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101 lines
3.7 KiB
101 lines
3.7 KiB
from detectors.general_detector.supervised_algorithm import SupervisedAlgorithm |
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import utils |
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# from grafana_data_provider import GrafanaDataProvider |
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from data_preprocessor import data_preprocessor |
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import pandas as pd |
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import logging |
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from urllib.parse import urlparse |
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import config |
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import os.path |
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import json |
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NANOSECONDS_IN_MS = 1000000 |
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logger = logging.getLogger('analytic_toolset') |
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class GeneralDetector: |
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def __init__(self, anomaly_name, data): |
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self.anomaly_name = anomaly_name |
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self.model = None |
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self.__load_model() |
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async def learn(self, segments): |
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logger.info("Start to learn for anomaly_name='%s'" % self.anomaly_name) |
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confidence = 0.02 |
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dataframe = self.data_prov.get_dataframe() |
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start_index, stop_index = 0, len(dataframe) |
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if len(segments) > 0: |
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confidence = 0.0 |
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min_time, max_time = utils.segments_box(segments) |
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dataframe = dataframe[dataframe['timestamp'] <= max_time] |
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dataframe = dataframe[dataframe['timestamp'] >= min_time] |
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train_augmented = self.preprocessor.get_augmented_data( |
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dataframe.index[0], |
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dataframe.index[-1], |
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segments |
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) |
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self.model = self.create_algorithm() |
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await self.model.fit(train_augmented, confidence) |
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if len(segments) > 0: |
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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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else: |
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last_prediction_time = 0 |
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self.__save_model() |
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logger.info("Learning is finished for anomaly_name='%s'" % self.anomaly_name) |
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return last_prediction_time |
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async def predict(self, last_prediction_time): |
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logger.info("Start to predict for anomaly type='%s'" % self.anomaly_name) |
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last_prediction_time = pd.to_datetime(last_prediction_time, unit='ms') |
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start_index = self.data_prov.get_upper_bound(last_prediction_time) |
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stop_index = self.data_prov.size() |
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last_prediction_time = int(last_prediction_time.value / NANOSECONDS_IN_MS) |
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predicted_anomalies = [] |
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if start_index < stop_index: |
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max_chunk_size = 50000 |
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predicted = pd.Series() |
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for index in range(start_index, stop_index, max_chunk_size): |
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chunk_start = index |
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chunk_finish = min(index + max_chunk_size, stop_index) |
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predict_augmented = self.preprocessor.get_augmented_data(chunk_start, chunk_finish) |
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assert(len(predict_augmented) == chunk_finish - chunk_start) |
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predicted_current = await self.model.predict(predict_augmented) |
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predicted = pd.concat([predicted, predicted_current]) |
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predicted_anomalies = self.preprocessor.inverse_transform_anomalies(predicted) |
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last_row = self.data_prov.get_data_range(stop_index - 1, stop_index) |
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last_dataframe_time = last_row.iloc[0]['timestamp'] |
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predicted_anomalies = utils.anomalies_to_timestamp(predicted_anomalies) |
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last_prediction_time = int(last_dataframe_time.timestamp() * 1000) |
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logger.info("Predicting is finished for anomaly type='%s'" % self.anomaly_name) |
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return predicted_anomalies, last_prediction_time |
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def synchronize_data(self): |
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self.data_prov.synchronize() |
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self.preprocessor.set_data_provider(self.data_prov) |
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self.preprocessor.synchronize() |
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def create_algorithm(self): |
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return SupervisedAlgorithm() |
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def __save_model(self): |
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pass |
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# TODO: use data_service to save anything |
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def __load_model(self): |
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pass |
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# TODO: use data_service to save anything
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