90 lines
2.8 KiB
Python
90 lines
2.8 KiB
Python
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"""
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==================================================
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Plot different SVM classifiers in the iris dataset
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==================================================
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Comparison of different linear SVM classifiers on a 2D projection of the iris
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dataset. We only consider the first 2 features of this dataset:
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- Sepal length
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- Sepal width
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This example shows how to plot the decision surface for four SVM classifiers
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with different kernels.
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The linear models ``LinearSVC()`` and ``SVC(kernel='linear')`` yield slightly
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different decision boundaries. This can be a consequence of the following
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differences:
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- ``LinearSVC`` minimizes the squared hinge loss while ``SVC`` minimizes the
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regular hinge loss.
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- ``LinearSVC`` uses the One-vs-All (also known as One-vs-Rest) multiclass
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reduction while ``SVC`` uses the One-vs-One multiclass reduction.
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Both linear models have linear decision boundaries (intersecting hyperplanes)
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while the non-linear kernel models (polynomial or Gaussian RBF) have more
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flexible non-linear decision boundaries with shapes that depend on the kind of
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kernel and its parameters.
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.. NOTE:: while plotting the decision function of classifiers for toy 2D
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datasets can help get an intuitive understanding of their respective
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expressive power, be aware that those intuitions don't always generalize to
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more realistic high-dimensional problems.
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"""
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import matplotlib.pyplot as plt
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from sklearn import datasets, svm
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from sklearn.inspection import DecisionBoundaryDisplay
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# import some data to play with
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iris = datasets.load_iris()
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# Take the first two features. We could avoid this by using a two-dim dataset
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X = iris.data[:, :2]
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y = iris.target
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# we create an instance of SVM and fit out data. We do not scale our
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# data since we want to plot the support vectors
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C = 1.0 # SVM regularization parameter
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models = (
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svm.SVC(kernel="linear", C=C),
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svm.LinearSVC(C=C, max_iter=10000),
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svm.SVC(kernel="rbf", gamma=0.7, C=C),
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svm.SVC(kernel="poly", degree=3, gamma="auto", C=C),
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)
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models = (clf.fit(X, y) for clf in models)
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# title for the plots
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titles = (
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"SVC with linear kernel",
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"LinearSVC (linear kernel)",
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"SVC with RBF kernel",
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"SVC with polynomial (degree 3) kernel",
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)
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# Set-up 2x2 grid for plotting.
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fig, sub = plt.subplots(2, 2)
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plt.subplots_adjust(wspace=0.4, hspace=0.4)
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X0, X1 = X[:, 0], X[:, 1]
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for clf, title, ax in zip(models, titles, sub.flatten()):
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disp = DecisionBoundaryDisplay.from_estimator(
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clf,
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X,
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response_method="predict",
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cmap=plt.cm.coolwarm,
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alpha=0.8,
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ax=ax,
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xlabel=iris.feature_names[0],
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ylabel=iris.feature_names[1],
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)
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ax.scatter(X0, X1, c=y, cmap=plt.cm.coolwarm, s=20, edgecolors="k")
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ax.set_xticks(())
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ax.set_yticks(())
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ax.set_title(title)
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plt.show()
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