.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/plot_linear_models.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_auto_examples_plot_linear_models.py>`
        to download the full example code

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_plot_linear_models.py:


Linear Models Optimization
===========================
Hyperparameter optimization for Ridge, Lasso, and ElasticNet regression.

.. GENERATED FROM PYTHON SOURCE LINES 6-16

.. code-block:: default


    from sklearn.datasets import load_diabetes
    from sklearn.linear_model import Ridge, Lasso, ElasticNet
    from sklearn.model_selection import train_test_split
    from sklearn.metrics import mean_squared_error, r2_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 Diabetes dataset...")
    data = load_diabetes()
    X, y = data.data, data.target

    print(f"Dataset shape: {X.shape}")





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    Loading Diabetes dataset...
    Dataset shape: (442, 10)




.. 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
    )








.. GENERATED FROM PYTHON SOURCE LINES 31-32

Optimize Ridge regression

.. GENERATED FROM PYTHON SOURCE LINES 32-52

.. code-block:: default

    print("\n=== Ridge Regression ===")
    hyperparam_space = HyperparameterSpaceBuilder.get_default_space(
        estimator_class=Ridge
    )

    opt = GeneticSearch(
        estimator_class=Ridge,
        hyperparam_space=hyperparam_space,
        generations=5,
        population_size=8,
        seed=42,
        use_mlflow=False,
        use_parallel=False
    )

    opt.fit(X_train, y_train)
    y_pred = opt.best_estimator_.predict(X_test)
    print(f"Ridge - Best params: {opt.best_params_}")
    print(f"Ridge - Test R2: {r2_score(y_test, y_pred):.4f}")





.. rst-class:: sphx-glr-script-out

 .. code-block:: none


    === Ridge Regression ===
    /home/docs/checkouts/readthedocs.org/user_builds/mloptimizer/checkouts/master/examples/plot_linear_models.py:37: UserWarning: Expected mutations per offspring is very low (0.32). With mutpb=0.8, indpb=0.2, and 2 hyperparameters, the population will converge prematurely. Recommended: mutpb >= 0.8, indpb >= 0.2 (gives ~0.3 mutations/offspring).
      opt = GeneticSearch(
    /home/docs/checkouts/readthedocs.org/user_builds/mloptimizer/checkouts/master/examples/plot_linear_models.py:37: UserWarning: Some hyperparameters have very small integer ranges (< 10 distinct values): 'solver' (6 values: 0 to 5). Small ranges limit search granularity. Consider increasing the range or scale for float types.
      opt = GeneticSearch(

    Genetic execution:   0%|          | 0/6 [00:00<?, ?it/s, best fitness=?]
    Genetic execution:  17%|█▋        | 1/6 [00:00<00:00, 645.97it/s, best fitness=58.2]
    Genetic execution:  17%|█▋        | 1/6 [00:00<00:00, 226.76it/s, best fitness=54.6]
    Genetic execution: 100%|██████████| 6/6 [00:00<00:00, 119.07it/s, best fitness=54.6]
    Ridge - Best params: {'alpha': 0.26, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'positive': False, 'random_state': 42, 'solver': 'saga', 'tol': 0.0001}
    Ridge - Test R2: 0.4599




.. GENERATED FROM PYTHON SOURCE LINES 53-54

Visualize Ridge optimization

.. GENERATED FROM PYTHON SOURCE LINES 54-64

.. code-block:: default

    population_df = opt.populations_
    g_logbook = plotly_logbook(opt.logbook_, population_df)
    g_logbook.update_layout(
        title="Ridge Regression Optimization Evolution",
        autosize=True,
        width=None,
        height=500
    )
    plotly.io.show(g_logbook, config={'responsive': True})




.. raw:: html
    :file: images/sphx_glr_plot_linear_models_001.html





.. GENERATED FROM PYTHON SOURCE LINES 65-66

Optimize ElasticNet

.. GENERATED FROM PYTHON SOURCE LINES 66-86

.. code-block:: default

    print("\n=== ElasticNet Regression ===")
    hyperparam_space = HyperparameterSpaceBuilder.get_default_space(
        estimator_class=ElasticNet
    )

    opt = GeneticSearch(
        estimator_class=ElasticNet,
        hyperparam_space=hyperparam_space,
        generations=5,
        population_size=8,
        seed=42,
        use_mlflow=False,
        use_parallel=False
    )

    opt.fit(X_train, y_train)
    y_pred = opt.best_estimator_.predict(X_test)
    print(f"ElasticNet - Best params: {opt.best_params_}")
    print(f"ElasticNet - Test R2: {r2_score(y_test, y_pred):.4f}")





.. rst-class:: sphx-glr-script-out

 .. code-block:: none


    === ElasticNet Regression ===
    /home/docs/checkouts/readthedocs.org/user_builds/mloptimizer/checkouts/master/examples/plot_linear_models.py:71: 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_linear_models.py:71: UserWarning: Some hyperparameters have very small integer ranges (< 10 distinct values): 'selection' (2 values: 0 to 1). Small ranges limit search granularity. Consider increasing the range or scale for float types.
      opt = GeneticSearch(

    Genetic execution:   0%|          | 0/6 [00:00<?, ?it/s, best fitness=?]
    Genetic execution:  17%|█▋        | 1/6 [00:00<00:00, 373.26it/s, best fitness=73.8]
    Genetic execution:  17%|█▋        | 1/6 [00:00<00:00, 156.00it/s, best fitness=69.4]
    Genetic execution:  17%|█▋        | 1/6 [00:00<00:00, 129.98it/s, best fitness=68.7]
    Genetic execution:  17%|█▋        | 1/6 [00:00<00:00, 98.35it/s, best fitness=58.2] 
    Genetic execution:  33%|███▎      | 2/6 [00:00<00:00, 137.47it/s, best fitness=53.8]
    Genetic execution: 100%|██████████| 6/6 [00:00<00:00, 140.47it/s, best fitness=53.8]
    ElasticNet - Best params: {'alpha': 0.0001, 'copy_X': True, 'fit_intercept': True, 'l1_ratio': 0.095, 'max_iter': 2000, 'positive': False, 'precompute': False, 'random_state': 42, 'selection': 'cyclic', 'tol': 0.0001, 'warm_start': False}
    ElasticNet - Test R2: 0.4579




.. GENERATED FROM PYTHON SOURCE LINES 87-88

Visualize ElasticNet optimization

.. GENERATED FROM PYTHON SOURCE LINES 88-98

.. code-block:: default

    population_df = opt.populations_
    g_logbook = plotly_logbook(opt.logbook_, population_df)
    g_logbook.update_layout(
        title="ElasticNet Regression Optimization Evolution",
        autosize=True,
        width=None,
        height=500
    )
    plotly.io.show(g_logbook, config={'responsive': True})




.. raw:: html
    :file: images/sphx_glr_plot_linear_models_002.html





.. GENERATED FROM PYTHON SOURCE LINES 99-100

Analyze optimization performance

.. GENERATED FROM PYTHON SOURCE LINES 100-105

.. 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: 28
    Total individuals in population history: 48
    Optimization time: 0.0440 seconds
    Time per evaluation: 0.0016 seconds





.. rst-class:: sphx-glr-timing

   **Total running time of the script:** (0 minutes 0.330 seconds)


.. _sphx_glr_download_auto_examples_plot_linear_models.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_linear_models.py <plot_linear_models.py>`

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: plot_linear_models.ipynb <plot_linear_models.ipynb>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_