REFACTOR-modin-project#7008: Remove 'check_exception_type' argument o…

…f 'eval_general' function

Signed-off-by: Anatoly Myachev <anatoly.myachev@intel.com>

modin/experimental/core/execution/native/implementations/hdk_on_native/test/test_dataframe.py

@@ -314,7 +314,6 @@ def test_read_csv_datetime(
         eval_io(
             fn_name="read_csv",
             md_extra_kwargs={"engine": engine},
-            check_exception_type=not skip_exc_type_check,
             raising_exceptions=None if skip_exc_type_check else io_ops_bad_exc,
             # read_csv kwargs
             filepath_or_buffer=pytest.csvs_names["test_read_csv_regular"],

modin/experimental/core/execution/native/implementations/hdk_on_native/test/utils.py

@@ -29,7 +29,6 @@ def eval_io(
     fn_name,
     comparator=df_equals,
     cast_to_str=False,
-    check_exception_type=True,
     raising_exceptions=io_ops_bad_exc,
     check_kwargs_callable=True,
     modin_warning=None,
@@ -75,7 +74,6 @@ def hdk_comparator(df1, df2, **kwargs):
         fn_name,
         comparator=hdk_comparator,
         cast_to_str=cast_to_str,
-        check_exception_type=check_exception_type,
         raising_exceptions=raising_exceptions,
         check_kwargs_callable=check_kwargs_callable,
         modin_warning=modin_warning,

modin/pandas/test/dataframe/test_default.py

@@ -598,7 +598,6 @@ def test_pivot(data, index, columns, values):
         index=index,
         columns=columns,
         values=values,
-        check_exception_type=None,
     )
 
 
@@ -660,7 +659,6 @@ def test_pivot_table_data(data, index, columns, values, aggfunc):
         columns=columns,
         values=values,
         aggfunc=aggfunc,
-        check_exception_type=None,
     )
 
 

modin/pandas/test/dataframe/test_map_metadata.py

@@ -439,7 +439,6 @@ def test_astype():
             if isinstance(df, pd.DataFrame)
             else pandas.Series([str, str], index=["col1", "col1"])
         ),
-        check_exception_type=True,
     )
 
 

modin/pandas/test/dataframe/test_reduce.py

@@ -389,10 +389,6 @@ def comparator(md_res, pd_res):
             ascending=ascending,
         ),
         comparator=comparator,
-        # Modin's `sort_values` does not validate `ascending` type and so
-        # does not raise an exception when it isn't a bool, when pandas do so,
-        # visit modin-issue#3388 for more info.
-        check_exception_type=None if sort and ascending is None else True,
     )
 
 

modin/pandas/test/dataframe/test_udf.py

@@ -80,7 +80,6 @@ def test_agg_apply(axis, func, op):
     eval_general(
         *create_test_dfs(test_data["float_nan_data"]),
         lambda df: getattr(df, op)(func, axis),
-        check_exception_type=True,
     )
 
 
@@ -95,7 +94,6 @@ def test_agg_apply_axis_names(axis, func, op):
     eval_general(
         *create_test_dfs(test_data["int_data"]),
         lambda df: getattr(df, op)(func, axis),
-        check_exception_type=True,
     )
 
 
@@ -521,6 +519,4 @@ def test_query_with_element_access_issue_4580(engine):
     ids=agg_func_keys + agg_func_except_keys,
 )
 def test_transform(data, func):
-    eval_general(
-        *create_test_dfs(data), lambda df: df.transform(func), check_exception_type=True
-    )
+    eval_general(*create_test_dfs(data), lambda df: df.transform(func))

modin/pandas/test/test_groupby.py

@@ -334,7 +334,6 @@ def test_mixed_dtypes_groupby(as_index):
         ]
         for func in agg_functions:
             eval_agg(modin_groupby, pandas_groupby, func)
-            eval_aggregate(modin_groupby, pandas_groupby, func)
 
         eval_general(modin_groupby, pandas_groupby, lambda df: df.last())
         eval_max(modin_groupby, pandas_groupby)
@@ -569,21 +568,14 @@ def maybe_get_columns(df, by):
     for func in agg_functions:
         # Pandas raises an exception when 'by' contains categorical key and `as_index=False`
         # because of this bug: https://github.com/pandas-dev/pandas/issues/36698
-        # Modin correctly processes the result, that's why `check_exception_type=None` in some cases
+        # Modin correctly processes the result
         is_pandas_bug_case = not as_index and col1_category and isinstance(func, dict)
-
-        eval_general(
-            modin_groupby,
-            pandas_groupby,
-            lambda grp: grp.agg(func),
-            check_exception_type=None if is_pandas_bug_case else True,
-        )
-        eval_general(
-            modin_groupby,
-            pandas_groupby,
-            lambda grp: grp.aggregate(func),
-            check_exception_type=None if is_pandas_bug_case else True,
-        )
+        if not is_pandas_bug_case:
+            eval_general(
+                modin_groupby,
+                pandas_groupby,
+                lambda grp: grp.agg(func),
+            )
 
     eval_general(modin_groupby, pandas_groupby, lambda df: df.last())
     eval_general(modin_groupby, pandas_groupby, lambda df: df.rank())
@@ -599,13 +591,13 @@ def maybe_get_columns(df, by):
     eval_ngroup(modin_groupby, pandas_groupby)
     # Pandas raising exception when 'by' contains categorical key and `as_index=False`
     # because of a bug: https://github.com/pandas-dev/pandas/issues/36698
-    # Modin correctly processes the result, so that's why `check_exception_type=None` in some cases
-    eval_general(
-        modin_groupby,
-        pandas_groupby,
-        lambda df: df.nunique(),
-        check_exception_type=None if (col1_category and not as_index) else True,
-    )
+    # Modin correctly processes the result
+    if not (col1_category and not as_index):
+        eval_general(
+            modin_groupby,
+            pandas_groupby,
+            lambda df: df.nunique(),
+        )
     # TypeError: category type does not support median operations
     eval_general(
         modin_groupby,
@@ -630,7 +622,6 @@ def maybe_get_columns(df, by):
                 modin_groupby,
                 pandas_groupby,
                 lambda df: df.transform(func),
-                check_exception_type=None,
             )
 
     pipe_functions = [lambda dfgb: dfgb.sum()]
@@ -655,7 +646,6 @@ def maybe_get_columns(df, by):
             modin_groupby,
             pandas_groupby,
             lambda df: df.size(),
-            check_exception_type=None,
         )
     eval_general(modin_groupby, pandas_groupby, lambda df: df.tail(n))
     eval_quantile(modin_groupby, pandas_groupby)
@@ -771,7 +761,6 @@ def test_single_group_row_groupby():
     ]
     for func in agg_functions:
         eval_agg(modin_groupby, pandas_groupby, func)
-        eval_aggregate(modin_groupby, pandas_groupby, func)
 
     eval_general(modin_groupby, pandas_groupby, lambda df: df.last())
     eval_rank(modin_groupby, pandas_groupby)
@@ -900,7 +889,6 @@ def test_large_row_groupby(is_by_category):
     ]
     for func in agg_functions:
         eval_agg(modin_groupby, pandas_groupby, func)
-        eval_aggregate(modin_groupby, pandas_groupby, func)
 
     eval_general(modin_groupby, pandas_groupby, lambda df: df.last())
     eval_rank(modin_groupby, pandas_groupby)
@@ -1157,7 +1145,6 @@ def test_series_groupby(by, as_index_series_or_dataframe):
         ]
         for func in agg_functions:
             eval_agg(modin_groupby, pandas_groupby, func)
-            eval_aggregate(modin_groupby, pandas_groupby, func)
 
         eval_general(modin_groupby, pandas_groupby, lambda df: df.last())
         eval_rank(modin_groupby, pandas_groupby)
@@ -1322,10 +1309,6 @@ def eval_std(modin_groupby, pandas_groupby, numeric_only=False):
     )
 
 
-def eval_aggregate(modin_groupby, pandas_groupby, func):
-    df_equals(modin_groupby.aggregate(func), pandas_groupby.aggregate(func))
-
-
 def eval_agg(modin_groupby, pandas_groupby, func):
     df_equals(modin_groupby.agg(func), pandas_groupby.agg(func))
 
@@ -2648,7 +2631,6 @@ def run_test(eval_function, *args, **kwargs):
     run_test(eval___getattr__, item="b")
     run_test(eval___getitem__, item="b")
     run_test(eval_agg, func=lambda df: df.mean())
-    run_test(eval_aggregate, func=lambda df: df.mean())
     run_test(eval_any)
     run_test(eval_apply, func=lambda df: df.mean())
     run_test(eval_count)

modin/pandas/test/test_series.py

@@ -3646,10 +3646,6 @@ def sort_sensitive_comparator(df1, df2):
             ascending=ascending,
         ),
         comparator=sort_sensitive_comparator,
-        # Modin's `sort_values` does not validate `ascending` type and so
-        # does not raise an exception when it isn't a bool, when pandas do so,
-        # visit modin-issue#3388 for more info.
-        check_exception_type=None if sort and ascending is None else True,
     )
 
     # from issue #2365
@@ -3665,10 +3661,6 @@ def sort_sensitive_comparator(df1, df2):
             ascending=ascending,
         ),
         comparator=sort_sensitive_comparator,
-        # Modin's `sort_values` does not validate `ascending` type and so
-        # does not raise an exception when it isn't a bool, when pandas do so,
-        # visit modin-issue#3388 for more info.
-        check_exception_type=None if sort and ascending is None else True,
     )
 
 

modin/pandas/test/utils.py

@@ -874,25 +874,18 @@ def eval_general(
     operation,
     comparator=df_equals,
     __inplace__=False,
-    check_exception_type=True,
     raising_exceptions=None,
     check_kwargs_callable=True,
     md_extra_kwargs=None,
     comparator_kwargs=None,
     **kwargs,
 ):
-    if raising_exceptions:
-        assert (
-            check_exception_type
-        ), "if raising_exceptions is not None or False, check_exception_type should be True"
     md_kwargs, pd_kwargs = {}, {}
 
     def execute_callable(fn, inplace=False, md_kwargs={}, pd_kwargs={}):
         try:
             pd_result = fn(pandas_df, **pd_kwargs)
         except Exception as pd_e:
-            if check_exception_type is None:
-                return None
             try:
                 if inplace:
                     _ = fn(modin_df, **md_kwargs)
@@ -902,16 +895,15 @@ def execute_callable(fn, inplace=False, md_kwargs={}, pd_kwargs={}):
                         fn(modin_df, **md_kwargs)
                     )  # force materialization
             except Exception as md_e:
-                if check_exception_type:
-                    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 raising_exceptions:
-                        assert not isinstance(
-                            md_e, tuple(raising_exceptions)
-                        ), f"not acceptable exception type: {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 raising_exceptions:
+                    assert not isinstance(
+                        md_e, tuple(raising_exceptions)
+                    ), f"not acceptable exception type: {md_e}"
             else:
                 raise NoModinException(
                     f"Modin doesn't throw an exception, while pandas does: [{repr(pd_e)}]"
@@ -949,7 +941,6 @@ def eval_io(
     fn_name,
     comparator=df_equals,
     cast_to_str=False,
-    check_exception_type=True,
     raising_exceptions=io_ops_bad_exc,
     check_kwargs_callable=True,
     modin_warning=None,
@@ -970,13 +961,9 @@ def eval_io(
         There could be some mismatches in dtypes, so we're
         casting the whole frame to `str` before comparison.
         See issue #1931 for details.
-    check_exception_type: bool
-        Check or not exception types in the case of operation fail
-        (compare exceptions types raised by Pandas and Modin).
     raising_exceptions: Exception or list of Exceptions
         Exceptions that should be raised even if they are raised
-        both by Pandas and Modin (check evaluated only if
-        `check_exception_type` passed as `True`).
+        both by Pandas and Modin.
     modin_warning: obj
         Warning that should be raised by Modin.
     modin_warning_str_match: str
@@ -997,7 +984,6 @@ def call_eval_general():
             pandas,
             applyier,
             comparator=comparator,
-            check_exception_type=check_exception_type,
             raising_exceptions=raising_exceptions,
             check_kwargs_callable=check_kwargs_callable,
             md_extra_kwargs=md_extra_kwargs,