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93 lines
3.3 KiB
93 lines
3.3 KiB
import config |
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import detectors |
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import logging |
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import pandas as pd |
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from typing import Optional, Union, Generator |
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from models import ModelCache |
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import concurrent.futures |
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import asyncio |
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from utils import get_data_chunks |
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logger = logging.getLogger('AnalyticUnitWorker') |
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class AnalyticUnitWorker: |
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CHUNK_WINDOW_SIZE_FACTOR = 100 |
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def __init__(self, analytic_unit_id: str, detector: detectors.Detector, executor: concurrent.futures.Executor): |
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self.analytic_unit_id = analytic_unit_id |
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self._detector = detector |
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self._executor: concurrent.futures.Executor = executor |
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self._training_future: asyncio.Future = None |
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async def do_train( |
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self, payload: Union[list, dict], data: pd.DataFrame, cache: Optional[ModelCache] |
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) -> Optional[ModelCache]: |
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cfuture: concurrent.futures.Future = self._executor.submit( |
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self._detector.train, data, payload, cache |
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) |
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self._training_future = asyncio.wrap_future(cfuture) |
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try: |
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new_cache: ModelCache = await asyncio.wait_for(self._training_future, timeout = config.LEARNING_TIMEOUT) |
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return new_cache |
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except asyncio.CancelledError: |
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return None |
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except asyncio.TimeoutError: |
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raise Exception('Timeout ({}s) exceeded while learning'.format(config.LEARNING_TIMEOUT)) |
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async def do_detect(self, data: pd.DataFrame, cache: Optional[ModelCache]) -> dict: |
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if cache is None: |
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msg = f'{self.analytic_unit_id} detection got invalid cache, skip detection' |
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logger.error(msg) |
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raise ValueError(msg) |
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window_size = self._detector.get_window_size(cache) |
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detection_result = { |
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'cache': None, |
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'segments': [], |
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'lastDetectionTime': None |
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} |
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for chunk in get_data_chunks(data, window_size, window_size * self.CHUNK_WINDOW_SIZE_FACTOR): |
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await asyncio.sleep(0) |
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detected = self._detector.detect(chunk, cache) |
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self.__append_detection_result(detection_result, detected) |
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return detection_result |
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def cancel(self): |
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if self._training_future is not None: |
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self._training_future.cancel() |
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async def consume_data(self, data: pd.DataFrame, cache: Optional[ModelCache]): |
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if cache is None: |
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msg = f'{self.analytic_unit_id} consume_data got invalid cache, skip detection' |
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logger.error(msg) |
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raise ValueError(msg) |
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window_size = self._detector.get_window_size(cache) |
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#TODO: make class DetectionResult |
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detection_result = { |
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'cache': None, |
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'segments': [], |
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'lastDetectionTime': None |
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} |
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#TODO: remove code duplication with do_detect |
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for chunk in get_data_chunks(data, window_size, window_size * self.CHUNK_WINDOW_SIZE_FACTOR): |
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await asyncio.sleep(0) |
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detected = self._detector.consume_data(chunk, cache) |
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self.__append_detection_result(detection_result, detected) |
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return detection_result |
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def __append_detection_result(self, detection_result: dict, new_chunk: dict): |
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if new_chunk is not None: |
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detection_result['cache'] = new_chunk['cache'] |
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detection_result['lastDetectionTime'] = new_chunk['lastDetectionTime'] |
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detection_result['segments'].extend(new_chunk['segments'])
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