max_depth = 3
# number positive/negative ligands
n_pos = 1
n_neg = 5
# 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 = 71
muv_tasks, dataset, transformers = load_muv_convmol()
# Define metric
metric = dc.metrics.Metric(
dc.metrics.roc_auc_score, verbosity="high", 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)
max_depth = 3 # number positive/negative ligands n_pos = 1 n_neg = 1 # 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 muv_tasks, dataset, transformers = load_muv_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) # Add layers