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

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

.. _sphx_glr_auto_examples_plot_quickstart.py:


Quickstart example
====================================
Quick example of use of the library to optimize a decision tree classifier.
Firstly, we import the necessary libraries to get data and plot the results.

.. GENERATED FROM PYTHON SOURCE LINES 7-12

.. code-block:: default


    from sklearn.tree import DecisionTreeClassifier
    from sklearn.datasets import load_iris
    from mloptimizer.interfaces import HyperparameterSpaceBuilder, GeneticSearch








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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 15-17

.. code-block:: default

    X, y = load_iris(return_X_y=True)








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Split the dataset into training and test sets

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.. code-block:: default

    from sklearn.model_selection import train_test_split

    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)








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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

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.. code-block:: default

    hyperparam_space = HyperparameterSpaceBuilder.get_default_space(estimator_class=DecisionTreeClassifier)








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The GeneticSearch class is the main wrapper for the optimization of a machine learning model.
We configure the genetic algorithm parameters:
- generations: Number of evolutionary iterations
- population_size: Number of hyperparameter configurations per generation
Note: These values are reduced for faster documentation builds.
For production use, consider generations=30-50 and population_size=50-100.

.. GENERATED FROM PYTHON SOURCE LINES 36-50

.. code-block:: default

    genetic_params = {
        'generations': 10,
        'population_size': 20,
        'seed': 0
    }

    opt = GeneticSearch(
        estimator_class=DecisionTreeClassifier,
        hyperparam_space=hyperparam_space,
        cv=5,
        scoring='accuracy',
        **genetic_params
    )








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To optimize the classifier we need to call the fit method.
The method finds the best hyperparameters and stores them in ``best_estimator_``.

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.. code-block:: default

    opt.fit(X, y)

    print(opt.best_estimator_)





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

 .. code-block:: none

    DecisionTreeClassifier(max_depth=20, random_state=424155)




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Train the classifier with the best hyperparameters found
Show the classification report and the confusion matrix

.. GENERATED FROM PYTHON SOURCE LINES 60-75

.. code-block:: default

    from sklearn.metrics import classification_report, confusion_matrix, \
        ConfusionMatrixDisplay
    import matplotlib.pyplot as plt

    y_pred = opt.predict(X_test)
    cm = confusion_matrix(y_test, y_pred)
    print(classification_report(y_test, y_pred))
    disp = ConfusionMatrixDisplay.from_predictions(
        y_test, y_pred, display_labels=opt.best_estimator_.classes_,
        cmap=plt.cm.Blues
    )
    disp.plot()
    plt.show()

    del opt



.. rst-class:: sphx-glr-horizontal


    *

      .. image-sg:: /auto_examples/images/sphx_glr_plot_quickstart_001.png
         :alt: plot quickstart
         :srcset: /auto_examples/images/sphx_glr_plot_quickstart_001.png
         :class: sphx-glr-multi-img

    *

      .. image-sg:: /auto_examples/images/sphx_glr_plot_quickstart_002.png
         :alt: plot quickstart
         :srcset: /auto_examples/images/sphx_glr_plot_quickstart_002.png
         :class: sphx-glr-multi-img


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

 .. code-block:: none

                  precision    recall  f1-score   support

               0       1.00      1.00      1.00        12
               1       1.00      1.00      1.00        10
               2       1.00      1.00      1.00         8

        accuracy                           1.00        30
       macro avg       1.00      1.00      1.00        30
    weighted avg       1.00      1.00      1.00        30






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

   **Total running time of the script:** (0 minutes 3.792 seconds)


.. _sphx_glr_download_auto_examples_plot_quickstart.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_quickstart.py <plot_quickstart.py>`

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

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


.. only:: html

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

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