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import logging as log
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import pandas as pd
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from typing import Optional
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from detectors import Detector
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from models import ModelCache
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from time import time
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from utils import convert_sec_to_ms, convert_pd_timestamp_to_ms
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logger = log.getLogger('THRESHOLD_DETECTOR')
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class ThresholdDetector(Detector):
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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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return {
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'cache': {
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'value': threshold['value'],
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'condition': threshold['condition']
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}
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}
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async def detect(self, dataframe: pd.DataFrame, cache: ModelCache) -> dict:
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if cache == None:
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raise 'Threshold detector error: cannot detect before learning'
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value = cache['value']
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condition = cache['condition']
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now = convert_sec_to_ms(time())
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segments = []
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dataframe_without_nans = dataframe.dropna()
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if len(dataframe_without_nans) == 0:
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if condition == 'NO_DATA':
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segments.append({ 'from': now, 'to': now , 'params': { value: 'NO_DATA' } })
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else:
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return None
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else:
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last_entry = dataframe_without_nans.iloc[-1]
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last_time = convert_pd_timestamp_to_ms(last_entry['timestamp'])
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last_value = float(last_entry['value'])
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segment = { 'from': last_time, 'to': last_time, 'params': { value: last_value } }
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if condition == '>':
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if last_value > value:
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segments.append(segment)
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elif condition == '>=':
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if last_value >= value:
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segments.append(segment)
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elif condition == '=':
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if last_value == value:
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segments.append(segment)
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elif condition == '<=':
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if last_value <= value:
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segments.append(segment)
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elif condition == '<':
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if last_value < value:
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segments.append(segment)
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return {
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'cache': cache,
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'segments': segments,
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'lastDetectionTime': now
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}
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def recieve_data(self, data: pd.DataFrame, cache: Optional[ModelCache]) -> Optional[dict]:
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result = self.detect(data, cache)
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return result if result else None
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