Support for attention coefficient extraction

.gitignore

@@ -132,4 +132,6 @@ models/*check*
 models/*handcrafted*
 
 data/pdb/*.txt
-data/tmp/*
+data/tmp/*
+predictions/.DS_Store
+.DS_Store

predict_bindingsites.py

@@ -74,7 +74,7 @@ def write_predictions(csv_file, residue_ids, sequence, interface_prediction):
     if interface_prediction.ndim == 1:
         columns.append('Binding site probability')
     else:
-        columns += ['Output %s' %i for i in range(len(interface_prediction) )]
+        columns += ['Output %s' %i for i in range(interface_prediction.shape[-1] )]
 
     with open(csv_file, 'w') as f:
         f.write(','.join(columns) + '\n' )
@@ -492,7 +492,7 @@ def predict_interface_residues(
             for attention_coeff,neighborhood_graph in zip(attention_coeffs,neighborhood_graphs):
                 aggregated_attention_coeff = np.zeros(len(attention_coeff),dtype=np.float32)
                 for s in range( len(attention_coeff) ):
-                    aggregated_attention_coeff[neighborhood_graph[s]] += np.maximum(sign*attention_coeff[s],0).mean(
+                    aggregated_attention_coeff[neighborhood_graph[s]] += np.maximum(sign*attention_coeff[s][:len(neighborhood_graph[s])],0).mean(
                         -1)  # Attention coefficient has size [N_aa,K_graph,nheads]. average over heads.
                     # aggregated_attention_coeff[neighborhood_graph[s]] += np.abs(attention_coeff[s]).mean(-1) # Attention coefficient has size [N_aa,K_graph,nheads]. average over heads.
                 aggregated_attention_coeffs.append(aggregated_attention_coeff)
@@ -556,7 +556,7 @@ def predict_interface_residues(
                 for attention_coeff,neighborhood_graph in zip(attention_coeffs,neighborhood_graphs):
                     aggregated_attention_coeff = np.zeros(len(attention_coeff),dtype=np.float32)
                     for s in range( len(attention_coeff) ):
-                        aggregated_attention_coeff[neighborhood_graph[s]] += np.maximum(sign*attention_coeff[s],0).mean(-1) # Attention coefficient has size [N_aa,K_graph,nheads]. average over heads.
+                        aggregated_attention_coeff[neighborhood_graph[s]] += np.maximum(sign*attention_coeff[s][:len(neighborhood_graph[s])],0).mean(-1) # Attention coefficient has size [N_aa,K_graph,nheads]. average over heads.
                     aggregated_attention_coeffs.append(aggregated_attention_coeff)
                 aggregated_attention_coeffs = np.array(aggregated_attention_coeffs)
                 if layer == 'attention_layer':
@@ -611,6 +611,7 @@ def predict_interface_residues(
             if not aggregate_models: # multioutput.
                 for layer_,prediction in zip(layer,predictions):
                     if layer_ is None:
+                        prediction = prediction[:,1]
                         csv_file = query_output_folder + 'predictions_' + query_name + '.csv'
                         chimera_file = query_output_folder + 'chimera_' + query_names[i]
                         annotated_pdb_file = query_output_folder + 'annotated_' + query_names[i] + ('.cif' if file_is_cif else '.pdb')