def main(config, print_metrics=True):
test = read_smiles_csv(config.test_path)
test_scaffolds = None
ptest = None
ptest_scaffolds = None
if config.test_scaffolds_path is not None:
test_scaffolds = read_smiles_csv(config.test_scaffolds_path)
if config.ptest_path is not None:
if not os.path.exists(config.ptest_path):
warnings.warn(f'{config.ptest_path} does not exist')
ptest = None
else:
ptest = np.load(config.ptest_path)['stats'].item()
if config.ptest_scaffolds_path is not None:
if not os.path.exists(config.ptest_scaffolds_path):
warnings.warn(f'{config.ptest_scaffolds_path} does not exist')
ptest_scaffolds = None
else:
ptest_scaffolds = np.load(
config.ptest_scaffolds_path)['stats'].item()
gen = read_smiles_csv(config.gen_path)
metrics = get_all_metrics(test,
gen,
k=config.ks,
n_jobs=config.n_jobs,
device=config.device,
test_scaffolds=test_scaffolds,
ptest=ptest,
ptest_scaffolds=ptest_scaffolds)
if print_metrics:
for name, value in metrics.items():
print('{},{}'.format(name, value))
else:
return metrics
def main(config, print_metrics=True):
test = None
test_scaffolds = None
ptest = None
ptest_scaffolds = None
train = None
if config.test_path:
test = read_smiles_csv(config.test_path)
if config.test_scaffolds_path is not None:
test_scaffolds = read_smiles_csv(config.test_scaffolds_path)
if config.train_path is not None:
train = read_smiles_csv(config.train_path)
if config.ptest_path is not None:
ptest = np.load(config.ptest_path, allow_pickle=True)['stats'].item()
if config.ptest_scaffolds_path is not None:
ptest_scaffolds = np.load(config.ptest_scaffolds_path,
allow_pickle=True)['stats'].item()
gen = read_smiles_csv(config.gen_path)
metrics = get_all_metrics(gen=gen,
k=config.ks,
n_jobs=config.n_jobs,
device=config.device,
test_scaffolds=test_scaffolds,
ptest=ptest,
ptest_scaffolds=ptest_scaffolds,
test=test,
train=train)
if print_metrics:
for name, value in metrics.items():
print('{},{}'.format(name, value))
else:
return metrics
def main(config, print_metrics=True):
ref = read_smiles_csv(config.ref_path)
gen = read_smiles_csv(config.gen_path)
metrics = get_all_metrics(ref, gen, k=config.ks, n_jobs=config.n_jobs,
gpu=config.device_code)
if print_metrics:
print('Metrics:')
for name, value in metrics.items():
print('\t' + name + ' = {}'.format(value))
else:
return metrics
def eval_metrics(eval_config, print_metrics=True):
# need to detect if file has the header or not
test = read_smiles_csv(model_config.test_path)
test_scaffolds = None
ptest = None
ptest_scaffolds = None
if model_config.test_scaffolds_path is not None:
test_scaffolds = read_smiles_csv(model_config.test_scaffolds_path)
if model_config.ptest_path is not None:
if not os.path.exists(model_config.ptest_path):
warnings.warn(f"{model_config.ptest_path} does not exist")
ptest = None
else:
ptest = np.load(model_config.ptest_path)["stats"].item()
if model_config.ptest_scaffolds_path is not None:
if not os.path.exists(model_config.ptest_scaffolds_path):
warnings.warn(f"{model_config.ptest_scaffolds_path} does not exist")
ptest_scaffolds = None
else:
ptest_scaffolds = np.load(model_config.ptest_scaffolds_path)["stats"].item()
gen = read_smiles_csv(model_config.gen_save)
metrics = get_all_metrics(
test,
gen,
k=model_config.ks,
n_jobs=model_config.n_jobs,
gpu=model_config.gpu,
test_scaffolds=test_scaffolds,
ptest=ptest,
ptest_scaffolds=ptest_scaffolds,
)
if print_metrics:
print("Metrics:")
for name, value in metrics.items():
print("\t" + name + " = {}".format(value))
return metrics
else:
return metrics