From 793c8186f1ec43c489644e7e60602cef53cc5385 Mon Sep 17 00:00:00 2001 From: Alexandr Velikiy <39257464+VargBurz@users.noreply.github.com> Date: Tue, 25 Sep 2018 16:47:58 +0300 Subject: [PATCH] Anti-segments in jumps model #142 (#172) --- analytics/models/jump_model.py | 45 +++++++++++++++++++++++++++++++--- 1 file changed, 42 insertions(+), 3 deletions(-) diff --git a/analytics/models/jump_model.py b/analytics/models/jump_model.py index 96843fb..8392d47 100644 --- a/analytics/models/jump_model.py +++ b/analytics/models/jump_model.py @@ -24,6 +24,8 @@ class JumpModel(Model): 'JUMP_HEIGHT': 1, 'JUMP_LENGTH': 1, 'WINDOW_SIZE': 240, + 'conv_del_min': 54000, + 'conv_del_max': 55000, } def do_fit(self, dataframe: pd.DataFrame, segments: list) -> None: @@ -39,8 +41,7 @@ class JumpModel(Model): 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 - + continue segment_min = min(segment_data) segment_max = max(segment_data) confidences.append(0.20 * (segment_max - segment_min)) @@ -80,6 +81,33 @@ class JumpModel(Model): convolve_jump = scipy.signal.fftconvolve(labeled_jump, self.model_jump) convolve_list.append(max(auto_convolve)) convolve_list.append(max(convolve_jump)) + + 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 + flat_segment = segment_data.rolling(window = 5).mean() + flat_segment_dropna = 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 = np.array(ax_list, np.float32) + antipeaks_kde = argrelextrema(np.array(ax_list), np.less)[0] + segment_median = ax_list[antipeaks_kde[0], 0] + cen_ind = utils.intersection_segment(flat_segment.tolist(), segment_median) #finds all interseprions with median + jump_center = cen_ind[0] + segment_cent_index = jump_center - 5 + segment_from_index + deleted_jump = data[segment_cent_index - self.state['WINDOW_SIZE'] : segment_cent_index + self.state['WINDOW_SIZE'] + 1] + deleted_jump = deleted_jump - min(labeled_jump) + del_conv_jump = scipy.signal.fftconvolve(deleted_jump, self.model_jump) + del_conv_list.append(max(del_conv_jump)) if len(confidences) > 0: self.state['confidence'] = float(min(confidences)) @@ -105,6 +133,16 @@ class JumpModel(Model): self.state['JUMP_LENGTH'] = int(max(jump_length_list)) else: self.state['JUMP_LENGTH'] = 1 + + 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) -> list: data = dataframe['value'] @@ -132,10 +170,11 @@ class JumpModel(Model): 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) + 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: delete_list.append(segment) for item in delete_list: