68 lines
2.4 KiB
Python
68 lines
2.4 KiB
Python
"""
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==============================================
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Plot randomly generated classification dataset
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==============================================
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Plot several randomly generated 2D classification datasets.
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This example illustrates the :func:`datasets.make_classification`
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:func:`datasets.make_blobs` and :func:`datasets.make_gaussian_quantiles`
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functions.
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For ``make_classification``, three binary and two multi-class classification
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datasets are generated, with different numbers of informative features and
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clusters per class. """
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print(__doc__)
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import matplotlib.pyplot as plt
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from sklearn.datasets import make_classification
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from sklearn.datasets import make_blobs
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from sklearn.datasets import make_gaussian_quantiles
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plt.figure(figsize=(8, 8))
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plt.subplots_adjust(bottom=.05, top=.9, left=.05, right=.95)
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plt.subplot(321)
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plt.title("One informative feature, one cluster per class", fontsize='small')
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X1, Y1 = make_classification(n_features=2, n_redundant=0, n_informative=1,
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n_clusters_per_class=1)
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plt.scatter(X1[:, 0], X1[:, 1], marker='o', c=Y1,
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s=25, edgecolor='k')
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plt.subplot(322)
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plt.title("Two informative features, one cluster per class", fontsize='small')
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X1, Y1 = make_classification(n_features=2, n_redundant=0, n_informative=2,
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n_clusters_per_class=1)
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plt.scatter(X1[:, 0], X1[:, 1], marker='o', c=Y1,
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s=25, edgecolor='k')
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plt.subplot(323)
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plt.title("Two informative features, two clusters per class",
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fontsize='small')
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X2, Y2 = make_classification(n_features=2, n_redundant=0, n_informative=2)
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plt.scatter(X2[:, 0], X2[:, 1], marker='o', c=Y2,
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s=25, edgecolor='k')
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plt.subplot(324)
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plt.title("Multi-class, two informative features, one cluster",
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fontsize='small')
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X1, Y1 = make_classification(n_features=2, n_redundant=0, n_informative=2,
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n_clusters_per_class=1, n_classes=3)
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plt.scatter(X1[:, 0], X1[:, 1], marker='o', c=Y1,
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s=25, edgecolor='k')
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plt.subplot(325)
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plt.title("Three blobs", fontsize='small')
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X1, Y1 = make_blobs(n_features=2, centers=3)
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plt.scatter(X1[:, 0], X1[:, 1], marker='o', c=Y1,
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s=25, edgecolor='k')
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plt.subplot(326)
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plt.title("Gaussian divided into three quantiles", fontsize='small')
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X1, Y1 = make_gaussian_quantiles(n_features=2, n_classes=3)
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plt.scatter(X1[:, 0], X1[:, 1], marker='o', c=Y1,
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s=25, edgecolor='k')
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plt.show()
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