.. _sphx_glr_auto_examples_under-sampling_plot_enn_renn_allknn.py:


==================
ENN, RENN, All-KNN
==================

An illustration of the ENN, RENN, and All-KNN method.





.. image:: /auto_examples/under-sampling/images/sphx_glr_plot_enn_renn_allknn_001.png
    :align: center


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

 Out::

    ENN
    Reduced 13.00%
    RENN
    Reduced 22.00%
    AllKNN
    Reduced 15.00%




|


.. 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 imblearn.under_sampling import (AllKNN, EditedNearestNeighbours,
                                         RepeatedEditedNearestNeighbours)

    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=0.4, weights=[0.4, 0.6],
                               n_informative=3, n_redundant=1, flip_y=0,
                               n_features=5, n_clusters_per_class=1,
                               n_samples=100, 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)

    # Three subplots, unpack the axes array immediately
    f, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2)

    c0, c1 = plot_resampling(ax1, X_vis, y, 'Original set')

    # Apply the ENN
    print('ENN')
    enn = EditedNearestNeighbours(return_indices=True)
    X_resampled, y_resampled, idx_resampled = enn.fit_sample(X, y)
    X_res_vis = pca.transform(X_resampled)
    idx_samples_removed = np.setdiff1d(np.arange(X_vis.shape[0]), idx_resampled)
    reduction_str = ('Reduced {:.2f}%'.format(100 * (1 - float(len(X_resampled)) /
                                                     len(X))))
    print(reduction_str)
    c3 = ax2.scatter(X_vis[idx_samples_removed, 0],
                     X_vis[idx_samples_removed, 1],
                     alpha=.2, label='Removed samples', c='g')
    plot_resampling(ax2, X_res_vis, y_resampled, 'ENN - ' + reduction_str)

    # Apply the RENN
    print('RENN')
    renn = RepeatedEditedNearestNeighbours(return_indices=True)
    X_resampled, y_resampled, idx_resampled = renn.fit_sample(X, y)
    X_res_vis = pca.transform(X_resampled)
    idx_samples_removed = np.setdiff1d(np.arange(X_vis.shape[0]), idx_resampled)
    reduction_str = ('Reduced {:.2f}%'.format(100 * (1 - float(len(X_resampled)) /
                                                     len(X))))
    print(reduction_str)
    ax3.scatter(X_vis[idx_samples_removed, 0],
                X_vis[idx_samples_removed, 1],
                alpha=.2, label='Removed samples', c='g')
    plot_resampling(ax3, X_res_vis, y_resampled, 'RENN - ' + reduction_str)

    # Apply the AllKNN
    print('AllKNN')
    allknn = AllKNN(return_indices=True)
    X_resampled, y_resampled, idx_resampled = allknn.fit_sample(X, y)
    X_res_vis = pca.transform(X_resampled)
    idx_samples_removed = np.setdiff1d(np.arange(X_vis.shape[0]), idx_resampled)
    reduction_str = ('Reduced {:.2f}%'.format(100 * (1 - float(len(X_resampled)) /
                                                     len(X))))
    print(reduction_str)
    ax4.scatter(X_vis[idx_samples_removed, 0],
                X_vis[idx_samples_removed, 1],
                alpha=.2, label='Removed samples', c='g')
    plot_resampling(ax4, X_res_vis, y_resampled, 'All-KNN - ' + reduction_str)

    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  1.585 seconds)



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  .. container:: sphx-glr-download

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



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

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

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