Loading deepchem/splits/splitters.py +3 −3 Original line number Diff line number Diff line Loading @@ -354,9 +354,9 @@ class SingletaskStratifiedSplitter(Splitter): while sortidx.shape[0] >= split_cd: sortidx_split, sortidx = np.split(sortidx, [split_cd]) shuffled = np.random.permutation(range(split_cd)) train_idx = np.hstack([train_idx, sortidx_split[shuffled(:train_cutoff)]]) valid_idx = np.hstack([valid_idx, sortidx_split[shuffled(train_cutoff:valid_cutoff)]]) test_idx = np.hstack([test_idx, sortidx_split[shuffled(valid_cutoff:)]]) train_idx = np.hstack([train_idx, sortidx_split[shuffled[:train_cutoff]]]) valid_idx = np.hstack([valid_idx, sortidx_split[shuffled[train_cutoff:valid_cutoff]]]) test_idx = np.hstack([test_idx, sortidx_split[shuffled[valid_cutoff:]]]) # Append remaining examples to train if sortidx.shape[0] > 0: np.hstack([train_idx, sortidx]) Loading deepchem/splits/tests/test_splitter.py +18 −0 Original line number Diff line number Diff line Loading @@ -72,6 +72,24 @@ class TestSplitters(unittest.TestCase): assert len(valid_data) == 1 assert len(test_data) == 1 def test_singletask_stratified_split(self): """ Test singletask SingletaskStratifiedSplitter class. """ solubility_dataset = dc.data.tests.load_solubility_data() stratified_splitter = dc.splits.ScaffoldSplitter() train_data, valid_data, test_data = \ stratified_splitter.train_valid_test_split( solubility_dataset, frac_train=0.8, frac_valid=0.1, frac_test=0.1) assert len(train_data) == 8 assert len(valid_data) == 1 assert len(test_data) == 1 merged_dataset = dc.data.DiskDataset.merge( [train_data, valid_data, test_data]) assert sorted(merged_dataset.ids) == ( sorted(solubility_dataset.ids)) def test_singletask_random_k_fold_split(self): """ Test singletask RandomSplitter class. Loading Loading
deepchem/splits/splitters.py +3 −3 Original line number Diff line number Diff line Loading @@ -354,9 +354,9 @@ class SingletaskStratifiedSplitter(Splitter): while sortidx.shape[0] >= split_cd: sortidx_split, sortidx = np.split(sortidx, [split_cd]) shuffled = np.random.permutation(range(split_cd)) train_idx = np.hstack([train_idx, sortidx_split[shuffled(:train_cutoff)]]) valid_idx = np.hstack([valid_idx, sortidx_split[shuffled(train_cutoff:valid_cutoff)]]) test_idx = np.hstack([test_idx, sortidx_split[shuffled(valid_cutoff:)]]) train_idx = np.hstack([train_idx, sortidx_split[shuffled[:train_cutoff]]]) valid_idx = np.hstack([valid_idx, sortidx_split[shuffled[train_cutoff:valid_cutoff]]]) test_idx = np.hstack([test_idx, sortidx_split[shuffled[valid_cutoff:]]]) # Append remaining examples to train if sortidx.shape[0] > 0: np.hstack([train_idx, sortidx]) Loading
deepchem/splits/tests/test_splitter.py +18 −0 Original line number Diff line number Diff line Loading @@ -72,6 +72,24 @@ class TestSplitters(unittest.TestCase): assert len(valid_data) == 1 assert len(test_data) == 1 def test_singletask_stratified_split(self): """ Test singletask SingletaskStratifiedSplitter class. """ solubility_dataset = dc.data.tests.load_solubility_data() stratified_splitter = dc.splits.ScaffoldSplitter() train_data, valid_data, test_data = \ stratified_splitter.train_valid_test_split( solubility_dataset, frac_train=0.8, frac_valid=0.1, frac_test=0.1) assert len(train_data) == 8 assert len(valid_data) == 1 assert len(test_data) == 1 merged_dataset = dc.data.DiskDataset.merge( [train_data, valid_data, test_data]) assert sorted(merged_dataset.ids) == ( sorted(solubility_dataset.ids)) def test_singletask_random_k_fold_split(self): """ Test singletask RandomSplitter class. Loading
