Ensure kwarg order matches sklearn expectations

python/cuml/cuml/cluster/dbscan.pyx

@@ -434,22 +434,22 @@ class DBSCAN(UniversalBase,
 
     @generate_docstring(skip_parameters_heading=True)
     @enable_device_interop
-    def fit(self, X, y=None, out_dtype="int32", sample_weight=None,
+    def fit(self, X, y=None, sample_weight=None, out_dtype="int32",
             convert_dtype=True) -> "DBSCAN":
         """
         Perform DBSCAN clustering from features.
 
         Parameters
         ----------
-        out_dtype: dtype Determines the precision of the output labels array.
-            default: "int32". Valid values are { "int32", np.int32,
-            "int64", np.int64}.
-
         sample_weight: array-like of shape (n_samples,), default=None
             Weight of each sample, such that a sample with a weight of at
             least min_samples is by itself a core sample; a sample with a
             negative weight may inhibit its eps-neighbor from being core.
             default: None (which is equivalent to weight 1 for all samples).
+
+        out_dtype: dtype Determines the precision of the output labels array.
+            default: "int32". Valid values are { "int32", np.int32,
+            "int64", np.int64}.
         """
         return self._fit(X, out_dtype, False, sample_weight)
 
@@ -459,21 +459,21 @@ class DBSCAN(UniversalBase,
                                        'description': 'Cluster labels',
                                        'shape': '(n_samples, 1)'})
     @enable_device_interop
-    def fit_predict(self, X, y=None, out_dtype="int32", sample_weight=None) -> CumlArray:
+    def fit_predict(self, X, y=None, sample_weight=None, out_dtype="int32") -> CumlArray:
         """
         Performs clustering on X and returns cluster labels.
 
         Parameters
         ----------
-        out_dtype: dtype Determines the precision of the output labels array.
-            default: "int32". Valid values are { "int32", np.int32,
-            "int64", np.int64}.
-
         sample_weight: array-like of shape (n_samples,), default=None
             Weight of each sample, such that a sample with a weight of at
             least min_samples is by itself a core sample; a sample with a
             negative weight may inhibit its eps-neighbor from being core.
             default: None (which is equivalent to weight 1 for all samples).
+
+        out_dtype: dtype Determines the precision of the output labels array.
+            default: "int32". Valid values are { "int32", np.int32,
+            "int64", np.int64}.
         """
         self.fit(X, out_dtype=out_dtype, sample_weight=sample_weight)
         return self.labels_

python/cuml/cuml/cluster/kmeans.pyx

@@ -703,8 +703,7 @@ class KMeans(UniversalBase,
                                        'description': 'Transformed data',
                                        'shape': '(n_samples, n_clusters)'})
     @enable_device_interop
-    def fit_transform(self, X, y=None, convert_dtype=False,
-                      sample_weight=None) -> CumlArray:
+    def fit_transform(self, X, y=None, sample_weight=None, convert_dtype=False) -> CumlArray:
         """
         Compute clustering and transform X to cluster-distance space.
 

python/cuml/cuml/linear_model/elastic_net.pyx

@@ -311,8 +311,7 @@ class ElasticNet(Base,
 
     @generate_docstring()
     @warn_legacy_device_interop
-    def fit(self, X, y, convert_dtype=True,
-            sample_weight=None) -> "ElasticNet":
+    def fit(self, X, y, sample_weight=None, convert_dtype=True) -> "ElasticNet":
         """
         Fit the model with X and y.
 

python/cuml/cuml/linear_model/linear_regression.pyx

@@ -345,8 +345,7 @@ class LinearRegression(Base,
 
     @generate_docstring()
     @warn_legacy_device_interop
-    def fit(self, X, y, convert_dtype=True,
-            sample_weight=None) -> "LinearRegression":
+    def fit(self, X, y, sample_weight=None, convert_dtype=True) -> "LinearRegression":
         """
         Fit the model with X and y.
 

python/cuml/cuml/linear_model/ridge.pyx

@@ -318,7 +318,7 @@ class Ridge(Base,
 
     @generate_docstring()
     @warn_legacy_device_interop
-    def fit(self, X, y, convert_dtype=True, sample_weight=None) -> "Ridge":
+    def fit(self, X, y, sample_weight=None, convert_dtype=True) -> "Ridge":
         """
         Fit the model with X and y.
         """

python/cuml/cuml/solvers/cd.pyx

@@ -216,7 +216,7 @@ class CD(Base,
         }[self.loss]
 
     @generate_docstring()
-    def fit(self, X, y, convert_dtype=True, sample_weight=None) -> "CD":
+    def fit(self, X, y, sample_weight=None, convert_dtype=True) -> "CD":
         """
         Fit the model with X and y.
 

python/cuml/cuml/tests/test_base.py

@@ -213,7 +213,7 @@ def test_base_children__get_param_names(child_class: str):
         }
     ],
 )
-def test_sklearn_methods_with_required_y_parameter(cls):
+def test_sklearn_methods_match_sklearn_interface(cls):
     optional_params = {
         inspect.Parameter.KEYWORD_ONLY,
         inspect.Parameter.POSITIONAL_OR_KEYWORD,
@@ -229,13 +229,24 @@ def test_sklearn_methods_with_required_y_parameter(cls):
         if (method := getattr(cls, name, None)) is None:
             # Method not defined, skip
             continue
-        params = list(inspect.signature(method).parameters.values())
+        sig = inspect.signature(method)
+        params = list(sig.parameters.values())
         # Assert method has a 2nd parameter named y, which is required by sklearn
         assert (
             len(params) > 2 and params[2].name == "y"
         ), f"`{name}` requires a `y` parameter, even if it's ignored"
+
         # Check that all remaining parameters are optional
         for param in params[3:]:
             assert (
                 param.kind in optional_params
             ), f"`{name}` parameter `{param.name}` must be optional"
+
+        # Check common kwargs have an expected order
+        i = 3
+        for kw_name in ["classes", "sample_weight"]:
+            if kw_name in sig.parameters:
+                assert (
+                    params[i].name == kw_name
+                ), f"`{kw_name}` should be before {params[i].name} in `{name}`"
+                i += 1