""" CatBoost optimization example ============================== A simple example showing hyperparameter optimization for CatBoost Classifier with genetic algorithms. """ from sklearn.datasets import load_breast_cancer from sklearn.model_selection import train_test_split from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score from catboost import CatBoostClassifier import plotly import os from mloptimizer.interfaces import HyperparameterSpaceBuilder, GeneticSearch from mloptimizer.application.reporting.plots import (plotly_search_space, plotly_logbook, plot_logbook) import matplotlib.pyplot as plt # %% # Load and prepare the dataset # ----------------------------- print("Loading Breast Cancer dataset...") data = load_breast_cancer() X, y = data.data, data.target print(f"Dataset shape: {X.shape}") print(f"Number of classes: {len(set(y))}") # %% # Split the data X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.2, random_state=42, stratify=y ) # %% # Define the CatBoost hyperparameter space # ----------------------------------------- # CatBoost has specific hyperparameters that differ from other gradient boosting libraries. # We can use the default hyperparameter space or build a custom one. # # Option 1: Load default space (recommended for quick start) hyperparam_space = HyperparameterSpaceBuilder.get_default_space( estimator_class=CatBoostClassifier ) # Option 2: Build custom space (uncomment to use) # hyperparam_space = (HyperparameterSpaceBuilder() # .add_int_param('depth', min_value=4, max_value=10) # .add_float_param('learning_rate', min_value=1, max_value=10, scale=100) # .add_int_param('iterations', min_value=50, max_value=200) # .add_float_param('subsample', min_value=700, max_value=1000, scale=1000) # .set_fixed_param('verbose', False) # .build()) print("\nHyperparameter space configuration:") print(f"Evolvable parameters: {list(hyperparam_space.evolvable_hyperparams.keys())}") print(f"Fixed parameters: {list(hyperparam_space.fixed_hyperparams.keys())}") # %% # Configure and run the genetic optimization # ------------------------------------------ # Genetic Algorithm Configuration: # - generations: Number of evolutionary iterations # - population_size: Number of configurations per generation # - n_elites: Number of best individuals preserved each generation # - seed: Random seed for reproducibility # Note: Small values for faster documentation builds. For production, increase to 20+ generations. genetic_params = { 'generations': 5, 'population_size': 8, 'n_elites': 2, 'seed': 42, 'use_parallel': False } opt = GeneticSearch( estimator_class=CatBoostClassifier, hyperparam_space=hyperparam_space, cv=3, scoring='accuracy', disable_file_output=False, **genetic_params ) print("\nStarting CatBoost optimization...") print(f"Generations: {opt.generations}") print(f"Population size: {opt.population_size}") # Run the optimization opt.fit(X_train, y_train) # %% # Get results and evaluate best_clf = opt.best_estimator_ y_pred = best_clf.predict(X_test) test_accuracy = accuracy_score(y_test, y_pred) precision = precision_score(y_test, y_pred, average='binary') recall = recall_score(y_test, y_pred, average='binary') f1 = f1_score(y_test, y_pred, average='binary') print(f"\nOptimization completed!") print(f"Best hyperparameters: {opt.best_params_}") print(f"\nTest performance:") print(f" Accuracy: {test_accuracy:.4f}") print(f" Precision: {precision:.4f}") print(f" Recall: {recall:.4f}") print(f" F1 Score: {f1:.4f}") # %% # Generate visualizations population_df = opt.populations_ # Search space visualization top_params = ['depth', 'learning_rate', 'n_estimators', 'l2_leaf_reg', 'fitness'] df_filtered = population_df[top_params] g_search_space = plotly_search_space(df_filtered, top_params) g_search_space.update_layout( title="CatBoost Hyperparameter Search Space - Breast Cancer Dataset", autosize=True, width=None, height=650 ) plotly.io.show(g_search_space, config={'responsive': True}) # %% # Simple logbook visualization g_logbook_s = plot_logbook(opt.logbook_) # plt.show() # Commented out for non-interactive environments # %% # Evolution logbook visualization g_logbook = plotly_logbook(opt.logbook_, population_df) g_logbook.update_layout( title="CatBoost Optimization Evolution - Breast Cancer Dataset", autosize=True, width=None, height=500 ) plotly.io.show(g_logbook, config={'responsive': True}) # %% # Analyze optimization results print("\n=== Optimization Analysis ===") print(f"Unique evaluations performed: {opt.n_trials_}") print(f"Total individuals in population history: {len(population_df)}") print(f"Optimization time: {opt.optimization_time_:.4f} seconds") print(f"Time per evaluation: {opt.optimization_time_ / opt.n_trials_:.4f} seconds") print(f"Generations completed: {opt.generations}") final_gen = population_df[population_df['population'] == opt.generations] initial_gen = population_df[population_df['population'] == 1] final_avg_fitness = final_gen['fitness'].mean() initial_avg_fitness = initial_gen['fitness'].mean() improvement = final_avg_fitness - initial_avg_fitness print(f"\nFitness progression:") print(f" Initial average fitness: {initial_avg_fitness:.4f}") print(f" Final average fitness: {final_avg_fitness:.4f}") print(f" Average improvement: {improvement:.4f}") # %% # Access generated files print("\n=== Generated Files ===") graphics_path = opt._optimizer_service.optimizer.tracker.graphics_path results_path = opt._optimizer_service.optimizer.tracker.results_path print(f"Graphics path: {graphics_path}") if os.path.exists(graphics_path): print(" Graphics files:", [f for f in os.listdir(graphics_path) if f.endswith('.html')]) print(f"Results path: {results_path}") if os.path.exists(results_path): print(" Results files:", [f for f in os.listdir(results_path) if f.endswith('.csv')]) # %% # CatBoost-specific features # -------------------------- # CatBoost offers several unique features: # # - **Automatic handling of categorical features**: No need for manual encoding # - **Balanced class weights**: Set via auto_class_weights='Balanced' (included in default space) # - **GPU acceleration**: Add 'task_type': 'GPU' to fixed parameters # - **Feature importance**: Access via ``best_clf.feature_importances_`` # # Example of adding categorical feature support: # # .. code-block:: python # # # If you have categorical features # cat_features = [0, 2, 4] # Indices of categorical columns # # hyperparam_space = (HyperparameterSpaceBuilder() # .add_int_param('depth', 4, 10) # .add_float_param('learning_rate', 1, 10, scale=100) # .set_fixed_param('cat_features', cat_features) # .set_fixed_param('verbose', False) # .build())