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91 lines
3.2 KiB
91 lines
3.2 KiB
from models import Model, AnalyticUnitCache |
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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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from scipy.signal import argrelextrema |
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import math |
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from scipy.stats import gaussian_kde |
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from scipy.stats import norm |
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from typing import Optional |
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WINDOW_SIZE = 150 |
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class GeneralModel(Model): |
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def __init__(self): |
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super() |
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self.segments = [] |
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self.ipats = [] |
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self.state = { |
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'convolve_max': WINDOW_SIZE, |
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} |
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self.all_conv = [] |
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def fit(self, dataframe: pd.DataFrame, segments: list, cache: Optional[AnalyticUnitCache]) -> AnalyticUnitCache: |
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if type(cache) is AnalyticUnitCache: |
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self.state = cache |
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self.segments = segments |
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data = dataframe['value'] |
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convolve_list = [] |
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for segment in segments: |
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if segment['labeled']: |
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segment_from_index = utils.timestamp_to_index(dataframe, pd.to_datetime(segment['from'], unit='ms')) |
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segment_to_index = utils.timestamp_to_index(dataframe, pd.to_datetime(segment['to'], unit='ms')) |
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segment_data = data[segment_from_index: segment_to_index + 1] |
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if len(segment_data) == 0: |
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continue |
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x = segment_from_index + int((segment_to_index - segment_from_index) / 2) |
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self.ipats.append(x) |
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segment_data = data[x - WINDOW_SIZE : x + WINDOW_SIZE] |
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segment_min = min(segment_data) |
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segment_data = segment_data - segment_min |
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convolve = scipy.signal.fftconvolve(segment_data, segment_data) |
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convolve_list.append(max(convolve)) |
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if len(convolve_list) > 0: |
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self.state['convolve_max'] = float(max(convolve_list)) |
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else: |
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self.state['convolve_max'] = WINDOW_SIZE / 3 |
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return self.state |
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def do_predict(self, dataframe: pd.DataFrame) -> list: |
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data = dataframe['value'] |
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pat_data = data[self.ipats[0] - WINDOW_SIZE: self.ipats[0] + WINDOW_SIZE] |
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x = min(pat_data) |
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pat_data = pat_data - x |
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y = max(pat_data) |
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for i in range(WINDOW_SIZE * 2, len(data)): |
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watch_data = data[i - WINDOW_SIZE * 2: i] |
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w = min(watch_data) |
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watch_data = watch_data - w |
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conv = scipy.signal.fftconvolve(pat_data, watch_data) |
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self.all_conv.append(max(conv)) |
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all_conv_peaks = utils.peak_finder(self.all_conv, WINDOW_SIZE * 2) |
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filtered = self.__filter_prediction(all_conv_peaks, data) |
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filtered = set(item + WINDOW_SIZE for item in filtered) |
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# TODO: convert from ns to ms more proper way (not dividing by 10^6) |
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return [(dataframe['timestamp'][x - 1].value / 1000000, dataframe['timestamp'][x + 1].value / 1000000) for x in filtered] |
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def __filter_prediction(self, segments: list, data: list): |
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if len(segments) == 0 or len(self.ipats) == 0: |
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return [] |
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delete_list = [] |
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for val in segments: |
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if self.all_conv[val] < self.state['convolve_max'] * 0.8: |
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delete_list.append(val) |
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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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