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66 lines
2.0 KiB
66 lines
2.0 KiB
import models |
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import utils |
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# from grafana_data_provider import GrafanaDataProvider |
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import logging |
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from urllib.parse import urlparse |
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import os.path |
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import json |
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import config |
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import pandas as pd |
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from detectors import Detector |
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logger = logging.getLogger('analytic_toolset') |
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def resolve_model_by_pattern(pattern: str) -> models.Model: |
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if pattern == 'PEAK': |
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return models.PeaksModel() |
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if pattern == 'DROP': |
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return models.StepModel() |
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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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class PatternDetector(Detector): |
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def __init__(self, pattern_type): |
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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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window_size = 100 |
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async def train(self, dataframe: pd.DataFrame, segments: list): |
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# TODO: pass only part of dataframe that has segments |
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self.model.fit(dataframe, data, segments) |
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self.__save_model() |
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return 0 |
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async def predict(self, data): |
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start_index = self.data_prov.get_upper_bound(last_prediction_time) |
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start_index = max(0, start_index - window_size) |
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dataframe = self.data_prov.get_data_range(start_index) |
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predicted_indexes = self.model.predict(dataframe) |
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predicted_indexes = [(x, y) for (x, y) in predicted_indexes if x >= start_index and y >= start_index] |
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predicted_times = self.data_prov.inverse_transform_indexes(predicted_indexes) |
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segments = [] |
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for time_value in predicted_times: |
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ts1 = int(time_value[0].timestamp() * 1000) |
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ts2 = int(time_value[1].timestamp() * 1000) |
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segments.append({ |
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'start': min(ts1, ts2), |
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'finish': max(ts1, ts2) |
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}) |
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last_dataframe_time = dataframe.iloc[-1]['timestamp'] |
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last_prediction_time = int(last_dataframe_time.timestamp() * 1000) |
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return segments, last_prediction_time
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