1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
|
// Copyright (C) 2012 Davis E. King (davis@dlib.net)
// License: Boost Software License See LICENSE.txt for the full license.
#include <dlib/svm.h>
#include "tester.h"
namespace
{
using namespace test;
using namespace dlib;
using namespace std;
logger dlog("test.active_learning");
// ----------------------------------------------------------------------------------------
typedef matrix<double, 0, 1> sample_type;
typedef radial_basis_kernel<sample_type> kernel_type;
// ----------------------------------------------------------------------------------------
void make_dataset (
std::vector<sample_type>& samples,
std::vector<double>& labels
)
{
for (int r = -10; r <= 10; ++r)
{
for (int c = -10; c <= 10; ++c)
{
sample_type samp(2);
samp(0) = r;
samp(1) = c;
samples.push_back(samp);
// if this point is less than 10 from the origin
if (sqrt((double)r*r + c*c) <= 8)
labels.push_back(+1);
else
labels.push_back(-1);
}
}
vector_normalizer<sample_type> normalizer;
normalizer.train(samples);
for (unsigned long i = 0; i < samples.size(); ++i)
samples[i] = normalizer(samples[i]);
randomize_samples(samples, labels);
/*
cout << "samples.size(): " << samples.size() << endl;
cout << "num +1 samples: "<< sum(mat(labels) > 0) << endl;
cout << "num -1 samples: "<< sum(mat(labels) < 0) << endl;
*/
empirical_kernel_map<kernel_type> ekm;
ekm.load(kernel_type(0.15), samples);
for (unsigned long i = 0; i < samples.size(); ++i)
samples[i] = ekm.project(samples[i]);
//cout << "dims: "<< ekm.out_vector_size() << endl;
}
// ----------------------------------------------------------------------------------------
double test_rank_unlabeled_training_samples (
const std::vector<sample_type>& samples,
const std::vector<double>& labels,
active_learning_mode mode,
int iterations,
bool pick_front
)
{
matrix<double,2,1> s;
s = sum(mat(labels) > 0), sum(mat(labels) < 0);
s /= labels.size();
svm_c_linear_dcd_trainer<linear_kernel<sample_type> > trainer;
trainer.set_c(25);
const unsigned long initial_size = 1;
std::vector<sample_type> tsamples(samples.begin(), samples.begin()+initial_size);
std::vector<double> tlabels(labels.begin(), labels.begin()+initial_size);
decision_function<linear_kernel<sample_type> > df;
double random_score = 0;
double active_learning_score = 0;
for (int i = 0; i < iterations; ++i)
{
print_spinner();
random_subset_selector<sample_type> sss = randomly_subsample(samples,50,i);
random_subset_selector<double> ssl = randomly_subsample(labels,50,i);
std::vector<unsigned long> results;
results = rank_unlabeled_training_samples(trainer, tsamples, tlabels, sss, mode);
const unsigned long idx = pick_front ? results.front() : results.back();
tsamples.push_back(sss[idx]);
tlabels.push_back(ssl[idx]);
df = trainer.train(tsamples, tlabels);
//cout << "tsamples.size(): " << tsamples.size() << endl;
const unsigned long num = tsamples.size();
const double active = test_binary_decision_function(df, samples, labels)*s;
//cout << "test: "<< active;
df = trainer.train(randomly_subsample(samples,num,i), randomly_subsample(labels,num,i));
const double random = test_binary_decision_function(df, samples, labels)*s;
//cout << "test: "<< random << endl;
active_learning_score += active;
random_score += random;
//cout << "\n\n***********\n\n" << flush;
}
dlog << LINFO << "pick_front: " << pick_front << " mode: "<< mode;
dlog << LINFO << "active_learning_score: "<< active_learning_score;
dlog << LINFO << "random_score: "<< random_score;
return active_learning_score / random_score;
}
// ----------------------------------------------------------------------------------------
class test_active_learning : public tester
{
public:
test_active_learning (
) :
tester ("test_active_learning",
"Runs tests on the active learning components.")
{}
void perform_test (
)
{
std::vector<sample_type> samples;
std::vector<double> labels;
print_spinner();
make_dataset(samples, labels);
dlog << LINFO << "samples.size(): "<< samples.size();
// When we pick the best/front ranked element then the active learning method
// shouldn't do much worse than random selection (and often much better).
DLIB_TEST(test_rank_unlabeled_training_samples(samples, labels, max_min_margin, 35, true) >= 0.97);
DLIB_TEST(test_rank_unlabeled_training_samples(samples, labels, ratio_margin, 25, true) >= 0.96);
// However, picking the worst ranked element should do way worse than random
// selection.
DLIB_TEST(test_rank_unlabeled_training_samples(samples, labels, max_min_margin, 25, false) < 0.8);
DLIB_TEST(test_rank_unlabeled_training_samples(samples, labels, ratio_margin, 25, false) < 0.8);
}
} a;
}
|
