mixle.data.validate module

Check that a dataset conforms to the spec a model expects, before fitting.

check_dataset(model, data) derives the model’s schema (mixle.data.schema.Schema.for_model()) and, over a sample of records, verifies each record (a) coerces to the schema’s logical types and (b) lies in the model’s support (finite log-density). It returns a report (and can raise on failure) so an invalid feed – wrong column types, out-of-support values, malformed records – is caught up front rather than surfacing later as a non-finite likelihood or a cryptic error deep in EM.

class DataReport(ok, n_checked, schema, issues=<factory>)

Bases: object

Result of checking sample records against a model-derived schema and support.

Parameters:

• ok (bool)

• n_checked (int)

• schema (list[tuple[str, str]])

• issues (list[str])

check_dataset(model, data, *, sample=1000, check_support=True, raise_on_error=False)

Validate data against the schema/support model expects (over the first sample records).

Records both type-coercion failures (wrong shape/dtype for a field) and, when check_support is True, support violations (a value the model assigns probability 0 -> -inf log-density). With raise_on_error the first batch of issues is raised as a ValueError.

Parameters:

• model (Any)

• data (Any)

• sample (int)

• check_support (bool)

• raise_on_error (bool)

Return type:

DataReport