hastic-server/analytics/jump_detector.py

import numpy as np
import pickle
import scipy.signal
from scipy.fftpack import fft
from scipy.signal import argrelextrema
import math

def is_intersect(target_segment, segments):
    for segment in segments:
        start = max(segment['start'], target_segment[0])
        finish = min(segment['finish'], target_segment[1])
        if start <= finish:
            return True
    return False

def exponential_smoothing(series, alpha):
    result = [series[0]]
    for n in range(1, len(series)):
        result.append(alpha * series[n] + (1 - alpha) * result[n-1])
    return result

class Jumpdetector:

    def __init__(self, pattern):
        self.pattern = pattern
        self.segments = []
        self.confidence = 1.5
        self.convolve_max = 120
    
    def fit(self, dataframe, segments):
            data = dataframe['value']
            confidences = []
            convolve_list = []
            for segment in segments:
                if segment['labeled']:
                    segment_data = data[segment['start'] : segment['finish'] + 1]
                    segment_min = min(segment_data)
                    segment_max = max(segment_data)
                    confidences.append(0.20 * (segment_max - segment_min))
                    flat_segment = segment_data.rolling(window=5).mean() #сглаживаем сегмент
                    # 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]
                    labeled_min = min(labeled_drop) 
                    for value in labeled_drop: # обрезаем
                        value = value - labeled_min
                    labeled_max = max(labeled_drop)
                    for value in labeled_drop: # нормируем
                        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

            if len(confidences) > 0:
                self.confidence = min(confidences)
            else:
                self.confidence = 1.5

            if len(convolve_list) > 0:
                self.convolve_max = max(convolve_list)
            else:
                self.convolve_max = 120 # макс метрика свертки равна отступу(120), вау!
        
    def logistic_sigmoid(x1, x2, alpha, height):
        distribution = []
        for i in range(x, y):
            F = 1 * height / (1 + math.exp(-i * alpha))
            distribution.append(F)
        return distribution
    
    def predict(self, dataframe):
        data = dataframe['value']

        result = self.__predict(data)
        result.sort()

        if len(self.segments) > 0:
            result = [segment for segment in result if not is_intersect(segment, self.segments)]
        return result

    def __predict(self, data):
            window_size = 24
            all_max_flatten_data = data.rolling(window=window_size).mean()
            extrema_list = []
            # добавить все пересечения экспоненты со сглаженным графиком
            # 
            for i in exponential_smoothing(data + self.confidence, 0.02):
                extrema_list.append(i)

            segments = []
            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)]

    def __filter_prediction(self, segments, all_max_flatten_data):
        delete_list = []
        variance_error = int(0.004 * len(all_max_flatten_data))
        if variance_error > 200:
            variance_error = 200
        for i in range(1, len(segments)):
            if segments[i] < segments[i - 1] + variance_error:
                delete_list.append(segments[i])
        for item in delete_list:
            segments.remove(item)

        # изменить секонд делит лист, сделать для свертки с сигмоидой
        delete_list = []
        pattern_data = all_max_flatten_data[segments[0] - 120 : segments[0] + 120]
        for segment in segments:
            convol_data = all_max_flatten_data[segment - 120 : segment + 120]
            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)
        for item in delete_list:
            segments.remove(item)

        return segments

    def save(self, model_filename):
        with open(model_filename, 'wb') as file:
            pickle.dump((self.confidence, self.convolve_max), file)

    def load(self, model_filename):
        try:
            with open(model_filename, 'rb') as file:
                (self.confidence, self.convolve_max) = pickle.load(file)
        except:
            pass