LabelRulesetExecution

Label ruleset execution step that applies validated rulesets to processed data to generate classification labels using priority-based rule evaluation with execution-time field validation

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

Compute

Compute kind	sklearn

Functionality

Label ruleset execution script. Applies validated rulesets to processed data to generate classification labels using priority-based rule evaluation.

Inputs (dependencies)

Input	Type	Required	Compatible producers
validated_ruleset	processing_output	yes	LabelRulesetGeneration, SlipboxKnowledgeRouting
input_data	processing_output	yes	TabularPreprocessing, BedrockProcessing, BedrockBatchProcessing, TemporalSequenceNormalization, TemporalFeatureEngineering, StratifiedSampling, MissingValueImputation, FeatureSelection, CurrencyConversion, RiskTableMapping

Outputs

Output	Type
processed_data	processing_output
execution_report	processing_output

Consumers (downstream steps)

Steps that declare this step as a compatible input source:

• ActiveSampleSelection

• LightGBMMTModelEval

• LightGBMMTModelInference

• PseudoLabelMerge

• PyTorchTraining

• XGBoostTraining

• XgboostMtModelEval

Framework requirements

Package	Version
pandas	>=1.3.0
pydantic	>=2.0.0

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