yapf formatting (7acf48f8) · Commits · 钟慕尧 / deepchem

deepchem/models/graph_models.py

+125 −112

Original line number	Diff line number	Diff line
		@@ -243,8 +243,7 @@ class WeaveModel(KerasModel):
		# Final atom-layer convolution. Note this differs slightly from the paper
		# since we use a tanh activation as default. This seems necessary for numerical
		# stability.
		dense1 = Dense(
		self.n_graph_feat,
		dense1 = Dense(self.n_graph_feat,
		activation=final_conv_activation_fn)(weave_layer_ind_A)
		if batch_normalize:
		dense1 = BatchNormalization(**batch_normalize_kwargs)(dense1)
		@@ -291,13 +290,15 @@ class WeaveModel(KerasModel):
		outputs = [output]
		output_types = ['prediction']
		loss = L2Loss()
		model = tf.keras.Model(
		inputs=[
		model = tf.keras.Model(inputs=[
		atom_features, pair_features, pair_split, atom_split, atom_to_pair
		],
		outputs=outputs)
		super(WeaveModel, self).__init__(
		model, loss, output_types=output_types, batch_size=batch_size, **kwargs)
		super(WeaveModel, self).__init__(model,
		loss,
		output_types=output_types,
		batch_size=batch_size,
		**kwargs)

		def compute_features_on_batch(self, X_b):
		"""Compute tensors that will be input into the model from featurized representation.
		@@ -360,17 +361,18 @@ class WeaveModel(KerasModel):
		# pair features
		pair_feat.append(mol.get_pair_features())

		return (np.concatenate(atom_feat, axis=0), np.concatenate(
		pair_feat, axis=0), np.array(pair_split), np.array(atom_split),
		return (np.concatenate(atom_feat, axis=0), np.concatenate(pair_feat,
		axis=0),
		np.array(pair_split), np.array(atom_split),
		np.concatenate(atom_to_pair, axis=0))

		def default_generator(
		self,
		def default_generator(self,
		dataset: Dataset,
		epochs: int = 1,
		mode: str = 'fit',
		deterministic: bool = True,
		pad_batches: bool = True) -> Iterable[Tuple[List, List, List]]:
		pad_batches: bool = True
		) -> Iterable[Tuple[List, List, List]]:
		"""Convert a dataset into the tensors needed for learning.

		Parameters
		@@ -392,14 +394,15 @@ class WeaveModel(KerasModel):
		"""

		for epoch in range(epochs):
		for (X_b, y_b, w_b, ids_b) in dataset.iterbatches(
		batch_size=self.batch_size,
		for (X_b, y_b, w_b,
		ids_b) in dataset.iterbatches(batch_size=self.batch_size,
		deterministic=deterministic,
		pad_batches=pad_batches):
		if y_b is not None:
		if self.mode == 'classification':
		y_b = to_one_hot(y_b.flatten(), self.n_classes).reshape(
		-1, self.n_tasks, self.n_classes)
		y_b = to_one_hot(y_b.flatten(),
		self.n_classes).reshape(-1, self.n_tasks,
		self.n_classes)
		inputs = self.compute_features_on_batch(X_b)
		yield (inputs, [y_b], [w_b])

		@@ -475,21 +478,23 @@ class DTNNModel(KerasModel):
		n_embedding=self.n_embedding)(atom_number)
		if self.dropout > 0.0:
		dtnn_embedding = Dropout(rate=self.dropout)(dtnn_embedding)
		dtnn_layer1 = layers.DTNNStep(
		n_embedding=self.n_embedding, n_distance=self.n_distance)([
		dtnn_embedding, distance, distance_membership_i,
		dtnn_layer1 = layers.DTNNStep(n_embedding=self.n_embedding,
		n_distance=self.n_distance)([
		dtnn_embedding, distance,
		distance_membership_i,
		distance_membership_j
		])
		if self.dropout > 0.0:
		dtnn_layer1 = Dropout(rate=self.dropout)(dtnn_layer1)
		dtnn_layer2 = layers.DTNNStep(
		n_embedding=self.n_embedding, n_distance=self.n_distance)([
		dtnn_layer1, distance, distance_membership_i, distance_membership_j
		dtnn_layer2 = layers.DTNNStep(n_embedding=self.n_embedding,
		n_distance=self.n_distance)([
		dtnn_layer1, distance,
		distance_membership_i,
		distance_membership_j
		])
		if self.dropout > 0.0:
		dtnn_layer2 = Dropout(rate=self.dropout)(dtnn_layer2)
		dtnn_gather = layers.DTNNGather(
		n_embedding=self.n_embedding,
		dtnn_gather = layers.DTNNGather(n_embedding=self.n_embedding,
		layer_sizes=[self.n_hidden],
		n_outputs=self.n_tasks,
		output_activation=self.output_activation)(
		@@ -499,8 +504,7 @@ class DTNNModel(KerasModel):

		n_tasks = self.n_tasks
		output = Dense(n_tasks)(dtnn_gather)
		model = tf.keras.Model(
		inputs=[
		model = tf.keras.Model(inputs=[
		atom_number, distance, atom_membership, distance_membership_i,
		distance_membership_j
		],
		@@ -540,8 +544,8 @@ class DTNNModel(KerasModel):

		atom_number = np.concatenate(atom_number).astype(np.int32)
		distance = np.concatenate(distance, axis=0)
		gaussian_dist = np.exp(
		-np.square(distance - self.steps) / (2 * self.step_size**2))
		gaussian_dist = np.exp(-np.square(distance - self.steps) /
		(2 * self.step_size**2))
		gaussian_dist = gaussian_dist.astype(np.float32)
		atom_mem = np.concatenate(atom_membership).astype(np.int32)
		dist_mem_i = np.concatenate(distance_membership_i).astype(np.int32)
		@@ -558,8 +562,8 @@ class DTNNModel(KerasModel):
		deterministic=True,
		pad_batches=True):
		for epoch in range(epochs):
		for (X_b, y_b, w_b, ids_b) in dataset.iterbatches(
		batch_size=self.batch_size,
		for (X_b, y_b, w_b,
		ids_b) in dataset.iterbatches(batch_size=self.batch_size,
		deterministic=deterministic,
		pad_batches=pad_batches):
		yield (self.compute_features_on_batch(X_b), [y_b], [w_b])
		@@ -659,15 +663,14 @@ class DAGModel(KerasModel):
		calculation_masks = Input(shape=(self.max_atoms,), dtype=tf.bool)
		membership = Input(shape=tuple(), dtype=tf.int32)
		n_atoms = Input(shape=tuple(), dtype=tf.int32)
		dag_layer1 = layers.DAGLayer(
		n_graph_feat=self.n_graph_feat,
		dag_layer1 = layers.DAGLayer(n_graph_feat=self.n_graph_feat,
		n_atom_feat=self.n_atom_feat,
		max_atoms=self.max_atoms,
		layer_sizes=self.layer_sizes,
		dropout=self.dropout,
		batch_size=batch_size)([
		atom_features, parents, calculation_orders, calculation_masks,
		n_atoms
		atom_features, parents, calculation_orders,
		calculation_masks, n_atoms
		])
		dag_gather = layers.DAGGather(
		n_graph_feat=self.n_graph_feat,
		@@ -678,8 +681,8 @@ class DAGModel(KerasModel):
		n_tasks = self.n_tasks
		if self.mode == 'classification':
		n_classes = self.n_classes
		logits = Reshape((n_tasks,
		n_classes))(Dense(n_tasks * n_classes)(dag_gather))
		logits = Reshape(
		(n_tasks, n_classes))(Dense(n_tasks * n_classes)(dag_gather))
		output = Softmax()(logits)
		outputs = [output, logits]
		output_types = ['prediction', 'loss']
		@@ -709,8 +712,11 @@ class DAGModel(KerasModel):
		n_atoms #, dropout_switch
		],
		outputs=outputs)
		super(DAGModel, self).__init__(
		model, loss, output_types=output_types, batch_size=batch_size, **kwargs)
		super(DAGModel, self).__init__(model,
		loss,
		output_types=output_types,
		batch_size=batch_size,
		**kwargs)

		def default_generator(self,
		dataset,
		@@ -720,14 +726,15 @@ class DAGModel(KerasModel):
		pad_batches=True):
		"""Convert a dataset into the tensors needed for learning"""
		for epoch in range(epochs):
		for (X_b, y_b, w_b, ids_b) in dataset.iterbatches(
		batch_size=self.batch_size,
		for (X_b, y_b, w_b,
		ids_b) in dataset.iterbatches(batch_size=self.batch_size,
		deterministic=deterministic,
		pad_batches=pad_batches):

		if y_b is not None and self.mode == 'classification':
		y_b = to_one_hot(y_b.flatten(), self.n_classes).reshape(
		-1, self.n_tasks, self.n_classes)
		y_b = to_one_hot(y_b.flatten(),
		self.n_classes).reshape(-1, self.n_tasks,
		self.n_classes)

		atoms_per_mol = [mol.get_num_atoms() for mol in X_b]
		n_atoms = sum(atoms_per_mol)
		@@ -817,8 +824,8 @@ class _GraphConvKerasModel(tf.keras.Model):
		]
		self.graph_pools = [layers.GraphPool() for _ in graph_conv_layers]
		self.dense = Dense(dense_layer_size, activation=tf.nn.relu)
		self.graph_gather = layers.GraphGather(
		batch_size=batch_size, activation_fn=tf.nn.tanh)
		self.graph_gather = layers.GraphGather(batch_size=batch_size,
		activation_fn=tf.nn.tanh)
		self.trim = TrimGraphOutput()
		if self.mode == 'classification':
		self.reshape_dense = Dense(n_tasks * n_classes)
		@@ -942,8 +949,7 @@ class GraphConvModel(KerasModel):
		self.n_classes = n_classes
		self.batch_size = batch_size
		self.uncertainty = uncertainty
		model = _GraphConvKerasModel(
		n_tasks,
		model = _GraphConvKerasModel(n_tasks,
		graph_conv_layers=graph_conv_layers,
		dense_layer_size=dense_layer_size,
		dropout=dropout,
		@@ -966,8 +972,11 @@ class GraphConvModel(KerasModel):
		else:
		output_types = ['prediction', 'embedding']
		loss = L2Loss()
		super(GraphConvModel, self).__init__(
		model, loss, output_types=output_types, batch_size=batch_size, **kwargs)
		super(GraphConvModel, self).__init__(model,
		loss,
		output_types=output_types,
		batch_size=batch_size,
		**kwargs)

		def default_generator(self,
		dataset,
		@@ -976,13 +985,14 @@ class GraphConvModel(KerasModel):
		deterministic=True,
		pad_batches=True):
		for epoch in range(epochs):
		for (X_b, y_b, w_b, ids_b) in dataset.iterbatches(
		batch_size=self.batch_size,
		for (X_b, y_b, w_b,
		ids_b) in dataset.iterbatches(batch_size=self.batch_size,
		deterministic=deterministic,
		pad_batches=pad_batches):
		if y_b is not None and self.mode == 'classification':
		y_b = to_one_hot(y_b.flatten(), self.n_classes).reshape(
		-1, self.n_tasks, self.n_classes)
		y_b = to_one_hot(y_b.flatten(),
		self.n_classes).reshape(-1, self.n_tasks,
		self.n_classes)
		multiConvMol = ConvMol.agglomerate_mols(X_b)
		n_samples = np.array(X_b.shape[0])
		inputs = [
		@@ -1076,9 +1086,10 @@ class MPNNModel(KerasModel):

		atom_embeddings = Dense(self.n_hidden)(message_passing)

		mol_embeddings = layers.SetGather(
		self.M, batch_size,
		n_hidden=self.n_hidden)([atom_embeddings, atom_split])
		mol_embeddings = layers.SetGather(self.M,
		batch_size,
		n_hidden=self.n_hidden)(
		[atom_embeddings, atom_split])

		dense1 = Dense(2 * self.n_hidden, activation=tf.nn.relu)(mol_embeddings)

		@@ -1108,13 +1119,15 @@ class MPNNModel(KerasModel):
		outputs = [output]
		output_types = ['prediction']
		loss = L2Loss()
		model = tf.keras.Model(
		inputs=[
		model = tf.keras.Model(inputs=[
		atom_features, pair_features, atom_split, atom_to_pair, n_samples
		],
		outputs=outputs)
		super(MPNNModel, self).__init__(
		model, loss, output_types=output_types, batch_size=batch_size, **kwargs)
		super(MPNNModel, self).__init__(model,
		loss,
		output_types=output_types,
		batch_size=batch_size,
		**kwargs)

		def default_generator(self,
		dataset,
		@@ -1123,16 +1136,17 @@ class MPNNModel(KerasModel):
		deterministic=True,
		pad_batches=True):
		for epoch in range(epochs):
		for (X_b, y_b, w_b, ids_b) in dataset.iterbatches(
		batch_size=self.batch_size,
		for (X_b, y_b, w_b,
		ids_b) in dataset.iterbatches(batch_size=self.batch_size,
		deterministic=deterministic,
		pad_batches=pad_batches):

		n_samples = np.array(X_b.shape[0])
		X_b = pad_features(self.batch_size, X_b)
		if y_b is not None and self.mode == 'classification':
		y_b = to_one_hot(y_b.flatten(), self.n_classes).reshape(
		-1, self.n_tasks, self.n_classes)
		y_b = to_one_hot(y_b.flatten(),
		self.n_classes).reshape(-1, self.n_tasks,
		self.n_classes)

		atom_feat = []
		pair_feat = []
		@@ -1214,8 +1228,7 @@ class DAGTensorGraph(DAGModel):

		def __init__(self, args, *kwargs):

		warnings.warn(
		TENSORGRAPH_DEPRECATION.format("DAGTensorGraph", "DAGModel"),
		warnings.warn(TENSORGRAPH_DEPRECATION.format("DAGTensorGraph", "DAGModel"),
		FutureWarning)

		super(DAGModel, self).__init__(args, *kwargs)

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