Function definition of predict_from_standalone_rf in data_predictor.py

    def predict_from_standalone_rf(self,train,test,valid,x,y,prediction_field_name):
        """Produces an H2O dataframe containing a field with predictions from random forest model.
        :param train: the training H2O dataframe
        :param test: the testing H2O dataframe
        :param valid: the validation H2O dataframe
        :param x: the feature variables
        :param y: the target variable
        :param prediction_field_name: the name to use for field to contain predictions
        :returns: the H2O dataframe with prediction field and all fields from the supplied dataframe
        """
        print("Random Forest")
        rf_standalone = H2ORandomForestEstimator(
            model_id="rf",
            stopping_rounds=2,
            score_each_iteration=True,
            sample_rate=.7,
            col_sample_rate_per_tree=.7,
            max_depth=16,
            ntrees=500,
            nfolds=5,
            seed=1000000)
        rf_standalone.train(x=x, y=y, training_frame=train, validation_frame=valid)
        print("Model type:", rf_standalone.type)
        if(rf_standalone.type == "classifier"):
            print("train[y].levels():",train[y].levels()[0])
            y_level_count = train[y].nlevels()[0]
            print("y_level_count:", y_level_count)
            if(y_level_count <= 2):
                print("AUC (training):", rf_standalone.auc(train=True))
                print("AUC (validation):", rf_standalone.auc(valid=True))
            else:
                print("Confusion Matrix (validation):", rf_standalone.confusion_matrix(data=valid))
                print("Hit Ratio Table (validation):", rf_standalone.hit_ratio_table(valid=True))
        else:
            print("r2 (training):", rf_standalone.r2(train=True))
            print("r2 (validation):", rf_standalone.r2(valid=True))
        rf_standalone_predictions = rf_standalone.predict(test)
        print("Random Forest Variable Importances")
        print(rf_standalone._model_json['output']['variable_importances'].as_data_frame())
        print("Best Random Forest Predictions:")
        print(rf_standalone_predictions.head(rows=5))
        return(self.set_prediction_field_name(rf_standalone_predictions,prediction_field_name))