pykrige.rk.Krige

class pykrige.rk.Krige(method='ordinary', variogram_model='linear', nlags=6, weight=False, n_closest_points=10, verbose=False, exact_values=True, pseudo_inv=False, pseudo_inv_type='pinv', variogram_parameters=None, variogram_function=None, anisotropy_scaling=(1.0, 1.0), anisotropy_angle=(0.0, 0.0, 0.0), enable_statistics=False, coordinates_type='euclidean', drift_terms=None, point_drift=None, ext_drift_grid=(None, None, None), functional_drift=None)[source]

Bases: RegressorMixin, BaseEstimator

A scikit-learn wrapper class for Ordinary and Universal Kriging.

This works with both Grid/RandomSearchCv for finding the best Krige parameters combination for a problem.

Parameters

  • method (str, optional) – type of kriging to be performed

  • variogram_model (str, optional) – variogram model to be used during Kriging

  • nlags (int) – see OK/UK class description

  • weight (bool) – see OK/UK class description

  • n_closest_points (int) – number of closest points to be used during Ordinary Kriging

  • verbose (bool) – see OK/UK class description

  • exact_values (bool) – see OK/UK class description

  • variogram_parameters (list or dict) – see OK/UK class description

  • variogram_function (callable) – see OK/UK class description

  • anisotropy_scaling (tuple) – single value for 2D (UK/OK) and two values in 3D (UK3D/OK3D)

  • anisotropy_angle (tuple) – single value for 2D (UK/OK) and three values in 3D (UK3D/OK3D)

  • enable_statistics (bool) – see OK class description

  • coordinates_type (str) – see OK/UK class description

  • drift_terms (list of strings) – see UK/UK3D class description

  • point_drift (array_like) – see UK class description

  • ext_drift_grid (tuple) – Holding the three values external_drift, external_drift_x and external_drift_z for the UK class

  • functional_drift (list of callable) – see UK/UK3D class description

Methods

execute(points, *args, **kwargs)

Execute.

fit(x, y, *args, **kwargs)

Fit the current model.

get_params([deep])

Get parameters for this estimator.

predict(x, *args, **kwargs)

Predict.

score(X, y[, sample_weight])

Return the coefficient of determination of the prediction.

set_params(**params)

Set the parameters of this estimator.
execute(points, *args, **kwargs)[source]

Execute.

Parameters

points (dict) –

Returns

  • Prediction array

  • Variance array

fit(x, y, *args, **kwargs)[source]

Fit the current model.

Parameters

  • x (ndarray) – array of Points, (x, y) pairs of shape (N, 2) for 2d kriging array of Points, (x, y, z) pairs of shape (N, 3) for 3d kriging

  • y (ndarray) – array of targets (N, )

get_params(deep=True)[source]

Get parameters for this estimator.

Parameters

deep (bool, default=True) – If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns

params – Parameter names mapped to their values.

Return type

dict

predict(x, *args, **kwargs)[source]

Predict.

Parameters

x (ndarray) – array of Points, (x, y) pairs of shape (N, 2) for 2d kriging array of Points, (x, y, z) pairs of shape (N, 3) for 3d kriging

Return type

Prediction array

score(X, y, sample_weight=None)[source]

Return the coefficient of determination of the prediction.

The coefficient of determination \(R^2\) is defined as \((1 - \frac{u}{v})\), where \(u\) is the residual sum of squares ((y_true - y_pred)** 2).sum() and \(v\) is the total sum of squares ((y_true - y_true.mean()) ** 2).sum(). The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of y, disregarding the input features, would get a \(R^2\) score of 0.0.

Parameters

  • X (array-like of shape (n_samples, n_features)) – Test samples. For some estimators this may be a precomputed kernel matrix or a list of generic objects instead with shape (n_samples, n_samples_fitted), where n_samples_fitted is the number of samples used in the fitting for the estimator.

  • y (array-like of shape (n_samples,) or (n_samples, n_outputs)) – True values for X.

  • sample_weight (array-like of shape (n_samples,), default=None) – Sample weights.

Returns

score – \(R^2\) of self.predict(X) wrt. y.

Return type

float

Notes

The \(R^2\) score used when calling score on a regressor uses multioutput='uniform_average' from version 0.23 to keep consistent with default value of r2_score(). This influences the score method of all the multioutput regressors (except for MultiOutputRegressor).

set_params(**params)[source]

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters

**params (dict) – Estimator parameters.

Returns

self – Estimator instance.

Return type

estimator instance