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Fix models for choosing the "wrong" patterns #195 (#202) 

* jump and drop not crash

* fix logiс of try/except block in drops

left one block

* fix logic of try/except in jump

left one block
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Alexandr Velikiy 6 years ago committed by GitHub
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8e41b0f125
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4 changed files with 51 additions and 30 deletions
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  1. 2
      analytics/.vscode/settings.json
  2. 38
      analytics/models/drop_model.py
  3. 40
      analytics/models/jump_model.py
  4. 1
      analytics/models/peak_model.py

2
analytics/.vscode/settings.json vendored
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@ -8,4 +8,4 @@
"files.exclude": {
"**/__pycache__/": true
}
}
}

38
analytics/models/drop_model.py
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@ -47,19 +47,24 @@ class DropModel(Model):
confidences.append(0.20 * (segment_max - segment_min))
flat_segment = segment_data.rolling(window = 5).mean()
pdf = gaussian_kde(flat_segment.dropna())
max_drop = max(flat_segment.dropna())
min_drop = min(flat_segment.dropna())
x = np.linspace(flat_segment.dropna().min() - 1, flat_segment.dropna().max() + 1, len(flat_segment.dropna()))
y = pdf(x)
ax_list = []
for i in range(len(x)):
ax_list.append([x[i], y[i]])
ax_list = list(zip(x, y))
ax_list = np.array(ax_list, np.float32)
antipeaks_kde = argrelextrema(np.array(ax_list), np.less)[0]
peaks_kde = argrelextrema(np.array(ax_list), np.greater)[0]
min_peak_index = peaks_kde[0]
max_peak_index = peaks_kde[1]
segment_median = ax_list[antipeaks_kde[0], 0]
segment_min_line = ax_list[min_peak_index, 0]
segment_max_line = ax_list[max_peak_index, 0]
try:
min_peak_index = peaks_kde[0]
segment_min_line = ax_list[min_peak_index, 0]
max_peak_index = peaks_kde[1]
segment_max_line = ax_list[max_peak_index, 0]
segment_median = ax_list[antipeaks_kde[0], 0]
except IndexError:
segment_max_line = max_drop
segment_min_line = min_drop
segment_median = (max_drop - min_drop) / 2 + min_drop
drop_height = 0.95 * (segment_max_line - segment_min_line)
drop_height_list.append(drop_height)
drop_length = utils.find_drop_length(segment_data, segment_min_line, segment_max_line)
@ -94,9 +99,7 @@ class DropModel(Model):
pdf = gaussian_kde(flat_segment_dropna)
x = np.linspace(flat_segment_dropna.min() - 1, flat_segment_dropna.max() + 1, len(flat_segment_dropna))
y = pdf(x)
ax_list = []
for i in range(len(x)):
ax_list.append([x[i], y[i]])
ax_list = list(zip(x, y))
ax_list = np.array(ax_list, np.float32)
antipeaks_kde = argrelextrema(np.array(ax_list), np.less)[0]
segment_median = ax_list[antipeaks_kde[0], 0]
@ -170,9 +173,16 @@ class DropModel(Model):
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'] + 1]
conv = scipy.signal.fftconvolve(convol_data, pattern_data)
if conv[self.state['WINDOW_SIZE']*2] > self.state['convolve_max'] * 1.2 or conv[self.state['WINDOW_SIZE']*2] < self.state['convolve_min'] * 0.8:
delete_list.append(segment)
elif max(conv) < self.state['conv_del_max'] * 1.02 and max(conv) > self.state['conv_del_min'] * 0.98:
upper_bound = self.state['convolve_max'] * 1.2
lower_bound = self.state['convolve_min'] * 0.8
delete_up_bound = self.state['conv_del_max'] * 1.02
delete_low_bound = self.state['conv_del_min'] * 0.98
try:
if max(conv) > upper_bound or max(conv) < lower_bound:
delete_list.append(segment)
elif max(conv) < delete_up_bound and max(conv) > delete_low_bound:
delete_list.append(segment)
except ValueError:
delete_list.append(segment)
else:
delete_list.append(segment)

40
analytics/models/jump_model.py
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@ -47,20 +47,25 @@ class JumpModel(Model):
confidences.append(0.20 * (segment_max - segment_min))
flat_segment = segment_data.rolling(window = 5).mean()
flat_segment_dropna = flat_segment.dropna()
min_jump = min(flat_segment_dropna)
max_jump = max(flat_segment_dropna)
pdf = gaussian_kde(flat_segment_dropna)
x = np.linspace(flat_segment_dropna.min() - 1, flat_segment_dropna.max() + 1, len(flat_segment_dropna))
y = pdf(x)
ax_list = []
for i in range(len(x)):
ax_list.append([x[i], y[i]])
ax_list = list(zip(x, y))
ax_list = np.array(ax_list, np.float32)
antipeaks_kde = argrelextrema(np.array(ax_list), np.less)[0]
peaks_kde = argrelextrema(np.array(ax_list), np.greater)[0]
min_peak_index = peaks_kde[0]
max_peak_index = peaks_kde[1]
segment_median = ax_list[antipeaks_kde[0], 0]
segment_min_line = ax_list[min_peak_index, 0]
segment_max_line = ax_list[max_peak_index, 0]
try:
min_peak_index = peaks_kde[0]
segment_min_line = ax_list[min_peak_index, 0]
max_peak_index = peaks_kde[1]
segment_max_line = ax_list[max_peak_index, 0]
segment_median = ax_list[antipeaks_kde[0], 0]
except IndexError:
segment_max_line = max_jump
segment_min_line = min_jump
segment_median = (max_jump - min_jump) / 2 + min_jump
jump_height = 0.95 * (segment_max_line - segment_min_line)
jump_height_list.append(jump_height)
jump_length = utils.find_jump_length(segment_data, segment_min_line, segment_max_line)
@ -95,9 +100,7 @@ class JumpModel(Model):
pdf = gaussian_kde(flat_segment_dropna)
x = np.linspace(flat_segment_dropna.min() - 1, flat_segment_dropna.max() + 1, len(flat_segment_dropna))
y = pdf(x)
ax_list = []
for i in range(len(x)):
ax_list.append([x[i], y[i]])
ax_list = list(zip(x, y))
ax_list = np.array(ax_list, np.float32)
antipeaks_kde = argrelextrema(np.array(ax_list), np.less)[0]
segment_median = ax_list[antipeaks_kde[0], 0]
@ -125,7 +128,7 @@ class JumpModel(Model):
self.state['convolve_min'] = self.state['WINDOW_SIZE']
if len(jump_height_list) > 0:
self.state['JUMP_HEIGHT'] = int(min(jump_height_list))
self.state['JUMP_HEIGHT'] = float(min(jump_height_list))
else:
self.state['JUMP_HEIGHT'] = 1
@ -167,13 +170,20 @@ class JumpModel(Model):
delete_list = []
pattern_data = self.model_jump
upper_bound = self.state['convolve_max'] * 1.2
lower_bound = self.state['convolve_min'] * 0.8
delete_up_bound = self.state['conv_del_max'] * 1.02
delete_low_bound = self.state['conv_del_min'] * 0.98
for segment in segments:
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'] + 1]
conv = scipy.signal.fftconvolve(convol_data, pattern_data)
if max(conv) > self.state['convolve_max'] * 1.2 or max(conv) < self.state['convolve_min'] * 0.8:
delete_list.append(segment)
elif max(conv) < self.state['conv_del_max'] * 1.02 and max(conv) > self.state['conv_del_min'] * 0.98:
try:
if max(conv) > upper_bound or max(conv) < lower_bound:
delete_list.append(segment)
elif max(conv) < delete_up_bound and max(conv) > delete_low_bound:
delete_list.append(segment)
except ValueError:
delete_list.append(segment)
else:
delete_list.append(segment)

1
analytics/models/peak_model.py
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@ -135,6 +135,7 @@ class PeakModel(Model):
if max(conv) > self.state['convolve_max'] * 1.05 or max(conv) < self.state['convolve_min'] * 0.95:
delete_list.append(segment)
elif max(conv) < self.state['conv_del_max'] * 1.02 and max(conv) > self.state['conv_del_min'] * 0.98:
print("this must be deleted: {0}, index: {1}".format(max(conv), segment))
delete_list.append(segment)
else:
delete_list.append(segment)

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