| 844 | |
| 845 | |
| 846 | class CustomReduction(Reduction): |
| 847 | _parameters = [ |
| 848 | "frame", |
| 849 | "meta", |
| 850 | "chunk_kwargs", |
| 851 | "aggregate_kwargs", |
| 852 | "combine_kwargs", |
| 853 | "split_every", |
| 854 | "token", |
| 855 | ] |
| 856 | |
| 857 | @functools.cached_property |
| 858 | def _name(self): |
| 859 | name = self.operand("token") or funcname(type(self)).lower() |
| 860 | return name + "-" + self.deterministic_token |
| 861 | |
| 862 | @classmethod |
| 863 | def chunk(cls, df, **kwargs): |
| 864 | func = kwargs.pop("func") |
| 865 | out = func(df, **kwargs) |
| 866 | # Return a dataframe so that the concatenated version is also a dataframe |
| 867 | return out.to_frame().T if is_series_like(out) else out |
| 868 | |
| 869 | @classmethod |
| 870 | def combine(cls, inputs: list, **kwargs): # type: ignore |
| 871 | func = kwargs.pop("func") |
| 872 | df = _concat(inputs) |
| 873 | out = func(df, **kwargs) |
| 874 | # Return a dataframe so that the concatenated version is also a dataframe |
| 875 | return out.to_frame().T if is_series_like(out) else out |
| 876 | |
| 877 | @classmethod |
| 878 | def aggregate(cls, inputs, **kwargs): |
| 879 | func = kwargs.pop("func") |
| 880 | df = _concat(inputs) |
| 881 | return func(df, **kwargs) |
| 882 | |
| 883 | @functools.cached_property |
| 884 | def _meta(self): |
| 885 | if self.operand("meta") is not no_default: |
| 886 | return self.operand("meta") |
| 887 | return super()._meta |
| 888 | |
| 889 | @property |
| 890 | def chunk_kwargs(self): |
| 891 | return self.operand("chunk_kwargs") |
| 892 | |
| 893 | @property |
| 894 | def combine_kwargs(self): |
| 895 | return self.operand("combine_kwargs") |
| 896 | |
| 897 | @property |
| 898 | def aggregate_kwargs(self): |
| 899 | return self.operand("aggregate_kwargs") |
| 900 | |
| 901 | def _simplify_up(self, parent, dependents): |
| 902 | return |
| 903 | |