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Definition

The column values match test checks if, for a given column, the values are the same between the current dataset and the reference dataset.

  • In development projects, the training set is used as the reference and the validation set as the current dataset.
  • In monitoring projects, the reference dataset is uploaded by the user and the production data is the current dataset.

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Taxonomy

  • Category: Consistency.
  • Task types: LLM, tabular classification, tabular regression, text classification.
  • Availability: .

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Why it matters

  • The column values match test can help verifying that key attributes or features in your dataset remain consistent over time or across different datasets.

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Test configuration examples

If you are writing a tests.json, here are a few valid configurations for the character length test:

[
  {
    "name": "Values in `output` and `target` match",
    "description": "Make sure that rows in your two datasets have the same values for target_column_name where reference_column_name is also the same",
    "type": "consistency",
    "subtype": "columnValuesMatch",
    "thresholds": [
      {
        "insightName": "columnValuesMatch",
        "insightParameters": [
          { "name": "reference_column_name", "value": "output" }, // Selects the column `output` as the reference column
          { "name": "target_column_name", "value": "target" } // Selects the column `target` as the target column
        ],
        "measurement": "failingRowPercentage", // Must be one of `failingRowPercentage` or `failingRowCount`
        "operator": "<=",
        "value": 0.0
      }
    ],
    "subpopulationFilters": null,
    "mode": "development",
    "usesValidationDataset": true,
    "usesTrainingDataset": true,
    "usesMlModel": false,
    "syncId": "b4dee7dc-4f15-48ca-a282-63e2c04e0689" // Some unique id
  }
]