Merge pull request #46 from salesforce/py3.7

Python 3.7 support

Author: yangwenzhuo08

.github/workflows/tests.yml

@@ -19,7 +19,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: ["3.8", "3.9", "3.10"]
+        python-version: ["3.7", "3.8", "3.9", "3.10"]
 
     steps:
     - uses: actions/checkout@v3

setup.py

@@ -38,23 +38,22 @@
         "toml>=0.10.2",
         "build>=0.7.0",
         "jupyter>=1.0.0",
-        "ipykernel>=6.19.4",
+        "ipykernel>=6.16",
         "pytest>=6.2.5",
     ]
 }
 extras_require["all"] = sum(extras_require.values(), [])
 
 setup(
     name="logai",
-    version="0.1.4",
+    version="0.1.5",
     description="LogAI is unified framework for AI-based log analytics",
     long_description_content_type="text/markdown",
     long_description=readme,
     author="Qian Cheng, Amrita Saha, Wenzhuo Yang, Chenghao Liu, Gerald Woo, Doyen Sahoo, Steven Hoi",
     author_email="logai@salesforce.com",
-    python_requires=">=3.8.0,<4",
+    python_requires=">=3.7.0,<4",
     install_requires=[
-        "scikit-learn>=1.2",
         "schema>=0.7.5",
         "salesforce-merlion>=1.0.0",
         "Cython>=0.29.30",

tests/logai/algorithms/anomaly_detection_algo/test_anomaly_detector_het.py

@@ -29,5 +29,3 @@ def test_het_anomaly_detector(self, log_counter_df):
         )
         res = model.fit_predict(counter_df)
         print(res.head())
-        return
-

tests/logai/algorithms/anomaly_detection_algo/test_dbl.py

@@ -44,9 +44,3 @@ def test_dbl(self, log_counter_df):
                 res = res.append(anom_score)
         assert (res.index == counter_df.index).all(), "Res.index should be identical to counter_df.index"
         assert len(res) == len(counter_df.index), "length of res should be equal to length of counter_df"
-
-        return
-
-
-
-

tests/logai/algorithms/anomaly_detection_algo/test_ets.py

@@ -24,7 +24,6 @@ def setup(self):
     def test_ets_detector(self, log_counter_df):
         counter_df = log_counter_df
 
-
         ts_df = counter_df[[constants.LOG_COUNTS]]
         ts_df.index = counter_df[constants.LOG_TIMESTAMPS]
         counter_df["attribute"] = counter_df.drop([constants.LOG_COUNTS, constants.LOG_TIMESTAMPS], axis=1).apply(
@@ -58,6 +57,3 @@ def test_ets_detector(self, log_counter_df):
         print(len(res), counter_df.shape[0])
         assert (res.index == counter_df.index).all(), "Res.index should be identical to counter_df.index"
         assert len(res) == len(counter_df.index), "length of res should be equal to length of counter_df"
-
-        return
-

tests/logai/algorithms/anomaly_detection_algo/test_one_class_svm.py

@@ -33,6 +33,3 @@ def test_fit_predict(self, log_features):
         assert isinstance(detector.model, OneClassSVM), "Model must be OneClassSVM"
         res = detector.predict(log_features)
         assert isinstance(res, pd.DataFrame), "result must be pd.DataFrame"
-
-
-

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'
-
-