RBF_SVM_AMAZ.ipynb

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      "name": "RBF-SVM_AMAZ.ipynb",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4VYQCKuefrqU"
      },
      "source": [
        "RBF KERNAL SVM FOR AMAZON DATASET\n",
        "===================="
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tsCD0OL4QOnn"
      },
      "source": [
        "import pandas as pd \n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plot\n",
        "from sklearn.feature_extraction.text import TfidfTransformer\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from sklearn.cross_validation import train_test_split\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.model_selection import RandomizedSearchCV\n",
        "from sklearn.svm import SVC\n",
        "from datetime import datetime\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn import cross_validation\n",
        "import seaborn as sns\n",
        "from sklearn.metrics import precision_recall_fscore_support, classification_report\n",
        "from sklearn import linear_model\n",
        "from sklearn.linear_model import SGDClassifier\n",
        "from sklearn.metrics import f1_score\n",
        "from sklearn.metrics import precision_score\n",
        "from sklearn.metrics import recall_score\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.metrics import confusion_matrix"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7Y-iGeTd5ZH1"
      },
      "source": [
        "from sklearn.model_selection import RandomizedSearchCV\n",
        "from sklearn.metrics import precision_recall_fscore_support, classification_report\n",
        "from sklearn.svm import SVC\n",
        "from datetime import datetime"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V_ACLvqTShZf",
        "outputId": "a6366ad2-0279-41ee-d190-15e96654692d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 369
        }
      },
      "source": [
        "data = pd.read_csv('final_clean.csv')\n",
        "data.head(2)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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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>150524</td>\n",
              "      <td>0006641040</td>\n",
              "      <td>ACITT7DI6IDDL</td>\n",
              "      <td>shari zychinski</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>939340800</td>\n",
              "      <td>EVERY book is educational</td>\n",
              "      <td>this witty little book makes my son laugh at l...</td>\n",
              "      <td>b'witti littl book make son laugh loud recit c...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>150506</td>\n",
              "      <td>0006641040</td>\n",
              "      <td>A2IW4PEEKO2R0U</td>\n",
              "      <td>Tracy</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1194739200</td>\n",
              "      <td>Love the book, miss the hard cover version</td>\n",
              "      <td>I grew up reading these Sendak books, and watc...</td>\n",
              "      <td>b'grew read sendak book watch realli rosi movi...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            ],
            "text/plain": [
              "       Id   ProductId          UserId      ProfileName  HelpfulnessNumerator  \\\n",
              "0  150524  0006641040   ACITT7DI6IDDL  shari zychinski                     0   \n",
              "1  150506  0006641040  A2IW4PEEKO2R0U            Tracy                     1   \n",
              "\n",
              "   HelpfulnessDenominator  Score        Time  \\\n",
              "0                       0      1   939340800   \n",
              "1                       1      1  1194739200   \n",
              "\n",
              "                                      Summary  \\\n",
              "0                   EVERY book is educational   \n",
              "1  Love the book, miss the hard cover version   \n",
              "\n",
              "                                                Text  \\\n",
              "0  this witty little book makes my son laugh at l...   \n",
              "1  I grew up reading these Sendak books, and watc...   \n",
              "\n",
              "                                         cleanedText  \n",
              "0  b'witti littl book make son laugh loud recit c...  \n",
              "1  b'grew read sendak book watch realli rosi movi...  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xaKDlR6D3eTu",
        "outputId": "e056a36a-032b-477a-9222-adc2ef9d4d18",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "final=data.sample(n=50000 ,replace=True,random_state=None, axis=0)\n",
        "final.shape\n",
        "#sampling 50k points"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(50000, 11)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LnKZnpVZvjme"
      },
      "source": [
        "X = final['Text']\n",
        "Y = final['Score']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lWpGGUVBSlnW"
      },
      "source": [
        "from sklearn import cross_validation\n",
        "X_train, X_test, Y_train, Y_test = cross_validation.train_test_split(X, Y, test_size=0.3, random_state=0)\n",
        "#train-test split"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hnhkVqgxgwst"
      },
      "source": [
        "TFIDF  featurization for modeling\n",
        "==="
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "elHQDtWEVMKb"
      },
      "source": [
        "tf_idf_vect = TfidfVectorizer()\n",
        "tfidf_train = tf_idf_vect.fit_transform(X_train)\n",
        "tfidf_test = tf_idf_vect.transform(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Xp4D5LsB0f6K"
      },
      "source": [
        "RANDOM SEARCH CV \n",
        "---------------"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "goYQzHqInaTH"
      },
      "source": [
        "\n",
        "startTime = datetime.now()\n",
        "\n",
        "def randomcv(train_feat,test_feat,y_train,y_test):\n",
        "    parameters = {'C':[10**-4,10**-3,10**-2, 10**0, 10**2,10**-3,10**4],\n",
        "              'gamma': [10**-4,10**-3,10**-2, 10**0, 10**2,10**-3,10**4]}\n",
        "    svm = SVC(kernel='rbf')\n",
        "    svm_rbf_cv= RandomizedSearchCV(svm, param_distributions=parameters, n_jobs=-1,scoring='f1',cv=3)\n",
        "    svm_rbf_cv.fit(train_feat, y_train)\n",
        "    print('Best Estimator:      ',svm_rbf_cv.best_estimator_)\n",
        "    print('Optimal parameters:  ',svm_rbf_cv.best_params_)\n",
        "    print('optimal score:       ',svm_rbf_cv.best_score_*(100))\n",
        "    print('-------------------------------------------------')\n",
        "    svm_rbf_cv.best_estimator_.fit(train_feat, y_train)\n",
        "\n",
        "    y_true, y_pred = y_test,svm_rbf_cv.best_estimator_.predict(test_feat)\n",
        "    confusion = confusion_matrix(y_true,y_pred)\n",
        "    accuracy = accuracy_score(y_true,y_pred)\n",
        "    acc=accuracy_score(y_train,svm_rbf_cv.best_estimator_.predict(train_feat))\n",
        "\n",
        "    #printing obtained performance metrics\n",
        "    print('Accuracy on training set is:',acc*100)\n",
        "    print('Accuracy on test set is:',accuracy*100)\n",
        "    print(\"Classification_Report\")\n",
        "\n",
        "    print(classification_report(y_true, y_pred))\n",
        "\n",
        "    print(precision_recall_fscore_support(y_true, y_pred, average='micro'))\n",
        "    print('<==========confusion matrix for test data=========>')\n",
        "    p= sns.heatmap(confusion, annot=True, fmt=\"d\", cmap=\"Spectral\",square=True)\n",
        "    print(datetime.now() - startTime)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O_OBiHiiFy67",
        "outputId": "a1589f86-0cf0-419d-8269-a46b36a21d73",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 725
        }
      },
      "source": [
        "randomcv(tfidf_train,tfidf_test,Y_train,Y_test)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "***********************************************\n",
            "Best Estimator:       SVC(C=100, cache_size=200, class_weight=None, coef0=0.0,\n",
            "  decision_function_shape='ovr', degree=3, gamma=0.01, kernel='rbf',\n",
            "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
            "  tol=0.001, verbose=False)\n",
            "Optimal parameters:   {'gamma': 0.01, 'C': 100}\n",
            "optimal score:        94.95379229403366\n",
            "-------------------------------------------------\n",
            "Accuracy on training set is: 96.03714285714285\n",
            "Accuracy on test set is: 91.75999999999999\n",
            "Classification_Report\n",
            "             precision    recall  f1-score   support\n",
            "\n",
            "          0       0.81      0.64      0.71      2406\n",
            "          1       0.93      0.97      0.95     12594\n",
            "\n",
            "avg / total       0.91      0.92      0.91     15000\n",
            "\n",
            "(0.9176, 0.9176, 0.9176, None)\n",
            "<==========confusion matrix for test data=========>\n",
            "5:03:36.596307\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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M/7xf3fpdJnt0lKre3S13zq/V1tAU7q6EXSTt/hvfPVYPzf+uJKnXFd1VebhO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\nYMn/AiueL0dNbN6OAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f7c4cfe2b70>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w0otHbO1gn-a"
      },
      "source": [
        "GRID-SEARCHCV\n",
        "---"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PDsguH-IFqyh"
      },
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "def grid(train_feat,test_feat,y_train,y_test):\n",
        "    parameters = {'C':[10**-4,10**-3,10**-2, 10**0, 10**2,10**-3,10**4],\n",
        "              'gamma': [10**-4,10**-3,10**-2, 10**0, 10**2,10**-3,10**4]}\n",
        "    svm = SVC(kernel='rbf')\n",
        "    svm_rbf= GridSearchCV(svm,param_grid=parameters,n_jobs=-1,scoring='f1',cv=3)\n",
        "    svm_rbf.fit(train_feat,y_train)\n",
        "    print('Best Estimator:      ',svm_rbf.best_estimator_)\n",
        "    print('Optimal parameters:  ',svm_rbf.best_params_)\n",
        "    print('optimal score:       ',svm_rbf.best_score_*(100))\n",
        "    print('-------------------------------------------------')\n",
        "    svm_rbf.best_estimator_.fit(train_feat, y_train)\n",
        "\n",
        "    y_true, y_pred = y_test,svm_rbf.best_estimator_.predict(test_feat)\n",
        "    confusion = confusion_matrix(y_true,y_pred)\n",
        "    accuracy = accuracy_score(y_true,y_pred)\n",
        "    acc=accuracy_score(y_train,svm_rbf.best_estimator_.predict(train_feat))\n",
        "\n",
        "    #printing obtained performance metrics\n",
        "    print('Accuracy on training set is:',acc*100)\n",
        "    print('Accuracy on test set is:',accuracy*100)\n",
        "    print(\"Classification_Report\")\n",
        "\n",
        "    print(classification_report(y_true, y_pred))\n",
        "    print(precision_recall_fscore_support(y_true, y_pred, average='micro'))\n",
        "    print('<==========confusion matrix for test data=========>')\n",
        "    p= sns.heatmap(confusion, annot=True, fmt=\"d\", cmap=\"Spectral\",square=True) "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YJB8tyoT434X",
        "outputId": "83647d35-5413-4323-b0fa-bfb3d02403ee",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 707
        }
      },
      "source": [
        "grid(tfidf_train,tfidf_test,Y_train,Y_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Best Estimator:       SVC(C=100, cache_size=200, class_weight=None, coef0=0.0,\n",
            "  decision_function_shape='ovr', degree=3, gamma=1, kernel='rbf',\n",
            "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
            "  tol=0.001, verbose=False)\n",
            "Optimal parameters:   {'C': 100, 'gamma': 1}\n",
            "optimal score:        95.4158674584948\n",
            "-------------------------------------------------\n",
            "Accuracy on training set is: 100.0\n",
            "Accuracy on test set is: 92.74\n",
            "Classification_Report\n",
            "             precision    recall  f1-score   support\n",
            "\n",
            "          0       0.86      0.64      0.73      2339\n",
            "          1       0.94      0.98      0.96     12661\n",
            "\n",
            "avg / total       0.92      0.93      0.92     15000\n",
            "\n",
            "(0.9274, 0.9274, 0.9274, None)\n",
            "<==========confusion matrix for test data=========>\n",
            "11:27:12.057100\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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pEsGKAODUESTVQHJysrKzsyVJW7dulc/nU8OGDSNcFQCcGp7+W03MmzdPn332\nmRzH0YwZM3TBBRdEuiTUYFu2bNETTzyhPXv2qE6dOmratKmeffZZxcfHR7o01EIECQDACENbAAAj\nBAkAwAhBAgAwQpAAAIwQJAAAIwQJAMAIQQIAMEKQAACM/BfZJ6CwplejIQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fb547a21748>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mawcNMILghq2"
      },
      "source": [
        "BAG OF WORDS\n",
        "===="
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VHXeMy-9glRi"
      },
      "source": [
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "\n",
        "\n",
        "count_vect = CountVectorizer() \n",
        "bow_train = count_vect.fit_transform(X_train)\n",
        "bow_test = count_vect.transform(X_test)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d1oQlBmmiit1",
        "outputId": "e92aca9b-07ed-4844-836f-62bb81577e71",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 708
        }
      },
      "source": [
        "randomcv(bow_train,bow_test,Y_train,Y_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Best Estimator:       SVC(C=100, cache_size=200, class_weight=None, coef0=0.0,\n",
            "  decision_function_shape='ovr', degree=3, gamma=0.001, kernel='rbf',\n",
            "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
            "  tol=0.001, verbose=False)\n",
            "Optimal parameters:   {'gamma': 0.001, 'C': 100}\n",
            "optimal score:        95.14935517587976\n",
            "-------------------------------------------------\n",
            "Accuracy on training set is: 99.16571428571429\n",
            "Accuracy on test set is: 92.08666666666666\n",
            "Classification_Report\n",
            "             precision    recall  f1-score   support\n",
            "\n",
            "          0       0.78      0.70      0.74      2406\n",
            "          1       0.94      0.96      0.95     12594\n",
            "\n",
            "avg / total       0.92      0.92      0.92     15000\n",
            "\n",
            "(0.9208666666666666, 0.9208666666666666, 0.9208666666666666, None)\n",
            "<==========confusion matrix for test data=========>\n",
            "5:27:27.363820\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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6d+9+ynUQJABgkMmhrVdeeUUXXXSRcnJyVFVVpTvuuEMJCQnKzc1V//79lZOTo7feekt9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\nSUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fa66bfccf98>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LQRsZk7yI2SO",
        "outputId": "61b05ac7-1442-47bc-f128-b76cb1dc7608",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        }
      },
      "source": [
        "grid(train_bow,test_bow,Y_train,Y_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Best Estimator:       SVC(C=100, cache_size=200, class_weight=None, coef0=0.0,\n",
            "  decision_function_shape='ovr', degree=3, gamma=0.001, kernel='rbf',\n",
            "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
            "  tol=0.001, verbose=False)\n",
            "Optimal parameters:   {'C': 100, 'gamma': 0.001}\n",
            "optimal score:        94.74515246713547\n",
            "-------------------------------------------------\n",
            "Accuracy on training set is: 97.77714285714286\n",
            "Accuracy on test set is: 91.74\n",
            "Classification_Report\n",
            "             precision    recall  f1-score   support\n",
            "\n",
            "          0       0.78      0.67      0.72      2357\n",
            "          1       0.94      0.96      0.95     12643\n",
            "\n",
            "avg / total       0.91      0.92      0.91     15000\n",
            "\n",
            "(0.9174, 0.9174, 0.9174, None)\n",
            "<==========confusion matrix for test data=========>\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2qFMhAsajfOh"
      },
      "source": [
        "TRAINING OUR OWN WORD 2 VEC\n",
        "====="
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zjgj7ZfaodKJ"
      },
      "source": [
        "def cleanhtml(sentence): #function to clean the word of any html-tags\n",
        "    cleanr = re.compile('<.*?>')\n",
        "    cleantext = re.sub(cleanr, ' ', sentence)\n",
        "    return cleantext\n",
        "def cleanpunc(sentence): #function to clean the word of any punctuation or special characters\n",
        "    cleaned = re.sub(r'[?|!|\\'|\"|#]',r'',sentence)\n",
        "    cleaned = re.sub(r'[.|,|)|(|\\|/]',r' ',cleaned)\n",
        "    return  cleaned\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vQ9Tyd_vozW6"
      },
      "source": [
        "import gensim\n",
        "import re\n",
        "i=0\n",
        "list_of_train=[]\n",
        "for sent in X_train:\n",
        "    filtered_sentence=[]\n",
        "    sent=cleanhtml(sent)\n",
        "    for w in sent.split():\n",
        "        for cleaned_words in cleanpunc(w).split():\n",
        "            if(cleaned_words.isalpha()):    \n",
        "                filtered_sentence.append(cleaned_words.lower())\n",
        "            else:\n",
        "                continue \n",
        "    list_of_train.append(filtered_sentence)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4m3xWXrspAR5"
      },
      "source": [
        "i =0\n",
        "list_of_test =[]\n",
        "for sent in X_test:\n",
        "    final_Xtest = []\n",
        "    sent = cleanhtml(sent)\n",
        "    for w in sent.split():\n",
        "        for cleaned_words in cleanpunc(w).split():\n",
        "            if(cleaned_words.isalpha()):\n",
        "                final_Xtest.append(cleaned_words.lower())\n",
        "            else:\n",
        "                continue\n",
        "    list_of_test.append(final_Xtest)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ozXCTbMYsniW"
      },
      "source": [
        "from gensim.models import Word2Vec\n",
        "w2v_train_model=Word2Vec(list_of_train,min_count=3,size=100,workers=5)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5EmggujDssfB"
      },
      "source": [
        "w2v_test_model=Word2Vec(list_of_test,min_count=3,size=100,workers=5)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oxXFW7aApjwp"
      },
      "source": [
        "AVERAGE W2V\n",
        "---"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XRsOs75FySxh",
        "outputId": "bed78c1b-7be1-4f6c-8fee-fa02ebd50220",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 764
        }
      },
      "source": [
        "randomcv(sent_train,sent_test,Y_train,Y_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Best Estimator:       SVC(C=1, cache_size=200, class_weight=None, coef0=0.0,\n",
            "  decision_function_shape='ovr', degree=3, gamma=1, kernel='rbf',\n",
            "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
            "  tol=0.001, verbose=False)\n",
            "Optimal parameters:   {'gamma': 1, 'C': 1}\n",
            "optimal score:        91.54013015057645\n",
            "-------------------------------------------------\n",
            "Accuracy on training set is: 84.39999999999999\n",
            "Accuracy on test set is: 84.15333333333334\n",
            "Classification_Report\n",
            "             precision    recall  f1-score   support\n",
            "\n",
            "          0       0.00      0.00      0.00      2377\n",
            "          1       0.84      1.00      0.91     12623\n",
            "\n",
            "avg / total       0.71      0.84      0.77     15000\n",
            "\n",
            "(0.8415333333333334, 0.8415333333333334, 0.8415333333333334, None)\n",
            "<==========confusion matrix for test data=========>\n",
            "0:34:19.734606\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
            "  'precision', 'predicted', average, warn_for)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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rxdAWABhkyRH2dqV27dqlwYMHS5LS0tLUvXt3SdLYsWNVVFQkr9crn88Xen1JSYm8Xq+8\nXq/Kysoa7AsHQQIABlmWFfZ2JUpKStS+fXt5PB4Fg0HNmTNH5eXlki4ETM+ePTVixAi9++67qq2t\nVUlJiUpLS3XTTTcpNTVVW7ZskSRt27ZNo0aNCusaGdoCAINMz5GUlZWF5jksy9LUqVM1Z84cxcbG\nqnPnzrr33nsVGxurqVOnaubMmbIsS8uWLZPD4dCsWbO0cOFCzZgxQwkJCVqzZk1YNVhBOwNjV2Cj\nY5zJ0yMKTfoBjTSa3jXPvGXkvKdq/m/Yx3aKmd2ElZjDv0gAgC0MbQGAQZbV9j+vEyQAYJCltn+z\nLYIEAAyiIwEA2EJHAgCwpTlukRJpBAkAGNQct0iJtLYflQAAo+hIAMAghrYAALYw2Q4AsIXlvwAA\nW+hIAAC20JEAAGyJhsn2tn+FAACj6EgAwCDmSAAAtjBHAgCwhY4EAGALHQkAwBY6EgCALSz/BQDg\nMuhIAMCgaPg9EoIErc59n82OdAlogzaZOnHQxrGtJIMIEgAwKRgI/1iCBABgK0haCYIEAEwiSAAA\ntkRBkLD8FwBgCx0JAJgUaPsdCUECACZFwdAWQQIAJhEkAABbCBIAgC3MkQAAbImCjoTlvwAAW+hI\nAMCkKOhICBIAMIkgAQDYEQz6wz62ldz8lyABAKNYtQUAsIWhLQCALVEQJCz/BQDYQkcCACZFQUdC\nkACASQQJAMAWVm0BAGyhIwEA2BIFQcKqLQCALXQkAGBSFHQkBAkAmGRwsn3Xrl26//771bNnT0lS\nr169dPfdd2vRokXy+/1KTk7WmjVr5PF4lJeXp02bNsnhcGjq1KmaMmWK6urqtHjxYp04cUJOp1Mr\nV65Ut27drroOggQATDLckQwbNkzr1q0LPc7MzNSMGTN066236qmnnlJubq4mTpyo9evXKzc3V263\nW5MnT1ZaWpry8/OVkJCg7Oxsbd++XdnZ2Vq7du1V18AcCQCYFAyEv4Vh165dGjdunCRpzJgxKigo\n0N69ezVgwADFx8crJiZGQ4YMUWFhoQoKCpSWliZJSklJUWFhYVjvSUcCACYZ/h7JkSNHNG/ePJ09\ne1bp6emqrq6Wx+ORJHXq1EllZWXy+XxKSkoKHZOUlHTRfofDIcuyVFtbGzr+ShEkAGBSIGjs1Dfc\ncIPS09N166236vjx45o9e7b8/s9//yQYvPR7X+3+y2FoCwBaqc6dO2vChAmyLEvdu3fXtddeq7Nn\nz6qmpkaSVFJSIq/XK6/XK5/PFzqutLQ0tL+srEySVFdXp2AweNXdiESQAIBZgUD422Xk5eXpl7/8\npSSprKxMp06d0qRJk7R161ZJ0rZt2zRq1CgNGjRI+/fvV3l5uSorK1VYWKihQ4cqNTVVW7ZskSTl\n5+dr+PDhYV0iQ1sAYJLBOZKxY8fqwQcf1Ntvv626ujotW7ZMffv21UMPPaScnBx17dpVEydOlNvt\nVkZGhubOnSvLsjR//nzFx8drwoQJ2rFjh6ZPny6Px6NVq1aFVYcVDHdQ7AptdIwzeXpEoXdvnxPp\nEtAGbfr1LCPnDf7p8bCPtfo+3ISVmENHAgAmcfdfAIAtBAkAwBaDy39bClZtAQBsoSMBAJMY2gIA\n2BIFQ1sECQCYREcCALCFIAEA2GHnO99WE9ZhEkECACZFQUfC8l8AgC10JABgUhR0JAQJAJjE8l8A\ngC10JAAAWwgSAIAtDG0BAGyJgo6E5b8AAFvoSADApCjoSAgSADCJORIAgC10JAAAWwgSAIAtDG0B\nAGyJgo6E5b8AAFvoSADAoKCfoS0AgB3MkQAAbKEjAQDYEaQjAQDYQkcCALDFz/JfAAAaRUcCAAYx\nR4Jm0WXM1/T1NfPk6hCrimMl2v691aoq9kW6LLQQTqelKbOH6NZ/+6oWzP1/On2qSpJ0+9QBGvnN\nr8hhWTr2l79p43/uVHVVnSRp7Ld7acId/SRJB/5wQi/+Yrf8/qC6fDlBd80brmuuiZU/ENB/v7JX\nH+08LkkaOrK7bp86QG63UxXl5/XCM7tU/OmZyFx0WxIFcyQMbUWYKy5Go195WB98P1v/1ecuHX+9\nQCk/fyDSZaEFuT9rjM5X1zXYN3Rkdw1LvV6PPPiGFqf/RsGgQsHRs2+yvnV7Xz2y8A0t+tGvFRPr\nVs++XklS+sLR+uCdo8q8N0/PPLVdP7g/VbFxbiVdG6e75g3X0yveVWZ6nnbvOKa700c296W2TYFg\n+FsrEXaQlJeXN2UdUavL2ME6d/SkTv3+sCTp8PNvqust/yJXh9gIV4aW4je/2qf/fnVfg30n/3pW\nz63boZqaegWD0pFDZbque6IkadS4m/Tu1sM6V35egUBQzzy1XYcOlMhyWPrNr/bpg3ePSpL+euyM\n6usDSu7cQX7/hdedKquUJP1x32f60nUJzXuhbVTQHwx7ay3CDpL09PSmrCNqJfT6ss59fCL0uL6y\nRudPlSvhpusiWBVako//fPEwZ/Hxs/rk47+FHg8c0lVHiy68rvsNHdUu1qWsFbdo1frbNXnm12Q5\nLAUDQe3+4JgCf/+ke2PPayVJnxWX6+zpah3ce1KS5HBYGjX2Rv1+93HTlxYdAoHwt1ai0TmSzZs3\nf+FzJSUlTV5MNHLFtZP/fMNhC3/1ebnax0SoIrQ2t03ur4TEGG17/ZAkKa69W736evXUo+/I5XZq\n8WNpKvusQu/97kjomKRr4/SjjG/opQ0fqrbWH9qf9q99NHHqQJV8dk5Pr3y3uS+lbWpFnUW4Gu1I\nXnjhBf35z3/W6dOnL9rq6+ubq8Y2rb6yRs527gb7nHExqq+ojlBFaE2mzBysoSO7a82yt1V7/sK/\nyeqqOu18/xPV1NSr4tx5vf/Ox+o/uEvomC91TVDm47fo9dwDKvifvzQ431uvH9L82b/Stt/+Sf+x\n6ttye5zNej1onRrtSNavX6/HH39cDz/8sDweT4Pndu3aZbSwaHH20Kf6ytSbQ4/dCe3VrmMHlR8u\njlxRaBUmThuonn2TtfIn21RT8/kHO19ppWLjPv9wEggEFfj7p+KOSbF6cOk45Wz6SB/u+DT0mi5f\nTlDHpDj9cd9nkqSd73+iWd8fpi7XJejTv5xupitqm6Jh+W+jHUmvXr307LPPyuW6OG8WL15srKho\ncjL/D2p/fWd5U/tLkvo98B0df32n6qtqIlwZWrIbeiQp9eYb9X+W5zcIEUnatf0T3ZzWU7Fxbrk9\nTqWM/ooO/j0g7po3XFt/+6cGISJJCQkx+sGCVCV2vLDIo2efZDldlko/q2ieC2rL/MHwt1bist8j\niY299Oqhfv36NXkx0chfU6v3pj+ukT+7T672MSo/Uqzt310d6bLQQiRcE6PM5beEHmc+nia/P6ii\nP5Yqrr1HS1bfGnruVFmlnnzkbe3+4Jiu656o5etuU915vwp3H9f773ysxI6xGjysm7pcd43GfrtX\n6LicTYX6w4d/1W9f269Fj46XZVmqr/PrP7PfV83/WnaMMLSiQAiXFQwGjV7lRsc4k6dHFHr39jmR\nLgFt0KZfzzJy3prl/xb2sTE/+U0TVmIO32wHAJOi4KaNBAkAGBT1k+0AAFwOHQkAmBQFk+0ECQCY\nFAVDWwQJABjUmm6+GC6CBABMoiMBANhiePnv6tWr9dFHH6m+vl4//OEP9c477+jgwYNKTLzwswJz\n587VzTffrLy8PG3atEkOh0NTp07VlClTVFdXp8WLF+vEiRNyOp1auXKlunXrdtU1ECQAYJDJ5b87\nd+7U4cOHlZOTo9OnT+uOO+7QiBEj9OMf/1hjxowJva6qqkrr169Xbm6u3G63Jk+erLS0NOXn5ysh\nIUHZ2dnavn27srOztXbt2quug+W/ANBKff3rX9fTTz8tSUpISFB1dbX8fv9Fr9u7d68GDBig+Ph4\nxcTEaMiQISosLFRBQYHS0tIkSSkpKSosLAyrDoIEAEwyeNNGp9OpuLg4SVJubq6++c1vyul06qWX\nXtLs2bP1wAMP6G9/+5t8Pp+SkpJCxyUlJamsrKzBfofDIcuyVFtbe9WXyNAWABgUbIY7pPzud79T\nbm6unn/+eR04cECJiYnq27evfvGLX+hnP/uZBg8e3LCmL7jFYri3XqQjAQCDggEr7O1KvP/++3rm\nmWe0YcMGxcfHa+TIkerbt68kaezYsSoqKpLX65XP9/lPNpeWlsrr9crr9aqsrEySVFdXp2AweNFv\nT10JggQADDL5k+3nzp3T6tWr9eyzz4ZWad177706fvy4pAs/QNizZ08NGjRI+/fvV3l5uSorK1VY\nWKihQ4cqNTVVW7ZskSTl5+dr+PDhYV0jQ1sAYFAweGWdRTjeeOMNnT59WgsWLAjtmzRpkhYsWKDY\n2FjFxcVp5cqViomJUUZGhubOnSvLsjR//nzFx8drwoQJ2rFjh6ZPny6Px6NVq1aFVQe/R4JWh98j\ngQmmfo/k9PdvufyLvkDHDduasBJzGNoCANjC0BYAGHSlk+atGUECAAZdyaR5a0eQAIBBdCQAAFua\n4wuJkUaQAIBBJpf/thQECQAYFA0dCct/AQC20JEAgEEBJtsBAHZEw9AWQQIABrH8FwBgCx0JAMAW\nlv8CAGyJhluksPwXAGALHQkAGMQcCQDAFlZtAQBsoSMBANjCN9sBALbQkQAAbImG75Gw/BcAYAsd\nCQAYxNAWAMAWlv8CAGyJhlukECQAYFDAH4x0CcYRJABgEB0JAMAWf6DtdyQs/wUA2EJHAgAGBfyR\nrsA8ggQADApEwdAWQQIABtGRAABsoSMBANjC8l8AgC3R8IVElv8CAGyhIwEAg/wMbQEA7IiGoS2C\nBAAMYrIdAGALy38BALbwhUQAgC3R0JGw/BcAYAsdCQAYxPJfAIAtLP8FANjC8l8AgC10JAAAW+hI\nAAC2sPwXAIDLoCMBAIP8hr/ZvmLFCu3du1eWZSkrK0sDBw40+4aXQJAAgEEmh7Z2796tY8eOKScn\nRx9//LGysrKUk5Nj7P2+CEECAAaZvNdWQUGBxo8fL0nq0aOHzp49q4qKCnXo0MHcm14CcyQAYFAg\nEAx7uxyfz6eOHTuGHiclJamsrMzk5VyS8Y7ku4G3Tb8Fosx3I10AcBWa8//AYDAyK8ToSACglfJ6\nvfL5fKHHpaWlSk5ObvY6CBIAaKVSU1O1detWSdLBgwfl9XqbfX5EYrIdAFqtIUOGqF+/fpo2bZos\ny9LSpUsjUocVjNSgGgCgTWBoCwBgC0ECALCFIGkhVqxYoTvvvFPTpk3Tvn37Il0O2oCioiKNHz9e\nL730UqRLQRvHZHsL0FJuc4C2o6qqSo899phGjhwZ6VIQBehIWoAvus0BEC6Px6MNGzbI6/VGuhRE\nAYKkBWgptzlA2+FyuRQTExPpMhAlCJIWiBXZAFoTgqQFaCm3OQCAcBAkLUBLuc0BAISDb7a3EE8+\n+aT27NkTus1Bnz59Il0SWrEDBw7oiSeeUHFxsVwulzp37qyf/vSnSkxMjHRpaIMIEgCALQxtAQBs\nIUgAALYQJAAAWwgSAIAtBAkAwBaCBABgC0ECALCFIAEA2PL/AZtnuperW6I0AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f1cd3e0f390>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vwy2eCpvy0Lw",
        "outputId": "9fa5f29c-f632-4e4a-8c4e-384a2e1ad180",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 764
        }
      },
      "source": [
        "grid(sent_train,sent_test,Y_train,Y_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Best Estimator:       SVC(C=0.0001, cache_size=200, class_weight=None, coef0=0.0,\n",
            "  decision_function_shape='ovr', degree=3, gamma=0.0001, kernel='rbf',\n",
            "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
            "  tol=0.001, verbose=False)\n",
            "Optimal parameters:   {'C': 0.0001, 'gamma': 0.0001}\n",
            "optimal score:        91.54013015057645\n",
            "-------------------------------------------------\n",
            "Accuracy on training set is: 84.39999999999999\n",
            "Accuracy on test set is: 84.15333333333334\n",
            "Classification_Report\n",
            "             precision    recall  f1-score   support\n",
            "\n",
            "          0       0.00      0.00      0.00      2377\n",
            "          1       0.84      1.00      0.91     12623\n",
            "\n",
            "avg / total       0.71      0.84      0.77     15000\n",
            "\n",
            "(0.8415333333333334, 0.8415333333333334, 0.8415333333333334, None)\n",
            "<==========confusion matrix for test data=========>\n",
            "4:57:56.237830\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
            "  'precision', 'predicted', average, warn_for)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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BALbwhUQAgC3R0JGw/BcAYAsdCQAYxPJfAIAtLP8FANjC8l8AgC10JAAAW+hI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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f1ce78b55c0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ud4IXLXty3dX"
      },
      "source": [
        "TFIDF-W2V featurization for  svm\n",
        "------"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ShGomnWzy4cY"
      },
      "source": [
        "tf_idf_vect = TfidfVectorizer()\n",
        "w2v_train = tf_idf_vect.fit_transform(X_train)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8bV-WM0Ey8DF"
      },
      "source": [
        "tfidf_vect = TfidfVectorizer()\n",
        "w2v_test = tfidf_vect.fit_transform(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MlhscDxV4BHB"
      },
      "source": [
        "tfidf_feat = tf_idf_vect.get_feature_names() # tfidf words/col-names\n",
        "# final_tf_idf is the sparse matrix with row= sentence, col=word and cell_val = tfidf\n",
        "\n",
        "tfidf_sent_train = []; # the tfidf-w2v for each sentence/review is stored in this list\n",
        "row=0;\n",
        "for sent in list_of_train: # for each review/sentence\n",
        "    sent_vec = np.zeros(100) # as word vectors are of zero length\n",
        "    weight_sum =0; # num of words with a valid vector in the sentence/review\n",
        "    for word in sent: # for each word in a review/sentence\n",
        "        try:\n",
        "            vec = w2v_train_model.wv[word]\n",
        "            # obtain the tf_idfidf of a word in a sentence/review\n",
        "            tfidf = w2v_train[row, tfidf_feat.index(word)]\n",
        "            sent_vec += (vec * tfidf)\n",
        "            weight_sum += tfidf\n",
        "        except:\n",
        "            pass\n",
        "    sent_vec /= weight_sum\n",
        "    tfidf_sent_train.append(sent_vec)\n",
        "    row += 1"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ILJWrEej-qge"
      },
      "source": [
        "tfidf_featt = tfidf_vect.get_feature_names() # tfidf words/col-names\n",
        "# final_tf_idf is the sparse matrix with row= sentence, col=word and cell_val = tfidf\n",
        "\n",
        "tfidf_sent_test = []; # the tfidf-w2v for each sentence/review is stored in this list\n",
        "row=0;\n",
        "for sent in list_of_test: # for each review/sentence\n",
        "    sent_vec = np.zeros(100) # as word vectors are of zero length\n",
        "    weight_sum =0; # num of words with a valid vector in the sentence/review\n",
        "    for word in sent: # for each word in a review/sentence\n",
        "        try:\n",
        "            vec = w2v_test_model.wv[word]\n",
        "            # obtain the tf_idfidf of a word in a sentence/review\n",
        "            tfidf = w2v_test[row, tfidf_featt.index(word)]\n",
        "            sent_vec += (vec * tfidf)\n",
        "            weight_sum += tfidf\n",
        "        except:\n",
        "            pass\n",
        "    sent_vec /= weight_sum\n",
        "    tfidf_sent_test.append(sent_vec)\n",
        "    row += 1\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lV_xMvg_SibL",
        "outputId": "2768a720-db6c-4b0e-fcbb-5cd134fed986",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 707
        }
      },
      "source": [
        "randomcv(tfidf_sent_train,tfidf_sent_test,Y_train,Y_test)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Best Estimator:       SVC(C=100, cache_size=200, class_weight=None, coef0=0.0,\n",
            "  decision_function_shape='ovr', degree=3, gamma=0.01, kernel='rbf',\n",
            "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
            "  tol=0.001, verbose=False)\n",
            "Optimal parameters:   {'gamma': 0.01, 'C': 100}\n",
            "optimal score:        93.23950055060955\n",
            "-------------------------------------------------\n",
            "Accuracy on training set is: 90.35428571428571\n",
            "Accuracy on test set is: 83.00666666666666\n",
            "Classification_Report\n",
            "             precision    recall  f1-score   support\n",
            "\n",
            "          0       0.26      0.04      0.08      2360\n",
            "          1       0.85      0.98      0.91     12640\n",
            "\n",
            "avg / total       0.75      0.83      0.78     15000\n",
            "\n",
            "(0.8300666666666666, 0.8300666666666666, 0.8300666666666666, None)\n",
            "<==========confusion matrix for test data=========>\n",
            "1:08:30.606334\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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JAFhEjwQAYIQeCQDACBUJAMAIFQkAwAgVCQDACMt/AQD4DlQkAGCRG76PxHqQ+JtR9ODk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\nrkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f2b90633358>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zXox2sKjRgsE",
        "outputId": "d5534f60-7b50-4afc-9c99-6105db85a6ba",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 690
        }
      },
      "source": [
        "grid(tfidf_sent_train,tfidf_sent_test,Y_train,Y_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Best Estimator:       SVC(C=100, cache_size=200, class_weight=None, coef0=0.0,\n",
            "  decision_function_shape='ovr', degree=3, gamma=0.01, kernel='rbf',\n",
            "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
            "  tol=0.001, verbose=False)\n",
            "Optimal parameters:   {'C': 100, 'gamma': 0.01}\n",
            "optimal score:        93.23156767259475\n",
            "-------------------------------------------------\n",
            "Accuracy on training set is: 90.25142857142858\n",
            "Accuracy on test set is: 84.30666666666666\n",
            "Classification_Report\n",
            "             precision    recall  f1-score   support\n",
            "\n",
            "          0       0.67      0.00      0.01      2357\n",
            "          1       0.84      1.00      0.91     12643\n",
            "\n",
            "avg / total       0.82      0.84      0.77     15000\n",
            "\n",
            "(0.8430666666666666, 0.8430666666666666, 0.8430666666666666, None)\n",
            "<==========confusion matrix for test data=========>\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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jxoyRJE2dOlXdu3dX586dtXDhQq1fv/68/S3r/OG2b9rWVAxtAYBBLpcr4ldTFRcXq0+f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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f0f410a5208>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Mbpt_EC--Tpy"
      },
      "source": [
        "from prettytable import PrettyTable"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qaAyESY690z5"
      },
      "source": [
        "x = PrettyTable()\n",
        "x.field_names = [\"Featurization\",\"BOW\",\"TFIDF\",\"AVG_W2V\",\"TFIDF_W2V\" ]\n",
        "x.add_row([\"Optimal C using RANDOM SEARCH\",100,100,1,100])\n",
        "x.add_row([\"Optimal GAMMA using RANDOM SEARCH\",0.001,0.01,1,0.01])\n",
        "x.add_row([\"Train Accuracy\",99.16,96.03,84.39,90.35])\n",
        "x.add_row([\"Test Accuracy\", 92.08,91.75,84.15,83.00])\n",
        "x.add_row([\"-----------------------------\",\"--------------\",\"--------------\",\"--------------\",\"-------------\",])\n",
        "\n",
        "\n",
        "x.add_row([\"Optimal C using Grid search\",100,100, 0.0001,100])\n",
        "x.add_row([\"Optimal GAMMA using Grid search\",0.001,1,0.0001,0.01])\n",
        "x.add_row([\"Train Accuracy\",97.77,100,84.39,90.25])\n",
        "x.add_row([\"Test Accuracy\", 91.74,95.41, 84.15,84.30])\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zdvXSNzG-QBJ",
        "outputId": "f7e50149-ca05-4b61-ab12-772e548bc926",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 251
        }
      },
      "source": [
        "\n",
        "print(x)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "+-----------------------------------+----------------+----------------+----------------+---------------+\n",
            "|           Featurization           |      BOW       |     TFIDF      |    AVG_W2V     |   TFIDF_W2V   |\n",
            "+-----------------------------------+----------------+----------------+----------------+---------------+\n",
            "|   Optimal C using RANDOM SEARCH   |      100       |      100       |       1        |      100      |\n",
            "| Optimal GAMMA using RANDOM SEARCH |     0.001      |      0.01      |       1        |      0.01     |\n",
            "|           Train Accuracy          |     99.16      |     96.03      |     84.39      |     90.35     |\n",
            "|           Test Accuracy           |     92.08      |     91.75      |     84.15      |      83.0     |\n",
            "|   -----------------------------   | -------------- | -------------- | -------------- | ------------- |\n",
            "|    Optimal C using Grid search    |      100       |      100       |     0.0001     |      100      |\n",
            "|  Optimal GAMMA using Grid search  |     0.001      |       1        |     0.0001     |      0.01     |\n",
            "|           Train Accuracy          |     97.77      |      100       |     84.39      |     90.25     |\n",
            "|           Test Accuracy           |     91.74      |     95.41      |     84.15      |      84.3     |\n",
            "+-----------------------------------+----------------+----------------+----------------+---------------+\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pjXhJxDTkpH-"
      },
      "source": [
        "BAG OF WORDS AND  FEATURIZATION GAVE OPTIMAL TRAIN AND TEST ACCURACY\n",
        "--"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CzdMMle5o-vJ"
      },
      "source": [
        "Lets implement SGD classifier with hinge loss which is equivalent to linearsvm\n",
        "========="
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Rt367V1n-dkQ",
        "outputId": "1ad5061e-1f23-41b7-f92b-4b3afbad7971",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        }
      },
      "source": [
        "\n",
        "clf = SGDClassifier(loss=\"hinge\", penalty=\"l2\",max_iter=100)#SGD classifier with hinge loss ehich is equivalent to linear svm\n",
        "clf.fit(tfidf_train,Y_train)\n",
        "predict=clf.predict(tfidf_train)#predicting train data using model\n",
        "\n",
        "pred = clf.predict(tfidf_test)#predicting test data using model\n",
        "f1score = f1_score(Y_test,pred,average='weighted')#obtaining various performance metrics\n",
        "precision = precision_score(Y_test,pred,average='weighted')\n",
        "recall = recall_score(Y_test,pred,average='weighted')\n",
        "confusion = confusion_matrix(Y_test,pred)\n",
        "accuracy = accuracy_score(Y_test,pred)\n",
        "acc=accuracy_score(Y_train,predict)\n",
        "#printing various performance metrics\n",
        "print('Accuracy on training set is:',acc*100)\n",
        "print('Accuracy on test set is:',accuracy*100)\n",
        "print('F1_score on test set is:',f1score*100)\n",
        "print('Precision on test set is:',precision*100)\n",
        "print('Recall on test set is:',recall*100)\n",
        "print('<=============confusion matrix============>')\n",
        "P= sns.heatmap(confusion, annot=True,cmap=\"Spectral\", fmt=\"d\",square=True)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy on training set is: 92.78285714285714\n",
            "Accuracy on test set is: 91.26666666666667\n",
            "F1_score on test set is: 90.15225787217284\n",
            "Precision on test set is: 91.20604356335488\n",
            "Recall on test set is: 91.26666666666667\n",
            "<=============confusion matrix============>\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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7dCRcRwIAMIWOBAAsVMdkOwDADDsMbREkAGAhlv8CAEyhIwEAmMLyXwCAKXa4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\nAIwzuwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f6ba70dd710>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OPeQwwbMoiUT",
        "outputId": "14d9e283-704f-4924-d56a-070053fd6378",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 233
        }
      },
      "source": [
        "names = tf_idf_vect.get_feature_names()\n",
        "feat = np.array(names)\n",
        "features = np.vstack((feat,np.absolute(clf.coef_)))\n",
        "features= pd.DataFrame(data=features.T,columns=('words','coefficients'))\n",
        "top_feat= features.sort_values('coefficients',axis=0,inplace=False,ascending=False,kind='quicksort')\n",
        "print('<=======> Top 10 important words <=======> ')\n",
        "print(top_feat[0:10])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<=======> Top 10 important words <=======> \n",
            "             words           coefficients\n",
            "34741    unrelated  9.594581085087672e-05\n",
            "2433    anticipate  8.581682631102269e-05\n",
            "9746       daytime  8.581682631102269e-05\n",
            "34338  ultraviolet  8.581682631102269e-05\n",
            "27618    replanted  8.581682631102269e-05\n",
            "1850          ahnd  8.581682631102269e-05\n",
            "33272       threee  8.581682631102269e-05\n",
            "32651        tanks  8.581682631102269e-05\n",
            "34681   unoccupied  8.581682631102269e-05\n",
            "14141        frech  8.581682631102269e-05\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "610S8iDgrxew"
      },
      "source": [
        "RBF kernal svm works well than linear svm\n",
        "-----\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t69b2u36tIf3"
      },
      "source": [
        "CONCLUSION\n",
        "----------\n",
        "1. Using various featurization(BOW,TFIDF,AVGW2V,TFIDFW2V) to get text vectors.\n",
        "2. Obtaining optimal parameters using hyperparameter tuning using grid search and random\n",
        "search .\n",
        "5. Obtaining various performance metrics.\n",
        "2.  Obtaining train and test accuracy\n",
        "4. Decide which featurization when modeled gives optimal perforamance."
      ]
    }
  ]
}