def _extract_and_write(self, X, neighbor_id_lists, distances_to_neighbors, fileName = "l2r_train", y = None):
labels_in_neighborhood = Parallel(n_jobs=self.n_jobs)(
delayed(_create_training_samples)(cur_doc, neighbor_list, X, y, cur_doc + 1, distances_to_neighbors,
self.count_concepts, self.count_terms, self.number_of_concepts,
self.ibm1 if self.n_jobs == 1 and self.translation_probability else None) for cur_doc, neighbor_list in enumerate(neighbor_id_lists))
doc_to_neighborhood_dict = self._merge_dicts(labels_in_neighborhood)
filenames = ["samples_" + str(qid + 1) + ".tmp" for qid in range(len(doc_to_neighborhood_dict))]
with open(fileName, 'w') as outfile:
for fname in filenames:
with open(fname) as infile:
for line in infile:
outfile.write(line)
outfile.write('\n')
return doc_to_neighborhood_dict
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