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112 lines
5.0 KiB
112 lines
5.0 KiB
from models import Model |
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import utils |
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import numpy as np |
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import pandas as pd |
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import scipy.signal |
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from scipy.fftpack import fft |
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import math |
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from scipy.signal import argrelextrema |
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from scipy.stats import gaussian_kde |
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class JumpModel(Model): |
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def __init__(self): |
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super() |
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self.segments = [] |
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self.state = { |
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'pattern_center': [], |
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'pattern_model': [], |
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'confidence': 1.5, |
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'convolve_max': 230, |
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'convolve_min': 230, |
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'JUMP_HEIGHT': 1, |
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'JUMP_LENGTH': 1, |
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'WINDOW_SIZE': 0, |
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'conv_del_min': 54000, |
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'conv_del_max': 55000, |
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} |
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def get_model_type(self) -> (str, bool): |
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model = 'jump' |
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type_model = True |
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return (model, type_model) |
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def find_segment_center(self, dataframe: pd.DataFrame, start: int, end: int) -> int: |
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data = dataframe['value'] |
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segment = data[start: end] |
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segment_center_index = utils.find_pattern_center(segment, start, 'jump') |
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return segment_center_index |
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def do_fit(self, dataframe: pd.DataFrame, labeled_segments: list, deleted_segments: list, learning_info: dict, id: str) -> None: |
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data = utils.cut_dataframe(dataframe) |
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data = data['value'] |
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window_size = self.state['WINDOW_SIZE'] |
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last_pattern_center = self.state.get('pattern_center', []) |
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self.state['pattern_center'] = list(set(last_pattern_center + learning_info['segment_center_list'])) |
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self.state['pattern_model'] = utils.get_av_model(learning_info['patterns_list']) |
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convolve_list = utils.get_convolve(self.state['pattern_center'], self.state['pattern_model'], data, window_size) |
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correlation_list = utils.get_correlation(self.state['pattern_center'], self.state['pattern_model'], data, window_size) |
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height_list = learning_info['patterns_value'] |
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del_conv_list = [] |
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delete_pattern_timestamp = [] |
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for segment in deleted_segments: |
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segment_cent_index = segment.center_index |
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delete_pattern_timestamp.append(segment.pattern_timestamp) |
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deleted_jump = utils.get_interval(data, segment_cent_index, window_size) |
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deleted_jump = utils.subtract_min_without_nan(deleted_jump) |
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del_conv_jump = scipy.signal.fftconvolve(deleted_jump, self.state['pattern_model']) |
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if len(del_conv_jump): del_conv_list.append(max(del_conv_jump)) |
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self._update_fiting_result(self.state, learning_info['confidence'], convolve_list, del_conv_list, height_list) |
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self.state['JUMP_HEIGHT'] = float(min(learning_info['pattern_height'], default = 1)) |
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self.state['JUMP_LENGTH'] = int(max(learning_info['pattern_width'], default = 1)) |
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def do_detect(self, dataframe: pd.DataFrame, id: str) -> list: |
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data = utils.cut_dataframe(dataframe) |
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data = data['value'] |
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possible_jumps = utils.find_jump(data, self.state['JUMP_HEIGHT'], self.state['JUMP_LENGTH'] + 1) |
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return self.__filter_detection(possible_jumps, data) |
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def __filter_detection(self, segments, data): |
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delete_list = [] |
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variance_error = self.state['WINDOW_SIZE'] |
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close_patterns = utils.close_filtering(segments, variance_error) |
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segments = utils.best_pattern(close_patterns, data, 'max') |
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if len(segments) == 0 or len(self.state.get('pattern_center', [])) == 0: |
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segments = [] |
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return segments |
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pattern_data = self.state['pattern_model'] |
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upper_bound = self.state['convolve_max'] * 1.2 |
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lower_bound = self.state['convolve_min'] * 0.8 |
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delete_up_bound = self.state['conv_del_max'] * 1.02 |
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delete_low_bound = self.state['conv_del_min'] * 0.98 |
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for segment in segments: |
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if segment > self.state['WINDOW_SIZE'] and segment < (len(data) - self.state['WINDOW_SIZE']): |
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convol_data = utils.get_interval(data, segment, self.state['WINDOW_SIZE']) |
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percent_of_nans = convol_data.isnull().sum() / len(convol_data) |
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if len(convol_data) == 0 or percent_of_nans > 0.5: |
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delete_list.append(segment) |
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continue |
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elif 0 < percent_of_nans <= 0.5: |
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nan_list = utils.find_nan_indexes(convol_data) |
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convol_data = utils.nan_to_zero(convol_data, nan_list) |
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pattern_data = utils.nan_to_zero(pattern_data, nan_list) |
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conv = scipy.signal.fftconvolve(convol_data, pattern_data) |
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try: |
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if max(conv) > upper_bound or max(conv) < lower_bound: |
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delete_list.append(segment) |
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elif max(conv) < delete_up_bound and max(conv) > delete_low_bound: |
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delete_list.append(segment) |
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except ValueError: |
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delete_list.append(segment) |
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else: |
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delete_list.append(segment) |
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for item in delete_list: |
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segments.remove(item) |
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return set(segments)
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