def test_sample_valid_molecules_if_not_enough_valid_generated():
# does not raise an exception if
molecules = ['invalid' for _ in range(20)]
molecules[-1] = 'CC'
molecules[-2] = 'CN'
generator = MockGenerator(molecules)
# samples a max of 9*2 molecules and just does not sample the good ones
# in this case the list of generated molecules is empty
assert not sample_valid_molecules(generator, 2, max_tries=9)
# with a max of 10*2 molecules two valid molecules can be sampled
generator = MockGenerator(molecules)
mols = sample_valid_molecules(generator, 2)
assert mols == ['CN', 'CC']
def assess_model(self, model: DistributionMatchingGenerator) -> DistributionLearningBenchmarkResult:
chemnet = self._load_chemnet()
start_time = time.time()
generated_molecules = sample_valid_molecules(model=model, number_molecules=self.number_samples)
end_time = time.time()
if len(generated_molecules) != self.number_samples:
logger.warning('The model could not generate enough valid molecules.')
mu_ref, cov_ref = self._calculate_distribution_statistics(chemnet, self.reference_molecules)
mu, cov = self._calculate_distribution_statistics(chemnet, generated_molecules)
FCD = fcd.calculate_frechet_distance(mu1=mu_ref, mu2=mu,
sigma1=cov_ref, sigma2=cov)
score = np.exp(-0.2 * FCD)
metadata = {
'number_reference_molecules': len(self.reference_molecules),
'number_generated_molecules': len(generated_molecules),
'FCD': FCD
}
return DistributionLearningBenchmarkResult(benchmark_name=self.name,
score=score,
sampling_time=end_time - start_time,
metadata=metadata)
def test_sample_valid_molecules_with_invalid_molecules():
generator = MockGenerator(
['invalid', 'invalid', 'invalid', 'CCCC', 'invalid', 'CC'])
mols = sample_valid_molecules(generator, 2)
assert mols == ['CCCC', 'CC']
def assess_model(
self, model: DistributionMatchingGenerator
) -> DistributionLearningBenchmarkResult:
start_time = time.time()
molecules = sample_valid_molecules(
model=model, number_molecules=self.number_samples)
end_time = time.time()
if len(molecules) != self.number_samples:
logger.warning(
'The model could not generate enough valid molecules. The score will be penalized.'
)
# canonicalize_list removes duplicates (and invalid molecules, but there shouldn't be any)
unique_molecules = canonicalize_list(molecules,
include_stereocenters=False)
unique_ratio = len(unique_molecules) / self.number_samples
metadata = {
'number_samples': self.number_samples,
'number_unique': len(unique_molecules)
}
return DistributionLearningBenchmarkResult(benchmark_name=self.name,
score=unique_ratio,
sampling_time=end_time -
start_time,
metadata=metadata)
def test_sample_valid_molecules_for_valid_only():
generator = MockGenerator(['CCCC', 'CC'])
mols = sample_valid_molecules(generator, 2)
assert mols == ['CCCC', 'CC']