WO for checking tests

onedal/datatypes/_data_conversion.py

@@ -31,14 +31,10 @@
     import dpnp
 
 
-def _apply_and_pass(func, *args, **kwargs):
+def _apply_and_pass(func, *args):
     if len(args) == 1:
-        return func(args[0], **kwargs)
-    return tuple(map(func, args, kwargs))
-
-
-def from_table(*args, sua_iface=None, xp=None):
-    return _apply_and_pass(convert_one_from_table, *args, sua_iface=sua_iface, xp=xp)
+        return func(args[0])
+    return tuple(map(func, args))
 
 
 # TODO:
@@ -47,6 +43,8 @@ def from_table(*args, sua_iface=None, xp=None):
 # sparse for sua data.
 # TODO:
 # update it for each of the datafrmae format.
+# TODO:
+# update func use with args and kwargs with _apply_and_pass.
 def convert_one_from_table(table, sua_iface=None, xp=None):
     # Currently only `__sycl_usm_array_interface__` protocol used to
     # convert into dpnp/dpctl tensors.
@@ -68,8 +66,12 @@ def convert_one_to_table(arg, sua_iface=None):
     return _backend.to_table(arg)
 
 
-def to_table(*args, sua_iface=None):
-    return _apply_and_pass(convert_one_to_table, *args, sua_iface=sua_iface)
+def from_table(*args):
+    return _apply_and_pass(convert_one_from_table, *args)
+
+
+def to_table(*args):
+    return _apply_and_pass(convert_one_to_table, *args)
 
 
 if _is_dpc_backend:

onedal/datatypes/tests/test_data.py

@@ -21,6 +21,10 @@
 from onedal import _backend
 from onedal._device_offload import dpctl_available, dpnp_available
 from onedal.datatypes import from_table, to_table
+
+# TODO:
+# re-impl and used from_table, to_table instead.
+from onedal.datatypes._data_conversion import convert_one_from_table, convert_one_to_table
 from onedal.primitives import linear_kernel
 from onedal.tests.utils._dataframes_support import (
     _convert_to_dataframe,
@@ -211,11 +215,13 @@ def test_input_sua_iface_zero_copy(dataframe, queue, order, dtype):
 
     sua_iface, X_dp_namespace, _ = _get_sycl_namespace(X_dp)
 
-    X_table = to_table(X_dp, sua_iface=sua_iface)
+    X_table = convert_one_to_table(X_dp, sua_iface=sua_iface)
 
     assert hasattr(X_table, "__sycl_usm_array_interface__")
 
-    X_dp_from_table = from_table(X_table, sua_iface=sua_iface, xp=X_dp_namespace)
+    X_dp_from_table = convert_one_from_table(
+        X_table, sua_iface=sua_iface, xp=X_dp_namespace
+    )
 
     _check_attributes_for_zero_copy(X_dp, X_dp_from_table, order)
 

sklearnex/tests/test_memory_usage.py

@@ -126,22 +126,27 @@ def gen_functions(functions):
 
 
 if dpctl_available:
-    from onedal.datatypes import from_table, to_table
+    # TODO:
+    # use  from_table, to_table.
+    from onedal.datatypes._data_conversion import (
+        convert_one_from_table,
+        convert_one_to_table,
+    )
 
     class DummyEstimatorWithTableConversions(BaseEstimator):
 
         # __name__ = 'DummyEstimatorWithTableConversions'
 
         def fit(self, X, y=None):
-            _, sua_iface, _ = _get_sycl_namespace(X)
-            X_table = to_table(X, sua_iface=sua_iface)
-            y_table = to_table(y, sua_iface=sua_iface)
+            sua_iface, _, _ = _get_sycl_namespace(X)
+            X_table = convert_one_to_table(X, sua_iface=sua_iface)
+            y_table = convert_one_to_table(y, sua_iface=sua_iface)
             return self
 
         def predict(self, X):
-            xp, sua_iface, _ = _get_sycl_namespace(X)
-            X_table = to_table(X, sua_iface=sua_iface)
-            returned_X = from_table(X_table, sua_iface=sua_iface, xp=xp)
+            sua_iface, xp, _ = _get_sycl_namespace(X)
+            X_table = convert_one_to_table(X, sua_iface=sua_iface)
+            returned_X = convert_one_from_table(X_table, sua_iface=sua_iface, xp=xp)
             return returned_X
 
     DUMMY_ESTIMATOR_WITH_TABLE_CONVERSIONS = {
@@ -209,14 +214,16 @@ def split_train_inference(kf, x, y, estimator, queue=None):
     return mem_tracks
 
 
-def _kfold_function_template(
-    estimator, dataframe, data_shape, queue=None, func=None, get_data_func=None
-):
+# TODO:
+# def _kfold_function_template(estimator, dataframe, data_shape, queue=None, func=None, get_data_func=None):
+# add custom get_data_func.
+def _kfold_function_template(estimator, dataframe, data_shape, queue=None, func=None):
     tracemalloc.start()
 
     n_samples, n_features = data_shape
-    get_data_func = get_data_func if get_data_func else gen_clsf_data
-    X, y, data_memory_size = get_data_func(n_samples, n_features)
+    # get_data_func = get_data_func if get_data_func else gen_clsf_data
+    # X, y, data_memory_size = get_data_func(n_samples, n_features)
+    X, y, data_memory_size = gen_clsf_data(n_samples, n_features)
     kf = KFold(n_splits=N_SPLITS)
     if func:
         X = func(X)