Black reformatting

Author: smlindauer

src/sasctl/pzmm/writeScoreCode.py

@@ -600,6 +600,7 @@ def score{modelPrefix}({inputVarList}):
                 model["scoreCodeType"] = "ds2MultiType"
                 modelRepo.update_model(model)
 
+    @classmethod
     def splitStringColumn(cls, inputSeries, otherVariable):
         """
         Splits a column of string values into a number of new variables equal

tests/integration/test_model_parameters.py

@@ -2,7 +2,7 @@
 import warnings
 from sasctl.pzmm.modelParameters import ModelParameters as mp
 
-pytestmark = pytest.mark.usefixtures('session')
+pytestmark = pytest.mark.usefixtures("session")
 
 
 @pytest.fixture
@@ -12,20 +12,20 @@ def train_data():
     try:
         import pandas as pd
     except ImportError:
-        pytest.skip('Package `pandas` not found.')
+        pytest.skip("Package `pandas` not found.")
 
     try:
         from sklearn import datasets
     except ImportError:
-        pytest.skip('Package `sklearn` not found.')
+        pytest.skip("Package `sklearn` not found.")
 
     raw = datasets.load_iris()
     iris = pd.DataFrame(raw.data, columns=raw.feature_names)
     iris = iris.join(pd.DataFrame(raw.target))
-    iris.columns = ['SepalLength', 'SepalWidth', 'PetalLength', 'PetalWidth', 'Species']
-    iris['Species'] = iris['Species'].astype('category')
+    iris.columns = ["SepalLength", "SepalWidth", "PetalLength", "PetalWidth", "Species"]
+    iris["Species"] = iris["Species"].astype("category")
     iris.Species.cat.categories = raw.target_names
-    return iris.iloc[:, 0:4], iris['Species']
+    return iris.iloc[:, 0:4], iris["Species"]
 
 
 @pytest.fixture
@@ -35,21 +35,18 @@ def sklearn_model(train_data):
     try:
         from sklearn.linear_model import LogisticRegression
     except ImportError:
-        pytest.skip('Package `sklearn` not found.')
+        pytest.skip("Package `sklearn` not found.")
 
     X, y = train_data
     with warnings.catch_warnings():
-        warnings.simplefilter('ignore')
-        model = LogisticRegression(multi_class='multinomial', solver='lbfgs')
+        warnings.simplefilter("ignore")
+        model = LogisticRegression(multi_class="multinomial", solver="lbfgs")
         model.fit(X, y)
     return model
 
+
 @pytest.mark.incremental
 class TestSklearnModel:
-    PROJECT_NAME="Test SKLearn Model"
-    MODEL_NAME="SKLearnModel"
-    PATH="."
-
-
-
-
+    PROJECT_NAME = "Test SKLearn Model"
+    MODEL_NAME = "SKLearnModel"
+    PATH = "."

tests/integration/test_text_categorization.py

@@ -45,7 +45,7 @@ def test_from_table():
 
 def test_service_removed_error():
     if current_session().version_info() < 4:
-        pytest.skip('Text Categorization service was not removed until Viya 4.')
+        pytest.skip("Text Categorization service was not removed until Viya 4.")
 
     with pytest.raises(RuntimeError):
-        tc.categorize('', None, id_column='', text_column='')
+        tc.categorize("", None, id_column="", text_column="")

tests/unit/test_model_parameters.py

@@ -24,32 +24,34 @@ def train_data():
     raw = datasets.load_iris()
     iris = pd.DataFrame(raw.data, columns=raw.feature_names)
     iris = iris.join(pd.DataFrame(raw.target))
-    iris.columns = ['SepalLength', 'SepalWidth', 'PetalLength', 'PetalWidth', 'Species']
-    iris['Species'] = iris['Species'].astype('category')
+    iris.columns = ["SepalLength", "SepalWidth", "PetalLength", "PetalWidth", "Species"]
+    iris["Species"] = iris["Species"].astype("category")
     iris.Species.cat.categories = raw.target_names
-    return iris.iloc[:, 0:4], iris['Species']
+    return iris.iloc[:, 0:4], iris["Species"]
 
 
 @pytest.fixture
 def sklearn_model(train_data):
     """Returns a simple Scikit-Learn model"""
     X, y = train_data
     with warnings.catch_warnings():
-        warnings.simplefilter('ignore')
-        model = LogisticRegression(multi_class='multinomial', solver='lbfgs')
+        warnings.simplefilter("ignore")
+        model = LogisticRegression(multi_class="multinomial", solver="lbfgs")
         model.fit(X, y)
     return model
 
 
 @pytest.mark.incremental
 class TestSKLearnModel:
-    PROJECT_NAME = 'PZMM SKLearn Test Project'
-    MODEL_NAME = 'SKLearnModel'
+    PROJECT_NAME = "PZMM SKLearn Test Project"
+    MODEL_NAME = "SKLearnModel"
 
     def test_generate_hyperparameters(self, sklearn_model):
         tmp_dir = tempfile.TemporaryDirectory()
         mp.generate_hyperparameters(sklearn_model, self.MODEL_NAME, Path(tmp_dir.name))
-        assert Path(Path(tmp_dir.name) / f'./{self.MODEL_NAME}Hyperparameters.json').exists()
+        assert Path(
+            Path(tmp_dir.name) / f"./{self.MODEL_NAME}Hyperparameters.json"
+        ).exists()
 
     def test_bad_model_hyperparameters(self, bad_model):
         tmp_dir = tempfile.TemporaryDirectory()

tests/unit/test_pageiterator.py

@@ -71,7 +71,9 @@ def test_no_paging_required():
     try:
         request.assert_not_called()
     except AssertionError as e:
-        raise AssertionError(f'method_calls={request.mock_calls}  call_args={request.call_args_list}')
+        raise AssertionError(
+            f"method_calls={request.mock_calls}  call_args={request.call_args_list}"
+        )
 
 
 def test_paging_required(paging):