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.. "auto_examples/model_selection/plot_grid_search_refit_callable.py"
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.. only:: html

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

        :ref:`Go to the end <sphx_glr_download_auto_examples_model_selection_plot_grid_search_refit_callable.py>`
        to download the full example code or to run this example in your browser via Binder

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

.. _sphx_glr_auto_examples_model_selection_plot_grid_search_refit_callable.py:


==================================================
Balance model complexity and cross-validated score
==================================================

This example balances model complexity and cross-validated score by
finding a decent accuracy within 1 standard deviation of the best accuracy
score while minimising the number of PCA components [1].

The figure shows the trade-off between cross-validated score and the number
of PCA components. The balanced case is when n_components=10 and accuracy=0.88,
which falls into the range within 1 standard deviation of the best accuracy
score.

[1] Hastie, T., Tibshirani, R.,, Friedman, J. (2001). Model Assessment and
Selection. The Elements of Statistical Learning (pp. 219-260). New York,
NY, USA: Springer New York Inc..

.. GENERATED FROM PYTHON SOURCE LINES 20-127



.. image-sg:: /auto_examples/model_selection/images/sphx_glr_plot_grid_search_refit_callable_001.png
   :alt: Balance model complexity and cross-validated score
   :srcset: /auto_examples/model_selection/images/sphx_glr_plot_grid_search_refit_callable_001.png
   :class: sphx-glr-single-img


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

 .. code-block:: none

    The best_index_ is 2
    The n_components selected is 10
    The corresponding accuracy score is 0.88






|

.. code-block:: default


    # Author: Wenhao Zhang <wenhaoz@ucla.edu>

    import matplotlib.pyplot as plt
    import numpy as np

    from sklearn.datasets import load_digits
    from sklearn.decomposition import PCA
    from sklearn.model_selection import GridSearchCV
    from sklearn.pipeline import Pipeline
    from sklearn.svm import LinearSVC


    def lower_bound(cv_results):
        """
        Calculate the lower bound within 1 standard deviation
        of the best `mean_test_scores`.

        Parameters
        ----------
        cv_results : dict of numpy(masked) ndarrays
            See attribute cv_results_ of `GridSearchCV`

        Returns
        -------
        float
            Lower bound within 1 standard deviation of the
            best `mean_test_score`.
        """
        best_score_idx = np.argmax(cv_results["mean_test_score"])

        return (
            cv_results["mean_test_score"][best_score_idx]
            - cv_results["std_test_score"][best_score_idx]
        )


    def best_low_complexity(cv_results):
        """
        Balance model complexity with cross-validated score.

        Parameters
        ----------
        cv_results : dict of numpy(masked) ndarrays
            See attribute cv_results_ of `GridSearchCV`.

        Return
        ------
        int
            Index of a model that has the fewest PCA components
            while has its test score within 1 standard deviation of the best
            `mean_test_score`.
        """
        threshold = lower_bound(cv_results)
        candidate_idx = np.flatnonzero(cv_results["mean_test_score"] >= threshold)
        best_idx = candidate_idx[
            cv_results["param_reduce_dim__n_components"][candidate_idx].argmin()
        ]
        return best_idx


    pipe = Pipeline(
        [
            ("reduce_dim", PCA(random_state=42)),
            ("classify", LinearSVC(random_state=42, C=0.01, dual="auto")),
        ]
    )

    param_grid = {"reduce_dim__n_components": [6, 8, 10, 12, 14]}

    grid = GridSearchCV(
        pipe,
        cv=10,
        n_jobs=1,
        param_grid=param_grid,
        scoring="accuracy",
        refit=best_low_complexity,
    )
    X, y = load_digits(return_X_y=True)
    grid.fit(X, y)

    n_components = grid.cv_results_["param_reduce_dim__n_components"]
    test_scores = grid.cv_results_["mean_test_score"]

    plt.figure()
    plt.bar(n_components, test_scores, width=1.3, color="b")

    lower = lower_bound(grid.cv_results_)
    plt.axhline(np.max(test_scores), linestyle="--", color="y", label="Best score")
    plt.axhline(lower, linestyle="--", color=".5", label="Best score - 1 std")

    plt.title("Balance model complexity and cross-validated score")
    plt.xlabel("Number of PCA components used")
    plt.ylabel("Digit classification accuracy")
    plt.xticks(n_components.tolist())
    plt.ylim((0, 1.0))
    plt.legend(loc="upper left")

    best_index_ = grid.best_index_

    print("The best_index_ is %d" % best_index_)
    print("The n_components selected is %d" % n_components[best_index_])
    print(
        "The corresponding accuracy score is %.2f"
        % grid.cv_results_["mean_test_score"][best_index_]
    )
    plt.show()


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

   **Total running time of the script:** (0 minutes 3.103 seconds)


.. _sphx_glr_download_auto_examples_model_selection_plot_grid_search_refit_callable.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example


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        :alt: Launch binder
        :width: 150 px



    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: plot_grid_search_refit_callable.py <plot_grid_search_refit_callable.py>`

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


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