estimator

py3dinterpolations.modelling.estimator

Cross-validation parameter estimation for interpolation models.

Estimator(griddata, params, scoring='neg_mean_absolute_error', verbose=3)

Parameter estimation via sklearn GridSearchCV.

Currently supports pykrige's Krige wrapper for cross-validation.

Parameters:

Name	Type	Description	Default
griddata	GridData	Training data.	required
params	dict[str, list[object]]	Parameter grid for GridSearchCV.	required
scoring	str	Scoring method. See sklearn docs.	'neg_mean_absolute_error'
verbose	int	Verbosity level (0-3).	3

Source code in py3dinterpolations/modelling/estimator.py

def __init__(
    self,
    griddata: GridData,
    params: dict[str, list[object]],
    scoring: str = "neg_mean_absolute_error",
    verbose: int = 3,
):
    self.estimator = GridSearchCV(
        Krige(),
        params,
        scoring=scoring,
        verbose=verbose,
    )
    self.estimator.fit(
        y=griddata.numpy_data[:, 3],
        X=griddata.numpy_data[:, 0:3],
    )