{ "cells": [ { "cell_type": "code", "execution_count": null, "id": "53854e75", "metadata": {}, "outputs": [], "source": [ "Author: Ujwal Watgule\n", "Contribution: This notebook demonstrates how to use DEAP (Distributed Evolutionary Algorithms in Python)\n", "for hyperparameter optimization in machine learning." ] }, { "cell_type": "code", "execution_count": null, "id": "ac756d61", "metadata": {}, "outputs": [], "source": [ "# 1. Introduction\n", "Evolutionary algorithms in DEAP can optimize machine learning hyperparameters more efficiently than grid/random search,\n", "especially in high-dimensional spaces. \n", "\n", "This tutorial shows how to:\n", "\n", "1. Set up a genetic algorithm (GA) in DEAP.\n", "\n", "2. Optimize a RandomForestClassifier from scikit-learn.\n", "\n", "3. Visualize the optimization process.\n", "\n", "Key Features Demonstrated:\n", " \n", "1. Custom fitness evaluation\n", "2. Evolutionary operators (selection, crossover, mutation)\n", "3. Statistical tracking and visualization" ] }, { "cell_type": "code", "execution_count": 1, "id": "58352f66", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Dataset: 150 samples, 4 features\n" ] } ], "source": [ "#2. Setup\n", "# Import Libraries\n", "\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from deap import base, creator, tools, algorithms\n", "from sklearn.ensemble import RandomForestClassifier\n", "from sklearn.datasets import load_iris\n", "from sklearn.model_selection import cross_val_score\n", "\n", "# Load Data\n", "\n", "data = load_iris()\n", "X, y = data.data, data.target\n", "print(f\"Dataset: {X.shape[0]} samples, {X.shape[1]} features\")" ] }, { "cell_type": "code", "execution_count": 2, "id": "f5573084", "metadata": {}, "outputs": [], "source": [ "# 3. Problem Definition\n", "# Goal: Optimize two hyperparameters for RandomForestClassifier:\n", " \n", "def ensure_valid_params(individual):\n", " \"\"\"Ensure n_estimators and max_depth stay within valid ranges\"\"\"\n", " n_estimators = max(1, int(individual[0])) # At least 1 tree\n", " max_depth = max(1, int(individual[1])) # At least depth 1\n", " return [n_estimators, max_depth]\n" ] }, { "cell_type": "code", "execution_count": 5, "id": "2e71bcde", "metadata": {}, "outputs": [], "source": [ "# 4. DEAP Implementation\n", "# Step 4.1: Define Fitness and Individual\n", "\n", "creator.create(\"FitnessMax\", base.Fitness, weights=(1.0,))\n", "creator.create(\"Individual\", list, fitness=creator.FitnessMax)\n", "\n", "# Step 4.2: Initialize Toolbox\n", "toolbox = base.Toolbox()\n", "\n", "# Initialize with valid ranges\n", "toolbox.register(\"attr_n_est\", np.random.randint, 10, 200) # 10-200 trees\n", "toolbox.register(\"attr_depth\", np.random.randint, 2, 20) # Depth 2-20\n", "toolbox.register(\"individual\", tools.initCycle, creator.Individual,\n", " (toolbox.attr_n_est, toolbox.attr_depth), n=1)\n", "toolbox.register(\"population\", tools.initRepeat, list, toolbox.individual)\n", "\n", "# Step 4.3: Register Evolutionary Operators\n", "\n", "# Evolutionary operators\n", "toolbox.register(\"mate\", tools.cxBlend, alpha=0.5)\n", "toolbox.register(\"mutate\", tools.mutGaussian, mu=0, sigma=5, indpb=0.2)\n", "toolbox.register(\"select\", tools.selTournament, tournsize=3)\n", "\n", "\n", "# Step 4.4: Define Fitness Evaluation\n", "\n", "def evaluate(individual):\n", " # Apply constraints first\n", " n_estimators, max_depth = ensure_valid_params(individual)\n", " \n", " try:\n", " model = RandomForestClassifier(\n", " n_estimators=n_estimators,\n", " max_depth=max_depth,\n", " random_state=42\n", " )\n", " score = cross_val_score(model, X, y, cv=3).mean()\n", " return (score,)\n", " except Exception as e:\n", " # Return worst possible score if error occurs\n", " return (0.0,)\n", " \n", "toolbox.register(\"evaluate\", evaluate)" ] }, { "cell_type": "code", "execution_count": 6, "id": "a548d76c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "gen\tnevals\tavg \tmax \n", "0 \t50 \t0.964533\t0.966667\n", "1 \t43 \t0.964 \t0.966667\n", "2 \t41 \t0.966 \t0.966667\n", "3 \t43 \t0.966267\t0.966667\n", "4 \t29 \t0.966533\t0.966667\n", "5 \t38 \t0.9664 \t0.966667\n", "6 \t41 \t0.966667\t0.966667\n", "7 \t34 \t0.966667\t0.966667\n", "8 \t40 \t0.965467\t0.966667\n", "9 \t36 \t0.965867\t0.966667\n", "10 \t40 \t0.966267\t0.966667\n", "11 \t44 \t0.966 \t0.966667\n", "12 \t40 \t0.965867\t0.966667\n", "13 \t38 \t0.965467\t0.966667\n", "14 \t35 \t0.965733\t0.966667\n", "15 \t41 \t0.966 \t0.966667\n", "16 \t40 \t0.966667\t0.966667\n", "17 \t37 \t0.966 \t0.966667\n", "18 \t36 \t0.966667\t0.966667\n", "19 \t28 \t0.9664 \t0.966667\n", "20 \t36 \t0.965067\t0.966667\n", "Best hyperparameters: n_estimators=199, max_depth=19\n", "Best accuracy: 0.9667\n" ] } ], "source": [ "# 5. Run the Evolutionary Algorithm\n", "# Step 5.1: Initialize Population and Statistics\n", "\n", "population = toolbox.population(n=50)\n", "stats = tools.Statistics(lambda ind: ind.fitness.values)\n", "stats.register(\"avg\", np.mean)\n", "stats.register(\"max\", np.max)\n", "\n", "# KEY FIX: Add halloffame to track best individual\n", "hof = tools.HallOfFame(1)\n", "\n", "# Step 5.2: Evolve!\n", "result, logbook = algorithms.eaSimple(\n", " population, toolbox, cxpb=0.7, mutpb=0.2, ngen=20,\n", " stats=stats, halloffame=hof, verbose=True\n", ")\n", "\n", "\n", "# Step 5.3: Extract Best Solution\n", "\n", "best_individual = hof[0]\n", "valid_params = ensure_valid_params(best_individual)\n", "print(f\"Best hyperparameters: n_estimators={valid_params[0]}, max_depth={valid_params[1]}\")\n", "print(f\"Best accuracy: {best_individual.fitness.values[0]:.4f}\")" ] }, { "cell_type": "code", "execution_count": 7, "id": "90cdeed7", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# 6. Visualization\n", "# Plot Fitness Over Generations\n", "\n", "gen = logbook.select(\"gen\")\n", "fit_max = logbook.select(\"max\")\n", "fit_avg = logbook.select(\"avg\")\n", "\n", "plt.figure(figsize=(10, 5))\n", "plt.plot(gen, fit_max, \"b-\", label=\"Max Fitness\")\n", "plt.plot(gen, fit_avg, \"r-\", label=\"Avg Fitness\")\n", "plt.xlabel(\"Generation\")\n", "plt.ylabel(\"Fitness (Accuracy)\")\n", "plt.title(\"Evolution of Hyperparameter Fitness\")\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "id": "da992209", "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": "3.10.9" } }, "nbformat": 4, "nbformat_minor": 5 }