.. _sphx_glr_auto_examples_applications_plot_over_sampling_benchmark_lfw.py:


==========================================================
Benchmark over-sampling methods in a face recognition task
==========================================================

In this face recognition example two faces are used from the LFW
(Faces in the Wild) dataset. Several implemented over-sampling
methods are used in conjunction with a 3NN classifier in order
to examine the improvement of the classifier's output quality
by using an over-sampler.





.. image:: /auto_examples/applications/images/sphx_glr_plot_over_sampling_benchmark_lfw_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 scipy import interp
    from sklearn import datasets, neighbors
    from sklearn.metrics import auc, roc_curve
    from sklearn.model_selection import StratifiedKFold

    from imblearn.over_sampling import ADASYN, SMOTE, RandomOverSampler
    from imblearn.pipeline import make_pipeline

    print(__doc__)

    LW = 2
    RANDOM_STATE = 42


    class DummySampler(object):

        def sample(self, X, y):
            return X, y

        def fit(self, X, y):
            return self

        def fit_sample(self, X, y):
            return self.sample(X, y)


    cv = StratifiedKFold(n_splits=3)

    # Load the dataset
    data = datasets.fetch_lfw_people()
    majority_person = 1871  # 530 photos of George W Bush
    minority_person = 531  # 29 photos of Bill Clinton
    majority_idxs = np.flatnonzero(data.target == majority_person)
    minority_idxs = np.flatnonzero(data.target == minority_person)
    idxs = np.hstack((majority_idxs, minority_idxs))

    X = data.data[idxs]
    y = data.target[idxs]
    y[y == majority_person] = 0
    y[y == minority_person] = 1

    classifier = ['3NN', neighbors.KNeighborsClassifier(3)]

    samplers = [
        ['Standard', DummySampler()],
        ['ADASYN', ADASYN(random_state=RANDOM_STATE)],
        ['ROS', RandomOverSampler(random_state=RANDOM_STATE)],
        ['SMOTE', SMOTE(random_state=RANDOM_STATE)],
    ]

    pipelines = [
        ['{}-{}'.format(sampler[0], classifier[0]),
         make_pipeline(sampler[1], classifier[1])]
        for sampler in samplers
    ]

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

    for name, pipeline in pipelines:
        mean_tpr = 0.0
        mean_fpr = np.linspace(0, 1, 100)
        for train, test in cv.split(X, y):
            probas_ = pipeline.fit(X[train], y[train]).predict_proba(X[test])
            fpr, tpr, thresholds = roc_curve(y[test], probas_[:, 1])
            mean_tpr += interp(mean_fpr, fpr, tpr)
            mean_tpr[0] = 0.0
            roc_auc = auc(fpr, tpr)

        mean_tpr /= cv.get_n_splits(X, y)
        mean_tpr[-1] = 1.0
        mean_auc = auc(mean_fpr, mean_tpr)
        plt.plot(mean_fpr, mean_tpr, linestyle='--',
                 label='{} (area = %0.2f)'.format(name) % mean_auc, lw=LW)

    plt.plot([0, 1], [0, 1], linestyle='--', lw=LW, color='k',
             label='Luck')

    # 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))
    plt.xlim([0, 1])
    plt.ylim([0, 1])
    plt.xlabel('False Positive Rate')
    plt.ylabel('True Positive Rate')
    plt.title('Receiver operating characteristic example')

    plt.legend(loc="lower right")

    plt.show()

**Total running time of the script:** ( 1 minutes  8.944 seconds)



.. container:: sphx-glr-footer


  .. container:: sphx-glr-download

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



  .. container:: sphx-glr-download

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

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

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