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62 lines
2.2 KiB
62 lines
2.2 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 |
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from models import ModelCache |
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from concurrent.futures import Executor, CancelledError |
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import asyncio |
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logger = logging.getLogger('AnalyticUnitWorker') |
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class AnalyticUnitWorker: |
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def __init__(self, analytic_unit_id: str, detector: detectors.Detector, executor: 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: Executor = executor |
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self._training_feature: asyncio.Future = None |
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self._detection_feature: asyncio.Future = None |
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self._recieve_feature: 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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) -> ModelCache: |
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self._training_feature = asyncio.get_event_loop().run_in_executor( |
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self._executor, self._detector.train, data, payload, cache |
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) |
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try: |
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new_cache: ModelCache = await self._training_feature |
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return new_cache |
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except CancelledError as e: |
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return cache |
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async def do_detect(self, data: pd.DataFrame, cache: Optional[ModelCache]) -> dict: |
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self._detection_feature = asyncio.get_event_loop().run_in_executor( |
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self._executor, self._detector.detect, data, cache |
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) |
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try: |
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detect_results = await self._detection_feature |
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return detect_results |
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except CancelledError as e: |
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return {} |
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def cancel(self): |
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if self._training_feature is not None: |
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self._training_feature.cancel() |
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if self._detection_feature is not None: |
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self._detection_feature.cancel() |
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if self._recieve_feature is not None: |
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self._recieve_feature.cancel() |
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async def recieve_data(self, data: pd.DataFrame, cache: Optional[ModelCache]): |
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self._recieve_feature = asyncio.get_event_loop().run_in_executor( |
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self._executor, self._detector.recieve_data, data, cache |
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) |
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try: |
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detect_results = await self._recieve_feature |
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return detect_results |
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except CancelledError as e: |
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return None
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