Modeling/Modeling_InitialData.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2215954f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initial imports\n",
    "import re\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "# plotting\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "# sklearn\n",
    "from sklearn.svm import LinearSVC\n",
    "from sklearn.naive_bayes import BernoulliNB\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.metrics import confusion_matrix, classification_report\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e0e2f314",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read cleaned data output file from Final_Data_Cleaning.ipynb, Combined_Tweet_Sentiments.csv\n",
    "combined_df = pd.read_csv(\"../Preprocessing/Resources/Combined_Tweet_Sentiments.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "bf3c2c36",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>['tim', 'tebow', 'availible', 'wow', 'jerry', ...</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>['aga', 'tayo', 'tomorrow', 'ah', ':)', 'good'...</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>['lunch', 'new', 'lil', 'spot', '...', 'cotton...</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>['snc', 'halloween', 'pr', 'pumped', 'let', 'w...</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>['louis', 'inspired', 'outfit', 'monday', 'zay...</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45451</th>\n",
       "      <td>['alright']</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45452</th>\n",
       "      <td>['mom', 'bought', 'ticket', 'section', 'sat', ...</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45453</th>\n",
       "      <td>['sat', 'metlife', 'annoying', 'didnt', 'let',...</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45454</th>\n",
       "      <td>['giant', 'niner', 'fan', 'wanna', 'sunday', '...</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45455</th>\n",
       "      <td>['mendez', 'told', \"he'd\", 'drive', 'metlife',...</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>45456 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   tweet  score\n",
       "0      ['tim', 'tebow', 'availible', 'wow', 'jerry', ...    1.0\n",
       "1      ['aga', 'tayo', 'tomorrow', 'ah', ':)', 'good'...    1.0\n",
       "2      ['lunch', 'new', 'lil', 'spot', '...', 'cotton...    1.0\n",
       "3      ['snc', 'halloween', 'pr', 'pumped', 'let', 'w...    1.0\n",
       "4      ['louis', 'inspired', 'outfit', 'monday', 'zay...    1.0\n",
       "...                                                  ...    ...\n",
       "45451                                        ['alright']    0.0\n",
       "45452  ['mom', 'bought', 'ticket', 'section', 'sat', ...    0.0\n",
       "45453  ['sat', 'metlife', 'annoying', 'didnt', 'let',...    0.0\n",
       "45454  ['giant', 'niner', 'fan', 'wanna', 'sunday', '...    0.0\n",
       "45455  ['mendez', 'told', \"he'd\", 'drive', 'metlife',...    0.0\n",
       "\n",
       "[45456 rows x 2 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check df\n",
    "combined_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1cc40d92",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dropping neutral tweets from dataframe as part of model optimization\n",
    "combined_df = combined_df[combined_df.score != 0.0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0b8c3591",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/samanthavillanueva/opt/anaconda3/envs/PythonData/lib/python3.7/site-packages/pandas/core/series.py:4515: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  method=method,\n"
     ]
    }
   ],
   "source": [
    "# Replace negative 1 scores with 0\n",
    "combined_df['score'].replace({-1.0: 0}, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ba59ae74",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Seperating features and labels \n",
    "X = combined_df['tweet']\n",
    "y = combined_df['score'].astype('int64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d9259f3e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Separating the 85% data for training data and 15% for testing data\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size = 0.15, random_state = 1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "23f08eef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No. of feature_words:  144779\n"
     ]
    }
   ],
   "source": [
    "# Fit the TF-IDF Vectorizer \n",
    "vectoriser = TfidfVectorizer(ngram_range=(1,2), max_features=500000)\n",
    "vectoriser.fit(X_train)\n",
    "print('No. of feature_words: ', len(vectoriser.get_feature_names()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a8f17133",
   "metadata": {},
   "outputs": [],
   "source": [
    "# TF-IDF Vectorizer \n",
    "X_train = vectoriser.transform(X_train)\n",
    "X_test  = vectoriser.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c23ebcc6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to evaluate model\n",
    "\n",
    "def model_Evaluate(model):\n",
    "    # Predict values for Test dataset\n",
    "    y_pred = model.predict(X_test)\n",
    "    # Print the evaluation metrics for the dataset\n",
    "    print(classification_report(y_test, y_pred))\n",
    "    \n",
    "    # Compute and plot the Confusion Matrix\n",
    "    cf_matrix = confusion_matrix(y_test, y_pred)\n",
    "    categories = ['Negative','Positive']\n",
    "    group_names = ['True Neg','False Pos', 'False Neg','True Pos']\n",
    "    group_percentages = ['{0:.2%}'.format(value) for value in cf_matrix.flatten() / np.sum(cf_matrix)]\n",
    "    labels = [f'{v1}n{v2}' for v1, v2 in zip(group_names,group_percentages)]\n",
    "    labels = np.asarray(labels).reshape(2,2)\n",
    "    sns.heatmap(cf_matrix, annot = labels, cmap = 'Blues',fmt = '',\n",
    "    xticklabels = categories, yticklabels = categories)\n",
    "    plt.xlabel(\"Predicted values\", fontdict = {'size':14}, labelpad = 10)\n",
    "    plt.ylabel(\"Actual values\" , fontdict = {'size':14}, labelpad = 10)\n",
    "    plt.title (\"Confusion Matrix\", fontdict = {'size':18}, pad = 20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3d41291b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.98      0.05      0.09      1090\n",
      "           1       0.72      1.00      0.84      2653\n",
      "\n",
      "    accuracy                           0.72      3743\n",
      "   macro avg       0.85      0.52      0.46      3743\n",
      "weighted avg       0.80      0.72      0.62      3743\n",
      "\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Bernoulli Naive Bayes Model\n",
    "BNBmodel = BernoulliNB()\n",
    "BNBmodel.fit(X_train, y_train)\n",
    "model_Evaluate(BNBmodel)\n",
    "y_pred = BNBmodel.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7ddd0241",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.79      0.58      0.67      1090\n",
      "           1       0.84      0.94      0.89      2653\n",
      "\n",
      "    accuracy                           0.83      3743\n",
      "   macro avg       0.82      0.76      0.78      3743\n",
      "weighted avg       0.83      0.83      0.82      3743\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LinearSVC Model\n",
    "SVCmodel = LinearSVC()\n",
    "SVCmodel.fit(X_train, y_train)\n",
    "model_Evaluate(SVCmodel)\n",
    "y_pred2 = SVCmodel.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6d33d320",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.82      0.49      0.61      1090\n",
      "           1       0.82      0.96      0.88      2653\n",
      "\n",
      "    accuracy                           0.82      3743\n",
      "   macro avg       0.82      0.72      0.75      3743\n",
      "weighted avg       0.82      0.82      0.80      3743\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LR Model\n",
    "LRmodel = LogisticRegression(C = 2, max_iter = 1000, n_jobs=-1)\n",
    "LRmodel.fit(X_train, y_train)\n",
    "model_Evaluate(LRmodel)\n",
    "y_pred3 = LRmodel.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3e312e2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# save vectorizer\n",
    "pickle.dump(vectoriser, open('vectorizer.pkl', 'wb'))\n",
    "\n",
    "# save chosen model --- Linear SVC\n",
    "pickle.dump(SVCmodel, open('model.pkl', 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b3e74963",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python (PythonData)",
   "language": "python",
   "name": "pythondata"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}