From 850f05498e0bed264715cccd9010b391f2752666 Mon Sep 17 00:00:00 2001
From: djm21 <david.moore@sas.com>
Date: Wed, 6 Mar 2024 19:53:14 -0600
Subject: [PATCH] fixed some bugs to allow for model card files to generate
 correctly.

---
 src/sasctl/pzmm/write_json_files.py | 34 +++++++++++++++--------------
 1 file changed, 18 insertions(+), 16 deletions(-)

diff --git a/src/sasctl/pzmm/write_json_files.py b/src/sasctl/pzmm/write_json_files.py
index 2618c209..d905acde 100644
--- a/src/sasctl/pzmm/write_json_files.py
+++ b/src/sasctl/pzmm/write_json_files.py
@@ -2208,7 +2208,7 @@ def generate_model_card(
         algorithm: str,
         train_data: pd.DataFrame,
         train_predictions: Union[pd.Series, list],
-        target_type: str = "Interval",
+        target_type: str = "interval",
         target_value: Union[str, int, float, None] = None,
         interval_vars: Optional[list] = [],
         class_vars: Optional[list] = [],
@@ -2237,10 +2237,10 @@ def generate_model_card(
         train_predictions : pandas.Series, list
             List of predictions made by the model on the training data.
         target_type : string
-            Type the model is targeting. Currently supports "Classification" and "Interval" types. 
+            Type the model is targeting. Currently supports "classification" and "interval" types. 
             The default value is "Interval".
         target_value : string, int, float, optional
-            Value the model is targeting for Classification models. This argument is not needed for
+            Value the model is targeting for classification models. This argument is not needed for
             Interval models. The default value is None.
         interval_vars : list, optional
             A list of interval variables. The default value is an empty list.
@@ -2255,14 +2255,14 @@ def generate_model_card(
         caslib: str, optional
             The caslib the training data will be stored on. The default value is "Public"
         """
-        if not target_value and target_type == "Classification":
+        if not target_value and target_type == "classification":
             raise RuntimeError(
-                "For the model card data to be properly generated on a Classification "
+                "For the model card data to be properly generated on a classification "
                 "model, a target value is required."
             )
-        if target_type not in ["Classification", "Interval"]:
+        if target_type not in ["classification", "interval"]:
             raise RuntimeError(
-                "Only Classification and Interval target types are currently accepted."
+                "Only classification and interval target types are currently accepted."
             )
         if selection_statistic not in cls.valid_params:
             raise RuntimeError(
@@ -2396,10 +2396,10 @@ def generate_outcome_average(
         Returns a dictionary with a key value pair that represents the outcome average.
         """
         output_var = train_data.drop(input_variables, axis=1)
-        if target_type == "Classification":
+        if target_type == "classification":
             value_counts = output_var[output_var.columns[0]].value_counts()
             return {'eventPercentage': value_counts[target_value]/sum(value_counts)}
-        elif target_type == "Interval":
+        elif target_type == "interval":
             return {'eventAverage': sum(value_counts[value_counts.columns[0]]) / len(value_counts)}
 
     @staticmethod
@@ -2480,8 +2480,8 @@ def update_model_properties(
                     "The ModelProperties.json file must be generated before the model card data "
                     "can be generated."
                     )
-            for key, value in update_dict:
-                model_files[PROP][key] = value
+            for key in update_dict:
+                model_files[PROP][key] = update_dict[key]
         else:
             if not Path.exists(Path(model_files) / PROP):
                 raise RuntimeError(
@@ -2490,8 +2490,8 @@ def update_model_properties(
                 )
             with open(Path(model_files) / PROP, 'r+') as properties_json:
                 model_properties = json.load(properties_json)
-                for key, value in update_dict:
-                    model_properties[key] = value
+                for key in update_dict:
+                    model_properties[key] = update_dict[key]
                 properties_json.seek(0)
                 properties_json.write(json.dumps(model_properties, indent=4, cls=NpEncoder))
                 properties_json.truncate()
@@ -2595,7 +2595,7 @@ def generate_variable_importance(
                 }
             })
         var_data = conn.dataPreprocess.transform(
-            table={"name": "test_data", "caslib": caslib},
+            table={"name": "train_data", "caslib": caslib},
             requestPackages=request_packages,
             evaluationStats=True,
             percentileMaxIterations=10, 
@@ -2623,7 +2623,10 @@ def generate_variable_importance(
                 },
                 "rowNumber": index+1
             })
-        with open('./dmcas_relativeimportance.json', 'r') as f:
+        json_template_path = (
+            Path(__file__).resolve().parent / f"template_files/{VARIMPORTANCES}"
+        )
+        with open(json_template_path, 'r') as f:
             relative_importance_json = json.load(f)
         relative_importance_json['data'] = relative_importances
 
@@ -2641,5 +2644,4 @@ def generate_variable_importance(
                 print(
                     f"{VARIMPORTANCES} was successfully written and saved to "
                     f"{Path(model_files) / VARIMPORTANCES}"
-           
                 )
\ No newline at end of file