Loading deepchem/splits/splitters.py +30 −20 Original line number Diff line number Diff line Loading @@ -852,7 +852,6 @@ class ScaffoldSplitter(Splitter): def split(self, dataset, seed=None, frac_train=.8, frac_valid=.1, frac_test=.1, Loading @@ -861,36 +860,47 @@ class ScaffoldSplitter(Splitter): Splits internal compounds into train/validation/test by scaffold. """ np.testing.assert_almost_equal(frac_train + frac_valid + frac_test, 1.) scaffold_sets = self.generate_scaffolds(dataset) train_cutoff = frac_train * len(dataset) valid_cutoff = (frac_train + frac_valid) * len(dataset) train_inds, valid_inds, test_inds = [], [], [] log("About to sort in scaffold sets", self.verbose) for scaffold_set in scaffold_sets: if len(train_inds) + len(scaffold_set) > train_cutoff: if len(train_inds) + len(valid_inds) + len(scaffold_set) > valid_cutoff: test_inds += scaffold_set else: valid_inds += scaffold_set else: train_inds += scaffold_set return train_inds, valid_inds, test_inds def generate_scaffolds(self, dataset, log_every_n=1000): """ Returns all scaffolds from the dataset """ scaffolds = {} log("About to generate scaffolds", self.verbose) data_len = len(dataset) log("About to generate scaffolds", self.verbose) for ind, smiles in enumerate(dataset.ids): if ind % log_every_n == 0: log("Generating scaffold %d/%d" % (ind, data_len), self.verbose) log(f"Generating scaffold {ind} {data_len}", self.verbose) scaffold = generate_scaffold(smiles) if scaffold not in scaffolds: scaffolds[scaffold] = [ind] else: scaffolds[scaffold].append(ind) # Sort from largest to smallest scaffold sets scaffolds = {key: sorted(value) for key, value in scaffolds.items()} scaffold_sets = [ scaffold_set for (scaffold, scaffold_set) in sorted( scaffolds.items(), key=lambda x: (len(x[1]), x[1][0]), reverse=True) ] train_cutoff = frac_train * len(dataset) valid_cutoff = (frac_train + frac_valid) * len(dataset) train_inds, valid_inds, test_inds = [], [], [] log("About to sort in scaffold sets", self.verbose) for scaffold_set in scaffold_sets: if len(train_inds) + len(scaffold_set) > train_cutoff: if len(train_inds) + len(valid_inds) + len(scaffold_set) > valid_cutoff: test_inds += scaffold_set else: valid_inds += scaffold_set else: train_inds += scaffold_set return train_inds, valid_inds, test_inds return scaffold_sets class FingerprintSplitter(Splitter): Loading deepchem/splits/test_scaffold_splitter.py 0 → 100644 +134 −0 Original line number Diff line number Diff line import unittest from unittest import TestCase import numpy as np import deepchem as dc from deepchem.splits import Splitter from deepchem.utils import ScaffoldGenerator from deepchem.utils.save import log from rdkit import Chem def generate_scaffold(smiles, include_chirality=False): """Compute the Bemis-Murcko scaffold for a SMILES string.""" mol = Chem.MolFromSmiles(smiles) engine = ScaffoldGenerator(include_chirality=include_chirality) scaffold = engine.get_scaffold(mol) return scaffold class ScaffoldSplitter(Splitter): """ Class for doing data splits based on the scaffold of small molecules. """ def split(self, dataset, frac_train=.8, frac_valid=.1, frac_test=.1, log_every_n=1000): """ Splits internal compounds into train/validation/test by scaffold. """ np.testing.assert_almost_equal(frac_train + frac_valid + frac_test, 1.) scaffold_sets = self.generate_scaffolds(dataset) train_cutoff = frac_train * len(dataset) valid_cutoff = (frac_train + frac_valid) * len(dataset) train_inds, valid_inds, test_inds = [], [], [] log("About to sort in scaffold sets", self.verbose) for scaffold_set in scaffold_sets: if len(train_inds) + len(scaffold_set) > train_cutoff: if len(train_inds) + len(valid_inds) + len(scaffold_set) > valid_cutoff: test_inds += scaffold_set else: valid_inds += scaffold_set else: train_inds += scaffold_set return train_inds, valid_inds, test_inds def generate_scaffolds(self, dataset, log_every_n=1000): """ Returns all scaffolds from the dataset """ scaffolds = {} data_len = len(dataset) log("About to generate scaffolds", self.verbose) for ind, smiles in enumerate(dataset.ids): if ind % log_every_n == 0: log(f"Generating scaffold {ind} {data_len}", self.verbose) scaffold = generate_scaffold(smiles) if scaffold not in scaffolds: scaffolds[scaffold] = [ind] else: scaffolds[scaffold].append(ind) # Sort from largest to smallest scaffold sets scaffolds = {key: sorted(value) for key, value in scaffolds.items()} scaffold_sets = [ scaffold_set for (scaffold, scaffold_set) in sorted( scaffolds.items(), key=lambda x: (len(x[1]), x[1][0]), reverse=True) ] return scaffold_sets class TestScaffoldSplitter(TestCase): def create_dataset(self): # Load Tox21 dataset tox21_tasks, tox21_datasets, transformers = dc.molnet.load_tox21( featurizer='GraphConv') return tox21_datasets def test_scaffolds(self): splitter = ScaffoldSplitter() train_dataset, valid_dataset, test_dataset = self.create_dataset() scaffolds_separate = splitter.generate_scaffolds(train_dataset) scaffolds_train, scaffolds_valid, _ = splitter.split(train_dataset) data_cnt = sum([len(sfd) for sfd in scaffolds_separate]) # The amount of datapoints has to be the same print(f"\nDatapoints count from generate_scaffolds: {data_cnt}") print( f"Datapoints count from train_dataset.X.shape[0]: {train_dataset.X.shape[0]}" ) self.assertTrue(data_cnt == train_dataset.X.shape[0]) # The number of scaffolds generated by the splitter # has to be smaller or equal than number of total molecules scaffolds_separate_cnt = len(scaffolds_separate) print(f"\nNumber of molecules: {train_dataset.X.shape[0]}") print( f"Number of scaffolds from generate_scaffolds() method: {scaffolds_separate_cnt}" ) self.assertTrue(scaffolds_separate_cnt <= train_dataset.X.shape[0]) if __name__ == "__main__": unittest.main() """class TestSpecifiedIndexSplitter(TestCase): def create_dataset(self): n_samples, n_features = 20, 10 X = np.random.random(size=(n_samples, n_features)) y = np.random.random(size=(n_samples, 1)) return deepchem.data.NumpyDataset(X, y) def test_split(self): ds = self.create_dataset() indexes = list(range(len(ds))) train, test = train_test_split(indexes) train, valid = train_test_split(train) splitter = SpecifiedIndexSplitter(train, valid, test) train_ds, valid_ds, test_ds = splitter.train_valid_test_split(ds) self.assertTrue(np.all(train_ds.X == ds.X[train])) self.assertTrue(np.all(valid_ds.X == ds.X[valid])) self.assertTrue(np.all(test_ds.X == ds.X[test])) """ Loading
deepchem/splits/splitters.py +30 −20 Original line number Diff line number Diff line Loading @@ -852,7 +852,6 @@ class ScaffoldSplitter(Splitter): def split(self, dataset, seed=None, frac_train=.8, frac_valid=.1, frac_test=.1, Loading @@ -861,36 +860,47 @@ class ScaffoldSplitter(Splitter): Splits internal compounds into train/validation/test by scaffold. """ np.testing.assert_almost_equal(frac_train + frac_valid + frac_test, 1.) scaffold_sets = self.generate_scaffolds(dataset) train_cutoff = frac_train * len(dataset) valid_cutoff = (frac_train + frac_valid) * len(dataset) train_inds, valid_inds, test_inds = [], [], [] log("About to sort in scaffold sets", self.verbose) for scaffold_set in scaffold_sets: if len(train_inds) + len(scaffold_set) > train_cutoff: if len(train_inds) + len(valid_inds) + len(scaffold_set) > valid_cutoff: test_inds += scaffold_set else: valid_inds += scaffold_set else: train_inds += scaffold_set return train_inds, valid_inds, test_inds def generate_scaffolds(self, dataset, log_every_n=1000): """ Returns all scaffolds from the dataset """ scaffolds = {} log("About to generate scaffolds", self.verbose) data_len = len(dataset) log("About to generate scaffolds", self.verbose) for ind, smiles in enumerate(dataset.ids): if ind % log_every_n == 0: log("Generating scaffold %d/%d" % (ind, data_len), self.verbose) log(f"Generating scaffold {ind} {data_len}", self.verbose) scaffold = generate_scaffold(smiles) if scaffold not in scaffolds: scaffolds[scaffold] = [ind] else: scaffolds[scaffold].append(ind) # Sort from largest to smallest scaffold sets scaffolds = {key: sorted(value) for key, value in scaffolds.items()} scaffold_sets = [ scaffold_set for (scaffold, scaffold_set) in sorted( scaffolds.items(), key=lambda x: (len(x[1]), x[1][0]), reverse=True) ] train_cutoff = frac_train * len(dataset) valid_cutoff = (frac_train + frac_valid) * len(dataset) train_inds, valid_inds, test_inds = [], [], [] log("About to sort in scaffold sets", self.verbose) for scaffold_set in scaffold_sets: if len(train_inds) + len(scaffold_set) > train_cutoff: if len(train_inds) + len(valid_inds) + len(scaffold_set) > valid_cutoff: test_inds += scaffold_set else: valid_inds += scaffold_set else: train_inds += scaffold_set return train_inds, valid_inds, test_inds return scaffold_sets class FingerprintSplitter(Splitter): Loading
deepchem/splits/test_scaffold_splitter.py 0 → 100644 +134 −0 Original line number Diff line number Diff line import unittest from unittest import TestCase import numpy as np import deepchem as dc from deepchem.splits import Splitter from deepchem.utils import ScaffoldGenerator from deepchem.utils.save import log from rdkit import Chem def generate_scaffold(smiles, include_chirality=False): """Compute the Bemis-Murcko scaffold for a SMILES string.""" mol = Chem.MolFromSmiles(smiles) engine = ScaffoldGenerator(include_chirality=include_chirality) scaffold = engine.get_scaffold(mol) return scaffold class ScaffoldSplitter(Splitter): """ Class for doing data splits based on the scaffold of small molecules. """ def split(self, dataset, frac_train=.8, frac_valid=.1, frac_test=.1, log_every_n=1000): """ Splits internal compounds into train/validation/test by scaffold. """ np.testing.assert_almost_equal(frac_train + frac_valid + frac_test, 1.) scaffold_sets = self.generate_scaffolds(dataset) train_cutoff = frac_train * len(dataset) valid_cutoff = (frac_train + frac_valid) * len(dataset) train_inds, valid_inds, test_inds = [], [], [] log("About to sort in scaffold sets", self.verbose) for scaffold_set in scaffold_sets: if len(train_inds) + len(scaffold_set) > train_cutoff: if len(train_inds) + len(valid_inds) + len(scaffold_set) > valid_cutoff: test_inds += scaffold_set else: valid_inds += scaffold_set else: train_inds += scaffold_set return train_inds, valid_inds, test_inds def generate_scaffolds(self, dataset, log_every_n=1000): """ Returns all scaffolds from the dataset """ scaffolds = {} data_len = len(dataset) log("About to generate scaffolds", self.verbose) for ind, smiles in enumerate(dataset.ids): if ind % log_every_n == 0: log(f"Generating scaffold {ind} {data_len}", self.verbose) scaffold = generate_scaffold(smiles) if scaffold not in scaffolds: scaffolds[scaffold] = [ind] else: scaffolds[scaffold].append(ind) # Sort from largest to smallest scaffold sets scaffolds = {key: sorted(value) for key, value in scaffolds.items()} scaffold_sets = [ scaffold_set for (scaffold, scaffold_set) in sorted( scaffolds.items(), key=lambda x: (len(x[1]), x[1][0]), reverse=True) ] return scaffold_sets class TestScaffoldSplitter(TestCase): def create_dataset(self): # Load Tox21 dataset tox21_tasks, tox21_datasets, transformers = dc.molnet.load_tox21( featurizer='GraphConv') return tox21_datasets def test_scaffolds(self): splitter = ScaffoldSplitter() train_dataset, valid_dataset, test_dataset = self.create_dataset() scaffolds_separate = splitter.generate_scaffolds(train_dataset) scaffolds_train, scaffolds_valid, _ = splitter.split(train_dataset) data_cnt = sum([len(sfd) for sfd in scaffolds_separate]) # The amount of datapoints has to be the same print(f"\nDatapoints count from generate_scaffolds: {data_cnt}") print( f"Datapoints count from train_dataset.X.shape[0]: {train_dataset.X.shape[0]}" ) self.assertTrue(data_cnt == train_dataset.X.shape[0]) # The number of scaffolds generated by the splitter # has to be smaller or equal than number of total molecules scaffolds_separate_cnt = len(scaffolds_separate) print(f"\nNumber of molecules: {train_dataset.X.shape[0]}") print( f"Number of scaffolds from generate_scaffolds() method: {scaffolds_separate_cnt}" ) self.assertTrue(scaffolds_separate_cnt <= train_dataset.X.shape[0]) if __name__ == "__main__": unittest.main() """class TestSpecifiedIndexSplitter(TestCase): def create_dataset(self): n_samples, n_features = 20, 10 X = np.random.random(size=(n_samples, n_features)) y = np.random.random(size=(n_samples, 1)) return deepchem.data.NumpyDataset(X, y) def test_split(self): ds = self.create_dataset() indexes = list(range(len(ds))) train, test = train_test_split(indexes) train, valid = train_test_split(train) splitter = SpecifiedIndexSplitter(train, valid, test) train_ds, valid_ds, test_ds = splitter.train_valid_test_split(ds) self.assertTrue(np.all(train_ds.X == ds.X[train])) self.assertTrue(np.all(valid_ds.X == ds.X[valid])) self.assertTrue(np.all(test_ds.X == ds.X[test])) """
