cat2cat.cat2cat_ml

Functions

cat2cat_ml_run(→ cat2cat_ml_run_results)

Run model diagnostics before using ML-based cat2cat weights.

Module Contents

cat2cat.cat2cat_ml.cat2cat_ml_run(mappings: cat2cat.dataclass.cat2cat_mappings, ml: cat2cat.dataclass.cat2cat_ml, **kwargs: Any) → cat2cat_ml_run_results

Run model diagnostics before using ML-based cat2cat weights.

This helper evaluates baseline and model-based classification quality within each mapping group and aggregates summary statistics across groups.

Parameters:

• mappings – Mapping configuration created with cat2cat_mappings.

• ml – ML configuration created with cat2cat_ml.

• **kwargs –

  Optional diagnostics settings: - test_prop (float): test split proportion in (0, 1).

  Default is 0.2.

  • split_seed (int): random seed for train/test split. Default is 42.

  • min_match (float): minimum fraction of records in ml.data whose category appears in the mapping table. Must be in [0, 1). Default is 0.8.

Returns:

object with per-group raw diagnostics and aggregated metrics such as mean accuracy, mean Brier score, mean P(true class), failure rates, and model-vs-baseline comparisons.

Return type:

cat2cat_ml_run_results

Raises:

• TypeError – if mappings or ml has invalid type.

• ValueError – if kwargs names/ranges are invalid or mapping coverage is below min_match.

Examples

>>> from sklearn.ensemble import RandomForestClassifier
>>> from cat2cat import cat2cat_ml_run
>>> from cat2cat.dataclass import cat2cat_mappings, cat2cat_ml
>>> from cat2cat.datasets import load_trans, load_occup
>>> trans = load_trans()
>>> occup = load_occup()
>>> data_2010 = occup.loc[occup.year == 2010, :].copy()
>>> mappings = cat2cat_mappings(trans, "backward")
>>> ml = cat2cat_ml(
...     data=data_2010,
...     cat_var="code",
...     features=["salary", "age", "edu", "sex"],
...     models=[RandomForestClassifier(n_estimators=50, random_state=1234)],
... )
>>> out = cat2cat_ml_run(mappings=mappings, ml=ml, test_prop=0.2)
>>> hasattr(out, "mean_acc")
True