Fixing yapf (46dea4da) · Commits · 钟慕尧 / deepchem

deepchem/feat/graph_features.py

+26 −28

Original line number	Diff line number	Diff line
		@@ -542,9 +542,8 @@ def pair_features(mol: RDKitMol,
		"Malformed molecule with bonds not in specified graph distance.")
		else:
		n = mapping[(int(a1), int(a2))]
		features[n, :bt_len] = np.asarray(bond_features_map[tuple(sorted(
		(a1, a2)))],
		dtype=float)
		features[n, :bt_len] = np.asarray(
		bond_features_map[tuple(sorted((a1, a2)))], dtype=float)
		for ring in rings:
		if a1 in ring:
		for a2 in ring:
		@@ -561,10 +560,8 @@ def pair_features(mol: RDKitMol,
		# graph distance between two atoms
		if graph_distance:
		# distance is a matrix of 1-hot encoded distances for all atoms
		distance = find_distance(a1,
		num_atoms,
		bond_adj_list,
		max_distance=max_distance)
		distance = find_distance(
		a1, num_atoms, bond_adj_list, max_distance=max_distance)
		for a2 in range(num_atoms):
		if (int(a1), int(a2)) not in mapping:
		# For ring pairs outside max pairs distance continue
		@@ -585,9 +582,7 @@ def pair_features(mol: RDKitMol,
		return features, pair_edges


		def find_distance(a1: RDKitAtom,
		num_atoms: int,
		bond_adj_list,
		def find_distance(a1: RDKitAtom, num_atoms: int, bond_adj_list,
		max_distance=7) -> np.ndarray:
		"""Computes distances from provided atom.

		@@ -712,9 +707,9 @@ class ConvMolFeaturizer(MolecularFeaturizer):
		self.atom_properties = list(atom_properties)
		self.per_atom_fragmentation = per_atom_fragmentation

		def featurize(self,
		molecules: Union[RDKitMol, str, Iterable[RDKitMol],
		Iterable[str]],
		def featurize(
		self,
		molecules: Union[RDKitMol, str, Iterable[RDKitMol], Iterable[str]],
		log_every_n: int = 1000) -> np.ndarray:
		"""
		Override parent: aim is to add handling atom-depleted molecules featurization
		@@ -732,8 +727,8 @@ class ConvMolFeaturizer(MolecularFeaturizer):
		features: np.ndarray
		A numpy array containing a featurized representation of `datapoints`.
		"""
		features = super(ConvMolFeaturizer, self).featurize(molecules,
		log_every_n=1000)
		features = super(ConvMolFeaturizer, self).featurize(
		molecules, log_every_n=1000)
		if self.per_atom_fragmentation:
		# create temporary valid ids serving to filter out failed featurizations from every sublist
		# of features (i.e. every molecules' frags list), and also totally failed sublists.
		@@ -806,9 +801,10 @@ class ConvMolFeaturizer(MolecularFeaturizer):

		# Get the node features
		idx_nodes = [(a.GetIdx(),
		np.concatenate(
		(atom_features(a, use_chirality=self.use_chirality),
		self._get_atom_properties(a)))) for a in mol.GetAtoms()]
		np.concatenate((atom_features(
		a, use_chirality=self.use_chirality),
		self._get_atom_properties(a))))
		for a in mol.GetAtoms()]

		idx_nodes.sort() # Sort by ind to ensure same order as rd_kit
		idx, nodes = list(zip(*idx_nodes))
		@@ -932,7 +928,8 @@ class WeaveFeaturizer(MolecularFeaturizer):
		"""Encodes mol as a WeaveMol object."""
		# Atom features
		idx_nodes = [(a.GetIdx(),
		atom_features(a,
		atom_features(
		a,
		explicit_H=self.explicit_H,
		use_chirality=self.use_chirality))
		for a in mol.GetAtoms()]
		@@ -956,7 +953,8 @@ class WeaveFeaturizer(MolecularFeaturizer):
		bond_adj_list[bond[1]].append(bond[0])

		# Calculate pair features
		pairs, pair_edges = pair_features(mol,
		pairs, pair_edges = pair_features(
		mol,
		bond_features_map,
		bond_adj_list,
		bt_len=self.bt_len,

deepchem/utils/test/test_evaluate.py

+4 −6

Original line number	Diff line number	Diff line
		"""Unit tests for evaluators."""
		import deepchem as dc
		import numpy as np
		import sklearn
		from deepchem.utils.evaluate import Evaluator
		from deepchem.utils.evaluate import GeneratorEvaluator
		from sklearn.ensemble import RandomForestClassifier
		@@ -77,9 +76,8 @@ def test_evaluate_multiclass_classification_singletask():
		y = np.random.randint(5, size=(100,))
		dataset = dc.data.NumpyDataset(X, y)
		model = dc.models.MultitaskClassifier(1, 5, n_classes=5)
		multitask_scores = model.evaluate(dataset,
		dc.metrics.roc_auc_score,
		n_classes=5)
		multitask_scores = model.evaluate(
		dataset, dc.metrics.roc_auc_score, n_classes=5)
		assert len(multitask_scores) == 1
		assert multitask_scores["metric-1"] >= 0

		@@ -296,8 +294,8 @@ def test_gc_binary_kappa_classification():
		model = dc.models.GraphConvModel(1, mode="classification")
		# TODO: Fix this case with correct thresholding
		evaluator = Evaluator(model, dataset, [])
		multitask_scores = evaluator.compute_model_performance(dc.metrics.kappa_score,
		n_classes=2)
		multitask_scores = evaluator.compute_model_performance(
		dc.metrics.kappa_score, n_classes=2)
		assert len(multitask_scores) == 1
		assert multitask_scores["metric-1"] <= 1
		assert multitask_scores["metric-1"] >= -1

docs/requirements.txt

+1 −1

Original line number	Diff line number	Diff line
		@@ -4,4 +4,4 @@ sphinx>=3.2,<4
		sphinx_rtd_theme>=0.5,<1
		tensorflow>=2.3,<2.4
		transformers
		torch==1.8.1
		torch==1.8.0

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