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86 lines
3.1 KiB
86 lines
3.1 KiB
import config |
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import detectors |
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import json |
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import logging |
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import sys |
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import traceback |
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import time |
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logger = logging.getLogger('WORKER') |
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class AnalyticUnitWorker(object): |
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models_cache = {} |
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# TODO: get task as an object built from json |
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async def do_task(self, task): |
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try: |
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type = task['type'] |
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analytic_unit_id = task['analyticUnitId'] |
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if type == "PREDICT": |
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last_prediction_time = task['lastPredictionTime'] |
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pattern = task['pattern'] |
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result = await self.do_predict(analytic_unit_id, last_prediction_time, pattern) |
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elif type == "LEARN": |
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segments = task['segments'] |
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pattern = task['pattern'] |
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result = await self.do_learn(analytic_unit_id, segments, pattern) |
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else: |
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result = { |
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'status': "FAILED", |
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'error': "unknown type " + str(type) |
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} |
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except Exception as e: |
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#traceback.extract_stack() |
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error_text = traceback.format_exc() |
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logger.error("do_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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result = { |
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'task': type, |
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'status': "FAILED", |
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'analyticUnitId': analytic_unit_id, |
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'error': str(e) |
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} |
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return result |
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async def do_learn(self, analytic_unit_id, segments, pattern): |
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model = self.get_model(analytic_unit_id, pattern) |
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model.synchronize_data() |
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last_prediction_time = await model.learn(segments) |
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# TODO: we should not do predict before labeling in all models, not just in drops |
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if pattern == 'DROP' and len(segments) == 0: |
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# TODO: move result to a class which renders to json for messaging to analytics |
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result = { |
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'status': 'SUCCESS', |
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'analyticUnitId': analytic_unit_id, |
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'segments': [], |
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'lastPredictionTime': last_prediction_time |
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} |
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else: |
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result = await self.do_predict(analytic_unit_id, last_prediction_time, pattern) |
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result['task'] = 'LEARN' |
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return result |
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async def do_predict(self, analytic_unit_id, last_prediction_time, pattern): |
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model = self.get_model(analytic_unit_id, pattern) |
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model.synchronize_data() |
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segments, last_prediction_time = await model.predict(last_prediction_time) |
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return { |
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'task': 'PREDICT', |
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'status': 'SUCCESS', |
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'analyticUnitId': analytic_unit_id, |
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'segments': segments, |
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'lastPredictionTime': last_prediction_time |
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} |
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def get_model(self, analytic_unit_id, pattern_type): |
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if analytic_unit_id not in self.models_cache: |
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if pattern_type == 'GENERAL': |
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model = detectors.GeneralDetector(analytic_unit_id) |
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else: |
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model = detectors.PatternDetector(analytic_unit_id, pattern_type) |
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self.models_cache[analytic_unit_id] = model |
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return self.models_cache[analytic_unit_id]
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