rf_metrics.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\user\\AppData\\Local\\Temp\\ipykernel_30128\\3070330209.py:2: DeprecationWarning: \n",
      "Pyarrow will become a required dependency of pandas in the next major release of pandas (pandas 3.0),\n",
      "(to allow more performant data types, such as the Arrow string type, and better interoperability with other libraries)\n",
      "but was not found to be installed on your system.\n",
      "If this would cause problems for you,\n",
      "please provide us feedback at https://github.com/pandas-dev/pandas/issues/54466\n",
      "        \n",
      "  import pandas as pd\n"
     ]
    }
   ],
   "source": [
    "#Load libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.manifold import TSNE\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import precision_score, recall_score, f1_score, accuracy_score\n",
    "from sklearn.metrics import roc_auc_score, roc_curve\n",
    "import xgboost as xgb\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "counts = pd.read_csv('C:/Users/user/Downloads/2117ensemble.csv')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "counts = counts.transpose()\n",
    "counts.columns = counts.iloc[0] \n",
    "counts = counts.drop(counts.index[0])\n",
    "counts = counts.astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample = pd.read_csv('c:/Users/user/Downloads/clin2117.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "labelencoder = LabelEncoder()\n",
    "sample['Label']=labelencoder.fit_transform(sample.iloc[:,3].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train shape (130, 18482)\n",
      "X_test shape (56, 18482)\n"
     ]
    }
   ],
   "source": [
    "# split data for train and test\n",
    "y =  sample['Label']\n",
    "X_train, X_test, y_train, y_test = train_test_split(counts,\n",
    "                                                    y,\n",
    "                                                    test_size = 0.3,random_state=132)\n",
    "                                                \n",
    "print(\"X_train shape\", X_train.shape)\n",
    "print(\"X_test shape\", X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9285714285714286\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "# Random Forest classifier\n",
    "rf_classifier = RandomForestClassifier(n_estimators=100, random_state=42)  \n",
    "\n",
    "# Train the Random Forest classifier\n",
    "rf_classifier.fit(X_train, y_train)\n",
    "\n",
    "# Make predictions on the test set\n",
    "y_pred = rf_classifier.predict(X_test)\n",
    "\n",
    "# Evaluate the classifier\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(\"Accuracy:\", accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bincount prediction: [35 21]\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "preds = rf_classifier.predict(X_test)\n",
    "prediction =[]\n",
    "for i in preds:\n",
    "    if i >=0.5:\n",
    "        prediction.append(1)\n",
    "    else:\n",
    "        prediction.append(0)\n",
    "print(\"bincount prediction:\", np.bincount(prediction))\n",
    "conf_matrix = confusion_matrix(y_true = y_test, y_pred=prediction)\n",
    "\n",
    "# Print the confusion matrix using Matplotlib\n",
    "fig, ax = plt.subplots(figsize=(5, 5))\n",
    "ax.matshow(conf_matrix, cmap=plt.cm.Reds, alpha=0.3)\n",
    "for i in range(conf_matrix.shape[0]):\n",
    "    for j in range(conf_matrix.shape[1]):\n",
    "        ax.text(x=j, y=i,s=conf_matrix[i, j], va='center', ha='center', size='xx-large')\n",
    " \n",
    "plt.xlabel('Predictions', fontsize=18)\n",
    "plt.ylabel('Actuals', fontsize=18)\n",
    "plt.title('Random Forest', fontsize=18)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sensitivity: 0.8695652173913043\n",
      "Specificity: 0.9696969696969697\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "# Assuming you have true labels (y_true) and predicted labels (y_pred)\n",
    "# Calculate the confusion matrix\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "\n",
    "# Extract true positives, false negatives, true negatives, and false positives\n",
    "tn, fp, fn, tp = cm.ravel()\n",
    "\n",
    "# Calculate sensitivity and specificity\n",
    "sensitivity = tp / (tp + fn)\n",
    "specificity = tn / (tn + fp)\n",
    "\n",
    "print(\"Sensitivity:\", sensitivity)\n",
    "print(\"Specificity:\", specificity)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.929\n",
      "F1 Score: 0.909\n",
      "Precision: 0.952\n",
      "Recall: 0.870\n"
     ]
    }
   ],
   "source": [
    "print('Accuracy: %.3f' % accuracy_score(y_test, y_pred))\n",
    "print('F1 Score: %.3f' % f1_score(y_test, y_pred))\n",
    "print('Precision: %.3f' % precision_score(y_test, y_pred))\n",
    "print('Recall: %.3f' % recall_score(y_test, y_pred))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "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": "3.11.5"
  }
 },
 "nbformat": 4,
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