if not ds.parser.kb.server_available:
logger.error("Server is not available. Please check the endpoint at: {}".format(ds.parser.kb.endpoint))
sys.exit(0)
output_file = 'lcquadtestanswer_output'
linker = Earl(path="data/LC-QUAD/entity_lcquad_test.json")
base_dir = "./output"
question_type_classifier_path = os.path.join(base_dir, "question_type_classifier")
double_relation_classifier_path = os.path.join(base_dir, "double_relation_classifier")
utility.makedirs(question_type_classifier_path)
utility.makedirs(double_relation_classifier_path)
question_type_classifier = SVMClassifier(os.path.join(question_type_classifier_path, "svm.model"))
double_relation_classifier = SVMClassifier(os.path.join(double_relation_classifier_path, "svm.model"))
stats = Stats()
parser = LC_QaudParser()
kb = parser.kb
o = Orchestrator(logger, question_type_classifier, double_relation_classifier, parser, question_type_classifier_path, True)
tmp = []
output = []
na_list = []
for qapair in ds.qapairs:
stats.inc("total")
output_row = {"question": qapair.question.text,
"id": qapair.id,
"query": qapair.sparql.query,
def bar_chart_per_feature(input_json):
stats_overall = Stats()
stats_features = Stats()
stats_with_answer = dict()
stats_without_answer = dict()
for item in input_json:
stats_overall.inc("total")
if "answer" in item:
stats_overall.inc(item["answer"])
for f in item["features"]:
stats_features.inc(f)
if item["answer"].startswith("-"):
if item["answer"] not in stats_without_answer:
stats_without_answer[item["answer"]] = Stats()
stats_without_answer[item["answer"]].inc(f)
else:
if item["answer"] not in stats_with_answer:
stats_with_answer[item["answer"]] = Stats()
stats_with_answer[item["answer"]].inc(f)
print stats_features
print "-" * 10, "covered"
stats_with_answer_keys = stats_with_answer.keys()
stats_with_answer_keys.sort()
for item in stats_with_answer_keys:
print "{}: {} -- ".format(item,
stats_overall[item]), stats_with_answer[item]
print "-" * 10, "not covered"
stats_without_answer_keys = stats_without_answer.keys()
stats_without_answer_keys.sort()
for item in stats_without_answer_keys:
print "{}: {} -- ".format(
item, stats_overall[item]), stats_without_answer[item]
print "-" * 100
keys = stats_features.dict.keys()
ind = range(len(stats_features.dict))
last = Stats()
plt_idx = []
colors = ["green", "yellowgreen", "lightgreen", "lime", "olive"]
fig = plt.figure()
ax = plt.subplot(111)
overall = [stats_features[key] for key in keys]
p0 = ax.bar(ind, overall, 0.35, color='red')
color_id = 0
for item in stats_with_answer_keys:
answered = [stats_with_answer[item][key] for key in keys]
tmp = [last[key] for key in keys]
plt_idx.append(
ax.bar(ind, answered, 0.2, color=colors[color_id], bottom=tmp))
last.dict = dict([(key, stats_with_answer[item][key] + last[key])
for key in keys])
color_id += 1
plt.xticks(ind, keys, rotation='vertical')
plt.subplots_adjust(bottom=0.2, left=0.1, right=0.7)
ax.legend([p0] + [item[0] for item in plt_idx],
["All"] + [item for item in stats_with_answer_keys],
loc='center left',
bbox_to_anchor=(1, 0.5))
plt.show()
def default(ds, id_to_include=[], n=-1):
stat = Stats()
stat["max_generated_queries"] = 0
for data in ds:
if (len(id_to_include) == 0) or (data["id"] in id_to_include):
stat.inc("total")
if "answer" in data:
stat.inc(data["answer"])
if stat["total"] == n:
break
if "generated_queries" in data:
number_of_quries = len(data["generated_queries"])
if number_of_quries > 0:
stat.inc("has_queries")
stat.inc("generated_queries", number_of_quries)
if number_of_quries > stat["max_generated_queries"]:
stat["max_generated_queries"] = number_of_quries
if number_of_quries > 3:
stat.inc("more_than_three_queries")
return stat
miss_match = False
for item in list1:
target_uri = item.uris[0]
found = False
for e2_item in list2:
if target_uri in e2_item.uris:
found = True
break
if not found:
miss_match = True
break
return miss_match
if __name__ == "__main__":
stats = Stats()
ds = LC_Qaud_Linked("../data/LC-QUAD/linked.json")
ds.load()
ds.parse()
goldLinker = GoldLinker()
# earl = Earl("../data/LC-QUAD/EARL/output.json")
earl = Earl("../data/LC-QUAD/TagMeRelnliod/output_2300.json")
for qapair in tqdm(ds.qapairs):
e1, r1 = goldLinker.do(qapair)
e2, r2 = earl.do(qapair, force_gold=False, top=100)
if e2 is None and r2 is None:
continue
import argparse
from common.utility.stats import Stats
if __name__ == "__main__":
parser = argparse.ArgumentParser(
description='Analyse the input of ranking model')
parser.add_argument("--file",
help="file name to load the results",
default="tmp",
dest="file_name")
args = parser.parse_args()
base_path = "../learning/treelstm/data/"
sets = ["train", "dev", "test"]
for item in sets:
stats = Stats()
with open("{}{}/{}/sim.txt".format(base_path, args.file_name,
item)) as file_reader:
file_data = file_reader.readlines()
for line in file_data:
stats.inc(line.replace("\n", ""))
print item
total = 0.0 + stats["1"] + stats["2"]
print stats["1"] / total, stats["2"] / total
from parser.lc_quad_linked import LC_Qaud_Linked
import json
from kb.dbpedia import DBpedia
from parser.answerparser import AnswerParser
from common.utility.stats import Stats
from common.container.answerset import AnswerSet
from tqdm import tqdm
stats = Stats()
with open("../output/nliwod_origin.json") as data_file:
nliwod = json.load(data_file)
ds = LC_Qaud_Linked(path="../data/LC-QUAD/linked_answer6.json")
ds.load()
ds.parse()
kb = DBpedia()
parser = AnswerParser(kb)
i = 0
for qapair in tqdm(ds.qapairs):
nliwod_row = nliwod[i]
if qapair.id == nliwod_row["q_id"]:
query = nliwod_row["candidate"]
raw_result = kb.query(query)
result = AnswerSet(raw_result[1], parser.parse_queryresult)
nliwod_row["answer"] = raw_result[1]
if qapair.answerset == result:
stats.inc("correct")
nliwod_row["correct"] = True
else: