diff --git a/chemprop/cli/chemprop_hpopt/mol/ray_results/TorchTrainer_2024-12-20_16-42-36/.validate_storage_marker b/chemprop/cli/chemprop_hpopt/mol/ray_results/TorchTrainer_2024-12-20_16-42-36/.validate_storage_marker new file mode 100644 index 000000000..e69de29bb diff --git a/chemprop/cli/chemprop_hpopt/mol/ray_results/TorchTrainer_2024-12-20_16-42-36/0e7392c7/error.txt b/chemprop/cli/chemprop_hpopt/mol/ray_results/TorchTrainer_2024-12-20_16-42-36/0e7392c7/error.txt new file mode 100644 index 000000000..a39c2acf1 --- /dev/null +++ b/chemprop/cli/chemprop_hpopt/mol/ray_results/TorchTrainer_2024-12-20_16-42-36/0e7392c7/error.txt @@ -0,0 +1,35 @@ +Failure # 1 (occurred at 2024-12-20_16-42-45) +ray::_Inner.train() (pid=14210, ip=127.0.0.1, actor_id=2c32c4e80ea58095ad1be8d001000000, repr=TorchTrainer) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/opt/anaconda3/envs/chemprop/lib/python3.11/site-packages/ray/tune/trainable/trainable.py", line 331, in train + raise skipped from exception_cause(skipped) + File "/opt/anaconda3/envs/chemprop/lib/python3.11/site-packages/ray/train/_internal/utils.py", line 57, in check_for_failure + ray.get(object_ref) + ^^^^^^^^^^^^^^^^^^^ + ^^^^^^^^^^^^^^^^^^^^^ + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +ray.exceptions.RayTaskError(ValueError): ray::_RayTrainWorker__execute.get_next() (pid=14211, ip=127.0.0.1, actor_id=ec31d849709d7a2717fe59d601000000, repr=) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/opt/anaconda3/envs/chemprop/lib/python3.11/site-packages/ray/train/_internal/worker_group.py", line 33, in __execute + raise skipped from exception_cause(skipped) + File "/opt/anaconda3/envs/chemprop/lib/python3.11/site-packages/ray/train/_internal/utils.py", line 176, in discard_return_wrapper + train_func(*args, **kwargs) + File "/Users/brianli/Documents/chemprop/chemprop/cli/hpopt.py", line 394, in + lambda config: train_model( + ^^^^^^^^^^^^ + File "/Users/brianli/Documents/chemprop/chemprop/cli/hpopt.py", line 333, in train_model + trainer = pl.Trainer( + ^^^^^^^^^^^ + File "/opt/anaconda3/envs/chemprop/lib/python3.11/site-packages/lightning/pytorch/utilities/argparse.py", line 70, in insert_env_defaults + return fn(self, **kwargs) + ^^^^^^^^^^^^^^^^^^ + File "/opt/anaconda3/envs/chemprop/lib/python3.11/site-packages/lightning/pytorch/trainer/trainer.py", line 395, in __init__ + self._accelerator_connector = _AcceleratorConnector( + ^^^^^^^^^^^^^^^^^^^^^^ + File "/opt/anaconda3/envs/chemprop/lib/python3.11/site-packages/lightning/pytorch/trainer/connectors/accelerator_connector.py", line 130, in __init__ + self._check_config_and_set_final_flags( + File "/opt/anaconda3/envs/chemprop/lib/python3.11/site-packages/lightning/pytorch/trainer/connectors/accelerator_connector.py", line 218, in _check_config_and_set_final_flags + raise ValueError( +ValueError: You set `strategy=` but strategies from the DDP family are not supported on the MPS accelerator. Either explicitly set `accelerator='cpu'` or change the strategy. diff --git a/chemprop/cli/chemprop_hpopt/mol/ray_results/TorchTrainer_2024-12-20_16-42-36/f8faf821/error.txt b/chemprop/cli/chemprop_hpopt/mol/ray_results/TorchTrainer_2024-12-20_16-42-36/f8faf821/error.txt new file mode 100644 index 000000000..b5adab6a6 --- /dev/null +++ b/chemprop/cli/chemprop_hpopt/mol/ray_results/TorchTrainer_2024-12-20_16-42-36/f8faf821/error.txt @@ -0,0 +1,35 @@ +Failure # 1 (occurred at 2024-12-20_16-42-43) +ray::_Inner.train() (pid=14206, ip=127.0.0.1, actor_id=2f76ed1f85d26f7b310fd19201000000, repr=TorchTrainer) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/opt/anaconda3/envs/chemprop/lib/python3.11/site-packages/ray/tune/trainable/trainable.py", line 331, in train + raise skipped from exception_cause(skipped) + File "/opt/anaconda3/envs/chemprop/lib/python3.11/site-packages/ray/train/_internal/utils.py", line 57, in check_for_failure + ray.get(object_ref) + ^^^^^^^^^^^^^^^^^^^ + ^^^^^^^^^^^^^^^^^^^^^ + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +ray.exceptions.RayTaskError(ValueError): ray::_RayTrainWorker__execute.get_next() (pid=14209, ip=127.0.0.1, actor_id=9faae5919004157f098a734001000000, repr=) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/opt/anaconda3/envs/chemprop/lib/python3.11/site-packages/ray/train/_internal/worker_group.py", line 33, in __execute + raise skipped from exception_cause(skipped) + File "/opt/anaconda3/envs/chemprop/lib/python3.11/site-packages/ray/train/_internal/utils.py", line 176, in discard_return_wrapper + train_func(*args, **kwargs) + File "/Users/brianli/Documents/chemprop/chemprop/cli/hpopt.py", line 394, in + lambda config: train_model( + ^^^^^^^^^^^^ + File "/Users/brianli/Documents/chemprop/chemprop/cli/hpopt.py", line 333, in train_model + trainer = pl.Trainer( + ^^^^^^^^^^^ + File "/opt/anaconda3/envs/chemprop/lib/python3.11/site-packages/lightning/pytorch/utilities/argparse.py", line 70, in insert_env_defaults + return fn(self, **kwargs) + ^^^^^^^^^^^^^^^^^^ + File "/opt/anaconda3/envs/chemprop/lib/python3.11/site-packages/lightning/pytorch/trainer/trainer.py", line 395, in __init__ + self._accelerator_connector = _AcceleratorConnector( + ^^^^^^^^^^^^^^^^^^^^^^ + File "/opt/anaconda3/envs/chemprop/lib/python3.11/site-packages/lightning/pytorch/trainer/connectors/accelerator_connector.py", line 130, in __init__ + self._check_config_and_set_final_flags( + File "/opt/anaconda3/envs/chemprop/lib/python3.11/site-packages/lightning/pytorch/trainer/connectors/accelerator_connector.py", line 218, in _check_config_and_set_final_flags + raise ValueError( +ValueError: You set `strategy=` but strategies from the DDP family are not supported on the MPS accelerator. Either explicitly set `accelerator='cpu'` or change the strategy. diff --git a/chemprop/cli/chemprop_hpopt/mol/ray_results/TorchTrainer_2024-12-21_18-45-46/.validate_storage_marker b/chemprop/cli/chemprop_hpopt/mol/ray_results/TorchTrainer_2024-12-21_18-45-46/.validate_storage_marker new file mode 100644 index 000000000..e69de29bb diff --git a/chemprop/cli/common.py b/chemprop/cli/common.py index 45392b19e..aaf79fa8c 100644 --- a/chemprop/cli/common.py +++ b/chemprop/cli/common.py @@ -28,7 +28,16 @@ def add_common_args(parser: ArgumentParser) -> ArgumentParser: action="store_true", help="Turn off using the first row in the input CSV as column names", ) - + data_args.add_argument( + "--is-mixed", + action="store_true", + help="If some of the targets are intended for molecules, some for atoms, and some for bonds", + ) + data_args.add_argument( + "--mixed-columns", + nargs="+", + help="Target columns that correspond to molecule (first index), atom (second index), and bond (third index) predictions", + ) dataloader_args = parser.add_argument_group("Dataloader args") dataloader_args.add_argument( "-n", @@ -120,6 +129,11 @@ def add_common_args(parser: ArgumentParser) -> ArgumentParser: featurization_args.add_argument( "--no-bond-feature-scaling", action="store_true", help="Turn off extra bond feature scaling" ) + featurization_args.add_argument( + "--no-bond-descriptor-scaling", + action="store_true", + help="Turn off extra bond descriptor scaling", + ) featurization_args.add_argument( "--atom-features-path", nargs="+", diff --git a/chemprop/cli/hpopt.py b/chemprop/cli/hpopt.py index 8f02605d0..ffb208f08 100644 --- a/chemprop/cli/hpopt.py +++ b/chemprop/cli/hpopt.py @@ -316,7 +316,10 @@ def train_model(config, args, train_dset, val_dset, logger, output_transform, in logger.info(model) if args.tracking_metric == "val_loss": - T_tracking_metric = model.criterion.__class__ + if args.is_mixed: + T_tracking_metric = model.criterion[0].__class__ + else: + T_tracking_metric = model.criterion.__class__ else: T_tracking_metric = MetricRegistry[args.tracking_metric] args.tracking_metric = "val/" + args.tracking_metric @@ -479,13 +482,35 @@ def main(args: Namespace): input_transforms = normalize_inputs(train_dset, val_dset, args) - if "regression" in args.task_type: + if args.is_mixed and "regression" in args.task_type: + output_transform = [] + mol_output_scaler = train_dset.mol_dataset.normalize_targets() + val_dset.mol_dataset.normalize_targets(mol_output_scaler) + output_transform.append(UnscaleTransform.from_standard_scaler(mol_output_scaler)) + atom_output_scaler = train_dset.atom_dataset.normalize_targets() + val_dset.atom_dataset.normalize_targets(atom_output_scaler) + output_transform.append(UnscaleTransform.from_standard_scaler(atom_output_scaler)) + bond_output_scaler = train_dset.bond_dataset.normalize_targets() + val_dset.bond_dataset.normalize_targets(bond_output_scaler) + output_transform.append(UnscaleTransform.from_standard_scaler(bond_output_scaler)) + logger.info( + f"Train data: mean = {mol_output_scaler.mean_} | std = {mol_output_scaler.scale_}" + ) + logger.info( + f"Train data: mean = {atom_output_scaler.mean_} | std = {atom_output_scaler.scale_}" + ) + logger.info( + f"Train data: mean = {bond_output_scaler.mean_} | std = {bond_output_scaler.scale_}" + ) + elif args.is_mixed: + output_transform = [None, None, None] + elif "regression" in args.task_type: output_scaler = train_dset.normalize_targets() val_dset.normalize_targets(output_scaler) logger.info(f"Train data: mean = {output_scaler.mean_} | std = {output_scaler.scale_}") - output_transform = UnscaleTransform.from_standard_scaler(output_scaler) + output_transform = [UnscaleTransform.from_standard_scaler(output_scaler)] else: - output_transform = None + output_transform = [None] train_loader = build_dataloader( train_dset, args.batch_size, args.num_workers, seed=args.data_seed diff --git a/chemprop/cli/predict.py b/chemprop/cli/predict.py index 5149f3d9e..2c2a5c899 100644 --- a/chemprop/cli/predict.py +++ b/chemprop/cli/predict.py @@ -16,8 +16,14 @@ process_common_args, validate_common_args, ) -from chemprop.cli.utils import LookupAction, Subcommand, build_data_from_files, make_dataset -from chemprop.models.utils import load_model, load_output_columns +from chemprop.cli.utils import ( + LookupAction, + Subcommand, + build_data_from_files, + build_mixed_data_from_files, + make_dataset, +) +from chemprop.models.utils import load_mixed_model, load_model, load_output_columns from chemprop.nn.metrics import LossFunctionRegistry from chemprop.nn.predictors import EvidentialFFN, MulticlassClassificationFFN, MveFFN from chemprop.uncertainty import ( @@ -196,19 +202,37 @@ def prepare_data_loader( featurization_kwargs = dict( molecule_featurizers=args.molecule_featurizers, keep_h=args.keep_h, add_h=args.add_h ) + if args.is_mixed: + datas, mol_cols, atom_cols, bond_cols = build_mixed_data_from_files( + data_path, + **format_kwargs, + p_descriptors=descriptors_path, + p_atom_feats=atom_feats_path, + p_bond_feats=bond_feats_path, + p_atom_descs=atom_descs_path, + **featurization_kwargs, + ) + args.mixed_columns = [mol_cols, atom_cols, bond_cols] + else: + datas = build_data_from_files( + data_path, + **format_kwargs, + p_descriptors=descriptors_path, + p_atom_feats=atom_feats_path, + p_bond_feats=bond_feats_path, + p_atom_descs=atom_descs_path, + **featurization_kwargs, + ) - datas = build_data_from_files( - data_path, - **format_kwargs, - p_descriptors=descriptors_path, - p_atom_feats=atom_feats_path, - p_bond_feats=bond_feats_path, - p_atom_descs=atom_descs_path, - **featurization_kwargs, - ) - - dsets = [make_dataset(d, args.rxn_mode, args.multi_hot_atom_featurizer_mode) for d in datas] - dset = data.MulticomponentDataset(dsets) if multicomponent else dsets[0] + dsets = [ + make_dataset(datas[d], args.rxn_mode, args.multi_hot_atom_featurizer_mode, d) + for d in range(len(datas)) + ] + dset = dsets[0] + if multicomponent: + dset = data.MulticomponentDataset(dsets) + elif args.is_mixed: + dset = data.MolAtomBondDataset(dsets[0], dsets[1], dsets[2]) return data.build_dataloader(dset, args.batch_size, args.num_workers, shuffle=False) @@ -216,10 +240,14 @@ def prepare_data_loader( def make_prediction_for_models( args: Namespace, model_paths: Iterator[Path], multicomponent: bool, output_path: Path ): - model = load_model(model_paths[0], multicomponent) - output_columns = load_output_columns(model_paths[0]) + if args.is_mixed: + models = [load_mixed_model(model_path) for model_path in model_paths] + args.uncertainty_method = "mixed_" + args.uncertainty_method + else: + models = [load_model(model_path, multicomponent) for model_path in model_paths] + output_columns, mixed_columns = load_output_columns(model_paths[0]) bounded = any( - isinstance(model.criterion, LossFunctionRegistry[loss_function]) + isinstance(models[0].criterion, LossFunctionRegistry[loss_function]) for loss_function in LossFunctionRegistry.keys() if "bounded" in loss_function ) @@ -234,6 +262,7 @@ def make_prediction_for_models( ) format_kwargs["target_cols"] = output_columns if args.evaluation_methods is not None else [] test_loader = prepare_data_loader(args, multicomponent, False, format_kwargs) + logger.info(f"test size: {len(test_loader.dataset)}") if args.cal_path is not None: format_kwargs["target_cols"] = output_columns @@ -244,94 +273,131 @@ def make_prediction_for_models( UncertaintyEstimatorRegistry[args.uncertainty_method], ensemble_size=args.dropout_sampling_size, dropout=args.uncertainty_dropout_p, - ) + ) # make is_mixed one if args.is_mixed is true. - models = [load_model(model_path, multicomponent) for model_path in model_paths] trainer = pl.Trainer( logger=False, enable_progress_bar=True, accelerator=args.accelerator, devices=args.devices ) test_individual_preds, test_individual_uncs = uncertainty_estimator( test_loader, models, trainer ) - test_preds = torch.mean(test_individual_preds, dim=0) - if not isinstance(uncertainty_estimator, NoUncertaintyEstimator): - test_uncs = torch.mean(test_individual_uncs, dim=0) - else: - test_uncs = None - - if args.calibration_method is not None: - uncertainty_calibrator = Factory.build( - UncertaintyCalibratorRegistry[args.calibration_method], - p=args.calibration_interval_percentile / 100, - alpha=args.conformal_alpha, - ) - cal_targets = cal_loader.dataset.Y - cal_mask = torch.from_numpy(np.isfinite(cal_targets)) - cal_targets = np.nan_to_num(cal_targets, nan=0.0) - cal_targets = torch.from_numpy(cal_targets) - cal_individual_preds, cal_individual_uncs = uncertainty_estimator( - cal_loader, models, trainer - ) - cal_preds = torch.mean(cal_individual_preds, dim=0) - cal_uncs = torch.mean(cal_individual_uncs, dim=0) - if isinstance(uncertainty_calibrator, MVEWeightingCalibrator): - uncertainty_calibrator.fit(cal_preds, cal_individual_uncs, cal_targets, cal_mask) - test_uncs = uncertainty_calibrator.apply(cal_individual_uncs) + if not args.is_mixed: + test_individual_preds = [test_individual_preds] + test_individual_uncs = [test_individual_uncs] + test_preds = [] + test_uncs = [] + for idx, individual_pred in enumerate(test_individual_preds): + test_preds.append(torch.mean(individual_pred, dim=0)) + + if not isinstance(uncertainty_estimator, NoUncertaintyEstimator): + test_uncs.append(torch.mean(test_individual_uncs[idx], dim=0)) else: - if isinstance(uncertainty_calibrator, RegressionCalibrator): - uncertainty_calibrator.fit(cal_preds, cal_uncs, cal_targets, cal_mask) + test_uncs = None + + if args.calibration_method is not None: + uncertainty_calibrator = Factory.build( + UncertaintyCalibratorRegistry[args.calibration_method], + p=args.calibration_interval_percentile / 100, + alpha=args.conformal_alpha, + ) + cal_targets = cal_loader.dataset.Y + cal_mask = torch.from_numpy(np.isfinite(cal_targets)) + cal_targets = np.nan_to_num(cal_targets, nan=0.0) + cal_targets = torch.from_numpy(cal_targets) + cal_individual_preds, cal_individual_uncs = uncertainty_estimator( + cal_loader, models, trainer + ) + cal_preds = torch.mean(cal_individual_preds, dim=0) + cal_uncs = torch.mean(cal_individual_uncs, dim=0) + if isinstance(uncertainty_calibrator, MVEWeightingCalibrator): + uncertainty_calibrator.fit(cal_preds, cal_individual_uncs, cal_targets, cal_mask) + test_uncs = uncertainty_calibrator.apply(cal_individual_uncs) else: - uncertainty_calibrator.fit(cal_uncs, cal_targets, cal_mask) - test_uncs = uncertainty_calibrator.apply(test_uncs) - for i in range(test_individual_uncs.shape[0]): - test_individual_uncs[i] = uncertainty_calibrator.apply(test_individual_uncs[i]) - - if args.evaluation_methods is not None: - uncertainty_evaluators = [ - Factory.build(UncertaintyEvaluatorRegistry[method]) - for method in args.evaluation_methods - ] - logger.info("Uncertainty evaluation metric:") - for evaluator in uncertainty_evaluators: - test_targets = test_loader.dataset.Y - test_mask = torch.from_numpy(np.isfinite(test_targets)) - test_targets = np.nan_to_num(test_targets, nan=0.0) - test_targets = torch.from_numpy(test_targets) - if isinstance(evaluator, RegressionEvaluator): - metric_value = evaluator.evaluate(test_preds, test_uncs, test_targets, test_mask) - else: - metric_value = evaluator.evaluate(test_uncs, test_targets, test_mask) - logger.info(f"{evaluator.alias}: {metric_value.tolist()}") - - if args.uncertainty_method == "none" and ( - isinstance(model.predictor, MveFFN) or isinstance(model.predictor, EvidentialFFN) - ): - test_preds = test_preds[..., 0] - test_individual_preds = test_individual_preds[..., 0] - - if output_columns is None: - output_columns = [ - f"pred_{i}" for i in range(test_preds.shape[1]) - ] # TODO: need to improve this for cases like multi-task MVE and multi-task multiclass - - save_predictions(args, model, output_columns, test_preds, test_uncs, output_path) + if isinstance(uncertainty_calibrator, RegressionCalibrator): + uncertainty_calibrator.fit(cal_preds, cal_uncs, cal_targets, cal_mask) + else: + uncertainty_calibrator.fit(cal_uncs, cal_targets, cal_mask) + print(args.uncertainty_method) + test_uncs[idx] = uncertainty_calibrator.apply(test_uncs[idx]) + for i in range(test_individual_uncs[idx].shape[0]): + test_individual_uncs[idx][i] = uncertainty_calibrator.apply( + test_individual_uncs[idx][i] + ) + + if args.evaluation_methods is not None: + uncertainty_evaluators = [ + Factory.build(UncertaintyEvaluatorRegistry[method]) + for method in args.evaluation_methods + ] + logger.info("Uncertainty evaluation metric:") + for evaluator in uncertainty_evaluators: + test_targets = test_loader.dataset.Y + test_mask = torch.from_numpy(np.isfinite(test_targets)) + test_targets = np.nan_to_num(test_targets, nan=0.0) + test_targets = torch.from_numpy(test_targets) + if isinstance(evaluator, RegressionEvaluator): + metric_value = evaluator.evaluate( + test_preds[idx], test_uncs[idx], test_targets, test_mask + ) + else: + metric_value = evaluator.evaluate(test_uncs[idx], test_targets, test_mask) + logger.info(f"{evaluator.alias}: {metric_value.tolist()}") + + if args.uncertainty_method == "none": + if args.is_mixed and ( + isinstance(models[0].predictors[0], MveFFN) + or isinstance(models[0].predictors[0], EvidentialFFN) + ): + test_preds[idx] = test_preds[idx][..., 0] + test_individual_preds[idx] = test_individual_preds[idx][..., 0] + elif isinstance(models[0].predictor, MveFFN) or isinstance( + models[0].predictor, EvidentialFFN + ): + test_preds[idx] = test_preds[idx][..., 0] + test_individual_preds[idx] = test_individual_preds[idx][..., 0] + + if output_columns is None: + print(test_preds[idx]) + output_columns = [ + f"pred_{i}" for i in range(test_preds[idx].shape[1]) + ] # TODO: need to improve this for cases like multi-task MVE and multi-task multiclass + + test_preds = test_preds[0] if not args.is_mixed and test_preds is not None else test_preds + test_uncs = test_uncs[0] if not args.is_mixed and test_uncs is not None else test_uncs + + save_predictions( + args, + models[0], + output_columns, + mixed_columns, + test_preds, + test_uncs, + output_path, + test_loader, + ) if len(model_paths) > 1: save_individual_predictions( args, - model, + models[0], model_paths, output_columns, + mixed_columns, test_individual_preds, test_individual_uncs, output_path, + test_loader, ) -def save_predictions(args, model, output_columns, test_preds, test_uncs, output_path): +def save_predictions( + args, model, output_columns, mixed_columns, test_preds, test_uncs, output_path, test_loader +): unc_columns = [f"{col}_unc" for col in output_columns] - if isinstance(model.predictor, MulticlassClassificationFFN): + if ( + args.is_mixed and isinstance(model.predictors[0], MulticlassClassificationFFN) + ) or isinstance(model.predictor, MulticlassClassificationFFN): output_columns = output_columns + [f"{col}_prob" for col in output_columns] predicted_class_labels = test_preds.argmax(axis=-1) formatted_probability_strings = np.apply_along_axis( @@ -344,7 +410,34 @@ def save_predictions(args, model, output_columns, test_preds, test_uncs, output_ df_test = pd.read_csv( args.test_path, header=None if args.no_header_row else "infer", index_col=False ) - df_test[output_columns] = test_preds + + if args.is_mixed: + mol_cols = mixed_columns[0] + atom_cols = mixed_columns[1] + bond_cols = mixed_columns[2] + + df_test.loc[:, mol_cols] = test_preds[0].tolist() + + for i in range(len(df_test)): + atom_slices = test_loader.dataset.atom_dataset._slices + if atom_slices is not None: + first_atom = atom_slices.index(i) + last_atom = first_atom + atom_slices.count(i) + atom_preds = test_preds[1][first_atom:last_atom] + df_test.loc[i, atom_cols] = [ + str(atom_preds[:, j].tolist()) for j in range(len(atom_cols)) + ] + + bond_slices = test_loader.dataset.bond_dataset._slices + if bond_slices is not None: + first_bond = bond_slices.index(i) + last_bond = first_bond + bond_slices.count(i) + bond_preds = test_preds[2][first_bond:last_bond] + df_test.loc[i, bond_cols] = [ + str(bond_preds[:, j].tolist()) for j in range(len(bond_cols)) + ] + else: + df_test[output_columns] = test_preds[0] if args.uncertainty_method not in ["none", "classification"]: df_test[unc_columns] = np.round(test_uncs, 6) @@ -362,15 +455,19 @@ def save_individual_predictions( model, model_paths, output_columns, + mixed_columns, test_individual_preds, test_individual_uncs, output_path, + test_loader, ): unc_columns = [ f"{col}_unc_model_{i}" for i in range(len(model_paths)) for col in output_columns ] - if isinstance(model.predictor, MulticlassClassificationFFN): + if ( + args.is_mixed and isinstance(model.predictors[0], MulticlassClassificationFFN) + ) or isinstance(model.predictor, MulticlassClassificationFFN): output_columns = [ item for i in range(len(model_paths)) @@ -378,29 +475,58 @@ def save_individual_predictions( for item in (f"{col}_model_{i}", f"{col}_prob_model_{i}") ] - predicted_class_labels = test_individual_preds.argmax(axis=-1) + predicted_class_labels = test_individual_preds[0].argmax(axis=-1) formatted_probability_strings = np.apply_along_axis( - lambda x: ",".join(map(str, x)), 3, test_individual_preds - ) - test_individual_preds = np.concatenate( - (predicted_class_labels, formatted_probability_strings), axis=-1 + lambda x: ",".join(map(str, x)), 3, test_individual_preds[0] ) + test_individual_preds = [ + np.concatenate((predicted_class_labels, formatted_probability_strings), axis=-1) + ] else: output_columns = [ f"{col}_model_{i}" for i in range(len(model_paths)) for col in output_columns ] - m, n, t = test_individual_preds.shape - test_individual_preds = np.transpose(test_individual_preds, (1, 0, 2)).reshape(n, m * t) + m, n, t = test_individual_preds[0].shape + test_individual_preds[0] = np.transpose(test_individual_preds[0], (1, 0, 2)).reshape( + n, m * t + ) # TODO: edit for atoms/bonds df_test = pd.read_csv( args.test_path, header=None if args.no_header_row else "infer", index_col=False ) - df_test[output_columns] = test_individual_preds + + if args.is_mixed: + mol_cols = mixed_columns[0] + atom_cols = mixed_columns[1] + bond_cols = mixed_columns[2] + + df_test.loc[:, mol_cols] = test_individual_preds[0].tolist() + + for i in range(len(df_test)): + atom_slices = test_loader.dataset.atom_dataset._slices + if atom_slices is not None: + first_atom = atom_slices.index(i) + last_atom = first_atom + atom_slices.count(i) + atom_preds = test_individual_preds[1][first_atom:last_atom] + df_test.loc[i, atom_cols] = [ + str(atom_preds[:, j].tolist()) for j in range(len(atom_cols)) + ] + + bond_slices = test_loader.dataset.bond_dataset._slices + if bond_slices is not None: + first_bond = bond_slices.index(i) + last_bond = first_bond + bond_slices.count(i) + bond_preds = test_individual_preds[2][first_bond:last_bond] + df_test.loc[i, bond_cols] = [ + str(bond_preds[:, j].tolist()) for j in range(len(bond_cols)) + ] + else: + df_test[output_columns] = test_individual_preds[0] if args.uncertainty_method not in ["none", "classification"]: - m, n, t = test_individual_uncs.shape - test_individual_uncs = np.transpose(test_individual_uncs, (1, 0, 2)).reshape(n, m * t) - df_test[unc_columns] = np.round(test_individual_uncs, 6) + m, n, t = test_individual_uncs[0].shape + test_individual_uncs[0] = np.transpose(test_individual_uncs[0], (1, 0, 2)).reshape(n, m * t) + df_test[unc_columns] = np.round(test_individual_uncs[0], 6) output_path = output_path.parent / Path( str(args.output.stem) + "_individual" + str(output_path.suffix) diff --git a/chemprop/cli/train.py b/chemprop/cli/train.py index a27aff11b..e007564c3 100644 --- a/chemprop/cli/train.py +++ b/chemprop/cli/train.py @@ -29,12 +29,15 @@ LookupAction, Subcommand, build_data_from_files, + build_mixed_data_from_files, get_column_names, make_dataset, parse_indices, ) from chemprop.cli.utils.args import uppercase from chemprop.data import ( + MockDataset, + MolAtomBondDataset, MoleculeDataset, MolGraphDataset, MulticomponentDataset, @@ -45,11 +48,19 @@ split_data_by_indices, ) from chemprop.data.datasets import _MolGraphDatasetMixin -from chemprop.models import MPNN, MulticomponentMPNN, save_model -from chemprop.nn import AggregationRegistry, LossFunctionRegistry, MetricRegistry, PredictorRegistry +from chemprop.models import MPNN, MolAtomBondMPNN, MulticomponentMPNN, save_model +from chemprop.nn import ( + AggregationRegistry, + FFNMockPredictor, + LossFunctionRegistry, + MetricRegistry, + PredictorRegistry, +) from chemprop.nn.message_passing import ( AtomMessagePassing, BondMessagePassing, + MixedAtomMessagePassing, + MixedBondMessagePassing, MulticomponentMessagePassing, ) from chemprop.nn.transforms import GraphTransform, ScaleTransform, UnscaleTransform @@ -569,12 +580,14 @@ def normalize_inputs(train_dset, val_dset, args): V_f_transforms = [nn.Identity()] * num_components E_f_transforms = [nn.Identity()] * num_components V_d_transforms = [None] * num_components + E_d_transforms = [None] * num_components graph_transforms = [] d_xd = train_dset.d_xd d_vf = train_dset.d_vf d_ef = train_dset.d_ef d_vd = train_dset.d_vd + d_ed = train_dset.d_ed if d_xd > 0 and not args.no_descriptor_scaling: scaler = train_dset.normalize_inputs("X_d") @@ -646,7 +659,22 @@ def normalize_inputs(train_dset, val_dset, args): ) V_d_transforms[i] = ScaleTransform.from_standard_scaler(scaler) - return X_d_transform, graph_transforms, V_d_transforms + if d_ed > 0 and not args.no_bond_descriptor_scaling: + scaler = train_dset.normalize_inputs("E_d") + val_dset.normalize_inputs("E_d", scaler) + + scalers = [scaler] if not isinstance(scaler, list) else scaler + + for i, scaler in enumerate(scalers): + if scaler is None: + continue + + logger.info( + f"Bond descriptors for mol {i}: loc = {np.array2string(scaler.mean_, precision=3)}, scale = {np.array2string(scaler.scale_, precision=3)}" + ) + E_d_transforms[i] = ScaleTransform.from_standard_scaler(scaler) + + return X_d_transform, graph_transforms, V_d_transforms, E_d_transforms def load_and_use_pretrained_model_scalers(model_path: Path, train_dset, val_dset) -> None: @@ -692,6 +720,11 @@ def load_and_use_pretrained_model_scalers(model_path: Path, train_dset, val_dset train_dsets[i].normalize_inputs("V_d", scaler) val_dsets[i].normalize_inputs("V_d", scaler) + if isinstance(blocks[i].E_d_transform, ScaleTransform): + scaler = blocks[i].E_d_transform.to_standard_scaler() + train_dsets[i].normalize_inputs("E_d", scaler) + val_dsets[i].normalize_inputs("E_d", scaler) + if isinstance(_model.predictor.output_transform, UnscaleTransform): scaler = _model.predictor.output_transform.to_standard_scaler() train_dset.normalize_targets(scaler) @@ -739,15 +772,28 @@ def save_smiles_splits(args: Namespace, output_dir, train_dset, val_dset, test_d def build_splits(args, format_kwargs, featurization_kwargs): """build the train/val/test splits""" logger.info(f"Pulling data from file: {args.data_path}") - all_data = build_data_from_files( - args.data_path, - p_descriptors=args.descriptors_path, - p_atom_feats=args.atom_features_path, - p_bond_feats=args.bond_features_path, - p_atom_descs=args.atom_descriptors_path, - **format_kwargs, - **featurization_kwargs, - ) + mol_cols, atom_cols, bond_cols = [], [], [] + if args.is_mixed: + all_data, mol_cols, atom_cols, bond_cols = build_mixed_data_from_files( + args.data_path, + p_descriptors=args.descriptors_path, + p_atom_feats=args.atom_features_path, + p_bond_feats=args.bond_features_path, + p_atom_descs=args.atom_descriptors_path, + **format_kwargs, + **featurization_kwargs, + ) + args.mixed_columns = [mol_cols, atom_cols, bond_cols] + else: + all_data = build_data_from_files( + args.data_path, + p_descriptors=args.descriptors_path, + p_atom_feats=args.atom_features_path, + p_bond_feats=args.bond_features_path, + p_atom_descs=args.atom_descriptors_path, + **format_kwargs, + **featurization_kwargs, + ) if args.splits_column is not None: df = pd.read_csv( @@ -770,12 +816,11 @@ def build_splits(args, format_kwargs, featurization_kwargs): splitting_data = all_data[args.split_key_molecule] if isinstance(splitting_data[0], ReactionDatapoint): splitting_mols = [datapoint.rct for datapoint in splitting_data] - else: + else: # works for is_mixed too since all_data[0] is coincidentally what we want 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 ) - train_data, val_data, test_data = split_data_by_indices( all_data, train_indices, val_indices, test_indices ) @@ -797,6 +842,9 @@ def summarize( ]: if isinstance(dataset, MulticomponentDataset): y = dataset.datasets[0].Y + elif isinstance(dataset, MolAtomBondDataset): + y = dataset.mol_dataset.Y + print(y) else: y = dataset.Y y_mean = np.nanmean(y, axis=0) @@ -871,7 +919,7 @@ def build_table(column_headers: list[str], table_rows: list[str], title: str | N def build_datasets(args, train_data, val_data, test_data): """build the train/val/test datasets, where :attr:`test_data` may be None""" - multicomponent = len(train_data) > 1 + multicomponent = len(train_data) > 1 and not args.is_mixed if multicomponent: train_dsets = [ make_dataset(data, args.rxn_mode, args.multi_hot_atom_featurizer_mode) @@ -891,6 +939,33 @@ def build_datasets(args, train_data, val_data, test_data): test_dset = MulticomponentDataset(test_dsets) else: test_dset = None + elif args.is_mixed: + train_dsets = [ + make_dataset(train_data[data], args.rxn_mode, args.multi_hot_atom_featurizer_mode, data) + if args.mixed_columns[data] + else MockDataset() + for data in range(len(train_data)) + ] + val_dsets = [ + make_dataset(val_data[data], args.rxn_mode, args.multi_hot_atom_featurizer_mode, data) + if args.mixed_columns[data] + else MockDataset() + for data in range(len(val_data)) + ] + train_dset = MolAtomBondDataset(train_dsets[0], train_dsets[1], train_dsets[2]) + val_dset = MolAtomBondDataset(val_dsets[0], val_dsets[1], val_dsets[2]) + if len(test_data[0]) > 0: + test_dsets = [ + make_dataset( + test_data[data], args.rxn_mode, args.multi_hot_atom_featurizer_mode, data + ) + if args.mixed_columns[data] + else MockDataset() + for data in range(len(test_data)) + ] + test_dset = MolAtomBondDataset(test_dsets[0], test_dsets[1], test_dsets[2]) + else: + test_dset = None else: train_data = train_data[0] val_data = val_data[0] @@ -914,13 +989,18 @@ def build_datasets(args, train_data, val_data, test_data): def build_model( args, - train_dset: MolGraphDataset | MulticomponentDataset, - output_transform: UnscaleTransform, - input_transforms: tuple[ScaleTransform, list[GraphTransform], list[ScaleTransform]], -) -> MPNN: - mp_cls = AtomMessagePassing if args.atom_messages else BondMessagePassing + train_dset: MolGraphDataset | MolAtomBondDataset | MulticomponentDataset, + output_transform: list[UnscaleTransform], + input_transforms: tuple[ + ScaleTransform, list[GraphTransform], list[ScaleTransform], list[ScaleTransform] + ], +) -> MPNN | MulticomponentMPNN: + if args.is_mixed: + mp_cls = MixedAtomMessagePassing if args.atom_messages else MixedBondMessagePassing + else: + mp_cls = AtomMessagePassing if args.atom_messages else BondMessagePassing - X_d_transform, graph_transforms, V_d_transforms = input_transforms + X_d_transform, graph_transforms, V_d_transforms, E_d_transforms = input_transforms if isinstance(train_dset, MulticomponentDataset): mp_blocks = [ mp_cls( @@ -932,12 +1012,18 @@ def build_model( if isinstance(train_dset.datasets[i], MoleculeDataset) else 0 ), + d_ed=( + train_dset.datasets[i].d_ed + if isinstance(train_dset.datasets[i], MoleculeDataset) + else 0 + ), bias=args.message_bias, depth=args.depth, undirected=args.undirected, dropout=args.dropout, activation=args.activation, V_d_transform=V_d_transforms[i], + E_d_transform=E_d_transforms[i], graph_transform=graph_transforms[i], ) for i in range(train_dset.n_components) @@ -957,18 +1043,38 @@ def build_model( d_xd = train_dset.datasets[0].d_xd n_tasks = train_dset.datasets[0].Y.shape[1] mpnn_cls = MulticomponentMPNN + elif isinstance(train_dset, MolAtomBondDataset): + mp_block = mp_cls( + train_dset.mol_dataset.featurizer.atom_fdim, + train_dset.mol_dataset.featurizer.bond_fdim, + d_h=args.message_hidden_dim, + d_vd=train_dset.d_vd, + d_ed=train_dset.d_ed, + bias=args.message_bias, + depth=args.depth, + undirected=args.undirected, + dropout=args.dropout, + activation=args.activation, + V_d_transform=V_d_transforms[0], + graph_transform=graph_transforms[0], + ) + d_xd = train_dset.d_xd + n_tasks = train_dset.mol_dataset.Y.shape[1] + mpnn_cls = MolAtomBondMPNN else: mp_block = mp_cls( train_dset.featurizer.atom_fdim, train_dset.featurizer.bond_fdim, d_h=args.message_hidden_dim, d_vd=train_dset.d_vd if isinstance(train_dset, MoleculeDataset) else 0, + d_ed=train_dset.d_ed if isinstance(train_dset, MoleculeDataset) else 0, bias=args.message_bias, depth=args.depth, undirected=args.undirected, dropout=args.dropout, activation=args.activation, V_d_transform=V_d_transforms[0], + E_d_transform=E_d_transforms[0], graph_transform=graph_transforms[0], ) d_xd = train_dset.d_xd @@ -994,37 +1100,61 @@ def build_model( else: metrics = None - predictor = Factory.build( - predictor_cls, - input_dim=mp_block.output_dim + d_xd, - n_tasks=n_tasks, - hidden_dim=args.ffn_hidden_dim, - n_layers=args.ffn_num_layers, - dropout=args.dropout, - activation=args.activation, - criterion=criterion, - n_classes=args.multiclass_num_classes, - output_transform=output_transform, - # spectral_activation=args.spectral_activation, TODO: Add in v2.1 - ) + input_dims = [mp_block.output_dim + d_xd] * 3 + input_dims[2] *= 2 # for bond edge input_dim + + predictors = [ + Factory.build( + predictor_cls + if not args.is_mixed or len(args.mixed_columns[i]) > 0 + else FFNMockPredictor, + input_dim=input_dims[i], + n_tasks=n_tasks, + hidden_dim=args.ffn_hidden_dim, + n_layers=args.ffn_num_layers, + dropout=args.dropout, + activation=args.activation, + criterion=criterion, + n_classes=args.multiclass_num_classes, + output_transform=output_transform[i], + # spectral_activation=args.spectral_activation, TODO: Add in v2.1 + ) + for i in range(len(output_transform)) + ] if args.loss_function is None: logger.info( f"No loss function was specified! Using class default: {predictor_cls._T_default_criterion}" ) - return mpnn_cls( - mp_block, - agg, - predictor, - args.batch_norm, - metrics, - args.warmup_epochs, - args.init_lr, - args.max_lr, - args.final_lr, - X_d_transform=X_d_transform, - ) + if args.is_mixed: + return mpnn_cls( + mp_block, + agg, + predictors[0], + predictors[1], + predictors[2], + args.batch_norm, + metrics, + args.warmup_epochs, + args.init_lr, + args.max_lr, + args.final_lr, + X_d_transform=X_d_transform, + ) + else: + return mpnn_cls( + mp_block, + agg, + predictors[0], + args.batch_norm, + metrics, + args.warmup_epochs, + args.init_lr, + args.max_lr, + args.final_lr, + X_d_transform=X_d_transform, + ) def train_model( @@ -1052,11 +1182,13 @@ def train_model( torch.manual_seed(seed) if args.checkpoint or args.model_frzn is not None: - mpnn_cls = ( - MulticomponentMPNN - if isinstance(train_loader.dataset, MulticomponentDataset) - else MPNN - ) + if isinstance(train_loader.dataset, MulticomponentDataset): + mpnn_cls = MulticomponentMPNN + elif isinstance(train_loader.dataset, MolAtomBondDataset): + mpnn_cls = MolAtomBondMPNN + else: + mpnn_cls = MPNN + model_path = model_paths[model_idx] if args.checkpoint else args.model_frzn model = mpnn_cls.load_from_file(model_path) @@ -1091,11 +1223,13 @@ def train_model( trainer_logger = CSVLogger(model_output_dir, "trainer_logs") if args.tracking_metric == "val_loss": - T_tracking_metric = model.criterion.__class__ + if args.is_mixed: + T_tracking_metric = model.criterion[0].__class__ + else: + T_tracking_metric = model.criterion.__class__ else: T_tracking_metric = MetricRegistry[args.tracking_metric] args.tracking_metric = "val/" + args.tracking_metric - monitor_mode = "max" if T_tracking_metric.higher_is_better else "min" logger.debug(f"Evaluation metric: '{T_tracking_metric.alias}', mode: '{monitor_mode}'") @@ -1155,36 +1289,83 @@ def train_model( else: predss = trainer.predict(dataloaders=test_loader) - preds = torch.concat(predss, 0) - if model.predictor.n_targets > 1: - preds = preds[..., 0] - preds = preds.numpy() - - evaluate_and_save_predictions( - preds, test_loader, model.metrics[:-1], model_output_dir, args - ) + if args.is_mixed: + preds = [] + preds.append(torch.concat([predss[0][0]], 0)) + preds.append(torch.concat([predss[0][1]], 0)) + preds.append(torch.concat([predss[0][2]], 0)) + else: + preds = [torch.concat(predss, 0)] + + if args.is_mixed and model.predictors[0].n_targets > 1 or model.predictor.n_targets > 1: + for i in range(len(preds)): + preds[i] = preds[i][..., 0] + + dfs = [] + for i in range(len(preds)): + if args.mixed_columns is not None and args.mixed_columns[i]: + preds[i] = preds[i].numpy() + dfs.append( + evaluate_and_save_predictions( + preds[i], test_loader, model.metrics[i][:-1], model_output_dir, args, i + ) + ) + else: + preds[0] = preds[0].numpy() + dfs.append( + evaluate_and_save_predictions( + preds[0], test_loader, model.metrics[:-1], model_output_dir, args, 0 + ) + ) + if len(dfs) == 3: + dfs[1] = dfs[1].drop(args.input_columns, axis=1) + dfs[2] = dfs[2].drop(args.input_columns, axis=1) + df_comb = pd.concat([dfs[0], dfs[1], dfs[2]], axis=1) + df_comb.to_csv(model_output_dir / "test_predictions.csv", index=False) + elif len(dfs) == 2: + dfs[1] = dfs[1].drop(args.input_columns, axis=1) + df_comb = pd.concat([dfs[0], dfs[1]], axis=1) + df_comb.to_csv(model_output_dir / "test_predictions.csv", index=False) + else: + df_comb = dfs[0] + df_comb.to_csv(model_output_dir / "test_predictions.csv", index=False) best_model_path = checkpointing.best_model_path model = model.__class__.load_from_checkpoint(best_model_path) p_model = model_output_dir / "best.pt" - save_model(p_model, model, args.target_columns) + save_model(p_model, model, args.target_columns, args.mixed_columns) logger.info(f"Best model saved to '{p_model}'") if args.remove_checkpoints: temp_dir.cleanup() -def evaluate_and_save_predictions(preds, test_loader, metrics, model_output_dir, args): +def evaluate_and_save_predictions(preds, test_loader, metrics, model_output_dir, args, index): if isinstance(test_loader.dataset, MulticomponentDataset): test_dset = test_loader.dataset.datasets[0] + elif isinstance(test_loader.dataset, MolAtomBondDataset): + if index == 0: + test_dset = test_loader.dataset.mol_dataset + elif index == 1: + test_dset = test_loader.dataset.atom_dataset + else: + test_dset = test_loader.dataset.bond_dataset else: test_dset = test_loader.dataset targets = test_dset.Y mask = torch.from_numpy(np.isfinite(targets)) targets = np.nan_to_num(targets, nan=0.0) - weights = torch.ones(len(test_dset)) - lt_mask = torch.from_numpy(test_dset.lt_mask) if test_dset.lt_mask[0] is not None else None - gt_mask = torch.from_numpy(test_dset.gt_mask) if test_dset.gt_mask[0] is not None else None + weights = torch.ones(targets.shape[0]) + lt_mask = ( + torch.from_numpy(test_dset.lt_mask) + if test_dset.lt_mask[0] is not None and test_dset.lt_mask[0][0] is not None + else None + ) + gt_mask = ( + torch.from_numpy(test_dset.gt_mask) + if test_dset.gt_mask[0] is not None and test_dset.gt_mask[0][0] is not None + else None + ) individual_scores = dict() for metric in metrics: @@ -1222,8 +1403,10 @@ def evaluate_and_save_predictions(preds, test_loader, metrics, model_output_dir, namess = list(zip(*names)) else: namess = [names] - - columns = args.input_columns + args.target_columns + if args.is_mixed: + columns = args.input_columns + args.mixed_columns[index] + else: + columns = args.input_columns + args.target_columns if "multiclass" in args.task_type: columns = columns + [f"{col}_prob" for col in args.target_columns] formatted_probability_strings = np.apply_along_axis( @@ -1236,7 +1419,8 @@ def evaluate_and_save_predictions(preds, test_loader, metrics, model_output_dir, ) else: df_preds = pd.DataFrame(list(zip(*namess, *preds.T)), columns=columns) - df_preds.to_csv(model_output_dir / "test_predictions.csv", index=False) + + return df_preds def main(args): @@ -1281,19 +1465,41 @@ def main(args): model_path = model_paths[0] if args.checkpoint else args.model_frzn load_and_use_pretrained_model_scalers(model_path, train_dset, val_dset) input_transforms = (None, None, None) - output_transform = None + output_transform = [None] else: input_transforms = normalize_inputs(train_dset, val_dset, args) - if "regression" in args.task_type: + if args.is_mixed and "regression" in args.task_type: + output_transform = [] + mol_output_scaler = train_dset.mol_dataset.normalize_targets() + val_dset.mol_dataset.normalize_targets(mol_output_scaler) + output_transform.append(UnscaleTransform.from_standard_scaler(mol_output_scaler)) + atom_output_scaler = train_dset.atom_dataset.normalize_targets() + val_dset.atom_dataset.normalize_targets(atom_output_scaler) + output_transform.append(UnscaleTransform.from_standard_scaler(atom_output_scaler)) + bond_output_scaler = train_dset.bond_dataset.normalize_targets() + val_dset.bond_dataset.normalize_targets(bond_output_scaler) + output_transform.append(UnscaleTransform.from_standard_scaler(bond_output_scaler)) + logger.info( + f"Train data: mean = {mol_output_scaler.mean_} | std = {mol_output_scaler.scale_}" + ) + logger.info( + f"Train data: mean = {atom_output_scaler.mean_} | std = {atom_output_scaler.scale_}" + ) + logger.info( + f"Train data: mean = {bond_output_scaler.mean_} | std = {bond_output_scaler.scale_}" + ) + elif args.is_mixed: + output_transform = [None, None, None] + elif "regression" in args.task_type: output_scaler = train_dset.normalize_targets() val_dset.normalize_targets(output_scaler) logger.info( f"Train data: mean = {output_scaler.mean_} | std = {output_scaler.scale_}" ) - output_transform = UnscaleTransform.from_standard_scaler(output_scaler) + output_transform = [UnscaleTransform.from_standard_scaler(output_scaler)] else: - output_transform = None + output_transform = [None] if not args.no_cache: train_dset.cache = True diff --git a/chemprop/cli/utils/__init__.py b/chemprop/cli/utils/__init__.py index 91d607b45..dc4829a42 100644 --- a/chemprop/cli/utils/__init__.py +++ b/chemprop/cli/utils/__init__.py @@ -3,6 +3,7 @@ from .command import Subcommand from .parsing import ( build_data_from_files, + build_mixed_data_from_files, get_column_names, make_datapoints, make_dataset, @@ -15,6 +16,7 @@ "LookupAction", "Subcommand", "build_data_from_files", + "build_mixed_data_from_files", "make_datapoints", "make_dataset", "get_column_names", diff --git a/chemprop/cli/utils/parsing.py b/chemprop/cli/utils/parsing.py index b6299eeed..c23166b7d 100644 --- a/chemprop/cli/utils/parsing.py +++ b/chemprop/cli/utils/parsing.py @@ -1,3 +1,4 @@ +import ast import logging from os import PathLike from typing import Mapping, Sequence @@ -6,7 +7,7 @@ import pandas as pd from chemprop.data.datapoints import MoleculeDatapoint, ReactionDatapoint -from chemprop.data.datasets import MoleculeDataset, ReactionDataset +from chemprop.data.datasets import AtomDataset, BondDataset, MoleculeDataset, ReactionDataset from chemprop.featurizers.atom import get_multi_hot_atom_featurizer from chemprop.featurizers.molecule import MoleculeFeaturizerRegistry from chemprop.featurizers.molgraph import ( @@ -59,6 +60,7 @@ def parse_csv( ) Y = df[target_cols] + weights = None if weight_col is None else df[weight_col].to_numpy(np.single) if bounded: @@ -73,6 +75,133 @@ def parse_csv( return smiss, rxnss, Y, weights, lt_mask, gt_mask +def mixed_parse_csv( + path: PathLike, + smiles_cols: Sequence[str] | None, + rxn_cols: Sequence[str] | None, + target_cols: Sequence[str] | None, + ignore_cols: Sequence[str] | None, + splits_col: str | None, + weight_col: str | None, + bounded: bool = False, + no_header_row: bool = False, +): + df = pd.read_csv(path, header=None if no_header_row else "infer", index_col=False) + + if smiles_cols is not None and rxn_cols is not None: + smiss = df[smiles_cols].T.values.tolist() + rxnss = df[rxn_cols].T.values.tolist() + input_cols = [*smiles_cols, *rxn_cols] + elif smiles_cols is not None and rxn_cols is None: + smiss = df[smiles_cols].T.values.tolist() + rxnss = None + input_cols = smiles_cols + elif smiles_cols is None and rxn_cols is not None: + smiss = None + rxnss = df[rxn_cols].T.values.tolist() + input_cols = rxn_cols + else: + smiss = df.iloc[:, [0]].T.values.tolist() + rxnss = None + input_cols = [df.columns[0]] + + if target_cols is None: + target_cols = list( + column + for column in df.columns + if column + not in set( # if splits or weight is None, df.columns will never have None + input_cols + (ignore_cols or []) + [splits_col] + [weight_col] + ) + ) + + flag, mol_Y, atom_Y, bond_Y = [], [], [], [] + for column in target_cols: + index = 0 + column_type = df.iloc[index][column] + if isinstance(column_type, float): + flag.append("mol") + else: + column_mol = make_mol(df.iloc[index][input_cols[0]], False, False) + column_type = ast.literal_eval(column_type) + while index < len(df) and column_mol.GetNumAtoms() == column_mol.GetNumBonds(): + index += 1 + column_mol = make_mol(df.iloc[index][input_cols[0]], False, False) + column_type = ast.literal_eval(df.iloc[index][column]) + flag.append("atom") if len(column_type) == column_mol.GetNumAtoms() else flag.append( + "bond" + ) + + mol_list = [] + for i in range(len(flag)): + if flag[i] == "mol": + mol_list.append(target_cols[i]) + mol_Y = df[mol_list] + + for molecule in range(len(df)): + atom_list_props = [] + bond_list_props = [] + for prop in range(len(target_cols)): + if flag[prop] == "mol": + continue + np_prop = np.array(ast.literal_eval(df.iloc[molecule][target_cols[prop]])) + np_prop = np.expand_dims(np_prop, axis=1) + atom_list_props.append(np_prop) if flag[prop] == "atom" else bond_list_props.append( + np_prop + ) + if len(atom_list_props) > 0: + atom_Y.append(np.hstack(atom_list_props)) + else: + atom_Y = df[[]] + atom_Y = atom_Y.to_numpy() + if len(bond_list_props) > 0: + bond_Y.append(np.hstack(bond_list_props)) + else: + bond_Y = df[[]] + bond_Y = bond_Y.to_numpy() + + weights = None if weight_col is None else df[weight_col].to_numpy(np.single) + lt_mask = [] + gt_mask = [] + if bounded: + mol_lt_mask = mol_Y.applymap(lambda x: "<" in x).to_numpy() + mol_gt_mask = mol_Y.applymap(lambda x: ">" in x).to_numpy() + lt_mask.append(mol_lt_mask) + gt_mask.append(mol_gt_mask) + + atom_dim = atom_Y[0].shape[1] + atom_lt_mask, atom_gt_mask = np.empty((0, atom_dim)), np.empty((0, atom_dim)) + for i in range(len(atom_Y)): + atom_lt_mask = np.vstack( + [atom_lt_mask, atom_Y[i].applymap(lambda x: "<" in x).to_numpy()] + ) + atom_gt_mask = np.vstack( + [atom_gt_mask, atom_Y[i].applymap(lambda x: ">" in x).to_numpy()] + ) + atom_Y[i] = atom_Y[i].applymap(lambda x: x.strip("<").strip(">")).to_numpy(np.single) + lt_mask.append(atom_lt_mask) + gt_mask.append(atom_gt_mask) + + bond_dim = bond_Y[0].shape[1] + bond_lt_mask, bond_gt_mask = np.empty((0, bond_dim)), np.empty((0, bond_dim)) + for i in range(len(bond_Y)): + bond_lt_mask = np.vstack( + [bond_lt_mask, bond_Y[i].applymap(lambda x: "<" in x).to_numpy()] + ) + bond_gt_mask = np.vstack( + [bond_gt_mask, bond_Y[i].applymap(lambda x: ">" in x).to_numpy()] + ) + bond_Y[i] = bond_Y[i].applymap(lambda x: x.strip("<").strip(">")).to_numpy(np.single) + lt_mask.append(bond_lt_mask) + gt_mask.append(bond_gt_mask) + else: + mol_Y = np.array(mol_Y) + lt_mask = None + gt_mask = None + + return smiss, rxnss, mol_Y, atom_Y, bond_Y, weights, lt_mask, gt_mask, flag + + def get_column_names( path: PathLike, smiles_cols: Sequence[str] | None, @@ -109,7 +238,7 @@ def get_column_names( def make_datapoints( smiss: list[list[str]] | None, rxnss: list[list[str]] | None, - Y: np.ndarray, + Y: np.ndarray | list[np.ndarray], weights: np.ndarray | None, lt_mask: np.ndarray | None, gt_mask: np.ndarray | None, @@ -117,6 +246,7 @@ def make_datapoints( V_fss: list[list[np.ndarray] | list[None]] | None, E_fss: list[list[np.ndarray] | list[None]] | None, V_dss: list[list[np.ndarray] | list[None]] | None, + keep_atom_map: bool, molecule_featurizers: list[str] | None, keep_h: bool, add_h: bool, @@ -205,17 +335,22 @@ def make_datapoints( N = len(smiss[0]) if len(smiss) > 0: - molss = [[make_mol(smi, keep_h, add_h) for smi in smis] for smis in smiss] + molss = [[make_mol(smi, keep_h, add_h, keep_atom_map) for smi in smis] for smis in smiss] if len(rxnss) > 0: rctss = [ [ - make_mol(f"{rct_smi}.{agt_smi}" if agt_smi else rct_smi, keep_h, add_h) + make_mol( + f"{rct_smi}.{agt_smi}" if agt_smi else rct_smi, keep_h, add_h, keep_atom_map + ) for rct_smi, agt_smi, _ in (rxn.split(">") for rxn in rxns) ] for rxns in rxnss ] pdtss = [ - [make_mol(pdt_smi, keep_h, add_h) for _, _, pdt_smi in (rxn.split(">") for rxn in rxns)] + [ + make_mol(pdt_smi, keep_h, add_h, keep_atom_map) + for _, _, pdt_smi in (rxn.split(">") for rxn in rxns) + ] for rxns in rxnss ] @@ -333,6 +468,7 @@ def build_data_from_files( bounded, no_header_row, ) + n_molecules = len(smiss) if smiss is not None else 0 n_datapoints = len(Y) @@ -341,7 +477,9 @@ def build_data_from_files( E_fss = load_input_feats_and_descs(p_bond_feats, n_molecules, n_datapoints, feat_desc="E_f") V_dss = load_input_feats_and_descs(p_atom_descs, n_molecules, n_datapoints, feat_desc="V_d") - mol_data, rxn_data = make_datapoints( + keep_atom_map = True if np.vstack(Y).shape[0] == n_molecules else False + + mol_data, mol_rxn_data = make_datapoints( smiss, rxnss, Y, @@ -352,10 +490,106 @@ def build_data_from_files( V_fss, E_fss, V_dss, + keep_atom_map, + **featurization_kwargs, + ) + + return mol_data + mol_rxn_data + + +def build_mixed_data_from_files( + p_data: PathLike, + no_header_row: bool, + smiles_cols: Sequence[str] | None, + rxn_cols: Sequence[str] | None, + target_cols: Sequence[str] | None, + ignore_cols: Sequence[str] | None, + splits_col: str | None, + weight_col: str | None, + bounded: bool, + p_descriptors: PathLike, + p_atom_feats: dict[int, PathLike], + p_bond_feats: dict[int, PathLike], + p_atom_descs: dict[int, PathLike], + **featurization_kwargs: Mapping, +) -> list[list[MoleculeDatapoint] | list[ReactionDatapoint]]: + smiss, rxnss, mol_Y, atom_Y, bond_Y, weights, lt_mask, gt_mask, flag = mixed_parse_csv( + p_data, + smiles_cols, + rxn_cols, + target_cols, + ignore_cols, + splits_col, + weight_col, + bounded, + no_header_row, + ) + + n_molecules = len(smiss) if smiss is not None else 0 + n_datapoints = len(mol_Y) + + X_ds = load_input_feats_and_descs(p_descriptors, None, None, feat_desc="X_d") + V_fss = load_input_feats_and_descs(p_atom_feats, n_molecules, n_datapoints, feat_desc="V_f") + E_fss = load_input_feats_and_descs(p_bond_feats, n_molecules, n_datapoints, feat_desc="E_f") + V_dss = load_input_feats_and_descs(p_atom_descs, n_molecules, n_datapoints, feat_desc="V_d") + + keep_atom_map = True if np.vstack(mol_Y).shape[0] == n_molecules else False + print(mol_Y.shape) + mol_data, mol_rxn_data = make_datapoints( + smiss, + rxnss, + mol_Y, + weights, + lt_mask if lt_mask is None else lt_mask[0], + gt_mask if gt_mask is None else gt_mask[0], + X_ds, + V_fss, + E_fss, + V_dss, + keep_atom_map, **featurization_kwargs, ) - return mol_data + rxn_data + atom_data, atom_rxn_data = make_datapoints( + smiss, + rxnss, + atom_Y, + weights, + lt_mask if lt_mask is None else lt_mask[1], + gt_mask if gt_mask is None else gt_mask[1], + X_ds, + V_fss, + E_fss, + V_dss, + keep_atom_map, + **featurization_kwargs, + ) + + bond_data, bond_rxn_data = make_datapoints( + smiss, + rxnss, + bond_Y, + weights, + lt_mask if lt_mask is None else lt_mask[2], + gt_mask if gt_mask is None else gt_mask[2], + X_ds, + V_fss, + E_fss, + V_dss, + keep_atom_map, + **featurization_kwargs, + ) + + mol_cols, atom_cols, bond_cols = [], [], [] + for i in range(len(flag)): + if flag[i] == "mol": + mol_cols.append(target_cols[i]) + elif flag[i] == "atom": + atom_cols.append(target_cols[i]) + else: + bond_cols.append(target_cols[i]) + + return mol_data + atom_data + bond_data, mol_cols, atom_cols, bond_cols def load_input_feats_and_descs( @@ -401,7 +635,8 @@ def make_dataset( data: Sequence[MoleculeDatapoint] | Sequence[ReactionDatapoint], reaction_mode: str, multi_hot_atom_featurizer_mode: str = "V2", -) -> MoleculeDataset | ReactionDataset: + index: int = 0, +) -> MoleculeDataset | AtomDataset | BondDataset | ReactionDataset: atom_featurizer = get_multi_hot_atom_featurizer(multi_hot_atom_featurizer_mode) if isinstance(data[0], MoleculeDatapoint): @@ -412,7 +647,14 @@ def make_dataset( extra_atom_fdim=extra_atom_fdim, extra_bond_fdim=extra_bond_fdim, ) - return MoleculeDataset(data, featurizer) + if index == 0: + return MoleculeDataset(data, featurizer) + elif index == 1: + return AtomDataset(data, featurizer) + elif index == 2: + return BondDataset(data, featurizer) + else: + raise IndexError("Index flag not in list of train_data, val_data, and test_data") featurizer = CondensedGraphOfReactionFeaturizer( mode_=reaction_mode, atom_featurizer=atom_featurizer diff --git a/chemprop/data/__init__.py b/chemprop/data/__init__.py index ea702bf9f..a0f56fb93 100644 --- a/chemprop/data/__init__.py +++ b/chemprop/data/__init__.py @@ -1,8 +1,18 @@ -from .collate import BatchMolGraph, TrainingBatch, collate_batch, collate_multicomponent +from .collate import ( + BatchMolGraph, + TrainingBatch, + collate_batch, + collate_multicomponent, + mixed_collate_batch, +) from .dataloader import build_dataloader from .datapoints import MoleculeDatapoint, ReactionDatapoint from .datasets import ( + AtomDataset, + BondDataset, Datum, + MockDataset, + MolAtomBondDataset, MoleculeDataset, MolGraphDataset, MulticomponentDataset, @@ -16,13 +26,18 @@ "BatchMolGraph", "TrainingBatch", "collate_batch", + "mixed_collate_batch", "collate_multicomponent", "build_dataloader", "MoleculeDatapoint", "ReactionDatapoint", "MoleculeDataset", + "AtomDataset", + "BondDataset", "ReactionDataset", "Datum", + "MockDataset", + "MolAtomBondDataset", "MulticomponentDataset", "MolGraphDataset", "MolGraph", diff --git a/chemprop/data/collate.py b/chemprop/data/collate.py index e9e9b59a8..40283a1c3 100644 --- a/chemprop/data/collate.py +++ b/chemprop/data/collate.py @@ -75,6 +75,7 @@ def to(self, device: str | torch.device): class TrainingBatch(NamedTuple): bmg: BatchMolGraph V_d: Tensor | None + E_d: Tensor | None X_d: Tensor | None Y: Tensor | None w: Tensor @@ -83,22 +84,55 @@ class TrainingBatch(NamedTuple): def collate_batch(batch: Iterable[Datum]) -> TrainingBatch: - mgs, V_ds, x_ds, ys, weights, lt_masks, gt_masks = zip(*batch) + mgs, V_ds, E_ds, x_ds, ys, weights, lt_masks, gt_masks = zip(*batch) + + # if ys[0] is not None: + # dim = ys[0].shape[1] + # np_y = np.empty((0, dim), float) + # for y in ys: + # np_y = np.vstack([np_y, y]) + # if lt_masks[0] is not None: + # dim = lt_masks[0].shape[1] + # np_lt = np.empty((0, dim)) + # for lt in lt_masks: + # np_lt = np.vstack([np_lt, lt]) + # if gt_masks[0] is not None: + # dim = gt_masks[0].shape[1] + # np_gt = np.empty((0, dim)) + # for gt in gt_masks: + # np_gt = np.vstack([np_gt, gt]) + + np_y = np.vstack(ys) + np_lt = np.vstack(lt_masks) + np_gt = np.vstack(gt_masks) + + if np_y.shape[0] == len(ys): + weights_tensor = torch.tensor(weights, dtype=torch.float).unsqueeze(1) + else: + num_atoms = torch.tensor([y.shape[0] for y in ys]) + weights_tensor = torch.tensor(weights, dtype=torch.float).unsqueeze(1) + weights_tensor = torch.repeat_interleave(weights_tensor, repeats=num_atoms) return TrainingBatch( BatchMolGraph(mgs), None if V_ds[0] is None else torch.from_numpy(np.concatenate(V_ds)).float(), + None if E_ds[0] is None else torch.from_numpy(np.concatenate(E_ds)).float(), None if x_ds[0] is None else torch.from_numpy(np.array(x_ds)).float(), - None if ys[0] is None else torch.from_numpy(np.array(ys)).float(), - torch.tensor(weights, dtype=torch.float).unsqueeze(1), - None if lt_masks[0] is None else torch.from_numpy(np.array(lt_masks)), - None if gt_masks[0] is None else torch.from_numpy(np.array(gt_masks)), + None if ys[0] is None else torch.from_numpy(np_y).float(), + weights_tensor, + None if lt_masks[0] is None else torch.from_numpy(np_lt), + None if gt_masks[0] is None else torch.from_numpy(np_gt), ) +def mixed_collate_batch(batches: Iterable[Iterable[Datum]]) -> list[TrainingBatch]: + return [collate_batch(batch) if batch[0] is not None else None for batch in zip(*batches)] + + class MulticomponentTrainingBatch(NamedTuple): bmgs: list[BatchMolGraph] V_ds: list[Tensor | None] + E_ds: list[Tensor | None] X_d: Tensor | None Y: Tensor | None w: Tensor @@ -112,6 +146,7 @@ def collate_multicomponent(batches: Iterable[Iterable[Datum]]) -> Multicomponent return MulticomponentTrainingBatch( [tb.bmg for tb in tbs], [tb.V_d for tb in tbs], + [tb.E_d for tb in tbs], tbs[0].X_d, tbs[0].Y, tbs[0].w, diff --git a/chemprop/data/dataloader.py b/chemprop/data/dataloader.py index e4728344e..93e135f22 100644 --- a/chemprop/data/dataloader.py +++ b/chemprop/data/dataloader.py @@ -2,13 +2,25 @@ from torch.utils.data import DataLoader -from chemprop.data.collate import collate_batch, collate_multicomponent -from chemprop.data.datasets import MoleculeDataset, MulticomponentDataset, ReactionDataset +from chemprop.data.collate import collate_batch, collate_multicomponent, mixed_collate_batch +from chemprop.data.datasets import ( + AtomDataset, + BondDataset, + MolAtomBondDataset, + MoleculeDataset, + MulticomponentDataset, + ReactionDataset, +) from chemprop.data.samplers import ClassBalanceSampler, SeededSampler def build_dataloader( - dataset: MoleculeDataset | ReactionDataset | MulticomponentDataset, + dataset: MoleculeDataset + | AtomDataset + | BondDataset + | MolAtomBondDataset + | ReactionDataset + | MulticomponentDataset, batch_size: int = 64, num_workers: int = 0, class_balance: bool = False, @@ -45,6 +57,8 @@ def build_dataloader( if isinstance(dataset, MulticomponentDataset): collate_fn = collate_multicomponent + elif isinstance(dataset, MolAtomBondDataset): + collate_fn = mixed_collate_batch else: collate_fn = collate_batch diff --git a/chemprop/data/datapoints.py b/chemprop/data/datapoints.py index 68d410d9f..8b187a0fa 100644 --- a/chemprop/data/datapoints.py +++ b/chemprop/data/datapoints.py @@ -48,9 +48,15 @@ class _MoleculeDatapointMixin: @classmethod def from_smi( - cls, smi: str, *args, keep_h: bool = False, add_h: bool = False, **kwargs + cls, + smi: str, + *args, + keep_h: bool = False, + add_h: bool = False, + keep_atom_map: bool = False, + **kwargs, ) -> _MoleculeDatapointMixin: - mol = make_mol(smi, keep_h, add_h) + mol = make_mol(smi, keep_h, add_h, keep_atom_map) kwargs["name"] = smi if "name" not in kwargs else kwargs["name"] @@ -73,6 +79,7 @@ class MoleculeDatapoint(_DatapointMixin, _MoleculeDatapointMixin): """A numpy array of shape ``V x d_vd``, where ``V`` is the number of atoms in the molecule, and ``d_vd`` is the number of additional descriptors that will be concatenated to atom-level descriptors *after* message passing""" + E_d: np.ndarray | None = None def __post_init__(self): if self.mol is None: @@ -85,6 +92,8 @@ def __post_init__(self): self.E_f[np.isnan(self.E_f)] = NAN_TOKEN if self.V_d is not None: self.V_d[np.isnan(self.V_d)] = NAN_TOKEN + if self.E_d is not None: + self.E_d[np.isnan(self.E_d)] = NAN_TOKEN super().__post_init__() @@ -106,6 +115,7 @@ def from_smi( *args, keep_h: bool = False, add_h: bool = False, + keep_atom_map: bool = False, **kwargs, ) -> _ReactionDatapointMixin: match rxn_or_smis: @@ -122,8 +132,8 @@ def from_smi( " a product SMILES strings!" ) - rct = make_mol(rct_smi, keep_h, add_h) - pdt = make_mol(pdt_smi, keep_h, add_h) + rct = make_mol(rct_smi, keep_h, add_h, keep_atom_map) + pdt = make_mol(pdt_smi, keep_h, add_h, keep_atom_map) kwargs["name"] = name if "name" not in kwargs else kwargs["name"] diff --git a/chemprop/data/datasets.py b/chemprop/data/datasets.py index 3f57ae7d1..40aefc181 100644 --- a/chemprop/data/datasets.py +++ b/chemprop/data/datasets.py @@ -22,6 +22,7 @@ class Datum(NamedTuple): mg: MolGraph V_d: np.ndarray | None + E_d: np.ndarray | None x_d: np.ndarray | None y: np.ndarray | None weight: float @@ -145,6 +146,207 @@ def _validate_attribute(self, X: np.ndarray, label: str): ) +@dataclass +class MockDataset(_MolGraphDatasetMixin, MolGraphDataset): + data = [] + featurizer = SimpleMoleculeMolGraphFeaturizer + + def __post_init__(self): + if self.data is None: + raise ValueError("Data cannot be None!") + + self.reset() + self.cache = False + + @cached_property + def _Y(self) -> np.ndarray: + """the raw targets of the dataset""" + return np.array([]) + + @property + def Y(self) -> np.ndarray: + """the (scaled) targets of the dataset""" + return self.__Y + + @Y.setter + def Y(self, Y: ArrayLike): + self._validate_attribute(Y, "targets") + + self.__Y = np.array(Y, float) + + def __getitem__(self, idx: int) -> Datum: + return None + + @property + def cache(self) -> bool: + return self.__cache + + @cache.setter + def cache(self, cache: bool = False): + self.__cache = cache + self._init_cache() + + def _init_cache(self): + """initialize the cache""" + self.mg_cache = (MolGraphCache if self.cache else MolGraphCacheOnTheFly)( + self.mols, self.V_fs, self.E_fs, self.featurizer + ) + + @cached_property + def _slices(self) -> list: + return None + + @property + def smiles(self) -> list[str]: + """the SMILES strings associated with the dataset""" + return [Chem.MolToSmiles(d.mol) for d in self.data] + + @property + def mols(self) -> list[Chem.Mol]: + """the molecules associated with the dataset""" + return [d.mol for d in self.data] + + @property + def _V_fs(self) -> list[np.ndarray]: + """the raw atom features of the dataset""" + return np.array([]) + + @property + def V_fs(self) -> list[np.ndarray]: + """the (scaled) atom descriptors of the dataset""" + return self.__V_fs + + @V_fs.setter + def V_fs(self, V_fs: list[np.ndarray]): + """the (scaled) atom features of the dataset""" + self._validate_attribute(V_fs, "atom features") + + self.__V_fs = V_fs + self._init_cache() + + @property + def _E_fs(self) -> list[np.ndarray]: + """the raw bond features of the dataset""" + return np.array([]) + + @property + def E_fs(self) -> list[np.ndarray]: + """the (scaled) bond features of the dataset""" + return self.__E_fs + + @E_fs.setter + def E_fs(self, E_fs: list[np.ndarray]): + self._validate_attribute(E_fs, "bond features") + + self.__E_fs = E_fs + self._init_cache() + + @property + def _V_ds(self) -> list[np.ndarray]: + """the raw atom descriptors of the dataset""" + return np.array([]) + + @property + def V_ds(self) -> list[np.ndarray]: + """the (scaled) atom descriptors of the dataset""" + return self.__V_ds + + @V_ds.setter + def V_ds(self, V_ds: list[np.ndarray]): + self._validate_attribute(V_ds, "atom descriptors") + + self.__V_ds = V_ds + + @property + def _E_ds(self) -> list[np.ndarray]: + return np.array([]) + + @property + def E_ds(self) -> list[np.ndarray]: + return self.__E_ds + + @E_ds.setter + def E_ds(self, E_ds: list[np.ndarray]): + self._validate_attribute(E_ds, "bond descriptors") + + self.__E_ds = E_ds + + @property + def d_vf(self) -> int: + """the extra atom feature dimension, if any""" + return 0 + + @property + def d_ef(self) -> int: + """the extra bond feature dimension, if any""" + return 0 + + @property + def d_vd(self) -> int: + """the extra atom descriptor dimension, if any""" + return 0 + + @property + def d_ed(self) -> int: + return 0 + + def normalize_inputs( + self, key: str = "X_d", scaler: StandardScaler | None = None + ) -> StandardScaler: + VALID_KEYS = {"X_d", "V_f", "E_f", "V_d", "E_d"} + + match key: + case "X_d": + X = None if self.d_xd == 0 else self.X_d + case "V_f": + X = None if self.d_vf == 0 else np.concatenate(self.V_fs, axis=0) + case "E_f": + X = None if self.d_ef == 0 else np.concatenate(self.E_fs, axis=0) + case "V_d": + X = None if self.d_vd == 0 else np.concatenate(self.V_ds, axis=0) + case "E_d": + X = None if self.d_ed == 0 else np.concatenate(self.E_ds, axis=0) + case _: + raise ValueError(f"Invalid feature key! got: {key}. expected one of: {VALID_KEYS}") + + if X is None: + return scaler + + if scaler is None: + scaler = StandardScaler().fit(X) + + match key: + case "X_d": + self.X_d = scaler.transform(X) + case "V_f": + self.V_fs = [scaler.transform(V_f) if V_f.size > 0 else V_f for V_f in self.V_fs] + case "E_f": + self.E_fs = [scaler.transform(E_f) if E_f.size > 0 else E_f for E_f in self.E_fs] + case "V_d": + self.V_ds = [scaler.transform(V_d) if V_d.size > 0 else V_d for V_d in self.V_ds] + case "E_d": + self.E_ds = [scaler.transform(E_d) if E_d.size > 0 else E_d for E_d in self.E_ds] + case _: + raise RuntimeError("unreachable code reached!") + + return scaler + + def normalize_targets(self, scaler: StandardScaler | None = None) -> StandardScaler: + scaler = StandardScaler() + scaler.mean_ = [0] + scaler.scale_ = [1] + return scaler + + def reset(self): + """Reset the atom and bond features; atom and extra descriptors; and targets of each + datapoint to their initial, unnormalized values.""" + super().reset() + self.__V_fs = self._V_fs + self.__E_fs = self._E_fs + self.__V_ds = self._V_ds + self.__E_ds = self._E_ds + + @dataclass class MoleculeDataset(_MolGraphDatasetMixin, MolGraphDataset): """A :class:`MoleculeDataset` composed of :class:`MoleculeDatapoint`\s @@ -178,7 +380,16 @@ def __getitem__(self, idx: int) -> Datum: d = self.data[idx] mg = self.mg_cache[idx] - return Datum(mg, self.V_ds[idx], self.X_d[idx], self.Y[idx], d.weight, d.lt_mask, d.gt_mask) + return Datum( + mg, + self.V_ds[idx], + self.E_ds[idx], + self.X_d[idx], + self.Y[idx], + d.weight, + d.lt_mask, + d.gt_mask, + ) @property def cache(self) -> bool: @@ -256,6 +467,20 @@ def V_ds(self, V_ds: list[np.ndarray]): self.__V_ds = V_ds + @property + def _E_ds(self) -> list[np.ndarray]: + return [d.E_d for d in self.data] + + @property + def E_ds(self) -> list[np.ndarray]: + return self.__E_ds + + @E_ds.setter + def E_ds(self, E_ds: list[np.ndarray]): + self._validate_attribute(E_ds, "bond descriptors") + + self.__E_ds = E_ds + @property def d_vf(self) -> int: """the extra atom feature dimension, if any""" @@ -271,10 +496,14 @@ def d_vd(self) -> int: """the extra atom descriptor dimension, if any""" return 0 if self.V_ds[0] is None else self.V_ds[0].shape[1] + @property + def d_ed(self) -> int: + return 0 if self.E_ds[0] is None else self.E_ds[0].shape[1] + def normalize_inputs( self, key: str = "X_d", scaler: StandardScaler | None = None ) -> StandardScaler: - VALID_KEYS = {"X_d", "V_f", "E_f", "V_d"} + VALID_KEYS = {"X_d", "V_f", "E_f", "V_d", "E_d"} match key: case "X_d": @@ -285,6 +514,8 @@ def normalize_inputs( X = None if self.d_ef == 0 else np.concatenate(self.E_fs, axis=0) case "V_d": X = None if self.d_vd == 0 else np.concatenate(self.V_ds, axis=0) + case "E_d": + X = None if self.d_ed == 0 else np.concatenate(self.E_ds, axis=0) case _: raise ValueError(f"Invalid feature key! got: {key}. expected one of: {VALID_KEYS}") @@ -303,6 +534,8 @@ def normalize_inputs( self.E_fs = [scaler.transform(E_f) if E_f.size > 0 else E_f for E_f in self.E_fs] case "V_d": self.V_ds = [scaler.transform(V_d) if V_d.size > 0 else V_d for V_d in self.V_ds] + case "E_d": + self.E_ds = [scaler.transform(E_d) if E_d.size > 0 else E_d for E_d in self.E_ds] case _: raise RuntimeError("unreachable code reached!") @@ -315,6 +548,188 @@ def reset(self): self.__V_fs = self._V_fs self.__E_fs = self._E_fs self.__V_ds = self._V_ds + self.__E_ds = self._E_ds + + +@dataclass +class AtomDataset(MoleculeDataset): + @cached_property + def _Y(self) -> np.ndarray: + return np.vstack([d.y for d in self.data]) + # dim = self.data[0].y.shape[1] + # raw_targets = np.empty((0, dim), float) + # for d in self.data: + # raw_targets = np.vstack([raw_targets, d.y]) + # return raw_targets + + @property + def Y(self) -> np.ndarray: + """the (scaled) targets of the dataset""" + return self.__Y + + @Y.setter + def Y(self, Y: ArrayLike): + self.__Y = np.array(Y, float) + + @cached_property + def _slices(self) -> list: + slice_indices = [] + index = 0 + for d in self.data: + slice_indices.extend([index] * d.mol.GetNumAtoms()) + index += 1 + return slice_indices + + @property + def gt_mask(self) -> np.ndarray: + return np.vstack([d.gt_mask for d in self.data]) + # dim = self.data[0].gt_mask.shape[1] + # temp_gt_mask = np.empty((0, dim)) + # for d in self.data: + # temp_gt_mask = np.vstack([temp_gt_mask, np.vstack(d.gt_mask)]) + # return temp_gt_mask + + @property + def lt_mask(self) -> np.ndarray: + return np.vstack([d.lt_mask for d in self.data]) + # dim = self.data[0].lt_mask.shape[1] + # temp_lt_mask = np.empty((0, dim)) + # for d in self.data: + # temp_lt_mask = np.vstack([temp_lt_mask, np.vstack(d.lt_mask)]) + # return temp_lt_mask + + def __getitem__(self, idx: int) -> Datum: + d = self.data[idx] + mg = self.mg_cache[idx] + slices = self._slices + ind_first = slices.index(idx) + ind_last = ind_first + slices.count(idx) + + # TODO: check for E_d? + + return Datum( + mg, + self.V_ds[idx], + self.E_ds[idx], + self.X_d[idx], + self.Y[ind_first:ind_last], + d.weight, + d.lt_mask, + d.gt_mask, + ) + + def reset(self): + """Reset the atom and bond features; atom and extra descriptors; and targets of each + datapoint to their initial, unnormalized values.""" + super().reset() + self.__Y = self._Y + + +@dataclass +class BondDataset(AtomDataset): + @cached_property + def _slices(self) -> list: + slice_indices = [] + index = 0 + for d in self.data: + slice_indices.extend([index] * d.mol.GetNumBonds()) + index += 1 + return slice_indices + + +@dataclass(repr=False, eq=False) +class MolAtomBondDataset(_MolGraphDatasetMixin, MolGraphDataset): + """A :class:`MulticomponentDataset` is a :class:`Dataset` composed of parallel + :class:`MoleculeDatasets` and :class:`ReactionDataset`\s""" + + mol_dataset: MoleculeDataset | MockDataset + atom_dataset: AtomDataset | MockDataset + bond_dataset: BondDataset | MockDataset + + def __post_init__(self): + self.datasets = [self.mol_dataset, self.atom_dataset, self.bond_dataset] + + def __len__(self) -> int: + return len(self.datasets[0]) + + @property + def n_components(self) -> int: + return len(self.datasets) + + def __getitem__(self, idx: int) -> list[Datum]: + mixed_list = [] + for dset in self.datasets: + mixed_list.append( + Datum( + dset[idx].mg, + dset[idx].V_d, + dset[idx].E_d, + dset[idx].x_d, + dset[idx].y, + dset[idx].weight, + dset[idx].lt_mask, + dset[idx].gt_mask, + ) + if dset[idx] is not None + else None + ) + + return mixed_list + + @property + def smiles(self) -> list[list[str]]: + return list(zip(*[dset.smiles for dset in self.datasets])) + + @property + def names(self) -> list[str]: + return self.datasets[0].names + + @property + def mols(self) -> list[list[Chem.Mol]]: + return list(zip(*[dset.mols for dset in self.datasets])) + + def normalize_targets(self, scaler: StandardScaler | None = None) -> StandardScaler: + return self.datasets[0].normalize_targets(scaler) + + def normalize_inputs( # CHANGE + self, key: str = "X_d", scaler: list[StandardScaler] | None = None + ) -> list[StandardScaler]: + match scaler: + case None: + return [ + dset.normalize_inputs(key) for dset in self.datasets + ] + case _: + assert len(scaler) == len( + self.datasets + ), "Number of scalers must match number of datasets!" + + return [ + dset.normalize_inputs(key, s) for dset, s in zip(self.datasets, scaler) + ] + + def reset(self): + return [dset.reset() for dset in self.datasets] + + @property + def d_xd(self) -> list[int]: + return self.datasets[0].d_xd + + @property + def d_vf(self) -> list[int]: + return sum(dset.d_vf for dset in self.datasets) + + @property + def d_ef(self) -> list[int]: + return sum(dset.d_ef for dset in self.datasets) + + @property + def d_vd(self) -> list[int]: + return sum(dset.d_vd for dset in self.datasets) + + @property + def d_ed(self) -> list[int]: + return sum(dset.d_ed for dset in self.datasets) @dataclass @@ -354,7 +769,7 @@ def __getitem__(self, idx: int) -> Datum: d = self.data[idx] mg = self.mg_cache[idx] - return Datum(mg, None, self.X_d[idx], self.Y[idx], d.weight, d.lt_mask, d.gt_mask) + return Datum(mg, None, None, self.X_d[idx], self.Y[idx], d.weight, d.lt_mask, d.gt_mask) @property def smiles(self) -> list[tuple]: @@ -376,6 +791,10 @@ def d_ef(self) -> int: def d_vd(self) -> int: return 0 + @property + def d_ed(self) -> int: + return 0 + @dataclass(repr=False, eq=False) class MulticomponentDataset(_MolGraphDatasetMixin, Dataset): @@ -457,3 +876,7 @@ def d_ef(self) -> list[int]: @property def d_vd(self) -> list[int]: return sum(dset.d_vd for dset in self.datasets) + + @property + def d_ed(self) -> list[int]: + return sum(dset.d_ed for dset in self.datasets) diff --git a/chemprop/models/__init__.py b/chemprop/models/__init__.py index 4cc506a0b..3f959065b 100644 --- a/chemprop/models/__init__.py +++ b/chemprop/models/__init__.py @@ -1,5 +1,12 @@ -from .model import MPNN +from .model import MPNN, MolAtomBondMPNN from .multi import MulticomponentMPNN -from .utils import load_model, save_model +from .utils import load_mixed_model, load_model, save_model -__all__ = ["MPNN", "MulticomponentMPNN", "load_model", "save_model"] +__all__ = [ + "MPNN", + "MolAtomBondMPNN", + "MulticomponentMPNN", + "load_model", + "load_mixed_model", + "save_model", +] diff --git a/chemprop/models/model.py b/chemprop/models/model.py index 0ae2bcba0..a9b34be73 100644 --- a/chemprop/models/model.py +++ b/chemprop/models/model.py @@ -139,7 +139,7 @@ def forward( return self.predictor(self.fingerprint(bmg, V_d, X_d)) def training_step(self, batch: TrainingBatch, batch_idx): - bmg, V_d, X_d, targets, weights, lt_mask, gt_mask = batch + bmg, V_d, E_d, X_d, targets, weights, lt_mask, gt_mask = batch mask = targets.isfinite() targets = targets.nan_to_num(nan=0.0) @@ -159,7 +159,7 @@ def on_validation_model_eval(self) -> None: def validation_step(self, batch: TrainingBatch, batch_idx: int = 0): self._evaluate_batch(batch, "val") - bmg, V_d, X_d, targets, weights, lt_mask, gt_mask = batch + bmg, V_d, E_d, X_d, targets, weights, lt_mask, gt_mask = batch mask = targets.isfinite() targets = targets.nan_to_num(nan=0.0) @@ -173,7 +173,7 @@ def test_step(self, batch: TrainingBatch, batch_idx: int = 0): self._evaluate_batch(batch, "test") def _evaluate_batch(self, batch: TrainingBatch, label: str) -> None: - bmg, V_d, X_d, targets, weights, lt_mask, gt_mask = batch + bmg, V_d, E_d, X_d, targets, weights, lt_mask, gt_mask = batch mask = targets.isfinite() targets = targets.nan_to_num(nan=0.0) @@ -208,8 +208,7 @@ def predict_step(self, batch: TrainingBatch, batch_idx: int, dataloader_idx: int * multiclass classification: ``n x t x c``, where ``c`` is the number of classes """ - bmg, X_vd, X_d, *_ = batch - + bmg, X_vd, X_ed, X_d, *_ = batch return self(bmg, X_vd, X_d) def configure_optimizers(self): @@ -239,7 +238,7 @@ def configure_optimizers(self): @classmethod def _load(cls, path, map_location, **submodules): - d = torch.load(path, map_location) + d = torch.load(path, map_location, weights_only=False) try: hparams = d["hyper_parameters"] @@ -283,7 +282,7 @@ def load_from_checkpoint( kwargs.update(submodules) state_dict = cls._add_metric_task_weights_to_state_dict(state_dict, hparams) - d = torch.load(checkpoint_path, map_location) + d = torch.load(checkpoint_path, map_location, weights_only=False) d["state_dict"] = state_dict buffer = io.BytesIO() torch.save(d, buffer) @@ -302,3 +301,335 @@ def load_from_file(cls, model_path, map_location=None, strict=True, **submodules model.load_state_dict(state_dict, strict=strict) return model + + +class MolAtomBondMPNN(pl.LightningModule): + def __init__( + self, + message_passing: MessagePassing, + agg: Aggregation, + mol_predictor: Predictor, + atom_predictor: Predictor, + bond_predictor: Predictor, + batch_norm: bool = False, + metrics: Iterable[ChempropMetric] | None = None, + warmup_epochs: int = 2, + init_lr: float = 1e-4, + max_lr: float = 1e-3, + final_lr: float = 1e-4, + X_d_transform: ScaleTransform | None = None, + ): + super().__init__() + # manually add X_d_transform to hparams to suppress lightning's warning about double saving + # its state_dict values. + self.save_hyperparameters( + ignore=[ + "X_d_transform", + "message_passing", + "agg", + "mol_predictor", + "atom_predictor", + "bond_predictor", + ] + ) + self.hparams["X_d_transform"] = X_d_transform + self.hparams.update( + { + "message_passing": message_passing.hparams, + "agg": agg.hparams, + "mol_predictor": mol_predictor.hparams, + "atom_predictor": atom_predictor.hparams, + "bond_predictor": bond_predictor.hparams, + } + ) + + self.message_passing = message_passing + self.agg = agg + self.bn = nn.BatchNorm1d(self.message_passing.output_dim) if batch_norm else nn.Identity() + self.predictors = nn.ModuleList([mol_predictor, atom_predictor, bond_predictor]) + + self.X_d_transform = X_d_transform if X_d_transform is not None else nn.Identity() + + self.metrics = nn.ModuleList( + [ + nn.ModuleList([*metrics, self.criterion[i].clone()]) + if metrics + else nn.ModuleList( + [self.predictors[i]._T_default_metric(), self.criterion[i].clone()] + ) + for i in range(3) + ] + ) + + self.warmup_epochs = warmup_epochs + self.init_lr = init_lr + self.max_lr = max_lr + self.final_lr = final_lr + + @property + def output_dim(self) -> list[int]: + return [ + self.predictors[0].output_dim, + self.predictors[1].output_dim, + self.predictors[2].output_dim, + ] + + @property + def n_tasks(self) -> list[int]: + return [self.predictors[0].n_tasks, self.predictors[1].n_tasks, self.predictors[2].n_tasks] + + @property + def n_targets(self) -> list[int]: + return [ + self.predictors[0].n_targets, + self.predictors[1].n_targets, + self.predictors[2].n_targets, + ] + + @property + def criterion(self) -> list[ChempropMetric]: + return [ + self.predictors[0].criterion, + self.predictors[1].criterion, + self.predictors[2].criterion, + ] + + def fingerprint( + self, + bmg: BatchMolGraph, + V_d: Tensor | None = None, + E_d: Tensor | None = None, + X_d: Tensor | None = None, + ) -> list[Tensor]: + """the learned fingerprints for the input molecules""" + H_v, H_b = self.message_passing(bmg, V_d, E_d) + H_g = self.agg(H_v, bmg.batch) + H_g = self.bn(H_g) + H_g = H_g if X_d is None else torch.cat((H_g, self.X_d_transform(X_d)), 1) + + H_b = torch.cat([H_b, H_b[bmg.rev_edge_index]], 1) + return [H_g, H_v, H_b] + + def encoding( + self, + bmg: BatchMolGraph, + V_d: Tensor | None = None, + E_d: Tensor | None = None, + X_d: Tensor | None = None, + i: int = -1, + ) -> list[Tensor]: + """Calculate the :attr:`i`-th hidden representation""" + H = self.fingerprint(bmg, V_d, E_d, X_d) + return [ + self.predictors[0].encode(H[0], i), + self.predictors[1].encode(H[1], i), + self.predictors[2].encode(H[2], i), + ] + + def forward( + self, + bmg: BatchMolGraph, + V_d: Tensor | None = None, + E_d: Tensor | None = None, + X_d: Tensor | None = None, + ) -> list[Tensor]: + """Generate predictions for the input molecules/reactions""" + H = self.fingerprint(bmg, V_d, E_d, X_d) + return [self.predictors[0](H[0]), self.predictors[1](H[1]), self.predictors[2](H[2])] + + def training_step(self, batch: list[TrainingBatch], batch_idx): + total_l = 0 + for batch_index, val in enumerate(batch): + if val is None: + continue + + bmg, V_d, E_d, X_d, targets, weights, lt_mask, gt_mask = batch[batch_index] + mask = targets.isfinite() + targets = targets.nan_to_num(nan=0.0) + Z = self.fingerprint(bmg, V_d, E_d, X_d) + preds = self.predictors[batch_index].train_step(Z[batch_index]) + if batch_index == 2: + preds = (preds[::2] + preds[1::2]) / 2 + l = self.criterion[batch_index](preds, targets, mask, weights, lt_mask, gt_mask) + total_l += l + + return total_l + + def on_validation_model_eval(self) -> None: + self.eval() + self.predictors[0].output_transform.train() + self.predictors[1].output_transform.train() + self.predictors[2].output_transform.train() + + def validation_step(self, batch: list[TrainingBatch], batch_idx: int = 0): + self._evaluate_batch(batch, "val") + + agg_metric = 0 + for batch_index, val in enumerate(batch): + if val is None: + continue + bmg, V_d, E_d, X_d, targets, weights, lt_mask, gt_mask = batch[batch_index] + mask = targets.isfinite() + targets = targets.nan_to_num(nan=0.0) + Z = self.fingerprint(bmg, V_d, E_d, X_d) + preds = self.predictors[batch_index].train_step(Z[batch_index]) + if batch_index == 2: + preds = (preds[::2] + preds[1::2]) / 2 + + self.metrics[batch_index][-1](preds, targets, mask, weights, lt_mask, gt_mask) + agg_metric += self.metrics[batch_index][-1].compute() + self.metrics[batch_index][-1].reset() + + self.log( + "val_loss", + agg_metric, + batch_size=len(batch[0][0] or batch[1][0] or batch[2][0]), + prog_bar=True, + ) + + def test_step(self, batch: list[TrainingBatch], batch_idx: int = 0): + self._evaluate_batch(batch, "test") + + def _evaluate_batch(self, batch: list[TrainingBatch], label: str) -> None: + for batch_index, val in enumerate(batch): + if val is None: + continue + + if batch_index == 0: + label = "mol_" + label + elif batch_index == 1: + label = "atom_" + label + else: + label = "bond_" + label + + bmg, V_d, E_d, X_d, targets, weights, lt_mask, gt_mask = batch[batch_index] + mask = targets.isfinite() + targets = targets.nan_to_num(nan=0.0) + preds = self(bmg, V_d, E_d, X_d) + preds = preds[batch_index] + if batch_index == 2: + preds = (preds[::2] + preds[1::2]) / 2 + weights = torch.ones_like(weights) + if self.predictors[batch_index].n_targets > 1: + preds = preds[..., 0] + + for m in self.metrics[batch_index][:-1]: + m.update(preds, targets, mask, weights, lt_mask, gt_mask) + self.log(f"{label}/{m.alias}", m, batch_size=len(batch[batch_index][0])) + + def predict_step( + self, batch: list[TrainingBatch], batch_idx: int, dataloader_idx: int = 0 + ) -> list[Tensor]: + """Return the predictions of the input batch + + Parameters + ---------- + batch : MixedTrainingBatch + the input batch + + Returns + ------- + Tensor + a tensor of varying shape depending on the task type: + + * regression/binary classification: ``n x (t * s)``, where ``n`` is the number of input + molecules/reactions, ``t`` is the number of tasks, and ``s`` is the number of targets + per task. The final dimension is flattened, so that the targets for each task are + grouped. I.e., the first ``t`` elements are the first target for each task, the second + ``t`` elements the second target, etc. + + * multiclass classification: ``n x t x c``, where ``c`` is the number of classes + """ + bmg, X_vd, X_ed, X_d, *_ = batch[0] or batch[1] or batch[2] + + predss = self(bmg, X_vd, X_ed, X_d) + predss[2] = (predss[2][::2] + predss[2][1::2]) / 2 + return predss + + def configure_optimizers(self): + opt = optim.Adam(self.parameters(), self.init_lr) + if self.trainer.train_dataloader is None: + # Loading `train_dataloader` to estimate number of training batches. + # Using this line of code can pypass the issue of using `num_training_batches` as described [here](https://github.com/Lightning-AI/pytorch-lightning/issues/16060). + self.trainer.estimated_stepping_batches + steps_per_epoch = self.trainer.num_training_batches + warmup_steps = self.warmup_epochs * steps_per_epoch + if self.trainer.max_epochs == -1: + warnings.warn( + "For infinite training, the number of cooldown epochs in learning rate scheduler is set to 100 times the number of warmup epochs." + ) + cooldown_steps = 100 * warmup_steps + else: + cooldown_epochs = self.trainer.max_epochs - self.warmup_epochs + cooldown_steps = cooldown_epochs * steps_per_epoch + + lr_sched = build_NoamLike_LRSched( + opt, warmup_steps, cooldown_steps, self.init_lr, self.max_lr, self.final_lr + ) + + lr_sched_config = {"scheduler": lr_sched, "interval": "step"} + + return {"optimizer": opt, "lr_scheduler": lr_sched_config} + + @classmethod + def _load(cls, path, map_location, **submodules): + d = torch.load(path, map_location, weights_only=False) + + try: + hparams = d["hyper_parameters"] + state_dict = d["state_dict"] + except KeyError: + raise KeyError(f"Could not find hyper parameters and/or state dict in {path}.") + + submodules |= { + key: hparams[key].pop("cls")(**hparams[key]) + for key in ( + "message_passing", + "agg", + "mol_predictor", + "atom_predictor", + "bond_predictor", + ) + if key not in submodules + } + + if not hasattr(submodules["mol_predictor"].criterion, "_defaults"): + submodules["mol_predictor"].criterion = submodules["mol_predictor"].criterion.__class__( + task_weights=submodules["mol_predictor"].criterion.task_weights + ) + + return submodules, state_dict, hparams + + @classmethod + def _add_metric_task_weights_to_state_dict(cls, state_dict, hparams): + return state_dict + + @classmethod + def load_from_checkpoint( + cls, checkpoint_path, map_location=None, hparams_file=None, strict=True, **kwargs + ) -> MolAtomBondMPNN: + submodules = { + k: v for k, v in kwargs.items() if k in ["message_passing", "agg", "predictor"] + } + submodules, state_dict, hparams = cls._load(checkpoint_path, map_location, **submodules) + kwargs.update(submodules) + + state_dict = cls._add_metric_task_weights_to_state_dict(state_dict, hparams) + d = torch.load(checkpoint_path, map_location, weights_only=False) + d["state_dict"] = state_dict + buffer = io.BytesIO() + torch.save(d, buffer) + buffer.seek(0) + + return super().load_from_checkpoint(buffer, map_location, hparams_file, strict, **kwargs) + + @classmethod + def load_from_file( + cls, model_path, map_location=None, strict=True, **submodules + ) -> MolAtomBondMPNN: + submodules, state_dict, hparams = cls._load(model_path, map_location, **submodules) + hparams.update(submodules) + model = cls(**hparams) + model.load_state_dict(state_dict, strict=strict) + + return model diff --git a/chemprop/models/utils.py b/chemprop/models/utils.py index dd16ef3b9..32837a521 100644 --- a/chemprop/models/utils.py +++ b/chemprop/models/utils.py @@ -2,16 +2,22 @@ import torch -from chemprop.models.model import MPNN +from chemprop.models.model import MPNN, MolAtomBondMPNN from chemprop.models.multi import MulticomponentMPNN -def save_model(path: PathLike, model: MPNN, output_columns: list[str] = None) -> None: +def save_model( + path: PathLike, + model: MPNN | MolAtomBondMPNN, + output_columns: list[str] = None, + mixed_columns: list[list[str]] = None, +) -> None: torch.save( { "hyper_parameters": model.hparams, "state_dict": model.state_dict(), "output_columns": output_columns, + "mixed_columns": mixed_columns, }, path, ) @@ -26,7 +32,12 @@ def load_model(path: PathLike, multicomponent: bool) -> MPNN: return model -def load_output_columns(path: PathLike) -> list[str] | None: +def load_mixed_model(path: PathLike) -> MolAtomBondMPNN: + model = MolAtomBondMPNN.load_from_file(path, map_location=torch.device("cpu")) + return model + + +def load_output_columns(path: PathLike) -> tuple[list[str] | None, list[list[str]] | None]: model_file = torch.load(path, map_location=torch.device("cpu")) - return model_file.get("output_columns") + return model_file.get("output_columns"), model_file.get("mixed_columns") diff --git a/chemprop/nn/__init__.py b/chemprop/nn/__init__.py index a6cdffb9d..f9c5270a1 100644 --- a/chemprop/nn/__init__.py +++ b/chemprop/nn/__init__.py @@ -3,6 +3,7 @@ AggregationRegistry, AttentiveAggregation, MeanAggregation, + NoAggregation, NormAggregation, SumAggregation, ) @@ -10,6 +11,8 @@ AtomMessagePassing, BondMessagePassing, MessagePassing, + MixedAtomMessagePassing, + MixedBondMessagePassing, MulticomponentMessagePassing, ) from .metrics import ( @@ -47,6 +50,7 @@ BinaryClassificationFFNBase, BinaryDirichletFFN, EvidentialFFN, + FFNMockPredictor, MulticlassClassificationFFN, MulticlassDirichletFFN, MveFFN, @@ -62,6 +66,7 @@ __all__ = [ "Aggregation", "AggregationRegistry", + "NoAggregation", "MeanAggregation", "SumAggregation", "NormAggregation", @@ -106,7 +111,9 @@ "QuantileLoss", "MessagePassing", "AtomMessagePassing", + "MixedAtomMessagePassing", "BondMessagePassing", + "MixedBondMessagePassing", "MulticomponentMessagePassing", "Predictor", "PredictorRegistry", @@ -115,6 +122,7 @@ "MveFFN", "DirichletLoss", "EvidentialFFN", + "FFNMockPredictor", "BinaryClassificationFFNBase", "BinaryClassificationFFN", "BinaryDirichletFFN", diff --git a/chemprop/nn/agg.py b/chemprop/nn/agg.py index 150851928..bb8a55596 100644 --- a/chemprop/nn/agg.py +++ b/chemprop/nn/agg.py @@ -9,6 +9,7 @@ __all__ = [ "Aggregation", "AggregationRegistry", + "NoAggregation", "MeanAggregation", "SumAggregation", "NormAggregation", @@ -62,6 +63,12 @@ def forward(self, H: Tensor, batch: Tensor) -> Tensor: AggregationRegistry = ClassRegistry[Aggregation]() +@AggregationRegistry.register("none") +class NoAggregation(Aggregation): + def forward(self, H: Tensor, batch: Tensor) -> Tensor: + return H + + @AggregationRegistry.register("mean") class MeanAggregation(Aggregation): r"""Average the graph-level representation: diff --git a/chemprop/nn/message_passing/__init__.py b/chemprop/nn/message_passing/__init__.py index 0ad9bd408..5be3018b6 100644 --- a/chemprop/nn/message_passing/__init__.py +++ b/chemprop/nn/message_passing/__init__.py @@ -1,10 +1,17 @@ -from .base import AtomMessagePassing, BondMessagePassing +from .base import ( + AtomMessagePassing, + BondMessagePassing, + MixedAtomMessagePassing, + MixedBondMessagePassing, +) from .multi import MulticomponentMessagePassing from .proto import MessagePassing __all__ = [ "MessagePassing", "AtomMessagePassing", + "MixedAtomMessagePassing", "BondMessagePassing", + "MixedBondMessagePassing", "MulticomponentMessagePassing", ] diff --git a/chemprop/nn/message_passing/base.py b/chemprop/nn/message_passing/base.py index 536731699..fcbc5a7c8 100644 --- a/chemprop/nn/message_passing/base.py +++ b/chemprop/nn/message_passing/base.py @@ -56,24 +56,31 @@ def __init__( activation: str | Activation = Activation.RELU, undirected: bool = False, d_vd: int | None = None, + d_ed: int | None = None, V_d_transform: ScaleTransform | None = None, + E_d_transform: ScaleTransform + | None = None, # should we change init here or localize to bond? graph_transform: GraphTransform | None = None, # layers_per_message: int = 1, ): super().__init__() # manually add V_d_transform and graph_transform to hparams to suppress lightning's warning # about double saving their state_dict values. - self.save_hyperparameters(ignore=["V_d_transform", "graph_transform"]) + self.save_hyperparameters(ignore=["V_d_transform", "E_d_transform", "graph_transform"]) self.hparams["V_d_transform"] = V_d_transform + self.hparams["E_d_transform"] = E_d_transform self.hparams["graph_transform"] = graph_transform self.hparams["cls"] = self.__class__ - self.W_i, self.W_h, self.W_o, self.W_d = self.setup(d_v, d_e, d_h, d_vd, bias) + self.W_i, self.W_h, self.W_o, self.W_d, *end = self.setup(d_v, d_e, d_h, d_vd, d_ed, bias) + self.W_o_b = end[0] if len(end) > 0 else None + self.W_ed = end[1] if len(end) > 0 else None self.depth = depth self.undirected = undirected self.dropout = nn.Dropout(dropout) self.tau = get_activation_function(activation) self.V_d_transform = V_d_transform if V_d_transform is not None else nn.Identity() + self.E_d_transform = E_d_transform if E_d_transform is not None else nn.Identity() self.graph_transform = graph_transform if graph_transform is not None else nn.Identity() @property @@ -87,8 +94,9 @@ def setup( d_e: int = DEFAULT_BOND_FDIM, d_h: int = DEFAULT_HIDDEN_DIM, d_vd: int | None = None, + d_ed: int | None = None, bias: bool = False, - ) -> tuple[nn.Module, nn.Module, nn.Module, nn.Module | None]: + ) -> tuple[nn.Module, nn.Module, nn.Module, nn.Module | None, nn.Module | None]: """setup the weight matrices used in the message passing update functions Parameters @@ -245,12 +253,12 @@ def setup( d_e: int = DEFAULT_BOND_FDIM, d_h: int = DEFAULT_HIDDEN_DIM, d_vd: int | None = None, + d_ed: int | None = None, bias: bool = False, ): W_i = nn.Linear(d_v + d_e, d_h, bias) W_h = nn.Linear(d_h, d_h, bias) W_o = nn.Linear(d_v + d_h, d_h) - # initialize W_d only when d_vd is neither 0 nor None W_d = nn.Linear(d_h + d_vd, d_h + d_vd) if d_vd else None return W_i, W_h, W_o, W_d @@ -268,6 +276,74 @@ def message(self, H: Tensor, bmg: BatchMolGraph) -> Tensor: return M_all - M_rev +class MixedBondMessagePassing(BondMessagePassing): + def setup( + self, + d_v: int = DEFAULT_ATOM_FDIM, + d_e: int = DEFAULT_BOND_FDIM, + d_h: int = DEFAULT_HIDDEN_DIM, + d_vd: int | None = None, + d_ed: int | None = None, + bias: bool = False, + ): + W_i = nn.Linear(d_v + d_e, d_h, bias) + W_h = nn.Linear(d_h, d_h, bias) + W_o = nn.Linear(d_v + d_h, d_h) + W_o_b = nn.Linear(d_e + d_h, d_h) + W_d = nn.Linear(d_h + d_vd, d_h + d_vd) if d_vd else None + W_ed = nn.Linear(d_h + d_ed, d_h + d_ed) if d_ed else None + + return W_i, W_h, W_o, W_d, W_o_b, W_ed + + def finalize( + self, H: Tensor, M: Tensor, V: Tensor, E: Tensor, V_d: Tensor | None, E_d: Tensor | None + ) -> tuple[Tensor]: + H_v = self.W_o(torch.cat((V, M), dim=1)) + H_v = self.tau(H_v) + H_v = self.dropout(H_v) + H_b = self.W_o_b(torch.cat((E, H), dim=1)) + H_b = self.tau(H_b) + H_b = self.dropout(H_b) + + if V_d is not None: + V_d = self.V_d_transform(V_d) + try: + H_v = self.W_d(torch.cat((H_v, V_d), dim=1)) # V x (d_o + d_vd) + H_v = self.dropout(H_v) + except RuntimeError: + raise InvalidShapeError("V_d", V_d.shape, [len(H_v), self.W_d.in_features]) + + if E_d is not None: + E_d = self.E_d_transform(E_d) + try: + H_b = self.W_ed(torch.cat((H_b, E_d), dim=1)) + H_b = self.dropout(H_b) + except RuntimeError: + raise InvalidShapeError("E_d", E_d.shape, [len(H_b), self.W_ed.in_features]) + + return H_v, H_b + + def forward( + self, bmg: BatchMolGraph, V_d: Tensor | None = None, E_d: Tensor | None = None + ) -> tuple[Tensor]: + bmg = self.graph_transform(bmg) + H_0 = self.initialize(bmg) + + H = self.tau(H_0) + for _ in range(1, self.depth): + if self.undirected: + H = (H + H[bmg.rev_edge_index]) / 2 + + M = self.message(H, bmg) + H = self.update(M, H_0) + + index_torch = bmg.edge_index[1].unsqueeze(1).repeat(1, H.shape[1]) + M = torch.zeros(len(bmg.V), H.shape[1], dtype=H.dtype, device=H.device).scatter_reduce_( + 0, index_torch, H, reduce="sum", include_self=False + ) + return self.finalize(H, M, bmg.V, bmg.E, V_d, E_d) + + class AtomMessagePassing(_MessagePassingBase): r"""A :class:`AtomMessagePassing` encodes a batch of molecular graphs by passing messages along atoms. @@ -296,12 +372,12 @@ def setup( d_e: int = DEFAULT_BOND_FDIM, d_h: int = DEFAULT_HIDDEN_DIM, d_vd: int | None = None, + d_ed: int | None = None, bias: bool = False, ): W_i = nn.Linear(d_v, d_h, bias) W_h = nn.Linear(d_e + d_h, d_h, bias) W_o = nn.Linear(d_v + d_h, d_h) - # initialize W_d only when d_vd is neither 0 nor None W_d = nn.Linear(d_h + d_vd, d_h + d_vd) if d_vd else None return W_i, W_h, W_o, W_d @@ -315,3 +391,71 @@ def message(self, H: Tensor, bmg: BatchMolGraph): return torch.zeros(len(bmg.V), H.shape[1], dtype=H.dtype, device=H.device).scatter_reduce_( 0, index_torch, H, reduce="sum", include_self=False )[bmg.edge_index[0]] + + +class MixedAtomMessagePassing(AtomMessagePassing): + def setup( + self, + d_v: int = DEFAULT_ATOM_FDIM, + d_e: int = DEFAULT_BOND_FDIM, + d_h: int = DEFAULT_HIDDEN_DIM, + d_vd: int | None = None, + d_ed: int | None = None, + bias: bool = False, + ): + W_i = nn.Linear(d_v + d_e, d_h, bias) + W_h = nn.Linear(d_h, d_h, bias) + W_o = nn.Linear(d_v + d_h, d_h) + W_o_b = nn.Linear(d_e + d_h, d_h) + W_d = nn.Linear(d_h + d_vd, d_h + d_vd) if d_vd else None + W_ed = nn.Linear(d_h + d_ed, d_h + d_ed) if d_ed else None + + return W_i, W_h, W_o, W_d, W_o_b, W_ed + + def finalize( + self, H: Tensor, M: Tensor, V: Tensor, E: Tensor, V_d: Tensor | None, E_d: Tensor | None + ) -> tuple[Tensor]: + H_v = self.W_o(torch.cat((V, M), dim=1)) + H_v = self.tau(H_v) + H_v = self.dropout(H_v) + H_b = self.W_o_b(torch.cat((E, H), dim=1)) + H_b = self.tau(H_b) + H_b = self.dropout(H_b) + + if V_d is not None: + V_d = self.V_d_transform(V_d) + try: + H_v = self.W_d(torch.cat((H_v, V_d), dim=1)) # V x (d_o + d_vd) + H_v = self.dropout(H_v) + except RuntimeError: + raise InvalidShapeError("V_d", V_d.shape, [len(H_v), self.W_d.in_features]) + + if E_d is not None: + E_d = self.E_d_transform(E_d) + try: + H_b = self.W_ed(torch.cat((H_b, V_d), dim=1)) + H_b = self.dropout(H_b) + except RuntimeError: + raise InvalidShapeError("E_d", E_d.shape, [len(H_b), self.W_ed.in_features]) + + return H_v, H_b + + def forward( + self, bmg: BatchMolGraph, V_d: Tensor | None = None, E_d: Tensor | None = None + ) -> tuple[Tensor]: + bmg = self.graph_transform(bmg) + H_0 = self.initialize(bmg) + + H = self.tau(H_0) + for _ in range(1, self.depth): + if self.undirected: + H = (H + H[bmg.rev_edge_index]) / 2 + + M = self.message(H, bmg) + H = self.update(M, H_0) + + index_torch = bmg.edge_index[1].unsqueeze(1).repeat(1, H.shape[1]) + M = torch.zeros(len(bmg.V), H.shape[1], dtype=H.dtype, device=H.device).scatter_reduce_( + 0, index_torch, H, reduce="sum", include_self=False + ) + return self.finalize(H, M, bmg.V, bmg.E, V_d, E_d) diff --git a/chemprop/nn/message_passing/proto.py b/chemprop/nn/message_passing/proto.py index d8223d504..1ed71c362 100644 --- a/chemprop/nn/message_passing/proto.py +++ b/chemprop/nn/message_passing/proto.py @@ -14,7 +14,9 @@ class MessagePassing(nn.Module, HasHParams): output_dim: int @abstractmethod - def forward(self, bmg: BatchMolGraph, V_d: Tensor | None = None) -> Tensor: + def forward( + self, bmg: BatchMolGraph, V_d: Tensor | None = None, E_d: Tensor | None = None + ) -> Tensor: """Encode a batch of molecular graphs. Parameters @@ -22,9 +24,13 @@ def forward(self, bmg: BatchMolGraph, V_d: Tensor | None = None) -> Tensor: bmg: BatchMolGraph the batch of :class:`~chemprop.featurizers.molgraph.MolGraph`\s to encode V_d : Tensor | None, default=None - an optional tensor of shape `V x d_vd` containing additional descriptors for each atom + an optional tensor of shape `V x d_vd` containing additional vertex descriptors for each atom in the batch. These will be concatenated to the learned atomic descriptors and transformed before the readout phase. + E_d: Tensor | None, default=None + an optional tensor of shape `V x d_ed` containing additional edge descriptors for each atom + in the batch. These will be concatenated to the learned bond descriptors and + transformed before the readout phase. Returns ------- diff --git a/chemprop/nn/predictors.py b/chemprop/nn/predictors.py index f61d3218c..cf92a1d0a 100644 --- a/chemprop/nn/predictors.py +++ b/chemprop/nn/predictors.py @@ -97,6 +97,33 @@ def encode(self, Z: Tensor, i: int) -> Tensor: PredictorRegistry = ClassRegistry[Predictor]() +class FFNMockPredictor(Predictor, HyperparametersMixin): + _T_default_criterion: ChempropMetric + _T_default_metric: ChempropMetric + output_transform = nn.Identity() + + @property + def input_dim(self) -> int: + return 0 + + @property + def output_dim(self) -> int: + return 0 + + @property + def n_tasks(self) -> int: + return 0 + + def forward(self, Z: Tensor) -> Tensor: + return torch.tensor([], device=Z.device()) + + def encode(self, Z: Tensor, i: int) -> Tensor: + return torch.tensor([], device=Z.device()) + + def criterion(self, *args, **kwargs) -> int: + return 0 + + class _FFNPredictorBase(Predictor, HyperparametersMixin): """A :class:`_FFNPredictorBase` is the base class for all :class:`Predictor`\s that use an underlying :class:`SimpleFFN` to map the learned fingerprint to the desired output. diff --git a/chemprop/uncertainty/estimator.py b/chemprop/uncertainty/estimator.py index e375c947a..ff9e5b4d8 100644 --- a/chemprop/uncertainty/estimator.py +++ b/chemprop/uncertainty/estimator.py @@ -6,7 +6,7 @@ from torch import Tensor from torch.utils.data import DataLoader -from chemprop.models.model import MPNN +from chemprop.models.model import MPNN, MolAtomBondMPNN from chemprop.utils.registry import ClassRegistry @@ -15,7 +15,10 @@ class UncertaintyEstimator(ABC): @abstractmethod def __call__( - self, dataloader: DataLoader, models: Iterable[MPNN], trainer: pl.Trainer + self, + dataloader: DataLoader, + models: Iterable[MPNN] | Iterable[MolAtomBondMPNN], + trainer: pl.Trainer, ) -> tuple[Tensor, Tensor]: """ Calculate the uncalibrated predictions and uncertainties for the dataloader. @@ -55,6 +58,7 @@ class NoUncertaintyEstimator(UncertaintyEstimator): def __call__( self, dataloader: DataLoader, models: Iterable[MPNN], trainer: pl.Trainer ) -> tuple[Tensor, Tensor]: + print("hi") predss = [] for model in models: preds = torch.concat(trainer.predict(model, dataloader), 0) @@ -62,6 +66,26 @@ def __call__( return torch.stack(predss), None +@UncertaintyEstimatorRegistry.register("mixed_none") +class MixedNoUncertaintyEstimator(UncertaintyEstimator): + def __call__( + self, dataloader: DataLoader, models: Iterable[MolAtomBondMPNN], trainer: pl.Trainer + ) -> tuple[Tensor, Tensor]: + preds = [torch.concat(type, 0) for type in zip(*trainer.predict(models[0], dataloader))] + predss = [] + for pred in preds: + predss.append([pred]) + if len(models) > 1: + for model_idx in range(1, len(models)): + preds = [ + torch.concat(type, 0) + for type in zip(*trainer.predict(models[model_idx], dataloader)) + ] + for pred_idx in range(len(preds)): + predss[pred_idx].append(preds[pred_idx]) + return [torch.stack(prediction) for prediction in predss], None + + @UncertaintyEstimatorRegistry.register("mve") class MVEEstimator(UncertaintyEstimator): """ diff --git a/chemprop/utils/utils.py b/chemprop/utils/utils.py index 4efd503b0..d0cda071c 100644 --- a/chemprop/utils/utils.py +++ b/chemprop/utils/utils.py @@ -3,6 +3,7 @@ from enum import StrEnum from typing import Iterable, Iterator +import numpy as np from rdkit import Chem @@ -32,7 +33,7 @@ def items(cls) -> Iterator[tuple[str, str]]: return zip(cls.keys(), cls.values()) -def make_mol(smi: str, keep_h: bool, add_h: bool) -> Chem.Mol: +def make_mol(smi: str, keep_h: bool, add_h: bool, keep_atom_map: bool = False) -> Chem.Mol: """build an RDKit molecule from a SMILES string. Parameters @@ -57,7 +58,16 @@ def make_mol(smi: str, keep_h: bool, add_h: bool) -> Chem.Mol: else: mol = Chem.MolFromSmiles(smi) - return Chem.AddHs(mol) if add_h else mol + mol = Chem.AddHs(mol) if add_h else mol + + if keep_atom_map and mol is not None: + atom_map_numbers = tuple(atom.GetAtomMapNum() for atom in mol.GetAtoms()) + for idx, map_num in enumerate(atom_map_numbers): + if idx + 1 != map_num: + new_order = np.argsort(atom_map_numbers).tolist() + return Chem.rdmolops.RenumberAtoms(mol, new_order) + + return mol def pretty_shape(shape: Iterable[int]) -> str: diff --git a/docs/source/tutorial/python/data/dataloaders.ipynb b/docs/source/tutorial/python/data/dataloaders.ipynb index ffefb5aa4..5fd7b410d 100644 --- a/docs/source/tutorial/python/data/dataloaders.ipynb +++ b/docs/source/tutorial/python/data/dataloaders.ipynb @@ -101,8 +101,8 @@ { "data": { "text/plain": [ - "TrainingBatch(bmg=, V_d=None, X_d=None, Y=tensor([[0.1604],\n", - " [0.5547]]), w=tensor([[1.],\n", + "TrainingBatch(bmg=, V_d=None, E_d=None, X_d=None, Y=tensor([[0.1968],\n", + " [0.8676]]), w=tensor([[1.],\n", " [1.]]), lt_mask=None, gt_mask=None)" ] }, @@ -149,7 +149,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "GPU available: False, used: False\n", + "GPU available: True (mps), used: True\n", "TPU available: False, using: 0 TPU cores\n", "HPU available: False, using: 0 HPUs\n" ] @@ -172,15 +172,23 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/knathan/anaconda3/envs/chemprop/lib/python3.11/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:475: Your `predict_dataloader`'s sampler has shuffling enabled, it is strongly recommended that you turn shuffling off for val/test dataloaders.\n", - "/home/knathan/anaconda3/envs/chemprop/lib/python3.11/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:424: The 'predict_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=11` in the `DataLoader` to improve performance.\n" + "/opt/anaconda3/envs/chemprop/lib/python3.11/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:475: Your `predict_dataloader`'s sampler has shuffling enabled, it is strongly recommended that you turn shuffling off for val/test dataloaders.\n", + "/opt/anaconda3/envs/chemprop/lib/python3.11/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:424: The 'predict_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=7` in the `DataLoader` to improve performance.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Predicting DataLoader 0: 100%|██████████| 1/1 [00:00<00:00, 90.89it/s] \n" + "Predicting DataLoader 0: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 14.32it/s]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/brianli/Documents/chemprop/chemprop/nn/message_passing/base.py:270: UserWarning: The operator 'aten::scatter_reduce.two_out' is not currently supported on the MPS backend and will fall back to run on the CPU. This may have performance implications. (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/mps/MPSFallback.mm:13.)\n", + " M_all = torch.zeros(len(bmg.V), H.shape[1], dtype=H.dtype, device=H.device).scatter_reduce_(\n" ] } ], @@ -197,7 +205,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Predicting DataLoader 0: 100%|██████████| 1/1 [00:00<00:00, 121.35it/s]\n" + "Predicting DataLoader 0: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 165.07it/s]\n" ] } ], @@ -229,7 +237,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 10, @@ -267,7 +275,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/knathan/chemprop/chemprop/data/dataloader.py:52: UserWarning: Dropping last batch of size 1 to avoid issues with batch normalization (dataset size = 3, batch_size = 2)\n", + "/Users/brianli/Documents/chemprop/chemprop/data/dataloader.py:61: UserWarning: Dropping last batch of size 1 to avoid issues with batch normalization (dataset size = 3, batch_size = 2)\n", " warnings.warn(\n" ] } @@ -310,7 +318,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 13, @@ -336,6 +344,10 @@ " [1.],\n", " [0.],\n", " [1.],\n", + " [0.],\n", + " [1.],\n", + " [0.],\n", + " [1.],\n", " [0.]])\n" ] } @@ -347,14 +359,14 @@ "\n", "dataloader = build_dataloader(dataset, class_balance=True)\n", "\n", - "_, _, _, Y, *_ = next(iter(dataloader))\n", + "_, _, _, _, Y, *_ = next(iter(dataloader))\n", "print(Y)" ] } ], "metadata": { "kernelspec": { - "display_name": "chemprop", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -368,9 +380,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.8" + "version": "3.11.9" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/docs/source/tutorial/python/models/basic_mpnn_model.ipynb b/docs/source/tutorial/python/models/basic_mpnn_model.ipynb index 27bec99e6..552837284 100644 --- a/docs/source/tutorial/python/models/basic_mpnn_model.ipynb +++ b/docs/source/tutorial/python/models/basic_mpnn_model.ipynb @@ -46,6 +46,7 @@ " (dropout): Dropout(p=0.0, inplace=False)\n", " (tau): ReLU()\n", " (V_d_transform): Identity()\n", + " (E_d_transform): Identity()\n", " (graph_transform): Identity()\n", " )\n", " (agg): NormAggregation()\n", @@ -117,6 +118,7 @@ " (dropout): Dropout(p=0.0, inplace=False)\n", " (tau): ReLU()\n", " (V_d_transform): Identity()\n", + " (E_d_transform): Identity()\n", " (graph_transform): Identity()\n", " )\n", " (agg): NormAggregation()\n", @@ -241,20 +243,20 @@ "dataset = MoleculeDataset([MoleculeDatapoint.from_smi(smi, y) for smi, y in zip(smis, ys)])\n", "dataloader = build_dataloader(dataset)\n", "batch = next(iter(dataloader))\n", - "bmg, V_d, X_d, *_ = batch" + "bmg, E_d, V_d, X_d, *_ = batch" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "tensor([[0.0333],\n", - " [0.0331],\n", - " [0.0332]], grad_fn=)" + "tensor([[0.0471],\n", + " [0.0468],\n", + " [0.0472]], grad_fn=)" ] }, "execution_count": 7, @@ -329,7 +331,7 @@ ], "metadata": { "kernelspec": { - "display_name": "chemprop", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -343,7 +345,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.8" + "version": "3.11.9" } }, "nbformat": 4, diff --git a/examples/atoms.ipynb b/examples/atoms.ipynb new file mode 100644 index 000000000..9f8207619 --- /dev/null +++ b/examples/atoms.ipynb @@ -0,0 +1,1481 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Training" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Import packages" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "from pathlib import Path\n", + "import numpy as np\n", + "import ast\n", + "\n", + "from lightning import pytorch as pl\n", + "from lightning.pytorch.callbacks import ModelCheckpoint\n", + "\n", + "from chemprop import data, featurizers, models, nn\n", + "from chemprop.utils import make_mol" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Change data inputs here" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "chemprop_dir = Path.cwd().parent\n", + "input_path = chemprop_dir / \"tests\" / \"data\" / \"atomic_regression_atom_mapped_input_copy.csv\" # path to your data .csv file\n", + "num_workers = 0 # number of workers for dataloader. 0 means using main process for data loading\n", + "smiles_column = 'smiles' # name of the column containing SMILES strings\n", + "target_columns = ['charges', 'charges2'] # list of names of the columns containing targets" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load data" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " charges \\\n", + "0 [-0.093451, -0.121894, 0.159898, -0.198989, 0.... \n", + "1 [-0.072057, 0.128813, -0.014234, -0.095166, -0... \n", + "2 [-0.47105, 0.575086, -0.557499, -0.171529, -0.... \n", + "3 [-0.220661, -0.131327, -0.220029, 0.338192, -0... \n", + "4 [-0.206838, -0.184143, -0.07144, 0.180829, -0.... \n", + "\n", + " charges2 \n", + "0 [-0.093451, -0.121894, 0.159898, -0.198989, 0.... \n", + "1 [-0.072057, 0.128813, -0.014234, -0.095166, -0... \n", + "2 [-0.47105, 0.575086, -0.557499, -0.171529, -0.... \n", + "3 [-0.220661, -0.131327, -0.220029, 0.338192, -0... \n", + "4 [-0.206838, -0.184143, -0.07144, 0.180829, -0.... " + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ys[:5] # show first 5 molecule targets" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "Y = []\n", + "for molecule in range(len(ys)):\n", + " list_props = []\n", + " for prop in target_columns:\n", + " np_prop = np.array(ast.literal_eval(ys.iloc[molecule][prop]))\n", + " np_prop = np.expand_dims(np_prop, axis=1)\n", + " list_props.append(np_prop)\n", + " Y.append(np.hstack(list_props))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Get molecule datapoints" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "all_data = [data.MoleculeDatapoint.from_smi(smi, y, keep_h = True) for smi, y in zip(smis, Y)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Perform data splitting for training, validation, and testing" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['SCAFFOLD_BALANCED',\n", + " 'RANDOM_WITH_REPEATED_SMILES',\n", + " 'RANDOM',\n", + " 'KENNARD_STONE',\n", + " 'KMEANS']" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# available split types\n", + "list(data.SplitType.keys())" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "mols = [d.mol for d in all_data] # RDkit Mol objects are use for structure based splits\n", + "train_indices, val_indices, test_indices = data.make_split_indices(mols, \"random\", (0.8, 0.1, 0.1))\n", + "train_data, val_data, test_data = data.split_data_by_indices(\n", + " all_data, train_indices, val_indices, test_indices\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Get AtomDataset" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "featurizer = featurizers.SimpleMoleculeMolGraphFeaturizer()\n", + "\n", + "train_dset = data.AtomDataset(train_data[0], featurizer)\n", + "scaler = train_dset.normalize_targets()\n", + "\n", + "val_dset = data.AtomDataset(val_data[0], featurizer)\n", + "val_dset.normalize_targets(scaler)\n", + "\n", + "test_dset = data.AtomDataset(test_data[0], featurizer)\n", + "\n", + "all_dset = data.AtomDataset(all_data, featurizer)\n", + "slices = all_dset._slices" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Get DataLoader" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "train_loader = data.build_dataloader(train_dset, num_workers=num_workers)\n", + "val_loader = data.build_dataloader(val_dset, num_workers=num_workers, shuffle=False)\n", + "test_loader = data.build_dataloader(test_dset, num_workers=num_workers, shuffle=False)\n", + "all_loader = data.build_dataloader(all_dset, num_workers=num_workers, shuffle=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Change Message-Passing Neural Network (MPNN) inputs here" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Message Passing\n", + "A `Message passing` constructs molecular graphs using message passing to learn node-level hidden representations.\n", + "\n", + "Options are `mp = nn.BondMessagePassing()` or `mp = nn.AtomMessagePassing()`" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "mp = nn.BondMessagePassing() #include why aggregation isn't used" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Feed-Forward Network (FFN)\n", + "\n", + "A `FFN` takes the aggregated representations and make target predictions.\n", + "\n", + "Available options can be found in `nn.PredictorRegistry`.\n", + "\n", + "For regression:\n", + "- `ffn = nn.RegressionFFN()`\n", + "- `ffn = nn.MveFFN()`\n", + "- `ffn = nn.EvidentialFFN()`\n", + "\n", + "For classification:\n", + "- `ffn = nn.BinaryClassificationFFN()`\n", + "- `ffn = nn.BinaryDirichletFFN()`\n", + "- `ffn = nn.MulticlassClassificationFFN()`\n", + "- `ffn = nn.MulticlassDirichletFFN()`\n", + "\n", + "For spectral:\n", + "- `ffn = nn.SpectralFFN()` # will be available in future version" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ClassRegistry {\n", + " 'regression': ,\n", + " 'regression-mve': ,\n", + " 'regression-evidential': ,\n", + " 'regression-quantile': ,\n", + " 'classification': ,\n", + " 'classification-dirichlet': ,\n", + " 'multiclass': ,\n", + " 'multiclass-dirichlet': ,\n", + " 'spectral': \n", + "}\n" + ] + } + ], + "source": [ + "print(nn.PredictorRegistry)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "output_transform = nn.UnscaleTransform.from_standard_scaler(scaler)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "ffn = nn.RegressionFFN(output_transform=output_transform, n_tasks=2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Batch Norm\n", + "A `Batch Norm` normalizes the outputs of the aggregation by re-centering and re-scaling.\n", + "\n", + "Whether to use batch norm" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "batch_norm = True" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Metrics\n", + "`Metrics` are the ways to evaluate the performance of model predictions.\n", + "\n", + "Available options can be found in `metrics.MetricRegistry`, including" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ClassRegistry {\n", + " 'mse': ,\n", + " 'mae': ,\n", + " 'rmse': ,\n", + " 'bounded-mse': ,\n", + " 'bounded-mae': ,\n", + " 'bounded-rmse': ,\n", + " 'r2': ,\n", + " 'binary-mcc': ,\n", + " 'multiclass-mcc': ,\n", + " 'roc': ,\n", + " 'prc': ,\n", + " 'accuracy': ,\n", + " 'f1': \n", + "}\n" + ] + } + ], + "source": [ + "print(nn.metrics.MetricRegistry)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "metric_list = [nn.metrics.RMSE(), nn.metrics.MAE()] # Only the first metric is used for training and early stopping" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Constructs MPNN" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "MPNN(\n", + " (message_passing): BondMessagePassing(\n", + " (W_i): Linear(in_features=86, out_features=300, bias=False)\n", + " (W_h): Linear(in_features=300, out_features=300, bias=False)\n", + " (W_o): Linear(in_features=372, out_features=300, bias=True)\n", + " (W_o_b): Linear(in_features=314, out_features=300, bias=True)\n", + " (dropout): Dropout(p=0.0, inplace=False)\n", + " (tau): ReLU()\n", + " (V_d_transform): Identity()\n", + " (E_d_transform): Identity()\n", + " (graph_transform): Identity()\n", + " )\n", + " (agg): NoAggregation()\n", + " (bn): BatchNorm1d(300, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (predictor): RegressionFFN(\n", + " (ffn): MLP(\n", + " (0): Sequential(\n", + " (0): Linear(in_features=300, out_features=300, bias=True)\n", + " )\n", + " (1): Sequential(\n", + " (0): ReLU()\n", + " (1): Dropout(p=0.0, inplace=False)\n", + " (2): Linear(in_features=300, out_features=2, bias=True)\n", + " )\n", + " )\n", + " (criterion): MSE(task_weights=[[1.0, 1.0]])\n", + " (output_transform): UnscaleTransform()\n", + " )\n", + " (X_d_transform): Identity()\n", + " (metrics): ModuleList(\n", + " (0): RMSE(task_weights=[[1.0]])\n", + " (1): MAE(task_weights=[[1.0]])\n", + " (2): MSE(task_weights=[[1.0, 1.0]])\n", + " )\n", + ")" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "agg = nn.NoAggregation()\n", + "atom_mpnn = models.MPNN(mp, agg, ffn, batch_norm, metric_list)\n", + "\n", + "atom_mpnn" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Set up trainer" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "GPU available: True (mps), used: True\n", + "TPU available: False, using: 0 TPU cores\n", + "HPU available: False, using: 0 HPUs\n", + "`Trainer(overfit_batches=1.0)` was configured so 100% of the batches will be used..\n" + ] + } + ], + "source": [ + "# Configure model checkpointing\n", + "checkpointing = ModelCheckpoint(\n", + " \"checkpoints\", # Directory where model checkpoints will be saved\n", + " \"best-{epoch}-{val_loss:.2f}\", # Filename format for checkpoints, including epoch and validation loss\n", + " \"val_loss\", # Metric used to select the best checkpoint (based on validation loss)\n", + " mode=\"min\", # Save the checkpoint with the lowest validation loss (minimization objective)\n", + " save_last=True, # Always save the most recent checkpoint, even if it's not the best\n", + ")\n", + "\n", + "trainer = pl.Trainer(\n", + " logger=False,\n", + " enable_checkpointing=True, # Use `True` if you want to save model checkpoints. The checkpoints will be saved in the `checkpoints` folder.\n", + " enable_progress_bar=True,\n", + " accelerator=\"auto\",\n", + " devices=1,\n", + " max_epochs=50, # number of epochs to train for\n", + " overfit_batches=1.00,\n", + " callbacks=[checkpointing],\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Start training" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/anaconda3/envs/chemprop/lib/python3.11/site-packages/lightning/pytorch/callbacks/model_checkpoint.py:654: Checkpoint directory /Users/brianli/Documents/chemprop/examples/checkpoints exists and is not empty.\n", + "Loading `train_dataloader` to estimate number of stepping batches.\n", + "/opt/anaconda3/envs/chemprop/lib/python3.11/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:251: You requested to overfit but enabled train dataloader shuffling. We are turning off the train dataloader shuffling for you.\n", + "/opt/anaconda3/envs/chemprop/lib/python3.11/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:424: The 'train_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=7` in the `DataLoader` to improve performance.\n", + "\n", + " | Name | Type | Params | Mode \n", + "---------------------------------------------------------------\n", + "0 | message_passing | BondMessagePassing | 322 K | train\n", + "1 | agg | NoAggregation | 0 | train\n", + "2 | bn | BatchNorm1d | 600 | train\n", + "3 | predictor | RegressionFFN | 90.9 K | train\n", + "4 | X_d_transform | Identity | 0 | train\n", + "5 | metrics | ModuleList | 0 | train\n", + "---------------------------------------------------------------\n", + "413 K Trainable params\n", + "0 Non-trainable params\n", + "413 K Total params\n", + "1.655 Total estimated model params size (MB)\n", + "27 Modules in train mode\n", + "0 Modules in eval mode\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Sanity Checking DataLoader 0: 0%| | 0/1 [00:00┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n", + "┃ Test metric ┃ DataLoader 0 ┃\n", + "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n", + "│ test/mae │ 0.19138887524604797 │\n", + "│ test/rmse │ 0.2649255096912384 │\n", + "└───────────────────────────┴───────────────────────────┘\n", + "\n" + ], + "text/plain": [ + "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n", + "┃\u001b[1m \u001b[0m\u001b[1m Test metric \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m DataLoader 0 \u001b[0m\u001b[1m \u001b[0m┃\n", + "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n", + "│\u001b[36m \u001b[0m\u001b[36m test/mae \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m 0.19138887524604797 \u001b[0m\u001b[35m \u001b[0m│\n", + "│\u001b[36m \u001b[0m\u001b[36m test/rmse \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m 0.2649255096912384 \u001b[0m\u001b[35m \u001b[0m│\n", + "└───────────────────────────┴───────────────────────────┘\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "results = trainer.test(atom_mpnn, test_loader)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Predictions" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/brianli/Documents/chemprop/chemprop/models/model.py:243: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", + " d = torch.load(path, map_location)\n", + "GPU available: True (mps), used: True\n", + "TPU available: False, using: 0 TPU cores\n", + "HPU available: False, using: 0 HPUs\n", + "/opt/anaconda3/envs/chemprop/lib/python3.11/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:475: Your `predict_dataloader`'s sampler has shuffling enabled, it is strongly recommended that you turn shuffling off for val/test dataloaders.\n", + "/opt/anaconda3/envs/chemprop/lib/python3.11/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:424: The 'predict_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=7` in the `DataLoader` to improve performance.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Predicting DataLoader 0: 100%|████████████████████| 7/7 [00:00<00:00, 12.10it/s]\n" + ] + } + ], + "source": [ + "from chemprop.models import load_model\n", + "import torch\n", + "\n", + "individual_preds = []\n", + "model = load_model(checkpointing.best_model_path, False)\n", + "trainer = pl.Trainer(\n", + " logger=False,\n", + " enable_progress_bar=True,\n", + " accelerator=\"auto\",\n", + " devices=1,\n", + ")\n", + "\n", + "predss = trainer.predict(model, train_loader)\n", + "individual_preds.append(torch.concat(predss, 0))\n", + "\n", + "average_preds = torch.mean(torch.stack(individual_preds).float(), dim=0)\n", + "test_path = chemprop_dir / \"tests\" / \"data\" / \"atomic_regression_atom_mapped_input_copy.csv\"\n", + "df_test = pd.read_csv(test_path, header=\"infer\", index_col=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Loaded Model" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "MPNN(\n", + " (message_passing): BondMessagePassing(\n", + " (W_i): Linear(in_features=86, out_features=300, bias=False)\n", + " (W_h): Linear(in_features=300, out_features=300, bias=False)\n", + " (W_o): Linear(in_features=372, out_features=300, bias=True)\n", + " (W_o_b): Linear(in_features=314, out_features=300, bias=True)\n", + " (dropout): Dropout(p=0.0, inplace=False)\n", + " (tau): ReLU()\n", + " (V_d_transform): Identity()\n", + " (E_d_transform): Identity()\n", + " (graph_transform): Identity()\n", + " )\n", + " (agg): NoAggregation()\n", + " (bn): BatchNorm1d(300, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (predictor): RegressionFFN(\n", + " (ffn): MLP(\n", + " (0): Sequential(\n", + " (0): Linear(in_features=300, out_features=300, bias=True)\n", + " )\n", + " (1): Sequential(\n", + " (0): ReLU()\n", + " (1): Dropout(p=0.0, inplace=False)\n", + " (2): Linear(in_features=300, out_features=2, bias=True)\n", + " )\n", + " )\n", + " (criterion): MSE(task_weights=[[1.0, 1.0]])\n", + " (output_transform): UnscaleTransform()\n", + " )\n", + " (X_d_transform): Identity()\n", + " (metrics): ModuleList(\n", + " (0): RMSE(task_weights=[[1.0]])\n", + " (1): MAE(task_weights=[[1.0]])\n", + " (2): MSE(task_weights=[[1.0, 1.0]])\n", + " )\n", + ")" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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45.0[1,3,5]
\n", + "
" + ], + "text/plain": [ + " molecule atom\n", + "0 1.0 [1,2]\n", + "1 2.0 [1,2,3]\n", + "2 3.0 [1,2,3,5]\n", + "3 4.0 [1,2,3,4]\n", + "4 5.0 [1,3,5]" + ] + }, + "execution_count": 88, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ys[:5] # show first 5 molecule targets" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "metadata": {}, + "outputs": [], + "source": [ + "flag = [] # mark which columns belong to which of (mol, atom, bond)\n", + "mol_Y, atom_Y, bond_Y = [], [], [] # target values for each type\n", + "for column in target_columns:\n", + " index = 0\n", + " column_type = df_input.iloc[index][column]\n", + " if isinstance(column_type, float):\n", + " for molecule in range(len(df_input)):\n", + " mol_Y.append([df_input.iloc[molecule][column]])\n", + " flag.append(\"mol\")\n", + " else:\n", + " column_mol = make_mol(df_input.iloc[index][smiles_column], False, False)\n", + " column_type = ast.literal_eval(column_type)\n", + " while index < len(df_input) and column_mol.GetNumAtoms() == column_mol.GetNumBonds():\n", + " index += 1\n", + " column_mol = make_mol(df_input.iloc[index][smiles_column], False, False)\n", + " column_type = ast.literal_eval(df_input.iloc[index][column])\n", + " flag.append(\"atom\") if len(column_type) == column_mol.GetNumAtoms() else flag.append(\"bond\")" + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "metadata": {}, + "outputs": [], + "source": [ + "for molecule in range(len(df_input)):\n", + " atom_list_props, bond_list_props = [], [] # list containing all target values, which we need to hstack for atom_Y and bond_Y\n", + " for prop in range(len(target_columns)):\n", + " if flag[prop] == \"mol\":\n", + " continue\n", + " np_prop = np.array(ast.literal_eval(df_input.iloc[molecule][target_columns[prop]]))\n", + " np_prop = np.expand_dims(np_prop, axis=1)\n", + " atom_list_props.append(np_prop) if flag[prop] == \"atom\" else bond_list_props.append(np_prop)\n", + " if len(atom_list_props) > 0:\n", + " atom_Y.append(np.hstack(atom_list_props))\n", + " else:\n", + " atom_Y = df_input[[]]\n", + " atom_Y = atom_Y.to_numpy()\n", + " if len(bond_list_props) > 0:\n", + " bond_Y.append(np.hstack(bond_list_props))\n", + " else:\n", + " bond_Y = df_input[[]]\n", + " bond_Y = bond_Y.to_numpy()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Get molecule datapoints" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "metadata": {}, + "outputs": [], + "source": [ + "mol_data = []\n", + "for smi, y in zip(smis, mol_Y):\n", + " mol_data.append(data.MoleculeDatapoint.from_smi(smi, y, keep_h=True))\n", + "\n", + "atom_data = []\n", + "for smi, y in zip(smis, atom_Y):\n", + " atom_data.append(data.MoleculeDatapoint.from_smi(smi, y, keep_h=True))\n", + "\n", + "bond_data = []\n", + "for smi, y in zip(smis, bond_Y):\n", + " bond_data.append(data.MoleculeDatapoint.from_smi(smi, y, keep_h=True))" + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "metadata": {}, + "outputs": [], + "source": [ + "all_data = []\n", + "all_data.append(mol_data)\n", + "all_data.append(atom_data)\n", + "all_data.append(bond_data)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Perform data splitting for training, validation, and testing" + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['SCAFFOLD_BALANCED',\n", + " 'RANDOM_WITH_REPEATED_SMILES',\n", + " 'RANDOM',\n", + " 'KENNARD_STONE',\n", + " 'KMEANS']" + ] + }, + "execution_count": 93, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# available split types\n", + "list(data.SplitType.keys())" + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "metadata": {}, + "outputs": [], + "source": [ + "mol = [d.mol for d in all_data[0]]\n", + "\n", + "train_indices, val_indices, test_indices = data.make_split_indices(mol, \"random\", (0.6, 0.2, 0.2))\n", + "train_data, val_data, test_data = data.split_data_by_indices(\n", + " all_data, train_indices, val_indices, test_indices\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Get All Datasets" + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/hx/9k64y_d101zg5t41l44wfs_w0000gn/T/ipykernel_38858/1057028705.py:6: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n", + " train_dsets.append(data.BondDataset(train_data[0][2], featurizer)) if bond_Y else train_dsets.append(data.MockDataset())\n" + ] + } + ], + "source": [ + "featurizer = featurizers.SimpleMoleculeMolGraphFeaturizer()\n", + "\n", + "train_dsets = []\n", + "train_dsets.append(data.MoleculeDataset(train_data[0][0], featurizer)) if mol_Y else train_dsets.append(data.MockDataset())\n", + "train_dsets.append(data.AtomDataset(train_data[0][1], featurizer)) if atom_Y else train_dsets.append(data.MockDataset())\n", + "train_dsets.append(data.BondDataset(train_data[0][2], featurizer)) if bond_Y else train_dsets.append(data.MockDataset())\n", + "\n", + "mol_scaler = train_dsets[0].normalize_targets()\n", + "atom_scaler = train_dsets[1].normalize_targets()\n", + "bond_scaler = train_dsets[2].normalize_targets()\n", + "train_dset = data.MolAtomBondDataset(train_dsets[0], train_dsets[1], train_dsets[2])" + ] + }, + { + "cell_type": "code", + "execution_count": 96, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/hx/9k64y_d101zg5t41l44wfs_w0000gn/T/ipykernel_38858/3338986635.py:4: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n", + " val_dsets.append(data.BondDataset(val_data[0][2], featurizer)) if bond_Y else val_dsets.append(data.MockDataset())\n" + ] + } + ], + "source": [ + "val_dsets = []\n", + "val_dsets.append(data.MoleculeDataset(val_data[0][0], featurizer)) if mol_Y else val_dsets.append(data.MockDataset())\n", + "val_dsets.append(data.AtomDataset(val_data[0][1], featurizer)) if atom_Y else val_dsets.append(data.MockDataset())\n", + "val_dsets.append(data.BondDataset(val_data[0][2], featurizer)) if bond_Y else val_dsets.append(data.MockDataset())\n", + "val_dsets[0].normalize_targets(mol_scaler)\n", + "val_dsets[1].normalize_targets(atom_scaler)\n", + "val_dsets[2].normalize_targets(bond_scaler)\n", + "val_dset = data.MolAtomBondDataset(val_dsets[0], val_dsets[1], val_dsets[2])" + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/hx/9k64y_d101zg5t41l44wfs_w0000gn/T/ipykernel_38858/3706167011.py:4: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n", + " test_dsets.append(data.BondDataset(test_data[0][2], featurizer)) if bond_Y else test_dsets.append(data.MockDataset())\n" + ] + } + ], + "source": [ + "test_dsets = []\n", + "test_dsets.append(data.MoleculeDataset(test_data[0][0], featurizer)) if mol_Y else test_dsets.append(data.MockDataset())\n", + "test_dsets.append(data.AtomDataset(test_data[0][1], featurizer)) if atom_Y else test_dsets.append(data.MockDataset())\n", + "test_dsets.append(data.BondDataset(test_data[0][2], featurizer)) if bond_Y else test_dsets.append(data.MockDataset())\n", + "test_dset = data.MolAtomBondDataset(test_dsets[0], test_dsets[1], test_dsets[2])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Get Atom/Bond Slices" + ] + }, + { + "cell_type": "code", + "execution_count": 98, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/hx/9k64y_d101zg5t41l44wfs_w0000gn/T/ipykernel_38858/1584694362.py:5: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n", + " all_dsets.append(data.BondDataset(all_data[2], featurizer)) if bond_Y else all_dsets.append(data.MockDataset())\n" + ] + } + ], + "source": [ + "all_dsets = []\n", + "all_dsets.append(data.MoleculeDataset(all_data[0], featurizer)) if mol_Y else all_dsets.append(data.MockDataset())\n", + "all_dsets.append(data.AtomDataset(all_data[1], featurizer)) if atom_Y else all_dsets.append(data.MockDataset())\n", + "atom_slices = all_dsets[1]._slices\n", + "all_dsets.append(data.BondDataset(all_data[2], featurizer)) if bond_Y else all_dsets.append(data.MockDataset())\n", + "bond_slices = all_dsets[2]._slices\n", + "all_dset = data.MolAtomBondDataset(all_dsets[0], all_dsets[1], all_dsets[2])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Get DataLoader" + ] + }, + { + "cell_type": "code", + "execution_count": 99, + "metadata": {}, + "outputs": [], + "source": [ + "train_loader = data.build_dataloader(train_dset, num_workers=num_workers, shuffle=False)\n", + "val_loader = data.build_dataloader(val_dset, num_workers=num_workers, shuffle=False)\n", + "test_loader = data.build_dataloader(test_dset, num_workers=num_workers, shuffle=False)\n", + "all_loader = data.build_dataloader(all_dset, num_workers=num_workers, shuffle=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Change Message-Passing Neural Network (MPNN) inputs here" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Message Passing\n", + "A `Message passing` constructs molecular graphs using message passing to learn node-level hidden representations.\n", + "\n", + "Options are `mp = nn.BondMessagePassing()` or `mp = nn.AtomMessagePassing()`" + ] + }, + { + "cell_type": "code", + "execution_count": 100, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "MixedBondMessagePassing(\n", + " (W_i): Linear(in_features=86, out_features=300, bias=False)\n", + " (W_h): Linear(in_features=300, out_features=300, bias=False)\n", + " (W_o): Linear(in_features=372, out_features=300, bias=True)\n", + " (W_o_b): Linear(in_features=314, out_features=300, bias=True)\n", + " (dropout): Dropout(p=0.0, inplace=False)\n", + " (tau): ReLU()\n", + " (V_d_transform): Identity()\n", + " (E_d_transform): Identity()\n", + " (graph_transform): Identity()\n", + ")" + ] + }, + "execution_count": 100, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mp = nn.MixedBondMessagePassing() #include why aggregation isn't used\n", + "mp" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Feed-Forward Network (FFN)\n", + "\n", + "A `FFN` takes the aggregated representations and make target predictions.\n", + "\n", + "Available options can be found in `nn.PredictorRegistry`.\n", + "\n", + "For regression:\n", + "- `ffn = nn.RegressionFFN()`\n", + "- `ffn = nn.MveFFN()`\n", + "- `ffn = nn.EvidentialFFN()`\n", + "\n", + "For classification:\n", + "- `ffn = nn.BinaryClassificationFFN()`\n", + "- `ffn = nn.BinaryDirichletFFN()`\n", + "- `ffn = nn.MulticlassClassificationFFN()`\n", + "- `ffn = nn.MulticlassDirichletFFN()`\n", + "\n", + "For spectral:\n", + "- `ffn = nn.SpectralFFN()` # will be available in future version" + ] + }, + { + "cell_type": "code", + "execution_count": 101, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ClassRegistry {\n", + " 'regression': ,\n", + " 'regression-mve': ,\n", + " 'regression-evidential': ,\n", + " 'regression-quantile': ,\n", + " 'classification': ,\n", + " 'classification-dirichlet': ,\n", + " 'multiclass': ,\n", + " 'multiclass-dirichlet': ,\n", + " 'spectral': \n", + "}\n" + ] + } + ], + "source": [ + "print(nn.PredictorRegistry)" + ] + }, + { + "cell_type": "code", + "execution_count": 102, + "metadata": {}, + "outputs": [], + "source": [ + "mol_output_transform = nn.UnscaleTransform.from_standard_scaler(mol_scaler)\n", + "atom_output_transform = nn.UnscaleTransform.from_standard_scaler(atom_scaler)\n", + "bond_output_transform = nn.UnscaleTransform.from_standard_scaler(bond_scaler)" + ] + }, + { + "cell_type": "code", + "execution_count": 103, + "metadata": {}, + "outputs": [], + "source": [ + "mol_ffn = nn.RegressionFFN(output_transform=mol_output_transform, n_tasks=1) # one ffn for each type\n", + "atom_ffn = nn.RegressionFFN(output_transform=atom_output_transform, n_tasks=1)\n", + "bond_ffn = nn.RegressionFFN(output_transform=bond_output_transform, input_dim=600, n_tasks=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Batch Norm\n", + "A `Batch Norm` normalizes the outputs of the aggregation by re-centering and re-scaling.\n", + "\n", + "Whether to use batch norm" + ] + }, + { + "cell_type": "code", + "execution_count": 104, + "metadata": {}, + "outputs": [], + "source": [ + "batch_norm = True" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Metrics\n", + "`Metrics` are the ways to evaluate the performance of model predictions.\n", + "\n", + "Available options can be found in `metrics.MetricRegistry`, including" + ] + }, + { + "cell_type": "code", + "execution_count": 105, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ClassRegistry {\n", + " 'mse': ,\n", + " 'mae': ,\n", + " 'rmse': ,\n", + " 'bounded-mse': ,\n", + " 'bounded-mae': ,\n", + " 'bounded-rmse': ,\n", + " 'r2': ,\n", + " 'binary-mcc': ,\n", + " 'multiclass-mcc': ,\n", + " 'roc': ,\n", + " 'prc': ,\n", + " 'accuracy': ,\n", + " 'f1': \n", + "}\n" + ] + } + ], + "source": [ + "print(nn.metrics.MetricRegistry)" + ] + }, + { + "cell_type": "code", + "execution_count": 106, + "metadata": {}, + "outputs": [], + "source": [ + "metric_list = [nn.metrics.RMSE(), nn.metrics.MAE()] # Only the first metric is used for training and early stopping" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Constructs MolAtomBondMPNN" + ] + }, + { + "cell_type": "code", + "execution_count": 107, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "MolAtomBondMPNN(\n", + " (message_passing): MixedBondMessagePassing(\n", + " (W_i): Linear(in_features=86, out_features=300, bias=False)\n", + " (W_h): Linear(in_features=300, out_features=300, bias=False)\n", + " (W_o): Linear(in_features=372, out_features=300, bias=True)\n", + " (W_o_b): Linear(in_features=314, out_features=300, bias=True)\n", + " (dropout): Dropout(p=0.0, inplace=False)\n", + " (tau): ReLU()\n", + " (V_d_transform): Identity()\n", + " (E_d_transform): Identity()\n", + " (graph_transform): Identity()\n", + " )\n", + " (agg): MeanAggregation()\n", + " (bn): BatchNorm1d(300, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (predictors): ModuleList(\n", + " (0-1): 2 x RegressionFFN(\n", + " (ffn): MLP(\n", + " (0): Sequential(\n", + " (0): Linear(in_features=300, out_features=300, bias=True)\n", + " )\n", + " (1): Sequential(\n", + " (0): ReLU()\n", + " (1): Dropout(p=0.0, inplace=False)\n", + " (2): Linear(in_features=300, out_features=1, bias=True)\n", + " )\n", + " )\n", + " (criterion): MSE(task_weights=[[1.0]])\n", + " (output_transform): UnscaleTransform()\n", + " )\n", + " (2): RegressionFFN(\n", + " (ffn): MLP(\n", + " (0): Sequential(\n", + " (0): Linear(in_features=600, out_features=300, bias=True)\n", + " )\n", + " (1): Sequential(\n", + " (0): ReLU()\n", + " (1): Dropout(p=0.0, inplace=False)\n", + " (2): Linear(in_features=300, out_features=1, bias=True)\n", + " )\n", + " )\n", + " (criterion): MSE(task_weights=[[1.0]])\n", + " (output_transform): UnscaleTransform()\n", + " )\n", + " )\n", + " (X_d_transform): Identity()\n", + " (metrics): ModuleList(\n", + " (0-2): 3 x ModuleList(\n", + " (0): RMSE(task_weights=[[1.0]])\n", + " (1): MAE(task_weights=[[1.0]])\n", + " (2): MSE(task_weights=[[1.0]])\n", + " )\n", + " )\n", + ")" + ] + }, + "execution_count": 107, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "agg = nn.MeanAggregation()\n", + "mol_atom_bond_mpnn = models.MolAtomBondMPNN(mp, agg, mol_ffn, atom_ffn, bond_ffn, batch_norm, metric_list)\n", + "\n", + "mol_atom_bond_mpnn" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Set up trainer" + ] + }, + { + "cell_type": "code", + "execution_count": 108, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "GPU available: True (mps), used: False\n", + "TPU available: False, using: 0 TPU cores\n", + "HPU available: False, using: 0 HPUs\n", + "/opt/anaconda3/envs/chemprop/lib/python3.11/site-packages/lightning/pytorch/trainer/setup.py:177: GPU available but not used. You can set it by doing `Trainer(accelerator='gpu')`.\n" + ] + } + ], + "source": [ + "# Configure model checkpointing\n", + "checkpointing = ModelCheckpoint(\n", + " \"checkpoints\", # Directory where model checkpoints will be saved\n", + " \"best-{epoch}-{val_loss:.2f}\", # Filename format for checkpoints, including epoch and validation loss\n", + " \"val_loss\", # Metric used to select the best checkpoint (based on validation loss)\n", + " mode=\"min\", # Save the checkpoint with the lowest validation loss (minimization objective)\n", + " save_last=True, # Always save the most recent checkpoint, even if it's not the best\n", + ")\n", + "\n", + "trainer = pl.Trainer(\n", + " logger=False,\n", + " enable_checkpointing=True, # Use `True` if you want to save model checkpoints. The checkpoints will be saved in the `checkpoints` folder.\n", + " enable_progress_bar=True,\n", + " accelerator=\"cpu\",\n", + " devices=1,\n", + " max_epochs=20, # number of epochs to train for\n", + " callbacks=[checkpointing],\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Start training" + ] + }, + { + "cell_type": "code", + "execution_count": 109, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/anaconda3/envs/chemprop/lib/python3.11/site-packages/lightning/pytorch/callbacks/model_checkpoint.py:654: Checkpoint directory /Users/brianli/Documents/chemprop/examples/checkpoints exists and is not empty.\n", + "Loading `train_dataloader` to estimate number of stepping batches.\n", + "/opt/anaconda3/envs/chemprop/lib/python3.11/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:424: The 'train_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=7` in the `DataLoader` to improve performance.\n", + "\n", + " | Name | Type | Params | Mode \n", + "--------------------------------------------------------------------\n", + "0 | message_passing | MixedBondMessagePassing | 322 K | train\n", + "1 | agg | MeanAggregation | 0 | train\n", + "2 | bn | BatchNorm1d | 600 | train\n", + "3 | predictors | ModuleList | 361 K | train\n", + "4 | X_d_transform | Identity | 0 | train\n", + "5 | metrics | ModuleList | 0 | train\n", + "--------------------------------------------------------------------\n", + "684 K Trainable params\n", + "0 Non-trainable params\n", + "684 K Total params\n", + "2.738 Total estimated model params size (MB)\n", + "53 Modules in train mode\n", + "0 Modules in eval mode\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " " + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/anaconda3/envs/chemprop/lib/python3.11/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:424: The 'val_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=7` in the `DataLoader` to improve performance.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 0: 100%|███████████████████████████████████| 1/1 [00:00<00:00, 111.72it/s]\n", + "Validation: | | 0/? [00:00┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n", + "┃ Test metric ┃ DataLoader 0 ┃\n", + "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n", + "│ atom_mol_test/mae │ 1.538080096244812 │\n", + "│ atom_mol_test/rmse │ 2.031416177749634 │\n", + "│ mol_test/mae │ 1.538080096244812 │\n", + "│ mol_test/rmse │ 2.031416177749634 │\n", + "└───────────────────────────┴───────────────────────────┘\n", + "\n" + ], + "text/plain": [ + "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n", + "┃\u001b[1m \u001b[0m\u001b[1m Test metric \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m DataLoader 0 \u001b[0m\u001b[1m \u001b[0m┃\n", + "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n", + "│\u001b[36m \u001b[0m\u001b[36m atom_mol_test/mae \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m 1.538080096244812 \u001b[0m\u001b[35m \u001b[0m│\n", + "│\u001b[36m \u001b[0m\u001b[36m atom_mol_test/rmse \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m 2.031416177749634 \u001b[0m\u001b[35m \u001b[0m│\n", + "│\u001b[36m \u001b[0m\u001b[36m mol_test/mae \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m 1.538080096244812 \u001b[0m\u001b[35m \u001b[0m│\n", + "│\u001b[36m \u001b[0m\u001b[36m mol_test/rmse \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m 2.031416177749634 \u001b[0m\u001b[35m \u001b[0m│\n", + "└───────────────────────────┴───────────────────────────┘\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "results = trainer.test(mol_atom_bond_mpnn, test_loader)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Predictions" + ] + }, + { + "cell_type": "code", + "execution_count": 111, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "GPU available: True (mps), used: False\n", + "TPU available: False, using: 0 TPU cores\n", + "HPU available: False, using: 0 HPUs\n" + ] + } + ], + "source": [ + "mol_individual_preds, atom_individual_preds, bond_individual_preds = [], [], []\n", + "model = load_mixed_model(checkpointing.best_model_path)\n", + "\n", + "trainer = pl.Trainer(\n", + " logger=False,\n", + " enable_progress_bar=True,\n", + " accelerator=\"cpu\",\n", + " devices=1,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 112, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/anaconda3/envs/chemprop/lib/python3.11/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:424: The 'predict_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=7` in the `DataLoader` to improve performance.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Predicting DataLoader 0: 100%|███████████████████| 1/1 [00:00<00:00, 309.13it/s]\n" + ] + } + ], + "source": [ + "predss = trainer.predict(model, all_loader)" + ] + }, + { + "cell_type": "code", + "execution_count": 113, + "metadata": {}, + "outputs": [], + "source": [ + "mol_individual_preds.append(torch.concat([predss[0][0]], 0))\n", + "atom_individual_preds.append(torch.concat([predss[0][1]], 0))\n", + "bond_individual_preds.append(torch.concat([predss[0][2]], 0))\n", + "\n", + "mol_average_preds = torch.mean(torch.stack(mol_individual_preds).float(), dim=0)\n", + "atom_average_preds = torch.mean(torch.stack(atom_individual_preds).float(), dim=0)\n", + "bond_average_preds = torch.mean(torch.stack(bond_individual_preds).float(), dim=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 114, + "metadata": {}, + "outputs": [], + "source": [ + "test_path = chemprop_dir / \"tests\" / \"data\" / \"mixed_regression_input.csv\"\n", + "df_test = pd.read_csv(test_path, header=\"infer\", index_col=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Loaded Model" + ] + }, + { + "cell_type": "code", + "execution_count": 115, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "MolAtomBondMPNN(\n", + " (message_passing): MixedBondMessagePassing(\n", + " (W_i): Linear(in_features=86, out_features=300, bias=False)\n", + " (W_h): Linear(in_features=300, out_features=300, bias=False)\n", + " (W_o): Linear(in_features=372, out_features=300, bias=True)\n", + " (W_o_b): Linear(in_features=314, out_features=300, bias=True)\n", + " (dropout): Dropout(p=0.0, inplace=False)\n", + " (tau): ReLU()\n", + " (V_d_transform): Identity()\n", + " (E_d_transform): Identity()\n", + " (graph_transform): Identity()\n", + " )\n", + " (agg): MeanAggregation()\n", + " (bn): BatchNorm1d(300, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (predictors): ModuleList(\n", + " (0-1): 2 x RegressionFFN(\n", + " (ffn): MLP(\n", + " (0): Sequential(\n", + " (0): Linear(in_features=300, out_features=300, bias=True)\n", + " )\n", + " (1): Sequential(\n", + " (0): ReLU()\n", + " (1): Dropout(p=0.0, inplace=False)\n", + " (2): Linear(in_features=300, out_features=1, bias=True)\n", + " )\n", + " )\n", + " (criterion): MSE(task_weights=[[1.0]])\n", + " (output_transform): UnscaleTransform()\n", + " )\n", + " (2): RegressionFFN(\n", + " (ffn): MLP(\n", + " (0): Sequential(\n", + " (0): Linear(in_features=600, out_features=300, bias=True)\n", + " )\n", + " (1): Sequential(\n", + " (0): ReLU()\n", + " (1): Dropout(p=0.0, inplace=False)\n", + " (2): Linear(in_features=300, out_features=1, bias=True)\n", + " )\n", + " )\n", + " (criterion): MSE(task_weights=[[1.0]])\n", + " (output_transform): UnscaleTransform()\n", + " )\n", + " )\n", + " (X_d_transform): Identity()\n", + " (metrics): ModuleList(\n", + " (0-2): 3 x ModuleList(\n", + " (0): RMSE(task_weights=[[1.0]])\n", + " (1): MAE(task_weights=[[1.0]])\n", + " (2): MSE(task_weights=[[1.0]])\n", + " )\n", + " )\n", + ")" + ] + }, + "execution_count": 115, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Output Predictions" + ] + }, + { + "cell_type": "code", + "execution_count": 116, + "metadata": {}, + "outputs": [], + "source": [ + "target_cols = df_test.columns.tolist()\n", + "mol_cols, atom_cols, bond_cols = [], [], []\n", + "for i in range(1, len(target_cols)):\n", + " index = 0\n", + " column_type = df_test.iloc[index][target_cols[i]]\n", + " if isinstance(column_type, float):\n", + " mol_cols.append(i)\n", + " else:\n", + " column_mol = make_mol(df_test.iloc[index][target_cols[0]], False, False)\n", + " column_type = ast.literal_eval(column_type)\n", + " while index < len(df_test) and column_mol.GetNumAtoms() == column_mol.GetNumBonds():\n", + " index += 1\n", + " column_mol = make_mol(df_test.iloc[index][target_cols[0]])\n", + " column_type = ast.literal_eval(df_test.iloc[index][target_cols[i]])\n", + " atom_cols.append(i) if len(column_type) == column_mol.GetNumAtoms() else bond_cols.append(i)" + ] + }, + { + "cell_type": "code", + "execution_count": 117, + "metadata": {}, + "outputs": [], + "source": [ + "df_test.iloc[:, mol_cols] = mol_average_preds.tolist()\n", + "\n", + "for i in range(len(df_test)):\n", + " if atom_slices is not None:\n", + " first_atom = atom_slices.index(i)\n", + " last_atom = first_atom + atom_slices.count(i)\n", + " atom_preds = atom_average_preds[first_atom:last_atom]\n", + " df_test.iloc[i, atom_cols] = [str(atom_preds[:,j].tolist()) for j in range(len(atom_cols))]\n", + "\n", + " if bond_slices is not None:\n", + " first_bond = bond_slices.index(i)\n", + " last_bond = first_bond + bond_slices.count(i)\n", + " bond_preds = bond_average_preds[first_atom:last_atom]\n", + " df_test.iloc[i, bond_cols] = [str(bond_preds[:,j].tolist()) for j in range(len(bond_cols))]" + ] + }, + { + "cell_type": "code", + "execution_count": 118, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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0CC5.342140[2.569624900817871, 2.569624900817871]
1CCC5.233238[2.4788389205932617, 2.5985231399536133, 2.478...
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" + ], + "text/plain": [ + " smiles molecule atom\n", + "0 CC 5.342140 [2.569624900817871, 2.569624900817871]\n", + "1 CCC 5.233238 [2.4788389205932617, 2.5985231399536133, 2.478...\n", + "2 CCCO 6.901553 [2.4710915088653564, 2.505635976791382, 2.7169...\n", + "3 CCOO 6.782914 [2.4493448734283447, 2.6188316345214844, 3.430...\n", + "4 COO 6.481140 [2.5182647705078125, 3.493091344833374, 3.4651...\n", + "5 CCOOO 6.913412 [2.451606273651123, 2.626923084259033, 3.22619...\n", + "6 COOO 6.779137 [2.5419275760650635, 3.3292524814605713, 3.465...\n", + "7 CO 6.977914 [2.758082389831543, 3.42410945892334]\n", + "8 CCO 6.894196 [2.4541964530944824, 2.7911367416381836, 3.389...\n", + "9 OO 7.735260 [3.6392979621887207, 3.6392979621887207]" + ] + }, + "execution_count": 118, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "output_path = chemprop_dir / \"tests\" / \"data\" / \"mixed_regression_output.csv\"\n", + "if output_path.suffix == \".pkl\":\n", + " df_test = df_test.reset_index(drop=True)\n", + " df_test.to_pickle(output_path)\n", + "else:\n", + " df_test.to_csv(output_path, index=False)\n", + "\n", + "df_test" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.9" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples/predicting_regression_multicomponent.ipynb b/examples/predicting_regression_multicomponent.ipynb index 15be04f71..d71472be3 100644 --- a/examples/predicting_regression_multicomponent.ipynb +++ b/examples/predicting_regression_multicomponent.ipynb @@ -1,588 +1,587 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Predicting Regression - Multicomponent" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Import packages" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "import torch\n", - "from lightning import pytorch as pl\n", - "from pathlib import Path\n", - "\n", - "from chemprop import data, featurizers\n", - "from chemprop.models import multi" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Change model input here" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "chemprop_dir = Path.cwd().parent\n", - "checkpoint_path = chemprop_dir / \"tests\" / \"data\" / \"example_model_v2_regression_mol+mol.ckpt\" # path to the checkpoint file. \n", - "# If the checkpoint file is generated using the training notebook, it will be in the `checkpoints` folder with name similar to `checkpoints/epoch=19-step=180.ckpt`." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Load model" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/knathan/chemprop/chemprop/models/multi.py:56: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", - " d = torch.load(path, map_location)\n", - "/home/knathan/chemprop/chemprop/models/model.py:286: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", - " d = torch.load(checkpoint_path, map_location)\n" - ] - }, - { - "data": { - "text/plain": [ - "MulticomponentMPNN(\n", - " (message_passing): MulticomponentMessagePassing(\n", - " (blocks): ModuleList(\n", - " (0-1): 2 x BondMessagePassing(\n", - " (W_i): Linear(in_features=86, out_features=300, bias=False)\n", - " (W_h): Linear(in_features=300, out_features=300, bias=False)\n", - " (W_o): Linear(in_features=372, out_features=300, bias=True)\n", - " (dropout): Dropout(p=0.0, inplace=False)\n", - " (tau): ReLU()\n", - " (V_d_transform): Identity()\n", - " (graph_transform): GraphTransform(\n", - " (V_transform): Identity()\n", - " (E_transform): Identity()\n", - " )\n", - " )\n", - " )\n", - " )\n", - " (agg): MeanAggregation()\n", - " (bn): BatchNorm1d(600, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " (predictor): RegressionFFN(\n", - " (ffn): MLP(\n", - " (0): Sequential(\n", - " (0): Linear(in_features=600, out_features=300, bias=True)\n", - " )\n", - " (1): Sequential(\n", - " (0): ReLU()\n", - " (1): Dropout(p=0.0, inplace=False)\n", - " (2): Linear(in_features=300, out_features=1, bias=True)\n", - " )\n", - " )\n", - " (criterion): MSE(task_weights=[[1.0]])\n", - " (output_transform): UnscaleTransform()\n", - " )\n", - " (X_d_transform): Identity()\n", - " (metrics): ModuleList(\n", - " (0-1): 2 x MSE(task_weights=[[1.0]])\n", - " )\n", - ")" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "mcmpnn = multi.MulticomponentMPNN.load_from_checkpoint(checkpoint_path)\n", - "mcmpnn" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Change predict input here" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "chemprop_dir = Path.cwd().parent\n", - "test_path = chemprop_dir / \"tests\" / \"data\" / \"regression\" / \"mol+mol\" / \"mol+mol.csv\" # path to your .csv file containing SMILES strings to make predictions for\n", - "smiles_columns = ['smiles', 'solvent'] # name of the column containing SMILES strings" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Load test smiles" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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............
95COc1ccc(C2CC(c3ccc(O)cc3)=NN2c2ccc(S(N)(=O)=O)...C1CCOC1359.0
96COc1ccc2c3c(c4ccc(OC)cc4c2c1)C1(c2ccccc2-c2ccc...C1CCCCC1386.0
97CCCCOc1c(C=C2N(C)c3ccccc3C2(C)C)c(=O)c1=OCCO425.0
98Cc1cc2ccc(-c3cccc4cccc(-c5ccc6cc(C)c(=O)oc6c5)...c1ccccc1324.0
99Cc1ccc(C(=O)c2c(C)c3ccc4cccc5c6cccc7ccc2c(c76)...ClCCl391.0
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100 rows × 3 columns

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" - ], - "text/plain": [ - " smiles solvent peakwavs_max\n", - "0 CCCCN1C(=O)C(=C/C=C/C=C/C=C2N(CCCC)c3ccccc3N2C... ClCCl 642.0\n", - "1 C(=C/c1cnccn1)\\c1ccc(N(c2ccccc2)c2ccc(/C=C/c3c... ClCCl 420.0\n", - "2 CN(C)c1ccc2c(-c3ccc(N)cc3C(=O)[O-])c3ccc(=[N+]... O 544.0\n", - "3 c1ccc2[nH]ccc2c1 O 290.0\n", - "4 CCN(CC)c1ccc2c(c1)OC1=C(/C=C/C3=[N+](C)c4ccc5c... ClC(Cl)Cl 736.0\n", - ".. ... ... ...\n", - "95 COc1ccc(C2CC(c3ccc(O)cc3)=NN2c2ccc(S(N)(=O)=O)... C1CCOC1 359.0\n", - "96 COc1ccc2c3c(c4ccc(OC)cc4c2c1)C1(c2ccccc2-c2ccc... C1CCCCC1 386.0\n", - "97 CCCCOc1c(C=C2N(C)c3ccccc3C2(C)C)c(=O)c1=O CCO 425.0\n", - "98 Cc1cc2ccc(-c3cccc4cccc(-c5ccc6cc(C)c(=O)oc6c5)... c1ccccc1 324.0\n", - "99 Cc1ccc(C(=O)c2c(C)c3ccc4cccc5c6cccc7ccc2c(c76)... ClCCl 391.0\n", - "\n", - "[100 rows x 3 columns]" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_test = pd.read_csv(test_path)\n", - "df_test" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Get smiles" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([['CCCCN1C(=O)C(=C/C=C/C=C/C=C2N(CCCC)c3ccccc3N2CCCC)C(=O)N(CCCC)C1=S',\n", - " 'ClCCl'],\n", - " ['C(=C/c1cnccn1)\\\\c1ccc(N(c2ccccc2)c2ccc(/C=C/c3cnccn3)cc2)cc1',\n", - " 'ClCCl'],\n", - " ['CN(C)c1ccc2c(-c3ccc(N)cc3C(=O)[O-])c3ccc(=[N+](C)C)cc-3oc2c1',\n", - " 'O'],\n", - " ['c1ccc2[nH]ccc2c1', 'O'],\n", - " ['CCN(CC)c1ccc2c(c1)OC1=C(/C=C/C3=[N+](C)c4ccc5ccccc5c4C3(C)C)CCCC1=C2c1ccccc1C(=O)O',\n", - " 'ClC(Cl)Cl']], dtype=object)" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "smiss = df_test[smiles_columns].values\n", - "smiss[:5]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Get molecule datapoints" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "n_componenets = len(smiles_columns)\n", - "test_datapointss = [[data.MoleculeDatapoint.from_smi(smi) for smi in smiss[:, i]] for i in range(n_componenets)]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Get molecule datasets" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "featurizer = featurizers.SimpleMoleculeMolGraphFeaturizer()\n", - "test_dsets = [data.MoleculeDataset(test_datapoints, featurizer) for test_datapoints in test_datapointss]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Get multicomponent dataset and data loader" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "test_mcdset = data.MulticomponentDataset(test_dsets)\n", - "test_loader = data.build_dataloader(test_mcdset, shuffle=False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Set up trainer" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "GPU available: False, used: False\n", - "TPU available: False, using: 0 TPU cores\n", - "HPU available: False, using: 0 HPUs\n", - "/home/knathan/anaconda3/envs/chemprop/lib/python3.11/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:424: The 'predict_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=11` in the `DataLoader` to improve performance.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Predicting DataLoader 0: 100%|██████████| 2/2 [00:01<00:00, 1.58it/s]\n" - ] - } - ], - "source": [ - "with torch.inference_mode():\n", - " trainer = pl.Trainer(\n", - " logger=None,\n", - " enable_progress_bar=True,\n", - " accelerator=\"auto\",\n", - " devices=1\n", - " )\n", - " test_preds = trainer.predict(mcmpnn, test_loader)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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smilessolventpeakwavs_maxpred
0CCCCN1C(=O)C(=C/C=C/C=C/C=C2N(CCCC)c3ccccc3N2C...ClCCl642.0454.898621
1C(=C/c1cnccn1)\\c1ccc(N(c2ccccc2)c2ccc(/C=C/c3c...ClCCl420.0453.561584
2CN(C)c1ccc2c(-c3ccc(N)cc3C(=O)[O-])c3ccc(=[N+]...O544.0448.694977
3c1ccc2[nH]ccc2c1O290.0448.159760
4CCN(CC)c1ccc2c(c1)OC1=C(/C=C/C3=[N+](C)c4ccc5c...ClC(Cl)Cl736.0456.897003
...............
95COc1ccc(C2CC(c3ccc(O)cc3)=NN2c2ccc(S(N)(=O)=O)...C1CCOC1359.0454.548584
96COc1ccc2c3c(c4ccc(OC)cc4c2c1)C1(c2ccccc2-c2ccc...C1CCCCC1386.0455.287140
97CCCCOc1c(C=C2N(C)c3ccccc3C2(C)C)c(=O)c1=OCCO425.0453.560364
98Cc1cc2ccc(-c3cccc4cccc(-c5ccc6cc(C)c(=O)oc6c5)...c1ccccc1324.0454.656891
99Cc1ccc(C(=O)c2c(C)c3ccc4cccc5c6cccc7ccc2c(c76)...ClCCl391.0453.118774
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" - ], - "text/plain": [ - " smiles solvent \\\n", - "0 CCCCN1C(=O)C(=C/C=C/C=C/C=C2N(CCCC)c3ccccc3N2C... ClCCl \n", - "1 C(=C/c1cnccn1)\\c1ccc(N(c2ccccc2)c2ccc(/C=C/c3c... ClCCl \n", - "2 CN(C)c1ccc2c(-c3ccc(N)cc3C(=O)[O-])c3ccc(=[N+]... O \n", - "3 c1ccc2[nH]ccc2c1 O \n", - "4 CCN(CC)c1ccc2c(c1)OC1=C(/C=C/C3=[N+](C)c4ccc5c... ClC(Cl)Cl \n", - ".. ... ... \n", - "95 COc1ccc(C2CC(c3ccc(O)cc3)=NN2c2ccc(S(N)(=O)=O)... C1CCOC1 \n", - "96 COc1ccc2c3c(c4ccc(OC)cc4c2c1)C1(c2ccccc2-c2ccc... C1CCCCC1 \n", - "97 CCCCOc1c(C=C2N(C)c3ccccc3C2(C)C)c(=O)c1=O CCO \n", - "98 Cc1cc2ccc(-c3cccc4cccc(-c5ccc6cc(C)c(=O)oc6c5)... c1ccccc1 \n", - "99 Cc1ccc(C(=O)c2c(C)c3ccc4cccc5c6cccc7ccc2c(c76)... ClCCl \n", - "\n", - " peakwavs_max pred \n", - "0 642.0 454.898621 \n", - "1 420.0 453.561584 \n", - "2 544.0 448.694977 \n", - "3 290.0 448.159760 \n", - "4 736.0 456.897003 \n", - ".. ... ... \n", - "95 359.0 454.548584 \n", - "96 386.0 455.287140 \n", - "97 425.0 453.560364 \n", - "98 324.0 454.656891 \n", - "99 391.0 453.118774 \n", - "\n", - "[100 rows x 4 columns]" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "test_preds = np.concatenate(test_preds, axis=0)\n", - "df_test['pred'] = test_preds\n", - "df_test" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "chemprop", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.8" - }, - "orig_nbformat": 4 + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Predicting Regression - Multicomponent" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Import packages" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import torch\n", + "from lightning import pytorch as pl\n", + "from pathlib import Path\n", + "\n", + "from chemprop import data, featurizers\n", + "from chemprop.models import multi" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Change model input here" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "chemprop_dir = Path.cwd().parent\n", + "checkpoint_path = chemprop_dir / \"tests\" / \"data\" / \"example_model_v2_regression_mol+mol.ckpt\" # path to the checkpoint file. \n", + "# If the checkpoint file is generated using the training notebook, it will be in the `checkpoints` folder with name similar to `checkpoints/epoch=19-step=180.ckpt`." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load model" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/knathan/chemprop/chemprop/models/multi.py:56: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", + " d = torch.load(path, map_location)\n", + "/home/knathan/chemprop/chemprop/models/model.py:286: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", + " d = torch.load(checkpoint_path, map_location)\n" + ] }, - "nbformat": 4, - "nbformat_minor": 2 + { + "data": { + "text/plain": [ + "MulticomponentMPNN(\n", + " (message_passing): MulticomponentMessagePassing(\n", + " (blocks): ModuleList(\n", + " (0-1): 2 x BondMessagePassing(\n", + " (W_i): Linear(in_features=86, out_features=300, bias=False)\n", + " (W_h): Linear(in_features=300, out_features=300, bias=False)\n", + " (W_o): Linear(in_features=372, out_features=300, bias=True)\n", + " (dropout): Dropout(p=0.0, inplace=False)\n", + " (tau): ReLU()\n", + " (V_d_transform): Identity()\n", + " (graph_transform): GraphTransform(\n", + " (V_transform): Identity()\n", + " (E_transform): Identity()\n", + " )\n", + " )\n", + " )\n", + " )\n", + " (agg): MeanAggregation()\n", + " (bn): BatchNorm1d(600, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (predictor): RegressionFFN(\n", + " (ffn): MLP(\n", + " (0): Sequential(\n", + " (0): Linear(in_features=600, out_features=300, bias=True)\n", + " )\n", + " (1): Sequential(\n", + " (0): ReLU()\n", + " (1): Dropout(p=0.0, inplace=False)\n", + " (2): Linear(in_features=300, out_features=1, bias=True)\n", + " )\n", + " )\n", + " (criterion): MSE(task_weights=[[1.0]])\n", + " (output_transform): UnscaleTransform()\n", + " )\n", + " (X_d_transform): Identity()\n", + " (metrics): ModuleList(\n", + " (0-1): 2 x MSE(task_weights=[[1.0]])\n", + " )\n", + ")" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mcmpnn = multi.MulticomponentMPNN.load_from_checkpoint(checkpoint_path)\n", + "mcmpnn" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Change predict input here" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "chemprop_dir = Path.cwd().parent\n", + "test_path = chemprop_dir / \"tests\" / \"data\" / \"regression\" / \"mol+mol\" / \"mol+mol.csv\" # path to your .csv file containing SMILES strings to make predictions for\n", + "smiles_columns = ['smiles', 'solvent'] # name of the column containing SMILES strings" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load test smiles" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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smilessolventpeakwavs_max
0CCCCN1C(=O)C(=C/C=C/C=C/C=C2N(CCCC)c3ccccc3N2C...ClCCl642.0
1C(=C/c1cnccn1)\\c1ccc(N(c2ccccc2)c2ccc(/C=C/c3c...ClCCl420.0
2CN(C)c1ccc2c(-c3ccc(N)cc3C(=O)[O-])c3ccc(=[N+]...O544.0
3c1ccc2[nH]ccc2c1O290.0
4CCN(CC)c1ccc2c(c1)OC1=C(/C=C/C3=[N+](C)c4ccc5c...ClC(Cl)Cl736.0
............
95COc1ccc(C2CC(c3ccc(O)cc3)=NN2c2ccc(S(N)(=O)=O)...C1CCOC1359.0
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99Cc1ccc(C(=O)c2c(C)c3ccc4cccc5c6cccc7ccc2c(c76)...ClCCl391.0
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100 rows × 3 columns

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" + ], + "text/plain": [ + " smiles solvent peakwavs_max\n", + "0 CCCCN1C(=O)C(=C/C=C/C=C/C=C2N(CCCC)c3ccccc3N2C... ClCCl 642.0\n", + "1 C(=C/c1cnccn1)\\c1ccc(N(c2ccccc2)c2ccc(/C=C/c3c... ClCCl 420.0\n", + "2 CN(C)c1ccc2c(-c3ccc(N)cc3C(=O)[O-])c3ccc(=[N+]... O 544.0\n", + "3 c1ccc2[nH]ccc2c1 O 290.0\n", + "4 CCN(CC)c1ccc2c(c1)OC1=C(/C=C/C3=[N+](C)c4ccc5c... ClC(Cl)Cl 736.0\n", + ".. ... ... ...\n", + "95 COc1ccc(C2CC(c3ccc(O)cc3)=NN2c2ccc(S(N)(=O)=O)... C1CCOC1 359.0\n", + "96 COc1ccc2c3c(c4ccc(OC)cc4c2c1)C1(c2ccccc2-c2ccc... C1CCCCC1 386.0\n", + "97 CCCCOc1c(C=C2N(C)c3ccccc3C2(C)C)c(=O)c1=O CCO 425.0\n", + "98 Cc1cc2ccc(-c3cccc4cccc(-c5ccc6cc(C)c(=O)oc6c5)... c1ccccc1 324.0\n", + "99 Cc1ccc(C(=O)c2c(C)c3ccc4cccc5c6cccc7ccc2c(c76)... ClCCl 391.0\n", + "\n", + "[100 rows x 3 columns]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_test = pd.read_csv(test_path)\n", + "df_test" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Get smiles" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([['CCCCN1C(=O)C(=C/C=C/C=C/C=C2N(CCCC)c3ccccc3N2CCCC)C(=O)N(CCCC)C1=S',\n", + " 'ClCCl'],\n", + " ['C(=C/c1cnccn1)\\\\c1ccc(N(c2ccccc2)c2ccc(/C=C/c3cnccn3)cc2)cc1',\n", + " 'ClCCl'],\n", + " ['CN(C)c1ccc2c(-c3ccc(N)cc3C(=O)[O-])c3ccc(=[N+](C)C)cc-3oc2c1',\n", + " 'O'],\n", + " ['c1ccc2[nH]ccc2c1', 'O'],\n", + " ['CCN(CC)c1ccc2c(c1)OC1=C(/C=C/C3=[N+](C)c4ccc5ccccc5c4C3(C)C)CCCC1=C2c1ccccc1C(=O)O',\n", + " 'ClC(Cl)Cl']], dtype=object)" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "smiss = df_test[smiles_columns].values\n", + "smiss[:5]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Get molecule datapoints" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "n_componenets = len(smiles_columns)\n", + "test_datapointss = [[data.MoleculeDatapoint.from_smi(smi) for smi in smiss[:, i]] for i in range(n_componenets)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Get molecule datasets" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "featurizer = featurizers.SimpleMoleculeMolGraphFeaturizer()\n", + "test_dsets = [data.MoleculeDataset(test_datapoints, featurizer) for test_datapoints in test_datapointss]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Get multicomponent dataset and data loader" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "test_mcdset = data.MulticomponentDataset(test_dsets)\n", + "test_loader = data.build_dataloader(test_mcdset, shuffle=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Set up trainer" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "GPU available: False, used: False\n", + "TPU available: False, using: 0 TPU cores\n", + "HPU available: False, using: 0 HPUs\n", + "/home/knathan/anaconda3/envs/chemprop/lib/python3.11/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:424: The 'predict_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=11` in the `DataLoader` to improve performance.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Predicting DataLoader 0: 100%|██████████| 2/2 [00:01<00:00, 1.58it/s]\n" + ] + } + ], + "source": [ + "with torch.inference_mode():\n", + " trainer = pl.Trainer(\n", + " logger=None,\n", + " enable_progress_bar=True,\n", + " accelerator=\"auto\",\n", + " devices=1\n", + " )\n", + " test_preds = trainer.predict(mcmpnn, test_loader)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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smilessolventpeakwavs_maxpred
0CCCCN1C(=O)C(=C/C=C/C=C/C=C2N(CCCC)c3ccccc3N2C...ClCCl642.0454.898621
1C(=C/c1cnccn1)\\c1ccc(N(c2ccccc2)c2ccc(/C=C/c3c...ClCCl420.0453.561584
2CN(C)c1ccc2c(-c3ccc(N)cc3C(=O)[O-])c3ccc(=[N+]...O544.0448.694977
3c1ccc2[nH]ccc2c1O290.0448.159760
4CCN(CC)c1ccc2c(c1)OC1=C(/C=C/C3=[N+](C)c4ccc5c...ClC(Cl)Cl736.0456.897003
...............
95COc1ccc(C2CC(c3ccc(O)cc3)=NN2c2ccc(S(N)(=O)=O)...C1CCOC1359.0454.548584
96COc1ccc2c3c(c4ccc(OC)cc4c2c1)C1(c2ccccc2-c2ccc...C1CCCCC1386.0455.287140
97CCCCOc1c(C=C2N(C)c3ccccc3C2(C)C)c(=O)c1=OCCO425.0453.560364
98Cc1cc2ccc(-c3cccc4cccc(-c5ccc6cc(C)c(=O)oc6c5)...c1ccccc1324.0454.656891
99Cc1ccc(C(=O)c2c(C)c3ccc4cccc5c6cccc7ccc2c(c76)...ClCCl391.0453.118774
\n", + "

100 rows × 4 columns

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" + ], + "text/plain": [ + " smiles solvent \\\n", + "0 CCCCN1C(=O)C(=C/C=C/C=C/C=C2N(CCCC)c3ccccc3N2C... ClCCl \n", + "1 C(=C/c1cnccn1)\\c1ccc(N(c2ccccc2)c2ccc(/C=C/c3c... ClCCl \n", + "2 CN(C)c1ccc2c(-c3ccc(N)cc3C(=O)[O-])c3ccc(=[N+]... O \n", + "3 c1ccc2[nH]ccc2c1 O \n", + "4 CCN(CC)c1ccc2c(c1)OC1=C(/C=C/C3=[N+](C)c4ccc5c... ClC(Cl)Cl \n", + ".. ... ... \n", + "95 COc1ccc(C2CC(c3ccc(O)cc3)=NN2c2ccc(S(N)(=O)=O)... C1CCOC1 \n", + "96 COc1ccc2c3c(c4ccc(OC)cc4c2c1)C1(c2ccccc2-c2ccc... C1CCCCC1 \n", + "97 CCCCOc1c(C=C2N(C)c3ccccc3C2(C)C)c(=O)c1=O CCO \n", + "98 Cc1cc2ccc(-c3cccc4cccc(-c5ccc6cc(C)c(=O)oc6c5)... c1ccccc1 \n", + "99 Cc1ccc(C(=O)c2c(C)c3ccc4cccc5c6cccc7ccc2c(c76)... ClCCl \n", + "\n", + " peakwavs_max pred \n", + "0 642.0 454.898621 \n", + "1 420.0 453.561584 \n", + "2 544.0 448.694977 \n", + "3 290.0 448.159760 \n", + "4 736.0 456.897003 \n", + ".. ... ... \n", + "95 359.0 454.548584 \n", + "96 386.0 455.287140 \n", + "97 425.0 453.560364 \n", + "98 324.0 454.656891 \n", + "99 391.0 453.118774 \n", + "\n", + "[100 rows x 4 columns]" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "test_preds = np.concatenate(test_preds, axis=0)\n", + "df_test['pred'] = test_preds\n", + "df_test" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.9" + } + }, + "nbformat": 4, + "nbformat_minor": 4 } diff --git a/examples/training.ipynb b/examples/training.ipynb index b7925260f..91d379224 100644 --- a/examples/training.ipynb +++ b/examples/training.ipynb @@ -1,861 +1,860 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Training" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Import packages" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "from pathlib import Path\n", - "\n", - "from lightning import pytorch as pl\n", - "from lightning.pytorch.callbacks import ModelCheckpoint\n", - "import pandas as pd\n", - "\n", - "from chemprop import data, featurizers, models, nn" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Change data inputs here" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "chemprop_dir = Path.cwd().parent\n", - "input_path = chemprop_dir / \"tests\" / \"data\" / \"regression\" / \"mol\" / \"mol.csv\" # path to your data .csv file\n", - "num_workers = 0 # number of workers for dataloader. 0 means using main process for data loading\n", - "smiles_column = 'smiles' # name of the column containing SMILES strings\n", - "target_columns = ['lipo'] # list of names of the columns containing targets" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Load data" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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smileslipo
0Cn1c(CN2CCN(CC2)c3ccc(Cl)cc3)nc4ccccc143.54
1COc1cc(OC)c(cc1NC(=O)CSCC(=O)O)S(=O)(=O)N2C(C)...-1.18
2COC(=O)[C@@H](N1CCc2sccc2C1)c3ccccc3Cl3.69
3OC[C@H](O)CN1C(=O)C(Cc2ccccc12)NC(=O)c3cc4cc(C...3.37
4Cc1cccc(C[C@H](NC(=O)c2cc(nn2C)C(C)(C)C)C(=O)N...3.10
.........
95CC(C)N(CCCNC(=O)Nc1ccc(cc1)C(C)(C)C)C[C@H]2O[C...2.20
96CCN(CC)CCCCNc1ncc2CN(C(=O)N(Cc3cccc(NC(=O)C=C)...2.04
97CCSc1c(Cc2ccccc2C(F)(F)F)sc3N(CC(C)C)C(=O)N(C)...4.49
98COc1ccc(Cc2c(N)n[nH]c2N)cc10.20
99CCN(CCN(C)C)S(=O)(=O)c1ccc(cc1)c2cnc(N)c(n2)C(...2.00
\n", - "

100 rows × 2 columns

\n", - "
" - ], - "text/plain": [ - " smiles lipo\n", - "0 Cn1c(CN2CCN(CC2)c3ccc(Cl)cc3)nc4ccccc14 3.54\n", - "1 COc1cc(OC)c(cc1NC(=O)CSCC(=O)O)S(=O)(=O)N2C(C)... -1.18\n", - "2 COC(=O)[C@@H](N1CCc2sccc2C1)c3ccccc3Cl 3.69\n", - "3 OC[C@H](O)CN1C(=O)C(Cc2ccccc12)NC(=O)c3cc4cc(C... 3.37\n", - "4 Cc1cccc(C[C@H](NC(=O)c2cc(nn2C)C(C)(C)C)C(=O)N... 3.10\n", - ".. ... ...\n", - "95 CC(C)N(CCCNC(=O)Nc1ccc(cc1)C(C)(C)C)C[C@H]2O[C... 2.20\n", - "96 CCN(CC)CCCCNc1ncc2CN(C(=O)N(Cc3cccc(NC(=O)C=C)... 2.04\n", - "97 CCSc1c(Cc2ccccc2C(F)(F)F)sc3N(CC(C)C)C(=O)N(C)... 4.49\n", - "98 COc1ccc(Cc2c(N)n[nH]c2N)cc1 0.20\n", - "99 CCN(CCN(C)C)S(=O)(=O)c1ccc(cc1)c2cnc(N)c(n2)C(... 2.00\n", - "\n", - "[100 rows x 2 columns]" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_input = pd.read_csv(input_path)\n", - "df_input" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Get SMILES and targets" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "smis = df_input.loc[:, smiles_column].values\n", - "ys = df_input.loc[:, target_columns].values" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array(['Cn1c(CN2CCN(CC2)c3ccc(Cl)cc3)nc4ccccc14',\n", - " 'COc1cc(OC)c(cc1NC(=O)CSCC(=O)O)S(=O)(=O)N2C(C)CCc3ccccc23',\n", - " 'COC(=O)[C@@H](N1CCc2sccc2C1)c3ccccc3Cl',\n", - " 'OC[C@H](O)CN1C(=O)C(Cc2ccccc12)NC(=O)c3cc4cc(Cl)sc4[nH]3',\n", - " 'Cc1cccc(C[C@H](NC(=O)c2cc(nn2C)C(C)(C)C)C(=O)NCC#N)c1'],\n", - " dtype=object)" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "smis[:5] # show first 5 SMILES strings" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 3.54],\n", - " [-1.18],\n", - " [ 3.69],\n", - " [ 3.37],\n", - " [ 3.1 ]])" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ys[:5] # show first 5 targets" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Get molecule datapoints" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "all_data = [data.MoleculeDatapoint.from_smi(smi, y) for smi, y in zip(smis, ys)]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Perform data splitting for training, validation, and testing" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['SCAFFOLD_BALANCED',\n", - " 'RANDOM_WITH_REPEATED_SMILES',\n", - " 'RANDOM',\n", - " 'KENNARD_STONE',\n", - " 'KMEANS']" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# available split types\n", - "list(data.SplitType.keys())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "ChemProp's `make_split_indices` function will always return a two- (if no validation) or three-length tuple.\n", - "Each member is a list of length `num_replicates`.\n", - "The inner lists then contain the actual indices for splitting.\n", - "\n", - "The type signature for this return type is `tuple[list[list[int]], ...]`." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "mols = [d.mol for d in all_data] # RDkit Mol objects are use for structure based splits\n", - "train_indices, val_indices, test_indices = data.make_split_indices(mols, \"random\", (0.8, 0.1, 0.1)) # unpack the tuple into three separate lists\n", - "train_data, val_data, test_data = data.split_data_by_indices(\n", - " all_data, train_indices, val_indices, test_indices\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "ChemProp's splitting function implements our preferred method of data splitting, which is random replication.\n", - "It's also possible to add your own custom cross-validation splitter, such as one of those as implemented in scikit-learn, as long as you get the data into the same `tuple[list[list[int]], ...]` data format with something like this:" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.model_selection import KFold\n", - "\n", - "k_splits = KFold(n_splits=5)\n", - "k_train_indices, k_val_indices, k_test_indices = [], [], []\n", - "for fold in k_splits.split(mols):\n", - " k_train_indices.append(fold[0])\n", - " k_val_indices.append([])\n", - " k_test_indices.append(fold[1])\n", - "k_train_data, _, k_test_data = data.split_data_by_indices(\n", - " all_data, k_train_indices, None, k_test_indices\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Get MoleculeDataset\n", - "Recall that the data is in a list equal in length to the number of replicates, so we select the zero index of the list to get the first replicate." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "featurizer = featurizers.SimpleMoleculeMolGraphFeaturizer()\n", - "\n", - "train_dset = data.MoleculeDataset(train_data[0], featurizer)\n", - "scaler = train_dset.normalize_targets()\n", - "\n", - "val_dset = data.MoleculeDataset(val_data[0], featurizer)\n", - "val_dset.normalize_targets(scaler)\n", - "\n", - "test_dset = data.MoleculeDataset(test_data[0], featurizer)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Get DataLoader" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "train_loader = data.build_dataloader(train_dset, num_workers=num_workers)\n", - "val_loader = data.build_dataloader(val_dset, num_workers=num_workers, shuffle=False)\n", - "test_loader = data.build_dataloader(test_dset, num_workers=num_workers, shuffle=False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Change Message-Passing Neural Network (MPNN) inputs here" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Message Passing\n", - "A `Message passing` constructs molecular graphs using message passing to learn node-level hidden representations.\n", - "\n", - "Options are `mp = nn.BondMessagePassing()` or `mp = nn.AtomMessagePassing()`" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "mp = nn.BondMessagePassing()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Aggregation\n", - "An `Aggregation` is responsible for constructing a graph-level representation from the set of node-level representations after message passing.\n", - "\n", - "Available options can be found in ` nn.agg.AggregationRegistry`, including\n", - "- `agg = nn.MeanAggregation()`\n", - "- `agg = nn.SumAggregation()`\n", - "- `agg = nn.NormAggregation()`" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ClassRegistry {\n", - " 'mean': ,\n", - " 'sum': ,\n", - " 'norm': \n", - "}\n" - ] - } - ], - "source": [ - "print(nn.agg.AggregationRegistry)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "agg = nn.MeanAggregation()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Feed-Forward Network (FFN)\n", - "\n", - "A `FFN` takes the aggregated representations and make target predictions.\n", - "\n", - "Available options can be found in `nn.PredictorRegistry`.\n", - "\n", - "For regression:\n", - "- `ffn = nn.RegressionFFN()`\n", - "- `ffn = nn.MveFFN()`\n", - "- `ffn = nn.EvidentialFFN()`\n", - "\n", - "For classification:\n", - "- `ffn = nn.BinaryClassificationFFN()`\n", - "- `ffn = nn.BinaryDirichletFFN()`\n", - "- `ffn = nn.MulticlassClassificationFFN()`\n", - "- `ffn = nn.MulticlassDirichletFFN()`\n", - "\n", - "For spectral:\n", - "- `ffn = nn.SpectralFFN()` # will be available in future version" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ClassRegistry {\n", - " 'regression': ,\n", - " 'regression-mve': ,\n", - " 'regression-evidential': ,\n", - " 'regression-quantile': ,\n", - " 'classification': ,\n", - " 'classification-dirichlet': ,\n", - " 'multiclass': ,\n", - " 'multiclass-dirichlet': ,\n", - " 'spectral': \n", - "}\n" - ] - } - ], - "source": [ - "print(nn.PredictorRegistry)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "output_transform = nn.UnscaleTransform.from_standard_scaler(scaler)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "ffn = nn.RegressionFFN(output_transform=output_transform)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Batch Norm\n", - "A `Batch Norm` normalizes the outputs of the aggregation by re-centering and re-scaling.\n", - "\n", - "Whether to use batch norm" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "batch_norm = True" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Metrics\n", - "`Metrics` are the ways to evaluate the performance of model predictions.\n", - "\n", - "Available options can be found in `metrics.MetricRegistry`, including" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ClassRegistry {\n", - " 'mse': ,\n", - " 'mae': ,\n", - " 'rmse': ,\n", - " 'bounded-mse': ,\n", - " 'bounded-mae': ,\n", - " 'bounded-rmse': ,\n", - " 'r2': ,\n", - " 'binary-mcc': ,\n", - " 'multiclass-mcc': ,\n", - " 'roc': ,\n", - " 'prc': ,\n", - " 'accuracy': ,\n", - " 'f1': \n", - "}\n" - ] - } - ], - "source": [ - "print(nn.metrics.MetricRegistry)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "metric_list = [nn.metrics.RMSE(), nn.metrics.MAE()] # Only the first metric is used for training and early stopping" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Constructs MPNN" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "MPNN(\n", - " (message_passing): BondMessagePassing(\n", - " (W_i): Linear(in_features=86, out_features=300, bias=False)\n", - " (W_h): Linear(in_features=300, out_features=300, bias=False)\n", - " (W_o): Linear(in_features=372, out_features=300, bias=True)\n", - " (dropout): Dropout(p=0.0, inplace=False)\n", - " (tau): ReLU()\n", - " (V_d_transform): Identity()\n", - " (graph_transform): Identity()\n", - " )\n", - " (agg): MeanAggregation()\n", - " (bn): BatchNorm1d(300, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " (predictor): RegressionFFN(\n", - " (ffn): MLP(\n", - " (0): Sequential(\n", - " (0): Linear(in_features=300, out_features=300, bias=True)\n", - " )\n", - " (1): Sequential(\n", - " (0): ReLU()\n", - " (1): Dropout(p=0.0, inplace=False)\n", - " (2): Linear(in_features=300, out_features=1, bias=True)\n", - " )\n", - " )\n", - " (criterion): MSE(task_weights=[[1.0]])\n", - " (output_transform): UnscaleTransform()\n", - " )\n", - " (X_d_transform): Identity()\n", - " (metrics): ModuleList(\n", - " (0): RMSE(task_weights=[[1.0]])\n", - " (1): MAE(task_weights=[[1.0]])\n", - " (2): MSE(task_weights=[[1.0]])\n", - " )\n", - ")" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "mpnn = models.MPNN(mp, agg, ffn, batch_norm, metric_list)\n", - "mpnn" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Set up trainer" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "GPU available: False, used: False\n", - "TPU available: False, using: 0 TPU cores\n", - "HPU available: False, using: 0 HPUs\n" - ] - } - ], - "source": [ - "# Configure model checkpointing\n", - "checkpointing = ModelCheckpoint(\n", - " \"checkpoints\", # Directory where model checkpoints will be saved\n", - " \"best-{epoch}-{val_loss:.2f}\", # Filename format for checkpoints, including epoch and validation loss\n", - " \"val_loss\", # Metric used to select the best checkpoint (based on validation loss)\n", - " mode=\"min\", # Save the checkpoint with the lowest validation loss (minimization objective)\n", - " save_last=True, # Always save the most recent checkpoint, even if it's not the best\n", - ")\n", - "\n", - "\n", - "trainer = pl.Trainer(\n", - " logger=False,\n", - " enable_checkpointing=True, # Use `True` if you want to save model checkpoints. The checkpoints will be saved in the `checkpoints` folder.\n", - " enable_progress_bar=True,\n", - " accelerator=\"auto\",\n", - " devices=1,\n", - " max_epochs=20, # number of epochs to train for\n", - " callbacks=[checkpointing], # Use the configured checkpoint callback\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Start training" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/knathan/anaconda3/envs/chemprop/lib/python3.11/site-packages/lightning/pytorch/callbacks/model_checkpoint.py:654: Checkpoint directory /home/knathan/chemprop/examples/checkpoints exists and is not empty.\n", - "Loading `train_dataloader` to estimate number of stepping batches.\n", - "/home/knathan/anaconda3/envs/chemprop/lib/python3.11/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:424: The 'train_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=11` in the `DataLoader` to improve performance.\n", - "\n", - " | Name | Type | Params | Mode \n", - "---------------------------------------------------------------\n", - "0 | message_passing | BondMessagePassing | 227 K | train\n", - "1 | agg | MeanAggregation | 0 | train\n", - "2 | bn | BatchNorm1d | 600 | train\n", - "3 | predictor | RegressionFFN | 90.6 K | train\n", - "4 | X_d_transform | Identity | 0 | train\n", - "5 | metrics | ModuleList | 0 | train\n", - "---------------------------------------------------------------\n", - "318 K Trainable params\n", - "0 Non-trainable params\n", - "318 K Total params\n", - "1.276 Total estimated model params size (MB)\n", - "25 Modules in train mode\n", - "0 Modules in eval mode\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " " - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/knathan/anaconda3/envs/chemprop/lib/python3.11/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:424: The 'val_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=11` in the `DataLoader` to improve performance.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 1: 0%| | 0/2 [00:00┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n", - "┃ Test metric ┃ DataLoader 0 ┃\n", - "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n", - "│ test/mae │ 0.643399715423584 │\n", - "│ test/rmse │ 0.9120855927467346 │\n", - "└───────────────────────────┴───────────────────────────┘\n", - "\n" - ], - "text/plain": [ - "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n", - "┃\u001b[1m \u001b[0m\u001b[1m Test metric \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m DataLoader 0 \u001b[0m\u001b[1m \u001b[0m┃\n", - "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n", - "│\u001b[36m \u001b[0m\u001b[36m test/mae \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m 0.643399715423584 \u001b[0m\u001b[35m \u001b[0m│\n", - "│\u001b[36m \u001b[0m\u001b[36m test/rmse \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m 0.9120855927467346 \u001b[0m\u001b[35m \u001b[0m│\n", - "└───────────────────────────┴───────────────────────────┘\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "results = trainer.test(dataloaders=test_loader)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "chemprop", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.8" - }, - "orig_nbformat": 4 + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Training" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Import packages" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from pathlib import Path\n", + "\n", + "from lightning import pytorch as pl\n", + "from lightning.pytorch.callbacks import ModelCheckpoint\n", + "import pandas as pd\n", + "\n", + "from chemprop import data, featurizers, models, nn" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Change data inputs here" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "chemprop_dir = Path.cwd().parent\n", + "input_path = chemprop_dir / \"tests\" / \"data\" / \"regression\" / \"mol\" / \"mol.csv\" # path to your data .csv file\n", + "num_workers = 0 # number of workers for dataloader. 0 means using main process for data loading\n", + "smiles_column = 'smiles' # name of the column containing SMILES strings\n", + "target_columns = ['lipo'] # list of names of the columns containing targets" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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smileslipo
0Cn1c(CN2CCN(CC2)c3ccc(Cl)cc3)nc4ccccc143.54
1COc1cc(OC)c(cc1NC(=O)CSCC(=O)O)S(=O)(=O)N2C(C)...-1.18
2COC(=O)[C@@H](N1CCc2sccc2C1)c3ccccc3Cl3.69
3OC[C@H](O)CN1C(=O)C(Cc2ccccc12)NC(=O)c3cc4cc(C...3.37
4Cc1cccc(C[C@H](NC(=O)c2cc(nn2C)C(C)(C)C)C(=O)N...3.10
.........
95CC(C)N(CCCNC(=O)Nc1ccc(cc1)C(C)(C)C)C[C@H]2O[C...2.20
96CCN(CC)CCCCNc1ncc2CN(C(=O)N(Cc3cccc(NC(=O)C=C)...2.04
97CCSc1c(Cc2ccccc2C(F)(F)F)sc3N(CC(C)C)C(=O)N(C)...4.49
98COc1ccc(Cc2c(N)n[nH]c2N)cc10.20
99CCN(CCN(C)C)S(=O)(=O)c1ccc(cc1)c2cnc(N)c(n2)C(...2.00
\n", + "

100 rows × 2 columns

\n", + "
" + ], + "text/plain": [ + " smiles lipo\n", + "0 Cn1c(CN2CCN(CC2)c3ccc(Cl)cc3)nc4ccccc14 3.54\n", + "1 COc1cc(OC)c(cc1NC(=O)CSCC(=O)O)S(=O)(=O)N2C(C)... -1.18\n", + "2 COC(=O)[C@@H](N1CCc2sccc2C1)c3ccccc3Cl 3.69\n", + "3 OC[C@H](O)CN1C(=O)C(Cc2ccccc12)NC(=O)c3cc4cc(C... 3.37\n", + "4 Cc1cccc(C[C@H](NC(=O)c2cc(nn2C)C(C)(C)C)C(=O)N... 3.10\n", + ".. ... ...\n", + "95 CC(C)N(CCCNC(=O)Nc1ccc(cc1)C(C)(C)C)C[C@H]2O[C... 2.20\n", + "96 CCN(CC)CCCCNc1ncc2CN(C(=O)N(Cc3cccc(NC(=O)C=C)... 2.04\n", + "97 CCSc1c(Cc2ccccc2C(F)(F)F)sc3N(CC(C)C)C(=O)N(C)... 4.49\n", + "98 COc1ccc(Cc2c(N)n[nH]c2N)cc1 0.20\n", + "99 CCN(CCN(C)C)S(=O)(=O)c1ccc(cc1)c2cnc(N)c(n2)C(... 2.00\n", + "\n", + "[100 rows x 2 columns]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_input = pd.read_csv(input_path)\n", + "df_input" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Get SMILES and targets" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "smis = df_input.loc[:, smiles_column].values\n", + "ys = df_input.loc[:, target_columns].values" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['Cn1c(CN2CCN(CC2)c3ccc(Cl)cc3)nc4ccccc14',\n", + " 'COc1cc(OC)c(cc1NC(=O)CSCC(=O)O)S(=O)(=O)N2C(C)CCc3ccccc23',\n", + " 'COC(=O)[C@@H](N1CCc2sccc2C1)c3ccccc3Cl',\n", + " 'OC[C@H](O)CN1C(=O)C(Cc2ccccc12)NC(=O)c3cc4cc(Cl)sc4[nH]3',\n", + " 'Cc1cccc(C[C@H](NC(=O)c2cc(nn2C)C(C)(C)C)C(=O)NCC#N)c1'],\n", + " dtype=object)" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "smis[:5] # show first 5 SMILES strings" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 3.54],\n", + " [-1.18],\n", + " [ 3.69],\n", + " [ 3.37],\n", + " [ 3.1 ]])" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ys[:5] # show first 5 targets" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Get molecule datapoints" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "all_data = [data.MoleculeDatapoint.from_smi(smi, y) for smi, y in zip(smis, ys)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Perform data splitting for training, validation, and testing" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['SCAFFOLD_BALANCED',\n", + " 'RANDOM_WITH_REPEATED_SMILES',\n", + " 'RANDOM',\n", + " 'KENNARD_STONE',\n", + " 'KMEANS']" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# available split types\n", + "list(data.SplitType.keys())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "ChemProp's `make_split_indices` function will always return a two- (if no validation) or three-length tuple.\n", + "Each member is a list of length `num_replicates`.\n", + "The inner lists then contain the actual indices for splitting.\n", + "\n", + "The type signature for this return type is `tuple[list[list[int]], ...]`." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "mols = [d.mol for d in all_data] # RDkit Mol objects are use for structure based splits\n", + "train_indices, val_indices, test_indices = data.make_split_indices(mols, \"random\", (0.8, 0.1, 0.1)) # unpack the tuple into three separate lists\n", + "train_data, val_data, test_data = data.split_data_by_indices(\n", + " all_data, train_indices, val_indices, test_indices\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "ChemProp's splitting function implements our preferred method of data splitting, which is random replication.\n", + "It's also possible to add your own custom cross-validation splitter, such as one of those as implemented in scikit-learn, as long as you get the data into the same `tuple[list[list[int]], ...]` data format with something like this:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.model_selection import KFold\n", + "\n", + "k_splits = KFold(n_splits=5)\n", + "k_train_indices, k_val_indices, k_test_indices = [], [], []\n", + "for fold in k_splits.split(mols):\n", + " k_train_indices.append(fold[0])\n", + " k_val_indices.append([])\n", + " k_test_indices.append(fold[1])\n", + "k_train_data, _, k_test_data = data.split_data_by_indices(\n", + " all_data, k_train_indices, None, k_test_indices\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Get MoleculeDataset\n", + "Recall that the data is in a list equal in length to the number of replicates, so we select the zero index of the list to get the first replicate." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "featurizer = featurizers.SimpleMoleculeMolGraphFeaturizer()\n", + "\n", + "train_dset = data.MoleculeDataset(train_data[0], featurizer)\n", + "scaler = train_dset.normalize_targets()\n", + "\n", + "val_dset = data.MoleculeDataset(val_data[0], featurizer)\n", + "val_dset.normalize_targets(scaler)\n", + "\n", + "test_dset = data.MoleculeDataset(test_data[0], featurizer)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Get DataLoader" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "train_loader = data.build_dataloader(train_dset, num_workers=num_workers)\n", + "val_loader = data.build_dataloader(val_dset, num_workers=num_workers, shuffle=False)\n", + "test_loader = data.build_dataloader(test_dset, num_workers=num_workers, shuffle=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Change Message-Passing Neural Network (MPNN) inputs here" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Message Passing\n", + "A `Message passing` constructs molecular graphs using message passing to learn node-level hidden representations.\n", + "\n", + "Options are `mp = nn.BondMessagePassing()` or `mp = nn.AtomMessagePassing()`" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "mp = nn.BondMessagePassing()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Aggregation\n", + "An `Aggregation` is responsible for constructing a graph-level representation from the set of node-level representations after message passing.\n", + "\n", + "Available options can be found in ` nn.agg.AggregationRegistry`, including\n", + "- `agg = nn.MeanAggregation()`\n", + "- `agg = nn.SumAggregation()`\n", + "- `agg = nn.NormAggregation()`" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ClassRegistry {\n", + " 'mean': ,\n", + " 'sum': ,\n", + " 'norm': \n", + "}\n" + ] + } + ], + "source": [ + "print(nn.agg.AggregationRegistry)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "agg = nn.MeanAggregation()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Feed-Forward Network (FFN)\n", + "\n", + "A `FFN` takes the aggregated representations and make target predictions.\n", + "\n", + "Available options can be found in `nn.PredictorRegistry`.\n", + "\n", + "For regression:\n", + "- `ffn = nn.RegressionFFN()`\n", + "- `ffn = nn.MveFFN()`\n", + "- `ffn = nn.EvidentialFFN()`\n", + "\n", + "For classification:\n", + "- `ffn = nn.BinaryClassificationFFN()`\n", + "- `ffn = nn.BinaryDirichletFFN()`\n", + "- `ffn = nn.MulticlassClassificationFFN()`\n", + "- `ffn = nn.MulticlassDirichletFFN()`\n", + "\n", + "For spectral:\n", + "- `ffn = nn.SpectralFFN()` # will be available in future version" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ClassRegistry {\n", + " 'regression': ,\n", + " 'regression-mve': ,\n", + " 'regression-evidential': ,\n", + " 'regression-quantile': ,\n", + " 'classification': ,\n", + " 'classification-dirichlet': ,\n", + " 'multiclass': ,\n", + " 'multiclass-dirichlet': ,\n", + " 'spectral': \n", + "}\n" + ] + } + ], + "source": [ + "print(nn.PredictorRegistry)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "output_transform = nn.UnscaleTransform.from_standard_scaler(scaler)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "ffn = nn.RegressionFFN(output_transform=output_transform)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Batch Norm\n", + "A `Batch Norm` normalizes the outputs of the aggregation by re-centering and re-scaling.\n", + "\n", + "Whether to use batch norm" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "batch_norm = True" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Metrics\n", + "`Metrics` are the ways to evaluate the performance of model predictions.\n", + "\n", + "Available options can be found in `metrics.MetricRegistry`, including" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ClassRegistry {\n", + " 'mse': ,\n", + " 'mae': ,\n", + " 'rmse': ,\n", + " 'bounded-mse': ,\n", + " 'bounded-mae': ,\n", + " 'bounded-rmse': ,\n", + " 'r2': ,\n", + " 'binary-mcc': ,\n", + " 'multiclass-mcc': ,\n", + " 'roc': ,\n", + " 'prc': ,\n", + " 'accuracy': ,\n", + " 'f1': \n", + "}\n" + ] + } + ], + "source": [ + "print(nn.metrics.MetricRegistry)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "metric_list = [nn.metrics.RMSE(), nn.metrics.MAE()] # Only the first metric is used for training and early stopping" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Constructs MPNN" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "MPNN(\n", + " (message_passing): BondMessagePassing(\n", + " (W_i): Linear(in_features=86, out_features=300, bias=False)\n", + " (W_h): Linear(in_features=300, out_features=300, bias=False)\n", + " (W_o): Linear(in_features=372, out_features=300, bias=True)\n", + " (dropout): Dropout(p=0.0, inplace=False)\n", + " (tau): ReLU()\n", + " (V_d_transform): Identity()\n", + " (graph_transform): Identity()\n", + " )\n", + " (agg): MeanAggregation()\n", + " (bn): BatchNorm1d(300, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (predictor): RegressionFFN(\n", + " (ffn): MLP(\n", + " (0): Sequential(\n", + " (0): Linear(in_features=300, out_features=300, bias=True)\n", + " )\n", + " (1): Sequential(\n", + " (0): ReLU()\n", + " (1): Dropout(p=0.0, inplace=False)\n", + " (2): Linear(in_features=300, out_features=1, bias=True)\n", + " )\n", + " )\n", + " (criterion): MSE(task_weights=[[1.0]])\n", + " (output_transform): UnscaleTransform()\n", + " )\n", + " (X_d_transform): Identity()\n", + " (metrics): ModuleList(\n", + " (0): RMSE(task_weights=[[1.0]])\n", + " (1): MAE(task_weights=[[1.0]])\n", + " (2): MSE(task_weights=[[1.0]])\n", + " )\n", + ")" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mpnn = models.MPNN(mp, agg, ffn, batch_norm, metric_list)\n", + "mpnn" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Set up trainer" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "GPU available: False, used: False\n", + "TPU available: False, using: 0 TPU cores\n", + "HPU available: False, using: 0 HPUs\n" + ] + } + ], + "source": [ + "# Configure model checkpointing\n", + "checkpointing = ModelCheckpoint(\n", + " \"checkpoints\", # Directory where model checkpoints will be saved\n", + " \"best-{epoch}-{val_loss:.2f}\", # Filename format for checkpoints, including epoch and validation loss\n", + " \"val_loss\", # Metric used to select the best checkpoint (based on validation loss)\n", + " mode=\"min\", # Save the checkpoint with the lowest validation loss (minimization objective)\n", + " save_last=True, # Always save the most recent checkpoint, even if it's not the best\n", + ")\n", + "\n", + "\n", + "trainer = pl.Trainer(\n", + " logger=False,\n", + " enable_checkpointing=True, # Use `True` if you want to save model checkpoints. The checkpoints will be saved in the `checkpoints` folder.\n", + " enable_progress_bar=True,\n", + " accelerator=\"auto\",\n", + " devices=1,\n", + " max_epochs=20, # number of epochs to train for\n", + " callbacks=[checkpointing], # Use the configured checkpoint callback\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Start training" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/knathan/anaconda3/envs/chemprop/lib/python3.11/site-packages/lightning/pytorch/callbacks/model_checkpoint.py:654: Checkpoint directory /home/knathan/chemprop/examples/checkpoints exists and is not empty.\n", + "Loading `train_dataloader` to estimate number of stepping batches.\n", + "/home/knathan/anaconda3/envs/chemprop/lib/python3.11/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:424: The 'train_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=11` in the `DataLoader` to improve performance.\n", + "\n", + " | Name | Type | Params | Mode \n", + "---------------------------------------------------------------\n", + "0 | message_passing | BondMessagePassing | 227 K | train\n", + "1 | agg | MeanAggregation | 0 | train\n", + "2 | bn | BatchNorm1d | 600 | train\n", + "3 | predictor | RegressionFFN | 90.6 K | train\n", + "4 | X_d_transform | Identity | 0 | train\n", + "5 | metrics | ModuleList | 0 | train\n", + "---------------------------------------------------------------\n", + "318 K Trainable params\n", + "0 Non-trainable params\n", + "318 K Total params\n", + "1.276 Total estimated model params size (MB)\n", + "25 Modules in train mode\n", + "0 Modules in eval mode\n" + ] }, - "nbformat": 4, - "nbformat_minor": 2 + { + "name": "stdout", + "output_type": "stream", + "text": [ + " " + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/knathan/anaconda3/envs/chemprop/lib/python3.11/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:424: The 'val_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=11` in the `DataLoader` to improve performance.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1: 0%| | 0/2 [00:00┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n", + "┃ Test metric ┃ DataLoader 0 ┃\n", + "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n", + "│ test/mae │ 0.643399715423584 │\n", + "│ test/rmse │ 0.9120855927467346 │\n", + "└───────────────────────────┴───────────────────────────┘\n", + "\n" + ], + "text/plain": [ + "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n", + "┃\u001b[1m \u001b[0m\u001b[1m Test metric \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m DataLoader 0 \u001b[0m\u001b[1m \u001b[0m┃\n", + "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n", + "│\u001b[36m \u001b[0m\u001b[36m test/mae \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m 0.643399715423584 \u001b[0m\u001b[35m \u001b[0m│\n", + "│\u001b[36m \u001b[0m\u001b[36m test/rmse \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m 0.9120855927467346 \u001b[0m\u001b[35m \u001b[0m│\n", + "└───────────────────────────┴───────────────────────────┘\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "results = trainer.test(dataloaders=test_loader)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.9" + } + }, + "nbformat": 4, + "nbformat_minor": 4 } diff --git a/tests/cli/test_cli_regression_mixed.py b/tests/cli/test_cli_regression_mixed.py new file mode 100644 index 000000000..4f92e5cd8 --- /dev/null +++ b/tests/cli/test_cli_regression_mixed.py @@ -0,0 +1,610 @@ +"""This tests the CLI functionality of training and predicting a regression model on a single molecule. +""" + +import json + +import pytest +import torch + +from chemprop.cli.hpopt import NO_HYPEROPT, NO_OPTUNA, NO_RAY +from chemprop.cli.main import main +from chemprop.cli.train import TrainSubcommand +from chemprop.models.model import MolAtomBondMPNN + +pytestmark = pytest.mark.CLI + + +@pytest.fixture +def data_path(data_dir): + return ( + str(data_dir / "regression" / "mixed" / "mixed.csv"), + str(data_dir / "regression" / "mixed" / "descriptors.npz"), + str(data_dir / "regression" / "mixed" / "atom_features.npz"), + str(data_dir / "regression" / "mixed" / "bond_features.npz"), + str(data_dir / "regression" / "mixed" / "atom_descriptors.npz"), + ) + + +@pytest.fixture +def model_path(data_dir): + return str(data_dir / "example_model_v2_regression_mol.pt") # fix + + +@pytest.fixture +def mve_model_path(data_dir): + return str(data_dir / "example_model_v2_regression_mve_mol.pt") # fix + + +@pytest.fixture +def evidential_model_path(data_dir): + return str(data_dir / "example_model_v2_regression_evidential_mol.pt") # fix + + +@pytest.fixture +def config_path(data_dir): + return str(data_dir / "regression" / "mixed" / "config.toml") + + +def test_train_quick(monkeypatch, data_path): + input_path, *_ = data_path + + args = [ + "chemprop", + "train", + "-i", + input_path, + "--epochs", + "3", + "--num-workers", + "0", + "--show-individual-scores", + "--is-mixed", + ] + + with monkeypatch.context() as m: + m.setattr("sys.argv", args) + main() + + +def test_train_config(monkeypatch, config_path, tmp_path): + args = [ + "chemprop", + "train", + "--config-path", + config_path, + "--epochs", + "3", + "--num-workers", + "0", + "--save-dir", + str(tmp_path), + "--is-mixed", + ] + + with monkeypatch.context() as m: + m.setattr("sys.argv", args) + main() + + new_config_path = tmp_path / "config.toml" + parser = TrainSubcommand.parser + + new_args = parser.parse_args(["--config-path", str(new_config_path)]) + old_args = parser.parse_args(["--config-path", str(config_path)]) + + for key, value in old_args.__dict__.items(): + if key not in ["config_path", "output_dir", "epochs"]: + assert getattr(new_args, key) == value + + assert new_args.epochs == 3 + + +def test_train_quick_features(monkeypatch, data_path): + ( + input_path, + descriptors_path, + atom_features_path, + bond_features_path, + atom_descriptors_path, + ) = data_path + + base_args = [ + "chemprop", + "train", + "-i", + input_path, + "--epochs", + "3", + "--num-workers", + "0", + "--descriptors-path", + descriptors_path, + "--atom-features-path", + atom_features_path, + "--bond-features-path", + bond_features_path, + "--atom-descriptors-path", + atom_descriptors_path, + "--is-mixed", + ] + + task_types = ["", "regression-mve", "regression-evidential", "regression-quantile"] + + for task_type in task_types: + args = base_args.copy() + + if task_type: + args += ["--task-type", task_type] + + if task_type == "regression-evidential": + args += ["--evidential-regularization", "0.2"] + + with monkeypatch.context() as m: + m.setattr("sys.argv", args) + main() + + +def test_predict_quick(monkeypatch, data_path, model_path): + input_path, *_ = data_path + args = ["chemprop", "predict", "-i", input_path, "--model-path", model_path, "--is-mixed"] + + with monkeypatch.context() as m: + m.setattr("sys.argv", args) + main() + + +def test_predict_mve_quick(monkeypatch, data_path, mve_model_path): + input_path, *_ = data_path + args = [ + "chemprop", + "predict", + "-i", + input_path, + "--model-path", + mve_model_path, + "--cal-path", + input_path, + "--uncertainty-method", + "mve", + "--calibration-method", + "zscaling", + "--evaluation-methods", + "nll-regression", + "miscalibration_area", + "ence", + "spearman", + "--is-mixed", + ] + + with monkeypatch.context() as m: + m.setattr("sys.argv", args) + main() + + +def test_predict_evidential_quick(monkeypatch, data_path, evidential_model_path): + input_path, *_ = data_path + args = [ + "chemprop", + "predict", + "-i", + input_path, + "--model-path", + evidential_model_path, + "--cal-path", + input_path, + "--uncertainty-method", + "evidential-total", + "--calibration-method", + "zscaling", + "--evaluation-methods", + "nll-regression", + "miscalibration_area", + "ence", + "spearman", + "--is-mixed", + ] + + with monkeypatch.context() as m: + m.setattr("sys.argv", args) + main() + + +@pytest.mark.parametrize("ffn_block_index", ["0", "1"]) +def test_fingerprint_quick(monkeypatch, data_path, model_path, ffn_block_index): + input_path, *_ = data_path + args = [ + "chemprop", + "fingerprint", + "-i", + input_path, + "--model-path", + model_path, + "--ffn-block-index", + ffn_block_index, + "--is-mixed", + ] + + with monkeypatch.context() as m: + m.setattr("sys.argv", args) + main() + + +def test_train_output_structure(monkeypatch, data_path, tmp_path): + input_path, *_ = data_path + args = [ + "chemprop", + "train", + "-i", + input_path, + "--epochs", + "3", + "--num-workers", + "0", + "--save-dir", + str(tmp_path), + "--save-smiles-splits", + "--is-mixed", + ] + + with monkeypatch.context() as m: + m.setattr("sys.argv", args) + main() + + assert (tmp_path / "model_0" / "best.pt").exists() + assert (tmp_path / "model_0" / "checkpoints" / "last.ckpt").exists() + assert (tmp_path / "model_0" / "trainer_logs" / "version_0").exists() + assert (tmp_path / "train_smiles.csv").exists() + assert (tmp_path / "model_0" / "test_predictions.csv").exists() + + +def test_train_output_structure_replicate_ensemble(monkeypatch, data_path, tmp_path): + input_path, *_ = data_path + args = [ + "chemprop", + "train", + "-i", + input_path, + "--epochs", + "3", + "--num-workers", + "0", + "--save-dir", + str(tmp_path), + "--save-smiles-splits", + "--split-type", + "random", + "--num-replicates", + "3", + "--ensemble-size", + "2", + "--metrics", + "mse", + "rmse", + "--molecule-featurizers", + "rdkit_2d", + "--is-mixed", + ] + + with monkeypatch.context() as m: + m.setattr("sys.argv", args) + main() + + assert (tmp_path / "replicate_2" / "model_1" / "best.pt").exists() + assert (tmp_path / "replicate_2" / "model_1" / "checkpoints" / "last.ckpt").exists() + assert (tmp_path / "replicate_2" / "model_1" / "trainer_logs" / "version_0").exists() + assert (tmp_path / "replicate_2" / "train_smiles.csv").exists() + + +def test_train_csv_splits(monkeypatch, data_dir, tmp_path): + input_path = str(data_dir / "regression" / "mol" / "mol_with_splits.csv") + args = [ + "chemprop", + "train", + "-i", + input_path, + "--smiles-columns", + "smiles", + "--target-columns", + "lipo", + "--splits-column", + "split", + "--epochs", + "3", + "--num-workers", + "0", + "--save-dir", + str(tmp_path), + "--is-mixed", + ] + + with monkeypatch.context() as m: + m.setattr("sys.argv", args) + main() + + +def test_train_splits_file(monkeypatch, data_path, tmp_path): + input_path, *_ = data_path + splits_file = str(tmp_path / "splits.json") + splits = [ + {"train": [1, 2], "val": "3-5", "test": "6,7"}, + {"val": [1, 2], "test": "3-5", "train": "6,7"}, + ] + + with open(splits_file, "w") as f: + json.dump(splits, f) + + args = [ + "chemprop", + "train", + "-i", + input_path, + "--epochs", + "3", + "--num-workers", + "0", + "--save-dir", + str(tmp_path), + "--splits-file", + splits_file, + "--is-mixed", + ] + + with monkeypatch.context() as m: + m.setattr("sys.argv", args) + main() + + +def test_predict_output_structure(monkeypatch, data_path, model_path, tmp_path): + input_path, *_ = data_path + args = [ + "chemprop", + "predict", + "-i", + input_path, + "--model-path", + model_path, + model_path, + "--output", + str(tmp_path / "preds.csv"), + "--is-mixed", + ] + + with monkeypatch.context() as m: + m.setattr("sys.argv", args) + main() + + assert (tmp_path / "preds.csv").exists() + assert (tmp_path / "preds_individual.csv").exists() + + +@pytest.mark.parametrize("ffn_block_index", ["0", "1"]) +def test_fingerprint_output_structure( + monkeypatch, data_path, model_path, tmp_path, ffn_block_index +): + input_path, *_ = data_path + args = [ + "chemprop", + "fingerprint", + "-i", + input_path, + "--model-path", + model_path, + "--output", + str(tmp_path / "fps.csv"), + "--ffn-block-index", + ffn_block_index, + "--is-mixed", + ] + + with monkeypatch.context() as m: + m.setattr("sys.argv", args) + main() + + assert (tmp_path / "fps_0.csv").exists() + + +def test_train_outputs(monkeypatch, data_path, tmp_path): + input_path, *_ = data_path + + args = [ + "chemprop", + "train", + "-i", + input_path, + "--epochs", + "3", + "--num-workers", + "0", + "--save-dir", + str(tmp_path), + "--is-mixed", + ] + + with monkeypatch.context() as m: + m.setattr("sys.argv", args) + main() + + checkpoint_path = tmp_path / "model_0" / "checkpoints" / "last.ckpt" + + model = MolAtomBondMPNN.load_from_checkpoint(checkpoint_path) + assert model is not None + + +def test_freeze_model(monkeypatch, data_path, model_path, tmp_path): + input_path, *_ = data_path + args = [ + "chemprop", + "train", + "-i", + input_path, + "--epochs", + "3", + "--num-workers", + "0", + "--save-dir", + str(tmp_path), + "--checkpoint", + model_path, + "--freeze-encoder", + "--frzn-ffn-layers", + "1", + "--accelerator", + "cpu", + "--is-mixed", + ] + + with monkeypatch.context() as m: + m.setattr("sys.argv", args) + main() + + checkpoint_path = tmp_path / "model_0" / "checkpoints" / "last.ckpt" + + trained_model = MolAtomBondMPNN.load_from_checkpoint(checkpoint_path) + frzn_model = MolAtomBondMPNN.load_from_file(model_path) + + assert torch.equal( + trained_model.message_passing.W_o.weight, frzn_model.message_passing.W_o.weight + ) + assert torch.equal( + trained_model.message_passing.W_o_b.weight, frzn_model.message_passing.W_o_b.weight + ) + assert torch.equal( + trained_model.mol_predictor.ffn[0][0].weight, frzn_model.mol_predictor.ffn[0][0].weight + ) + assert torch.equal( + trained_model.atom_predictor.ffn[0][0].weight, frzn_model.atom_predictor.ffn[0][0].weight + ) + assert torch.equal( + trained_model.bond_predictor.ffn[0][0].weight, frzn_model.bond_predictor.ffn[0][0].weight + ) + + +def test_checkpoint_model(monkeypatch, data_path, model_path, tmp_path): + input_path, *_ = data_path + args = [ + "chemprop", + "train", + "-i", + input_path, + "--epochs", + "3", + "--num-workers", + "0", + "--save-dir", + str(tmp_path), + "--checkpoint", + model_path, + "--is-mixed", + ] + + with monkeypatch.context() as m: + m.setattr("sys.argv", args) + main() + + checkpoint_path = tmp_path / "model_0" / "checkpoints" / "last.ckpt" + + model = MolAtomBondMPNN.load_from_checkpoint(checkpoint_path) + assert model is not None + + +@pytest.mark.skipif(NO_RAY or NO_OPTUNA, reason="Optuna not installed") +def test_optuna_quick(monkeypatch, data_path, tmp_path): + input_path, *_ = data_path + + args = [ + "chemprop", + "hpopt", + "-i", + input_path, + "--epochs", + "6", + "--hpopt-save-dir", + str(tmp_path), + "--raytune-num-samples", + "2", + "--raytune-search-algorithm", + "optuna", + "--molecule-featurizers", + "morgan_count", + "--search-parameter-keywords", + "all", + "--accelerator", + "cpu", + "--is-mixed", + ] + + with monkeypatch.context() as m: + m.setattr("sys.argv", args) + main() + + assert (tmp_path / "best_config.toml").exists() + assert (tmp_path / "best_checkpoint.ckpt").exists() + assert (tmp_path / "all_progress.csv").exists() + assert (tmp_path / "ray_results").exists() + + args = [ + "chemprop", + "train", + "--config-path", + str(tmp_path / "best_config.toml"), + "--save-dir", + str(tmp_path), + "--is-mixed", + ] + + with monkeypatch.context() as m: + m.setattr("sys.argv", args) + main() + + assert (tmp_path / "model_0" / "best.pt").exists() + + +@pytest.mark.skipif(NO_RAY or NO_HYPEROPT, reason="Ray and/or Hyperopt not installed") +def test_hyperopt_quick(monkeypatch, data_path, tmp_path): + input_path, *_ = data_path + + args = [ + "chemprop", + "hpopt", + "-i", + input_path, + "--epochs", + "6", + "--hpopt-save-dir", + str(tmp_path), + "--raytune-num-samples", + "2", + "--raytune-search-algorithm", + "hyperopt", + "--molecule-featurizers", + "morgan_binary", + "--search-parameter-keywords", + "all", + "--accelerator", + "cpu", + "--is-mixed", + ] + + with monkeypatch.context() as m: + m.setattr("sys.argv", args) + main() + + assert (tmp_path / "best_config.toml").exists() + assert (tmp_path / "best_checkpoint.ckpt").exists() + assert (tmp_path / "all_progress.csv").exists() + assert (tmp_path / "ray_results").exists() + + args = [ + "chemprop", + "train", + "--config-path", + str(tmp_path / "best_config.toml"), + "--save-dir", + str(tmp_path), + "--is-mixed", + ] + + with monkeypatch.context() as m: + m.setattr("sys.argv", args) + main() + + assert (tmp_path / "model_0" / "best.pt").exists() diff --git a/tests/cli/test_cli_regression_mol.py b/tests/cli/test_cli_regression_mol.py index faffbc3e7..6c3db1c21 100644 --- a/tests/cli/test_cli_regression_mol.py +++ b/tests/cli/test_cli_regression_mol.py @@ -434,6 +434,8 @@ def test_freeze_model(monkeypatch, data_path, model_path, tmp_path): "--freeze-encoder", "--frzn-ffn-layers", "1", + "--accelerator", + "cpu", ] with monkeypatch.context() as m: @@ -501,6 +503,8 @@ def test_optuna_quick(monkeypatch, data_path, tmp_path): "morgan_count", "--search-parameter-keywords", "all", + "--accelerator", + "cpu", ] with monkeypatch.context() as m: @@ -549,6 +553,8 @@ def test_hyperopt_quick(monkeypatch, data_path, tmp_path): "morgan_binary", "--search-parameter-keywords", "all", + "--accelerator", + "cpu", ] with monkeypatch.context() as m: diff --git a/tests/conftest.py b/tests/conftest.py index 03e59a495..24b3338fa 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -1,3 +1,4 @@ +import ast from pathlib import Path import numpy as np @@ -60,6 +61,24 @@ def mol_regression_data(data_dir): return smis, Y +@pytest.fixture +def atom_regression_data(data_dir): + df = pd.read_csv(data_dir / "regression/atoms.csv") + smis = df.loc[:, "smiles"].values + target_columns = ["charges"] + ys = df.loc[:, target_columns] + Y = [] + for molecule in range(len(ys)): + list_props = [] + for prop in target_columns: + np_prop = np.array(ast.literal_eval(ys.iloc[molecule][prop])) + np_prop = np.expand_dims(np_prop, axis=1) + list_props.append(np_prop) + Y.append(np.hstack(list_props)) + + return smis, Y + + @pytest.fixture def rxn_regression_data(data_dir): df = pd.read_csv(data_dir / "regression/rxn/rxn.csv") diff --git a/tests/data/.DS_Store b/tests/data/.DS_Store new file mode 100644 index 000000000..f23f89a91 Binary files /dev/null and b/tests/data/.DS_Store differ diff --git a/tests/data/atomic_bond_regression.csv b/tests/data/atomic_bond_regression.csv new file mode 100644 index 000000000..4d1749351 --- /dev/null +++ b/tests/data/atomic_bond_regression.csv @@ -0,0 +1,501 @@ 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+[S:1]=[C:2]([N:3]1[C:4]([H:21])([H:22])[C:5]([H:23])([H:24])[O:6][C:7]([H:25])([H:26])[C:8]1([H:27])[H:28])[c:9]1[c:10]([H:29])[c:11]([O:12][C:13]([H:30])([H:31])[H:32])[c:14]([O:15][C:16]([H:33])([H:34])[H:35])[c:17]([O:18][C:19]([H:36])([H:37])[H:38])[c:20]1[H:39],[],[] +[C:1]([C:2]([C:3]([H:24])([H:25])[H:26])([C:4]([C:5]([c:6]1[n:7][o:8][c:9]([C:10]([C:11]([H:31])([H:32])[H:33])([N:12]([H:34])[H:35])[c:13]2[c:14]([H:36])[c:15]([H:37])[c:16]([H:38])[c:17]([H:39])[c:18]2[H:40])[n:19]1)([H:29])[H:30])([H:27])[H:28])[H:23])([H:20])([H:21])[H:22],[],[] +[C:1]([C:2]1([H:24])[C:3]([H:25])([H:26])[N:4]([C:5]([n:6]2[n:7][n:8][c:9](-[c:10]3[c:11]([H:29])[c:12]([H:30])[c:13]([Cl:14])[c:15]([H:31])[c:16]3[H:32])[n:17]2)([H:27])[H:28])[C:18]([H:33])([H:34])[C:19]([H:35])([H:36])[O:20]1)([H:21])([H:22])[H:23],[],[] diff --git a/tests/data/mixed_regression_input.csv b/tests/data/mixed_regression_input.csv new file mode 100644 index 000000000..5dcf4dc59 --- /dev/null +++ b/tests/data/mixed_regression_input.csv @@ -0,0 +1,11 @@ +smiles,molecule,atom +CC,1.0,"[1,2]" +CCC,2.0,"[1,2,3]" +CCCO,3.0,"[1,2,3,5]" +CCOO,4.0,"[1,2,3,4]" +COO,5.0,"[1,3,5]" +CCOOO,6.0,"[1,7,3,4,5]" +COOO,7.0,"[2,5,3,2]" +CO,8.0,"[1,3]" +CCO,9.0,"[1,3,5]" +OO,10.0,"[5,7]" \ No newline at end of file diff --git a/tests/data/mixed_regression_output.csv b/tests/data/mixed_regression_output.csv new file mode 100644 index 000000000..6cc261a9f --- /dev/null +++ b/tests/data/mixed_regression_output.csv @@ -0,0 +1,11 @@ +smiles,molecule,atom +CC,5.342139720916748,"[2.569624900817871, 2.569624900817871]" +CCC,5.2332377433776855,"[2.4788389205932617, 2.5985231399536133, 2.4788389205932617]" +CCCO,6.901552677154541,"[2.4710915088653564, 2.505635976791382, 2.7169690132141113, 3.3837594985961914]" +CCOO,6.782913684844971,"[2.4493448734283447, 2.6188316345214844, 3.430971622467041, 3.431046962738037]" +COO,6.48114013671875,"[2.5182647705078125, 3.493091344833374, 3.4651689529418945]" +CCOOO,6.913412094116211,"[2.451606273651123, 2.626923084259033, 3.226198673248291, 3.446617841720581, 3.376347541809082]" +COOO,6.779136657714844,"[2.5419275760650635, 3.3292524814605713, 3.46563458442688, 3.3711774349212646]" +CO,6.977913856506348,"[2.758082389831543, 3.42410945892334]" +CCO,6.894195556640625,"[2.4541964530944824, 2.7911367416381836, 3.389388084411621]" +OO,7.735259532928467,"[3.6392979621887207, 3.6392979621887207]" diff --git a/tests/data/regression/atoms.csv b/tests/data/regression/atoms.csv new file mode 100644 index 000000000..119b3efb4 --- /dev/null +++ b/tests/data/regression/atoms.csv @@ -0,0 +1,501 @@ +smiles,charges +[S:1]1[S:2][C:3]2([C:23]([H:41])([H:42])[H:43])[N:4]([C:20]([C:21]([C:22]([H:38])([H:39])[H:40])([H:36])[H:37])([H:34])[H:35])[C:5](=[O:6])[C:7]13[N:8]([c:9]1[c:10]([c:11]([H:24])[c:12]([H:25])[c:13]([H:26])[c:14]1[H:27])[C:15]3([C:16]([H:28])([H:29])[H:30])[C:17]([H:31])([H:32])[H:33])[C:18]2=[O:19],"[-0.093451, -0.121894, 0.159898, -0.198989, 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mol_atom_bond_mpnn(request): + message_passing, agg = request.param + mol_ffn = nn.RegressionFFN() + atom_ffn = nn.RegressionFFN() + bond_ffn = nn.RegressionFFN() + + return models.MolAtomBondMPNN(message_passing, agg, mol_ffn, atom_ffn, bond_ffn, True) + + @pytest.fixture(scope="session") def classification_mpnn_dirichlet(request): agg = nn.SumAggregation() diff --git a/tests/integration/test_classification_mol.py b/tests/integration/test_classification_mol.py index c84a13c96..741211464 100644 --- a/tests/integration/test_classification_mol.py +++ b/tests/integration/test_classification_mol.py @@ -83,7 +83,7 @@ def test_overfit(classification_mpnn, dataloader): targetss = [] masks = [] for batch in dataloader: - bmg, _, _, targets, *_ = batch + bmg, _, _, _, targets, *_ = batch preds = classification_mpnn(bmg) not_nan = ~targets.isnan() predss.append(preds) @@ -120,7 +120,7 @@ def test_dirichlet_overfit(classification_mpnn_dirichlet, dataloader): targetss = [] masks = [] for batch in dataloader: - bmg, _, _, targets, *_ = batch + bmg, _, _, _, targets, *_ = batch preds = classification_mpnn_dirichlet(bmg) not_nan = ~targets.isnan() predss.append(preds) diff --git a/tests/integration/test_classification_mol_multiclass.py b/tests/integration/test_classification_mol_multiclass.py index e19737a3c..f30ad5e98 100644 --- a/tests/integration/test_classification_mol_multiclass.py +++ b/tests/integration/test_classification_mol_multiclass.py @@ -86,7 +86,7 @@ def test_overfit(classification_mpnn_multiclass, dataloader): predss = [] targetss = [] for batch in dataloader: - bmg, _, _, targets, *_ = batch + bmg, _, _, _, targets, *_ = batch preds = classification_mpnn_multiclass(bmg) preds = preds.transpose(1, 2) predss.append(preds) @@ -122,7 +122,7 @@ def test_dirichlet_overfit(classification_mpnn_multiclass_dirichlet, dataloader) predss = [] targetss = [] for batch in dataloader: - bmg, _, _, targets, *_ = batch + bmg, _, _, _, targets, *_ = batch preds = classification_mpnn_multiclass_dirichlet(bmg) preds = preds.transpose(1, 2) predss.append(preds) diff --git a/tests/integration/test_output_transform.py b/tests/integration/test_output_transform.py index 1422d2158..c9c6dccca 100644 --- a/tests/integration/test_output_transform.py +++ b/tests/integration/test_output_transform.py @@ -42,7 +42,7 @@ def test_output_transform(data): mpnn.train() predss = [] for batch in train_loader: - bmg, _, _, targets, *_ = batch + bmg, _, _, _, targets, *_ = batch preds = mpnn(bmg) predss.append(preds) diff --git a/tests/integration/test_regression_atom.py b/tests/integration/test_regression_atom.py new file mode 100644 index 000000000..6496c7735 --- /dev/null +++ b/tests/integration/test_regression_atom.py @@ -0,0 +1,79 @@ +"""This integration test is designed to ensure that the chemprop model can _overfit_ the training +data. A small enough dataset should be memorizable by even a moderately sized model, so this test +should generally pass.""" + +from lightning import pytorch as pl +import pytest +import torch +from torch.utils.data import DataLoader + +from chemprop import nn +from chemprop.data import AtomDataset, MoleculeDatapoint, collate_batch + +pytestmark = [ + pytest.mark.parametrize( + "mpnn", + [ + (nn.BondMessagePassing(), nn.NoAggregation()), + (nn.AtomMessagePassing(), nn.NoAggregation()), + ], + indirect=True, + ), + pytest.mark.integration, +] + + +@pytest.fixture +def data(atom_regression_data): + smis, Y = atom_regression_data + + return [ + MoleculeDatapoint.from_smi(smi, y, keep_h=True, keep_atom_map=True) + for smi, y in zip(smis, Y) + ] + + +@pytest.fixture +def dataloader(data): + dset = AtomDataset(data) + dset.normalize_targets() + + return DataLoader(dset, 32, collate_fn=collate_batch) + + +def test_quick(mpnn, dataloader): + trainer = pl.Trainer( + logger=False, + enable_checkpointing=False, + enable_progress_bar=False, + enable_model_summary=False, + accelerator="cpu", + devices=1, + fast_dev_run=True, + ) + trainer.fit(mpnn, dataloader, None) + + +def test_overfit(mpnn, dataloader): + trainer = pl.Trainer( + logger=False, + enable_checkpointing=False, + enable_progress_bar=True, + enable_model_summary=False, + accelerator="cpu", + devices=1, + max_epochs=100, + overfit_batches=1.00, + ) + trainer.fit(mpnn, dataloader) + + errors = [] + for batch in dataloader: + bmg, _, _, targets, *_ = batch + preds = mpnn(bmg) + errors.append(preds - targets) + + errors = torch.cat(errors) + mse = errors.square().mean().item() + + assert mse <= 0.05 diff --git a/tests/integration/test_regression_mixed.py b/tests/integration/test_regression_mixed.py new file mode 100644 index 000000000..4a5ea6345 --- /dev/null +++ b/tests/integration/test_regression_mixed.py @@ -0,0 +1,125 @@ +"""This integration test is designed to ensure that the chemprop model can _overfit_ the training +data. A small enough dataset should be memorizable by even a moderately sized model, so this test +should generally pass.""" + +from lightning import pytorch as pl +import pytest +import torch +from torch.utils.data import DataLoader + +from chemprop import nn +from chemprop.data import ( + AtomDataset, + BondDataset, + MockDataset, + MolAtomBondDataset, + MoleculeDatapoint, + MoleculeDataset, + mixed_collate_batch, +) + +pytestmark = [ + pytest.mark.parametrize( + "mol_atom_bond_mpnn", + [ + (nn.MixedAtomMessagePassing(), nn.MeanAggregation()), + (nn.MixedBondMessagePassing(), nn.MeanAggregation()), + ], + indirect=True, + ), + pytest.mark.integration, +] + + +@pytest.fixture +def data(mixed_regression_data): + smis, mol_Y, atom_Y, bond_Y = mixed_regression_data + + all_data = [] + all_data.append( + [ + MoleculeDatapoint.from_smi(smi, y, keep_h=True, keep_atom_map=True) + for smi, y in zip(smis, mol_Y) + ] + ) + all_data.append( + [ + MoleculeDatapoint.from_smi(smi, y, keep_h=True, keep_atom_map=True) + for smi, y in zip(smis, atom_Y) + ] + ) + all_data.append( + [ + MoleculeDatapoint.from_smi(smi, y, keep_h=True, keep_atom_map=True) + for smi, y in zip(smis, bond_Y) + ] + ) + return all_data + + +@pytest.fixture +def dataloader(data): + dsets = [] + if data[0]: + dset = MoleculeDataset(data) + dset.normalize_targets() + dsets.append(dset) + else: + dsets.append(MockDataset()) + + if data[1]: + dset = AtomDataset(data) + dset.normalize_targets() + dsets.append(dset) + else: + dsets.append(MockDataset()) + + if data[2]: + dset = BondDataset(data) + dset.normalize_targets() + dsets.append(dset) + else: + dsets.append(MockDataset()) + + dset = MolAtomBondDataset(dsets[0], dsets[1], dsets[2]) + return DataLoader(dset, 32, collate_fn=mixed_collate_batch) + + +def test_quick(mol_atom_bond_mpnn, dataloader): + trainer = pl.Trainer( + logger=False, + enable_checkpointing=False, + enable_progress_bar=False, + enable_model_summary=False, + accelerator="cpu", + devices=1, + fast_dev_run=True, + ) + trainer.fit(mol_atom_bond_mpnn, dataloader, None) + + +def test_overfit(mol_atom_bond_mpnn, dataloader): + trainer = pl.Trainer( + logger=False, + enable_checkpointing=False, + enable_progress_bar=True, + enable_model_summary=False, + accelerator="cpu", + devices=1, + max_epochs=100, + overfit_batches=1.00, + ) + trainer.fit(mol_atom_bond_mpnn, dataloader) + + errors = [] + for batch in dataloader: + bmg, _, _, _, mol_targets, *_ = batch[0] + _, _, _, _, atom_targets, *_ = batch[1] + _, _, _, _, bond_targets, *_ = batch[2] + preds = mol_atom_bond_mpnn(bmg) + errors.append(preds[0] + preds[1] + preds[2] - mol_targets - atom_targets - bond_targets) + + errors = torch.cat(errors) + mse = errors.square().mean().item() + + assert mse <= 0.05 diff --git a/tests/integration/test_regression_mol+mol.py b/tests/integration/test_regression_mol+mol.py index cf7f4f8eb..ee50a26fa 100644 --- a/tests/integration/test_regression_mol+mol.py +++ b/tests/integration/test_regression_mol+mol.py @@ -77,7 +77,7 @@ def test_overfit(mcmpnn, dataloader): errors = [] for batch in dataloader: - bmg, _, _, targets, *_ = batch + bmg, _, _, _, targets, *_ = batch preds = mcmpnn(bmg) errors.append(preds - targets) diff --git a/tests/integration/test_regression_mol.py b/tests/integration/test_regression_mol.py index 5238bac01..9e4e3ccf3 100644 --- a/tests/integration/test_regression_mol.py +++ b/tests/integration/test_regression_mol.py @@ -67,7 +67,7 @@ def test_overfit(mpnn, dataloader): errors = [] for batch in dataloader: - bmg, _, _, targets, *_ = batch + bmg, _, _, _, targets, *_ = batch preds = mpnn(bmg) errors.append(preds - targets) diff --git a/tests/integration/test_regression_rxn+mol.py b/tests/integration/test_regression_rxn+mol.py index 1e064370d..400cce672 100644 --- a/tests/integration/test_regression_rxn+mol.py +++ b/tests/integration/test_regression_rxn+mol.py @@ -80,7 +80,7 @@ def test_overfit(mcmpnn, dataloader): errors = [] for batch in dataloader: - bmg, _, _, targets, *_ = batch + bmg, _, _, _, targets, *_ = batch preds = mcmpnn(bmg) errors.append(preds - targets)