqN8zIKJ/yf16rUlO9ZYrFAjpdnmPY+g1Ev3Y1qqxMArZ+g+bYUWx9B+Q7FiFEwZBkSghRuuj1uCtWQvPn7wBo/jpEwM8/kTVtFrb+9+G8tRWuBo1Q2Ww+zZTQMG9iczUZc95BlZODeUBvsFp93vPExpH5xlzO/X6E1PUb8URFEXJfH1Tnz13xeP8kcOqzZ3zK1WdOX4gr9GLhFR5NZe/WA0UbgH7NKgxLl+Bs3KR038koRDEjyZQQonSx2dAc+QtPXLnc19YLSZNO762iPnaUgJ07fJo52iShX7MS/pVk/ZsnNp60T9agOXwI80MDfe4avNiBGleTZmQ//hSqnBw0Fy75KboAVHbf43vi4nHHxKJbs8qnXL/6UzzBZly16uQ/ZqMRe49eGN6dj379Z9j6yKyUEDdTibugvmXLFqZMmYLH4+Gee+5h8ODBRR2SEMJPdpeH7/46z59HThMcbKRVzRjKWa7tUUyao0fR7t4JgPrcOYwL56PKyMDWfyAA7mrVccfFEzhpIjlPTUSVlYVp5lQ8sXE+x8l5fDyWzm2xdL8d66Mjc+/m+3kfSlgYDBviU9dTqTLpn6zB0v12goc9Qua8d1FlZRLSuwe2e/rirpqAymHHOOdN3FHRuKrVyI0loTr6DevRrV+LJy4OT0wsnphYcsaNJ+jx0SihYTiS2hHw3XYMixaQPXHSNe8jZes/kNDFC1CMRuw9el5TGyFEwShRyZTb7Wby5MksXLiQ6OhoevXqRfv27UlISCjq0IQQNyjL7mLax7uI/nUvzf/ex5lACzNqtab3nbfQKiEi3/amOW9gmvMGAJ6wMFy16pC+fBWuho1zK+j1ZCz8gKCnxmJ+6D7csXHk/Hccuu1b0Rz4zXscd0I10j77gsAXJxH035G5ZTVqkD1h0mX7dVevQfryVYT0+A9Bj40ka8YruGrVwTh/DprkEyhGI87GTUlfvsq7qN36wCNof95H8JhhqNPSyH78KXKemIBt4CBw2DHNm41x/hw8sfFkPz8F69AR13weXQ0a4Y6Nw3lrKxRzyDW3E0L4T6UoilLUQVyrvXv38uabb7JgQe4GefPmzQNgyJAhl63vdLpJS8u5afHlx2IxFat4CpuMt/QqyLF+sP0Qmo+WMPT3r/hnRdAxo4UnOw9n1uD2BOqK/jNfSfjZan4/QFjrZqR9sgZnm7Z+HaskjLcgyXhLlsjI4KIOIY+i/y11HVJSUoiJuXi3TXR0NPv27btifY1GhcViuhmhXRONRl2s4ilsMt7SqyDH+uOB47x4fA9azcUlnJUcGdRLOcThjFa0TjBfpfXNUax/tufOofrjd9TPTUKpU5fAO7tccaH6tSrW4y0EMl7hrxKVTF1uEk11lV8abrdSrLLvkv5p4HrJeEuvghyry+VGcblxuz2+b7jdZGbaisU5Lc4/W/3HnxI8ehjuatXJfOttXOnW/BvloziPtzDIeEuW4jgzVaLu5ouJieHUqYu3LqekpBAVFXWVFkKI4q5hrXJ8Xr6BT9kpfTD7YqtTP67oZ6WKO3uf/pxNSSd12y5ciQ2LOhwhyqQSNTNVr149jhw5wrFjx4iOjmbdunW8/PLLRR2WEMIP3ZtWZtpft3EuKJTmx/Zz2hjCZ3XbcU/HRIL0JepXlBCijCpRv6m0Wi3PPvssDz/8MG63m549e1KtWrWiDksI4QeLMYBJfVuw5WACmw+dJNgcyKi68VQJDyzq0IQQ4pqUqGQKICkpiaSkpKIOQwhRgEw6DbfXjuH22vk/zkUIIYqbErVmSgghhBCiuJFkSgghhBDCD5JMCSGEEEL4QZIpIYQQQgg/SDIlhBBCCOEHSaaEEEIIIfwgyZQQQgghhB8kmRJCCCGE8IMkU0IIIYQQfpBkSgghhBDCD5JMCSGEEEL4QZIpIYQQQgg/SDIlhBBCCOEHSaaEEEIIIfwgyZQQQgghhB8kmRJCCCGE8IMkU0Jcg2OZRxm28REavleb8vMiabC4Fvet78N3ydu9dT7+9WOWHlhy02Pbk7KbmTun5imfuXMqNd+tdNPjEUKIskaSKSHykWZLpcuKDvx+/gATm0/io/+s4MlmE1Gp1Ow6tdNb75PfiiaZ2nv6B17aPf2m9yuEECKXtqgDEKK4++zwas7knGbTvd8SaYrMLYyHvrUGoChKofSpKAp2tx2D1lAoxxdCCFFwZGZKiHyk29PRaXSEGkLzvKdSqQAY+dVQVv7+Kd8mbyNqtpmo2WafS28Lfp7HLUsaUG5uBM0+SGTuT2/6HOefS3I7Tn5H54+TKD8vkjWHVlJvUXVm7ZqWp9/uq7ow6PP+LD2whPFbxwF4+71rVVefuj+f+YkuK9pT8e1o2i9vxY7kb/Mc74NfF9P6o2aUmxtBo/fq8Mbe/7v+EyWEEGWUzEwJkY/6kYnY3XaGb3yEYQ1GUS8yEbXK93PIY02eIMWWzLnsVGa0eRmAuKB4AN7/dRHjt45jaOII2pXvwPYTW5m0fSIOt4NRjR7zHsPqsjLyqyGMaDiGqpYEYkyx9K7Rj2UHPuTxJk95E7cj6X+xI/lb3uu6lMbRTXk0cSRzfnqD9XdvBCBYZ/Y55oivhjI0cThRpihe2j2dQRv6sWfgr5gCTAC8ufc1pn7/PCMajOHW+FbsO/MjM75/EZPWyEP1hhTeiRVCiFJCkikh8tGmXFuGJA7n7Z9ms/LgCoICgkkq345BdR4iqXw7ACqHVCHUEIbD6aJJTDNvW4/iYdauafSp2Z/JLXNnqtpV6ECGI53X9rzC4PrDvJfyrC4rk1tOo0vlO7zt+9UawOt7X2F78lZaxbcBYOnvS4gwRtKhQie0ai0VzBUAfPr9h9Vl5cVW02ldLgmAqMAYOixvxY6T22lfoROZjgxe2jWd/zYex7im4wFoW749Oa4cXtk9i0F1Hkaj1hT0KRVCiFJFLvMJcQ1eaDmN7/rvYVKLF2kZ34pNRzdy72d3sWj/gqu2S846wansk3SrepdP+V0JPcl0ZPDbuV+8ZSpUdKjQyadeFUsCLeJaehe2K4rCx78v5Z4afdCq8/8sFKAOoGV8a+/rGqE1L8SVDMCuUzvJcWXTrWoPXB6X96t1fBJnrKdJzj6Rbx9CCFHWycyUENeoSkhVhjccxfCGozhnPce9n93F1O+f5/46D3ovwf1bSs4pACKNUT7lkabc16n2VG+ZRW9Bp9HlOUa/mgN5cstjTG/9EntO/8CxzKP0rTngmmIO1gX7XJL85/h2tw2A87ZzALRemndWC+BE1gnKB1e4pr6EEKKskmRKiBsQbgynb83+TNj2BGesZ4gyRV22XrQpBoCz1jM+5WdyTgMQqr+4qP1KCVm3hB5M3PYkaw6tYtuJLTSObkKNsJoFMQxv/0u6LvcmeJdKsFQrkH6EEKI0k2RKiHyctZ4lwhiRp/xw+iH0Gj3mCwu+dRqdd8bnH3FB8cQExrLm0Co6VOzsLV996FOCdWZqhdfJt3+j1kiPar14d/98Dqb+wfMtfTfoDFDnzjbZXLbr3kqhSUwzjFojp3JO0anS7dfVVgghRC5JpoTIx7IDH7Liz+XcW6MPdcLr4fQ42Xr8Gxbuf4dBdR/yJjA1wmvw2R9rWH94LXFBccQExhITGMu4puN5fPNoQg1hJJVrx3fJ21m0fwETm0+65uSnf62BLP5lQW5ildDT571qodUBeHvfHFrHtyFYZyYh9NpmlEL0Fh5vOp6ntz3J8cyjNI9tiYKHQ2kH2XZiK4u7fHgdZ0oIIcomSaZEmbMvOYOVPx8iJSuLutFR3F2/IjHmKyc1HSt25mjmET74dTEnsk6gUWmoFFKZqa1nMbD2IG+9oY0fZdfxHxizaRhp9jQeb/IUTzSbwMDag3C47cz7aTbz980hNiie51tOYWjiiGuOuUFUI2ID47g1rhVmfYjPe81jb2V4g9HM3zeHKTueo0VcS1bdtf6ajz2y4RhiTDHM2zebOT++iV5roGpIVbr/K2kTQghxeSqlsLZwLgacTjdpaTlFHYaXxWIqVvEUtuI43vW/JjN7x3dkaTeg1qShOCsRoerMrP+0pFK4ya9jF+Z4fz9/gNZLm/FJtzW0Kde2UPq4HsXxZ1uYZLylm4y3ZImMDC7qEPKQrRFEmZHjcPPO9/uxGhcSYPwDje402sCdnGYFC3b+VtThXdZ52zl2nvyeCVvHUSusNq3jk4o6JCGEEP8iyZQoM/48k4VDdRy1NtOnPMD4B3tPnMdTDCdpvzyygTtXdiYl5xSvt59zxTv+hBBCFB1ZMyXKDEOABpWSd22U4tGj06gojmlKn5r96VOzf1GHIYQQ4ipkZkqUGdUiA4kwlsNpq+QtUxTAeiudqleQWR8hhBA3RGamRJmhVqmY2LExT3/uIjX7Z2yeYwSpEqkeWoOBTaoUdXhCCCFKKEmmRJmSEBHIwj5JfH+kPuetTqqEmagfb0Yts1JCCCFukCRToswxBmhoWy3vjuZCCCHEjZA1U0IIIYQQfpBkSgghhBDCD5JMCSGEEEL4QZIpIYQQQgg/SDIlhBBCCOEHSaaEEEIIIfwgyZQQQgghhB8kmRJCCCGE8IMkU0IIIa4qMsqMYcG8Aj2m5tCfmGZORZWe5lOuX7qEyCgzZGXdlP6EKAiSTAkhhLjpNIcOEvjSdFTp6T7ljo63kbp+I5hMN6U/IQqCPE5GCCFEsaFEROCKkMc9iZJFZqaEEEJcl5C7umJ+cCD6FcsJa5ZIeJV4QvrcjTr5hE8942svE9YskYjykYTXrkpI7x6oUlII2L6VkAG9AQhvUo/IKDNhjesCeS/zqY/+TWSUGf3qTwkaO5rwquUIS6yJacYU8Hh8+tP8sh/zgHsJTyhPRKVYLLe1JWDz11ftT4iCIDNTQgghrpt2z27Up06S9fxUVDYrQROfJGjsKDI+WgGAftmHmP7vZbKfeR53zVqozp9Ht+0bVDnZuOonkvXcFIKem0j6wiV4oqNBr79qf4GTn8V+RzcyFryHbus3BL48A3fNWti73w2A5s8/sPynM+6EamTNehVPaBjan/aiST6B/c7u192fENdDkikhhBDXTZWZSfqHH6NYQgFQn04h6JnxYLWC0UjA3h9wtm2P7cFHvG0c/+nm/d6dkACAq159PBUq5tufs/mtZE+emvt92/bovt6Ibt0abzJlemkaitlM2poNYDR6691of0JcD7nMJ4QQ4rq5GjTyJlIAruo1AVCfTM59Xbc+uq++xDRjCto9u8Ht9qs/xyWJEYCrRg00ycne17qtW3ITqwuJlBA3kyRTQgghrpsSEuJboNMBoLLbAbD1G0j2hGfRr1lJ6O3tCa9TFdP0F244qcrTX4AOLvQFoEo9jyc65oaOLYS/JJkSQghR8NRqrENHkLp9N+f2/krOsFGY/u9lDO8vKpTulNAw1CmnCuXYQuRHkikhhBCFyhNfDuuox3BXroLmjwMAKAG+M1n+crRJQr9mJdhsl32/oPsT4lKyAF0IIcoQp9vD76ez8ChQIyoIvbZwPlMHjR2NEhqKs3FTFLOZgG1b0Bw+hPOZyQC4E6oBYHjvXex39UQxmnDXrnPD/eU8Ph5L57ZYut+O9dGRuXfz/bwPJSwMW7+BBd6fEJeSZEoIIcqIPcfTWLh2D3Gnj6FRPMyLrMDALg1oXjm8wPtyNm2G8f1FGN57F5XdjrtSFbJeeQNH1/8A4ClfgaznpmB8Zy7Gd+bhiYvn/A/7b7g/d0I10j77gsAXJxH035G5ZTVqkD1hUqH0J8SlVIqiKEUdRGFxOt2kpeUUdRheFoupWMVT2GS8pVdZGiuUjvGmZNp5YeEmnt20gOpZpwH4yxTOpLYPMW5Qe8qHXrwLrjSM93rIeEuWyMjgog4hD1kzJYQQZcCWA6fo+Nt2byIFUDnnHF1/+YbNv5y4SkshRH4kmRJCiDIgNTWLchmn85SXyzpLWmpmEUQkROkhyZQQQpQBlctHsLtc7TzlP8TWoHLF6CKISIjSQ5IpIYQoA1onRHCwZmOWVGpBhtZApkbPxxWa8GOd5rStEVXU4QlRosndfEIIUQYYAjRM6N2Mj8uF8dDvHVEUhUbVYpnQqhpBevlTIIQ/5H+QEEKUEWEmHUM61WZwx1oAqFSqIo5IiNJBkikhhChjJIkSomDJmikhhBBCCD9IMiWEEEII4QdJpoQQQggh/CDJlBBCCCGEHySZEkIIIYTwgyRTQgghhBB+kGRKCCGEEMIPkkwJIYQQQvhBkikhhBBCCD9IMiWEEEII4QdJpoQQQggh/CDJlBBCCCGEHySZEkLcNOpjRwke9ghhDWsTUT6SsAa1MN/Xh4Dvtnvr6Fd/in7pkpsem3bPbkwzp95Q24DtW4mMMqP57VdvWWSUGcOCed7XIXd1xfzgQL/jFEIUP9qiDkAIUTao0lKxdOmAJzqG7ImT8MTEoj52FP2G9Wh37cTZoiUA+tUrUZ0/h71P/5san3bvDwS+NJ2cJyYUyvGzZrwCAfIrV4jSSP5nCyFuCv1nq1GfOU3qpm9RIiO95fa+A0BRCqdTRQG7HQyGwjn+dXDXqFnUIQghColc5hNC3BSq9HTQ6VBCQy/zpgqA4JFD0a9dje7bbURGmYmMMvtcejMsmEfoLQ2IKBdBWLNEjHPf9DmMaeZUwmtWQrvjOyydk4goH4l+zUrC6lXHNGtanm5DunfBPKg/+qVLCB4/DsDbb8hdXS9W3L8fc79ehFeOI7xyHOaH7kOVknJd4//3ZT5vrD//hKVLeyIqRmNp34qAHd/6NrTbCRr3X8ITyhNeoyKBzz2Ncd5bREaZr6t/IUThkWRKCHFTuOonorLbCR7+CNqf9oLHk6dO9mNP4GjVBme9RFLXbyR1/UZsA+4HwPD+IoLHj8NxW1fS31+GvVsPAidNxPj6Kz7HUFmtmEcOwdb/ftKXfoqrURPsvfthWPahzwyY+shfBOz4FlvfATg63kbOoyMBvP1mzcg9rvrwIbRt26Cy28l8620yX5+D5vffCBl4r98zaiqrleARQ7He9yAZ774Peh3mQf0gJ8dbJ3DyMxiWLSHn8SfJmPMO6hPHMc558ypHFULcbHKZTwhxUzjbtCVnyHCMb8/GsHIFnqBgnEntsA56CGdSOwA8laugWELB48HVpNnFxh4PplnTsPXpT/bk3JkqZ7sOqDLSMb32CtbBw7yX8lRWK1mTxjHuNgAAIABJREFUp+Hocoe3ua3fAEyvv0LA9q04W7UBwLB0CUpEJI4OnUCrxV2hAoBvv0DgS9MhOob0j1aATgeAu04dQm9tgm7jFzg63X7D50RltZL14nScrZNyjxsVQ1iHVgTs2I6zfSdU589hfH8R2U9MxDp0xIVxdyS0zS033KcQouDJzJQQ4qbJfmEa57/bQ9akF3G2bIVu00ZC7r0Lw6IFV22nTj6B5tRJ7N3u8im339UTdWYG2t9+8ZYpKlVugnQJd5UEHC1aYvjnLkFFwfDxUmz39AHt1T9TBmzZjKd7d1CrweUClwt3hUp4yldA++Pe6xh9XkpAAM6WrS/GeWFdlSY5GQDtb7+istlw3H7JJUeVCkfnLn71K4QoWJJMCSFuKk+VqliHjyLj/WWc++EXXHXrEzj1+ateMlOnnMptGxnle6wLr1Wpqd4yxWLxziBdytZvIPq1q1FlZRKw9Rs0x45i6zsg33jV58+heWkWkXFhPl+av4+gTj5xTWO+EiU4ODdJ+8c/cdttuX2fzl2X5QkP92nnCY/wq18hRMGSZKoUOJZ5lGEbH6Hhe7UpPy+SBotrcd/6PnyXfHHvntUHP2XpgZu/d8/SA0uImm0my5kFwNGMv4mabebLI5/f9FhE8aOEh2Pr2x91WhqqM2euWM8THQOA+qxvHfWZ07nHuXRR+4XF7P9m79YDRRuAfs0qDEuX4Gzc5JrusFMsobgfGUzql5vzfOX8d1y+7f3hiYoGQH3unE+5+tzZQu1XCHF9ZM1UCZdmS6XLig5Em2KY2HwSMYGxHMs4yoYj69l1aict4nL37ll9cCXnbefoU/Pm7t3zb9GBMay/eyPVQqsXaRzi5lOdPYsSkXdGRXP4EIpej2LOvTtN0QWgvjAz8w9PXDzumFh0a1bh6NDZW65f/SmeYDOuWnXyD8BoxN6jF4Z356M9+AdZz/9rg86AC7NCNpvPVgqONknoftmP68WGV0zUCourVm0UgwHd5+uwjhyTW6go6L6UDyNCFCeSTJVwnx1ezZmc02y691siTRf27omHvrUGoBTS3j2KomB32zFor3/vHr1GT5OYZvlXFMVetsPFz8kZeBSoF2sm2HD1XyeGZR+iX7Ec+719cNWpB04nuq3fYFz4DrZBD3kTGHdCdfQb1qNbvxZPXByemFg8MbHkjBtP0OOjUULDcCS1I+C77RgWLSB74qRr3kfK1n8goYsXoBiN2Hv09HnPXS03wTe+PQdn6zYowWbcCdXIGTce/e3tMffrha3fQJSwcNQnk9F9swlbn/4+a54KmhIWjnXA/QTOmgoBWlzVa2D4aAmqzEyUm5zYCSGuTC7zlXDp9nR0Gh2hhrx796gu/LId+dVQ1h5ezbfJ24iabSZqtpmZOy9+Kl/w8zxuWdKAcnMjaPZBInN/8r3teubOqdR8txI7Tn5H54+TKD8vkjWHVlJvUXVm7cq7d0/3VV0Y9PnlZ8Aud5mv8ft1mbR9InN/epPExTWptqACg78cRLo9zadtqu08YzePpvbCqpSfF0nXFR35IWXXtZ8sUWA2H0hh3Nub2D1/KT+9/SFPvP0VG387ddU2jo6dcTVthuGDxZjv64t58AMEbP2GrKmzyHphuree9YFHcCS1J3jMMEI7t8Xw3kIAbAMHkTV1Jvr1nxEy4F4Mn35C9vNTsI567JrjdjVohDs2DnvXO1HMIT7vOZvfSs7w0Rjnz8Fye3uCHh8NgLtqNVxbt4PRRPDYUYT07UngzKkoOj3uylWuue8blf3sC9h698c0azrmIQ/hiYzMTeqCZZ8pIYoLlVJY0xfFgNPpJi0tJ/+KN4nFYirweLYc30yvNd3okdCTYQ1GUS8yEbXKN0f+K/0wYzePIt2ezow2LwMQFxRPXFA87/+6iLGbRzE0cQTtyndg+4mtvLH3VSY2n8SoRrl/pGbunMpbP75GlCmaEQ3HUNWSQIwplqUHlrDq4Ap2DdjnTdyOpP/FLUsa8F7XpfRu0JO5O+Yz6utHOfxIMkEBQRzN+JsmH9Tjg67L6Fwp946kxu/XxaN4qB1ehwfqPkxyVjLPbp/AvTX6MDPpVQDsbjtdV3Qk3ZHOuCZPEWmKZNH+BWw9sYUd/fcSbYou0PN6Iwrj51scJafbmLl4E5M3zqOCNXfhd4o+mKfaPsLwQR1JiAgs4givTPP7AcJaNyPtkzU427S95nbF7Wcb0rMbuJykry6cy33FbbyFrayN99INXxWDAU94BK7Ehrl7rt1W/O8UjYwMLuoQ8pDLfCVcm3JtGZI4nLd/ms3KgysICggmqXw7BtV5iKTyuXv3VA6pgkUfikfx+Fxi8ygeZu2aRp+a/ZncMnemql2FDmQ40nltzysMrj/MeynP6rIyueU0ulS+uHdPv1oDeH3vK2xP3kqr+Ny9e5b+voQIYyQdKvjemp4frVrL4i4foVXn/pP8I/UAK/9c4U2mPvl9GQfO/8rWPt9TxZJwYeztaPFhI+b8+AbP3frijZw+cQO2HjhFp9+2eRMpgGh7Jt1+2cTW/TVJaFujCKO7PNX5c2gOHiRwxou4atX27utUEgRs24J2z25c9RJRuZzoV32Kbutm0he8V9ShiRIs59GR2O/sjsrlQn3iOLov1mO+rw+2Pv3Jem12UYdX4shlvlLghZbT+K7/Hia1eJGW8a3YdHQj9352F4v2X33vnuSsE5zKPkm3qr5799yV0JNMRwa/nbu4d48KVZ4EqYolgRZxLb13CSqKwse/L+WeGn28SdG1ahXfxqdN9dCanLWeweF2ALDl+CbqRzaggrkSLo8Ll8cFwK1xrfjptH97/Yjrk5mZQ1R2ap7ySGsGmRnZRRBR/nRfbsByZ2fUKafIfH3OTV9I7g8lMBD952sxP3w/5gcGoP35JzJen4PjzrvybyzEFbgrVMDVpBnO5rdi73kvmW8vIuvVNzF+9AH6pVe589vtBofj5gVaQkgyVUpUCanK8IajeL/rMn4Y+At1I+oz9fvnr7oIPSUnd41LpNF3755IU+7rVPvFP5gWvQWdJu/ePf1qDmTtodVkOTLZeuIbjmUepW/N/Pfu+Tezznf9ik6jQ0HB4cn9T3vOdp4fUnYRNzfM5+ujAx9wIuv4dfcnbly1StHsqJjIv/9l7ShXh2pV44okpvzY+/TnbEo6qdt24UpsWNThXBdXw8akff415w4d5+zxs6Ru+R57n6K9K1eUTrZ+A3E2boJx8cUP4sEjh2LplIRu/VpCWzcjonwkAXt2o045RdDoYYQ1qU9EhShCmzfENG2yT6KlPvo3kVFm9Cs/IXjUo4RXiScssSb6j5cCYHzj/wirV53wWpUJnPyszyOmNH/+QfDgQYQ1qEVExWhCWzfDOO+tyz6GqjiQy3ylULgxnL41+zNh2xOcsZ4hyhR12XrRpty9e85afffuOZOTu3dPqP7ionbVFT7Jd0vowcRtT7Lm0Cq2ndhC4+gm1AjLf++e6xWqD6VBZEPvZb9L6TT6Au9PXFmLSqFsqteQOWmn6X5kF2pF4fPyDfij7i30rSabSQpRkjmS2mF6/VVwOiEgAADNsb8JnPwMOWOfxBMZhbtCRVTnzqFYQsmaPBXFYkFz6CCmWdNQnz1H1suv+Rwz8IVJ2HveS8a772P46H2CRw5F+/M+NMePkfnaWwT89COB017AVa8+9h69AFCfTMZdtRr2nr1RgoLQ7t+Haea03K1LXnzuJp+V/BVJMjVjxgw2bdpEQEAAFSpUYNq0aZgv7DEzb948PvnkE9RqNU8//TStW+fedrxlyxamTJmCx+PhnnvuYfDgwUURerFz1nqWCGPeP2CH0w+h1+gx63LPq04TgN3tu3dPXFA8MYGxrDm0ig4VL+7ds/rQpwTrzNQKz3/vHqPWSI9qvXh3/3wOpv7B8y2n5tvmRrQul8TmHV8TH1T+4hYQokjotGpeuK8V75UL5+n9rVAUhYa1yjGhaWUCdfL5TIiSzBMbj8rlQpWaihKV+0Fcff48aR+vwV2v/sWKcfFkPz/F+9LZrDmKyUTwmOFkTZvl8xQCZ6s2uVuYAK7GTdB/thrdF+tJ/fYH0Ghwtu+EbsM6dOvXepMpZ5u2F28SURSct7RAZbVifH+xJFP/aNmyJWPHjkWr1TJr1izmzZvHuHHjOHjwIOvWrWPdunWkpKTwwAMP8MUXXwAwefJkFi5cSHR0NL169aJ9+/YkJCQURfiFKi3HyS+nMtGqVSTGmzEEaK5af9mBD1nx53LurdGHOuH1cHqcbD3+DQv3v8Ogug95F5AnWKqz4a/1rD+8lrigOGICY4kJjGVc0/E8vnk0oYYwksq147vk7Szav4CJzSdd8z5S/WsNZPEvC3ITq4Se+Te4AffW6MviX96lx+quDGswiormSpy3nWfv6R+IMkUxNHFEofQrLi/YoKX/rVXpf2vVog5FCFGQLrM0xB0b55tIXahnfHs2hvcXoTn6NyrbxQ/r6uPH8FS5+LvBcckNH0qwGU94BM5bW4Hm4t83d+UqaI5fsmTDZsP02ssYVixHfeI4Kqfz4nsuV77P1LzZiiSaVq1aeb9v0KABGzZsAOCrr77ijjvuQKfTUb58eSpWrMi+ffsAqFixIuXLlwfgjjvu4Kuvvip1ydTKfcdZtPtnHOo/UKHHpErgybaNaVox7x5S/+hYsTNHM4/wwa+LOZF1Ao1KQ6WQykxtPYuBtQd56z1Q9xF+PruPMZuGkWZP4/EmT/FEswkMrD0Ih9vOvJ9mM3/fHGKD4nm+5ZTrSk4aRDUiNjCOW+NaYdaH5N/gBhi0BlZ2X8uMnVOYuXMqZ6yniTBG0jCqMbdV6pr/AYQQQuRLfSoZJSDA5xFN/34mJoBx3lsEPvc0OaMew9miJYrFgnbvHoKfGovKbvepq4T86++CTpdnnzcCdHBJu8AXnsW45D2yxz6Fq34iSkgIug3rCHxlVu6lvqAg/wdbgIo8tVuxYgVduuTua5GSkkJiYqL3vejoaFJSch/0GRMT41P+T5J1NRqNCovFVMAR3ziNRn3FePYeS2Phnu+xBb+LWpODAqQ7Ipj+zSMse7ALEUGXXxd0i6URt1SZm2/fFouJ1X1XXfa9sa3/y9jW/71i26mdX2Rq5ytvPfDrmV85mZ3MI00e8hmfRqNmaPNHGNr8kUviqIVjgsun/aGRh/Mc89/tACyYeOvON3mLN/PULw6u9vMtbcrSWEHGW9qVtfECmIw6DJcZs2brZpRGjbBE5iY7Gp0WtJo850ezfg1Kz17oZk7nnwt6qmN/ARAcbACLCdKMAAQG6jFd0l6tVqE3aAm49O+FTguX/By0a1fjGTYc/TMT+Oevn3rr1wUx9EJRaMnUoEGDOHs278M4x4wZQ8eOHQGYM2cOGo2Gbt26AVz2zjOVSoXnMqv3r7Qg+lJut1KsNmK72sZwy3YeIF31P3Rk4XZfKNScJt3zPev21qN7vdibF+g1Om87x8HUg8zY+SK1wmrT0NLcZ3xlbSO8sjTesjRWkPGWdmVtvJFAjtWB7V9jNnz4PsG7dpHxxlzsF94LdrjQuD15zo8lKwe3SkPmJeUh778PQGamDXdaDuoMK+FAdrYdxyX1wjwKdpuL7EvK/t1PeI4Vm6Im5586bjehS5cW1CkocIWWTC1atOiq769cuZLNmzezaNEib2IUExPDqVMXH0mRkpJC1IUFcFcqLy3O5eSg1mTmKXcqZ0m1Wosgovx9eWQDo78eRrXQ6rzV4e1rSnCFEEIUnPM5Dv46l4PZoCUhIvCafw9rjh5Fu3tn7qadySfQbViHfvVKrP0GYu/dL9/2zqR2GN+Zi7NRE9yVKmNYsRzNX3/5OxwvR1I7jAvn465cBSU0FMO781HZi+/+VkVymW/Lli3Mnz+fDz74AKPR6C1v3749Y8eO5YEHHiAlJYUjR45Qv359FEXhyJEjHDt2jOjoaNatW8fLL79cFKEXmibl4th7tiboLy7AUxQVgTSkbkxYEUZ2ZX1q9qdPTdnvRgghbjaPovDRt4fYvusPap07yilTKJQvz8hujYgx53/zkGnOG5jmvIGi1+c+TqZBIzLeW3rNj5PJHvsk6nNnCZz+AgD2O7qRNXUGIQN6+zWuf2RNnUXwuDEEPfU4GAzYevfD0fVOgseOKpDjF7QieTZfp06dcDgcWCwWABITE5k8eTKQe+lvxYoVaDQaJkyYQFJS7l0A33zzDVOnTsXtdtOzZ08effTRfPspSc/mS7c6GbnyW47ZP0dt2I+iBKC2N6dheHum/6cJGnXJm/Upa1PnZWm8ZWmsIOMt7UrieL/89RQ/LF3LMzuXEeR2oACfx9ZlTcd+TB14K+qrzFCVxPFeqjg+m08edHwT5fcP+Gy2g2V7D7Ptr2QCNGpuq1GJnvXL57s9QnFV0v/DXq+yNN6yNFaQ8ZZ2JXG84xduYfSaN6mZleItU4D/th3CvQ/dSe2YKyccJXG8lyqOyVSR380nLooI1DG8VU2Gtyr4HcSFEEKUHmk5DuKtaT5lKqBcxhlSrc7LNxKFRp7NJ4QQQpQwlWMs/BBawafModLwc1QVqoaXrW0eigNJpoQQQogSplurGrzbrAdbw6viUGk4agxleqO7qVWvyjUtQBcFSy7zCSGEECVMzehghvRtw6q4CF5JySDEoKVN4wS6NYgv6tDKJEmmhBBCiBKoTkwwdXrfUtRhCOQynxBCCCGEXySZEkIIIYTwgyRTQgghhBB+kGRKCCGEEMIPkkwJIYQQQvhBkikhhBBCCD9IMiWEEEII4QdJpoQQQggh/CDJlBBCCCGEHySZEkIIIYTwgyRTQgghhBB+kGRKCCGEEMIPkkwJIYQQQvhBkikhhBBCCD9IMiWEEEII4QdJpoQQQggh/CDJlBBCCCGEHySZEkIIIYTwgyRTQgghhBB+kGRKCCGEEMIPkkwJIYQQQvhBkikhhBBCCD9IMiWEEEII4QdJpoQQQggh/CDJlBBCCCGEHySZEkIIIYTwgyRTQgghhBB+kGRKCCGEEMIPkkwJIYQQQvhBkikhhBBCCD9IMiWEEEII4QdJpoQQQggh/CDJlBBCCCGEHySZEkIIIYTwgyRTQgghhBB+kGRKCCGEEMIPkkwJIYQQQvhBkikhhBBCCD9IMiWEEEII4QdJpoQQQggh/CDJlBBClFDqY0cJHvYIYQ1rE1E+krAGtTDf14eA77YXbD9H/yYyyozuy88L9LhClBbaog5ACCHE9VOlpWLp0gFPdAzZEyfhiYlFfewo+g3r0e7aibNFy6IOUYgyQ5IpIYQogfSfrUZ95jSpm75FiYz0ltv7DgBFKcLIboDVCkZjUUchxA2Ty3xCCFECqdLTQadDCQ29zJsq77faXd9jHtibsHrViagUQ2i7lug/WZanifrYUYKHPEB4zUpoQ4IJTWqBfsVy38NarQSNHU141XKEJdbENGMKeDze9zV//kHw4EGENahFRMVoQls3wzjvLZ86Adu3EhllJuDrjZgH9iaiUixB4x8vgDMiRNGRmSkhhCiBXPUTUdntBA9/BOuwUbjqJYI67+djzfFjOJs2x3b/gyh6AwE7dxA8ehio1djvvgcA1ZkzWLp2BKORrOemYKpeBdvuvahPnPA5VuDkZ7Hf0Y2MBe+h2/oNgS/PwF2zFvbudwOgPpmMu2o17D17owQFod2/D9PMaWCzYR091udYwf8dga1vf6yDh6HoDYV0loS4OSSZEkKIEsjZpi05Q4ZjfHs2hpUr8AQF40xqh3XQQziT2nnr2Xv0uthIUXC2aIk6ORnDB4u9yZRp3luoMzM4v3ELnugYjBYT1kYt8vbZ/FayJ0/N/b5te3Rfb0S3bo03mXK2aYuzTduLfd3SApXVivH9xXmSKXu3u8h56pkCPCNCFB1JpoQQooTKfmEa1gceRv/5OgJ2bEe3aSO69Z+RNeMVbIMeAnIXqptmTkW/YT3qk8mo3G4A3LFx3uMEbPsGR7uOeKJjrtqfo217n9euGjXQHD9+scBmw/TayxhWLEd94jgqp/OSyi7QXvyT4+h4240OW4hiR9ZMCSFECeapUhXr8FFkvL+Mcz/8gqtufQKnPu9dhB486lEMqz4lZ/go0pevIvXLzVj7DURlt3mPoT5/Hk90dL59KSEhvgUBOrDbvS8DX3gW05w3sA58gPQPPyH1y81kPzYu902bzaepJzLqBkcsRPEjM1NCCFFKKOHh2Pr2J3jCE6jOnEExm9H97wuypr3knakCUHnm+7TzhIWhTknxu3/9mlVYHxqCdeQYb5nuf19cvvIli+SFKOlkZkoIIUog1dmzly3XHD6EotejmM2oHHZUbjeKXn+xXVYmui/W+7Rxtm6LbtNXqE6f9i8mmw1Fp7tY4HajX7XCr2MKURLIzJQQQhQxl9vD90fT+OXPk+gNOm6tFUu1yKCrtjEs+xD9iuXY7+2Dq049cDrRbf0G48J3cmehDAYUgwFnw0YEvjwDJSgY1GpMb7yCEhyCKivDe6ycIcPRL/8IS7fbyBnzOKoaVTHs2YcqJ8dnlik/jqR2GBfOx125CkpoKIZ356OyO274vAhRUkgyJYQQRcju8vDSqj0E7PuRtod2k6UzMLdWEm06NKZ74wpXbOfo2BnN0SMYPlicu4WBRoO7UmWyps7CNnCQt17GnAUEPz4a88gheELDsD44GJU1B+O7b3vrKBERpK39ksDJzxL0zHhUDjvGylXJGf3YdY0la+osgseNIeipx8FgwNa7H46udxI8dtR1nxchShKVopS0rXKvndPpJi0tp6jD8LJYTMUqnsIm4y29ytJYoXDHu/6XU/z13nLG7/kUNbm/jtMCjIzsOIKJD3ckxnzz92CSn2/pVtLHGxkZXNQh5CFrpoQQogjt+ekw/zm0w5tIAVicVlof2s3uY2lFGJkQ4lpJMiWEEMWQuvReNBCi1JFkSgg/Rc02EzXbjG6qlgrzokha2px398/Ho3jyb3wTYlvw87yiDkNcRcN6lVlfuSmXpk7pWgNbqzahUTlLkcUlhLh2sgBdiALwaOJI+jXozenUVD7/ay1PbRmLonh4qN6QIo1r/d0bqWCuVKQxiKvrUDOKWc1a8azeRNvDP5ClM7CuVhJJbeoTFyLPrBOiJJBkSogCUMFcgVvim5MWmEPrckn8nvo7i/YvuGIyZXVZMWqNhR5Xk5hmhd6H8I8hQMNTdzdmR6Mq7PyjBXqDjgdrx1MzuvgtshVCXJ5c5hOiECRGNuBY5lEAjmb8TdRsM5/8sYzhGweT8E55Bqzv7a37wa+Laf1RM8rNjaDRe3V4Y+//+Rxr5FdD6fRxEv87soFWHzWl4tvR9Fvbi1TbeQ6nH6LHqjuo9HYMnT5O4pez+33a/vsy346T33HnytuoMj+eKvPjabesJWsOrvRpk188ouAFaNS0rhrOo13q82C7mpJICVHCyMyUEIXgWMZRIk2+zx577tunuaPKnbxz22I0Kg0Ab+59janfP8+IBmO4Nb4V+878yIzvX8SkNfrMap3IOsaMXVN5qtkzWF05TNj2BGM3j+ZY5lEG1L6fEQ1H8+KO5xnyvwfY2mcnqss8qiPTkcGAdfdye+WujG3yJIqi8Nv5X0l3pHvrXGs8QgghLpJkSogC4FE8uDwushyZrDv8GWsPr2Zw/WE+dRpHN2VGm1e8rzMdGby0azr/bTyOcU3HA9C2fHtyXDm8snsWg+o8jEadm3Sl2lJZd/dGKodUAeDXc7/w1o+v8Ub7ufSu2Q8ABYV+6+7hz9Q/qB5WI0+Mh9IOkuFIZ3rrlwjS5c58tKvQ4YbiEUIIcZFc5hOiAEzc9iSm6QaqvBPPqK8fpVf13oxrNt6nTqeKt/m83nVqJzmubLpV7YHL4/J+tY5P4oz1NMnZJ7x1ywdX8CZSgPf71uWS8pSdzE6+bIyVzJUJDAhi6P8e4vO/1pFu993D6HriEUIIcZHMTAlRAIY3GM2Ahn1xWlVUNFe67OLyf1/2O287B0DrpZdfJH4i6wTlg3MfJxKi971FXqfJfZisWR/iLQtQ55bZ3bbLHs9iCGX5nSt5add0Hvnifjx4aFuuPVNbz6JSSOXrikcIIcRFkkwJUQDKBZejcWyTqz6i4d+rmEL1oQAs6bo8T6IFkGCpVpAhAtA05haW3bkSq8vKluObeXb7eB7d+BCf9/y6SOIRQojSQJIpIYpIk5hmGLVGTuWcolOl229q30atkdsqdeHAuV95bc8rRR6PEEKUZJJMCXEJt0fh4JlsXB4PVSMCMQQU3oLrEL2Fx5uO5+ltT3I88yjNY1ui4OFQ2kG2ndjK4i4fFmh//zuygQ8PfECXyndQLqg8J7OTee/XhbQq16ZI4hFCiNJCkikhLjiQksmUr/aQ5jgBKgd6yjGiZSJtEyILrc+RDccQY4ph3r7ZzPnxTfRaA1VDqtI9oWeB91U5pCoqVEzdMZmz1jOEGyPoVPF2JjZ/tkjiEUKI0kKlKKX3aZpOp/uqa1huNovFVKziKWwlabzpVicPL/+Gs5qFaPXHAXC7LARZH+TlOztQLTIo32OUpPH6qyyNFWS8pZ2Mt2SJjCx+m9rK1ghCANv/OkeaZ7c3kQLQaNPI0PyPNb/8XYSRCSGEKO4kmRICSMnMwcHJPOUqTSqnMjOLICIhhBAlhSRTQgDVoywEqurkKVe7qlAvJroIIhJCCFFSSDIlBNCsgoWKQXVxZ7fG4zGgKFqc2XWJULela+34og5PCCFEMSZ38wkBBGjUTL+jKYt2hbLpYFtcHoX/b+9+Y5sq2zCAX8dWzMTRRl05aBaSIW7EKPvwCsr6btrZDixlY3bfNGFImGGxdCRL/BMWFQKBxAyNCalmZCSYmAzoiBCD2gZGw8wSwxwQg/PPwhq3U2FbiyAt3Z73A+9OtpXB4Di6nl2/T93dZr2vrae5z3NOe/6T+zg2LC/A43PnpLs9IiKawThMEf2fKeszSuwYAAAK5UlEQVRBbC5eAs9/CyAAPCBN/M5yIiKiVBymiCaQJCnl0i9EREST4TlTRERERBpwmCIiIiLSgMMUERERkQYcpoiIiIg04DBFREREpAGHKSIiIiINOEwRERERacBhioiIiEgDDlNEREREGnCYIiIiItKAwxQRERGRBhymiIiIiDTgMEVERESkQVqHqaamJuTn52NgYAAAIITA9u3bYbfb4XK5cP78efWxfr8fDocDDocDfr8/XS0TERERjWNM1xP39fXh9OnTeOKJJ9RaW1sbenp68O233+Knn37CBx98gJaWFgwNDeGzzz7DoUOHIEkSKisrYbPZYDKZ0tU+EREREYA0rkzt3LkT9fX1kCRJrQUCAVRUVECSJBQWFiIWiyESiSAUCqGoqAhmsxkmkwlFRUU4depUulonIiIiUqVlZSoQCMBisaCgoGBcXVEUyLKs/izLMhRFSanPnz8fiqLc8XkMBglm88P/XuMaGQwPzKh+phvz6tdsygowr94xL2k1bcPUunXrcOnSpZS61+uFz+fDvn37Uu4TQqTUJEmatH4nw8MCQ0PXptjx9DObH55R/Uw35tWv2ZQVYF69Y97MkpOTne4WUkzbMNXc3HzL+oULFxAOh1FeXg4A6O/vR2VlJVpaWiDLMvr7+9XH9vf3w2KxQJZldHR0qHVFUbBs2bLpap2IiIhoyu77OVP5+flob29HMBhEMBiELMs4fPgwcnJyYLPZ0NraCiEEOjs7kZ2dDYvFAqvVilAohGg0img0ilAoBKvVer9bJyIiIkqRtk/z3UpJSQlOnjwJu92OrKws7NixAwBgNpuxadMmuN1uAEBtbS3MZvMdf9+DDxpm3HLgTOtnujGvfs2mrADz6h3zkhaSuNUJSUREREQ0JfwGdCIiIiINOEwRERERacBhioiIiEgDDlNEREREGnCYIiIiItKAw9Q0ampqQn5+PgYGBgDc/Ib37du3w263w+Vy4fz58+pj/X4/HA4HHA4H/H5/ulq+J3v27IHL5UJ5eTnWr1+vXupHr3l37dqFlStXwuVyoba2FrFYTL3P5/PBbrejrKxs3PUj29raUFZWBrvdjs8//zwdbd+zb775Bk6nEwUFBTh79uy4+/SYdyI9ZRn17rvv4sUXX8Tq1avV2tDQEKqrq+FwOFBdXY1oNArg9ttxJujr68Mbb7yBVatWwel0Yv/+/QD0mzcej8PtdmPNmjVwOp349NNPAQC9vb2oqqqCw+GA1+tFIpEAACQSCXi9XtjtdlRVVSEcDqez/cwlaFr8+eefYv369eKll14Sly9fFkIIceLECfHmm2+KkZERcebMGeF2u4UQQgwODgqbzSYGBwfF0NCQsNlsYmhoKJ3t35UrV66ot/fv3y+2bt0qhNBv3lOnTokbN24IIYTYvXu32L17txBCiO7ubuFyuUQ8HhcXL14UpaWlIplMimQyKUpLS8XFixdFPB4XLpdLdHd3pzPCXfn111/Fb7/9Jl5//XXR1dWl1vWadyw9ZRmro6NDnDt3TjidTrW2a9cu4fP5hBBC+Hw+9XU92XacKRRFEefOnRNC3Hyvcjgcoru7W7d5R0ZGxN9//y2EECKRSAi32y3OnDkjPB6POHr0qBBCiK1bt4ovv/xSCCHEgQMH1Pfso0ePis2bN6en8QzHlalpsnPnTtTX14+7hmAgEEBFRQUkSUJhYSFisRgikQhCoRCKiopgNpthMplQVFQ0bi9/pnvkkUfU2//884+aWa95rVYrjMab33dbWFioXgIpEAjA6XRizpw5yM3NxcKFC9HV1YWuri4sXLgQubm5mDNnDpxOJwKBQDoj3JVFixYhLy8vpa7XvGPpKctYzz//PEwm07ja6PYKABUVFfj+++/H1Sdux5nCYrHgmWeeAXDzvSovLw+Koug2ryRJmDt3LgAgmUwimUxCkiT88MMPKCsrAwCsXbtWfR0Hg0GsXbsWAFBWVob29vZbXg+Xbo/D1DQIBAKwWCwoKCgYV1cUBbIsqz/LsgxFUVLq8+fPVw+VZYrGxkaUlJTg66+/xubNmwHoO++oQ4cOobi4GEBq3tFceso71mzIq6csd3L58mVYLBYANweQ0dMTJtuOM1E4HMbPP/+MpUuX6jrv8PAwysvLsWLFCqxYsQK5ubmYN2+euhM4NpOiKFiwYAEAwGg0Ijs7G4ODg2nrPVPNqMvJZJJ169bh0qVLKXWv1wufz4d9+/al3HeraV+SpEnrM8nt8r7yyiuoq6tDXV0dfD4fDhw4AI/Ho+u8ALB3714YDAasWbMGwOT/35GRkVvWZ5Kp5J0ok/NOVSa8VqebXv4GV69ehcfjwXvvvTduNX0iPeQ1GAw4cuQIYrEYamtr8fvvv6c8ZjSTHvLOBBym7lFzc/Mt6xcuXEA4HEZ5eTkAoL+/H5WVlWhpaYEsy+ohodH7LBYLZFlGR0eHWlcUBcuWLZvW/u/WZHknWr16NWpqauDxeHSd1+/348SJE2hublbfeCbmVRRF3fOdrD5TTPX/O1Ym552q22XUm8ceewyRSAQWiwWRSASPPvoogNS/weh2nElu3LgBj8cDl8sFh8MBQN95R82bNw/Lly9HZ2cnYrEYkskkjEbjuEyyLKOvrw+yLCOZTOLKlStTuvYtjcfDfP+y/Px8tLe3IxgMIhgMQpZlHD58GDk5ObDZbGhtbYUQAp2dncjOzobFYoHVakUoFEI0GkU0GkUoFILVak13lCnr6elRbweDQfX8Gr3mbWtrwxdffIG9e/ciKytLrdtsNhw7dgyJRAK9vb3o6enBc889h2effRY9PT3o7e1FIpHAsWPHYLPZ0pjg3zEb8uopy52Mbq8A0NraitLS0nH1idtxphBC4P3330deXh6qq6vVul7zDgwMqJ8wvn79Ok6fPo1FixZh+fLlOH78OICbO4Ojr2ObzaZ+ovr48eN44YUXuDJ1D7gydR+VlJTg5MmTsNvtyMrKwo4dOwAAZrMZmzZtgtvtBgDU1tZm1J7Bxx9/jD/++AOSJOHJJ5/Ehx9+CEC/ebdt24ZEIqG+MS9duhQfffQRFi9ejFWrVuHVV1+FwWBAQ0MDDAYDAKChoQEbNmzA8PAwXnvtNSxevDidEe7Kd999h23btmFgYAA1NTVYsmQJmpqadJt3LKPRqJssY23ZsgUdHR0YHBxEcXEx3n77bWzcuBFerxcHDx7EggUL8MknnwCYfDvOFD/++COOHDmCp59+Wj1isGXLFt3mjUQieOeddzA8PAwhBFauXImXX34ZTz31FOrq6rBnzx4sWbIEVVVVAAC32436+nrY7XaYTCY0NjamOUFmkgRP2yciIiK6ZzzMR0RERKQBhykiIiIiDThMEREREWnAYYqIiIhIAw5TRERERBpwmCIiIiLSgMMUERERkQYcpogoY3V1dcHlciEej+PatWtwOp345Zdf0t0WEc0y/NJOIspojY2NSCQSuH79OmRZRk1NTbpbIqJZhsMUEWW0RCIBt9uNhx56CF999ZV6SRsiovuFh/mIKKNFo1Fcu3YNV69eRTweT3c7RDQLcWWKiDLaW2+9BafTiXA4jL/++gsNDQ3pbomIZhmuTBFRxmptbYXRaITL5cLGjRtx9uxZtLe3p7stIppluDJFREREpAFXpoiIiIg04DBFREREpAGHKSIiIiINOEwRERERacBhioiIiEgDDlNEREREGnCYIiIiItKAwxQRERGRBv8DalKkxfNBfOUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 648x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "word_vectors_plot(model, \"story\", [t[0] for t in model.wv.most_similar(positive=[\"story\"], \n",
    "                                                                             topn=16)][8:])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Wordcloud visualization of positive words in reviews"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "superb exceptional terrific phenomenal stellar excellent firstrate fabulous topnotch magnificent marvelous splendid superlative fine fantastic flawless masterful impeccable wonderful kudos\n"
     ]
    }
   ],
   "source": [
    "pos_lst=[t[0] for t in model.wv.most_similar(positive=[\"outstanding\"],topn=20)]\n",
    "pos_wrd=' '.join(pos_lst)\n",
    "print(pos_wrd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "wordcloud = WordCloud(background_color='white',\n",
    "                      max_words=200000,\n",
    "                      max_font_size=400).generate(pos_wrd)\n",
    "plt.figure()\n",
    "plt.imshow(wordcloud)\n",
    "plt.axis(\"off\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Wordcloud visualization of negative words in reviews"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "terrible horrible atrocious horrid dreadful horrendous lousy sucks pathetic appalling abysmal laughable bad lame crappy pitiful stinks poor sucked unwatchable\n"
     ]
    }
   ],
   "source": [
    "neg_lst=[n[0] for n in model.wv.most_similar(positive=[\"awful\"],topn=20)]\n",
    "neg_wrd=' '.join(neg_lst)\n",
    "print(neg_wrd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "wordcloud = WordCloud(background_color='black',\n",
    "                      max_words=100,\n",
    "                      max_font_size=50).generate(neg_wrd)\n",
    "plt.figure()\n",
    "plt.imshow(wordcloud)\n",
    "plt.axis(\"off\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **Generating Feature vectors**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Function to generate feature vectors\n",
    "\"\"\"\n",
    "\n",
    "def generate_feature_vectors(doc, model):\n",
    "    vec = np.zeros(150).reshape((1, 150))  \n",
    "    count = 0\n",
    "    for word in gensim.utils.simple_preprocess(doc):\n",
    "        if model.__contains__(word.strip()):\n",
    "            count = count + 1\n",
    "            vec += model[word.strip()]\n",
    "    vec = vec / count\n",
    "    return vec\n",
    "\n",
    "def generate_features(model, data):\n",
    "    features = np.concatenate([generate_feature_vectors(s, model) for s in data])\n",
    "    return features\n",
    "\n",
    "\"\"\"\n",
    "Generating train, test and validation vectors\n",
    "\"\"\"\n",
    "\n",
    "training_vectors = generate_features(model, x_train)\n",
    "test_vectors = generate_features(model, x_test)\n",
    "validation_vectors = generate_features(model, x_validation)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####  Word2Vec Word Embedding Based Sentiment Analysis using LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "***** Word2Vec Word Embedding Based Sentiment Analysis using LogisticRegression *******\n",
      "\n",
      "LogisticRegression Performance : \n",
      "\n",
      "Train-Set Score : 0.8753\n",
      "Train-Set Accuracy : 0.8753\n",
      "\n",
      "Evaluation on Validation-Set : \n",
      "Classification report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.86      0.87      2513\n",
      "           1       0.86      0.88      0.87      2487\n",
      "\n",
      "   micro avg       0.87      0.87      0.87      5000\n",
      "   macro avg       0.87      0.87      0.87      5000\n",
      "weighted avg       0.87      0.87      0.87      5000\n",
      "\n",
      "Confusion matrix:\n",
      " [[2152  361]\n",
      " [ 293 2194]]\n",
      "Validation-Set Score : 0.8692\n",
      "Validation-Set Accuracy:0.8692\n",
      "\n",
      "Evaluation on Test-Set : \n",
      "Classification report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.86      0.87      2529\n",
      "           1       0.86      0.88      0.87      2471\n",
      "\n",
      "   micro avg       0.87      0.87      0.87      5000\n",
      "   macro avg       0.87      0.87      0.87      5000\n",
      "weighted avg       0.87      0.87      0.87      5000\n",
      "\n",
      "Confusion matrix:\n",
      " [[2185  344]\n",
      " [ 303 2168]]\n",
      "Test-Set Score : 0.8706\n",
      "Test-Set Accuracy:0.8706\n"
     ]
    }
   ],
   "source": [
    "lr = LogisticRegression()\n",
    "lr.fit(training_vectors, y_train)\n",
    "print(\"***** Word2Vec Word Embedding Based Sentiment Analysis using LogisticRegression *******\\n\")\n",
    "print(\"LogisticRegression Performance : \\n\")\n",
    "print('Train-Set Score : {:.4f}'.format(lr.score(training_vectors, y_train)))\n",
    "print('Train-Set Accuracy : {:.4f}'.format(accuracy_score(y_train,lr.predict(training_vectors))))\n",
    "\n",
    "print(\"\\nEvaluation on Validation-Set : \")\n",
    "pred_val = lr.predict(validation_vectors)\n",
    "print(\"Classification report:\\n {}\".format(classification_report(y_validation, pred_val)))\n",
    "print(\"Confusion matrix:\\n {}\".format(confusion_matrix(y_validation, pred_val)))\n",
    "print('Validation-Set Score : {:.4f}'.format(lr.score(validation_vectors, y_validation)))\n",
    "print('Validation-Set Accuracy:{:.4f}'.format(accuracy_score(y_validation, pred_val)))\n",
    "\n",
    "print(\"\\nEvaluation on Test-Set : \")\n",
    "pred = lr.predict(test_vectors)\n",
    "print(\"Classification report:\\n {}\".format(classification_report(y_test, pred)))\n",
    "print(\"Confusion matrix:\\n {}\".format(confusion_matrix(y_test, pred)))\n",
    "print('Test-Set Score : {:.4f}'.format(lr.score(test_vectors, y_test)))\n",
    "print('Test-Set Accuracy:{:.4f}'.format(accuracy_score(y_test, pred)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####  Word2Vec Word Embedding Based Sentiment Analysis using SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "***** Word2Vec Word Embedding Based Sentiment Analysis using SVC *******\n",
      "\n",
      "SVC with linear kernel Performance : \n",
      "\n",
      "Train-Set Score : 0.8755\n",
      "Train-Set Accuracy : 0.8755\n",
      "\n",
      "Evaluation on Validation-Set : \n",
      "Classification report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.86      0.87      2513\n",
      "           1       0.86      0.89      0.87      2487\n",
      "\n",
      "   micro avg       0.87      0.87      0.87      5000\n",
      "   macro avg       0.87      0.87      0.87      5000\n",
      "weighted avg       0.87      0.87      0.87      5000\n",
      "\n",
      "Confusion matrix:\n",
      " [[2151  362]\n",
      " [ 281 2206]]\n",
      "Validation-Set Score : 0.8714\n",
      "Validation-Set Accuracy:0.8714\n",
      "\n",
      "Evaluation on Test-Set : \n",
      "Classification report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.86      0.87      2529\n",
      "           1       0.86      0.88      0.87      2471\n",
      "\n",
      "   micro avg       0.87      0.87      0.87      5000\n",
      "   macro avg       0.87      0.87      0.87      5000\n",
      "weighted avg       0.87      0.87      0.87      5000\n",
      "\n",
      "Confusion matrix:\n",
      " [[2182  347]\n",
      " [ 308 2163]]\n",
      "Test-Set Score : 0.8690\n",
      "Test-Set Accuracy: 0.8690\n"
     ]
    }
   ],
   "source": [
    "svm = SVC(kernel='linear')\n",
    "svm.fit(training_vectors, y_train)\n",
    "print(\"***** Word2Vec Word Embedding Based Sentiment Analysis using SVC *******\\n\")\n",
    "print(\"SVC with linear kernel Performance : \\n\")\n",
    "print('Train-Set Score : {:.4f}'.format(svm.score(training_vectors, y_train)))\n",
    "print('Train-Set Accuracy : {:.4f}'.format(accuracy_score(y_train,svm.predict(training_vectors))))\n",
    "\n",
    "print(\"\\nEvaluation on Validation-Set : \")\n",
    "pred_val = svm.predict(validation_vectors)\n",
    "print(\"Classification report:\\n {}\".format(classification_report(y_validation, pred_val)))\n",
    "print(\"Confusion matrix:\\n {}\".format(confusion_matrix(y_validation, pred_val)))\n",
    "print('Validation-Set Score : {:.4f}'.format(svm.score(validation_vectors, y_validation)))\n",
    "print('Validation-Set Accuracy:{:.4f}'.format(accuracy_score(y_validation, pred_val)))\n",
    "\n",
    "print(\"\\nEvaluation on Test-Set : \")\n",
    "pred = svm.predict(test_vectors)\n",
    "print(\"Classification report:\\n {}\".format(classification_report(y_test, pred)))\n",
    "print(\"Confusion matrix:\\n {}\".format(confusion_matrix(y_test, pred)))\n",
    "print('Test-Set Score : {:.4f}'.format(svm.score(test_vectors, y_test)))\n",
    "print(\"Test-Set Accuracy: {:.4f}\".format(accuracy_score(y_test, pred)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Word2Vec Word Embedding Based Sentiment Analysis using XGBClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "***** Word2Vec Word Embedding Based Sentiment Analysis using XGBClassifier *******\n",
      "\n",
      "XGBClassifier Performance : \n",
      "\n",
      "Train-Set Score : 0.8695\n",
      "Train-Set Accuracy : 0.8695\n",
      "\n",
      "Evaluation on Validation-Set : \n",
      "Classification report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.87      0.83      0.85      2513\n",
      "           1       0.84      0.87      0.86      2487\n",
      "\n",
      "   micro avg       0.85      0.85      0.85      5000\n",
      "   macro avg       0.85      0.85      0.85      5000\n",
      "weighted avg       0.85      0.85      0.85      5000\n",
      "\n",
      "Confusion matrix:\n",
      " [[2096  417]\n",
      " [ 313 2174]]\n",
      "Validation-Set Score : 0.8540\n",
      "Validation-Set Accuracy:0.8540\n",
      "\n",
      "Evaluation on Test-Set : \n",
      "Classification report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.86      0.84      0.85      2529\n",
      "           1       0.84      0.86      0.85      2471\n",
      "\n",
      "   micro avg       0.85      0.85      0.85      5000\n",
      "   macro avg       0.85      0.85      0.85      5000\n",
      "weighted avg       0.85      0.85      0.85      5000\n",
      "\n",
      "Confusion matrix:\n",
      " [[2127  402]\n",
      " [ 345 2126]]\n",
      "Test-Set Score : 0.8506\n",
      "Test-Set Accuracy: 0.8506\n"
     ]
    }
   ],
   "source": [
    "xgb = XGBClassifier()\n",
    "xgb.fit(training_vectors, y_train)\n",
    "print(\"***** Word2Vec Word Embedding Based Sentiment Analysis using XGBClassifier *******\\n\")\n",
    "print(\"XGBClassifier Performance : \\n\")\n",
    "print('Train-Set Score : {:.4f}'.format(xgb.score(training_vectors, y_train)))\n",
    "print('Train-Set Accuracy : {:.4f}'.format(accuracy_score(y_train,xgb.predict(training_vectors))))\n",
    "\n",
    "print(\"\\nEvaluation on Validation-Set : \")\n",
    "pred_val = xgb.predict(validation_vectors)\n",
    "print(\"Classification report:\\n {}\".format(classification_report(y_validation, pred_val)))\n",
    "print(\"Confusion matrix:\\n {}\".format(confusion_matrix(y_validation, pred_val)))\n",
    "print('Validation-Set Score : {:.4f}'.format(xgb.score(validation_vectors, y_validation)))\n",
    "print('Validation-Set Accuracy:{:.4f}'.format(accuracy_score(y_validation, pred_val)))\n",
    "\n",
    "\n",
    "print(\"\\nEvaluation on Test-Set : \")\n",
    "pred = xgb.predict(test_vectors)\n",
    "print(\"Classification report:\\n {}\".format(classification_report(y_test, pred)))\n",
    "print(\"Confusion matrix:\\n {}\".format(confusion_matrix(y_test, pred)))\n",
    "print(\"Test-Set Score : {:.4f}\".format(xgb.score(test_vectors, y_test)))\n",
    "print(\"Test-Set Accuracy: {:.4f}\".format(accuracy_score(y_test, pred)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Sentiment Analysis using Keras Convolutional Neural Networks(CNN) \n",
    "\n",
    "[Keras reference](https://blog.keras.io/using-pre-trained-word-embeddings-in-a-keras-model.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x_train[2] :  famous famous tension release style cartoon semimain character terrible peril rescued hero last second particular casper one remember death actually takes hand even death still happy endingthe constant famous studios cartoons virtue always triumphs popeye always gets spinach time baby huey always outfoxes fox little audery always learns lesson fs cartoons really dark depressingyou give credit much love looney tunes tom jerry nt think anyone putting better cartoon product time paramount color animation music great winston sharples editing voices consistent glowing example best art form offer\n",
      "\n",
      " X_token_train[2] :  [647, 4052, 647, 3, 68, 177, 10, 373, 252, 10055, 9061, 1046, 3, 2853, 340, 253, 202, 38, 1851, 951, 4999, 2666, 2037, 9756, 6701, 17593, 180, 11092, 17049, 3613, 372, 356, 21986, 350, 6591, 1093, 104, 1051, 12429, 483, 2111, 605, 384, 3116, 3147, 781, 4628, 3614, 126, 699, 2, 809, 5037, 5215, 2612, 70, 141, 67006, 11092, 10384, 1025, 726, 86, 4407, 801, 1004, 8712, 17, 84, 104, 13, 1004, 3406, 10384, 2047, 351, 249, 5479, 4522, 4794, 214, 2344, 6, 801, 6955, 826, 1075, 617, 283, 1037, 12673, 10384, 2651, 214, 87, 3902, 3087, 12176, 364, 5092, 576, 3341, 525, 1841, 36, 12, 1254, 142, 104, 11714, 303, 1492, 1031, 6479, 1058, 895, 15230, 9173, 42623, 96, 689, 33989, 1683, 59, 2288, 752, 315, 474, 1440, 187, 870, 114, 175, 650, 2, 119, 880, 4629, 93, 11936, 3923, 644, 50676, 4, 1151, 7020, 4072, 385, 797, 14, 7, 195, 4967, 175, 5656, 7086, 91, 208, 1176, 95, 162, 1441, 400, 31, 17, 302, 9454, 2818, 415, 4, 11, 395, 225, 36, 501]\n",
      "\n",
      " vocab_size :  158132\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "Create the tokenizer\n",
    "\"\"\"\n",
    "number_of_words=len(vocabulary)\n",
    "tokenizer = Tokenizer(num_words=number_of_words)\n",
    "\n",
    "\"\"\"\n",
    "Fit the tokenizer\n",
    "\"\"\"\n",
    "tokenizer.fit_on_texts(x_train)\n",
    "\n",
    "\"\"\"\n",
    "Sequence encode\n",
    "\"\"\"\n",
    "X_token_train = tokenizer.texts_to_sequences(x_train)\n",
    "X_token_test = tokenizer.texts_to_sequences(x_test)\n",
    "X_token_validation = tokenizer.texts_to_sequences(x_validation)\n",
    "\n",
    "\"\"\"\n",
    "Adding 1 because of reserved 0 index\n",
    "\"\"\"\n",
    "vocabulary_size = len(tokenizer.word_index) + 1  \n",
    "\n",
    "print(\"x_train[2] : \",x_train[2])\n",
    "print(\"\\n X_token_train[2] : \",X_token_train[2])\n",
    "print(\"\\n vocab_size : \",vocabulary_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "famous : 662\n",
      "cartoon : 877\n",
      "studios : 2378\n",
      "love : 36\n",
      "baby : 806\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "Checking the index of each word by looking at the word_index dictionary of the Tokenizer object\n",
    "\"\"\"\n",
    "for word in ['famous','cartoon','studios', 'love', 'baby']:\n",
    "    print('{} : {}'.format(word, tokenizer.word_index[word]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Pad sequences\n",
    "\"\"\"\n",
    "max_length = 1500\n",
    "\n",
    "X_token_train = pad_sequences(X_token_train, padding='post', maxlen=max_length)\n",
    "X_token_test = pad_sequences(X_token_test, padding='post', maxlen=max_length)\n",
    "X_token_validation = pad_sequences(X_token_validation, padding='post', maxlen=max_length)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /opt/conda/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Colocations handled automatically by placer.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "W0507 14:54:43.152259 140072427317056 deprecation.py:323] From /opt/conda/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Colocations handled automatically by placer.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "embedding_1 (Embedding)      (None, 1500, 100)         15813200  \n",
      "_________________________________________________________________\n",
      "conv1d_1 (Conv1D)            (None, 1496, 128)         64128     \n",
      "_________________________________________________________________\n",
      "global_max_pooling1d_1 (Glob (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 10)                1290      \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 1)                 11        \n",
      "=================================================================\n",
      "Total params: 15,878,629\n",
      "Trainable params: 15,878,629\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "CPU times: user 128 ms, sys: 0 ns, total: 128 ms\n",
      "Wall time: 123 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\"\"\"\n",
    "Create model\n",
    "\"\"\"\n",
    "embedding_dimension = 100\n",
    "\n",
    "keras_cnn_model = Sequential()\n",
    "keras_cnn_model.add(Embedding(input_dim=vocabulary_size, \n",
    "                              output_dim=embedding_dimension, \n",
    "                              input_length=max_length))\n",
    "keras_cnn_model.add(Conv1D(128, 5, activation='relu'))\n",
    "keras_cnn_model.add(GlobalMaxPooling1D())\n",
    "keras_cnn_model.add(Dense(10, activation='relu'))\n",
    "keras_cnn_model.add(Dense(1, activation='sigmoid'))\n",
    "\n",
    "\"\"\"\n",
    "Compile network\n",
    "\"\"\"\n",
    "keras_cnn_model.compile(optimizer='adam',\n",
    "                        loss='binary_crossentropy',\n",
    "                        metrics=['accuracy'])\n",
    "keras_cnn_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /opt/conda/lib/python3.6/site-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.cast instead.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "W0507 14:54:43.302245 140072427317056 deprecation.py:323] From /opt/conda/lib/python3.6/site-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.cast instead.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /opt/conda/lib/python3.6/site-packages/tensorflow/python/ops/math_grad.py:102: div (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Deprecated in favor of operator or tf.math.divide.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "W0507 14:54:43.437217 140072427317056 deprecation.py:323] From /opt/conda/lib/python3.6/site-packages/tensorflow/python/ops/math_grad.py:102: div (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Deprecated in favor of operator or tf.math.divide.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " **** Sentiment Analysis Using Keras Convolutional Neural Networks(CNN) ****\n",
      "\n",
      "Train-Set Accuracy: 0.9994\n",
      "\n",
      "Evaluation on Validation-Set : \n",
      "Classification report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.86      0.90      0.88      2513\n",
      "           1       0.89      0.86      0.87      2487\n",
      "\n",
      "   micro avg       0.88      0.88      0.88      5000\n",
      "   macro avg       0.88      0.88      0.88      5000\n",
      "weighted avg       0.88      0.88      0.88      5000\n",
      "\n",
      "Confusion matrix:\n",
      " [[2252  261]\n",
      " [ 354 2133]]\n",
      "Validation-Set Accuracy: 0.8770\n",
      "\n",
      "Evaluation on Test-Set : \n",
      "Classification report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.86      0.91      0.88      2529\n",
      "           1       0.90      0.85      0.87      2471\n",
      "\n",
      "   micro avg       0.88      0.88      0.88      5000\n",
      "   macro avg       0.88      0.88      0.88      5000\n",
      "weighted avg       0.88      0.88      0.88      5000\n",
      "\n",
      "Confusion matrix:\n",
      " [[2302  227]\n",
      " [ 379 2092]]\n",
      "Test-Set Accuracy:  0.8788\n",
      "CPU times: user 7h 16min 38s, sys: 1h 34min 29s, total: 8h 51min 8s\n",
      "Wall time: 1h 20min 30s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\"\"\"\n",
    "Fit network\n",
    "\"\"\"\n",
    "keras_cnn_model.fit(X_token_train, y_train,\n",
    "                    epochs=5,\n",
    "                    verbose=False,\n",
    "                    validation_data=(X_token_validation, y_validation),\n",
    "                    batch_size=10)\n",
    "\n",
    "\"\"\"\n",
    "Evaluate\n",
    "\"\"\"\n",
    "print(\"\\n **** Sentiment Analysis Using Keras Convolutional Neural Networks(CNN) ****\\n\")\n",
    "loss, accuracy = keras_cnn_model.evaluate(X_token_train, y_train, verbose=False)\n",
    "print(\"Train-Set Accuracy: {:.4f}\".format(accuracy))\n",
    "\n",
    "\n",
    "print(\"\\nEvaluation on Validation-Set : \")\n",
    "pred_val=keras_cnn_model.predict_classes(X_token_validation)\n",
    "print(\"Classification report:\\n {}\".format(classification_report(y_validation, pred_val)))\n",
    "print(\"Confusion matrix:\\n {}\".format(confusion_matrix(y_validation, pred_val)))\n",
    "loss, accuracy = keras_cnn_model.evaluate(X_token_validation, y_validation, verbose=False)\n",
    "print(\"Validation-Set Accuracy: {:.4f}\".format(accuracy))\n",
    "\n",
    "print(\"\\nEvaluation on Test-Set : \")\n",
    "pred=keras_cnn_model.predict_classes(X_token_test)\n",
    "print(\"Classification report:\\n {}\".format(classification_report(y_test, pred)))\n",
    "print(\"Confusion matrix:\\n {}\".format(confusion_matrix(y_test, pred)))\n",
    "loss, accuracy = keras_cnn_model.evaluate(X_token_test, y_test, verbose=False)\n",
    "print(\"Test-Set Accuracy:  {:.4f}\".format(accuracy))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Sentiment Analysis Using Pre-trained Word2Vec Word Embedding To Keras CNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Vocabulary size : 74272\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "Vocabulary size\n",
    "\"\"\"\n",
    "num_of_words = list(model.wv.vocab)\n",
    "print('Vocabulary size : %d' % len(num_of_words))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "W0507 16:15:14.071372 140072427317056 smart_open_lib.py:385] this function is deprecated, use smart_open.open instead\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 15.1 s, sys: 288 ms, total: 15.4 s\n",
      "Wall time: 15.6 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\"\"\"\n",
    "Save model in ASCII\n",
    "\"\"\"\n",
    "file_name = 'movie_embedding_word2vec.txt'\n",
    "model.wv.save_word2vec_format(file_name, binary=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Load word embedding\n",
    "\"\"\"\n",
    "def load_word_embedding(file_name):\n",
    "    word_embedding = dict()\n",
    "    file = open(file_name,'r')\n",
    "    lines = file.readlines()[1:]\n",
    "    file.close()\n",
    "    \n",
    "    \"\"\"\n",
    "    Mapping words to vectors\n",
    "    \"\"\"\n",
    "    for line in lines:\n",
    "        line_parts = line.split()\n",
    "        word_embedding[line_parts[0]] = np.asarray(line_parts[1:], dtype='float32')\n",
    "        \n",
    "    return word_embedding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Create a weight matrix for the Embedding layer\n",
    "\"\"\"\n",
    "def get_embedding_weight_matrix(wrd_embedding, vocabulary):\n",
    "    \n",
    "    vocabulary_size = len(vocabulary) + 1\n",
    "    \"\"\"\n",
    "    Define weight matrix dimensions with all 0\n",
    "    \"\"\"\n",
    "    embedding_weight_matrix = np.zeros((vocabulary_size, 150))\n",
    "    \"\"\"\n",
    "    Step vocab, store vectors using the Tokenizer's integer mapping\n",
    "    \"\"\"\n",
    "    for wrd, i in vocabulary.items():\n",
    "        vector = wrd_embedding.get(wrd)\n",
    "        if vector is not None:\n",
    "            embedding_weight_matrix[i] = vector\n",
    "    return embedding_weight_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Completed creation of raw word2vec word embedding\n",
      "Completed creation of embedding weight vectors\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "Load embedding from file\n",
    "\"\"\"\n",
    "raw_w2v_embedding = load_word_embedding('movie_embedding_word2vec.txt')\n",
    "\n",
    "print('Completed creation of raw word2vec word embedding')\n",
    "\n",
    "\"\"\"\n",
    "Get weight vectors in the right order\n",
    "\"\"\"\n",
    "embedding_weight_vectors = get_embedding_weight_matrix(raw_w2v_embedding, tokenizer.word_index)\n",
    "\n",
    "print('Completed creation of embedding weight vectors')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Completed creation of embedding layer\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "Create the embedding layer\n",
    "\"\"\"\n",
    "embedding_layer = Embedding(vocabulary_size, \n",
    "                            150, \n",
    "                            weights=[embedding_weight_vectors], \n",
    "                            input_length=max_length, \n",
    "                            trainable=False)\n",
    "\n",
    "print('Completed creation of embedding layer')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "embedding_2 (Embedding)      (None, 1500, 150)         23719800  \n",
      "_________________________________________________________________\n",
      "conv1d_2 (Conv1D)            (None, 1496, 128)         96128     \n",
      "_________________________________________________________________\n",
      "max_pooling1d_1 (MaxPooling1 (None, 748, 128)          0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 95744)             0         \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 1)                 95745     \n",
      "=================================================================\n",
      "Total params: 23,911,673\n",
      "Trainable params: 191,873\n",
      "Non-trainable params: 23,719,800\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "Create model\n",
    "\"\"\"\n",
    "keras_cnn_w2v_model = Sequential()\n",
    "keras_cnn_w2v_model.add(embedding_layer)\n",
    "keras_cnn_w2v_model.add(Conv1D(filters=128, kernel_size=5, activation='relu'))\n",
    "keras_cnn_w2v_model.add(MaxPooling1D(pool_size=2))\n",
    "keras_cnn_w2v_model.add(Flatten())\n",
    "keras_cnn_w2v_model.add(Dense(1, activation='sigmoid'))\n",
    "keras_cnn_w2v_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " **** Sentiment Analysis Using Pre-trained Word2Vec Word Embedding To Keras CNN ****\n",
      "\n",
      "Train-Set Accuracy: 0.9593\n",
      "\n",
      "Evaluation on Validation-Set : \n",
      "Classification report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.84      0.85      0.85      2513\n",
      "           1       0.85      0.84      0.84      2487\n",
      "\n",
      "   micro avg       0.84      0.84      0.84      5000\n",
      "   macro avg       0.84      0.84      0.84      5000\n",
      "weighted avg       0.84      0.84      0.84      5000\n",
      "\n",
      "Confusion matrix:\n",
      " [[2137  376]\n",
      " [ 402 2085]]\n",
      "Validation-Set Accuracy: 0.8444\n",
      "\n",
      "Evaluation on Test-Set : \n",
      "Classification report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.83      0.83      0.83      2529\n",
      "           1       0.83      0.82      0.82      2471\n",
      "\n",
      "   micro avg       0.83      0.83      0.83      5000\n",
      "   macro avg       0.83      0.83      0.83      5000\n",
      "weighted avg       0.83      0.83      0.83      5000\n",
      "\n",
      "Confusion matrix:\n",
      " [[2110  419]\n",
      " [ 447 2024]]\n",
      "Test-Set Accuracy:  0.8268\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "Compile network\n",
    "\"\"\"\n",
    "keras_cnn_w2v_model.compile(loss='binary_crossentropy', \n",
    "                            optimizer='adam', \n",
    "                            metrics=['accuracy'])\n",
    "\n",
    "\"\"\"\n",
    "Fit network\n",
    "\"\"\"\n",
    "keras_cnn_w2v_model.fit(X_token_train, y_train,\n",
    "                        epochs=5,\n",
    "                        verbose=False,\n",
    "                        validation_data=(X_token_validation, y_validation),\n",
    "                        batch_size=10)\n",
    "\n",
    "\"\"\"\n",
    "Evaluate\n",
    "\"\"\"\n",
    "print(\"\\n **** Sentiment Analysis Using Pre-trained Word2Vec Word Embedding To Keras CNN ****\\n\")\n",
    "loss, accuracy = keras_cnn_w2v_model.evaluate(X_token_train, y_train, verbose=False)\n",
    "print(\"Train-Set Accuracy: {:.4f}\".format(accuracy))\n",
    "\n",
    "\n",
    "print(\"\\nEvaluation on Validation-Set : \")\n",
    "pred_val=keras_cnn_w2v_model.predict_classes(X_token_validation)\n",
    "print(\"Classification report:\\n {}\".format(classification_report(y_validation, pred_val)))\n",
    "print(\"Confusion matrix:\\n {}\".format(confusion_matrix(y_validation, pred_val)))\n",
    "loss, accuracy = keras_cnn_w2v_model.evaluate(X_token_validation, y_validation, verbose=False)\n",
    "print(\"Validation-Set Accuracy: {:.4f}\".format(accuracy))\n",
    "\n",
    "print(\"\\nEvaluation on Test-Set : \")\n",
    "pred=keras_cnn_w2v_model.predict_classes(X_token_test)\n",
    "print(\"Classification report:\\n {}\".format(classification_report(y_test, pred)))\n",
    "print(\"Confusion matrix:\\n {}\".format(confusion_matrix(y_test, pred)))\n",
    "loss, accuracy = keras_cnn_w2v_model.evaluate(X_token_test, y_test, verbose=False)\n",
    "print(\"Test-Set Accuracy:  {:.4f}\".format(accuracy))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Sentiment Analysis  Using Pre-trained Word2Vec Word Embedding To Keras CNN And Bidirectional LSTM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /opt/conda/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "W0507 16:42:33.180719 140072427317056 deprecation.py:506] From /opt/conda/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "embedding_3 (Embedding)      (None, 1500, 150)         23719800  \n",
      "_________________________________________________________________\n",
      "conv1d_3 (Conv1D)            (None, 1496, 128)         96128     \n",
      "_________________________________________________________________\n",
      "max_pooling1d_2 (MaxPooling1 (None, 748, 128)          0         \n",
      "_________________________________________________________________\n",
      "bidirectional_1 (Bidirection (None, 128)               98816     \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 1)                 129       \n",
      "=================================================================\n",
      "Total params: 23,914,873\n",
      "Trainable params: 195,073\n",
      "Non-trainable params: 23,719,800\n",
      "_________________________________________________________________\n",
      "CPU times: user 1.48 s, sys: 776 ms, total: 2.26 s\n",
      "Wall time: 1.05 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\"\"\"\n",
    "Create model\n",
    "\"\"\"\n",
    "keras_cnn_bidir_lstm_w2v_model = Sequential()\n",
    "keras_cnn_bidir_lstm_w2v_model.add(Embedding(vocabulary_size, \n",
    "                                             150, \n",
    "                                             weights=[embedding_weight_vectors],\n",
    "                                             input_length=max_length,  \n",
    "                                             trainable=False))\n",
    "keras_cnn_bidir_lstm_w2v_model.add(Conv1D(128, 5, activation='relu'))\n",
    "keras_cnn_bidir_lstm_w2v_model.add(MaxPooling1D(pool_size=2))\n",
    "keras_cnn_bidir_lstm_w2v_model.add(Bidirectional(LSTM(64)))\n",
    "keras_cnn_bidir_lstm_w2v_model.add(Dropout(0.5))\n",
    "keras_cnn_bidir_lstm_w2v_model.add(Dense(1, activation='sigmoid'))\n",
    "\n",
    "\"\"\"\n",
    "Compile network\n",
    "\"\"\"\n",
    "keras_cnn_bidir_lstm_w2v_model.compile(loss='binary_crossentropy', \n",
    "                                       optimizer='adam', \n",
    "                                       metrics=['accuracy'])\n",
    "keras_cnn_bidir_lstm_w2v_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 15h 18min 43s, sys: 1h 22min 27s, total: 16h 41min 10s\n",
      "Wall time: 2h 23min 51s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\"\"\"\n",
    "Fit train data\n",
    "\"\"\"\n",
    "keras_cnn_bidir_lstm_w2v_model.fit(X_token_train, y_train,\n",
    "                                   epochs=5,\n",
    "                                   verbose=False,\n",
    "                                   validation_data=(X_token_validation, y_validation),\n",
    "                                   batch_size=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "**** Sentiment Analysis  Using Pre-trained Word2Vec Word Embedding To Keras CNN And Bidirectional LSTM ****\n",
      "\n",
      "Train-Set Accuracy: 0.9356\n",
      "\n",
      "Evaluation on Validation-Set : \n",
      "Classification report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.90      0.87      0.88      2513\n",
      "           1       0.87      0.90      0.89      2487\n",
      "\n",
      "   micro avg       0.89      0.89      0.89      5000\n",
      "   macro avg       0.89      0.89      0.89      5000\n",
      "weighted avg       0.89      0.89      0.89      5000\n",
      "\n",
      "Confusion matrix:\n",
      " [[2184  329]\n",
      " [ 244 2243]]\n",
      "Validation-Set Accuracy: 0.8854\n",
      "\n",
      "Evaluation on Test-Set : \n",
      "Classification report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.91      0.87      0.89      2529\n",
      "           1       0.87      0.91      0.89      2471\n",
      "\n",
      "   micro avg       0.89      0.89      0.89      5000\n",
      "   macro avg       0.89      0.89      0.89      5000\n",
      "weighted avg       0.89      0.89      0.89      5000\n",
      "\n",
      "Confusion matrix:\n",
      " [[2208  321]\n",
      " [ 228 2243]]\n",
      "Test-Set Accuracy:  0.8902\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "Evaluate\n",
    "\"\"\"\n",
    "print(\"**** Sentiment Analysis  Using Pre-trained Word2Vec Word Embedding To Keras CNN And Bidirectional LSTM ****\\n\")\n",
    "loss, accuracy = keras_cnn_bidir_lstm_w2v_model.evaluate(X_token_train, y_train, verbose=False)\n",
    "print(\"Train-Set Accuracy: {:.4f}\".format(accuracy))\n",
    "\n",
    "print(\"\\nEvaluation on Validation-Set : \")\n",
    "pred_val=keras_cnn_bidir_lstm_w2v_model.predict_classes(X_token_validation)\n",
    "print(\"Classification report:\\n {}\".format(classification_report(y_validation, pred_val)))\n",
    "print(\"Confusion matrix:\\n {}\".format(confusion_matrix(y_validation, pred_val)))\n",
    "loss, accuracy = keras_cnn_bidir_lstm_w2v_model.evaluate(X_token_validation, y_validation, verbose=False)\n",
    "print(\"Validation-Set Accuracy: {:.4f}\".format(accuracy))\n",
    "\n",
    "print(\"\\nEvaluation on Test-Set : \")\n",
    "pred=keras_cnn_bidir_lstm_w2v_model.predict_classes(X_token_test)\n",
    "print(\"Classification report:\\n {}\".format(classification_report(y_test, pred)))\n",
    "print(\"Confusion matrix:\\n {}\".format(confusion_matrix(y_test, pred)))\n",
    "loss, accuracy = keras_cnn_bidir_lstm_w2v_model.evaluate(X_token_test, y_test, verbose=False)\n",
    "print(\"Test-Set Accuracy:  {:.4f}\".format(accuracy))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Sentiment Analysis Using Pre-trained Word2Vec Word Embedding To Keras Bidirectional LSTM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "embedding_4 (Embedding)      (None, 1500, 150)         23719800  \n",
      "_________________________________________________________________\n",
      "bidirectional_2 (Bidirection (None, 128)               110080    \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "dense_5 (Dense)              (None, 1)                 129       \n",
      "=================================================================\n",
      "Total params: 23,830,009\n",
      "Trainable params: 110,209\n",
      "Non-trainable params: 23,719,800\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "Create model\n",
    "\"\"\"\n",
    "\n",
    "keras_bidir_lstm_w2v_model = Sequential()\n",
    "keras_bidir_lstm_w2v_model.add(Embedding(vocabulary_size, \n",
    "                                         150, \n",
    "                                         weights=[embedding_weight_vectors],\n",
    "                                         input_length=max_length,  \n",
    "                                         trainable=False))\n",
    "keras_bidir_lstm_w2v_model.add(Bidirectional(LSTM(64)))\n",
    "keras_bidir_lstm_w2v_model.add(Dropout(0.5))\n",
    "keras_bidir_lstm_w2v_model.add(Dense(1, activation='sigmoid'))\n",
    "\n",
    "\n",
    "\"\"\"\n",
    "Compile network\n",
    "\"\"\"\n",
    "keras_bidir_lstm_w2v_model.compile(loss='binary_crossentropy', \n",
    "                                   optimizer='adam', \n",
    "                                   metrics=['accuracy'])\n",
    "keras_bidir_lstm_w2v_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 23h 22min 14s, sys: 2h 34min 46s, total: 1d 1h 57min\n",
      "Wall time: 3h 43min 52s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\"\"\"\n",
    "Fit train data\n",
    "\"\"\"\n",
    "keras_bidir_lstm_w2v_model.fit(X_token_train, y_train,\n",
    "                               epochs=5,\n",
    "                               verbose=False,\n",
    "                               validation_data=(X_token_validation, y_validation),\n",
    "                               batch_size=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "**** Sentiment Analysis Using Pre-trained Word2Vec Word Embedding To Keras Bidirectional LSTM ****\n",
      "\n",
      "Train-Set Accuracy: 0.9011\n",
      "\n",
      "Evaluation on Validation-Set : \n",
      "Classification report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.90      0.86      0.88      2513\n",
      "           1       0.86      0.90      0.88      2487\n",
      "\n",
      "   micro avg       0.88      0.88      0.88      5000\n",
      "   macro avg       0.88      0.88      0.88      5000\n",
      "weighted avg       0.88      0.88      0.88      5000\n",
      "\n",
      "Confusion matrix:\n",
      " [[2152  361]\n",
      " [ 239 2248]]\n",
      "Validation-Set Accuracy: 0.8800\n",
      "\n",
      "Evaluation on Test-Set : \n",
      "Classification report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.90      0.86      0.88      2529\n",
      "           1       0.86      0.90      0.88      2471\n",
      "\n",
      "   micro avg       0.88      0.88      0.88      5000\n",
      "   macro avg       0.88      0.88      0.88      5000\n",
      "weighted avg       0.88      0.88      0.88      5000\n",
      "\n",
      "Confusion matrix:\n",
      " [[2176  353]\n",
      " [ 254 2217]]\n",
      "Test-Set Accuracy:  0.8786\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "Evaluate\n",
    "\"\"\"\n",
    "print(\"**** Sentiment Analysis Using Pre-trained Word2Vec Word Embedding To Keras Bidirectional LSTM ****\\n\")\n",
    "loss, accuracy = keras_bidir_lstm_w2v_model.evaluate(X_token_train, y_train, verbose=False)\n",
    "print(\"Train-Set Accuracy: {:.4f}\".format(accuracy))\n",
    "\n",
    "print(\"\\nEvaluation on Validation-Set : \")\n",
    "pred_val=keras_bidir_lstm_w2v_model.predict_classes(X_token_validation)\n",
    "print(\"Classification report:\\n {}\".format(classification_report(y_validation, pred_val)))\n",
    "print(\"Confusion matrix:\\n {}\".format(confusion_matrix(y_validation, pred_val)))\n",
    "loss, accuracy = keras_bidir_lstm_w2v_model.evaluate(X_token_validation, y_validation, verbose=False)\n",
    "print(\"Validation-Set Accuracy: {:.4f}\".format(accuracy))\n",
    "\n",
    "print(\"\\nEvaluation on Test-Set : \")\n",
    "pred=keras_bidir_lstm_w2v_model.predict_classes(X_token_test)\n",
    "print(\"Classification report:\\n {}\".format(classification_report(y_test, pred)))\n",
    "print(\"Confusion matrix:\\n {}\".format(confusion_matrix(y_test, pred)))\n",
    "\n",
    "loss, accuracy = keras_bidir_lstm_w2v_model.evaluate(X_token_test, y_test, verbose=False)\n",
    "print(\"Test-Set Accuracy:  {:.4f}\".format(accuracy))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## About Doc2Vec\n",
    "\n",
    "* **Doc2Vec** is a generalization of the Word2Vec algorithm and applies at the document level. According to [Mikolov et al. (2014)](https://arxiv.org/pdf/1405.4053.pdf), paragraphs in a document are mapped to a vector representation called paragraph vector. This is then combined with the word vectors by averaging or concatenating to predict the next word in a context. The paragraph vector is just like another word vector but it represents the missing context of the the topic of the paragraph.\n",
    "* **PV-DM or DM:** is the Doc2Vec model analogous to CBOW in Word2Vec. The document vectors are obtained by training a neural network on the task of inferring a centre word based on context words and a context paragraph. See image below for architecture ([image credit: Google](https://www.google.com/search?newwindow=1&biw=1855&bih=952&tbm=isch&sa=1&ei=vQvOXO-dH8rj_AasiKywBw&q=doc2vec+dm&oq=doc2vec+dm&gs_l=img.3..0i24l3.2275.7633..7995...0.0..0.145.386.4j1......1....1..gws-wiz-img.......0j0i30.DJjU4rFKil8))\n",
    "\n",
    "<span style=\"display:block;text-align:center\">![DM](dm.png)\n",
    "\n",
    "* **PV-DBOW or DBOW:** is the Doc2Vec model analogous to Skip-gram in Word2Vec. The document vectors are obtained by training a neural network on the task of predicting a probability distribution of words in a paragraph given a randomly-sampled word from the document. See image below for architecture ([image credit: Google](https://www.google.com/search?newwindow=1&biw=1855&bih=952&tbm=isch&sa=1&ei=5QvOXPTaNsmIggfb6JGoAw&q=doc2vec+dbow&oq=doc2vec+dbow&gs_l=img.3..0i24l3.20722.21318..21542...0.0..0.69.203.3......0....1..gws-wiz-img.5MtfgvxyAEI))\n",
    "\n",
    "<span style=\"display:block;text-align:center\">![DBOW](dbow.png)\n",
    "\n",
    "Below we will see the usage gensim python library with comaprisons of each of these models and their combinations. That is:\n",
    "1. **DBOW (Distributed Bag of Words)**\n",
    "2. **DMC (Distributed Memory Concatenated)**\n",
    "3. **DMM (Distributed Memory Mean)**\n",
    "4. **DBOW + DMC**\n",
    "5. **DBOW + DMM**\n",
    "    \n",
    "[Doc2Vec reference](https://radimrehurek.com/gensim/models/doc2vec.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Function to labelize the reviews\n",
    "\"\"\"\n",
    "def labelize_review(reviews,label):\n",
    "    labelized_review = []\n",
    "    prefix = label\n",
    "    for indx, rvw in zip(reviews.index, reviews):\n",
    "        labelized_review.append(LabeledSentence(rvw.split(), [prefix + '_%s' % indx]))\n",
    "    return labelized_review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "labelize the reviews\n",
    "\"\"\"\n",
    "all_reviews_d2v = labelize_review(all_reviews, 'all')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **Distributed Bag Of Words (DBOW)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "W0507 23:03:09.999566 140072427317056 base_any2vec.py:723] consider setting layer size to a multiple of 4 for greater performance\n",
      "100%|██████████| 50000/50000 [00:00<00:00, 2264718.52it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 5.04 s, sys: 28 ms, total: 5.07 s\n",
      "Wall time: 5.07 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\"\"\"\n",
    "Create doc2vec DBOW model\n",
    "\"\"\"\n",
    "dbow_model = Doc2Vec(dm=0, \n",
    "                     size=150, \n",
    "                     negative=5, \n",
    "                     min_count=2, \n",
    "                     workers=10, \n",
    "                     alpha=0.065, \n",
    "                     min_alpha=0.065)\n",
    "\n",
    "dbow_model.build_vocab([review for review in tqdm(all_reviews_d2v)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 50000/50000 [00:00<00:00, 2235316.94it/s]\n",
      "100%|██████████| 50000/50000 [00:00<00:00, 2449457.47it/s]\n",
      "100%|██████████| 50000/50000 [00:00<00:00, 2604445.99it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 53.7 s, sys: 1.56 s, total: 55.2 s\n",
      "Wall time: 18.7 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "\"\"\"\n",
    "Train the model\n",
    "\"\"\"\n",
    "\n",
    "for epoch in range(3):\n",
    "    dbow_model.train(utils.shuffle([review for review in tqdm(all_reviews_d2v)]), \n",
    "                     total_examples=len(all_reviews_d2v), \n",
    "                     epochs=1)\n",
    "    dbow_model.alpha -= 0.002\n",
    "    dbow_model.min_alpha = dbow_model.alpha"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Function to generate vectors from corpus\n",
    "\"\"\"\n",
    "\n",
    "def generate_vectors(model, corpus, size):\n",
    "    vectors = np.zeros((len(corpus), size))\n",
    "    n = 0\n",
    "    for indx in corpus.index:\n",
    "        prefix = 'all_' + str(indx)\n",
    "        vectors[n] = model.docvecs[prefix]\n",
    "        n += 1\n",
    "    return vectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Generating train, test and validation vectors\n",
    "\"\"\"\n",
    "\n",
    "train_vectors_dbow = generate_vectors(dbow_model, x_train, 150)\n",
    "test_vectors_dbow = generate_vectors(dbow_model, x_test, 150)\n",
    "validation_vectors_dbow = generate_vectors(dbow_model, x_validation, 150)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Doc2Vec Distributed Bag Of Words(DBOW) Based Sentiment Analysis using LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "**** Doc2Vec Distributed Bag Of Words(DBOW) Based Sentiment Analysis using LogisticRegression ****\n",
      "\n",
      "LogisticRegression Performance : \n",
      "\n",
      "Train-Set Score : 0.8732\n",
      "Train-Set Accuracy : 0.8732\n",
      "\n",
      "Evaluation on Validation-Set : \n",
      "Classification report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.87      0.87      2513\n",
      "           1       0.87      0.88      0.87      2487\n",
      "\n",
      "   micro avg       0.87      0.87      0.87      5000\n",
      "   macro avg       0.87      0.87      0.87      5000\n",
      "weighted avg       0.87      0.87      0.87      5000\n",
      "\n",
      "Confusion matrix:\n",
      " [[2177  336]\n",
      " [ 295 2192]]\n",
      "Validation-Set Score : 0.8738\n",
      "Validation-Set Accuracy:0.8738\n",
      "\n",
      "Evaluation on Test-Set : \n",
      "Classification report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.87      0.88      2529\n",
      "           1       0.87      0.88      0.88      2471\n",
      "\n",
      "   micro avg       0.88      0.88      0.88      5000\n",
      "   macro avg       0.88      0.88      0.88      5000\n",
      "weighted avg       0.88      0.88      0.88      5000\n",
      "\n",
      "Confusion matrix:\n",
      " [[2205  324]\n",
      " [ 287 2184]]\n",
      "Test-Set Score : 0.8778\n",
      "Test-Set Accuracy:0.8778\n"
     ]
    }
   ],
   "source": [
    "logreg_dbow = LogisticRegression()\n",
    "logreg_dbow.fit(train_vectors_dbow, y_train)\n",
    "print(\"**** Doc2Vec Distributed Bag Of Words(DBOW) Based Sentiment Analysis using LogisticRegression ****\\n\")\n",
    "print(\"LogisticRegression Performance : \\n\")\n",
    "print('Train-Set Score : {:.4f}'.format(logreg_dbow.score(train_vectors_dbow, y_train)))\n",
    "print('Train-Set Accuracy : {:.4f}'.format(accuracy_score(y_train,logreg_dbow.predict(train_vectors_dbow))))\n",
    "\n",
    "print(\"\\nEvaluation on Validation-Set : \")\n",
    "pred_val = logreg_dbow.predict(validation_vectors_dbow)\n",
    "print(\"Classification report:\\n {}\".format(classification_report(y_validation, pred_val)))\n",
    "print(\"Confusion matrix:\\n {}\".format(confusion_matrix(y_validation, pred_val)))\n",
    "print('Validation-Set Score : {:.4f}'.format(logreg_dbow.score(validation_vectors_dbow, y_validation)))\n",
    "print('Validation-Set Accuracy:{:.4f}'.format(accuracy_score(y_validation, pred_val)))\n",
    "\n",
    "print(\"\\nEvaluation on Test-Set : \")\n",
    "pred = logreg_dbow.predict(test_vectors_dbow)\n",
    "print(\"Classification report:\\n {}\".format(classification_report(y_test, pred)))\n",
    "print(\"Confusion matrix:\\n {}\".format(confusion_matrix(y_test, pred)))\n",
    "print('Test-Set Score : {:.4f}'.format(logreg_dbow.score(test_vectors_dbow, y_test)))\n",
    "print('Test-Set Accuracy:{:.4f}'.format(accuracy_score(y_test, pred)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Doc2Vec Distributed Bag Of Words(DBOW) Based Sentiment Analysis using SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "**** Doc2Vec Distributed Bag Of Words(DBOW) Based Sentiment Analysis using SVC ****\n",
      "\n",
      "SVC with linear kernel Performance : \n",
      "\n",
      "Train-Set Score : 0.8738\n",
      "Train-Set Accuracy : 0.8738\n",
      "\n",
      "Evaluation on Validation-Set : \n",
      "Classification report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.87      0.87      2513\n",
      "           1       0.87      0.88      0.87      2487\n",
      "\n",
      "   micro avg       0.87      0.87      0.87      5000\n",
      "   macro avg       0.87      0.87      0.87      5000\n",
      "weighted avg       0.87      0.87      0.87      5000\n",
      "\n",
      "Confusion matrix:\n",
      " [[2178  335]\n",
      " [ 294 2193]]\n",
      "Validation-Set Score : 0.8742\n",
      "Validation-Set Accuracy:0.8742\n",
      "\n",
      "Evaluation on Test-Set : \n",
      "Classification report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.87      0.88      2529\n",
      "           1       0.87      0.88      0.88      2471\n",
      "\n",
      "   micro avg       0.88      0.88      0.88      5000\n",
      "   macro avg       0.88      0.88      0.88      5000\n",
      "weighted avg       0.88      0.88      0.88      5000\n",
      "\n",
      "Confusion matrix:\n",
      " [[2204  325]\n",
      " [ 292 2179]]\n",
      "Test-Set Score : 0.8766\n",
      "Test-Set Accuracy: 0.8766\n"
     ]
    }
   ],
   "source": [
    "svm_dbow = SVC(kernel='linear')\n",
    "svm_dbow.fit(train_vectors_dbow, y_train)\n",
    "print(\"**** Doc2Vec Distributed Bag Of Words(DBOW) Based Sentiment Analysis using SVC ****\\n\")\n",
    "print(\"SVC with linear kernel Performance : \\n\")\n",
    "print('Train-Set Score : {:.4f}'.format(svm_dbow.score(train_vectors_dbow, y_train)))\n",
    "print('Train-Set Accuracy : {:.4f}'.format(accuracy_score(y_train,svm_dbow.predict(train_vectors_dbow))))\n",
    "\n",
    "print(\"\\nEvaluation on Validation-Set : \")\n",
    "pred_val = svm_dbow.predict(validation_vectors_dbow)\n",
    "print(\"Classification report:\\n {}\".format(classification_report(y_validation, pred_val)))\n",
    "print(\"Confusion matrix:\\n {}\".format(confusion_matrix(y_validation, pred_val)))\n",
    "print('Validation-Set Score : {:.4f}'.format(svm_dbow.score(validation_vectors_dbow, y_validation)))\n",
    "print('Validation-Set Accuracy:{:.4f}'.format(accuracy_score(y_validation, pred_val)))\n",
    "\n",
    "print(\"\\nEvaluation on Test-Set : \")\n",
    "pred = svm_dbow.predict(test_vectors_dbow)\n",
    "print(\"Classification report:\\n {}\".format(classification_report(y_test, pred)))\n",
    "print(\"Confusion matrix:\\n {}\".format(confusion_matrix(y_test, pred)))\n",
    "print('Test-Set Score : {:.4f}'.format(svm_dbow.score(test_vectors_dbow, y_test)))\n",
    "print(\"Test-Set Accuracy: {:.4f}\".format(accuracy_score(y_test, pred)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Doc2Vec Distributed Bag Of Words(DBOW) Based Sentiment Analysis using XGBClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "**** Doc2Vec Distributed Bag Of Words(DBOW) Based Sentiment Analysis using XGBClassifier ****\n",
      "\n",
      "XGBClassifier Performance : \n",
      "\n",
      "Train-Set Score : 0.8682\n",
      "Train-Set Accuracy : 0.8682\n",
      "\n",
      "Evaluation on Validation-Set : \n",
      "Classification report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.86      0.84      0.85      2513\n",
      "           1       0.84      0.86      0.85      2487\n",
      "\n",
      "   micro avg       0.85      0.85      0.85      5000\n",
      "   macro avg       0.85      0.85      0.85      5000\n",
      "weighted avg       0.85      0.85      0.85      5000\n",
      "\n",
      "Confusion matrix:\n",
      " [[2110  403]\n",
      " [ 347 2140]]\n",
      "Validation-Set Score : 0.8500\n",
      "Validation-Set Accuracy:0.8500\n",
      "\n",
      "Evaluation on Test-Set : \n",
      "Classification report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.86      0.85      0.86      2529\n",
      "           1       0.85      0.86      0.85      2471\n",
      "\n",
      "   micro avg       0.86      0.86      0.86      5000\n",
      "   macro avg       0.86      0.86      0.86      5000\n",
      "weighted avg       0.86      0.86      0.86      5000\n",
      "\n",
      "Confusion matrix:\n",
      " [[2152  377]\n",
      " [ 345 2126]]\n",
      "Test-Set Score : 0.8556\n",
      "Test-Set Accuracy: 0.8556\n"
     ]
    }
   ],
   "source": [
    "xgb_dbow = XGBClassifier()\n",
    "xgb_dbow.fit(train_vectors_dbow, y_train)\n",
    "\n",
    "print(\"**** Doc2Vec Distributed Bag Of Words(DBOW) Based Sentiment Analysis using XGBClassifier ****\\n\")\n",
    "print(\"XGBClassifier Performance : \\n\")\n",
    "print('Train-Set Score : {:.4f}'.format(xgb_dbow.score(train_vectors_dbow, y_train)))\n",
    "print('Train-Set Accuracy : {:.4f}'.format(accuracy_score(y_train,xgb_dbow.predict(train_vectors_dbow))))\n",
    "\n",
    "print(\"\\nEvaluation on Validation-Set : \")\n",
    "pred_val = xgb_dbow.predict(validation_vectors_dbow)\n",
    "print(\"Classification report:\\n {}\".format(classification_report(y_validation, pred_val)))\n",
    "print(\"Confusion matrix:\\n {}\".format(confusion_matrix(y_validation, pred_val)))\n",
    "print('Validation-Set Score : {:.4f}'.format(xgb_dbow.score(validation_vectors_dbow, y_validation)))\n",
    "print('Validation-Set Accuracy:{:.4f}'.format(accuracy_score(y_validation, pred_val)))\n",
    "\n",
    "\n",
    "print(\"\\nEvaluation on Test-Set : \")\n",
    "pred = xgb_dbow.predict(test_vectors_dbow)\n",
    "print(\"Classification report:\\n {}\".format(classification_report(y_test, pred)))\n",
    "print(\"Confusion matrix:\\n {}\".format(confusion_matrix(y_test, pred)))\n",
    "print(\"Test-Set Score : {:.4f}\".format(xgb_dbow.score(test_vectors_dbow, y_test)))\n",
    "print(\"Test-Set Accuracy: {:.4f}\".format(accuracy_score(y_test, pred)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **Distributed Momory (concatenated)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "W0507 23:11:45.659167 140072427317056 base_any2vec.py:723] consider setting layer size to a multiple of 4 for greater performance\n",
      "100%|██████████| 50000/50000 [00:00<00:00, 2415906.73it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 4.47 s, sys: 0 ns, total: 4.47 s\n",
      "Wall time: 4.47 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\"\"\"\n",
    "Create Doc2Vec DMC model\n",
    "\"\"\"\n",
    "\n",
    "dmc_model = Doc2Vec(dm=1, \n",
    "                    dm_concat=1, \n",
    "                    size=150, \n",
    "                    window=10, \n",
    "                    negative=5, \n",
    "                    min_count=2, \n",
    "                    workers=10,\n",
    "                    alpha=0.065,\n",
    "                    min_alpha=0.065)\n",
    "\n",
    "dmc_model.build_vocab([review for review in tqdm(all_reviews_d2v)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 50000/50000 [00:00<00:00, 2471657.55it/s]\n",
      "100%|██████████| 50000/50000 [00:00<00:00, 2204836.20it/s]\n",
      "100%|██████████| 50000/50000 [00:00<00:00, 2605740.41it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 8min 57s, sys: 8.31 s, total: 9min 6s\n",
      "Wall time: 1min 26s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "\"\"\"\n",
    "Train the model\n",
    "\"\"\"\n",
    "\n",
    "for epoch in range(3):\n",
    "    dmc_model.train(utils.shuffle([review for review in tqdm(all_reviews_d2v)]), \n",
    "                    total_examples=len(all_reviews_d2v), \n",
    "                    epochs=1)\n",
    "    dmc_model.alpha -= 0.002\n",
    "    dmc_model.min_alpha = dmc_model.alpha"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Generating train, test and validation vectors\n",
    "\"\"\"\n",
    "train_vectors_dmc = generate_vectors(dmc_model, x_train, 150)\n",
    "test_vectors_dmc = generate_vectors(dmc_model, x_test, 150)\n",
    "validation_vectors_dmc = generate_vectors(dmc_model, x_validation, 150)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Doc2Vec DMC Based Sentiment Analysis using LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "**** Doc2Vec Distributed Momory (concatenated) Based Sentiment Analysis using LogisticRegression ****\n",
      "\n",
      "LogisticRegression Performance : \n",
      "\n",
      "Train-Set Score : 0.5939\n",
      "Train-Set Accuracy : 0.5939\n",
      "\n",
      "Evaluation on Validation-Set : \n",
      "Classification report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.58      0.62      0.60      2513\n",
      "           1       0.59      0.55      0.57      2487\n",
      "\n",
      "   micro avg       0.59      0.59      0.59      5000\n",
      "   macro avg       0.59      0.59      0.59      5000\n",
      "weighted avg       0.59      0.59      0.59      5000\n",
      "\n",
      "Confusion matrix:\n",
      " [[1553  960]\n",
      " [1109 1378]]\n",
      "Validation-Set Score : 0.5862\n",
      "Validation-Set Accuracy:0.5862\n",
      "\n",
      "Evaluation on Test-Set : \n",
      "Classification report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.60      0.63      0.61      2529\n",
      "           1       0.60      0.57      0.58      2471\n",
      "\n",
      "   micro avg       0.60      0.60      0.60      5000\n",
      "   macro avg       0.60      0.60      0.60      5000\n",
      "weighted avg       0.60      0.60      0.60      5000\n",
      "\n",
      "Confusion matrix:\n",
      " [[1586  943]\n",
      " [1064 1407]]\n",
      "Test-Set Score : 0.5986\n",
      "Test-Set Accuracy:0.5986\n"
     ]
    }
   ],
   "source": [
    "logreg_dmc = LogisticRegression()\n",
    "logreg_dmc.fit(train_vectors_dmc, y_train)\n",
    "print(\"**** Doc2Vec Distributed Momory (concatenated) Based Sentiment Analysis using LogisticRegression ****\\n\")\n",
    "print(\"LogisticRegression Performance : \\n\")\n",
    "print('Train-Set Score : {:.4f}'.format(logreg_dmc.score(train_vectors_dmc, y_train)))\n",
    "print('Train-Set Accuracy : {:.4f}'.format(accuracy_score(y_train,logreg_dmc.predict(train_vectors_dmc))))\n",
    "\n",
    "print(\"\\nEvaluation on Validation-Set : \")\n",
    "pred_val = logreg_dmc.predict(validation_vectors_dmc)\n",
    "print(\"Classification report:\\n {}\".format(classification_report(y_validation, pred_val)))\n",
    "print(\"Confusion matrix:\\n {}\".format(confusion_matrix(y_validation, pred_val)))\n",
    "print('Validation-Set Score : {:.4f}'.format(logreg_dmc.score(validation_vectors_dmc, y_validation)))\n",
    "print('Validation-Set Accuracy:{:.4f}'.format(accuracy_score(y_validation, pred_val)))\n",
    "\n",
    "print(\"\\nEvaluation on Test-Set : \")\n",
    "pred = logreg_dmc.predict(test_vectors_dmc)\n",
    "print(\"Classification report:\\n {}\".format(classification_report(y_test, pred)))\n",
    "print(\"Confusion matrix:\\n {}\".format(confusion_matrix(y_test, pred)))\n",
    "print('Test-Set Score : {:.4f}'.format(logreg_dmc.score(test_vectors_dmc, y_test)))\n",
    "print('Test-Set Accuracy:{:.4f}'.format(accuracy_score(y_test, pred)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Doc2Vec DMC Based Sentiment Analysis using SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "**** Doc2Vec Distributed Momory (concatenated) Based Sentiment Analysis using SVC ****\n",
      "\n",
      "SVC with linear kernel Performance : \n",
      "\n",
      "Train-Set Score : 0.5933\n",
      "Train-Set Accuracy : 0.5933\n",
      "\n",
      "Evaluation on Validation-Set : \n",
      "Classification report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.58      0.65      0.61      2513\n",
      "           1       0.60      0.52      0.56      2487\n",
      "\n",
      "   micro avg       0.59      0.59      0.59      5000\n",
      "   macro avg       0.59      0.59      0.58      5000\n",
      "weighted avg       0.59      0.59      0.58      5000\n",
      "\n",
      "Confusion matrix:\n",
      " [[1637  876]\n",
      " [1192 1295]]\n",
      "Validation-Set Score : 0.5864\n",
      "Validation-Set Accuracy:0.5864\n",
      "\n",
      "Evaluation on Test-Set : \n",
      "Classification report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.59      0.65      0.62      2529\n",
      "           1       0.60      0.53      0.56      2471\n",
      "\n",
      "   micro avg       0.59      0.59      0.59      5000\n",
      "   macro avg       0.59      0.59      0.59      5000\n",
      "weighted avg       0.59      0.59      0.59      5000\n",
      "\n",
      "Confusion matrix:\n",
      " [[1654  875]\n",
      " [1157 1314]]\n",
      "Test-Set Score : 0.5936\n",
      "Test-Set Accuracy:0.5936\n"
     ]
    }
   ],
   "source": [
    "svm_dmc = SVC(kernel='linear')\n",
    "svm_dmc.fit(train_vectors_dmc, y_train)\n",
    "\n",
    "print(\"**** Doc2Vec Distributed Momory (concatenated) Based Sentiment Analysis using SVC ****\\n\")\n",
    "print(\"SVC with linear kernel Performance : \\n\")\n",
    "print('Train-Set Score : {:.4f}'.format(svm_dmc.score(train_vectors_dmc, y_train)))\n",
    "print('Train-Set Accuracy : {:.4f}'.format(accuracy_score(y_train,svm_dmc.predict(train_vectors_dmc))))\n",
    "\n",
    "print(\"\\nEvaluation on Validation-Set : \")\n",
    "pred_val = svm_dmc.predict(validation_vectors_dmc)\n",
    "print(\"Classification report:\\n {}\".format(classification_report(y_validation, pred_val)))\n",
    "print(\"Confusion matrix:\\n {}\".format(confusion_matrix(y_validation, pred_val)))\n",
    "print('Validation-Set Score : {:.4f}'.format(svm_dmc.score(validation_vectors_dmc, y_validation)))\n",
    "print('Validation-Set Accuracy:{:.4f}'.format(accuracy_score(y_validation, pred_val)))\n",
    "\n",
    "print(\"\\nEvaluation on Test-Set : \")\n",
    "pred = svm_dmc.predict(test_vectors_dmc)\n",
    "print(\"Classification report:\\n {}\".format(classification_report(y_test, pred)))\n",
    "print(\"Confusion matrix:\\n {}\".format(confusion_matrix(y_test, pred)))\n",
    "print('Test-Set Score : {:.4f}'.format(svm_dmc.score(test_vectors_dmc, y_test)))\n",
    "print('Test-Set Accuracy:{:.4f}'.format(accuracy_score(y_test, pred)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Doc2Vec DMC Based Sentiment Analysis using XGBClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "**** Doc2Vec Distributed Momory (concatenated) Based Sentiment Analysis using XGBClassifier ****\n",
      "\n",
      "XGBClassifier Performance : \n",
      "\n",
      "Train-Set Score : 0.6214\n",
      "Train-Set Accuracy : 0.6214\n",
      "\n",
      "Evaluation on Validation-Set : \n",
      "Classification report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.60      0.58      0.59      2513\n",
      "           1       0.59      0.61      0.60      2487\n",
      "\n",
      "   micro avg       0.60      0.60      0.60      5000\n",
      "   macro avg       0.60      0.60      0.60      5000\n",
      "weighted avg       0.60      0.60      0.60      5000\n",
      "\n",
      "Confusion matrix:\n",
      " [[1460 1053]\n",
      " [ 971 1516]]\n",
      "Validation-Set Score : 0.5952\n",
      "Validation-Set Accuracy:0.5952\n",
      "\n",
      "Evaluation on Test-Set : \n",
      "Classification report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.61      0.57      0.59      2529\n",
      "           1       0.59      0.62      0.60      2471\n",
      "\n",
      "   micro avg       0.60      0.60      0.60      5000\n",
      "   macro avg       0.60      0.60      0.60      5000\n",
      "weighted avg       0.60      0.60      0.60      5000\n",
      "\n",
      "Confusion matrix:\n",
      " [[1452 1077]\n",
      " [ 944 1527]]\n",
      "Test-Set Score : 0.5958\n",
      "Test-Set Accuracy:0.5958\n"
     ]
    }
   ],
   "source": [
    "xgb_dmc = XGBClassifier()\n",
    "xgb_dmc.fit(train_vectors_dmc, y_train)\n",
    "\n",
    "print(\"**** Doc2Vec Distributed Momory (concatenated) Based Sentiment Analysis using XGBClassifier ****\\n\")\n",
    "print(\"XGBClassifier Performance : \\n\")\n",
    "print('Train-Set Score : {:.4f}'.format(xgb_dmc.score(train_vectors_dmc, y_train)))\n",
    "print('Train-Set Accuracy : {:.4f}'.format(accuracy_score(y_train,xgb_dmc.predict(train_vectors_dmc))))\n",
    "\n",
    "print(\"\\nEvaluation on Validation-Set : \")\n",
    "pred_val = xgb_dmc.predict(validation_vectors_dmc)\n",
    "print(\"Classification report:\\n {}\".format(classification_report(y_validation, pred_val)))\n",
    "print(\"Confusion matrix:\\n {}\".format(confusion_matrix(y_validation, pred_val)))\n",
    "print('Validation-Set Score : {:.4f}'.format(xgb_dmc.score(validation_vectors_dmc, y_validation)))\n",
    "print('Validation-Set Accuracy:{:.4f}'.format(accuracy_score(y_validation, pred_val)))\n",
    "\n",
    "print(\"\\nEvaluation on Test-Set : \")\n",
    "pred = xgb_dmc.predict(test_vectors_dmc)\n",
    "print(\"Classification report:\\n {}\".format(classification_report(y_test, pred)))\n",
    "print(\"Confusion matrix:\\n {}\".format(confusion_matrix(y_test, pred)))\n",
    "print('Test-Set Score : {:.4f}'.format(xgb_dmc.score(test_vectors_dmc, y_test)))\n",
    "print('Test-Set Accuracy:{:.4f}'.format(accuracy_score(y_test, pred)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Distributed Memory (mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "W0507 23:28:03.707127 140072427317056 base_any2vec.py:723] consider setting layer size to a multiple of 4 for greater performance\n",
      "100%|██████████| 50000/50000 [00:00<00:00, 2326782.13it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 5.15 s, sys: 0 ns, total: 5.15 s\n",
      "Wall time: 5.15 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\"\"\"\n",
    "Create doc2vec DMM model\n",
    "\"\"\"\n",
    "dmm_model = Doc2Vec(dm=1, \n",
    "                    dm_mean=1, \n",
    "                    size=150, \n",
    "                    window=10, \n",
    "                    negative=5, \n",
    "                    min_count=2, \n",
    "                    workers=10, \n",
    "                    alpha=0.065, \n",
    "                    min_alpha=0.065)\n",
    "\n",
    "dmm_model.build_vocab([review for review in tqdm(all_reviews_d2v)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 50000/50000 [00:00<00:00, 2346936.45it/s]\n",
      "100%|██████████| 50000/50000 [00:00<00:00, 2296864.36it/s]\n",
      "100%|██████████| 50000/50000 [00:00<00:00, 2294928.98it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1min 26s, sys: 4.28 s, total: 1min 30s\n",
      "Wall time: 24.6 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "\"\"\"\n",
    "Train the model\n",
    "\"\"\"\n",
    "\n",
    "for epoch in range(3):\n",
    "    dmm_model.train(utils.shuffle([review for review in tqdm(all_reviews_d2v)]), \n",
    "                    total_examples=len(all_reviews_d2v), \n",
    "                    epochs=1)\n",
    "    dmm_model.alpha -= 0.002\n",
    "    dmm_model.min_alpha = dmm_model.alpha"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Generating train, test and validation vectors\n",
    "\"\"\"\n",
    "\n",
    "train_vectors_dmm = generate_vectors(dmm_model, x_train, 150)\n",
    "test_vectors_dmm = generate_vectors(dmm_model, x_test, 150)\n",
    "validation_vectors_dmm = generate_vectors(dmm_model, x_validation, 150)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Doc2Vec DMM Based Sentiment Analysis using LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "**** Doc2Vec Distributed Memory(mean) Based Sentiment Analysis using LogisticRegression ****\n",
      "\n",
      "LogisticRegression Performance : \n",
      "\n",
      "Train-Set Score : 0.8187\n",
      "Train-Set Accuracy : 0.8187\n",
      "\n",
      "Evaluation on Validation-Set : \n",
      "Classification report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.83      0.81      0.82      2513\n",
      "           1       0.81      0.83      0.82      2487\n",
      "\n",
      "   micro avg       0.82      0.82      0.82      5000\n",
      "   macro avg       0.82      0.82      0.82      5000\n",
      "weighted avg       0.82      0.82      0.82      5000\n",
      "\n",
      "Confusion matrix:\n",
      " [[2037  476]\n",
      " [ 426 2061]]\n",
      "Validation-Set Score : 0.8196\n",
      "Validation-Set Accuracy:0.8196\n",
      "\n",
      "Evaluation on Test-Set : \n",
      "Classification report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.82      0.82      0.82      2529\n",
      "           1       0.81      0.82      0.82      2471\n",
      "\n",
      "   micro avg       0.82      0.82      0.82      5000\n",
      "   macro avg       0.82      0.82      0.82      5000\n",
      "weighted avg       0.82      0.82      0.82      5000\n",
      "\n",
      "Confusion matrix:\n",
      " [[2064  465]\n",
      " [ 448 2023]]\n",
      "Test-Set Score : 0.8174\n",
      "Test-Set Accuracy:0.8174\n"
     ]
    }
   ],
   "source": [
    "logreg_dmm = LogisticRegression()\n",
    "logreg_dmm.fit(train_vectors_dmm, y_train)\n",
    "print(\"**** Doc2Vec Distributed Memory(mean) Based Sentiment Analysis using LogisticRegression ****\\n\")\n",
    "print(\"LogisticRegression Performance : \\n\")\n",
    "print('Train-Set Score : {:.4f}'.format(logreg_dmm.score(train_vectors_dmm, y_train)))\n",
    "print('Train-Set Accuracy : {:.4f}'.format(accuracy_score(y_train,logreg_dmm.predict(train_vectors_dmm))))\n",
    "\n",
    "print(\"\\nEvaluation on Validation-Set : \")\n",
    "pred_val = logreg_dmm.predict(validation_vectors_dmm)\n",
    "print(\"Classification report:\\n {}\".format(classification_report(y_validation, pred_val)))\n",
    "print(\"Confusion matrix:\\n {}\".format(confusion_matrix(y_validation, pred_val)))\n",
    "print('Validation-Set Score : {:.4f}'.format(logreg_dmm.score(validation_vectors_dmm, y_validation)))\n",
    "print('Validation-Set Accuracy:{:.4f}'.format(accuracy_score(y_validation, pred_val)))\n",
    "\n",
    "print(\"\\nEvaluation on Test-Set : \")\n",
    "pred = logreg_dmm.predict(test_vectors_dmm)\n",
    "print(\"Classification report:\\n {}\".format(classification_report(y_test, pred)))\n",
    "print(\"Confusion matrix:\\n {}\".format(confusion_matrix(y_test, pred)))\n",
    "print('Test-Set Score : {:.4f}'.format(logreg_dmm.score(test_vectors_dmm, y_test)))\n",
    "print('Test-Set Accuracy:{:.4f}'.format(accuracy_score(y_test, pred)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Doc2Vec DMM Based Sentiment Analysis using SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "**** Doc2Vec Distributed Memory(mean) Based Sentiment Analysis using SVC ****\n",
      "\n",
      "SVC With Linear Kernel Performance : \n",
      "\n",
      "Train-Set Score : 0.8193\n",
      "Train-Set Accuracy : 0.8193\n",
      "\n",
      "Evaluation on Validation-Set : \n",
      "Classification report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.83      0.81      0.82      2513\n",
      "           1       0.81      0.83      0.82      2487\n",
      "\n",
      "   micro avg       0.82      0.82      0.82      5000\n",
      "   macro avg       0.82      0.82      0.82      5000\n",
      "weighted avg       0.82      0.82      0.82      5000\n",
      "\n",
      "Confusion matrix:\n",
      " [[2028  485]\n",
      " [ 423 2064]]\n",
      "Validation-Set Score : 0.8184\n",
      "Validation-Set Accuracy:0.8184\n",
      "\n",
      "Evaluation on Test-Set : \n",
      "Classification report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.82      0.82      0.82      2529\n",
      "           1       0.81      0.82      0.82      2471\n",
      "\n",
      "   micro avg       0.82      0.82      0.82      5000\n",
      "   macro avg       0.82      0.82      0.82      5000\n",
      "weighted avg       0.82      0.82      0.82      5000\n",
      "\n",
      "Confusion matrix:\n",
      " [[2062  467]\n",
      " [ 438 2033]]\n",
      "Test-Set Score : 0.8190\n",
      "Test-Set Accuracy:0.8190\n"
     ]
    }
   ],
   "source": [
    "svm_dmm = SVC(kernel='linear')\n",
    "svm_dmm.fit(train_vectors_dmm, y_train)\n",
    "print(\"**** Doc2Vec Distributed Memory(mean) Based Sentiment Analysis using SVC ****\\n\")\n",
    "print(\"SVC With Linear Kernel Performance : \\n\")\n",
    "print('Train-Set Score : {:.4f}'.format(svm_dmm.score(train_vectors_dmm, y_train)))\n",
    "print('Train-Set Accuracy : {:.4f}'.format(accuracy_score(y_train,svm_dmm.predict(train_vectors_dmm))))\n",
    "\n",
    "print(\"\\nEvaluation on Validation-Set : \")\n",
    "pred_val = svm_dmm.predict(validation_vectors_dmm)\n",
    "print(\"Classification report:\\n {}\".format(classification_report(y_validation, pred_val)))\n",
    "print(\"Confusion matrix:\\n {}\".format(confusion_matrix(y_validation, pred_val)))\n",
    "print('Validation-Set Score : {:.4f}'.format(svm_dmm.score(validation_vectors_dmm, y_validation)))\n",
    "print('Validation-Set Accuracy:{:.4f}'.format(accuracy_score(y_validation, pred_val)))\n",
    "\n",
    "print(\"\\nEvaluation on Test-Set : \")\n",
    "pred = svm_dmm.predict(test_vectors_dmm)\n",
    "print(\"Classification report:\\n {}\".format(classification_report(y_test, pred)))\n",
    "print(\"Confusion matrix:\\n {}\".format(confusion_matrix(y_test, pred)))\n",
    "print('Test-Set Score : {:.4f}'.format(svm_dmm.score(test_vectors_dmm, y_test)))\n",
    "print('Test-Set Accuracy:{:.4f}'.format(accuracy_score(y_test, pred)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Doc2Vec DMM Based Sentiment Analysis using XGBClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "**** Doc2Vec Distributed Memory(mean) Based Sentiment Analysis using XGBClassifier ****\n",
      "\n",
      "XGBClassifier Performance : \n",
      "\n",
      "Train-Set Score : 0.8115\n",
      "Train-Set Accuracy : 0.8115\n",
      "\n",
      "Evaluation on Validation-Set : \n",
      "Classification report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.79      0.77      0.78      2513\n",
      "           1       0.78      0.80      0.79      2487\n",
      "\n",
      "   micro avg       0.79      0.79      0.79      5000\n",
      "   macro avg       0.79      0.79      0.79      5000\n",
      "weighted avg       0.79      0.79      0.79      5000\n",
      "\n",
      "Confusion matrix:\n",
      " [[1945  568]\n",
      " [ 503 1984]]\n",
      "Validation-Set Score : 0.7858\n",
      "Validation-Set Accuracy:0.7858\n",
      "\n",
      "Evaluation on Test-Set : \n",
      "Classification report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.80      0.78      0.79      2529\n",
      "           1       0.78      0.80      0.79      2471\n",
      "\n",
      "   micro avg       0.79      0.79      0.79      5000\n",
      "   macro avg       0.79      0.79      0.79      5000\n",
      "weighted avg       0.79      0.79      0.79      5000\n",
      "\n",
      "Confusion matrix:\n",
      " [[1981  548]\n",
      " [ 492 1979]]\n",
      "Test-Set Score : 0.7920\n",
      "Test-Set Accuracy:0.7920\n"
     ]
    }
   ],
   "source": [
    "xgb_dmm = XGBClassifier()\n",
    "xgb_dmm.fit(train_vectors_dmm, y_train)\n",
    "\n",
    "print(\"**** Doc2Vec Distributed Memory(mean) Based Sentiment Analysis using XGBClassifier ****\\n\")\n",
    "print(\"XGBClassifier Performance : \\n\")\n",
    "print('Train-Set Score : {:.4f}'.format(xgb_dmm.score(train_vectors_dmm, y_train)))\n",
    "print('Train-Set Accuracy : {:.4f}'.format(accuracy_score(y_train,xgb_dmm.predict(train_vectors_dmm))))\n",
    "\n",
    "print(\"\\nEvaluation on Validation-Set : \")\n",
    "pred_val = xgb_dmm.predict(validation_vectors_dmm)\n",
    "print(\"Classification report:\\n {}\".format(classification_report(y_validation, pred_val)))\n",
    "print(\"Confusion matrix:\\n {}\".format(confusion_matrix(y_validation, pred_val)))\n",
    "print('Validation-Set Score : {:.4f}'.format(xgb_dmm.score(validation_vectors_dmm, y_validation)))\n",
    "print('Validation-Set Accuracy:{:.4f}'.format(accuracy_score(y_validation, pred_val)))\n",
    "\n",
    "print(\"\\nEvaluation on Test-Set : \")\n",
    "pred = xgb_dmm.predict(test_vectors_dmm)\n",
    "print(\"Classification report:\\n {}\".format(classification_report(y_test, pred)))\n",
    "print(\"Confusion matrix:\\n {}\".format(confusion_matrix(y_test, pred)))\n",
    "print('Test-Set Score : {:.4f}'.format(xgb_dmm.score(test_vectors_dmm, y_test)))\n",
    "print('Test-Set Accuracy:{:.4f}'.format(accuracy_score(y_test, pred)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Combination of Doc2Vec Distributed Bag Of Words (DBOW) And Distributed Memory(Concatenated)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, I have the document vectors from three different models, now I can concatenate them in combination to see how it affects the performance. Below I defined a function to concatenate document vectors from different models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Function to concatenate document vectors from different models\n",
    "\"\"\"\n",
    "\n",
    "def generate_concat_vectors(model1,model2, corpus, size):\n",
    "    vectors = np.zeros((len(corpus), size))\n",
    "    n = 0\n",
    "    for indx in corpus.index:\n",
    "        prefix = 'all_' + str(indx)\n",
    "        vectors[n] = np.append(model1.docvecs[prefix],model2.docvecs[prefix])\n",
    "        n += 1\n",
    "    return vectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Generating train, test and validation document vectors\n",
    "\"\"\"\n",
    "train_vectors_dbow_dmc = generate_concat_vectors(dbow_model,dmc_model, x_train, 300)\n",
    "test_vectors_dbow_dmc = generate_concat_vectors(dbow_model,dmc_model, x_test, 300)\n",
    "validation_vectors_dbow_dmc = generate_concat_vectors(dbow_model,dmc_model, x_validation, 300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Combination of Doc2Vec DBOW And DMC Based Sentiment Analysis using LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "**** Combination of Doc2Vec DBOW And DMC Based Sentiment Analysis using LogisticRegression ****\n",
      "\n",
      "LogisticRegression Performance : \n",
      "\n",
      "Train-Set Score : 0.8741\n",
      "Train-Set Accuracy : 0.8741\n",
      "\n",
      "Evaluation on Validation-Set : \n",
      "Classification report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.87      0.87      2513\n",
      "           1       0.87      0.88      0.87      2487\n",
      "\n",
      "   micro avg       0.87      0.87      0.87      5000\n",
      "   macro avg       0.87      0.87      0.87      5000\n",
      "weighted avg       0.87      0.87      0.87      5000\n",
      "\n",
      "Confusion matrix:\n",
      " [[2179  334]\n",
      " [ 297 2190]]\n",
      "Validation-Set Score : 0.8738\n",
      "Validation-Set Accuracy:0.8738\n",
      "\n",
      "Evaluation on Test-Set : \n",
      "Classification report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.89      0.87      0.88      2529\n",
      "           1       0.87      0.88      0.88      2471\n",
      "\n",
      "   micro avg       0.88      0.88      0.88      5000\n",
      "   macro avg       0.88      0.88      0.88      5000\n",
      "weighted avg       0.88      0.88      0.88      5000\n",
      "\n",
      "Confusion matrix:\n",
      " [[2204  325]\n",
      " [ 286 2185]]\n",
      "Test-Set Score : 0.8778\n",
      "Test-Set Accuracy:0.8778\n"
     ]
    }
   ],
   "source": [
    "logreg_dbow_dmc = LogisticRegression()\n",
    "logreg_dbow_dmc.fit(train_vectors_dbow_dmc, y_train)\n",
    "print(\"**** Combination of Doc2Vec DBOW And DMC Based Sentiment Analysis using LogisticRegression ****\\n\")\n",
    "print(\"LogisticRegression Performance : \\n\")\n",
    "print('Train-Set Score : {:.4f}'.format(logreg_dbow_dmc.score(train_vectors_dbow_dmc, y_train)))\n",
    "print('Train-Set Accuracy : {:.4f}'.format(accuracy_score(y_train,logreg_dbow_dmc.predict(train_vectors_dbow_dmc))))\n",
    "\n",
    "print(\"\\nEvaluation on Validation-Set : \")\n",
    "pred_val = logreg_dbow_dmc.predict(validation_vectors_dbow_dmc)\n",
    "print(\"Classification report:\\n {}\".format(classification_report(y_validation, pred_val)))\n",
    "print(\"Confusion matrix:\\n {}\".format(confusion_matrix(y_validation, pred_val)))\n",
    "print('Validation-Set Score : {:.4f}'.format(logreg_dbow_dmc.score(validation_vectors_dbow_dmc, y_validation)))\n",
    "print('Validation-Set Accuracy:{:.4f}'.format(accuracy_score(y_validation, pred_val)))\n",
    "\n",
    "print(\"\\nEvaluation on Test-Set : \")\n",
    "pred = logreg_dbow_dmc.predict(test_vectors_dbow_dmc)\n",
    "print(\"Classification report:\\n {}\".format(classification_report(y_test, pred)))\n",
    "print(\"Confusion matrix:\\n {}\".format(confusion_matrix(y_test, pred)))\n",
    "print('Test-Set Score : {:.4f}'.format(logreg_dbow_dmc.score(test_vectors_dbow_dmc, y_test)))\n",
    "print('Test-Set Accuracy:{:.4f}'.format(accuracy_score(y_test, pred)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Combination of Doc2Vec DBOW And DMC Based Sentiment Analysis using SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "**** Combination of Doc2Vec DBOW And DMC Based Sentiment Analysis using SVC ****\n",
      "\n",
      "SVC with linear kernel Performance : \n",
      "\n",
      "Train-Set Score : 0.8751\n",
      "Train-Set Accuracy : 0.8751\n",
      "\n",
      "Evaluation on Validation-Set : \n",
      "Classification report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.87      0.87      2513\n",
      "           1       0.87      0.88      0.87      2487\n",
      "\n",
      "   micro avg       0.87      0.87      0.87      5000\n",
      "   macro avg       0.87      0.87      0.87      5000\n",
      "weighted avg       0.87      0.87      0.87      5000\n",
      "\n",
      "Confusion matrix:\n",
      " [[2181  332]\n",
      " [ 296 2191]]\n",
      "Validation-Set Score : 0.8744\n",
      "Validation-Set Accuracy:0.8744\n",
      "\n",
      "Evaluation on Test-Set : \n",
      "Classification report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.89      0.87      0.88      2529\n",
      "           1       0.87      0.88      0.88      2471\n",
      "\n",
      "   micro avg       0.88      0.88      0.88      5000\n",
      "   macro avg       0.88      0.88      0.88      5000\n",
      "weighted avg       0.88      0.88      0.88      5000\n",
      "\n",
      "Confusion matrix:\n",
      " [[2210  319]\n",
      " [ 286 2185]]\n",
      "Test-Set Score : 0.8790\n",
      "Test-Set Accuracy:0.8790\n"
     ]
    }
   ],
   "source": [
    "svm_dbow_dmc = SVC(kernel='linear')\n",
    "svm_dbow_dmc.fit(train_vectors_dbow_dmc, y_train)\n",
    "print(\"**** Combination of Doc2Vec DBOW And DMC Based Sentiment Analysis using SVC ****\\n\")\n",
    "print(\"SVC with linear kernel Performance : \\n\")\n",
    "print('Train-Set Score : {:.4f}'.format(svm_dbow_dmc.score(train_vectors_dbow_dmc, y_train)))\n",
    "print('Train-Set Accuracy : {:.4f}'.format(accuracy_score(y_train,svm_dbow_dmc.predict(train_vectors_dbow_dmc))))\n",
    "\n",
    "print(\"\\nEvaluation on Validation-Set : \")\n",
    "pred_val = svm_dbow_dmc.predict(validation_vectors_dbow_dmc)\n",
    "print(\"Classification report:\\n {}\".format(classification_report(y_validation, pred_val)))\n",
    "print(\"Confusion matrix:\\n {}\".format(confusion_matrix(y_validation, pred_val)))\n",
    "print('Validation-Set Score : {:.4f}'.format(svm_dbow_dmc.score(validation_vectors_dbow_dmc, y_validation)))\n",
    "print('Validation-Set Accuracy:{:.4f}'.format(accuracy_score(y_validation, pred_val)))\n",
    "\n",
    "print(\"\\nEvaluation on Test-Set : \")\n",
    "pred = svm_dbow_dmc.predict(test_vectors_dbow_dmc)\n",
    "print(\"Classification report:\\n {}\".format(classification_report(y_test, pred)))\n",
    "print(\"Confusion matrix:\\n {}\".format(confusion_matrix(y_test, pred)))\n",
    "print('Test-Set Score : {:.4f}'.format(svm_dbow_dmc.score(test_vectors_dbow_dmc, y_test)))\n",
    "print('Test-Set Accuracy:{:.4f}'.format(accuracy_score(y_test, pred)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Combination of Doc2Vec DBOW And DMC Based Sentiment Analysis using XGBClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "**** Combination of Doc2Vec DBOW And DMC Based Sentiment Analysis using XGBClassifier ****\n",
      "\n",
      "XGBClassifier Performance : \n",
      "\n",
      "Train-Set Score : 0.8692\n",
      "Train-Set Accuracy : 0.8692\n",
      "\n",
      "Evaluation on Validation-Set : \n",
      "Classification report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.86      0.84      0.85      2513\n",
      "           1       0.84      0.86      0.85      2487\n",
      "\n",
      "   micro avg       0.85      0.85      0.85      5000\n",
      "   macro avg       0.85      0.85      0.85      5000\n",
      "weighted avg       0.85      0.85      0.85      5000\n",
      "\n",
      "Confusion matrix:\n",
      " [[2114  399]\n",
      " [ 346 2141]]\n",
      "Validation-Set Score : 0.8510\n",
      "Validation-Set Accuracy:0.8510\n",
      "\n",
      "Evaluation on Test-Set : \n",
      "Classification report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.86      0.86      0.86      2529\n",
      "           1       0.85      0.86      0.86      2471\n",
      "\n",
      "   micro avg       0.86      0.86      0.86      5000\n",
      "   macro avg       0.86      0.86      0.86      5000\n",
      "weighted avg       0.86      0.86      0.86      5000\n",
      "\n",
      "Confusion matrix:\n",
      " [[2164  365]\n",
      " [ 348 2123]]\n",
      "Test-Set Score : 0.8574\n",
      "Test-Set Accuracy:0.8574\n"
     ]
    }
   ],
   "source": [
    "xgb_dbow_dmc =XGBClassifier()\n",
    "xgb_dbow_dmc.fit(train_vectors_dbow_dmc, y_train)\n",
    "print(\"**** Combination of Doc2Vec DBOW And DMC Based Sentiment Analysis using XGBClassifier ****\\n\")\n",
    "print(\"XGBClassifier Performance : \\n\")\n",
    "print('Train-Set Score : {:.4f}'.format(xgb_dbow_dmc.score(train_vectors_dbow_dmc, y_train)))\n",
    "print('Train-Set Accuracy : {:.4f}'.format(accuracy_score(y_train,xgb_dbow_dmc.predict(train_vectors_dbow_dmc))))\n",
    "\n",
    "print(\"\\nEvaluation on Validation-Set : \")\n",
    "pred_val = xgb_dbow_dmc.predict(validation_vectors_dbow_dmc)\n",
    "print(\"Classification report:\\n {}\".format(classification_report(y_validation, pred_val)))\n",
    "print(\"Confusion matrix:\\n {}\".format(confusion_matrix(y_validation, pred_val)))\n",
    "print('Validation-Set Score : {:.4f}'.format(xgb_dbow_dmc.score(validation_vectors_dbow_dmc, y_validation)))\n",
    "print('Validation-Set Accuracy:{:.4f}'.format(accuracy_score(y_validation, pred_val)))\n",
    "\n",
    "print(\"\\nEvaluation on Test-Set : \")\n",
    "pred = xgb_dbow_dmc.predict(test_vectors_dbow_dmc)\n",
    "print(\"Classification report:\\n {}\".format(classification_report(y_test, pred)))\n",
    "print(\"Confusion matrix:\\n {}\".format(confusion_matrix(y_test, pred)))\n",
    "print('Test-Set Score : {:.4f}'.format(xgb_dbow_dmc.score(test_vectors_dbow_dmc, y_test)))\n",
    "print('Test-Set Accuracy:{:.4f}'.format(accuracy_score(y_test, pred)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Combination of Doc2Vec Distributed Bag Of Words (DBOW) And Distributed Memory(Mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Generating train, test and validation document vectors\n",
    "\"\"\"\n",
    "train_vectors_dbow_dmm = generate_concat_vectors(dbow_model,dmm_model, x_train, 300)\n",
    "test_vectors_dbow_dmm = generate_concat_vectors(dbow_model,dmm_model, x_test, 300)\n",
    "validation_vectors_dbow_dmm = generate_concat_vectors(dbow_model,dmm_model, x_validation, 300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Combination of Doc2Vec DBOW And DMM Based Sentiment Analysis using LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "**** Combination of Doc2Vec DBOW And DMM Based Sentiment Analysis using LogisticRegression ****\n",
      "\n",
      "LogisticRegression Performance : \n",
      "\n",
      "Train-Set Score : 0.8813\n",
      "Train-Set Accuracy : 0.8813\n",
      "\n",
      "Evaluation on Validation-Set : \n",
      "Classification report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.87      0.87      2513\n",
      "           1       0.87      0.88      0.87      2487\n",
      "\n",
      "   micro avg       0.87      0.87      0.87      5000\n",
      "   macro avg       0.87      0.87      0.87      5000\n",
      "weighted avg       0.87      0.87      0.87      5000\n",
      "\n",
      "Confusion matrix:\n",
      " [[2182  331]\n",
      " [ 298 2189]]\n",
      "Validation-Set Score : 0.8742\n",
      "Validation-Set Accuracy:0.8742\n",
      "\n",
      "Evaluation on Test-Set : \n",
      "Classification report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.89      0.87      0.88      2529\n",
      "           1       0.87      0.89      0.88      2471\n",
      "\n",
      "   micro avg       0.88      0.88      0.88      5000\n",
      "   macro avg       0.88      0.88      0.88      5000\n",
      "weighted avg       0.88      0.88      0.88      5000\n",
      "\n",
      "Confusion matrix:\n",
      " [[2206  323]\n",
      " [ 275 2196]]\n",
      "Test-Set Score : 0.8804\n",
      "Test-Set Accuracy:0.8804\n"
     ]
    }
   ],
   "source": [
    "logreg_dbow_dmm = LogisticRegression()\n",
    "logreg_dbow_dmm.fit(train_vectors_dbow_dmm, y_train)\n",
    "print(\"**** Combination of Doc2Vec DBOW And DMM Based Sentiment Analysis using LogisticRegression ****\\n\")\n",
    "print(\"LogisticRegression Performance : \\n\")\n",
    "print('Train-Set Score : {:.4f}'.format(logreg_dbow_dmm.score(train_vectors_dbow_dmm, y_train)))\n",
    "print('Train-Set Accuracy : {:.4f}'.format(accuracy_score(y_train,logreg_dbow_dmm.predict(train_vectors_dbow_dmm))))\n",
    "\n",
    "print(\"\\nEvaluation on Validation-Set : \")\n",
    "pred_val = logreg_dbow_dmm.predict(validation_vectors_dbow_dmm)\n",
    "print(\"Classification report:\\n {}\".format(classification_report(y_validation, pred_val)))\n",
    "print(\"Confusion matrix:\\n {}\".format(confusion_matrix(y_validation, pred_val)))\n",
    "print('Validation-Set Score : {:.4f}'.format(logreg_dbow_dmm.score(validation_vectors_dbow_dmm, y_validation)))\n",
    "print('Validation-Set Accuracy:{:.4f}'.format(accuracy_score(y_validation, pred_val)))\n",
    "\n",
    "print(\"\\nEvaluation on Test-Set : \")\n",
    "pred = logreg_dbow_dmm.predict(test_vectors_dbow_dmm)\n",
    "print(\"Classification report:\\n {}\".format(classification_report(y_test, pred)))\n",
    "print(\"Confusion matrix:\\n {}\".format(confusion_matrix(y_test, pred)))\n",
    "print('Test-Set Score : {:.4f}'.format(logreg_dbow_dmm.score(test_vectors_dbow_dmm, y_test)))\n",
    "print('Test-Set Accuracy:{:.4f}'.format(accuracy_score(y_test, pred)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Combination of Doc2Vec DBOW And DMM Based Sentiment Analysis using SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "**** Combination of Doc2Vec DBOW And DMM Based Sentiment Analysis using SVC ****\n",
      "\n",
      "SVC with linear kernel Performance : \n",
      "\n",
      "Train-Set Score : 0.8809\n",
      "Train-Set Accuracy : 0.8809\n",
      "\n",
      "Evaluation on Validation-Set : \n",
      "Classification report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.87      0.88      2513\n",
      "           1       0.87      0.88      0.88      2487\n",
      "\n",
      "   micro avg       0.88      0.88      0.88      5000\n",
      "   macro avg       0.88      0.88      0.88      5000\n",
      "weighted avg       0.88      0.88      0.88      5000\n",
      "\n",
      "Confusion matrix:\n",
      " [[2180  333]\n",
      " [ 289 2198]]\n",
      "Validation-Set Score : 0.8756\n",
      "Validation-Set Accuracy:0.8756\n",
      "\n",
      "Evaluation on Test-Set : \n",
      "Classification report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.89      0.87      0.88      2529\n",
      "           1       0.87      0.88      0.88      2471\n",
      "\n",
      "   micro avg       0.88      0.88      0.88      5000\n",
      "   macro avg       0.88      0.88      0.88      5000\n",
      "weighted avg       0.88      0.88      0.88      5000\n",
      "\n",
      "Confusion matrix:\n",
      " [[2209  320]\n",
      " [ 287 2184]]\n",
      "Test-Set Score : 0.8786\n",
      "Test-Set Accuracy:0.8786\n"
     ]
    }
   ],
   "source": [
    "svm_dbow_dmm = SVC(kernel='linear')\n",
    "svm_dbow_dmm.fit(train_vectors_dbow_dmm, y_train)\n",
    "print(\"**** Combination of Doc2Vec DBOW And DMM Based Sentiment Analysis using SVC ****\\n\")\n",
    "print(\"SVC with linear kernel Performance : \\n\")\n",
    "print('Train-Set Score : {:.4f}'.format(svm_dbow_dmm.score(train_vectors_dbow_dmm, y_train)))\n",
    "print('Train-Set Accuracy : {:.4f}'.format(accuracy_score(y_train,svm_dbow_dmm.predict(train_vectors_dbow_dmm))))\n",
    "\n",
    "print(\"\\nEvaluation on Validation-Set : \")\n",
    "pred_val = svm_dbow_dmm.predict(validation_vectors_dbow_dmm)\n",
    "print(\"Classification report:\\n {}\".format(classification_report(y_validation, pred_val)))\n",
    "print(\"Confusion matrix:\\n {}\".format(confusion_matrix(y_validation, pred_val)))\n",
    "print('Validation-Set Score : {:.4f}'.format(svm_dbow_dmm.score(validation_vectors_dbow_dmm, y_validation)))\n",
    "print('Validation-Set Accuracy:{:.4f}'.format(accuracy_score(y_validation, pred_val)))\n",
    "\n",
    "print(\"\\nEvaluation on Test-Set : \")\n",
    "pred = svm_dbow_dmm.predict(test_vectors_dbow_dmm)\n",
    "print(\"Classification report:\\n {}\".format(classification_report(y_test, pred)))\n",
    "print(\"Confusion matrix:\\n {}\".format(confusion_matrix(y_test, pred)))\n",
    "print('Test-Set Score : {:.4f}'.format(svm_dbow_dmm.score(test_vectors_dbow_dmm, y_test)))\n",
    "print('Test-Set Accuracy:{:.4f}'.format(accuracy_score(y_test, pred)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Combination of Doc2Vec DBOW And DMM Based Sentiment Analysis using XGBClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "**** Combination of Doc2Vec DBOW And DMM Based Sentiment Analysis using XGBClassifier ****\n",
      "\n",
      "XGBClassifier Performance : \n",
      "\n",
      "Train-Set Score : 0.8736\n",
      "Train-Set Accuracy : 0.8736\n",
      "\n",
      "Evaluation on Validation-Set : \n",
      "Classification report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.87      0.84      0.85      2513\n",
      "           1       0.84      0.87      0.86      2487\n",
      "\n",
      "   micro avg       0.85      0.85      0.85      5000\n",
      "   macro avg       0.86      0.85      0.85      5000\n",
      "weighted avg       0.86      0.85      0.85      5000\n",
      "\n",
      "Confusion matrix:\n",
      " [[2113  400]\n",
      " [ 326 2161]]\n",
      "Validation-Set Score : 0.8548\n",
      "Validation-Set Accuracy:0.8548\n",
      "\n",
      "Evaluation on Test-Set : \n",
      "Classification report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.86      0.86      0.86      2529\n",
      "           1       0.85      0.86      0.86      2471\n",
      "\n",
      "   micro avg       0.86      0.86      0.86      5000\n",
      "   macro avg       0.86      0.86      0.86      5000\n",
      "weighted avg       0.86      0.86      0.86      5000\n",
      "\n",
      "Confusion matrix:\n",
      " [[2166  363]\n",
      " [ 342 2129]]\n",
      "Test-Set Score : 0.8590\n",
      "Test-Set Accuracy:0.8590\n"
     ]
    }
   ],
   "source": [
    "xgb_dbow_dmm =XGBClassifier()\n",
    "xgb_dbow_dmm.fit(train_vectors_dbow_dmm, y_train)\n",
    "print(\"**** Combination of Doc2Vec DBOW And DMM Based Sentiment Analysis using XGBClassifier ****\\n\")\n",
    "print(\"XGBClassifier Performance : \\n\")\n",
    "print('Train-Set Score : {:.4f}'.format(xgb_dbow_dmm.score(train_vectors_dbow_dmm, y_train)))\n",
    "print('Train-Set Accuracy : {:.4f}'.format(accuracy_score(y_train,xgb_dbow_dmm.predict(train_vectors_dbow_dmm))))\n",
    "\n",
    "print(\"\\nEvaluation on Validation-Set : \")\n",
    "pred_val = xgb_dbow_dmm.predict(validation_vectors_dbow_dmm)\n",
    "print(\"Classification report:\\n {}\".format(classification_report(y_validation, pred_val)))\n",
    "print(\"Confusion matrix:\\n {}\".format(confusion_matrix(y_validation, pred_val)))\n",
    "print('Validation-Set Score : {:.4f}'.format(xgb_dbow_dmm.score(validation_vectors_dbow_dmm, y_validation)))\n",
    "print('Validation-Set Accuracy:{:.4f}'.format(accuracy_score(y_validation, pred_val)))\n",
    "\n",
    "print(\"\\nEvaluation on Test-Set : \")\n",
    "pred = xgb_dbow_dmm.predict(test_vectors_dbow_dmm)\n",
    "print(\"Classification report:\\n {}\".format(classification_report(y_test, pred)))\n",
    "print(\"Confusion matrix:\\n {}\".format(confusion_matrix(y_test, pred)))\n",
    "print('Test-Set Score : {:.4f}'.format(xgb_dbow_dmm.score(test_vectors_dbow_dmm, y_test)))\n",
    "print('Test-Set Accuracy:{:.4f}'.format(accuracy_score(y_test, pred)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Sentiment Analysis Using Combination of Doc2Vec DBOW And DMC Document Embedding and Keras Neural Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_6 (Dense)              (None, 128)               38528     \n",
      "_________________________________________________________________\n",
      "dense_7 (Dense)              (None, 128)               16512     \n",
      "_________________________________________________________________\n",
      "dense_8 (Dense)              (None, 1)                 129       \n",
      "=================================================================\n",
      "Total params: 55,169\n",
      "Trainable params: 55,169\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "Create model\n",
    "\"\"\"\n",
    "keras_d2v_combo_dbow_dmc_model = Sequential()\n",
    "keras_d2v_combo_dbow_dmc_model.add(Dense(128, activation='relu', input_dim=300))\n",
    "keras_d2v_combo_dbow_dmc_model.add(Dense(128, activation='relu'))\n",
    "keras_d2v_combo_dbow_dmc_model.add(Dense(1, activation='sigmoid'))\n",
    "\n",
    "\"\"\"\n",
    "Compile network\n",
    "\"\"\"\n",
    "keras_d2v_combo_dbow_dmc_model.compile(optimizer='adam',\n",
    "                                       loss='binary_crossentropy',\n",
    "                                       metrics=['accuracy'])\n",
    "keras_d2v_combo_dbow_dmc_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "**** Sentiment Analysis Using Combination of Doc2Vec DBOW And DMC Document Embedding and Keras Neural Network ****\n",
      "\n",
      "Train-Set Accuracy: 0.9088\n",
      "\n",
      "Evaluation on Validation-Set : \n",
      "Classification report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.86      0.87      2513\n",
      "           1       0.86      0.89      0.88      2487\n",
      "\n",
      "   micro avg       0.87      0.87      0.87      5000\n",
      "   macro avg       0.87      0.87      0.87      5000\n",
      "weighted avg       0.87      0.87      0.87      5000\n",
      "\n",
      "Confusion matrix:\n",
      " [[2166  347]\n",
      " [ 282 2205]]\n",
      "Validation-Set Accuracy: 0.8742\n",
      "\n",
      "Evaluation on Test-Set : \n",
      "Classification report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.86      0.87      2529\n",
      "           1       0.86      0.88      0.87      2471\n",
      "\n",
      "   micro avg       0.87      0.87      0.87      5000\n",
      "   macro avg       0.87      0.87      0.87      5000\n",
      "weighted avg       0.87      0.87      0.87      5000\n",
      "\n",
      "Confusion matrix:\n",
      " [[2187  342]\n",
      " [ 285 2186]]\n",
      "Test-Set Accuracy:  0.8746\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "Fit network\n",
    "\"\"\"\n",
    "keras_d2v_combo_dbow_dmc_model.fit(train_vectors_dbow_dmc, y_train,\n",
    "                                   validation_data=(validation_vectors_dbow_dmc, y_validation),\n",
    "                                   epochs=5, \n",
    "                                   batch_size=10, \n",
    "                                   verbose=False)\n",
    "\n",
    "\"\"\"\n",
    "Evaluate\n",
    "\"\"\"\n",
    "print(\"**** Sentiment Analysis Using Combination of Doc2Vec DBOW And DMC Document Embedding and Keras Neural Network ****\\n\")\n",
    "loss, accuracy = keras_d2v_combo_dbow_dmc_model.evaluate(train_vectors_dbow_dmc, y_train, verbose=False)\n",
    "print(\"Train-Set Accuracy: {:.4f}\".format(accuracy))\n",
    "\n",
    "print(\"\\nEvaluation on Validation-Set : \")\n",
    "pred_val=keras_d2v_combo_dbow_dmc_model.predict_classes(validation_vectors_dbow_dmc)\n",
    "print(\"Classification report:\\n {}\".format(classification_report(y_validation, pred_val)))\n",
    "print(\"Confusion matrix:\\n {}\".format(confusion_matrix(y_validation, pred_val)))\n",
    "loss, accuracy = keras_d2v_combo_dbow_dmc_model.evaluate(validation_vectors_dbow_dmc, y_validation, verbose=False)\n",
    "print(\"Validation-Set Accuracy: {:.4f}\".format(accuracy))\n",
    "\n",
    "print(\"\\nEvaluation on Test-Set : \")\n",
    "pred=keras_d2v_combo_dbow_dmc_model.predict_classes(test_vectors_dbow_dmc)\n",
    "print(\"Classification report:\\n {}\".format(classification_report(y_test, pred)))\n",
    "print(\"Confusion matrix:\\n {}\".format(confusion_matrix(y_test, pred)))\n",
    "\n",
    "loss, accuracy = keras_d2v_combo_dbow_dmc_model.evaluate(test_vectors_dbow_dmc, y_test, verbose=False)\n",
    "print(\"Test-Set Accuracy:  {:.4f}\".format(accuracy))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Sentiment Analysis Using Combination of Doc2Vec DBOW And DMM Document Embedding and Keras Neural Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_9 (Dense)              (None, 128)               38528     \n",
      "_________________________________________________________________\n",
      "dense_10 (Dense)             (None, 128)               16512     \n",
      "_________________________________________________________________\n",
      "dense_11 (Dense)             (None, 1)                 129       \n",
      "=================================================================\n",
      "Total params: 55,169\n",
      "Trainable params: 55,169\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "Create model\n",
    "\"\"\"\n",
    "keras_d2v_combo_dbow_dmm_model = Sequential()\n",
    "keras_d2v_combo_dbow_dmm_model.add(Dense(128, activation='relu', input_dim=300))\n",
    "keras_d2v_combo_dbow_dmm_model.add(Dense(128, activation='relu'))\n",
    "keras_d2v_combo_dbow_dmm_model.add(Dense(1, activation='sigmoid'))\n",
    "\n",
    "\"\"\"\n",
    "Compile network\n",
    "\"\"\"\n",
    "keras_d2v_combo_dbow_dmm_model.compile(optimizer='adam',\n",
    "                                       loss='binary_crossentropy',\n",
    "                                       metrics=['accuracy'])\n",
    "keras_d2v_combo_dbow_dmm_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "**** Sentiment Analysis Using Combination of Doc2Vec DBOW And DMM Document Embedding and Keras Neural Network ****\n",
      "\n",
      "Train-Set Accuracy: 0.9295\n",
      "\n",
      "Evaluation on Validation-Set : \n",
      "Classification report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.90      0.84      0.87      2513\n",
      "           1       0.85      0.90      0.88      2487\n",
      "\n",
      "   micro avg       0.87      0.87      0.87      5000\n",
      "   macro avg       0.87      0.87      0.87      5000\n",
      "weighted avg       0.87      0.87      0.87      5000\n",
      "\n",
      "Confusion matrix:\n",
      " [[2118  395]\n",
      " [ 244 2243]]\n",
      "Validation-Set Accuracy: 0.8722\n",
      "\n",
      "Evaluation on Test-Set : \n",
      "Classification report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.89      0.85      0.87      2529\n",
      "           1       0.85      0.90      0.87      2471\n",
      "\n",
      "   micro avg       0.87      0.87      0.87      5000\n",
      "   macro avg       0.87      0.87      0.87      5000\n",
      "weighted avg       0.87      0.87      0.87      5000\n",
      "\n",
      "Confusion matrix:\n",
      " [[2142  387]\n",
      " [ 253 2218]]\n",
      "Test-Set Accuracy:  0.8720\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "Fit network\n",
    "\"\"\"\n",
    "keras_d2v_combo_dbow_dmm_model.fit(train_vectors_dbow_dmm, y_train,\n",
    "                                   validation_data=(validation_vectors_dbow_dmm, y_validation),\n",
    "                                   epochs=5, \n",
    "                                   batch_size=10, \n",
    "                                   verbose=False)\n",
    "\n",
    "\"\"\"\n",
    "Evaluate\n",
    "\"\"\"\n",
    "print(\"**** Sentiment Analysis Using Combination of Doc2Vec DBOW And DMM Document Embedding and Keras Neural Network ****\\n\")\n",
    "loss, accuracy = keras_d2v_combo_dbow_dmm_model.evaluate(train_vectors_dbow_dmm, y_train, verbose=False)\n",
    "print(\"Train-Set Accuracy: {:.4f}\".format(accuracy))\n",
    "\n",
    "print(\"\\nEvaluation on Validation-Set : \")\n",
    "pred_val=keras_d2v_combo_dbow_dmm_model.predict_classes(validation_vectors_dbow_dmm)\n",
    "print(\"Classification report:\\n {}\".format(classification_report(y_validation, pred_val)))\n",
    "print(\"Confusion matrix:\\n {}\".format(confusion_matrix(y_validation, pred_val)))\n",
    "loss, accuracy = keras_d2v_combo_dbow_dmm_model.evaluate(validation_vectors_dbow_dmm, y_validation, verbose=False)\n",
    "print(\"Validation-Set Accuracy: {:.4f}\".format(accuracy))\n",
    "\n",
    "print(\"\\nEvaluation on Test-Set : \")\n",
    "pred=keras_d2v_combo_dbow_dmm_model.predict_classes(test_vectors_dbow_dmm)\n",
    "print(\"Classification report:\\n {}\".format(classification_report(y_test, pred)))\n",
    "print(\"Confusion matrix:\\n {}\".format(confusion_matrix(y_test, pred)))\n",
    "\n",
    "loss, accuracy = keras_d2v_combo_dbow_dmm_model.evaluate(test_vectors_dbow_dmm, y_test, verbose=False)\n",
    "print(\"Test-Set Accuracy:  {:.4f}\".format(accuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time elapsed :  9.7  in hours\n"
     ]
    }
   ],
   "source": [
    "print(\"Time elapsed : \",(round(((time.time()-program_start_time)/3600),2)),\" in hours\")"
   ]
  }
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