.. _sphx_glr_auto_examples_under-sampling_plot_one_sided_selection.py:


===================
One-sided selection
===================

An illustration of the one-sided selection method.





.. image:: /auto_examples/under-sampling/images/sphx_glr_plot_one_sided_selection_001.png
    :align: center





.. code-block:: python


    # Authors: 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 imblearn.under_sampling import OneSidedSelection

    print(__doc__)

    # Generate the dataset
    X, y = make_classification(n_classes=2, class_sep=2, weights=[0.1, 0.9],
                               n_informative=3, n_redundant=1, flip_y=0,
                               n_features=20, n_clusters_per_class=1,
                               n_samples=200, random_state=10)

    # Instanciate a PCA object for the sake of easy visualisation
    pca = PCA(n_components=2)
    # Fit and transform x to visualise inside a 2D feature space
    X_vis = pca.fit_transform(X)

    # Apply One-Sided Selection
    oss = OneSidedSelection(return_indices=True)
    X_resampled, y_resampled, idx_resampled = oss.fit_sample(X, y)
    X_res_vis = pca.transform(X_resampled)

    fig = plt.figure()
    ax = fig.add_subplot(1, 1, 1)

    idx_samples_removed = np.setdiff1d(np.arange(X_vis.shape[0]),
                                       idx_resampled)

    idx_class_0 = y_resampled == 0
    plt.scatter(X_res_vis[idx_class_0, 0], X_res_vis[idx_class_0, 1],
                alpha=.8, label='Class #0')
    plt.scatter(X_res_vis[~idx_class_0, 0], X_res_vis[~idx_class_0, 1],
                alpha=.8, label='Class #1')
    plt.scatter(X_vis[idx_samples_removed, 0], X_vis[idx_samples_removed, 1],
                alpha=.8, label='Removed samples')

    # make nice plotting
    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, 6])
    ax.set_ylim([-6, 6])

    plt.title('Under-sampling using one-sided selection')
    plt.legend()
    plt.tight_layout()
    plt.show()

**Total running time of the script:** ( 0 minutes  0.136 seconds)



.. container:: sphx-glr-footer


  .. container:: sphx-glr-download

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



  .. container:: sphx-glr-download

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

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

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