updates to write_json_files to work better with prediction models

src/sasctl/pzmm/write_json_files.py

@@ -1260,7 +1260,7 @@ def calculate_model_statistics(
             if not partition:
                 continue
 
-            data = cls.stat_dataset_to_dataframe(data, target_value)
+            data = cls.stat_dataset_to_dataframe(data, target_value, target_type)
 
             conn.upload(
                 data,
@@ -1392,6 +1392,7 @@ def check_for_data(
     def stat_dataset_to_dataframe(
         data: Union[DataFrame, List[list], Type["numpy.array"]],
         target_value: Union[str, int, float] = None,
+        target_type: str = 'classification'
     ) -> DataFrame:
         """
         Convert the user supplied statistical dataset from either a pandas DataFrame,
@@ -1439,13 +1440,15 @@ def stat_dataset_to_dataframe(
         if isinstance(data, pd.DataFrame):
             if len(data.columns) == 2:
                 data.columns = ["actual", "predict"]
-                data["predict_proba"] = data["predict"].gt(target_value).astype(int)
+                if target_type == 'classification':
+                    data["predict_proba"] = data["predict"].gt(target_value).astype(int)
             elif len(data.columns) == 3:
                 data.columns = ["actual", "predict", "predict_proba"]
         elif isinstance(data, list):
             if len(data) == 2:
                 data = pd.DataFrame({"actual": data[0], "predict": data[1]})
-                data["predict_proba"] = data["predict"].gt(target_value).astype(int)
+                if target_type == 'classification':
+                    data["predict_proba"] = data["predict"].gt(target_value).astype(int)
             elif len(data) == 3:
                 data = pd.DataFrame(
                     {
@@ -1457,7 +1460,8 @@ def stat_dataset_to_dataframe(
         elif isinstance(data, np.ndarray):
             if len(data) == 2:
                 data = pd.DataFrame({"actual": data[0, :], "predict": data[1, :]})
-                data["predict_proba"] = data["predict"].gt(target_value).astype(int)
+                if target_type == 'classification':
+                    data["predict_proba"] = data["predict"].gt(target_value).astype(int)
             elif len(data) == 3:
                 data = pd.DataFrame(
                     {"actual": data[0], "predict": data[1], "predict_proba": data[2]}
@@ -2366,7 +2370,8 @@ def generate_model_card(
         )
 
         # Generates dmcas_misc.json file
-        cls.generate_misc(model_files)
+        if target_type == 'classification':
+            cls.generate_misc(model_files)
 
     @staticmethod
     def upload_training_data(
@@ -2617,7 +2622,7 @@ def generate_variable_importance(
         if target_type == "classification":
             method = "DTREE"
             treeCrit = "Entropy"
-        elif target_type == "interval":
+        elif target_type == "prediction":
             method = "RTREE"
             treeCrit = "RSS"
         else:
@@ -2743,14 +2748,14 @@ def generate_misc(cls, model_files: Union[str, Path, dict]):
         if isinstance(model_files, dict):
             if ROC not in model_files:
                 raise RuntimeError(
-                    "The ModelProperties.json file must be generated before the model card data "
+                    "The dmcas_roc.json file must be generated before the model card data "
                     "can be generated."
                 )
             roc_table = model_files[ROC]
         else:
             if not Path.exists(Path(model_files) / ROC):
                 raise RuntimeError(
-                    "The ModelProperties.json file must be generated before the model card data "
+                    "The dmcas_roc.json file must be generated before the model card data "
                     "can be generated."
                 )
             with open(Path(model_files) / ROC, "r") as roc_file: