def test_search_filenames(use_hashing):
cache_dir = check_cache()
fe = FeatureVectorizer(cache_dir=cache_dir, mode='w')
uuid = fe.setup(use_hashing=use_hashing)
fe.ingest(data_dir, file_pattern='.*\d.txt')
assert fe.db_ is not None
for low, high, step in [(0, 1, 1), (0, 4, 1), (3, 1, -1)]:
idx_slice = list(range(low, high, step))
filenames_slice = [fe.filenames_[idx] for idx in idx_slice]
idx0 = fe.db_._search_filenames(filenames_slice)
assert_equal(idx0, idx_slice)
assert_equal(filenames_slice, fe[idx0])
with pytest.raises(NotFound):
fe.db_._search_filenames(['DOES_NOT_EXIST.txt'])
if not use_hashing:
n_top_words = 5
terms = fe.query_features([2, 3, 5], n_top_words=n_top_words)
assert len(terms) == n_top_words
fe.list_datasets()
def _list(args):
cache_dir = _parse_cache_dir(args.cache_dir)
fe = FeatureVectorizer(cache_dir)
res = fe.list_datasets()
res = sorted(res, key=lambda row: row['creation_date'], reverse=True)
for row in res:
print(' * Processed dataset {}'.format(row['id']))
print(' - data_dir: {}'.format(row['data_dir']))
print(' - creation_date: {}'.format(row['creation_date']))
for method in ['lsi', 'categorizer', 'cluster', 'dupdet', 'threading']:
dpath = os.path.join(fe.cache_dir, row['id'], method)
if not os.path.exists(dpath):
continue
mid_list = os.listdir(dpath)
if mid_list:
print(' # {}'.format(method))
for mid in mid_list:
print(' * {}'.format(mid))
print(' ')
