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66 lines
2.1 KiB
66 lines
2.1 KiB
from typing import Dict |
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import pandas as pd |
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import logging, traceback |
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import detectors |
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from analytic_unit_worker import AnalyticUnitWorker |
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logger = logging.getLogger('AnalyticUnitManager') |
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AnalyticUnitId = str |
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analytic_workers: Dict[AnalyticUnitId, AnalyticUnitWorker] = dict() |
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def get_detector_by_type(analytic_unit_type) -> detectors.Detector: |
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return detectors.PatternDetector(analytic_unit_type) |
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def ensure_worker(analytic_unit_id, analytic_unit_type) -> AnalyticUnitWorker: |
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if analytic_unit_id in analytic_workers: |
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# TODO: check that type is the same |
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return analytic_workers[analytic_unit_id] |
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detector = get_detector_by_type(analytic_unit_type) |
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worker = AnalyticUnitWorker(analytic_unit_id, detector) |
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analytic_workers[analytic_unit_id] = worker |
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return worker |
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async def handle_analytic_task(task): |
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try: |
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payload = task['payload'] |
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worker = ensure_worker(task['analyticUnitId'], payload['pattern']) |
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data = prepare_data(payload['data']) |
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result_payload = {} |
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if task['type'] == 'LEARN': |
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result_payload = await worker.do_learn(payload['segments'], data, payload['cache']) |
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elif task['type'] == 'PREDICT': |
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result_payload = await worker.do_predict(data, payload['cache']) |
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else: |
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raise ValueError('Unknown task type "%s"' % task['type']) |
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return { |
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'status': 'SUCCESS', |
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'payload': result_payload |
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} |
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except Exception as e: |
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error_text = traceback.format_exc() |
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logger.error("handle_analytic_task exception: '%s'" % error_text) |
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# TODO: move result to a class which renders to json for messaging to analytics |
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return { |
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'status': 'FAILED', |
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'error': str(e) |
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} |
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def prepare_data(data: list): |
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""" |
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Takes list |
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- converts it into pd.DataFrame, |
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- converts 'timestamp' column to pd.Datetime, |
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- subtracts min value from dataset |
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""" |
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data = pd.DataFrame(data, columns=['timestamp', 'value']) |
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data['timestamp'] = pd.to_datetime(data['timestamp'], unit='ms') |
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data['value'] = data['value'] - min(data['value']) |
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return data
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