Updates listed in the PR. Also, bug fixes. (d214cfe3) · Commits · 钟慕尧 / deepchem

deepchem/feat/molecule_featurizers/one_hot_featurizer.py

+35 −4

Original line number	Diff line number	Diff line
		@@ -6,6 +6,9 @@ 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 Featurizer
		from deepchem.feat.base_classes import MolecularFeaturizer
		from typing import Any, Iterable
		from icecream import ic

		logger = logging.getLogger(__name__)

		@@ -16,7 +19,7 @@ ZINC_CHARSET = [
		]


		class OneHotFeaturizer(Featurizer):
		class OneHotFeaturizer(MolecularFeaturizer):
		"""Encodes SMILES as a one-hot array.

		This featurizer encodes SMILES string as a one-hot array.
		@@ -42,7 +45,34 @@ class OneHotFeaturizer(Featurizer):
		self.charset = charset
		self.max_length = max_length

		def _featurize(self, string: str) -> np.ndarray:
		def featurize(self, datapoints: Iterable[Any],
		log_every_n: int = 1000) -> np.ndarray:
		datapoints = list(datapoints)
		if (len(datapoints) < 1):
		print("No datapoints are present in the parameter Iterable, so we return an empty array.")
		return np.array([])
		ic(type(datapoints[0]))
		# Featurize Mol data
		if (type(datapoints[0]) == RDKitMol): # Mol
		return MolecularFeaturizer.featurize(self, datapoints, log_every_n)
		# Featurize str data
		elif (type(datapoints[0]) == str): # String
		return Featurizer.featurize(self, datapoints, log_every_n)
		else:
		print("One hot featurizer only supports strings and mols at this time, so returning {}")
		return np.array([])

		def _featurize(self, datapoint: Any):
		ic(datapoint)
		if (type(datapoint)==str):
		return self._featurizeString(datapoint)
		elif (type(datapoint)==RDKitMol):
		return self._featurizeMol(datapoint)
		else:
		print("One hot featurizer only supports strings and mols at this time, so returning {}")
		return np.array([])

		def _featurizeString(self, string: str) -> np.ndarray:
		"""Compute one-hot featurization of string.

		Parameters
		@@ -57,19 +87,20 @@ class OneHotFeaturizer(Featurizer):
		The index of unknown character is `len(charset)`.
		"""
		# validation
		if len(string) > 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([])

		string = self.pad_string(string)
		ic(string)
		return np.array([
		one_hot_encode(val, self.charset, include_unknown_set=True)
		for val in string
		])

		def featurizeMol(self, mol: RDKitMol) -> np.ndarray:
		def _featurizeMol(self, mol: RDKitMol) -> np.ndarray:
		"""Compute one-hot featurization of this molecule.

		Parameters

deepchem/feat/tests/test_one_hot_featurizer.py

+5 −5

Original line number	Diff line number	Diff line
		@@ -18,7 +18,7 @@ class TestOneHotFeaturizert(unittest.TestCase):
		string = "abcdefghijklmnopqrstuvwxyz"
		length = len(string) + 1
		featurizer = OneHotFeaturizer()
		feature = featurizer(string) # Implicit call to _featurize()
		feature = featurizer([string]) # Implicit call to featurize()
		assert feature.shape == (1, 100, length)
		# untransform
		undo_string = featurizer.untransform(feature[0])
		@@ -33,7 +33,7 @@ class TestOneHotFeaturizert(unittest.TestCase):
		smiles = 'CC(=O)Oc1ccccc1C(=O)O'
		mol = Chem.MolFromSmiles(smiles)
		featurizer = OneHotFeaturizer()
		feature = featurizer.featurizeMol(mol)
		feature = featurizer([mol])
		assert feature.shape == (1, 100, length)
		# untranform
		undo_smiles = featurizer.untransform(feature[0])
		@@ -46,7 +46,7 @@ class TestOneHotFeaturizert(unittest.TestCase):
		string = "abcdefghijklmnopqrstuvwxyz"
		length = len(string) + 1
		featurizer = OneHotFeaturizer(max_length=120)
		feature = featurizer(string)
		feature = featurizer([string])
		assert feature.shape == (1, 120, length)
		# untranform
		undo_string = featurizer.untransform(feature[0])
		@@ -61,7 +61,7 @@ class TestOneHotFeaturizert(unittest.TestCase):
		smiles = 'CC(=O)Oc1ccccc1C(=O)O'
		mol = Chem.MolFromSmiles(smiles)
		featurizer = OneHotFeaturizer(max_length=120)
		feature = featurizer.featurizeMol(mol)
		feature = featurizer([mol])
		assert feature.shape == (1, 120, length)
		# untranform
		undo_smiles = featurizer.untransform(feature[0])
		@@ -76,7 +76,7 @@ class TestOneHotFeaturizert(unittest.TestCase):
		smiles = 'CN=C=O'
		mol = Chem.MolFromSmiles(smiles)
		featurizer = OneHotFeaturizer(charset=charset, max_length=100)
		feature = featurizer(mol)
		feature = featurizer([mol])
		assert np.allclose(feature[0][0], np.array([1, 0, 0, 0, 0, 0, 0]))
		assert np.allclose(feature[0][1], np.array([0, 1, 0, 0, 0, 0, 0]))
		assert np.allclose(feature[0][2], np.array([0, 0, 1, 0, 0, 0, 0]))

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