amazonNLP.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c7640a6c",
   "metadata": {},
   "source": [
    "# Natural Language Processing with Amazon Reviews\n",
    "\n",
    "Developed for the second project of the Artificial Intelligence course\n",
    "\n",
    "## Setup and Environment\n",
    "\n",
    "After installing Python 3 and the necessary packages, run the following code to import them:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c28d746a",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import pandas as pd \n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sb\n",
    "import time\n",
    "from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.svm import LinearSVC\n",
    "from sklearn.linear_model import LogisticRegression, Perceptron\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import *\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "\n",
    "from sklearn.model_selection import PredefinedSplit\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from sklearn.experimental import enable_halving_search_cv\n",
    "from sklearn.model_selection import HalvingGridSearchCV\n",
    "\n",
    "MAX_FEATURES = 10000"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23e738a9",
   "metadata": {},
   "source": [
    "## Implementation work"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83a47e52",
   "metadata": {},
   "source": [
    "### Generating a data set\n",
    "#### Obtaining features from the Bag-of-Words model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f839739c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "train = pd.read_csv('./data/train/train_preprocessed20k.csv')\n",
    "test = pd.read_csv('./data/test/test_preprocessed20k.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b57253aa",
   "metadata": {},
   "source": [
    "Please choose one of the following vectorizers:\n",
    "#### Creating regular CountVectorizers (default)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "9c6883c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "titleVectorizer = CountVectorizer(max_features=MAX_FEATURES)\n",
    "textVectorizer = CountVectorizer(max_features=MAX_FEATURES)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f00ce050",
   "metadata": {},
   "source": [
    "#### Creating 1-hot vectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a89c77a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "titleVectorizer = CountVectorizer(binary=True, max_features=MAX_FEATURES)\n",
    "textVectorizer = CountVectorizer(binary=True, max_features=MAX_FEATURES)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc81a00a",
   "metadata": {},
   "source": [
    "#### Creating TF-IDF vectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c86fb0d4",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "titleVectorizer = TfidfVectorizer(max_features=MAX_FEATURES)\n",
    "textVectorizer = TfidfVectorizer(max_features=MAX_FEATURES)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f5c0e16",
   "metadata": {},
   "source": [
    "#### Creating bi-gram CountVectorizors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "5a49379a",
   "metadata": {},
   "outputs": [],
   "source": [
    "titleVectorizer = CountVectorizer(ngram_range=(1, 2), max_features=MAX_FEATURES)\n",
    "textVectorizer = CountVectorizer(ngram_range=(1, 2), max_features=MAX_FEATURES)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff1fe250",
   "metadata": {},
   "source": [
    "#### Creating bi-gram TF-IDF vectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "87a4a810",
   "metadata": {},
   "outputs": [],
   "source": [
    "titleVectorizer = TfidfVectorizer(ngram_range=(1, 2), max_features=MAX_FEATURES)\n",
    "textVectorizer = TfidfVectorizer(ngram_range=(1, 2), max_features=MAX_FEATURES)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c42ca485",
   "metadata": {},
   "source": [
    "#### Creating tri-gram CountVectorizors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "0ce17fd6",
   "metadata": {},
   "outputs": [],
   "source": [
    "titleVectorizer = CountVectorizer(ngram_range=(1, 3), max_features=MAX_FEATURES)\n",
    "textVectorizer = CountVectorizer(ngram_range=(1, 3), max_features=MAX_FEATURES)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd345607",
   "metadata": {},
   "source": [
    "#### Creating tri-gram TF-IDF vectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3c90931c",
   "metadata": {},
   "outputs": [],
   "source": [
    "titleVectorizer = TfidfVectorizer(ngram_range=(1, 3), max_features=MAX_FEATURES)\n",
    "textVectorizer = TfidfVectorizer(ngram_range=(1, 3), max_features=MAX_FEATURES)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0f77787",
   "metadata": {},
   "source": [
    "#### Transforming the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "4449108b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(20000, 10000)\n",
      "(20000, 10000)\n",
      "(20000, 20000)\n"
     ]
    }
   ],
   "source": [
    "trainTitleArr = list(map(str, train['review_title']))\n",
    "trainTextArr = list(map(str, train['review_text']))\n",
    "\n",
    "titleWords = titleVectorizer.fit_transform(trainTitleArr).toarray()\n",
    "textWords = textVectorizer.fit_transform(trainTextArr).toarray()\n",
    "\n",
    "allWords = np.array([np.concatenate((titleWords[i], textWords[i])) for i in range(len(titleWords))])\n",
    "\n",
    "print(titleWords.shape)\n",
    "print(textWords.shape)\n",
    "print(allWords.shape)\n",
    "\n",
    "y = train['class_index']\n",
    "\n",
    "# Test variables\n",
    "testTitleArr = list(map(str, test['review_title']))\n",
    "testTextArr = list(map(str, test['review_text']))\n",
    "\n",
    "testTitleVector = titleVectorizer.transform(testTitleArr).toarray()\n",
    "testTextVector = textVectorizer.transform(testTextArr).toarray()\n",
    "\n",
    "testAllWords = np.array([np.concatenate((testTitleVector[i], testTextVector[i])) for i in range(len(testTitleVector))])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d872871e",
   "metadata": {},
   "source": [
    "### Training Classifiers and Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e7025165",
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict(clf, **arg):\n",
    "    # Train with allWords\n",
    "    clf.fit(allWords, y, **arg)\n",
    "\n",
    "    return clf.predict(testAllWords)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65b2efd3",
   "metadata": {},
   "source": [
    "### Splitting in 3 datasets: Train, Parameter Fine-Tuning and Test."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9f8ffcf0",
   "metadata": {},
   "outputs": [],
   "source": [
    "def fullPredict(clf, param_grid, **arg):\n",
    "    # Splitting test dataset into test and parameter fine tuning\n",
    "    X_train, X_paramTuning, y_train, y_paramTuning= train_test_split(allWords, y, test_size=0.3, random_state=0)\n",
    "    \n",
    "    # Train with allWords\n",
    "    clf.fit(X_train, y_train, **arg)\n",
    "\n",
    "    grid = HalvingGridSearchCV(clf, param_grid)\n",
    "\n",
    "    grid.fit(X_paramTuning, y_paramTuning, **arg)\n",
    "\n",
    "    print(\"Best grid params:\", grid.best_params_)\n",
    "\n",
    "    return grid.predict(testAllWords)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9ede350",
   "metadata": {},
   "source": [
    "### Plot Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "3834f945",
   "metadata": {},
   "outputs": [],
   "source": [
    "def prepare_statistic(data):\n",
    "    x = ['Accuracy', 'Precision', 'Recall', 'F1']\n",
    "    title = data.iloc[0]\n",
    "    y = data.iloc[1:].tolist()\n",
    "    return x, y, title\n",
    "\n",
    "def plot_all_metrics(data, figLabel):\n",
    "    fig, axs = plt.subplots(2, 3, sharex=True, sharey=True)\n",
    "    fig.set_figwidth(20)\n",
    "    fig.set_figheight(7)\n",
    "    fig.suptitle(figLabel)\n",
    "    for i in range(2):\n",
    "        for j in range(3):\n",
    "            index = i*3 + j\n",
    "            currData = data[index]\n",
    "            x, y, title = prepare_statistic(currData)\n",
    "            axis = axs[i, j]\n",
    "            axis.set_ylim(bottom=0.0, top=1.0)\n",
    "            axis.bar(x, y)\n",
    "            axis.set_title(title)\n",
    "            axis.grid(True)\n",
    "    plt.show()\n",
    "\n",
    "def plot_bar(x, y, title, y_label, x_label):\n",
    "    fig = plt.figure()\n",
    "    ax = fig.add_axes([0,0,3,1.5])\n",
    "    ax.bar(x, y)\n",
    "    ax.set_title(title)\n",
    "    ax.set_ylabel(y_label)\n",
    "    ax.set_xlabel(x_label)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20db34f2",
   "metadata": {},
   "source": [
    "### Analyze Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0fa05366",
   "metadata": {},
   "outputs": [],
   "source": [
    "def analyze(prediction, test, classifierLabel):\n",
    "    y_test = test['class_index']\n",
    "\n",
    "    # confusion matrix\n",
    "    confusionMatrix = confusion_matrix(y_test, prediction)\n",
    "    print(\"Confusion Matrix:\")\n",
    "    print(confusionMatrix)\n",
    "\n",
    "    plot_data = []\n",
    "\n",
    "    # Accuracy is a metric that takes into account all the classes\n",
    "    accuracy = accuracy_score(y_test, prediction)\n",
    "    precision = precision_score(y_test, prediction, average='weighted')\n",
    "    recall = recall_score(y_test, prediction, average='weighted')\n",
    "    f1 = f1_score(y_test, prediction, average='weighted')\n",
    "\n",
    "    print(\"Accuracy: \", accuracy * 100, \" %\")\n",
    "    print(\"Precision: \", precision * 100, \" %\")\n",
    "    print('Recall: ', recall * 100, \" %\")\n",
    "    print('F1: ', f1 * 100, \" %\")\n",
    "\n",
    "    plot_data.append(pd.Series(['All Ratings', accuracy, precision, recall, f1]))\n",
    "    \n",
    "    # Calculate metrics for individual classes\n",
    "    for class_index in range(1, 6, 1):\n",
    "        precision = precision_score(y_test, prediction, average='weighted', labels=[class_index])\n",
    "        recall = recall_score(y_test, prediction, average='weighted', labels=[class_index])\n",
    "        f1 = f1_score(y_test, prediction, average='weighted', labels=[class_index])\n",
    "        plot_data.append(pd.Series([str(class_index)+' Star Rating', accuracy, precision, recall, f1]))\n",
    "\n",
    "\n",
    "    # Plot the data\n",
    "    plot_all_metrics(plot_data, classifierLabel)\n",
    "\n",
    "    return accuracy\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19b98685",
   "metadata": {},
   "source": [
    "### K-Nearest Neighbor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "72624c21",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "K-Nearest Neighbor Results:\n",
      "Confusion Matrix:\n",
      "[[394 237  84  50  35]\n",
      " [227 346 114  71  42]\n",
      " [131 284 187 127  71]\n",
      " [116 178 138 202 166]\n",
      " [107 138  84 179 292]]\n",
      "Accuracy:  35.525  %\n",
      "Precision:  36.15289329332526  %\n",
      "Recall:  35.525  %\n",
      "F1:  35.13603159158251  %\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x504 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "begin = time.time()\n",
    "prediction = predict(KNeighborsClassifier(n_neighbors=5))\n",
    "end = time.time()\n",
    "# prediction = fullPredict(KNeighborsClassifier(), {\"n_neighbors\": [5, 15, 30, 40], \"leaf_size\": [10, 20, 30, 35]})\n",
    "print(\"K-Nearest Neighbor Results:\")\n",
    "accuracy = analyze(prediction, test, \"K-Nearest Neighbor\")\n",
    "knn_metrics = (accuracy, end - begin)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7eaad8f-0f60-42af-9747-d1669b2c1cf2",
   "metadata": {},
   "source": [
    "### Multinomial Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "84fb6fec-daf4-4abf-861b-537f454143c3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MultinomialNB Results:\n",
      "Confusion Matrix:\n",
      "[[494 191  55  22  38]\n",
      " [186 320 206  55  33]\n",
      " [ 81 179 330 146  64]\n",
      " [ 34  58 150 348 210]\n",
      " [ 34  29  38 169 530]]\n",
      "Accuracy:  50.55  %\n",
      "Precision:  50.14687333198059  %\n",
      "Recall:  50.55  %\n",
      "F1:  50.30221722566489  %\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x504 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "begin = time.time()\n",
    "prediction = predict(MultinomialNB(), sample_weight=1.0)\n",
    "end = time.time()\n",
    "# prediction = fullPredict(MultinomialNB(), {\"alpha\": [0.0001, 0.5, 1.0]}, sample_weight=1.0)\n",
    "\n",
    "print(\"MultinomialNB Results:\")\n",
    "accuracy = analyze(prediction, test, 'Multinomial Naive Bayes')\n",
    "nb_metrics = (accuracy, end - begin)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2153922a-923c-4d3e-b54d-663d2940311e",
   "metadata": {},
   "source": [
    "### Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "70143506-709a-44e8-8024-4e74a3760a57",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RandomForestClassifier Results:\n",
      "Confusion Matrix:\n",
      "[[505 125  63  36  71]\n",
      " [247 253 177  66  57]\n",
      " [110 177 304 129  80]\n",
      " [ 66  61 177 257 239]\n",
      " [ 47  35  48 142 528]]\n",
      "Accuracy:  46.175  %\n",
      "Precision:  45.02750311012402  %\n",
      "Recall:  46.175  %\n",
      "F1:  45.192344109995176  %\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x504 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "begin = time.time()\n",
    "prediction = predict(RandomForestClassifier(n_estimators=100), sample_weight=1.0) \n",
    "#prediction = fullPredict(RandomForestClassifier(), {\"n_estimators\": [50, 100]} , sample_weight=1.0)\n",
    "end = time.time()\n",
    "\n",
    "print(\"RandomForestClassifier Results:\")\n",
    "accuracy = analyze(prediction, test, 'Random Forest')\n",
    "rforest_metrics = (accuracy, end - begin)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25173d2c-fb89-4769-8c29-7d7051e24a76",
   "metadata": {},
   "source": [
    "### Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "72b2289b-c402-4699-bfd6-13a85b28543d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DecisionTreeClassifier Results:\n",
      "Confusion Matrix:\n",
      "[[322 184 136  87  71]\n",
      " [200 217 192 100  91]\n",
      " [120 159 239 168 114]\n",
      " [ 94 116 174 216 200]\n",
      " [ 85  82 118 189 326]]\n",
      "Accuracy:  33.0  %\n",
      "Precision:  32.948182586013765  %\n",
      "Recall:  33.0  %\n",
      "F1:  32.95775200086782  %\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x504 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "begin = time.time()\n",
    "prediction = predict(DecisionTreeClassifier(), sample_weight=1.0)\n",
    "# prediction = fullPredict(DecisionTreeClassifier(), {\"criterion\": [\"gini\", \"entropy\", \"log_loss\"], \"splitter\": [\"best\", \"random\"]} , sample_weight=1.0)\n",
    "end = time.time()\n",
    "\n",
    "print(\"DecisionTreeClassifier Results:\")\n",
    "accuracy = analyze(prediction, test, 'Decision Tree')\n",
    "dtree_metrics = (accuracy, end - begin)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3dae7ab-a235-4ddb-932d-43885ae6eb33",
   "metadata": {},
   "source": [
    "### Perceptron"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "8423c33b-12a9-4e43-9c6c-55ffef0b0397",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Perceptron Results:\n",
      "Confusion Matrix:\n",
      "[[450 227  55  33  35]\n",
      " [221 319 150  80  30]\n",
      " [ 85 230 250 175  60]\n",
      " [ 46  91 146 323 194]\n",
      " [ 35  66  51 239 409]]\n",
      "Accuracy:  43.775  %\n",
      "Precision:  44.0958200492072  %\n",
      "Recall:  43.775  %\n",
      "F1:  43.78283995408955  %\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x504 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "begin = time.time()\n",
    "prediction = predict(Perceptron(), sample_weight=1.0)\n",
    "# prediction = fullPredict(Perceptron(), {\"alpha\": [0.0001, 0.001]} , sample_weight=1.0)\n",
    "end = time.time()\n",
    "\n",
    "print(\"Perceptron Results:\")\n",
    "accuracy = analyze(prediction, test, 'Perceptron')\n",
    "perceptron_metrics = (accuracy, end - begin)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a5bc0ee-4846-47fc-ad85-d9ce622c250f",
   "metadata": {},
   "source": [
    "### Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "f2802cfd-e474-4094-8ec0-cf71b38781a7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LogisticRegression Results:\n",
      "Confusion Matrix:\n",
      "[[537 156  53  27  27]\n",
      " [208 318 190  60  24]\n",
      " [ 76 187 338 151  48]\n",
      " [ 33  44 158 356 209]\n",
      " [ 32  27  34 174 533]]\n",
      "Accuracy:  52.05  %\n",
      "Precision:  51.50178930175616  %\n",
      "Recall:  52.05  %\n",
      "F1:  51.71183756238891  %\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x504 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "begin = time.time()\n",
    "prediction = predict(LogisticRegression(class_weight='balanced', solver='saga'), sample_weight=1.0)\n",
    "# Best grid params: {'class_weight': 'balanced', 'solver': 'saga'} este foi o resultado do grid search\n",
    "#prediction = fullPredict(LogisticRegression(), {#\"penalty\": [\"l1\", \"l2\", \"elastic\", \"none\"],\n",
    "#    \"class_weight\": [\"balanced\", None], \"solver\": [\"newton-cg\", \"lbfgs\", \"sag\", \"saga\"]\n",
    "#}, sample_weight=1.0)\n",
    "end = time.time()\n",
    "\n",
    "print(\"LogisticRegression Results:\")\n",
    "accuracy = analyze(prediction, test, 'Logistic Regression')\n",
    "logregression_metrics = (accuracy, end - begin)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49b8f22b-96f3-4977-869d-574fdfe5289d",
   "metadata": {},
   "source": [
    "### Linear SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "e3947cd4-d0a6-43a6-8c5e-1f8f74704ba8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LinearSVC Results:\n",
      "Confusion Matrix:\n",
      "[[541 150  50  25  34]\n",
      " [230 297 180  61  32]\n",
      " [ 79 181 327 150  63]\n",
      " [ 40  45 149 340 226]\n",
      " [ 35  24  33 169 539]]\n",
      "Accuracy:  51.1  %\n",
      "Precision:  50.25501481048139  %\n",
      "Recall:  51.1  %\n",
      "F1:  50.50968634488595  %\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x504 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "begin = time.time()\n",
    "prediction = predict(LinearSVC(C= 0.2, class_weight= 'balanced', dual=True, loss='squared_hinge', multi_class='ovr'), sample_weight=1.0)\n",
    "# Best grid params: {'C': 0.2, 'class_weight': 'balanced', 'dual': True, 'loss': 'squared_hinge', 'multi_class': 'ovr'}\n",
    "# prediction = fullPredict(LinearSVC(), {\n",
    "#    \"class_weight\": [\"balanced\", None], \"loss\": [\"hinge\", \"squared_hinge\"], \"multi_class\": [\"ovr\", \"crammer_singer\"], \"C\": [0.2, 0.5, 1.0, 2.0], \"dual\": [True]\n",
    "# }, sample_weight=1.0)\n",
    "end = time.time()\n",
    "\n",
    "print(\"LinearSVC Results:\")\n",
    "accuracy = analyze(prediction, test, 'Linear SVC')\n",
    "linearSVC_metrics = (accuracy, end - begin)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "415ecb19",
   "metadata": {},
   "source": [
    "### Neural Network (Multi-Layer Perceptron)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "66c77fe8-4486-42e7-a500-0594c36d7106",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NeuralNetwork Results:\n",
      "Confusion Matrix:\n",
      "[[477 186  78  31  28]\n",
      " [220 283 199  66  32]\n",
      " [ 70 195 304 164  67]\n",
      " [ 35  63 176 318 208]\n",
      " [ 37  35  59 215 454]]\n",
      "Accuracy:  45.9  %\n",
      "Precision:  45.76779513773086  %\n",
      "Recall:  45.9  %\n",
      "F1:  45.82161236099417  %\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x504 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "begin = time.time()\n",
    "prediction = predict(MLPClassifier())\n",
    "end = time.time()\n",
    "\n",
    "print(\"NeuralNetwork Results:\")\n",
    "accuracy = analyze(prediction, test, 'Neural Network (Multi-Layer Perceptron)')\n",
    "neuralnet_metrics = (accuracy, end - begin)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc8ae583",
   "metadata": {},
   "source": [
    "### Comparing Classifiers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "031f2a3d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "models=['K-NN', 'Naive Bayes', 'Random Forest', 'Decision Tree', 'Perceptron', 'Logistic Regression', 'Linear SVC', 'Neural Network']\n",
    "accuracies=[knn_metrics[0], nb_metrics[0], rforest_metrics[0], dtree_metrics[0], perceptron_metrics[0], logregression_metrics[0], linearSVC_metrics[0], neuralnet_metrics[0]]\n",
    "execTimes=[knn_metrics[1], nb_metrics[1], rforest_metrics[1], dtree_metrics[1], perceptron_metrics[1], logregression_metrics[1], linearSVC_metrics[1], neuralnet_metrics[1]]\n",
    "\n",
    "plot_bar(models, accuracies, 'ML Models Accuracy', 'Accuracy', '')\n",
    "\n",
    "plot_bar(models, execTimes, 'ML Models Time', 'Time (s)', '')"
   ]
  }
 ],
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