.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/plot_logistic_regression.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_logistic_regression.py: Logistic Regression Optimization ================================= Hyperparameter optimization for Logistic Regression with regularization. .. GENERATED FROM PYTHON SOURCE LINES 6-16 .. code-block:: default from sklearn.datasets import load_breast_cancer from sklearn.linear_model import LogisticRegression from sklearn.model_selection import train_test_split from sklearn.metrics import accuracy_score, f1_score import numpy as np import plotly from mloptimizer.interfaces import HyperparameterSpaceBuilder, GeneticSearch from mloptimizer.application.reporting.plots import plotly_search_space, plotly_logbook .. GENERATED FROM PYTHON SOURCE LINES 17-18 Load and prepare the dataset .. GENERATED FROM PYTHON SOURCE LINES 18-24 .. code-block:: default print("Loading Breast Cancer dataset...") data = load_breast_cancer() X, y = data.data, data.target print(f"Dataset shape: {X.shape}") .. rst-class:: sphx-glr-script-out .. code-block:: none Loading Breast Cancer dataset... Dataset shape: (569, 30) .. GENERATED FROM PYTHON SOURCE LINES 25-26 Split the data .. GENERATED FROM PYTHON SOURCE LINES 26-30 .. 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 31-32 Define the hyperparameter space .. GENERATED FROM PYTHON SOURCE LINES 32-36 .. code-block:: default hyperparam_space = HyperparameterSpaceBuilder.get_default_space( estimator_class=LogisticRegression ) .. GENERATED FROM PYTHON SOURCE LINES 37-38 Configure and run the genetic optimization .. GENERATED FROM PYTHON SOURCE LINES 38-58 .. code-block:: default genetic_params = { 'generations': 5, 'population_size': 8, 'n_elites': 2, 'seed': 42, 'use_mlflow': False, 'use_parallel': False } opt = GeneticSearch( estimator_class=LogisticRegression, hyperparam_space=hyperparam_space, cv=3, scoring='accuracy', **genetic_params ) print("Starting Logistic Regression 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_logistic_regression.py:47: UserWarning: Expected mutations per offspring is very low (0.48). With mutpb=0.8, indpb=0.2, and 3 hyperparameters, the population will converge prematurely. Recommended: mutpb >= 0.8, indpb >= 0.2 (gives ~0.5 mutations/offspring). opt = GeneticSearch( /home/docs/checkouts/readthedocs.org/user_builds/mloptimizer/checkouts/master/examples/plot_logistic_regression.py:47: UserWarning: Some hyperparameters have very small integer ranges (< 10 distinct values): 'penalty' (2 values: 0 to 1), 'solver' (2 values: 0 to 1). Small ranges limit search granularity. Consider increasing the range or scale for float types. opt = GeneticSearch( Starting Logistic Regression optimization... Genetic execution: 0%| | 0/6 [00:00 
GeneticSearch(cv=StratifiedKFold(n_splits=3, random_state=42, shuffle=True),
                  estimator_class=<class 'sklearn.linear_model._logistic.LogisticRegression'>,
                  generations=5,
                  hyperparam_space=HyperparameterSpace(fixed_hyperparams={'max_iter': 1000}, evolvable_hyperparams={'C': Hyperparam('C', 1, 1000, 'float', 100), 'penalty': Hyperparam('penalty', 0, 1, 'list', ['l1', 'l2']), 'solver': Hyperparam('solver', 0, 1, 'list', ['liblinear', 'saga'])}),
                  n_elites=2, population_size=8, scoring='accuracy', seed=42,
                  use_parallel=False)
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.. GENERATED FROM PYTHON SOURCE LINES 59-60 Evaluate the optimized model .. GENERATED FROM PYTHON SOURCE LINES 60-70 .. code-block:: default best_clf = opt.best_estimator_ y_pred = best_clf.predict(X_test) test_accuracy = accuracy_score(y_test, y_pred) test_f1 = f1_score(y_test, y_pred, average='binary') print(f"\nOptimization completed!") print(f"Best parameters: {opt.best_params_}") print(f"Test accuracy: {test_accuracy:.4f}") print(f"Test F1: {test_f1:.4f}") .. rst-class:: sphx-glr-script-out .. code-block:: none Optimization completed! Best parameters: {'C': 7.6, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 1000, 'multi_class': 'deprecated', 'n_jobs': None, 'penalty': 'l1', 'random_state': 42, 'solver': 'liblinear', 'tol': 0.0001, 'verbose': 0, 'warm_start': False} Test accuracy: 0.9825 Test F1: 0.9861 .. GENERATED FROM PYTHON SOURCE LINES 71-72 Visualize the search space .. GENERATED FROM PYTHON SOURCE LINES 72-84 .. code-block:: default population_df = opt.populations_ top_params = ['C', 'penalty', 'solver', 'fitness'] df_filtered = population_df[top_params] g_search_space = plotly_search_space(df_filtered, top_params) g_search_space.update_layout( title="Logistic Regression Hyperparameter Search Space", autosize=True, width=None, height=650 ) plotly.io.show(g_search_space, config={'responsive': True}) .. raw:: html :file: images/sphx_glr_plot_logistic_regression_001.html .. GENERATED FROM PYTHON SOURCE LINES 85-86 Visualize the optimization evolution .. GENERATED FROM PYTHON SOURCE LINES 86-95 .. code-block:: default g_logbook = plotly_logbook(opt.logbook_, population_df) g_logbook.update_layout( title="Logistic Regression Optimization Evolution", autosize=True, width=None, height=500 ) plotly.io.show(g_logbook, config={'responsive': True}) .. raw:: html :file: images/sphx_glr_plot_logistic_regression_002.html .. GENERATED FROM PYTHON SOURCE LINES 96-97 Analyze optimization performance .. GENERATED FROM PYTHON SOURCE LINES 97-102 .. code-block:: default print("\n=== Optimization Performance ===") print(f"Unique evaluations performed: {opt.n_trials_}") print(f"Total individuals in population history: {len(opt.populations_)}") print(f"Optimization time: {opt.optimization_time_:.4f} seconds") print(f"Time per evaluation: {opt.optimization_time_ / opt.n_trials_:.4f} seconds") .. rst-class:: sphx-glr-script-out .. code-block:: none === Optimization Performance === Unique evaluations performed: 33 Total individuals in population history: 48 Optimization time: 12.9662 seconds Time per evaluation: 0.3929 seconds .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 14.071 seconds) .. _sphx_glr_download_auto_examples_plot_logistic_regression.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_logistic_regression.py ` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_logistic_regression.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_