Fix for PYL-W0102: Dangerous default argument

Author: smlindauer

src/sasctl/pzmm/importModel.py

@@ -121,7 +121,7 @@ def pzmmImportModel(
         inputDF,
         targetDF,
         predictmethod,
-        metrics=["EM_EVENTPROBABILITY", "EM_CLASSIFICATION"],
+        metrics=None,
         projectVersion="latest",
         modelFileName=None,
         pyPath=None,
@@ -171,7 +171,7 @@ def pzmmImportModel(
             the format() command.
             For example: '{}.predict_proba({})'.
         metrics : string list, optional
-            The scoring metrics for the model. The default is a set of two
+            The scoring metrics for the model. The default is a list of two
             metrics: EM_EVENTPROBABILITY and EM_CLASSIFICATION.
         projectVersion : string, optional
             The project version to import the model in to on SAS Model Manager. The default value
@@ -201,6 +201,10 @@ def pzmmImportModel(
             Model details from an MLFlow model. This dictionary is created by the readMLModelFile function.
             By default None.
         """
+        # Set metrics internal to function call if no value is given
+        if metrics is None:
+            metrics = ["EM_EVENTPROBABILITY", "EM_CLASSIFICATION"]
+
         # Initialize no score code or binary H2O model flags
         noScoreCode = False
         binaryModel = False

src/sasctl/pzmm/writeScoreCode.py

@@ -17,7 +17,7 @@ def writeScoreCode(
         modelPrefix,
         predictMethod,
         modelFileName,
-        metrics=["EM_EVENTPROBABILITY", "EM_CLASSIFICATION"],
+        metrics=None,
         pyPath=Path.cwd(),
         threshPrediction=None,
         otherVariable=False,
@@ -120,6 +120,10 @@ def writeScoreCode(
         pickleType : string, optional
             Indicator for MLFlow models, which may pickle by non-standard methods. By default 'pickle'.
         """
+        # Set metrics internal to function call if no value is given
+        if metrics is None:
+            metrics = ["EM_EVENTPROBABILITY", "EM_CLASSIFICATION"]
+
         # Check if binary string model
         if binaryString is not None:
             isBinaryString = True