Revise for Python3.7

Author: yangwenzhuo08

tests/logai/algorithms/feature_extraction_algo/test_label_encoding.py

@@ -31,7 +31,3 @@ def test_model_creation_2d_attributes(self):
 
  assert isinstance(res, pd.DataFrame), "results not in pandas.DataFrame"
  assert (res.columns == ["animals_categorical", "moves_categorical"]).all(), "results does not have correct column names"
-
-
-
-

tests/logai/algorithms/nn_model/test_tokenizer.py

@@ -23,5 +23,3 @@ def tokenize(self):
  tokenizer.normalizer = normalizers.Sequence(
  [normalizers.NFD(), normalizers.StripAccents()])
  tokenizer.pre_tokenizer = pre_tokenizers.BertPreTokenizer()
-
- return

tests/logai/algorithms/nn_model/test_trainer.py

@@ -38,5 +38,3 @@ def test_train_pred(self, log_pattern):
 
  for l in y_label_pred:
  assert l in [0, 1], 'result should be either 0 or 1'
-
-

tests/logai/algorithms/nn_model/test_transformers.py

@@ -24,4 +24,3 @@ def test_fit_parse(self, logrecord_body):
  parsed_loglines = parser.parse(logrecord_body['logline'])
 
  assert isinstance(parsed_loglines, pd.Series), 'parse returns pandas.Series'
-

tests/logai/algorithms/parsing_algo/test_ael.py

@@ -52,5 +52,3 @@ def test_fit_parse(self, logrecord_body):
  assert parser.clusters_counter > 0, "log cluster number should be greater than zero after fit"
  assert isinstance(parser, Drain)
  assert isinstance(parsed_loglines, pd.Series), 'parse returns pandas.Series'
-
-

tests/logai/algorithms/parsing_algo/test_drain.py

@@ -22,5 +22,3 @@ def test_fit_parse(self, logrecord_body):
  parsed_loglines = parser.parse(logrecord_body['logline'])
  assert isinstance(parser, IPLoM)
  assert isinstance(parsed_loglines, pd.Series), 'parse returns pandas.Series'
-
-

tests/logai/algorithms/parsing_algo/test_iplom.py

@@ -36,4 +36,3 @@ def test_fit_predict(self, logrecord_body):
  res = model.transform(loglines)
  assert isinstance(res, pd.Series), "result is not a pandas.Series"
  assert len(loglines) == len(res), "result length should match input"
- return

tests/logai/algorithms/vectorization_algo/test_tfidf.py

@@ -77,6 +77,4 @@ def test_fit_predict_counter(self, log_counter_df):
  labels = anomaly_detector.predict(test)
 
  assert isinstance(labels, pd.DataFrame), 'labels are not pd.DataFrame'
-
  assert 'anom_score' in labels.columns, 'labels must contain anom_score column'
-