LIBLINEAR FAQ

Some questions are listed in LIBSVM FAQ.

Table of Contents

Please check our explanation on the LIBLINEAR webpage. Also see appendix C of our SVM guide.

Please see the descriptions at LIBLINEAR page.

Please cite the following paper:

R.-E. Fan, K.-W. Chang, C.-J. Hsieh, X.-R. Wang, and C.-J. Lin. LIBLINEAR: A Library for Large Linear Classification, Journal of Machine Learning Research 9(2008), 1871-1874. Software available at http://www.csie.ntu.edu.tw/~cjlin/liblinear

The bibtex format is

@Article{REF08a,
  author = 	 {Rong-En Fan and Kai-Wei Chang and Cho-Jui Hsieh and Xiang-Rui Wang and Chih-Jen Lin},
  title = 	 {{LIBLINEAR}: A Library for Large Linear Classification},
  journal = 	 {Journal of Machine Learning Research},
  year = 	 {2008},
  volume =	 {9},
  pages =	 {1871--1874}
}

See the change log and directory for earlier/current versions.

Generally we recommend linear SVM as its training is faster and the accuracy is competitive. However, if you would like to have probability outputs, you may consider logistic regression.

Moreover, try L2 regularization first unless you need a sparse model. For most cases, L1 regularization does not give higher accuracy but may be slightly slower in training.

Among L2-regularized SVM solvers, try the default one (L2-loss SVC dual) first. If it is too slow, use the option -s 2 to solve the primal problem.

Please check this page

[rcv1_test_labels,rcv1_test_inst] = libsvmread('../rcv1_test.binary');
save rcv1_test.mat rcv1_test_labels rcv1_test_inst;

save -mat7-binary rcv1_test.mat rcv1_test_labels rcv1_test_inst;

load rcv1_test.mat

help save

-s 2

They should be very similar. However, sometimes the difference may not be small. Note that LIBLINEAR does not use the bias term b by default. If you observe very different results, try to set -B 1 for LIBLINEAR. This will add the bias term to the loss function as well as the regularization term (w^Tw + b^2). Then, results should be closer.

To make results exactly the same as LIBSVM, you can

• modify the primal-based solver for L2-loss SVC; see the FAQ below
• modify LIBSVM to solve L2-loss SVC; see LIBSVM FAQ: "I would like to solve L2-loss SVM (i.e., error term is quadratic). How should I modify the code ?"

For some multi-class data, the difference between LIBSVM and LIBLINEAR may be significant. The reason is that LIBSVM uses the 1-vs-1 strategy, while LIBLINEAR uses 1-vs-the rest.

Take L2-regularized L2-loss SVC as an example. If -B 1 is specified, LIBLINEAR solves

min_{w,b} w^Tw/2 + b^2/2 + C \sum max(0, 1- (y_i w^Tx_i+b))^2.

Now we would like to solve

min_{w,b} w^Tw/2 + C \sum max(0, 1- (y_i w^Tx_i+b))^2.

It's difficult to modify dual-based solvers for the above problem. However, primal-based solvers can be easily changed by modifying function evaluation, gradient evaluation, and Hessian-vector products. First, in l2r_l2_svc_fun::fun for function evaluation, modify

	for(i=0;i<w_size;i++)
		f += w[i]*w[i];

	for(i=0;i<w_size-1;i++)
		f += w[i]*w[i];

	for(i=0;i<w_size;i++)
		g[i] = w[i] + g[i];

	g[w_size-1] -= w[w_size-1];

	for(i=0;i<w_size;i++)
		Hs[i] = s[i] + 2*Hs[i];

	Hs[w_size-1] -= s[w_size-1];

For L2-regularized logistic regression, the modification is exactly the same.

For L2-regularized L2-loss SVR, the modification for function and gradient evaluation is the same. However, its Hessian-vector product is by the code of SVC through inheritance. Therefore, you need to modify l2r_l2_svc_fun::Hv.

This FAQ is prepared by Pin-Yen Lin.

After version 2.0, an option -C is provided to find C. For example, you can run

> train -C data_file  

The -C option is available for classification only at this moment. For regression, you can use gridregression.py from libsvm tools. Several options must be specified.

1. `-svmtrain train': use the command `train' of LIBLINEAR
2. `-log2g null': do not grid with `g'

> python gridregression.py -log2c -3,0,1 -log2g null -log2p -1,0,1 -svmtrain ./train -s 11 heart_scale

We guess that you are comparing

> time ./train -s 0 -v 5 -e 0.001 data

1. The above timeing of LIBLINEAR includes time for reading data, but in the paper we exclude that part.
2. In the paper, to conduct 5-fold (or 2-fold) CV we group folds used for training as a separate matrix, but LIBLINEAR simply uses pointers of the corresponding instances. Therefore, in doing matrix-vector multiplications, the former sequentially uses rows in a continuous segment of the memory, but the latter does not. Thus, LIBLINEAR may be slower but it saves the memory.

We carefully studied such issues, and decided to use the current setting. For data classification, one doesn't need very accurate solution, so numerical issues are less important. Moreover, log1p is not available on all platforms. Please let us know if you observe any numerical problems.

Assume k is the total number of classes and n is the number of features. In the model file, after the parameters, there is an n*k matrix W, whose columns are obtained from solving two-class problems: 1 vs rest, 2 vs rest, 3 vs rest, ...., k vs rest. For example, if there are 4 classes, the file looks like:

+-------+-------+-------+-------+
| w_1vR | w_2vR | w_3vR | w_4vR |
+-------+-------+-------+-------+

Please see the answer in LIBSVM faq.

To correctly obtain decision values, you need to check the array

label

LIBSVM uses more advanced techniques for SVM probability outputs. The code is a bit complicated so we haven't decided if including it is suitable or not.

If you really would like to have probability outputs for SVM in LIBLINEAR, you can consider using the simple probability model of logistic regression. Simply modify the following subrutine in linear.cpp.

int check_probability_model(const struct model *model_)
{
	return (model_->param.solver_type==L2R_LR ||

int check_probability_model(const struct model *model_)
{
	return 1;

Some LIBLINEAR solvers consider the primal problem, so support vectors are not obtained during the training procedure. For dual solvers, we output only the primal weight vector w, so support vectors are not stored in the model. This is different from LIBSVM.

To know support vectors, you can modify the following loop in solve_l2r_l1l2_svc() of linear.cpp to print out indices:

	for(i=0; i<l; i++)
	{
		v += alpha[i]*(alpha[i]*diag[GETI(i)] - 2);
		if(alpha[i] > 0)
			++nSV;
	}

Please see multi-core LIBLINEAR page for details. This extension can dramatically reduce the running time on a shared-memory system.

This FAQ is for solvers. For multiclass classification, please check How to speedup multiclass classification using OpenMP instead.

Please take the following steps. Note that it works only for -s 0, 1, 2, 3, 5, 6, 7.

In Makefile, add -fopenmp to CFLAGS.

In linear.cpp, replace the following segment of code

				model_->w=Malloc(double, w_size*nr_class);
				double *w=Malloc(double, w_size);
				for(i=0;i<nr_class;i++)
				{
					int si = start[i];
					int ei = si+count[i];

					k=0;
					for(; k<si; k++)
						sub_prob.y[k] = -1;
					for(; k<ei; k++)
						sub_prob.y[k] = +1;
					for(; k<sub_prob.l; k++)
						sub_prob.y[k] = -1;

					if(param->init_sol != NULL)
						for(j=0;j<w_size;j++)
							w[j] = param->init_sol[j*nr_class+i];
					else
						for(j=0;j<w_size;j++)
							w[j] = 0;

					train_one(&sub_prob, param, w, weighted_C[i], param->C);

					for(j=0;j<w_size;j++)
						model_->w[j*nr_class+i] = w[j];
				}
				free(w);

				model_->w=Malloc(double, w_size*nr_class);
#pragma omp parallel for private(i, j, k) 
				for(i=0;i<nr_class;i++)
				{
					problem sub_prob_omp;
					sub_prob_omp.l = l;
					sub_prob_omp.n = n;
					sub_prob_omp.x = x;
					sub_prob_omp.y = Malloc(double,l);

					int si = start[i];
					int ei = si+count[i];

					double *w=Malloc(double, w_size);

					k=0;
					for(; k<si; k++)
						sub_prob_omp.y[k] = -1;
					for(; k<ei; k++)
						sub_prob_omp.y[k] = +1;
					for(; k<sub_prob_omp.l; k++)
						sub_prob_omp.y[k] = -1;

					if(param->init_sol != NULL)
						for(j=0;j<w_size;j++)
							w[j] = param->init_sol[j*nr_class+i];
					else
						for(j=0;j<w_size;j++)
							w[j] = 0;

					train_one(&sub_prob_omp, param, w, weighted_C[i], param->C);

					for(j=0;j<w_size;j++)
						model_->w[j*nr_class+i] = w[j];
					free(sub_prob_omp.y);
					free(w);
				}

%export OMP_NUM_THREADS=8
%time ./train -s 2 rcv1_test.multiclass
2m4.019s
%time ./train -s 1 rcv1_test.multiclass
0m45.349s

%time ./train -s 2 rcv1_test.multiclass
6m52.237s
%time ./train -s 1 rcv1_test.multiclass
1m51.739s

If you use solvers -s 0, -s 2, or -s 11, please directly use multi-core LIBLINEAR.

For parameter search (i.e, -C option), which is available for -s 0, 2, 11 only, please also check multi-core LIBLINEAR for details.

For cross validation using other solvers, please modify LIBLINEAR by the following steps.

In Makefile, add -fopenmp to CFLAGS.

In linear.cpp, add a line of code in the cross_validation function:

#pragma omp parallel for private(i) schedule(dynamic)
	for(i=0;i<nr_fold;i++)
	{
		int begin = fold_start[i];
		int end = fold_start[i+1];

We take an example of using 5 threads on the data set rcv1_test.binary. Here we assume Bash is used.

> export OMP_NUM_THREADS=5
> ./train -s 0 -v 5 rcv1_test.binary

cross-validation time (standard LIBLINEAR): 103.24(sec)
cross-validation time (5 threads): 24.99(sec)

Note: It will be useless to assign the number of threads more than the number of CV folds.

Please check this page

    rand()
  

    (rand()*32768+rand())