syne_tune/optimizer/schedulers/searchers/bayesopt/gpautograd/gp_regression.py [121:140]:
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            log_msg += ("{} of the {} restarts failed with the following exceptions:\n".format(
                n_starts - n_succeeded, n_starts))
            copy_params = {
                param.name: param.data()
                for param in self.likelihood.collect_params().values()}
            for i, ret_info in enumerate(ret_infos):
                if ret_info is not None:
                    log_msg += ("- Restart {}: Exception {}\n".format(
                        i, ret_info['type']))
                    log_msg += ("  Message: {}\n".format(ret_info['msg']))
                    log_msg += ("  Args: {}\n".format(ret_info['args']))
                    # Set parameters in order to print them. These are the
                    # parameters for which the evaluation failed
                    self._set_likelihood_params(ret_info['params'])
                    log_msg += ("  Params: " + str(self.get_params()))
                    logger.info(log_msg)
            # Restore parameters
            self._set_likelihood_params(copy_params)
            if n_succeeded == 0:
                logger.info("All restarts failed: Skipping hyperparameter fitting for now")
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syne_tune/optimizer/schedulers/searchers/bayesopt/gpautograd/learncurve/gpiss_model.py [153:172]:
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            log_msg += ("{} of the {} restarts failed with the following exceptions:\n".format(
                n_starts - n_succeeded, n_starts))
            copy_params = {
                param.name: param.data()
                for param in self.likelihood.collect_params().values()}
            for i, ret_info in enumerate(ret_infos):
                if ret_info is not None:
                    log_msg += ("- Restart {}: Exception {}\n".format(
                        i, ret_info['type']))
                    log_msg += ("  Message: {}\n".format(ret_info['msg']))
                    log_msg += ("  Args: {}\n".format(ret_info['args']))
                    # Set parameters in order to print them. These are the
                    # parameters for which the evaluation failed
                    self._set_likelihood_params(ret_info['params'])
                    log_msg += ("  Params: " + str(self.get_params()))
                    logger.info(log_msg)
            # Restore parameters
            self._set_likelihood_params(copy_params)
            if n_succeeded == 0:
                logger.info("All restarts failed: Skipping hyperparameter fitting for now")
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