Loading examples/splitters/random_split.py +10 −4 Original line number Diff line number Diff line import deepchem as dc mols = ['C1=CC2=C(C=C1)C1=CC=CC=C21', 'O=C1C=CC(=O)C2=C1OC=CO2', 'C1=C[N]C=C1', 'C1=CC=CC=C[C+]1', 'C1=[C]NC=C1', 'N[C@@H](C)C(=O)O', 'N[C@H](C)C(=O)O', 'CC', 'O=C=O', 'C#N', 'CCN(CC)CC', 'CC(=O)O', 'C1CCCCC1', 'c1ccccc1'] mols = [ 'C1=CC2=C(C=C1)C1=CC=CC=C21', 'O=C1C=CC(=O)C2=C1OC=CO2', 'C1=C[N]C=C1', 'C1=CC=CC=C[C+]1', 'C1=[C]NC=C1', 'N[C@@H](C)C(=O)O', 'N[C@H](C)C(=O)O', 'CC', 'O=C=O', 'C#N', 'CCN(CC)CC', 'CC(=O)O', 'C1CCCCC1', 'c1ccccc1' ] print("Original set of molecules") print(mols) splitter = dc.splits.RandomSplitter(seed=123) train, valid, test = splitter.train_valid_test_split(mols) # TODO once improved splitting API is merged in swap out for simpler # API dataset = dc.data.NumpyDataset(X=mols, ids=mols) splitter = dc.splits.RandomSplitter() train, valid, test = splitter.train_valid_test_split(dataset) # The return values are dc.data.Dataset objects so we need to extract # the ids print("Training set") Loading @@ -14,4 +21,3 @@ print("Valid set") print(valid.ids) print("Test set") print(test.ids) examples/splitters/scaffold_split.py +9 −4 Original line number Diff line number Diff line import deepchem as dc mols = ['C1=CC2=C(C=C1)C1=CC=CC=C21', 'O=C1C=CC(=O)C2=C1OC=CO2', 'C1=C[N]C=C1', 'C1=CC=CC=C[C+]1', 'C1=[C]NC=C1', 'N[C@@H](C)C(=O)O', 'N[C@H](C)C(=O)O', 'CC', 'O=C=O', 'C#N', 'CCN(CC)CC', 'CC(=O)O', 'C1CCCCC1', 'c1ccccc1'] mols = [ 'C1=CC2=C(C=C1)C1=CC=CC=C21', 'O=C1C=CC(=O)C2=C1OC=CO2', 'C1=C[N]C=C1', 'C1=CC=CC=C[C+]1', 'C1=[C]NC=C1', 'N[C@@H](C)C(=O)O', 'N[C@H](C)C(=O)O', 'CC', 'O=C=O', 'C#N', 'CCN(CC)CC', 'CC(=O)O', 'C1CCCCC1', 'c1ccccc1' ] print("Original set of molecules") print(mols) splitter = dc.splits.ScaffoldSplitter(seed=123) train, valid, test = splitter.train_valid_test_split(mols) # TODO: Once improved splitting API is merged in swap to simpler API dataset = dc.data.NumpyDataset(X=mols, ids=mols) splitter = dc.splits.ScaffoldSplitter() train, valid, test = splitter.train_valid_test_split(dataset) # The return values are dc.data.Dataset objects so we need to extract # the ids print("Training set") Loading @@ -14,4 +20,3 @@ print("Valid set") print(valid.ids) print("Test set") print(test.ids) Loading
examples/splitters/random_split.py +10 −4 Original line number Diff line number Diff line import deepchem as dc mols = ['C1=CC2=C(C=C1)C1=CC=CC=C21', 'O=C1C=CC(=O)C2=C1OC=CO2', 'C1=C[N]C=C1', 'C1=CC=CC=C[C+]1', 'C1=[C]NC=C1', 'N[C@@H](C)C(=O)O', 'N[C@H](C)C(=O)O', 'CC', 'O=C=O', 'C#N', 'CCN(CC)CC', 'CC(=O)O', 'C1CCCCC1', 'c1ccccc1'] mols = [ 'C1=CC2=C(C=C1)C1=CC=CC=C21', 'O=C1C=CC(=O)C2=C1OC=CO2', 'C1=C[N]C=C1', 'C1=CC=CC=C[C+]1', 'C1=[C]NC=C1', 'N[C@@H](C)C(=O)O', 'N[C@H](C)C(=O)O', 'CC', 'O=C=O', 'C#N', 'CCN(CC)CC', 'CC(=O)O', 'C1CCCCC1', 'c1ccccc1' ] print("Original set of molecules") print(mols) splitter = dc.splits.RandomSplitter(seed=123) train, valid, test = splitter.train_valid_test_split(mols) # TODO once improved splitting API is merged in swap out for simpler # API dataset = dc.data.NumpyDataset(X=mols, ids=mols) splitter = dc.splits.RandomSplitter() train, valid, test = splitter.train_valid_test_split(dataset) # The return values are dc.data.Dataset objects so we need to extract # the ids print("Training set") Loading @@ -14,4 +21,3 @@ print("Valid set") print(valid.ids) print("Test set") print(test.ids)
examples/splitters/scaffold_split.py +9 −4 Original line number Diff line number Diff line import deepchem as dc mols = ['C1=CC2=C(C=C1)C1=CC=CC=C21', 'O=C1C=CC(=O)C2=C1OC=CO2', 'C1=C[N]C=C1', 'C1=CC=CC=C[C+]1', 'C1=[C]NC=C1', 'N[C@@H](C)C(=O)O', 'N[C@H](C)C(=O)O', 'CC', 'O=C=O', 'C#N', 'CCN(CC)CC', 'CC(=O)O', 'C1CCCCC1', 'c1ccccc1'] mols = [ 'C1=CC2=C(C=C1)C1=CC=CC=C21', 'O=C1C=CC(=O)C2=C1OC=CO2', 'C1=C[N]C=C1', 'C1=CC=CC=C[C+]1', 'C1=[C]NC=C1', 'N[C@@H](C)C(=O)O', 'N[C@H](C)C(=O)O', 'CC', 'O=C=O', 'C#N', 'CCN(CC)CC', 'CC(=O)O', 'C1CCCCC1', 'c1ccccc1' ] print("Original set of molecules") print(mols) splitter = dc.splits.ScaffoldSplitter(seed=123) train, valid, test = splitter.train_valid_test_split(mols) # TODO: Once improved splitting API is merged in swap to simpler API dataset = dc.data.NumpyDataset(X=mols, ids=mols) splitter = dc.splits.ScaffoldSplitter() train, valid, test = splitter.train_valid_test_split(dataset) # The return values are dc.data.Dataset objects so we need to extract # the ids print("Training set") Loading @@ -14,4 +20,3 @@ print("Valid set") print(valid.ids) print("Test set") print(test.ids)
