Tentative updates to CHANGELOG.md and __init__.py, as well as fixing example in write_score_code.py.

Author: djm21

CHANGELOG.md

@@ -5,6 +5,15 @@ 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()`
+  
+**Bugfixes**
+- Reworked `write_score_code.py` to allow for proper execution of single line scoring.
+
 v1.10 (2023-08-31)
 ----------
 **Improvements**

src/sasctl/__init__.py

@@ -4,7 +4,7 @@
 # Copyright © 2019, SAS Institute Inc., Cary, NC, USA.  All Rights Reserved.
 # SPDX-License-Identifier: Apache-2.0
 
-__version__ = "1.10.0"
+__version__ = "1.10.1"
 __author__ = "SAS"
 __credits__ = [
     "Yi Jian Ching",

src/sasctl/pzmm/write_score_code.py

@@ -1453,11 +1453,16 @@ def _binary_target(
                 )
                 """
     if input_array.shape[0] == 1:
-        return prediction[1][0], float(prediction[1][2])
+        if prediction[1][1] > 0.5:
+            Classification = '1'
+        else:
+            Classification = '0'
+        return EM_CLASSIFICATION, float(prediction[1][1])
     else:
-        output_table = prediction.drop(prediction.columns[1], axis=1)
-        output_table.columns = ['Classification', 'Probability']
-        return output_table
+        output_table = prediction.drop(prediction.columns[2], axis=1)
+        classifications = np.where(prediction[prediction.columns[1]] > 0.5, '0', '1')
+        output_table.columns = ['EM_CLASSIFICATION', 'EM_EVENTPROBABILITY']
+        output_table['EM_CLASSIFICATION'] = classifications
                 """
             # Calculate the classification; return the classification and probability
             elif sum(returns) == 0 and len(returns) == 1:
@@ -1787,7 +1792,7 @@ def _nonbinary_targets(
             elif len(returns) == 1:
                 cls.score_code += (
                     f"{'':4}if input_array.shape[0] == 1:\n"
-                    f"{'':8}return prediction[0]\n"
+                    f"{'':8}return prediction[0][0]\n"
                     f"{'':4}else:\n"
                     f"{'':8}return pd.DataFrame({{'{metrics}': prediction}})"
                 )