def standardize_oia_repo(output_file_path, old_standard):
"""
@param output_file_path:
@type output_file_path:
@return:
@rtype:
"""
source_oia_repo = OIARepo()
target_oia_repo = OIARepo(output_file_path)
standardizer = OIAStandardizer()
context = UD2OIAContext()
options = WorkFlowOptions()
for source in ["train", "dev", "test"]:
standardized_graphs = []
for oia in tqdm.tqdm(source_oia_repo.all(source),
"Standardize {}:".format(source)):
origin_oia_graph = OIAGraph.parse(oia)
uri = origin_oia_graph.meta["uri"]
oia_graph = copy.deepcopy(origin_oia_graph)
logger.info("Standardizing {}:{}".format(source, uri))
if old_standard:
upgrade_to_new_standard(oia_graph)
logger.info("Update to new standard {}:{}".format(source, uri))
try:
standardizer.standardize(None, oia_graph, context, options)
except Exception as e:
logger.error("Sentence {0} standardize error".format(uri))
logger.error("Sentence = " + " ".join(oia_graph.words))
raise e
standardized_graphs.append(oia_graph)
target_oia_repo.insert(source, standardized_graphs)
def evaluate(lang, input_path, source, output_path, debug, use_ud):
"""
:param source:
:return:
"""
if not debug:
logger.remove()
logger.add(sys.stderr, level="INFO")
ud_repo = get_udrepo(lang)
oia_repo = get_oiarepo(lang, input_path)
total_node_pred_num = 0
total_node_true_num = 0
total_node_match_num = 0
total_edge_pred_num = 0
total_edge_true_num = 0
total_edge_match_num = 0
total_exact_same = 0
total_graph_num = 0
total_empty_num = 0
item_measure = []
samples = getattr(ud_repo, source)
logger.info("evaluation sample num = {0}".format(len(samples)))
oia_visualizer = OIAGraphVisualizer()
results = dict()
for oia_data in oia_repo.all(source):
ground_oia_graph = OIAGraph.parse(oia_data)
uri = ground_oia_graph.meta['uri']
# try:
# ground_oia_graph = OIAGraph.parse(oia_repo.get(source, index))
# except Exception as e:
# logger.error("error in get labeled oia for {0}_{1}".format(source, index))
# continue
result = ud_repo.get(int(uri), source)
if len(result) == 0:
logger.error("cannot find the original ud data ")
continue
index, sentence, ud_data = result[0]
logger.info("evaluating uri {0}".format(index))
try:
if use_ud:
pred_oia_graph, _ = ud2oia(lang, index, ud_data, debug=debug)
else:
pred_oia_graph, _ = sentence2oia(lang,
index,
sentence,
debug=debug)
except Exception as e:
logger.error("error in generating oia for {0}_{1}".format(
source, index))
traceback.print_exc()
continue
results[index] = (pred_oia_graph, ground_oia_graph)
try:
eval_result = graph_match_metric(pred_oia_graph, ground_oia_graph)
except Exception as e:
logger.error("error in evaluating {0}_{1}".format(source, index))
traceback.print_exc()
continue
((node_pred_num, node_true_num,
node_match_num), (edge_pred_num, edge_true_num, edge_match_num),
exact_same) = eval_result
node_recall = float(
node_match_num) / node_true_num if node_true_num > 0 else 1
node_prec = float(
node_match_num) / node_pred_num if node_pred_num > 0 else 1
if node_true_num == 0 and node_pred_num == 0:
node_f1 = 1
elif node_match_num == 0:
node_f1 = 0
else:
node_f1 = 2 * node_recall * node_prec / (node_recall + node_prec)
edge_recall = float(
edge_match_num) / edge_true_num if edge_true_num > 0 else 1
edge_prec = float(
edge_match_num) / edge_pred_num if edge_pred_num > 0 else 1
if edge_true_num == 0 and edge_pred_num == 0:
edge_f1 = 1
elif edge_match_num == 0:
edge_f1 = 0
else:
edge_f1 = 2 * edge_recall * edge_prec / (edge_recall + edge_prec)
item_measure.append((index, node_f1, edge_f1))
total_node_pred_num += node_pred_num
total_node_true_num += node_true_num
total_node_match_num += node_match_num
total_edge_pred_num += edge_pred_num
total_edge_true_num += edge_true_num
total_edge_match_num += edge_match_num
total_exact_same += exact_same
total_graph_num += 1
logger.info("Evaluation Results:")
logger.info("Error Graph Num = {0}".format(total_empty_num))
logger.info("Type\tTrueNum\tPredNum\tMatchNum\tRecall\tPrecision")
logger.info("Node\t{0}\t{1}\t{2}\t{3}\t{4}".format(
total_node_true_num,
total_node_pred_num,
total_node_match_num,
float(total_node_match_num) / total_node_true_num,
float(total_node_match_num) / total_node_pred_num,
))
logger.info("Edge\t{0}\t{1}\t{2}\t{3}\t{4}".format(
total_edge_true_num,
total_edge_pred_num,
total_edge_match_num,
float(total_edge_match_num) / total_edge_true_num,
float(total_edge_match_num) / total_edge_pred_num,
))
logger.info("Graph\t{0}\t{1}\t{2}\t{3}\t{4}".format(
total_graph_num,
total_graph_num,
total_exact_same,
float(total_exact_same) / total_graph_num,
float(total_exact_same) / total_graph_num,
))
logger.info("Worst Items by node f1")
item_measure.sort(key=lambda x: x[1])
for index, node_f1, edge_f1 in item_measure:
if node_f1 < 0.8:
pred_oia_graph, ground_oia_graph = results[index]
pred_img = oia_visualizer.visualize(pred_oia_graph,
return_img=True)
ground_img = oia_visualizer.visualize(ground_oia_graph,
return_img=True)
image = make_pair_image(pred_img, ground_img)
image.save(
os.path.join(output_path,
"node_error_{0}_{1}.png".format(source, index)))
logger.info("{0}\t{1}\t{2}".format(index, node_f1, edge_f1))
logger.info("Worst Items by edge f1")
item_measure.sort(key=lambda x: x[2])
for index, node_f1, edge_f1 in item_measure:
if edge_f1 < 0.6:
pred_oia_graph, ground_oia_graph = results[index]
pred_img = oia_visualizer.visualize(pred_oia_graph,
return_img=True)
ground_img = oia_visualizer.visualize(ground_oia_graph,
return_img=True)
image = make_pair_image(pred_img, ground_img)
image.save(
os.path.join(output_path,
"label_error_{0}_{1}.png".format(source, index)))
logger.info("{0}\t{1}\t{2}".format(index, node_f1, edge_f1))
