yapfed (0041dbf3) · Commits · 钟慕尧 / deepchem

deepchem/models/tensorgraph/layers.py

+4 −4

Original line number	Diff line number	Diff line
		@@ -1436,8 +1436,8 @@ class NeighborList(Layer):
		# List of length N_atoms each of shape (M_nbrs)
		padded_dists = [
		tf.reduce_sum((atom_coord - padded_nbr_coord)**2, axis=1)
		for (atom_coord,
		padded_nbr_coord) in zip(atom_coords, padded_nbr_coords)
		for (atom_coord, padded_nbr_coord
		) in zip(atom_coords, padded_nbr_coords)
		]

		padded_closest_nbrs = [
		@@ -1448,8 +1448,8 @@ class NeighborList(Layer):
		# N_atoms elts of size (M_nbrs,) each
		padded_neighbor_list = [
		tf.gather(padded_atom_nbrs, padded_closest_nbr)
		for (padded_atom_nbrs,
		padded_closest_nbr) in zip(padded_nbrs, padded_closest_nbrs)
		for (padded_atom_nbrs, padded_closest_nbr
		) in zip(padded_nbrs, padded_closest_nbrs)
		]

		neighbor_list = tf.stack(padded_neighbor_list)

deepchem/models/tensorgraph/models/graph_models.py

+3 −3

Original line number	Diff line number	Diff line
		@@ -154,8 +154,7 @@ class WeaveTensorGraph(TensorGraph):
		C0, C1 = np.meshgrid(np.arange(n_atoms), np.arange(n_atoms))
		atom_to_pair.append(
		np.transpose(
		np.array([C1.flatten() + start,
		C0.flatten() + start])))
		np.array([C1.flatten() + start, C0.flatten() + start])))
		# number of pairs for each atom
		pair_split.extend(C1.flatten() + start)
		start = start + n_atoms
		@@ -484,7 +483,8 @@ class GraphConvTensorGraph(TensorGraph):

		"""
		self.n_tasks = n_tasks
		self.error_bars = True if 'error_bars' in kwargs and kwargs['error_bars'] else False
		self.error_bars = True if 'error_bars' in kwargs and kwargs[
		'error_bars'] else False
		kwargs['use_queue'] = False
		super(GraphConvTensorGraph, self).__init__(**kwargs)
		self.build_graph()

deepchem/molnet/run_benchmark_models.py

+15 −8

Original line number	Diff line number	Diff line
		@@ -19,6 +19,7 @@ from sklearn.ensemble import RandomForestRegressor
		from sklearn.svm import SVC
		from sklearn.kernel_ridge import KernelRidge


		def benchmark_classification(train_dataset,
		valid_dataset,
		test_dataset,
		@@ -326,11 +327,14 @@ def benchmark_classification(train_dataset,
		gamma = hyper_parameters['gamma']
		nb_epoch = None

		# Building scikit random forest model
		# Building scikit learn Kernel SVM model
		def model_builder(model_dir_kernelsvm):
		sklearn_model = SVC(
		C=C, gamma=gamma, class_weight="balanced", probability=True)
		sklearn_model = SVC(C=C,
		gamma=gamma,
		class_weight="balanced",
		probability=True)
		return deepchem.models.SklearnModel(sklearn_model, model_dir_kernelsvm)

		model = deepchem.models.multitask.SingletaskToMultitask(tasks,
		model_builder)

		@@ -661,11 +665,13 @@ def benchmark_regression(train_dataset,
		learning_rate = hyper_parameters['learning_rate']
		layer_structures = hyper_parameters['layer_structures']

		assert len(n_features) == 2, 'DTNN is only applicable to qm datasets'
		assert len(n_features) == 2, 'ANI is only applicable to qm datasets'
		max_atoms = n_features[0]
		atom_number_cases = np.unique(np.concatenate([train_dataset.X[:,:,0],
		valid_dataset.X[:,:,0],
		test_dataset.X[:,:,0]]))
		atom_number_cases = np.unique(
		np.concatenate([
		train_dataset.X[:, :, 0], valid_dataset.X[:, :, 0],
		test_dataset.X[:, :, 0]
		]))

		atom_number_cases = atom_number_cases.astype(int).tolist()
		try:
		@@ -713,10 +719,11 @@ def benchmark_regression(train_dataset,
		gamma = hyper_parameters['gamma']
		nb_epoch = None

		# Building scikit random forest model
		# Building scikit learn Kernel Ridge Regression model
		def model_builder(model_dir_krr):
		sklearn_model = KernelRidge(kernel="rbf", alpha=alpha, gamma=gamma)
		return deepchem.models.SklearnModel(sklearn_model, model_dir_krr)

		model = deepchem.models.multitask.SingletaskToMultitask(tasks,
		model_builder)

examples/delaney/delaney_krr.py

+3 −2

Original line number	Diff line number	Diff line
		@@ -27,9 +27,10 @@ metric = dc.metrics.Metric(dc.metrics.pearson_r2_score, np.mean)


		def model_builder(model_dir):
		sklearn_model = KernelRidge(
		kernel="rbf", alpha=1e-3, gamma=0.05)
		sklearn_model = KernelRidge(kernel="rbf", alpha=1e-3, gamma=0.05)
		return dc.models.SklearnModel(sklearn_model, model_dir)


		model_dir = tempfile.mkdtemp()
		model = dc.models.SingletaskToMultitask(delaney_tasks, model_builder, model_dir)

examples/tox21/tox21_KernelSVM.py

+4 −2

Original line number	Diff line number	Diff line
		@@ -26,10 +26,12 @@ train_dataset, valid_dataset, test_dataset = tox21_datasets
		# Fit models
		metric = dc.metrics.Metric(dc.metrics.roc_auc_score, np.mean)


		def model_builder(model_dir):
		sklearn_model = SVC(
		C=1.0, class_weight="balanced", probability=True)
		sklearn_model = SVC(C=1.0, class_weight="balanced", probability=True)
		return dc.models.SklearnModel(sklearn_model, model_dir)


		model_dir = tempfile.mkdtemp()
		model = dc.models.SingletaskToMultitask(tox21_tasks, model_builder, model_dir)

Admin message