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104 lines
4.3 KiB
104 lines
4.3 KiB
from analytic_types import AnalyticUnitId |
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from models import Model, ModelState, AnalyticSegment, ModelType |
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from typing import Union, List, Generator |
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import utils |
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import utils.meta |
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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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from scipy.signal import argrelextrema |
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from scipy.stats.stats import pearsonr |
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from scipy.stats import gaussian_kde |
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from scipy.stats import norm |
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import logging |
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from typing import Optional, List, Tuple |
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import math |
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from analytic_types import AnalyticUnitId, TimeSeries |
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from analytic_types.learning_info import LearningInfo |
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PEARSON_FACTOR = 0.7 |
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@utils.meta.JSONClass |
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class GeneralModelState(ModelState): |
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def __init__(self, **kwargs): |
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super().__init__(**kwargs) |
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class GeneralModel(Model): |
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def get_model_type(self) -> ModelType: |
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return ModelType.GENERAL |
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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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center_ind = start + math.ceil((end - start) / 2) |
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return center_ind |
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def get_state(self, cache: Optional[dict] = None) -> GeneralModelState: |
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return GeneralModelState.from_json(cache) |
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def do_fit( |
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self, |
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dataframe: pd.DataFrame, |
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labeled_segments: List[AnalyticSegment], |
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deleted_segments: List[AnalyticSegment], |
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learning_info: LearningInfo |
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) -> None: |
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data = utils.cut_dataframe(dataframe) |
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data = data['value'] |
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last_pattern_center = self.state.pattern_center |
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self.state.pattern_center = utils.remove_duplicates_and_sort(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, self.state.window_size) |
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correlation_list = utils.get_correlation(self.state.pattern_center, self.state.pattern_model, data, self.state.window_size) |
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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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del_mid_index = segment.center_index |
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delete_pattern_timestamp.append(segment.pattern_timestamp) |
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deleted_pat = utils.get_interval(data, del_mid_index, self.state.window_size) |
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deleted_pat = utils.subtract_min_without_nan(deleted_pat) |
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del_conv_pat = scipy.signal.fftconvolve(deleted_pat, self.state.pattern_model) |
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if len(del_conv_pat): del_conv_list.append(max(del_conv_pat)) |
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self.state.convolve_min, self.state.convolve_max = utils.get_min_max(convolve_list, self.state.window_size / 3) |
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self.state.conv_del_min, self.state.conv_del_max = utils.get_min_max(del_conv_list, self.state.window_size) |
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def do_detect(self, dataframe: pd.DataFrame) -> TimeSeries: |
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data = utils.cut_dataframe(dataframe) |
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data = data['value'] |
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pat_data = self.state.pattern_model |
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if pat_data.count(0) == len(pat_data): |
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raise ValueError('Labeled patterns must not be empty') |
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window_size = self.state.window_size |
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all_corr = utils.get_correlation_gen(data, window_size, pat_data) |
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all_corr_peaks = utils.find_peaks(all_corr, window_size * 2) |
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filtered = self.__filter_detection(all_corr_peaks, data) |
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filtered = list(filtered) |
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return [(item, item + window_size * 2) for item in filtered] |
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def __filter_detection(self, segments: Generator[int, None, None], data: pd.Series) -> Generator[int, None, None]: |
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if not self.state.pattern_center: |
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return [] |
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window_size = self.state.window_size |
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pattern_model = self.state.pattern_model |
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for ind, val in segments: |
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watch_data = data[ind - window_size: ind + window_size + 1] |
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watch_data = utils.subtract_min_without_nan(watch_data) |
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convolve_segment = scipy.signal.fftconvolve(watch_data, pattern_model) |
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if len(convolve_segment) > 0: |
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watch_conv = max(convolve_segment) |
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else: |
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continue |
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if watch_conv < self.state.convolve_min * 0.8 or val < PEARSON_FACTOR: |
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continue |
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if watch_conv < self.state.conv_del_max * 1.02 and watch_conv > self.state.conv_del_min * 0.98: |
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continue |
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yield ind
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