cat2cat.cat2cat_ml_utils

Functions

safe_nanmean(→ float)
resolve_ml_models(→ List[Tuple[str, ...)
prepare_ml_frames(→ Tuple[pandas.DataFrame, ...)
brier_score(→ float)
mean_true_probability(→ float)
apply_ml_fallback(→ None)
_feature_is_direct(→ bool)
_weight_column_method_name(→ str)

Module Contents

cat2cat.cat2cat_ml_utils.safe_nanmean(values: Sequence[Any], ndigits: int = 3) → float

cat2cat.cat2cat_ml_utils.resolve_ml_models(ml: cat2cat.dataclass.cat2cat_ml) → List[Tuple[str, sklearn.base.ClassifierMixin]]

cat2cat.cat2cat_ml_utils.prepare_ml_frames(ml: cat2cat.dataclass.cat2cat_ml, target_data: pandas.DataFrame | None = None) → Tuple[pandas.DataFrame, pandas.DataFrame | None, List[str]]

cat2cat.cat2cat_ml_utils.brier_score(prob_matrix: pandas.DataFrame, true_cats: Sequence[Any], classes: Sequence[Any]) → float

cat2cat.cat2cat_ml_utils.mean_true_probability(prob_matrix: pandas.DataFrame, true_cats: Sequence[Any]) → float

cat2cat.cat2cat_ml_utils.apply_ml_fallback(target_df: pandas.DataFrame, ml_names: Sequence[str], on_fail: str, fail_warn: bool) → None

cat2cat.cat2cat_ml_utils._feature_is_direct(feature: Any) → bool

cat2cat.cat2cat_ml_utils._weight_column_method_name(ml_name: str) → str