111 lines
3.0 KiB
Python
111 lines
3.0 KiB
Python
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"""
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==================
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Two-class AdaBoost
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==================
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This example fits an AdaBoosted decision stump on a non-linearly separable
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classification dataset composed of two "Gaussian quantiles" clusters
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(see :func:`sklearn.datasets.make_gaussian_quantiles`) and plots the decision
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boundary and decision scores. The distributions of decision scores are shown
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separately for samples of class A and B. The predicted class label for each
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sample is determined by the sign of the decision score. Samples with decision
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scores greater than zero are classified as B, and are otherwise classified
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as A. The magnitude of a decision score determines the degree of likeness with
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the predicted class label. Additionally, a new dataset could be constructed
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containing a desired purity of class B, for example, by only selecting samples
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with a decision score above some value.
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"""
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# Author: Noel Dawe <noel.dawe@gmail.com>
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#
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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_gaussian_quantiles
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from sklearn.ensemble import AdaBoostClassifier
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from sklearn.inspection import DecisionBoundaryDisplay
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from sklearn.tree import DecisionTreeClassifier
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# Construct dataset
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X1, y1 = make_gaussian_quantiles(
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cov=2.0, n_samples=200, n_features=2, n_classes=2, random_state=1
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)
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X2, y2 = make_gaussian_quantiles(
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mean=(3, 3), cov=1.5, n_samples=300, n_features=2, n_classes=2, random_state=1
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)
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X = np.concatenate((X1, X2))
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y = np.concatenate((y1, -y2 + 1))
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# Create and fit an AdaBoosted decision tree
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bdt = AdaBoostClassifier(
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DecisionTreeClassifier(max_depth=1), algorithm="SAMME", n_estimators=200
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)
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bdt.fit(X, y)
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plot_colors = "br"
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plot_step = 0.02
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class_names = "AB"
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plt.figure(figsize=(10, 5))
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# Plot the decision boundaries
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ax = plt.subplot(121)
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disp = DecisionBoundaryDisplay.from_estimator(
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bdt,
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X,
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cmap=plt.cm.Paired,
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response_method="predict",
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ax=ax,
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xlabel="x",
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ylabel="y",
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)
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x_min, x_max = disp.xx0.min(), disp.xx0.max()
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y_min, y_max = disp.xx1.min(), disp.xx1.max()
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plt.axis("tight")
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# Plot the training points
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for i, n, c in zip(range(2), class_names, plot_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=c,
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s=20,
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edgecolor="k",
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label="Class %s" % n,
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)
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plt.xlim(x_min, x_max)
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plt.ylim(y_min, y_max)
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plt.legend(loc="upper right")
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plt.title("Decision Boundary")
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# Plot the two-class decision scores
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twoclass_output = bdt.decision_function(X)
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plot_range = (twoclass_output.min(), twoclass_output.max())
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plt.subplot(122)
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for i, n, c in zip(range(2), class_names, plot_colors):
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plt.hist(
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twoclass_output[y == i],
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bins=10,
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range=plot_range,
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facecolor=c,
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label="Class %s" % n,
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alpha=0.5,
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edgecolor="k",
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)
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x1, x2, y1, y2 = plt.axis()
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plt.axis((x1, x2, y1, y2 * 1.2))
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plt.legend(loc="upper right")
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plt.ylabel("Samples")
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plt.xlabel("Score")
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plt.title("Decision Scores")
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plt.tight_layout()
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plt.subplots_adjust(wspace=0.35)
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plt.show()
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