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,
WINDOW_WIDTHS = range(1, 51, 1)
TEST_FILE = 'sequences/random_walk_added_noise'
# set up anomaly detectors
anomaly_detectors = []
ad_config = defaults.DEFAULT_KNN_CONFIG
for width in WINDOW_WIDTHS:
filter_config = {'method': 'sliding_window', 'width': width, 'step': 1}
ad_config['evaluation_filter_config'] = filter_config
ad_config['reference_filter_config'] = filter_config
anomaly_detectors.append(
anomaly_detection.create_anomaly_detector(**ad_config))
# init test
test = [utils.load_sequence(TEST_FILE)]
test_suite = utils.TestSuite(anomaly_detectors, WINDOW_WIDTHS, [test],
['test'])
# execute test
test_suite.evaluate(display_progress=True)
# get plot
results = test_suite.results
fig1, plot1 = utils.plot_normalized_anomaly_vector_heat_map(results,
WINDOW_WIDTHS,
ylabel='w')
fig2, plot2 = utils.plot_mean_error_values(results,
WINDOW_WIDTHS,
WINDOW_WIDTHS,
xlabel='w')
fig3, plot3 = utils.plot_execution_times(results,
WINDOW_WIDTHS,
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
ERRORS = [
'equal_support_distance', 'full_support_distance', 'best_support_distance',
'normalized_euclidean_distance'
]
ERROR_LABELS = [
'Equal support', 'Full support', 'Best support', 'Normalized Euclidean'
]
TEST_FILE = 'sequences/random_walk_added_noise'
# set up anomaly detectors
anomaly_detectors = []
ad_config = defaults.DEFAULT_KNN_CONFIG
for context_width in CONTEXT_WIDTHS:
ad_config['context_config'] = {
'method': 'local_symmetric',
'width': context_width
}
anomaly_detectors.append(
anomaly_detection.create_anomaly_detector(**ad_config))
# init test
test = [utils.load_sequence(TEST_FILE)]
test_suite = utils.TestSuite(anomaly_detectors, CONTEXT_WIDTHS, [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,
CONTEXT_WIDTHS,
ylabel='m')
fig2, plot2 = utils.plot_mean_error_values(results,
CONTEXT_WIDTHS,
CONTEXT_WIDTHS,
xlabel='m')
fig3, plot3 = utils.plot_execution_times(results,
CONTEXT_WIDTHS,