I'm trying to pass a .arff file to LinearRegression object and while doing so it gives me this exception Cannot handle multi-valued nominal class!.
What actually happening is i'm performing Attribute selection using CFSSubsetEval evaluater and search as GreedyStepwise after doing so, passing those attributes to LinearRegression as follows
LinearRegression rl=new LinearRegression(); rl.buildClassifier(data);
data is the Instance object which has the data from .arff file which previously converted to nominal values using weka only. Am i doing anything wrong here? I was trying to search for this error on google but couldn't find one.
Code
package com.attribute;
import java.io.BufferedReader;
import java.io.FileReader;
import java.util.Random;
import weka.attributeSelection.AttributeSelection;
import weka.attributeSelection.CfsSubsetEval;
import weka.attributeSelection.GreedyStepwise;
import weka.classifiers.Evaluation;
import weka.classifiers.functions.LinearRegression;
import weka.classifiers.meta.AttributeSelectedClassifier;
import weka.classifiers.trees.J48;
import weka.core.Instances;
import weka.core.Utils;
import weka.filters.supervised.attribute.NominalToBinary;
/**
* performs attribute selection using CfsSubsetEval and GreedyStepwise
* (backwards) and trains J48 with that. Needs 3.5.5 or higher to compile.
*
* @author FracPete (fracpete at waikato dot ac dot nz)
*/
public class AttributeSelectionTest2 {
/**
* uses the meta-classifier
*/
protected static void useClassifier(Instances data) throws Exception {
System.out.println("\n1. Meta-classfier");
AttributeSelectedClassifier classifier = new AttributeSelectedClassifier();
CfsSubsetEval eval = new CfsSubsetEval();
GreedyStepwise search = new GreedyStepwise();
search.setSearchBackwards(true);
J48 base = new J48();
classifier.setClassifier(base);
classifier.setEvaluator(eval);
classifier.setSearch(search);
Evaluation evaluation = new Evaluation(data);
evaluation.crossValidateModel(classifier, data, 10, new Random(1));
System.out.println(evaluation.toSummaryString());
}
/**
* uses the low level approach
*/
protected static void useLowLevel(Instances data) throws Exception {
System.out.println("\n3. Low-level");
AttributeSelection attsel = new AttributeSelection();
CfsSubsetEval eval = new CfsSubsetEval();
GreedyStepwise search = new GreedyStepwise();
search.setSearchBackwards(true);
attsel.setEvaluator(eval);
attsel.setSearch(search);
attsel.SelectAttributes(data);
int[] indices = attsel.selectedAttributes();
System.out.println("selected attribute indices (starting with 0):\n"
+ Utils.arrayToString(indices));
useLinearRegression(indices, data);
}
protected static void useLinearRegression(int[] indices, Instances data) throws Exception{
System.out.println("\n 4. Linear-Regression on above selected attributes");
BufferedReader reader = new BufferedReader(new FileReader(
"C:/Entertainement/MS/Fall 2014/spdb/project 4/healthcare.arff"));
Instances data1 = new Instances(reader);
data.setClassIndex(data.numAttributes() - 1);
/*NominalToBinary nb = new NominalToBinary();
for(int i=0;i<=20; i++){
//Still coding left here, create an Instance variable to store the data from 'data' variable for given indices
Instances data_lr=data1.
}*/
LinearRegression rl=new LinearRegression(); //Creating a LinearRegression Object to pass data1
rl.buildClassifier(data1);
}
/**
* takes a dataset as first argument
*
* @param args
* the commandline arguments
* @throws Exception
* if something goes wrong
*/
public static void main(String[] args) throws Exception {
// load data
System.out.println("\n0. Loading data");
BufferedReader reader = new BufferedReader(new FileReader(
"C:/Entertainement/MS/Fall 2014/spdb/project 4/healthcare.arff"));
Instances data = new Instances(reader);
if (data.classIndex() == -1)
data.setClassIndex(data.numAttributes() - 14);
// 1. meta-classifier
useClassifier(data);
// 2. filter
//useFilter(data);
// 3. low-level
useLowLevel(data);
}
}
NOTE : As i have not written code to build an instance variable with 'indices' attributes, i'm (for the sake of program to run) loading data from the same original file.
I don't know how to upload a file for sample data, but it looks something like this. [link] (https://scontent-a-dfw.xx.fbcdn.net/hphotos-xfa1/t31.0-8/p552x414/10496920_756438941076936_8448023649960186530_o.jpg)
