def model_performance(self, test_data=None, train=False, valid=False, xval=False):
        """
        Generate model metrics for this model on test_data.

        :param H2OFrame test_data: Data set for which model metrics shall be computed against. All three of train,
            valid and xval arguments are ignored if test_data is not None.
        :param bool train: Report the training metrics for the model.
        :param bool valid: Report the validation metrics for the model.
        :param bool xval: Report the cross-validation metrics for the model. If train and valid are True, then it
            defaults to True.

        :returns: An object of class H2OModelMetrics.
        """
        if test_data is None:
            if not train and not valid and not xval: train = True  # default to train
            if train: return self._model_json["output"]["training_metrics"]
            if valid: return self._model_json["output"]["validation_metrics"]
            if xval: return self._model_json["output"]["cross_validation_metrics"]

        else:  # cases dealing with test_data not None
            if not isinstance(test_data, h2o.H2OFrame):
                raise ValueError("`test_data` must be of type H2OFrame.  Got: " + type(test_data))
            if (self._model_json["response_column_name"] != None) and not(self._model_json["response_column_name"] in test_data.names):
                print("WARNING: Model metrics cannot be calculated and metric_json is empty due to the absence of the response column in your dataset.")
                return
            res = h2o.api("POST /3/ModelMetrics/models/%s/frames/%s" % (self.model_id, test_data.frame_id))

            # FIXME need to do the client-side filtering...  (PUBDEV-874)
            raw_metrics = None
            for mm in res["model_metrics"]:
                if mm["frame"] is not None and mm["frame"]["name"] == test_data.frame_id:
                    raw_metrics = mm
                    break
            return self._metrics_class(raw_metrics, algo=self._model_json["algo"])
def model_performance(self, test_data=None, train=False, valid=False, xval=False):
        """
        Generate model metrics for this model on test_data.

        :param H2OFrame test_data: Data set for which model metrics shall be computed against. All three of train,
            valid and xval arguments are ignored if test_data is not None.
        :param bool train: Report the training metrics for the model.
        :param bool valid: Report the validation metrics for the model.
        :param bool xval: Report the cross-validation metrics for the model. If train and valid are True, then it
            defaults to True.

        :returns: An object of class H2OModelMetrics.
        """
        if test_data is None:
            if not train and not valid and not xval: train = True  # default to train
            if train: return self._model_json["output"]["training_metrics"]
            if valid: return self._model_json["output"]["validation_metrics"]
            if xval: return self._model_json["output"]["cross_validation_metrics"]

        else:  # cases dealing with test_data not None
            if not isinstance(test_data, h2o.H2OFrame):
                raise ValueError("`test_data` must be of type H2OFrame.  Got: " + type(test_data))
            if (self._model_json["response_column_name"] != None) and not(self._model_json["response_column_name"] in test_data.names):
                print("WARNING: Model metrics cannot be calculated and metric_json is empty due to the absence of the response column in your dataset.")
                return
            res = h2o.api("POST /3/ModelMetrics/models/%s/frames/%s" % (self.model_id, test_data.frame_id))

            # FIXME need to do the client-side filtering...  (PUBDEV-874)
            raw_metrics = None
            for mm in res["model_metrics"]:
                if mm["frame"] is not None and mm["frame"]["name"] == test_data.frame_id:
                    raw_metrics = mm
                    break
            return self._metrics_class(raw_metrics, algo=self._model_json["algo"])
def model_performance ( self , test_data = None , train = False , valid = False , xval = False ) : if test_data is None : if not train and not valid and not xval : train = True # default to train if train : return self . _model_json [ "output" ] [ "training_metrics" ] if valid : return self . _model_json [ "output" ] [ "validation_metrics" ] if xval : return self . _model_json [ "output" ] [ "cross_validation_metrics" ] else : # cases dealing with test_data not None if not isinstance ( test_data , h2o . H2OFrame ) : raise ValueError ( "`test_data` must be of type H2OFrame.  Got: " + type ( test_data ) ) if ( self . _model_json [ "response_column_name" ] != None ) and not ( self . _model_json [ "response_column_name" ] in test_data . names ) : print ( "WARNING: Model metrics cannot be calculated and metric_json is empty due to the absence of the response column in your dataset." ) return res = h2o . api ( "POST /3/ModelMetrics/models/%s/frames/%s" % ( self . model_id , test_data . frame_id ) ) # FIXME need to do the client-side filtering...  (PUBDEV-874) raw_metrics = None for mm in res [ "model_metrics" ] : if mm [ "frame" ] is not None and mm [ "frame" ] [ "name" ] == test_data . frame_id : raw_metrics = mm break return self . _metrics_class ( raw_metrics , algo = self . _model_json [ "algo" ] )
Generate model metrics for this model on test_data.

        :param H2OFrame test_data: Data set for which model metrics shall be computed against. All three of train,
            valid and xval arguments are ignored if test_data is not None.
        :param bool train: Report the training metrics for the model.
        :param bool valid: Report the validation metrics for the model.
        :param bool xval: Report the cross-validation metrics for the model. If train and valid are True, then it
            defaults to True.

        :returns: An object of class H2OModelMetrics.
Generate model metrics for this model on test_data .