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@@ -3,3 +3,5 @@ pyod.egg-info/ | |
.cache/ | ||
.pytest_cache | ||
__pycache__ | ||
.idea/ | ||
.vscode/ |
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"""Example of using INNE for outlier detection | ||
""" | ||
# Author: Xin Han <xinhan197@gmail.com> | ||
# License: BSD 2 clause | ||
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from __future__ import division | ||
from __future__ import print_function | ||
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import os | ||
import sys | ||
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# 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__"), '..'))) | ||
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from pyod.models.inne import INNE | ||
from pyod.utils.data import generate_data | ||
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from pyod.utils.data import evaluate_print | ||
from pyod.utils.example import visualize | ||
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if __name__ == "__main__": | ||
contamination = 0.1 # percentage of outliers | ||
n_train = 200 # number of training points | ||
n_test = 100 # number of testing points | ||
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# 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) | ||
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# train INNE detector | ||
clf_name = 'INNE' | ||
clf = INNE(contamination=contamination, max_samples=4) | ||
clf.fit(X_train) | ||
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# 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 | ||
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# 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 | ||
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# 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) | ||
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# 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=False) |
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