# num 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 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'))
# 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 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'))