.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/plot_search_space.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_search_space.py>`
        to download the full example code

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

.. _sphx_glr_auto_examples_plot_search_space.py:


Search space graph
====================================
mloptimizer provides a function to plot the search space of the optimization.

.. GENERATED FROM PYTHON SOURCE LINES 6-17

.. code-block:: default


    from sklearn.tree import DecisionTreeClassifier
    from mloptimizer.domain.evaluation import kfold_stratified_score
    from mloptimizer.application.reporting.plots import plotly_search_space
    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 18-20

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 20-22

.. code-block:: default

    X, y = load_iris(return_X_y=True)








.. GENERATED FROM PYTHON SOURCE LINES 23-26

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 26-28

.. code-block:: default

    hyperparam_space = HyperparameterSpaceBuilder.get_default_space(estimator_class=DecisionTreeClassifier)








.. GENERATED FROM PYTHON SOURCE LINES 29-30

The GeneticSearch class is used to optimize the hyperparameters of a machine learning model.

.. GENERATED FROM PYTHON SOURCE LINES 30-38

.. code-block:: default

    cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)
    opt = GeneticSearch(
            estimator_class=DecisionTreeClassifier,
            hyperparam_space=hyperparam_space,
            **{"generations": 30, "population_size": 100},
            cv=cv
            )








.. GENERATED FROM PYTHON SOURCE LINES 39-40

To optimizer the classifier we need to call the fit method.

.. GENERATED FROM PYTHON SOURCE LINES 40-44

.. code-block:: default

    opt.fit(X, y)
    clf = opt.best_estimator_






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

 .. code-block:: none


    Genetic execution:   0%|          | 0/31 [00:00<?, ?it/s, best fitness=?]
    Genetic execution:   3%|▎         | 1/31 [00:00<00:00, 77.34it/s, best fitness=0.94]
    Genetic execution:   3%|▎         | 1/31 [00:00<00:13,  2.29it/s, best fitness=0.96]
    Genetic execution:   6%|▋         | 2/31 [00:00<00:11,  2.55it/s, best fitness=0.96]
    Genetic execution:  10%|▉         | 3/31 [00:01<00:13,  2.04it/s, best fitness=0.96]
    Genetic execution:  13%|█▎        | 4/31 [00:02<00:14,  1.84it/s, best fitness=0.96]
    Genetic execution:  16%|█▌        | 5/31 [00:02<00:14,  1.74it/s, best fitness=0.96]
    Genetic execution:  19%|█▉        | 6/31 [00:03<00:14,  1.68it/s, best fitness=0.96]
    Genetic execution:  23%|██▎       | 7/31 [00:03<00:14,  1.65it/s, best fitness=0.96]
    Genetic execution:  26%|██▌       | 8/31 [00:04<00:14,  1.62it/s, best fitness=0.96]
    Genetic execution:  29%|██▉       | 9/31 [00:05<00:13,  1.60it/s, best fitness=0.96]
    Genetic execution:  32%|███▏      | 10/31 [00:05<00:13,  1.59it/s, best fitness=0.96]
    Genetic execution:  35%|███▌      | 11/31 [00:06<00:12,  1.58it/s, best fitness=0.96]
    Genetic execution:  39%|███▊      | 12/31 [00:07<00:12,  1.57it/s, best fitness=0.96]
    Genetic execution:  42%|████▏     | 13/31 [00:07<00:11,  1.57it/s, best fitness=0.96]
    Genetic execution:  45%|████▌     | 14/31 [00:08<00:10,  1.56it/s, best fitness=0.96]
    Genetic execution:  48%|████▊     | 15/31 [00:09<00:10,  1.56it/s, best fitness=0.96]
    Genetic execution:  52%|█████▏    | 16/31 [00:09<00:09,  1.55it/s, best fitness=0.96]
    Genetic execution:  55%|█████▍    | 17/31 [00:10<00:09,  1.55it/s, best fitness=0.96]
    Genetic execution:  58%|█████▊    | 18/31 [00:11<00:08,  1.55it/s, best fitness=0.96]
    Genetic execution:  61%|██████▏   | 19/31 [00:11<00:07,  1.54it/s, best fitness=0.96]
    Genetic execution:  65%|██████▍   | 20/31 [00:12<00:07,  1.54it/s, best fitness=0.96]
    Genetic execution:  68%|██████▊   | 21/31 [00:12<00:06,  1.54it/s, best fitness=0.96]
    Genetic execution:  71%|███████   | 22/31 [00:13<00:05,  1.53it/s, best fitness=0.96]
    Genetic execution:  74%|███████▍  | 23/31 [00:14<00:05,  1.53it/s, best fitness=0.96]
    Genetic execution:  77%|███████▋  | 24/31 [00:14<00:04,  1.53it/s, best fitness=0.96]
    Genetic execution:  81%|████████  | 25/31 [00:15<00:03,  1.53it/s, best fitness=0.96]
    Genetic execution:  84%|████████▍ | 26/31 [00:16<00:03,  1.52it/s, best fitness=0.96]
    Genetic execution:  87%|████████▋ | 27/31 [00:16<00:02,  1.52it/s, best fitness=0.96]
    Genetic execution:  90%|█████████ | 28/31 [00:17<00:01,  1.52it/s, best fitness=0.96]
    Genetic execution:  94%|█████████▎| 29/31 [00:18<00:01,  1.51it/s, best fitness=0.96]
    Genetic execution:  97%|█████████▋| 30/31 [00:18<00:00,  1.51it/s, best fitness=0.96]
    Genetic execution: 100%|██████████| 31/31 [00:19<00:00,  1.51it/s, best fitness=0.96]
    Genetic execution: 100%|██████████| 31/31 [00:20<00:00,  1.53it/s, best fitness=0.96]




.. GENERATED FROM PYTHON SOURCE LINES 45-46

Following we can generate the plot of the search space

.. GENERATED FROM PYTHON SOURCE LINES 46-53

.. code-block:: default

    population_df = opt.populations_
    param_names = list(opt.get_evolvable_hyperparams().keys())
    param_names.append("fitness")
    df = population_df[param_names]
    g_search_space = plotly_search_space(df, param_names)
    plotly.io.show(g_search_space)




.. raw:: html
    :file: images/sphx_glr_plot_search_space_001.html





.. GENERATED FROM PYTHON SOURCE LINES 54-55

At the end of the evolution the graph is saved as an html at the path:

.. GENERATED FROM PYTHON SOURCE LINES 55-59

.. 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

    ./20250807_114202_DecisionTreeClassifier/graphics
    ['search_space.html', 'logbook.html']




.. GENERATED FROM PYTHON SOURCE LINES 60-61

The data to generate the graph is available at the path:

.. GENERATED FROM PYTHON SOURCE LINES 61-65

.. 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

    ./20250807_114202_DecisionTreeClassifier/results
    ['populations.csv', 'logbook.csv']





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

   **Total running time of the script:** (0 minutes 24.327 seconds)


.. _sphx_glr_download_auto_examples_plot_search_space.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_search_space.py <plot_search_space.py>`

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

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


.. only:: html

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

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