ML_Final.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np, pandas as pd\n",
    "import json\n",
    "import ast \n",
    "from textblob import TextBlob\n",
    "import nltk\n",
    "import torch\n",
    "import pickle\n",
    "from scipy import spatial\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "import spacy\n",
    "from nltk import Tree\n",
    "from nltk.stem.lancaster import LancasterStemmer\n",
    "st = LancasterStemmer()\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer, TfidfTransformer\n",
    "#en_nlp = spacy.load('en')\n",
    "from sklearn import linear_model\n",
    "from sklearn import metrics\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler, MinMaxScaler,QuantileTransformer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Convert JSON to Pandas Dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_json(\"/Users/devshreepatel/Desktop/LR/data/train-v1.1.json\") #Read the train data JSON File\n",
    "valid = pd.read_json(\"/Users/devshreepatel/Desktop/LR/data/dev-v1.1.json\") #Read the valid data JSON File"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((442, 2), (48, 2))"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape, valid.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "contexts = []       \n",
    "questions = []\n",
    "answers_text = []\n",
    "answers_start = []\n",
    "for i in range(train.shape[0]):\n",
    "    topic = train.iloc[i,0]['paragraphs'] # To select each paragraph from data and gather corresponding info of it.\n",
    "    for sub_para in topic:\n",
    "        for q_a in sub_para['qas']:\n",
    "            questions.append(q_a['question']) # To get Question of each paragraph \n",
    "            answers_start.append(q_a['answers'][0]['answer_start']) # To get the start index of answer\n",
    "            answers_text.append(q_a['answers'][0]['text']) # To get the Answer for the question\n",
    "            contexts.append(sub_para['context']) # To get the context of the paragraph\n",
    "df = pd.DataFrame({\"context\":contexts, \"question\": questions, \"answer_start\": answers_start, \"text\": answers_text})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(87599, 4)"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(\"data/train.csv\", index = None) # Change JSON file to CSV file"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create dictionary of sentence embeddings for faster computation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "paras = list(df[\"context\"].drop_duplicates().reset_index(drop= True)) # Create List of all Contexts and drop duplicates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18891"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(paras)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "blob = TextBlob(\" \".join(paras)) \n",
    "sentences = [item.raw for item in blob.sentences]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "92659"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(sentences)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from models import InferSent\n",
    "V = 2\n",
    "MODEL_PATH = '/Users/devshreepatel/Desktop/LR/encoder/infersent%s.pkl' % V\n",
    "params_model = {'bsize': 64, 'word_emb_dim': 300, 'enc_lstm_dim': 2048,\n",
    "                'pool_type': 'max', 'dpout_model': 0.0, 'version': V} # Model Parameters For Infersent Model\n",
    "infersent = InferSent(params_model) # Infersent Pre Trained encoder by Facebook\n",
    "infersent.load_state_dict(torch.load(MODEL_PATH)) # Load Dictionary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "W2V_PATH = '/Users/devshreepatel/Desktop/LR/data/fastText/crawl-300d-2M.vec'\n",
    "infersent.set_w2v_path(W2V_PATH) # Set the Word To Vector Path for embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "InferSent(\n",
       "  (enc_lstm): LSTM(300, 2048, bidirectional=True)\n",
       ")"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "infersent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 89231(/109703) words with w2v vectors\n",
      "Vocab size : 89231\n"
     ]
    }
   ],
   "source": [
    "infersent.build_vocab(sentences, tokenize=True) # Using Function of InferSent Class to build vocabulary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generating word embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dict_embeddings = {}\n",
    "for i in range(len(sentences)):\n",
    "    print(i)\n",
    "    dict_embeddings[sentences[i]] = infersent.encode([sentences[i]], tokenize=True) # Encode the sentences in dict_embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "questions = list(df[\"question\"]) #Use the question column from df and create a list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "87599"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(questions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(len(questions)):\n",
    "    print(i)\n",
    "    dict_embeddings[questions[i]] = infersent.encode([questions[i]], tokenize=True) # Generate encoding for questions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create dictionary with each Sentence and corresponding word embeddings array "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "d1 = {key:dict_embeddings[key] for i, key in enumerate(dict_embeddings) if i % 2 == 0} \n",
    "d2 = {key:dict_embeddings[key] for i, key in enumerate(dict_embeddings) if i % 2 == 1}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv(\"data/train.csv\") # Load the train data CSV file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(87599, 4)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading Embedding dictionary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Open the pickle file 1 with word embeddings and load to d1\n",
    "with open(\"data/dict_1.pickle\", \"rb\") as f:\n",
    "    d1 = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Open the pickle file 2 with word embeddings and load to d2\n",
    "with open(\"data/dict_2.pickle\", \"rb\") as f:\n",
    "    d2 = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Concatenate the two dictionaries with word embeddings in one dict_emb\n",
    "dict_emb = dict(d1)\n",
    "dict_emb.update(d2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "179862"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(dict_emb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Pre-Processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This function sets the value of column Target which is the sentence number in the paragraph which contains the answer.\n",
    "def get_target(x):\n",
    "    idx = -1\n",
    "    for i in range(len(x[\"sentences\"])):\n",
    "        if x[\"text\"] in x[\"sentences\"][i]: idx = i\n",
    "    return idx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>context</th>\n",
       "      <th>question</th>\n",
       "      <th>answer_start</th>\n",
       "      <th>text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Architecturally, the school has a Catholic cha...</td>\n",
       "      <td>To whom did the Virgin Mary allegedly appear i...</td>\n",
       "      <td>515</td>\n",
       "      <td>Saint Bernadette Soubirous</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Architecturally, the school has a Catholic cha...</td>\n",
       "      <td>What is in front of the Notre Dame Main Building?</td>\n",
       "      <td>188</td>\n",
       "      <td>a copper statue of Christ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Architecturally, the school has a Catholic cha...</td>\n",
       "      <td>The Basilica of the Sacred heart at Notre Dame...</td>\n",
       "      <td>279</td>\n",
       "      <td>the Main Building</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "                                             context  \\\n",
       "0  Architecturally, the school has a Catholic cha...   \n",
       "1  Architecturally, the school has a Catholic cha...   \n",
       "2  Architecturally, the school has a Catholic cha...   \n",
       "\n",
       "                                            question  answer_start  \\\n",
       "0  To whom did the Virgin Mary allegedly appear i...           515   \n",
       "1  What is in front of the Notre Dame Main Building?           188   \n",
       "2  The Basilica of the Sacred heart at Notre Dame...           279   \n",
       "\n",
       "                         text  \n",
       "0  Saint Bernadette Soubirous  \n",
       "1   a copper statue of Christ  \n",
       "2           the Main Building  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(87599, 4)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use of dropna function to drop null values using inplace=True to make the changes in CSV file automatic \n",
    "train.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Processing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This function to add the extra columns to the training data: sentences, target, sent_emb, quest_emb\n",
    "def process_data(train):\n",
    "    \n",
    "    print(\"step 1\")\n",
    "    train['sentences'] = train['context'].apply(lambda x: [item.raw for item in TextBlob(x).sentences])\n",
    "    \n",
    "    print(\"step 2\")\n",
    "    train[\"target\"] = train.apply(get_target, axis = 1)\n",
    "    \n",
    "    print(\"step 3\")\n",
    "    train['sent_emb'] = train['sentences'].apply(lambda x: [dict_emb[item][0] if item in\n",
    "                                                           dict_emb else np.zeros(4096) for item in x])\n",
    "    print(\"step 4\")\n",
    "    train['quest_emb'] = train['question'].apply(lambda x: dict_emb[x] if x in dict_emb else np.zeros(4096) )\n",
    "        \n",
    "    return train   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "step 1\n",
      "step 2\n",
      "step 3\n",
      "step 4\n"
     ]
    }
   ],
   "source": [
    "# Run this to process data to add columns \n",
    "train = process_data(train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(87598, 8)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>context</th>\n",
       "      <th>question</th>\n",
       "      <th>answer_start</th>\n",
       "      <th>text</th>\n",
       "      <th>sentences</th>\n",
       "      <th>target</th>\n",
       "      <th>sent_emb</th>\n",
       "      <th>quest_emb</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Architecturally, the school has a Catholic cha...</td>\n",
       "      <td>To whom did the Virgin Mary allegedly appear i...</td>\n",
       "      <td>515</td>\n",
       "      <td>Saint Bernadette Soubirous</td>\n",
       "      <td>[Architecturally, the school has a Catholic ch...</td>\n",
       "      <td>5</td>\n",
       "      <td>[[0.0074688885, -0.050863117, 0.007364763, -0....</td>\n",
       "      <td>[[0.0074688885, 0.024210272, 0.069616325, -0.0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Architecturally, the school has a Catholic cha...</td>\n",
       "      <td>What is in front of the Notre Dame Main Building?</td>\n",
       "      <td>188</td>\n",
       "      <td>a copper statue of Christ</td>\n",
       "      <td>[Architecturally, the school has a Catholic ch...</td>\n",
       "      <td>2</td>\n",
       "      <td>[[0.0074688885, -0.050863117, 0.007364763, -0....</td>\n",
       "      <td>[[0.0074688885, -0.033483382, 0.040545918, -0....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Architecturally, the school has a Catholic cha...</td>\n",
       "      <td>The Basilica of the Sacred heart at Notre Dame...</td>\n",
       "      <td>279</td>\n",
       "      <td>the Main Building</td>\n",
       "      <td>[Architecturally, the school has a Catholic ch...</td>\n",
       "      <td>3</td>\n",
       "      <td>[[0.0074688885, -0.050863117, 0.007364763, -0....</td>\n",
       "      <td>[[0.0074688885, -0.043944724, 0.14385942, -0.0...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             context  \\\n",
       "0  Architecturally, the school has a Catholic cha...   \n",
       "1  Architecturally, the school has a Catholic cha...   \n",
       "2  Architecturally, the school has a Catholic cha...   \n",
       "\n",
       "                                            question  answer_start  \\\n",
       "0  To whom did the Virgin Mary allegedly appear i...           515   \n",
       "1  What is in front of the Notre Dame Main Building?           188   \n",
       "2  The Basilica of the Sacred heart at Notre Dame...           279   \n",
       "\n",
       "                         text  \\\n",
       "0  Saint Bernadette Soubirous   \n",
       "1   a copper statue of Christ   \n",
       "2           the Main Building   \n",
       "\n",
       "                                           sentences  target  \\\n",
       "0  [Architecturally, the school has a Catholic ch...       5   \n",
       "1  [Architecturally, the school has a Catholic ch...       2   \n",
       "2  [Architecturally, the school has a Catholic ch...       3   \n",
       "\n",
       "                                            sent_emb  \\\n",
       "0  [[0.0074688885, -0.050863117, 0.007364763, -0....   \n",
       "1  [[0.0074688885, -0.050863117, 0.007364763, -0....   \n",
       "2  [[0.0074688885, -0.050863117, 0.007364763, -0....   \n",
       "\n",
       "                                           quest_emb  \n",
       "0  [[0.0074688885, 0.024210272, 0.069616325, -0.0...  \n",
       "1  [[0.0074688885, -0.033483382, 0.040545918, -0....  \n",
       "2  [[0.0074688885, -0.043944724, 0.14385942, -0.0...  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predicted Cosine & Euclidean Index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Find the Cosine similarity Between Question and Answer \n",
    "def cosine_sim(x):\n",
    "    li = []\n",
    "    for item in x[\"sent_emb\"]:\n",
    "        li.append(spatial.distance.cosine(item,x[\"quest_emb\"][0]))\n",
    "    return li   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "#This function is to find the argmin between all the distances of a single sentence array to find the predicted target ...\n",
    "#sentence number\n",
    "def pred_idx(distances):\n",
    "    return np.argmin(distances)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The euclidean distance and cosine similarity are calculated and predictions are made using above function and stored \n",
    "def predictions(train):\n",
    "    \n",
    "    train[\"cosine_sim\"] = train.apply(cosine_sim, axis = 1)\n",
    "    train[\"diff\"] = (train[\"quest_emb\"] - train[\"sent_emb\"])**2\n",
    "    train[\"euclidean_dis\"] = train[\"diff\"].apply(lambda x: list(np.sum(x, axis = 1)))\n",
    "    del train[\"diff\"]\n",
    "    \n",
    "    print(\"cosine start\")\n",
    "    \n",
    "    train[\"pred_idx_cos\"] = train[\"cosine_sim\"].apply(lambda x: pred_idx(x))\n",
    "    train[\"pred_idx_euc\"] = train[\"euclidean_dis\"].apply(lambda x: pred_idx(x))\n",
    "    \n",
    "    return train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Call to make the predictions\n",
    "predicted = predictions(train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(87598, 12)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predicted.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>context</th>\n",
       "      <th>question</th>\n",
       "      <th>answer_start</th>\n",
       "      <th>text</th>\n",
       "      <th>sentences</th>\n",
       "      <th>target</th>\n",
       "      <th>sent_emb</th>\n",
       "      <th>quest_emb</th>\n",
       "      <th>cosine_sim</th>\n",
       "      <th>euclidean_dis</th>\n",
       "      <th>pred_idx_cos</th>\n",
       "      <th>pred_idx_euc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Architecturally, the school has a Catholic cha...</td>\n",
       "      <td>To whom did the Virgin Mary allegedly appear i...</td>\n",
       "      <td>515</td>\n",
       "      <td>Saint Bernadette Soubirous</td>\n",
       "      <td>['Architecturally, the school has a Catholic c...</td>\n",
       "      <td>5</td>\n",
       "      <td>[array([ 0.00746889, -0.05086312,  0.00736476,...</td>\n",
       "      <td>[[ 0.00746889  0.02421027  0.06961633 ...  0.0...</td>\n",
       "      <td>[0.6835565567016602, 0.5527453124523163, 0.574...</td>\n",
       "      <td>[7.301305, 6.4764132, 7.214262, 6.9176197, 6.5...</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Architecturally, the school has a Catholic cha...</td>\n",
       "      <td>What is in front of the Notre Dame Main Building?</td>\n",
       "      <td>188</td>\n",
       "      <td>a copper statue of Christ</td>\n",
       "      <td>['Architecturally, the school has a Catholic c...</td>\n",
       "      <td>2</td>\n",
       "      <td>[array([ 0.00746889, -0.05086312,  0.00736476,...</td>\n",
       "      <td>[[ 0.00746889 -0.03348338  0.04054592 ... -0.0...</td>\n",
       "      <td>[0.6624992787837982, 0.5191770792007446, 0.592...</td>\n",
       "      <td>[5.717004, 5.076439, 6.3408985, 3.5867877, 4.8...</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Architecturally, the school has a Catholic cha...</td>\n",
       "      <td>The Basilica of the Sacred heart at Notre Dame...</td>\n",
       "      <td>279</td>\n",
       "      <td>the Main Building</td>\n",
       "      <td>['Architecturally, the school has a Catholic c...</td>\n",
       "      <td>3</td>\n",
       "      <td>[array([ 0.00746889, -0.05086312,  0.00736476,...</td>\n",
       "      <td>[[ 0.00746889 -0.04394472  0.14385942 ...  0.0...</td>\n",
       "      <td>[0.6164608299732208, 0.4940056800842285, 0.521...</td>\n",
       "      <td>[5.3495297, 4.8541417, 5.6286764, 2.8828857, 3...</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             context  \\\n",
       "0  Architecturally, the school has a Catholic cha...   \n",
       "1  Architecturally, the school has a Catholic cha...   \n",
       "2  Architecturally, the school has a Catholic cha...   \n",
       "\n",
       "                                            question  answer_start  \\\n",
       "0  To whom did the Virgin Mary allegedly appear i...           515   \n",
       "1  What is in front of the Notre Dame Main Building?           188   \n",
       "2  The Basilica of the Sacred heart at Notre Dame...           279   \n",
       "\n",
       "                         text  \\\n",
       "0  Saint Bernadette Soubirous   \n",
       "1   a copper statue of Christ   \n",
       "2           the Main Building   \n",
       "\n",
       "                                           sentences  target  \\\n",
       "0  ['Architecturally, the school has a Catholic c...       5   \n",
       "1  ['Architecturally, the school has a Catholic c...       2   \n",
       "2  ['Architecturally, the school has a Catholic c...       3   \n",
       "\n",
       "                                            sent_emb  \\\n",
       "0  [array([ 0.00746889, -0.05086312,  0.00736476,...   \n",
       "1  [array([ 0.00746889, -0.05086312,  0.00736476,...   \n",
       "2  [array([ 0.00746889, -0.05086312,  0.00736476,...   \n",
       "\n",
       "                                           quest_emb  \\\n",
       "0  [[ 0.00746889  0.02421027  0.06961633 ...  0.0...   \n",
       "1  [[ 0.00746889 -0.03348338  0.04054592 ... -0.0...   \n",
       "2  [[ 0.00746889 -0.04394472  0.14385942 ...  0.0...   \n",
       "\n",
       "                                          cosine_sim  \\\n",
       "0  [0.6835565567016602, 0.5527453124523163, 0.574...   \n",
       "1  [0.6624992787837982, 0.5191770792007446, 0.592...   \n",
       "2  [0.6164608299732208, 0.4940056800842285, 0.521...   \n",
       "\n",
       "                                       euclidean_dis  pred_idx_cos  \\\n",
       "0  [7.301305, 6.4764132, 7.214262, 6.9176197, 6.5...             5   \n",
       "1  [5.717004, 5.076439, 6.3408985, 3.5867877, 4.8...             3   \n",
       "2  [5.3495297, 4.8541417, 5.6286764, 2.8828857, 3...             3   \n",
       "\n",
       "   pred_idx_euc  \n",
       "0             5  \n",
       "1             3  \n",
       "2             3  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predicted.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'[0.6835565567016602, 0.5527453124523163, 0.5747938454151154, 0.624308854341507, 0.619026243686676, 0.25835102796554565, 0.5743658542633057]'"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predicted[\"cosine_sim\"][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'[7.301305, 6.4764132, 7.214262, 6.9176197, 6.519748, 3.6410067, 6.9080434]'"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predicted[\"euclidean_dis\"][0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate the accuracy between predicted and target values\n",
    "def accuracy(target, predicted):\n",
    "    acc = (target==predicted).sum()/len(target)\n",
    "    return acc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Accuracy for  euclidean Distance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.488778282609192\n"
     ]
    }
   ],
   "source": [
    "print(accuracy(predicted[\"target\"], predicted[\"pred_idx_euc\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Accuracy for Cosine Similarity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5972282472202561\n"
     ]
    }
   ],
   "source": [
    "print(accuracy(predicted[\"target\"], predicted[\"pred_idx_cos\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Write back the final data with predicted values to a CSV file\n",
    "predicted.to_csv(\"train_detect_sent.csv\", index=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Read the new CSV file\n",
    "predicted = pd.read_csv(\"train_detect_sent.csv\").reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Use spaCy Open source Library to load the data needed for to process for English Language\n",
    "en_nlp=spacy.load('en')\n",
    "doc = en_nlp(predicted.iloc[0,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        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>context</th>\n",
       "      <th>question</th>\n",
       "      <th>answer_start</th>\n",
       "      <th>text</th>\n",
       "      <th>sentences</th>\n",
       "      <th>target</th>\n",
       "      <th>sent_emb</th>\n",
       "      <th>quest_emb</th>\n",
       "      <th>cosine_sim</th>\n",
       "      <th>euclidean_dis</th>\n",
       "      <th>pred_idx_cos</th>\n",
       "      <th>pred_idx_euc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Architecturally, the school has a Catholic cha...</td>\n",
       "      <td>To whom did the Virgin Mary allegedly appear i...</td>\n",
       "      <td>515</td>\n",
       "      <td>Saint Bernadette Soubirous</td>\n",
       "      <td>['Architecturally, the school has a Catholic c...</td>\n",
       "      <td>5</td>\n",
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       "      <td>[7.301305, 6.4764132, 7.214262, 6.9176197, 6.5...</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Architecturally, the school has a Catholic cha...</td>\n",
       "      <td>What is in front of the Notre Dame Main Building?</td>\n",
       "      <td>188</td>\n",
       "      <td>a copper statue of Christ</td>\n",
       "      <td>['Architecturally, the school has a Catholic c...</td>\n",
       "      <td>2</td>\n",
       "      <td>[array([ 0.00746889, -0.05086312,  0.00736476,...</td>\n",
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       "      <td>[0.6624992787837982, 0.5191770792007446, 0.592...</td>\n",
       "      <td>[5.717004, 5.076439, 6.3408985, 3.5867877, 4.8...</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Architecturally, the school has a Catholic cha...</td>\n",
       "      <td>The Basilica of the Sacred heart at Notre Dame...</td>\n",
       "      <td>279</td>\n",
       "      <td>the Main Building</td>\n",
       "      <td>['Architecturally, the school has a Catholic c...</td>\n",
       "      <td>3</td>\n",
       "      <td>[array([ 0.00746889, -0.05086312,  0.00736476,...</td>\n",
       "      <td>[[ 0.00746889 -0.04394472  0.14385942 ...  0.0...</td>\n",
       "      <td>[0.6164608299732208, 0.4940056800842285, 0.521...</td>\n",
       "      <td>[5.3495297, 4.8541417, 5.6286764, 2.8828857, 3...</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Architecturally, the school has a Catholic cha...</td>\n",
       "      <td>What is the Grotto at Notre Dame?</td>\n",
       "      <td>381</td>\n",
       "      <td>a Marian place of prayer and reflection</td>\n",
       "      <td>['Architecturally, the school has a Catholic c...</td>\n",
       "      <td>4</td>\n",
       "      <td>[array([ 0.00746889, -0.05086312,  0.00736476,...</td>\n",
       "      <td>[[ 0.00746889 -0.02113509  0.08985032 ...  0.0...</td>\n",
       "      <td>[0.6685060262680054, 0.6087148487567902, 0.695...</td>\n",
       "      <td>[6.2550764, 6.3554144, 7.8789406, 4.5443306, 4...</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Architecturally, the school has a Catholic cha...</td>\n",
       "      <td>What sits on top of the Main Building at Notre...</td>\n",
       "      <td>92</td>\n",
       "      <td>a golden statue of the Virgin Mary</td>\n",
       "      <td>['Architecturally, the school has a Catholic c...</td>\n",
       "      <td>1</td>\n",
       "      <td>[array([ 0.00746889, -0.05086312,  0.00736476,...</td>\n",
       "      <td>[[ 0.00746889 -0.02515129  0.08926863 ...  0.0...</td>\n",
       "      <td>[0.7792749553918839, 0.528475284576416, 0.5735...</td>\n",
       "      <td>[7.5543714, 5.692802, 6.6871767, 4.9444075, 5....</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87593</th>\n",
       "      <td>Kathmandu Metropolitan City (KMC), in order to...</td>\n",
       "      <td>In what US state did Kathmandu first establish...</td>\n",
       "      <td>229</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>['Kathmandu Metropolitan City (KMC), in order ...</td>\n",
       "      <td>1</td>\n",
       "      <td>[array([ 0.00746889, -0.05648201,  0.10629363,...</td>\n",
       "      <td>[[ 0.00746889 -0.03403048  0.06046114 ...  0.0...</td>\n",
       "      <td>[0.41991251707077026, 0.3826998472213745, 0.48...</td>\n",
       "      <td>[4.760933, 4.116004, 6.391081, 5.7619123]</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87594</th>\n",
       "      <td>Kathmandu Metropolitan City (KMC), in order to...</td>\n",
       "      <td>What was Yangon previously known as?</td>\n",
       "      <td>414</td>\n",
       "      <td>Rangoon</td>\n",
       "      <td>['Kathmandu Metropolitan City (KMC), in order ...</td>\n",
       "      <td>2</td>\n",
       "      <td>[array([ 0.00746889, -0.05648201,  0.10629363,...</td>\n",
       "      <td>[[ 0.00746889 -0.05823731 -0.00707996 ... -0.0...</td>\n",
       "      <td>[0.7255948781967163, 0.698612630367279, 0.7624...</td>\n",
       "      <td>[7.763542, 7.0988116, 9.420061, 9.603617]</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87595</th>\n",
       "      <td>Kathmandu Metropolitan City (KMC), in order to...</td>\n",
       "      <td>With what Belorussian city does Kathmandu have...</td>\n",
       "      <td>476</td>\n",
       "      <td>Minsk</td>\n",
       "      <td>['Kathmandu Metropolitan City (KMC), in order ...</td>\n",
       "      <td>2</td>\n",
       "      <td>[array([ 0.00746889, -0.05648201,  0.10629363,...</td>\n",
       "      <td>[[ 0.00746889 -0.05044248  0.06999452 ...  0.0...</td>\n",
       "      <td>[0.4843701124191284, 0.5179441869258881, 0.530...</td>\n",
       "      <td>[5.394357, 5.4583673, 6.8717723, 6.434966]</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87596</th>\n",
       "      <td>Kathmandu Metropolitan City (KMC), in order to...</td>\n",
       "      <td>In what year did Kathmandu create its initial ...</td>\n",
       "      <td>199</td>\n",
       "      <td>1975</td>\n",
       "      <td>['Kathmandu Metropolitan City (KMC), in order ...</td>\n",
       "      <td>1</td>\n",
       "      <td>[array([ 0.00746889, -0.05648201,  0.10629363,...</td>\n",
       "      <td>[[ 0.00746889 -0.03403048  0.05538646 ...  0.0...</td>\n",
       "      <td>[0.49502629041671753, 0.4206138253211975, 0.52...</td>\n",
       "      <td>[5.523418, 4.4599686, 6.844032, 6.0133524]</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87597</th>\n",
       "      <td>Kathmandu Metropolitan City (KMC), in order to...</td>\n",
       "      <td>What is KMC an initialism of?</td>\n",
       "      <td>0</td>\n",
       "      <td>Kathmandu Metropolitan City</td>\n",
       "      <td>['Kathmandu Metropolitan City (KMC), in order ...</td>\n",
       "      <td>0</td>\n",
       "      <td>[array([ 0.00746889, -0.05648201,  0.10629363,...</td>\n",
       "      <td>[[ 0.00746889 -0.05823731  0.08288754 ... -0.0...</td>\n",
       "      <td>[0.6605812907218933, 0.7128670513629913, 0.849...</td>\n",
       "      <td>[7.2158666, 7.3852386, 10.605681, 9.032009]</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>87598 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 context  \\\n",
       "0      Architecturally, the school has a Catholic cha...   \n",
       "1      Architecturally, the school has a Catholic cha...   \n",
       "2      Architecturally, the school has a Catholic cha...   \n",
       "3      Architecturally, the school has a Catholic cha...   \n",
       "4      Architecturally, the school has a Catholic cha...   \n",
       "...                                                  ...   \n",
       "87593  Kathmandu Metropolitan City (KMC), in order to...   \n",
       "87594  Kathmandu Metropolitan City (KMC), in order to...   \n",
       "87595  Kathmandu Metropolitan City (KMC), in order to...   \n",
       "87596  Kathmandu Metropolitan City (KMC), in order to...   \n",
       "87597  Kathmandu Metropolitan City (KMC), in order to...   \n",
       "\n",
       "                                                question  answer_start  \\\n",
       "0      To whom did the Virgin Mary allegedly appear i...           515   \n",
       "1      What is in front of the Notre Dame Main Building?           188   \n",
       "2      The Basilica of the Sacred heart at Notre Dame...           279   \n",
       "3                      What is the Grotto at Notre Dame?           381   \n",
       "4      What sits on top of the Main Building at Notre...            92   \n",
       "...                                                  ...           ...   \n",
       "87593  In what US state did Kathmandu first establish...           229   \n",
       "87594               What was Yangon previously known as?           414   \n",
       "87595  With what Belorussian city does Kathmandu have...           476   \n",
       "87596  In what year did Kathmandu create its initial ...           199   \n",
       "87597                      What is KMC an initialism of?             0   \n",
       "\n",
       "                                          text  \\\n",
       "0                   Saint Bernadette Soubirous   \n",
       "1                    a copper statue of Christ   \n",
       "2                            the Main Building   \n",
       "3      a Marian place of prayer and reflection   \n",
       "4           a golden statue of the Virgin Mary   \n",
       "...                                        ...   \n",
       "87593                                   Oregon   \n",
       "87594                                  Rangoon   \n",
       "87595                                    Minsk   \n",
       "87596                                     1975   \n",
       "87597              Kathmandu Metropolitan City   \n",
       "\n",
       "                                               sentences  target  \\\n",
       "0      ['Architecturally, the school has a Catholic c...       5   \n",
       "1      ['Architecturally, the school has a Catholic c...       2   \n",
       "2      ['Architecturally, the school has a Catholic c...       3   \n",
       "3      ['Architecturally, the school has a Catholic c...       4   \n",
       "4      ['Architecturally, the school has a Catholic c...       1   \n",
       "...                                                  ...     ...   \n",
       "87593  ['Kathmandu Metropolitan City (KMC), in order ...       1   \n",
       "87594  ['Kathmandu Metropolitan City (KMC), in order ...       2   \n",
       "87595  ['Kathmandu Metropolitan City (KMC), in order ...       2   \n",
       "87596  ['Kathmandu Metropolitan City (KMC), in order ...       1   \n",
       "87597  ['Kathmandu Metropolitan City (KMC), in order ...       0   \n",
       "\n",
       "                                                sent_emb  \\\n",
       "0      [array([ 0.00746889, -0.05086312,  0.00736476,...   \n",
       "1      [array([ 0.00746889, -0.05086312,  0.00736476,...   \n",
       "2      [array([ 0.00746889, -0.05086312,  0.00736476,...   \n",
       "3      [array([ 0.00746889, -0.05086312,  0.00736476,...   \n",
       "4      [array([ 0.00746889, -0.05086312,  0.00736476,...   \n",
       "...                                                  ...   \n",
       "87593  [array([ 0.00746889, -0.05648201,  0.10629363,...   \n",
       "87594  [array([ 0.00746889, -0.05648201,  0.10629363,...   \n",
       "87595  [array([ 0.00746889, -0.05648201,  0.10629363,...   \n",
       "87596  [array([ 0.00746889, -0.05648201,  0.10629363,...   \n",
       "87597  [array([ 0.00746889, -0.05648201,  0.10629363,...   \n",
       "\n",
       "                                               quest_emb  \\\n",
       "0      [[ 0.00746889  0.02421027  0.06961633 ...  0.0...   \n",
       "1      [[ 0.00746889 -0.03348338  0.04054592 ... -0.0...   \n",
       "2      [[ 0.00746889 -0.04394472  0.14385942 ...  0.0...   \n",
       "3      [[ 0.00746889 -0.02113509  0.08985032 ...  0.0...   \n",
       "4      [[ 0.00746889 -0.02515129  0.08926863 ...  0.0...   \n",
       "...                                                  ...   \n",
       "87593  [[ 0.00746889 -0.03403048  0.06046114 ...  0.0...   \n",
       "87594  [[ 0.00746889 -0.05823731 -0.00707996 ... -0.0...   \n",
       "87595  [[ 0.00746889 -0.05044248  0.06999452 ...  0.0...   \n",
       "87596  [[ 0.00746889 -0.03403048  0.05538646 ...  0.0...   \n",
       "87597  [[ 0.00746889 -0.05823731  0.08288754 ... -0.0...   \n",
       "\n",
       "                                              cosine_sim  \\\n",
       "0      [0.6835565567016602, 0.5527453124523163, 0.574...   \n",
       "1      [0.6624992787837982, 0.5191770792007446, 0.592...   \n",
       "2      [0.6164608299732208, 0.4940056800842285, 0.521...   \n",
       "3      [0.6685060262680054, 0.6087148487567902, 0.695...   \n",
       "4      [0.7792749553918839, 0.528475284576416, 0.5735...   \n",
       "...                                                  ...   \n",
       "87593  [0.41991251707077026, 0.3826998472213745, 0.48...   \n",
       "87594  [0.7255948781967163, 0.698612630367279, 0.7624...   \n",
       "87595  [0.4843701124191284, 0.5179441869258881, 0.530...   \n",
       "87596  [0.49502629041671753, 0.4206138253211975, 0.52...   \n",
       "87597  [0.6605812907218933, 0.7128670513629913, 0.849...   \n",
       "\n",
       "                                           euclidean_dis  pred_idx_cos  \\\n",
       "0      [7.301305, 6.4764132, 7.214262, 6.9176197, 6.5...             5   \n",
       "1      [5.717004, 5.076439, 6.3408985, 3.5867877, 4.8...             3   \n",
       "2      [5.3495297, 4.8541417, 5.6286764, 2.8828857, 3...             3   \n",
       "3      [6.2550764, 6.3554144, 7.8789406, 4.5443306, 4...             3   \n",
       "4      [7.5543714, 5.692802, 6.6871767, 4.9444075, 5....             3   \n",
       "...                                                  ...           ...   \n",
       "87593          [4.760933, 4.116004, 6.391081, 5.7619123]             1   \n",
       "87594          [7.763542, 7.0988116, 9.420061, 9.603617]             1   \n",
       "87595         [5.394357, 5.4583673, 6.8717723, 6.434966]             0   \n",
       "87596         [5.523418, 4.4599686, 6.844032, 6.0133524]             1   \n",
       "87597        [7.2158666, 7.3852386, 10.605681, 9.032009]             0   \n",
       "\n",
       "       pred_idx_euc  \n",
       "0                 5  \n",
       "1                 3  \n",
       "2                 3  \n",
       "3                 3  \n",
       "4                 3  \n",
       "...             ...  \n",
       "87593             1  \n",
       "87594             1  \n",
       "87595             0  \n",
       "87596             1  \n",
       "87597             0  \n",
       "\n",
       "[87598 rows x 12 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predicted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(87598, 12)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predicted.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Architecturally, the school has a Catholic character.',\n",
       " \"Atop the Main Building's gold dome is a golden statue of the Virgin Mary.\",\n",
       " 'Immediately in front of the Main Building and facing it, is a copper statue of Christ with arms upraised with the legend \"Venite Ad Me Omnes\".',\n",
       " 'Next to the Main Building is the Basilica of the Sacred Heart.',\n",
       " 'Immediately behind the basilica is the Grotto, a Marian place of prayer and reflection.',\n",
       " 'It is a replica of the grotto at Lourdes, France where the Virgin Mary reputedly appeared to Saint Bernadette Soubirous in 1858.',\n",
       " 'At the end of the main drive (and in a direct line that connects through 3 statues and the Gold Dome), is a simple, modern stone statue of Mary.']"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#ast.literal_eval to print sentence-wise\n",
    "ast.literal_eval(predicted[\"sentences\"][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Here all the paragraphs with number of sentences 10 or less are taken from data\n",
    "predicted = predicted[predicted[\"sentences\"].apply(lambda x: len(ast.literal_eval(x)))<11].reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Every array value of cosine similarity and euclidean distance for each sentence are separated in columns as we have to work\n",
    "#on the numerical distance data and every Question Answer pair has array for euclidean distance and cosine similarity\n",
    "def create_features(predicted):\n",
    "    train = pd.DataFrame()\n",
    "     \n",
    "    for k in range(len(predicted[\"euclidean_dis\"])):\n",
    "        dis = ast.literal_eval(predicted[\"euclidean_dis\"][k])\n",
    "        for i in range(len(dis)):\n",
    "            train.loc[k, \"column_euc_\"+\"%s\"%i] = dis[i]\n",
    "    \n",
    "    print(\"Finished\")\n",
    "    \n",
    "    for k in range(len(predicted[\"cosine_sim\"])):\n",
    "        dis = ast.literal_eval(predicted[\"cosine_sim\"][k].replace(\"nan\",\"1\"))\n",
    "        for i in range(len(dis)):\n",
    "            train.loc[k, \"column_cos_\"+\"%s\"%i] = dis[i]\n",
    "            \n",
    "    train[\"target\"] = predicted[\"target\"]\n",
    "    return train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finished\n"
     ]
    }
   ],
   "source": [
    "# Call to create features and convert distances to columns\n",
    "train = create_features(predicted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.to_csv(\"train_final.csv\", index=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "train2.to_csv(\"train_2.csv\",index=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## start here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Read the new CSV file\n",
    "train1 = pd.read_csv(\"train_final.csv\").reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>column_euc_0</th>\n",
       "      <th>column_euc_1</th>\n",
       "      <th>column_euc_2</th>\n",
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       "      <th>column_euc_9</th>\n",
       "      <th>...</th>\n",
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       "      <td>0.552745</td>\n",
       "      <td>0.574794</td>\n",
       "      <td>0.624309</td>\n",
       "      <td>0.619026</td>\n",
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       "      <td>0.574366</td>\n",
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       "      <td>5.076439</td>\n",
       "      <td>6.340898</td>\n",
       "      <td>3.586788</td>\n",
       "      <td>4.826955</td>\n",
       "      <td>7.294750</td>\n",
       "      <td>6.392016</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>5.349530</td>\n",
       "      <td>4.854142</td>\n",
       "      <td>5.628676</td>\n",
       "      <td>2.882886</td>\n",
       "      <td>3.573071</td>\n",
       "      <td>6.292664</td>\n",
       "      <td>5.433945</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.494006</td>\n",
       "      <td>0.521783</td>\n",
       "      <td>0.316489</td>\n",
       "      <td>0.421474</td>\n",
       "      <td>0.506190</td>\n",
       "      <td>0.533841</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6.255076</td>\n",
       "      <td>6.355414</td>\n",
       "      <td>7.878941</td>\n",
       "      <td>4.544331</td>\n",
       "      <td>4.577210</td>\n",
       "      <td>7.192090</td>\n",
       "      <td>7.930581</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.608715</td>\n",
       "      <td>0.695520</td>\n",
       "      <td>0.465277</td>\n",
       "      <td>0.498301</td>\n",
       "      <td>0.558794</td>\n",
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       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.554371</td>\n",
       "      <td>5.692802</td>\n",
       "      <td>6.687177</td>\n",
       "      <td>4.944407</td>\n",
       "      <td>5.559352</td>\n",
       "      <td>7.806414</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.528475</td>\n",
       "      <td>0.573556</td>\n",
       "      <td>0.488644</td>\n",
       "      <td>0.583152</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>85115</th>\n",
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       "      <td>9.603617</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.698613</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85116</th>\n",
       "      <td>5.394357</td>\n",
       "      <td>5.458367</td>\n",
       "      <td>6.871772</td>\n",
       "      <td>6.434966</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.517944</td>\n",
       "      <td>0.530936</td>\n",
       "      <td>0.525017</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85117</th>\n",
       "      <td>5.523418</td>\n",
       "      <td>4.459969</td>\n",
       "      <td>6.844032</td>\n",
       "      <td>6.013352</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.420614</td>\n",
       "      <td>0.527569</td>\n",
       "      <td>0.487704</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85118</th>\n",
       "      <td>7.215867</td>\n",
       "      <td>7.385239</td>\n",
       "      <td>10.605681</td>\n",
       "      <td>9.032009</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.712867</td>\n",
       "      <td>0.849905</td>\n",
       "      <td>0.757005</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>85119 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       column_euc_0  column_euc_1  column_euc_2  column_euc_3  column_euc_4  \\\n",
       "0          7.301305      6.476413      7.214262      6.917620      6.519748   \n",
       "1          5.717004      5.076439      6.340898      3.586788      4.826955   \n",
       "2          5.349530      4.854142      5.628676      2.882886      3.573071   \n",
       "3          6.255076      6.355414      7.878941      4.544331      4.577210   \n",
       "4          7.554371      5.692802      6.687177      4.944407      5.559352   \n",
       "...             ...           ...           ...           ...           ...   \n",
       "85114      4.760933      4.116004      6.391081      5.761912           NaN   \n",
       "85115      7.763542      7.098812      9.420061      9.603617           NaN   \n",
       "85116      5.394357      5.458367      6.871772      6.434966           NaN   \n",
       "85117      5.523418      4.459969      6.844032      6.013352           NaN   \n",
       "85118      7.215867      7.385239     10.605681      9.032009           NaN   \n",
       "\n",
       "       column_euc_5  column_euc_6  column_euc_7  column_euc_8  column_euc_9  \\\n",
       "0          3.641007      6.908043           NaN           NaN           NaN   \n",
       "1          7.294750      6.392016           NaN           NaN           NaN   \n",
       "2          6.292664      5.433945           NaN           NaN           NaN   \n",
       "3          7.192090      7.930581           NaN           NaN           NaN   \n",
       "4          7.806414      6.598026           NaN           NaN           NaN   \n",
       "...             ...           ...           ...           ...           ...   \n",
       "85114           NaN           NaN           NaN           NaN           NaN   \n",
       "85115           NaN           NaN           NaN           NaN           NaN   \n",
       "85116           NaN           NaN           NaN           NaN           NaN   \n",
       "85117           NaN           NaN           NaN           NaN           NaN   \n",
       "85118           NaN           NaN           NaN           NaN           NaN   \n",
       "\n",
       "       ...  column_cos_1  column_cos_2  column_cos_3  column_cos_4  \\\n",
       "0      ...      0.552745      0.574794      0.624309      0.619026   \n",
       "1      ...      0.519177      0.592157      0.395893      0.572930   \n",
       "2      ...      0.494006      0.521783      0.316489      0.421474   \n",
       "3      ...      0.608715      0.695520      0.465277      0.498301   \n",
       "4      ...      0.528475      0.573556      0.488644      0.583152   \n",
       "...    ...           ...           ...           ...           ...   \n",
       "85114  ...      0.382700      0.485660      0.461544           NaN   \n",
       "85115  ...      0.698613      0.762461      0.821555           NaN   \n",
       "85116  ...      0.517944      0.530936      0.525017           NaN   \n",
       "85117  ...      0.420614      0.527569      0.487704           NaN   \n",
       "85118  ...      0.712867      0.849905      0.757005           NaN   \n",
       "\n",
       "       column_cos_5  column_cos_6  column_cos_7  column_cos_8  column_cos_9  \\\n",
       "0          0.258351      0.574366           NaN           NaN           NaN   \n",
       "1          0.594844      0.632305           NaN           NaN           NaN   \n",
       "2          0.506190      0.533841           NaN           NaN           NaN   \n",
       "3          0.558794      0.736132           NaN           NaN           NaN   \n",
       "4          0.594557      0.594187           NaN           NaN           NaN   \n",
       "...             ...           ...           ...           ...           ...   \n",
       "85114           NaN           NaN           NaN           NaN           NaN   \n",
       "85115           NaN           NaN           NaN           NaN           NaN   \n",
       "85116           NaN           NaN           NaN           NaN           NaN   \n",
       "85117           NaN           NaN           NaN           NaN           NaN   \n",
       "85118           NaN           NaN           NaN           NaN           NaN   \n",
       "\n",
       "       target  \n",
       "0           5  \n",
       "1           2  \n",
       "2           3  \n",
       "3           4  \n",
       "4           1  \n",
       "...       ...  \n",
       "85114       1  \n",
       "85115       2  \n",
       "85116       2  \n",
       "85117       1  \n",
       "85118       0  \n",
       "\n",
       "[85119 rows x 21 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "column_euc_0    29.927914\n",
       "column_euc_1    40.290561\n",
       "column_euc_2    33.286404\n",
       "column_euc_3    34.140771\n",
       "column_euc_4    36.115491\n",
       "column_euc_5    35.623532\n",
       "column_euc_6    39.640774\n",
       "column_euc_7          NaN\n",
       "column_euc_8          NaN\n",
       "column_euc_9          NaN\n",
       "column_cos_0     1.491517\n",
       "column_cos_1     1.489619\n",
       "column_cos_2     1.455926\n",
       "column_cos_3     1.490736\n",
       "column_cos_4     1.415583\n",
       "column_cos_5     1.355459\n",
       "column_cos_6     1.418385\n",
       "column_cos_7          NaN\n",
       "column_cos_8          NaN\n",
       "column_cos_9          NaN\n",
       "target           9.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Finding Maximum for all the columns\n",
    "train1.apply(max, axis = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#As for all the sentences the number of array values in cosine similarity and euclidean distance array are not same so we\n",
    "#replace those values accordingly by 60 and 1\n",
    "subset1 = train1.iloc[:,:10].fillna(60)\n",
    "subset2 = train1.iloc[:,10:].fillna(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>column_euc_0</th>\n",
       "      <th>column_euc_1</th>\n",
       "      <th>column_euc_2</th>\n",
       "      <th>column_euc_3</th>\n",
       "      <th>column_euc_4</th>\n",
       "      <th>column_euc_5</th>\n",
       "      <th>column_euc_6</th>\n",
       "      <th>column_euc_7</th>\n",
       "      <th>column_euc_8</th>\n",
       "      <th>column_euc_9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.301305</td>\n",
       "      <td>6.476413</td>\n",
       "      <td>7.214262</td>\n",
       "      <td>6.917620</td>\n",
       "      <td>6.519748</td>\n",
       "      <td>3.641007</td>\n",
       "      <td>6.908043</td>\n",
       "      <td>60.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.717004</td>\n",
       "      <td>5.076439</td>\n",
       "      <td>6.340898</td>\n",
       "      <td>3.586788</td>\n",
       "      <td>4.826955</td>\n",
       "      <td>7.294750</td>\n",
       "      <td>6.392016</td>\n",
       "      <td>60.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.349530</td>\n",
       "      <td>4.854142</td>\n",
       "      <td>5.628676</td>\n",
       "      <td>2.882886</td>\n",
       "      <td>3.573071</td>\n",
       "      <td>6.292664</td>\n",
       "      <td>5.433945</td>\n",
       "      <td>60.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   column_euc_0  column_euc_1  column_euc_2  column_euc_3  column_euc_4  \\\n",
       "0      7.301305      6.476413      7.214262      6.917620      6.519748   \n",
       "1      5.717004      5.076439      6.340898      3.586788      4.826955   \n",
       "2      5.349530      4.854142      5.628676      2.882886      3.573071   \n",
       "\n",
       "   column_euc_5  column_euc_6  column_euc_7  column_euc_8  column_euc_9  \n",
       "0      3.641007      6.908043          60.0          60.0          60.0  \n",
       "1      7.294750      6.392016          60.0          60.0          60.0  \n",
       "2      6.292664      5.433945          60.0          60.0          60.0  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " subset1.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# After the update of NaN values in both euclidean and cosine subsets they are concatenated for final use\n",
    "train2 = pd.concat([subset1, subset2],axis=1, join_axes=[subset1.index])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "column_euc_0    29.927914\n",
       "column_euc_1    60.000000\n",
       "column_euc_2    60.000000\n",
       "column_euc_3    60.000000\n",
       "column_euc_4    60.000000\n",
       "column_euc_5    60.000000\n",
       "column_euc_6    60.000000\n",
       "column_euc_7    60.000000\n",
       "column_euc_8    60.000000\n",
       "column_euc_9    60.000000\n",
       "column_cos_0     1.491517\n",
       "column_cos_1     1.489619\n",
       "column_cos_2     1.455926\n",
       "column_cos_3     1.490736\n",
       "column_cos_4     1.415583\n",
       "column_cos_5     1.355459\n",
       "column_cos_6     1.418385\n",
       "column_cos_7     1.354246\n",
       "column_cos_8     1.315012\n",
       "column_cos_9     1.159472\n",
       "target           9.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Maximum for all columns\n",
    "train2.apply(max, axis = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Normalization of final train data between 0 to 1\n",
    "scaler = MinMaxScaler()\n",
    "X = scaler.fit_transform(train2.iloc[:,:-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(85119, 20)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape  #printing shape of X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.23388115, 0.10277595, 0.1112316 , ..., 0.71921987, 0.74207714,\n",
       "        0.84381084],\n",
       "       [0.18023799, 0.07930797, 0.09652653, ..., 0.71921987, 0.74207714,\n",
       "        0.84381084],\n",
       "       [0.1677956 , 0.07558157, 0.08453465, ..., 0.71921987, 0.74207714,\n",
       "        0.84381084],\n",
       "       ...,\n",
       "       [0.16931342, 0.0857103 , 0.10546501, ..., 0.71921987, 0.74207714,\n",
       "        0.84381084],\n",
       "       [0.17368332, 0.06897399, 0.10499793, ..., 0.71921987, 0.74207714,\n",
       "        0.84381084],\n",
       "       [0.23098827, 0.11801073, 0.16833388, ..., 0.71921987, 0.74207714,\n",
       "        0.84381084]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        5\n",
       "1        2\n",
       "2        3\n",
       "3        4\n",
       "4        1\n",
       "        ..\n",
       "85114    1\n",
       "85115    2\n",
       "85116    2\n",
       "85117    1\n",
       "85118    0\n",
       "Name: target, Length: 85119, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train1.iloc[:,-1]  #Printing last column of all rows"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Obtaining Eigen Values and Vectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#calculating eigen values and eigen vectors\n",
    "eigvals, eigvecs = np.linalg.eig(np.matmul(X.T,X)) #fetching eigen values and eigen vectors\n",
    "\n",
    "idx = eigvals.argsort()[::-1]   #Sorting the eigen values in descending order\n",
    "\n",
    "eigvals = eigvals[idx]  #Getting eigen values in that order\n",
    "\n",
    "eigvecs = eigvecs[:,idx]  #Fetching the eigen vectors corresponding to eigen values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20, 20)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.matmul(X.T,X).shape  #Displaying shape of matrix multiplication of X Transpose and X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([6.82719996e+05, 2.69746040e+04, 1.25780234e+04, 6.35226721e+03,\n",
       "       4.69533312e+03, 3.65588802e+03, 2.76153078e+03, 1.85492816e+03,\n",
       "       1.24731151e+03, 1.03511958e+03, 7.08657571e+02, 4.46083726e+02,\n",
       "       3.15441411e+02, 2.77861826e+02, 2.56116509e+02, 1.99118547e+02,\n",
       "       1.82047965e+02, 1.04305528e+02, 6.93401787e+01, 3.42316421e+01])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eigvals #Displaying eigen values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 6.93143340e-02,  4.40384176e-02, -5.71697628e-02,\n",
       "        -4.60286463e-02,  7.86862204e-02,  3.20841027e-03,\n",
       "        -1.31578012e-01,  1.50141754e-01,  3.53369469e-02,\n",
       "         1.56050849e-01, -1.60877628e-01, -8.87047165e-04,\n",
       "         2.98537964e-02,  1.61594223e-02,  6.01674787e-02,\n",
       "        -9.28056556e-01, -1.55344508e-01,  3.91988386e-03,\n",
       "         2.45299369e-03, -1.06887941e-02],\n",
       "       [ 4.33177706e-02, -2.12674931e-02, -1.13206060e-01,\n",
       "         7.27892948e-02,  1.55998821e-01, -1.76245008e-01,\n",
       "        -3.21386712e-02,  1.70006201e-01, -7.76755829e-01,\n",
       "        -2.27542320e-01, -1.13777166e-01, -1.85875251e-01,\n",
       "        -2.42610748e-01, -3.64104969e-01,  6.47918536e-03,\n",
       "        -1.11368453e-02, -1.64065350e-03, -2.54489850e-04,\n",
       "        -1.34752509e-03,  1.21274016e-03],\n",
       "       [ 6.92391377e-02, -1.84138391e-01, -3.49158152e-01,\n",
       "         3.74848458e-01,  3.40053068e-01, -5.50567252e-01,\n",
       "         2.71750119e-01, -2.09923960e-01,  1.82398663e-01,\n",
       "         1.01959324e-02, -1.11341373e-01, -1.97443682e-01,\n",
       "        -5.62247225e-02,  2.71087707e-01,  2.46963093e-02,\n",
       "        -1.29461171e-02, -3.52436917e-03,  1.69073416e-03,\n",
       "         1.71143655e-03, -9.26549625e-05],\n",
       "       [ 1.26333243e-01, -4.59498420e-01, -4.88984695e-01,\n",
       "         3.46438727e-01, -2.64667924e-01,  4.91095033e-01,\n",
       "        -1.47923556e-02, -5.02255115e-03, -5.89979785e-02,\n",
       "        -3.40374254e-03, -1.09420076e-01, -2.83221040e-02,\n",
       "         2.40932913e-01, -7.53541646e-02, -1.49247977e-01,\n",
       "        -5.97803903e-03,  1.20704200e-04,  2.62359682e-04,\n",
       "        -1.58949281e-03,  5.86534249e-04],\n",
       "       [ 2.02580496e-01, -5.89068509e-01, -2.20851077e-03,\n",
       "        -5.54580891e-01, -2.79774881e-01, -3.48638559e-01,\n",
       "        -1.13396163e-02, -3.96978133e-02, -3.19569764e-02,\n",
       "         2.15012179e-02, -9.93361361e-02,  8.66816004e-02,\n",
       "         8.01637601e-02, -2.82142242e-02,  2.67168121e-01,\n",
       "         3.48355967e-02, -6.46798401e-02,  7.08141554e-04,\n",
       "        -1.65248647e-03,  1.01643921e-03],\n",
       "       [ 2.65062204e-01, -3.91524575e-01,  4.65107205e-01,\n",
       "         3.38512777e-02,  5.16109289e-01,  3.06403828e-01,\n",
       "         2.94816593e-01,  7.03737236e-02, -2.42725239e-02,\n",
       "         3.63890838e-02, -7.17073280e-02,  1.24079942e-01,\n",
       "        -5.26701995e-02,  1.93437506e-02, -8.98979136e-03,\n",
       "        -4.52357855e-02,  2.76756402e-01,  5.03519865e-02,\n",
       "        -6.81701547e-05, -1.60495473e-03],\n",
       "       [ 3.05746300e-01, -9.86849405e-02,  4.08917483e-01,\n",
       "         4.40147408e-01, -1.73046590e-01, -1.81366653e-01,\n",
       "        -5.34582230e-01, -2.91342830e-01, -6.90368213e-02,\n",
       "         8.54779317e-02, -3.04062082e-02,  9.11637517e-02,\n",
       "        -5.94084369e-02,  2.33940126e-02, -6.06730808e-02,\n",
       "         1.27429438e-03, -3.85444212e-02, -2.67062525e-01,\n",
       "         2.02087148e-02,  7.45460394e-03],\n",
       "       [ 3.27774222e-01,  1.02656530e-01,  1.68766958e-01,\n",
       "         2.54587554e-01, -3.93887140e-01, -1.80434211e-01,\n",
       "         3.32536141e-01,  5.62119949e-01,  1.44029726e-01,\n",
       "        -2.09371260e-01, -7.61519805e-02,  9.48903217e-02,\n",
       "        -6.28046029e-02,  2.36939580e-02, -7.28414712e-02,\n",
       "         9.02144887e-03, -9.71430885e-02,  7.77837804e-02,\n",
       "        -2.63996305e-01,  2.73223689e-03],\n",
       "       [ 3.39132720e-01,  2.05910602e-01, -3.22840783e-02,\n",
       "        -1.90323046e-02, -1.80131863e-01,  1.56037868e-02,\n",
       "         3.71339939e-01, -1.92077981e-01, -2.90880760e-01,\n",
       "         6.47169149e-01,  1.90631557e-01, -9.61612739e-03,\n",
       "        -3.60923985e-02,  1.21662724e-02, -4.79511770e-02,\n",
       "         1.26982223e-02, -8.78926024e-02,  1.10605853e-01,\n",
       "         1.39196373e-01,  2.24920089e-01],\n",
       "       [ 3.44329067e-01,  2.43659150e-01, -1.38189178e-01,\n",
       "        -1.60795488e-01,  3.11279589e-02,  1.09561623e-01,\n",
       "         7.23498836e-02, -3.46425071e-01,  3.38316186e-03,\n",
       "        -2.97451262e-01, -3.91499306e-01,  3.67594474e-01,\n",
       "        -1.75807605e-01,  5.53742188e-02, -1.62928326e-01,\n",
       "         3.04664663e-02, -1.96128982e-01,  1.56046271e-01,\n",
       "         2.01551937e-01, -3.11647195e-01],\n",
       "       [ 1.27181796e-01,  1.01469444e-01, -1.09476080e-01,\n",
       "        -9.82448918e-02,  1.61960531e-01,  1.62218557e-02,\n",
       "        -3.00511189e-01,  3.51107051e-01,  1.76644466e-01,\n",
       "         4.54861841e-01, -5.75888740e-01, -1.78933582e-01,\n",
       "        -8.43018421e-02, -6.81444776e-02, -2.79410527e-02,\n",
       "         3.07565657e-01,  5.36889760e-02, -1.59293904e-03,\n",
       "        -1.60376153e-03,  5.57872180e-03],\n",
       "       [ 1.29253005e-01,  8.27519184e-02, -1.36795170e-01,\n",
       "        -5.05473349e-02,  1.97939954e-01, -6.53613925e-02,\n",
       "        -2.21711914e-01,  2.95787812e-01, -3.51406361e-01,\n",
       "        -1.44647982e-02,  1.38052352e-01,  3.05684035e-01,\n",
       "         4.14742616e-01,  5.96662977e-01, -2.43396796e-02,\n",
       "         9.98628673e-02,  1.59601558e-02,  7.46367424e-04,\n",
       "         5.46985014e-03, -3.69929319e-03],\n",
       "       [ 1.37775250e-01,  2.19402175e-02, -2.26821204e-01,\n",
       "         6.77804753e-02,  2.65704617e-01, -2.07547242e-01,\n",
       "        -9.14936290e-02,  1.02743632e-01,  2.07255305e-01,\n",
       "         1.03764009e-01,  3.02367368e-01,  5.10010026e-01,\n",
       "         1.56667549e-01, -5.98653424e-01, -4.79202720e-02,\n",
       "         3.58061924e-02,  8.63229190e-03, -4.68743068e-03,\n",
       "        -5.33722996e-03,  6.16461325e-04],\n",
       "       [ 1.59102369e-01, -6.92562582e-02, -2.54587819e-01,\n",
       "         3.41076148e-02,  1.33390323e-02,  2.02323177e-01,\n",
       "        -1.86511829e-01,  1.43563777e-01,  9.04586779e-02,\n",
       "         8.10704445e-03,  3.32242000e-01,  1.06913615e-01,\n",
       "        -6.82856532e-01,  2.05154591e-01,  4.09978704e-01,\n",
       "         2.84996886e-02,  5.47768359e-03, -8.80014912e-04,\n",
       "         5.25151154e-03, -1.88686551e-03],\n",
       "       [ 1.94193897e-01, -1.12481258e-01, -8.21366037e-02,\n",
       "        -2.83902605e-01,  5.25234123e-03, -8.85272440e-02,\n",
       "        -1.48356339e-01,  6.38563632e-02,  1.00180280e-01,\n",
       "        -5.99266480e-02,  2.93499652e-01, -2.40916243e-01,\n",
       "        -2.18212180e-01,  7.49965543e-02, -7.61760917e-01,\n",
       "        -8.13306285e-02,  1.74213223e-01, -3.57720782e-03,\n",
       "         4.62621552e-03, -2.80440388e-03],\n",
       "       [ 2.24992433e-01, -4.14095198e-02,  8.75339229e-02,\n",
       "        -6.64023369e-02,  2.69877045e-01,  1.50820300e-01,\n",
       "        -1.62697903e-02,  5.47167445e-02,  9.09008029e-02,\n",
       "        -1.02355498e-01,  1.95670081e-01, -3.39656855e-01,\n",
       "         1.43813185e-01, -5.51238059e-02,  3.55998670e-02,\n",
       "         1.35317876e-01, -7.76674652e-01, -1.33469423e-01,\n",
       "        -1.60941479e-04,  4.82204686e-03],\n",
       "       [ 2.28857563e-01,  6.22261464e-02,  6.08527942e-02,\n",
       "         6.84474487e-02,  7.64970585e-03, -2.11013923e-02,\n",
       "        -2.59711749e-01, -9.72934606e-02,  5.87773799e-02,\n",
       "        -1.01030721e-01,  1.21100922e-01, -2.75982877e-01,\n",
       "         1.81923026e-01, -6.88165503e-02,  1.80911722e-01,\n",
       "        -4.21221703e-03,  1.38870844e-01,  8.12636944e-01,\n",
       "        -5.14074996e-02, -2.37675911e-02],\n",
       "       [ 2.42992945e-01,  1.30095534e-01, -1.28184769e-02,\n",
       "         1.13498690e-02, -7.81230514e-02, -2.10198961e-02,\n",
       "         5.42262276e-02,  1.69256091e-01,  1.16521457e-01,\n",
       "        -1.92605309e-01,  6.60141674e-02, -2.29435325e-01,\n",
       "         1.72421574e-01, -6.76835208e-02,  1.90725698e-01,\n",
       "        -3.05143194e-02,  2.79151761e-01, -2.48192001e-01,\n",
       "         7.52717137e-01, -1.43199798e-02],\n",
       "       [ 2.54688982e-01,  1.70810234e-01, -8.45857733e-02,\n",
       "        -8.71179204e-02, -3.63154623e-03,  5.10275300e-02,\n",
       "         6.99773205e-02, -1.14439836e-01, -3.98711571e-02,\n",
       "         9.20624545e-02,  1.24550663e-01, -2.21479849e-01,\n",
       "         1.50126133e-01, -5.27149404e-02,  1.71910074e-01,\n",
       "        -2.25746989e-02,  2.57519677e-01, -3.06663564e-01,\n",
       "        -4.31001972e-01, -6.28068762e-01],\n",
       "       [ 2.91513361e-01,  2.08474788e-01, -1.35436998e-01,\n",
       "        -1.52335456e-01,  7.35648726e-02,  9.42257547e-02,\n",
       "        -2.95461323e-02, -1.99887052e-01,  6.44943526e-02,\n",
       "        -2.75198249e-01, -1.18220390e-01, -4.41836376e-02,\n",
       "         8.08450440e-02, -2.49590767e-02,  1.10471151e-01,\n",
       "        -3.00629427e-02,  1.93954923e-01, -2.22745772e-01,\n",
       "        -3.38798198e-01,  6.75858022e-01]])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eigvecs #Displaying eigen vectors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculating PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Function for normalisation\n",
    "def normalize(X):\n",
    "    mu = np.mean(X)  #Finding mean of X\n",
    "    std = np.std(X, axis=0)  #Finding standard deviation of X\n",
    "    std_filled = std.copy()  #Storing std in a new variable\n",
    "    std_filled[std==0] = 1.  #Placing 1 where value is 0 in std\n",
    "    Xbar =  (X-mu)/std_filled  #Calculating normalised X -> Xbar        \n",
    "    return Xbar, mu, std   #Returns Xbar, mean and standard deviation\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The covariance matrix can be calculated as :\n",
    "<img src = \"cov.png\">\n",
    "Matrix after reducing the dimensions can be formed as :\n",
    "<img src = \"compress.png\">\n",
    "Projection matrix can be calculated as : \n",
    "<img src = \"proj.png\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Xbar,mu,std=normalize(X)  #Normalising the matrix X by calling normalise function\n",
    "covariance=np.matmul(Xbar.T,Xbar)  #Finding covariance matrix by multiplying normalised matrix and its transpose\n",
    "S=covariance  #Storing in variable S\n",
    "eigvals,eigvecs=np.linalg.eig(S)  #Calculating eigen values and vectors of covariance matrix\n",
    "idx = eigvals.argsort()[::-1]   #Sorting the eigen values in descending order\n",
    "eigvals = eigvals[idx]  #Getting eigen values in that order\n",
    "eigvecs = eigvecs[:,idx]  #Fetching the eigen vectors corresponding to eigen values\n",
    "B = np.stack(eigvecs[:,:15])  #Selecting eigen vectors only upto 15 features\n",
    "P = np.matmul(B,B.T)  #Matrix multiplication of B and B transpose\n",
    "X_reconstruct=np.matmul(P,X.T)  #Calculating reconstruction matrix by (X_Transpose * P)\n",
    "X_reconstruct=X_reconstruct.T  #Taking transpose of reconstructed matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "B.shape  #Displaying shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.24158254, 0.10599513, 0.1244064 , ..., 0.81955732, 0.79702055,\n",
       "        0.88208912],\n",
       "       [0.19165141, 0.0840962 , 0.11565174, ..., 0.84154205, 0.8239859 ,\n",
       "        0.90735149],\n",
       "       [0.17831808, 0.08003549, 0.10139699, ..., 0.83145445, 0.81476258,\n",
       "        0.90213154],\n",
       "       ...,\n",
       "       [0.16671408, 0.08491819, 0.09548483, ..., 0.69922149, 0.69996569,\n",
       "        0.82594423],\n",
       "       [0.171072  , 0.06815381, 0.09532167, ..., 0.70030721, 0.70146704,\n",
       "        0.82603383],\n",
       "       [0.22772874, 0.11697466, 0.15639542, ..., 0.69501229, 0.69287508,\n",
       "        0.82119588]])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_reconstruct  #Displaying reconstructed X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(85119, 20)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_reconstruct.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PCA vs Variance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "std=np.std(B,axis=0)  #standard deviation\n",
    "var=np.square(std)  #variance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.04999991, 0.00506238, 0.04707097, 0.04843382, 0.04975318,\n",
       "       0.04989952, 0.04999965, 0.04999718, 0.04998623, 0.04999498,\n",
       "       0.04999799, 0.04985184, 0.04996196, 0.0499981 , 0.04999549])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "var"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "no_of_comp=[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15] #,6,7,8,9,10,11,12,13,14,15,16,17"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.scatter(no_of_comp,var)  #Plotting variance along number of components\n",
    "plt.title('PCA and variance')  #title\n",
    "plt.ylabel('variance')  # Y axis label\n",
    "plt.xlabel('PCA')  # X axis label\n",
    "plt.show()  #Displaying figure"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Mean Square error "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.06693887014469453"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.square(X_reconstruct - X).sum(axis=1).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting eigen values vs indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "#plt.plot(idx,eigvals)\n",
    "plt.scatter(idx,eigvals)\n",
    "plt.title('Eigen values vs indices')\n",
    "plt.ylabel('Eigen values')\n",
    "plt.xlabel('indices')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the training data and testing data with 80:20 ratio\n",
    "train_x, test_x, train_y, test_y = train_test_split(X,\n",
    "train1.iloc[:,-1], train_size=0.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>column_euc_0</th>\n",
       "      <th>column_euc_1</th>\n",
       "      <th>column_euc_2</th>\n",
       "      <th>column_euc_3</th>\n",
       "      <th>column_euc_4</th>\n",
       "      <th>column_euc_5</th>\n",
       "      <th>column_euc_6</th>\n",
       "      <th>column_euc_7</th>\n",
       "      <th>column_euc_8</th>\n",
       "      <th>column_euc_9</th>\n",
       "      <th>...</th>\n",
       "      <th>column_cos_1</th>\n",
       "      <th>column_cos_2</th>\n",
       "      <th>column_cos_3</th>\n",
       "      <th>column_cos_4</th>\n",
       "      <th>column_cos_5</th>\n",
       "      <th>column_cos_6</th>\n",
       "      <th>column_cos_7</th>\n",
       "      <th>column_cos_8</th>\n",
       "      <th>column_cos_9</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.301305</td>\n",
       "      <td>6.476413</td>\n",
       "      <td>7.214262</td>\n",
       "      <td>6.917620</td>\n",
       "      <td>6.519748</td>\n",
       "      <td>3.641007</td>\n",
       "      <td>6.908043</td>\n",
       "      <td>60.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.552745</td>\n",
       "      <td>0.574794</td>\n",
       "      <td>0.624309</td>\n",
       "      <td>0.619026</td>\n",
       "      <td>0.258351</td>\n",
       "      <td>0.574366</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.717004</td>\n",
       "      <td>5.076439</td>\n",
       "      <td>6.340898</td>\n",
       "      <td>3.586788</td>\n",
       "      <td>4.826955</td>\n",
       "      <td>7.294750</td>\n",
       "      <td>6.392016</td>\n",
       "      <td>60.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.519177</td>\n",
       "      <td>0.592157</td>\n",
       "      <td>0.395893</td>\n",
       "      <td>0.572930</td>\n",
       "      <td>0.594844</td>\n",
       "      <td>0.632305</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.349530</td>\n",
       "      <td>4.854142</td>\n",
       "      <td>5.628676</td>\n",
       "      <td>2.882886</td>\n",
       "      <td>3.573071</td>\n",
       "      <td>6.292664</td>\n",
       "      <td>5.433945</td>\n",
       "      <td>60.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.494006</td>\n",
       "      <td>0.521783</td>\n",
       "      <td>0.316489</td>\n",
       "      <td>0.421474</td>\n",
       "      <td>0.506190</td>\n",
       "      <td>0.533841</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6.255076</td>\n",
       "      <td>6.355414</td>\n",
       "      <td>7.878941</td>\n",
       "      <td>4.544331</td>\n",
       "      <td>4.577210</td>\n",
       "      <td>7.192090</td>\n",
       "      <td>7.930581</td>\n",
       "      <td>60.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.608715</td>\n",
       "      <td>0.695520</td>\n",
       "      <td>0.465277</td>\n",
       "      <td>0.498301</td>\n",
       "      <td>0.558794</td>\n",
       "      <td>0.736132</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.554371</td>\n",
       "      <td>5.692802</td>\n",
       "      <td>6.687177</td>\n",
       "      <td>4.944407</td>\n",
       "      <td>5.559352</td>\n",
       "      <td>7.806414</td>\n",
       "      <td>6.598026</td>\n",
       "      <td>60.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.528475</td>\n",
       "      <td>0.573556</td>\n",
       "      <td>0.488644</td>\n",
       "      <td>0.583152</td>\n",
       "      <td>0.594557</td>\n",
       "      <td>0.594187</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   column_euc_0  column_euc_1  column_euc_2  column_euc_3  column_euc_4  \\\n",
       "0      7.301305      6.476413      7.214262      6.917620      6.519748   \n",
       "1      5.717004      5.076439      6.340898      3.586788      4.826955   \n",
       "2      5.349530      4.854142      5.628676      2.882886      3.573071   \n",
       "3      6.255076      6.355414      7.878941      4.544331      4.577210   \n",
       "4      7.554371      5.692802      6.687177      4.944407      5.559352   \n",
       "\n",
       "   column_euc_5  column_euc_6  column_euc_7  column_euc_8  column_euc_9  ...  \\\n",
       "0      3.641007      6.908043          60.0          60.0          60.0  ...   \n",
       "1      7.294750      6.392016          60.0          60.0          60.0  ...   \n",
       "2      6.292664      5.433945          60.0          60.0          60.0  ...   \n",
       "3      7.192090      7.930581          60.0          60.0          60.0  ...   \n",
       "4      7.806414      6.598026          60.0          60.0          60.0  ...   \n",
       "\n",
       "   column_cos_1  column_cos_2  column_cos_3  column_cos_4  column_cos_5  \\\n",
       "0      0.552745      0.574794      0.624309      0.619026      0.258351   \n",
       "1      0.519177      0.592157      0.395893      0.572930      0.594844   \n",
       "2      0.494006      0.521783      0.316489      0.421474      0.506190   \n",
       "3      0.608715      0.695520      0.465277      0.498301      0.558794   \n",
       "4      0.528475      0.573556      0.488644      0.583152      0.594557   \n",
       "\n",
       "   column_cos_6  column_cos_7  column_cos_8  column_cos_9  target  \n",
       "0      0.574366           1.0           1.0           1.0       5  \n",
       "1      0.632305           1.0           1.0           1.0       2  \n",
       "2      0.533841           1.0           1.0           1.0       3  \n",
       "3      0.736132           1.0           1.0           1.0       4  \n",
       "4      0.594187           1.0           1.0           1.0       1  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        0.683557\n",
       "1        0.662499\n",
       "2        0.616461\n",
       "3        0.668506\n",
       "4        0.779275\n",
       "           ...   \n",
       "85114    0.419913\n",
       "85115    0.725595\n",
       "85116    0.484370\n",
       "85117    0.495026\n",
       "85118    0.660581\n",
       "Name: column_cos_0, Length: 85119, dtype: float64"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train2.iloc[:,10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Applying Logistic Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hypothesis for logistic regression :\n",
    "<img src=\"hypothesis.png\">\n",
    "It can be written as :\n",
    "<img src = \"hypo.png\">\n",
    "Cost function is defined as :\n",
    "<img src = \"lr2.png\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Multinomial Logistic regression Train Accuracy :  0.627021073500257\n",
      "Multinomial Logistic regression Test Accuracy :  0.621828007518797\n"
     ]
    }
   ],
   "source": [
    "#Applying Logistic Regression using sklearn library\n",
    "mul_lr = linear_model.LogisticRegression(multi_class='multinomial', solver='saga',max_iter=200)\n",
    "mul_lr.fit(train_x, train_y)\n",
    "\n",
    "print(\"Multinomial Logistic regression Train Accuracy : \", metrics.accuracy_score(train_y, mul_lr.predict(train_x)))\n",
    "print(\"Multinomial Logistic regression Test Accuracy : \", metrics.accuracy_score(test_y, mul_lr.predict(test_x)))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Applying Logistic Regression Using Regularization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cost function for logistic regression with regularization:\n",
    "<img src = \"regularisation.png\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Multinomial Logistic regression Train Accuracy :  0.6199133563404068\n",
      "Multinomial Logistic regression Test Accuracy :  0.6188322368421053\n"
     ]
    }
   ],
   "source": [
    "#Applying Logistic Regression using sklearn library L2 Regularization\n",
    "mul_lr = linear_model.LogisticRegression(multi_class='multinomial', solver='saga',max_iter=200,penalty='l2')\n",
    "mul_lr.fit(train_x, train_y)\n",
    "\n",
    "print(\"Multinomial Logistic regression Train Accuracy : \", metrics.accuracy_score(train_y, mul_lr.predict(train_x)))\n",
    "print(\"Multinomial Logistic regression Test Accuracy : \", metrics.accuracy_score(test_y, mul_lr.predict(test_x)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Plot two graphs 'Accuracy v/s Epoch' and 'Loss v/s Epoch'\n",
    "plt.plot(history.history['acc'])\n",
    "plt.plot(history.history['val_acc'])\n",
    "plt.title('Model accuracy')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['Test', 'Train'], loc='upper left')\n",
    "plt.show()\n",
    "\n",
    "# Plot training & validation loss values\n",
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('Model loss')\n",
    "plt.ylabel('Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['Test', 'Train'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {
    "A%20%281%29.png": {
     "image/png": 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"
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![L%20%281%29.png](attachment:L%20%281%29.png)![A%20%281%29.png](attachment:A%20%281%29.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "y=mul_lr.predict(train_x) #Predicting values of y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_y=train_y.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_y=train_y.to_numpy() #Converting to numpy array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Counting number of unmatched samples\n",
    "count=0\n",
    "for i in range(68095):\n",
    "    if y[i]!=train_y[i]:  #Condition for inequality\n",
    "        count=count+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3800866436595932"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#loss\n",
    "(count)/68095"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.2268742198399294"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#mean square error\n",
    "(np.square(y - train_y).sum())/68095"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6199133563404068"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#fscore\n",
    "from sklearn.metrics import f1_score\n",
    "f1_score(train_y, y, average='micro')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import RandomizedSearchCV, GridSearchCV"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center> Mathematics for Random Forest : <br> </center> <br>\n",
    "The importance of each feature on a decision tree is given as :\n",
    "<img src = \"feature.png\">\n",
    "where, \n",
    "<img src = \"fini.png\">\n",
    "Normalising of features :\n",
    "<img src = \"normfi.png\">\n",
    "The final feature importance is computed as :\n",
    "<img src = \"rffi.png\">\n",
    "<center> T = total number of trees </center>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Multinomial Logistic regression Train Accuracy :  0.761715250752625\n",
      "Multinomial Logistic regression Test Accuracy :  0.6051456766917294\n"
     ]
    }
   ],
   "source": [
    "rf = RandomForestClassifier(min_samples_leaf=3, n_estimators=5)\n",
    "rf.fit(train_x, train_y)\n",
    "\n",
    "print(\"Random Forest Train Accuracy : \", metrics.accuracy_score(train_y, rf.predict(train_x)))\n",
    "print(\"Random Forest Test Accuracy \", metrics.accuracy_score(test_y, rf.predict(test_x)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## XG BOOST Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xgboost as xgb\n",
    "from xgboost import plot_tree"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center> Mathematics behind XGBoost : <br> </center>\n",
    "Need for taylor approximation : <br> <br>\n",
    "The original objective function of XGBoost should be converted to a function in the Euclidean domain, in order to be able to use traditional optimization techniques. <br> <br>\n",
    "Taylor Approximation:\n",
    "<img src = \"taylor.png\"> <br>\n",
    "By the above formulation, the error will be reduced for each sequential generation of tree since the boosting will produce trees based on previous errors. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Multinomial Logistic regression Train Accuracy :  0.6661428886114986\n",
      "Multinomial Logistic regression Test Accuracy :  0.6524906015037594\n"
     ]
    }
   ],
   "source": [
    "xg = xgb.XGBClassifier(max_depth=3)\n",
    "xg.fit(train_x, train_y)\n",
    "\n",
    "print(\"XgBoost Train Accuracy : \", metrics.accuracy_score(train_y, xg.predict(train_x)))\n",
    "print(\"XgBoost Test Accuracy : \", metrics.accuracy_score(test_y, xg.predict(test_x)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 10800x7200 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "xgb.plot_tree(xg, num_trees=2)\n",
    "fig = matplotlib.pyplot.gcf()\n",
    "fig.set_size_inches(150, 100)\n",
    "fig.savefig('tree.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Support Vector Machine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import svm  #Importing module for svm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf=svm.SVC(kernel='linear', random_state=1)  #Choosing linear kernel and random_state as 1 for svm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1.0, break_ties=False, cache_size=200, class_weight=None, coef0=0.0,\n",
       "    decision_function_shape='ovr', degree=3, gamma='scale', kernel='linear',\n",
       "    max_iter=-1, probability=False, random_state=1, shrinking=True, tol=0.001,\n",
       "    verbose=False)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(train_x,train_y)  #Training on svm"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center> Problem formulation </center> <br>\n",
    "Equation of two-dimensional hyperplane is given as : <br>\n",
    "<img src = \"form.png\">\n",
    "<img src = \"formhn1.png\">\n",
    "<img src = \"formhn2.png\">\n",
    "Two hyperplanes passing through support vectors of class +1 and -1 can be given as : <br>\n",
    "<img src = \"formh1.png\">\n",
    "The equation of margin is given as : <br>\n",
    "<img src = \"formhm.png\">\n",
    "The parameters w and b will keep updating till an optimal hyperplane is obtained which will maximise the margin. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVM train Accuracy :  0.6212937807474851\n",
      "SVM test Accuracy :  0.6214755639097744\n"
     ]
    }
   ],
   "source": [
    "print(\"SVM train Accuracy : \", metrics.accuracy_score(train_y, clf.predict(train_x)))\n",
    "print(\"SVM test Accuracy : \", metrics.accuracy_score(test_y, clf.predict(test_x)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(52295, 20)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.support_vectors_.shape #shape of support vectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  440,   663,  1135, ..., 65574, 66581, 68051], dtype=int32)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.support_ ##indices of support vectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVM train Accuracy :  0.6290476540127763\n",
      "SVM test Accuracy :  0.6272908834586466\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "clf = SVC(kernel='poly', C=10000, gamma=0.01, random_state=1) #when c=50 accuracy-around 49\n",
    "clf.fit(train_x,train_y)\n",
    "print(\"SVM train Accuracy : \", metrics.accuracy_score(train_y, clf.predict(train_x)))\n",
    "print(\"SVM test Accuracy : \", metrics.accuracy_score(test_y, clf.predict(test_x)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVM train Accuracy :  0.6704750715911594\n",
      "SVM test Accuracy :  0.6630639097744361\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "clf = SVC(kernel='rbf', C=10000, gamma=0.01, random_state=1)\n",
    "clf.fit(train_x,train_y)\n",
    "print(\"SVM train Accuracy : \", metrics.accuracy_score(train_y, clf.predict(train_x)))\n",
    "print(\"SVM test Accuracy : \", metrics.accuracy_score(test_y, clf.predict(test_x)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total length to check for best model and parameters:  57\n",
      "Best Parameters:  {'C': 1000, 'gamma': 0.1, 'kernel': 'rbf'}\n",
      "Best Estimator:  SVC(C=1000, break_ties=False, cache_size=200, class_weight=None, coef0=0.0,\n",
      "    decision_function_shape='ovr', degree=3, gamma=0.1, kernel='rbf',\n",
      "    max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
      "    tol=0.001, verbose=False)\n",
      "Best Score:  0.43\n"
     ]
    }
   ],
   "source": [
    "#Importing modules from sklearn for svm\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import ParameterGrid\n",
    "\n",
    "#Defining a parameter grid with kernels, regularisation parameters and kernel coefficients\n",
    "param_grid = [{'kernel': ['linear']}, {'kernel': ['poly', 'rbf'], 'C': [0.1, 1,100, 1000],\n",
    "'gamma': [0.0001, 0.001, 0.005, 0.1, 1, 3, 5]}]\n",
    "\n",
    "#Printing to search for best model and corresponding parameters\n",
    "print(\"Total length to check for best model and parameters: \",len(list(ParameterGrid(param_grid))))\n",
    "\n",
    "#Performing grid search to look for the best tuning hyperparameters\n",
    "gsc = GridSearchCV(SVC(),param_grid,scoring='accuracy',cv=5)\n",
    "grid_result = gsc.fit(train_x, train_y)  #Trainging with the obtained parameters\n",
    "\n",
    "#Printing Best values of the parameters\n",
    "print(\"Best Parameters: \", grid_result.best_params_)\n",
    "print(\"Best Estimator: \", grid_result.best_estimator_)\n",
    "print(\"Best Score: \", grid_result.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVM train Accuracy :  0.515265437991042\n",
      "SVM test Accuracy :  0.5118068609022557\n"
     ]
    }
   ],
   "source": [
    "print(\"SVM train Accuracy : \", metrics.accuracy_score(train_y, grid_result.predict(train_x)))\n",
    "print(\"SVM test Accuracy : \", metrics.accuracy_score(test_y, grid_result.predict(test_x)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gaussian Mixture Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt \n",
    "from pandas import DataFrame \n",
    "from sklearn import datasets \n",
    "from sklearn.mixture import GaussianMixture "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "d1 = pd.DataFrame(X[:,0]) #euclidean distance column 1\n",
    "d2 = pd.DataFrame(X[:,10]) #cosine similarity column 1\n",
    "\n",
    "d = pd.concat([d1, d2],axis=1, join_axes=[d1.index])\n",
    "# plot the data \n",
    "#temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "gmm = GaussianMixture(n_components = 10) \n",
    "gmm.fit(d) \n",
    "\n",
    "# Assign a label to each sample \n",
    "labels = gmm.predict(d) \n",
    "d['labels']= labels \n",
    "d0 = d[d['labels']== 0] \n",
    "d1 = d[d['labels']== 1] \n",
    "d2 = d[d['labels']== 2]\n",
    "d3 = d[d['labels']== 3]\n",
    "d4 = d[d['labels']== 4]\n",
    "d5 = d[d['labels']== 5]\n",
    "d6 = d[d['labels']== 6]\n",
    "d7 = d[d['labels']== 7]\n",
    "d8 = d[d['labels']== 8]\n",
    "d9 = d[d['labels']== 9]\n",
    "\n",
    "\n",
    "  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot three clusters in same plot \n",
    "plt.scatter(d0.iloc[:,0], d0.iloc[:,1], c ='red' ,marker='*') \n",
    "plt.scatter(d1.iloc[:,0], d1.iloc[:,1], c ='yellow',marker='s') \n",
    "plt.scatter(d2.iloc[:,0], d2.iloc[:,1], c ='green',marker='p') \n",
    "plt.scatter(d3.iloc[:,0], d3.iloc[:,1], c ='blue') \n",
    "plt.scatter(d4.iloc[:,0], d4.iloc[:,1], c ='black') \n",
    "plt.scatter(d5.iloc[:,0], d5.iloc[:,1], c ='magenta') \n",
    "plt.scatter(d6.iloc[:,0], d6.iloc[:,1], c ='cyan') \n",
    "plt.scatter(d7.iloc[:,0], d7.iloc[:,1], c ='red',marker='+')\n",
    "plt.scatter(d8.iloc[:,0], d8.iloc[:,1], c ='yellow',marker='1') \n",
    "plt.scatter(d9.iloc[:,0], d9.iloc[:,1], c ='green',marker='2') \n",
    "plt.xlabel('Euclidean Distance') \n",
    "# frequency label \n",
    "plt.ylabel('Cosine Similarity') \n",
    "# plot title \n",
    "plt.title('Gaussian Mixture Model') \n",
    "# function to show the plot \n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "distributions transformed\n"
     ]
    }
   ],
   "source": [
    "qt = QuantileTransformer(n_quantiles = len(train2.iloc[:,0]), output_distribution = 'normal')\n",
    "\n",
    "## transforming above distributions to Normal distribution ##\n",
    "X = train2.iloc[:,:-1].to_numpy()\n",
    "#Y = train2.iloc[:,1].to_numpy().reshape(-1,1)\n",
    "\n",
    "X = qt.fit_transform(X)\n",
    "#Y = qt.fit_transform(Y)\n",
    "print('distributions transformed')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sentiment Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Error loading vader_lexicon: <urlopen error [SSL:\n",
      "[nltk_data]     CERTIFICATE_VERIFY_FAILED] certificate verify failed:\n",
      "[nltk_data]     unable to get local issuer certificate (_ssl.c:1108)>\n"
     ]
    }
   ],
   "source": [
    "#Importing nltk library for Valence Aware Dictionary and Sentiment Reasoner (VADER)\n",
    "\n",
    "import nltk\n",
    "nltk.download('vader_lexicon')  #Vader is a tool for sentiment analysis\n",
    "from nltk.sentiment.vader import SentimentIntensityAnalyzer\n",
    "sid = SentimentIntensityAnalyzer()  #Initialising sid with SentimentIntensityAnalyzer\n",
    "a = [0]*(len(paras)+1)  \n",
    "b = [0]*(len(paras)+1)\n",
    "for i in range(0,len(paras)-1):\n",
    "    a[i] = sid.polarity_scores(paras[i])  #Polarity indices are obtained using polarity_scores method\n",
    "    b[i] = a[i][\"compound\"]  #The Compound score is a metric that calculates the sum of all the ratings which have been normalized between -1 and +1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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dLcDHa9mPmZnVR6U9iwkR8TTwMeCiiDiA7NyDmZmNApUmizGS2oAjgZ80MB4zMyuhSpPF14DrgTUR8VtJuwOrGxeWmZmVSaWXzm6IiJdPakfEQ5L+tUExmZlZyVTas/hOhWVmZjYCDdqzkPQu4N3ArpLyVz69kewGgGZmNgoUDUONBXZM6+2UK3+aV75AZ2ZmI9ygySIifgn8UtIFEbG2STGZmVnJVHqCewdJy4AZ+W0i4oONCMrMzMql0mRxBXAOcC6wrXHhmJlZGVWaLLZGxPcbGomZmZVWpZfO/ljSpyW1Sdq599HQyMzMrDQq7Vn03iL887myAHavbzhmZlZGFSWLiJjZ6EDMzKy8Kr1F+dH9lUfERfUNx8zMyqjSYah35qbHkf263V2Ak4WZ2ShQ6TDUSfl5SROBSxsSkZmZlU6tv8H9HODzGGZmo0Sl5yx+THb1E2Q3ENwTuLxRQZmZWblUes7in3PTW4G1EdHVgHjMzKyEKhqGSjcU/D3ZnWcnAS82MigzMyuXipKFpCOB3wAfJ/sd7tsl+RblZmajRKXDUKcD74yIjQCSdgV+AVzZqMDMzKw8Kr0a6nW9iSLZXMW2ryFpoqQrJf1e0ipJ70r3m7pB0ur0d1JaV5K+LWmNpHsk7Vvrfs3MrDaVHvCvk3S9pGMkHQP8FLh2CPs9G7guIt4KzAZWAUuAGyNiFnBjmgc4DJiVHosB3/3WzKzJin6Dew9gt4j4vKSPAQelRbcCF9eyQ0kTgPcBxwBExIvAi5LmAe9Pq10I3Ax8EZgHXBQRAdyWeiVtEbGhlv2bmVn1is5ZfAs4DSAifgT8CEDS29Oyv6phnzOBx4EfSJoN3AmcTJaUehPAo8BuaXoKsC63fVcqe1WykLSYrOdBW1sbnZ2dNYQGPT09NW/bSI6remWNbfzYMcyePrl0sZW1vcoaF5Q3tkbEVZQsdouIe/sWRsS9kmYMYZ/7AidFxO2SzuaVIafe+kNS9Lv1ACJiGbAMoKOjI9rb22sKrru7m1q3bSTHVb2yxta58gHuXruJUxaVK7aytldZ44LyxtaIuIrOWUwcZNnra9xnF9AVEben+SvJksdjktoA0t/eE+rrgWm57aemMjMza5KiZHGHpP/Zt1DSIrLho6pFxKPAOklvSUVzgJXAcl75kaWFwDVpejlwdLoq6kDgKZ+vMDNrrqJhqFOAqyV9gleSQwcwFvjoEPZ7EnCxpLHAQ8CxZInrcknHA2vJvvwH2VVXc4E1wPNpXTMza6JBk0VEPAa8W9IHgL1T8U8j4qah7DQiOsmSTl9z+lk3gBOHsj8zMxuaSn/PYgWwosGxmJlZSdX8LWwzMxs9nCzMzKyQk4WZmRVysjAzs0JOFmZmJXfsGUs59oylLY3BycLMbBhpVeJwsjAzs0JOFmZmVsjJwsyshMpwniLPycLMzAo5WZiZWSEnCzOzYaqZQ1VOFmZmVsjJwszMCjlZmJlZIScLMzMr5GRhZmaFnCzMzEqibF/Ey3OyMDOzQk4WZmYtVObeRJ6ThZmZFXKyMDOzQi1LFpK2k/Q7ST9J8zMl3S5pjaTLJI1N5Tuk+TVp+YxWxWxmVg/DZegpr5U9i5OBVbn5bwBLI2IP4Eng+FR+PPBkKl+a1jMzsyZqSbKQNBX4b8C5aV7AB4Er0yoXAvPT9Lw0T1o+J61vZmZNMqZF+/0W8AVgpzS/C9AdEVvTfBcwJU1PAdYBRMRWSU+l9TflK5S0GFgM0NbWRmdnZ02B9fT01LxtIzmu6pU1tvFjxzB7+uTSxVbW9iprXFB7bLOnTwags7OzbtP1iGswTU8Wkj4CbIyIOyW9v171RsQyYBlAR0dHtLe311RPd3c3tW7bSI6remWNrXPlA9y9dhOnLCpXbGVtr7LGBbXHdvZVKwA4ZVF73abrEddgWtGzeA9wuKS5wDjgjcDZwERJY1LvYiqwPq2/HpgGdEkaA0wANjc/bDOz0avp5ywi4rSImBoRM4AFwE0R8QlgBXBEWm0hcE2aXp7mSctviohoYshmZqNemb5n8UXgVElryM5JnJfKzwN2SeWnAktaFJ+Z2ajVqhPcAETEzcDNafohYP9+1tkCfLypgZmZ2auUqWdhZmYl1dKehZlZmfV+y/oHX/9szdvWun3ZuGdhZmaF3LMwM2uwkdDLcM/CzMwKOVmYmVkhJwszMyvkZGFmo8Zw/B2JsnCyMDMbgtGSgHw1lJlZlXqTw8I5+7Q4kuZxz8LMzAo5WRQYLV1MM7PBeBjKzKxORvIHS/cszMyskHsWZtZUI+HWF6ORk4WZtUwzEke1d44dyp1mRzIPQ1XBJ7ttNPD73PrjZGFmVoHRnkSdLMxs1BvtiaASThZmZiNAoxOek0U//CnDzOzVnCzMbNjyB7vmcbIwM7NCTU8WkqZJWiFppaT7JZ2cyneWdIOk1envpFQuSd+WtEbSPZL2bXbMZlYZf9IfuVrxpbytwOci4i5JOwF3SroBOAa4MSLOkrQEWAJ8ETgMmJUeBwDfT39bKv/FHX+Jx+rN33K2sml6zyIiNkTEXWn6GWAVMAWYB1yYVrsQmJ+m5wEXReY2YKKktiaHbWY2qrX0dh+SZgD7ALcDu0XEhrToUWC3ND0FWJfbrCuVbciVIWkxsBigra2Nzs7OmmLq6elh9vTJAHR2dlY93Sg9PT0Nrb9WZY0Lyhvb+LFjmD198qCx9b6noLHvq7ze9hrK+7mSbfPPLW+gbQZ7HavZ32DPrdb/+YGOF4Op5bhS7XQj3vstSxaSdgSuAk6JiKclvbwsIkJSVFNfRCwDlgF0dHREe3t7TXF1d3dz99pNAJyyqJ2zr1pR1XSjdHd3U+tzaqSyxgXlja1z5QPcvXbToO+X3vcUNPZ9ldfbXkN5Pw+0bSXnMQbaZuGcfQZ8HSuJtZL/1Wr/z3un2/eY1u/xouh51rq/Sqcb8d5vydVQkrYnSxQXR8SPUvFjvcNL6e/GVL4emJbbfGoqM7MSq+fJbp84b71WXA0l4DxgVUT8a27RcmBhml4IXJMrPzpdFXUg8FRuuMrMzJqgFcNQ7wE+BdwrqXdQ7UvAWcDlko4H1gJHpmXXAnOBNcDzwLHNDbc6vjLKRiq/t0e3pieLiPhPQAMsntPP+gGc2NCgzKxm9Uwi9arLia3+/A1uG9Y8ll0dt5fVysmigUb7P+Zof/5mI4mTRZOU8cBZxpisefKv/7FnLOWPf9pYsEW5+f3cWP4N7hao97hsPeoy689IHvsfyc+tEdyzKCl/SrKBjPb3xmh//q3iZNFilbzxR8IQgTWXD6hWb04WNuKN5ANn3/MOI/V5Wus5WZi1kA/wNlw4WZRIpUNSzTy41Gt/A9VTxoPlcIrVrFl8NdQoM9yvABnsCrCR9NzMysY9CyuNZnxyb8Q+Bjpv4J6IjSTuWYwQfT9xj5RP2bXGf+wZSzl4zylD3t9wb0ezenHPwuqqjOddzGzonCysYj7Im41eThZmZlbI5yzsNSoZp8//PrLH9c1GPvcsRgGfRzCzoXKyMDOzQk4WZmZWyMnCzKrmYcvRxye4rfR8UBoaX4Bg9eCehY1aw+nT8XCK1UYmJwuzJvDB3oa7YZMsJB0q6Q+S1kha0up4bHTzwd9Gm2GRLCRtB3wPOAzYCzhK0l6tjcrMbPQYFskC2B9YExEPRcSLwKXAvBbHZGY2aigiWh1DIUlHAIdGxKI0/ynggIj4TG6dxcDiNPsW4A817m4ysGkI4TaK46peWWNzXNUpa1xQ3thqjWt6ROza34IRc+lsRCwDlg21Hkl3RERHHUKqK8dVvbLG5riqU9a4oLyxNSKu4TIMtR6YlpufmsrMzKwJhkuy+C0wS9JMSWOBBcDyFsdkZjZqDIthqIjYKukzwPXAdsD5EXF/g3Y35KGsBnFc1StrbI6rOmWNC8obW93jGhYnuM3MrLWGyzCUmZm1kJOFmZkVGpXJQtLHJd0v6c+SBry8bKBbjKQT7ben8svSSfd6xLWzpBskrU5/J/WzzgckdeYeWyTNT8sukPRwbll7s+JK623L7Xt5rryV7dUu6db0et8j6a9zy+raXkW3pJG0Q3r+a1J7zMgtOy2V/0HSIUOJo8bYTpW0MrXRjZKm55b1+7o2Ka5jJD2e2/+i3LKF6bVfLWlhk+NamovpAUnduWWNbK/zJW2UdN8AyyXp2ynueyTtm1s2tPaKiFH3APYk++LezUDHAOtsBzwI7A6MBe4G9krLLgcWpOlzgL+tU1z/G1iSppcA3yhYf2fgCWB8mr8AOKIB7VVRXMCzA5S3rL2ANwOz0vSbgA3AxHq312Dvl9w6nwbOSdMLgMvS9F5p/R2Amame7er4+lUS2wdy76O/7Y1tsNe1SXEdA3y3n213Bh5Kfyel6UnNiqvP+ieRXXTT0PZKdb8P2Be4b4Dlc4GfAQIOBG6vV3uNyp5FRKyKiKJvePd7ixFJAj4IXJnWuxCYX6fQ5qX6Kq33COBnEfF8nfY/kGrjelmr2ysiHoiI1Wn6T8BGoN9vqA5RJbekycd7JTAntc884NKIeCEiHgbWpPqaFltErMi9j24j+y5Tow3lNj6HADdExBMR8SRwA3Boi+I6CrikTvseVET8iuwD4kDmARdF5jZgoqQ26tBeozJZVGgKsC4335XKdgG6I2Jrn/J62C0iNqTpR4HdCtZfwGvfpGem7udSSTs0Oa5xku6QdFvv0Bglai9J+5N9UnwwV1yv9hro/dLvOqk9niJrn0q2HYpq6z+e7NNpr/5e12bG9d/Ta3SlpN4v5zayzSquOw3XzQRuyhU3qr0qMVDsQ26vYfE9i1pI+gXwF/0sOj0irml2PL0Giys/ExEhacDrmtOnhbeTffek12lkB82xZNdZfxH4hybGNT0i1kvaHbhJ0r1kB8Sa1bm9/h1YGBF/TsU1t9dIJemTQAdwcK74Na9rRDzYfw1192Pgkoh4QdLfkPXMPtikfVdiAXBlRGzLlbWyvRpmxCaLiPjQEKsY6BYjm8m6dmPSp8Oqbj0yWFySHpPUFhEb0sFt4yBVHQlcHREv5eru/ZT9gqQfAH/fzLgiYn36+5Ckm4F9gKtocXtJeiPwU7IPCrfl6q65vfpRyS1petfpkjQGmED2fmr07Wwqql/Sh8iS8MER8UJv+QCvaz0OfoVxRcTm3Oy5ZOeperd9f59tb65DTBXFlbMAODFf0MD2qsRAsQ+5vTwMNbB+bzES2dmiFWTnCwAWAvXqqSxP9VVS72vGSdMBs/c8wXyg3ysmGhGXpEm9wziSJgPvAVa2ur3Sa3c12TjulX2W1bO9KrklTT7eI4CbUvssBxYou1pqJjAL+M0QYqk6Nkn7AP8HODwiNubK+31dmxhXW272cGBVmr4e+HCKbxLwYV7dy25oXCm2t5KdLL41V9bI9qrEcuDodFXUgcBT6UPR0NurUWfty/wAPko2ZvcC8BhwfSp/E3Btbr25wANknwpOz5XvTvbPvAa4AtihTnHtAtwIrAZ+AeycyjuAc3PrzSD7pPC6PtvfBNxLdtD7IbBjs+IC3p32fXf6e3wZ2gv4JPAS0Jl7tDeivfp7v5ANax2epsel578mtcfuuW1PT9v9ATisAe/5oth+kf4XettoedHr2qS4/gm4P+1/BfDW3LbHpbZcAxzbzLjS/FeBs/ps1+j2uoTsir6XyI5hxwMnACek5SL7obgH0/47ctsOqb18uw8zMyvkYSgzMyvkZGFmZoWcLMzMrJCThZmZFXKyMDOzQk4WZhXK3U30PklXSBpfsP6zNezjWkkT0+PTtUdrVl9OFmaV64mI9ojYG3iR7Pr2ukhfonpdRMyNiG5gItldas1KwcnCrDa3AHvAy78FcV96nNJ3RUk7KvuNiLsk3StpXiqfoew3Ey4i+2LgNEl/TN/8PQv4y9ST+aaki/I3pZN0cW89Zs3gL+WZVUjSsxGxY7qv01XAdWTfxL6A7LcDBNwOfDIiftdn/fER8XRKBLeR3dJjOtnvCrw70j2rJP2R7BvoOwI/Sb0YJB0MfDYi5kuaQPYt61nxyt18zRrKPQuzyr1eUidwB/AIcB5wENkNHZ+LiGeBHwHv7bOdgH+UdA/ZbTWm8Mrt1NdG7uaGA4mIX5Ldr3rFwzUAAADsSURBVGhXsvuCXeVEYc00Yu86a9YAPRHxqp9eze5BWOgTZD+4tF9EvJR6D+PSsueq2P9FZPe6WgAcW8V2ZkPmnoXZ0NwCzJc0XtIbyG5SeUufdSYAG1Oi+ADZ8FORZ4Cd+pRdAJwCEBHNvJOpmXsWZkMREXdJuoBXbil+bkT8rs9qFwM/Tj8GdQfw+wrq3Szp15LuI/vp3M9HxGOSVgH/r45PwawiPsFtNkyk73XcC+wbEUP6BUKzankYymwYSL9itwr4jhOFtYJ7FmZmVsg9CzMzK+RkYWZmhZwszMyskJOFmZkVcrIwM7NC/x9uxG8EaQea4QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy\n",
    "\n",
    "b1=numpy.array(b)  #Converting the compound scores obtained to numpy array\n",
    "b2=pd.DataFrame(b1)  #Converting to dataframe\n",
    "\n",
    "#Plotting a histogram\n",
    "b2.plot.hist(grid=True, bins=100,rwidth=0.7,\n",
    "                   color='#607c8e')\n",
    "\n",
    "#Setting title, x-axis label and y-axis label\n",
    "plt.title('Sentiment Analysis')\n",
    "plt.xlabel('Polarity')\n",
    "plt.ylabel('Counts')\n",
    "plt.grid(axis='y', alpha=0.75)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x14c4f5cd0>"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting a histogram\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import plotly\n",
    "from plotly import __version__\n",
    "%matplotlib inline\n",
    "import cufflinks as cf\n",
    "from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot\n",
    "init_notebook_mode(connected=True)\n",
    "cf.go_offline()\n",
    "df=pd.DataFrame(b)\n",
    "df.plot(\n",
    "    kind='hist',\n",
    "    bins=100)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "linkText": "Export to plot.ly",
        "plotlyServerURL": "https://plot.ly",
        "showLink": true
       },
       "data": [
        {
         "histfunc": "count",
         "histnorm": "",
         "marker": {
          "color": "rgba(255, 153, 51, 1.0)",
          "line": {
           "color": "#000000",
           "width": 1.3
          }
         },
         "name": "0",
         "nbinsx": 100,
         "opacity": 0.8,
         "orientation": "v",
         "type": "histogram",
         "x": [
          0,
          0.5106,
          0.886,
          0.6486,
          0.8519,
          0.8442,
          0.9524,
          0.5574,
          0.8271,
          0.9501,
          0.9827,
          0.0516,
          0.8885,
          0.891,
          0.7845,
          0.872,
          0.7469,
          0.8955,
          -0.7796,
          0.0772,
          0.936,
          -0.8403,
          0.9783,
          0.765,
          0.4019,
          0.7178,
          0.8807,
          0.5994,
          0.9059,
          0,
          0.902,
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          0.765,
          0.5859,
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          0.8402,
          0.6808,
          0.5994,
          0.4767,
          0.9147,
          0.9136,
          0,
          0.6124,
          0.6249,
          -0.6705,
          -0.5267,
          0.4678,
          0.9402,
          0.9798,
          0.9683,
          0.6597,
          0.1911,
          0.9744,
          0.9449,
          0.9393,
          0.872,
          0.7964,
          0.9863,
          0,
          0.93,
          0.8623,
          0.9898,
          -0.9781,
          0.9062,
          0.8625,
          0.9834,
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          0.9371,
          0.7096,
          0.9947,
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          0.9186,
          0.8689,
          0.6369,
          0.9877,
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          0.9842,
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          0.9769,
          0.9942,
          0.5994,
          -0.8405,
          0.9552,
          -0.128,
          0.9698,
          -0.0772,
          0.34,
          -0.8658,
          0.8977,
          0.8487,
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          0.9868,
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          -0.765,
          0.9074,
          0.2732,
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          0.6486,
          0.2732,
          0.7579,
          0.9559,
          0.9889,
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          0.3612,
          0.128,
          -0.128,
          0.7506,
          0.9331,
          0.6249,
          0.875,
          0.4939,
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          -0.2023,
          0,
          -0.0516,
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          -0.0516,
          -0.6124,
          0.6956,
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          0.6908,
          0.9246,
          0.1779,
          -0.5994,
          -0.9682,
          0.4215,
          0,
          0.9678,
          -0.9274,
          -0.4215,
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          0.7579,
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   ],
   "source": [
    "#Plotting a histogram\n",
    "\n",
    "df=pd.DataFrame(b)\n",
    "df.iplot(\n",
    "    kind='hist',\n",
    "    bins=100,\n",
    "    xTitle='polarity',\n",
    "    linecolor='black',\n",
    "    yTitle='count',\n",
    "    title='Sentiment Polarity Distribution')"
   ]
  }
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