Loading deepchem/trans/tests/test_rxn_transform.py +38 −16 Original line number Diff line number Diff line Loading @@ -10,24 +10,46 @@ reactions: np.ndarray = np.array( ], 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 """ 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. """ """Tests the source/target split from an input reaction SMILES.""" trans = RxnSplitTransformer(sep_reagent=True) split_reactions = trans.transform_array(X=reactions, y=[], w=[], ids=[]) assert split_reactions[0].shape == (3, 2) # Should check for equality of split_reactions and sep_split! # def test_mixing(self): # """ # Tests the reagent - reactant mixing option. # """ # WIP # pass 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 +2 −4 Original line number Diff line number Diff line Loading @@ -2502,7 +2502,7 @@ class RxnSplitTransformer(Transformer): ['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 diabled, you get the following outputs 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([])) Loading @@ -2529,8 +2529,6 @@ class RxnSplitTransformer(Transformer): dataset: dc.data.Dataset object, optional (default None) Dataset to be transformed. """ # the transformer would have to split the source and target sequences # would also consider adding the option of separating the reagent here. self.sep_reagent = sep_reagent super(RxnSplitTransformer, self).__init__(transform_X=True, dataset=dataset) Loading Loading
deepchem/trans/tests/test_rxn_transform.py +38 −16 Original line number Diff line number Diff line Loading @@ -10,24 +10,46 @@ reactions: np.ndarray = np.array( ], 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 """ 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. """ """Tests the source/target split from an input reaction SMILES.""" trans = RxnSplitTransformer(sep_reagent=True) split_reactions = trans.transform_array(X=reactions, y=[], w=[], ids=[]) assert split_reactions[0].shape == (3, 2) # Should check for equality of split_reactions and sep_split! # def test_mixing(self): # """ # Tests the reagent - reactant mixing option. # """ # WIP # pass 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 +2 −4 Original line number Diff line number Diff line Loading @@ -2502,7 +2502,7 @@ class RxnSplitTransformer(Transformer): ['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 diabled, you get the following outputs 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([])) Loading @@ -2529,8 +2529,6 @@ class RxnSplitTransformer(Transformer): dataset: dc.data.Dataset object, optional (default None) Dataset to be transformed. """ # the transformer would have to split the source and target sequences # would also consider adding the option of separating the reagent here. self.sep_reagent = sep_reagent super(RxnSplitTransformer, self).__init__(transform_X=True, dataset=dataset) Loading
