MovieSentimentsMain.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "fee35677",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import ast\n",
    "from tabulate import tabulate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "723ec2d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('corpus.txt', 'r') as f:\n",
    "        corpus = f.read()\n",
    "        f.close()\n",
    "corpus = ast.literal_eval(corpus)\n",
    "%run MovieSentimentsDataCleaning.ipynb\n",
    "data = dataCleaning()\n",
    "'''np.random.seed(10)\n",
    "remove_n = 5000\n",
    "drop_indices = np.random.choice(data.index, remove_n, replace=False)\n",
    "data = data.drop(drop_indices)\n",
    "indicesList = sorted(drop_indices, reverse=True)\n",
    "for indx in indicesList:\n",
    "    if indx < len(corpus):\n",
    "       corpus.pop(indx)'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "01ee9bf2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NB Run 1 Accuracy: 0.494\n",
      "DT Run 1 Accuracy: 0.548\n",
      "NB Run 2 Accuracy: 0.524\n",
      "DT Run 2 Accuracy: 0.568\n",
      "NB Run 3 Accuracy: 0.518\n",
      "DT Run 3 Accuracy: 0.562\n",
      "NB Run 4 Accuracy: 0.506\n",
      "DT Run 4 Accuracy: 0.526\n",
      "NB Run 5 Accuracy: 0.544\n",
      "DT Run 5 Accuracy: 0.544\n",
      "NB Run 6 Accuracy: 0.558\n",
      "DT Run 6 Accuracy: 0.568\n",
      "NB Run 7 Accuracy: 0.512\n",
      "DT Run 7 Accuracy: 0.574\n",
      "NB Run 8 Accuracy: 0.56\n",
      "DT Run 8 Accuracy: 0.552\n",
      "NB Run 9 Accuracy: 0.514\n",
      "DT Run 9 Accuracy: 0.538\n",
      "NB Run 10 Accuracy: 0.53\n",
      "DT Run 10 Accuracy: 0.538\n",
      "FastText Run 1 Accuracy: 0.576\n",
      "FastText Run 2 Accuracy: 0.594\n",
      "FastText Run 3 Accuracy: 0.606\n",
      "FastText Run 4 Accuracy: 0.634\n",
      "FastText Run 5 Accuracy: 0.568\n",
      "FastText Run 6 Accuracy: 0.6\n",
      "FastText Run 7 Accuracy: 0.6\n",
      "FastText Run 8 Accuracy: 0.558\n",
      "FastText Run 9 Accuracy: 0.59\n",
      "FastText Run 10 Accuracy: 0.576\n",
      "Naive Bayes Accuracies: [0.494, 0.524, 0.518, 0.506, 0.544, 0.558, 0.512, 0.56, 0.514, 0.53]\n",
      "Decision Tree Accuracies: [0.548, 0.568, 0.562, 0.526, 0.544, 0.568, 0.574, 0.552, 0.538, 0.538]\n",
      "Fast Text Accuracies: [0.576, 0.594, 0.606, 0.634, 0.568, 0.6, 0.6, 0.558, 0.59, 0.576]\n"
     ]
    }
   ],
   "source": [
    "%run MovieSentimentsFastText.ipynb\n",
    "%run MovieSentimentsModel.ipynb\n",
    "\n",
    "NB_accuracies, NB_matrices, DT_accuracies, DT_matrices = runModels(data, corpus)\n",
    "FT_accuracies, FT_precisions, FT_recalls = fastTextRun(data, corpus)\n",
    "print(f'Naive Bayes Accuracies: {NB_accuracies}')\n",
    "print(f'Decision Tree Accuracies: {DT_accuracies}')\n",
    "print(f'Fast Text Accuracies: {FT_accuracies}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a57f5e34",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Naive Bayes Average Accuracy: 0.526\n",
      "Decision Tree Average Accuracy: 0.5518000000000001\n",
      "Fast Text Average Accuracy: 0.5902\n"
     ]
    }
   ],
   "source": [
    "NB_accuracy = sum(NB_accuracies)/len(NB_accuracies)\n",
    "DT_accuracy = sum(DT_accuracies)/len(DT_accuracies)\n",
    "FT_accuracy = sum(FT_accuracies)/len(FT_accuracies)\n",
    "print(f'Naive Bayes Average Accuracy: {NB_accuracy}')\n",
    "print(f'Decision Tree Average Accuracy: {DT_accuracy}')\n",
    "print(f'Fast Text Average Accuracy: {FT_accuracy}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4e00aa03",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Author: Isaac Bland\n",
    "runs = ['1','2','3','4','5','6','7','8','9','10']\n",
    "X_axis = np.arange(len(runs))\n",
    "\n",
    "ax = plt.gca()\n",
    "ax.set_ylim([0,1])\n",
    "plt.bar(X_axis - 0.2, NB_accuracies, 0.4, label='GNB')\n",
    "plt.bar(X_axis, DT_accuracies, 0.4, label='DT')\n",
    "plt.bar(X_axis + 0.2, FT_accuracies, 0.4, label='FT')\n",
    "\n",
    "plt.xticks(X_axis, runs)\n",
    "plt.xlabel(\"Run Number\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.title(\"Accuracy Comparison of the Three Models Over the 10 Runs\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6939a930",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_confusion_matrix(cm, classes, normalize=False, title='Confusion matrix', cmap=plt.cm.Blues):\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title)\n",
    "    plt.colorbar()\n",
    "    tick_marks = np.arange(len(classes))\n",
    "    plt.xticks(tick_marks, classes, rotation=45)\n",
    "    plt.yticks(tick_marks, classes)\n",
    "    fmt = '.2f' if normalize else 'd'\n",
    "    thresh = cm.max() / 2\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        plt.text(j, i, format(cm[i, j], fmt), horizontalalignment=\"center\", color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "        plt.tight_layout()\n",
    "        plt.ylabel('True label')\n",
    "        plt.xlabel('Predicted label')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6be8aa5e",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "class_names = ['good', 'bad']\n",
    "NB_precisions = []\n",
    "NB_recalls = []\n",
    "for cnf_matrix in NB_matrices:\n",
    "    plt.figure()\n",
    "    tn, fp, fn, tp = cnf_matrix.ravel() \n",
    "    NB_precisions.append(tp / (tp + fp))\n",
    "    NB_recalls.append(tp / (tp + fn))\n",
    "    plot_confusion_matrix(cnf_matrix, classes=class_names, title=\"Naive Bayes Confusion Matrix\")\n",
    "    plt.show()\n",
    "\n",
    "DT_precisions = []\n",
    "DT_recalls = []\n",
    "for cnf_matrix in DT_matrices:\n",
    "    plt.figure()\n",
    "    tn, fp, fn, tp = cnf_matrix.ravel() \n",
    "    DT_precisions.append(tp / (tp + fp))\n",
    "    DT_recalls.append(tp / (tp + fn))\n",
    "    plot_confusion_matrix(cnf_matrix, classes=class_names, title=\"Decision Tree Confusion Matrix\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c05d17f8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Naive Bayes Average Precision: 0.5338113207203357\n",
      "Decision Tree Average Precision: 0.537871204812934\n",
      "Fast Text Average Precision: 0.5916843699388885\n",
      "Naive Bayes Average Recall: 0.48447762870878586\n",
      "Decision Tree Average Recall: 0.7929756940413687\n",
      "Fast Text Average Recall: nan\n"
     ]
    }
   ],
   "source": [
    "NB_precision = sum(NB_precisions)/len(NB_precisions)\n",
    "DT_precision = sum(DT_precisions)/len(DT_precisions)\n",
    "FT_precision = sum(FT_precisions)/len(FT_precisions)\n",
    "print(f'Naive Bayes Average Precision: {NB_precision}')\n",
    "print(f'Decision Tree Average Precision: {DT_precision}')\n",
    "print(f'Fast Text Average Precision: {FT_precision}')\n",
    "\n",
    "NB_recall = sum(NB_recalls)/len(NB_recalls)\n",
    "DT_recall = sum(DT_recalls)/len(DT_recalls)\n",
    "FT_recall = sum(FT_recalls)/len(FT_recalls)\n",
    "print(f'Naive Bayes Average Recall: {NB_recall}')\n",
    "print(f'Decision Tree Average Recall: {DT_recall}')\n",
    "print(f'Fast Text Average Recall: {FT_recall}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a147fe17",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "╒═══════════════╤════════════╤═════════════╤════════════╕\n",
      "│ Model         │   Accuracy │   Precision │     Recall │\n",
      "╞═══════════════╪════════════╪═════════════╪════════════╡\n",
      "│ Naive Bayes   │     0.526  │    0.533811 │   0.484478 │\n",
      "├───────────────┼────────────┼─────────────┼────────────┤\n",
      "│ Decision Tree │     0.5518 │    0.537871 │   0.792976 │\n",
      "├───────────────┼────────────┼─────────────┼────────────┤\n",
      "│ Fast Text     │     0.5902 │    0.591684 │ nan        │\n",
      "╘═══════════════╧════════════╧═════════════╧════════════╛\n"
     ]
    }
   ],
   "source": [
    "#Author: Isaac Bland\n",
    "table = [['Model', 'Accuracy', 'Precision', 'Recall'], ['Naive Bayes', NB_accuracy, NB_precision, NB_recall], ['Decision Tree', DT_accuracy, DT_precision, DT_recall], ['Fast Text', FT_accuracy, FT_precision, FT_recall]]\n",
    "print(tabulate(table, headers='firstrow', tablefmt='fancy_grid'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2e954194",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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