[testing] Remove sklearn mixin dependence from test_patching (#1795)

* Update test_patching.py

* Update _utils.py

* formatting

* Update _utils.py

* Update _utils.py

* Update test_patching.py

Author: icfaust

sklearnex/tests/_utils.py

@@ -104,10 +104,13 @@ def _load_all_models(with_sklearnex=True, estimator=True):
 def gen_models_info(algorithms):
     output = []
     for i in algorithms:
-        # split handles SPECIAL_INSTANCES or custom inputs
-        # custom sklearn inputs must be a dict of estimators
-        # with keys set by the __str__ method
-        est = PATCHED_MODELS[i.split("(")[0]]
+
+        if i in PATCHED_MODELS:
+            est = PATCHED_MODELS[i]
+        elif i in SPECIAL_INSTANCES:
+            est = SPECIAL_INSTANCES[i].__class__
+        else:
+            raise KeyError(f"Unrecognized sklearnex estimator: {i}")
 
         methods = set()
         candidates = set(
@@ -118,7 +121,11 @@ def gen_models_info(algorithms):
             if issubclass(est, mixin):
                 methods |= candidates & set(method)
 
-        output += [[i, j] for j in methods]
+        output += [[i, j] for j in methods] if methods else [[i, None]]
+
+    # In the case that no methods are available, set method to None.
+    # This will allow estimators without mixins to still test the fit
+    # method in various tests.
     return output
 
 

sklearnex/tests/test_patching.py

@@ -26,14 +26,7 @@
 import numpy as np
 import numpy.random as nprnd
 import pytest
-from sklearn.base import (
-    BaseEstimator,
-    ClassifierMixin,
-    ClusterMixin,
-    OutlierMixin,
-    RegressorMixin,
-    TransformerMixin,
-)
+from sklearn.base import BaseEstimator
 
 from daal4py.sklearn._utils import sklearn_check_version
 from onedal.tests.utils._dataframes_support import (
@@ -149,16 +142,17 @@ def test_standard_estimator_patching(caplog, dataframe, queue, dtype, estimator,
             and dtype in [np.uint32, np.uint64]
         ):
             pytest.skip("Windows segmentation fault for Ridge.predict for unsigned ints")
-        elif not hasattr(est, method):
+        elif method and not hasattr(est, method):
             pytest.skip(f"sklearn available_if prevents testing {estimator}.{method}")
 
         X, y = gen_dataset(est, queue=queue, target_df=dataframe, dtype=dtype)
         est.fit(X, y)
 
-        if method != "score":
-            getattr(est, method)(X)
-        else:
-            est.score(X, y)
+        if method:
+            if method != "score":
+                getattr(est, method)(X)
+            else:
+                est.score(X, y)
     assert all(
         [
             "running accelerated version" in i.message
@@ -186,12 +180,15 @@ def test_special_estimator_patching(caplog, dataframe, queue, dtype, estimator,
         X, y = gen_dataset(est, queue=queue, target_df=dataframe, dtype=dtype)
         est.fit(X, y)
 
-        if not hasattr(est, method):
+        if method and not hasattr(est, method):
             pytest.skip(f"sklearn available_if prevents testing {estimator}.{method}")
-        if method != "score":
-            getattr(est, method)(X)
-        else:
-            est.score(X, y)
+
+        if method:
+            if method != "score":
+                getattr(est, method)(X)
+            else:
+                est.score(X, y)
+
     assert all(
         [
             "running accelerated version" in i.message
@@ -336,18 +333,6 @@ def test_if_estimator_inherits_sklearn(estimator):
         ), f"{estimator} does not inherit from the patched sklearn estimator"
     else:
         assert issubclass(est, BaseEstimator)
-        assert any(
-            [
-                issubclass(est, i)
-                for i in [
-                    ClassifierMixin,
-                    ClusterMixin,
-                    OutlierMixin,
-                    RegressorMixin,
-                    TransformerMixin,
-                ]
-            ]
-        ), f"{estimator} does not inherit a sklearn Mixin"
 
 
 @pytest.mark.parametrize("estimator", UNPATCHED_MODELS.keys())