.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/plot_evolution.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_evolution.py: Evolution (logbook) graph ==================================== mloptimizer provides a function to plot the evolution of the fitness function. .. GENERATED FROM PYTHON SOURCE LINES 6-16 .. code-block:: default from sklearn.tree import DecisionTreeClassifier from mloptimizer.application.reporting.plots import plotly_logbook from mloptimizer.domain.evaluation import kfold_stratified_score import plotly import os from sklearn.datasets import load_iris from mloptimizer.interfaces import HyperparameterSpaceBuilder, GeneticSearch from sklearn.model_selection import StratifiedKFold .. GENERATED FROM PYTHON SOURCE LINES 17-19 Load the iris dataset to obtain a vector of features X and a vector of labels y. Another dataset or a custom one can be used .. GENERATED FROM PYTHON SOURCE LINES 19-21 .. code-block:: default X, y = load_iris(return_X_y=True) .. GENERATED FROM PYTHON SOURCE LINES 22-25 Define the HyperparameterSpace, you can use the default hyperparameters for the machine learning model that you want to optimize. In this case we use the default hyperparameters for a DecisionTreeClassifier. Another dataset or a custom one can be used .. GENERATED FROM PYTHON SOURCE LINES 25-27 .. code-block:: default hyperparam_space = HyperparameterSpaceBuilder.get_default_space(estimator_class=DecisionTreeClassifier) .. GENERATED FROM PYTHON SOURCE LINES 28-35 The GeneticSearch class is the main wrapper for the optimization of a machine learning model. Configure genetic algorithm parameters: - generations: Number of evolutionary iterations - population_size: Number of individuals per generation - n_elites: Number of best individuals preserved each generation - seed: Random seed for reproducibility Note: Values reduced for faster documentation builds. For production, use larger values. .. GENERATED FROM PYTHON SOURCE LINES 35-51 .. code-block:: default genetic_params = { 'generations': 10, 'population_size': 20, 'n_elites': 2, 'seed': 42 } cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=42) opt = GeneticSearch( estimator_class=DecisionTreeClassifier, hyperparam_space=hyperparam_space, cv=cv, scoring='accuracy', **genetic_params ) .. GENERATED FROM PYTHON SOURCE LINES 52-53 To optimize the classifier we need to call the fit method. .. GENERATED FROM PYTHON SOURCE LINES 53-56 .. code-block:: default opt.fit(X, y) .. raw:: html

GeneticSearch(cv=StratifiedKFold(n_splits=5, random_state=42, shuffle=True),
                  estimator_class=<class 'sklearn.tree._classes.DecisionTreeClassifier'>,
                  generations=10,
                  hyperparam_space=HyperparameterSpace(fixed_hyperparams={'criterion': 'gini'}, evolvable_hyperparams={'min_samples_split': Hyperparam('min_samples_split', 2, 50, 'int'), 'min_samples_leaf': Hyperparam('min_samples_leaf', 1, 20, 'int'), 'max_depth': Hyperparam('max_depth', 2, 20, 'int'), 'min_impurity_decrease': Hyperparam('min_impurity_decrease', 0, 150, 'float', 1000), 'ccp_alpha': Hyperparam('ccp_alpha', 0, 300, 'float', 100000)}),
                  n_elites=2, scoring='accuracy', seed=42)

In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.

.. GENERATED FROM PYTHON SOURCE LINES 57-58 We can plot the evolution of the fitness function. .. GENERATED FROM PYTHON SOURCE LINES 58-63 .. code-block:: default population_df = opt.populations_ g_logbook = plotly_logbook(opt.logbook_, population_df) g_logbook.update_layout(autosize=True, width=None, height=450) plotly.io.show(g_logbook, config={'responsive': True}) .. raw:: html :file: images/sphx_glr_plot_evolution_001.html .. GENERATED FROM PYTHON SOURCE LINES 64-65 Alternatively, we can use the simpler plot_logbook function. .. GENERATED FROM PYTHON SOURCE LINES 65-71 .. code-block:: default from mloptimizer.application.reporting.plots import plot_logbook import matplotlib.pyplot as plt g_logbook = plot_logbook(opt.logbook_) plt.show() .. image-sg:: /auto_examples/images/sphx_glr_plot_evolution_002.png :alt: plot evolution :srcset: /auto_examples/images/sphx_glr_plot_evolution_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 72-73 At the end of the evolution the graph is saved as an html at the path: .. GENERATED FROM PYTHON SOURCE LINES 73-77 .. code-block:: default print(opt._optimizer_service.optimizer.tracker.graphics_path) print(os.listdir(opt._optimizer_service.optimizer.tracker.graphics_path)) .. rst-class:: sphx-glr-script-out .. code-block:: none None ['plot_evolution.py', 'plot_mlp_neural_network.py', 'plot_hist_gradient_boosting.py', 'regression_example.py', 'plot_lightgbm_classifier.py', 'plot_xgboost_example.py', 'plot_catboost_example.py', 'plot_search_space.py', 'plot_xgboost_hyperparam_opt_comparison.py', 'plot_logistic_regression.py', 'plot_lightgbm_regressor.py', 'plot_quickstart.py', 'plot_linear_models.py', 'plot_adaboost.py', 'README.rst'] .. GENERATED FROM PYTHON SOURCE LINES 78-79 The data to generate the graph is available at the path: .. GENERATED FROM PYTHON SOURCE LINES 79-83 .. code-block:: default print(opt._optimizer_service.optimizer.tracker.results_path) print(os.listdir(opt._optimizer_service.optimizer.tracker.results_path)) del opt .. rst-class:: sphx-glr-script-out .. code-block:: none None ['plot_evolution.py', 'plot_mlp_neural_network.py', 'plot_hist_gradient_boosting.py', 'regression_example.py', 'plot_lightgbm_classifier.py', 'plot_xgboost_example.py', 'plot_catboost_example.py', 'plot_search_space.py', 'plot_xgboost_hyperparam_opt_comparison.py', 'plot_logistic_regression.py', 'plot_lightgbm_regressor.py', 'plot_quickstart.py', 'plot_linear_models.py', 'plot_adaboost.py', 'README.rst'] .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 4.676 seconds) .. _sphx_glr_download_auto_examples_plot_evolution.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_evolution.py ` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_evolution.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_