.. _sphx_glr_auto_examples_under-sampling_plot_instance_hardness_threshold.py:


===========================
Instance Hardness Threshold
===========================

An illustration of the instance hardness threshold method.





.. image:: /auto_examples/under-sampling/images/sphx_glr_plot_instance_hardness_threshold_001.png
    :align: center





.. code-block:: python


    # Authors: Dayvid Oliveira
    #          Christos Aridas
    #          Guillaume Lemaitre <g.lemaitre58@gmail.com>
    # License: MIT

    import matplotlib.pyplot as plt
    import numpy as np
    from sklearn.datasets import make_classification
    from sklearn.decomposition import PCA
    from sklearn.linear_model import LogisticRegression

    from imblearn.under_sampling import InstanceHardnessThreshold

    print(__doc__)


    def plot_resampling(ax, X, y, title):
        c0 = ax.scatter(X[y == 0, 0], X[y == 0, 1], label="Class #0", alpha=0.5)
        c1 = ax.scatter(X[y == 1, 0], X[y == 1, 1], label="Class #1", alpha=0.5)
        ax.set_title(title)
        ax.spines['top'].set_visible(False)
        ax.spines['right'].set_visible(False)
        ax.get_xaxis().tick_bottom()
        ax.get_yaxis().tick_left()
        ax.spines['left'].set_position(('outward', 10))
        ax.spines['bottom'].set_position(('outward', 10))
        ax.set_xlim([-6, 8])
        ax.set_ylim([-6, 6])

        return c0, c1


    # Generate the dataset
    X, y = make_classification(n_classes=2, class_sep=1., weights=[0.1, 0.9],
                               n_informative=3, n_redundant=1, flip_y=0,
                               n_features=20, n_clusters_per_class=1,
                               n_samples=100, random_state=10)

    pca = PCA(n_components=2)
    X_vis = pca.fit_transform(X)

    # Two subplots, unpack the axes array immediately
    f, axs = plt.subplots(2, 2)

    axs = [a for ax in axs for a in ax]
    for ax, ratio in zip(axs, [0.0, 0.4, 0.7, 1.0]):
        if ratio == 0.0:
            c0, c1 = plot_resampling(ax, X_vis, y, 'Original set')
        else:
            iht = InstanceHardnessThreshold(ratio=ratio,
                                            estimator=LogisticRegression(),
                                            return_indices=True)
            X_res, y_res, idx_res = iht.fit_sample(X, y)
            X_res_vis = pca.transform(X_res)
            plot_resampling(ax, X_res_vis, y_res,
                            'Instance Hardness Threshold ({})'.format(ratio))
            # plot samples which have been removed
            idx_samples_removed = np.setdiff1d(np.arange(X_vis.shape[0]),
                                               idx_res)
            c3 = ax.scatter(X_vis[idx_samples_removed, 0],
                            X_vis[idx_samples_removed, 1],
                            alpha=.2, label='Removed samples')

    plt.figlegend((c0, c1, c3), ('Class #0', 'Class #1', 'Removed samples'),
                  loc='lower center', ncol=3, labelspacing=0.)
    plt.tight_layout(pad=3)
    plt.show()

**Total running time of the script:** ( 0 minutes  0.493 seconds)



.. container:: sphx-glr-footer


  .. container:: sphx-glr-download

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



  .. container:: sphx-glr-download

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

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

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