n_pos = 10 n_neg = 10 # Set batch sizes for network test_batch_size = 128 support_batch_size = n_pos + n_neg nb_epochs = 1 n_train_trials = 2000 n_eval_trials = 20 learning_rate = 1e-4 log_every_n_samples = 50 # Number of features on conv-mols n_feat = 75 sider_tasks, sider_dataset, _ = load_sider_convmol() sider_dataset = to_numpy_dataset(sider_dataset) tox21_tasks, tox21_dataset, _ = load_tox21_convmol() tox21_dataset = to_numpy_dataset(tox21_dataset) # Define metric metric = dc.metrics.Metric(dc.metrics.roc_auc_score, mode="classification") # Train support model on train support_model = dc.nn.SequentialSupportGraph(n_feat) # Add layers support_model.add(dc.nn.GraphConv(64, activation='relu')) support_model.add(dc.nn.GraphPool()) support_model.add(dc.nn.GraphConv(128, activation='relu')) support_model.add(dc.nn.GraphPool()) support_model.add(dc.nn.GraphConv(64, activation='relu')) support_model.add(dc.nn.GraphPool())
max_depth = 3
# number positive/negative ligands
n_pos = 10
n_neg = 10
# Set batch sizes for network
test_batch_size = 128
support_batch_size = n_pos + n_neg
nb_epochs = 1
n_train_trials = 2000
n_eval_trials = 20
learning_rate = 1e-4
log_every_n_samples = 50
# Number of features on conv-mols
n_feat = 75
tox21_tasks, dataset, transformers = load_tox21_convmol()
# Define metric
metric = dc.metrics.Metric(
dc.metrics.roc_auc_score, mode="classification")
task_splitter = dc.splits.TaskSplitter()
fold_datasets = task_splitter.k_fold_split(dataset, K)
train_folds = fold_datasets[:-1]
train_dataset = dc.splits.merge_fold_datasets(train_folds)
test_dataset = fold_datasets[-1]
# Train support model on train
support_model = dc.nn.SequentialSupportGraph(n_feat)
n_pos = 10 n_neg = 10 # Set batch sizes for network test_batch_size = 128 support_batch_size = n_pos + n_neg nb_epochs = 1 n_train_trials = 2000 n_eval_trials = 20 learning_rate = 1e-4 log_every_n_samples = 50 # Number of features on conv-mols n_feat = 75 sider_tasks, sider_dataset, _ = load_sider_convmol() sider_dataset = to_numpy_dataset(sider_dataset) tox21_tasks, tox21_dataset, _ = load_tox21_convmol() tox21_dataset = to_numpy_dataset(tox21_dataset) # Define metric metric = dc.metrics.Metric(dc.metrics.roc_auc_score, mode="classification") # Train support model on train support_model = dc.nn.SequentialSupportGraph(n_feat) # Add layers support_model.add(dc.nn.GraphConv(64, activation='relu')) support_model.add(dc.nn.GraphPool()) support_model.add(dc.nn.GraphConv(128, activation='relu')) support_model.add(dc.nn.GraphPool()) support_model.add(dc.nn.GraphConv(64, activation='relu')) support_model.add(dc.nn.GraphPool())
