pykrige.rk.RegressionKriging

class pykrige.rk.RegressionKriging(regression_model=SVR(C=1.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma='auto', kernel='rbf', max_iter=-1, shrinking=True, tol=0.001, verbose=False), method='ordinary', variogram_model='linear', n_closest_points=10, nlags=6, weight=False, verbose=False)

This is an implementation of Regression-Kriging as described here: https://en.wikipedia.org/wiki/Regression-Kriging

__init__(regression_model=SVR(C=1.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma='auto', kernel='rbf', max_iter=-1, shrinking=True, tol=0.001, verbose=False), method='ordinary', variogram_model='linear', n_closest_points=10, nlags=6, weight=False, verbose=False)

Parameters:

• regression_model (machine learning model instance from sklearn) –
• method (str, optional) – type of kriging to be performed
• variogram_model (str, optional) – variogram model to be used during Kriging
• n_closest_points (int) – number of closest points to be used during Ordinary Kriging
• nlags (int) – see OK/UK class description
• weight (bool) – see OK/UK class description
• verbose (bool) – see OK/UK class description

Methods

__init__([regression_model, cache_size, …])	param regression_model:
fit(p, x, y)	fit the regression method and also Krige the residual
krige_residual(x)	param x:ndarray of (x, y) points. Needs to be a (Ns, 2) array
predict(p, x)	param p:(Ns, d) array of predictor variables (Ns samples, d dimensions)
score(p, x, y[, sample_weight])	Overloading default regression score method

fit(p, x, y)

fit the regression method and also Krige the residual

Parameters:

• p (ndarray) – (Ns, d) array of predictor variables (Ns samples, d dimensions) for regression
• x (ndarray) – ndarray of (x, y) points. Needs to be a (Ns, 2) array corresponding to the lon/lat, for example 2d regression kriging. array of Points, (x, y, z) pairs of shape (N, 3) for 3d kriging
• y (ndarray) – array of targets (Ns, )

krige_residual(x)

Parameters:x (ndarray) – ndarray of (x, y) points. Needs to be a (Ns, 2) array corresponding to the lon/lat, for example. Returns:residual – kriged residual values Return type:ndarray

predict(p, x)

Parameters:

• p (ndarray) – (Ns, d) array of predictor variables (Ns samples, d dimensions) for regression
• x (ndarray) – ndarray of (x, y) points. Needs to be a (Ns, 2) array corresponding to the lon/lat, for example. array of Points, (x, y, z) pairs of shape (N, 3) for 3d kriging

Returns:

pred – The expected value of ys for the query inputs, of shape (Ns,).

Return type:

ndarray

score(p, x, y, sample_weight=None)

Overloading default regression score method

Parameters:

• p (ndarray) – (Ns, d) array of predictor variables (Ns samples, d dimensions) for regression
• x (ndarray) – ndarray of (x, y) points. Needs to be a (Ns, 2) array corresponding to the lon/lat, for example. array of Points, (x, y, z) pairs of shape (N, 3) for 3d kriging
• y (ndarray) – array of targets (Ns, )