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Changed the interface to the ridge regression trainer objects so that

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they report the entire set of LOO prediction values rather than a
summary statistic like mean squared error.
This commit is contained in:
Davis King 2011-08-24 21:34:28 -04:00
parent 9b863dc9d0
commit 82a3e62577
4 changed files with 59 additions and 62 deletions
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19
dlib/svm/krr_trainer.h
View File

@ -189,7 +189,8 @@ namespace dlib
const in_scalar_vector_type& y
) const
{
scalar_type temp, temp2;
std::vector<scalar_type> temp;
scalar_type temp2;
return do_train(vector_to_matrix(x), vector_to_matrix(y), false, temp, temp2);
}
@ -200,11 +201,11 @@ namespace dlib
const decision_function<kernel_type> train (
const in_sample_vector_type& x,
const in_scalar_vector_type& y,
scalar_type& looe
std::vector<scalar_type>& loo_values
) const
{
scalar_type temp;
return do_train(vector_to_matrix(x), vector_to_matrix(y), true, looe, temp);
return do_train(vector_to_matrix(x), vector_to_matrix(y), true, loo_values, temp);
}
template <
@ -214,11 +215,11 @@ namespace dlib
const decision_function<kernel_type> train (
const in_sample_vector_type& x,
const in_scalar_vector_type& y,
scalar_type& looe,
std::vector<scalar_type>& loo_values,
scalar_type& lambda_used
) const
{
return do_train(vector_to_matrix(x), vector_to_matrix(y), true, looe, lambda_used);
return do_train(vector_to_matrix(x), vector_to_matrix(y), true, loo_values, lambda_used);
}
@ -231,8 +232,8 @@ namespace dlib
const decision_function<kernel_type> do_train (
const in_sample_vector_type& x,
const in_scalar_vector_type& y,
bool output_looe,
scalar_type& best_looe,
const bool output_loo_values,
std::vector<scalar_type>& loo_values,
scalar_type& the_lambda
) const
{
@ -311,8 +312,8 @@ namespace dlib
decision_function<linear_kernel<matrix<scalar_type,0,0,mem_manager_type> > > lin_df;
if (output_looe)
lin_df = trainer.train(proj_x,y, best_looe, the_lambda);
if (output_loo_values)
lin_df = trainer.train(proj_x,y, loo_values, the_lambda);
else
lin_df = trainer.train(proj_x,y);

24
dlib/svm/krr_trainer_abstract.h
View File

@ -275,7 +275,7 @@ namespace dlib
const decision_function<kernel_type> train (
const in_sample_vector_type& x,
const in_scalar_vector_type& y,
scalar_type& looe
std::vector<scalar_type>& loo_values
) const;
/*!
requires
@ -283,12 +283,10 @@ namespace dlib
ensures
- returns train(x,y)
(i.e. executes train(x,y) and returns its result)
- if (will_use_regression_loss_for_loo_cv())
- #looe == the mean squared error as determined by leave-one-out
cross-validation.
- else
- #looe == the fraction of samples misclassified as determined by
leave-one-out cross-validation.
- #loo_values.size() == y.size()
- for all valid i:
- #loo_values[i] == leave-one-out prediction for the value of y(i) based
on all the training samples other than (x(i),y(i)).
!*/
template <
@ -298,7 +296,7 @@ namespace dlib
const decision_function<kernel_type> train (
const in_sample_vector_type& x,
const in_scalar_vector_type& y,
scalar_type& looe,
std::vector<scalar_type>& loo_values,
scalar_type& lambda_used
) const;
/*!
@ -307,12 +305,10 @@ namespace dlib
ensures
- returns train(x,y)
(i.e. executes train(x,y) and returns its result)
- if (will_use_regression_loss_for_loo_cv())
- #looe == the mean squared error as determined by leave-one-out
cross-validation.
- else
- #looe == the fraction of samples misclassified as determined by
leave-one-out cross-validation.
- #loo_values.size() == y.size()
- for all valid i:
- #loo_values[i] == leave-one-out prediction for the value of y(i) based
on all the training samples other than (x(i),y(i)).
- #lambda_used == the value of lambda used to generate the
decision_function. Note that this lambda value is always
equal to get_lambda() if get_lambda() isn't 0.

54
dlib/svm/rr_trainer.h
View File

@ -133,7 +133,8 @@ namespace dlib
const in_scalar_vector_type& y
) const
{
scalar_type temp, temp2;
std::vector<scalar_type> temp;
scalar_type temp2;
return do_train(vector_to_matrix(x), vector_to_matrix(y), false, temp, temp2);
}
@ -144,11 +145,11 @@ namespace dlib
const decision_function<kernel_type> train (
const in_sample_vector_type& x,
const in_scalar_vector_type& y,
scalar_type& looe
std::vector<scalar_type>& loo_values
) const
{
scalar_type temp;
return do_train(vector_to_matrix(x), vector_to_matrix(y), true, looe, temp);
return do_train(vector_to_matrix(x), vector_to_matrix(y), true, loo_values, temp);
}
template <
@ -158,11 +159,11 @@ namespace dlib
const decision_function<kernel_type> train (
const in_sample_vector_type& x,
const in_scalar_vector_type& y,
scalar_type& looe,
std::vector<scalar_type>& loo_values,
scalar_type& lambda_used
) const
{
return do_train(vector_to_matrix(x), vector_to_matrix(y), true, looe, lambda_used);
return do_train(vector_to_matrix(x), vector_to_matrix(y), true, loo_values, lambda_used);
}
@ -175,8 +176,8 @@ namespace dlib
const decision_function<kernel_type> do_train (
const in_sample_vector_type& x,
const in_scalar_vector_type& y,
bool output_looe,
scalar_type& best_looe,
const bool output_loo_values,
std::vector<scalar_type>& loo_values,
scalar_type& the_lambda
) const
{
@ -279,7 +280,7 @@ namespace dlib
// We can save some work by pre-multiplying the x vectors by trans(V)
// and saving the result so we don't have to recompute it over and over later.
matrix<column_matrix_type,0,1,mem_manager_type > Vx;
if (lambda == 0 || output_looe)
if (lambda == 0 || output_loo_values)
{
// Save the transpose of V into a temporary because the subsequent matrix
// vector multiplies will be faster (because of better cache locality).
@ -299,9 +300,11 @@ namespace dlib
// If we need to automatically select a lambda then do so using the LOOE trick described
// above.
bool did_loov = false;
scalar_type best_looe = std::numeric_limits<scalar_type>::max();
if (lambda == 0)
{
best_looe = std::numeric_limits<scalar_type>::max();
did_loov = true;
// Compute leave one out errors for a bunch of different lambdas and pick the best one.
for (long idx = 0; idx < lams.size(); ++idx)
@ -342,16 +345,7 @@ namespace dlib
}
}
// mark that we saved the looe to best_looe already
output_looe = false;
best_looe /= x.size();
if (verbose)
{
using namespace std;
cout << "Using lambda: " << the_lambda << endl;
cout << "LOO Error: " << best_looe << endl;
}
}
@ -370,8 +364,10 @@ namespace dlib
// If we haven't done this already and we are supposed to then compute the LOO error rate for
// the current lambda and store the result in best_looe.
if (output_looe)
if (output_loo_values)
{
loo_values.resize(x.size());
did_loov = true;
best_looe = 0;
for (long i = 0; i < x.size(); ++i)
{
@ -385,18 +381,26 @@ namespace dlib
loov = 0;
best_looe += loss(loov, y(i));
loo_values[i] = loov;
}
best_looe /= x.size();
if (verbose)
{
using namespace std;
cout << "Using lambda: " << the_lambda << endl;
cout << "LOO Error: " << best_looe << endl;
}
}
else
{
loo_values.clear();
}
if (verbose && did_loov)
{
using namespace std;
cout << "Using lambda: " << the_lambda << endl;
if (use_regression_loss)
cout << "LOO Mean Squared Error: " << best_looe << endl;
else
cout << "LOO Classification Accuracy: " << best_looe << endl;
}
// convert w into a proper decision function
decision_function<kernel_type> df;

24
dlib/svm/rr_trainer_abstract.h
View File

@ -208,7 +208,7 @@ namespace dlib
const decision_function<kernel_type> train (
const in_sample_vector_type& x,
const in_scalar_vector_type& y,
scalar_type& looe
std::vector<scalar_type>& loo_values
) const;
/*!
requires
@ -216,12 +216,10 @@ namespace dlib
ensures
- returns train(x,y)
(i.e. executes train(x,y) and returns its result)
- if (will_use_regression_loss_for_loo_cv())
- #looe == the mean squared error as determined by leave-one-out
cross-validation.
- else
- #looe == the fraction of samples misclassified as determined by
leave-one-out cross-validation.
- #loo_values.size() == y.size()
- for all valid i:
- #loo_values[i] == leave-one-out prediction for the value of y(i) based
on all the training samples other than (x(i),y(i)).
!*/
template <
@ -231,7 +229,7 @@ namespace dlib
const decision_function<kernel_type> train (
const in_sample_vector_type& x,
const in_scalar_vector_type& y,
scalar_type& looe,
std::vector<scalar_type>& loo_values,
scalar_type& lambda_used
) const;
/*!
@ -240,12 +238,10 @@ namespace dlib
ensures
- returns train(x,y)
(i.e. executes train(x,y) and returns its result)
- if (will_use_regression_loss_for_loo_cv())
- #looe == the mean squared error as determined by leave-one-out
cross-validation.
- else
- #looe == the fraction of samples misclassified as determined by
leave-one-out cross-validation.
- #loo_values.size() == y.size()
- for all valid i:
- #loo_values[i] == leave-one-out prediction for the value of y(i) based
on all the training samples other than (x(i),y(i)).
- #lambda_used == the value of lambda used to generate the
decision_function. Note that this lambda value is always
equal to get_lambda() if get_lambda() isn't 0.