'MPNNPOMModel' object is not callable

[Abhishek-Sood]

So i was using gnn explainer in this GNN based model using OPENPOM model and pytorch's GNNExplainer algorithm. But i was encountering error while generating explanation using the below code. Any help would be beneficial.

input_file = 'curated_GS_LF_merged_4983.csv' # or new downloaded file path

featurizer = GraphFeaturizer()
smiles_field = 'nonStereoSMILES' # column that contains SMILES
loader = dc.data.CSVLoader(tasks=TASKS,
feature_field=smiles_field,
featurizer=featurizer)
dataset = loader.create_dataset(inputs=[input_file])
n_tasks = len(dataset.tasks)
len(dataset)

randomstratifiedsplitter = dc.splits.RandomStratifiedSplitter()
train_dataset, test_dataset, valid_dataset = randomstratifiedsplitter.train_valid_test_split(dataset, frac_train = 0.8, frac_valid = 0.1, frac_test = 0.1, seed = 1)

print("train_dataset: ", len(train_dataset))
print("valid_dataset: ", len(valid_dataset))
print("test_dataset: ", len(test_dataset))
train_ratios = get_class_imbalance_ratio(train_dataset)
assert len(train_ratios) == n_tasks
learning_rate = dc.models.optimizers.ExponentialDecay(initial_rate=0.001, decay_rate=0.5, decay_steps=32*20, staircase=True)


model = MPNNPOMModel(n_tasks = n_tasks, # general configuration
                     batch_size = 128,
                     learning_rate = learning_rate,
                     
                     class_imbalance_ratio = train_ratios, # loss calculation
                     loss_aggr_type = 'sum',
                     
                     node_out_feats = 100, # for node and edge feature representation
                     edge_hidden_feats = 75,
                     edge_out_feats = 100,
                     
                     num_step_message_passing = 5,  # for message passing and aggregation
                     mpnn_residual = True,
                     message_aggregator_type = 'sum',
                     
                     
                     mode = 'classification',
                     
                     number_atom_features = GraphConvConstants.ATOM_FDIM, #number of input features per atom
                     number_bond_features = GraphConvConstants.BOND_FDIM, #number of input features per bond
                     
                     n_classes = 1,  #suggest its a binary classification problem as per the dataset where 0 is no odor and 1 is odor
                     readout_type = 'global_sum_pooling',
                     num_step_set2set = 3,
                     num_layer_set2set = 2,
                     ffn_hidden_list = [392, 392],
                     ffn_embeddings = 256,
                     ffn_activation = 'relu',
                     ffn_dropout_p = 0.12,
                     ffn_dropout_at_input_no_act = False,
                     weight_decay = 1e-5,
                     self_loop = False,
                     optimizer_name = 'adam',
                     log_frequency = 32,
                     model_dir = './experiments',
                     device_name ='cpu')
explainer = Explainer(
    model=model,
    algorithm=GNNExplainer(epochs=200),
    explanation_type='model',
    node_mask_type='attributes',
    edge_mask_type='object',
    model_config=dict(
        mode='multiclass_classification',
        task_level='graph',
        return_type='log_probs',
    ),
)

batch = torch.zeros(data.x.size(0), dtype=torch.long)

with torch.no_grad():
    output = model(data.x, data.edge_index, batch)
print(f"Model output shape: {output.shape}")
print("Forward pass successful!")
print("Running explainer...")
exp = explainer(
    x=data.x,
    edge_index=data.edge_index,
    target=label_id,
    batch=batch
)
print(f"Explanation for '{TASKS[label_id]}' odor:")
print(f"Node importance: max={exp.node_mask.max().item():.3f}, min={exp.node_mask.min().item():.3f}")
print(f"Edge importance: max={exp.edge_mask.max().item():.3f}, min={exp.edge_mask.min().item():.3f}")
   
    # Optionally, if you need to visualize the results
print("Explanation generated successfully!")

while the explainer uses mode='multiclass_classification', and is set as multiclass, and the problem that openpom solves is multi label, i was handling it already from extracting 571 smile strings that have only single label as their discriptor, so it sorts of represent multi class so considering that the dataset is already converted to PyG format . So please help how to resolve this error.