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122 lines
5.3 KiB
122 lines
5.3 KiB
from models import Model, ModelState, AnalyticSegment |
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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 import gaussian_kde |
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from typing import Optional, List, Tuple |
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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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from analytic_types import AnalyticUnitId, TimeSeries |
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from analytic_types.learning_info import LearningInfo |
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@utils.meta.JSONClass |
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class DropModelState(ModelState): |
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def __init__( |
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self, |
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confidence: float = 0, |
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drop_height: float = 0, |
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drop_length: float = 0, |
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**kwargs |
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): |
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super().__init__(**kwargs) |
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self.confidence = confidence |
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self.drop_height = drop_height |
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self.drop_length = drop_length |
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class DropModel(Model): |
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def get_model_type(self) -> (str, bool): |
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model = 'drop' |
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type_model = False |
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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, 'drop') |
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return segment_center_index |
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def get_state(self, cache: Optional[dict] = None) -> DropModelState: |
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return DropModelState.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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window_size = self.state.window_size |
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last_pattern_center = self.state.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_drop = utils.get_interval(data, segment_cent_index, window_size) |
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deleted_drop = utils.subtract_min_without_nan(deleted_drop) |
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del_conv_drop = scipy.signal.fftconvolve(deleted_drop, self.state.pattern_model) |
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if len(del_conv_drop): del_conv_list.append(max(del_conv_drop)) |
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self._update_fiting_result(self.state, learning_info.confidence, convolve_list, del_conv_list) |
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self.state.drop_height = int(min(learning_info.pattern_height, default = 1)) |
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self.state.drop_length = int(max(learning_info.pattern_width, default = 1)) |
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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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possible_drops = utils.find_drop(data, self.state.drop_height, self.state.drop_length + 1) |
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result = self.__filter_detection(possible_drops, data) |
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return [(val - 1, val + 1) for val in result] |
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def __filter_detection(self, segments: List[int], data: list): |
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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, 'min') |
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if len(segments) == 0 or len(self.state.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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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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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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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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