def main():
dataset = CompositionData(data_path=args.data_path, fea_path=args.fea_path)
orig_atom_fea_len = dataset.atom_fea_dim
if args.test_path:
print("using independent test set: {}".format(args.test_path))
train_set = dataset
test_set = CompositionData(data_path=args.test_path,
fea_path=args.fea_path)
else:
print("using {} of training set as test set".format(args.test_size))
indices = list(range(len(dataset)))
train_idx, test_idx = split(indices,
random_state=args.seed,
test_size=args.test_size)
train_set = torch.utils.data.Subset(dataset, train_idx[0::args.sample])
test_set = torch.utils.data.Subset(dataset, test_idx)
if not os.path.isdir("models/"):
os.makedirs("models/")
if not os.path.isdir("runs/"):
os.makedirs("runs/")
if not os.path.isdir("results/"):
os.makedirs("results/")
ensemble(args.data_id, train_set, test_set, args.ensemble,
orig_atom_fea_len)
def train_roost(args,
model_name,
csv_train,
csv_val=None,
val_frac=0.0,
resume=False,
transfer=None,
fine_tune=None):
args.data_path = f'data/datasets/{csv_train}'
args.val_size = val_frac
dataset = CompositionData(data_path=args.data_path, fea_path=args.fea_path)
orig_atom_fea_len = dataset.atom_fea_dim
args.fea_len = orig_atom_fea_len
if resume:
args.resume = resume
else:
if transfer is not None:
args.transfer = transfer
elif fine_tune is not None:
args.fine_tune = fine_tune
if csv_val is None:
indices = list(range(len(dataset)))
train_idx, val_idx = split(indices,
random_state=args.seed,
test_size=args.val_size)
train_set = torch.utils.data.Subset(dataset, train_idx[0::args.sample])
val_set = torch.utils.data.Subset(dataset, val_idx)
else:
train_set = dataset
val_set = CompositionData(data_path=f'data/datasets/{csv_val}',
fea_path=args.fea_path)
if not os.path.isdir("models/"):
os.makedirs("models/")
if not os.path.isdir("runs/"):
os.makedirs("runs/")
if not os.path.isdir("results/"):
os.makedirs("results/")
ensemble(model_name, args.fold_id, train_set, val_set, args.ensemble,
orig_atom_fea_len, args)
def predict_roost(args, model_name, csv_pred):
fold_id = args.fold_id
ensemble_folds = args.ensemble
fea_len = args.fea_len
args.data_path = f'data/datasets/{csv_pred}'
dataset = CompositionData(data_path=args.data_path, fea_path=args.fea_path)
hold_out_set = dataset
test_ensemble(model_name, fold_id, ensemble_folds, hold_out_set, fea_len,
args)
def generate_standard_model(mat_prop, device):
args = input_parser()
args.device = device
args.optim = 'AdamW'
args.epochs = 300
args.fea_path = "data/embeddings/matscholar-embedding.json"
model_name = f'{mat_prop}_model_{args.epochs}_epochs'
csv_train = f'aflow/{mat_prop}/train.csv'
csv_val = f'aflow/{mat_prop}/val.csv'
csv_test = f'aflow/{mat_prop}/test.csv'
# define dataset
dataset = CompositionData(data_path=args.data_path, fea_path=args.fea_path)
orig_atom_fea_len = dataset.atom_fea_dim
args.fea_len = orig_atom_fea_len
# train and test model
train_roost(args, model_name, csv_train, csv_val=csv_val)
predict_roost(args, model_name, csv_test)
def generate_cgcnn_aflow_transfer_model(mat_prop):
args = input_parser()
args.optim = 'AdamW'
args.epochs = 2
args.fea_path = "data/embeddings/matscholar-embedding.json"
model_name = f'{mat_prop}_cgcnn_aflow_transfer_model_{args.epochs}_epochs'
csv_train = 'cgcnn_aflow/{mat_prop}_cgcnn_pred.csv'
csv_val = 'aflow/{mat_prop}/val.csv'
csv_test = 'aflow/{mat_prop}/test.csv'
dataset = CompositionData(data_path=args.data_path,
fea_path=args.fea_path)
orig_atom_fea_len = dataset.atom_fea_dim
args.fea_len = orig_atom_fea_len
train_roost(args, model_name, csv_train, csv_val=csv_val)
predict_roost(args, model_name, csv_test)
def run():
args = input_parser()
args.optim = 'AdamW'
args.epochs = 300
args.fea_path = "data/embeddings/matscholar-embedding.json"
model_name = f'shear_train_{args.epochs}_epochs'
model_name = 'finetune_500'
# model_name = 'shear_cgcnn_aflow_fine_tune_500_epoch'
# csv_train = 'cgcnn_predictions/shear_train_and_cgcnn_aflow_pcd_train.csv'
csv_train = 'shear_train.csv'
csv_val = 'shear_val.csv'
csv_test = 'shear_test.csv'
trained_model = 'models/checkpoint_shear_cgcnn_aflow_fine_tune_500_epoch'
dataset = CompositionData(data_path=args.data_path,
fea_path=args.fea_path)
orig_atom_fea_len = dataset.atom_fea_dim
args.fea_len = orig_atom_fea_len
# train_roost(args, model_name, csv_train, val_frac=0.05)
# train_roost(args, model_name, csv_train, csv_val=csv_val)
# train_roost(args, model_name, csv_train, csv_val=csv_val, resume=True)
# train_roost(args, model_name, csv_train, csv_val=csv_val,
# fine_tune=trained_model)
# train_roost(args, model_name, csv_train, csv_val=csv_val,
# transfer=trained_model)
predict_roost(args, model_name, csv_test)
df_results = pd.read_csv(f'results/test_results_{model_name}.csv')
y_act, y_pred = df_results['target'], df_results['pred-0']
# choose to "log10" or "unlog10" your data before metrics/plots
if False:
y_act, y_pred = np.log10(y_act), np.log10(y_pred)
if False:
y_act, y_pred = 10**y_act, 10**y_pred
r2 = r2_score(y_act, y_pred)
mae = mean_absolute_error(y_act, y_pred)
print(f'-------------------')
print(f'r2: {r2:0.4f}, mae: {mae:0.4f}')
pva_plot(y_act, y_pred, model_name)
def main():
dataset = CompositionData(data_path=args.data_path, fea_path=args.fea_path)
orig_atom_fea_len = dataset.atom_fea_dim
indices = list(range(len(dataset)))
train_idx, test_idx = split(indices,
random_state=args.seed,
test_size=args.test_size)
train_set = torch.utils.data.Subset(dataset, train_idx[0::args.sample])
test_set = torch.utils.data.Subset(dataset, test_idx)
if not os.path.isdir("models/"):
os.makedirs("models/")
if not os.path.isdir("runs/"):
os.makedirs("runs/")
if not os.path.isdir("results/"):
os.makedirs("results/")
ensemble(args.fold_id, train_set, test_set, args.ensemble,
orig_atom_fea_len)