def backward_attributes(dictionary): """ add new attributes to old instance or update attribute types """ if "name" not in dictionary: dictionary["name"] = "default_gp" if "per_atom_par" not in dictionary: dictionary["per_atom_par"] = True if "optimization_algorithm" not in dictionary: dictionary["opt_algorithm"] = "L-BFGS-B" if "hyps_mask" not in dictionary: dictionary["hyps_mask"] = None if "parallel" not in dictionary: dictionary["parallel"] = False if "component" not in dictionary: dictionary["component"] = "mc" if "training_structures" not in dictionary: # Environments of each structure dictionary["training_structures"] = [] dictionary["energy_labels"] = [] # Energies of training structures dictionary["energy_labels_np"] = np.empty(0, ) if "training_labels" not in dictionary: dictionary["training_labels"] = [] dictionary["training_labels_np"] = np.empty(0, ) if "energy_noise" not in dictionary: dictionary["energy_noise"] = 0.01 if not isinstance(dictionary["cutoffs"], dict): dictionary["cutoffs"] = Parameters.cutoff_array_to_dict( dictionary["cutoffs"]) dictionary["hyps_mask"] = Parameters.backward( dictionary["kernels"], deepcopy(dictionary["hyps_mask"])) if "logger_name" not in dictionary: dictionary["logger_name"] = None
def backward_attributes(dictionary): """ add new attributes to old instance or update attribute types """ if 'name' not in dictionary: dictionary['name'] = 'default_gp' if 'per_atom_par' not in dictionary: dictionary['per_atom_par'] = True if 'optimization_algorithm' not in dictionary: dictionary['opt_algorithm'] = 'L-BFGS-B' if 'hyps_mask' not in dictionary: dictionary['hyps_mask'] = None if 'parallel' not in dictionary: dictionary['parallel'] = False if 'component' not in dictionary: dictionary['component'] = 'mc' if 'training_structures' not in dictionary: # Environments of each structure dictionary['training_structures'] = [] dictionary['energy_labels'] = [] # Energies of training structures dictionary['energy_labels_np'] = np.empty(0, ) if 'training_labels' not in dictionary: dictionary['training_labels'] = [] dictionary['training_labels_np'] = np.empty(0, ) if 'energy_noise' not in dictionary: dictionary['energy_noise'] = 0.01 if not isinstance(dictionary['cutoffs'], dict): dictionary['cutoffs'] = Parameters.cutoff_array_to_dict( dictionary['cutoffs']) dictionary['hyps_mask'] = Parameters.backward( dictionary['kernels'], deepcopy(dictionary['hyps_mask'])) if 'logger_name' not in dictionary: dictionary['logger_name'] = None