mirror of https://github.com/explosion/spaCy.git
172 lines
6.2 KiB
Python
172 lines
6.2 KiB
Python
# coding: utf-8
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from __future__ import unicode_literals
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from .train_descriptions import EntityEncoder
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from . import wikidata_processor as wd, wikipedia_processor as wp
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from spacy.kb import KnowledgeBase
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import csv
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import datetime
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INPUT_DIM = 300 # dimension of pre-trained input vectors
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DESC_WIDTH = 64 # dimension of output entity vectors
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def create_kb(nlp, max_entities_per_alias, min_entity_freq, min_occ,
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entity_def_output, entity_descr_output,
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count_input, prior_prob_input, wikidata_input):
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# Create the knowledge base from Wikidata entries
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kb = KnowledgeBase(vocab=nlp.vocab, entity_vector_length=DESC_WIDTH)
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# disable this part of the pipeline when rerunning the KB generation from preprocessed files
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read_raw_data = True
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if read_raw_data:
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print()
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print(" * _read_wikidata_entities", datetime.datetime.now())
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title_to_id, id_to_descr = wd.read_wikidata_entities_json(wikidata_input)
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# write the title-ID and ID-description mappings to file
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_write_entity_files(entity_def_output, entity_descr_output, title_to_id, id_to_descr)
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else:
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# read the mappings from file
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title_to_id = get_entity_to_id(entity_def_output)
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id_to_descr = get_id_to_description(entity_descr_output)
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print()
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print(" * _get_entity_frequencies", datetime.datetime.now())
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print()
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entity_frequencies = wp.get_all_frequencies(count_input=count_input)
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# filter the entities for in the KB by frequency, because there's just too much data (8M entities) otherwise
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filtered_title_to_id = dict()
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entity_list = []
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description_list = []
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frequency_list = []
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for title, entity in title_to_id.items():
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freq = entity_frequencies.get(title, 0)
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desc = id_to_descr.get(entity, None)
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if desc and freq > min_entity_freq:
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entity_list.append(entity)
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description_list.append(desc)
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frequency_list.append(freq)
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filtered_title_to_id[title] = entity
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print("Kept", len(filtered_title_to_id.keys()), "out of", len(title_to_id.keys()),
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"titles with filter frequency", min_entity_freq)
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print()
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print(" * train entity encoder", datetime.datetime.now())
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print()
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encoder = EntityEncoder(nlp, INPUT_DIM, DESC_WIDTH)
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encoder.train(description_list=description_list, to_print=True)
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print()
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print(" * get entity embeddings", datetime.datetime.now())
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print()
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embeddings = encoder.apply_encoder(description_list)
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print()
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print(" * adding", len(entity_list), "entities", datetime.datetime.now())
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kb.set_entities(entity_list=entity_list, prob_list=frequency_list, vector_list=embeddings)
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print()
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print(" * adding aliases", datetime.datetime.now())
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print()
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_add_aliases(kb, title_to_id=filtered_title_to_id,
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max_entities_per_alias=max_entities_per_alias, min_occ=min_occ,
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prior_prob_input=prior_prob_input)
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print()
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print("kb size:", len(kb), kb.get_size_entities(), kb.get_size_aliases())
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print("done with kb", datetime.datetime.now())
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return kb
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def _write_entity_files(entity_def_output, entity_descr_output, title_to_id, id_to_descr):
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with open(entity_def_output, mode='w', encoding='utf8') as id_file:
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id_file.write("WP_title" + "|" + "WD_id" + "\n")
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for title, qid in title_to_id.items():
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id_file.write(title + "|" + str(qid) + "\n")
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with open(entity_descr_output, mode='w', encoding='utf8') as descr_file:
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descr_file.write("WD_id" + "|" + "description" + "\n")
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for qid, descr in id_to_descr.items():
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descr_file.write(str(qid) + "|" + descr + "\n")
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def get_entity_to_id(entity_def_output):
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entity_to_id = dict()
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with open(entity_def_output, 'r', encoding='utf8') as csvfile:
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csvreader = csv.reader(csvfile, delimiter='|')
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# skip header
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next(csvreader)
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for row in csvreader:
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entity_to_id[row[0]] = row[1]
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return entity_to_id
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def get_id_to_description(entity_descr_output):
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id_to_desc = dict()
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with open(entity_descr_output, 'r', encoding='utf8') as csvfile:
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csvreader = csv.reader(csvfile, delimiter='|')
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# skip header
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next(csvreader)
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for row in csvreader:
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id_to_desc[row[0]] = row[1]
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return id_to_desc
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def _add_aliases(kb, title_to_id, max_entities_per_alias, min_occ, prior_prob_input):
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wp_titles = title_to_id.keys()
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# adding aliases with prior probabilities
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# we can read this file sequentially, it's sorted by alias, and then by count
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with open(prior_prob_input, mode='r', encoding='utf8') as prior_file:
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# skip header
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prior_file.readline()
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line = prior_file.readline()
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previous_alias = None
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total_count = 0
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counts = []
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entities = []
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while line:
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splits = line.replace('\n', "").split(sep='|')
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new_alias = splits[0]
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count = int(splits[1])
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entity = splits[2]
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if new_alias != previous_alias and previous_alias:
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# done reading the previous alias --> output
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if len(entities) > 0:
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selected_entities = []
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prior_probs = []
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for ent_count, ent_string in zip(counts, entities):
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if ent_string in wp_titles:
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wd_id = title_to_id[ent_string]
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p_entity_givenalias = ent_count / total_count
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selected_entities.append(wd_id)
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prior_probs.append(p_entity_givenalias)
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if selected_entities:
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try:
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kb.add_alias(alias=previous_alias, entities=selected_entities, probabilities=prior_probs)
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except ValueError as e:
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print(e)
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total_count = 0
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counts = []
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entities = []
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total_count += count
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if len(entities) < max_entities_per_alias and count >= min_occ:
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counts.append(count)
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entities.append(entity)
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previous_alias = new_alias
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line = prior_file.readline()
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