def process_semistructured(corpus_dir, outfile, language, processes,
sourced_only, genealogics, dump_unresolved):
""" Processes the corpus and extracts semi-structured data serialized into QuickStatements.
Needs a second pass on genealogics to correctly resolve family members.
"""
resolver = SemistructuredSerializer(language, sourced_only, )
genealogics_url_to_id, count, skipped = resolver.process_corpus(
io.load_scraped_items(corpus_dir), outfile, dump_unresolved, genealogics, processes
)
logger.info('Done, produced %d statements, skipped %d names', count, skipped)
if not genealogics:
logger.info("Dataset serialized to '%s'" % outfile.name)
return
logger.info('Starting second pass on genealogics ...')
genealogics_data = resolver.resolve_genealogics_family(genealogics, genealogics_url_to_id)
for success, item in genealogics_data:
if success:
outfile.write(item.encode('utf8'))
outfile.write('\n')
count += 1
if count % 10000 == 0:
logger.info('Produced %d statements so far, skipped %d names', count, skipped)
else:
skipped += 1
if dump_unresolved:
dump_unresolved.write(json.dumps(item))
dump_unresolved.write('\n')
logger.info('Done, produced %d statements, skipped %d names', count, skipped)
logger.info("Dataset serialized to '%s'" % outfile.name)
def about_biographies_count(corpus):
""" Finds how many items have/don't have a biography
"""
count = with_bio = characters = 0
for doc in load_scraped_items(corpus):
count += 1
if doc.get('bio') and len(doc['bio']) > 5:
with_bio += 1
characters += len(doc['bio'])
print 'Total number of items:', count
print 'Items with a biography %d (%.2f %%)' % (with_bio,
100. * with_bio / count)
print 'Cumulative length of biographies: %d characters' % characters
try:
import matplotlib.pyplot as plt
except ImportError:
logger.warn('Cannot import matplotlib, skipping chart')
return
plt.bar([0, 1], [count - with_bio, with_bio], width=0.75)
plt.xticks([0.375, 1.375], ['Without Biography', 'With Biography'])
plt.grid(True, axis='y')
plt.xlim((-0.5, 2.25))
plt.show()
def about_biographies_length(corpus, bins, log_y):
""" Computes an histogram of biography length
"""
lengths = []
for doc in load_scraped_items(corpus):
if len(doc.get('bio') or '') > 5:
lengths.append(len(doc['bio']))
width = float(max(lengths)) / bins
buckets = defaultdict(int)
for each in lengths:
buckets[int(each / width)] += 1
for i in xrange(max(buckets.keys())):
print '%d - %d: %d' % (i * width, (i + 1) * width - 1, buckets[i])
try:
import matplotlib.pyplot as plt
except ImportError:
logger.warn('Cannot import matplotlib, skipping chart')
return
plt.title('Biography length distribution for %d items' % len(lengths))
plt.xlabel('Biography length in characters')
plt.ylabel('Number of items')
plt.hist(lengths, bins=bins, log=log_y)
plt.grid(True)
plt.tight_layout()
plt.show()
def main(corpus, document_key, pos_tag_key, language_code, tagger, outfile,
tt_home, batch_size):
""" Perform part-of-speech (POS) tagging over an input corpus.
"""
if tagger == 'tt':
pos_tagger = TTPosTagger(language_code, tt_home)
logger.info(
"About to perform part-of-speech tagging with TreeTagger ...")
else:
pos_tagger = NLTKPosTagger(language_code)
logger.info(
"About to perform part-of-speech tagging with NLTK tagger ...")
corpus = load_scraped_items(corpus)
total = 0
for i, tagged_document in enumerate(
pos_tagger.tag_many(corpus, document_key, pos_tag_key,
batch_size)):
total += 1
outfile.write(json.dumps(tagged_document) + '\n')
if (i + 1) % 10000 == 0:
logger.info('processed %d items', i + 1)
logger.info("Done, total tagged items: %d" % total)
return 0
def main(corpus, lemma_to_tokens, language_code, strategy, outfile, processes,
sentences_key, document_key, match_base_form):
""" Extract corpus sentences containing at least one token in the given set. """
corpus = load_scraped_items(corpus)
updated = extract_sentences(corpus, sentences_key, document_key, language_code,
json.load(lemma_to_tokens), strategy, match_base_form, processes)
for item in updated:
outfile.write(json.dumps(item) + '\n')
logger.info("Dumped sentences to '%s'" % outfile.name)
return 0
def about_sources(corpus, processes, with_bio):
""" Items' sources
"""
def worker(items):
sources = defaultdict(int)
for doc in items:
url = doc.get('url')
if not url:
logger.warn('found an item without URL, name: %s, bio: %s',
doc.get('name'),
doc.get('bio', '')[:100] + ' ...')
sources['_skipped_'] += 1
continue
elif with_bio and len(doc.get('bio') or '') < 5:
continue
parsed = urlparse(url)
if parsed.netloc:
sources[parsed.netloc] += 1
else:
logger.warn('cannot parse URL: %s', url)
sources['_skipped_'] += 1
return sources
aggregated_sources = defaultdict(int)
corpus = parallel.make_batches(load_scraped_items(corpus), 1000)
for sources in parallel.map(worker, corpus, processes):
for k, v in sources.iteritems():
aggregated_sources[k] += v
aggregated_sources = sorted(aggregated_sources.items(),
key=lambda (_, v): v,
reverse=True)
for source, count in aggregated_sources:
print source, count
try:
import matplotlib.pyplot as plt
except ImportError:
logger.warn('Cannot import matplotlib, skipping chart')
return
count = sum(c for s, c in aggregated_sources)
display_sources = filter(lambda (s, v): float(v) / count >= 0.01,
aggregated_sources)
sources, values = map(list, zip(*display_sources))
sources.append('Rest')
values.append(count - sum(values))
plt.pie(values, labels=sources)
plt.axis('equal')
plt.show()
def main(corpus, lemma_to_tokens, language_code, strategy, outfile, processes,
sentences_key, document_key, match_base_form):
""" Extract corpus sentences containing at least one token in the given set. """
corpus = load_scraped_items(corpus)
updated = extract_sentences(corpus, sentences_key,
document_key, language_code,
json.load(lemma_to_tokens), strategy,
match_base_form, processes)
for item in updated:
outfile.write(json.dumps(item) + '\n')
logger.info("Dumped sentences to '%s'" % outfile.name)
return 0
def produce_lemma_tokens(pos_tagged_path, pos_tag_key, language):
""" Extracts a map from lemma to all its tokens
:param str pos_tagged_path: path of the pos-tagged corpus
:param str pos_tag_key: where the pos tag data is in each item
:param language: language of the corpus
:return: mapping from lemma to tokens
:rtype: dict
"""
corpus = load_scraped_items(pos_tagged_path)
lemma_tokens = defaultdict(set)
for item in corpus:
for token, pos, lemma in item.get(pos_tag_key, []):
if pos.startswith(VERBAL_PREFIXES[language]):
lemma_tokens[lemma.lower()].add(token.lower())
return lemma_tokens
def preprocess_corpus(corpus_dir, document_key, output_dir, items_per_file,
min_length):
""" Remove items without text documents or whose text document is too short """
filename = 'corpus-%d.jsonlines'
count = 0
current_file = open(os.path.join(output_dir, filename % 0), 'w')
for item in load_scraped_items(corpus_dir):
if item.get(document_key) and len(item[document_key]) > min_length:
count += 1
if count % items_per_file == 0:
fname = filename % (count / items_per_file)
logger.info('processed %d items so far, continuing in %s' %
(count, fname))
current_file.close()
current_file = open(os.path.join(output_dir, fname), 'w')
item['id'] = hashlib.sha1(
item[document_key].encode('utf8')).hexdigest()
json.dump(item, current_file)
current_file.write('\n')
def main(corpus, document_key, pos_tag_key, language_code, tagger, outfile, tt_home, batch_size):
""" Perform part-of-speech (POS) tagging over an input corpus.
"""
if tagger == 'tt':
pos_tagger = TTPosTagger(language_code, tt_home)
logger.info("About to perform part-of-speech tagging with TreeTagger ...")
else:
pos_tagger = NLTKPosTagger(language_code)
logger.info("About to perform part-of-speech tagging with NLTK tagger ...")
corpus = load_scraped_items(corpus)
total = 0
for i, tagged_document in enumerate(pos_tagger.tag_many(corpus, document_key, pos_tag_key, batch_size)):
total += 1
outfile.write(json.dumps(tagged_document) + '\n')
if (i + 1) % 10000 == 0:
logger.info('processed %d items', i + 1)
logger.info("Done, total tagged items: %d" % total)
return 0
def __init__(self, corpus_path, pos_tag_key):
self.tags = self._flatten(item.get(pos_tag_key) for item in load_scraped_items(corpus_path))
