Merge branch 'scoring_services' of https://github.com/sassoftware/python-sasctl into scoring_services

Author: samyarpotlapalli

.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: |

CHANGELOG.md

@@ -5,6 +5,16 @@ Unreleased
  - Add `model_info` class to better capture model information.
  - Test `/examples` Jupyter notebooks within normal test suite.
 
+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**

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
@@ -206,3 +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.

examples/register_Azure_OpenAI_model_using_REST_calls.ipynb

@@ -4,7 +4,7 @@
 # Copyright © 2019, SAS Institute Inc., Cary, NC, USA.  All Rights Reserved.
 # SPDX-License-Identifier: Apache-2.0
 
-__version__ = "1.10.3"
+__version__ = "1.10.4"
 __author__ = "SAS"
 __credits__ = [
     "Yi Jian Ching",

src/sasctl/__init__.py

@@ -355,17 +355,17 @@ def write_model_properties_json(
                 )
 
         if not target_values:
-            model_function = model_function if model_function else "Prediction"
+            model_function = model_function if model_function else "prediction"
             target_level = "Interval"
             target_event = ""
             event_prob_var = ""
         elif isinstance(target_values, list) and len(target_values) == 2:
-            model_function = model_function if model_function else "Classification"
+            model_function = model_function if model_function else "classification"
             target_level = "Binary"
             target_event = str(target_values[0])
             event_prob_var = f"P_{target_values[0]}"
         elif isinstance(target_values, list) and len(target_values) > 2:
-            model_function = model_function if model_function else "Classification"
+            model_function = model_function if model_function else "classification"
             target_level = "Nominal"
             target_event = ""
             event_prob_var = ""
@@ -2410,7 +2410,7 @@ def upload_training_data(
                 "either delete/rename the old table or give a new name to the current table."
             )
 
-        return server + "/" + caslib + "/" + train_data_name
+        return server + "/" + caslib + "/" + train_data_name.upper()
 
     @staticmethod
     def generate_outcome_average(
@@ -2730,7 +2730,7 @@ def generate_variable_importance(
     @classmethod
     def generate_misc(cls, model_files: Union[str, Path, dict]):
         """
-        Generates the dmcas_relativeimportance.json file, which is used to determine variable importance
+        Generates the dmcas_misc.json file, which is used to determine variable importance
 
         Parameters
         ----------

src/sasctl/pzmm/write_json_files.py

@@ -633,12 +633,12 @@ def pytest_runtest_makereport(item, call):
             # The id of the test is deteremined by its parameterization.  We just want to know if the test was
             # for Viya 3.5 or 2020.01, 2022.09, etc.  Try to check the parameter assigned to known fixtures like
             # `session`.  If that fails, we'll just use the id generated by pytest.
-            if "session" in item.callspec.params:
-                key = item.callspec.params["session"]
-            elif "cas_session" in item.callspec.params:
-                key = item.callspec.params["cas_session"]
-            else:
-                key = item.callspec.id
+            # if "session" in item.callspec.params:
+            #     key = item.callspec.params["session"]
+            # elif "cas_session" in item.callspec.params:
+            #     key = item.callspec.params["cas_session"]
+            # else:
+            key = item.callspec.id
 
             # Track that this test was the last test to fail for this Viya version
             parent._previousfailed[key] = item

tests/conftest.py

@@ -262,15 +262,15 @@ def test_write_model_properties_json():
         target_values=None,
     )
     assert "ModelProperties.json" in prop_dict
-    assert json.loads(prop_dict["ModelProperties.json"])["function"] == "Prediction"
+    assert json.loads(prop_dict["ModelProperties.json"])["function"] == "prediction"
     assert json.loads(prop_dict["ModelProperties.json"])["targetLevel"] == "Interval"
     assert json.loads(prop_dict["ModelProperties.json"])["targetEvent"] == ""
     assert json.loads(prop_dict["ModelProperties.json"])["eventProbVar"] == ""
 
     prop_dict = jf.write_model_properties_json(
         model_name="Test_Model", target_variable="BAD", target_values=[1, 0]
     )
-    assert json.loads(prop_dict["ModelProperties.json"])["function"] == "Classification"
+    assert json.loads(prop_dict["ModelProperties.json"])["function"] == "classification"
     assert json.loads(prop_dict["ModelProperties.json"])["targetLevel"] == "Binary"
     assert json.loads(prop_dict["ModelProperties.json"])["targetEvent"] == "1"
     assert json.loads(prop_dict["ModelProperties.json"])["eventProbVar"] == "P_1"

tests/unit/test_write_json_files.py

@@ -12,16 +12,14 @@
 
 
 [tox]
-envlist = py{36,37,38,39,310,311}-tests-{clean,unit,integration}
+envlist = py{38,39,310,311}-tests-{clean,unit,integration}
 
 # Allow execution even if all Python versions are not present
 skip_missing_interpreters = {env:TOX_SKIP_MISSING_INTERPRETERS:True}
 
 # Required by tox-gh-actions GH action.  Maps GH Python runtime to tox envlist.
 [gh-actions]
 python =
-    3.6: py36
-    3.7: py37
     3.8: py38
     3.9: py39
     3.10: py310
@@ -32,8 +30,6 @@ skip_install =
     clean: true
 
 basepython =
-    py36: python3.6
-    py37: python3.7
     py38: python3.8
     py39: python3.9
     py310: python3.10
@@ -45,12 +41,13 @@ deps =
     tests: pytest-cov
     tests: betamax >= 0.8.1
     tests: betamax_serializers >= 0.2.0
-    tests: scikit-learn
+    tests: scikit-learn < 1.5.0
     tests: pandas < 2.0.0
+    tests: numpy < 2.0.0
     tests: cython               # required to install pandas from source
     tests: swat >= 1.8
     tests: kerberos ; platform_system != "Windows" and platform_system != "Darwin"
-    tests: xgboost == 0.82
+    tests: xgboost == 1.7.3
     tests: urllib3 < 2.0.0
     tests: nbconvert
     tests: nbformat