euclidean_dists = []
for transformation, name, heat_map_plot in zip(TRANSFORMATIONS, TRANSFORMATION_NAMES, heat_map_plots):
# 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')
import defaults
"""
Visualizes the impact of the kNN k values on anomaly detection accuracy by
evaluating the accuracy of a given kNN anomaly detection problem on a
single sequence.
"""
K_VALUES = range(1, 101, 1)
TEST_FILE = 'sequences/random_walk_added_noise'
# 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
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
fig1, plot1 = utils.plot_normalized_anomaly_vector_heat_map(results, K_VALUES, ylabel='k')
fig2, plot2 = utils.plot_mean_error_values(results, K_VALUES, K_VALUES, xlabel='k')
fig3, plot3 = utils.plot_execution_times(results, K_VALUES, K_VALUES, xlabel='k')
TRANSFORMATION_NAMES,
heat_map_plots):
# 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)
K_VALUES = range(1, 50, 1)
W_VALUES = range(1, 50, 1)
TEST_FILE = 'sequences/random_walk_added_noise'
# generate anomaly_detectors
ad_config = defaults.DEFAULT_KNN_CONFIG
anomaly_detectors = []
ad_label_matrix = []
for k in K_VALUES:
ad_row = []
for w in W_VALUES:
ad_config['evaluator_config']['k'] = k
ad_config['evaluation_filter_config']['width'] = w
ad_config['reference_filter_config']['width'] = w
ad = anomaly_detection.create_anomaly_detector(**ad_config)
anomaly_detectors.append(ad)
ad_row.append(str(k) + ',' + str(w))
ad_label_matrix.append(ad_row)
ad_label_list = sum(ad_label_matrix, [])
test = [utils.load_sequence(TEST_FILE)]
test_suite = utils.TestSuite(anomaly_detectors, ad_label_list, [test], 'test')
#execute
test_suite.evaluate(display_progress=True)
results = test_suite.results
# plot the distances
