Per Column Transformer

[AKuederle]

First suggestion for a PerColumnTransformer

class PerColumnTransformer(BaseTransformer, TrainableTransformerMixin):
    transformer: BaseTransformer
    per_column_transformer: OptimizableParameter[Optional[List[Tuple[str, BaseTransformer]]]]

    def __init__(
        self, transformer: BaseTransformer, *, per_col_transformer: Optional[List[Tuple[str, BaseTransformer]]] = None
    ):
        self.transformer = transformer
        self.per_col_transformer = per_col_transformer

    def transform(self, data: SingleSensorData, **kwargs) -> Self:
        self.data = data
        transformed_data = {}
        # We either used the per column transformer that are already created during self_optimize or we create them
        # here.
        # Note, that we don't store them in the parameters, when they are created here, because this is the "action"
        # method and we don't want to modify parameters in an action method.
        per_col_transformer = self.per_col_transformer or [
            (col_name, self.transformer.clone()) for col_name in data.columns
        ]
        for col_name, transformer in per_col_transformer:
            transformed_data[col_name] = transformer.clone().transform(data[col_name], **kwargs).transformed_data_
        self.transformed_data_ = pd.concat(transformed_data, axis=1)
        return self

    def self_optimize(self, data: Sequence[SingleSensorData], **kwargs) -> Self:
        # If there is no per_col_transformer set yet, we create one transformer for each column.
        if self.per_col_transformer is None:
            self.per_col_transformer = [(col_name, self.transformer.clone()) for col_name in data[0].columns]
        optimized_transformers = []
        for col_name, transformer in self.per_col_transformer:
            if isinstance(transformer, TrainableTransformerMixin):
                transformer = transformer.self_optimize([d[[col_name]] for d in data], **kwargs)
            optimized_transformers.append((col_name, transformer.clone()))

        self.per_col_transformer = optimized_transformers
        return self