| 842 | |
| 843 | |
| 844 | class CustomReduction(Reduction): |
| 845 | _parameters = [ |
| 846 | "frame", |
| 847 | "meta", |
| 848 | "chunk_kwargs", |
| 849 | "aggregate_kwargs", |
| 850 | "combine_kwargs", |
| 851 | "split_every", |
| 852 | "token", |
| 853 | ] |
| 854 | |
| 855 | @functools.cached_property |
| 856 | def _name(self): |
| 857 | name = self.operand("token") or funcname(type(self)).lower() |
| 858 | return f"{name}-{self.deterministic_token}" |
| 859 | |
| 860 | @classmethod |
| 861 | def chunk(cls, df, **kwargs): |
| 862 | func = kwargs.pop("func") |
| 863 | out = func(df, **kwargs) |
| 864 | # Return a dataframe so that the concatenated version is also a dataframe |
| 865 | return out.to_frame().T if is_series_like(out) else out |
| 866 | |
| 867 | @classmethod |
| 868 | def combine(cls, inputs: list, **kwargs): |
| 869 | func = kwargs.pop("func") |
| 870 | df = _concat(inputs) |
| 871 | out = func(df, **kwargs) |
| 872 | # Return a dataframe so that the concatenated version is also a dataframe |
| 873 | return out.to_frame().T if is_series_like(out) else out |
| 874 | |
| 875 | @classmethod |
| 876 | def aggregate(cls, inputs, **kwargs): |
| 877 | func = kwargs.pop("func") |
| 878 | df = _concat(inputs) |
| 879 | return func(df, **kwargs) |
| 880 | |
| 881 | @functools.cached_property |
| 882 | def _meta(self): |
| 883 | if self.operand("meta") is not no_default: |
| 884 | return self.operand("meta") |
| 885 | return super()._meta |
| 886 | |
| 887 | @property |
| 888 | def chunk_kwargs(self): |
| 889 | return self.operand("chunk_kwargs") |
| 890 | |
| 891 | @property |
| 892 | def combine_kwargs(self): |
| 893 | return self.operand("combine_kwargs") |
| 894 | |
| 895 | @property |
| 896 | def aggregate_kwargs(self): |
| 897 | return self.operand("aggregate_kwargs") |
| 898 | |
| 899 | def _simplify_up(self, parent, dependents): |
| 900 | return |
| 901 | |