def test_trough_antisegments(self):
data_val = [
9.0, 9.0, 9.0, 9.0, 7.0, 4.0, 7.0, 9.0, 9.0, 9.0, 5.0, 1.0, 5.0,
9.0, 9.0, 9.0, 9.0, 9.0, 9.0, 9.0
]
dataframe = create_dataframe(data_val)
segments = [{
'_id': 'Esl7uetLhx4lCqHa',
'analyticUnitId': 'opnICRJwOmwBELK8',
'from': 1523889000010,
'to': 1523889000012,
'labeled': True,
'deleted': False
}, {
'_id': 'Esl7uetLhx4lCqHa',
'analyticUnitId': 'opnICRJwOmwBELK8',
'from': 1523889000003,
'to': 1523889000005,
'labeled': False,
'deleted': True
}]
segments = [Segment.from_json(segment) for segment in segments]
try:
model = models.TroughModel()
model_name = model.__class__.__name__
model.state = model.get_state(None)
model.fit(dataframe, segments, 'test')
except ValueError:
self.fail('Model {} raised unexpectedly'.format(model_name))
def test_trough_model_for_cache(self):
cache = {
'patternCenter': [2, 6],
'patternModel': [5, 0.5, 4],
'confidence': 2,
'convolveMax': 8,
'convolveMin': 7,
'window_size': 1,
'convDelMin': 0,
'convDelMax': 0,
}
data_val = [
5.0, 5.0, 1.0, 4.0, 5.0, 5.0, 0.0, 4.0, 5.0, 5.0, 6.0, 1.0, 5.0,
5.0, 5.0
]
dataframe = create_dataframe(data_val)
segments = [{
'_id': 'Esl7uetLhx4lCqHa',
'analyticUnitId': 'opnICRJwOmwBELK8',
'from': 1523889000010,
'to': 1523889000012,
'labeled': True,
'deleted': False
}]
segments = [Segment.from_json(segment) for segment in segments]
model = models.TroughModel()
model.state = model.get_state(cache)
result = model.fit(dataframe, segments, 'test')
self.assertEqual(len(result.pattern_center), 3)
def resolve_model_by_pattern(pattern: str) -> models.Model:
if pattern == 'GENERAL':
return models.GeneralModel()
if pattern == 'PEAK':
return models.PeakModel()
if pattern == 'TROUGH':
return models.TroughModel()
if pattern == 'DROP':
return models.DropModel()
if pattern == 'JUMP':
return models.JumpModel()
if pattern == 'CUSTOM':
return models.CustomModel()
raise ValueError('Unknown pattern "%s"' % pattern)
def test_trough_model_for_cache(self):
cache = {
'pattern_center': [2, 6],
'pattern_model': [5, 0.5, 4],
'confidence': 2,
'convolve_max': 8,
'convolve_min': 7,
'WINDOW_SIZE': 1,
'conv_del_min': 0,
'conv_del_max': 0,
}
data_val = [5.0, 5.0, 1.0, 4.0, 5.0, 5.0, 0.0, 4.0, 5.0, 5.0, 6.0, 1.0, 5.0, 5.0, 5.0]
dataframe = create_dataframe(data_val)
segments = [{'_id': 'Esl7uetLhx4lCqHa', 'analyticUnitId': 'opnICRJwOmwBELK8', 'from': 1523889000010, 'to': 1523889000012, 'labeled': True, 'deleted': False}]
model = models.TroughModel()
result = model.fit(dataframe, segments, cache)
self.assertEqual(len(result['pattern_center']), 3)
def test_models_with_corrupted_dataframe(self):
data = [[1523889000000 + i, float('nan')] for i in range(10)]
dataframe = pd.DataFrame(data, columns=['timestamp', 'value'])
segments = []
model_instances = [
models.JumpModel(),
models.DropModel(),
models.GeneralModel(),
models.PeakModel(),
models.TroughModel()
]
try:
for model in model_instances:
model_name = model.__class__.__name__
model.fit(dataframe, segments, dict())
except ValueError:
self.fail('Model {} raised unexpectedly'.format(model_name))
def test_models_with_corrupted_dataframe(self):
data = [[1523889000000 + i, float('nan')] for i in range(10)]
dataframe = pd.DataFrame(data, columns=['timestamp', 'value'])
segments = []
model_instances = [
models.JumpModel(),
models.DropModel(),
models.GeneralModel(),
models.PeakModel(),
models.TroughModel()
]
for model in model_instances:
model_name = model.__class__.__name__
model.state = model.get_state(None)
with self.assertRaises(AssertionError):
model.fit(dataframe, segments, 'test')
def test_random_dataset_for_random_model(self):
data = create_random_model(random.randint(1, 100))
data = create_dataframe(data)
model_instances = [models.PeakModel(), models.TroughModel()]
cache = {
'patternCenter': [5, 50],
'patternModel': [],
'windowSize': 2,
'convolveMin': 0,
'convolveMax': 0,
'confidence': 0,
'heightMax': 0,
'heightMin': 0,
'convDelMin': 0,
'convDelMax': 0,
}
ws = random.randint(1, int(len(data['value'] / 2)))
pattern_model = create_random_model(ws)
convolve = scipy.signal.fftconvolve(pattern_model, pattern_model)
confidence = 0.2 * (data['value'].max() - data['value'].min())
cache['windowSize'] = ws
cache['patternModel'] = pattern_model
cache['convolveMin'] = max(convolve)
cache['convolveMax'] = max(convolve)
cache['confidence'] = confidence
cache['heightMax'] = data['value'].max()
cache['heightMin'] = confidence
try:
for model in model_instances:
model_name = model.__class__.__name__
model.state = model.get_state(cache)
model.detect(data, 'test')
except ValueError:
self.fail(
'Model {} raised unexpectedly with dataset {} and cache {}'.
format(model_name, data['value'], cache))
