def test_find_drop_parameters_height(self):
segment = [
5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 3, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1
]
segment = pd.Series(segment)
drop_height = [3.5, 4]
self.assertGreaterEqual(
utils.find_parameters(segment, 0, 'drop')[0], drop_height[0])
self.assertLessEqual(
utils.find_parameters(segment, 0, 'drop')[0], drop_height[1])
def test_find_jump_parameters_height(self):
segment = [
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 3, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
5, 5, 5, 5, 5
]
segment = pd.Series(segment)
jump_height = [3.5, 4]
self.assertGreaterEqual(
utils.find_parameters(segment, 0, 'jump')[0], jump_height[0])
self.assertLessEqual(
utils.find_parameters(segment, 0, 'jump')[0], jump_height[1])
def get_parameters_from_segments(self, dataframe: pd.DataFrame, labeled: List[dict], deleted: List[dict], model: ModelType) -> dict:
logging.debug('Start parsing segments')
learning_info = LearningInfo()
data = dataframe['value']
for segment in labeled:
confidence = utils.find_confidence(segment.data)[0]
learning_info.confidence.append(confidence)
segment_center = segment.center_index
learning_info.segment_center_list.append(segment_center)
learning_info.pattern_timestamp.append(segment.pattern_timestamp)
aligned_segment = utils.get_interval(data, segment_center, self.state.window_size)
aligned_segment = utils.subtract_min_without_nan(aligned_segment)
if len(aligned_segment) == 0:
logging.warning('cant add segment to learning because segment is empty where segments center is: {}, window_size: {}, and len_data: {}'.format(
segment_center, self.state.window_size, len(data)))
continue
learning_info.patterns_list.append(aligned_segment)
# TODO: use Triangle/Stair types
if model == ModelType.PEAK or model == ModelType.TROUGH:
learning_info.pattern_height.append(utils.find_confidence(aligned_segment)[1])
learning_info.patterns_value.append(aligned_segment.values.max())
if model == ModelType.JUMP or model == ModelType.DROP:
pattern_height, pattern_length = utils.find_parameters(segment.data, segment.from_index, model.value)
learning_info.pattern_height.append(pattern_height)
learning_info.pattern_width.append(pattern_length)
learning_info.patterns_value.append(aligned_segment.values[self.state.window_size])
logging.debug('Parsing segments ended correctly with learning_info: {}'.format(learning_info))
return learning_info
def test_find_drop_parameters_length(self):
segment = [
5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 3, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1
]
segment = pd.Series(segment)
drop_length = 2
self.assertEqual(
utils.find_parameters(segment, 0, 'drop')[1], drop_length)
def test_find_jump_parameters_length(self):
segment = [
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 3, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
5, 5, 5, 5, 5
]
segment = pd.Series(segment)
jump_length = 2
self.assertEqual(
utils.find_parameters(segment, 0, 'jump')[1], jump_length)
