Merge 4825059 into caf49bf

examples/sos_example.py

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+from __future__ import division
+from __future__ import print_function
+
+import os
+import sys
+
+# temporary solution for relative imports in case pyod is not installed
+# if pyod is installed, no need to use the following line
+sys.path.append(
+    os.path.abspath(os.path.join(os.path.dirname("__file__"), '..')))
+
+from sklearn.utils import check_X_y
+import matplotlib.pyplot as plt
+from matplotlib.lines import Line2D
+
+from pyod.models.sos import SOS
+from pyod.utils.data import generate_data
+from pyod.utils.data import get_color_codes
+from pyod.utils.data import evaluate_print
+
+def visualize(clf_name, X_train, y_train, X_test, y_test, y_train_pred,
+              y_test_pred, show_figure=True,
+              save_figure=False):  # pragma: no cover
+    """
+    Utility function for visualizing the results in examples
+    Internal use only
+
+    :param clf_name: The name of the detector
+    :type clf_name: str
+
+    :param X_train: The training samples
+    :param X_train: numpy array of shape (n_samples, n_features)
+
+    :param y_train: The ground truth of training samples
+    :type y_train: list or array of shape (n_samples,)
+
+    :param X_test: The test samples
+    :type X_test: numpy array of shape (n_samples, n_features)
+
+    :param y_test: The ground truth of test samples
+    :type y_test: list or array of shape (n_samples,)
+
+    :param y_train_pred: The predicted outlier scores on the training samples
+    :type y_train_pred: numpy array of shape (n_samples, n_features)
+
+    :param y_test_pred: The predicted outlier scores on the test samples
+    :type y_test_pred: numpy array of shape (n_samples, n_features)
+
+    :param show_figure: If set to True, show the figure
+    :type show_figure: bool, optional (default=True)
+
+    :param save_figure: If set to True, save the figure to the local
+    :type save_figure: bool, optional (default=False)
+    """
+
+    if X_train.shape[1] != 2 or X_test.shape[1] != 2:
+        raise ValueError("Input data has to be 2-d for visualization. The "
+                         "input data has {shape}.".format(shape=X_train.shape))
+
+    X_train, y_train = check_X_y(X_train, y_train)
+    X_test, y_test = check_X_y(X_test, y_test)
+    c_train = get_color_codes(y_train)
+    c_test = get_color_codes(y_test)
+
+    fig = plt.figure(figsize=(12, 10))
+    plt.suptitle("Demo of {clf_name}".format(clf_name=clf_name))
+
+    fig.add_subplot(221)
+    plt.scatter(X_train[:, 0], X_train[:, 1], c=c_train)
+    plt.title('Train ground truth')
+    legend_elements = [Line2D([0], [0], marker='o', color='w', label='normal',
+                              markerfacecolor='b', markersize=8),
+                       Line2D([0], [0], marker='o', color='w', label='outlier',
+                              markerfacecolor='r', markersize=8)]
+
+    plt.legend(handles=legend_elements, loc=4)
+
+    fig.add_subplot(222)
+    plt.scatter(X_test[:, 0], X_test[:, 1], c=c_test)
+    plt.title('Test ground truth')
+    plt.legend(handles=legend_elements, loc=4)
+
+    fig.add_subplot(223)
+    plt.scatter(X_train[:, 0], X_train[:, 1], c=y_train_pred)
+    plt.title('Train prediction by {clf_name}'.format(clf_name=clf_name))
+    legend_elements = [Line2D([0], [0], marker='o', color='w', label='normal',
+                              markerfacecolor='0', markersize=8),
+                       Line2D([0], [0], marker='o', color='w', label='outlier',
+                              markerfacecolor='yellow', markersize=8)]
+    plt.legend(handles=legend_elements, loc=4)
+
+    fig.add_subplot(224)
+    plt.scatter(X_test[:, 0], X_test[:, 1], c=y_test_pred)
+    plt.title('Test prediction by {clf_name}'.format(clf_name=clf_name))
+    plt.legend(handles=legend_elements, loc=4)
+
+    if save_figure:
+        plt.savefig('{clf_name}.png'.format(clf_name=clf_name), dpi=300)
+    if show_figure:
+        plt.show()
+    return
+
+if __name__ == "__main__":
+    contamination = 0.1  # percentage of outliers
+    n_train = 100  # number of training points
+    n_test = 100  # number of testing points
+
+    #Generate sample data
+    X_train, y_train, X_test, y_test = \
+        generate_data(n_train=n_train,
+                      n_test=n_test,
+                      n_features=2,
+                      contamination=contamination,
+                      random_state=42)
+
+    # train kNN detector
+    clf_name = 'SOS'
+    clf = SOS()
+    clf.fit(X_train)
+    # get the prediction labels and outlier scores of the training data
+    y_train_pred = clf.labels_  # binary labels (0: inliers, 1: outliers)
+    y_train_scores = clf.decision_scores_  # raw outlier scores
+    # get the prediction on the test data
+    y_test_pred = clf.predict(X_test)  # outlier labels (0 or 1)
+    y_test_scores = clf.decision_function(X_test)  # outlier scores
+
+    # evaluate and print the results
+    print("\nOn Training Data:")
+    evaluate_print(clf_name, y_train, y_train_scores)
+    print("\nOn Test Data:")
+    evaluate_print(clf_name, y_test, y_test_scores)
+
+    # visualize the results
+    visualize(clf_name, X_train, y_train, X_test, y_test, y_train_pred,
+              y_test_pred, show_figure=True, save_figure=True)