Python plot_mean_error_values Exemples

Exemple #1

Fichier : context_width_evaluation.py Projet : rm/ad-eval

import defaults

"""
Visualizes the impact of context width on anomaly detection accuracy by
evaluating the accuracy of a given kNN anomaly detection problem on a
single sequence.
"""

CONTEXT_WIDTHS = range(20, 400, 1)
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, CONTEXT_WIDTHS, xlabel='m')

Exemple #2

Fichier : k_value_evaluation.py Projet : zandy19/ad-eval

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')

Exemple #3

Fichier : sliding_window_step_evaluation.py Projet : rm/ad-eval

"""
Visualizes the impact of the sliding window step on anomaly detection accuracy
by evaluating the accuracy of a given (sliding window) kNN anomaly detection
problem on a single sequence.
"""

STEP_VALUES = range(1, 11, 1)
TEST_FILE = 'sequences/random_walk_added_noise'

# set up anomaly detectors
anomaly_detectors = []
ad_config = defaults.DEFAULT_KNN_CONFIG
for step_value in STEP_VALUES:
    filter_config = {'method': 'sliding_window', 'width': 10, 'step': step_value}
    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, STEP_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, STEP_VALUES, ylabel='s')
fig2, plot2 = utils.plot_mean_error_values(results, STEP_VALUES, STEP_VALUES, xlabel='s')
fig3, plot3 = utils.plot_execution_times(results, STEP_VALUES, STEP_VALUES, xlabel='s')

Exemple #4

    filter_config = {
        'method': 'sliding_window',
        'width': 10,
        'step': step_value
    }
    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, STEP_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,
                                                            STEP_VALUES,
                                                            ylabel='s')
fig2, plot2 = utils.plot_mean_error_values(results,
                                           STEP_VALUES,
                                           STEP_VALUES,
                                           xlabel='s')
fig3, plot3 = utils.plot_execution_times(results,
                                         STEP_VALUES,
                                         STEP_VALUES,
                                         xlabel='s')

Exemple #5

Fichier : sliding_window_width_evaluation.py Projet : zandy19/ad-eval

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,
                                         WINDOW_WIDTHS,
                                         xlabel='w')

Exemple #6

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,
                                         CONTEXT_WIDTHS,
                                         xlabel='m')

Exemple #7

Fichier : sliding_window_width_evaluation.py Projet : rm/ad-eval

"""
Visualizes the impact of the sliding window width on anomaly detection accuracy
by evaluating the accuracy of a given (sliding window) kNN anomaly detection
problem on a single sequence.
"""

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, WINDOW_WIDTHS, xlabel='w')