h2o-py/h2o/grid/grid_search.py
def get_grid(self, sort_by=None, decreasing=None):
        """
        Retrieve an H2OGridSearch instance.

        Optionally specify a metric by which to sort models and a sort order.
        Note that if neither cross-validation nor a validation frame is used in the grid search, then the
        training metrics will display in the "get grid" output. If a validation frame is passed to the grid, and
        ``nfolds = 0``, then the validation metrics will display. However, if ``nfolds`` > 1, then cross-validation
        metrics will display even if a validation frame is provided.

        :param str sort_by: A metric by which to sort the models in the grid space. Choices are: ``"logloss"``,
            ``"residual_deviance"``, ``"mse"``, ``"auc"``, ``"r2"``, ``"accuracy"``, ``"precision"``, ``"recall"``,
            ``"f1"``, etc.
        :param bool decreasing: Sort the models in decreasing order of metric if true, otherwise sort in increasing
            order (default).

        :returns: A new H2OGridSearch instance optionally sorted on the specified metric.
        """
        if sort_by is None and decreasing is None: return self

        grid_json = h2o.api("GET /99/Grids/%s" % self._id, data={"sort_by": sort_by, "decreasing": decreasing})
        grid = H2OGridSearch(self.model, self.hyper_params, self._id)
        grid.models = [h2o.get_model(key['name']) for key in grid_json['model_ids']]  # reordered
        first_model_json = h2o.api("GET /99/Models/%s" % grid_json['model_ids'][0]['name'])['models'][0]
        model_class = H2OGridSearch._metrics_class(first_model_json)
        m = model_class()
        m._id = self._id
        m._grid_json = grid_json
        # m._metrics_class = metrics_class
        m._parms = grid._parms
        H2OEstimator.mixin(grid, model_class)
        grid.__dict__.update(m.__dict__.copy())
        return grid
def get_grid(self, sort_by=None, decreasing=None):
        if sort_by is None and decreasing is None: return self

        grid_json = h2o.api("GET /99/Grids/%s" % self._id, data={"sort_by": sort_by, "decreasing": decreasing})
        grid = H2OGridSearch(self.model, self.hyper_params, self._id)
        grid.models = [h2o.get_model(key['name']) for key in grid_json['model_ids']]  # reordered
        first_model_json = h2o.api("GET /99/Models/%s" % grid_json['model_ids'][0]['name'])['models'][0]
        model_class = H2OGridSearch._metrics_class(first_model_json)
        m = model_class()
        m._id = self._id
        m._grid_json = grid_json
        # m._metrics_class = metrics_class
        m._parms = grid._parms
        H2OEstimator.mixin(grid, model_class)
        grid.__dict__.update(m.__dict__.copy())
        return grid
def get_grid ( self , sort_by = None , decreasing = None ) : if sort_by is None and decreasing is None : return self grid_json = h2o . api ( "GET /99/Grids/%s" % self . _id , data = { "sort_by" : sort_by , "decreasing" : decreasing } ) grid = H2OGridSearch ( self . model , self . hyper_params , self . _id ) grid . models = [ h2o . get_model ( key [ 'name' ] ) for key in grid_json [ 'model_ids' ] ] # reordered first_model_json = h2o . api ( "GET /99/Models/%s" % grid_json [ 'model_ids' ] [ 0 ] [ 'name' ] ) [ 'models' ] [ 0 ] model_class = H2OGridSearch . _metrics_class ( first_model_json ) m = model_class ( ) m . _id = self . _id m . _grid_json = grid_json # m._metrics_class = metrics_class m . _parms = grid . _parms H2OEstimator . mixin ( grid , model_class ) grid . __dict__ . update ( m . __dict__ . copy ( ) ) return grid
Retrieve an H2OGridSearch instance.

        Optionally specify a metric by which to sort models and a sort order.
        Note that if neither cross-validation nor a validation frame is used in the grid search, then the
        training metrics will display in the "get grid" output. If a validation frame is passed to the grid, and
        ``nfolds = 0``, then the validation metrics will display. However, if ``nfolds`` > 1, then cross-validation
        metrics will display even if a validation frame is provided.

        :param str sort_by: A metric by which to sort the models in the grid space. Choices are: ``"logloss"``,
            ``"residual_deviance"``, ``"mse"``, ``"auc"``, ``"r2"``, ``"accuracy"``, ``"precision"``, ``"recall"``,
            ``"f1"``, etc.
        :param bool decreasing: Sort the models in decreasing order of metric if true, otherwise sort in increasing
            order (default).

        :returns: A new H2OGridSearch instance optionally sorted on the specified metric.
Retrieve an H2OGridSearch instance .