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

Introduction

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

Prototype

DenseMatrix entropy

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Usage

From source file:com.ml.ira.algos.RunLogistic.java

License:Apache License

static void mainToOutput(String[] args, PrintWriter output) throws Exception {
    if (parseArgs(args)) {
        if (!showAuc && !showConfusion && !showScores) {
            showAuc = true;/* w  ww . j  a v  a 2 s .c  om*/
            showConfusion = true;
        }

        Auc collector = new Auc();
        LogisticModelParameters lmp;
        if (modelFile.startsWith("hdfs://")) {
            lmp = LogisticModelParameters.loadFrom(new Path(modelFile));
        } else {
            lmp = LogisticModelParameters.loadFrom(new File(modelFile));
        }
        CsvRecordFactory csv = lmp.getCsvRecordFactory();
        OnlineLogisticRegression lr = lmp.createRegression();
        BufferedReader in = TrainLogistic.open(inputFile);
        //String line = in.readLine();
        //csv.firstLine(line);
        String line;
        if (fieldNames != null && fieldNames.equalsIgnoreCase("internal")) {
            csv.firstLine(lmp.getFieldNames());
        } else {
            csv.firstLine(in.readLine());
        }
        line = in.readLine();
        if (showScores) {
            output.println("\"target\",\"model-output\",\"log-likelihood\"");
        }
        while (line != null) {
            Vector v = new SequentialAccessSparseVector(lmp.getNumFeatures());
            int target = csv.processLine(line, v);

            double score = lr.classifyScalar(v);
            if (showScores) {
                output.printf(Locale.ENGLISH, "%d,%.3f,%.6f%n", target, score, lr.logLikelihood(target, v));
            }
            collector.add(target, score);
            line = in.readLine();
        }

        if (showAuc) {
            output.printf(Locale.ENGLISH, "AUC = %.2f%n", collector.auc());
        }
        if (showConfusion) {
            Matrix m = collector.confusion();
            output.printf(Locale.ENGLISH, "confusion: [[%.1f, %.1f], [%.1f, %.1f]]%n", m.get(0, 0), m.get(1, 0),
                    m.get(0, 1), m.get(1, 1));
            m = collector.entropy();
            output.printf(Locale.ENGLISH, "entropy: [[%.1f, %.1f], [%.1f, %.1f]]%n", m.get(0, 0), m.get(1, 0),
                    m.get(0, 1), m.get(1, 1));
        }
    }
}

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From source file:haflow.component.mahout.logistic.RunLogistic.java

License:Apache License

static void mainToOutput(String[] args) throws Exception {
    if (parseArgs(args)) {
        if (!showAuc && !showConfusion && !showScores) {
            showAuc = true;/*from   w ww.  j  av  a2 s  . c  o m*/
            showConfusion = true;
        }

        //PrintWriter output=new PrintWriter(new FileOutputStream(outputFile),true);

        PrintWriter output = new PrintWriter(HdfsUtil.writeHdfs(outputFile), true);
        PrintWriter acc_output = new PrintWriter(HdfsUtil.writeHdfs(accurateFile), true);
        Auc collector = new Auc();
        LogisticModelParameters lmp = LogisticModelParameters.loadFrom(HdfsUtil.open(modelFile));

        CsvRecordFactory csv = lmp.getCsvRecordFactory();
        OnlineLogisticRegression lr = lmp.createRegression();
        BufferedReader in = new BufferedReader(new InputStreamReader(HdfsUtil.open(inputFile)));
        String line = in.readLine();
        csv.firstLine(line);
        line = in.readLine();
        if (showScores) {
            output.println("\"target\",\"model-output\",\"log-likelihood\"");
        }
        while (line != null) {
            Vector v = new SequentialAccessSparseVector(lmp.getNumFeatures());
            int target = csv.processLine(line, v);

            double score = lr.classifyScalar(v);
            if (showScores) {
                output.printf(Locale.ENGLISH, "%d,%.3f,%.6f%n", target, score, lr.logLikelihood(target, v));
            }
            collector.add(target, score);
            line = in.readLine();
        }

        if (showAuc) {
            acc_output.printf(Locale.ENGLISH, "AUC , %.2f%n", collector.auc());
        }
        if (showConfusion) {
            Matrix m = collector.confusion();
            acc_output.printf(Locale.ENGLISH, "confusion, [[%.1f  %.1f], [%.1f  %.1f]]%n", m.get(0, 0),
                    m.get(1, 0), m.get(0, 1), m.get(1, 1));
            m = collector.entropy();
            acc_output.printf(Locale.ENGLISH, "entropy, [[%.1f  %.1f], [%.1f  %.1f]]%n", m.get(0, 0),
                    m.get(1, 0), m.get(0, 1), m.get(1, 1));
        }
        output.close();
        acc_output.close();
    }
}

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