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78 lines
2.5 KiB
78 lines
2.5 KiB
import logging |
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import pandas as pd |
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from typing import Optional, Union, List, Tuple |
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from analytic_types.data_bucket import DataBucket |
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from detectors import Detector |
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from models import ModelCache |
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import utils |
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logger = logging.getLogger('ANOMALY_DETECTOR') |
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class AnomalyDetector(Detector): |
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def __init__(self, *args, **kwargs): |
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self.bucket = DataBucket() |
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def train(self, dataframe: pd.DataFrame, payload: Union[list, dict], cache: Optional[ModelCache]) -> ModelCache: |
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return { |
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'cache': { |
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'confidence': payload['confidence'], |
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'alpha': payload['alpha'] |
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} |
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} |
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def detect(self, dataframe: pd.DataFrame, cache: Optional[ModelCache]) -> dict: |
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data = dataframe['value'] |
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last_values = None |
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if cache is not None: |
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last_values = cache['last_values'] |
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#TODO detection code here |
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smoth_data = utils.exponential_smoothing(data, cache['alpha']) |
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upper_bound = utils.exponential_smoothing(data + cache['confidence'], cache['alpha']) |
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lower_bound = utils.exponential_smoothing(data - cache['confidence'], cache['alpha']) |
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segemnts = [] |
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for idx, val in enumerate(data.values): |
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if val > upper_bound[idx] or val < lower_bound[idx]: |
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segemnts.append(idx) |
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last_detection_time = dataframe['timestamp'][-1] |
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return { |
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'cache': cache, |
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'segments': segemnts, |
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'lastDetectionTime': last_detection_time |
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} |
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def consume_data(self, data: pd.DataFrame, cache: Optional[ModelCache]) -> Optional[dict]: |
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self.detect(data, cache) |
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def __smooth_data(self, dataframe: pd.DataFrame) -> List[Tuple[int, float]]: |
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''' |
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smooth data using exponential smoothing/moving average/weighted_average |
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''' |
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def __get_confidence_window(self, smooth_data: pd.Series, condfidence: float) -> Tuple[pd.Series, pd.Series]: |
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''' |
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build confidence interval above and below smoothed data |
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''' |
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def __get_dependency_level(self, alpha: float) -> int: |
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''' |
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get the number of values that will affect the next value |
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''' |
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for level in range(1, 100): |
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if (1 - alpha) ** level < 0.1: |
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break |
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return level |
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def get_window_size(self, cache: Optional[ModelCache]) -> int: |
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if cache is None: |
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raise ValueError('anomaly detector got None cache') |
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#TODO: calculate value based on `alpha` value from cache |
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return 1
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