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Anti-segments in peaks model #142 (#170)

pull/1/head
Alexandr Velikiy 6 years ago committed by rozetko
parent
commit
4824b22df8
  1. 30
      analytics/models/peak_model.py

30
analytics/models/peak_model.py

@ -23,6 +23,8 @@ class PeakModel(Model):
'convolve_max': 570000, 'convolve_max': 570000,
'convolve_min': 530000, 'convolve_min': 530000,
'WINDOW_SIZE': 240, 'WINDOW_SIZE': 240,
'conv_del_min': 54000,
'conv_del_max': 55000,
} }
def do_fit(self, dataframe: pd.DataFrame, segments: list) -> None: def do_fit(self, dataframe: pd.DataFrame, segments: list) -> None:
@ -47,13 +49,27 @@ class PeakModel(Model):
patterns_list.append(labeled_peak) patterns_list.append(labeled_peak)
self.model_peak = utils.get_av_model(patterns_list) self.model_peak = utils.get_av_model(patterns_list)
for n in range(len(segments)): for n in range(len(segments)): #labeled segments
labeled_peak = data[self.ipeaks[n] - self.state['WINDOW_SIZE']: self.ipeaks[n] + self.state['WINDOW_SIZE'] + 1] labeled_peak = data[self.ipeaks[n] - self.state['WINDOW_SIZE']: self.ipeaks[n] + self.state['WINDOW_SIZE'] + 1]
labeled_peak = labeled_peak - min(labeled_peak) labeled_peak = labeled_peak - min(labeled_peak)
auto_convolve = scipy.signal.fftconvolve(labeled_peak, labeled_peak) auto_convolve = scipy.signal.fftconvolve(labeled_peak, labeled_peak)
convolve_peak = scipy.signal.fftconvolve(labeled_peak, self.model_peak) convolve_peak = scipy.signal.fftconvolve(labeled_peak, self.model_peak)
convolve_list.append(max(auto_convolve)) convolve_list.append(max(auto_convolve))
convolve_list.append(max(convolve_peak)) convolve_list.append(max(convolve_peak))
del_conv_list = []
for segment in segments:
if segment['deleted']:
segment_from_index = utils.timestamp_to_index(dataframe, pd.to_datetime(segment['from'], unit='ms'))
segment_to_index = utils.timestamp_to_index(dataframe, pd.to_datetime(segment['to'], unit='ms'))
segment_data = data[segment_from_index: segment_to_index + 1]
if len(segment_data) == 0:
continue
del_max_index = segment_data.idxmax()
deleted_peak = data[del_max_index - self.state['WINDOW_SIZE']: del_max_index + self.state['WINDOW_SIZE'] + 1]
deleted_peak = deleted_peak - min(deleted_peak)
del_conv_peak = scipy.signal.fftconvolve(deleted_peak, self.model_peak)
del_conv_list.append(max(del_conv_peak))
if len(confidences) > 0: if len(confidences) > 0:
self.state['confidence'] = float(min(confidences)) self.state['confidence'] = float(min(confidences))
@ -69,6 +85,16 @@ class PeakModel(Model):
self.state['convolve_min'] = float(min(convolve_list)) self.state['convolve_min'] = float(min(convolve_list))
else: else:
self.state['convolve_min'] = self.state['WINDOW_SIZE'] self.state['convolve_min'] = self.state['WINDOW_SIZE']
if len(del_conv_list) > 0:
self.state['conv_del_min'] = float(min(del_conv_list))
else:
self.state['conv_del_min'] = self.state['WINDOW_SIZE']
if len(del_conv_list) > 0:
self.state['conv_del_max'] = float(max(del_conv_list))
else:
self.state['conv_del_max'] = self.state['WINDOW_SIZE']
def do_predict(self, dataframe: pd.DataFrame): def do_predict(self, dataframe: pd.DataFrame):
data = dataframe['value'] data = dataframe['value']
@ -108,6 +134,8 @@ class PeakModel(Model):
conv = scipy.signal.fftconvolve(convol_data, pattern_data) conv = scipy.signal.fftconvolve(convol_data, pattern_data)
if max(conv) > self.state['convolve_max'] * 1.05 or max(conv) < self.state['convolve_min'] * 0.95: if max(conv) > self.state['convolve_max'] * 1.05 or max(conv) < self.state['convolve_min'] * 0.95:
delete_list.append(segment) delete_list.append(segment)
if max(conv) < self.state['conv_del_max'] * 1.02 and max(conv) > self.state['conv_del_min'] * 0.98:
delete_list.append(segment)
else: else:
delete_list.append(segment) delete_list.append(segment)
# TODO: implement filtering # TODO: implement filtering

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