def test():
print('Loading amr data')
paragraphs = generate_paragraphs('amr_test.txt', k=5)
print('%d total cleaned paragraphs' % len(paragraphs))
paragraphs = paragraphs
print('Testing Subgraph Selection Scorer')
test_instances, test_labels = gen_subgraph_data(paragraphs, k=1)
subgraph_scorer = SubgraphSelectionScorer()
subgraph_scorer.load()
subgraph_scorer.test(test_instances, test_labels)
print('Testing Order Scorer')
test_instances, test_labels, test_weights = gen_order_data(paragraphs)
order_scorer = OrderScorer()
order_scorer.load()
order_scorer.test(test_instances, test_labels)
print('Testing Pipeline Scorer')
pipeline_scorer = PipelineScorer()
pipeline_scorer.load()
baseline_graphs = pipeline_scorer.test(paragraphs, subgraph_strategy='baseline', order_strategy='baseline', processes=3)
greedy_graphs = pipeline_scorer.test(paragraphs, subgraph_strategy='greedy', order_strategy='greedy', processes=3)
anneal_graphs = pipeline_scorer.test(paragraphs, subgraph_strategy='greedy', order_strategy='anneal', processes=3)
pickle.dump(baseline_graphs, open('baseline_graphs.pickle', 'wb'))
pickle.dump(greedy_graphs, open('greedy_graphs.pickle', 'wb'))
pickle.dump(anneal_graphs, open('anneal_graphs.pickle', 'wb'))
def example():
entries = FileParser().parse('paper_example_amr.txt')
swg = SlidingWindowGenerator(entries)
paragraphs = swg.generate(k=3)[:1]
#paragraphs[0].paragraph_graph().draw()
pipeline_scorer = PipelineScorer()
pipeline_scorer.load()
#greedy_graphs = pipeline_scorer.test(paragraphs, subgraph_strategy='greedy', order_strategy='greedy', processes=1)
#graphs = pipeline_scorer.test(paragraphs, subgraph_strategy='greedy', order_strategy='anneal', processes=1)
graphs = pipeline_scorer.test(paragraphs, subgraph_strategy='baseline', order_strategy='baseline', processes=1)
p = graphs[0]
subgraphs = [p.get_subgraph(r) for r in p.get_ordered_root_sets()]
print(subgraphs)
for i, s in enumerate(subgraphs):
s.draw('random_gv%d' % i)
def train():
print('Loading amr data')
paragraphs = generate_paragraphs('amr.txt', k=5)
print('%d total cleaned paragraphs' % len(paragraphs))
print('Training Subgraph Selection Scorer')
train_instances, train_labels = gen_subgraph_data(paragraphs)
subgraph_scorer = SubgraphSelectionScorer()
subgraph_scorer.train(train_instances, train_labels, update_cache=True)
print('Training Order Scorer')
train_instances, train_labels, train_weights = gen_order_data(paragraphs)
order_scorer = OrderScorer()
order_scorer.train(train_instances, train_labels, train_weights)
print('Training Pipeline Scorer')
pipeline_scorer = PipelineScorer()
subgraph_optimizer = SubgraphOptimizer(subgraph_scorer)
order_optimizer = OrderOptimizer(order_scorer)
pipeline_scorer.train(subgraph_optimizer, order_optimizer)
