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 metadata routing of this object.
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_fit_request
(*[, x])Request metadata passed to the
fit
method.set_params
(**params)Set the parameters of this estimator.
set_predict_request
(*[, x])Request metadata passed to the
predict
method.set_score_request
(*[, sample_weight])Request metadata passed to the
score
method.- 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_metadata_routing()[source]
Get metadata routing of this object.
Please check User Guide on how the routing mechanism works.
- Returns:
routing – A
MetadataRequest
encapsulating routing information.- Return type:
MetadataRequest
- 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)
, 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)
w.r.t. 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
).
- set_fit_request(*, x: bool | None | str = '$UNCHANGED$') Krige [source]
Request metadata passed to the
fit
method.Note that this method is only relevant if
enable_metadata_routing=True
(seesklearn.set_config()
). Please see User Guide on how the routing mechanism works.The options for each parameter are:
True
: metadata is requested, and passed tofit
if provided. The request is ignored if metadata is not provided.False
: metadata is not requested and the meta-estimator will not pass it tofit
.None
: metadata is not requested, and the meta-estimator will raise an error if the user provides it.str
: metadata should be passed to the meta-estimator with this given alias instead of the original name.
The default (
sklearn.utils.metadata_routing.UNCHANGED
) retains the existing request. This allows you to change the request for some parameters and not others.Added in version 1.3.
Note
This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a
Pipeline
. Otherwise it has no effect.- Parameters:
x (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for
x
parameter infit
.- Returns:
self – The updated object.
- Return type:
object
- 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
- set_predict_request(*, x: bool | None | str = '$UNCHANGED$') Krige [source]
Request metadata passed to the
predict
method.Note that this method is only relevant if
enable_metadata_routing=True
(seesklearn.set_config()
). Please see User Guide on how the routing mechanism works.The options for each parameter are:
True
: metadata is requested, and passed topredict
if provided. The request is ignored if metadata is not provided.False
: metadata is not requested and the meta-estimator will not pass it topredict
.None
: metadata is not requested, and the meta-estimator will raise an error if the user provides it.str
: metadata should be passed to the meta-estimator with this given alias instead of the original name.
The default (
sklearn.utils.metadata_routing.UNCHANGED
) retains the existing request. This allows you to change the request for some parameters and not others.Added in version 1.3.
Note
This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a
Pipeline
. Otherwise it has no effect.- Parameters:
x (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for
x
parameter inpredict
.- Returns:
self – The updated object.
- Return type:
object
- set_score_request(*, sample_weight: bool | None | str = '$UNCHANGED$') Krige [source]
Request metadata passed to the
score
method.Note that this method is only relevant if
enable_metadata_routing=True
(seesklearn.set_config()
). Please see User Guide on how the routing mechanism works.The options for each parameter are:
True
: metadata is requested, and passed toscore
if provided. The request is ignored if metadata is not provided.False
: metadata is not requested and the meta-estimator will not pass it toscore
.None
: metadata is not requested, and the meta-estimator will raise an error if the user provides it.str
: metadata should be passed to the meta-estimator with this given alias instead of the original name.
The default (
sklearn.utils.metadata_routing.UNCHANGED
) retains the existing request. This allows you to change the request for some parameters and not others.Added in version 1.3.
Note
This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a
Pipeline
. Otherwise it has no effect.- Parameters:
sample_weight (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for
sample_weight
parameter inscore
.- Returns:
self – The updated object.
- Return type:
object