dlib C++ Library - custom_trainer_ex.cpp
// The contents of this file are in the public domain. See LICENSE_FOR_EXAMPLE_PROGRAMS.txt
/*
This example program shows you how to create your own custom binary classification
trainer object and use it with the multiclass classification tools in the dlib C++
library. This example assumes you have already become familiar with the concepts
introduced in the multiclass_classification_ex.cpp example program.
In this example we will create a very simple trainer object that takes a binary
classification problem and produces a decision rule which says a test point has the
same class as whichever centroid it is closest to.
The multiclass training dataset will consist of four classes. Each class will be a blob
of points in one of the quadrants of the cartesian plane. For fun, we will use
std::string labels and therefore the labels of these classes will be the following:
"upper_left",
"upper_right",
"lower_left",
"lower_right"
*/
#include <dlib/svm_threaded.h>
#include <iostream>
#include <vector>
#include <dlib/rand.h>
using namespace std;
using namespace dlib;
// Our data will be 2-dimensional data. So declare an appropriate type to contain these points.
typedef matrix<double,2,1> sample_type;
// ----------------------------------------------------------------------------------------
struct custom_decision_function
{
/*!
WHAT THIS OBJECT REPRESENTS
This object is the representation of our binary decision rule.
!*/
// centers of the two classes
sample_type positive_center, negative_center;
double operator() (
const sample_type& x
) const
{
// if x is closer to the positive class then return +1
if (length(positive_center - x) < length(negative_center - x))
return +1;
else
return -1;
}
};
// Later on in this example we will save our decision functions to disk. This
// pair of routines is needed for this functionality.
void serialize (const custom_decision_function& item, std::ostream& out)
{
// write the state of item to the output stream
serialize(item.positive_center, out);
serialize(item.negative_center, out);
}
void deserialize (custom_decision_function& item, std::istream& in)
{
// read the data from the input stream and store it in item
deserialize(item.positive_center, in);
deserialize(item.negative_center, in);
}
// ----------------------------------------------------------------------------------------
class simple_custom_trainer
{
/*!
WHAT THIS OBJECT REPRESENTS
This is our example custom binary classifier trainer object. It simply
computes the means of the +1 and -1 classes, puts them into our
custom_decision_function, and returns the results.
Below we define the train() function. I have also included the
requires/ensures definition for a generic binary classifier's train()
!*/
public:
custom_decision_function train (
const std::vector<sample_type>& samples,
const std::vector<double>& labels
) const
/*!
requires
- is_binary_classification_problem(samples, labels) == true
(e.g. labels consists of only +1 and -1 values, samples.size() == labels.size())
ensures
- returns a decision function F with the following properties:
- if (new_x is a sample predicted have +1 label) then
- F(new_x) >= 0
- else
- F(new_x) < 0
!*/
{
sample_type positive_center, negative_center;
// compute sums of each class
positive_center = 0;
negative_center = 0;
for (unsigned long i = 0; i < samples.size(); ++i)
{
if (labels[i] == +1)
positive_center += samples[i];
else // this is a -1 sample
negative_center += samples[i];
}
// divide by number of +1 samples
positive_center /= sum(mat(labels) == +1);
// divide by number of -1 samples
negative_center /= sum(mat(labels) == -1);
custom_decision_function df;
df.positive_center = positive_center;
df.negative_center = negative_center;
return df;
}
};
// ----------------------------------------------------------------------------------------
void generate_data (
std::vector<sample_type>& samples,
std::vector<string>& labels
);
/*!
ensures
- make some four class data as described above.
- each class will have 50 samples in it
!*/
// ----------------------------------------------------------------------------------------
int main()
{
std::vector<sample_type> samples;
std::vector<string> labels;
// First, get our labeled set of training data
generate_data(samples, labels);
cout << "samples.size(): "<< samples.size() << endl;
// Define the trainer we will use. The second template argument specifies the type
// of label used, which is string in this case.
typedef one_vs_one_trainer<any_trainer<sample_type>, string> ovo_trainer;
ovo_trainer trainer;
// Now tell the one_vs_one_trainer that, by default, it should use the simple_custom_trainer
// to solve the individual binary classification subproblems.
trainer.set_trainer(simple_custom_trainer());
// Next, to make things a little more interesting, we will setup the one_vs_one_trainer
// to use kernel ridge regression to solve the upper_left vs lower_right binary classification
// subproblem.
typedef radial_basis_kernel<sample_type> rbf_kernel;
krr_trainer<rbf_kernel> rbf_trainer;
rbf_trainer.set_kernel(rbf_kernel(0.1));
trainer.set_trainer(rbf_trainer, "upper_left", "lower_right");
// Now let's do 5-fold cross-validation using the one_vs_one_trainer we just setup.
// As an aside, always shuffle the order of the samples before doing cross validation.
// For a discussion of why this is a good idea see the svm_ex.cpp example.
randomize_samples(samples, labels);
cout << "cross validation: \n" << cross_validate_multiclass_trainer(trainer, samples, labels, 5) << endl;
// This dataset is very easy and everything is correctly classified. Therefore, the output of
// cross validation is the following confusion matrix.
/*
50 0 0 0
0 50 0 0
0 0 50 0
0 0 0 50
*/
// We can also obtain the decision rule as always.
one_vs_one_decision_function<ovo_trainer> df = trainer.train(samples, labels);
cout << "predicted label: "<< df(samples[0]) << ", true label: "<< labels[0] << endl;
cout << "predicted label: "<< df(samples[90]) << ", true label: "<< labels[90] << endl;
// The output is:
/*
predicted label: upper_right, true label: upper_right
predicted label: lower_left, true label: lower_left
*/
// Finally, let's save our multiclass decision rule to disk. Remember that we have
// to specify the types of binary decision function used inside the one_vs_one_decision_function.
one_vs_one_decision_function<ovo_trainer,
custom_decision_function, // This is the output of the simple_custom_trainer
decision_function<radial_basis_kernel<sample_type> > // This is the output of the rbf_trainer
> df2, df3;
df2 = df;
// save to a file called df.dat
serialize("df.dat") << df2;
// load the function back in from disk and store it in df3.
deserialize("df.dat") >> df3;
// Test df3 to see that this worked.
cout << endl;
cout << "predicted label: "<< df3(samples[0]) << ", true label: "<< labels[0] << endl;
cout << "predicted label: "<< df3(samples[90]) << ", true label: "<< labels[90] << endl;
// Test df3 on the samples and labels and print the confusion matrix.
cout << "test deserialized function: \n" << test_multiclass_decision_function(df3, samples, labels) << endl;
}
// ----------------------------------------------------------------------------------------
void generate_data (
std::vector<sample_type>& samples,
std::vector<string>& labels
)
{
const long num = 50;
sample_type m;
dlib::rand rnd;
// add some points in the upper right quadrant
m = 10, 10;
for (long i = 0; i < num; ++i)
{
samples.push_back(m + randm(2,1,rnd));
labels.push_back("upper_right");
}
// add some points in the upper left quadrant
m = -10, 10;
for (long i = 0; i < num; ++i)
{
samples.push_back(m + randm(2,1,rnd));
labels.push_back("upper_left");
}
// add some points in the lower right quadrant
m = 10, -10;
for (long i = 0; i < num; ++i)
{
samples.push_back(m + randm(2,1,rnd));
labels.push_back("lower_right");
}
// add some points in the lower left quadrant
m = -10, -10;
for (long i = 0; i < num; ++i)
{
samples.push_back(m + randm(2,1,rnd));
labels.push_back("lower_left");
}
}
// ----------------------------------------------------------------------------------------