def execute_experiment(features_hypers1, features_hypers2):
experiment_id, gfre_vec = gfr_pairwise_experiment(dataset_name,
nb_samples,
features_hypers1,
features_hypers2,
two_split=two_split,
train_ratio=train_ratio,
seed=seed,
noise_removal=False,
regularizer=regularizer,
compute_distortion=False,
one_direction=True)
experiment_ids.append(experiment_id)
print(gfre_vec)
}
}]
for nb_local_envs in nbs_local_envs:
hash_value = lfre_pairwise_experiment(dataset_name,
nb_samples,
features_hypers1,
features_hypers2,
nb_local_envs,
two_split,
seed,
train_ratio,
regularizer,
inner_epsilon,
outer_epsilon,
one_direction=False)
experiment_ids.append(hash_value)
print(f"nb_local_envs={nb_local_envs} hash_value={hash_value}")
print(experiment_ids)
hash_value, _ = gfr_pairwise_experiment(dataset_name,
nb_samples,
features_hypers1,
features_hypers2,
two_split=two_split,
train_ratio=train_ratio,
seed=seed,
noise_removal=False,
regularizer=regularizer,
compute_distortion=False)
print(f"GFRE={hash_value}")
"interaction_cutoff": cutoff,
"max_radial": max_radial,
"max_angular": max_angular,
"gaussian_sigma_constant": sigma,
"gaussian_sigma_type": "Constant",
"cutoff_smooth_width": cutoff_smooth_width,
"normalize": normalize
}
} for max_radial, max_angular in max_radials_angulars]
hash_value, _ = gfr_pairwise_experiment(
dataset_name,
nb_samples,
features_hypers1,
features_hypers2,
two_split=two_split,
train_ratio=train_ratio,
seed=seed,
noise_removal=False,
regularizer=regularizer,
set_methane_dataset_to_same_species=False)
hash_values.append(hash_value)
print('"' + ' '.join(hash_values).replace(' ', '" "') + '" ')
print(f"soap_{dataset_name}_hash_value = " + '[' + '"' +
' '.join(hash_values).replace(' ', '", "') + '"]')
hash_values = []
for feature_count in feature_counts:
features_hypers1 = [{