61 lines
2.3 KiB
Python
61 lines
2.3 KiB
Python
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"""
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==============================================
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Plot randomly generated classification dataset
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==============================================
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This example plots several randomly generated classification datasets.
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For easy visualization, all datasets have 2 features, plotted on the x and y
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axis. The color of each point represents its class label.
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The first 4 plots use the :func:`~sklearn.datasets.make_classification` with
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different numbers of informative features, clusters per class and classes.
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The final 2 plots use :func:`~sklearn.datasets.make_blobs` and
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:func:`~sklearn.datasets.make_gaussian_quantiles`.
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"""
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import matplotlib.pyplot as plt
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from sklearn.datasets import make_blobs, make_classification, make_gaussian_quantiles
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plt.figure(figsize=(8, 8))
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plt.subplots_adjust(bottom=0.05, top=0.9, left=0.05, right=0.95)
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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(
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n_features=2, n_redundant=0, n_informative=1, n_clusters_per_class=1
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)
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plt.scatter(X1[:, 0], X1[:, 1], marker="o", c=Y1, 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(
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n_features=2, n_redundant=0, n_informative=2, n_clusters_per_class=1
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)
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plt.scatter(X1[:, 0], X1[:, 1], marker="o", c=Y1, s=25, edgecolor="k")
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plt.subplot(323)
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plt.title("Two informative features, two clusters per class", 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, s=25, edgecolor="k")
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plt.subplot(324)
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plt.title("Multi-class, two informative features, one cluster", fontsize="small")
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X1, Y1 = make_classification(
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n_features=2, n_redundant=0, n_informative=2, n_clusters_per_class=1, n_classes=3
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)
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plt.scatter(X1[:, 0], X1[:, 1], marker="o", c=Y1, 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, 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, s=25, edgecolor="k")
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plt.show()
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