.. 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








.. GENERATED FROM PYTHON SOURCE LINES 13-15

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)








.. GENERATED FROM PYTHON SOURCE LINES 18-19

Split the dataset into training and test sets

.. GENERATED FROM PYTHON SOURCE LINES 19-23

.. 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)








.. GENERATED FROM PYTHON SOURCE LINES 24-27

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 27-29

.. 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.

.. GENERATED FROM PYTHON SOURCE LINES 31-36

.. code-block:: default

    opt = GeneticSearch(
            estimator_class=DecisionTreeClassifier, hyperparam_space=hyperparam_space,
            **{"generations": 30, "population_size": 100}
        )








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To optimizer the classifier we need to call the optimize_clf method.
The method returns the best classifier with the best hyperparameters found.

.. GENERATED FROM PYTHON SOURCE LINES 39-43

.. code-block:: default

    opt.fit(X, y)

    print(opt.best_estimator_)





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

 .. code-block:: none

    WARNING:root:The folder . already exists and it will be used
    INFO:mloptimizer.log:Initiating genetic optimization...
    INFO:mloptimizer.log:Algorithm: Optimizer

    Genetic execution:   0%|          | 0/31 [00:00<?, ?it/s, best fitness=?]
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    DecisionTreeClassifier(ccp_alpha=0.0025, max_depth=12,
                           min_impurity_decrease=0.006, min_samples_split=18,
                           random_state=619697)




.. GENERATED FROM PYTHON SOURCE LINES 44-46

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

.. GENERATED FROM PYTHON SOURCE LINES 46-61

.. 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        10
               1       1.00      0.88      0.93         8
               2       0.92      1.00      0.96        12

        accuracy                           0.97        30
       macro avg       0.97      0.96      0.96        30
    weighted avg       0.97      0.97      0.97        30






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

   **Total running time of the script:** (0 minutes 8.023 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>`_