def test_three_value_segment(self):
data_val = [
1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 2.0, 5.0, 5.0, 1.0, 1.0, 1.0, 1.0,
9.0, 9.0, 9.0, 9.0, 2.0, 3.0, 4.0, 5.0, 4.0, 2.0, 1.0, 3.0, 4.0
]
dataframe = create_dataframe(data_val)
segments = [{
'_id': 'Esl7uetLhx4lCqHa',
'analyticUnitId': 'opnICRJwOmwBELK8',
'from': 1523889000004,
'to': 1523889000006,
'labeled': True,
'deleted': False
}]
segments = [Segment.from_json(segment) for segment in segments]
model_instances = [
models.GeneralModel(),
models.PeakModel(),
]
try:
for model in model_instances:
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_general_antisegments(self):
data_val = [
1.0, 2.0, 1.0, 2.0, 5.0, 6.0, 3.0, 2.0, 1.0, 1.0, 8.0, 9.0, 8.0,
1.0, 2.0, 3.0, 2.0, 1.0, 1.0, 2.0
]
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.GeneralModel()
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_general_for_two_labeling(self):
data_val = [1.0, 2.0, 5.0, 2.0, 1.0, 1.0, 3.0, 6.0, 4.0, 2.0, 1.0, 0, 0]
dataframe = create_dataframe(data_val)
segments = [{'_id': 'Esl7uetLhx4lCqHa', 'analyticUnitId': 'opnICRJwOmwBELK8', 'from': 1523889000001, 'to': 1523889000003, 'labeled': True, 'deleted': False}]
model = models.GeneralModel()
model.fit(dataframe, segments, dict())
result = len(data_val) + 1
for _ in range(2):
model.do_detect(dataframe)
max_pattern_index = max(model.do_detect(dataframe))
self.assertLessEqual(max_pattern_index, result)
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_problem_data_for_random_model(self):
problem_data = [
2.0, 3.0, 3.0, 3.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0,
2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0,
5.0, 5.0, 5.0, 5.0, 2.0, 2.0, 2.0, 2.0, 3.0, 3.0, 3.0, 3.0, 3.0,
3.0, 2.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0,
3.0, 2.0, 2.0, 2.0, 2.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0,
2.0, 2.0, 2.0, 6.0, 7.0, 8.0, 8.0, 4.0, 2.0, 2.0, 3.0, 3.0, 3.0,
4.0, 4.0, 4.0, 4.0, 3.0, 3.0, 3.0, 3.0, 2.0, 2.0, 2.0, 2.0, 2.0,
2.0, 2.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 2.0, 2.0, 2.0, 2.0,
2.0, 2.0, 3.0, 4.0, 4.0, 4.0, 4.0, 4.0, 6.0, 5.0, 4.0, 4.0, 3.0,
3.0, 3.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 2.0, 3.0, 3.0, 2.0,
2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 8.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0,
2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0
]
data = create_dataframe(problem_data)
cache = {
'patternCenter': [5, 50],
'patternModel': [],
'windowSize': 2,
'convolveMin': 0,
'convolveMax': 0,
'convDelMin': 0,
'convDelMax': 0,
}
max_ws = 20
iteration = 1
for ws in range(1, max_ws):
for _ in range(iteration):
pattern_model = create_random_model(ws)
convolve = scipy.signal.fftconvolve(pattern_model,
pattern_model)
cache['windowSize'] = ws
cache['patternModel'] = pattern_model
cache['convolveMin'] = max(convolve)
cache['convolveMax'] = max(convolve)
try:
model = models.GeneralModel()
model.state = model.get_state(cache)
model_name = model.__class__.__name__
model.detect(data, 'test')
except ValueError:
self.fail(
'Model {} raised unexpectedly with av_model {} and window size {}'
.format(model_name, pattern_model, ws))
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')