grid_search.py 文件源码

def fit(self, frame):
        """Fit the grid search.

        Parameters
        ----------

        frame : H2OFrame, shape=(n_samples, n_features)
            The training frame on which to fit.
        """
        sampled_params = ParameterSampler(self.param_grid,
                                          self.n_iter,
                                          random_state=self.random_state)

        # set our score class
        self.scoring_class_ = GainsStatisticalReport(**self.grsttngs_)

        # we can do this once to avoid many as_data_frame operations
        exp, loss, prem = _val_exp_loss_prem(self.exposure_feature, self.loss_feature, self.premium_feature)
        self.extra_args_ = {
            'expo': _as_numpy(frame[exp]),
            'loss': _as_numpy(frame[loss]),
            'prem': _as_numpy(frame[prem]) if prem is not None else None
        }

        # for validation set
        self.extra_names_ = {
            'expo': exp,
            'loss': loss,
            'prem': prem
        }

        # do fit
        the_fit = self._fit(frame, sampled_params)

        # clear extra_args_, because they might take lots of mem
        # we can do this because a re-fit will re-assign them anyways.
        # don't delete the extra_names_ though, because they're used in
        # scoring the incoming frame.
        del self.extra_args_

        return the_fit