def test__evaluate_signal_detrend(self, detrend_signal_mock, load_signal_mock,
load_pipeline_mock, analyze_mock, load_anomalies_mock):
train = Mock(autospec=pd.DataFrame)
test = Mock(autospec=pd.DataFrame)
detrend_signal_mock.side_effect = [train, test]
load_signal_mock.side_effect = [train, test]
load_pipeline_mock.return_value = self.pipeline
detrend = True
returned = benchmark._evaluate_signal(
self.pipeline, self.signal, self.hyper, self.metrics, test_split=True, detrend=detrend)
expected_return = self.set_score(1, ANY, ANY)
assert returned == expected_return
expected_calls = [
call('signal-name-train'),
call('signal-name-test')
]
assert load_signal_mock.call_args_list == expected_calls
expected_calls = [
call(train, 'value'),
call(test, 'value')
]
assert detrend_signal_mock.call_args_list == expected_calls
load_pipeline_mock.assert_called_once_with(self.pipeline, self.hyper)
analyze_mock.assert_called_once_with(self.pipeline, train, test)
load_anomalies_mock.assert_called_once_with(self.signal)
def test__evaluate_signal_exception(
self, load_signal_mock, load_pipeline_mock, analyze_mock, load_anomalies_mock):
train = Mock(autospec=pd.DataFrame)
test = Mock(autospec=pd.DataFrame)
load_signal_mock.side_effect = [train, test]
load_pipeline_mock.return_value = self.pipeline
analyze_mock.side_effect = Exception("failed analyze.")
returned = benchmark._evaluate_signal(
self.pipeline, self.signal, self.hyper, self.metrics, True)
expected_return = self.set_score(0, ANY, ANY)
expected_return['status'] = 'ERROR'
assert returned == expected_return
expected_calls = [
call('signal-name-train'),
call('signal-name-test')
]
assert load_signal_mock.call_args_list == expected_calls
load_pipeline_mock.assert_called_once_with(self.pipeline, self.hyper)
analyze_mock.assert_called_once_with(self.pipeline, train, test)
assert load_anomalies_mock.called
def test__evaluate_signal_exception_confusion_matrix(
self, load_signal_mock, load_pipeline_mock, analyze_mock, load_anomalies_mock):
anomalies = pd.DataFrame({
'start': [10, 35],
'end': [20, 40]
})
train = Mock(autospec=pd.DataFrame)
test = Mock(autospec=pd.DataFrame)
load_signal_mock.side_effect = [train, test]
load_pipeline_mock.return_value = self.pipeline
load_anomalies_mock.return_value = anomalies
analyze_mock.side_effect = Exception("failed analyze.")
metrics = {'confusion_matrix': Mock(autospec=contextual_confusion_matrix)}
metrics = {**metrics, **self.metrics}
returned = benchmark._evaluate_signal(
self.pipeline, self.signal, self.hyper, metrics, True)
expected_return = self.set_score(0, ANY, ANY)
expected_return['status'] = 'ERROR'
expected_return['tn'] = None
expected_return['fp'] = 0
expected_return['fn'] = 2
expected_return['tp'] = 0
assert returned == expected_return
def test__evaluate_signal_no_test_split(self, load_signal_mock,
load_pipeline_mock, analyze_mock,
load_anomalies_mock):
train = test = Mock(autospec=pd.DataFrame)
load_signal_mock.side_effect = [train, test]
load_pipeline_mock.return_value = self.pipeline
test_split = False
returned = benchmark._evaluate_signal(self.pipeline,
self.name,
self.dataset,
self.signal,
self.hyper,
self.metrics,
test_split=test_split)
expected_return = self.set_score(1, ANY, test_split)
assert returned == expected_return
expected_calls = [call('signal-name')]
assert load_signal_mock.call_args_list == expected_calls
load_pipeline_mock.assert_called_once_with(self.pipeline, self.hyper)
analyze_mock.assert_called_once_with(self.pipeline, train, test)
load_anomalies_mock.assert_called_once_with(self.signal)
