| """ | |
| This is a module for GP Interpolation | |
| """ | |
| import numpy as np | |
| from ..base import Base | |
| from GPy.models import GPRegression | |
| from GPy.kern import RBF | |
| class GP(Base): | |
| """A class that is declared for performing GP interpolation. | |
| GP interpolation (usually) works on the principle of finding the | |
| best unbiased predictor. | |
| Parameters | |
| ---------- | |
| type : str, optional | |
| This parameter defines the type of Kriging under consideration. This | |
| implementation uses PyKrige package (https://github.com/bsmurphy/PyKrige). | |
| The user needs to choose between "Ordinary" and "Universal". | |
| """ | |
| def __init__( | |
| self, | |
| kernel=RBF(2, ARD=True), | |
| ): | |
| super().__init__() | |
| self.kernel = kernel | |
| def _fit(self, X, y, n_restarts=5, verbose=False, random_state=None): | |
| """Fit method for GP Interpolation | |
| This function shouldn't be called directly. | |
| """ | |
| np.random.seed(random_state) | |
| if len(y.shape) == 1: | |
| y = y.reshape(-1, 1) | |
| self.model = GPRegression(X, y, self.kernel) | |
| self.model.optimize_restarts(n_restarts, verbose=verbose) | |
| return self | |
| def _predict_grid(self, x1lim, x2lim): | |
| """The function that is called to return the interpolated data in Kriging Interpolation | |
| in a grid. This method shouldn't be called directly""" | |
| lims = (*x1lim, *x2lim) | |
| x1min, x1max, x2min, x2max = lims | |
| x1 = np.linspace(x1min, x1max, self.resolution) | |
| x2 = np.linspace(x2min, x2max, self.resolution) | |
| X1, X2 = np.meshgrid(x1, x2) | |
| X = np.array([(i, j) for i, j in zip(X1.ravel(), X2.ravel())]) | |
| predictions = self.model.predict(X)[0].reshape(len(x1), len(x2)) | |
| return predictions.ravel() | |
| def _predict(self, X, return_variance=False): | |
| """This function should be called to return the interpolated data in kriging | |
| in a pointwise manner. This method shouldn't be called directly.""" | |
| predictions, variance = self.model.predict(X) | |
| if return_variance: | |
| return predictions.ravel(), variance | |
| else: | |
| return predictions.ravel() | |