JABS Behavior (jabs-behavior)
Prediction Postprocessing
The jabs.behavior.postprocessing module provides tools for refining behavior predictions generated by the JABS framework. It includes classes and functions to manipulate and enhance the quality of behavior event predictions through various postprocessing techniques.
- BehaviorEvents: Run-length encoding of JABS predictions for efficient behavior event manipulation
- Postprocessing Stages: Configurable pipeline stages for refining behavior predictions:
BoutDurationFilterStage: Remove short behavior bouts below a minimum durationBoutStitchingStage: Combine behavior bouts separated by short gapsGapInterpolationStage: Fill gaps in predictions (NONE labels)
- PostprocessingPipeline: Sequential application of multiple Stages
Usage
import numpy as np
from jabs.behavior.events import BehaviorEvents, ClassLabels
from jabs.behavior.postprocessing import BoutDurationFilterStage, BoutStitchingStage, PostprocessingPipeline
# Create behavior events from a prediction vector
predictions = np.array([0, 0, 1, 1, 0, 1, 1, 1, 0])
events = BehaviorEvents.from_vector(predictions)
# Apply postprocessing pipeline
config = {
BoutStitchingStage.name: {"max_stitch_gap": 2},
BoutDurationFilterStage.name: {"min_duration": 3},
}
pipeline = PostprocessingPipeline(config)
refined_predictions = pipeline.run(predictions)
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Metadata
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Release files for jabs-behavior 0.47.0
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