93 lines
2.3 KiB
Python
93 lines
2.3 KiB
Python
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"""
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=================================================
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SVM: Separating hyperplane for unbalanced classes
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=================================================
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Find the optimal separating hyperplane using an SVC for classes that
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are unbalanced.
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We first find the separating plane with a plain SVC and then plot
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(dashed) the separating hyperplane with automatically correction for
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unbalanced classes.
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.. currentmodule:: sklearn.linear_model
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.. note::
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This example will also work by replacing ``SVC(kernel="linear")``
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with ``SGDClassifier(loss="hinge")``. Setting the ``loss`` parameter
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of the :class:`SGDClassifier` equal to ``hinge`` will yield behaviour
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such as that of a SVC with a linear kernel.
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For example try instead of the ``SVC``::
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clf = SGDClassifier(n_iter=100, alpha=0.01)
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"""
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import matplotlib.lines as mlines
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import matplotlib.pyplot as plt
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from sklearn import svm
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from sklearn.datasets import make_blobs
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from sklearn.inspection import DecisionBoundaryDisplay
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# we create two clusters of random points
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n_samples_1 = 1000
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n_samples_2 = 100
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centers = [[0.0, 0.0], [2.0, 2.0]]
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clusters_std = [1.5, 0.5]
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X, y = make_blobs(
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n_samples=[n_samples_1, n_samples_2],
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centers=centers,
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cluster_std=clusters_std,
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random_state=0,
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shuffle=False,
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)
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# fit the model and get the separating hyperplane
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clf = svm.SVC(kernel="linear", C=1.0)
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clf.fit(X, y)
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# fit the model and get the separating hyperplane using weighted classes
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wclf = svm.SVC(kernel="linear", class_weight={1: 10})
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wclf.fit(X, y)
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# plot the samples
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plt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.Paired, edgecolors="k")
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# plot the decision functions for both classifiers
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ax = plt.gca()
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disp = DecisionBoundaryDisplay.from_estimator(
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clf,
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X,
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plot_method="contour",
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colors="k",
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levels=[0],
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alpha=0.5,
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linestyles=["-"],
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ax=ax,
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)
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# plot decision boundary and margins for weighted classes
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wdisp = DecisionBoundaryDisplay.from_estimator(
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wclf,
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X,
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plot_method="contour",
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colors="r",
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levels=[0],
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alpha=0.5,
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linestyles=["-"],
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ax=ax,
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)
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plt.legend(
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[
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mlines.Line2D([], [], color="k", label="non weighted"),
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mlines.Line2D([], [], color="r", label="weighted"),
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],
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["non weighted", "weighted"],
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loc="upper right",
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)
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plt.show()
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