Judgemental_Statements_Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "To9ENLU90WGl"
   },
   "outputs": [],
   "source": [
    "#!pip install transformers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "fvFvBLJV0Dkv"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import cross_val_score\n",
    "import torch\n",
    "import transformers as ppb\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "GPQwJ3jo-TIQ",
    "outputId": "d2583bdf-7996-4b9d-9c8d-cd5eaa276d83"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mounted at /content/drive\n"
     ]
    }
   ],
   "source": [
    "from google.colab import drive\n",
    "drive.mount('/content/drive', force_remount=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zQ-42fh0hjsF"
   },
   "source": [
    "## Importing the dataset\n",
    "We'll use pandas to read the dataset and load it into a dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "cyoj29J24hPX"
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('/content/drive/My Drive/Colab Notebooks/NLP/Judgement Classification/Mydata01.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "MXd2yS0y-wm-",
    "outputId": "97422824-7df3-4f19-98b1-250e0a1a1e44"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(320, 2)"
      ]
     },
     "execution_count": 6,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 195
    },
    "id": "x8Gwa1Kx_qmr",
    "outputId": "1d75e36a-6522-4506-d6af-18ed0d1afea3"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>People from Pune are arrogant</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>I have often experienced lack of respect while...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Varanasi is super clean now!</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Varanasi is much more clean now. I felt really...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Tamilians eat monotonous food.</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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       "                                                   0  1\n",
       "0                      People from Pune are arrogant  0\n",
       "1  I have often experienced lack of respect while...  1\n",
       "2                       Varanasi is super clean now!  0\n",
       "3  Varanasi is much more clean now. I felt really...  1\n",
       "4                     Tamilians eat monotonous food.  0"
      ]
     },
     "execution_count": 7,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "PRc2L89hh1Tf"
   },
   "source": [
    "Lets see how many sentences are labeled as \"Judgemental\" (value 0) and how many are labeled \"Non Judgmental\" (having the value 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ZxbedMIlNT-L",
    "outputId": "b58b1879-0ecd-4e0e-9c14-9c93e9b19b55"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    161\n",
       "0    159\n",
       "Name: 1, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['1'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7_MO08_KiAOb"
   },
   "source": [
    "## Loading the Pre-trained BERT model\n",
    "Let's now load a pre-trained BERT model. "
   ]
  },
  {
   "cell_type": "code",
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      "\n"
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      "\n"
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    },
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      "\n"
     ]
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   ],
   "source": [
    "# For DistilBERT:\n",
    "model_class, tokenizer_class, pretrained_weights = (ppb.DistilBertModel, ppb.DistilBertTokenizer, 'distilbert-base-uncased')\n",
    "\n",
    "# Load pretrained model/tokenizer\n",
    "tokenizer = tokenizer_class.from_pretrained(pretrained_weights)\n",
    "model = model_class.from_pretrained(pretrained_weights)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lZDBMn3wiSX6"
   },
   "source": [
    "Right now, the variable `model` holds a pretrained distilBERT model -- a version of BERT that is smaller, but much faster and requiring a lot less memory.\n",
    "\n",
    "## Model #1: Preparing the Dataset\n",
    "Before we can hand our sentences to BERT, we need to so some minimal processing to put them in the format it requires.\n",
    "\n",
    "### Tokenization\n",
    "Our first step is to tokenize the sentences -- break them up into word and subwords in the format BERT is comfortable with."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "Dg82ndBA5xlN"
   },
   "outputs": [],
   "source": [
    "tokenized = df['0'].apply((lambda x: tokenizer.encode(x, add_special_tokens=True)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "URn-DWJt5xhP"
   },
   "outputs": [],
   "source": [
    "max_len = 0\n",
    "for i in tokenized.values:\n",
    "    if len(i) > max_len:\n",
    "        max_len = len(i)\n",
    "\n",
    "padded = np.array([i + [0]*(max_len-len(i)) for i in tokenized.values])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Mdjg306wjjmL"
   },
   "source": [
    "Our dataset is now in the `padded` variable, we can view its dimensions below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "jdi7uXo95xeq",
    "outputId": "091e2b23-87bc-46dd-d20c-330c0d268ed0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(320, 41)"
      ]
     },
     "execution_count": 12,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(padded).shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "sDZBsYSDjzDV"
   },
   "source": [
    "### Masking\n",
    "If we directly send `padded` to BERT, that would slightly confuse it. We need to create another variable to tell it to ignore (mask) the padding we've added when it's processing its input. That's what attention_mask is:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "4K_iGRNa_Ozc",
    "outputId": "2b7e2599-0e48-45c0-a454-d26c0f001752"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(320, 41)"
      ]
     },
     "execution_count": 13,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "attention_mask = np.where(padded != 0, 1, 0)\n",
    "attention_mask.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "39UVjAV56PJz"
   },
   "outputs": [],
   "source": [
    "input_ids = torch.tensor(padded)  \n",
    "attention_mask = torch.tensor(attention_mask)\n",
    "\n",
    "with torch.no_grad():\n",
    "    last_hidden_states = model(input_ids, attention_mask=attention_mask)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "C9t60At16PVs"
   },
   "outputs": [],
   "source": [
    "features = last_hidden_states[0][:,0,:].numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "nkMZt0ffcYWd",
    "outputId": "462c9bdb-4e9c-46a4-fc61-7991351a8bda"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(320, 768)"
      ]
     },
     "execution_count": 16,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_VZVU66Gurr-"
   },
   "source": [
    "The labels indicating which sentence is positive and negative now go into the `labels` variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "id": "JD3fX2yh6PTx"
   },
   "outputs": [],
   "source": [
    "labels = df['1']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "iaoEvM2evRx1"
   },
   "source": [
    "## Model #2: Train/Test Split\n",
    "Let's now split our datset into a training set and testing set (even though we're using 2,000 sentences from the SST2 training set)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "id": "ddAqbkoU6PP9"
   },
   "outputs": [],
   "source": [
    "train_features, test_features, train_labels, test_labels = train_test_split(features, labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "VSxEOMGWvv4A"
   },
   "source": [
    "# **Logistic Regression**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "cyEwr7yYD3Ci",
    "outputId": "822d3154-80be-4045-9d30-6d76f4ed83ca"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best parameters:  {'C': 5.263252631578947}\n",
      "best scrores:  0.8083333333333332\n"
     ]
    }
   ],
   "source": [
    "parameters = {'C': np.linspace(0.0001, 100, 20)}\n",
    "grid_search = GridSearchCV(LogisticRegression(), parameters)\n",
    "grid_search.fit(train_features, train_labels)\n",
    "\n",
    "print('best parameters: ', grid_search.best_params_)\n",
    "print('best scrores: ', grid_search.best_score_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KCT9u8vAwnID"
   },
   "source": [
    "We now train the LogisticRegression model. If you've chosen to do the gridsearch, you can plug the value of C into the model declaration (e.g. `LogisticRegression(C=5.2)`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "gG-EVWx4CzBc",
    "outputId": "a6838c37-3436-48d0-ca2f-16591536cba3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
       "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
       "                   random_state=42, solver='lbfgs', tol=0.0001, verbose=0,\n",
       "                   warm_start=False)"
      ]
     },
     "execution_count": 20,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr_clf = LogisticRegression(random_state=42)#,C=5.2)\n",
    "lr_clf.fit(train_features, train_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "iCoyxRJ7ECTA",
    "outputId": "fdff7fd8-66d2-4737-b918-99604ff04501"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression Train Accuracy : 0.9666666666666667\n",
      "Logistic Regression Test Accuracy : 0.8625\n"
     ]
    }
   ],
   "source": [
    "print(\"Logistic Regression Train Accuracy :\",lr_clf.score(train_features, train_labels))\n",
    "print(\"Logistic Regression Test Accuracy :\",lr_clf.score(test_features, test_labels))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TxqUnYv18F9E"
   },
   "source": [
    "# Lets Check Precision , Recall and Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Sft8mNVUpLGR",
    "outputId": "2f5a610c-9552-4568-adce-56a7eea2e291"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 precision    recall  f1-score   support\n",
      "\n",
      "    Judgemental       0.97      0.77      0.86        43\n",
      "Non Judgemental       0.78      0.97      0.87        37\n",
      "\n",
      "       accuracy                           0.86        80\n",
      "      macro avg       0.88      0.87      0.86        80\n",
      "   weighted avg       0.88      0.86      0.86        80\n",
      "\n",
      "[[33 10]\n",
      " [ 1 36]]\n"
     ]
    }
   ],
   "source": [
    "import sklearn.metrics as metrics\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "\n",
    "predictions = lr_clf.predict(test_features)\n",
    "\n",
    "report = metrics.classification_report(test_labels, predictions,target_names=['Judgemental','Non Judgemental'])\n",
    "print(report) \n",
    "\n",
    "confusion_matrix = confusion_matrix(test_labels, predictions)\n",
    "print(confusion_matrix)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "H80Gx8aE8Tbn"
   },
   "source": [
    "# RoC and AUC of Logisitc Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 295
    },
    "id": "BOAnUxRigAxC",
    "outputId": "b313e7c0-1aec-426f-acff-1fc846859d8c"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.metrics import roc_curve\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "logit_roc_auc = roc_auc_score(test_labels, lr_clf.predict(test_features))\n",
    "fpr, tpr, thresholds = roc_curve(test_labels, lr_clf.predict_proba(test_features)[:,1])\n",
    "plt.figure()\n",
    "plt.plot(fpr, tpr, label='LR AUC = %0.2f)' % logit_roc_auc)\n",
    "plt.plot([0, 1], [0, 1],'r--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('FPR (1-Specificity)')\n",
    "plt.ylabel('TPR (Sensitivity)')\n",
    "plt.title('ROC : DistilBERT + LR')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.savefig('Log_ROC')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "K423AogmwHh6"
   },
   "source": [
    "# **Gaussin Naive Bayes**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "1VrGfsT5ogY3",
    "outputId": "8935ea06-a47d-4e60-fc91-9ba6045f3eef"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GaussianNB(priors=None, var_smoothing=1e-09)"
      ]
     },
     "execution_count": 25,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_NB = GaussianNB()\n",
    "model_NB.fit(train_features, train_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "EAjRjlhYoglN",
    "outputId": "7b97d1cc-696c-4dda-aa61-95261eafcb47"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gaussian NB Train Accuracy : 0.7583333333333333\n",
      "Gaussian NB Test Accuracy : 0.75\n"
     ]
    }
   ],
   "source": [
    "print(\"Gaussian NB Train Accuracy :\",model_NB.score(train_features, train_labels))\n",
    "print(\"Gaussian NB Test Accuracy :\",model_NB.score(test_features, test_labels))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "JPvTJ0ik8eZ_"
   },
   "source": [
    "# Lets Check Precision , Recall and Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "BV4pLLWPt-H_",
    "outputId": "8455dc72-c1fb-4bb1-9676-400f578c165e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 precision    recall  f1-score   support\n",
      "\n",
      "    Judgemental       0.77      0.77      0.77        43\n",
      "Non Judgemental       0.73      0.73      0.73        37\n",
      "\n",
      "       accuracy                           0.75        80\n",
      "      macro avg       0.75      0.75      0.75        80\n",
      "   weighted avg       0.75      0.75      0.75        80\n",
      "\n",
      "[[33 10]\n",
      " [10 27]]\n"
     ]
    }
   ],
   "source": [
    "import sklearn.metrics as metrics\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "\n",
    "predictions = model_NB.predict(test_features)\n",
    "\n",
    "report = metrics.classification_report(test_labels, predictions,target_names=['Judgemental','Non Judgemental'])\n",
    "print(report) \n",
    "\n",
    "confusion_matrix = confusion_matrix(test_labels, predictions)\n",
    "print(confusion_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Sg69dAWLvlxc"
   },
   "outputs": [],
   "source": [
    "#print(test_labels,predictions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "oo87N1jg8uDr"
   },
   "source": [
    "# RoC and AUC of Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 295
    },
    "id": "1YmeZoE-p3ZW",
    "outputId": "8be6218c-8da8-491d-dba4-72b27054a5ad"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.metrics import roc_curve\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "logit_roc_auc = roc_auc_score(test_labels, model_NB.predict(test_features))\n",
    "fpr, tpr, thresholds = roc_curve(test_labels, model_NB.predict_proba(test_features)[:,1])\n",
    "plt.figure()\n",
    "plt.plot(fpr, tpr, label='LR AUC = %0.2f)' % logit_roc_auc)\n",
    "plt.plot([0, 1], [0, 1],'r--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('FPR (1-Specificity)')\n",
    "plt.ylabel('TPR (Sensitivity)')\n",
    "plt.title('ROC : DistilBERT + LR')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.savefig('Log_ROC')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "u9xy5Ls081SG"
   },
   "source": [
    "# **Comparison of LR and NB**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "z3QnI4_O87Ec",
    "outputId": "f82e8a31-68b0-4b13-bfa7-7a29aadf099c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression Train Accuracy : 0.9666666666666667\n",
      "Logistic Regression Test Accuracy : 0.8625\n",
      "Gaussian NB Train Accuracy : 0.7583333333333333\n",
      "Gaussian NB Test Accuracy : 0.75\n"
     ]
    }
   ],
   "source": [
    "print(\"Logistic Regression Train Accuracy :\",lr_clf.score(train_features, train_labels))\n",
    "print(\"Logistic Regression Test Accuracy :\",lr_clf.score(test_features, test_labels))\n",
    "print(\"Gaussian NB Train Accuracy :\",model_NB.score(train_features, train_labels))\n",
    "print(\"Gaussian NB Test Accuracy :\",model_NB.score(test_features, test_labels))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "p1cGgNGb9JBP"
   },
   "source": [
    "# **Our Decision**\n",
    "\n",
    "We find Logisitic Regression is better fit here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gsBiyE0sOlhz"
   },
   "source": [
    "# **Lets Validate it**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "W3SXHcjAmDBQ",
    "outputId": "c49cd3db-fcde-4303-f376-03b6ec088811"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4, 1)"
      ]
     },
     "execution_count": 31,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tst = pd.DataFrame([\"I dont think he will be able to do that\",\"Boys are dumb\",\"New Delhi is the capital of India\",\"Doing Yoga everyday keeps us fit\"])\n",
    "df_tst.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "id": "EJQuqV6cnWQu"
   },
   "outputs": [],
   "source": [
    "#tokenized_tst = df_tst['Statement'].apply((lambda x: tokenizer.encode(x, add_special_tokens=True)))\n",
    "tokenized_tst = df_tst[0].apply((lambda x: tokenizer.encode(x, add_special_tokens=True)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "id": "4x2pmAItbeAk"
   },
   "outputs": [],
   "source": [
    "max_len = 0\n",
    "for i in tokenized_tst.values:\n",
    "    if len(i) > max_len:\n",
    "        max_len = len(i)\n",
    "\n",
    "padded_tst = np.array([i + [0]*(max_len-len(i)) for i in tokenized_tst.values])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "zHyYRpaCc8Fc",
    "outputId": "0276579a-1e97-4da9-a2ad-bb0e8bfd512d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4, 13)"
      ]
     },
     "execution_count": 34,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(padded_tst).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
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    "id": "lZZdKc-_cwL7",
    "outputId": "6c913a07-6937-4b00-c68b-72367a6316c0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4, 13)"
      ]
     },
     "execution_count": 35,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "attention_mask_tst = np.where(padded_tst != 0, 1, 0)\n",
    "attention_mask_tst.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "id": "iORrw3NQdEan"
   },
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   "source": [
    "input_ids_tst = torch.tensor(padded_tst)  \n",
    "attention_mask_tst = torch.tensor(attention_mask_tst)\n",
    "\n",
    "with torch.no_grad():\n",
    "    last_hidden_states_tst = model(input_ids_tst, attention_mask=attention_mask_tst)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "id": "a6AL660ock5W"
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   "outputs": [],
   "source": [
    "features_tst = last_hidden_states_tst[0][:,0,:].numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "id": "oFFVwiw6a2n5"
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    "y_prob = lr_clf.predict_proba(features_tst)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {
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    },
    "id": "2KG6u7jCf8Dc",
    "outputId": "95fcf0f3-9dd7-40e2-8345-3b162ce04548"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.87298594, 0.12701406],\n",
       "       [0.97573595, 0.02426405],\n",
       "       [0.07432295, 0.92567705],\n",
       "       [0.8919436 , 0.1080564 ]])"
      ]
     },
     "execution_count": 39,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_prob"
   ]
  },
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   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "TSca1WGPa20S",
    "outputId": "43f33046-304f-45b7-b5ad-ce9df64c21a1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 1, 0])"
      ]
     },
     "execution_count": 40,
     "metadata": {
      "tags": []
     },
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   ],
   "source": [
    "predicted_indices = (y_prob[:, 1] > 0.5).astype(int)\n",
    "predicted_indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "id": "qP-IuNWza25q"
   },
   "outputs": [],
   "source": [
    "df_tst = np.array(df_tst)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "id": "BWB4RbMnY2f4"
   },
   "outputs": [],
   "source": [
    "labels = ['Judgemental','Non Judgemental']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "6_l4AxdJY9_w",
    "outputId": "b29e0826-813b-4b51-f6ef-a0ff41bb14d6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['I dont think he will be able to do that']  =>  Judgemental  with  87.29859368671632\n",
      "['Boys are dumb']  =>  Judgemental  with  97.57359543590825\n",
      "['New Delhi is the capital of India']  =>  Non Judgemental  with  92.56770513385308\n",
      "['Doing Yoga everyday keeps us fit']  =>  Judgemental  with  89.19435959714033\n"
     ]
    }
   ],
   "source": [
    "for i, index in enumerate(predicted_indices):\n",
    "    print(df_tst[i], \" => \", labels[index], ' with ', 100*y_prob[i, index])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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   "source": []
  }
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