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 do_fit(
self,
dataframe: pd.DataFrame,
labeled_segments: List[AnalyticSegment],
deleted_segments: List[AnalyticSegment],
learning_info: LearningInfo
) -> None:
data = utils.cut_dataframe(dataframe)
data = data['value']
self.state.pattern_center = list(set(self.state.pattern_center + learning_info.segment_center_list))
self.state.pattern_model = utils.get_av_model(learning_info.patterns_list)
convolve_list = utils.get_convolve(self.state.pattern_center, self.state.pattern_model, data, self.state.window_size)
correlation_list = utils.get_correlation(self.state.pattern_center, self.state.pattern_model, data, self.state.window_size)
height_list = learning_info.patterns_value
del_conv_list = []
delete_pattern_width = []
delete_pattern_height = []
delete_pattern_timestamp = []
for segment in deleted_segments:
delete_pattern_timestamp.append(segment.pattern_timestamp)
deleted = utils.get_interval(data, segment.center_index, self.state.window_size)
deleted = utils.subtract_min_without_nan(deleted)
del_conv = scipy.signal.fftconvolve(deleted, self.state.pattern_model)
if len(del_conv):
del_conv_list.append(max(del_conv))
delete_pattern_height.append(utils.find_confidence(deleted)[1])
self._update_fiting_result(self.state, learning_info.confidence, convolve_list, del_conv_list, height_list)
def do_fit(self, dataframe: pd.DataFrame, labeled_segments: list,
deleted_segments: list, learning_info: dict) -> None:
data = utils.cut_dataframe(dataframe)
data = data['value']
window_size = self.state['WINDOW_SIZE']
last_pattern_center = self.state.get('pattern_center', [])
self.state['pattern_center'] = list(
set(last_pattern_center + learning_info['segment_center_list']))
self.state['pattern_model'] = utils.get_av_model(
learning_info['patterns_list'])
convolve_list = utils.get_convolve(self.state['pattern_center'],
self.state['pattern_model'], data,
window_size)
correlation_list = utils.get_correlation(self.state['pattern_center'],
self.state['pattern_model'],
data, window_size)
height_list = learning_info['patterns_value']
del_conv_list = []
delete_pattern_width = []
delete_pattern_height = []
delete_pattern_timestamp = []
for segment in deleted_segments:
del_min_index = segment.center_index
delete_pattern_timestamp.append(segment.pattern_timestamp)
deleted = utils.get_interval(data, del_min_index, window_size)
deleted = utils.subtract_min_without_nan(deleted)
del_conv = scipy.signal.fftconvolve(deleted,
self.state['pattern_model'])
if len(del_conv): del_conv_list.append(max(del_conv))
delete_pattern_height.append(utils.find_confidence(deleted)[1])
delete_pattern_width.append(utils.find_width(deleted, False))
self._update_fiting_result(self.state, learning_info['confidence'],
convolve_list, del_conv_list, height_list)
def test_confidence_with_nan_value(self):
data = [np.nan, np.nan, 0, 8]
utils_result = utils.find_confidence(data)[0]
result = 4.0
self.assertTrue(
math.isclose(utils_result, result, rel_tol=RELATIVE_TOLERANCE))
def test_confidence_all_nan_value(self):
segment = [np.nan, np.nan, np.nan, np.nan]
self.assertEqual(utils.find_confidence(segment)[0], 0)
def test_confidence_all_normal_value(self):
segment = [1, 2, 0, 6, 8, 5, 3]
utils_result = utils.find_confidence(segment)[0]
result = 4.0
self.assertTrue(
math.isclose(utils_result, result, rel_tol=RELATIVE_TOLERANCE))
