Merge pull request #1 from deepchem/master (2582b55f) · 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.

+3 −3

Original line number	Diff line number	Diff line
		@@ -520,7 +520,7 @@ class Dataset(object):
		try:
		import tensorflow as tf
		except:
		raise ValueError("This method requires TensorFlow to be installed.")
		raise ImportError("This method requires TensorFlow to be installed.")

		# Retrieve the first sample so we can determine the dtypes.
		X, y, w, ids = next(self.itersamples())
		@@ -943,7 +943,7 @@ class NumpyDataset(Dataset):
		try:
		from deepchem.data.pytorch_datasets import _TorchNumpyDataset
		except:
		raise ValueError("This method requires PyTorch to be installed.")
		raise ImportError("This method requires PyTorch to be installed.")

		pytorch_ds = _TorchNumpyDataset(
		numpy_dataset=self,
		@@ -1829,7 +1829,7 @@ class DiskDataset(Dataset):
		try:
		from deepchem.data.pytorch_datasets import _TorchDiskDataset
		except:
		raise ValueError("This method requires PyTorch to be installed.")
		raise ImportError("This method requires PyTorch to be installed.")

		pytorch_ds = _TorchDiskDataset(
		disk_dataset=self,

+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

+3 −3

Original line number	Diff line number	Diff line
		@@ -255,7 +255,7 @@ class MolecularFeaturizer(Featurizer):
		from rdkit.Chem import rdmolops
		from rdkit.Chem.rdchem import Mol
		except ModuleNotFoundError:
		raise ValueError("This class requires RDKit to be installed.")
		raise ImportError("This class requires RDKit to be installed.")

		# Special case handling of single molecule
		if isinstance(molecules, str) or isinstance(molecules, Mol):
		@@ -337,7 +337,7 @@ class MaterialStructureFeaturizer(Featurizer):
		try:
		from pymatgen import Structure
		except ModuleNotFoundError:
		raise ValueError("This class requires pymatgen to be installed.")
		raise ImportError("This class requires pymatgen to be installed.")

		structures = list(structures)
		features = []
		@@ -400,7 +400,7 @@ class MaterialCompositionFeaturizer(Featurizer):
		try:
		from pymatgen import Composition
		except ModuleNotFoundError:
		raise ValueError("This class requires pymatgen to be installed.")
		raise ImportError("This class requires pymatgen to be installed.")

		compositions = list(compositions)
		features = []