h2o-py/h2o/grid/grid_search.py
def get_hyperparams_dict(self, id, display=True):
        """
        Derived and returned the model parameters used to train the particular grid search model.

        :param str id: The model id of the model with hyperparameters of interest.
        :param bool display: Flag to indicate whether to display the hyperparameter names.

        :returns: A dict of model pararmeters derived from the hyper-parameters used to train this particular model.
        """
        idx = id if is_type(id, int) else self.model_ids.index(id)
        model = self[idx]

        model_params = dict()

        # if cross-validation is turned on, parameters in one of the fold model actual contains the max_runtime_secs
        # parameter and not the main model that is returned.
        if model._is_xvalidated:
            model = h2o.get_model(model._xval_keys[0])

        for param_name in self.hyper_names:
            model_params[param_name] = model.params[param_name]['actual'][0] if \
                isinstance(model.params[param_name]['actual'], list) else model.params[param_name]['actual']

        if display: print('Hyperparameters: [' + ', '.join(list(self.hyper_params.keys())) + ']')
        return model_params
def get_hyperparams_dict(self, id, display=True):
        """
        Derived and returned the model parameters used to train the particular grid search model.

        :param str id: The model id of the model with hyperparameters of interest.
        :param bool display: Flag to indicate whether to display the hyperparameter names.

        :returns: A dict of model pararmeters derived from the hyper-parameters used to train this particular model.
        """
        idx = id if is_type(id, int) else self.model_ids.index(id)
        model = self[idx]

        model_params = dict()

        # if cross-validation is turned on, parameters in one of the fold model actual contains the max_runtime_secs
        # parameter and not the main model that is returned.
        if model._is_xvalidated:
            model = h2o.get_model(model._xval_keys[0])

        for param_name in self.hyper_names:
            model_params[param_name] = model.params[param_name]['actual'][0] if \
                isinstance(model.params[param_name]['actual'], list) else model.params[param_name]['actual']

        if display: print('Hyperparameters: [' + ', '.join(list(self.hyper_params.keys())) + ']')
        return model_params
Derived and returned the model parameters used to train the particular grid search model.

        :param str id: The model id of the model with hyperparameters of interest.
        :param bool display: Flag to indicate whether to display the hyperparameter names.

        :returns: A dict of model pararmeters derived from the hyper-parameters used to train this particular model.