def test_detect_visualization(self):
pipeline = load_pipeline('dummy')
pipeline['outputs'] = {
'visualization': [{
'name':
'y_hat',
'variable':
'orion.primitives.estimators.MeanEstimator#1.y'
}]
}
orion = Orion(pipeline)
orion.fit(self.clean)
events, visualization = orion.detect(self.anomalous,
visualization=True)
pd.testing.assert_frame_equal(self.events, events)
assert isinstance(visualization, dict)
assert 'y_hat' in visualization
y_hat = visualization['y_hat']
np.testing.assert_array_equal(y_hat, np.ones(len(self.anomalous)))
class TestOrion:
@classmethod
def setup_class(cls):
cls.clean = pd.DataFrame({
'timestamp': list(range(100)),
'value': [1] * 100,
})
cls.anomalous = pd.DataFrame({
'timestamp': list(range(100, 200)),
'value': [1] * 45 + [10] * 10 + [1] * 45
})
cls.events = pd.DataFrame([{
'start': 145,
'end': 155,
'severity': 9.0
}],
columns=['start', 'end', 'severity'])
cls.all_data = pd.concat((cls.clean, cls.anomalous))
cls.all_events = pd.DataFrame([{
'start': 145,
'end': 155,
'severity': 4.275
}],
columns=['start', 'end', 'severity'])
def setup(self):
self.orion = Orion('dummy')
def test_fit(self):
self.orion.fit(self.clean)
def test_detect(self):
self.orion.fit(self.clean)
events = self.orion.detect(self.anomalous)
pd.testing.assert_frame_equal(self.events, events)
def test_detect_no_visualization(self):
self.orion.fit(self.clean)
events, visualization = self.orion.detect(self.anomalous,
visualization=True)
pd.testing.assert_frame_equal(self.events, events)
assert visualization == {}
def test_detect_visualization(self):
pipeline = load_pipeline('dummy')
pipeline['outputs'] = {
'visualization': [{
'name':
'y_hat',
'variable':
'orion.primitives.estimators.MeanEstimator#1.y'
}]
}
orion = Orion(pipeline)
orion.fit(self.clean)
events, visualization = orion.detect(self.anomalous,
visualization=True)
pd.testing.assert_frame_equal(self.events, events)
assert isinstance(visualization, dict)
assert 'y_hat' in visualization
y_hat = visualization['y_hat']
np.testing.assert_array_equal(y_hat, np.ones(len(self.anomalous)))
def test_fit_detect(self):
events = self.orion.fit_detect(self.all_data)
pd.testing.assert_frame_equal(self.all_events, events)
def test_save_load(self, tmpdir):
path = os.path.join(tmpdir, 'some/path.pkl')
self.orion.save(path)
new_orion = Orion.load(path)
assert new_orion == self.orion
def test_evaluate(self):
self.orion.fit(self.clean)
scores = self.orion.evaluate(data=self.anomalous,
ground_truth=self.events)
expected = pd.Series({
'accuracy': 1.0,
'f1': 1.0,
'recall': 1.0,
'precision': 1.0,
})
pd.testing.assert_series_equal(expected, scores)
def test_evaluate_fit(self):
scores = self.orion.evaluate(
data=self.all_data,
ground_truth=self.all_events,
fit=True,
)
expected = pd.Series({
'accuracy': 1.0,
'f1': 1.0,
'recall': 1.0,
'precision': 1.0,
})
pd.testing.assert_series_equal(expected, scores)
def test_evaluate_train_data(self):
scores = self.orion.evaluate(data=self.anomalous,
ground_truth=self.events,
fit=True,
train_data=self.clean)
expected = pd.Series({
'accuracy': 1.0,
'f1': 1.0,
'recall': 1.0,
'precision': 1.0,
})
pd.testing.assert_series_equal(expected, scores)