def test_get_epithet_of_author(self):
"""Test get_epithet_of_author()."""
epithet = get_epithet_of_author('0016')
self.assertEqual(epithet, 'Historici/-ae')
for file in files:
filepath = os.path.join(user_dir, file)
with open(filepath) as fo:
#TODO rm words less the 3 chars long
yield file[3:-4], fo.read()
t0 = dt.datetime.utcnow()
map_id_author = get_id_author()
df = pandas.DataFrame(columns=['id', 'author' 'text', 'epithet'])
for _id, text in stream_lemmatized_files('tlg_lemmatized_no_accents_no_stops'):
author = map_id_author[_id]
epithet = get_epithet_of_author(_id)
df = df.append({'id': _id, 'author': author, 'text': text, 'epithet': epithet}, ignore_index=True)
print(df.shape)
print('... finished in {}'.format(dt.datetime.utcnow() - t0))
print('Number of texts:', len(df))
text_list = df['text'].tolist()
# make a list of short texts to drop
# For pres, get distributions of words per doc
short_text_drop_index = [index if len(text) > 500 else None for index, text in enumerate(text_list) ] # ~100 words
t0 = dt.datetime.utcnow()
with open(filepath) as fo:
#TODO rm words less the 3 chars long
yield file[3:-4], fo.read()
# In[3]:
t0 = dt.datetime.utcnow()
map_id_author = get_id_author()
df = pandas.DataFrame(columns=['id', 'author' 'text', 'epithet'])
for _id, text in stream_lemmatized_files('tlg_lemmatized_no_accents_no_stops'):
author = map_id_author[_id]
epithet = get_epithet_of_author(_id)
df = df.append(
{
'id': _id,
'author': author,
'text': text,
'epithet': epithet
},
ignore_index=True)
print(df.shape)
print('... finished in {}'.format(dt.datetime.utcnow() - t0))
print('Number of texts:', len(df))
# In[4]:
def test_get_epithet_of_author(self):
"""Test get_epithet_of_author()."""
epithet = get_epithet_of_author('0016')
self.assertEqual(epithet, 'Historici/-ae')
def test_get_epithet_of_author(self):
"""Test get_epithet_of_author()."""
epithet = get_epithet_of_author("0016")
self.assertEqual(epithet, "Historici/-ae")
