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109 lines
3.4 KiB
109 lines
3.4 KiB
import config |
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from anomaly_model import AnomalyModel |
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from pattern_detection_model import PatternDetectionModel |
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import queue |
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import threading |
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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 Worker(object): |
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models_cache = {} |
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thread = None |
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queue = queue.Queue() |
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def start(self): |
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self.thread = threading.Thread(target=self.run) |
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self.thread.start() |
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def stop(self): |
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if self.thread: |
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self.queue.put(None) |
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self.thread.join() |
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def run(self): |
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while True: |
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task = self.queue.get() |
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if task['type'] == "stop": |
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break |
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self.do_task(task) |
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self.queue.task_done() |
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def add_task(self, task): |
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self.queue.put(task) |
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def do_task(self, task): |
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try: |
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type = task['type'] |
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predictor_id = task['predictor_id'] |
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if type == "predict": |
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last_prediction_time = task['last_prediction_time'] |
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pattern = task['pattern'] |
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result = self.do_predict(predictor_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 = self.do_learn(predictor_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("Exception: '%s'" % error_text) |
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result = { |
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'task': type, |
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'status': "failed", |
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'predictor_id': predictor_id, |
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'error': str(e) |
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} |
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return result |
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def do_learn(self, predictor_id, segments, pattern): |
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model = self.get_model(predictor_id, pattern) |
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model.synchronize_data() |
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last_prediction_time = 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 == 'drops' and len(segments) == 0: |
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result = { |
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'status': 'success', |
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'predictor_id': predictor_id, |
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'segments': [], |
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'last_prediction_time': last_prediction_time |
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} |
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else: |
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result = self.do_predict(predictor_id, last_prediction_time, pattern) |
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result['task'] = 'learn' |
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return result |
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def do_predict(self, predictor_id, last_prediction_time, pattern): |
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model = self.get_model(predictor_id, pattern) |
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model.synchronize_data() |
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segments, last_prediction_time = 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': predictor_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, predictor_id, pattern): |
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if predictor_id not in self.models_cache: |
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if pattern.find('general') != -1: |
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model = AnomalyModel(predictor_id) |
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else: |
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model = PatternDetectionModel(predictor_id, pattern) |
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self.models_cache[predictor_id] = model |
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return self.models_cache[predictor_id] |
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