splearn/pipeline.py
def fit(self, Z, **fit_params):
        """TODO: rewrite docstring
        Fit all transformers using X.
        Parameters
        ----------
        X : array-like or sparse matrix, shape (n_samples, n_features)
            Input data, used to fit transformers.
        """
        fit_params_steps = dict((step, {})
                                for step, _ in self.transformer_list)

        for pname, pval in six.iteritems(fit_params):
            step, param = pname.split('__', 1)
            fit_params_steps[step][param] = pval

        transformers = Parallel(n_jobs=self.n_jobs, backend="threading")(
            delayed(_fit_one_transformer)(trans, Z, **fit_params_steps[name])
            for name, trans in self.transformer_list)
        self._update_transformer_list(transformers)
        return self
def fit(self, Z, **fit_params):
        """TODO: rewrite docstring
        Fit all transformers using X.
        Parameters
        ----------
        X : array-like or sparse matrix, shape (n_samples, n_features)
            Input data, used to fit transformers.
        """
        fit_params_steps = dict((step, {})
                                for step, _ in self.transformer_list)

        for pname, pval in six.iteritems(fit_params):
            step, param = pname.split('__', 1)
            fit_params_steps[step][param] = pval

        transformers = Parallel(n_jobs=self.n_jobs, backend="threading")(
            delayed(_fit_one_transformer)(trans, Z, **fit_params_steps[name])
            for name, trans in self.transformer_list)
        self._update_transformer_list(transformers)
        return self
def fit ( self , Z , * * fit_params ) : fit_params_steps = dict ( ( step , { } ) for step , _ in self . transformer_list ) for pname , pval in six . iteritems ( fit_params ) : step , param = pname . split ( '__' , 1 ) fit_params_steps [ step ] [ param ] = pval transformers = Parallel ( n_jobs = self . n_jobs , backend = "threading" ) ( delayed ( _fit_one_transformer ) ( trans , Z , * * fit_params_steps [ name ] ) for name , trans in self . transformer_list ) self . _update_transformer_list ( transformers ) return self
TODO: rewrite docstring
        Fit all transformers using X.
        Parameters
        ----------
        X : array-like or sparse matrix, shape (n_samples, n_features)
            Input data, used to fit transformers.
TODO : rewrite docstring Fit all transformers using X . Parameters ---------- X : array - like or sparse matrix shape ( n_samples n_features ) Input data used to fit transformers .