5 Amazon Food Reviews - SVM.ipynb

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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Amazon Food Reviews - [SVM]\n",
    "\n",
    "Data Source:https://www.kaggle.com/snap/amazon-fine-food-reviews\n",
    "\n",
    "This dataset consists of reviews of fine foods from Amazon. The data span a period of more than 10 years, including all ~500,000 reviews up to October 2012. Reviews include product and user information, ratings, and a plain text review. It also includes reviews from all other Amazon categories.\n",
    "\n",
    "![alt text](http://nycdatascience.com/blog/wp-content/uploads/2016/04/NewCover.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Excerpt \n",
    "1. Applied SVM with rbf(radial basis function) kernel on Different Featurization of Data viz. BOW(uni-gram,bi-gram), tfidf, Avg-Word2Vec(using Word2Vec model pretrained on Google News) and tf-idf-Word2Vec <br>\n",
    "2. Used both Grid Search & Randomized Search Cross Validation <br>\n",
    "3. Evaluated the test data on various performance metrics like accuracy, f1-score, precision, recall,etc. also plotted Confusion matrix using seaborne<br>\n",
    "4. Evaluated SGDClassifier on the best resulting featurization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data includes:\n",
    "- Reviews from Oct 1999 - Oct 2012\n",
    "- 568,454 reviews\n",
    "- 256,059 users\n",
    "- 74,258 products\n",
    "- 260 users with > 50 reviews\n",
    "\n",
    "#### Attribute Information:\n",
    "\n",
    "1. Id\n",
    "2. ProductId - unique identifier for the product\n",
    "3. UserId - unqiue identifier for the user\n",
    "4. ProfileName\n",
    "5. HelpfulnessNumerator - number of users who found the review helpful\n",
    "6. HelpfulnessDenominator - number of users who indicated whether they found the review helpful or not\n",
    "7. Score - rating between 1 and 5\n",
    "8. Time - timestamp for the review\n",
    "9. Summary - brief summary of the review\n",
    "10. Text - text of the review"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![alt text](http://nycdatascience.com/blog/wp-content/uploads/2016/04/AmazonReview.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### Objective:- Review Polarity\n",
    "Given a review, determine the review is positive or negative\n",
    "\n",
    "#### Using text review to decide the polarity\n",
    "Take the summary and text of review and analyze it using NLP whether the customer feedback/review is positive or negative\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # !pip install --upgrade pip\n",
    "# # !pip install qtconsole ipywidgets widgetsnbextension\n",
    "# # !pip install nltk\n",
    "# # # import nltk\n",
    "# # # nltk.download(\"stopwords\")\n",
    "# # !pip install seaborn\n",
    "# # !pip install gensim\n",
    "# !pip install sklearn-evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Imports\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import sqlite3 as sql\n",
    "import seaborn as sns\n",
    "from time import time\n",
    "import random\n",
    "import gensim\n",
    "import warnings\n",
    "\n",
    "#Metrics\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import precision_score\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.metrics import recall_score\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "%matplotlib inline \n",
    "# sets the backend of matplotlib to the 'inline' backend:\n",
    "#With this backend, the output of plotting commands is displayed inline within frontends like the Jupyter notebook,\n",
    "#directly below the code cell that produced it. The resulting plots will then also be stored in the notebook document.\n",
    "\n",
    "#Functions to save objects for later use and retireve it\n",
    "import pickle\n",
    "def savetofile(obj,filename):\n",
    "    pickle.dump(obj,open(filename+\".p\",\"wb\"))\n",
    "def openfromfile(filename):\n",
    "    temp = pickle.load(open(filename+\".p\",\"rb\"))\n",
    "    return temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !wget --header=\"Host: e-2106e5ff6b.cognitiveclass.ai\" --header=\"User-Agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/67.0.3396.99 Safari/537.36\" --header=\"Accept: text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8\" --header=\"Accept-Language: en-US,en;q=0.9\" --header=\"Cookie: _ga=GA1.2.1009651095.1527270727; _xsrf=2|d66eb8d7|8e30b1015ec501038d0632ff567bddb6|1529904261; session=.eJxVj9tugkAURX_FnGdi5FaBxKSgoiZKtYq0Ng0ZYIBRGBQGEY3_XjBt2r6us1f2PjdwjzhPEcWUgcbyEnOASha7LDtgCtoNOg3_gBoJaneSiIVh7x0UXf0wWsTyNNkZorCZKbm5Z6ZAgrAotpbZz3aC4fRn0vyqWFYujKlOp97YWF963kVdE10I-KoKzFLE7OS9rtUzNmRnPlrOlq6a6Updnh00TspafjLEGJ18aSjtfRu4Zo0HGsD9885BgRL2GNiilX18tye70-LNXLw4puw7vLzaWrWdxCN701OH0WAAjVQWOHcJDbPWxCkiSSPnxD_UKCCs-bLP889Ry7t-lgIHIckL5lKU4iaoPzINTdAvnJRH1rJ_mc4PbQu_L39r2i2V55IANEWWROn-BWX4gJ4.Dh-C7A.53fm96PBqDQvenTjy0oa1UWqE_8\" --header=\"Connection: keep-alive\" \"https://e-2106e5ff6b.cognitiveclass.ai/files/Amazon%20Fine%20Food%20Reviews%20Dataset/tfidf_w2v_vec_google.p?download=1\" -O \"tfidf_w2v_vec_google.p\" -c"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>Id</th>\n",
       "      <th>ProductId</th>\n",
       "      <th>UserId</th>\n",
       "      <th>ProfileName</th>\n",
       "      <th>HelpfulnessNumerator</th>\n",
       "      <th>HelpfulnessDenominator</th>\n",
       "      <th>Score</th>\n",
       "      <th>Time</th>\n",
       "      <th>Summary</th>\n",
       "      <th>Text</th>\n",
       "      <th>CleanedText</th>\n",
       "      <th>CleanedText_NoStem</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>B001E4KFG0</td>\n",
       "      <td>A3SGXH7AUHU8GW</td>\n",
       "      <td>delmartian</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Positive</td>\n",
       "      <td>1303862400</td>\n",
       "      <td>Good Quality Dog Food</td>\n",
       "      <td>I have bought several of the Vitality canned d...</td>\n",
       "      <td>b'bought sever vital can dog food product foun...</td>\n",
       "      <td>b'bought several vitality canned dog food prod...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>B00813GRG4</td>\n",
       "      <td>A1D87F6ZCVE5NK</td>\n",
       "      <td>dll pa</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Negative</td>\n",
       "      <td>1346976000</td>\n",
       "      <td>Not as Advertised</td>\n",
       "      <td>Product arrived labeled as Jumbo Salted Peanut...</td>\n",
       "      <td>b'product arriv label jumbo salt peanutsth pea...</td>\n",
       "      <td>b'product arrived labeled jumbo salted peanuts...</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>B000LQOCH0</td>\n",
       "      <td>ABXLMWJIXXAIN</td>\n",
       "      <td>Natalia Corres \"Natalia Corres\"</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Positive</td>\n",
       "      <td>1219017600</td>\n",
       "      <td>\"Delight\" says it all</td>\n",
       "      <td>This is a confection that has been around a fe...</td>\n",
       "      <td>b'confect around centuri light pillowi citrus ...</td>\n",
       "      <td>b'confection around centuries light pillowy ci...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>B000UA0QIQ</td>\n",
       "      <td>A395BORC6FGVXV</td>\n",
       "      <td>Karl</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>Negative</td>\n",
       "      <td>1307923200</td>\n",
       "      <td>Cough Medicine</td>\n",
       "      <td>If you are looking for the secret ingredient i...</td>\n",
       "      <td>b'look secret ingredi robitussin believ found ...</td>\n",
       "      <td>b'looking secret ingredient robitussin believe...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>B006K2ZZ7K</td>\n",
       "      <td>A1UQRSCLF8GW1T</td>\n",
       "      <td>Michael D. Bigham \"M. Wassir\"</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Positive</td>\n",
       "      <td>1350777600</td>\n",
       "      <td>Great taffy</td>\n",
       "      <td>Great taffy at a great price.  There was a wid...</td>\n",
       "      <td>b'great taffi great price wide assort yummi ta...</td>\n",
       "      <td>b'great taffy great price wide assortment yumm...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  Id   ProductId          UserId                      ProfileName  \\\n",
       "0      0   1  B001E4KFG0  A3SGXH7AUHU8GW                       delmartian   \n",
       "1      1   2  B00813GRG4  A1D87F6ZCVE5NK                           dll pa   \n",
       "2      2   3  B000LQOCH0   ABXLMWJIXXAIN  Natalia Corres \"Natalia Corres\"   \n",
       "3      3   4  B000UA0QIQ  A395BORC6FGVXV                             Karl   \n",
       "4      4   5  B006K2ZZ7K  A1UQRSCLF8GW1T    Michael D. Bigham \"M. Wassir\"   \n",
       "\n",
       "   HelpfulnessNumerator  HelpfulnessDenominator     Score        Time  \\\n",
       "0                     1                       1  Positive  1303862400   \n",
       "1                     0                       0  Negative  1346976000   \n",
       "2                     1                       1  Positive  1219017600   \n",
       "3                     3                       3  Negative  1307923200   \n",
       "4                     0                       0  Positive  1350777600   \n",
       "\n",
       "                 Summary                                               Text  \\\n",
       "0  Good Quality Dog Food  I have bought several of the Vitality canned d...   \n",
       "1      Not as Advertised  Product arrived labeled as Jumbo Salted Peanut...   \n",
       "2  \"Delight\" says it all  This is a confection that has been around a fe...   \n",
       "3         Cough Medicine  If you are looking for the secret ingredient i...   \n",
       "4            Great taffy  Great taffy at a great price.  There was a wid...   \n",
       "\n",
       "                                         CleanedText  \\\n",
       "0  b'bought sever vital can dog food product foun...   \n",
       "1  b'product arriv label jumbo salt peanutsth pea...   \n",
       "2  b'confect around centuri light pillowi citrus ...   \n",
       "3  b'look secret ingredi robitussin believ found ...   \n",
       "4  b'great taffi great price wide assort yummi ta...   \n",
       "\n",
       "                                  CleanedText_NoStem  \n",
       "0  b'bought several vitality canned dog food prod...  \n",
       "1  b'product arrived labeled jumbo salted peanuts...  \n",
       "2  b'confection around centuries light pillowy ci...  \n",
       "3  b'looking secret ingredient robitussin believe...  \n",
       "4  b'great taffy great price wide assortment yumm...  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Using sqlite3 to retrieve data from sqlite file\n",
    "\n",
    "con = sql.connect(\"final.sqlite\")#Loading Cleaned/ Preprocesed text that we did in Text Preprocessing\n",
    "\n",
    "#Using pandas functions to query from sql table\n",
    "df = pd.read_sql_query(\"\"\"\n",
    "SELECT * FROM Reviews \n",
    "\"\"\",con)\n",
    "\n",
    "#Reviews is the name of the table given\n",
    "#Taking only the data where score != 3 as score 3 will be neutral and it won't help us much\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe thead th {\n",
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       "    .dataframe tbody tr th {\n",
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       "      <th>HelpfulnessDenominator</th>\n",
       "      <th>Time</th>\n",
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       "      <th>count</th>\n",
       "      <td>364171.000000</td>\n",
       "      <td>364171.000000</td>\n",
       "      <td>364171.000000</td>\n",
       "      <td>364171.000000</td>\n",
       "      <td>3.641710e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>241825.377603</td>\n",
       "      <td>261814.561014</td>\n",
       "      <td>1.739021</td>\n",
       "      <td>2.186841</td>\n",
       "      <td>1.296135e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>154519.869452</td>\n",
       "      <td>166958.768333</td>\n",
       "      <td>6.723921</td>\n",
       "      <td>7.348482</td>\n",
       "      <td>4.864772e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.393408e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>104427.500000</td>\n",
       "      <td>113379.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.270858e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>230033.000000</td>\n",
       "      <td>249445.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.311379e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>376763.500000</td>\n",
       "      <td>407408.500000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.332893e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>525813.000000</td>\n",
       "      <td>568454.000000</td>\n",
       "      <td>866.000000</td>\n",
       "      <td>878.000000</td>\n",
       "      <td>1.351210e+09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               index             Id  HelpfulnessNumerator  \\\n",
       "count  364171.000000  364171.000000         364171.000000   \n",
       "mean   241825.377603  261814.561014              1.739021   \n",
       "std    154519.869452  166958.768333              6.723921   \n",
       "min         0.000000       1.000000              0.000000   \n",
       "25%    104427.500000  113379.500000              0.000000   \n",
       "50%    230033.000000  249445.000000              0.000000   \n",
       "75%    376763.500000  407408.500000              2.000000   \n",
       "max    525813.000000  568454.000000            866.000000   \n",
       "\n",
       "       HelpfulnessDenominator          Time  \n",
       "count           364171.000000  3.641710e+05  \n",
       "mean                 2.186841  1.296135e+09  \n",
       "std                  7.348482  4.864772e+07  \n",
       "min                  0.000000  9.393408e+08  \n",
       "25%                  0.000000  1.270858e+09  \n",
       "50%                  1.000000  1.311379e+09  \n",
       "75%                  2.000000  1.332893e+09  \n",
       "max                878.000000  1.351210e+09  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "364171"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape\n",
    "df['Score'].size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## For EDA and Text Preprecessing Refer other ipynb notebook<b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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       "      <td>B001E4KFG0</td>\n",
       "      <td>A3SGXH7AUHU8GW</td>\n",
       "      <td>delmartian</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1303862400</td>\n",
       "      <td>Good Quality Dog Food</td>\n",
       "      <td>I have bought several of the Vitality canned d...</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>B00813GRG4</td>\n",
       "      <td>A1D87F6ZCVE5NK</td>\n",
       "      <td>dll pa</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1346976000</td>\n",
       "      <td>Not as Advertised</td>\n",
       "      <td>Product arrived labeled as Jumbo Salted Peanut...</td>\n",
       "      <td>b'product arriv label jumbo salt peanutsth pea...</td>\n",
       "      <td>b'product arrived labeled jumbo salted peanuts...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>B000LQOCH0</td>\n",
       "      <td>ABXLMWJIXXAIN</td>\n",
       "      <td>Natalia Corres \"Natalia Corres\"</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1219017600</td>\n",
       "      <td>\"Delight\" says it all</td>\n",
       "      <td>This is a confection that has been around a fe...</td>\n",
       "      <td>b'confect around centuri light pillowi citrus ...</td>\n",
       "      <td>b'confection around centuries light pillowy ci...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>B000UA0QIQ</td>\n",
       "      <td>A395BORC6FGVXV</td>\n",
       "      <td>Karl</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1307923200</td>\n",
       "      <td>Cough Medicine</td>\n",
       "      <td>If you are looking for the secret ingredient i...</td>\n",
       "      <td>b'look secret ingredi robitussin believ found ...</td>\n",
       "      <td>b'looking secret ingredient robitussin believe...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>B006K2ZZ7K</td>\n",
       "      <td>A1UQRSCLF8GW1T</td>\n",
       "      <td>Michael D. Bigham \"M. Wassir\"</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1350777600</td>\n",
       "      <td>Great taffy</td>\n",
       "      <td>Great taffy at a great price.  There was a wid...</td>\n",
       "      <td>b'great taffi great price wide assort yummi ta...</td>\n",
       "      <td>b'great taffy great price wide assortment yumm...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  Id   ProductId          UserId                      ProfileName  \\\n",
       "0      0   1  B001E4KFG0  A3SGXH7AUHU8GW                       delmartian   \n",
       "1      1   2  B00813GRG4  A1D87F6ZCVE5NK                           dll pa   \n",
       "2      2   3  B000LQOCH0   ABXLMWJIXXAIN  Natalia Corres \"Natalia Corres\"   \n",
       "3      3   4  B000UA0QIQ  A395BORC6FGVXV                             Karl   \n",
       "4      4   5  B006K2ZZ7K  A1UQRSCLF8GW1T    Michael D. Bigham \"M. Wassir\"   \n",
       "\n",
       "   HelpfulnessNumerator  HelpfulnessDenominator  Score        Time  \\\n",
       "0                     1                       1      0  1303862400   \n",
       "1                     0                       0      1  1346976000   \n",
       "2                     1                       1      0  1219017600   \n",
       "3                     3                       3      1  1307923200   \n",
       "4                     0                       0      0  1350777600   \n",
       "\n",
       "                 Summary                                               Text  \\\n",
       "0  Good Quality Dog Food  I have bought several of the Vitality canned d...   \n",
       "1      Not as Advertised  Product arrived labeled as Jumbo Salted Peanut...   \n",
       "2  \"Delight\" says it all  This is a confection that has been around a fe...   \n",
       "3         Cough Medicine  If you are looking for the secret ingredient i...   \n",
       "4            Great taffy  Great taffy at a great price.  There was a wid...   \n",
       "\n",
       "                                         CleanedText  \\\n",
       "0  b'bought sever vital can dog food product foun...   \n",
       "1  b'product arriv label jumbo salt peanutsth pea...   \n",
       "2  b'confect around centuri light pillowi citrus ...   \n",
       "3  b'look secret ingredi robitussin believ found ...   \n",
       "4  b'great taffi great price wide assort yummi ta...   \n",
       "\n",
       "                                  CleanedText_NoStem  \n",
       "0  b'bought several vitality canned dog food prod...  \n",
       "1  b'product arrived labeled jumbo salted peanuts...  \n",
       "2  b'confection around centuries light pillowy ci...  \n",
       "3  b'looking secret ingredient robitussin believe...  \n",
       "4  b'great taffy great price wide assortment yumm...  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Score as positive/negative -> 0/1\n",
    "def polarity(x):\n",
    "    if x == \"Positive\":\n",
    "        return 0\n",
    "    else:\n",
    "        return 1\n",
    "df[\"Score\"] = df[\"Score\"].map(polarity) #Map all the scores as the function polarity i.e. positive or negative\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
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       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>Id</th>\n",
       "      <th>ProductId</th>\n",
       "      <th>UserId</th>\n",
       "      <th>ProfileName</th>\n",
       "      <th>HelpfulnessNumerator</th>\n",
       "      <th>HelpfulnessDenominator</th>\n",
       "      <th>Score</th>\n",
       "      <th>Time</th>\n",
       "      <th>Summary</th>\n",
       "      <th>Text</th>\n",
       "      <th>CleanedText</th>\n",
       "      <th>CleanedText_NoStem</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>169263</th>\n",
       "      <td>212454</td>\n",
       "      <td>230265</td>\n",
       "      <td>B00004RYGX</td>\n",
       "      <td>AZRJH4JFB59VC</td>\n",
       "      <td>Lynwood E. Hines</td>\n",
       "      <td>21</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>997228800</td>\n",
       "      <td>Great movie, terrible DVD</td>\n",
       "      <td>I am continually amazed at the shoddy treatmen...</td>\n",
       "      <td>b'continu amaz shoddi treatment movi get dvd r...</td>\n",
       "      <td>b'continually amazed shoddy treatment movies g...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169255</th>\n",
       "      <td>212446</td>\n",
       "      <td>230257</td>\n",
       "      <td>B00004RYGX</td>\n",
       "      <td>A1OP3SQP78M1PP</td>\n",
       "      <td>James Gowen</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1015718400</td>\n",
       "      <td>A movie to \"literally die for!!!\"</td>\n",
       "      <td>Beetlejuice is not a movie that you can't watc...</td>\n",
       "      <td>b'beetlejuic not movi cant watch time one funi...</td>\n",
       "      <td>b'beetlejuice not movie cant watch times one f...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>316046</th>\n",
       "      <td>443662</td>\n",
       "      <td>479723</td>\n",
       "      <td>B00005U2FA</td>\n",
       "      <td>A3TO9GEQEGKFDC</td>\n",
       "      <td>N. Smith \"emerald999\"</td>\n",
       "      <td>35</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>1020211200</td>\n",
       "      <td>I love this thing</td>\n",
       "      <td>The wine saver is great in so many ways. Obvio...</td>\n",
       "      <td>b'wine saver great mani way obvious wonder abl...</td>\n",
       "      <td>b'wine saver great many ways obviously wonderf...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178946</th>\n",
       "      <td>226060</td>\n",
       "      <td>245108</td>\n",
       "      <td>B001O8NLV2</td>\n",
       "      <td>A356HBGSVZ5NRH</td>\n",
       "      <td>B.P. \"tilley_traveler\"</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>1038009600</td>\n",
       "      <td>Perpetually Delicious - Tazo Makes The Best!</td>\n",
       "      <td>For me, when the days get colder nothing is as...</td>\n",
       "      <td>b'day get colder noth reward simpl cup hot tea...</td>\n",
       "      <td>b'days get colder nothing rewarding simple cup...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>280071</th>\n",
       "      <td>388413</td>\n",
       "      <td>419994</td>\n",
       "      <td>B0000A0BS5</td>\n",
       "      <td>A238V1XTSK9NFE</td>\n",
       "      <td>Andrew Lynn</td>\n",
       "      <td>46</td>\n",
       "      <td>59</td>\n",
       "      <td>1</td>\n",
       "      <td>1064361600</td>\n",
       "      <td>Not actually for use in espresso machines</td>\n",
       "      <td>Few things I'd like to point out:&lt;p&gt;1.  &amp;quot;...</td>\n",
       "      <td>b'thing like point not roast drink northern it...</td>\n",
       "      <td>b'things like point not roast drink northern i...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55686</th>\n",
       "      <td>61299</td>\n",
       "      <td>66610</td>\n",
       "      <td>B0000SY9U4</td>\n",
       "      <td>A3EEDHNHI4WNSH</td>\n",
       "      <td>Joanna J. Young</td>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>1067472000</td>\n",
       "      <td>Yummy</td>\n",
       "      <td>If you've never tried Kona coffee, Aloha Islan...</td>\n",
       "      <td>b'youv never tri kona coffe aloha island defin...</td>\n",
       "      <td>b'youve never tried kona coffee aloha island d...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36307</th>\n",
       "      <td>38888</td>\n",
       "      <td>42226</td>\n",
       "      <td>B0000A0BS8</td>\n",
       "      <td>A23GFTVIETX7DS</td>\n",
       "      <td>Debbie Lee Wesselmann</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1067904000</td>\n",
       "      <td>Five stars if you like Starbucks</td>\n",
       "      <td>This blend is one of Starbucks' gentler blends...</td>\n",
       "      <td>b'blend one starbuck gentler blend like tast s...</td>\n",
       "      <td>b'blend one starbucks gentler blends like tast...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10496</th>\n",
       "      <td>10992</td>\n",
       "      <td>11991</td>\n",
       "      <td>B0000T15M8</td>\n",
       "      <td>A2928LJN5IISB4</td>\n",
       "      <td>chatchi</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1067990400</td>\n",
       "      <td>The fruits of my labor</td>\n",
       "      <td>When chatchi's favorite afternoon treat became...</td>\n",
       "      <td>b'chatchi favorit afternoon treat becam myster...</td>\n",
       "      <td>b'chatchis favorite afternoon treat became mys...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199797</th>\n",
       "      <td>255320</td>\n",
       "      <td>276802</td>\n",
       "      <td>B0000DCXFY</td>\n",
       "      <td>AQFIH82DRPMW</td>\n",
       "      <td>Patrick O'Brien</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1069027200</td>\n",
       "      <td>Great for Crostini</td>\n",
       "      <td>While Pesto Genovese is my favorite pesto for ...</td>\n",
       "      <td>b'pesto genoves favorit pesto capelli dang fam...</td>\n",
       "      <td>b'pesto genovese favorite pesto capelli dangel...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>208301</th>\n",
       "      <td>267390</td>\n",
       "      <td>289844</td>\n",
       "      <td>B0000E5JRW</td>\n",
       "      <td>A12PEEUG7CN2BO</td>\n",
       "      <td>B. Kalafut</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>1069718400</td>\n",
       "      <td>The genuine article</td>\n",
       "      <td>Red beans and rice is such a simple dish.  Eve...</td>\n",
       "      <td>b'red bean rice simpl dish everybodi new orlea...</td>\n",
       "      <td>b'red beans rice simple dish everybody new orl...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         index      Id   ProductId          UserId             ProfileName  \\\n",
       "169263  212454  230265  B00004RYGX   AZRJH4JFB59VC        Lynwood E. Hines   \n",
       "169255  212446  230257  B00004RYGX  A1OP3SQP78M1PP             James Gowen   \n",
       "316046  443662  479723  B00005U2FA  A3TO9GEQEGKFDC   N. Smith \"emerald999\"   \n",
       "178946  226060  245108  B001O8NLV2  A356HBGSVZ5NRH  B.P. \"tilley_traveler\"   \n",
       "280071  388413  419994  B0000A0BS5  A238V1XTSK9NFE             Andrew Lynn   \n",
       "55686    61299   66610  B0000SY9U4  A3EEDHNHI4WNSH         Joanna J. Young   \n",
       "36307    38888   42226  B0000A0BS8  A23GFTVIETX7DS   Debbie Lee Wesselmann   \n",
       "10496    10992   11991  B0000T15M8  A2928LJN5IISB4                 chatchi   \n",
       "199797  255320  276802  B0000DCXFY    AQFIH82DRPMW         Patrick O'Brien   \n",
       "208301  267390  289844  B0000E5JRW  A12PEEUG7CN2BO              B. Kalafut   \n",
       "\n",
       "        HelpfulnessNumerator  HelpfulnessDenominator  Score        Time  \\\n",
       "169263                    21                      23      1   997228800   \n",
       "169255                     0                       0      0  1015718400   \n",
       "316046                    35                      35      0  1020211200   \n",
       "178946                    14                      14      0  1038009600   \n",
       "280071                    46                      59      1  1064361600   \n",
       "55686                     23                      23      0  1067472000   \n",
       "36307                      5                       5      0  1067904000   \n",
       "10496                      5                       5      0  1067990400   \n",
       "199797                     5                       5      0  1069027200   \n",
       "208301                     6                       8      0  1069718400   \n",
       "\n",
       "                                             Summary  \\\n",
       "169263                     Great movie, terrible DVD   \n",
       "169255             A movie to \"literally die for!!!\"   \n",
       "316046                             I love this thing   \n",
       "178946  Perpetually Delicious - Tazo Makes The Best!   \n",
       "280071     Not actually for use in espresso machines   \n",
       "55686                                          Yummy   \n",
       "36307               Five stars if you like Starbucks   \n",
       "10496                         The fruits of my labor   \n",
       "199797                            Great for Crostini   \n",
       "208301                           The genuine article   \n",
       "\n",
       "                                                     Text  \\\n",
       "169263  I am continually amazed at the shoddy treatmen...   \n",
       "169255  Beetlejuice is not a movie that you can't watc...   \n",
       "316046  The wine saver is great in so many ways. Obvio...   \n",
       "178946  For me, when the days get colder nothing is as...   \n",
       "280071  Few things I'd like to point out:<p>1.  &quot;...   \n",
       "55686   If you've never tried Kona coffee, Aloha Islan...   \n",
       "36307   This blend is one of Starbucks' gentler blends...   \n",
       "10496   When chatchi's favorite afternoon treat became...   \n",
       "199797  While Pesto Genovese is my favorite pesto for ...   \n",
       "208301  Red beans and rice is such a simple dish.  Eve...   \n",
       "\n",
       "                                              CleanedText  \\\n",
       "169263  b'continu amaz shoddi treatment movi get dvd r...   \n",
       "169255  b'beetlejuic not movi cant watch time one funi...   \n",
       "316046  b'wine saver great mani way obvious wonder abl...   \n",
       "178946  b'day get colder noth reward simpl cup hot tea...   \n",
       "280071  b'thing like point not roast drink northern it...   \n",
       "55686   b'youv never tri kona coffe aloha island defin...   \n",
       "36307   b'blend one starbuck gentler blend like tast s...   \n",
       "10496   b'chatchi favorit afternoon treat becam myster...   \n",
       "199797  b'pesto genoves favorit pesto capelli dang fam...   \n",
       "208301  b'red bean rice simpl dish everybodi new orlea...   \n",
       "\n",
       "                                       CleanedText_NoStem  \n",
       "169263  b'continually amazed shoddy treatment movies g...  \n",
       "169255  b'beetlejuice not movie cant watch times one f...  \n",
       "316046  b'wine saver great many ways obviously wonderf...  \n",
       "178946  b'days get colder nothing rewarding simple cup...  \n",
       "280071  b'things like point not roast drink northern i...  \n",
       "55686   b'youve never tried kona coffee aloha island d...  \n",
       "36307   b'blend one starbucks gentler blends like tast...  \n",
       "10496   b'chatchis favorite afternoon treat became mys...  \n",
       "199797  b'pesto genovese favorite pesto capelli dangel...  \n",
       "208301  b'red beans rice simple dish everybody new orl...  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Taking Sample Data\n",
    "n_samples = 25000\n",
    "df_sample = df.sample(n_samples)\n",
    "\n",
    "###Sorting as we want according to time series\n",
    "df_sample.sort_values('Time',inplace=True) \n",
    "df_sample.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Saving all samples in disk to as to test to test on the same sample for each of all Algo\n",
    "savetofile(df_sample,\"sample_svm\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#Opening from samples from file\n",
    "df_sample = openfromfile(\"sample_svm\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Support Vector Machine Model  using Different Featurization in NLP"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![alt text](https://chrisalbon.com/images/machine_learning_flashcards/Support_Vector_Classifier_print.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bag of Words (BoW)\n",
    "\n",
    "A commonly used model in methods of Text Classification. As part of the BOW model, a piece of text (sentence or a document) is represented as a bag or multiset of words, disregarding grammar and even word order and the frequency or occurrence of each word is used as a feature for training a classifier.<br>\n",
    "OR <br>\n",
    "Simply,Converting a collection of text documents to a matrix of token counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Data Size:  (17500, 27066)\n",
      "Test Data Size:  (7500, 27066)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import preprocessing\n",
    "\n",
    "#Breaking into Train and test\n",
    "X_train, X_test, y_train, y_test = train_test_split(df_sample['CleanedText'].values,df_sample['Score'].values,test_size=0.3,shuffle=False)\n",
    "\n",
    "#Text -> Uni gram Vectors\n",
    "uni_gram = CountVectorizer() \n",
    "X_train = uni_gram.fit_transform(X_train)\n",
    "#Normalize Data\n",
    "X_train = preprocessing.normalize(X_train)\n",
    "print(\"Train Data Size: \",X_train.shape)\n",
    "X_test = uni_gram.transform(X_test)\n",
    "#Normalize Data\n",
    "X_test = preprocessing.normalize(X_test)\n",
    "print(\"Test Data Size: \",X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1600, 27066) (1590, 27066)\n",
      "(3190, 27066) (1590, 27066)\n",
      "(4780, 27066) (1590, 27066)\n",
      "(6370, 27066) (1590, 27066)\n",
      "(7960, 27066) (1590, 27066)\n",
      "(9550, 27066) (1590, 27066)\n",
      "(11140, 27066) (1590, 27066)\n",
      "(12730, 27066) (1590, 27066)\n",
      "(14320, 27066) (1590, 27066)\n",
      "(15910, 27066) (1590, 27066)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import TimeSeriesSplit\n",
    "tscv = TimeSeriesSplit(n_splits=10)\n",
    "for train, cv in tscv.split(X_train):\n",
    "#     print(\"%s %s\" % (train, cv))\n",
    "    print(X_train[train].shape, X_train[cv].shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![i](https://i.stack.imgur.com/fXZ6k.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Finding the best 'alpha' using Forward Chaining Cross Validation or Time Series CV "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 0 ns, sys: 0 ns, total: 0 ns\n",
      "Wall time: 7.39 µs\n",
      "Fitting 10 folds for each of 225 candidates, totalling 2250 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:   29.3s\n",
      "[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed: 12.8min\n",
      "[Parallel(n_jobs=-1)]: Done 410 tasks      | elapsed: 34.6min\n",
      "[Parallel(n_jobs=-1)]: Done 760 tasks      | elapsed: 59.6min\n",
      "[Parallel(n_jobs=-1)]: Done 1210 tasks      | elapsed: 89.3min\n",
      "[Parallel(n_jobs=-1)]: Done 1760 tasks      | elapsed: 114.9min\n",
      "[Parallel(n_jobs=-1)]: Done 2250 out of 2250 | elapsed: 123.5min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best HyperParameter:  {'C': 10, 'gamma': 0.5}\n",
      "Best Accuracy: 90.30%\n"
     ]
    }
   ],
   "source": [
    "%time\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "clf = SVC()\n",
    "param_grid = {'gamma':[1000,500,100,50,10,5,1,0.5,0.1,0.05,0.01,0.005,0.001,0.0005,0.0001],\n",
    "             'C':[1000,500,100,50,10,5,1,0.5,0.1,0.05,0.01,0.005,0.001,0.0005,0.0001]} #params we need to try on classifier\n",
    "tscv = TimeSeriesSplit(n_splits=10) #For time based splitting\n",
    "gsv = GridSearchCV(clf,param_grid,cv=tscv,verbose=1,n_jobs=-1)\n",
    "gsv.fit(X_train,y_train)\n",
    "print(\"Best HyperParameter: \",gsv.best_params_)\n",
    "print(\"Best Accuracy: %.2f%%\"%(gsv.best_score_*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on test set: 90.640%\n",
      "Precision on test set: 0.800\n",
      "Recall on test set: 0.622\n",
      "F1-Score on test set: 0.700\n",
      "Confusion Matrix of test set:\n",
      " [ [TN  FP]\n",
      " [FN TP] ]\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x3fff2efb5630>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x3fff2efb0d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Testing Accuracy on Test data\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "clf = SVC(C=10,gamma=0.5)\n",
    "clf.fit(X_train,y_train)\n",
    "y_pred = clf.predict(X_test)\n",
    "print(\"Accuracy on test set: %0.3f%%\"%(accuracy_score(y_test, y_pred)*100))\n",
    "print(\"Precision on test set: %0.3f\"%(precision_score(y_test, y_pred)))\n",
    "print(\"Recall on test set: %0.3f\"%(recall_score(y_test, y_pred)))\n",
    "print(\"F1-Score on test set: %0.3f\"%(f1_score(y_test, y_pred)))\n",
    "print(\"Confusion Matrix of test set:\\n [ [TN  FP]\\n [FN TP] ]\\n\")\n",
    "df_cm = pd.DataFrame(confusion_matrix(y_test, y_pred), range(2),range(2))\n",
    "sns.set(font_scale=1.4)#for label size\n",
    "sns.heatmap(df_cm, annot=True,annot_kws={\"size\": 16}, fmt='g')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Using Randomized Search CV to find best parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 0 ns, sys: 0 ns, total: 0 ns\n",
      "Wall time: 7.39 µs\n",
      "Fitting 10 folds for each of 15 candidates, totalling 150 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done 150 out of 150 | elapsed: 155.2min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best HyperParameter:  {'gamma': 0.05, 'C': 50}\n",
      "Best Accuracy: 89.96%\n"
     ]
    }
   ],
   "source": [
    "%time\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from sklearn.svm import SVC\n",
    "from scipy.stats import uniform as sp_rand\n",
    "\n",
    "clf = SVC()\n",
    "#params we need to try on classifier\n",
    "param_dist = {'gamma':[1000,500,100,50,10,5,1,0.5,0.1,0.05,0.01,0.005,0.001,0.0005,0.0001],\n",
    "             'C':[1000,500,100,50,10,5,1,0.5,0.1,0.05,0.01,0.005,0.001,0.0005,0.0001]} \n",
    "tscv = TimeSeriesSplit(n_splits=10) #For time based splitting\n",
    "gsv = RandomizedSearchCV(clf,param_dist,cv=tscv,verbose=1,n_iter=15)\n",
    "gsv.fit(X_train,y_train)\n",
    "print(\"Best HyperParameter: \",gsv.best_params_)\n",
    "print(\"Best Accuracy: %.2f%%\"%(gsv.best_score_*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on test set: 89.960%\n",
      "Precision on test set: 0.756\n",
      "Recall on test set: 0.632\n",
      "F1-Score on test set: 0.688\n",
      "Confusion Matrix of test set:\n",
      " [ [TN  FP]\n",
      " [FN TP] ]\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x3fff2efb0860>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x3fff2efbb6d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Testing Accuracy on Test data\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "clf = SVC(C=50,gamma=0.05)\n",
    "clf.fit(X_train,y_train)\n",
    "y_pred = clf.predict(X_test)\n",
    "print(\"Accuracy on test set: %0.3f%%\"%(accuracy_score(y_test, y_pred)*100))\n",
    "print(\"Precision on test set: %0.3f\"%(precision_score(y_test, y_pred)))\n",
    "print(\"Recall on test set: %0.3f\"%(recall_score(y_test, y_pred)))\n",
    "print(\"F1-Score on test set: %0.3f\"%(f1_score(y_test, y_pred)))\n",
    "print(\"Confusion Matrix of test set:\\n [ [TN  FP]\\n [FN TP] ]\\n\")\n",
    "df_cm = pd.DataFrame(confusion_matrix(y_test, y_pred), range(2),range(2))\n",
    "sns.set(font_scale=1.4)#for label size\n",
    "sns.heatmap(df_cm, annot=True,annot_kws={\"size\": 16}, fmt='g')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### bi-gram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Data Size:  (17500, 27066)\n",
      "Test Data Size:  (7500, 27066)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import preprocessing\n",
    "\n",
    "#Breaking into Train and test\n",
    "X_train, X_test, y_train, y_test = train_test_split(df_sample['CleanedText'].values,df_sample['Score'].values,test_size=0.3,shuffle=False)\n",
    "\n",
    "#taking one words and two consecutive words together\n",
    "bi_gram = CountVectorizer() \n",
    "X_train = bi_gram.fit_transform(X_train)\n",
    "#Normalize Data\n",
    "X_train = preprocessing.normalize(X_train)\n",
    "print(\"Train Data Size: \",X_train.shape)\n",
    "X_test = bi_gram.transform(X_test)\n",
    "#Normalize Data\n",
    "X_test = preprocessing.normalize(X_test)\n",
    "print(\"Test Data Size: \",X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 0 ns, sys: 0 ns, total: 0 ns\n",
      "Wall time: 7.87 µs\n",
      "Fitting 10 folds for each of 225 candidates, totalling 2250 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:   26.7s\n",
      "[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed: 12.3min\n",
      "[Parallel(n_jobs=-1)]: Done 410 tasks      | elapsed: 33.9min\n",
      "[Parallel(n_jobs=-1)]: Done 760 tasks      | elapsed: 59.0min\n",
      "[Parallel(n_jobs=-1)]: Done 1210 tasks      | elapsed: 88.7min\n",
      "[Parallel(n_jobs=-1)]: Done 1760 tasks      | elapsed: 114.4min\n",
      "[Parallel(n_jobs=-1)]: Done 2250 out of 2250 | elapsed: 123.3min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best HyperParameter:  {'C': 10, 'gamma': 0.5}\n",
      "Best Accuracy: 90.30%\n"
     ]
    }
   ],
   "source": [
    "%time\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "clf = SVC()\n",
    "param_grid = {'gamma':[1000,500,100,50,10,5,1,0.5,0.1,0.05,0.01,0.005,0.001,0.0005,0.0001],\n",
    "             'C':[1000,500,100,50,10,5,1,0.5,0.1,0.05,0.01,0.005,0.001,0.0005,0.0001]} #params we need to try on classifier\n",
    "tscv = TimeSeriesSplit(n_splits=10) #For time based splitting\n",
    "gsv = GridSearchCV(clf,param_grid,cv=tscv,verbose=1,n_jobs=-1)\n",
    "gsv.fit(X_train,y_train)\n",
    "print(\"Best HyperParameter: \",gsv.best_params_)\n",
    "print(\"Best Accuracy: %.2f%%\"%(gsv.best_score_*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on test set: 90.640%\n",
      "Precision on test set: 0.800\n",
      "Recall on test set: 0.622\n",
      "F1-Score on test set: 0.700\n",
      "Confusion Matrix of test set:\n",
      " [ [TN  FP]\n",
      " [FN TP] ]\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x3fff2eb09dd8>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x3fff2efbb0b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Testing Accuracy on Test data\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "clf = SVC(C=10,gamma=0.5)\n",
    "clf.fit(X_train,y_train)\n",
    "y_pred = clf.predict(X_test)\n",
    "print(\"Accuracy on test set: %0.3f%%\"%(accuracy_score(y_test, y_pred)*100))\n",
    "print(\"Precision on test set: %0.3f\"%(precision_score(y_test, y_pred)))\n",
    "print(\"Recall on test set: %0.3f\"%(recall_score(y_test, y_pred)))\n",
    "print(\"F1-Score on test set: %0.3f\"%(f1_score(y_test, y_pred)))\n",
    "print(\"Confusion Matrix of test set:\\n [ [TN  FP]\\n [FN TP] ]\\n\")\n",
    "df_cm = pd.DataFrame(confusion_matrix(y_test, y_pred), range(2),range(2))\n",
    "sns.set(font_scale=1.4)#for label size\n",
    "sns.heatmap(df_cm, annot=True,annot_kws={\"size\": 16}, fmt='g')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Using Randomized Search CV to find best parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 0 ns, sys: 0 ns, total: 0 ns\n",
      "Wall time: 7.87 µs\n",
      "Fitting 10 folds for each of 15 candidates, totalling 150 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done 150 out of 150 | elapsed: 126.9min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best HyperParameter:  {'gamma': 0.5, 'C': 5}\n",
      "Best Accuracy: 90.29%\n"
     ]
    }
   ],
   "source": [
    "%time\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from sklearn.svm import SVC\n",
    "from scipy.stats import uniform as sp_rand\n",
    "\n",
    "clf = SVC()\n",
    "#params we need to try on classifier\n",
    "param_dist = {'gamma':[1000,500,100,50,10,5,1,0.5,0.1,0.05,0.01,0.005,0.001,0.0005,0.0001],\n",
    "             'C':[1000,500,100,50,10,5,1,0.5,0.1,0.05,0.01,0.005,0.001,0.0005,0.0001]} \n",
    "tscv = TimeSeriesSplit(n_splits=10) #For time based splitting\n",
    "gsv = RandomizedSearchCV(clf,param_dist,cv=tscv,verbose=1,n_iter=15)\n",
    "gsv.fit(X_train,y_train)\n",
    "print(\"Best HyperParameter: \",gsv.best_params_)\n",
    "print(\"Best Accuracy: %.2f%%\"%(gsv.best_score_*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on test set: 90.707%\n",
      "Precision on test set: 0.803\n",
      "Recall on test set: 0.623\n",
      "F1-Score on test set: 0.702\n",
      "Confusion Matrix of test set:\n",
      " [ [TN  FP]\n",
      " [FN TP] ]\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x3fff2eb05eb8>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x3fff2efbb9b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Testing Accuracy on Test data\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "clf = SVC(C=5,gamma=0.5)\n",
    "clf.fit(X_train,y_train)\n",
    "y_pred = clf.predict(X_test)\n",
    "print(\"Accuracy on test set: %0.3f%%\"%(accuracy_score(y_test, y_pred)*100))\n",
    "print(\"Precision on test set: %0.3f\"%(precision_score(y_test, y_pred)))\n",
    "print(\"Recall on test set: %0.3f\"%(recall_score(y_test, y_pred)))\n",
    "print(\"F1-Score on test set: %0.3f\"%(f1_score(y_test, y_pred)))\n",
    "print(\"Confusion Matrix of test set:\\n [ [TN  FP]\\n [FN TP] ]\\n\")\n",
    "df_cm = pd.DataFrame(confusion_matrix(y_test, y_pred), range(2),range(2))\n",
    "sns.set(font_scale=1.4)#for label size\n",
    "sns.heatmap(df_cm, annot=True,annot_kws={\"size\": 16}, fmt='g')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# tf-idf\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![alt text](https://s3-ap-south-1.amazonaws.com/av-blog-media/wp-content/uploads/2017/01/11181616/image-4.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Data Size:  (17500, 378093)\n",
      "Test Data Size:  (7500, 378093)\n",
      "CPU times: user 3.8 s, sys: 28 ms, total: 3.83 s\n",
      "Wall time: 3.83 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import preprocessing\n",
    "\n",
    "#Breaking into Train and test\n",
    "X_train, X_test, y_train, y_test = train_test_split(df_sample['CleanedText'].values,df_sample['Score'].values,test_size=0.3,shuffle=False)\n",
    "\n",
    "tfidf = TfidfVectorizer(ngram_range=(1,2)) #Using bi-grams\n",
    "X_train = tfidf.fit_transform(X_train)\n",
    "#Normalize Data\n",
    "X_train = preprocessing.normalize(X_train)\n",
    "print(\"Train Data Size: \",X_train.shape)\n",
    "X_test = tfidf.transform(X_test)\n",
    "#Normalize Data\n",
    "X_test = preprocessing.normalize(X_test)\n",
    "print(\"Test Data Size: \",X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 0 ns, sys: 0 ns, total: 0 ns\n",
      "Wall time: 6.91 µs\n",
      "Fitting 10 folds for each of 225 candidates, totalling 2250 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:   47.2s\n",
      "[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed: 26.9min\n",
      "[Parallel(n_jobs=-1)]: Done 410 tasks      | elapsed: 72.6min\n",
      "[Parallel(n_jobs=-1)]: Done 760 tasks      | elapsed: 123.9min\n",
      "[Parallel(n_jobs=-1)]: Done 1210 tasks      | elapsed: 180.8min\n",
      "[Parallel(n_jobs=-1)]: Done 1760 tasks      | elapsed: 227.8min\n",
      "[Parallel(n_jobs=-1)]: Done 2250 out of 2250 | elapsed: 241.7min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best HyperParameter:  {'C': 1000, 'gamma': 0.005}\n",
      "Best Accuracy: 89.87%\n"
     ]
    }
   ],
   "source": [
    "%time\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "clf = SVC()\n",
    "param_grid = {'gamma':[1000,500,100,50,10,5,1,0.5,0.1,0.05,0.01,0.005,0.001,0.0005,0.0001],\n",
    "             'C':[1000,500,100,50,10,5,1,0.5,0.1,0.05,0.01,0.005,0.001,0.0005,0.0001]} #params we need to try on classifier\n",
    "tscv = TimeSeriesSplit(n_splits=10) #For time based splitting\n",
    "gsv = GridSearchCV(clf,param_grid,cv=tscv,verbose=1,n_jobs=-1)\n",
    "gsv.fit(X_train,y_train)\n",
    "print(\"Best HyperParameter: \",gsv.best_params_)\n",
    "print(\"Best Accuracy: %.2f%%\"%(gsv.best_score_*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on test set: 91.667%\n",
      "Precision on test set: 0.838\n",
      "Recall on test set: 0.651\n",
      "F1-Score on test set: 0.733\n",
      "Confusion Matrix of test set:\n",
      " [ [TN  FP]\n",
      " [FN TP] ]\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x3fff27f7cef0>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x3fff2eef6278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Testing Accuracy on Test data\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "clf = SVC(C=1000,gamma=0.005)\n",
    "clf.fit(X_train,y_train)\n",
    "y_pred = clf.predict(X_test)\n",
    "print(\"Accuracy on test set: %0.3f%%\"%(accuracy_score(y_test, y_pred)*100))\n",
    "print(\"Precision on test set: %0.3f\"%(precision_score(y_test, y_pred)))\n",
    "print(\"Recall on test set: %0.3f\"%(recall_score(y_test, y_pred)))\n",
    "print(\"F1-Score on test set: %0.3f\"%(f1_score(y_test, y_pred)))\n",
    "print(\"Confusion Matrix of test set:\\n [ [TN  FP]\\n [FN TP] ]\\n\")\n",
    "df_cm = pd.DataFrame(confusion_matrix(y_test, y_pred), range(2),range(2))\n",
    "sns.set(font_scale=1.4)#for label size\n",
    "sns.heatmap(df_cm, annot=True,annot_kws={\"size\": 16}, fmt='g')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Using Randomized Search CV to find best parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 0 ns, sys: 0 ns, total: 0 ns\n",
      "Wall time: 7.63 µs\n",
      "Fitting 10 folds for each of 15 candidates, totalling 150 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done 150 out of 150 | elapsed: 327.5min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best HyperParameter:  {'gamma': 0.01, 'C': 1000}\n",
      "Best Accuracy: 89.86%\n"
     ]
    }
   ],
   "source": [
    "%time\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from sklearn.svm import SVC\n",
    "from scipy.stats import uniform as sp_rand\n",
    "\n",
    "clf = SVC()\n",
    "#params we need to try on classifier\n",
    "param_dist = {'gamma':[1000,500,100,50,10,5,1,0.5,0.1,0.05,0.01,0.005,0.001,0.0005,0.0001],\n",
    "             'C':[1000,500,100,50,10,5,1,0.5,0.1,0.05,0.01,0.005,0.001,0.0005,0.0001]} \n",
    "tscv = TimeSeriesSplit(n_splits=10) #For time based splitting\n",
    "gsv = RandomizedSearchCV(clf,param_dist,cv=tscv,verbose=1,n_iter=15)\n",
    "gsv.fit(X_train,y_train)\n",
    "print(\"Best HyperParameter: \",gsv.best_params_)\n",
    "print(\"Best Accuracy: %.2f%%\"%(gsv.best_score_*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on test set: 91.640%\n",
      "Precision on test set: 0.837\n",
      "Recall on test set: 0.650\n",
      "F1-Score on test set: 0.732\n",
      "Confusion Matrix of test set:\n",
      " [ [TN  FP]\n",
      " [FN TP] ]\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x3fff27d0e7b8>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x3fff2eb08e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Testing Accuracy on Test data\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "clf = SVC(C=1000,gamma=0.01)\n",
    "clf.fit(X_train,y_train)\n",
    "y_pred = clf.predict(X_test)\n",
    "print(\"Accuracy on test set: %0.3f%%\"%(accuracy_score(y_test, y_pred)*100))\n",
    "print(\"Precision on test set: %0.3f\"%(precision_score(y_test, y_pred)))\n",
    "print(\"Recall on test set: %0.3f\"%(recall_score(y_test, y_pred)))\n",
    "print(\"F1-Score on test set: %0.3f\"%(f1_score(y_test, y_pred)))\n",
    "print(\"Confusion Matrix of test set:\\n [ [TN  FP]\\n [FN TP] ]\\n\")\n",
    "df_cm = pd.DataFrame(confusion_matrix(y_test, y_pred), range(2),range(2))\n",
    "sns.set(font_scale=1.4)#for label size\n",
    "sns.heatmap(df_cm, annot=True,annot_kws={\"size\": 16}, fmt='g')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using SGDClassifier with hinge loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 0 ns, sys: 0 ns, total: 0 ns\n",
      "Wall time: 12.6 µs\n",
      "Fitting 10 folds for each of 48 candidates, totalling 480 fits\n",
      "Best HyperParameter:  {'alpha': 1e-05, 'penalty': 'l1'}\n",
      "Best Accuracy: 90.48%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done 480 out of 480 | elapsed:   30.4s finished\n"
     ]
    }
   ],
   "source": [
    "%time\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "from sklearn.model_selection import TimeSeriesSplit\n",
    "\n",
    "clf = SGDClassifier()\n",
    "#params we need to try on classifier\n",
    "param_dist = {'penalty':['l1','l2','elasticnet'],\n",
    "             'alpha':[500,100,50,10,5,1,0.5,0.1,0.05,0.01,0.005,0.001,0.0005,0.0001,0.00005,0.00001]} \n",
    "tscv = TimeSeriesSplit(n_splits=10) #For time based splitting\n",
    "gsv = GridSearchCV(clf,param_dist,cv=tscv,verbose=1)\n",
    "gsv.fit(X_train,y_train)\n",
    "print(\"Best HyperParameter: \",gsv.best_params_)\n",
    "print(\"Best Accuracy: %.2f%%\"%(gsv.best_score_*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on test set: 91.040%\n",
      "Precision on test set: 0.767\n",
      "Recall on test set: 0.704\n",
      "F1-Score on test set: 0.734\n",
      "Confusion Matrix of test set:\n",
      " [ [TN  FP]\n",
      " [FN TP] ]\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x3fff181514a8>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x3fff1674af98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Testing Accuracy on Test data\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "\n",
    "clf = SGDClassifier(alpha=1e-05,penalty='l1')\n",
    "clf.fit(X_train,y_train)\n",
    "y_pred = clf.predict(X_test)\n",
    "print(\"Accuracy on test set: %0.3f%%\"%(accuracy_score(y_test, y_pred)*100))\n",
    "print(\"Precision on test set: %0.3f\"%(precision_score(y_test, y_pred)))\n",
    "print(\"Recall on test set: %0.3f\"%(recall_score(y_test, y_pred)))\n",
    "print(\"F1-Score on test set: %0.3f\"%(f1_score(y_test, y_pred)))\n",
    "print(\"Confusion Matrix of test set:\\n [ [TN  FP]\\n [FN TP] ]\\n\")\n",
    "df_cm = pd.DataFrame(confusion_matrix(y_test, y_pred), range(2),range(2))\n",
    "sns.set(font_scale=1.4)#for label size\n",
    "sns.heatmap(df_cm, annot=True,annot_kws={\"size\": 16}, fmt='g')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gensim\n",
    "Gensim is a robust open-source vector space modeling and topic modeling toolkit implemented in Python. It uses NumPy, SciPy and optionally Cython for performance. Gensim is specifically designed to handle large text collections, using data streaming and efficient incremental algorithms, which differentiates it from most other scientific software packages that only target batch and in-memory processing."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Word2Vec\n",
    "[Refer Docs] :https://radimrehurek.com/gensim/models/word2vec.html "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from gensim.models import KeyedVectors\n",
    "\n",
    "#Loading the model from file in the disk\n",
    "w2vec_model = KeyedVectors.load_word2vec_format('GoogleNews-vectors-negative300.bin', binary=True) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3000000"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w2v_vocub = w2vec_model.wv.vocab\n",
    "len(w2v_vocub)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Avg Word2Vec\n",
    "* One of the most naive but good ways to convert a sentence into a vector\n",
    "* Convert all the words to vectors and then just take the avg of the vectors the resulting vector represent the sentence "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 11.9 s, sys: 40 ms, total: 11.9 s\n",
      "Wall time: 11.9 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "avg_vec_google = [] #List to store all the avg w2vec's \n",
    "# no_datapoints = 364170\n",
    "# sample_cols = random.sample(range(1, no_datapoints), 20001)\n",
    "for sent in df_sample['CleanedText_NoStem']:\n",
    "    cnt = 0 #to count no of words in each reviews\n",
    "    sent_vec = np.zeros(300) #Initializing with zeroes\n",
    "#     print(\"sent:\",sent) \n",
    "    sent = sent.decode(\"utf-8\") \n",
    "    for word in sent.split():\n",
    "        try:\n",
    "#             print(word)\n",
    "            wvec = w2vec_model.wv[word] #Vector of each using w2v model\n",
    "#             print(\"wvec:\",wvec)\n",
    "            sent_vec += wvec #Adding the vectors\n",
    "#             print(\"sent_vec:\",sent_vec)\n",
    "            cnt += 1\n",
    "        except: \n",
    "            pass #When the word is not in the dictionary then do nothing  \n",
    "#     print(sent_vec)\n",
    "    sent_vec /= cnt #Taking average of vectors sum of the particular review\n",
    "#     print(\"avg_vec:\",sent_vec)\n",
    "    avg_vec_google.append(sent_vec) #Storing the avg w2vec's for each review\n",
    "#     print(\"*******************************************************************\")\n",
    "# print(avg_vec_google)\n",
    "avg_vec_google = np.array(avg_vec_google)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.isnan(avg_vec_google).any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(25000, 300)\n",
      "(25000,)\n"
     ]
    }
   ],
   "source": [
    "mask = ~np.any(np.isnan(avg_vec_google), axis=1)\n",
    "# print(mask)\n",
    "avg_vec_google_new = avg_vec_google[mask]\n",
    "df_sample_new = df_sample['Score'][mask]\n",
    "print(avg_vec_google_new.shape)\n",
    "print(df_sample_new.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import preprocessing\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "avg_vec_norm = preprocessing.normalize(avg_vec_google_new)\n",
    "\n",
    "#Not shuffling the data as we want it on time basis\n",
    "X_train, X_test, y_train, y_test = train_test_split(avg_vec_norm,df_sample_new.values,test_size=0.3,shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 0 ns, sys: 0 ns, total: 0 ns\n",
      "Wall time: 8.82 µs\n",
      "Fitting 10 folds for each of 225 candidates, totalling 2250 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:  1.0min\n",
      "[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed: 21.4min\n",
      "[Parallel(n_jobs=-1)]: Done 410 tasks      | elapsed: 57.5min\n",
      "[Parallel(n_jobs=-1)]: Done 760 tasks      | elapsed: 100.5min\n",
      "[Parallel(n_jobs=-1)]: Done 1210 tasks      | elapsed: 150.8min\n",
      "[Parallel(n_jobs=-1)]: Done 1760 tasks      | elapsed: 194.6min\n",
      "[Parallel(n_jobs=-1)]: Done 2250 out of 2250 | elapsed: 211.4min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best HyperParameter:  {'C': 5, 'gamma': 1}\n",
      "Best Accuracy: 90.00%\n"
     ]
    }
   ],
   "source": [
    "%time\n",
    "from sklearn.model_selection import TimeSeriesSplit\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "clf = SVC()\n",
    "param_grid = {'gamma':[1000,500,100,50,10,5,1,0.5,0.1,0.05,0.01,0.005,0.001,0.0005,0.0001],\n",
    "             'C':[1000,500,100,50,10,5,1,0.5,0.1,0.05,0.01,0.005,0.001,0.0005,0.0001]} #params we need to try on classifier\n",
    "tscv = TimeSeriesSplit(n_splits=10) #For time based splitting\n",
    "gsv = GridSearchCV(clf,param_grid,cv=tscv,verbose=1,n_jobs=-1)\n",
    "gsv.fit(X_train,y_train)\n",
    "print(\"Best HyperParameter: \",gsv.best_params_)\n",
    "print(\"Best Accuracy: %.2f%%\"%(gsv.best_score_*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on test set: 89.573%\n",
      "Precision on test set: 0.801\n",
      "Recall on test set: 0.540\n",
      "F1-Score on test set: 0.645\n",
      "Confusion Matrix of test set:\n",
      " [ [TN  FP]\n",
      " [FN TP] ]\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x3fffa111edd8>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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NaHxt4vXVa6ivb9Byi0VNnnwDhw8fZulLr7UYN+mW6+jRo0eryzICRkODx0dn12pCT0hI4M9//jPJycmsXr2a/Px8PvnkE1avXk1ycjKpqancdttt7THX08IVoy4D4IMNn7i1f/DxJwzo74efrw+joy7l2/IKNhZsNvura2pY98HHjI66xGwbHXUpR44cIXfNerPtyJF63vr3e1x2cThnnnlmo/O//ta/CT5vqLlHXazjjDPO4KYbr2XVW2sabU88Xlzc9Xy6eQuffvp5O82uE2swPD86uVbX0CdNmkS/fv147rnneOWVV8xf77p3786FF15Ieno6V1999Smf6OniFyNHcHF4KHMence+/VUEDDqbt9e9T96GTaT96V7gaKIOHX4BSal/Zcb03+Hdpw9Zi5dhGAbxk24wxzo/OIirrvgF6f/I5PCRegIGDWDZa2/wzZ69pKc0fkLclqIv2bb9KxLvmtpu31dO3nXXHd3iGh4eAsBVsTGUuypwlVfw3o+2sV5zzRh8ffu1epHTETaci4ZfwH2Jc07dpK2kCz3L5YQen3v48GH27Tu6Papfv36cccYZJ3xCKzw+t7qmhr898xy5a9+n6kA1Q4cEcFvcjVxz5WgzprLqAH/NyGLNex9y6NBhQoefT+Jd0zj/Z4FuY31fV8c/nn2eN95ex4HqaoadF8g9dyRw8X//8f/Yw397hmWvruSd5Yvx8+l3yr9ne7Ly43ObewTsu+/mccUvf/gP/tVXsom87GICfurg8OHDzY73xONzuOP23zJkaARlZa5m46ygLR6fW5M6yePYXslLTvp8HUnPQ5fTgpUTuvzv2iShJ0/0OLZX6ksnfb6OpFv/RcTautCSixK6iFibBS52ekoJXUQszQrbET2lhC4i1qYKXUTEIpTQRUQswgK39HtKCV1ELE3vFBURsQoldBERi9AuFxERi1CFLiJiEV0ooesl0SJiaUZ9g8fHiXjmmWcYNmwYqampP5zLMJg3bx5RUVGEhIQwefLkRi8AqqysJDExEafTidPpJDExkaqqKreYoqIi4uLiCAkJITo6moyMDI9eOK6ELiLWdgqeh15YWMjLL7/MsGHD3NqzsrLIzs5m9uzZ5OTk4OPjQ3x8PNXV1WbMjBkz2LJlC1lZWcyfP58tW7Ywc+YPj8uurq4mISEBX19fcnJymDVrFgsWLGDhwoWtzksJXUQszWgwPD48ceDAAe677z4eeughvL29fziPYbBo0SKmTZtGbGwswcHBpKenU1NTw8qVKwEoKSlh/fr1pKamEh4ejsPhYM6cOaxdu5bt248+iXbFihUcPHiQ9PR0goODiY2NZerUqSxcuLDVKl0JXUSsrY0r9NmzZxMbG8vIkSPd2ktLSykvLycyMtJs8/LyYsSIERQUFABQUFCA3W4nPDzcjHE6ndjtdjOmsLCQiIgIvLy8zJioqCjKysooLS1tcW5K6CJibQ0ncLTi5Zdf5uuvv+aPf/xjo77y8nIA/Pz83Np9fX1xuY6+iMTlcuHj44PNZjP7bTYbPj4+bjG+vr5uYxwb81hMc7TLRUQszTjSNvvQt2/fzhNPPMGSJUuafN/vMT9O1p72G4bRKMkf3+/J2EroImJtbXRfUWFhIfv27WPcuHFmW319PRs3buSll14y18nLy8sZOHCgGVNRUWFW2H5+flRUVLglcMMw2Ldvn1mV+/n5NarEKyoqABpV7sfTkouIWFpbXRQdM2YMr7/+OsuXLzeP4cOHc80117B8+XKGDh2Kv78/eXl55mfq6urIz8/H4XAA4HA4qK2tNdfL4ei6em1trRkTFhZGfn4+dXV1ZkxeXh79+/cnICCgxTmqQhcRa2ujCr1v37707dvXrc1ut+Pt7U1wcDAAU6ZM4ZlnniEwMJBzzz2Xp59+GrvdztixYwEICgoiOjqalJQU/vKXv2AYBikpKYwePZrAwKMvkB83bhxPPvkkSUlJ3HHHHXz11VdkZmZy5513aslFRLq29nza4tSpU6mrqyM1NZXKykpCQ0PJzs6md+/eZsxjjz1GWloaCQkJAMTExJCcnGz29+nTh+zsbFJTU5kwYQLe3t4kJCQQHx/f6vlthie3H7Whw67t7Xk66SR6Doru6CnIaejIoW9Oeozvrh3lcazPv9496fN1JFXoImJpxpGOnkH7UUIXEUszus7Tc5XQRcTilNBFRKxBFbqIiEUooYuIWIRR3/LebStRQhcRS1OFLiJiEUaDKnQREUtQhS4iYhGGoQpdRMQSVKGLiFhEg3a5iIhYgy6KiohYhBK6iIhFtO8DwjuWErqIWJoqdBERi9C2RRERi6jXLhcREWtQhS4iYhFaQxcRsQjtchERsQhV6CIiFlHf0K2jp9BulNBFxNK05CIiYhEN2uUiImINXWnbYtdZXBKRLskwPD9as2TJEsaNG0d4eDjh4eHcdNNNrFu37kfnMpg3bx5RUVGEhIQwefJktm3b5jZGZWUliYmJOJ1OnE4niYmJVFVVucUUFRURFxdHSEgI0dHRZGRkYHgwwXav0IcG/7q9TymdwJC+Azp6CmJRbbnkMmDAAO677z7OPfdcGhoaWL58OdOnT+eVV17h/PPPJysri+zsbB555BGGDh3Kk08+SXx8PG+99Ra9e/cGYMaMGezZs4esrCxsNhuzZs1i5syZPPPMMwBUV1eTkJBAREQEOTk57Nixg6SkJOx2OwkJCS3OTxW6iFhafUM3j4/WjBkzhlGjRjFkyBCGDh3KPffcQ69evSgsLMQwDBYtWsS0adOIjY0lODiY9PR0ampqWLlyJQAlJSWsX7+e1NRUwsPDcTgczJkzh7Vr17J9+3YAVqxYwcGDB0lPTyc4OJjY2FimTp3KwoULW63SldBFxNKMEzhORH19PW+88Qa1tbU4HA5KS0spLy8nMjLSjPHy8mLEiBEUFBQAUFBQgN1uJzw83IxxOp3Y7XYzprCwkIiICLy8vMyYqKgoysrKKC0tbXFOuigqIpbW1rtcioqKmDhxInV1ddjtdjIyMhg2bBibNm0CwM/Pzy3e19eXsrIyAFwuFz4+PthsP8zJZrPh4+ODy+UyYwYMcF+CPDamy+Vi8ODBzc5NCV1ELK2td7kMHTqU5cuXU1VVRW5uLvfffz+LFy82+3+crJvSVL9hGI2S/PH9noythC4iltbQxuOdeeaZDBkyBICLLrqIzz77jOeee4477rgDgPLycgYOHGjGV1RUmBW2n58fFRUVbgncMAz27duHr6+vGXOsWv/xGIAZ0xytoYuIpRnYPD7+Fw0NDRw6dIiAgAD8/f3Jy8sz++rq6sjPz8fhcADgcDiora0118vh6Lr6sXV4gLCwMPLz86mrqzNj8vLy6N+/PwEBAS3ORQldRCztiGHz+GjNY489Rn5+PqWlpRQVFfH444+zYcMGxo0bh81mY8qUKWRmZpKbm0txcbG53XDs2LEABAUFER0dTUpKCoWFhRQUFJCSksLo0aMJDAwEYNy4cfTs2ZOkpCSKi4vJzc0lMzOT+Ph4LbmISNf2v1beTXG5XCQmJlJeXk6fPn0YNmwYWVlZREdHAzB16lTq6upITU2lsrKS0NBQsrOzzT3ocPQ/hbS0NHNPeUxMDMnJyWZ/nz59yM7OJjU1lQkTJuDt7U1CQgLx8fGtzs9meHL7URsK8BnenqeTTuIn3c7s6CnIaajEtemkx3h7wE0ex/7y22Unfb6OpApdRCytLSv0050SuohYWlvvcjmdKaGLiKXVq0IXEbGGLvQGOiV0EbG2BlXoIiLW0IXeQKeELiLWpouiIiIW0dDK3ZVWooQuIpZW39ETaEdK6CJiadrlIiJiEdrlIiJiEdrlIiJiEVpyERGxCG1bFBGxiHpV6CIi1qAKXUTEIpTQRUQswoNXhVqGErqIWJoqdBERi9Ct/yIiFqF96CIiFqElFxERi1BCFxGxCD3LRUTEIrSGLiJiEV1pl0u3jp6AiMip1IDh8dGaZ599lgkTJhAeHs6ll17K7bffTnFxsVuMYRjMmzePqKgoQkJCmDx5Mtu2bXOLqaysJDExEafTidPpJDExkaqqKreYoqIi4uLiCAkJITo6moyMDAyj5TkqoYuIpTWcwNGaDRs2cMstt/DSSy/x/PPP0717d+Lj49m/f78Zk5WVRXZ2NrNnzyYnJwcfHx/i4+Oprq42Y2bMmMGWLVvIyspi/vz5bNmyhZkzZ5r91dXVJCQk4OvrS05ODrNmzWLBggUsXLiwxflpyUVELK0tL4ouWLDA7edHH32UiIgINm3aRExMDIZhsGjRIqZNm0ZsbCwA6enpjBw5kpUrVzJx4kRKSkpYv349L774IuHh4QDMmTOHSZMmsX37dgIDA1mxYgUHDx4kPT0dLy8vgoODKSkpYeHChcTHx2Nr5sXXqtBFxNLaskI/Xk1NDQ0NDfTt2xeA0tJSysvLiYyMNGO8vLwYMWIEBQUFABQUFGC3281kDuB0OrHb7WZMYWEhEREReHl5mTFRUVGUlZVRWlra7HyU0EXE0o7YDI+PE/XQQw9xwQUX4HA4ACgvLwfAz8/PLc7X1xeXywWAy+XCx8fHrcq22Wz4+Pi4xfj6+rqNcWzMYzFN0ZKLiFjaqdqH/vDDD/PJJ5+wdOlSunfv7tbX3JJIS/2GYTRK8sf3tza2KnQRsbRTseQyd+5c3njjDZ5//nkGDx5stvv7+wM/VOrHVFRUmBW2n58fFRUVbjtWDMNg3759ZlXu5+fXqBKvqKgAaFS5/5gSuohYWltuWwRIS0tj5cqVPP/88wQFBbn1BQQE4O/vT15entlWV1dHfn6+uSzjcDiora0118vh6Lp6bW2tGRMWFkZ+fj51dXVmTF5eHv379ycgIKDZuSmhi4ilGSdwtGbOnDm8+uqrPP744/Tt25fy8nLKy8upqakBji6HTJkyhczMTHJzcykuLiYpKQm73c7YsWMBCAoKIjo6mpSUFAoLCykoKCAlJYXRo0cTGBgIwLhx4+jZsydJSUkUFxeTm5tLZmZmiztcAGxGazvV21iAz/D2PJ10Ej/pdmZHT0FOQyWuTSc9xn3n3uxx7GNfLW2xf9iwYU2233nnndx1113A0eWTjIwMli1bRmVlJaGhoSQnJxMcHGzG79+/n7S0NNasWQNATEwMycnJ5m4ZOHpjUWpqKps3b8bb25uJEycyffp0JXQ5/SmhS1PaIqHfc+5Ej2P/76uXTvp8HUlLLm0oZkw0OSufo+jrDWzd+RFv/HsZl0VfbPZ7e/flr3+fw+Zt6ynetYGlr2Zx/gU/azRO6Xf/afL4+fCmqwM5PS35VyYlrk1NHguXZQDQq7edB+bczZJ/ZVK44z1KXJu4JNLZ5HgJd0wic8nf+PDz1ZS4NvGHmb9vz6/TaZ3KfeinG21bbCOTfnsDaY/+iefmL+Xvjz1Lt242Lhx+Pj179jRjsl+cx0+HnMPs++dSWVnFnXffxssrsokddT17dn/rNt6yF19jyXP/dGvbXrKzXb6LtI2UxIfp3ae3W5tjRAiz0mbwzlvvAnBWP2+uv+VaPt/8BR+s+5irxl3R7Hg3Tb6O6gPVvP3mOibF33BK524lRhd6gK4SehsIGDyIOXPvJy3lcRY884LZ/u6aH650X/mr0Vwy0smNv44n7/2NAHyy4VM+LFzNHXclkPzAw25j7t1dxqb8ze3zBeSU+LJ4R6O2myaPp67uECtfWw3AN7v24PzZaAAu+8XFLSb0qyKvxzAMunfvroR+AqxQeXtKSy5tYGLceBoaGnhh4cvNxlz5q9Hs3f2tmcwBDhyo5u231nHl1aPbY5rSwX7i9RN+de0Y1qx+j8r9Va1/4DjtfLnLMtp62+LpTAm9DYy4JJwvt+3g19f9ivc/WcVXZYW8n/8mv/3dDxdjgs8P4osvvmz02eIvviRg8CDsvXq6tU9JuImSPZvYVrqRZcsXcPGl4Y0+K51L7NgY+vTpzavLVnb0VLqUtty2eLprk4S+Z88eHnjggbYYqlMacLY/Q4OGMGvODJ76+3xumTCN99Z9yEN/ncXvfh8HwFlneTdZle3/b5v3Wd5m2yvLXudPiWncPP427r9nDv18zmLZvxYwMnJE+3whOSXG3zgWV1kF777zQUeYt/j7AAAJcklEQVRPpUs5guHx0dm1SUKvrKxk+fLlbTFUp9StWzf69OlN0r2pvLjoFfLWb+BP9/2Fte+sZ/rdtwFHbzho6lfmpraU/vGOB3j9tbfY8NEmXv3nSsZfPZlv95aT+OBdp/qryCnS/2w/IkddzL9eWUV9fVd6h07HM07gT2fn0UXR1pL17t2722QyndW+fUcfbv/eujy39nfX5jF6TDQDzvZn//5Kzurn3eiz3t5HbySo3F/Z7Pg11bX8O/c9JsZd14azlvb0mxuuoXv37rz60usdPZUupytdFPUooSclJbltvzteV79YU/xFCc4RYY3aj93R1dDQQNEXJYwaPbJRzM+GBVG6aze1NQdbPEdzFb50DuNvvIYtnxXxxefbWg+WNmWFyttTHi259O/fn/T0dAoKCpo8li5t+XZZq1u18t8AXB4T6dY+KiaS3d/spbysgrdXrWXgoLO59LIIs793n1788qrLeXvV2hbH792nF1dc+QsKP9E2xs7oorALCL7gPF0M7SC6seg4F154IZ9//jlXXnllk/1dvXpc8/Z7fPDexzzyRAr9fPrx9c5Srvn1L7k8JpJ7pj8IQO6qteRvKOQfzz5CWsrjVO6v5M67p2Kz2Xhq3g/vCfz9nbcSdN655K3fwLd7yzln8EBuv/NW/Pv7cde0+zvqK8pJGH/jWA4fPsyKV1Y12T/qisvoae/JsJ8fvWv44suc9PM5i4O1B3n33z8s410UdgHnDB5Et25H67Dzgoea+9bXvfMB3x/8/hR/k86pvgvlJo+e5ZKfn09NTQ2jRo1qsr+2tpb//Oc/XHzxxU32/5hVn+XSu08vkmbfzTW/vhLvs/pSsm0HT/5tPstfedOMOeusvsz6SyJXXR3DT35yJp9s/JQ5s/7K1s+LzJgxsaOYfvdtBJ13Ln369qb6QA0bPy7gH48/S+Gm/3TEV2sXVn2WS48ePcj7z2oKP/mMaZPubjLm3U0rCfjpoEbtpV/vZlT4WPPnR+f9mQk3/7rJMX7huIZvdu1pm0mfRtriWS63DBnvceyLO1876fN1JD2cS04LVk3ocnLaIqHfPOQ3Hscu3dm5d+vp1n8RsTQrrI17SgldRCzNCrf0e0oJXUQsrSttW1RCFxFL60q7XJTQRcTStOQiImIRuigqImIRWkMXEbEILbmIiFhEV3osiRK6iFhavSp0ERFr0JKLiIhFaMlFRMQiVKGLiFhEV9q22CYviRYROV3VG4bHR2s2btzI7bffTnR0NMOGDePVV1916zcMg3nz5hEVFUVISAiTJ09m2zb31w5WVlaSmJiI0+nE6XSSmJhIVVWVW0xRURFxcXGEhIQQHR1NRkaGR0tHSugiYmkNGB4framtrSU4OJgHH3wQLy+vRv1ZWVlkZ2cze/ZscnJy8PHxIT4+nurqajNmxowZbNmyhaysLObPn8+WLVuYOXOm2V9dXU1CQgK+vr7k5OQwa9YsFixYwMKFCxud73hK6CJiaW2Z0EeNGsW9997LVVddZb4K8BjDMFi0aBHTpk0jNjaW4OBg0tPTqampYeXKo++TLSkpYf369aSmphIeHo7D4WDOnDmsXbuW7du3A7BixQoOHjxIeno6wcHBxMbGMnXqVBYuXNhqla6ELiKWZhiGx8fJKC0tpby8nMjIH14W7+XlxYgRIygoKACgoKAAu91OeHi4GeN0OrHb7WZMYWEhERERbr8BREVFUVZWRmlpaYtzUEIXEUtrywq9JeXl5QD4+fm5tfv6+uJyuQBwuVz4+Phgs9nMfpvNho+Pj1uMr6+v2xjHxjwW0xztchERS2vvXS4/Ttae9huG0SjJH9/vydiq0EXE0uqNBo+Pk+Hv7w/8UKkfU1FRYVbYfn5+VFRUuC3vGIbBvn37zKrcz8+vUSVeUVEB0KhyP54SuohYWnutoQcEBODv709eXp7ZVldXR35+Pg6HAwCHw0Ftba25Xg5H19Vra2vNmLCwMPLz86mrqzNj8vLy6N+/PwEBAS3OQQldRCytLdfQa2pq2Lp1K1u3bqWhoYHdu3ezdetWdu/ejc1mY8qUKWRmZpKbm0txcTFJSUnY7XbGjh0LQFBQENHR0aSkpFBYWEhBQQEpKSmMHj2awMBAAMaNG0fPnj1JSkqiuLiY3NxcMjMziY+Pb305x2jnBx0E+Axvz9NJJ/GTbmd29BTkNFTi2nTSY4ScPdLj2M17P2yx/+OPP2bKlCmN2sePH88jjzyCYRhkZGSwbNkyKisrCQ0NJTk5meDgYDN2//79pKWlsWbNGgBiYmJITk6mb9++ZkxRURGpqals3rwZb29vJk6cyPTp05XQpXNQQpemtEVCHz7gUo9j//PtRyd9vo6kXS4iYmld6VkuSugiYmknu3ulM1FCFxFLa9Dz0EVErEFLLiIiFqEKXUTEIlShi4hYRL1R39FTaDdK6CJiaXpJtIiIRegl0SIiFqEKXUTEIrTLRUTEIrTLRUTEInTrv4iIRWgNXUTEIrSGLiJiEarQRUQsQvvQRUQsQhW6iIhFaJeLiIhF6KKoiIhFaMlFRMQidKeoiIhFqEIXEbGIrrSGbjO60n9fIiIW1q2jJyAiIm1DCV1ExCKU0EVELEIJXUTEIpTQRUQsQgldRMQilNBFRCxCCb2DLFmyhJiYGC666CKuu+468vPzO3pK0oE2btzI7bffTnR0NMOGDePVV1/t6ClJJ6SE3gHefPNN5s6dy+23387y5ctxOBxMnTqV3bt3d/TUpIPU1tYSHBzMgw8+iJeXV0dPRzop3SnaAW644QaGDRtGWlqa2XbllVcSGxvLjBkzOnBmcjpwOBzMnj2b6667rqOnIp2MKvR2dujQIT7//HMiIyPd2iMjIykoKOigWYmIFSiht7N9+/ZRX1+Pn5+fW7uvry/l5eUdNCsRsQIl9A5is9k8ahMR8ZQSejvr168f3bt3b1SNV1RUNKraRUROhBJ6OzvzzDO58MILycvLc2vPy8vD4XB00KxExAr0gosOEB8fz8yZMwkJCSE8PJylS5dSVlbGxIkTO3pq0kFqamr4+uuvAWhoaGD37t1s3boVb29vBg0a1MGzk85C2xY7yJIlS1iwYAFlZWUEBwfzwAMPMGLEiI6elnSQjz/+mClTpjRqHz9+PI888kgHzEg6IyV0ERGL0Bq6iIhFKKGLiFiEErqIiEUooYuIWIQSuoiIRSihi4hYhBK6iIhFKKGLiFiEErqIiEX8fyS56PXq6B17AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x3fffa1122240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Testing Accuracy on Test data\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "clf = SVC(C=5,gamma=1)\n",
    "clf.fit(X_train,y_train)\n",
    "y_pred = clf.predict(X_test)\n",
    "print(\"Accuracy on test set: %0.3f%%\"%(accuracy_score(y_test, y_pred)*100))\n",
    "print(\"Precision on test set: %0.3f\"%(precision_score(y_test, y_pred)))\n",
    "print(\"Recall on test set: %0.3f\"%(recall_score(y_test, y_pred)))\n",
    "print(\"F1-Score on test set: %0.3f\"%(f1_score(y_test, y_pred)))\n",
    "print(\"Confusion Matrix of test set:\\n [ [TN  FP]\\n [FN TP] ]\\n\")\n",
    "df_cm = pd.DataFrame(confusion_matrix(y_test, y_pred), range(2),range(2))\n",
    "sns.set(font_scale=1.4)#for label size\n",
    "sns.heatmap(df_cm, annot=True,annot_kws={\"size\": 16}, fmt='g')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Using Randomized Search CV to find best parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 0 ns, sys: 0 ns, total: 0 ns\n",
      "Wall time: 8.34 µs\n",
      "Fitting 10 folds for each of 15 candidates, totalling 150 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done 150 out of 150 | elapsed: 294.9min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best HyperParameter:  {'gamma': 0.005, 'C': 500}\n",
      "Best Accuracy: 89.19%\n"
     ]
    }
   ],
   "source": [
    "%time\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from sklearn.svm import SVC\n",
    "from scipy.stats import uniform as sp_rand\n",
    "\n",
    "clf = SVC()\n",
    "#params we need to try on classifier\n",
    "param_dist = {'gamma':[1000,500,100,50,10,5,1,0.5,0.1,0.05,0.01,0.005,0.001,0.0005,0.0001],\n",
    "             'C':[1000,500,100,50,10,5,1,0.5,0.1,0.05,0.01,0.005,0.001,0.0005,0.0001]} \n",
    "tscv = TimeSeriesSplit(n_splits=10) #For time based splitting\n",
    "gsv = RandomizedSearchCV(clf,param_dist,cv=tscv,verbose=1,n_iter=15)\n",
    "gsv.fit(X_train,y_train)\n",
    "print(\"Best HyperParameter: \",gsv.best_params_)\n",
    "print(\"Best Accuracy: %.2f%%\"%(gsv.best_score_*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on test set: 88.800%\n",
      "Precision on test set: 0.786\n",
      "Recall on test set: 0.497\n",
      "F1-Score on test set: 0.609\n",
      "Confusion Matrix of test set:\n",
      " [ [TN  FP]\n",
      " [FN TP] ]\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x3ffe14311ef0>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x3ffe142fd668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Testing Accuracy on Test data\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "clf = SVC(C=500,gamma=0.005)\n",
    "clf.fit(X_train,y_train)\n",
    "y_pred = clf.predict(X_test)\n",
    "print(\"Accuracy on test set: %0.3f%%\"%(accuracy_score(y_test, y_pred)*100))\n",
    "print(\"Precision on test set: %0.3f\"%(precision_score(y_test, y_pred)))\n",
    "print(\"Recall on test set: %0.3f\"%(recall_score(y_test, y_pred)))\n",
    "print(\"F1-Score on test set: %0.3f\"%(f1_score(y_test, y_pred)))\n",
    "print(\"Confusion Matrix of test set:\\n [ [TN  FP]\\n [FN TP] ]\\n\")\n",
    "df_cm = pd.DataFrame(confusion_matrix(y_test, y_pred), range(2),range(2))\n",
    "sns.set(font_scale=1.4)#for label size\n",
    "sns.heatmap(df_cm, annot=True,annot_kws={\"size\": 16}, fmt='g')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tf-idf W2Vec\n",
    "* Another way to covert sentence into vectors\n",
    "* Take weighted sum of the vectors divided by the sum of all the tfidf's \n",
    "<br>i.e. (tfidf(word) x w2v(word))/sum(tfidf's)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(25000, 589499)\n",
      "CPU times: user 6.15 s, sys: 16 ms, total: 6.16 s\n",
      "Wall time: 6.16 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "#Taking Sample Data as it was taking more that 10 hours to computer this block\n",
    "n_samples = 25000\n",
    "df_sample_new = df_sample.sample(n_samples)\n",
    "\n",
    "###Sorting as we want according to time series\n",
    "df_sample_new.sort_values('Time',inplace=True) \n",
    "\n",
    "###tf-idf with No Stemming\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "tfidf = TfidfVectorizer(ngram_range=(1,2)) #Using bi-grams\n",
    "\n",
    "tfidf_vec_new = tfidf.fit_transform(df_sample_new['CleanedText_NoStem'].values)\n",
    "\n",
    "print(tfidf_vec_new.shape)\n",
    "\n",
    "# tf-idf came up with 2.9 million features for the data corpus\n",
    "# from sklearn.decomposition import TruncatedSVD\n",
    "\n",
    "# tsvd_tfidf_ns = TruncatedSVD(n_components=300)#No of components as total dimensions\n",
    "# tsvd_tfidf_vec_ns = tsvd_tfidf_ns.fit_transform(tfidf_vec_ns)\n",
    "# print(tsvd_tfidf_ns.explained_variance_ratio_[:].sum())\n",
    "features = tfidf.get_feature_names()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "tfidf_w2v_vec_google = []\n",
    "review = 0\n",
    "\n",
    "for sent in df_sample_new['CleanedText_NoStem'].values:\n",
    "    cnt = 0 \n",
    "    weighted_sum  = 0\n",
    "    sent_vec = np.zeros(300)\n",
    "    sent = sent.decode(\"utf-8\") \n",
    "    for word in sent.split():\n",
    "        try:\n",
    "#             print(word)\n",
    "            wvec = w2vec_model.wv[word] #Vector of each using w2v model\n",
    "#             print(\"w2vec:\",wvec)\n",
    "#             print(\"tfidf:\",tfidf_vec_ns[review,features.index(word)])\n",
    "            tfidf_vec = tfidf_vec_new[review,features.index(word)]\n",
    "            sent_vec += (wvec * tfidf_vec)\n",
    "            weighted_sum += tfidf_vec\n",
    "        except:\n",
    "#             print(review)\n",
    "            pass\n",
    "    sent_vec /= weighted_sum\n",
    "#     print(sent_vec)\n",
    "    tfidf_w2v_vec_google.append(sent_vec)\n",
    "    review += 1\n",
    "tfidf_w2v_vec_google = np.array(tfidf_w2v_vec_google)\n",
    "savetofile(tfidf_w2v_vec_google,\"tfidf_w2v_vec_google\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Precomputed File\n",
    "tfidf_w2v_vec_google = openfromfile(\"tfidf_w2v_vec_google\")\n",
    "#Loading the same samples as using precomuted file\n",
    "df_sample_new = openfromfile(\"df_sample_new_tfidfw2vec\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import preprocessing\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "tfidfw2v_vecs_norm = preprocessing.normalize(tfidf_w2v_vec_google)\n",
    "\n",
    "#Not shuffling the data as we want it on time basis\n",
    "X_train, X_test, y_train, y_test = train_test_split(tfidfw2v_vecs_norm,df_sample_new['Score'].values,test_size=0.3,shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 0 ns, sys: 0 ns, total: 0 ns\n",
      "Wall time: 13.1 µs\n",
      "Fitting 10 folds for each of 225 candidates, totalling 2250 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:   57.9s\n",
      "[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed: 24.2min\n",
      "[Parallel(n_jobs=-1)]: Done 410 tasks      | elapsed: 65.3min\n",
      "[Parallel(n_jobs=-1)]: Done 760 tasks      | elapsed: 112.6min\n",
      "[Parallel(n_jobs=-1)]: Done 1210 tasks      | elapsed: 163.2min\n",
      "[Parallel(n_jobs=-1)]: Done 1760 tasks      | elapsed: 214.0min\n",
      "[Parallel(n_jobs=-1)]: Done 2250 out of 2250 | elapsed: 235.1min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best HyperParameter:  {'C': 5, 'gamma': 0.5}\n",
      "Best Accuracy: 88.08%\n"
     ]
    }
   ],
   "source": [
    "%time\n",
    "from sklearn.model_selection import TimeSeriesSplit\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "clf = SVC()\n",
    "param_grid = {'gamma':[1000,500,100,50,10,5,1,0.5,0.1,0.05,0.01,0.005,0.001,0.0005,0.0001],\n",
    "             'C':[1000,500,100,50,10,5,1,0.5,0.1,0.05,0.01,0.005,0.001,0.0005,0.0001]} #params we need to try on classifier\n",
    "tscv = TimeSeriesSplit(n_splits=10) #For time based splitting\n",
    "gsv = GridSearchCV(clf,param_grid,cv=tscv,verbose=1,n_jobs=-1)\n",
    "gsv.fit(X_train,y_train)\n",
    "print(\"Best HyperParameter: \",gsv.best_params_)\n",
    "print(\"Best Accuracy: %.2f%%\"%(gsv.best_score_*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on test set: 88.707%\n",
      "Precision on test set: 0.777\n",
      "Recall on test set: 0.421\n",
      "F1-Score on test set: 0.546\n",
      "Confusion Matrix of test set:\n",
      " [ [TN  FP]\n",
      " [FN TP] ]\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x3fff2d37d390>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x3fff2d283dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Testing Accuracy on Test data\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "clf = SVC(C=5,gamma=0.5)\n",
    "clf.fit(X_train,y_train)\n",
    "y_pred = clf.predict(X_test)\n",
    "print(\"Accuracy on test set: %0.3f%%\"%(accuracy_score(y_test, y_pred)*100))\n",
    "print(\"Precision on test set: %0.3f\"%(precision_score(y_test, y_pred)))\n",
    "print(\"Recall on test set: %0.3f\"%(recall_score(y_test, y_pred)))\n",
    "print(\"F1-Score on test set: %0.3f\"%(f1_score(y_test, y_pred)))\n",
    "print(\"Confusion Matrix of test set:\\n [ [TN  FP]\\n [FN TP] ]\\n\")\n",
    "df_cm = pd.DataFrame(confusion_matrix(y_test, y_pred), range(2),range(2))\n",
    "sns.set(font_scale=1.4)#for label size\n",
    "sns.heatmap(df_cm, annot=True,annot_kws={\"size\": 16}, fmt='g')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Using Randomized Search CV to find best parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 0 ns, sys: 0 ns, total: 0 ns\n",
      "Wall time: 7.63 µs\n",
      "Fitting 10 folds for each of 15 candidates, totalling 150 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done 150 out of 150 | elapsed: 224.7min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best HyperParameter:  {'gamma': 0.05, 'C': 1000}\n",
      "Best Accuracy: 87.45%\n"
     ]
    }
   ],
   "source": [
    "%time\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "clf = SVC()\n",
    "#params we need to try on classifier\n",
    "param_dist = {'gamma':[1000,500,100,50,10,5,1,0.5,0.1,0.05,0.01,0.005,0.001,0.0005,0.0001],\n",
    "             'C':[1000,500,100,50,10,5,1,0.5,0.1,0.05,0.01,0.005,0.001,0.0005,0.0001]} \n",
    "tscv = TimeSeriesSplit(n_splits=10) #For time based splitting\n",
    "gsv = RandomizedSearchCV(clf,param_dist,cv=tscv,verbose=1,n_iter=15)\n",
    "gsv.fit(X_train,y_train)\n",
    "print(\"Best HyperParameter: \",gsv.best_params_)\n",
    "print(\"Best Accuracy: %.2f%%\"%(gsv.best_score_*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on test set: 88.840%\n",
      "Precision on test set: 0.741\n",
      "Recall on test set: 0.475\n",
      "F1-Score on test set: 0.579\n",
      "Confusion Matrix of test set:\n",
      " [ [TN  FP]\n",
      " [FN TP] ]\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x3fff2dc43748>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x3fff2dc43fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Testing Accuracy on Test data\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "clf = SVC(C=1000,gamma=0.05)\n",
    "clf.fit(X_train,y_train)\n",
    "y_pred = clf.predict(X_test)\n",
    "print(\"Accuracy on test set: %0.3f%%\"%(accuracy_score(y_test, y_pred)*100))\n",
    "print(\"Precision on test set: %0.3f\"%(precision_score(y_test, y_pred)))\n",
    "print(\"Recall on test set: %0.3f\"%(recall_score(y_test, y_pred)))\n",
    "print(\"F1-Score on test set: %0.3f\"%(f1_score(y_test, y_pred)))\n",
    "print(\"Confusion Matrix of test set:\\n [ [TN  FP]\\n [FN TP] ]\\n\")\n",
    "df_cm = pd.DataFrame(confusion_matrix(y_test, y_pred), range(2),range(2))\n",
    "sns.set(font_scale=1.4)#for label size\n",
    "sns.heatmap(df_cm, annot=True,annot_kws={\"size\": 16}, fmt='g')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Performance Table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image](https://image.ibb.co/mvrHz8/5_SVM.png)"
   ]
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    "## Conclusions<br>\n",
    "\n",
    "1. Support Vector Machine(SVM) gave the best result better than other algos close to Logistic Regression\n",
    "2. Tf-idf Featurization(C=1000,gamma=0.005) gave the best results with accuracy of 91.667% and F1-score of 0.733\n",
    "3. SVM with RBF kernel the separating plane exists in another space - a result of kernel transformation of the original space. Its coefficients are not directly related to the input space. Hence we can't get the feature importance<br>\n",
    "4. Also tried SGDClassifier with the best result i.e. with tfidf featurization it was very quick and gave around same score in just seconds with accuracy of 91.04% and F1-score of 0.734 with (alpha=1e-05,penalty='l1')"
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