def create_feature(data_file):
if os.path.exists(feature_output_file(data_file)):
print('File exists {}.'.format(feature_output_file(data_file)))
return
question1_vectors, question2_vectors = sentence2vec(data_file)
print(sys.argv[0], data_file, file=sys.stderr)
df = pd.DataFrame()
column_name = 'f{0}'.format(os.path.basename(feature_output_file(data_file)).split('_')[0])
df[column_name] = np.nan_to_num([kurtosis(x) for x in np.nan_to_num(question2_vectors)])
df[[column_name]].to_csv(feature_output_file(data_file), index=False, float_format='%.5f')
def create_feature(data_file, model: gensim.models.KeyedVectors):
if os.path.exists(feature_output_file(data_file)):
print('File exists {}.'.format(feature_output_file(data_file)))
return
question1_vectors, question2_vectors = sentence2vec(data_file)
print(sys.argv[0], data_file, file=sys.stderr)
df = pd.DataFrame()
column_name = 'f{0}'.format(
os.path.basename(feature_output_file(data_file)).split('_')[0])
values = []
df[column_name] = np.nan_to_num([
jaccard(x, y) for (x, y) in zip(np.nan_to_num(question1_vectors),
np.nan_to_num(question2_vectors))
])
df[[column_name]].to_csv(feature_output_file(data_file),
index=False,
float_format='%.5f')