.. _sphx_glr_auto_examples_over-sampling_plot_smote.py:


=====
SMOTE
=====

An illustration of the SMOTE method and its variant.





.. image:: /auto_examples/over-sampling/images/sphx_glr_plot_smote_001.png
    :align: center





.. code-block:: python


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

    import matplotlib.pyplot as plt
    from sklearn.datasets import make_classification
    from sklearn.decomposition import PCA

    from imblearn.over_sampling import SMOTE

    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=[0.3, 0.7],
                               n_informative=3, n_redundant=1, flip_y=0,
                               n_features=20, n_clusters_per_class=1,
                               n_samples=80, 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 regular SMOTE
    kind = ['regular', 'borderline1', 'borderline2', 'svm']
    sm = [SMOTE(kind=k) for k in kind]
    X_resampled = []
    y_resampled = []
    X_res_vis = []
    for method in sm:
        X_res, y_res = method.fit_sample(X, y)
        X_resampled.append(X_res)
        y_resampled.append(y_res)
        X_res_vis.append(pca.transform(X_res))

    # Two subplots, unpack the axes array immediately
    f, ((ax1, ax2), (ax3, ax4), (ax5, ax6)) = plt.subplots(3, 2)
    # Remove axis for second plot
    ax2.axis('off')
    ax_res = [ax3, ax4, ax5, ax6]

    c0, c1 = plot_resampling(ax1, X_vis, y, 'Original set')
    for i in range(len(kind)):
        plot_resampling(ax_res[i], X_res_vis[i], y_resampled[i],
                        'SMOTE {}'.format(kind[i]))

    ax2.legend((c0, c1), ('Class #0', 'Class #1'), loc='center',
               ncol=1, labelspacing=0.)
    plt.tight_layout()
    plt.show()

**Total running time of the script:** ( 0 minutes  0.805 seconds)



.. container:: sphx-glr-footer


  .. container:: sphx-glr-download

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



  .. container:: sphx-glr-download

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

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

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