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73 lines
2.3 KiB
73 lines
2.3 KiB
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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