71 lines
2.3 KiB
Python
71 lines
2.3 KiB
Python
"""
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====================================================
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Plot multinomial and One-vs-Rest Logistic Regression
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====================================================
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Plot decision surface of multinomial and One-vs-Rest Logistic Regression.
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The hyperplanes corresponding to the three One-vs-Rest (OVR) classifiers
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are represented by the dashed lines.
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"""
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# Authors: Tom Dupre la Tour <tom.dupre-la-tour@m4x.org>
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# License: BSD 3 clause
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import matplotlib.pyplot as plt
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import numpy as np
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from sklearn.datasets import make_blobs
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from sklearn.inspection import DecisionBoundaryDisplay
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from sklearn.linear_model import LogisticRegression
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from sklearn.multiclass import OneVsRestClassifier
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# make 3-class dataset for classification
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centers = [[-5, 0], [0, 1.5], [5, -1]]
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X, y = make_blobs(n_samples=1000, centers=centers, random_state=40)
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transformation = [[0.4, 0.2], [-0.4, 1.2]]
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X = np.dot(X, transformation)
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for multi_class in ("multinomial", "ovr"):
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clf = LogisticRegression(solver="sag", max_iter=100, random_state=42)
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if multi_class == "ovr":
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clf = OneVsRestClassifier(clf)
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clf.fit(X, y)
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# print the training scores
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print("training score : %.3f (%s)" % (clf.score(X, y), multi_class))
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_, ax = plt.subplots()
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DecisionBoundaryDisplay.from_estimator(
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clf, X, response_method="predict", cmap=plt.cm.Paired, ax=ax
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)
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plt.title("Decision surface of LogisticRegression (%s)" % multi_class)
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plt.axis("tight")
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# Plot also the training points
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colors = "bry"
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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(X[idx, 0], X[idx, 1], c=color, edgecolor="black", s=20)
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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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if multi_class == "ovr":
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coef = np.concatenate([est.coef_ for est in clf.estimators_])
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intercept = np.concatenate([est.intercept_ for est in clf.estimators_])
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else:
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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.show()
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