Merge branch 'master' into model_cards

Author: djm21

src/sasctl/_services/model_repository.py

@@ -359,10 +359,9 @@ def create_model(
 
         model["scoreCodeType"] = score_code_type or model.get("scoreCodeType")
         model["trainTable"] = training_table or model.get("trainTable")
-        model[
-            "classificationEventProbabilityVariableName"
-        ] = event_prob_variable or model.get(
-            "classificationEventProbabilityVariableName"
+        model["classificationEventProbabilityVariableName"] = (
+            event_prob_variable
+            or model.get("classificationEventProbabilityVariableName")
         )
         model["classificationTargetEventValue"] = event_target_value or model.get(
             "classificationTargetEventValue"

src/sasctl/pzmm/mlflow_model.py

@@ -39,14 +39,20 @@ def read_mlflow_model_file(cls, m_path=Path.cwd()):
             var_dict = {
                 "python_version": m_yml["flavors"]["python_function"]["python_version"],
                 "model_path": m_yml["flavors"]["python_function"]["model_path"],
-                "serialization_format": m_yml["flavors"]["sklearn"]["serialization_format"],
+                "serialization_format": m_yml["flavors"]["sklearn"][
+                    "serialization_format"
+                ],
                 "run_id": m_yml["run_id"],
-                "mlflowPath": m_path
+                "mlflowPath": m_path,
             }
         except KeyError:
-            raise ValueError("This MLFlow model type is not currently supported.") from None
+            raise ValueError(
+                "This MLFlow model type is not currently supported."
+            ) from None
         except TypeError:
-            raise ValueError("This MLFlow model type is not currently supported.") from None
+            raise ValueError(
+                "This MLFlow model type is not currently supported."
+            ) from None
 
         # Read in the input and output variables
         try:
@@ -57,5 +63,5 @@ def read_mlflow_model_file(cls, m_path=Path.cwd()):
                 "Improper or unset signature values for model. No input or output "
                 "dicts could be generated. "
             ) from None
-        
+
         return var_dict, inputs_dict, outputs_dict

src/sasctl/pzmm/write_json_files.py

@@ -877,9 +877,9 @@ def assess_model_bias(
             # run assessBias, if levels=None then assessBias treats the input like a regression problem
             tables = conn.fairaitools.assessbias(
                 modelTableType="None",
-                predictedVariables=pred_values
-                if pred_values is not None
-                else prob_values,
+                predictedVariables=(
+                    pred_values if pred_values is not None else prob_values
+                ),
                 response=actual_values,
                 responseLevels=levels,
                 sensitiveVariable=x,
@@ -1294,6 +1294,7 @@ def calculate_model_statistics(
                 .to_dict()
             )
             json_dict[0]["data"][i]["dataMap"].update(fitstat_dict)
+
             if target_type == 'classification':
                 roc_df = pd.DataFrame(conn.CASTable("ROC", caslib="Public").to_frame())
                 roc_dict = cls.apply_dataframe_to_json(json_dict[1]["data"], i, roc_df)
@@ -1308,6 +1309,7 @@ def calculate_model_statistics(
                             "_Gamma_": roc_dict[j]["dataMap"]["_Gamma_"],
                             "_Tau_": roc_dict[j]["dataMap"]["_Tau_"]
                         }
+                    
                     json_dict[0]["data"][i]["dataMap"].update(fitstat_data)
 
             lift_df = pd.DataFrame(conn.CASTable("Lift", caslib="Public").to_frame())

src/sasctl/pzmm/write_score_code.py

@@ -475,19 +475,7 @@ def _write_imports(
 import tensorflow as tf
 
             """
-        elif binary_string:
-            cls.score_code += (
-                f'import codecs\n\nbinary_string = "{binary_string}"'
-                f"\nmodel = {pickle_type}.loads(codecs.decode(binary_string"
-                '.encode(), "base64"))\n\n'
-            )
-            """
-import codecs
 
-binary_string = "<binary string>"
-model = pickle.load(codecs.decode(binary_string.encode(), "base64"))
-
-            """
 
     @classmethod
     def _viya35_model_load(