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import logging as log
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import operator
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import pandas as pd
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import numpy as np
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from typing import Optional, List
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from analytic_types import ModelCache
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from analytic_types.detector_typing import DetectionResult
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from analytic_types.segment import Segment
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from detectors import Detector
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from time import time
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import utils
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logger = log.getLogger('THRESHOLD_DETECTOR')
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class ThresholdDetector(Detector):
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WINDOW_SIZE = 3
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def __init__(self):
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pass
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def train(self, dataframe: pd.DataFrame, threshold: dict, cache: Optional[ModelCache]) -> ModelCache:
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time_step = utils.find_interval(dataframe)
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return {
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'cache': {
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'value': threshold['value'],
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'condition': threshold['condition'],
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'timeStep': time_step
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}
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}
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def detect(self, dataframe: pd.DataFrame, cache: ModelCache) -> DetectionResult:
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if cache is None or cache == {}:
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raise ValueError('Threshold detector error: cannot detect before learning')
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if len(dataframe) == 0:
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return None
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value = cache['value']
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condition = cache['condition']
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segments = []
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for index, row in dataframe.iterrows():
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current_value = row['value']
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current_timestamp = utils.convert_pd_timestamp_to_ms(row['timestamp'])
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segment = Segment(current_timestamp, current_timestamp)
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# TODO: merge segments
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if pd.isnull(current_value):
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if condition == 'NO_DATA':
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segment.message = 'NO_DATA detected'
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segments.append(segment)
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continue
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comparators = {
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'>': operator.gt,
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'<': operator.lt,
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'=': operator.eq,
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'>=': operator.ge,
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'<=': operator.le
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}
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assert condition in comparators.keys(), f'condition {condition} not allowed'
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if comparators[condition](current_value, value):
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segment.message = f"{current_value} {condition} threshold's value {value}"
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segments.append(segment)
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last_entry = dataframe.iloc[-1]
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last_detection_time = utils.convert_pd_timestamp_to_ms(last_entry['timestamp'])
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return DetectionResult(cache, segments, last_detection_time)
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def consume_data(self, data: pd.DataFrame, cache: Optional[ModelCache]) -> Optional[DetectionResult]:
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result = self.detect(data, cache)
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return result if result else None
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def get_window_size(self, cache: Optional[ModelCache]) -> int:
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return self.WINDOW_SIZE
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def concat_detection_results(self, detections: List[DetectionResult]) -> DetectionResult:
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result = DetectionResult()
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time_step = detections[0].cache['timeStep']
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for detection in detections:
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result.segments.extend(detection.segments)
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result.last_detection_time = detection.last_detection_time
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result.cache = detection.cache
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result.segments = utils.merge_intersecting_segments(result.segments, time_step)
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return result
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