71 lines
2.3 KiB
Python
71 lines
2.3 KiB
Python
"""
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==========================================
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IsolationForest example
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==========================================
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An example using :class:`sklearn.ensemble.IsolationForest` for anomaly
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detection.
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The IsolationForest 'isolates' observations by randomly selecting a feature
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and then randomly selecting a split value between the maximum and minimum
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values of the selected feature.
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Since recursive partitioning can be represented by a tree structure, the
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number of splittings required to isolate a sample is equivalent to the path
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length from the root node to the terminating node.
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This path length, averaged over a forest of such random trees, is a measure
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of normality and our decision function.
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Random partitioning produces noticeable shorter paths for anomalies.
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Hence, when a forest of random trees collectively produce shorter path lengths
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for particular samples, they are highly likely to be anomalies.
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"""
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print(__doc__)
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import numpy as np
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import matplotlib.pyplot as plt
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from sklearn.ensemble import IsolationForest
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rng = np.random.RandomState(42)
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# Generate train data
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X = 0.3 * rng.randn(100, 2)
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X_train = np.r_[X + 2, X - 2]
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# Generate some regular novel observations
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X = 0.3 * rng.randn(20, 2)
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X_test = np.r_[X + 2, X - 2]
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# Generate some abnormal novel observations
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X_outliers = rng.uniform(low=-4, high=4, size=(20, 2))
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# fit the model
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clf = IsolationForest(max_samples=100, random_state=rng)
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clf.fit(X_train)
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y_pred_train = clf.predict(X_train)
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y_pred_test = clf.predict(X_test)
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y_pred_outliers = clf.predict(X_outliers)
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# plot the line, the samples, and the nearest vectors to the plane
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xx, yy = np.meshgrid(np.linspace(-5, 5, 50), np.linspace(-5, 5, 50))
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Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()])
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Z = Z.reshape(xx.shape)
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plt.title("IsolationForest")
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plt.contourf(xx, yy, Z, cmap=plt.cm.Blues_r)
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b1 = plt.scatter(X_train[:, 0], X_train[:, 1], c='white',
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s=20, edgecolor='k')
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b2 = plt.scatter(X_test[:, 0], X_test[:, 1], c='green',
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s=20, edgecolor='k')
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c = plt.scatter(X_outliers[:, 0], X_outliers[:, 1], c='red',
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s=20, edgecolor='k')
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plt.axis('tight')
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plt.xlim((-5, 5))
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plt.ylim((-5, 5))
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plt.legend([b1, b2, c],
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["training observations",
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"new regular observations", "new abnormal observations"],
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loc="upper left")
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plt.show()
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