import models import logging import config import pandas as pd from detectors import Detector logger = logging.getLogger('PATTERN_DETECTOR') def resolve_model_by_pattern(pattern: str) -> models.Model: if pattern == 'PEAK': return models.PeaksModel() if pattern == 'DROP': return models.StepModel() 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): # TODO: pass only part of dataframe that has segments self.model.fit(dataframe, segments) # TODO: save model after fit return 0 async def predict(self, data): start_index = self.data_prov.get_upper_bound(last_prediction_time) start_index = max(0, start_index - window_size) dataframe = self.data_prov.get_data_range(start_index) predicted_indexes = self.model.predict(dataframe) predicted_indexes = [(x, y) for (x, y) in predicted_indexes if x >= start_index and y >= start_index] predicted_times = self.data_prov.inverse_transform_indexes(predicted_indexes) segments = [] for time_value in predicted_times: ts1 = int(time_value[0].timestamp() * 1000) ts2 = int(time_value[1].timestamp() * 1000) segments.append({ 'start': min(ts1, ts2), 'finish': max(ts1, ts2) }) last_dataframe_time = dataframe.iloc[-1]['timestamp'] last_prediction_time = int(last_dataframe_time.timestamp() * 1000) return segments, last_prediction_time