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