Source code for diffprivlib.mechanisms.transforms.base

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# Copyright (C) IBM Corporation 2019
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"""
Core utilities for DP transformers.
"""
from diffprivlib.mechanisms.base import DPMachine


class DPTransformer(DPMachine):
    """
    Base class for DP transformers.  DP Transformers are simple wrappers for DP Mechanisms to allow mechanisms to be
    used with data types and structures outside their scope.

    A :class:`.DPTransformer` must be initiated with a :class:`.DPMachine` (either another :class:`.DPTransformer`, or a
    :class:`.DPMechanism`).  This allows many instances of :class:`.DPTransformer` to be chained together, but the chain
    must terminate with a :class:`.DPMechanism`.

    """
    def __init__(self, parent):
        if not isinstance(parent, DPMachine):
            raise TypeError("Data transformer must take a DPMachine as input")

        self.parent = parent

    def pre_transform(self, value):
        """Performs no transformation on the input data, and is ingested by the mechanism as-is.

        Parameters
        ----------
        value : float or string
            Input value to be transformed.

        Returns
        -------
        float or string
            Transformed input value
        """
        return value

    def post_transform(self, value):
        """Performs no transformation on the output of the mechanism, and is returned as-is.

        Parameters
        ----------
        value : float or string
            Mechanism output to be transformed.

        Returns
        -------
        float or string
            Transformed output value.

        """
        return value

    def randomise(self, value):
        """
        Randomise the given value using the :class:`.DPMachine`.

        Parameters
        ----------
        value : float or string
            Value to be randomised.

        Returns
        -------
        float or string
            Randomised value, same type as `value`.

        """
        transformed_value = self.pre_transform(value)
        noisy_value = self.parent.randomise(transformed_value)
        output_value = self.post_transform(noisy_value)
        return output_value