ROC.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Import modules/packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import auc\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import StratifiedKFold"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Make output directory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "cwd = os.getcwd()\n",
    "path = cwd + \"\\output\"\n",
    "if not os.path.exists(path):\n",
    "    os.mkdir(path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Load data and assign response (y) and predictor (X) variables. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(88, 1)\n"
     ]
    }
   ],
   "source": [
    "y = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, \n",
    "     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
    "     0, 0, 0, 0, 1, 1, 1, 1,\n",
    "     1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
    "     1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n",
    "y = np.array(y)\n",
    "\n",
    "X = [[1.191072215], [-0.150732988], [1.02539397], [0.192915327], [0.237137539], [0.533956707], [-0.228751412], [0.524620627], \n",
    "     [1.081934102], [0.875224248], [1.387946695], [0.682203195], [0.650375449], [1.146245458], [0.371088942], [1.396910481], \n",
    "     [0.319208586], [0.61971852], [0.654233226], [0.562634732], [0.114221031], [0.481049931], [-0.428311114], [1.255137404], \n",
    "     [1.041066024], [0.969473169], [-0.731967289], [0.802590184], [0.550082823], [-1.272879776], [0.427473371], [-0.451440377], \n",
    "     [1.455265987], [0.906650123], [1.186416787], [1.468974794], [0.956555924], [-0.05405806], [1.015775188], [1.052885522], \n",
    "     [0.483556833], [0.589072028], [-0.029023829], [0.239449596], [-1.288667521], [-1.863513657], [0.588107367], [-1.285098534],\n",
    "     [-2.19697431], [-1.946418018], [-1.853475609], [-0.654115876], [0.725006684], [-1.457779057], \n",
    "     [-1.53202234], [-1.560086252], [0.901247496], [-1.95333853], [0.419436844], [0.594437252], [-0.10963962], [-1.435731885], \n",
    "     [-0.217053693], [-2.446162331], [-0.26829819], [0.494217693], [0.198844212], [0.219697953], [0.343805283], [-1.236012131], \n",
    "     [-0.794290172], [0.968658121], [-0.624976831], [0.401511012], [-0.321885188], [-1.102287759], [-0.266741162], [0.925130918], \n",
    "     [0.246887668], [0.487255428], [-1.108491516], [-1.066809261], [-1.564944354], [-0.705496938], [-1.287848124], \n",
    "     [-1.057692382], [-0.803777606], [1.394033023]]\n",
    "\n",
    "X = np.array(X)\n",
    "print(X.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Fit model and calculate ROC metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.7386363636363636\n",
      "AUC: 0.8243801652892563\n",
      "Confusion matrix:\n",
      "[[36  8]\n",
      " [15 29]]\n",
      "Classification report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.71      0.82      0.76        44\n",
      "           1       0.78      0.66      0.72        44\n",
      "\n",
      "    accuracy                           0.74        88\n",
      "   macro avg       0.74      0.74      0.74        88\n",
      "weighted avg       0.74      0.74      0.74        88\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "model = LogisticRegression().fit(X, y)\n",
    "fpr, tpr, _ = metrics.roc_curve(y, model.predict_proba(X)[:,1])\n",
    "roc_auc = metrics.roc_auc_score(y, model.predict_proba(X)[:,1])\n",
    "\n",
    "print(\"Accuracy:\", metrics.accuracy_score(y, model.predict(X)))\n",
    "print(\"AUC:\", roc_auc)\n",
    "print(\"Confusion matrix:\")\n",
    "print(metrics.confusion_matrix(y, model.predict(X)))\n",
    "print(\"Classification report:\")\n",
    "print(metrics.classification_report(y, model.predict(X)))\n",
    "print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Time to visualize the results generated above"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.figure(figsize=(10, 10))\n",
    "plt.plot(fpr, tpr, label = \"ROC curve (AUC = %0.2f)\" % roc_auc)\n",
    "plt.plot([0, 1], [0, 1], linestyle=\"--\", lw=2, color=\"black\", label=\"Chance\", alpha=0.8)\n",
    "plt.xlim([-0.05, 1.05])\n",
    "plt.ylim([-0.05, 1.05])\n",
    "plt.title(\"ROC curve\")\n",
    "plt.xlabel(\"False positive rate\")\n",
    "plt.ylabel(\"True positive rate\")\n",
    "plt.legend(loc='best')\n",
    "plt.savefig('output\\TrueROC.png', dpi=100)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ROC with k-fold cross validation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Calculate ROC folds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k = 5 #change as necessary\n",
    "groups = np.array([])\n",
    "for i in range(len(y)):\n",
    "    groups = np.append(groups, i)\n",
    "kfold = StratifiedKFold(n_splits=k)\n",
    "kfold.get_n_splits(X, y, groups)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fold 1\n",
      "Accuracy: 0.7142857142857143\n",
      "AUC: 0.8163265306122449\n",
      "Confusion matrix:\n",
      "[[28  7]\n",
      " [13 22]]\n",
      "Classification report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.68      0.80      0.74        35\n",
      "           1       0.76      0.63      0.69        35\n",
      "\n",
      "    accuracy                           0.71        70\n",
      "   macro avg       0.72      0.71      0.71        70\n",
      "weighted avg       0.72      0.71      0.71        70\n",
      "\n",
      "\n",
      "Fold 2\n",
      "Accuracy: 0.7142857142857143\n",
      "AUC: 0.8073469387755102\n",
      "Confusion matrix:\n",
      "[[27  8]\n",
      " [12 23]]\n",
      "Classification report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.69      0.77      0.73        35\n",
      "           1       0.74      0.66      0.70        35\n",
      "\n",
      "    accuracy                           0.71        70\n",
      "   macro avg       0.72      0.71      0.71        70\n",
      "weighted avg       0.72      0.71      0.71        70\n",
      "\n",
      "\n",
      "Fold 3\n",
      "Accuracy: 0.7571428571428571\n",
      "AUC: 0.8253061224489796\n",
      "Confusion matrix:\n",
      "[[29  6]\n",
      " [11 24]]\n",
      "Classification report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.72      0.83      0.77        35\n",
      "           1       0.80      0.69      0.74        35\n",
      "\n",
      "    accuracy                           0.76        70\n",
      "   macro avg       0.76      0.76      0.76        70\n",
      "weighted avg       0.76      0.76      0.76        70\n",
      "\n",
      "\n",
      "Fold 4\n",
      "Accuracy: 0.7605633802816901\n",
      "AUC: 0.8476190476190476\n",
      "Confusion matrix:\n",
      "[[29  6]\n",
      " [11 25]]\n",
      "Classification report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.72      0.83      0.77        35\n",
      "           1       0.81      0.69      0.75        36\n",
      "\n",
      "    accuracy                           0.76        71\n",
      "   macro avg       0.77      0.76      0.76        71\n",
      "weighted avg       0.77      0.76      0.76        71\n",
      "\n",
      "\n",
      "Fold 5\n",
      "Accuracy: 0.7323943661971831\n",
      "AUC: 0.8206349206349206\n",
      "Confusion matrix:\n",
      "[[30  6]\n",
      " [13 22]]\n",
      "Classification report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.70      0.83      0.76        36\n",
      "           1       0.79      0.63      0.70        35\n",
      "\n",
      "    accuracy                           0.73        71\n",
      "   macro avg       0.74      0.73      0.73        71\n",
      "weighted avg       0.74      0.73      0.73        71\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "fprdict = list()\n",
    "tprdict = list()\n",
    "roc_aucdict = list()\n",
    "acc_score = []\n",
    "\n",
    "for i, (train_index, test_index) in enumerate(kfold.split(X, y)):\n",
    "    X_train, X_test = X[train_index], X[test_index]\n",
    "    y_train, y_test = y[train_index], y[test_index]\n",
    "    \n",
    "    model = LogisticRegression().fit(X_train, y_train)\n",
    "    fpr, tpr, _ = metrics.roc_curve(y_train, model.predict_proba(X_train)[:,1])\n",
    "    roc_auc = metrics.roc_auc_score(y_train, model.predict_proba(X_train)[:,1])\n",
    "\n",
    "    fprdict.append(fpr)\n",
    "    tprdict.append(tpr)\n",
    "    roc_aucdict.append(roc_auc)\n",
    "    \n",
    "    print('Fold', (i+1))\n",
    "    print(\"Accuracy:\", metrics.accuracy_score(y_train, model.predict(X_train)))\n",
    "    print(\"AUC:\", roc_auc)\n",
    "    print(\"Confusion matrix:\")\n",
    "    print(metrics.confusion_matrix(y_train, model.predict(X_train)))\n",
    "    print(\"Classification report:\")\n",
    "    print(metrics.classification_report(y_train, model.predict(X_train)))\n",
    "    print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Calculate the ROC mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "longest = len(max(tprdict,key=len))\n",
    "n = 0\n",
    "\n",
    "for i in tprdict:\n",
    "    if len(i) < longest:\n",
    "        temparray = list(i)\n",
    "        end = (longest - len(i)) / 2\n",
    "        for j in range(int(end)):\n",
    "            temparray.insert(0, 0)\n",
    "            temparray.insert(-1, 1)\n",
    "        tprdict[n] = temparray\n",
    "    n = n + 1\n",
    "\n",
    "n = 0\n",
    "for i in fprdict:\n",
    "    if len(i) < longest:\n",
    "        temparray = list(i)\n",
    "        end = (longest - len(i)) / 2\n",
    "        for j in range(int(end)):\n",
    "            temparray.insert(0, 0)\n",
    "            temparray.insert(-1, 1)\n",
    "        fprdict[n] = temparray\n",
    "    n = n + 1\n",
    "    \n",
    "mean_tpr = np.mean(tprdict, axis=0)\n",
    "mean_tpr[-1] = 1.0\n",
    "mean_fpr = np.mean(fprdict, axis=0)\n",
    "mean_fpr[-1] = 1.0\n",
    "mean_auc = np.mean(roc_aucdict, axis=0)\n",
    "std_auc = np.std(roc_aucdict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Calculate the ROC standard deviation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "std_tpr = np.std(tprdict, axis=0)\n",
    "tprs_upper = np.minimum(mean_tpr + std_tpr, 1)\n",
    "tprs_lower = np.maximum(mean_tpr - std_tpr, 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Time to visualize the results generated above"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.figure(figsize=(10, 10))\n",
    "plt.plot([0, 1], [0, 1], linestyle=\"--\", color=\"black\", lw=2, alpha=0.8, label=\"Chance\")\n",
    "\n",
    "for i in range(k):\n",
    "    plt.plot(fprdict[i], tprdict[i], label = ((\"ROC fold {}\".format(i)) + \" (AUC = %0.2f)\" % roc_aucdict[i]))\n",
    "\n",
    "plt.plot(mean_fpr, mean_tpr, label = (\"Mean ROC (AUC = %0.2f $\\pm$ %0.2f)\" % (mean_auc, std_auc)))\n",
    "plt.fill_between(mean_fpr, tprs_lower, tprs_upper, color=\"grey\", alpha=0.2, label=r\"$\\pm$ 1 std. dev.\")\n",
    "plt.xlim([-0.05, 1.05])\n",
    "plt.ylim([-0.05, 1.05])\n",
    "plt.title(\"ROC curve\")\n",
    "plt.xlabel(\"False positive rate\")\n",
    "plt.ylabel(\"True positive rate\")\n",
    "plt.legend(loc='best')\n",
    "plt.savefig('output\\KmeansROC.png', dpi=100)\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}