PseudoLabelMerge

Pseudo label merge step that intelligently combines labeled base data with pseudo-labeled or augmented samples for Semi-Supervised Learning (SSL) and Active Learning workflows with split-aware merge, auto-inferred split ratios, and provenance tracking

SageMaker step type	Processing
Node type	internal (consumes upstream, produces downstream)
Container entry point	pseudo_label_merge.py
Interface file	steps/interfaces/pseudo_label_merge.step.yaml

Compute

Compute kind	sklearn

Functionality

Pseudo label merge script. Intelligently merges labeled base data with pseudo-labeled or augmented samples for SSL and Active Learning workflows.

Inputs (dependencies)

Input	Type	Required	Compatible producers
base_data	processing_output	yes	TabularPreprocessing, RiskTableMapping, MissingValueImputation, FeatureSelection, StratifiedSampling, TemporalSequenceNormalization, TemporalFeatureEngineering, LabelRulesetExecution
augmentation_data	processing_output	yes	ActiveSampleSelection, XGBoostModelInference, LightGBMModelInference, PyTorchModelInference, XGBoostModelEval, LightGBMModelEval, PyTorchModelEval, BedrockBatchProcessing, BedrockProcessing, LabelRulesetExecution

Outputs

Output	Type
merged_data	processing_output

Consumers (downstream steps)

No cataloged step lists this step as a compatible source (it may be a terminal/sink step, or consumed via a generic source name).

Framework requirements

Package	Version
pandas	>=1.3.0
numpy	>=1.21.0

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