.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/plot_catboost_example.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_plot_catboost_example.py: CatBoost optimization example ============================== A simple example showing hyperparameter optimization for CatBoost Classifier with genetic algorithms. .. GENERATED FROM PYTHON SOURCE LINES 7-19 .. code-block:: default 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 .. GENERATED FROM PYTHON SOURCE LINES 20-22 Load and prepare the dataset ----------------------------- .. GENERATED FROM PYTHON SOURCE LINES 22-29 .. code-block:: default 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))}") .. rst-class:: sphx-glr-script-out .. code-block:: none Loading Breast Cancer dataset... Dataset shape: (569, 30) Number of classes: 2 .. GENERATED FROM PYTHON SOURCE LINES 30-31 Split the data .. GENERATED FROM PYTHON SOURCE LINES 31-35 .. code-block:: default X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.2, random_state=42, stratify=y ) .. GENERATED FROM PYTHON SOURCE LINES 36-42 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) .. GENERATED FROM PYTHON SOURCE LINES 42-59 .. code-block:: default 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())}") .. rst-class:: sphx-glr-script-out .. code-block:: none Hyperparameter space configuration: Evolvable parameters: ['learning_rate', 'depth', 'n_estimators', 'subsample', 'l2_leaf_reg', 'colsample_bylevel', 'random_strength'] Fixed parameters: ['auto_class_weights', 'bootstrap_type', 'allow_writing_files', 'verbose', 'thread_count'] .. GENERATED FROM PYTHON SOURCE LINES 60-68 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. .. GENERATED FROM PYTHON SOURCE LINES 68-92 .. code-block:: default 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) .. rst-class:: sphx-glr-script-out .. code-block:: none /home/docs/checkouts/readthedocs.org/user_builds/mloptimizer/checkouts/master/examples/plot_catboost_example.py:76: UserWarning: Some hyperparameters have very small integer ranges (< 10 distinct values): 'depth' (7 values: 4 to 10). Small ranges limit search granularity. Consider increasing the range or scale for float types. opt = GeneticSearch( Starting CatBoost optimization... Generations: 5 Population size: 8 Genetic execution: 0%| | 0/6 [00:00 
GeneticSearch(cv=StratifiedKFold(n_splits=3, random_state=42, shuffle=True),
                  estimator_class=<class 'catboost.core.CatBoostClassifier'>,
                  generations=5,
                  hyperparam_space=HyperparameterSpace(fixed_hyperparams={'auto_class_weights': 'Balanced', 'bootstrap_type': 'Bernoulli', 'allow_writing_files': False, 'verbose': False, 'thread_count': 1}, evolvable_hyperparams={'l...ram('n_estimators', 100, 500, 'int'), 'subsample': Hyperparam('subsample', 700, 1000, 'float', 1000), 'l2_leaf_reg': Hyperparam('l2_leaf_reg', 1, 10, 'int'), 'colsample_bylevel': Hyperparam('colsample_bylevel', 50, 100, 'float', 100), 'random_strength': Hyperparam('random_strength', 1, 10, 'int')}),
                  n_elites=2, population_size=8, scoring='accuracy', seed=42,
                  use_parallel=False)
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.. GENERATED FROM PYTHON SOURCE LINES 93-94 Get results and evaluate .. GENERATED FROM PYTHON SOURCE LINES 94-109 .. code-block:: default 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}") .. rst-class:: sphx-glr-script-out .. code-block:: none Optimization completed! Best hyperparameters: {'learning_rate': 0.02, 'depth': 4, 'l2_leaf_reg': 5, 'thread_count': 1, 'verbose': False, 'auto_class_weights': 'Balanced', 'random_strength': 8, 'allow_writing_files': False, 'bootstrap_type': 'Bernoulli', 'subsample': 0.835, 'n_estimators': 479, 'colsample_bylevel': 0.86, 'random_state': 42} Test performance: Accuracy: 0.9649 Precision: 0.9722 Recall: 0.9722 F1 Score: 0.9722 .. GENERATED FROM PYTHON SOURCE LINES 110-111 Generate visualizations .. GENERATED FROM PYTHON SOURCE LINES 111-125 .. code-block:: default 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}) .. raw:: html :file: images/sphx_glr_plot_catboost_example_001.html .. GENERATED FROM PYTHON SOURCE LINES 126-127 Simple logbook visualization .. GENERATED FROM PYTHON SOURCE LINES 127-130 .. code-block:: default g_logbook_s = plot_logbook(opt.logbook_) # plt.show() # Commented out for non-interactive environments .. image-sg:: /auto_examples/images/sphx_glr_plot_catboost_example_002.png :alt: plot catboost example :srcset: /auto_examples/images/sphx_glr_plot_catboost_example_002.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none /home/docs/checkouts/readthedocs.org/user_builds/mloptimizer/envs/master/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead. with pd.option_context('mode.use_inf_as_na', True): /home/docs/checkouts/readthedocs.org/user_builds/mloptimizer/envs/master/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead. with pd.option_context('mode.use_inf_as_na', True): .. GENERATED FROM PYTHON SOURCE LINES 131-132 Evolution logbook visualization .. GENERATED FROM PYTHON SOURCE LINES 132-141 .. code-block:: default 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}) .. raw:: html :file: images/sphx_glr_plot_catboost_example_003.html .. GENERATED FROM PYTHON SOURCE LINES 142-143 Analyze optimization results .. GENERATED FROM PYTHON SOURCE LINES 143-162 .. code-block:: default 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}") .. rst-class:: sphx-glr-script-out .. code-block:: none === Optimization Analysis === Unique evaluations performed: 28 Total individuals in population history: 48 Optimization time: 109.3850 seconds Time per evaluation: 3.9066 seconds Generations completed: 5 Fitness progression: Initial average fitness: 0.9772 Final average fitness: 0.9783 Average improvement: 0.0011 .. GENERATED FROM PYTHON SOURCE LINES 163-164 Access generated files .. GENERATED FROM PYTHON SOURCE LINES 164-176 .. code-block:: default 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')]) .. rst-class:: sphx-glr-script-out .. code-block:: none === Generated Files === Graphics path: ./20260406_230846_CatBoostClassifier/graphics Graphics files: ['logbook.html', 'search_space.html'] Results path: ./20260406_230846_CatBoostClassifier/results Results files: ['logbook.csv', 'populations.csv'] .. GENERATED FROM PYTHON SOURCE LINES 177-199 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()) .. rst-class:: sphx-glr-timing **Total running time of the script:** (1 minutes 51.535 seconds) .. _sphx_glr_download_auto_examples_plot_catboost_example.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_catboost_example.py ` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_catboost_example.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_