From 90231e8c939ef645d0b1c492819eb8618e2213fb Mon Sep 17 00:00:00 2001 From: Andrei Paleyes Date: Fri, 21 May 2021 09:25:09 +0100 Subject: [PATCH] Fix after GPy version update --- emukit/model_wrappers/gpy_model_wrappers.py | 4 ++-- requirements/requirements.txt | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/emukit/model_wrappers/gpy_model_wrappers.py b/emukit/model_wrappers/gpy_model_wrappers.py index 0bb9560e..da38098a 100644 --- a/emukit/model_wrappers/gpy_model_wrappers.py +++ b/emukit/model_wrappers/gpy_model_wrappers.py @@ -105,7 +105,7 @@ def get_covariance_between_points(self, X1: np.ndarray, X2: np.ndarray) -> np.nd argument to the posterior covariance function. :return: An array of shape n_points x 1 of posterior covariances between X1 and X2 """ - return self.model.posterior_covariance_between_points(X1, X2) + return self.model.posterior_covariance_between_points(X1, X2, include_likelihood=False) @property def X(self) -> np.ndarray: @@ -239,7 +239,7 @@ def calculate_variance_reduction(self, x_train_new: np.ndarray, x_test: np.ndarr """ fidelities_train_new = x_train_new[:, -1] y_metadata = {'output_index': fidelities_train_new.astype(int)} - covariance = self.gpy_model.posterior_covariance_between_points(x_train_new, x_test) + covariance = self.gpy_model.posterior_covariance_between_points(x_train_new, x_test, include_likelihood=False) variance_prediction = self.gpy_model.predict(x_train_new, Y_metadata=y_metadata)[1] return covariance**2 / variance_prediction diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 482b3b8b..47f48d79 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -3,6 +3,6 @@ numpy>=1.14.5 # This is unfortunate - we don't need matplotlib # but until GPy and GPyOpt get their dependencies straight # we need GPy's plotting extra to ensure smooth installation -GPy[plotting]>=1.9.9 +GPy[plotting]>=1.10.0 emcee>=2.2.1 scipy>=1.1.0