.. _sphx_glr_auto_examples_under-sampling_plot_nearmiss.py:


==================
Nearmiss 1 & 2 & 3
==================

An illustration of the nearmiss 1 & 2 & 3 method.





.. image:: /auto_examples/under-sampling/images/sphx_glr_plot_nearmiss_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 NearMiss

    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=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 Nearmiss
    version = [1, 2, 3]
    nm = [NearMiss(version=v, return_indices=True) for v in version]

    X_resampled = []
    y_resampled = []
    X_res_vis = []
    idx_samples_removed = []
    for method in nm:
        X_res, y_res, idx_res = method.fit_sample(X, y)
        X_resampled.append(X_res)
        y_resampled.append(y_res)
        X_res_vis.append(pca.transform(X_res))
        idx_samples_removed = np.setdiff1d(np.arange(X_vis.shape[0]),
                                           idx_res)

    # Two subplots, unpack the axes array immediately
    f, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2)
    ax_res = [ax2, ax3, ax4]

    c0, c1 = plot_resampling(ax1, X_vis, y, 'Original set')
    for i in range(len(version)):
        # plot the missing samples
        c3 = ax_res[i].scatter(X_vis[idx_samples_removed, 0],
                               X_vis[idx_samples_removed, 1],
                               alpha=.2, label='Removed samples',
                               c='g')
        plot_resampling(ax_res[i], X_res_vis[i], y_resampled[i],
                        'Nearmiss {}'.format(version[i]))

    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  0.579 seconds)



.. container:: sphx-glr-footer


  .. container:: sphx-glr-download

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



  .. container:: sphx-glr-download

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

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

    `Generated by Sphinx-Gallery <https://sphinx-gallery.readthedocs.io>`_