def set_test_file(self, test_file_location):
self.test_file_iterator = ConllReader(
test_file_location,
self.kb_prefix,
max_elements=self.max_elements_to_read,
disambiguation=self.disambiguation,
score_transform=self.score_transform)
def construct_evaluator(self, settings, file):
evaluation_type = settings["evaluation"]["type"]
method = settings["evaluation"]["method"]
prefix = settings["endpoint"]["prefix"] if "prefix" in settings[
"endpoint"] else ""
file_reader = ConllReader(settings["dataset"][file],
entity_prefix=prefix)
if evaluation_type == "cutoff":
cutoff = float(settings["evaluation"]["cutoff"])
evaluator = Evaluator(file_reader,
cutoff,
self.logger,
method=method)
return evaluator
database = HypergraphInterface(database_interface,
expansion_strategy,
prefix=prefix)
candidate_generator = NeighborhoodCandidateGenerator(
database, neighborhood_search_scope=1, extra_literals=True)
e_indexer = LazyIndexer((40000, 1))
r_indexer = FreebaseRelationIndexer((6000, 1), 10)
database = IndexedInterface(database, e_indexer, r_indexer)
candidate_generator = CandidateGeneratorCache(candidate_generator,
disk_cache=disk_cache)
train_file_iterator = ConllReader("data/webquestions/train.split.conll",
entity_prefix=prefix)
def project_from_name_wrapper(iterator, skip=True):
for example in iterator:
names = example["gold_entities"]
graph = example["neighborhood"]
name_projection_dictionary = graph.get_inverse_name_connections(names)
gold_list = []
for name, l in name_projection_dictionary.items():
if len(l) > 0:
gold_list.extend(l)
elif graph.has_index(name):
gold_list.append(graph.to_index(name))
from experiment_construction.candidate_generator_construction.candidate_generator_cache import CandidateGeneratorCache
from helpers.read_conll_files import ConllReader
database_interface = FreebaseInterface()
expansion_strategy = OnlyFreebaseExpansionStrategy()
prefix = "http://rdf.freebase.com/ns/"
disk_cache = "/datastore/michael_cache/webquestions.1neighbors.cache"
database = HypergraphInterface(database_interface,
expansion_strategy,
prefix=prefix)
candidate_generator = NeighborhoodCandidateGenerator(
database, neighborhood_search_scope=1, extra_literals=True)
candidate_generator = CandidateGeneratorCache(candidate_generator,
disk_cache=disk_cache)
train_file_iterator = ConllReader("data/webquestions/train.split.conll")
epoch_iterator = train_file_iterator.iterate()
epoch_iterator = candidate_generator.enrich(epoch_iterator)
for example in epoch_iterator:
for edge in example["neighborhood"].entity_to_event_edges:
print(edge[1])
for edge in example["neighborhood"].event_to_entity_edges:
print(edge[1])
for edge in example["neighborhood"].entity_to_entity_edges:
print(edge[1])
from helpers.read_conll_files import ConllReader
import random
reader = ConllReader(
"/home/michael/Projects/QuestionAnswering/GCNQA/data/webquestions/train.internal.conll"
)
sentences = []
for line in reader.iterate():
sentence = " ".join([w[1] for w in line["sentence"]])
sentences.append(sentence)
subset = random.sample(sentences, 50)
counter = {}
for example in subset:
print(example)
classification = input()
if classification not in counter:
counter[classification] = 0
counter[classification] += 1
total = sum([v for k, v in counter.items()])
print("class\tcount\t%")
for k, v in counter.items():
print(str(k) + ": \t" + str(v) + " \t" + str(v / total * 100))
def __init__(self, validation_file_location, prefix):
self.validation_file_iterator = ConllReader(validation_file_location,
entity_prefix=prefix)
self.evaluator = Evaluator(self.validation_file_iterator)