PiperMetricGeneration¶

PIPER metric generation step; recomputes ROC/PR curves and emits PIPER .metric + paired data CSVs flat to the output root for PIPER rendering


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

Compute¶


Compute kind	`sklearn`

Functionality¶

PIPER metric generation script. Loads prediction data, recomputes ROC and PR curves, and emits PIPER .metric JSON files with paired 2-column data CSVs written flat to the output root for PIPER rendering.

Inputs (dependencies)¶

Input	Type	Required	Compatible producers
`eval_output`	`processing_output`	yes	XGBoostModelInference, XGBoostModelEval, LightGBMMTModelInference, LightGBMModelInference, PyTorchModelInference, TabularPreprocessing, CradleDataLoading, RiskTableMapping, CurrencyConversion

Outputs¶

Output	Type
`metric_output`	`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`
`scikit-learn`	`>=1.0.0`

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