Added a bunch of unit tests for the various forms of structured svm.

dlib/test/CMakeLists.txt

@@ -101,6 +101,7 @@ set (tests
    svm_c_linear.cpp
    svm.cpp
    svm_multiclass_linear.cpp
+   svm_struct.cpp
    symmetric_matrix_cache.cpp
    thread_pool.cpp
    threads.cpp

dlib/test/makefile

@@ -111,6 +111,7 @@ SRC += string.cpp
 SRC += svm_c_linear.cpp
 SRC += svm.cpp
 SRC += svm_multiclass_linear.cpp
+SRC += svm_struct.cpp
 SRC += symmetric_matrix_cache.cpp
 SRC += thread_pool.cpp
 SRC += threads.cpp

dlib/test/svm_struct.cpp

+// Copyright (C) 2011  Davis E. King (davis@dlib.net)
+// License: Boost Software License   See LICENSE.txt for the full license.
+
+#include <sstream>
+#include <string>
+#include <cstdlib>
+#include <ctime>
+#include <dlib/svm_threaded.h>
+
+#include "tester.h"
+
+namespace  
+{
+    using namespace test;
+    using namespace dlib;
+    using namespace std;
+
+    logger dlog("test.svm_struct");
+
+
+    template <
+        typename matrix_type,
+        typename sample_type,
+        typename label_type
+        >
+    class test_multiclass_svm_problem : public structural_svm_problem_threaded<matrix_type,
+                                                                 std::vector<std::pair<unsigned long,typename matrix_type::type> > > 
+    {
+
+    public:
+        typedef typename matrix_type::type scalar_type;
+        typedef std::vector<std::pair<unsigned long,scalar_type> > feature_vector_type;
+
+        test_multiclass_svm_problem (
+            const std::vector<sample_type>& samples_,
+            const std::vector<label_type>& labels_
+        ) :
+            structural_svm_problem_threaded<matrix_type,
+                std::vector<std::pair<unsigned long,typename matrix_type::type> > >(2),
+            samples(samples_),
+            labels(labels_),
+            dims(10+1) // +1 for the bias
+        {
+            for (int i = 0; i < 10; ++i)
+            {
+                distinct_labels.push_back(i);
+            }
+        }
+
+        virtual long get_num_dimensions (
+        ) const
+        {
+            return dims*10;
+        }
+
+        virtual long get_num_samples (
+        ) const 
+        {
+            return static_cast<long>(samples.size());
+        }
+
+        virtual void get_truth_joint_feature_vector (
+            long idx,
+            feature_vector_type& psi
+        ) const 
+        {
+            sparse_vector::assign(psi, samples[idx]);
+            // Add a constant -1 to account for the bias term.
+            psi.push_back(std::make_pair(dims-1,static_cast<scalar_type>(-1)));
+
+            // Find which distinct label goes with this psi.
+            const long label_idx = index_of_max(vector_to_matrix(distinct_labels) == labels[idx]);
+
+            offset_feature_vector(psi, dims*label_idx);
+        }
+
+        virtual void separation_oracle (
+            const long idx,
+            const matrix_type& current_solution,
+            scalar_type& loss,
+            feature_vector_type& psi
+        ) const 
+        {
+            scalar_type best_val = -std::numeric_limits<scalar_type>::infinity();
+            unsigned long best_idx = 0;
+
+            // Figure out which label is the best.  That is, what label maximizes
+            // LOSS(idx,y) + F(x,y).  Note that y in this case is given by distinct_labels[i].
+            for (unsigned long i = 0; i < distinct_labels.size(); ++i)
+            {
+                using dlib::sparse_vector::dot;
+                using dlib::dot;
+                // Compute the F(x,y) part:
+                // perform: temp == dot(relevant part of current solution, samples[idx]) - current_bias
+                scalar_type temp = dot(rowm(current_solution, range(i*dims, (i+1)*dims-2)), samples[idx]) - current_solution((i+1)*dims-1);
+
+                // Add the LOSS(idx,y) part:
+                if (labels[idx] != distinct_labels[i])
+                    temp += 1;
+
+                // Now temp == LOSS(idx,y) + F(x,y).  Check if it is the biggest we have seen.
+                if (temp > best_val)
+                {
+                    best_val = temp;
+                    best_idx = i;
+                }
+            }
+
+            sparse_vector::assign(psi, samples[idx]);
+            // add a constant -1 to account for the bias term
+            psi.push_back(std::make_pair(dims-1,static_cast<scalar_type>(-1)));
+
+            offset_feature_vector(psi, dims*best_idx);
+
+            if (distinct_labels[best_idx] == labels[idx])
+                loss = 0;
+            else
+                loss = 1;
+        }
+
+    private:
+
+        void offset_feature_vector (
+            feature_vector_type& sample,
+            const unsigned long val
+        ) const
+        {
+            if (val != 0)
+            {
+                for (typename feature_vector_type::iterator i = sample.begin(); i != sample.end(); ++i)
+                {
+                    i->first += val;
+                }
+            }
+        }
+
+
+        const std::vector<sample_type>& samples;
+        const std::vector<label_type>& labels;
+        std::vector<label_type> distinct_labels;
+        const long dims;
+    };
+
+// ----------------------------------------------------------------------------------------
+
+    template <
+        typename K,
+        typename label_type_ = typename K::scalar_type 
+        >
+    class test_svm_multiclass_linear_trainer2
+    {
+    public:
+        typedef label_type_ label_type;
+        typedef K kernel_type;
+        typedef typename kernel_type::scalar_type scalar_type;
+        typedef typename kernel_type::sample_type sample_type;
+        typedef typename kernel_type::mem_manager_type mem_manager_type;
+
+        typedef multiclass_linear_decision_function<kernel_type, label_type> trained_function_type;
+
+
+        test_svm_multiclass_linear_trainer2 (
+        ) :
+            C(10),
+            eps(1e-4),
+            verbose(false)
+        {
+        }
+
+        trained_function_type train (
+            const std::vector<sample_type>& all_samples,
+            const std::vector<label_type>& all_labels
+        ) const
+        {
+            scalar_type svm_objective = 0;
+            return train(all_samples, all_labels, svm_objective);
+        }
+
+        trained_function_type train (
+            const std::vector<sample_type>& all_samples,
+            const std::vector<label_type>& all_labels,
+            scalar_type& svm_objective
+        ) const
+        {
+            // make sure requires clause is not broken
+            DLIB_ASSERT(is_learning_problem(all_samples,all_labels),
+                "\t trained_function_type test_svm_multiclass_linear_trainer2::train(all_samples,all_labels)"
+                << "\n\t invalid inputs were given to this function"
+                << "\n\t all_samples.size():     " << all_samples.size() 
+                << "\n\t all_labels.size():      " << all_labels.size() 
+                );
+
+            typedef matrix<scalar_type,0,1> w_type;
+            w_type weights;
+            std::vector<sample_type> samples1(all_samples.begin(), all_samples.begin()+all_samples.size()/2);
+            std::vector<sample_type> samples2(all_samples.begin()+all_samples.size()/2, all_samples.end());
+
+            std::vector<label_type> labels1(all_labels.begin(), all_labels.begin()+all_labels.size()/2);
+            std::vector<label_type> labels2(all_labels.begin()+all_labels.size()/2, all_labels.end());
+            test_multiclass_svm_problem<w_type, sample_type, label_type> problem1(samples1, labels1);
+            test_multiclass_svm_problem<w_type, sample_type, label_type> problem2(samples2, labels2);
+            problem1.set_max_cache_size(3);
+            problem2.set_max_cache_size(0);
+
+            svm_struct_processing_node node1(problem1, 12345, 3);
+            svm_struct_processing_node node2(problem2, 12346, 0);
+
+            solver.set_inactive_plane_threshold(50);
+            solver.set_subproblem_epsilon(1e-4);
+
+            svm_struct_controller_node controller;
+            controller.set_c(C);
+            controller.set_epsilon(eps);
+            if (verbose)
+                controller.be_verbose();
+            controller.add_processing_node("127.0.0.1", 12345);
+            controller.add_processing_node("127.0.0.1", 12346);
+            svm_objective = controller(solver, weights);
+
+
+
+            trained_function_type df;
+
+            const long dims = sparse_vector::max_index_plus_one(all_samples);
+            df.labels  = select_all_distinct_labels(all_labels);
+            df.weights = colm(reshape(weights, df.labels.size(), dims+1), range(0,dims-1));
+            df.b       = colm(reshape(weights, df.labels.size(), dims+1), dims);
+            return df;
+        }
+
+    private:
+        scalar_type C;
+        scalar_type eps;
+        bool verbose;
+        mutable oca solver;
+    };
+
+// ----------------------------------------------------------------------------------------
+
+    template <
+        typename K,
+        typename label_type_ = typename K::scalar_type 
+        >
+    class test_svm_multiclass_linear_trainer3
+    {
+    public:
+        typedef label_type_ label_type;
+        typedef K kernel_type;
+        typedef typename kernel_type::scalar_type scalar_type;
+        typedef typename kernel_type::sample_type sample_type;
+        typedef typename kernel_type::mem_manager_type mem_manager_type;
+
+        typedef multiclass_linear_decision_function<kernel_type, label_type> trained_function_type;
+
+
+        test_svm_multiclass_linear_trainer3 (
+        ) :
+            C(10),
+            eps(1e-4),
+            verbose(false)
+        {
+        }
+
+        trained_function_type train (
+            const std::vector<sample_type>& all_samples,
+            const std::vector<label_type>& all_labels
+        ) const
+        {
+            scalar_type svm_objective = 0;
+            return train(all_samples, all_labels, svm_objective);
+        }
+
+        trained_function_type train (
+            const std::vector<sample_type>& all_samples,
+            const std::vector<label_type>& all_labels,
+            scalar_type& svm_objective
+        ) const
+        {
+            // make sure requires clause is not broken
+            DLIB_ASSERT(is_learning_problem(all_samples,all_labels),
+                "\t trained_function_type test_svm_multiclass_linear_trainer3::train(all_samples,all_labels)"
+                << "\n\t invalid inputs were given to this function"
+                << "\n\t all_samples.size():     " << all_samples.size() 
+                << "\n\t all_labels.size():      " << all_labels.size() 
+                );
+
+            typedef matrix<scalar_type,0,1> w_type;
+            w_type weights;
+            test_multiclass_svm_problem<w_type, sample_type, label_type> problem(all_samples, all_labels);
+            problem.set_max_cache_size(0);
+
+            problem.set_c(C);
+            problem.set_epsilon(eps);
+
+            if (verbose)
+                problem.be_verbose();
+            
+            solver.set_inactive_plane_threshold(50);
+            solver.set_subproblem_epsilon(1e-4);
+            svm_objective = solver(problem, weights);
+
+
+            trained_function_type df;
+
+            const long dims = sparse_vector::max_index_plus_one(all_samples);
+            df.labels  = select_all_distinct_labels(all_labels);
+            df.weights = colm(reshape(weights, df.labels.size(), dims+1), range(0,dims-1));
+            df.b       = colm(reshape(weights, df.labels.size(), dims+1), dims);
+            return df;
+        }
+
+    private:
+        scalar_type C;
+        scalar_type eps;
+        bool verbose;
+        mutable oca solver;
+    };
+
+// ----------------------------------------------------------------------------------------
+
+    template <
+        typename K,
+        typename label_type_ = typename K::scalar_type 
+        >
+    class test_svm_multiclass_linear_trainer4
+    {
+    public:
+        typedef label_type_ label_type;
+        typedef K kernel_type;
+        typedef typename kernel_type::scalar_type scalar_type;
+        typedef typename kernel_type::sample_type sample_type;
+        typedef typename kernel_type::mem_manager_type mem_manager_type;
+
+        typedef multiclass_linear_decision_function<kernel_type, label_type> trained_function_type;
+
+
+        test_svm_multiclass_linear_trainer4 (
+        ) :
+            C(10),
+            eps(1e-4),
+            verbose(false)
+        {
+        }
+
+        trained_function_type train (
+            const std::vector<sample_type>& all_samples,
+            const std::vector<label_type>& all_labels
+        ) const
+        {
+            scalar_type svm_objective = 0;
+            return train(all_samples, all_labels, svm_objective);
+        }
+
+        trained_function_type train (
+            const std::vector<sample_type>& all_samples,
+            const std::vector<label_type>& all_labels,
+            scalar_type& svm_objective
+        ) const
+        {
+            // make sure requires clause is not broken
+            DLIB_ASSERT(is_learning_problem(all_samples,all_labels),
+                "\t trained_function_type test_svm_multiclass_linear_trainer4::train(all_samples,all_labels)"
+                << "\n\t invalid inputs were given to this function"
+                << "\n\t all_samples.size():     " << all_samples.size() 
+                << "\n\t all_labels.size():      " << all_labels.size() 
+                );
+
+            typedef matrix<scalar_type,0,1> w_type;
+            w_type weights;
+            test_multiclass_svm_problem<w_type, sample_type, label_type> problem(all_samples, all_labels);
+            problem.set_max_cache_size(3);
+
+            problem.set_c(C);
+            problem.set_epsilon(eps);
+
+            if (verbose)
+                problem.be_verbose();
+            
+            solver.set_inactive_plane_threshold(50);
+            solver.set_subproblem_epsilon(1e-4);
+            svm_objective = solver(problem, weights);
+
+
+            trained_function_type df;
+
+            const long dims = sparse_vector::max_index_plus_one(all_samples);
+            df.labels  = select_all_distinct_labels(all_labels);
+            df.weights = colm(reshape(weights, df.labels.size(), dims+1), range(0,dims-1));
+            df.b       = colm(reshape(weights, df.labels.size(), dims+1), dims);
+            return df;
+        }
+
+    private:
+        scalar_type C;
+        scalar_type eps;
+        bool verbose;
+        mutable oca solver;
+    };
+
+// ----------------------------------------------------------------------------------------
+
+    template <
+        typename K,
+        typename label_type_ = typename K::scalar_type 
+        >
+    class test_svm_multiclass_linear_trainer5
+    {
+    public:
+        typedef label_type_ label_type;
+        typedef K kernel_type;
+        typedef typename kernel_type::scalar_type scalar_type;
+        typedef typename kernel_type::sample_type sample_type;
+        typedef typename kernel_type::mem_manager_type mem_manager_type;
+
+        typedef multiclass_linear_decision_function<kernel_type, label_type> trained_function_type;
+
+
+        test_svm_multiclass_linear_trainer5 (
+        ) :
+            C(10),
+            eps(1e-4),
+            verbose(false)
+        {
+        }
+
+        trained_function_type train (
+            const std::vector<sample_type>& all_samples,
+            const std::vector<label_type>& all_labels
+        ) const
+        {
+            scalar_type svm_objective = 0;
+            return train(all_samples, all_labels, svm_objective);
+        }
+
+        trained_function_type train (
+            const std::vector<sample_type>& all_samples,
+            const std::vector<label_type>& all_labels,
+            scalar_type& svm_objective
+        ) const
+        {
+            // make sure requires clause is not broken
+            DLIB_ASSERT(is_learning_problem(all_samples,all_labels),
+                "\t trained_function_type test_svm_multiclass_linear_trainer5::train(all_samples,all_labels)"
+                << "\n\t invalid inputs were given to this function"
+                << "\n\t all_samples.size():     " << all_samples.size() 
+                << "\n\t all_labels.size():      " << all_labels.size() 
+                );
+
+            typedef matrix<scalar_type,0,1> w_type;
+            w_type weights;
+            multiclass_svm_problem<w_type, sample_type, label_type> problem(all_samples, all_labels);
+            problem.set_max_cache_size(3);
+
+            problem.set_c(C);
+            problem.set_epsilon(eps);
+
+            if (verbose)
+                problem.be_verbose();
+            
+            solver.set_inactive_plane_threshold(50);
+            solver.set_subproblem_epsilon(1e-4);
+            svm_objective = solver(problem, weights);
+
+
+            trained_function_type df;
+
+            const long dims = sparse_vector::max_index_plus_one(all_samples);
+            df.labels  = select_all_distinct_labels(all_labels);
+            df.weights = colm(reshape(weights, df.labels.size(), dims+1), range(0,dims-1));
+            df.b       = colm(reshape(weights, df.labels.size(), dims+1), dims);
+            return df;
+        }
+
+    private:
+        scalar_type C;
+        scalar_type eps;
+        bool verbose;
+        mutable oca solver;
+    };
+
+
+// ----------------------------------------------------------------------------------------
+
+    typedef matrix<double,10,1> sample_type;
+    typedef double scalar_type;
+
+    void make_dataset (
+        std::vector<sample_type>& samples,
+        std::vector<scalar_type>& labels
+    )
+    {
+        samples.clear();
+        labels.clear();
+        dlib::rand rnd;
+        for (int i = 0; i < 10; ++i)
+        {
+            for (int j = 0; j < 100; ++j)
+            {
+                sample_type samp;
+                samp = 0;
+                samp(i) = 10*rnd.get_random_double()+1;
+
+                samples.push_back(samp);
+                labels.push_back(i);
+            }
+        }
+    }
+
+// ----------------------------------------------------------------------------------------
+
+    class test_svm_struct : public tester
+    {
+    public:
+        test_svm_struct (
+        ) :
+            tester ("test_svm_struct",
+                    "Runs tests on the structural svm components.")
+        {}
+
+        void perform_test (
+        )
+        {
+            typedef linear_kernel<sample_type> kernel_type;
+            svm_multiclass_linear_trainer<kernel_type> trainer1;
+            test_svm_multiclass_linear_trainer2<kernel_type> trainer2;
+            test_svm_multiclass_linear_trainer3<kernel_type> trainer3;
+            test_svm_multiclass_linear_trainer4<kernel_type> trainer4;
+            test_svm_multiclass_linear_trainer5<kernel_type> trainer5;
+
+            trainer1.set_epsilon(1e-4);
+            trainer1.set_c(10);
+
+            std::vector<sample_type> samples;
+            std::vector<scalar_type> labels;
+            make_dataset(samples, labels);
+
+
+            multiclass_linear_decision_function<kernel_type,double> df1, df2, df3, df4, df5;
+            double obj1, obj2, obj3, obj4, obj5;
+
+            // Solve a multiclass SVM a whole bunch of different ways and make sure
+            // they all give the same answer.
+            print_spinner();
+            df1 = trainer1.train(samples, labels, obj1);
+            print_spinner();
+            df2 = trainer2.train(samples, labels, obj2);
+            print_spinner();
+            df3 = trainer3.train(samples, labels, obj3);
+            print_spinner();
+            df4 = trainer4.train(samples, labels, obj4);
+            print_spinner();
+            df5 = trainer5.train(samples, labels, obj5);
+            print_spinner();
+
+            dlog << LINFO << "obj1: "<< obj1;
+            dlog << LINFO << "obj2: "<< obj2;
+            dlog << LINFO << "obj3: "<< obj3;
+            dlog << LINFO << "obj4: "<< obj4;
+            dlog << LINFO << "obj5: "<< obj5;
+            DLIB_TEST(std::abs(obj1 - obj2) < 1e-2);
+            DLIB_TEST(std::abs(obj1 - obj3) < 1e-2);
+            DLIB_TEST(std::abs(obj1 - obj4) < 1e-2);
+            DLIB_TEST(std::abs(obj1 - obj5) < 1e-2);
+
+            dlog << LINFO << "weight error: "<< max(abs(df1.weights - df2.weights));
+            dlog << LINFO << "weight error: "<< max(abs(df1.weights - df3.weights));
+            dlog << LINFO << "weight error: "<< max(abs(df1.weights - df4.weights));
+            dlog << LINFO << "weight error: "<< max(abs(df1.weights - df5.weights));
+
+            DLIB_TEST(max(abs(df1.weights - df2.weights)) < 1e-2);
+            DLIB_TEST(max(abs(df1.weights - df3.weights)) < 1e-2);
+            DLIB_TEST(max(abs(df1.weights - df4.weights)) < 1e-2);
+            DLIB_TEST(max(abs(df1.weights - df5.weights)) < 1e-2);
+
+            dlog << LINFO << "b error: "<< max(abs(df1.b - df2.b));
+            dlog << LINFO << "b error: "<< max(abs(df1.b - df3.b));
+            dlog << LINFO << "b error: "<< max(abs(df1.b - df4.b));
+            dlog << LINFO << "b error: "<< max(abs(df1.b - df5.b));
+            DLIB_TEST(max(abs(df1.b - df2.b)) < 1e-2);
+            DLIB_TEST(max(abs(df1.b - df3.b)) < 1e-2);
+            DLIB_TEST(max(abs(df1.b - df4.b)) < 1e-2);
+            DLIB_TEST(max(abs(df1.b - df5.b)) < 1e-2);
+
+            matrix<double> res = test_multiclass_decision_function(df1, samples, labels);
+            dlog << LINFO << res;
+            dlog << LINFO << "accuracy: " << sum(diag(res))/sum(res);
+            DLIB_TEST(sum(diag(res)) == 1000);
+
+            res = test_multiclass_decision_function(df2, samples, labels);
+            dlog << LINFO << res;
+            dlog << LINFO << "accuracy: " << sum(diag(res))/sum(res);
+            DLIB_TEST(sum(diag(res)) == 1000);
+
+            res = test_multiclass_decision_function(df3, samples, labels);
+            dlog << LINFO << res;
+            dlog << LINFO << "accuracy: " << sum(diag(res))/sum(res);
+            DLIB_TEST(sum(diag(res)) == 1000);
+
+            res = test_multiclass_decision_function(df4, samples, labels);
+            dlog << LINFO << res;
+            dlog << LINFO << "accuracy: " << sum(diag(res))/sum(res);
+            DLIB_TEST(sum(diag(res)) == 1000);
+
+            res = test_multiclass_decision_function(df5, samples, labels);
+            dlog << LINFO << res;
+            dlog << LINFO << "accuracy: " << sum(diag(res))/sum(res);
+            DLIB_TEST(sum(diag(res)) == 1000);
+        }
+    } a;
+
+
+
+}
+
+
+
+