# set up anomaly detectors
anomaly_detectors = []
ad_config = defaults.DEFAULT_KNN_CONFIG
for k_value in K_VALUES:
ad_config['evaluator_config']['k'] = k_value
if transformation is None:
if 'representation_config' in ad_config:
ad_config.pop('representation_config')
else:
ad_config['representation_config'] = {'method': transformation}
anomaly_detectors.append(anomaly_detection.create_anomaly_detector(**ad_config))
# init test
test = [utils.load_sequence(TEST_FILE)]
test_suite = utils.TestSuite(anomaly_detectors, K_VALUES, [test], ['test'])
# execute test
test_suite.evaluate(display_progress=True)
# get plots
results = test_suite.results
utils.plots.plot_normalized_anomaly_vector_heat_map(results, K_VALUES, plot=heat_map_plot)
heat_map_plot.set_title(name)
heat_map_plot.set_ylabel('k')
full_support_dists.append(results.get_anomaly_detector_averages(K_VALUES, 'full_support_distance'))
equal_support_dists.append(results.get_anomaly_detector_averages(K_VALUES, 'equal_support_distance'))
euclidean_dists.append(results.get_anomaly_detector_averages(K_VALUES, 'normalized_euclidean_distance'))
# set up anomaly detectors
anomaly_detectors = []
ad_config = defaults.DEFAULT_KNN_CONFIG
for k_value in K_VALUES:
ad_config['evaluator_config']['k'] = k_value
if transformation is None:
if 'representation_config' in ad_config:
ad_config.pop('representation_config')
else:
ad_config['representation_config'] = {'method': transformation}
anomaly_detectors.append(
anomaly_detection.create_anomaly_detector(**ad_config))
# init test
test = [utils.load_sequence(TEST_FILE)]
test_suite = utils.TestSuite(anomaly_detectors, K_VALUES, [test], ['test'])
# execute test
test_suite.evaluate(display_progress=True)
# get plots
results = test_suite.results
utils.plots.plot_normalized_anomaly_vector_heat_map(results,
K_VALUES,
plot=heat_map_plot)
heat_map_plot.set_title(name)
heat_map_plot.set_ylabel('k')
full_support_dists.append(