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]¶ 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
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__init__
(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]¶ Initialize self. See help(type(self)) for accurate signature.
Methods
__init__
([method, variogram_model, nlags, …])Initialize self. 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 \(R^2\) 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
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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, )
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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
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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 Returns: Return type: Prediction array
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score
(X, y, sample_weight=None)[source]¶ Return the coefficient of determination \(R^2\) of the prediction.
The coefficient \(R^2\) 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)
, wheren_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 usesmultioutput='uniform_average'
from version 0.23 to keep consistent with default value ofr2_score()
. This influences thescore
method of all the multioutput regressors (except forMultiOutputRegressor
).- 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
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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