Best_models.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Optimal Model Evaluation\n",
    "### Ziad Al-Ziadi - NM427 Neural Computing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this Notebook, we will compare our optimal MLP and optimal SVM on the test dataset to see which model performs optimally. Both the optimal MLP and SVM will be loaded in as pickle files that we created in their respective notebooks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt \n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.metrics import log_loss, roc_auc_score, roc_curve, auc, plot_roc_curve, plot_confusion_matrix,confusion_matrix, classification_report\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.model_selection import GroupKFold\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim \n",
    "from torch.autograd import Variable\n",
    "from torch.utils.data import DataLoader\n",
    "from torch.nn import CrossEntropyLoss, MSELoss\n",
    "from torch.nn.modules.loss import _WeightedLoss\n",
    "import torch.nn.functional as F\n",
    "\n",
    "import pickle\n",
    "\n",
    "pd.set_option(\"display.max_rows\", 250)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reading in the dataset\n",
    "\n",
    "path = \"diabetes.csv\"\n",
    "\n",
    "diabetes = pd.read_csv(path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "postive = diabetes.loc[diabetes[\"Outcome\"] == 1] # has diabetes\n",
    "negative = diabetes.loc[diabetes[\"Outcome\"] == 0] # does not have "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "postive = postive.replace({\"Glucose\": 0},\n",
    "                            np.mean(postive[\"Glucose\"]))\n",
    "\n",
    "postive = postive.replace({\"BloodPressure\": 0},\n",
    "                            np.mean(postive[\"BloodPressure\"]))\n",
    "\n",
    "\n",
    "postive = postive.replace({\"SkinThickness\": 0},\n",
    "                            np.mean(postive[\"SkinThickness\"]))\n",
    "\n",
    "postive = postive.replace({\"BMI\": 0},\n",
    "                            np.mean(postive[\"BMI\"]))\n",
    "\n",
    "postive = postive.replace({\"Insulin\": 0},\n",
    "                            np.mean(postive[\"Insulin\"]))\n",
    "\n",
    "negative = negative.replace({\"Glucose\": 0},\n",
    "                            np.mean(negative[\"Glucose\"]))\n",
    "\n",
    "negative = negative.replace({\"BloodPressure\": 0},\n",
    "                            np.mean(negative[\"BloodPressure\"]))\n",
    "\n",
    "negative = negative.replace({\"SkinThickness\": 0},\n",
    "                            np.mean(negative[\"SkinThickness\"]))\n",
    "\n",
    "negative = negative.replace({\"BMI\": 0},\n",
    "                            np.mean(negative[\"BMI\"]))\n",
    "\n",
    "negative = negative.replace({\"Insulin\": 0},\n",
    "                            np.mean(negative[\"Insulin\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "diabetes = pd.concat([postive, negative])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = diabetes[\"Outcome\"]\n",
    "diabetes.drop([\"Outcome\"], axis = 1, inplace = True)\n",
    "X = diabetes\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 42)\n",
    "\n",
    "X, X_validation, y, y_validation = train_test_split(X_train, y_train, test_size = 0.25,random_state = 42)\n",
    "\n",
    "scaler = StandardScaler()\n",
    "\n",
    "scaler.fit_transform(X_train)\n",
    "scaler.fit_transform(X_test)\n",
    "scaler.fit_transform(X_validation)\n",
    "\n",
    "# here we convert our sets to tensors \n",
    "\n",
    "X_train = torch.Tensor(X_train.to_numpy())\n",
    "X_test = torch.Tensor(X_test.to_numpy())\n",
    "X_validation = torch.Tensor(X_validation.to_numpy())\n",
    "\n",
    "X_train = torch.FloatTensor(X_train)\n",
    "X_test = torch.FloatTensor(X_test)\n",
    "X_validation = torch.FloatTensor(X_validation)\n",
    "\n",
    "y_train = torch.LongTensor(y_train.values)\n",
    "y_test = torch.LongTensor(y_test.values)\n",
    "y_validation = torch.LongTensor(y_validation.values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multi-Layer Perceptron"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "class MLPThree(nn.Module):\n",
    "    def __init__(self,hiddenOne = 25, hiddenTwo = 20):\n",
    "        super().__init__()\n",
    "        self.fc1 = nn.Linear(8,hiddenOne)\n",
    "        self.fc2 = nn.Linear(hiddenOne,hiddenTwo)\n",
    "        self.output = nn.Linear(hiddenTwo,2)\n",
    "    def forward(self,x):\n",
    "        x = F.leaky_relu(self.fc1(x))\n",
    "        x = F.leaky_relu(self.fc2(x))\n",
    "        x = self.output(x)\n",
    "        return x\n",
    "\n",
    "with open(\"optimal_mlp\", \"rb\") as op_mlp:\n",
    "    optimal_mlp = pickle.load(op_mlp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
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   ],
   "source": [
    "# Test accuracy for our optimal MLP\n",
    "\n",
    "predictions=[]\n",
    "for i,data in enumerate(X_test):\n",
    "    y_pred=optimal_mlp(data)\n",
    "    predictions.append(y_pred.argmax().item())\n",
    "\n",
    "fpr, tpr, _  = roc_curve(y_test, predictions)\n",
    "roc_auc = auc(fpr, tpr)\n",
    "\n",
    "plt.style.use(\"seaborn\")\n",
    "plt.figure()\n",
    "lw = 2\n",
    "plt.plot(fpr, tpr, color='darkorange',\n",
    "         lw=lw, label='ROC curve (AUC = %0.2f)' % roc_auc)\n",
    "plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('ROC for MLP 3 (Testing)')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score for MLP:  87.01298701298701\n",
      "---\n",
      "Classification Report for MLP:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.91      0.89      0.90        97\n",
      "           1       0.81      0.84      0.83        57\n",
      "\n",
      "    accuracy                           0.87       154\n",
      "   macro avg       0.86      0.86      0.86       154\n",
      "weighted avg       0.87      0.87      0.87       154\n",
      "\n",
      "---\n",
      "Confusion Matrix:\n",
      " [[86 11]\n",
      " [ 9 48]]\n"
     ]
    }
   ],
   "source": [
    "acc_score = accuracy_score(y_test, predictions)\n",
    "print(\"Accuracy score for MLP: \", acc_score*100)\n",
    "print(\"---\")\n",
    "print(\"Classification Report for MLP:\\n\",classification_report(y_test, predictions))\n",
    "print(\"---\")\n",
    "print(\"Confusion Matrix:\\n\", confusion_matrix(y_test,predictions ))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Suport Vector Machine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "path = \"diabetes.csv\"\n",
    "\n",
    "diabetes = pd.read_csv(path)\n",
    "\n",
    "postive = postive.replace({\"Glucose\": 0},\n",
    "                            np.mean(postive[\"Glucose\"]))\n",
    "\n",
    "postive = postive.replace({\"BloodPressure\": 0},\n",
    "                            np.mean(postive[\"BloodPressure\"]))\n",
    "\n",
    "\n",
    "postive = postive.replace({\"SkinThickness\": 0},\n",
    "                            np.mean(postive[\"SkinThickness\"]))\n",
    "\n",
    "postive = postive.replace({\"BMI\": 0},\n",
    "                            np.mean(postive[\"BMI\"]))\n",
    "\n",
    "postive = postive.replace({\"Insulin\": 0},\n",
    "                            np.mean(postive[\"Insulin\"]))\n",
    "                            \n",
    "negative = negative.replace({\"Glucose\": 0},\n",
    "                            np.mean(negative[\"Glucose\"]))\n",
    "\n",
    "negative = negative.replace({\"BloodPressure\": 0},\n",
    "                            np.mean(negative[\"BloodPressure\"]))\n",
    "\n",
    "\n",
    "negative = negative.replace({\"SkinThickness\": 0},\n",
    "                            np.mean(negative[\"SkinThickness\"]))\n",
    "\n",
    "negative = negative.replace({\"BMI\": 0},\n",
    "                            np.mean(negative[\"BMI\"]))\n",
    "\n",
    "negative = negative.replace({\"Insulin\": 0},\n",
    "                            np.mean(negative[\"Insulin\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.55000589, -1.14041655,  0.08730665, ..., -0.50984268,\n",
       "        -0.72905992, -0.90004854],\n",
       "       [ 1.56751679,  1.32652046,  1.19271999, ...,  0.35311676,\n",
       "         2.59987394,  0.6786243 ],\n",
       "       [-0.85250913,  0.85490015,  1.19271999, ..., -0.3307378 ,\n",
       "         1.56998506, -0.82111489],\n",
       "       ...,\n",
       "       [-1.15501238, -0.81391018, -0.83387113, ...,  0.76017316,\n",
       "         0.55743437, -0.66324761],\n",
       "       [-1.15501238,  0.56467227,  1.92966222, ...,  1.37889876,\n",
       "        -0.53833973, -0.58431397],\n",
       "       [-0.85250913, -1.7571508 , -2.30775558, ..., -1.91011675,\n",
       "        -0.40310178, -0.90004854]])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = diabetes[\"Outcome\"]\n",
    "diabetes.drop([\"Outcome\"], axis = 1, inplace = True)\n",
    "X = diabetes\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 42)\n",
    "\n",
    "X, X_valdation, y, y_validation = train_test_split(X_train, y_train, test_size = 0.25,random_state = 42)\n",
    "\n",
    "scaler = StandardScaler()\n",
    "\n",
    "scaler.fit_transform(X_train)\n",
    "scaler.fit_transform(X_test)\n",
    "scaler.fit_transform(X_validation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"optimal_svm\", \"rb\") as op_svm:\n",
    "    optimal_svm = pickle.load(op_svm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_true_test, y_pred_test = y_test, optimal_svm.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
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   "source": [
    "lw = 2\n",
    "plt.style.use(\"seaborn\")\n",
    "fig, ax_roc = plt.subplots(1, figsize = (7, 7))\n",
    "plot_roc_curve(optimal_svm, X_test, y_test, ax = ax_roc, color = \"darkorange\")\n",
    "plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')\n",
    "ax_roc.set_title('ROC curve for our Test set')\n",
    "ax_roc.grid(linestyle='--')\n",
    "\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accuracy score for SVM:  75.32467532467533\n",
      "---\n",
      "Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.81      0.81      0.81        99\n",
      "           1       0.65      0.65      0.65        55\n",
      "\n",
      "    accuracy                           0.75       154\n",
      "   macro avg       0.73      0.73      0.73       154\n",
      "weighted avg       0.75      0.75      0.75       154\n",
      "\n",
      "---\n",
      "Confusion Matrix:\n",
      " [[80 19]\n",
      " [19 36]]\n"
     ]
    }
   ],
   "source": [
    "acc_score = accuracy_score(y_test, y_pred_test)\n",
    "print(\"Test accuracy score for SVM: \", acc_score*100)\n",
    "print(\"---\")\n",
    "print(\"Classification Report:\\n\", classification_report(y_true_test, y_pred_test))\n",
    "print(\"---\")\n",
    "print(\"Confusion Matrix:\\n\", confusion_matrix(y_true_test, y_pred_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion\n",
    "We can see that our MLP model outperforms our SVM model in accuracy by a very tight, almost negligible, margin. Interestingly, the SVM obtains a better AUC score. With respect to the confusion matrices, we see that with some metrics such as recall, the SVM scores better, however the confusion matrix of the MLP scores better overall. In conclusion, we can say that the MLP is narrowly more optimal than the SVM. Further, minor improvements made to the MLP will substantially improve its performance. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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