PR changes

Signed-off-by: arunjose696 <arunjose696@gmail.com>

modin/core/storage_formats/pandas/native_query_compiler.py

@@ -25,14 +25,13 @@
 from pandas.core.dtypes.common import is_list_like, is_scalar
 
 from modin.core.storage_formats.base.query_compiler import BaseQueryCompiler
+from modin.core.storage_formats.pandas.query_compiler_caster import QueryCompilerCaster
 from modin.utils import (
     MODIN_UNNAMED_SERIES_LABEL,
     _inherit_docstrings,
     try_cast_to_pandas,
 )
 
-from .query_compiler_validator import QueryCompilerCaster
-
 
 def _get_axis(axis):
     """

modin/core/storage_formats/pandas/query_compiler.py

@@ -66,6 +66,7 @@
     extract_dtype,
 )
 from modin.core.storage_formats import BaseQueryCompiler
+from modin.core.storage_formats.pandas.query_compiler_caster import QueryCompilerCaster
 from modin.error_message import ErrorMessage
 from modin.logging import get_logger
 from modin.utils import (
@@ -79,7 +80,6 @@
 from .aggregations import CorrCovBuilder
 from .groupby import GroupbyReduceImpl, PivotTableImpl
 from .merge import MergeImpl
-from .query_compiler_validator import QueryCompilerCaster
 from .utils import get_group_names, merge_partitioning
 
 if TYPE_CHECKING:

modin/core/storage_formats/pandas/query_compiler_validator.py → modin/core/storage_formats/pandas/query_compiler_caster.py

@@ -119,8 +119,7 @@ def decorator(obj: Fn) -> Fn:
             # This is required because inspect converts class methods to member functions
             current_class_attrs = vars(obj)
             for key in current_class_attrs:
-                if key in current_class_attrs:
-                    all_attrs[key] = current_class_attrs[key]
+                all_attrs[key] = current_class_attrs[key]
 
             for attr_name, attr_value in all_attrs.items():
                 if isinstance(

modin/tests/pandas/native_df_mode/test_binary.py

@@ -17,11 +17,11 @@
 import pytest
 
 from modin.config import NativeDataframeMode, NPartitions
+from modin.tests.pandas.native_df_mode.utils import eval_general_interop
 from modin.tests.pandas.utils import (
     create_test_dfs,
     default_to_pandas_ignore_string,
     df_equals,
-    eval_general_interop,
     test_data,
     test_data_keys,
     test_data_values,

modin/tests/pandas/native_df_mode/test_default.py

@@ -23,12 +23,12 @@
 import modin.pandas as pd
 from modin.config import NativeDataframeMode, NPartitions
 from modin.pandas.io import to_pandas
+from modin.tests.pandas.native_df_mode.utils import eval_general_interop
 from modin.tests.pandas.utils import (
     create_test_dfs,
     create_test_series,
     default_to_pandas_ignore_string,
     df_equals,
-    eval_general_interop,
     test_data,
     test_data_diff_dtype,
     test_data_keys,

modin/tests/pandas/native_df_mode/test_indexing.py

@@ -19,6 +19,7 @@
 
 import modin.pandas as pd
 from modin.config import NativeDataframeMode, NPartitions
+from modin.tests.pandas.native_df_mode.utils import eval_general_interop
 from modin.tests.pandas.utils import (
     RAND_HIGH,
     RAND_LOW,
@@ -27,7 +28,6 @@
     default_to_pandas_ignore_string,
     df_equals,
     eval_general,
-    eval_general_interop,
     test_data,
     test_data_keys,
     test_data_values,

modin/tests/pandas/native_df_mode/test_join_sort.py

@@ -21,13 +21,13 @@
 import modin.pandas as pd
 from modin.config import NativeDataframeMode, NPartitions
 from modin.pandas.io import to_pandas
+from modin.tests.pandas.native_df_mode.utils import eval_general_interop
 from modin.tests.pandas.utils import (
     create_test_dfs,
     create_test_series,
     default_to_pandas_ignore_string,
     df_equals,
     eval_general,
-    eval_general_interop,
     random_state,
     test_data_keys,
     test_data_values,

modin/tests/pandas/native_df_mode/utils.py

@@ -0,0 +1,108 @@
+# Licensed to Modin Development Team under one or more contributor license agreements.
+# See the NOTICE file distributed with this work for additional information regarding
+# copyright ownership.  The Modin Development Team licenses this file to you under the
+# Apache License, Version 2.0 (the "License"); you may not use this file except in
+# compliance with the License.  You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software distributed under
+# the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
+# ANY KIND, either express or implied. See the License for the specific language
+# governing permissions and limitations under the License.
+from modin.config import Engine
+from modin.tests.pandas.utils import NoModinException, create_test_dfs, df_equals
+from modin.utils import try_cast_to_pandas
+
+
+def eval_general_interop(
+    data,
+    backend,
+    operation,
+    df_mode_pair,
+    comparator=df_equals,
+    __inplace__=False,
+    expected_exception=None,
+    check_kwargs_callable=True,
+    md_extra_kwargs=None,
+    comparator_kwargs=None,
+    **kwargs,
+):
+    df_mode1, df_mode2 = df_mode_pair
+    modin_df1, pandas_df1 = create_test_dfs(data, backend=backend, df_mode=df_mode1)
+    modin_df2, pandas_df2 = create_test_dfs(data, backend=backend, df_mode=df_mode2)
+    md_kwargs, pd_kwargs = {}, {}
+
+    def execute_callable(fn, inplace=False, md_kwargs={}, pd_kwargs={}):
+        try:
+            pd_result = fn(pandas_df1, pandas_df2, **pd_kwargs)
+        except Exception as pd_e:
+            try:
+                if inplace:
+                    _ = fn(modin_df1, modin_df2, **md_kwargs)
+                    try_cast_to_pandas(modin_df1)  # force materialization
+                else:
+                    try_cast_to_pandas(
+                        fn(modin_df1, modin_df2, **md_kwargs)
+                    )  # force materialization
+            except Exception as md_e:
+                assert isinstance(
+                    md_e, type(pd_e)
+                ), "Got Modin Exception type {}, but pandas Exception type {} was expected".format(
+                    type(md_e), type(pd_e)
+                )
+                if expected_exception:
+                    if Engine.get() == "Ray":
+                        from ray.exceptions import RayTaskError
+
+                        # unwrap ray exceptions from remote worker
+                        if isinstance(md_e, RayTaskError):
+                            md_e = md_e.args[0]
+                    assert (
+                        type(md_e) is type(expected_exception)
+                        and md_e.args == expected_exception.args
+                    ), f"not acceptable Modin's exception: [{repr(md_e)}]"
+                    assert (
+                        pd_e.args == expected_exception.args
+                    ), f"not acceptable Pandas' exception: [{repr(pd_e)}]"
+                elif expected_exception is False:
+                    # The only way to disable exception message checking.
+                    pass
+                else:
+                    # It’s not enough that Modin and pandas have the same types of exceptions;
+                    # we need to explicitly specify the instance of an exception
+                    # (using `expected_exception`) in tests so that we can check exception messages.
+                    # This allows us to eliminate situations where exceptions are thrown
+                    # that we don't expect, which could hide different bugs.
+                    raise pd_e
+            else:
+                raise NoModinException(
+                    f"Modin doesn't throw an exception, while pandas does: [{repr(pd_e)}]"
+                )
+        else:
+            md_result = fn(modin_df1, modin_df2, **md_kwargs)
+            return (md_result, pd_result) if not inplace else (modin_df1, pandas_df1)
+
+    for key, value in kwargs.items():
+        if check_kwargs_callable and callable(value):
+            values = execute_callable(value)
+            # that means, that callable raised an exception
+            if values is None:
+                return
+            else:
+                md_value, pd_value = values
+        else:
+            md_value, pd_value = value, value
+
+        md_kwargs[key] = md_value
+        pd_kwargs[key] = pd_value
+
+        if md_extra_kwargs:
+            assert isinstance(md_extra_kwargs, dict)
+            md_kwargs.update(md_extra_kwargs)
+
+    values = execute_callable(
+        operation, md_kwargs=md_kwargs, pd_kwargs=pd_kwargs, inplace=__inplace__
+    )
+    if values is not None:
+        comparator(*values, **(comparator_kwargs or {}))

modin/tests/pandas/utils.py

@@ -915,99 +915,6 @@ class NoModinException(Exception):
     pass
 
 
-def eval_general_interop(
-    data,
-    backend,
-    operation,
-    df_mode_pair,
-    comparator=df_equals,
-    __inplace__=False,
-    expected_exception=None,
-    check_kwargs_callable=True,
-    md_extra_kwargs=None,
-    comparator_kwargs=None,
-    **kwargs,
-):
-    df_mode1, df_mode2 = df_mode_pair
-    modin_df1, pandas_df1 = create_test_dfs(data, backend=backend, df_mode=df_mode1)
-    modin_df2, pandas_df2 = create_test_dfs(data, backend=backend, df_mode=df_mode2)
-    md_kwargs, pd_kwargs = {}, {}
-
-    def execute_callable(fn, inplace=False, md_kwargs={}, pd_kwargs={}):
-        try:
-            pd_result = fn(pandas_df1, pandas_df2, **pd_kwargs)
-        except Exception as pd_e:
-            try:
-                if inplace:
-                    _ = fn(modin_df1, modin_df2, **md_kwargs)
-                    try_cast_to_pandas(modin_df1)  # force materialization
-                else:
-                    try_cast_to_pandas(
-                        fn(modin_df1, modin_df2, **md_kwargs)
-                    )  # force materialization
-            except Exception as md_e:
-                assert isinstance(
-                    md_e, type(pd_e)
-                ), "Got Modin Exception type {}, but pandas Exception type {} was expected".format(
-                    type(md_e), type(pd_e)
-                )
-                if expected_exception:
-                    if Engine.get() == "Ray":
-                        from ray.exceptions import RayTaskError
-
-                        # unwrap ray exceptions from remote worker
-                        if isinstance(md_e, RayTaskError):
-                            md_e = md_e.args[0]
-                    assert (
-                        type(md_e) is type(expected_exception)
-                        and md_e.args == expected_exception.args
-                    ), f"not acceptable Modin's exception: [{repr(md_e)}]"
-                    assert (
-                        pd_e.args == expected_exception.args
-                    ), f"not acceptable Pandas' exception: [{repr(pd_e)}]"
-                elif expected_exception is False:
-                    # The only way to disable exception message checking.
-                    pass
-                else:
-                    # It’s not enough that Modin and pandas have the same types of exceptions;
-                    # we need to explicitly specify the instance of an exception
-                    # (using `expected_exception`) in tests so that we can check exception messages.
-                    # This allows us to eliminate situations where exceptions are thrown
-                    # that we don't expect, which could hide different bugs.
-                    raise pd_e
-            else:
-                raise NoModinException(
-                    f"Modin doesn't throw an exception, while pandas does: [{repr(pd_e)}]"
-                )
-        else:
-            md_result = fn(modin_df1, modin_df2, **md_kwargs)
-            return (md_result, pd_result) if not inplace else (modin_df1, pandas_df1)
-
-    for key, value in kwargs.items():
-        if check_kwargs_callable and callable(value):
-            values = execute_callable(value)
-            # that means, that callable raised an exception
-            if values is None:
-                return
-            else:
-                md_value, pd_value = values
-        else:
-            md_value, pd_value = value, value
-
-        md_kwargs[key] = md_value
-        pd_kwargs[key] = pd_value
-
-        if md_extra_kwargs:
-            assert isinstance(md_extra_kwargs, dict)
-            md_kwargs.update(md_extra_kwargs)
-
-    values = execute_callable(
-        operation, md_kwargs=md_kwargs, pd_kwargs=pd_kwargs, inplace=__inplace__
-    )
-    if values is not None:
-        comparator(*values, **(comparator_kwargs or {}))
-
-
 def eval_general(
     modin_df,
     pandas_df,