name: ROC curve
description: Calculates Receiver Operating Characteristic curve. See https://en.wikipedia.org/wiki/Receiver_operating_characteristic
inputs:
  - {name: Predictions dir,   type: GCSPath,  description: 'GCS path of prediction file pattern.'}  #TODO: Replace dir data + schema files # type: {GCSPath: {path_type: Directory}}
  - {name: True class,        type: String, default: 'true',            description: 'The true class label for the sample. Default is "true".'}
  - {name: True score column, type: String, default: 'true',            description: 'The name of the column for positive probability.'}
  - {name: Target lambda,     type: String, default: '',                description: 'Text of Python lambda function which returns boolean value indicating whether the classification result is correct.\nFor example, "lambda x: x[''a''] and x[''b'']". If missing, input must have a "target" column.'}
  - {name: Output dir,        type: GCSPath,  description: 'GCS path of the output directory.'}     #TODO: Replace dir with single file # type: {GCSPath: {path_type: Directory}}
outputs:
  - {name: MLPipeline UI metadata, type: UI metadata}
  - {name: MLPipeline Metrics,     type: Metrics}
implementation:
  container:
    image: gcr.io/ml-pipeline/ml-pipeline-local-confusion-matrix:1.7.0
    command: [python2, /ml/roc.py]
    args: [
      --predictions,        {inputValue: Predictions dir},
      --trueclass,          {inputValue: True class},
      --true_score_column,  {inputValue: True score column},
      --target_lambda,      {inputValue: Target lambda},
      --output,             {inputValue: Output dir},
      --ui-metadata-output-path, {outputPath: MLPipeline UI metadata},
      --metrics-output-path, {outputPath: MLPipeline Metrics},
    ]