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57 lines
1.8 KiB
57 lines
1.8 KiB
from abc import ABC, abstractmethod |
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from pandas import DataFrame |
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from typing import Optional, Union, List |
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from analytic_types import ModelCache, TimeSeries |
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from analytic_types.detector_typing import DetectionResult, ProcessingResult |
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from analytic_types.segment import Segment |
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class Detector(ABC): |
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@abstractmethod |
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def train(self, dataframe: DataFrame, payload: Union[list, dict], cache: Optional[ModelCache]) -> ModelCache: |
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""" |
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Should be thread-safe to other detectors' train method |
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""" |
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pass |
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@abstractmethod |
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def detect(self, dataframe: DataFrame, cache: Optional[ModelCache]) -> DetectionResult: |
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pass |
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@abstractmethod |
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def consume_data(self, data: DataFrame, cache: Optional[ModelCache]) -> Optional[DetectionResult]: |
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pass |
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@abstractmethod |
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def get_window_size(self, cache: Optional[ModelCache]) -> int: |
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pass |
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def is_detection_intersected(self) -> bool: |
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return True |
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def concat_detection_results(self, detections: List[DetectionResult]) -> 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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return result |
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class ProcessingDetector(Detector): |
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@abstractmethod |
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def process_data(self, data: TimeSeries, cache: Optional[ModelCache]) -> ProcessingResult: |
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pass |
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def concat_processing_results(self, processing_results: List[ProcessingResult]) -> Optional[ProcessingResult]: |
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if len(processing_results) == 0: |
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return None |
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united_result = ProcessingResult() |
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for result in processing_results: |
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united_result.data.extend(result.data) |
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return united_result
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