Example usage for org.apache.mahout.classifier.evaluation Auc Auc

Introduction

In this page you can find the example usage for org.apache.mahout.classifier.evaluation Auc Auc.

Prototype

public Auc(double threshold) 

Source Link

Document

Allocates a new data-structure for accumulating information about AUC and a few other accuracy measures.

Usage

From source file:br.com.sitedoph.mahout_examples.BankMarketingClassificationMain.java

License:Apache License

public static void main(String[] args) throws Exception {
    List<TelephoneCall> calls = Lists.newArrayList(new TelephoneCallParser("bank-full.csv"));

    double heldOutPercentage = 0.10;

    double biggestScore = 0.0;

    for (int run = 0; run < 20; run++) {
        Collections.shuffle(calls);
        int cutoff = (int) (heldOutPercentage * calls.size());
        List<TelephoneCall> testAccuracyData = calls.subList(0, cutoff);
        List<TelephoneCall> trainData = calls.subList(cutoff, calls.size());

        List<TelephoneCall> testUnknownData = new ArrayList<>();

        testUnknownData.add(getUnknownTelephoneCall(trainData));

        OnlineLogisticRegression lr = new OnlineLogisticRegression(NUM_CATEGORIES, TelephoneCall.FEATURES,
                new L1()).learningRate(1).alpha(1).lambda(0.000001).stepOffset(10000).decayExponent(0.2);

        for (int pass = 0; pass < 20; pass++) {
            for (TelephoneCall observation : trainData) {
                lr.train(observation.getTarget(), observation.asVector());
            }//from ww w  . jav a 2 s.  c o m
            Auc eval = new Auc(0.5);
            for (TelephoneCall testCall : testAccuracyData) {
                biggestScore = evaluateTheCallAndGetBiggestScore(biggestScore, lr, eval, testCall);
            }
            System.out.printf("run: %-5d pass: %-5d current learning rate: %-5.4f \teval auc %-5.4f\n", run,
                    pass, lr.currentLearningRate(), eval.auc());

            for (TelephoneCall testCall : testUnknownData) {
                final double score = lr.classifyScalar(testCall.asVector());
                System.out.println(" score: " + score + " accuracy " + eval.auc() + " call fields: "
                        + testCall.getFields());
            }
        }
    }
}

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From source file:OpioidePrescriberClassification.Driver.java

public static void main(String args[]) throws Exception {
    List<Opioides> calls = Lists.newArrayList(new Parser("/input1/try.csv"));
    double heldOutPercentage = 0.10;
    //        for (int run = 0; run < 20; run++) 
    {//ww  w.j  a v  a2 s.c om
        //            Random random = RandomUtils.getRandom();
        Collections.shuffle(calls);
        int cutoff = (int) (heldOutPercentage * calls.size());
        List<Opioides> test = calls.subList(0, cutoff);
        List<Opioides> train = calls.subList(cutoff, calls.size());

        OnlineLogisticRegression lr = new OnlineLogisticRegression(NUM_CATEGORIES, Opioides.FEATURES, new L1())
                .learningRate(1).alpha(1).lambda(0.000001).stepOffset(10000).decayExponent(0.2);

        //            for (int pass = 0; pass < 2 ; pass++)
        {
            System.err.println("pass");
            for (Opioides observation : train) {
                lr.train(observation.getTarget(), observation.asVector());
            }
            //                if (pass % 2 == 0) 
            {
                Auc eval = new Auc(0.5);
                for (Opioides testCall : test) {
                    eval.add(testCall.getTarget(), lr.classifyScalar(testCall.asVector()));
                }
                System.out.printf("%d, %.4f, %.4f\n", 1, lr.currentLearningRate(), eval.auc());
            }
        }
    }
}

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