Validate.assert_dimension_scores()

Raise an AssertionError if any dimension’s health score falls below a minimum.

Usage

Source

Validate.assert_dimension_scores(
    thresholds=None,
    message=None,
    by=None,
)

The assert_dimension_scores() method checks each data quality dimension’s score (from get_dimension_scores()) against a minimum acceptable value. This is useful in automated testing and CI environments where you want to fail the run when, say, the completeness score drops below 95.

Parameters

thresholds: dict[str, float] | None = None

A mapping of dimension name to a minimum acceptable score (0-100). If None, the minimums set via config(dimension_thresholds=...) are used. A dimension present in the thresholds but absent from the validation is ignored.

message: str | None = None

Custom error message to use if the assertion fails. If None, a default message that lists the offending dimensions (with actual vs. required scores) is generated.

by: str | None = None

The name of a column that steps are segmented by (with segments=), to require the minimums of every segment rather than of the whole validation. The scores of each segment are those of get_dimension_scores(by=).

Returns

None

Raises

AssertionError

If any dimension’s score is below its specified minimum.

Examples

Let’s validate the small_table dataset with a not-null check (a completeness step) and a range check (a validity step).

import pointblank as pb

small_table = pb.load_dataset(dataset="small_table", tbl_type="polars")

validation = (
    pb.Validate(data=small_table)
    .col_vals_not_null(columns="c")
    .col_vals_gt(columns="d", value=1000)
    .interrogate()
)

validation.get_dimension_scores()

{'completeness': 84.62, 'validity': 53.85}

Requiring completeness of at least 80 and validity of at least 50 passes, so nothing happens:

validation.assert_dimension_scores(thresholds={"completeness": 80, "validity": 50})

Raising the completeness minimum to 95 makes the assertion fail. The AssertionError is caught here so that its message can be shown:

try:
    validation.assert_dimension_scores(thresholds={"completeness": 95, "validity": 50})
except AssertionError as e:
    print(e)

Dimension health score(s) below the required minimum: completeness (84.62% < 95%)

The message lists each dimension below its minimum, with the actual and required scores. In a test suite or a CI job, leave the error uncaught so that it fails the run. To use the same minimums everywhere, set them once with config(dimension_thresholds=...) and call assert_dimension_scores() without thresholds=.

See Also

get_dimension_scores()

Get the scores without raising.

config

Set default per-dimension thresholds globally.