feat(hyperparameters): get_hyperparameters as staticmethod

sklearnex/_utils.py

@@ -16,7 +16,6 @@
 
 import logging
 import os
-import sys
 import warnings
 from abc import ABC
 
@@ -106,14 +105,26 @@ def register_hyperparameters(hyperparameters_map):
     Adds `get_hyperparameters` method to class.
     """
 
-    def wrap_class(estimator_class):
-        def get_hyperparameters(self, op):
-            return hyperparameters_map[op]
+    def decorator(cls):
+        """Add `get_hyperparameters()` static method"""
 
-        estimator_class.get_hyperparameters = get_hyperparameters
-        return estimator_class
+        class StaticHyperparametersAccessor:
+            """Like a @staticmethod, but additionally raises a Warning when called on an instance."""
 
-    return wrap_class
+            def __get__(self, instance, _):
+                if instance is not None:
+                    warnings.warn(
+                        "Hyperparameters are static variables and can not be modified per instance."
+                    )
+                return self.get_hyperparameters
+
+            def get_hyperparameters(self, op):
+                return hyperparameters_map[op]
+
+        cls.get_hyperparameters = StaticHyperparametersAccessor()
+        return cls
+
+    return decorator
 
 
 # This abstract class is meant to generate a clickable doc link for classses

sklearnex/ensemble/tests/test_forest.py

@@ -51,7 +51,7 @@ def test_sklearnex_import_rf_classifier(dataframe, queue, block, trees, rows, sc
     X = _convert_to_dataframe(X, sycl_queue=queue, target_df=dataframe)
     y = _convert_to_dataframe(y, sycl_queue=queue, target_df=dataframe)
     rf = RandomForestClassifier(max_depth=2, random_state=0).fit(X, y)
-    hparams = rf.get_hyperparameters("infer")
+    hparams = RandomForestClassifier.get_hyperparameters("infer")
     if hparams and block is not None:
         hparams.block_size = block
         hparams.min_trees_for_threading = trees

sklearnex/linear_model/tests/test_incremental_linear.py

@@ -41,7 +41,7 @@ def test_sklearnex_fit_on_gold_data(dataframe, queue, fit_intercept, macro_block
 
     inclin = IncrementalLinearRegression(fit_intercept=fit_intercept)
     if macro_block is not None:
-        hparams = inclin.get_hyperparameters("fit")
+        hparams = IncrementalLinearRegression.get_hyperparameters("fit")
         hparams.cpu_macro_block = macro_block
         hparams.gpu_macro_block = macro_block
     inclin.fit(X_df, y_df)
@@ -72,7 +72,7 @@ def test_sklearnex_partial_fit_on_gold_data(
 
     inclin = IncrementalLinearRegression()
     if macro_block is not None:
-        hparams = inclin.get_hyperparameters("fit")
+        hparams = IncrementalLinearRegression.get_hyperparameters("fit")
         hparams.cpu_macro_block = macro_block
         hparams.gpu_macro_block = macro_block
     for i in range(2):
@@ -113,7 +113,7 @@ def test_sklearnex_partial_fit_multitarget_on_gold_data(
 
     inclin = IncrementalLinearRegression()
     if macro_block is not None:
-        hparams = inclin.get_hyperparameters("fit")
+        hparams = IncrementalLinearRegression.get_hyperparameters("fit")
         hparams.cpu_macro_block = macro_block
         hparams.gpu_macro_block = macro_block
     for i in range(2):
@@ -176,7 +176,7 @@ def test_sklearnex_partial_fit_on_random_data(
 
     inclin = IncrementalLinearRegression(fit_intercept=fit_intercept)
     if macro_block is not None:
-        hparams = inclin.get_hyperparameters("fit")
+        hparams = IncrementalLinearRegression.get_hyperparameters("fit")
         hparams.cpu_macro_block = macro_block
         hparams.gpu_macro_block = macro_block
     for i in range(num_blocks):

sklearnex/linear_model/tests/test_linear.py

@@ -61,7 +61,7 @@ def test_sklearnex_import_linear(
 
     linreg = LinearRegression()
     if daal_check_version((2024, "P", 0)) and macro_block is not None:
-        hparams = linreg.get_hyperparameters("fit")
+        hparams = LinearRegression.get_hyperparameters("fit")
         hparams.cpu_macro_block = macro_block
         hparams.gpu_macro_block = macro_block
 

sklearnex/preview/covariance/tests/test_covariance.py

@@ -36,7 +36,7 @@ def test_sklearnex_import_covariance(dataframe, queue, macro_block, assume_cente
     X = _convert_to_dataframe(X, sycl_queue=queue, target_df=dataframe)
     empcov = EmpiricalCovariance(assume_centered=assume_centered)
     if daal_check_version((2024, "P", 0)) and macro_block is not None:
-        hparams = empcov.get_hyperparameters("fit")
+        hparams = EmpiricalCovariance.get_hyperparameters("fit")
         hparams.cpu_macro_block = macro_block
     result = empcov.fit(X)
 

sklearnex/spmd/linear_model/tests/test_incremental_linear_spmd.py

@@ -86,11 +86,11 @@ def test_incremental_linear_regression_fit_spmd_gold(
     inclin = IncrementalLinearRegression(fit_intercept=fit_intercept)
 
     if macro_block is not None:
-        hparams = inclin.get_hyperparameters("fit")
+        hparams = IncrementalLinearRegression.get_hyperparameters("fit")
         hparams.cpu_macro_block = macro_block
         hparams.gpu_macro_block = macro_block
 
-        hparams_spmd = inclin_spmd.get_hyperparameters("fit")
+        hparams_spmd = IncrementalLinearRegression_SPMD.get_hyperparameters("fit")
         hparams_spmd.cpu_macro_block = macro_block
         hparams_spmd.gpu_macro_block = macro_block
 
@@ -159,11 +159,11 @@ def test_incremental_linear_regression_partial_fit_spmd_gold(
     inclin = IncrementalLinearRegression(fit_intercept=fit_intercept)
 
     if macro_block is not None:
-        hparams = inclin.get_hyperparameters("fit")
+        hparams = IncrementalLinearRegression.get_hyperparameters("fit")
         hparams.cpu_macro_block = macro_block
         hparams.gpu_macro_block = macro_block
 
-        hparams_spmd = inclin_spmd.get_hyperparameters("fit")
+        hparams_spmd = IncrementalLinearRegression_SPMD.get_hyperparameters("fit")
         hparams_spmd.cpu_macro_block = macro_block
         hparams_spmd.gpu_macro_block = macro_block
 
@@ -225,11 +225,11 @@ def test_incremental_linear_regression_fit_spmd_random(
     inclin = IncrementalLinearRegression(fit_intercept=fit_intercept)
 
     if macro_block is not None:
-        hparams = inclin.get_hyperparameters("fit")
+        hparams = IncrementalLinearRegression.get_hyperparameters("fit")
         hparams.cpu_macro_block = macro_block
         hparams.gpu_macro_block = macro_block
 
-        hparams_spmd = inclin_spmd.get_hyperparameters("fit")
+        hparams_spmd = IncrementalLinearRegression_SPMD.get_hyperparameters("fit")
         hparams_spmd.cpu_macro_block = macro_block
         hparams_spmd.gpu_macro_block = macro_block
 
@@ -298,11 +298,11 @@ def test_incremental_linear_regression_partial_fit_spmd_random(
     inclin = IncrementalLinearRegression(fit_intercept=fit_intercept)
 
     if macro_block is not None:
-        hparams = inclin.get_hyperparameters("fit")
+        hparams = IncrementalLinearRegression.get_hyperparameters("fit")
         hparams.cpu_macro_block = macro_block
         hparams.gpu_macro_block = macro_block
 
-        hparams_spmd = inclin_spmd.get_hyperparameters("fit")
+        hparams_spmd = IncrementalLinearRegression_SPMD.get_hyperparameters("fit")
         hparams_spmd.cpu_macro_block = macro_block
         hparams_spmd.gpu_macro_block = macro_block
 

sklearnex/tests/test_hyperparameters.py

@@ -0,0 +1,26 @@
+import pytest
+
+from sklearnex._utils import register_hyperparameters
+
+
+def test_register_hyperparameters():
+    hyperparameters_map = {"op": "hyperparameters"}
+
+    @register_hyperparameters(hyperparameters_map)
+    class Test:
+        pass
+
+    # assert the correct value is returned
+    assert Test.get_hyperparameters("op") == "hyperparameters"
+
+
+def test_register_hyperparameters_issues_warning():
+    hyperparameters_map = {"op": "hyperparameters"}
+
+    @register_hyperparameters(hyperparameters_map)
+    class Test:
+        pass
+
+    # assert a warning is issued when trying to modify the hyperparameters per instance
+    with pytest.warns(Warning):
+        Test().get_hyperparameters("op")