coco
642 lines
22 KiB
C++
642 lines
22 KiB
C++
// Copyright (C) 2011 Davis E. King (davis@dlib.net)
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// License: Boost Software License See LICENSE.txt for the full license.
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#include <sstream>
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#include <string>
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#include <cstdlib>
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#include <ctime>
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#include <dlib/svm_threaded.h>
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#include "tester.h"
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namespace
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{
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using namespace test;
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using namespace dlib;
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using namespace std;
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logger dlog("test.svm_struct");
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template <
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typename matrix_type,
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typename sample_type,
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typename label_type
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>
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class test_multiclass_svm_problem : public structural_svm_problem_threaded<matrix_type,
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std::vector<std::pair<unsigned long,typename matrix_type::type> > >
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{
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public:
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typedef typename matrix_type::type scalar_type;
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typedef std::vector<std::pair<unsigned long,scalar_type> > feature_vector_type;
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test_multiclass_svm_problem (
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const std::vector<sample_type>& samples_,
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const std::vector<label_type>& labels_
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) :
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structural_svm_problem_threaded<matrix_type,
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std::vector<std::pair<unsigned long,typename matrix_type::type> > >(2),
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samples(samples_),
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labels(labels_),
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dims(10+1) // +1 for the bias
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{
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for (int i = 0; i < 10; ++i)
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{
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distinct_labels.push_back(i);
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}
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}
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virtual long get_num_dimensions (
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) const
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{
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return dims*10;
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}
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virtual long get_num_samples (
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) const
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{
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return static_cast<long>(samples.size());
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}
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virtual void get_truth_joint_feature_vector (
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long idx,
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feature_vector_type& psi
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) const
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{
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assign(psi, samples[idx]);
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// Add a constant -1 to account for the bias term.
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psi.push_back(std::make_pair(dims-1,static_cast<scalar_type>(-1)));
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// Find which distinct label goes with this psi.
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const long label_idx = index_of_max(mat(distinct_labels) == labels[idx]);
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offset_feature_vector(psi, dims*label_idx);
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}
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virtual void separation_oracle (
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const long idx,
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const matrix_type& current_solution,
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scalar_type& loss,
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feature_vector_type& psi
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) const
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{
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scalar_type best_val = -std::numeric_limits<scalar_type>::infinity();
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unsigned long best_idx = 0;
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// Figure out which label is the best. That is, what label maximizes
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// LOSS(idx,y) + F(x,y). Note that y in this case is given by distinct_labels[i].
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for (unsigned long i = 0; i < distinct_labels.size(); ++i)
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{
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// Compute the F(x,y) part:
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// perform: temp == dot(relevant part of current solution, samples[idx]) - current_bias
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scalar_type temp = dot(rowm(current_solution, range(i*dims, (i+1)*dims-2)), samples[idx]) - current_solution((i+1)*dims-1);
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// Add the LOSS(idx,y) part:
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if (labels[idx] != distinct_labels[i])
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temp += 1;
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// Now temp == LOSS(idx,y) + F(x,y). Check if it is the biggest we have seen.
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if (temp > best_val)
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{
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best_val = temp;
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best_idx = i;
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}
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}
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assign(psi, samples[idx]);
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// add a constant -1 to account for the bias term
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psi.push_back(std::make_pair(dims-1,static_cast<scalar_type>(-1)));
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offset_feature_vector(psi, dims*best_idx);
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if (distinct_labels[best_idx] == labels[idx])
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loss = 0;
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else
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loss = 1;
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}
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private:
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void offset_feature_vector (
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feature_vector_type& sample,
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const unsigned long val
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) const
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{
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if (val != 0)
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{
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for (typename feature_vector_type::iterator i = sample.begin(); i != sample.end(); ++i)
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{
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i->first += val;
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}
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}
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}
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const std::vector<sample_type>& samples;
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const std::vector<label_type>& labels;
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std::vector<label_type> distinct_labels;
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const long dims;
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};
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// ----------------------------------------------------------------------------------------
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template <
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typename K,
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typename label_type_ = typename K::scalar_type
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>
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class test_svm_multiclass_linear_trainer2
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{
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public:
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typedef label_type_ label_type;
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typedef K kernel_type;
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typedef typename kernel_type::scalar_type scalar_type;
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typedef typename kernel_type::sample_type sample_type;
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typedef typename kernel_type::mem_manager_type mem_manager_type;
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typedef multiclass_linear_decision_function<kernel_type, label_type> trained_function_type;
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test_svm_multiclass_linear_trainer2 (
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) :
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C(10),
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eps(1e-4),
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verbose(false)
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{
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}
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trained_function_type train (
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const std::vector<sample_type>& all_samples,
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const std::vector<label_type>& all_labels
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) const
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{
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scalar_type svm_objective = 0;
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return train(all_samples, all_labels, svm_objective);
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}
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trained_function_type train (
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const std::vector<sample_type>& all_samples,
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const std::vector<label_type>& all_labels,
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scalar_type& svm_objective
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) const
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{
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// make sure requires clause is not broken
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DLIB_ASSERT(is_learning_problem(all_samples,all_labels),
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"\t trained_function_type test_svm_multiclass_linear_trainer2::train(all_samples,all_labels)"
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<< "\n\t invalid inputs were given to this function"
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<< "\n\t all_samples.size(): " << all_samples.size()
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<< "\n\t all_labels.size(): " << all_labels.size()
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);
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typedef matrix<scalar_type,0,1> w_type;
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w_type weights;
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std::vector<sample_type> samples1(all_samples.begin(), all_samples.begin()+all_samples.size()/2);
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std::vector<sample_type> samples2(all_samples.begin()+all_samples.size()/2, all_samples.end());
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std::vector<label_type> labels1(all_labels.begin(), all_labels.begin()+all_labels.size()/2);
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std::vector<label_type> labels2(all_labels.begin()+all_labels.size()/2, all_labels.end());
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test_multiclass_svm_problem<w_type, sample_type, label_type> problem1(samples1, labels1);
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test_multiclass_svm_problem<w_type, sample_type, label_type> problem2(samples2, labels2);
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problem1.set_max_cache_size(3);
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problem2.set_max_cache_size(0);
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svm_struct_processing_node node1(problem1, 12345, 3);
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svm_struct_processing_node node2(problem2, 12346, 0);
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solver.set_inactive_plane_threshold(50);
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solver.set_subproblem_epsilon(1e-4);
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svm_struct_controller_node controller;
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controller.set_c(C);
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controller.set_epsilon(eps);
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if (verbose)
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controller.be_verbose();
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controller.add_processing_node("127.0.0.1", 12345);
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controller.add_processing_node("localhost:12346");
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svm_objective = controller(solver, weights);
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trained_function_type df;
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const long dims = max_index_plus_one(all_samples);
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df.labels = select_all_distinct_labels(all_labels);
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df.weights = colm(reshape(weights, df.labels.size(), dims+1), range(0,dims-1));
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df.b = colm(reshape(weights, df.labels.size(), dims+1), dims);
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return df;
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}
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private:
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scalar_type C;
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scalar_type eps;
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bool verbose;
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mutable oca solver;
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};
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// ----------------------------------------------------------------------------------------
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template <
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typename K,
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typename label_type_ = typename K::scalar_type
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>
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class test_svm_multiclass_linear_trainer3
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{
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public:
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typedef label_type_ label_type;
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typedef K kernel_type;
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typedef typename kernel_type::scalar_type scalar_type;
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typedef typename kernel_type::sample_type sample_type;
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typedef typename kernel_type::mem_manager_type mem_manager_type;
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typedef multiclass_linear_decision_function<kernel_type, label_type> trained_function_type;
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test_svm_multiclass_linear_trainer3 (
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) :
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C(10),
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eps(1e-4),
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verbose(false)
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{
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}
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trained_function_type train (
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const std::vector<sample_type>& all_samples,
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const std::vector<label_type>& all_labels
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) const
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{
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scalar_type svm_objective = 0;
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return train(all_samples, all_labels, svm_objective);
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}
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trained_function_type train (
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const std::vector<sample_type>& all_samples,
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const std::vector<label_type>& all_labels,
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scalar_type& svm_objective
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) const
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{
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// make sure requires clause is not broken
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DLIB_ASSERT(is_learning_problem(all_samples,all_labels),
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"\t trained_function_type test_svm_multiclass_linear_trainer3::train(all_samples,all_labels)"
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<< "\n\t invalid inputs were given to this function"
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<< "\n\t all_samples.size(): " << all_samples.size()
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<< "\n\t all_labels.size(): " << all_labels.size()
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);
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typedef matrix<scalar_type,0,1> w_type;
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w_type weights;
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test_multiclass_svm_problem<w_type, sample_type, label_type> problem(all_samples, all_labels);
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problem.set_max_cache_size(0);
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problem.set_c(C);
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problem.set_epsilon(eps);
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if (verbose)
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problem.be_verbose();
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solver.set_inactive_plane_threshold(50);
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solver.set_subproblem_epsilon(1e-4);
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svm_objective = solver(problem, weights);
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trained_function_type df;
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const long dims = max_index_plus_one(all_samples);
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df.labels = select_all_distinct_labels(all_labels);
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df.weights = colm(reshape(weights, df.labels.size(), dims+1), range(0,dims-1));
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df.b = colm(reshape(weights, df.labels.size(), dims+1), dims);
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return df;
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}
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private:
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scalar_type C;
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scalar_type eps;
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bool verbose;
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mutable oca solver;
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};
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// ----------------------------------------------------------------------------------------
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template <
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typename K,
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typename label_type_ = typename K::scalar_type
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>
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class test_svm_multiclass_linear_trainer4
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{
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public:
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typedef label_type_ label_type;
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typedef K kernel_type;
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typedef typename kernel_type::scalar_type scalar_type;
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typedef typename kernel_type::sample_type sample_type;
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typedef typename kernel_type::mem_manager_type mem_manager_type;
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typedef multiclass_linear_decision_function<kernel_type, label_type> trained_function_type;
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test_svm_multiclass_linear_trainer4 (
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) :
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C(10),
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eps(1e-4),
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verbose(false)
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{
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}
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trained_function_type train (
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const std::vector<sample_type>& all_samples,
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const std::vector<label_type>& all_labels
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) const
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{
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scalar_type svm_objective = 0;
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return train(all_samples, all_labels, svm_objective);
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}
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trained_function_type train (
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const std::vector<sample_type>& all_samples,
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const std::vector<label_type>& all_labels,
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scalar_type& svm_objective
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) const
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{
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// make sure requires clause is not broken
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DLIB_ASSERT(is_learning_problem(all_samples,all_labels),
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"\t trained_function_type test_svm_multiclass_linear_trainer4::train(all_samples,all_labels)"
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<< "\n\t invalid inputs were given to this function"
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<< "\n\t all_samples.size(): " << all_samples.size()
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<< "\n\t all_labels.size(): " << all_labels.size()
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);
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typedef matrix<scalar_type,0,1> w_type;
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w_type weights;
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test_multiclass_svm_problem<w_type, sample_type, label_type> problem(all_samples, all_labels);
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problem.set_max_cache_size(3);
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problem.set_c(C);
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problem.set_epsilon(eps);
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if (verbose)
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problem.be_verbose();
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solver.set_inactive_plane_threshold(50);
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solver.set_subproblem_epsilon(1e-4);
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svm_objective = solver(problem, weights);
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trained_function_type df;
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const long dims = max_index_plus_one(all_samples);
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df.labels = select_all_distinct_labels(all_labels);
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df.weights = colm(reshape(weights, df.labels.size(), dims+1), range(0,dims-1));
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df.b = colm(reshape(weights, df.labels.size(), dims+1), dims);
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return df;
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}
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private:
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scalar_type C;
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scalar_type eps;
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bool verbose;
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mutable oca solver;
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};
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// ----------------------------------------------------------------------------------------
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template <
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typename K,
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typename label_type_ = typename K::scalar_type
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>
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class test_svm_multiclass_linear_trainer5
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{
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public:
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typedef label_type_ label_type;
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typedef K kernel_type;
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typedef typename kernel_type::scalar_type scalar_type;
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typedef typename kernel_type::sample_type sample_type;
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typedef typename kernel_type::mem_manager_type mem_manager_type;
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typedef multiclass_linear_decision_function<kernel_type, label_type> trained_function_type;
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test_svm_multiclass_linear_trainer5 (
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) :
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C(10),
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eps(1e-4),
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verbose(false)
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{
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}
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trained_function_type train (
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const std::vector<sample_type>& all_samples,
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const std::vector<label_type>& all_labels
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) const
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{
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scalar_type svm_objective = 0;
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return train(all_samples, all_labels, svm_objective);
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}
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trained_function_type train (
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const std::vector<sample_type>& all_samples,
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const std::vector<label_type>& all_labels,
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scalar_type& svm_objective
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) const
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{
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// make sure requires clause is not broken
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DLIB_ASSERT(is_learning_problem(all_samples,all_labels),
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"\t trained_function_type test_svm_multiclass_linear_trainer5::train(all_samples,all_labels)"
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<< "\n\t invalid inputs were given to this function"
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<< "\n\t all_samples.size(): " << all_samples.size()
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<< "\n\t all_labels.size(): " << all_labels.size()
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);
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typedef matrix<scalar_type,0,1> w_type;
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w_type weights;
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const long dims = max_index_plus_one(all_samples);
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trained_function_type df;
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df.labels = select_all_distinct_labels(all_labels);
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multiclass_svm_problem<w_type, sample_type, label_type> problem(all_samples, all_labels, df.labels, dims, 4);
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problem.set_max_cache_size(3);
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problem.set_c(C);
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problem.set_epsilon(eps);
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if (verbose)
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problem.be_verbose();
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solver.set_inactive_plane_threshold(50);
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solver.set_subproblem_epsilon(1e-4);
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svm_objective = solver(problem, weights);
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df.weights = colm(reshape(weights, df.labels.size(), dims+1), range(0,dims-1));
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df.b = colm(reshape(weights, df.labels.size(), dims+1), dims);
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return df;
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}
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private:
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scalar_type C;
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scalar_type eps;
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bool verbose;
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mutable oca solver;
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};
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// ----------------------------------------------------------------------------------------
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typedef matrix<double,10,1> sample_type;
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typedef double scalar_type;
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void make_dataset (
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std::vector<sample_type>& samples,
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std::vector<scalar_type>& labels,
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int num,
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dlib::rand& rnd
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)
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{
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samples.clear();
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labels.clear();
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for (int i = 0; i < 10; ++i)
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{
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for (int j = 0; j < num; ++j)
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{
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sample_type samp;
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samp = 0;
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samp(i) = 10*rnd.get_random_double()+1;
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samples.push_back(samp);
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labels.push_back(i);
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}
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}
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}
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// ----------------------------------------------------------------------------------------
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class test_svm_struct : public tester
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{
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public:
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test_svm_struct (
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) :
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tester ("test_svm_struct",
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"Runs tests on the structural svm components.")
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{}
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void run_test (
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const std::vector<sample_type>& samples,
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const std::vector<scalar_type>& labels,
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const double true_obj
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)
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{
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typedef linear_kernel<sample_type> kernel_type;
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svm_multiclass_linear_trainer<kernel_type> trainer1;
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test_svm_multiclass_linear_trainer2<kernel_type> trainer2;
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test_svm_multiclass_linear_trainer3<kernel_type> trainer3;
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test_svm_multiclass_linear_trainer4<kernel_type> trainer4;
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test_svm_multiclass_linear_trainer5<kernel_type> trainer5;
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trainer1.set_epsilon(1e-4);
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trainer1.set_c(10);
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multiclass_linear_decision_function<kernel_type,double> df1, df2, df3, df4, df5;
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double obj1, obj2, obj3, obj4, obj5;
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// Solve a multiclass SVM a whole bunch of different ways and make sure
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// they all give the same answer.
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print_spinner();
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df1 = trainer1.train(samples, labels, obj1);
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print_spinner();
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df2 = trainer2.train(samples, labels, obj2);
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print_spinner();
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df3 = trainer3.train(samples, labels, obj3);
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print_spinner();
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df4 = trainer4.train(samples, labels, obj4);
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print_spinner();
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df5 = trainer5.train(samples, labels, obj5);
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print_spinner();
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dlog << LINFO << "obj1: "<< obj1;
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dlog << LINFO << "obj2: "<< obj2;
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dlog << LINFO << "obj3: "<< obj3;
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dlog << LINFO << "obj4: "<< obj4;
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dlog << LINFO << "obj5: "<< obj5;
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DLIB_TEST(std::abs(obj1 - obj2) < 1e-2);
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DLIB_TEST(std::abs(obj1 - obj3) < 1e-2);
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DLIB_TEST(std::abs(obj1 - obj4) < 1e-2);
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DLIB_TEST(std::abs(obj1 - obj5) < 1e-2);
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DLIB_TEST(std::abs(obj1 - true_obj) < 1e-2);
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DLIB_TEST(std::abs(obj2 - true_obj) < 1e-2);
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DLIB_TEST(std::abs(obj3 - true_obj) < 1e-2);
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DLIB_TEST(std::abs(obj4 - true_obj) < 1e-2);
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DLIB_TEST(std::abs(obj5 - true_obj) < 1e-2);
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dlog << LINFO << "weight error: "<< max(abs(df1.weights - df2.weights));
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dlog << LINFO << "weight error: "<< max(abs(df1.weights - df3.weights));
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dlog << LINFO << "weight error: "<< max(abs(df1.weights - df4.weights));
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dlog << LINFO << "weight error: "<< max(abs(df1.weights - df5.weights));
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DLIB_TEST(max(abs(df1.weights - df2.weights)) < 1e-2);
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DLIB_TEST(max(abs(df1.weights - df3.weights)) < 1e-2);
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DLIB_TEST(max(abs(df1.weights - df4.weights)) < 1e-2);
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DLIB_TEST(max(abs(df1.weights - df5.weights)) < 1e-2);
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dlog << LINFO << "b error: "<< max(abs(df1.b - df2.b));
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dlog << LINFO << "b error: "<< max(abs(df1.b - df3.b));
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dlog << LINFO << "b error: "<< max(abs(df1.b - df4.b));
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dlog << LINFO << "b error: "<< max(abs(df1.b - df5.b));
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DLIB_TEST(max(abs(df1.b - df2.b)) < 1e-2);
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DLIB_TEST(max(abs(df1.b - df3.b)) < 1e-2);
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DLIB_TEST(max(abs(df1.b - df4.b)) < 1e-2);
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DLIB_TEST(max(abs(df1.b - df5.b)) < 1e-2);
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matrix<double> res = test_multiclass_decision_function(df1, samples, labels);
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dlog << LINFO << res;
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dlog << LINFO << "accuracy: " << sum(diag(res))/sum(res);
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DLIB_TEST(sum(diag(res)) == samples.size());
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res = test_multiclass_decision_function(df2, samples, labels);
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dlog << LINFO << res;
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dlog << LINFO << "accuracy: " << sum(diag(res))/sum(res);
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DLIB_TEST(sum(diag(res)) == samples.size());
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res = test_multiclass_decision_function(df3, samples, labels);
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dlog << LINFO << res;
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dlog << LINFO << "accuracy: " << sum(diag(res))/sum(res);
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DLIB_TEST(sum(diag(res)) == samples.size());
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res = test_multiclass_decision_function(df4, samples, labels);
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dlog << LINFO << res;
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dlog << LINFO << "accuracy: " << sum(diag(res))/sum(res);
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DLIB_TEST(sum(diag(res)) == samples.size());
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res = test_multiclass_decision_function(df5, samples, labels);
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dlog << LINFO << res;
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|
dlog << LINFO << "accuracy: " << sum(diag(res))/sum(res);
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DLIB_TEST(sum(diag(res)) == samples.size());
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}
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void perform_test (
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|
)
|
|
{
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|
std::vector<sample_type> samples;
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|
std::vector<scalar_type> labels;
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|
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dlib::rand rnd;
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dlog << LINFO << "test with 100 samples per class";
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make_dataset(samples, labels, 100, rnd);
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run_test(samples, labels, 1.155);
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|
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dlog << LINFO << "test with 1 sample per class";
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|
make_dataset(samples, labels, 1, rnd);
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|
run_test(samples, labels, 0.251);
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|
|
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dlog << LINFO << "test with 2 sample per class";
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|
make_dataset(samples, labels, 2, rnd);
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|
run_test(samples, labels, 0.444);
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|
}
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} a;
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}
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