Fix errors when calling df.agg("nunique")

mars/dataframe/groupby/aggregation.py

@@ -25,7 +25,7 @@
 from ... import opcodes as OperandDef
 from ...config import options
 from ...core.custom_log import redirect_custom_log
-from ...core import ENTITY_TYPE, OutputType
+from ...core import ENTITY_TYPE, OutputType, recursive_tile
 from ...core.context import get_context
 from ...core.operand import OperandStage
 from ...serialization.serializables import (
@@ -51,6 +51,7 @@
     ReductionCompiler,
     ReductionSteps,
     ReductionAggStep,
+    CustomReduction,
 )
 from ..reduction.aggregation import is_funcs_aggregate, normalize_reduction_funcs
 from ..utils import parse_index, build_concatenated_rows_frame, is_cudf
@@ -63,6 +64,8 @@
 
 _support_get_group_without_as_index = pd_release_version[:2] > (1, 0)
 
+_FUNCS_PREFER_SHUFFLE = {"nunique"}
+
 
 class SizeRecorder:
     def __init__(self):
@@ -94,6 +97,7 @@ def get(self):
     "skew": lambda x, bias=False: x.skew(bias=bias),
     "kurt": lambda x, bias=False: x.kurt(bias=bias),
     "kurtosis": lambda x, bias=False: x.kurtosis(bias=bias),
+    "nunique": lambda x: x.nunique(),
 }
 _series_col_name = "col_name"
 
@@ -161,6 +165,8 @@ class DataFrameGroupByAgg(DataFrameOperand, DataFrameOperandMixin):
     method = StringField("method")
     use_inf_as_na = BoolField("use_inf_as_na")
 
+    map_on_shuffle = AnyField("map_on_shuffle")
+
     # for chunk
     combine_size = Int32Field("combine_size")
     chunk_store_limit = Int64Field("chunk_store_limit")
@@ -419,10 +425,29 @@ def _tile_with_shuffle(
         in_df: TileableType,
         out_df: TileableType,
         func_infos: ReductionSteps,
+        agg_chunks: List[ChunkType] = None,
     ):
-        # First, perform groupby and aggregation on each chunk.
-        agg_chunks = cls._gen_map_chunks(op, in_df.chunks, out_df, func_infos)
-        return cls._perform_shuffle(op, agg_chunks, in_df, out_df, func_infos)
+        if op.map_on_shuffle is None:
+            op.map_on_shuffle = all(
+                agg_fun.custom_reduction is None for agg_fun in func_infos.agg_funcs
+            )
+
+        if not op.map_on_shuffle:
+            groupby_params = op.groupby_params.copy()
+            selection = groupby_params.pop("selection", None)
+            groupby = in_df.groupby(**groupby_params)
+            if selection:
+                groupby = groupby[selection]
+            result = groupby.transform(
+                op.raw_func, _call_agg=True, index=out_df.index_value
+            )
+            return (yield from recursive_tile(result))
+        else:
+            # First, perform groupby and aggregation on each chunk.
+            agg_chunks = agg_chunks or cls._gen_map_chunks(
+                op, in_df.chunks, out_df, func_infos
+            )
+            return cls._perform_shuffle(op, agg_chunks, in_df, out_df, func_infos)
 
     @classmethod
     def _perform_shuffle(
@@ -622,8 +647,10 @@ def _tile_auto(
         else:
             # otherwise, use shuffle
             logger.debug("Choose shuffle method for groupby operand %s", op)
-            return cls._perform_shuffle(
-                op, chunks + left_chunks, in_df, out_df, func_infos
+            return (
+                yield from cls._tile_with_shuffle(
+                    op, in_df, out_df, func_infos, chunks + left_chunks
+                )
             )
 
     @classmethod
@@ -636,12 +663,16 @@ def tile(cls, op: "DataFrameGroupByAgg"):
         func_infos = cls._compile_funcs(op, in_df)
 
         if op.method == "auto":
-            if len(in_df.chunks) <= op.combine_size:
+            if set(op.func) & _FUNCS_PREFER_SHUFFLE:
+                return (
+                    yield from cls._tile_with_shuffle(op, in_df, out_df, func_infos)
+                )
+            elif len(in_df.chunks) <= op.combine_size:
                 return cls._tile_with_tree(op, in_df, out_df, func_infos)
             else:
                 return (yield from cls._tile_auto(op, in_df, out_df, func_infos))
         if op.method == "shuffle":
-            return cls._tile_with_shuffle(op, in_df, out_df, func_infos)
+            return (yield from cls._tile_with_shuffle(op, in_df, out_df, func_infos))
         elif op.method == "tree":
             return cls._tile_with_tree(op, in_df, out_df, func_infos)
         else:  # pragma: no cover
@@ -694,39 +725,66 @@ def _pack_inputs(agg_funcs: List[ReductionAggStep], in_data):
         return out_dict
 
     @staticmethod
-    def _do_custom_agg(op, custom_reduction, *input_objs):
+    def _do_custom_agg_single(op, custom_reduction: CustomReduction, input_obj):
+        if op.stage == OperandStage.map:
+            if custom_reduction.pre_with_agg:
+                apply_fun = custom_reduction.pre
+            else:
+
+                def apply_fun(obj):
+                    return custom_reduction.agg(custom_reduction.pre(obj))
+
+        elif op.stage == OperandStage.agg:
+            if custom_reduction.post_with_agg:
+                apply_fun = custom_reduction.post
+            else:
+
+                def apply_fun(obj):
+                    return custom_reduction.post(custom_reduction.agg(obj))
+
+        else:
+            apply_fun = custom_reduction.agg
+
+        res = input_obj.apply(apply_fun)
+        return (res,)
+
+    @staticmethod
+    def _do_custom_agg_multiple(op, custom_reduction: CustomReduction, *input_objs):
         xdf = cudf if op.gpu else pd
         results = []
         out = op.outputs[0]
         for group_key in input_objs[0].groups.keys():
             group_objs = [o.get_group(group_key) for o in input_objs]
+
             agg_done = False
             if op.stage == OperandStage.map:
-                result = custom_reduction.pre(group_objs[0])
+                res_tuple = custom_reduction.pre(group_objs[0])
                 agg_done = custom_reduction.pre_with_agg
-                if not isinstance(result, tuple):
-                    result = (result,)
+                if not isinstance(res_tuple, tuple):
+                    res_tuple = (res_tuple,)
             else:
-                result = group_objs
+                res_tuple = group_objs
 
             if not agg_done:
-                result = custom_reduction.agg(*result)
-                if not isinstance(result, tuple):
-                    result = (result,)
+                res_tuple = custom_reduction.agg(*res_tuple)
+                if not isinstance(res_tuple, tuple):
+                    res_tuple = (res_tuple,)
 
             if op.stage == OperandStage.agg:
-                result = custom_reduction.post(*result)
-                if not isinstance(result, tuple):
-                    result = (result,)
-
-            if out.ndim == 2:
-                result = tuple(r.to_frame().T for r in result)
-                if op.stage == OperandStage.agg:
-                    result = tuple(r.astype(out.dtypes) for r in result)
-            else:
-                result = tuple(xdf.Series(r) for r in result)
+                res_tuple = custom_reduction.post(*res_tuple)
+                if not isinstance(res_tuple, tuple):
+                    res_tuple = (res_tuple,)
+
+            new_res_list = []
+            for r in res_tuple:
+                if out.ndim == 2 and r.ndim == 1:
+                    r = r.to_frame().T
+                elif out.ndim < 2:
+                    if getattr(r, "ndim", 0) == 2:
+                        r = r.iloc[0, :]
+                    else:
+                        r = xdf.Series(r)
 
-            for r in result:
                 if len(input_objs[0].grouper.names) == 1:
                     r.index = xdf.Index(
                         [group_key], name=input_objs[0].grouper.names[0]
@@ -735,7 +793,21 @@ def _do_custom_agg(op, custom_reduction, *input_objs):
                     r.index = xdf.MultiIndex.from_tuples(
                         [group_key], names=input_objs[0].grouper.names
                     )
-            results.append(result)
+
+                if op.groupby_params.get("selection"):
+                    # correct columns for groupby-selection-agg paradigms
+                    selection = op.groupby_params["selection"]
+                    r.columns = [selection] if input_objs[0].ndim == 1 else selection
+
+                if out.ndim == 2 and op.stage == OperandStage.agg:
+                    dtype_cols = set(out.dtypes.index) & set(r.columns)
+                    conv_dtypes = {
+                        k: v for k, v in out.dtypes.items() if k in dtype_cols
+                    }
+                    r = r.astype(conv_dtypes)
+                new_res_list.append(r)
+
+            results.append(tuple(new_res_list))
         if not results and op.stage == OperandStage.agg:
             empty_df = pd.DataFrame(
                 [], columns=out.dtypes.index, index=out.index_value.to_pandas()[:0]
@@ -745,6 +817,13 @@ def _do_custom_agg(op, custom_reduction, *input_objs):
             concat_result = tuple(xdf.concat(parts) for parts in zip(*results))
         return concat_result
 
+    @classmethod
+    def _do_custom_agg(cls, op, custom_reduction, *input_objs, output_limit: int = 1):
+        if output_limit == 1:
+            return cls._do_custom_agg_single(op, custom_reduction, input_objs[0])
+        else:
+            return cls._do_custom_agg_multiple(op, custom_reduction, *input_objs)
+
     @staticmethod
     def _do_predefined_agg(input_obj, agg_func, single_func=False, **kwds):
         ndim = getattr(input_obj, "ndim", None) or input_obj.obj.ndim
@@ -839,12 +918,16 @@ def _wrapped_func(col):
             _agg_func_name,
             custom_reduction,
             _output_key,
-            _output_limit,
+            output_limit,
             kwds,
         ) in op.agg_funcs:
             input_obj = ret_map_groupbys[input_key]
             if map_func_name == "custom_reduction":
-                agg_dfs.extend(cls._do_custom_agg(op, custom_reduction, input_obj))
+                agg_dfs.extend(
+                    cls._do_custom_agg(
+                        op, custom_reduction, input_obj, output_limit=output_limit
+                    )
+                )
             else:
                 single_func = map_func_name == op.raw_func
                 agg_dfs.append(
@@ -885,12 +968,16 @@ def _execute_combine(cls, ctx, op: "DataFrameGroupByAgg"):
             agg_func_name,
             custom_reduction,
             output_key,
-            _output_limit,
+            output_limit,
             kwds,
         ) in op.agg_funcs:
             input_obj = in_data_dict[output_key]
             if agg_func_name == "custom_reduction":
-                combines.extend(cls._do_custom_agg(op, custom_reduction, *input_obj))
+                combines.extend(
+                    cls._do_custom_agg(
+                        op, custom_reduction, *input_obj, output_limit=output_limit
+                    )
+                )
             else:
                 combines.append(
                     cls._do_predefined_agg(input_obj, agg_func_name, **kwds)
@@ -925,15 +1012,15 @@ def _execute_agg(cls, ctx, op: "DataFrameGroupByAgg"):
             agg_func_name,
             custom_reduction,
             output_key,
-            _output_limit,
+            output_limit,
             kwds,
         ) in op.agg_funcs:
             if agg_func_name == "custom_reduction":
                 input_obj = tuple(
                     cls._get_grouped(op, o, ctx) for o in in_data_dict[output_key]
                 )
                 in_data_dict[output_key] = cls._do_custom_agg(
-                    op, custom_reduction, *input_obj
+                    op, custom_reduction, *input_obj, output_limit=output_limit
                 )[0]
             else:
                 input_obj = cls._get_grouped(op, in_data_dict[output_key], ctx)
@@ -1017,7 +1104,15 @@ def execute(cls, ctx, op: "DataFrameGroupByAgg"):
             pd.reset_option("mode.use_inf_as_na")
 
 
-def agg(groupby, func=None, method="auto", combine_size=None, *args, **kwargs):
+def agg(
+    groupby,
+    func=None,
+    method="auto",
+    combine_size=None,
+    map_on_shuffle=None,
+    *args,
+    **kwargs,
+):
     """
     Aggregate using one or more operations on grouped data.
 
@@ -1033,7 +1128,11 @@ def agg(groupby, func=None, method="auto", combine_size=None, *args, **kwargs):
         in distributed mode and use 'tree' in local mode.
     combine_size : int
         The number of chunks to combine when method is 'tree'
-
+    map_on_shuffle : bool
+        When not specified, will decide whether to perform aggregation on the
+        map stage of shuffle (currently no aggregation when there is custom
+        reduction in functions). Otherwise, whether to call map on map stage
+        of shuffle is determined by the value.
 
     Returns
     -------
@@ -1080,5 +1179,6 @@ def agg(groupby, func=None, method="auto", combine_size=None, *args, **kwargs):
         combine_size=combine_size or options.combine_size,
         chunk_store_limit=options.chunk_store_limit,
         use_inf_as_na=use_inf_as_na,
+        map_on_shuffle=map_on_shuffle,
     )
     return agg_op(groupby)

mars/dataframe/groupby/tests/test_groupby_execution.py

@@ -1241,13 +1241,16 @@ def test_groupby_nunique(setup):
     # test with as_index=False
     mdf = md.DataFrame(df1, chunk_size=13)
     if _agg_size_as_frame:
+        res = mdf.groupby("b", as_index=False)["a"].nunique().execute().fetch()
+        expected = df1.groupby("b", as_index=False)["a"].nunique()
         pd.testing.assert_frame_equal(
-            mdf.groupby("b", as_index=False)["a"]
-            .nunique()
-            .execute()
-            .fetch()
-            .sort_values(by="b", ignore_index=True),
-            df1.groupby("b", as_index=False)["a"]
-            .nunique()
-            .sort_values(by="b", ignore_index=True),
+            res.sort_values(by="b", ignore_index=True),
+            expected.sort_values(by="b", ignore_index=True),
+        )
+
+        res = mdf.groupby("b", as_index=False)[["a", "c"]].nunique().execute().fetch()
+        expected = df1.groupby("b", as_index=False)[["a", "c"]].nunique()
+        pd.testing.assert_frame_equal(
+            res.sort_values(by="b", ignore_index=True),
+            expected.sort_values(by="b", ignore_index=True),
         )

mars/dataframe/merge/concat.py

@@ -324,7 +324,10 @@ def _auto_concat_dataframe_chunks(chunk, inputs):
             )
 
             if chunk.op.axis is not None:
-                return xdf.concat(inputs, axis=op.axis)
+                try:
+                    return xdf.concat(inputs, axis=op.axis)
+                except:
+                    raise
 
             # auto generated concat when executing a DataFrame
             if len(inputs) == 1:

mars/dataframe/reduction/aggregation.py

@@ -78,6 +78,7 @@ def where_function(cond, var1, var2):
     "skew": lambda x, skipna=True, bias=False: x.skew(skipna=skipna, bias=bias),
     "kurt": lambda x, skipna=True, bias=False: x.kurt(skipna=skipna, bias=bias),
     "kurtosis": lambda x, skipna=True, bias=False: x.kurtosis(skipna=skipna, bias=bias),
+    "nunique": lambda x: x.nunique(),
 }
 
 

mars/dataframe/reduction/core.py

@@ -655,6 +655,8 @@ class CustomReduction:
 
     # set to True when pre() already performs aggregation
     pre_with_agg = False
+    # set to True when post() already performs aggregation
+    post_with_agg = False
 
     def __init__(self, name=None, is_gpu=None):
         self.name = name or "<custom>"
@@ -972,13 +974,15 @@ def _compile_function(self, func, func_name=None, ndim=1) -> ReductionSteps:
             else:
                 map_func_name, agg_func_name = step_func_name, step_func_name
 
+            op_custom_reduction = getattr(t.op, "custom_reduction", None)
+
             # build agg description
             agg_funcs.append(
                 ReductionAggStep(
                     agg_input_key,
                     map_func_name,
                     agg_func_name,
-                    custom_reduction,
+                    op_custom_reduction or custom_reduction,
                     t.key,
                     output_limit,
                     t.op.get_reduction_args(axis=self._axis),