from data_provider import DataProvider import logging from urllib.parse import urlparse import os.path import json import config import pandas as pd logger = logging.getLogger('analytic_toolset') def segments_box(segments): max_time = 0 min_time = float("inf") for segment in segments: min_time = min(min_time, segment['start']) max_time = max(max_time, segment['finish']) min_time = pd.to_datetime(min_time, unit='ms') max_time = pd.to_datetime(max_time, unit='ms') return min_time, max_time class PatternDetectionModel: def __init__(self, analytic_unit_id, pattern): self.analytic_unit_id = analytic_unit_id self.pattern = pattern self.__load_anomaly_config() parsedUrl = urlparse(self.anomaly_config['panelUrl']) origin = parsedUrl.scheme + '://' + parsedUrl.netloc datasource = self.anomaly_config['datasource'] metric_name = self.anomaly_config['metric']['targets'][0] target_filename = os.path.join(config.METRICS_FOLDER, metric_name + ".json") datasource['origin'] = origin dataset_filename = os.path.join(config.DATASET_FOLDER, metric_name + ".csv") with open(target_filename, 'r') as file: target = json.load(file) self.data_prov = DataProvider(datasource, target, dataset_filename) self.model = None self.__load_model(pattern) def learn(self, segments): self.model = self.__create_model(self.pattern) window_size = 200 dataframe = self.data_prov.get_dataframe() segments = self.data_prov.transform_anomalies(segments) # TODO: pass only part of dataframe that has segments self.model.fit(dataframe, segments) self.__save_model() return 0 def predict(self, last_prediction_time): if self.model is None: return [], last_prediction_time window_size = 100 last_prediction_time = pd.to_datetime(last_prediction_time, unit='ms') 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 # return predicted_anomalies, last_prediction_time def synchronize_data(self): self.data_prov.synchronize() def __create_model(self, pattern): if pattern == "peaks": from peaks_detector import PeaksDetector return PeaksDetector() if pattern == "jumps" or pattern == "drops": from step_detector import StepDetector return StepDetector(pattern) def __load_anomaly_config(self): with open(os.path.join(config.ANALYTIC_UNITS_FOLDER, self.analytic_unit_id + ".json"), 'r') as config_file: self.anomaly_config = json.load(config_file) def __save_model(self): logger.info("Save model '%s'" % self.analytic_unit_id) model_filename = os.path.join(config.MODELS_FOLDER, self.analytic_unit_id + ".m") self.model.save(model_filename) def __load_model(self, pattern): logger.info("Load model '%s'" % self.analytic_unit_id) model_filename = os.path.join(config.MODELS_FOLDER, self.pattern + ".m") if os.path.exists(model_filename): self.model = self.__create_model(pattern) self.model.load(model_filename)