Change default testing versions to mirror currently supported python versions

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

@@ -21,7 +21,7 @@ jobs:
       LANG: en_US.UTF-8
     strategy:
       matrix:
-        python-version: ['3.8', '3.9', '3.10', '3.11']
+        python-version: ['3.10', '3.11', '3.12', '3.13', '3.14']
         os-version: ['ubuntu-latest', 'windows-latest', 'macos-latest']
 #        os-version: [ubuntu-latest, windows-latest, macos-latest]
 
@@ -40,11 +40,15 @@ jobs:
           sudo apt-get update
           sudo apt-get install build-essential libkrb5-dev
 
+      - name: Install dependencies (macOS)
+        if: startsWith(matrix.os-version, 'macos')
+        run: brew install libomp
+
       - name: Install dependencies (Common)
         run: |
           # Setup tox & code coverage
           pip install --upgrade pip
-          pip install tox tox-gh-actions pytest pytest-cov 'scikit-learn<=1.5.0' 'numpy<2.0.0'
+          pip install tox tox-gh-actions pytest pytest-cov 'scikit-learn' 'numpy'
 
       - name: Run Tests
         run: |

tests/conftest.py

@@ -532,9 +532,7 @@ def sklearn_classification_model(iris_dataset):
 
     with warnings.catch_warnings():
         warnings.simplefilter("ignore")
-        model = sk.LogisticRegression(
-            multi_class="multinomial", solver="lbfgs", max_iter=1000
-        )
+        model = sk.LogisticRegression(solver="lbfgs", max_iter=1000)
         model.fit(X, y)
     return model
 

tests/integration/test_model_parameters.py

@@ -42,7 +42,7 @@ def sklearn_model(train_data):
     X, y = train_data
     with warnings.catch_warnings():
         warnings.simplefilter("ignore")
-        model = LogisticRegression(multi_class="multinomial", solver="lbfgs")
+        model = LogisticRegression(solver="lbfgs")
         model.fit(X, y)
     return model
 

tests/integration/test_pymas.py

@@ -63,9 +63,7 @@ def sklearn_model(train_data):
     X, y = train_data
     with warnings.catch_warnings():
         warnings.simplefilter("ignore")
-        model = LogisticRegression(
-            multi_class="multinomial", solver="lbfgs", max_iter=10000
-        )
+        model = LogisticRegression(solver="lbfgs", max_iter=10000)
         model.fit(X, y)
     return model
 

tests/integration/test_tasks.py

@@ -40,9 +40,7 @@ def sklearn_logistic_model():
 
     with warnings.catch_warnings():
         warnings.simplefilter("ignore")
-        model = LogisticRegression(
-            multi_class="multinomial", solver="lbfgs", max_iter=10000
-        )
+        model = LogisticRegression(solver="lbfgs", max_iter=10000)
         model.fit(iris.iloc[:, 0:4], iris["Species"])
 
     return model, iris.iloc[:, 0:4]

tests/unit/test_model_parameters.py

@@ -1,11 +1,13 @@
-import ast
 import copy
 import json
 import tempfile
+import unittest
+import uuid
 import warnings
 from pathlib import Path
 from unittest import mock
 
+import numpy as np
 import pandas as pd
 import pytest
 from requests.models import Response
@@ -14,9 +16,6 @@
 
 from sasctl import RestObj, current_session
 from sasctl.pzmm import ModelParameters as mp
-import unittest
-import uuid
-import numpy as np
 
 
 class BadModel:
@@ -47,9 +46,7 @@ def sklearn_model(train_data):
     X, y = train_data
     with warnings.catch_warnings():
         warnings.simplefilter("ignore")
-        model = LogisticRegression(
-            multi_class="multinomial", solver="lbfgs", max_iter=1000
-        )
+        model = LogisticRegression(solver="lbfgs", max_iter=1000)
         model.fit(X, y)
     return model
 

tests/unit/test_write_json_files.py

@@ -74,9 +74,7 @@ def sklearn_model(train_data):
     X, y = train_data
     with warnings.catch_warnings():
         warnings.simplefilter("ignore")
-        model = LogisticRegression(
-            multi_class="multinomial", solver="lbfgs", max_iter=1000
-        )
+        model = LogisticRegression(solver="lbfgs", max_iter=1000)
         model.fit(X, y)
     return model
 

tests/unit/test_write_score_code.py

@@ -11,6 +11,7 @@
 
 import pandas as pd
 import pytest
+from packaging.version import Version
 
 from sasctl import current_session
 from sasctl.core import RestObj, VersionInfo
@@ -1165,7 +1166,10 @@ def test_input_var_lists():
     data = pd.DataFrame(data=[[1, "A"], [5, "B"]], columns=["First", "Second"])
     var_list, dtypes_list = sc._input_var_lists(data)
     assert var_list == ["First", "Second"]
-    assert dtypes_list == ["int64", "object"]
+    if Version(pd.__version__) < Version("3.0.0"):
+        assert dtypes_list == ["int64", "object"]
+    else:
+        assert dtypes_list == ["int64", "str"]
 
     data = [{"name": "First", "type": "int"}, {"name": "Second", "type": "string"}]
     var_list, dtypes_list = sc._input_var_lists(data)

tox.ini

@@ -13,18 +13,19 @@
 
 [tox]
 requires = tox>=4
-envlist = py{38,39,310,311}-tests-{clean,unit,integration}
+envlist = py{310,311,312,313,314}-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 =
-    3.8: py38
-    3.9: py39
     3.10: py310
     3.11: py311
+    3.12: py312
+    3.13: py313
+    3.14: py314
 
 [testenv]
 description = run standard unit and integration tests
@@ -43,14 +44,14 @@ deps =
     tests: pytest-cov
     tests: betamax >= 0.8.1
     tests: betamax_serializers >= 0.2.0
-    tests: scikit-learn < 1.5.0
-    tests: pandas < 2.0.0
-    tests: numpy < 2.0.0
+    tests: scikit-learn
+    tests: pandas
+    tests: numpy
     tests: cython               # required to install pandas from source
     tests: swat >= 1.8
     tests: kerberos ; platform_system != "Windows" and platform_system != "Darwin"
-    tests: xgboost == 1.7.3
-    tests: urllib3 < 2.0.0
+    tests: xgboost
+    tests: urllib3
     tests: nbconvert
     tests: nbformat
 #    tests: torch