93 lines
3.0 KiB
Python
93 lines
3.0 KiB
Python
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"""
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=================================================
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Outlier detection with Local Outlier Factor (LOF)
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=================================================
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The Local Outlier Factor (LOF) algorithm is an unsupervised anomaly detection
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method which computes the local density deviation of a given data point with
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respect to its neighbors. It considers as outliers the samples that have a
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substantially lower density than their neighbors. This example shows how to use
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LOF for outlier detection which is the default use case of this estimator in
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scikit-learn. Note that when LOF is used for outlier detection it has no
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`predict`, `decision_function` and `score_samples` methods. See the :ref:`User
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Guide <outlier_detection>` for details on the difference between outlier
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detection and novelty detection and how to use LOF for novelty detection.
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The number of neighbors considered (parameter `n_neighbors`) is typically set 1)
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greater than the minimum number of samples a cluster has to contain, so that
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other samples can be local outliers relative to this cluster, and 2) smaller
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than the maximum number of close by samples that can potentially be local
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outliers. In practice, such information is generally not available, and taking
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`n_neighbors=20` appears to work well in general.
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"""
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# %%
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# Generate data with outliers
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# ---------------------------
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# %%
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import numpy as np
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np.random.seed(42)
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X_inliers = 0.3 * np.random.randn(100, 2)
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X_inliers = np.r_[X_inliers + 2, X_inliers - 2]
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X_outliers = np.random.uniform(low=-4, high=4, size=(20, 2))
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X = np.r_[X_inliers, X_outliers]
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n_outliers = len(X_outliers)
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ground_truth = np.ones(len(X), dtype=int)
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ground_truth[-n_outliers:] = -1
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# %%
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# Fit the model for outlier detection (default)
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# ---------------------------------------------
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#
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# Use `fit_predict` to compute the predicted labels of the training samples
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# (when LOF is used for outlier detection, the estimator has no `predict`,
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# `decision_function` and `score_samples` methods).
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from sklearn.neighbors import LocalOutlierFactor
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clf = LocalOutlierFactor(n_neighbors=20, contamination=0.1)
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y_pred = clf.fit_predict(X)
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n_errors = (y_pred != ground_truth).sum()
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X_scores = clf.negative_outlier_factor_
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# %%
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# Plot results
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# ------------
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# %%
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import matplotlib.pyplot as plt
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from matplotlib.legend_handler import HandlerPathCollection
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def update_legend_marker_size(handle, orig):
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"Customize size of the legend marker"
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handle.update_from(orig)
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handle.set_sizes([20])
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plt.scatter(X[:, 0], X[:, 1], color="k", s=3.0, label="Data points")
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# plot circles with radius proportional to the outlier scores
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radius = (X_scores.max() - X_scores) / (X_scores.max() - X_scores.min())
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scatter = plt.scatter(
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X[:, 0],
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X[:, 1],
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s=1000 * radius,
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edgecolors="r",
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facecolors="none",
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label="Outlier scores",
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)
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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.xlabel("prediction errors: %d" % (n_errors))
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plt.legend(
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handler_map={scatter: HandlerPathCollection(update_func=update_legend_marker_size)}
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)
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plt.title("Local Outlier Factor (LOF)")
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plt.show()
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