def _get_data(args):
key_from = 'news:{}:'.format(args.date_from.replace('-', ''))
key_to = 'news:{};'.format(args.date_to.replace('-', ''))
db = datastore.corpus_db()
for key, doc in db.range(key_from, key_to):
if not any(uri in doc['url'] for uri in DISCARD_URLS):
yield doc
def _get_data(args):
key_from = 'news:{}:'.format(args.date_from.replace('-', ''))
key_to = 'news:{};'.format(args.date_to.replace('-', ''))
db = datastore.corpus_db()
for key, doc in db.range(key_from, key_to):
if not any(uri in doc['url'] for uri in DISCARD_URLS):
yield doc
def main(args):
# search corpus index
db = datastore.corpus_db()
sconn = xappy.SearchConnection(settings.XAPIAN_DB)
query = ' '.join(args.query)
print "Search {} documents for '{}'".format(
sconn.get_doccount(), args.query
)
q = sconn.query_parse(query, default_op=sconn.OP_AND)
if args.category:
qc = q.compose(q.OP_OR, [
sconn.query_field('category', c) for c in args.category
])
q = q & qc
if args.date:
qd = q.compose(q.OP_OR, [
sconn.query_field('date', d) for d in args.date
])
q = q & qd
if args.date_start and args.date_end:
qr = sconn.query_range('date', args.date_start, args.date_end)
q = q.filter(qr)
if args.sort:
sortby = [tuple(args.sort.split(','))]
else:
sortby = None
print 'Query: {!r}'.format(q)
results = execute_query(sconn, q, args.offset, args.limit,
getfacets=args.facet,
allowfacets=('category',),
sortby=sortby)
if results.estimate_is_exact:
print "Found {} results".format(results.matches_estimated)
else:
print "Found approximately {} results".format(results.matches_estimated)
for i, result in enumerate(results, 1):
doc = db[result.id]
try:
cat = result.get_terms('category').next()
except StopIteration:
cat = 'none'
try:
date = result.get_terms('date').next()
except StopIteration:
date = 'none'
print "{:2}. {} -- {} -- {}\n\t{}\n\t{}\n".format(
i, cat, doc['headline'], date, doc['url'], result.id)
from IPython import embed; embed()
def load_jsonlines(stream):
# do batch write
db = datastore.corpus_db()
with db.write_batch() as wb:
for n, line in enumerate(stream, 1):
doc = preprocess(json.loads(line))
key = get_key(doc)
wb[key] = doc
return n
def main(args):
# index corpus
db = datastore.corpus_db()
if args.prefix:
items = db.range(args.prefix, next_key(args.prefix))
elif args.from_classified:
items = get_classified_items(args.from_classified, db)
else:
items = db.range()
count = search.index(items, 'corpus', create=args.create)
print "Indexed {} documents".format(count)
def _get_data(category):
# load data from xapian categories
db = datastore.corpus_db()
sconn = xappy.SearchConnection(settings.XAPIAN_DB)
q = sconn.query_field('category', category)
offset = 0
limit = 1000
while True:
results = sconn.search(q, offset, offset + limit)
for key, doc in db.range(key_from, key_to):
if not any(uri in doc['url'] for uri in DISCARD_URLS):
yield doc
def _get_data(category):
# load data from xapian categories
db = datastore.corpus_db()
sconn = xappy.SearchConnection(settings.XAPIAN_DB)
q = sconn.query_field('category', category)
offset = 0
limit = 1000
while True:
results = sconn.search(q, offset, offset + limit)
for key, doc in db.range(key_from, key_to):
if not any(uri in doc['url'] for uri in DISCARD_URLS):
yield doc
def load_csv(stream):
# read first row as fields
fields = csv.reader(stream).next()
if any(f not in fields for f in REQUIRED_FIELDS):
raise ValueError(
"Required fields: {}".format(','.join(REQUIRED_FIELDS))
)
reader = csv.DictReader(stream, fields)
# do batch write
db = datastore.corpus_db()
with db.write_batch() as wb:
for n, doc in enumerate(reader, 1):
doc = preprocess(dict(
(k, v.decode('utf-8')) for k, v in doc.iteritems()
))
key = get_key(doc)
wb[key] = doc
return n
def load_keys(fromkey, offset, limit):
tokey = next_key(fromkey)
db = datastore.corpus_db()
it = db.range(fromkey, tokey)
return [v for k, v in itertools.islice(it, offset, offset + limit)]
def load_keys(fromkey, offset, limit):
tokey = next_key(fromkey)
db = datastore.corpus_db()
it = db.range(fromkey, tokey)
return [v for k,v in itertools.islice(it, offset, offset + limit)]
def main(args):
# search corpus index
db = datastore.corpus_db()
sconn = xappy.SearchConnection(settings.XAPIAN_DB)
query = ' '.join(args.query)
print "Search {} documents for '{}'".format(sconn.get_doccount(),
args.query)
q = sconn.query_parse(query, default_op=sconn.OP_AND)
if args.category:
qc = q.compose(
q.OP_OR, [sconn.query_field('category', c) for c in args.category])
q = q & qc
if args.date:
qd = q.compose(q.OP_OR,
[sconn.query_field('date', d) for d in args.date])
q = q & qd
if args.date_start and args.date_end:
qr = sconn.query_range('date', args.date_start, args.date_end)
q = q.filter(qr)
if args.sort:
sortby = [tuple(args.sort.split(','))]
else:
sortby = None
print 'Query: {!r}'.format(q)
results = execute_query(sconn,
q,
args.offset,
args.limit,
getfacets=args.facet,
allowfacets=('category', ),
sortby=sortby)
if results.estimate_is_exact:
print "Found {} results".format(results.matches_estimated)
else:
print "Found approximately {} results".format(
results.matches_estimated)
for i, result in enumerate(results, 1):
doc = db[result.id]
try:
cat = result.get_terms('category').next()
except StopIteration:
cat = 'none'
try:
date = result.get_terms('date').next()
except StopIteration:
date = 'none'
print "{:2}. {} -- {} -- {}\n\t{}\n\t{}\n".format(
i, cat, doc['headline'], date, doc['url'], result.id)
from IPython import embed
embed()