{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Basic Binary Classification with LogisticNet\n",
    "\n",
    "This notebook demonstrates the basic usage of `LogisticNet` for binary classification, including fitting the model, making predictions, and evaluating accuracy.\n",
    "\n",
    "## Setup\n",
    "We use a synthetic dataset from `sklearn.datasets.make_classification` for reproducibility."
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-22T14:39:38.037332Z",
     "start_time": "2025-07-22T14:39:36.408324Z"
    }
   },
   "source": [
    "import numpy as np\n",
    "from sklearn.datasets import make_classification\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score\n",
    "from glmpynet import LogisticRegression\n",
    "\n",
    "# Generate synthetic dataset\n",
    "X, y = make_classification(n_samples=200, n_features=20, n_classes=2, random_state=42)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Instantiate and fit LogisticNet\n",
    "model = LogisticRegression()\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "# Predict and evaluate\n",
    "y_pred = model.predict(X_test)\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(f\"Accuracy: {accuracy:.2f}\")\n",
    "\n",
    "# Predict probabilities\n",
    "y_proba = model.predict_proba(X_test)\n",
    "print(f\"Sample probabilities:\\n{y_proba[:5]}\")"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.88\n",
      "Sample probabilities:\n",
      "[[0.94532103 0.05467897]\n",
      " [0.98537361 0.01462639]\n",
      " [0.9899398  0.0100602 ]\n",
      " [0.00795596 0.99204404]\n",
      " [0.0825289  0.9174711 ]]\n"
     ]
    }
   ],
   "execution_count": 1
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Explanation\n",
    "- `LogisticRegression()` is instantiated with default parameters (`C=1.0`, `penalty='l2'`).\n",
    "- The model is trained on a synthetic dataset with 200 samples and 20 features.\n",
    "- Predictions and probabilities are computed, with accuracy typically around 0.85-0.95 for this dataset.\n",
    "- When transitioning to `glmnet`, expect similar accuracy but potential differences in probabilities due to elastic-net regularization."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.11.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}