diff --git a/analytics/detectors/jump_detector.py b/analytics/detectors/jump_detector.py index 6fb6fdf..93672f3 100644 --- a/analytics/detectors/jump_detector.py +++ b/analytics/detectors/jump_detector.py @@ -6,13 +6,15 @@ from scipy.fftpack import fft from scipy.signal import argrelextrema import math +WINDOW_SIZE = 120 class JumpDetector: def __init__(self): self.segments = [] self.confidence = 1.5 - self.convolve_max = 120 + self.convolve_max = WINDOW_SIZE + self.size = 50 async def fit(self, dataframe, segments): #self.alpha_finder() @@ -27,36 +29,23 @@ class JumpDetector: confidences.append(0.20 * (segment_max - segment_min)) flat_segment = segment_data.rolling(window=4).mean() #сглаживаем сегмент kde_segment = flat_data.dropna().plot.kde() # distribution density - ax = flat_data.dropna().plot.kde() - ax_list = ax.get_lines()[0].get_xydata() - mids = argrelextrema(np.array(ax_list), np.less)[0] + ax_list = kde_segment.get_lines()[0].get_xydata() #take coordinates of kde + mids = argrelextrema(np.array(ax_list), np.less)[0] maxs = argrelextrema(np.array(ax_list), np.greater)[0] min_peak = maxs[0] max_peak = maxs[1] min_line = ax_list[min_peak, 0] max_line = ax_list[max_peak, 0] sigm_heidht = max_line - min_line - pat_sigm = utils.logistic_sigmoid(-120, 120, 1, sigm_heidht) + pat_sigm = utils.logistic_sigmoid(-WINDOW_SIZE, WINDOW_SIZE, 1, sigm_heidht) for i in range(0, len(pat_sigm)): pat_sigm[i] = pat_sigm[i] + min_line - cen_ind = utils.intersection_segment(flat_segment, mids[0]) - c = [] - for i in range(len(cen_ind)): - x = cen_ind[i] - cx = scipy.signal.fftconvolve(pat_sigm, flat_data[x-120:x+120]) - c.append(cx[240]) - - # в идеале нужно посмотреть гистограмму сегмента и выбрать среднее значение, - # далее от него брать + -120 - segment_summ = 0 - for val in flat_segment: - segment_summ += val - segment_mid = segment_summ / len(flat_segment) #посчитать нормально среднее значение/медиану - for ind in range(1, len(flat_segment) - 1): - if flat_segment[ind + 1] > segment_mid and flat_segment[ind - 1] < segment_mid: - flat_mid_index = ind # найти пересечение средней и графика, получить его индекс - segment_mid_index = flat_mid_index - 5 - labeled_drop = data[segment_mid_index - 120 : segment_mid_index + 120] + cen_ind = utils.intersection_segment(flat_segment, mids[0]) #finds all interseprions with median + c = [] # choose the correct one interseption by convolve + jump_center = utils.find_jump_center(cen_ind) + + segment_cent_index = jump_center - 4 + labeled_drop = data[segment_cent_index - WINDOW_SIZE : segment_cent_index + WINDOW_SIZE] labeled_min = min(labeled_drop) for value in labeled_drop: # обрезаем value = value - labeled_min @@ -65,7 +54,9 @@ class JumpDetector: value = value / labeled_max convolve = scipy.signal.fftconvolve(labeled_drop, labeled_drop) convolve_list.append(max(convolve)) # сворачиваем паттерн - # плюс надо впихнуть сюда логистическую сигмоиду и поиск альфы + # TODO: add convolve with alpha sigmoid + # TODO: add size of jump rize + if len(confidences) > 0: self.confidence = min(confidences) @@ -75,7 +66,7 @@ class JumpDetector: if len(convolve_list) > 0: self.convolve_max = max(convolve_list) else: - self.convolve_max = 120 # макс метрика свертки равна отступу(120), вау! + self.convolve_max = WINDOW_SIZE # макс метрика свертки равна отступу(WINDOW_SIZE), вау! async def predict(self, dataframe): data = dataframe['value'] @@ -90,10 +81,10 @@ class JumpDetector: async def __predict(self, data): window_size = 24 all_max_flatten_data = data.rolling(window=window_size).mean() - all_mins = argrelextrema(np.array(all_max_flatten_data), np.less)[0] - extrema_list = [] - # добавить все пересечения экспоненты со сглаженным графиком - + all_mins = argrelextrema(np.array(all_max_flatten_data), np.less)[0] + possible_jumps = utils.find_all_jumps(all_max_flatten_data, 50, self.confidence) + + ''' for i in utils.exponential_smoothing(data + self.confidence, 0.02): extrema_list.append(i) @@ -101,8 +92,9 @@ class JumpDetector: for i in all_mins: if all_max_flatten_data[i] > extrema_list[i]: segments.append(i - window_size) + ''' - return [(x - 1, x + 1) for x in self.__filter_prediction(segments, all_max_flatten_data)] + return [(x - 1, x + 1) for x in self.__filter_prediction(possible_jumps, all_max_flatten_data)] def __filter_prediction(self, segments, all_max_flatten_data): delete_list = [] @@ -116,10 +108,14 @@ class JumpDetector: segments.remove(item) # изменить секонд делит лист, сделать для свертки с сигмоидой + # !!!!!!!! + # написать фильтрацию паттернов-джампов! посмотерть каждый сегмент, обрезать его + # отнормировать, сравнить с выбранным патерном. + # !!!!!!!! delete_list = [] - pattern_data = all_max_flatten_data[segments[0] - 120 : segments[0] + 120] + pattern_data = all_max_flatten_data[segments[0] - WINDOW_SIZE : segments[0] + WINDOW_SIZE] for segment in segments: - convol_data = all_max_flatten_data[segment - 120 : segment + 120] + convol_data = all_max_flatten_data[segment - WINDOW_SIZE : segment + WINDOW_SIZE] conv = scipy.signal.fftconvolve(pattern_data, convol_data) if max(conv) > self.convolve_max * 1.1 or max(conv) < self.convolve_max * 0.9: delete_list.append(segment) diff --git a/analytics/utils/__init__.py b/analytics/utils/__init__.py index 72ca4cc..ca5b90e 100644 --- a/analytics/utils/__init__.py +++ b/analytics/utils/__init__.py @@ -61,27 +61,27 @@ def segments_box(segments): return min_time, max_time def intersection_segment(data, median): + """ + Finds all intersections between flatten data and median + """ cen_ind = [] for i in range(1, len(data)-1): if data[i - 1] < median and data[i + 1] > median: cen_ind.append(i) del_ind = [] - for i in range(1,len(cen_ind)): + for i in range(1, len(cen_ind)): if cen_ind[i] == cen_ind[i - 1] + 1: del_ind.append(i - 1) - del_ind = del_ind[::-1] - for i in del_ind: - del cen_ind[i] - return cen_ind + + return [x for (idx, x) in enumerate(cen_ind) if idx not in del_ind] -def logistic_sigmoid(self, x1, x2, alpha, height): - distribution = [] - for i in range(x1, x2): - F = 1 * height / (1 + math.exp(-i * alpha)) - distribution.append(F) - return distribution +def logistic_sigmoid_distribution(self, x1, x2, alpha, height): + return map(lambda x: logistic_sigmoid(x, alpha, height), range(x1, x2)) -def findOneJump(data, x, size, height, err): +def logistic_sigmoid(x, alpha, height): + return height / (1 + math.exp(-x * alpha)) + +def find_one_jump(data, x, size, height, err): l = [] for i in range(x + 1, x + size): if (data[i] > data[x] and data[x + size] > data[x] + height): @@ -91,10 +91,20 @@ def findOneJump(data, x, size, height, err): else: return 0 -def findAllJumps(data, size, height): +def find_all_jumps(data, size, height): possible_jump_list = [] for i in range(len(data - size)): - x = findOneJump(data, i, size, height, 0.9) + x = find_one_jump(data, i, size, height, 0.9) if x > 0: possible_jump_list.append(x) return possible_jump_list + +def find_jump_center(cen_ind): + jump_center = cen_ind[0] + for i in range(len(cen_ind)): + x = cen_ind[i] + cx = scipy.signal.fftconvolve(pat_sigm, flat_data[x - WINDOW_SIZE : x + WINDOW_SIZE]) + c.append(cx[2 * WINDOW_SIZE]) + if i > 0 and cx > c[i - 1]: + jump_center = x + return jump_center