AutoGluon Tabular trains a state-of-the-art tabular model using stacking ensemble model. Here I introduce a simple Graphviz based function to visualize the stack ensemble model.
def generate_model_visual(predictor, model_image_fname='model.png'):
G = predictor._trainer.model_graph
remove = [node for node,degree in dict(G.degree()).items() if degree < 1]
G.remove_nodes_from(remove)
root_node = [n for n,d in G.out_degree() if d==0]
best_model_node = predictor.get_model_best()
A = nx.nx_agraph.to_agraph(G)
A.graph_attr['label'] = 'Ensemble stack (Blue box is the best model)'
A.graph_attr['labelloc']='t'
A.graph_attr.update(rankdir='BT')
A.node_attr.update(fontsize=10)
A.node_attr.update(shape='rectangle')
for node in A.iternodes():
node.attr['label'] = f"{node.name}\nVal score: {float(node.attr['val_score']):.4f}"
if node.name == best_model_node:
node.attr['style'] = 'filled'
node.attr['fillcolor'] = '#ff9900'
node.attr['shape'] = 'box3d'
elif nx.has_path(G, node.name, best_model_node):
node.attr['style'] = 'filled'
node.attr['fillcolor'] = '#ffcc00'
A.draw(model_image_fname, format='png', prog='dot')This is a stacked ensemble model trained by executing fit().
This is a new stacked ensemble model created by fit_weighted_ensemble() on the previous trained model. As seen here, this helps understading how AutoGluon stacks models to get the best model.
