Loading deepchem/models/torch_models/cgcnn.py +8 −6 Original line number Diff line number Diff line Loading @@ -253,12 +253,14 @@ class CGCNNModel(TorchModel): Here is a simple example of code that uses the CGCNNModel with materials dataset. >> import deepchem as dc >> dataset_config = {"reload": False, "featurizer": dc.feat.CGCNNFeaturizer, "transformers": []} >> tasks, datasets, transformers = dc.molnet.load_perovskite(**dataset_config) >> train, valid, test = datasets >> model = dc.models.CGCNNModel(mode='regression', batch_size=32, learning_rate=0.001) >> model.fit(train, nb_epoch=50) Examples -------- >>> import deepchem as dc >>> dataset_config = {"reload": False, "featurizer": dc.feat.CGCNNFeaturizer(), "transformers": []} >>> tasks, datasets, transformers = dc.molnet.load_perovskite(**dataset_config) >>> train, valid, test = datasets >>> model = dc.models.CGCNNModel(mode='regression', batch_size=32, learning_rate=0.001) >>> avg_loss = model.fit(train, nb_epoch=50) This model takes arbitary crystal structures as an input, and predict material properties using the element information and connection of atoms in the crystal. If you want to get Loading deepchem/molnet/load_function/material_datasets/load_perovskite.py +5 −8 Original line number Diff line number Diff line Loading @@ -30,7 +30,7 @@ class _PerovskiteLoader(_MolnetLoader): def load_perovskite( featurizer: Union[dc.feat.Featurizer, str] = dc.feat.SineCoulombMatrix(), featurizer: Union[dc.feat.Featurizer, str] = dc.feat.CGCNNFeaturizer(), splitter: Union[dc.splits.Splitter, str, None] = 'random', transformers: List[Union[TransformerGenerator, str]] = ['normalization'], reload: bool = True, Loading Loading @@ -93,13 +93,10 @@ def load_perovskite( Examples -------- >>> >> import deepchem as dc >> tasks, datasets, transformers = dc.molnet.load_perovskite() >> train_dataset, val_dataset, test_dataset = datasets >> n_tasks = len(tasks) >> n_features = train_dataset.get_data_shape()[0] >> model = dc.models.MultitaskRegressor(n_tasks, n_features) >>> import deepchem as dc >>> tasks, datasets, transformers = dc.molnet.load_perovskite() >>> train_dataset, val_dataset, test_dataset = datasets >>> model = dc.models.CGCNNModel(mode='regression', batch_size=32, learning_rate=0.001) """ loader = _PerovskiteLoader(featurizer, splitter, transformers, Loading deepchem/trans/__init__.py +1 −0 Original line number Diff line number Diff line Loading @@ -21,4 +21,5 @@ from deepchem.trans.transformers import ImageTransformer from deepchem.trans.transformers import DataTransforms from deepchem.trans.transformers import Transformer from deepchem.trans.transformers import FlatteningTransformer from deepchem.trans.transformers import RxnSplitTransformer from deepchem.trans.duplicate import DuplicateBalancingTransformer deepchem/trans/tests/test_rxn_transform.py 0 → 100644 +55 −0 Original line number Diff line number Diff line import unittest import numpy as np from deepchem.trans.transformers import RxnSplitTransformer reactions: np.ndarray = np.array( [ "CC(C)C[Mg+].CON(C)C(=O)c1ccc(O)nc1>C1CCOC1.[Cl-]>CC(C)CC(=O)c1ccc(O)nc1", "CCn1cc(C(=O)O)c(=O)c2cc(F)c(-c3ccc(N)cc3)cc21.O=CO>>CCn1cc(C(=O)O)c(=O)c2cc(F)c(-c3ccc(NC=O)cc3)cc21" ], dtype=object) split: np.ndarray = np.array( [[ "CC(C)C[Mg+].CON(C)C(=O)c1ccc(O)nc1>C1CCOC1.[Cl-]", "CC(C)CC(=O)c1ccc(O)nc1" ], [ "CCn1cc(C(=O)O)c(=O)c2cc(F)c(-c3ccc(N)cc3)cc21.O=CO>", "CCn1cc(C(=O)O)c(=O)c2cc(F)c(-c3ccc(NC=O)cc3)cc21" ]], dtype=object) sep: np.ndarray = np.array( [[ "CC(C)C[Mg+].CON(C)C(=O)c1ccc(O)nc1.C1CCOC1.[Cl-]>", "CC(C)CC(=O)c1ccc(O)nc1" ], [ "CCn1cc(C(=O)O)c(=O)c2cc(F)c(-c3ccc(N)cc3)cc21.O=CO>", "CCn1cc(C(=O)O)c(=O)c2cc(F)c(-c3ccc(NC=O)cc3)cc21" ]], dtype=object) class TestRxnSplitTransformer(unittest.TestCase): """ Tests the Reaction split transformer for the source/target splitting and for the reagent mixing operation. """ def test_split(self): """Tests the source/target split from an input reaction SMILES.""" trans = RxnSplitTransformer(sep_reagent=True) split_reactions = trans.transform_array( X=reactions, y=np.array([]), w=np.array([]), ids=np.array([])) assert split_reactions[0].shape == (2, 2) assert (split_reactions[0] == split).all() def test_mixing(self): """Tests the reagent - reactant mixing toggle.""" trans = RxnSplitTransformer(sep_reagent=False) split_reactions = trans.transform_array( X=reactions, y=np.array([]), w=np.array([]), ids=np.array([])) assert split_reactions[0].shape == (2, 2) assert (split_reactions[0] == sep).all() deepchem/trans/transformers.py +113 −0 Original line number Diff line number Diff line Loading @@ -2469,3 +2469,116 @@ class DataTransforms(object): image = Image.fromarray(self.Image) image = image.filter(ImageFilter.MedianFilter(size=size)) return np.array(image) class RxnSplitTransformer(Transformer): """Splits the reaction SMILES input into the source and target strings required for machine translation tasks. The input is expected to be in the form reactant>reagent>product. The source string would be reactants>reagents and the target string would be the products. The transformer can also separate the reagents from the reactants for a mixed training mode. During mixed training, the source string is transformed from reactants>reagent to reactants.reagent> . This can be toggled (default True) by setting the value of sep_reagent while calling the transformer. Examples -------- >>> # When mixed training is toggled. >>> import numpy as np >>> from deepchem.trans.transformers import RxnSplitTransformer >>> reactions = np.array( [ "CC(C)C[Mg+].CON(C)C(=O)c1ccc(O)nc1>C1CCOC1.[Cl-]>CC(C)CC(=O)c1ccc(O)nc1", "CCn1cc(C(=O)O)c(=O)c2cc(F)c(-c3ccc(N)cc3)cc21.O=CO>>CCn1cc(C(=O)O)c(=O)c2cc(F)c(-c3ccc(NC=O)cc3)cc21" ], dtype=object) >>> trans = RxnSplitTransformer(sep_reagent=True) >>> split_reactions = trans.transform_array(X=reactions, y=np.array([]), w=np.array([]), ids=np.array([])) >>> split_reactions (array([['CC(C)C[Mg+].CON(C)C(=O)c1ccc(O)nc1>C1CCOC1.[Cl-]', 'CC(C)CC(=O)c1ccc(O)nc1'], ['CCn1cc(C(=O)O)c(=O)c2cc(F)c(-c3ccc(N)cc3)cc21.O=CO>', 'CCn1cc(C(=O)O)c(=O)c2cc(F)c(-c3ccc(NC=O)cc3)cc21']], dtype='<U51'), array([], dtype=float64), array([], dtype=float64), array([], dtype=float64)) When mixed training is disabled, you get the following outputs: >>> trans_disable = RxnSplitTransformer(sep_reagent=False) >>> split_reactions = trans_disable.transform_array(X=reactions, y=np.array([]), w=np.array([]), ids=np.array([])) >>> split_reactions (array([['CC(C)C[Mg+].CON(C)C(=O)c1ccc(O)nc1.C1CCOC1.[Cl-]>', 'CC(C)CC(=O)c1ccc(O)nc1'], ['CCn1cc(C(=O)O)c(=O)c2cc(F)c(-c3ccc(N)cc3)cc21.O=CO>', 'CCn1cc(C(=O)O)c(=O)c2cc(F)c(-c3ccc(NC=O)cc3)cc21']], dtype='<U51'), array([], dtype=float64), array([], dtype=float64), array([], dtype=float64)) Note ---- This class only transforms the feature field of a reaction dataset like USPTO. """ def __init__(self, sep_reagent: bool = True, dataset: Optional[Dataset] = None): """Initializes the Reaction split Transformer. Parameters ---------- sep_reagent: bool, optional (default True) To separate the reagent and reactants for training. dataset: dc.data.Dataset object, optional (default None) Dataset to be transformed. """ self.sep_reagent = sep_reagent super(RxnSplitTransformer, self).__init__(transform_X=True, dataset=dataset) def transform_array( self, X: np.ndarray, y: np.ndarray, w: np.ndarray, ids: np.ndarray) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]: """Transform the data in a set of (X, y, w, ids) arrays. Parameters ---------- X: np.ndarray Array of features(the reactions) y: np.ndarray Array of labels w: np.ndarray Array of weights. ids: np.ndarray Array of weights. Returns ------- Xtrans: np.ndarray Transformed array of features ytrans: np.ndarray Transformed array of labels wtrans: np.ndarray Transformed array of weights idstrans: np.ndarray Transformed array of ids """ reactant = list(map(lambda x: x.split('>')[0], X)) reagent = list(map(lambda x: x.split('>')[1], X)) product = list(map(lambda x: x.split('>')[2], X)) if self.sep_reagent: source = [x + '>' + y for x, y in zip(reactant, reagent)] else: source = [ x + '.' + y + '>' if y else x + '>' + y for x, y in zip(reactant, reagent) ] target = product X = np.column_stack((source, target)) return (X, y, w, ids) def untransform(self, z): """Not Implemented.""" raise NotImplementedError("Cannot untransform the source/target split.") Loading
deepchem/models/torch_models/cgcnn.py +8 −6 Original line number Diff line number Diff line Loading @@ -253,12 +253,14 @@ class CGCNNModel(TorchModel): Here is a simple example of code that uses the CGCNNModel with materials dataset. >> import deepchem as dc >> dataset_config = {"reload": False, "featurizer": dc.feat.CGCNNFeaturizer, "transformers": []} >> tasks, datasets, transformers = dc.molnet.load_perovskite(**dataset_config) >> train, valid, test = datasets >> model = dc.models.CGCNNModel(mode='regression', batch_size=32, learning_rate=0.001) >> model.fit(train, nb_epoch=50) Examples -------- >>> import deepchem as dc >>> dataset_config = {"reload": False, "featurizer": dc.feat.CGCNNFeaturizer(), "transformers": []} >>> tasks, datasets, transformers = dc.molnet.load_perovskite(**dataset_config) >>> train, valid, test = datasets >>> model = dc.models.CGCNNModel(mode='regression', batch_size=32, learning_rate=0.001) >>> avg_loss = model.fit(train, nb_epoch=50) This model takes arbitary crystal structures as an input, and predict material properties using the element information and connection of atoms in the crystal. If you want to get Loading
deepchem/molnet/load_function/material_datasets/load_perovskite.py +5 −8 Original line number Diff line number Diff line Loading @@ -30,7 +30,7 @@ class _PerovskiteLoader(_MolnetLoader): def load_perovskite( featurizer: Union[dc.feat.Featurizer, str] = dc.feat.SineCoulombMatrix(), featurizer: Union[dc.feat.Featurizer, str] = dc.feat.CGCNNFeaturizer(), splitter: Union[dc.splits.Splitter, str, None] = 'random', transformers: List[Union[TransformerGenerator, str]] = ['normalization'], reload: bool = True, Loading Loading @@ -93,13 +93,10 @@ def load_perovskite( Examples -------- >>> >> import deepchem as dc >> tasks, datasets, transformers = dc.molnet.load_perovskite() >> train_dataset, val_dataset, test_dataset = datasets >> n_tasks = len(tasks) >> n_features = train_dataset.get_data_shape()[0] >> model = dc.models.MultitaskRegressor(n_tasks, n_features) >>> import deepchem as dc >>> tasks, datasets, transformers = dc.molnet.load_perovskite() >>> train_dataset, val_dataset, test_dataset = datasets >>> model = dc.models.CGCNNModel(mode='regression', batch_size=32, learning_rate=0.001) """ loader = _PerovskiteLoader(featurizer, splitter, transformers, Loading
deepchem/trans/__init__.py +1 −0 Original line number Diff line number Diff line Loading @@ -21,4 +21,5 @@ from deepchem.trans.transformers import ImageTransformer from deepchem.trans.transformers import DataTransforms from deepchem.trans.transformers import Transformer from deepchem.trans.transformers import FlatteningTransformer from deepchem.trans.transformers import RxnSplitTransformer from deepchem.trans.duplicate import DuplicateBalancingTransformer
deepchem/trans/tests/test_rxn_transform.py 0 → 100644 +55 −0 Original line number Diff line number Diff line import unittest import numpy as np from deepchem.trans.transformers import RxnSplitTransformer reactions: np.ndarray = np.array( [ "CC(C)C[Mg+].CON(C)C(=O)c1ccc(O)nc1>C1CCOC1.[Cl-]>CC(C)CC(=O)c1ccc(O)nc1", "CCn1cc(C(=O)O)c(=O)c2cc(F)c(-c3ccc(N)cc3)cc21.O=CO>>CCn1cc(C(=O)O)c(=O)c2cc(F)c(-c3ccc(NC=O)cc3)cc21" ], dtype=object) split: np.ndarray = np.array( [[ "CC(C)C[Mg+].CON(C)C(=O)c1ccc(O)nc1>C1CCOC1.[Cl-]", "CC(C)CC(=O)c1ccc(O)nc1" ], [ "CCn1cc(C(=O)O)c(=O)c2cc(F)c(-c3ccc(N)cc3)cc21.O=CO>", "CCn1cc(C(=O)O)c(=O)c2cc(F)c(-c3ccc(NC=O)cc3)cc21" ]], dtype=object) sep: np.ndarray = np.array( [[ "CC(C)C[Mg+].CON(C)C(=O)c1ccc(O)nc1.C1CCOC1.[Cl-]>", "CC(C)CC(=O)c1ccc(O)nc1" ], [ "CCn1cc(C(=O)O)c(=O)c2cc(F)c(-c3ccc(N)cc3)cc21.O=CO>", "CCn1cc(C(=O)O)c(=O)c2cc(F)c(-c3ccc(NC=O)cc3)cc21" ]], dtype=object) class TestRxnSplitTransformer(unittest.TestCase): """ Tests the Reaction split transformer for the source/target splitting and for the reagent mixing operation. """ def test_split(self): """Tests the source/target split from an input reaction SMILES.""" trans = RxnSplitTransformer(sep_reagent=True) split_reactions = trans.transform_array( X=reactions, y=np.array([]), w=np.array([]), ids=np.array([])) assert split_reactions[0].shape == (2, 2) assert (split_reactions[0] == split).all() def test_mixing(self): """Tests the reagent - reactant mixing toggle.""" trans = RxnSplitTransformer(sep_reagent=False) split_reactions = trans.transform_array( X=reactions, y=np.array([]), w=np.array([]), ids=np.array([])) assert split_reactions[0].shape == (2, 2) assert (split_reactions[0] == sep).all()
deepchem/trans/transformers.py +113 −0 Original line number Diff line number Diff line Loading @@ -2469,3 +2469,116 @@ class DataTransforms(object): image = Image.fromarray(self.Image) image = image.filter(ImageFilter.MedianFilter(size=size)) return np.array(image) class RxnSplitTransformer(Transformer): """Splits the reaction SMILES input into the source and target strings required for machine translation tasks. The input is expected to be in the form reactant>reagent>product. The source string would be reactants>reagents and the target string would be the products. The transformer can also separate the reagents from the reactants for a mixed training mode. During mixed training, the source string is transformed from reactants>reagent to reactants.reagent> . This can be toggled (default True) by setting the value of sep_reagent while calling the transformer. Examples -------- >>> # When mixed training is toggled. >>> import numpy as np >>> from deepchem.trans.transformers import RxnSplitTransformer >>> reactions = np.array( [ "CC(C)C[Mg+].CON(C)C(=O)c1ccc(O)nc1>C1CCOC1.[Cl-]>CC(C)CC(=O)c1ccc(O)nc1", "CCn1cc(C(=O)O)c(=O)c2cc(F)c(-c3ccc(N)cc3)cc21.O=CO>>CCn1cc(C(=O)O)c(=O)c2cc(F)c(-c3ccc(NC=O)cc3)cc21" ], dtype=object) >>> trans = RxnSplitTransformer(sep_reagent=True) >>> split_reactions = trans.transform_array(X=reactions, y=np.array([]), w=np.array([]), ids=np.array([])) >>> split_reactions (array([['CC(C)C[Mg+].CON(C)C(=O)c1ccc(O)nc1>C1CCOC1.[Cl-]', 'CC(C)CC(=O)c1ccc(O)nc1'], ['CCn1cc(C(=O)O)c(=O)c2cc(F)c(-c3ccc(N)cc3)cc21.O=CO>', 'CCn1cc(C(=O)O)c(=O)c2cc(F)c(-c3ccc(NC=O)cc3)cc21']], dtype='<U51'), array([], dtype=float64), array([], dtype=float64), array([], dtype=float64)) When mixed training is disabled, you get the following outputs: >>> trans_disable = RxnSplitTransformer(sep_reagent=False) >>> split_reactions = trans_disable.transform_array(X=reactions, y=np.array([]), w=np.array([]), ids=np.array([])) >>> split_reactions (array([['CC(C)C[Mg+].CON(C)C(=O)c1ccc(O)nc1.C1CCOC1.[Cl-]>', 'CC(C)CC(=O)c1ccc(O)nc1'], ['CCn1cc(C(=O)O)c(=O)c2cc(F)c(-c3ccc(N)cc3)cc21.O=CO>', 'CCn1cc(C(=O)O)c(=O)c2cc(F)c(-c3ccc(NC=O)cc3)cc21']], dtype='<U51'), array([], dtype=float64), array([], dtype=float64), array([], dtype=float64)) Note ---- This class only transforms the feature field of a reaction dataset like USPTO. """ def __init__(self, sep_reagent: bool = True, dataset: Optional[Dataset] = None): """Initializes the Reaction split Transformer. Parameters ---------- sep_reagent: bool, optional (default True) To separate the reagent and reactants for training. dataset: dc.data.Dataset object, optional (default None) Dataset to be transformed. """ self.sep_reagent = sep_reagent super(RxnSplitTransformer, self).__init__(transform_X=True, dataset=dataset) def transform_array( self, X: np.ndarray, y: np.ndarray, w: np.ndarray, ids: np.ndarray) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]: """Transform the data in a set of (X, y, w, ids) arrays. Parameters ---------- X: np.ndarray Array of features(the reactions) y: np.ndarray Array of labels w: np.ndarray Array of weights. ids: np.ndarray Array of weights. Returns ------- Xtrans: np.ndarray Transformed array of features ytrans: np.ndarray Transformed array of labels wtrans: np.ndarray Transformed array of weights idstrans: np.ndarray Transformed array of ids """ reactant = list(map(lambda x: x.split('>')[0], X)) reagent = list(map(lambda x: x.split('>')[1], X)) product = list(map(lambda x: x.split('>')[2], X)) if self.sep_reagent: source = [x + '>' + y for x, y in zip(reactant, reagent)] else: source = [ x + '.' + y + '>' if y else x + '>' + y for x, y in zip(reactant, reagent) ] target = product X = np.column_stack((source, target)) return (X, y, w, ids) def untransform(self, z): """Not Implemented.""" raise NotImplementedError("Cannot untransform the source/target split.")
