h2o-py/h2o/model/model_base.py
def predict(self, test_data, custom_metric = None, custom_metric_func = None):
        """
        Predict on a dataset.

        :param H2OFrame test_data: Data on which to make predictions.
        :param custom_metric:  custom evaluation function defined as class reference, the class get uploaded
        into cluster
        :param custom_metric_func: custom evaluation function reference, e.g, result of upload_custom_metric

        :returns: A new H2OFrame of predictions.
        """
        # Upload evaluation function into DKV
        if custom_metric:
            assert_satisfies(custom_metric_func, custom_metric_func is None,
                             "The argument 'eval_func_ref' cannot be specified when eval_func is specified, ")
            eval_func_ref = h2o.upload_custom_metric(custom_metric)
        if not isinstance(test_data, h2o.H2OFrame): raise ValueError("test_data must be an instance of H2OFrame")
        j = H2OJob(h2o.api("POST /4/Predictions/models/%s/frames/%s" % (self.model_id, test_data.frame_id), data = {'custom_metric_func': custom_metric_func}),
                   self._model_json["algo"] + " prediction")
        j.poll()
        return h2o.get_frame(j.dest_key)
def predict(self, test_data, custom_metric = None, custom_metric_func = None):
        """
        Predict on a dataset.

        :param H2OFrame test_data: Data on which to make predictions.
        :param custom_metric:  custom evaluation function defined as class reference, the class get uploaded
        into cluster
        :param custom_metric_func: custom evaluation function reference, e.g, result of upload_custom_metric

        :returns: A new H2OFrame of predictions.
        """
        # Upload evaluation function into DKV
        if custom_metric:
            assert_satisfies(custom_metric_func, custom_metric_func is None,
                             "The argument 'eval_func_ref' cannot be specified when eval_func is specified, ")
            eval_func_ref = h2o.upload_custom_metric(custom_metric)
        if not isinstance(test_data, h2o.H2OFrame): raise ValueError("test_data must be an instance of H2OFrame")
        j = H2OJob(h2o.api("POST /4/Predictions/models/%s/frames/%s" % (self.model_id, test_data.frame_id), data = {'custom_metric_func': custom_metric_func}),
                   self._model_json["algo"] + " prediction")
        j.poll()
        return h2o.get_frame(j.dest_key)
def predict ( self , test_data , custom_metric = None , custom_metric_func = None ) : # Upload evaluation function into DKV if custom_metric : assert_satisfies ( custom_metric_func , custom_metric_func is None , "The argument 'eval_func_ref' cannot be specified when eval_func is specified, " ) eval_func_ref = h2o . upload_custom_metric ( custom_metric ) if not isinstance ( test_data , h2o . H2OFrame ) : raise ValueError ( "test_data must be an instance of H2OFrame" ) j = H2OJob ( h2o . api ( "POST /4/Predictions/models/%s/frames/%s" % ( self . model_id , test_data . frame_id ) , data = { 'custom_metric_func' : custom_metric_func } ) , self . _model_json [ "algo" ] + " prediction" ) j . poll ( ) return h2o . get_frame ( j . dest_key )
Predict on a dataset.

        :param H2OFrame test_data: Data on which to make predictions.
        :param custom_metric:  custom evaluation function defined as class reference, the class get uploaded
        into cluster
        :param custom_metric_func: custom evaluation function reference, e.g, result of upload_custom_metric

        :returns: A new H2OFrame of predictions.
Predict on a dataset .