LR.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import random\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.utils import shuffle\n",
    "from sklearn.metrics import classification_report, accuracy_score, confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set the random seed for reproducibility\n",
    "random.seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load Dataset\n",
    "file_path = \"/Users/Sebastiano/ML_MRI copy.xlsx\"\n",
    "df = pd.read_excel(file_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop unwanted columns\n",
    "df = df.drop(['Patient', 'Gender', 'Age', 'Event', 'Scale factor', 'SNR'], axis='columns')\n",
    "\n",
    "# Normalize the selected features\n",
    "features_to_normalize = df.columns.difference(['NP-SLE'])\n",
    "scaler = MinMaxScaler()\n",
    "df[features_to_normalize] = scaler.fit_transform(df[features_to_normalize])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Separate features and target variable\n",
    "X = df.drop(['NP-SLE'], axis=1)\n",
    "y = df['NP-SLE']\n",
    "\n",
    "# Split the dataset into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Hyperparameters: {'C': 4.281332398719396, 'penalty': 'l1'}\n",
      "Accuracy on Test Set: 0.6\n",
      "Confusion Matrix:\n",
      " [[0 2 0]\n",
      " [2 0 0]\n",
      " [0 0 6]]\n",
      "Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.00      0.00      0.00         2\n",
      "           1       0.00      0.00      0.00         2\n",
      "           2       1.00      1.00      1.00         6\n",
      "\n",
      "    accuracy                           0.60        10\n",
      "   macro avg       0.33      0.33      0.33        10\n",
      "weighted avg       0.60      0.60      0.60        10\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Hyperparameter Tuning\n",
    "param_grid = {\n",
    "    'C': np.logspace(-4, 4, 20),\n",
    "    'penalty': ['l1', 'l2']\n",
    "}\n",
    "\n",
    "# Create Logistic Regression classifier\n",
    "logistic_regression = LogisticRegression(solver='liblinear')\n",
    "\n",
    "# Initialize GridSearchCV\n",
    "grid_search = GridSearchCV(logistic_regression, param_grid, cv=5, verbose=0, scoring='accuracy')\n",
    "\n",
    "# Fit the GridSearchCV\n",
    "grid_search.fit(X_train, y_train)\n",
    "\n",
    "# Get the best parameters\n",
    "best_params = grid_search.best_params_\n",
    "print(\"Best Hyperparameters:\", best_params)\n",
    "\n",
    "# Train the best model\n",
    "best_model = grid_search.best_estimator_\n",
    "best_model.fit(X_train, y_train)\n",
    "\n",
    "# Predict on the test set\n",
    "y_pred = best_model.predict(X_test)\n",
    "\n",
    "# Evaluation\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "conf_matrix = confusion_matrix(y_test, y_pred)\n",
    "class_report = classification_report(y_test, y_pred, zero_division=0)\n",
    "\n",
    "# Print results\n",
    "print(\"Accuracy on Test Set:\", accuracy)\n",
    "print(\"Confusion Matrix:\\n\", conf_matrix)\n",
    "print(\"Classification Report:\\n\", class_report)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Hyperparameters: {'C': 4.281332398719396, 'penalty': 'l1'}\n",
      "Cross-Validation Scores: [0.875      0.5        0.85714286 0.71428571 0.85714286]\n",
      "Average Cross-Validation Accuracy: 0.7607142857142858\n",
      "Accuracy on Test Set: 0.6\n",
      "Confusion Matrix:\n",
      " [[0 2 0]\n",
      " [2 0 0]\n",
      " [0 0 6]]\n",
      "Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.00      0.00      0.00         2\n",
      "           1       0.00      0.00      0.00         2\n",
      "           2       1.00      1.00      1.00         6\n",
      "\n",
      "    accuracy                           0.60        10\n",
      "   macro avg       0.33      0.33      0.33        10\n",
      "weighted avg       0.60      0.60      0.60        10\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV, cross_val_score\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report\n",
    "import numpy as np\n",
    "\n",
    "# Hyperparameter Tuning\n",
    "param_grid = {\n",
    "    'C': np.logspace(-4, 4, 20),\n",
    "    'penalty': ['l1', 'l2']\n",
    "}\n",
    "\n",
    "# Create Logistic Regression classifier\n",
    "logistic_regression = LogisticRegression(solver='liblinear')\n",
    "\n",
    "# Initialize GridSearchCV\n",
    "grid_search = GridSearchCV(logistic_regression, param_grid, cv=5, verbose=0, scoring='accuracy')\n",
    "\n",
    "# Fit the GridSearchCV\n",
    "grid_search.fit(X_train, y_train)\n",
    "\n",
    "# Get the best parameters\n",
    "best_params = grid_search.best_params_\n",
    "print(\"Best Hyperparameters:\", best_params)\n",
    "\n",
    "# Train the best model\n",
    "best_model = grid_search.best_estimator_\n",
    "\n",
    "# Perform 5-fold cross-validation\n",
    "cv_scores = cross_val_score(best_model, X_train, y_train, cv=5)\n",
    "\n",
    "# Print cross-validation scores\n",
    "print(\"Cross-Validation Scores:\", cv_scores)\n",
    "print(\"Average Cross-Validation Accuracy:\", cv_scores.mean())\n",
    "\n",
    "# Predict on the test set\n",
    "y_pred = best_model.predict(X_test)\n",
    "\n",
    "# Evaluation\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "conf_matrix = confusion_matrix(y_test, y_pred)\n",
    "class_report = classification_report(y_test, y_pred, zero_division=0)\n",
    "\n",
    "# Print results\n",
    "print(\"Accuracy on Test Set:\", accuracy)\n",
    "print(\"Confusion Matrix:\\n\", conf_matrix)\n",
    "print(\"Classification Report:\\n\", class_report)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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