yobx.sklearn.model_selection.halving_search#

yobx.sklearn.model_selection.halving_search.sklearn_halving_grid_search_cv(g: GraphBuilderExtendedProtocol, sts: Dict, outputs: List[str], estimator: HalvingGridSearchCV, X: str, name: str = 'halving_grid_search_cv') → str | Tuple[str, ...][source]#

Converts a sklearn.model_selection.HalvingGridSearchCV into ONNX by delegating to the converter registered for best_estimator_.

After fitting, HalvingGridSearchCV exposes the best model found during successive-halving grid search via its best_estimator_ attribute. The ONNX graph produced here is therefore identical to converting that best estimator directly.

X ──[best_estimator_ converter]──► output(s)

Parameters:

g – the graph builder to add nodes to
sts – shapes and types defined by scikit-learn
outputs – desired output tensor names for the result
estimator – a fitted HalvingGridSearchCV
X – name of the input tensor
name – prefix used for names of nodes added by this converter

Returns:

name of the output tensor, or a tuple of output tensor names

Raises:

AttributeError – if estimator has not been fitted yet (i.e. best_estimator_ does not exist)

yobx.sklearn.model_selection.halving_search.sklearn_halving_random_search_cv(g: GraphBuilderExtendedProtocol, sts: Dict, outputs: List[str], estimator: HalvingRandomSearchCV, X: str, name: str = 'halving_random_search_cv') → str | Tuple[str, ...][source]#

Converts a sklearn.model_selection.HalvingRandomSearchCV into ONNX by delegating to the converter registered for best_estimator_.

After fitting, HalvingRandomSearchCV exposes the best model found during successive-halving randomized search via its best_estimator_ attribute. The ONNX graph produced here is therefore identical to converting that best estimator directly.

X ──[best_estimator_ converter]──► output(s)