def predict(self, dataset_type: str, preds_path: str, save_dir: str):
# Set up command line arguments for predicting
raw_predict_args = self.create_raw_predict_args(
dataset_type=dataset_type,
preds_path=preds_path,
checkpoint_dir=save_dir
)
# Predict
with patch('sys.argv', raw_predict_args):
chemprop_predict()
def predict(self,
dataset_type: str,
preds_path: str,
save_dir: str,
model_type: str = 'chemprop',
flags: List[str] = None):
# Set up command line arguments
raw_args = self.create_raw_predict_args(dataset_type=dataset_type,
preds_path=preds_path,
checkpoint_dir=save_dir,
flags=flags)
# Predict
with patch('sys.argv', raw_args):
command_line = ' '.join(raw_args[1:])
if model_type == 'chemprop':
print(f'python predict.py {command_line}')
chemprop_predict()
else:
print(f'python sklearn_predict.py {command_line}')
sklearn_predict()
"""Loads a trained chemprop model checkpoint and makes predictions on a dataset."""
from chemprop.train import chemprop_predict
if __name__ == '__main__':
chemprop_predict()
"""Loads a trained chemprop model checkpoint and makes predictions on a dataset."""
from chemprop.train import chemprop_predict
if __name__ == '__main__':
preds = chemprop_predict()
print(preds)