Add python 3.12 to test matrix

Signed-off-by: Keith Battocchi <[email protected]>

Author: kbattocchi

.github/workflows/ci.yml

@@ -203,7 +203,7 @@ jobs:
     strategy:
       matrix:
         os: [ubuntu-latest, windows-latest, macos-latest]
-        python-version: ['3.8', '3.9', '3.10', '3.11']
+        python-version: ['3.8', '3.9', '3.10', '3.11', '3.12']
         kind: [serial, other, dml, main, treatment, ray]
         exclude:
           # Serial tests fail randomly on mac sometimes, so we don't run them there
@@ -212,6 +212,9 @@ jobs:
           # Ray tests run out of memory on Windows
           - os: windows-latest
             kind: ray
+          # Ray doesn't currently support Python 3.12
+          - python-version: '3.12'
+            kind: ray
         # Assign the correct package and testing options for each kind of test
         include:
           - kind: serial

econml/dowhy.py

@@ -36,8 +36,8 @@ class DoWhyWrapper:
     """
 
     def __init__(self, cate_estimator):
-        from pkg_resources import parse_version
-        if parse_version(dowhy.__version__) >= parse_version('0.11'):
+        from packaging.version import parse
+        if parse(dowhy.__version__) >= parse('0.11'):
             warnings.warn("econml has not been tested with dowhy versions >= 0.11")
         self._cate_estimator = cate_estimator
 

econml/sklearn_extensions/model_selection.py

@@ -825,9 +825,9 @@ def _cross_val_predict(estimator, X, y=None, *, groups=None, cv=None,
     parallel = Parallel(n_jobs=n_jobs, verbose=verbose,
                         pre_dispatch=pre_dispatch)
 
-    from pkg_resources import parse_version
+    from packaging.version import parse
     # verbose was removed from sklearn's non-public _fit_and_predict method in 1.4
-    if parse_version(sklearn.__version__) < parse_version("1.4"):
+    if parse(sklearn.__version__) < parse("1.4"):
         predictions = parallel(delayed(_fit_and_predict)(
             clone(estimator, safe=safe), X, y, train, test, verbose, fit_params, method)
             for train, test in splits)

econml/tests/test_dml.py

@@ -787,7 +787,7 @@ def true_fn(x):
             np.testing.assert_array_less(mean_truth - .03, inf_sum.ci_upper)
             tables = est.summary().tables
             for t in range(4):
-                np.testing.assert_allclose(np.array(tables[t].data[1:])[:, 1].astype(np.float),
+                np.testing.assert_allclose(np.array(tables[t].data[1:])[:, 1].astype(float),
                                            mean_truth, rtol=0, atol=.06)
 
             if it == 0:

econml/tests/test_orf.py

@@ -316,8 +316,8 @@ def predict(self, X):
             "Estimators of type {} do not accept weights. "
             "Consider using the class WeightedModelWrapper from econml.utilities to build a weighted model."
         )
-        self.assertRaisesRegexp(TypeError, weights_error_msg.format("NoWeightModel"),
-                                est.effect, X=TestOrthoForest.X)
+        self.assertRaisesRegex(TypeError, weights_error_msg.format("NoWeightModel"),
+                               est.effect, X=TestOrthoForest.X)
 
     def _test_te(self, learner_instance, expected_te, tol, treatment_type='continuous'):
         # Compute the treatment effect on test points

econml/utilities.py

@@ -1516,8 +1516,8 @@ def one_hot_encoder(sparse=False, **kwargs):
     Wrapper for sklearn's OneHotEncoder that handles the name change from `sparse` to `sparse_output`
     between sklearn versions 1.1 and 1.2.
     """
-    from pkg_resources import parse_version
-    if parse_version(sklearn.__version__) < parse_version("1.2"):
+    from packaging.version import parse
+    if parse(sklearn.__version__) < parse("1.2"):
         return OneHotEncoder(sparse=sparse, **kwargs)
     else:
         return OneHotEncoder(sparse_output=sparse, **kwargs)

pyproject.toml

@@ -28,7 +28,8 @@ dependencies = [
     "statsmodels >= 0.10",
     "pandas > 1.0",
     "shap >= 0.38.1, < 0.44.0",
-    "lightgbm"
+    "lightgbm",
+    "packaging"
 ]
 
 [project.urls]