h2o-py/h2o/estimators/pca.py
def init_for_pipeline(self):
        """
        Returns H2OPCA object which implements fit and transform method to be used in sklearn.Pipeline properly.
        All parameters defined in self.__params, should be input parameters in H2OPCA.__init__ method.

        :returns: H2OPCA object
        """
        import inspect
        from h2o.transforms.decomposition import H2OPCA
        # check which parameters can be passed to H2OPCA init
        var_names = list(dict(inspect.getmembers(H2OPCA.__init__.__code__))['co_varnames'])
        parameters = {k: v for k, v in self._parms.items() if k in var_names}
        return H2OPCA(**parameters)
def init_for_pipeline(self):
        """
        Returns H2OPCA object which implements fit and transform method to be used in sklearn.Pipeline properly.
        All parameters defined in self.__params, should be input parameters in H2OPCA.__init__ method.

        :returns: H2OPCA object
        """
        import inspect
        from h2o.transforms.decomposition import H2OPCA
        # check which parameters can be passed to H2OPCA init
        var_names = list(dict(inspect.getmembers(H2OPCA.__init__.__code__))['co_varnames'])
        parameters = {k: v for k, v in self._parms.items() if k in var_names}
        return H2OPCA(**parameters)
Returns H2OPCA object which implements fit and transform method to be used in sklearn.Pipeline properly.
        All parameters defined in self.__params, should be input parameters in H2OPCA.__init__ method.

        :returns: H2OPCA object