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Code Issues Releases Wiki Activity

Added rank_unlabeled_training_samples()

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Davis King 2012-12-27 11:52:06 -05:00
parent 015643e078
commit b5e8d9d835
3 changed files with 238 additions and 0 deletions
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1
dlib/svm.h
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@ -47,6 +47,7 @@
#include "svm/svm_multiclass_linear_trainer.h"
#include "svm/sequence_labeler.h"
#include "svm/assignment_function.h"
#include "svm/active_learning.h"
#endif // DLIB_SVm_HEADER

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dlib/svm/active_learning.h Normal file
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// Copyright (C) 2012 Davis E. King (davis@dlib.net)
// License: Boost Software License See LICENSE.txt for the full license.
#ifndef DLIB_ACTIVE_LEARnING_H__
#define DLIB_ACTIVE_LEARnING_H__
#include "active_learning_abstract.h"
#include "svm_c_linear_dcd_trainer.h"
#include <vector>
namespace dlib
{
enum active_learning_mode
{
max_min_margin,
ratio_margin
};
// ----------------------------------------------------------------------------------------
template <
typename kernel_type,
typename in_sample_vector_type,
typename in_scalar_vector_type,
typename in_sample_vector_type2
>
std::vector<unsigned long> impl_rank_unlabeled_training_samples (
const svm_c_linear_dcd_trainer<kernel_type>& trainer,
const in_sample_vector_type& samples,
const in_scalar_vector_type& labels,
const in_sample_vector_type2& unlabeled_samples,
const active_learning_mode mode
)
{
DLIB_ASSERT(is_vector(unlabeled_samples) &&
(samples.size() == 0 || is_learning_problem(samples, labels)) ,
"\t std::vector<unsigned long> rank_unlabeled_training_samples()"
<< "\n\t Invalid inputs were given to this function"
<< "\n\t is_vector(unlabeled_samples): " << is_vector(unlabeled_samples)
<< "\n\t is_learning_problem(samples, labels): " << is_learning_problem(samples, labels)
<< "\n\t samples.size(): " << samples.size()
<< "\n\t labels.size(): " << labels.size()
);
// If there aren't any training samples then all unlabeled_samples are equally good.
// So just report an arbitrary ordering.
if (samples.size() == 0 || unlabeled_samples.size() == 0)
{
std::vector<unsigned long> ret(unlabeled_samples.size());
for (unsigned long i = 0; i < ret.size(); ++i)
ret[i] = i;
return ret;
}
// We are going to score each unlabeled sample and put the score and index into
// results. Then at the end of this function we just sort it and return the indices.
std::vector<std::pair<double, unsigned long> > results;
results.resize(unlabeled_samples.size());
// make sure we use this trainer's ability to warm start itself since that will make
// this whole function run a lot faster. But first, we need to find out what the state
// we will be warm starting from is.
typedef typename svm_c_linear_dcd_trainer<kernel_type>::optimizer_state optimizer_state;
optimizer_state state;
trainer.train(samples, labels, state); // call train() just to get state
decision_function<kernel_type> df;
std::vector<typename kernel_type::sample_type> temp_samples;
std::vector<typename kernel_type::scalar_type> temp_labels;
temp_samples.reserve(samples.size()+1);
temp_labels.reserve(labels.size()+1);
temp_samples.assign(samples.begin(), samples.end());
temp_labels.assign(labels.begin(), labels.end());
temp_samples.resize(temp_samples.size()+1);
temp_labels.resize(temp_labels.size()+1);
for (unsigned long i = 0; i < unlabeled_samples.size(); ++i)
{
temp_samples.back() = unlabeled_samples(i);
// figure out the margin for each possible labeling of this sample.
optimizer_state temp(state);
temp_labels.back() = +1;
df = trainer.train(temp_samples, temp_labels, temp);
const double margin_p = temp_labels.back()*df(temp_samples.back());
temp = state;
temp_labels.back() = -1;
df = trainer.train(temp_samples, temp_labels, temp);
const double margin_n = temp_labels.back()*df(temp_samples.back());
if (mode == max_min_margin)
{
// The score for this sample is its min possible margin over possible labels.
// Therefore, this score measures how much flexibility we have to label this
// sample however we want. The intuition being that the most useful points to
// label are the ones that are still free to obtain either label.
results[i] = std::make_pair(std::min(margin_p, margin_n), i);
}
else
{
// In this case, the score for the sample is a ratio that tells how close the
// two margin values are to each other. The closer they are the better. So in
// this case we are saying we are looking for samples that have the same
// preference for either class label.
if (std::abs(margin_p) >= std::abs(margin_n))
{
if (margin_p != 0)
results[i] = std::make_pair(margin_n/margin_p, i);
else // if both are == 0 then say 0/0 == 1
results[i] = std::make_pair(1, i);
}
else
{
results[i] = std::make_pair(margin_p/margin_n, i);
}
}
}
// sort the results so the highest scoring samples come first.
std::sort(results.rbegin(), results.rend());
// transfer results into a vector with just sample indices so we can return it.
std::vector<unsigned long> ret(results.size());
for (unsigned long i = 0; i < ret.size(); ++i)
ret[i] = results[i].second;
return ret;
}
// ----------------------------------------------------------------------------------------
template <
typename kernel_type,
typename in_sample_vector_type,
typename in_scalar_vector_type,
typename in_sample_vector_type2
>
std::vector<unsigned long> rank_unlabeled_training_samples (
const svm_c_linear_dcd_trainer<kernel_type>& trainer,
const in_sample_vector_type& samples,
const in_scalar_vector_type& labels,
const in_sample_vector_type2& unlabeled_samples,
const active_learning_mode mode = max_min_margin
)
{
return impl_rank_unlabeled_training_samples(trainer,
mat(samples),
mat(labels),
mat(unlabeled_samples),
mode);
}
// ----------------------------------------------------------------------------------------
}
#endif // DLIB_ACTIVE_LEARnING_H__

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dlib/svm/active_learning_abstract.h Normal file
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// Copyright (C) 2012 Davis E. King (davis@dlib.net)
// License: Boost Software License See LICENSE.txt for the full license.
#undef DLIB_ACTIVE_LEARnING_ABSTRACT_H__
#ifdef DLIB_ACTIVE_LEARnING_ABSTRACT_H__
#include "svm_c_linear_dcd_trainer_abstract.h"
#include <vector>
namespace dlib
{
// ----------------------------------------------------------------------------------------
enum active_learning_mode
{
max_min_margin,
ratio_margin
};
// ----------------------------------------------------------------------------------------
template <
typename kernel_type,
typename in_sample_vector_type,
typename in_scalar_vector_type,
typename in_sample_vector_type2
>
std::vector<unsigned long> rank_unlabeled_training_samples (
const svm_c_linear_dcd_trainer<kernel_type>& trainer,
const in_sample_vector_type& samples,
const in_scalar_vector_type& labels,
const in_sample_vector_type2& unlabeled_samples,
const active_learning_mode mode = max_min_margin
);
/*!
requires
- if (samples.size() != 0) then
- it must be legal to call trainer.train(samples, labels)
- is_learning_problem(samples, labels) == true
- unlabeled_samples must contain the same kind of vectors as samples.
- unlabeled_samples, samples, and labels must be matrices or types of
objects convertible to a matrix via mat().
- is_vector(unlabeled_samples) == true
ensures
- Suppose that we wish to learn a binary classifier by calling
trainer.train(samples, labels) but we are also interested in selecting one of
the elements of unlabeled_samples to add to our training data. Since doing
this requires us to find out the label of the sample, a potentially tedious
or expensive process, we would like to select the "best" element from
unlabeled_samples for labeling. The rank_unlabeled_training_samples()
attempts to find this "best" element. In particular, this function returns a
ranked list of all the elements in unlabeled_samples such that that the
"best" elements come first.
- The method used by this function is described in the paper:
Support Vector Machine Active Learning with Applications to Text Classification
by Simon Tong and Daphne Koller
In particular, this function implements the MaxMin Margin and Ratio Margin
selection strategies described in the paper. Moreover, the mode argument
to this function selects which of these strategies is used.
- returns a std::vector V such that:
- V contains a list of all the indices from unlabeled_samples. Moreover,
they are ordered so that the most useful samples come first.
- V.size() == unlabeled_samples.size()
- unlabeled_samples[V[0]] == The best sample to add into the training set.
- unlabeled_samples[V[1]] == The second best sample to add into the training set.
- unlabeled_samples[V[i]] == The i-th best sample to add into the training set.
!*/
// ----------------------------------------------------------------------------------------
}
#endif // DLIB_ACTIVE_LEARnING_ABSTRACT_H__