Source code for secml.ml.features.c_preprocess

"""
.. module:: CPreProcess
   :synopsis: Common interface for feature preprocessing algorithms.

.. moduleauthor:: Battista Biggio <battista.biggio@unica.it>
.. moduleauthor:: Marco Melis <marco.melis@unica.it>

"""
from abc import ABCMeta, abstractmethod
from secml.ml import CModule


[docs]class CPreProcess(CModule, metaclass=ABCMeta): """Common interface for feature preprocessing algorithms. Parameters ---------- preprocess : CPreProcess or str or None, optional Features preprocess to be applied to input data. Can be a CPreProcess subclass or a string with the type of the desired preprocessor. If None, input data is used as is. """ __super__ = 'CPreProcess' def __init__(self, preprocess=None): CModule.__init__(self, preprocess=preprocess)
[docs] def fit(self, x, y=None): """Fit the preprocessor. Parameters ---------- x : CArray Array to be used as training set. Each row must correspond to one single patterns, so each column is a different feature. y : CArray or None, optional Flat array with the label of each pattern. Can be None if not required by the preprocessing algorithm. Returns ------- CPreProcess Instance of the trained normalizer. """ return CModule.fit(self, x, y)
[docs] def fit_transform(self, x, y=None): """Fit preprocessor using data and then transform data. This method is equivalent to call fit(data) and transform(data) in sequence, but it's useful when data is both the training array and the array to be transformed. Parameters ---------- x : CArray Array to be transformed. Each row must correspond to one single patterns, so each column is a different feature. y : CArray or None, optional Flat array with the label of each pattern. Can be None if not required by the preprocessing algorithm. Returns ------- CArray Transformed input data. See Also -------- fit : fit the preprocessor. transform : transform input data. """ return self.fit_forward(x=x, y=y, caching=False)
[docs] def transform(self, x): """Apply the transformation algorithm on data. Parameters ---------- x : CArray Array to be transformed. Shape of input array depends on the algorithm itself. Returns ------- CArray Transformed input data. """ return self.forward(x, caching=False)
def _inverse_transform(self, x): raise NotImplementedError( "inverting this transformation is not supported.")
[docs] def inverse_transform(self, x): """Revert data to original form. Parameters ---------- x : CArray Transformed array to be reverted to original form. Shape of input array depends on the algorithm itself. Returns ------- CArray Original input data. Warnings -------- Reverting a transformed array is not always possible. See description of each preprocessor for details. """ self._check_is_fitted() v = self._inverse_transform(x) # Revert data using the inner preprocess, if defined if self.preprocess is not None: return self.preprocess.inverse_transform(v) return v
# Same doc for the protected method _inverse_transform.__doc__ = inverse_transform.__doc__