Loading deepchem/feat/molecule_featurizers/one_hot_featurizer.py +89 −27 Original line number Diff line number Diff line Loading @@ -5,7 +5,8 @@ import numpy as np from deepchem.utils.typing import RDKitMol from deepchem.utils.molecule_feature_utils import one_hot_encode from deepchem.feat.base_classes import MolecularFeaturizer from deepchem.feat.base_classes import Featurizer from typing import Any, Iterable logger = logging.getLogger(__name__) Loading @@ -16,14 +17,19 @@ ZINC_CHARSET = [ ] class OneHotFeaturizer(MolecularFeaturizer): """Encodes SMILES as a one-hot array. class OneHotFeaturizer(Featurizer): """Encodes any arbitrary string or molecule as a one-hot array. This featurizer encodes SMILES string as a one-hot array. This featurizer encodes the characters within any given string as a one-hot array. It also works with RDKit molecules: it can convert RDKit molecules to SMILES strings and then one-hot encode the characters in said strings. Note ---- This class requires RDKit to be installed. This class needs RDKit to be installed in order to accept RDKit molecules as inputs. It does not need RDKit to be installed to work with arbitrary strings. """ def __init__(self, charset: List[str] = ZINC_CHARSET, max_length: int = 100): Loading @@ -42,57 +48,113 @@ class OneHotFeaturizer(MolecularFeaturizer): self.charset = charset self.max_length = max_length def _featurize(self, mol: RDKitMol) -> np.ndarray: """Compute one-hot featurization of this molecule. def featurize(self, datapoints: Iterable[Any], log_every_n: int = 1000) -> np.ndarray: """Featurize strings or mols. Parameters ---------- mol: rdkit.Chem.rdchem.Mol RDKit Mol object datapoints: list A list of either strings or RDKit molecules. log_every_n: int, optional (default 1000) How many elements are featurized every time a featurization is logged. """ datapoints = list(datapoints) if (len(datapoints) < 1): return np.array([]) # Featurize data using featurize() in grandparent class return Featurizer.featurize(self, datapoints, log_every_n) def _featurize(self, datapoint: Any): # Featurize str data if (type(datapoint) == str): return self._featurize_string(datapoint) # Featurize mol data else: return self._featurize_mol(datapoint) def _featurize_string(self, string: str) -> np.ndarray: """Compute one-hot featurization of string. Parameters ---------- string: str An arbitrary string to be featurized. Returns ------- np.ndarray An one hot vector encoded from SMILES. An one hot vector encoded from arbitrary input string. The shape is `(max_length, len(charset) + 1)`. The index of unknown character is `len(charset)`. """ try: from rdkit import Chem except ModuleNotFoundError: raise ImportError("This class requires RDKit to be installed.") smiles = Chem.MolToSmiles(mol) # validation if len(smiles) > self.max_length: if (len(string) > self.max_length): logger.info( "The length of {} is longer than `max_length`. So we return an empty array." ) return np.array([]) smiles = self.pad_smile(smiles) string = self.pad_string(string) # Padding return np.array([ one_hot_encode(val, self.charset, include_unknown_set=True) for val in smiles for val in string ]) def _featurize_mol(self, mol: RDKitMol) -> np.ndarray: """Compute one-hot featurization of this molecule. Parameters ---------- mol: rdKit.Chem.rdchem.Mol RDKit Mol object Returns ------- np.ndarray An one hot vector encoded from SMILES. The shape is '(max_length, len(charset) + 1)' The index of unknown character is 'len(charset)'. """ try: from rdkit import Chem except ModuleNotFoundError: raise ImportError("This class requires RDKit to be installed.") smiles = Chem.MolToSmiles(mol) # Convert mol to SMILES string. return self._featurize_string(smiles) # Use string featurization. def pad_smile(self, smiles: str) -> str: """Pad SMILES string to `self.pad_length` Parameters ---------- smiles: str The smiles string to be padded. The SMILES string to be padded. Returns ------- str SMILES string space padded to self.pad_length """ return smiles.ljust(self.max_length) return self.pad_string(smiles) def pad_string(self, string: str) -> str: """Pad string to `self.pad_length` Parameters ---------- string: str The string to be padded. Returns ------- str String space padded to self.pad_length """ return string.ljust(self.max_length) def untransform(self, one_hot_vectors: np.ndarray) -> str: """Convert from one hot representation back to SMILES """Convert from one hot representation back to original string Parameters ---------- Loading @@ -102,13 +164,13 @@ class OneHotFeaturizer(MolecularFeaturizer): Returns ------- str SMILES string for an one hot encoded array. Original string for an one hot encoded array. """ smiles = "" string = "" for one_hot in one_hot_vectors: try: idx = np.argmax(one_hot) smiles += self.charset[idx] string += self.charset[idx] except IndexError: smiles += "" return smiles string += "" return string deepchem/feat/tests/test_one_hot_featurizer.py +55 −7 Original line number Diff line number Diff line Loading @@ -11,9 +11,24 @@ class TestOneHotFeaturizert(unittest.TestCase): Test OneHotFeaturizer. """ def test_onehot_featurizer(self): def test_onehot_featurizer_arbitrary(self): """ Test simple one hot encoding. Test simple one hot encoding for arbitrary string. """ string = "abcdefghijklmnopqrstuvwxyzwebhasw" charset = "abcdefghijklmnopqrstuvwxyz" length = len(charset) + 1 defaultMaxLength = 100 featurizer = OneHotFeaturizer(charset) feature = featurizer([string]) # Implicit call to featurize() assert feature.shape == (1, defaultMaxLength, length) # untransform undo_string = featurizer.untransform(feature[0]) assert string == undo_string def test_onehot_featurizer_SMILES(self): """ Test simple one hot encoding for SMILES strings. """ from rdkit import Chem length = len(ZINC_CHARSET) + 1 Loading @@ -21,13 +36,27 @@ class TestOneHotFeaturizert(unittest.TestCase): mol = Chem.MolFromSmiles(smiles) featurizer = OneHotFeaturizer() feature = featurizer([mol]) assert feature.shape == (1, 100, length) defaultMaxLength = 100 assert feature.shape == (1, defaultMaxLength, length) # untranform undo_smiles = featurizer.untransform(feature[0]) assert smiles == undo_smiles def test_onehot_featurizer_with_max_length(self): def test_onehot_featurizer_arbitrary_with_max_length(self): """ Test one hot encoding with max_length. """ string = "abcdefghijklmnopqrstuvwxyzvewqmc" charset = "abcdefghijklmnopqrstuvwxyz" length = len(charset) + 1 featurizer = OneHotFeaturizer(charset, max_length=120) feature = featurizer([string]) assert feature.shape == (1, 120, length) # untranform undo_string = featurizer.untransform(feature[0]) assert string == undo_string def test_onehot_featurizer_SMILES_with_max_length(self): """ Test one hot encoding with max_length. """ Loading @@ -38,12 +67,11 @@ class TestOneHotFeaturizert(unittest.TestCase): featurizer = OneHotFeaturizer(max_length=120) feature = featurizer([mol]) assert feature.shape == (1, 120, length) # untranform undo_smiles = featurizer.untransform(feature[0]) assert smiles == undo_smiles def test_correct_transformation(self): def test_correct_transformation_SMILES(self): """ Test correct one hot encoding. """ Loading @@ -62,3 +90,23 @@ class TestOneHotFeaturizert(unittest.TestCase): # untranform undo_smiles = featurizer.untransform(feature[0]) assert smiles == undo_smiles def test_correct_transformation_arbitrary(self): """ Test correct one hot encoding. """ charset = "1234567890" string = "12345" featurizer = OneHotFeaturizer(charset=charset, max_length=100) feature = featurizer([string]) assert np.allclose(feature[0][0], np.array([1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])) assert np.allclose(feature[0][1], np.array([0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0])) assert np.allclose(feature[0][2], np.array([0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0])) assert np.allclose(feature[0][3], np.array([0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0])) assert np.allclose(feature[0][4], np.array([0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0])) assert "This test case has not yet been written." Loading
deepchem/feat/molecule_featurizers/one_hot_featurizer.py +89 −27 Original line number Diff line number Diff line Loading @@ -5,7 +5,8 @@ import numpy as np from deepchem.utils.typing import RDKitMol from deepchem.utils.molecule_feature_utils import one_hot_encode from deepchem.feat.base_classes import MolecularFeaturizer from deepchem.feat.base_classes import Featurizer from typing import Any, Iterable logger = logging.getLogger(__name__) Loading @@ -16,14 +17,19 @@ ZINC_CHARSET = [ ] class OneHotFeaturizer(MolecularFeaturizer): """Encodes SMILES as a one-hot array. class OneHotFeaturizer(Featurizer): """Encodes any arbitrary string or molecule as a one-hot array. This featurizer encodes SMILES string as a one-hot array. This featurizer encodes the characters within any given string as a one-hot array. It also works with RDKit molecules: it can convert RDKit molecules to SMILES strings and then one-hot encode the characters in said strings. Note ---- This class requires RDKit to be installed. This class needs RDKit to be installed in order to accept RDKit molecules as inputs. It does not need RDKit to be installed to work with arbitrary strings. """ def __init__(self, charset: List[str] = ZINC_CHARSET, max_length: int = 100): Loading @@ -42,57 +48,113 @@ class OneHotFeaturizer(MolecularFeaturizer): self.charset = charset self.max_length = max_length def _featurize(self, mol: RDKitMol) -> np.ndarray: """Compute one-hot featurization of this molecule. def featurize(self, datapoints: Iterable[Any], log_every_n: int = 1000) -> np.ndarray: """Featurize strings or mols. Parameters ---------- mol: rdkit.Chem.rdchem.Mol RDKit Mol object datapoints: list A list of either strings or RDKit molecules. log_every_n: int, optional (default 1000) How many elements are featurized every time a featurization is logged. """ datapoints = list(datapoints) if (len(datapoints) < 1): return np.array([]) # Featurize data using featurize() in grandparent class return Featurizer.featurize(self, datapoints, log_every_n) def _featurize(self, datapoint: Any): # Featurize str data if (type(datapoint) == str): return self._featurize_string(datapoint) # Featurize mol data else: return self._featurize_mol(datapoint) def _featurize_string(self, string: str) -> np.ndarray: """Compute one-hot featurization of string. Parameters ---------- string: str An arbitrary string to be featurized. Returns ------- np.ndarray An one hot vector encoded from SMILES. An one hot vector encoded from arbitrary input string. The shape is `(max_length, len(charset) + 1)`. The index of unknown character is `len(charset)`. """ try: from rdkit import Chem except ModuleNotFoundError: raise ImportError("This class requires RDKit to be installed.") smiles = Chem.MolToSmiles(mol) # validation if len(smiles) > self.max_length: if (len(string) > self.max_length): logger.info( "The length of {} is longer than `max_length`. So we return an empty array." ) return np.array([]) smiles = self.pad_smile(smiles) string = self.pad_string(string) # Padding return np.array([ one_hot_encode(val, self.charset, include_unknown_set=True) for val in smiles for val in string ]) def _featurize_mol(self, mol: RDKitMol) -> np.ndarray: """Compute one-hot featurization of this molecule. Parameters ---------- mol: rdKit.Chem.rdchem.Mol RDKit Mol object Returns ------- np.ndarray An one hot vector encoded from SMILES. The shape is '(max_length, len(charset) + 1)' The index of unknown character is 'len(charset)'. """ try: from rdkit import Chem except ModuleNotFoundError: raise ImportError("This class requires RDKit to be installed.") smiles = Chem.MolToSmiles(mol) # Convert mol to SMILES string. return self._featurize_string(smiles) # Use string featurization. def pad_smile(self, smiles: str) -> str: """Pad SMILES string to `self.pad_length` Parameters ---------- smiles: str The smiles string to be padded. The SMILES string to be padded. Returns ------- str SMILES string space padded to self.pad_length """ return smiles.ljust(self.max_length) return self.pad_string(smiles) def pad_string(self, string: str) -> str: """Pad string to `self.pad_length` Parameters ---------- string: str The string to be padded. Returns ------- str String space padded to self.pad_length """ return string.ljust(self.max_length) def untransform(self, one_hot_vectors: np.ndarray) -> str: """Convert from one hot representation back to SMILES """Convert from one hot representation back to original string Parameters ---------- Loading @@ -102,13 +164,13 @@ class OneHotFeaturizer(MolecularFeaturizer): Returns ------- str SMILES string for an one hot encoded array. Original string for an one hot encoded array. """ smiles = "" string = "" for one_hot in one_hot_vectors: try: idx = np.argmax(one_hot) smiles += self.charset[idx] string += self.charset[idx] except IndexError: smiles += "" return smiles string += "" return string
deepchem/feat/tests/test_one_hot_featurizer.py +55 −7 Original line number Diff line number Diff line Loading @@ -11,9 +11,24 @@ class TestOneHotFeaturizert(unittest.TestCase): Test OneHotFeaturizer. """ def test_onehot_featurizer(self): def test_onehot_featurizer_arbitrary(self): """ Test simple one hot encoding. Test simple one hot encoding for arbitrary string. """ string = "abcdefghijklmnopqrstuvwxyzwebhasw" charset = "abcdefghijklmnopqrstuvwxyz" length = len(charset) + 1 defaultMaxLength = 100 featurizer = OneHotFeaturizer(charset) feature = featurizer([string]) # Implicit call to featurize() assert feature.shape == (1, defaultMaxLength, length) # untransform undo_string = featurizer.untransform(feature[0]) assert string == undo_string def test_onehot_featurizer_SMILES(self): """ Test simple one hot encoding for SMILES strings. """ from rdkit import Chem length = len(ZINC_CHARSET) + 1 Loading @@ -21,13 +36,27 @@ class TestOneHotFeaturizert(unittest.TestCase): mol = Chem.MolFromSmiles(smiles) featurizer = OneHotFeaturizer() feature = featurizer([mol]) assert feature.shape == (1, 100, length) defaultMaxLength = 100 assert feature.shape == (1, defaultMaxLength, length) # untranform undo_smiles = featurizer.untransform(feature[0]) assert smiles == undo_smiles def test_onehot_featurizer_with_max_length(self): def test_onehot_featurizer_arbitrary_with_max_length(self): """ Test one hot encoding with max_length. """ string = "abcdefghijklmnopqrstuvwxyzvewqmc" charset = "abcdefghijklmnopqrstuvwxyz" length = len(charset) + 1 featurizer = OneHotFeaturizer(charset, max_length=120) feature = featurizer([string]) assert feature.shape == (1, 120, length) # untranform undo_string = featurizer.untransform(feature[0]) assert string == undo_string def test_onehot_featurizer_SMILES_with_max_length(self): """ Test one hot encoding with max_length. """ Loading @@ -38,12 +67,11 @@ class TestOneHotFeaturizert(unittest.TestCase): featurizer = OneHotFeaturizer(max_length=120) feature = featurizer([mol]) assert feature.shape == (1, 120, length) # untranform undo_smiles = featurizer.untransform(feature[0]) assert smiles == undo_smiles def test_correct_transformation(self): def test_correct_transformation_SMILES(self): """ Test correct one hot encoding. """ Loading @@ -62,3 +90,23 @@ class TestOneHotFeaturizert(unittest.TestCase): # untranform undo_smiles = featurizer.untransform(feature[0]) assert smiles == undo_smiles def test_correct_transformation_arbitrary(self): """ Test correct one hot encoding. """ charset = "1234567890" string = "12345" featurizer = OneHotFeaturizer(charset=charset, max_length=100) feature = featurizer([string]) assert np.allclose(feature[0][0], np.array([1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])) assert np.allclose(feature[0][1], np.array([0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0])) assert np.allclose(feature[0][2], np.array([0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0])) assert np.allclose(feature[0][3], np.array([0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0])) assert np.allclose(feature[0][4], np.array([0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0])) assert "This test case has not yet been written."
