68c9bab4a5c6821c2a4395763c676ba796406c49,dask_ml/preprocessing/_encoders.py,OneHotEncoder,_fit,#OneHotEncoder#Any#Any#,157

Before Change

                self.categories_.append(cats)
                self.dtypes_.append(None)
        else:
            if not (X.dtypes == "category").all():
                raise ValueError("All columns must be Categorical dtype.")
            if self.categories == "auto":
                for col in X.columns:
                    Xi = X[col]
                    cats = _encode(Xi, uniques=Xi.cat.categories)
                    self.categories_.append(cats)
                    self.dtypes_.append(Xi.dtype)
            else:
                raise ValueError(
                    "Cannot specify "categories" with DataFrame input. "
                    "Use a categorical dtype instead."
                )

        self.categories_ = dask.compute(self.categories_)[0]

    def transform(
        self, X: Union[ArrayLike, DataFrameType]

After Change

                self.categories_.append(cats)
                self.dtypes_.append(None)
        else:
            for i in range(len(X.columns)):
                Xi = X.iloc[:, i]
                if self.categories != "auto":
                    categories = self.categories[i]
                    Xi = Xi.astype(pd.CategoricalDtype(categories))
                else:
                    if not pd.api.types.is_categorical_dtype(Xi.dtype):
                        raise ValueError(
                            "All columns must be Categorical dtype when "
                            ""categories=\"auto\""."
                        )

                cats = _encode(Xi, uniques=Xi.cat.categories)
                self.categories_.append(cats)
                self.dtypes_.append(Xi.dtype)

        self.categories_ = dask.compute(self.categories_)[0]

    def transform(
        self, X: Union[ArrayLike, DataFrameType]

Instances