def methanol_gp():
the_gp = GaussianProcess(
kernel_name="2+3_mc",
hyps=np.array([
3.75996759e-06,
1.53990678e-02,
2.50624782e-05,
5.07884426e-01,
1.70172923e-03,
]),
cutoffs=np.array([5, 3]),
hyp_labels=["l2", "s2", "l3", "s3", "n0"],
maxiter=1,
opt_algorithm="L-BFGS-B",
)
with open(path.join(TEST_FILE_DIR, "methanol_envs.json"), "r") as f:
dicts = [loads(s) for s in f.readlines()]
for cur_dict in dicts:
force = cur_dict["forces"]
env = AtomicEnvironment.from_dict(cur_dict)
the_gp.add_one_env(env, force)
the_gp.set_L_alpha()
return the_gp
def methanol_gp():
the_gp = GaussianProcess(kernel_name="2+3_mc",
hyps=np.array([
3.75996759e-06, 1.53990678e-02,
2.50624782e-05, 5.07884426e-01, 1.70172923e-03
]),
cutoffs=np.array([7, 7]),
hyp_labels=['l2', 's2', 'l3', 's3', 'n0'],
maxiter=1,
opt_algorithm='L-BFGS-B')
with open('./test_files/methanol_envs.json') as f:
dicts = [loads(s) for s in f.readlines()]
for cur_dict in dicts:
force = cur_dict['forces']
env = AtomicEnvironment.from_dict(cur_dict)
the_gp.add_one_env(env, force)
the_gp.set_L_alpha()
return the_gp
def test_prediction():
"""
Test that prediction functions works.
The RBCM in the 1-expert case *does not* reduce to a GP's predictions,
because the way the mean and variance is computed for each expert
is weighted based on the expert's performance on the entire dataset in a way
that does not yield 1 in the absence of other experts.
Hence, perform the relevant transformations on a GP's prediction
and check it against the RBCM's.
:return:
"""
prior_var = 0.1
rbcm = RobustBayesianCommitteeMachine(
ndata_per_expert=100,
prior_variance=prior_var,
)
gp = GaussianProcess()
envs = methanol_envs[:10]
for env in envs:
rbcm.add_one_env(env, env.force)
gp.add_one_env(env, env.force, train=False)
struc = methanol_frames[-1]
gp.update_db(struc, forces=struc.forces)
rbcm.update_db(struc, forces=struc.forces)
test_env = methanol_envs[-1]
for d in [1, 2, 3]:
assert np.array_equal(gp.hyps, rbcm.hyps)
rbcm_pred = rbcm.predict(test_env, d)
gp_pred = gp.predict(test_env, d)
gp_kv = get_kernel_vector(
gp.name,
gp.kernel,
gp.energy_force_kernel,
test_env,
d,
gp.hyps,
cutoffs=gp.cutoffs,
hyps_mask=gp.hyps_mask,
n_cpus=1,
n_sample=gp.n_sample,
)
gp_mean = np.matmul(gp_kv, gp.alpha)
assert gp_mean == gp_pred[0]
gp_self_kern = gp.kernel(
env1=test_env,
env2=test_env,
d1=d,
d2=d,
hyps=gp.hyps,
cutoffs=np.array((7, 3.5)),
)
gp_var_i = gp_self_kern - np.matmul(np.matmul(gp_kv.T, gp.ky_mat_inv),
gp_kv)
gp_beta = 0.5 * (np.log(prior_var) - np.log(gp_var_i))
mean = gp_mean * gp_beta / gp_var_i
var = gp_beta / gp_var_i + (1 - gp_beta) / prior_var
pred_var = 1.0 / var
pred_mean = pred_var * mean
assert pred_mean == rbcm_pred[0]
assert pred_var == rbcm_pred[1]
