pykrige.rk.RegressionKriging¶
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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]¶ 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
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__init__
(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]¶ Initialize self. See help(type(self)) for accurate signature.
Methods
__init__
([regression_model, method, …])Initialize self. 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, )
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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
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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
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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, )