概率预测与lilinear (java)，与直接使用分类器内部的代码

Question

考虑以下线性(http://liblinear.bwaldvogel.de/):

)的用法

    double C = 1.0; // cost of constraints violation
    double eps = 0.01; // stopping criteria
    Parameter param = new Parameter(SolverType.L2R_L2LOSS_SVC, C, eps);
    Problem problem = new Problem();
    double[] GROUPS_ARRAY = {1, 0, 0, 0};
    problem.y = GROUPS_ARRAY;
    int NUM_OF_TS_EXAMPLES = 4;
    problem.l = NUM_OF_TS_EXAMPLES;
     problem.n = 2;
    FeatureNode[] instance1 = { new FeatureNode(1, 1), new FeatureNode(2, 1) };
    FeatureNode[] instance2 = { new FeatureNode(1, -1), new FeatureNode(2, 1) };
    FeatureNode[] instance3 = { new FeatureNode(1, -1), new FeatureNode(2, -1) };
    FeatureNode[] instance4 = { new FeatureNode(1, 1), new FeatureNode(2, -1) };
    FeatureNode[] instance5 = { new FeatureNode(1, 1), new FeatureNode(2, -0.1) };
    FeatureNode[] instance6 = { new FeatureNode(1, -0.1), new FeatureNode(2, 1) };
    FeatureNode[] instance7 = { new FeatureNode(1, -0.1), new FeatureNode(2, -0.1) };
    FeatureNode[][] testSetWithUnknown = {
            instance5,
            instance6, 
            instance7
        };
    FeatureNode[][] trainingSetWithUnknown = {
            instance1,
            instance2, 
            instance3, 
            instance4
        };
    problem.x = trainingSetWithUnknown;
    Model m = Linear.train(problem, param); 
    for( int i = 0; i < trainingSetWithUnknown.length; i++)
        System.out.println(" Train.instance =  " + i + " =>  " + Linear.predict(m, trainingSetWithUnknown[i]) ); 
    System.out.println("---------------------"); 
    for( int i = 0; i < testSetWithUnknown.length; i++)
        System.out.println(" Test.instance =  " + i + " =>  " + Linear.predict(m, testSetWithUnknown[i]) ); 

输出如下:

iter  1 act 1.778e+00 pre 1.778e+00 delta 6.285e-01 f 4.000e+00 |g| 5.657e+00 CG   1
 Train.instance =  0 =>  1.0
 Train.instance =  1 =>  0.0
 Train.instance =  2 =>  0.0
 Train.instance =  3 =>  0.0
---------------------
 Test.instance =  0 =>  1.0
 Test.instance =  1 =>  1.0
 Test.instance =  2 =>  0.0

我需要的不是整数(硬)预测，而是概率预测。有一个命令行选项-b，但我找不到在代码中直接使用该函数的任何内容。此外，查看代码内部(https://github.com/bwaldvogel/liblinear-java/blob/master/src/main/java/de/bwaldvogel/liblinear/Predict.java);显然，在代码中没有直接使用概率预测的选项。对吗?

更新:我最终使用线性代码形式https://github.com/bwaldvogel/liblinear-java。在Predict.java文件中，我更改了

private static boolean       flag_predict_probability = true;

private static boolean       flag_predict_probability = false;

和used

SolverType.L2R_LR

但是仍然得到整数类。任何想法?

Answer 1

要使用概率需要更改代码。预测是在

中进行的。

public static double predictValues(Model Model, Feature[] x, double[] dec_values) {

line .java文件中的函数:

    if (model.nr_class == 2) {
        System.out.println("Two classes "); 
        if (model.solverType.isSupportVectorRegression()) { 
            System.out.println("Support vector");
            return dec_values[0];
        }
        else { 
            System.out.println("Not Support vector");
            return (dec_values[0] > 0) ? model.label[0] : model.label[1];
        }
    } 

需要改成

    if (model.nr_class == 2) {
        System.out.println("Two classes "); 
        if (model.solverType.isSupportVectorRegression()) { 
            System.out.println("Support vector");
            return dec_values[0];
        }
        else { 
            System.out.println("Not Support vector");
            return dec_values[0]; 
        }    
    } 

请注意，输出仍然不是概率，而是权重和特征值的线性组合。如果你把它赋给softmax函数它就会变成[0,1]内的概率。

同时，确保选择逻辑回归:

     SolverType.L2R_LR