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147 lines
6.5 KiB
147 lines
6.5 KiB
from models import Model, ModelState, AnalyticSegment, ModelType |
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from analytic_types import TimeSeries |
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from analytic_types.learning_info import LearningInfo |
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from scipy.fftpack import fft |
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from typing import Optional, List |
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from enum import Enum |
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import scipy.signal |
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import utils |
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import utils.meta |
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import pandas as pd |
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import numpy as np |
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import operator |
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POSITIVE_SEGMENT_MEASUREMENT_ERROR = 0.2 |
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NEGATIVE_SEGMENT_MEASUREMENT_ERROR = 0.02 |
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@utils.meta.JSONClass |
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class StairModelState(ModelState): |
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def __init__( |
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self, |
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confidence: float = 0, |
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stair_height: float = 0, |
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stair_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.stair_height = stair_height |
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self.stair_length = stair_length |
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class StairModel(Model): |
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def get_state(self, cache: Optional[dict] = None) -> StairModelState: |
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return StairModelState.from_json(cache) |
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def get_stair_indexes(self, data: pd.Series, height: float, length: int) -> List[int]: |
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"""Get list of start stair segment indexes. |
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Keyword arguments: |
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data -- data, that contains stair (jump or drop) segments |
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length -- maximum count of values in the stair |
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height -- the difference between stair max_line and min_line(see utils.find_parameters) |
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""" |
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indexes = [] |
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for i in range(len(data) - length - 1): |
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is_stair = self.is_stair_in_segment(data.values[i:i + length + 1], height) |
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if is_stair == True: |
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indexes.append(i) |
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return indexes |
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def is_stair_in_segment(self, segment: np.ndarray, height: float) -> bool: |
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if len(segment) < 2: |
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return False |
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comparison_operator = operator.ge |
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if self.get_model_type() == ModelType.DROP: |
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comparison_operator = operator.le |
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height = -height |
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return comparison_operator(max(segment[1:]), segment[0] + height) |
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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, self.get_model_type().value) |
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return segment_center_index |
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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 = 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, 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_stair = utils.get_interval(data, segment_cent_index, window_size) |
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deleted_stair = utils.subtract_min_without_nan(deleted_stair) |
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del_conv_stair = scipy.signal.fftconvolve(deleted_stair, self.state.pattern_model) |
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if len(del_conv_stair) > 0: |
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del_conv_list.append(max(del_conv_stair)) |
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self._update_fitting_result(self.state, learning_info.confidence, convolve_list, del_conv_list) |
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self.state.stair_height = int(min(learning_info.pattern_height, default = 1)) |
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self.state.stair_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_stairs = self.get_stair_indexes(data, self.state.stair_height, self.state.stair_length + 1) |
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result = self.__filter_detection(possible_stairs, data) |
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return [(val - 1, val + 1) for val in result] |
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def __filter_detection(self, segments_indexes: 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_segments = utils.close_filtering(segments_indexes, variance_error) |
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segments_indexes = utils.best_pattern(close_segments, data, self.get_extremum_type().value) |
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if len(segments_indexes) == 0 or len(self.state.pattern_center) == 0: |
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return [] |
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pattern_data = self.state.pattern_model |
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for segment_index in segments_indexes: |
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if segment_index <= self.state.window_size or segment_index >= (len(data) - self.state.window_size): |
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delete_list.append(segment_index) |
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continue |
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convol_data = utils.get_interval(data, segment_index, 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_index) |
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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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if len(conv) == 0: |
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delete_list.append(segment_index) |
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continue |
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upper_bound = self.state.convolve_max * (1 + POSITIVE_SEGMENT_MEASUREMENT_ERROR) |
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lower_bound = self.state.convolve_min * (1 - POSITIVE_SEGMENT_MEASUREMENT_ERROR) |
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delete_up_bound = self.state.conv_del_max * (1 + NEGATIVE_SEGMENT_MEASUREMENT_ERROR) |
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delete_low_bound = self.state.conv_del_min * (1 - NEGATIVE_SEGMENT_MEASUREMENT_ERROR) |
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max_conv = max(conv) |
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if max_conv > upper_bound or max_conv < lower_bound: |
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delete_list.append(segment_index) |
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elif max_conv < delete_up_bound and max_conv > delete_low_bound: |
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delete_list.append(segment_index) |
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for item in delete_list: |
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segments_indexes.remove(item) |
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segments_indexes = utils.remove_duplicates_and_sort(segments_indexes) |
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return segments_indexes
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