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45 lines
1.6 KiB
45 lines
1.6 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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import concurrent.futures |
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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: 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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return self._detector.detect(data, cache) |
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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 recieve_data(self, data: pd.DataFrame, cache: Optional[ModelCache]): |
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return self._detector.recieve_data(data, cache)
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