def predict(self, dest_data_type=DataType.Test):
self.relation_collections[dest_data_type].reset_labels()
for index, relation_groups in enumerate(
self.relation_collections[dest_data_type].
iter_by_linked_relations_groups(self.Settings.BatchSize)):
batch = Batch(relation_groups, self.Settings.GroupSize)
feed_dict = self.create_feed_dict(batch, dest_data_type)
result = self.sess.run([self.network.Labels], feed_dict=feed_dict)
uint_labels = result[0]
for group_index, group in enumerate(batch.iter_groups):
for relation in group:
assert (isinstance(relation, ExtractedRelation))
self.relation_collections[dest_data_type].apply_label(
label=Label.from_uint(int(uint_labels[group_index])),
relation_id=relation.relation_id)
for news_ID in self.io.get_data_indices(dest_data_type):
collection = OpinionCollection(None, self.synonyms,
self.Settings.Stemmer)
self.relation_collections[dest_data_type].fill_opinion_collection(
collection,
news_ID,
lambda labels: labels[0],
debug_check_collection=False)
collection.save(
self.io.get_opinion_output_filepath(
news_ID, self.io.get_model_root(dest_data_type)))
return self._evaluate(dest_data_type, self.Settings.Stemmer)
def apply_labels(self, uint_labels, minibatch):
"""
uint_labels: list of int
each label could be as follows: 0 -- neutral, and 1 -- positive, 2 -- negative
Applying labels for each bag. It is supposed that labels and bags have
the same order.
"""
assert (isinstance(uint_labels, list))
assert (isinstance(minibatch, MiniBatch))
index = 0
for bag in minibatch.bags:
for sample in bag.samples:
label = Label.from_uint(uint_labels[index])
self._find_relation_and_set_label(sample.position, label)
index += 1
def predict(self, dest_data_type=DataType.Test):
def calculate_label(relation_labels):
assert(isinstance(relation_labels, list))
label = None
if self.Settings.RelationLabelCalculationMode == LabelCalculationMode.FIRST_APPEARED:
label = relation_labels[0]
if self.Settings.RelationLabelCalculationMode == LabelCalculationMode.AVERAGE:
label = Label.from_int(np.sign(sum([l.to_int() for l in relation_labels])))
if DebugKeys.PredictLabel:
print [l.to_int() for l in relation_labels]
print "Result: {}".format(label.to_int())
return label
assert(isinstance(dest_data_type, unicode))
self._relations_collections[dest_data_type].reset_labels()
prediction_collection = RelationPredictionResultCollection(len(self._relations_collections[dest_data_type]))
for bags_group in self.bags_collection[dest_data_type].iter_by_groups(self.Settings.BagsPerMinibatch):
minibatch = MiniBatch(bags_group)
feed_dict = self.create_feed_dict(minibatch, data_type=dest_data_type)
log_names, log_params = self.network.Log
result = self.sess.run([self.network.Labels, self.network.Output] + log_params, feed_dict=feed_dict)
uint_labels = result[0]
output = result[1]
if DebugKeys.PredictBatchDisplayLog:
self._display_log(log_names, result[2:])
# apply labels
sample_indices_count = 0
for sample_index, sample in enumerate(minibatch.iter_by_samples()):
label = Label.from_uint(int(uint_labels[sample_index]))
self._relations_collections[dest_data_type].apply_label(label, sample.RelationID)
prediction_collection.add(sample.RelationID, RelationPredictionResult(output[sample_index]))
sample_indices_count += 1
assert(sample_indices_count == len(uint_labels))
assert(self._relations_collections[dest_data_type].debug_check_all_relations_has_labels())
self._relations_collections[dest_data_type].debug_labels_statistic(dest_data_type)
# Compose Result
self._relations_collections[dest_data_type].save(
self.io.get_relations_filepath(data_type=dest_data_type,
epoch=self._last_fit_epoch_index))
prediction_collection.save(
self.io.get_relations_prediction_filepath(data_type=dest_data_type,
epoch=self._last_fit_epoch_index))
for news_ID in self.io.get_data_indices(dest_data_type):
collection = OpinionCollection(None, self.synonyms, self.settings.Stemmer)
self._relations_collections[dest_data_type].fill_opinion_collection(collection, news_ID, calculate_label)
collection.save(self.io.get_opinion_output_filepath(news_ID, self.io.get_model_root(dest_data_type)))
return self._evaluate(dest_data_type, self.Settings.Stemmer)