def test_run_selected_algorithm(self, timeMock):
timeMock.return_value, timeseries = self.data(time())
result, ensemble, datapoint = algorithms.run_selected_algorithm(
timeseries, "test.metric")
self.assertTrue(result)
self.assertTrue(len(filter(None, ensemble)) >= settings.CONSENSUS)
self.assertEqual(datapoint, 1000)
def test_run_selected_algorithm(self, timeMock):
timeMock.return_value, timeseries = self.data(time())
# @modified 20200206 - Feature #3400: Identify air gaps in the metric data
# Added the airgapped_metrics list
# result, ensemble, datapoint = algorithms.run_selected_algorithm(timeseries, "test.metric")
airgapped_metrics = [
'test.metric.airgapped.1', 'test.metric.airgapped.2'
]
# @modified 20200520 - Feature #3400: Identify air gaps in the metric data
# Feature #3508: ionosphere.untrainable_metrics
# Feature #3504: Handle airgaps in batch metrics
# Added airgapped_metrics_filled, run_negatives_present and
# check_for_airgaps_only
# result, ensemble, datapoint = algorithms.run_selected_algorithm(timeseries, "test.metric", airgapped_metrics)
airgapped_metrics_filled = []
run_negatives_present = False
check_for_airgaps_only = False
# @modified 20200604 - Feature #3566: custom_algorithms
# Added algorithms_run
# @modified 20210519 - Feature #4076: CUSTOM_STALE_PERIOD
# Added custom_stale_metrics_dict
custom_stale_metrics_dict = {}
result, ensemble, datapoint, negatives_found, algorithms_run = algorithms.run_selected_algorithm(
timeseries, 'test.metric', airgapped_metrics,
airgapped_metrics_filled, run_negatives_present,
check_for_airgaps_only, custom_stale_metrics_dict)
self.assertTrue(result)
# @modified 20200808 - Bug #3666: Failing algorithm_tests on Python 3.8.3
# self.assertTrue(len(filter(None, ensemble)) >= settings.CONSENSUS)
self.assertTrue(
len(list(filter(None, ensemble))) >= settings.CONSENSUS)
self.assertEqual(datapoint, 1000)
def test_run_selected_algorithm(self, timeMock):
timeMock.return_value, timeseries = self.data(time())
# @modified 20200206 - Feature #3400: Identify air gaps in the metric data
# Added the airgapped_metrics list
# result, ensemble, datapoint = algorithms.run_selected_algorithm(timeseries, "test.metric")
airgapped_metrics = [
'test.metric.airgapped.1', 'test.metric.airgapped.2'
]
# @modified 20200520 - Feature #3400: Identify air gaps in the metric data
# Feature #3508: ionosphere.untrainable_metrics
# Feature #3504: Handle airgaps in batch metrics
# Added airgapped_metrics_filled, run_negatives_present and
# check_for_airgaps_only
# result, ensemble, datapoint = algorithms.run_selected_algorithm(timeseries, "test.metric", airgapped_metrics)
airgapped_metrics_filled = []
run_negatives_present = False
check_for_airgaps_only = False
result, ensemble, datapoint, negatives_found = algorithms.run_selected_algorithm(
timeseries, 'test.metric', airgapped_metrics,
airgapped_metrics_filled, run_negatives_present,
check_for_airgaps_only)
self.assertTrue(result)
self.assertTrue(len(filter(None, ensemble)) >= settings.CONSENSUS)
self.assertEqual(datapoint, 1000)
def test_run_selected_algorithm(self, timeMock):
timeMock.return_value, timeseries = self.data(time())
# @modified 20200206 - Feature #3400: Identify air gaps in the metric data
# Added the airgapped_metrics list
# result, ensemble, datapoint = algorithms.run_selected_algorithm(timeseries, "test.metric")
airgapped_metrics = [
'test.metric.airgapped.1', 'test.metric.airgapped.2'
]
result, ensemble, datapoint = algorithms.run_selected_algorithm(
timeseries, "test.metric", airgapped_metrics)
self.assertTrue(result)
self.assertTrue(len(filter(None, ensemble)) >= settings.CONSENSUS)
self.assertEqual(datapoint, 1000)
def test_run_selected_algorithm_runs_novel_algorithm(self, timeMock,
algorithmsListMock, consensusMock):
"""
Assert that a user can add their own custom algorithm.
This mocks out settings.ALGORITHMS and settings.CONSENSUS to use only a
single custom-defined function (alwaysTrue)
"""
algorithmsListMock.__iter__.return_value = ['alwaysTrue']
consensusMock = 1
timeMock.return_value, timeseries = self.data(time())
alwaysTrue = Mock(return_value=True)
with patch.dict(algorithms.__dict__, {'alwaysTrue': alwaysTrue}):
result, ensemble, tail_avg = algorithms.run_selected_algorithm(timeseries)
alwaysTrue.assert_called_with(timeseries)
self.assertTrue(result)
self.assertEqual(ensemble, [True])
self.assertEqual(tail_avg, 334)
def test_run_selected_algorithm_runs_novel_algorithm(
self, timeMock, algorithmsListMock, consensusMock):
"""
Assert that a user can add their own custom algorithm.
This mocks out settings.ALGORITHMS and settings.CONSENSUS to use only a
single custom-defined function (alwaysTrue)
"""
algorithmsListMock.__iter__.return_value = ['alwaysTrue']
consensusMock = 1
timeMock.return_value, timeseries = self.data(time())
alwaysTrue = Mock(return_value=True)
with patch.dict(algorithms.__dict__, {'alwaysTrue': alwaysTrue}):
result, ensemble, tail_avg = algorithms.run_selected_algorithm(
timeseries)
alwaysTrue.assert_called_with(timeseries)
self.assertTrue(result)
self.assertEqual(ensemble, [True])
self.assertEqual(tail_avg, 334)
def test_run_selected_algorithm(self, timeMock):
timeMock.return_value, timeseries = self.data(time())
result, ensemble, datapoint = algorithms.run_selected_algorithm(timeseries, "test.metric")
self.assertTrue(result)
self.assertTrue(len(filter(None, ensemble)) >= settings.CONSENSUS)
self.assertEqual(datapoint, 1000)