85 lines
1.9 KiB
Python
85 lines
1.9 KiB
Python
"""
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========================================
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Plot multi-class SGD on the iris dataset
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========================================
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Plot decision surface of multi-class SGD on iris dataset.
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The hyperplanes corresponding to the three one-versus-all (OVA) classifiers
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are represented by the dashed lines.
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"""
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import matplotlib.pyplot as plt
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import numpy as np
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from sklearn import datasets
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from sklearn.inspection import DecisionBoundaryDisplay
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from sklearn.linear_model import SGDClassifier
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# import some data to play with
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iris = datasets.load_iris()
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# we only take the first two features. We could
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# avoid this ugly slicing 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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colors = "bry"
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# shuffle
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idx = np.arange(X.shape[0])
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np.random.seed(13)
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np.random.shuffle(idx)
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X = X[idx]
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y = y[idx]
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# standardize
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mean = X.mean(axis=0)
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std = X.std(axis=0)
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X = (X - mean) / std
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clf = SGDClassifier(alpha=0.001, max_iter=100).fit(X, y)
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ax = plt.gca()
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DecisionBoundaryDisplay.from_estimator(
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clf,
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X,
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cmap=plt.cm.Paired,
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ax=ax,
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response_method="predict",
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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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plt.axis("tight")
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# Plot also the training points
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for i, color in zip(clf.classes_, colors):
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idx = np.where(y == i)
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plt.scatter(
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X[idx, 0],
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X[idx, 1],
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c=color,
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label=iris.target_names[i],
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edgecolor="black",
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s=20,
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)
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plt.title("Decision surface of multi-class SGD")
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plt.axis("tight")
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# Plot the three one-against-all classifiers
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xmin, xmax = plt.xlim()
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ymin, ymax = plt.ylim()
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coef = clf.coef_
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intercept = clf.intercept_
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def plot_hyperplane(c, color):
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def line(x0):
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return (-(x0 * coef[c, 0]) - intercept[c]) / coef[c, 1]
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plt.plot([xmin, xmax], [line(xmin), line(xmax)], ls="--", color=color)
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for i, color in zip(clf.classes_, colors):
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plot_hyperplane(i, color)
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plt.legend()
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plt.show()
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