Merge branch 'master' into doc-fix

Author: jlwalke2

.conda/meta.yaml

@@ -21,6 +21,7 @@ requirements:
     - python >=3.6
     - pip
   run:
+    - dill
     - python >=3.6
     - pandas
     - requests

.github/workflows/build-test-deploy.yml

@@ -21,7 +21,7 @@ jobs:
       LANG: en_US.UTF-8
     strategy:
       matrix:
-        python-version: ['3.6', '3.7', '3.8', '3.9', '3.10']
+        python-version: ['3.8', '3.9', '3.10', '3.11']
         os-version: ['ubuntu-20.04', 'windows-latest', 'macos-latest']
 # Pinned Ubuntu version to 20.04 since no Python 3.6 builds available on ubuntu-latest (22.04) as of 2022-12-7.
 #        os-version: [ubuntu-latest, windows-latest, macos-latest]
@@ -45,7 +45,7 @@ jobs:
         run: |
           # Setup tox & code coverage
           pip install --upgrade pip
-          pip install tox tox-gh-actions pytest pytest-cov scikit-learn
+          pip install tox tox-gh-actions pytest pytest-cov 'scikit-learn<=1.5.0' 'numpy<2.0.0'
 
       - name: Run Tests
         run: |

.github/workflows/codeql-analysis.yml

@@ -29,9 +29,9 @@ jobs:
 
     # Initializes the CodeQL tools for scanning.
     - name: Initialize CodeQL
-      uses: github/codeql-action/init@v1
+      uses: github/codeql-action/init@v2
       with:
         languages: python
 
     - name: Perform CodeQL Analysis
-      uses: github/codeql-action/analyze@v1
+      uses: github/codeql-action/analyze@v2

CHANGELOG.md

@@ -1,9 +1,52 @@
-Unreleased
+v1.10.6 (2024-08-26)
 ----------
 **Improvements**
  - Refactor `tasks.py` to utilize `sasctl.pzmm` functions.
  - Add `model_info` class to better capture model information.
- - Test `/examples` Jupyter notebooks within normal test suite.
+
+v1.10.5 (2024-08-01)
+----------
+**Buxfixes**
+- Updated `write_json_files.py` to allow for better support for prediction models
+- Fixed issues relating to model card support.
+
+v1.10.4 (2024-07-08)
+----------
+**Improvements**
+- Added example Jupyter notebook for OpenAI models.
+
+**Buxfixes**
+- Dropped support for Python 3.6 and Python 3.7, as those are no longer officially supported versions.
+- Added `dmcas_misc.json` template file for model card generation.
+- Updated generation of `ModelProperties.json` to allow for model card generation immediately upon upload.
+
+v1.10.3 (2024-04-12)
+----------
+**Bugfixes**
+- Updated all examples to use current versions of sasctl functions
+- Fixed bug in `generate_model_card` that threw an error when trying to generate the `dmcas_misc.json` file
+
+v1.10.2 (2024-04-10)
+----------
+**Improvements**
+- Introduced `generate_model_card` into `write_json_files.py` to allow for python models to work with planned model card tab in SAS Model Manager.
+
+**Bugfixes**
+- Allow for score code to impute NaN values in tables that have been loaded into SAS Model Manager.
+- Fix issue where target_value was not being properly set during score code generation
+- Updated `pzmm_generate_requrirements_json.ipynb` so the requirements file is generated properly.
+- Added missing statistics to `dmcas_fitstat.json` file.
+
+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)
 ----------

examples/README.md

@@ -37,6 +37,8 @@ Tasks and Services
 - [Modeling with Python & SAS AutoML](#modeling-with-python--sas-automl)
 - [Making direct REST API calls](#making-direct-rest-api-calls)
 
+- [Register an Azure OpenAI GPT model using REST API calls](#register-an-azure-openai-gpt-model-using-rest-api-calls)
+
 ---
 Register binary classification models
 -------------------------------------
@@ -94,11 +96,11 @@ Generates a requirements.json file which includes the minimal number of dependen
 
 Create and update custom model KPIs
 -----------------------------------
-Filename: [pzmm_custom_kpis.ipynb](pzmm_custom_kpis.ipynb)
+Filename: [pzmm_custom_kpis.ipynb](pzmm_custom_kpi_model_parameters.ipynb)
 
 Level: Intermediate
 
-Create and update custom model parameters and kpis on SAS Model Manager
+Create and update custom model parameters and kpis on SAS Model Manager.
 
 
 Register a SAS classification model
@@ -125,9 +127,10 @@ Register a SAS deep learning model
 ----------------------------------
 Filename: [register_sas_dlpy_model.py](register_sas_dlpy_model.py)
 
+
 Level: Beginner
 
-Creates a SAS deep learning model using [dlpy](https://github.com/sassoftware/python-dlpy) and registers the model in SAS Model Manager.
+Creates a SAS deep learning model using [dlpy](https://github.com/sassoftware/python-dlpy) and registers the model in SAS Model Manager. (WARNING: Does not work with Python 3.10 and later)
 
 
 
@@ -205,3 +208,11 @@ Level: Advanced
 Demonstrates using `sasctl` to make REST calls over HTTP(S) directly to the SAS microservices.
 
 Use if you need to customize behavior or use functionality not yet exposed through higher-level `sasctl` functions.
+
+Register an Azure OpenAI GPT Model Using REST API Calls
+--------------------------
+Filename: [register_Azure_OpenAI_model_using_REST_calls.ipynb](register_Azure_OpenAI_model_using_REST_calls.ipynb)
+
+Level: Intermediate
+
+Leverages a GPT-3.5-Turbo model from Azure OpenAI in SAS® Model Manager and SAS® Intelligent Decisioning.