(self, X, y=None)
return Xt
Fit-transform a clone of `transformer` to each element in the
        collection `X`.

        Parameters
        ----------
        X : list of length n_samples, or ndarray of shape (n_samples, ...)
            Collection of inputs to be fit-transformed by `transformer`.

        y : None
            There is no need for a target in a transformer, yet the pipeline
            API requires this parameter.

        Returns
        -------
        Xt : list of length n_samples, or ndarray of shape (n_samples, ...)
            Collection of outputs. It is a list unless all outputs have the
            same shape, in which case it is converted to an ndarray.
Fit-transform a clone of `transformer` to each element in the
        collection `X`.
Fit - transform a clone of transformer to each element in the collection X .
def fit_transform(self, X, y=None):
        """Fit-transform a clone of `transformer` to each element in the
        collection `X`.

        Parameters
        ----------
        X : list of length n_samples, or ndarray of shape (n_samples, ...)
            Collection of inputs to be fit-transformed by `transformer`.

        y : None
            There is no need for a target in a transformer, yet the pipeline
            API requires this parameter.

        Returns
        -------
        Xt : list of length n_samples, or ndarray of shape (n_samples, ...)
            Collection of outputs. It is a list unless all outputs have the
            same shape, in which case it is converted to an ndarray.

        """
        Xt = check_collection(X, accept_sparse=True, accept_large_sparse=True,
                              force_all_finite=False)
        self._validate_transformer()

        Xt = Parallel(n_jobs=self.n_jobs, prefer=self.parallel_backend_prefer,
                      require=self.parallel_backend_require)(
            delayed(clone(self.transformer).fit_transform)(x) for x in Xt
            )

        x0_shape = Xt[0].shape
        if reduce(and_, (x.shape == x0_shape for x in Xt), True):
            Xt = np.asarray(Xt)

        return Xt
def fit_transform ( self , X , y = None ) : Xt = check_collection ( X , accept_sparse = True , accept_large_sparse = True , force_all_finite = False ) self . _validate_transformer ( ) Xt = Parallel ( n_jobs = self . n_jobs , prefer = self . parallel_backend_prefer , require = self . parallel_backend_require ) ( delayed ( clone ( self . transformer ) . fit_transform ) ( x ) for x in Xt ) x0_shape = Xt [ 0 ] . shape if reduce ( and_ , ( x . shape == x0_shape for x in Xt ) , True ) : Xt = np . asarray ( Xt ) return Xt