src/gluonts/nursery/SCott/pts/transform/convert.py [139:291]:
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        data[self.output_field] = output
        for fname in self.cols_to_drop:
            del data[fname]
        return data


class ConcatFeatures(SimpleTransformation):
    """
    Concatenate fields together using ``np.concatenate``.

    Fields with value ``None`` are ignored.

    Parameters
    ----------
    output_field
        Field name to use for the output
    input_fields
        Fields to stack together
    drop_inputs
        If set to true the input fields will be dropped.
    """

    @validated()
    def __init__(
        self,
        output_field: str,
        input_fields: List[str],
        drop_inputs: bool = True,
    ) -> None:
        self.output_field = output_field
        self.input_fields = input_fields
        self.cols_to_drop = (
            []
            if not drop_inputs
            else [
                fname for fname in self.input_fields if fname != output_field
            ]
        )

    def transform(self, data: DataEntry) -> DataEntry:
        r = [
            data[fname]
            for fname in self.input_fields
            if data[fname] is not None
        ]
        output = np.concatenate(r)
        data[self.output_field] = output
        for fname in self.cols_to_drop:
            del data[fname]
        return data


class SwapAxes(SimpleTransformation):
    """
    Apply `np.swapaxes` to fields.

    Parameters
    ----------
    input_fields
        Field to apply to
    axes
        Axes to use
    """

    @validated()
    def __init__(self, input_fields: List[str], axes: Tuple[int, int]) -> None:
        self.input_fields = input_fields
        self.axis1, self.axis2 = axes

    def transform(self, data: DataEntry) -> DataEntry:
        for field in self.input_fields:
            data[field] = self.swap(data[field])
        return data

    def swap(self, v):
        if isinstance(v, np.ndarray):
            return np.swapaxes(v, self.axis1, self.axis2)
        if isinstance(v, list):
            return [self.swap(x) for x in v]
        else:
            raise ValueError(
                f"Unexpected field type {type(v).__name__}, expected "
                f"np.ndarray or list[np.ndarray]"
            )


class ListFeatures(SimpleTransformation):
    """
    Creates a new field which contains a list of features.

    Parameters
    ----------
    output_field
        Field name for output
    input_fields
        Fields to combine into list
    drop_inputs
        If true the input fields will be removed from the result.
    """

    @validated()
    def __init__(
        self,
        output_field: str,
        input_fields: List[str],
        drop_inputs: bool = True,
    ) -> None:
        self.output_field = output_field
        self.input_fields = input_fields
        self.cols_to_drop = (
            []
            if not drop_inputs
            else [
                fname for fname in self.input_fields if fname != output_field
            ]
        )

    def transform(self, data: DataEntry) -> DataEntry:
        data[self.output_field] = [data[fname] for fname in self.input_fields]
        for fname in self.cols_to_drop:
            del data[fname]
        return data


class TargetDimIndicator(SimpleTransformation):
    """
    Label-encoding of the target dimensions.
    """

    @validated()
    def __init__(self, field_name: str, target_field: str) -> None:
        self.field_name = field_name
        self.target_field = target_field

    def transform(self, data: DataEntry) -> DataEntry:
        data[self.field_name] = np.arange(0, data[self.target_field].shape[0])
        return data


class SampleTargetDim(FlatMapTransformation):
    """
    Samples random dimensions from the target at training time.
    """

    @validated()
    def __init__(
        self,
        field_name: str,
        target_field: str,
        observed_values_field: str,
        num_samples: int,
        shuffle: bool = True,
    ) -> None:
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src/gluonts/transform/convert.py [210:362]:
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        data[self.output_field] = output
        for fname in self.cols_to_drop:
            del data[fname]
        return data


class ConcatFeatures(SimpleTransformation):
    """
    Concatenate fields together using ``np.concatenate``.

    Fields with value ``None`` are ignored.

    Parameters
    ----------
    output_field
        Field name to use for the output
    input_fields
        Fields to stack together
    drop_inputs
        If set to true the input fields will be dropped.
    """

    @validated()
    def __init__(
        self,
        output_field: str,
        input_fields: List[str],
        drop_inputs: bool = True,
    ) -> None:
        self.output_field = output_field
        self.input_fields = input_fields
        self.cols_to_drop = (
            []
            if not drop_inputs
            else [
                fname for fname in self.input_fields if fname != output_field
            ]
        )

    def transform(self, data: DataEntry) -> DataEntry:
        r = [
            data[fname]
            for fname in self.input_fields
            if data[fname] is not None
        ]
        output = np.concatenate(r)
        data[self.output_field] = output
        for fname in self.cols_to_drop:
            del data[fname]
        return data


class SwapAxes(SimpleTransformation):
    """
    Apply `np.swapaxes` to fields.

    Parameters
    ----------
    input_fields
        Field to apply to
    axes
        Axes to use
    """

    @validated()
    def __init__(self, input_fields: List[str], axes: Tuple[int, int]) -> None:
        self.input_fields = input_fields
        self.axis1, self.axis2 = axes

    def transform(self, data: DataEntry) -> DataEntry:
        for field in self.input_fields:
            data[field] = self.swap(data[field])
        return data

    def swap(self, v):
        if isinstance(v, np.ndarray):
            return np.swapaxes(v, self.axis1, self.axis2)
        if isinstance(v, list):
            return [self.swap(x) for x in v]
        else:
            raise ValueError(
                f"Unexpected field type {type(v).__name__}, expected "
                f"np.ndarray or list[np.ndarray]"
            )


class ListFeatures(SimpleTransformation):
    """
    Creates a new field which contains a list of features.

    Parameters
    ----------
    output_field
        Field name for output
    input_fields
        Fields to combine into list
    drop_inputs
        If true the input fields will be removed from the result.
    """

    @validated()
    def __init__(
        self,
        output_field: str,
        input_fields: List[str],
        drop_inputs: bool = True,
    ) -> None:
        self.output_field = output_field
        self.input_fields = input_fields
        self.cols_to_drop = (
            []
            if not drop_inputs
            else [
                fname for fname in self.input_fields if fname != output_field
            ]
        )

    def transform(self, data: DataEntry) -> DataEntry:
        data[self.output_field] = [data[fname] for fname in self.input_fields]
        for fname in self.cols_to_drop:
            del data[fname]
        return data


class TargetDimIndicator(SimpleTransformation):
    """
    Label-encoding of the target dimensions.
    """

    @validated()
    def __init__(self, field_name: str, target_field: str) -> None:
        self.field_name = field_name
        self.target_field = target_field

    def transform(self, data: DataEntry) -> DataEntry:
        data[self.field_name] = np.arange(0, data[self.target_field].shape[0])
        return data


class SampleTargetDim(FlatMapTransformation):
    """
    Samples random dimensions from the target at training time.
    """

    @validated()
    def __init__(
        self,
        field_name: str,
        target_field: str,
        observed_values_field: str,
        num_samples: int,
        shuffle: bool = True,
    ) -> None:
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