def test_jump_antisegments(self):
data_val = [
1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 5.0, 5.0, 5.0, 1.0, 1.0, 1.0, 1.0,
9.0, 9.0, 9.0, 9.0, 9.0, 1.0, 1.0
]
dataframe = create_dataframe(data_val)
segments = [{
'_id': 'Esl7uetLhx4lCqHa',
'analyticUnitId': 'opnICRJwOmwBELK8',
'from': 1523889000010,
'to': 1523889000016,
'labeled': True,
'deleted': False
}, {
'_id': 'Esl7uetLhx4lCqHa',
'analyticUnitId': 'opnICRJwOmwBELK8',
'from': 1523889000002,
'to': 1523889000008,
'labeled': False,
'deleted': True
}]
segments = [Segment.from_json(segment) for segment in segments]
try:
model = models.JumpModel()
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_jump_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 = [
1.0, 1.0, 1.0, 4.0, 4.0, 0.0, 0.0, 5.0, 5.0, 0.0, 0.0, 4.0, 4.0,
4.0, 4.0
]
dataframe = create_dataframe(data_val)
segments = [{
'_id': 'Esl7uetLhx4lCqHa',
'analyticUnitId': 'opnICRJwOmwBELK8',
'from': 152388900009,
'to': 1523889000013,
'labeled': True,
'deleted': False
}]
segments = [Segment.from_json(segment) for segment in segments]
model = models.JumpModel()
model.state = model.get_state(cache)
result = model.fit(dataframe, segments, 'test')
self.assertEqual(len(result.pattern_center), 3)
def test_value_error_dataset_input_should_have_multiple_elements(self):
data_val = [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 5.0, 4.0, 5.0, 5.0, 6.0, 5.0, 1.0, 2.0, 3.0, 4.0, 5.0,3.0,3.0,2.0,7.0,8.0,9.0,8.0,7.0,6.0]
dataframe = create_dataframe(data_val)
segments = [{'_id': 'Esl7uetLhx4lCqHa', 'analyticUnitId': 'opnICRJwOmwBELK8', 'from': 1523889000007, 'to': 1523889000011, 'labeled': True, 'deleted': False}]
try:
model = models.JumpModel()
model_name = model.__class__.__name__
model.fit(dataframe, segments, dict())
except ValueError:
self.fail('Model {} raised unexpectedly'.format(model_name))
def test_jump_empty_segment(self):
data_val = [1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 5.0, 5.0, 5.0, 1.0, 1.0, 1.0, 1.0, 9.0, 9.0, 9.0, 9.0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
dataframe = create_dataframe(data_val)
segments = [{'_id': 'Esl7uetLhx4lCqHa', 'analyticUnitId': 'opnICRJwOmwBELK8', 'from': 1523889000019, 'to': 1523889000025, 'labeled': True, 'deleted': False},
{'_id': 'Esl7uetLhx4lCqHa', 'analyticUnitId': 'opnICRJwOmwBELK8', 'from': 1523889000002, 'to': 1523889000008, 'labeled': True, 'deleted': False}]
try:
model = models.JumpModel()
model_name = model.__class__.__name__
model.fit(dataframe, segments, dict())
except ValueError:
self.fail('Model {} raised unexpectedly'.format(model_name))
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_jump_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 = [1.0, 1.0, 1.0, 4.0, 4.0, 0.0, 0.0, 5.0, 5.0, 0.0, 0.0, 4.0, 4.0, 4.0, 4.0]
dataframe = create_dataframe(data_val)
segments = [{'_id': 'Esl7uetLhx4lCqHa', 'analyticUnitId': 'opnICRJwOmwBELK8', 'from': 152388900009, 'to': 1523889000013, 'labeled': True, 'deleted': False}]
model = models.JumpModel()
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')