def ingest_docket(agency, docket, docs, ngrams=None):
print "Beginning processing %s at %s" % (docket, datetime.now())
parser_tag = "%s-grams" % ngrams if ngrams else 'sentence'
c = Corpus(metadata=dict(docket=docket, agency=agency, parser=parser_tag))
if ngrams:
i = DocumentIngester(c, parser=ngram_parser(int(ngrams)))
else:
i = DocumentIngester(c)
i.ingest(docs)
print "Finished processing at %s" % datetime.now()
print "Added %d documents in corpus %d" % (len(docs), c.id)
def ingest_single_parse(docket_id, deletions, insertions, parser):
if parser not in ('sentence', '4-gram'):
raise "Parser must be one of 'sentence' or '4-gram'. Got '%s'." % parser
corpora = get_corpora_by_metadata('docket_id', docket_id)
parsed_corpora = [c for c in corpora if c.metadata.get('parser') == parser]
if len(parsed_corpora) == 0:
dockets = list(Docket.objects(id=docket_id).only('agency'))
docket = dockets[0] if dockets else Docket()
c = Corpus(metadata=dict(docket_id=docket_id,
agency_id=docket.agency if docket.
agency else docket_id.split("-")[0],
parser=parser))
print "Created new corpus #%s for %s parse." % (c.id, parser)
elif len(parsed_corpora) == 1:
c = parsed_corpora[0]
print "Updating existing corpus #%s for %s parse." % (c.id, parser)
print "Deleting documents at %s..." % datetime.now()
c.delete_by_metadata(
'document_id',
deletions + [d['metadata']['document_id'] for d in insertions])
else:
raise "More than one sentence parse for docket %s found. Shouldn't happen--will need ot manually remove extra corpora." % docket_id
print "Inserting documents at %s..." % datetime.now()
if parser == 'sentence':
i = DocumentIngester(c,
parser=sentence_parse,
compute_similarities=False)
elif parser == '4-gram':
i = DocumentIngester(c,
parser=ngram_parser(4),
compute_similarities=True)
i.ingest(insertions)
print "Removing hierarchy, if cached, at %s..." % datetime.now()
c.delete_hierarchy_cache()
def repair_missing_sims(docket_id):
"""Repair the situation where a docket is correct in Mongo and Postgres,
but the similarity directory is missing."""
with transaction.commit_on_success():
c = get_dual_corpora_by_metadata('docket_id', docket_id)
if c and not bsims.exists(c.id):
print "Docket %s (id=%s) missing similarities. Starting recomputation at %s..." % (
docket_id, c.id, datetime.now())
i = DocumentIngester(c)
i.compute_similarities()
def handle(self, ls_docs_path, **options):
docs = list()
for doc in json.load(open(ls_docs_path, 'r')):
docs += [d['text'] for d in doc['documents'] if d.get('text')]
cleaned_docs = [d.encode('ascii', 'replace') for d in docs]
print "Beginning processing at %s" % datetime.now()
with transaction.commit_on_success():
c = Corpus()
if options.get('ngrams'):
i = DocumentIngester(c,
parser=ngram_parser(int(
options['ngrams'])))
else:
i = DocumentIngester(c)
i.ingest(cleaned_docs)
print "Finished processing at %s" % datetime.now()
print "Added %d documents in corpus %d" % (len(cleaned_docs), c.id)