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import models
import logging
import config
import pandas as pd
from typing import Optional
from detectors import Detector
logger = logging.getLogger('PATTERN_DETECTOR')
def resolve_model_by_pattern(pattern: str) -> models.Model:
if pattern == 'GENERAL':
return models.GeneralModel()
if pattern == 'PEAK':
return models.PeakModel()
if pattern == 'TROUGH':
return models.TroughModel()
if pattern == 'DROP':
return models.DropModel()
if pattern == 'JUMP':
return models.JumpModel()
if pattern == 'CUSTOM':
return models.CustomModel()
raise ValueError('Unknown pattern "%s"' % pattern)
class PatternDetector(Detector):
def __init__(self, pattern_type):
self.pattern_type = pattern_type
self.model = resolve_model_by_pattern(self.pattern_type)
window_size = 100
async def train(self, dataframe: pd.DataFrame, segments: list, cache: Optional[models.AnalyticUnitCache]) -> models.AnalyticUnitCache:
# TODO: pass only part of dataframe that has segments
new_cache = self.model.fit(dataframe, segments, cache)
return {
'cache': new_cache
}
async def predict(self, dataframe: pd.DataFrame, cache: Optional[models.AnalyticUnitCache]) -> dict:
# TODO: split and sleep (https://github.com/hastic/hastic-server/pull/124#discussion_r214085643)
predicted = self.model.predict(dataframe, cache)
segments = [{ 'from': segment[0], 'to': segment[1] } for segment in predicted['segments']]
newCache = predicted['cache']
last_dataframe_time = dataframe.iloc[-1]['timestamp']
last_prediction_time = last_dataframe_time.value
return {
'cache': newCache,
'segments': segments,
'lastPredictionTime': last_prediction_time
}