Merge pull request #182 from sassoftware/score-code-update

Score code update branch merge

Author: djm21

CHANGELOG.md

@@ -5,6 +5,17 @@ Unreleased
  - Add `model_info` class to better capture model information.
  - Test `/examples` Jupyter notebooks within normal test suite.
 
+v1.10.1 (2023-08-24)
+----------
+**Improvements**
+- Introduced ability to specify the target index of a binary model when creating score code.
+  - index can be specified in `pzmm.import_model.ImportModel.import_model()`
+  - Relevant examples updated to include target_index.
+  
+**Bugfixes**
+- Reworked `write_score_code.py` to allow for proper execution of single line scoring.
+- Added template files for `assess_model_bias.py` to allow for proper execution
+
 v1.10 (2023-08-31)
 ----------
 **Improvements**

examples/pzmm_binary_classification_model_import.ipynb

@@ -814,7 +814,8 @@
     "        predict_method=[dtc.predict_proba, [int, int]], # What is the predict method and what does it return?\n",
     "        score_metrics=score_metrics, # What are the output variables?\n",
     "        overwrite_model=True, # Overwrite the model if it already exists?\n",
-    "        target_values=[\"1\", \"0\"], # What are the expected values of the target variable?\n",
+    "        target_values=[\"0\", \"1\"], # What are the expected values of the target variable?\n",
+    "        target_index=1, # What is the index of the target value in target_values?\n",
     "        model_file_name=prefix + \".pickle\", # How was the model file serialized?\n",
     "        missing_values=True # Does the data include missing values?\n",
     "    )\n",

examples/pzmm_h2o_model_import.ipynb

@@ -753,7 +753,7 @@
     "   input_data=x, # What does example input data look like?\n",
     "   predict_method=[model.predict, [int, int]], # What is the predict method and what does it return?\n",
     "   overwrite_model=True, # Overwrite model if it arleady exists\n",
-    "   target_values=[\"1\", \"0\"], # What are the expecte values of the target variable?\n",
+    "   target_values=[\"0\", \"1\"], # What are the expecte values of the target variable?\n",
     "   score_metrics=score_metrics, # What are the output variables?\n",
     "   model_file_name = model_prefix + \".h5\", # How was the model file serialized?\n",
     "   missing_values = True, # Does the data include missing values?\n",

examples/pzmm_tensorflow_keras_model_import.ipynb

@@ -198,6 +198,7 @@ def import_model(
         predict_threshold: Optional[float] = None,
         target_values: Optional[List[str]] = None,
         overwrite_project_properties: Optional[bool] = False,
+        target_index: Optional[int] = None,
         **kwargs,
     ) -> Tuple[RestObj, Union[dict, str, Path]]:
         """
@@ -275,10 +276,16 @@ def import_model(
         target_values : list of strings, optional
             A list of target values for the target variable. This argument and the
             score_metrics argument dictate the handling of the predicted values from
-            the prediction method. The default value is None.
+            the prediction method. The order of the target values should reflect the
+            order of the related probabilities in the model. The default value is None.
         overwrite_project_properties : bool, optional
             Set whether the project properties should be overwritten when attempting to
             import the model. The default value is False.
+        target_index : int, optional
+            Sets the index of success for a binary model. If target_values are given, this
+            index should match the index of the target outcome in target_values. If target_values
+            are not given, this index should indicate whether the the target probability variable
+            is the first or second variable returned by the model. The default value is 1.
         kwargs : dict, optional
             Other keyword arguments are passed to the following function:
                 * sasctl.pzmm.ScoreCode.write_score_code(...,
@@ -352,6 +359,7 @@ def import_model(
                 target_values=target_values,
                 missing_values=missing_values,
                 score_cas=score_cas,
+                target_index=target_index,
                 **kwargs,
             )
             if score_code_dict:
@@ -451,6 +459,7 @@ def import_model(
                 target_values=target_values,
                 missing_values=missing_values,
                 score_cas=score_cas,
+                target_index=target_index,
                 **kwargs,
             )
             if score_code_dict: