Merge branch 'master' into fix-docs-build-0 (208050eb) · Commits · 钟慕尧 / deepchem

contrib/rl/mcts.py

+5 −2

Original line number	Diff line number	Diff line
		@@ -5,7 +5,10 @@ from deepchem.models.optimizers import Adam
		from deepchem.models.tensorgraph.layers import Feature, Weights, Label, Layer
		import numpy as np
		import tensorflow as tf
		import collections
		try:
		from collections.abc import Sequence as SequenceCollection
		except:
		from collections import Sequence as SequenceCollection
		import copy
		import time

		@@ -109,7 +112,7 @@ class MCTS(object):
		self.n_search_episodes = n_search_episodes
		self.discount_factor = discount_factor
		self.value_weight = value_weight
		self._state_is_list = isinstance(env.state_shape[0], collections.Sequence)
		self._state_is_list = isinstance(env.state_shape[0], SequenceCollection)
		if optimizer is None:
		self._optimizer = Adam(learning_rate=0.001, beta1=0.9, beta2=0.999)
		else:

+10 −5

Original line number	Diff line number	Diff line
		@@ -6,6 +6,11 @@ import warnings
		import numpy as np
		import tensorflow as tf

		try:
		from collections.abc import Sequence as SequenceCollection
		except:
		from collections import Sequence as SequenceCollection

		from deepchem.nn import model_ops

		class RobustMultitaskClassifier(MultiTaskClassifier):
		@@ -73,15 +78,15 @@ class RobustMultitaskClassifier(MultiTaskClassifier):

		n_layers = len(layer_sizes)
		assert n_layers == len(bypass_layer_sizes)
		if not isinstance(weight_init_stddevs, collections.Sequence):
		if not isinstance(weight_init_stddevs, SequenceCollection):
		weight_init_stddevs = [weight_init_stddevs] * n_layers
		if not isinstance(bypass_weight_init_stddevs, collections.Sequence):
		if not isinstance(bypass_weight_init_stddevs, SequenceCollection):
		bypass_weight_init_stddevs = [bypass_weight_init_stddevs] * n_layers
		if not isinstance(bias_init_consts, collections.Sequence):
		if not isinstance(bias_init_consts, SequenceCollection):
		bias_init_consts = [bias_init_consts] * n_layers
		if not isinstance(dropouts, collections.Sequence):
		if not isinstance(dropouts, SequenceCollection):
		dropouts = [dropouts] * n_layers
		if not isinstance(activation_fns, collections.Sequence):
		if not isinstance(activation_fns, SequenceCollection):
		activation_fns = [activation_fns] * n_layers

		# Add the input features.

+2 −0

Original line number	Diff line number	Diff line
		@@ -21,10 +21,12 @@ from deepchem.feat.molecule_featurizers import BPSymmetryFunctionInput
		from deepchem.feat.molecule_featurizers import CircularFingerprint
		from deepchem.feat.molecule_featurizers import CoulombMatrix
		from deepchem.feat.molecule_featurizers import CoulombMatrixEig
		from deepchem.feat.molecule_featurizers import MACCSKeysFingerprint
		from deepchem.feat.molecule_featurizers import MordredDescriptors
		from deepchem.feat.molecule_featurizers import Mol2VecFingerprint
		from deepchem.feat.molecule_featurizers import MolGraphConvFeaturizer
		from deepchem.feat.molecule_featurizers import OneHotFeaturizer
		from deepchem.feat.molecule_featurizers import PubChemFingerprint
		from deepchem.feat.molecule_featurizers import RawFeaturizer
		from deepchem.feat.molecule_featurizers import RDKitDescriptors
		from deepchem.feat.molecule_featurizers import SmilesToImage

+2 −0

Original line number	Diff line number	Diff line
		@@ -4,9 +4,11 @@ from deepchem.feat.molecule_featurizers.bp_symmetry_function_input import BPSymm
		from deepchem.feat.molecule_featurizers.circular_fingerprint import CircularFingerprint
		from deepchem.feat.molecule_featurizers.coulomb_matrices import CoulombMatrix
		from deepchem.feat.molecule_featurizers.coulomb_matrices import CoulombMatrixEig
		from deepchem.feat.molecule_featurizers.maccs_keys_fingerprint import MACCSKeysFingerprint
		from deepchem.feat.molecule_featurizers.mordred_descriptors import MordredDescriptors
		from deepchem.feat.molecule_featurizers.mol2vec_fingerprint import Mol2VecFingerprint
		from deepchem.feat.molecule_featurizers.one_hot_featurizer import OneHotFeaturizer
		from deepchem.feat.molecule_featurizers.pubchem_fingerprint import PubChemFingerprint
		from deepchem.feat.molecule_featurizers.raw_featurizer import RawFeaturizer
		from deepchem.feat.molecule_featurizers.rdkit_descriptors import RDKitDescriptors
		from deepchem.feat.molecule_featurizers.smiles_to_image import SmilesToImage

0 → 100644

+47 −0

Original line number	Diff line number	Diff line
		import numpy as np

		from deepchem.utils.typing import RDKitMol
		from deepchem.feat.base_classes import MolecularFeaturizer


		class MACCSKeysFingerprint(MolecularFeaturizer):
		"""MACCS Keys Fingerprint.

		The MACCS (Molecular ACCess System) keys are one of the most commonly used structural keys.
		Please confirm the details in [1]_, [2]_.

		References
		----------
		.. [1] Durant, Joseph L., et al. "Reoptimization of MDL keys for use in drug discovery."
		Journal of chemical information and computer sciences 42.6 (2002): 1273-1280.
		.. [2] https://github.com/rdkit/rdkit/blob/master/rdkit/Chem/MACCSkeys.py

		Notes
		-----
		This class requires RDKit to be installed.
		"""

		def __init__(self):
		"""Initialize this featurizer."""
		try:
		from rdkit.Chem.AllChem import GetMACCSKeysFingerprint # noqa
		except ModuleNotFoundError:
		raise ValueError("This class requires RDKit to be installed.")

		self.calculator = GetMACCSKeysFingerprint

		def _featurize(self, mol: RDKitMol) -> np.ndarray:
		"""
		Calculate MACCS keys fingerprint.

		Parameters
		----------
		mol: rdkit.Chem.rdchem.Mol
		RDKit Mol object

		Returns
		-------
		np.ndarray
		1D array of RDKit descriptors for `mol`. The length is 167.
		"""
		return self.calculator(mol)