SVM_on_AFFR.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing the dependencies:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import sqlite3    \n",
    "import pickle    \n",
    "import time\n",
    "\n",
    "from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "from sklearn import svm\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import classification_report\n",
    "\n",
    "\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix\n",
    "from sklearn.model_selection import train_test_split, cross_val_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reading the cleaned sqlite file. \n",
    "###### (cleanning process show in previous file.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\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>1</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>bought sever vital can dog food product found ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\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>0</td>\n",
       "      <td>1346976000</td>\n",
       "      <td>Not as Advertised</td>\n",
       "      <td>Product arrived labeled as Jumbo Salted Peanut...</td>\n",
       "      <td>product arriv label jumbo salt peanut peanut a...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\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>1</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>confect around centuri light pillowi citrus ge...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\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>0</td>\n",
       "      <td>1307923200</td>\n",
       "      <td>Cough Medicine</td>\n",
       "      <td>If you are looking for the secret ingredient i...</td>\n",
       "      <td>look secret ingredi robitussin believ found go...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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>1</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>great taffi great price wide assort yummi taff...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id   ProductId          UserId                      ProfileName  \\\n",
       "0   1  B001E4KFG0  A3SGXH7AUHU8GW                       delmartian   \n",
       "1   2  B00813GRG4  A1D87F6ZCVE5NK                           dll pa   \n",
       "2   3  B000LQOCH0   ABXLMWJIXXAIN  Natalia Corres \"Natalia Corres\"   \n",
       "3   4  B000UA0QIQ  A395BORC6FGVXV                             Karl   \n",
       "4   5  B006K2ZZ7K  A1UQRSCLF8GW1T    Michael D. Bigham \"M. Wassir\"   \n",
       "\n",
       "   HelpfulnessNumerator  HelpfulnessDenominator  Score        Time  \\\n",
       "0                     1                       1      1  1303862400   \n",
       "1                     0                       0      0  1346976000   \n",
       "2                     1                       1      1  1219017600   \n",
       "3                     3                       3      0  1307923200   \n",
       "4                     0                       0      1  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  bought sever vital can dog food product found ...  \n",
       "1  product arriv label jumbo salt peanut peanut a...  \n",
       "2  confect around centuri light pillowi citrus ge...  \n",
       "3  look secret ingredi robitussin believ found go...  \n",
       "4  great taffi great price wide assort yummi taff...  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conn = sqlite3.connect('C:\\\\Users\\\\HP\\\\Desktop\\\\GRE\\\\ML\\\\final.sqlite')\n",
    "final = pd.read_sql_query(\"\"\"SELECT * FROM Reviews\"\"\", conn)\n",
    "conn.close()\n",
    "final.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### using sample dataset since svm takes quit a long time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "negative = final[final['Score'] == 0][['Score', 'Time', 'CleanedText']]\n",
    "positive = final[final['Score'] == 1][['Score', 'Time', 'CleanedText']]\n",
    "data = pd.concat([negative.iloc[:5,:], positive.iloc[:5,:]], axis = 0, ignore_index = True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### sorting data based on time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Score</th>\n",
       "      <th>Time</th>\n",
       "      <th>CleanedText</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>117879</th>\n",
       "      <td>1</td>\n",
       "      <td>939340800</td>\n",
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       "      <td>1</td>\n",
       "      <td>940809600</td>\n",
       "      <td>rememb see show air televis year ago child sis...</td>\n",
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       "      <th>298565</th>\n",
       "      <td>1</td>\n",
       "      <td>944092800</td>\n",
       "      <td>beetlejuic well written movi everyth excel act...</td>\n",
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       "      <th>169198</th>\n",
       "      <td>1</td>\n",
       "      <td>944438400</td>\n",
       "      <td>twist rumplestiskin captur film star michael k...</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>169259</th>\n",
       "      <td>1</td>\n",
       "      <td>947376000</td>\n",
       "      <td>one movi movi collect fill comedi action whate...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169184</th>\n",
       "      <td>0</td>\n",
       "      <td>948240000</td>\n",
       "      <td>alway enjoy movi funni entertain hesit pick cl...</td>\n",
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       "    <tr>\n",
       "      <th>63292</th>\n",
       "      <td>1</td>\n",
       "      <td>948672000</td>\n",
       "      <td>bought apart infest fruit fli hour trap quot a...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169284</th>\n",
       "      <td>1</td>\n",
       "      <td>951523200</td>\n",
       "      <td>happen say name three time michael keaten star...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298566</th>\n",
       "      <td>1</td>\n",
       "      <td>959990400</td>\n",
       "      <td>get crazi realli imposs today find french vhs ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Score       Time                                        CleanedText\n",
       "117879      1  939340800  witti littl book make son laugh loud recit car...\n",
       "117856      1  940809600  rememb see show air televis year ago child sis...\n",
       "298565      1  944092800  beetlejuic well written movi everyth excel act...\n",
       "169198      1  944438400  twist rumplestiskin captur film star michael k...\n",
       "298564      1  946857600  beetlejuic excel funni movi keaton hilari wack...\n",
       "169259      1  947376000  one movi movi collect fill comedi action whate...\n",
       "169184      0  948240000  alway enjoy movi funni entertain hesit pick cl...\n",
       "63292       1  948672000  bought apart infest fruit fli hour trap quot a...\n",
       "169284      1  951523200  happen say name three time michael keaten star...\n",
       "298566      1  959990400  get crazi realli imposs today find french vhs ..."
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = final[['Score', 'Time', 'CleanedText']].copy()\n",
    "data.sort_values('Time', inplace = True)\n",
    "data.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ### Unbalanced dataset:\n",
    "(can be balanced by upsampling the negative datapoints.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    306779\n",
       "0     57078\n",
       "Name: Score, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Score'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Deleting the final variable inorder to empty space:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "del final"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Separating the target variable from the features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data['CleanedText']\n",
    "y = data['Score']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Splitting the training and testing data:\n",
    "(70:30 ratio)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_raw, X_test_raw, y_train, y_test = train_test_split(X[:8000], y[:8000], test_size = 0.3, shuffle = False )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((5600,), (5600,), (2400,), (2400,))"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_raw.shape, y_train.shape, X_test_raw.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['witti littl book make son laugh loud recit car drive along alway sing refrain learn whale india droop rose love new word book introduc silli classic book will bet son still abl recit memori colleg',\n",
       "       'rememb see show air televis year ago child sister later bought day thirti someth use seri book amp song student teach preschool amp turn whole school purchas along book children amp tradit live',\n",
       "       'beetlejuic well written movi everyth excel act special effect delight chose view movi',\n",
       "       ...,\n",
       "       'famili love marinad smoki sweet flavor tast wonder chicken love dice chicken use fill soft taco burrito',\n",
       "       'difficult oatmeal quinoa cook breakfast small grain must shaken apart ad water frequent stir requir avoid sticki gel like mass bottom pan minor annoy asid teff excel flavor nutrit benefit includ rda iron great fruit nut breakfast base veget casserol',\n",
       "       'spent time recent year old man actual quit famous main thing mind maintain sens regular much younger sens import maintain balanc high fiber diet even age product tast wonder job nuff said'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_raw.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Applying Count BOW text preprocessing technique on training data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "bow = CountVectorizer()\n",
    "X_train = bow.fit_transform(X_train_raw.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5600, 12476)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Applying BOW vectorizer text preprocessing technique on testing data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test = bow.transform(X_test_raw.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2400, 12476)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Standardizing  features by removing the mean and scaling to unit variance:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\python\\lib\\site-packages\\sklearn\\utils\\validation.py:475: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n"
     ]
    }
   ],
   "source": [
    "s = StandardScaler(with_mean=False) \n",
    "\n",
    "X_train= s.fit_transform(X_train)\n",
    "\n",
    "X_test = s.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Performing cross validation to find the best hyperparameters(c, gamma),kernels & finding accuracy on training data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time required =515.1245949268341s\n"
     ]
    }
   ],
   "source": [
    "start = time.time()\n",
    "clf = SVC()\n",
    "param_grid = [{'kernel':['rbf'],'gamma':[50,5,10,0.5],\n",
    "             'C':[10,0.1,0.001] }]\n",
    "gsv = GridSearchCV(clf,param_grid,cv=5,n_jobs=-1)\n",
    "gsv.fit(X_train,y_train)\n",
    "end = time.time()\n",
    "print(\"Time required =\"+str(end-start)+\"s\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best HyperParameter:  {'C': 10, 'gamma': 50, 'kernel': 'rbf'}\n",
      "Best Accuracy: 88.34%\n"
     ]
    }
   ],
   "source": [
    "\n",
    "print(\"Best HyperParameter: \",gsv.best_params_)\n",
    "print(\"Best Accuracy: %.2f%%\"%(gsv.best_score_*100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### appying model on testing data and finding accuracy on it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy = 89.41666666666667\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[   0,  254],\n",
       "       [   0, 2146]], dtype=int64)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = SVC(C= 10, gamma = 0.5 )\n",
    "clf.fit(X_train , y_train)\n",
    "y_pred = clf.predict(X_test)\n",
    "acc = accuracy_score(y_test, y_pred) * 100\n",
    "print(\"Accuracy =\", acc)\n",
    "confusion_matrix(y_test, y_pred)"
   ]
  }
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