def fit(self, X, y=None):
        '''
        Fit the transform

        Parameters
        ----------
        X : array-like, shape [n_series, ...]
            Segmented time series data and (optionally) contextual data
        y : None
            There is no need of a target in a transformer, yet the pipeline API requires this
            parameter.

        Returns
        -------
        self : object
            Returns self.
        '''
        Xt, Xc = get_ts_data_parts(X)
        self.f_labels = []

        # calculated features (prefix with the FeatureRep name and correct the index)
        for name, trans, cols in self.transformers:
            indices = self._retrieve_indices(cols)
            trans.fit(self._select(Xt, cols))
            for label, index in zip(trans.f_labels, indices):
                self.f_labels.append(name + '_' + label.rsplit('_', 1)[0] + '_' + str(index))

        # contextual features
        if Xc is not None:
            Ns = len(np.atleast_1d(Xc[0]))
            self.f_labels += ['context_' + str(i) for i in range(Ns)]

        return self
def fit ( self , X , y = None ) : Xt , Xc = get_ts_data_parts ( X ) self . f_labels = [ ] for name , trans , cols in self . transformers : indices = self . _retrieve_indices ( cols ) trans . fit ( self . _select ( Xt , cols ) ) for label , index in zip ( trans . f_labels , indices ) : self . f_labels . append ( name + '_' + label . rsplit ( '_' , 1 ) [ 0 ] + '_' + str ( index ) ) if Xc is not None : Ns = len ( np . atleast_1d ( Xc [ 0 ] ) ) self . f_labels += [ 'context_' + str ( i ) for i in range ( Ns ) ] return self
Fit the transform

        Parameters
        ----------
        X : array-like, shape [n_series, ...]
            Segmented time series data and (optionally) contextual data
        y : None
            There is no need of a target in a transformer, yet the pipeline API requires this
            parameter.

        Returns
        -------
        self : object
            Returns self.