pykrige.rk.Krige

class pykrige.rk.Krige(method='ordinary', variogram_model='linear', nlags=6, weight=False, n_closest_points=10, verbose=False)

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.

__init__(method='ordinary', variogram_model='linear', nlags=6, weight=False, n_closest_points=10, verbose=False)

Methods

__init__([method, variogram_model, nlags, …])
execute(points, *args, **kwargs)	param points:
fit(x, y, *args, **kwargs)	param x:array of Points, (x, y) pairs of shape (N, 2) for 2d kriging
get_params([deep])	Get parameters for this estimator.
predict(x, *args, **kwargs)	param x:array of Points, (x, y) pairs of shape (N, 2) for 2d kriging
score(X, y[, sample_weight])	Returns the coefficient of determination R^2 of the prediction.
set_params(**params)	Set the parameters of this estimator.

execute(points, *args, **kwargs)

Parameters:

• points (dict) –
• Returns –
• ------- –
• array (Variance) –
• array –

fit(x, y, *args, **kwargs)

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)

Get parameters for this estimator.

Parameters:deep (boolean, optional) – 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:mapping of string to any

predict(x, *args, **kwargs)

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

score(X, y, sample_weight=None)

Returns the coefficient of determination R^2 of the prediction.

The coefficient R^2 is defined as (1 - 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, shape = (n_samples, n_features)) – Test samples.
• y (array-like, shape = (n_samples) or (n_samples, n_outputs)) – True values for X.
• sample_weight (array-like, shape = [n_samples], optional) – Sample weights.

Returns:

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

Return type:

float

set_params(**params)

Set the parameters of this estimator.

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

Returns: Return type:self