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Example: adding a separate test path flag #4

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45 changes: 42 additions & 3 deletions chemprop/cli/train.py
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Original file line number Diff line number Diff line change
Expand Up @@ -469,6 +469,18 @@ def add_train_args(parser: ArgumentParser) -> ArgumentParser:
help="Seed for PyTorch randomness (e.g., random initial weights)",
)

test_args = parser.add_argument_group("separate test args")
test_args.add_argument("--separate-test-path", type=Path)
test_args.add_argument("--separate-test-descriptors-path", type=Path)
test_args.add_argument("--separate-test-atom-features-path", type=Path)
test_args.add_argument("--separate-test-bond-features-path", type=Path)
test_args.add_argument("--separate-test-atom-descriptors-path", type=Path)
test_args.add_argument("--separate-test-no-header-row", action="store_true")
test_args.add_argument("--separate-test-smiles-columns", nargs="+")
test_args.add_argument("--separate-test-reaction-columns", nargs="+")
test_args.add_argument("--separate-test-target-columns", nargs="+")
test_args.add_argument("--separate-test-ignore-columns", nargs="+")

return parser


Expand Down Expand Up @@ -772,13 +784,40 @@ def build_splits(args, format_kwargs, featurization_kwargs):
splitting_mols = [datapoint.rct for datapoint in splitting_data]
else:
splitting_mols = [datapoint.mol for datapoint in splitting_data]
train_indices, val_indices, test_indices = make_split_indices(
splitting_mols, args.split, args.split_sizes, args.data_seed, args.num_replicates
)

if args.split_sizes[2] == 0:
split_sizes = (args.split_sizes[0], 0.0, args.split_sizes[1])
train_indices, test_indices, val_indices = make_split_indices(
splitting_mols, args.split, split_sizes, args.data_seed, args.num_replicates
)
else:
train_indices, val_indices, test_indices = make_split_indices(
splitting_mols, args.split, args.split_sizes, args.data_seed, args.num_replicates
)

train_data, val_data, test_data = split_data_by_indices(
all_data, train_indices, val_indices, test_indices
)

if args.separate_test_path:
test_data = build_data_from_files(
p_data=args.separate_test_path,
no_header_row=args.separate_test_no_header_row or args.no_header_row,
smiles_cols=args.separate_test_smiles_columns or args.smiles_columns,
rxn_cols=args.separate_test_reaction_columns or args.reaction_columns,
target_cols=args.separate_test_target_columns or args.target_columns,
ignore_cols=args.separate_test_ignore_columns or args.ignore_columns,
splits_col=None,
weight_col=None,
bounded=args.metrics and any(["bounded" in metrics for metrics in args.metrics]),
p_descriptors=args.separate_test_descriptors_path,
p_atom_feats=args.separate_test_atom_features_path,
p_bond_feats=args.separate_test_bond_features_path,
p_atom_descs=args.separate_test_atom_descriptors_path,
**featurization_kwargs,
)
test_data = [test_data] * len(train_data)

for i_split in range(len(train_data)):
sizes = [len(train_data[i_split][0]), len(val_data[i_split][0]), len(test_data[i_split][0])]
logger.info(f"train/val/test split_{i_split} sizes: {sizes}")
Expand Down