def test_env_methods(structure, mask, cutoff, result):
if mask is True:
mask = generate_mask(cutoff)
else:
mask = None
structure = deepcopy(structure)
structure.forces = np.random.random(size=(len(structure), 3))
env_test = AtomicEnvironment(structure,
atom=0,
cutoffs=cutoff,
cutoffs_mask=mask)
assert np.array_equal(structure.forces[0], env_test.force)
assert (
str(env_test) ==
f"Atomic Env. of Type 1 surrounded by {result[0]} atoms of Types [1, 2, 3]"
)
the_dict = env_test.as_dict()
assert isinstance(the_dict, dict)
the_str = env_test.as_str()
assert dumps(the_dict, cls=NumpyEncoder) == the_str
for key in ["positions", "cell", "atom", "cutoffs", "species"]:
assert key in the_dict.keys()
# This saves a few seconds, the masked envs take longer to read/write
if not mask:
with open("test_environment.json", "w") as f:
f.write(env_test.as_str())
remade_env = AtomicEnvironment.from_file("test_environment.json")
else:
remade_env = AtomicEnvironment.from_dict(the_dict)
assert isinstance(remade_env, AtomicEnvironment)
assert np.array_equal(remade_env.bond_array_2, env_test.bond_array_2)
if len(cutoff) > 1:
assert np.array_equal(remade_env.bond_array_3, env_test.bond_array_3)
if len(cutoff) > 2:
assert np.array_equal(remade_env.q_array, env_test.q_array)
if not mask:
remove("test_environment.json")
from flare.rbcm import RobustBayesianCommitteeMachine
from flare.gp import GaussianProcess
import os as os
import numpy as np
from flare.struc import Structure
from flare.env import AtomicEnvironment
from flare.gp_algebra import get_kernel_vector
TEST_DIR = os.path.dirname(__file__)
TEST_FILE_DIR = os.path.join(TEST_DIR, "test_files")
methanol_frames = Structure.from_file(
os.path.join(TEST_FILE_DIR, "methanol_frames.json"))
methanol_envs = AtomicEnvironment.from_file(
os.path.join(TEST_FILE_DIR, "methanol_envs.json"))
def test_basic():
rbcm = RobustBayesianCommitteeMachine()
assert isinstance(rbcm, RobustBayesianCommitteeMachine)
def test_expert_growth_and_training():
"""
Test that as data is added the data is allocated to experts correctly
and that various training algorithms can run correctly on it
:return:
"""
def test_passive_learning():
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",
)
frames = Structure.from_file(
path.join(TEST_FILE_DIR, "methanol_frames.json"))
envs = AtomicEnvironment.from_file(
path.join(TEST_FILE_DIR, "methanol_envs.json"))
cur_gp = deepcopy(the_gp)
tt = TrajectoryTrainer(frames=None, gp=cur_gp)
# TEST ENVIRONMENT ADDITION
envs_species = set(Z_to_element(env.ctype) for env in envs)
tt.run_passive_learning(environments=envs, post_build_matrices=False)
assert cur_gp.training_statistics["N"] == len(envs)
assert set(cur_gp.training_statistics["species"]) == envs_species
# TEST FRAME ADDITION: ALL ARE ADDED
cur_gp = deepcopy(the_gp)
tt.gp = cur_gp
tt.run_passive_learning(frames=frames, post_build_matrices=False)
assert len(cur_gp.training_data) == sum([len(fr) for fr in frames])
# TEST FRAME ADDITION: MAX OUT MODEL SIZE AT 1
cur_gp = deepcopy(the_gp)
tt.gp = cur_gp
tt.run_passive_learning(frames=frames,
max_model_size=1,
post_training_iterations=1)
assert len(cur_gp.training_data) == 1
# TEST FRAME ADDITION: EXCLUDE OXYGEN, LIMIT CARBON TO 1, 1 H PER FRAME
cur_gp = deepcopy(the_gp)
tt.gp = cur_gp
tt.run_passive_learning(
frames=frames,
max_model_elts={
"O": 0,
"C": 1,
"H": 5
},
max_elts_per_frame={"H": 1},
post_build_matrices=False,
)
assert "O" not in cur_gp.training_statistics["species"]
assert cur_gp.training_statistics["envs_by_species"]["C"] == 1
assert cur_gp.training_statistics["envs_by_species"]["H"] == 5
