.. _sphx_glr_auto_examples_over-sampling_plot_adasyn.py:


======
ADASYN
======

An illustration of the Adaptive Synthetic Sampling Approach for Imbalanced
Learning ADASYN method.





.. image:: /auto_examples/over-sampling/images/sphx_glr_plot_adasyn_001.png
    :align: center





.. code-block:: python


    # Authors: 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 ADASYN

    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 the random over-sampling
    ada = ADASYN()
    X_resampled, y_resampled = ada.fit_sample(X, y)
    X_res_vis = pca.transform(X_resampled)

    # Two subplots, unpack the axes array immediately
    f, (ax1, ax2) = plt.subplots(1, 2)

    c0 = ax1.scatter(X_vis[y == 0, 0], X_vis[y == 0, 1], label="Class #0",
                     alpha=0.5)
    c1 = ax1.scatter(X_vis[y == 1, 0], X_vis[y == 1, 1], label="Class #1",
                     alpha=0.5)
    ax1.set_title('Original set')

    ax2.scatter(X_res_vis[y_resampled == 0, 0], X_res_vis[y_resampled == 0, 1],
                label="Class #0", alpha=.5)
    ax2.scatter(X_res_vis[y_resampled == 1, 0], X_res_vis[y_resampled == 1, 1],
                label="Class #1", alpha=.5)
    ax2.set_title('ADASYN')

    # make nice plotting
    for ax in (ax1, ax2):
        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])

    plt.figlegend((c0, c1), ('Class #0', 'Class #1'), loc='lower center',
                  ncol=2, labelspacing=0.)
    plt.tight_layout(pad=3)
    plt.show()

**Total running time of the script:** ( 0 minutes  0.272 seconds)



.. container:: sphx-glr-footer


  .. container:: sphx-glr-download

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



  .. container:: sphx-glr-download

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

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

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