scikit-learn
diff --git a/‎examples/neighbors/plot_lof_outlier_detection.py‎
Lines changed: 48 additions & 29 deletions b/‎examples/neighbors/plot_lof_outlier_detection.py‎
Lines changed: 48 additions & 29 deletions
@@ -6,56 +6,74 @@
 The Local Outlier Factor (LOF) algorithm is an unsupervised anomaly detection
 method which computes the local density deviation of a given data point with
 respect to its neighbors. It considers as outliers the samples that have a
-substantially lower density than their neighbors. This example shows how to
-use LOF for outlier detection which is the default use case of this estimator
-in scikit-learn. Note that when LOF is used for outlier detection it has no
-predict, decision_function and score_samples methods. See
-:ref:`User Guide <outlier_detection>`: for details on the difference between
-outlier detection and novelty detection and how to use LOF for novelty
-detection.
-
-The number of neighbors considered (parameter n_neighbors) is typically
-set 1) greater than the minimum number of samples a cluster has to contain,
-so that other samples can be local outliers relative to this cluster, and 2)
-smaller than the maximum number of close by samples that can potentially be
-local outliers.
-In practice, such information is generally not available, and taking
-n_neighbors=20 appears to work well in general.
+substantially lower density than their neighbors. This example shows how to use
+LOF for outlier detection which is the default use case of this estimator in
+scikit-learn. Note that when LOF is used for outlier detection it has no
+`predict`, `decision_function` and `score_samples` methods. See the :ref:`User
+Guide <outlier_detection>` for details on the difference between outlier
+detection and novelty detection and how to use LOF for novelty detection.
+
+The number of neighbors considered (parameter `n_neighbors`) is typically set 1)
+greater than the minimum number of samples a cluster has to contain, so that
+other samples can be local outliers relative to this cluster, and 2) smaller
+than the maximum number of close by samples that can potentially be local
+outliers. In practice, such information is generally not available, and taking
+`n_neighbors=20` appears to work well in general.
 
 """
 
+# %%
+# Generate data with outliers
+# ---------------------------
+
+# %%
 import numpy as np
-import matplotlib.pyplot as plt
-from sklearn.neighbors import LocalOutlierFactor
 
 np.random.seed(42)
 
-# Generate train data
 X_inliers = 0.3 * np.random.randn(100, 2)
 X_inliers = np.r_[X_inliers + 2, X_inliers - 2]
-
-# Generate some outliers
 X_outliers = np.random.uniform(low=-4, high=4, size=(20, 2))
 X = np.r_[X_inliers, X_outliers]
 
 n_outliers = len(X_outliers)
 ground_truth = np.ones(len(X), dtype=int)
 ground_truth[-n_outliers:] = -1
 
-# fit the model for outlier detection (default)
+# %%
+# Fit the model for outlier detection (default)
+# ---------------------------------------------
+#
+# Use `fit_predict` to compute the predicted labels of the training samples
+# (when LOF is used for outlier detection, the estimator has no `predict`,
+# `decision_function` and `score_samples` methods).
+
+from sklearn.neighbors import LocalOutlierFactor
+
 clf = LocalOutlierFactor(n_neighbors=20, contamination=0.1)
-# use fit_predict to compute the predicted labels of the training samples
-# (when LOF is used for outlier detection, the estimator has no predict,
-# decision_function and score_samples methods).
 y_pred = clf.fit_predict(X)
 n_errors = (y_pred != ground_truth).sum()
 X_scores = clf.negative_outlier_factor_
 
-plt.title("Local Outlier Factor (LOF)")
+# %%
+# Plot results
+# ------------
+
+# %%
+import matplotlib.pyplot as plt
+from matplotlib.legend_handler import HandlerPathCollection
+
+
+def update_legend_marker_size(handle, orig):
+    "Customize size of the legend marker"
+    handle.update_from(orig)
+    handle.set_sizes([20])
+
+
 plt.scatter(X[:, 0], X[:, 1], color="k", s=3.0, label="Data points")
 # plot circles with radius proportional to the outlier scores
 radius = (X_scores.max() - X_scores) / (X_scores.max() - X_scores.min())
-plt.scatter(
+scatter = plt.scatter(
     X[:, 0],
     X[:, 1],
     s=1000 * radius,
@@ -67,7 +85,8 @@
 plt.xlim((-5, 5))
 plt.ylim((-5, 5))
 plt.xlabel("prediction errors: %d" % (n_errors))
-legend = plt.legend(loc="upper left")
-legend.legendHandles[0]._sizes = [10]
-legend.legendHandles[1]._sizes = [20]
+plt.legend(
+    handler_map={scatter: HandlerPathCollection(update_func=update_legend_marker_size)}
+)
+plt.title("Local Outlier Factor (LOF)")
 plt.show()