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import models
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import logging
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import config
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import pandas as pd
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from typing import Optional
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from detectors import Detector
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from buckets import DataBucket
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from models import ModelCache
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logger = logging.getLogger('PATTERN_DETECTOR')
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def resolve_model_by_pattern(pattern: str) -> models.Model:
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if pattern == 'GENERAL':
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return models.GeneralModel()
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if pattern == 'PEAK':
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return models.PeakModel()
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if pattern == 'TROUGH':
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return models.TroughModel()
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if pattern == 'DROP':
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return models.DropModel()
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if pattern == 'JUMP':
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return models.JumpModel()
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if pattern == 'CUSTOM':
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return models.CustomModel()
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raise ValueError('Unknown pattern "%s"' % pattern)
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AnalyticUnitId = str
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class PatternDetector(Detector):
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def __init__(self, pattern_type: str, analytic_unit_id: AnalyticUnitId):
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self.analytic_unit_id = analytic_unit_id
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self.pattern_type = pattern_type
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self.model = resolve_model_by_pattern(self.pattern_type)
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self.window_size = 150
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self.bucket = DataBucket()
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self.bucket_full_reported = False
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def train(self, dataframe: pd.DataFrame, segments: list, cache: Optional[models.ModelCache]) -> models.ModelCache:
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# TODO: pass only part of dataframe that has segments
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new_cache = self.model.fit(dataframe, segments, cache)
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return {
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'cache': new_cache
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}
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def detect(self, dataframe: pd.DataFrame, cache: Optional[models.ModelCache]) -> dict:
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logger.debug('Unit {} got {} data points for detection'.format(self.analytic_unit_id, len(dataframe)))
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# TODO: split and sleep (https://github.com/hastic/hastic-server/pull/124#discussion_r214085643)
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detected = self.model.detect(dataframe, cache)
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segments = [{ 'from': segment[0], 'to': segment[1] } for segment in detected['segments']]
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newCache = detected['cache']
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last_dataframe_time = dataframe.iloc[-1]['timestamp']
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# TODO: convert from nanoseconds to millisecond in a better way: not by dividing by 10^6
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last_detection_time = last_dataframe_time.value / 1000000
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return {
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'cache': newCache,
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'segments': segments,
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'lastDetectionTime': last_detection_time
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}
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def recieve_data(self, data: pd.DataFrame, cache: Optional[ModelCache]) -> Optional[dict]:
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self.bucket.receive_data(data.dropna())
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if len(self.bucket.data) >= self.window_size and cache != None:
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if not self.bucket_full_reported:
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logging.debug('{} unit`s bucket full, run detect'.format(self.analytic_unit_id))
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self.bucket_full_reported = True
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res = self.detect(self.bucket.data, cache)
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excess_data = len(self.bucket.data) - self.window_size
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self.bucket.drop_data(excess_data)
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return res
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else:
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filling = len(self.bucket.data)*100 / self.window_size
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logging.debug('bucket for {} {}% full'.format(self.analytic_unit_id, filling))
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return None
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