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88 lines
3.0 KiB
88 lines
3.0 KiB
import logging as log |
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import pandas as pd |
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import numpy as np |
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from typing import Optional, List |
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from analytic_types import ModelCache |
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from analytic_types.detector_typing import DetectionResult |
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from analytic_types.segment import Segment |
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from detectors import Detector |
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from time import time |
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import utils |
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logger = log.getLogger('THRESHOLD_DETECTOR') |
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class ThresholdDetector(Detector): |
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WINDOW_SIZE = 3 |
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def __init__(self): |
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pass |
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def train(self, dataframe: pd.DataFrame, threshold: dict, cache: Optional[ModelCache]) -> ModelCache: |
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return { |
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'cache': { |
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'value': threshold['value'], |
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'condition': threshold['condition'] |
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} |
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} |
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def detect(self, dataframe: pd.DataFrame, cache: ModelCache) -> DetectionResult: |
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if cache is None or cache == {}: |
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raise ValueError('Threshold detector error: cannot detect before learning') |
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if len(dataframe) == 0: |
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return None |
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value = cache['value'] |
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condition = cache['condition'] |
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segments = [] |
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for index, row in dataframe.iterrows(): |
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current_value = row['value'] |
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current_timestamp = utils.convert_pd_timestamp_to_ms(row['timestamp']) |
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segment = Segment(current_timestamp, current_timestamp) |
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# TODO: merge segments |
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if pd.isnull(current_value): |
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if condition == 'NO_DATA': |
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segments.append(segment) |
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continue |
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if condition == '>': |
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if current_value > value: |
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segments.append(segment) |
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elif condition == '>=': |
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if current_value >= value: |
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segments.append(segment) |
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elif condition == '=': |
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if current_value == value: |
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segments.append(segment) |
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elif condition == '<=': |
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if current_value <= value: |
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segments.append(segment) |
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elif condition == '<': |
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if current_value < value: |
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segments.append(segment) |
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last_entry = dataframe.iloc[-1] |
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last_detection_time = utils.convert_pd_timestamp_to_ms(last_entry['timestamp']) |
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return DetectionResult(cache, segments, last_detection_time) |
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def consume_data(self, data: pd.DataFrame, cache: Optional[ModelCache]) -> Optional[DetectionResult]: |
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result = self.detect(data, cache) |
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return result if result else None |
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def get_window_size(self, cache: Optional[ModelCache]) -> int: |
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return self.WINDOW_SIZE |
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def concat_detection_results(self, detections: List[DetectionResult], time_step: int) -> DetectionResult: |
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result = DetectionResult() |
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for detection in detections: |
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result.segments.extend(detection.segments) |
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result.last_detection_time = detection.last_detection_time |
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result.cache = detection.cache |
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result.segments = utils.merge_intersecting_segments(result.segments, time_step) |
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return result
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