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Common model in jumps (#165)

- add common model
- add convolve window
- add WINDOW_SIZE
pull/1/head
Alexandr Velikiy 6 years ago committed by rozetko
parent
commit
40fd1e296b
  1. 48
      analytics/models/jump_model.py

48
analytics/models/jump_model.py

@ -16,11 +16,14 @@ class JumpModel(Model):
super() super()
self.segments = [] self.segments = []
self.ijumps = [] self.ijumps = []
self.model_jump = []
self.state = { self.state = {
'confidence': 1.5, 'confidence': 1.5,
'convolve_max': 230, 'convolve_max': 230,
'convolve_min': 230,
'JUMP_HEIGHT': 1, 'JUMP_HEIGHT': 1,
'JUMP_LENGTH': 1, 'JUMP_LENGTH': 1,
'WINDOW_SIZE': 240,
} }
def do_fit(self, dataframe: pd.DataFrame, segments: list) -> None: def do_fit(self, dataframe: pd.DataFrame, segments: list) -> None:
@ -29,14 +32,15 @@ class JumpModel(Model):
convolve_list = [] convolve_list = []
jump_height_list = [] jump_height_list = []
jump_length_list = [] jump_length_list = []
patterns_list = []
for segment in segments: for segment in segments:
if segment['labeled']: if segment['labeled']:
segment_from_index = utils.timestamp_to_index(dataframe, pd.to_datetime(segment['from'], unit='ms')) 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_to_index = utils.timestamp_to_index(dataframe, pd.to_datetime(segment['to'], unit='ms'))
segment_data = data[segment_from_index: segment_to_index + 1] segment_data = data[segment_from_index: segment_to_index + 1]
if len(segment_data) == 0: if len(segment_data) == 0:
continue continue
segment_min = min(segment_data) segment_min = min(segment_data)
segment_max = max(segment_data) segment_max = max(segment_data)
confidences.append(0.20 * (segment_max - segment_min)) confidences.append(0.20 * (segment_max - segment_min))
@ -64,12 +68,18 @@ class JumpModel(Model):
jump_center = cen_ind[0] jump_center = cen_ind[0]
segment_cent_index = jump_center - 5 + segment_from_index segment_cent_index = jump_center - 5 + segment_from_index
self.ijumps.append(segment_cent_index) self.ijumps.append(segment_cent_index)
labeled_jump = data[segment_cent_index - self.state['WINDOW_SIZE'] : segment_cent_index + self.state['WINDOW_SIZE']] labeled_jump = data[segment_cent_index - self.state['WINDOW_SIZE'] : segment_cent_index + self.state['WINDOW_SIZE'] + 1]
labeled_min = min(labeled_jump) labeled_jump = labeled_jump - min(labeled_jump)
for value in labeled_jump: patterns_list.append(labeled_jump)
value = value - labeled_min
convolve = scipy.signal.fftconvolve(labeled_jump, labeled_jump) self.model_jump = utils.get_av_model(patterns_list)
convolve_list.append(max(convolve)) for n in range(len(segments)):
labeled_jump = data[self.ijumps[n] - self.state['WINDOW_SIZE']: self.ijumps[n] + self.state['WINDOW_SIZE'] + 1]
labeled_jump = labeled_jump - min(labeled_jump)
auto_convolve = scipy.signal.fftconvolve(labeled_jump, labeled_jump)
convolve_jump = scipy.signal.fftconvolve(labeled_jump, self.model_jump)
convolve_list.append(max(auto_convolve))
convolve_list.append(max(convolve_jump))
if len(confidences) > 0: if len(confidences) > 0:
self.state['confidence'] = float(min(confidences)) self.state['confidence'] = float(min(confidences))
@ -81,6 +91,11 @@ class JumpModel(Model):
else: else:
self.state['convolve_max'] = self.state['WINDOW_SIZE'] self.state['convolve_max'] = self.state['WINDOW_SIZE']
if len(convolve_list) > 0:
self.state['convolve_min'] = float(min(convolve_list))
else:
self.state['convolve_min'] = self.state['WINDOW_SIZE']
if len(jump_height_list) > 0: if len(jump_height_list) > 0:
self.state['JUMP_HEIGHT'] = int(min(jump_height_list)) self.state['JUMP_HEIGHT'] = int(min(jump_height_list))
else: else:
@ -100,25 +115,26 @@ class JumpModel(Model):
def __filter_prediction(self, segments, data): def __filter_prediction(self, segments, data):
delete_list = [] delete_list = []
variance_error = int(0.004 * len(data)) variance_error = int(0.004 * len(data))
if variance_error > 50: if variance_error > self.state['WINDOW_SIZE']:
variance_error = 50 variance_error = self.state['WINDOW_SIZE']
for i in range(1, len(segments)): for i in range(1, len(segments)):
if segments[i] < segments[i - 1] + variance_error: if segments[i] < segments[i - 1] + variance_error:
delete_list.append(segments[i]) delete_list.append(segments[i])
#for item in delete_list: for item in delete_list:
#segments.remove(item) segments.remove(item)
delete_list = []
if len(segments) == 0 or len(self.ijumps) == 0 : if len(segments) == 0 or len(self.ijumps) == 0 :
segments = [] segments = []
return segments return segments
pattern_data = data[self.ijumps[0] - self.state['WINDOW_SIZE'] : self.ijumps[0] + self.state['WINDOW_SIZE']] delete_list = []
pattern_data = self.model_jump
for segment in segments: for segment in segments:
if segment > self.state['WINDOW_SIZE'] and segment < (len(data) - self.state['WINDOW_SIZE']): if segment > self.state['WINDOW_SIZE'] and segment < (len(data) - self.state['WINDOW_SIZE']):
convol_data = data[segment - self.state['WINDOW_SIZE'] : segment + self.state['WINDOW_SIZE']] convol_data = data[segment - self.state['WINDOW_SIZE'] : segment + self.state['WINDOW_SIZE'] + 1]
conv = scipy.signal.fftconvolve(pattern_data, convol_data) conv = scipy.signal.fftconvolve(convol_data, pattern_data)
if max(conv) > self.state['convolve_max'] * 1.2 or max(conv) < self.state['convolve_max'] * 0.8: if max(conv) > self.state['convolve_max'] * 1.2 or max(conv) < self.state['convolve_min'] * 0.8:
delete_list.append(segment) delete_list.append(segment)
else: else:
delete_list.append(segment) delete_list.append(segment)

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