Brain MRI NaiveBayes.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "fabbf7ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "import sklearn\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import accuracy_score \n",
    "from sklearn.decomposition import PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "efda165d",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_target = np.load(\"labels.npz\")['a']\n",
    "X_features = np.load(\"features.npz\")['a']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "297ee424",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5752481713688611\n"
     ]
    }
   ],
   "source": [
    "model=GaussianNB()\n",
    "Xtrain, Xtest, ytrain, ytest = train_test_split(X_features, y_target, test_size=0.20, random_state = 42, shuffle = True, # shuffle dataset\n",
    "stratify = y_target)\n",
    "#model.fit(X_features, y_target)\n",
    "model.fit(Xtest, ytest)\n",
    "score_kfold=cross_val_score(model, X_features, y_target, cv=10).mean() #cv is the number of folds\n",
    "print(score_kfold)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "47545b96",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average CV Score:  0.5590607553366175\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import KFold, cross_val_score, LeaveOneOut \n",
    "\n",
    "k_folds = KFold(n_splits = 5)\n",
    "\n",
    "scores = cross_val_score(model, X_features, y_target, cv = k_folds) \n",
    "print(\"Average CV Score: \", scores.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "115e173e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average CV Score using LOO:  0.5774971297359357\n"
     ]
    }
   ],
   "source": [
    "loo = LeaveOneOut()\n",
    "scores = cross_val_score(model, X_features, y_target, cv = loo) \n",
    "print(\"Average CV Score using LOO: \", scores.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ba622ef4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# predict probabilities\n",
    "\n",
    "pred_prob = model.predict_proba(X_features)\n",
    "from sklearn.metrics import roc_curve\n",
    "\n",
    "# roc curve for models\n",
    "fpr, tpr, thresh = roc_curve(y_target, pred_prob[:,1], pos_label=1)\n",
    "\n",
    "# roc curve for tpr = fpr \n",
    "random_probs = [0 for i in range(len(y_target))]\n",
    "p_fpr, p_tpr, _ = roc_curve(y_target, random_probs, pos_label=1)\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('seaborn')\n",
    "\n",
    "# plot roc curves\n",
    "plt.plot(fpr, tpr, linestyle='--',color='orange', label='Naive Bayes')\n",
    "plt.plot(p_fpr, p_tpr, linestyle='--', color='blue')\n",
    "# title\n",
    "plt.title('ROC curve')\n",
    "# x label\n",
    "plt.xlabel('False Positive Rate')\n",
    "# y label\n",
    "plt.ylabel('True Positive rate')\n",
    "\n",
    "plt.legend(loc='best')\n",
    "plt.savefig('ROC',dpi=300)\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "8b4d29fd",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\user\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\sklearn\\utils\\deprecation.py:87: FutureWarning: Function plot_confusion_matrix is deprecated; Function `plot_confusion_matrix` is deprecated in 1.0 and will be removed in 1.2. Use one of the class methods: ConfusionMatrixDisplay.from_predictions or ConfusionMatrixDisplay.from_estimator.\n",
      "  warnings.warn(msg, category=FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x1e3d6e79a00>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import plot_confusion_matrix\n",
    "plot_confusion_matrix(model,\n",
    "                      Xtest, ytest,\n",
    "                      display_labels=[\"ASD\",\"TD\"], cmap=\"Blues\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "facd8fde",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1       0.59      0.60      0.60        81\n",
      "           2       0.65      0.64      0.65        94\n",
      "\n",
      "    accuracy                           0.62       175\n",
      "   macro avg       0.62      0.62      0.62       175\n",
      "weighted avg       0.62      0.62      0.62       175\n",
      "\n",
      "0.5591274397244547\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "print(metrics.classification_report(ytest, model.predict(Xtest)))\n",
    "\n",
    "print(accuracy_score(y_target, model.predict(X_features)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b5d4e7db",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}