pykrige.rk.RegressionKriging
- class pykrige.rk.RegressionKriging(regression_model=SVR(), method='ordinary', variogram_model='linear', n_closest_points=10, nlags=6, weight=False, 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:
object
An implementation of Regression-Kriging.
As described here: https://en.wikipedia.org/wiki/Regression-Kriging
- 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
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
fit
(p, x, y)Fit the regression method and also Krige the residual.
krige_residual
(x, **kwargs)Calculate the residuals.
predict
(p, x, **kwargs)Predict.
score
(p, x, y[, sample_weight])Overloading default regression score method.
- fit(p, x, y)[source]
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, **kwargs)[source]
Calculate the residuals.
- 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, **kwargs)[source]
Predict.
- 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, **kwargs)[source]
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, )