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progress bar in iterative algorithms #4305

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merged 4 commits into from Jun 12, 2018
Merged

progress bar in iterative algorithms #4305

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496d3f2
progress bar in neuralnets
shubham808 May 16, 2018
7c3e8a7
progress bar in iterative algorithms
shubham808 May 19, 2018
12032f0
use class_name::method_name as prefix
shubham808 Jun 4, 2018
a2ab4f9
SG_PROGRESS and SG_SPROGRESS
shubham808 Jun 6, 2018
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2 changes: 1 addition & 1 deletion src/gpl
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Submodule gpl updated 1 files
+1 −1 shogun/multiclass/LaRank.cpp
29 changes: 24 additions & 5 deletions src/shogun/base/progress.h
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Expand Up @@ -58,6 +58,12 @@

namespace shogun
{
#define SG_PROGRESS(...) \
progress( \
std::string(this->get_name()) + "::" + std::string(__FUNCTION__), \
*this->io, __VA_ARGS__)

#define SG_SPROGRESS(...) progress(__FUNCTION__, __VA_ARGS__)
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cool!
How does it look like with this? Can you post and example?


/** Possible print modes */
enum SG_PRG_MODE
Expand Down Expand Up @@ -712,7 +718,7 @@ namespace shogun
*/
template <typename T>
inline PRange<T> progress(
Range<T> range, const SGIO& io, std::string prefix = "PROGRESS: ",
std::string prefix, const SGIO& io, Range<T> range,
SG_PRG_MODE mode = UTF8,
std::function<bool()> condition = []() { return true; })
{
Expand All @@ -733,8 +739,7 @@ namespace shogun
*/
template <typename T>
inline PRange<T> progress(
Range<T> range, std::string prefix = "PROGRESS: ",
SG_PRG_MODE mode = UTF8,
std::string prefix, Range<T> range, SG_PRG_MODE mode = UTF8,
std::function<bool()> condition = []() { return true; })
{
return PRange<T>(range, *sg_io, prefix, mode, condition);
Expand All @@ -746,9 +751,23 @@ namespace shogun
* @param condition premature stopping condition
*/
template <typename T>
inline PRange<T> progress(Range<T> range, std::function<bool()> condition)
inline PRange<T> progress(
std::string prefix, Range<T> range, std::function<bool()> condition)
{
return PRange<T>(range, *sg_io, prefix, UTF8, condition);
}
/** Creates @ref PRange given a range and a stopping condition
*
* @param range range used
* @param io SGIO object
* @param condition premature stopping condition
*/
template <typename T>
inline PRange<T> progress(
std::string prefix, const SGIO& io, Range<T> range,
std::function<bool()> condition)
{
return PRange<T>(range, *sg_io, "PROGRESS: ", UTF8, condition);
return PRange<T>(range, io, prefix, UTF8, condition);
}
};
#endif /* __SG_PROGRESS_H__ */
12 changes: 7 additions & 5 deletions src/shogun/classifier/AveragedPerceptron.cpp
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Expand Up @@ -5,11 +5,12 @@
* Michele Mazzoni
*/

#include <shogun/base/progress.h>
#include <shogun/classifier/AveragedPerceptron.h>
#include <shogun/labels/Labels.h>
#include <shogun/mathematics/Math.h>
#include <shogun/labels/BinaryLabels.h>
#include <shogun/labels/Labels.h>
#include <shogun/lib/Signal.h>
#include <shogun/mathematics/Math.h>

using namespace shogun;

Expand Down Expand Up @@ -69,8 +70,8 @@ bool CAveragedPerceptron::train_machine(CFeatures* data)
for (int32_t i=0; i<num_feat; i++)
w[i]=1.0/num_feat;

//loop till we either get everything classified right or reach max_iter

auto pb = SG_PROGRESS(range(max_iter));
// loop till we either get everything classified right or reach max_iter
while (!converged && iter < max_iter)
{
COMPUTATION_CONTROLLERS
Expand All @@ -95,8 +96,9 @@ bool CAveragedPerceptron::train_machine(CFeatures* data)
tmp_bias+=bias;
}
iter++;
pb.print_progress();
}

pb.complete();
if (converged)
SG_INFO("Averaged Perceptron algorithm converged after %d iterations.\n", iter)
else
Expand Down
5 changes: 4 additions & 1 deletion src/shogun/classifier/Perceptron.cpp
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Expand Up @@ -5,6 +5,7 @@
* Michele Mazzoni, Heiko Strathmann, Fernando Iglesias
*/

#include <shogun/base/progress.h>
#include <shogun/base/range.h>
#include <shogun/classifier/Perceptron.h>
#include <shogun/features/iterators/DotIterator.h>
Expand Down Expand Up @@ -81,6 +82,7 @@ bool CPerceptron::train_machine(CFeatures* data)
{
w = get_w();
}
auto pb = SG_PROGRESS(range(max_iter));
//loop till we either get everything classified right or reach max_iter
while (!converged && iter < max_iter)
{
Expand All @@ -105,8 +107,9 @@ bool CPerceptron::train_machine(CFeatures* data)
}

iter++;
pb.print_progress();
}

pb.complete();
if (converged)
SG_INFO("Perceptron algorithm converged after %d iterations.\n", iter)
else
Expand Down
8 changes: 4 additions & 4 deletions src/shogun/classifier/svm/LibLinear.cpp
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Expand Up @@ -324,7 +324,7 @@ void CLibLinear::solve_l2r_l1l2_svc(
index[i] = i;
}

auto pb = progress(range(10));
auto pb = SG_PROGRESS(range(10));
CTime start_time;
while (iter < get_max_iterations())
{
Expand Down Expand Up @@ -538,7 +538,7 @@ void CLibLinear::solve_l1r_l2_svc(
}
}

auto pb = progress(range(10));
auto pb = SG_PROGRESS(range(10));
CTime start_time;
while (iter < get_max_iterations())
{
Expand Down Expand Up @@ -913,7 +913,7 @@ void CLibLinear::solve_l1r_lr(
}
}

auto pb = progress(range(10));
auto pb = SG_PROGRESS(range(10));
CTime start_time;
while (iter < get_max_iterations())
{
Expand Down Expand Up @@ -1270,7 +1270,7 @@ void CLibLinear::solve_l2r_lr_dual(
index[i] = i;
}

auto pb = progress(range(10));
auto pb = SG_PROGRESS(range(10));
while (iter < max_iter)
{
for (i = 0; i < l; i++)
Expand Down
6 changes: 5 additions & 1 deletion src/shogun/classifier/svm/MPDSVM.cpp
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Expand Up @@ -5,11 +5,12 @@
* Evan Shelhamer, Sergey Lisitsyn
*/

#include <shogun/base/progress.h>
#include <shogun/classifier/svm/MPDSVM.h>
#include <shogun/io/SGIO.h>
#include <shogun/lib/Signal.h>
#include <shogun/lib/common.h>
#include <shogun/mathematics/Math.h>
#include <shogun/lib/Signal.h>

using namespace shogun;

Expand Down Expand Up @@ -91,6 +92,7 @@ bool CMPDSVM::train_machine(CFeatures* data)
dalphas[i]=-1; //CSVC
}

auto pb = SG_PROGRESS(range(maxiter));
// go ...
while (niter++ < maxiter)
{
Expand Down Expand Up @@ -222,8 +224,10 @@ bool CMPDSVM::train_machine(CFeatures* data)
dalphas[i]+= F[i] * etachange;
//dalphas[i]+= F[i] * etachange[0] + F[i+n] * etachange[1];
}
pb.print_progress();
}

pb.complete();
if (niter >= maxiter)
SG_WARNING("increase maxiter ... \n")

Expand Down
19 changes: 9 additions & 10 deletions src/shogun/classifier/svm/NewtonSVM.cpp
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Expand Up @@ -7,15 +7,16 @@
#include <shogun/lib/config.h>

#ifdef HAVE_LAPACK
#include <shogun/base/progress.h>
#include <shogun/classifier/svm/NewtonSVM.h>
#include <shogun/mathematics/Math.h>
#include <shogun/mathematics/linalg/LinalgNamespace.h>
#include <shogun/machine/LinearMachine.h>
#include <shogun/features/DotFeatures.h>
#include <shogun/labels/Labels.h>
#include <shogun/labels/BinaryLabels.h>
#include <shogun/mathematics/lapack.h>
#include <shogun/labels/Labels.h>
#include <shogun/lib/Signal.h>
#include <shogun/machine/LinearMachine.h>
#include <shogun/mathematics/Math.h>
#include <shogun/mathematics/lapack.h>
#include <shogun/mathematics/linalg/LinalgNamespace.h>

//#define DEBUG_NEWTON
//#define V_NEWTON
Expand Down Expand Up @@ -80,6 +81,7 @@ bool CNewtonSVM::train_machine(CFeatures* data)
float64_t obj, *grad=SG_MALLOC(float64_t, x_d+1);
float64_t t;

auto pb = SG_PROGRESS(range(num_iter));
while (iter <= num_iter)
{
COMPUTATION_CONTROLLERS
Expand Down Expand Up @@ -204,13 +206,10 @@ bool CNewtonSVM::train_machine(CFeatures* data)

if (newton_decrement*2<prec*obj)
break;
}

if (iter > num_iter)
{
SG_PRINT("Maximum number of Newton steps reached. Try larger lambda")
pb.print_progress();
}

pb.complete();
#ifdef V_NEWTON
SG_PRINT("FINAL W AND BIAS Vector=\n\n")
CMath::display_matrix(weights, x_d+1, 1);
Expand Down
9 changes: 5 additions & 4 deletions src/shogun/classifier/svm/OnlineSVMSGD.cpp
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Expand Up @@ -5,12 +5,13 @@
* Thoralf Klein, Viktor Gal, Evan Shelhamer, Bjoern Esser
*/

#include <shogun/classifier/svm/OnlineSVMSGD.h>
#include <shogun/mathematics/Math.h>
#include <shogun/mathematics/linalg/LinalgNamespace.h>
#include <shogun/base/Parameter.h>
#include <shogun/base/progress.h>
#include <shogun/classifier/svm/OnlineSVMSGD.h>
#include <shogun/lib/Signal.h>
#include <shogun/loss/HingeLoss.h>
#include <shogun/mathematics/Math.h>
#include <shogun/mathematics/linalg/LinalgNamespace.h>

using namespace shogun;

Expand Down Expand Up @@ -89,7 +90,7 @@ bool COnlineSVMSGD::train(CFeatures* data)
is_log_loss = true;

int32_t vec_count;
for (int32_t e = 0; e < epochs; e++)
for (auto e : SG_PROGRESS(range(epochs)))
{
COMPUTATION_CONTROLLERS
vec_count=0;
Expand Down
9 changes: 5 additions & 4 deletions src/shogun/classifier/svm/SGDQN.cpp
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Expand Up @@ -5,12 +5,13 @@
* Sergey Lisitsyn, Thoralf Klein, Evan Shelhamer, Bjoern Esser
*/

#include <shogun/classifier/svm/SGDQN.h>
#include <shogun/base/Parameter.h>
#include <shogun/base/progress.h>
#include <shogun/classifier/svm/SGDQN.h>
#include <shogun/labels/BinaryLabels.h>
#include <shogun/lib/Signal.h>
#include <shogun/mathematics/Math.h>
#include <shogun/loss/HingeLoss.h>
#include <shogun/labels/BinaryLabels.h>
#include <shogun/mathematics/Math.h>

using namespace shogun;

Expand Down Expand Up @@ -129,7 +130,7 @@ bool CSGDQN::train(CFeatures* data)
if ((loss_type == L_LOGLOSS) || (loss_type == L_LOGLOSSMARGIN))
is_log_loss = true;

for (int32_t e = 0; e < epochs; e++)
for (auto e : SG_PROGRESS(range(epochs)))
{
COMPUTATION_CONTROLLERS
count = skip;
Expand Down
2 changes: 1 addition & 1 deletion src/shogun/classifier/svm/SVMLight.cpp
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Expand Up @@ -642,7 +642,7 @@ int32_t CSVMLight::optimize_to_convergence(int32_t* docs, int32_t* label, int32_
CTime start_time;
mkl_converged=false;

auto pb = progress(range(10), *this->io);
auto pb = SG_PROGRESS(range(10));
#ifdef CYGWIN
for (;((iteration<100 || (!mkl_converged && callback) ) || (retrain && (!terminate))); iteration++){
#else
Expand Down
14 changes: 8 additions & 6 deletions src/shogun/clustering/GMM.cpp
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Expand Up @@ -7,8 +7,9 @@
*/
#include <shogun/lib/config.h>

#include <shogun/base/some.h>
#include <shogun/base/Parameter.h>
#include <shogun/base/progress.h>
#include <shogun/base/some.h>
#include <shogun/clustering/GMM.h>
#include <shogun/clustering/KMeans.h>
#include <shogun/distance/EuclideanDistance.h>
Expand Down Expand Up @@ -149,7 +150,7 @@ float64_t CGMM::train_em(float64_t min_cov, int32_t max_iter, float64_t min_chan
SGVector<float64_t> logPxy(num_vectors * m_components.size());
SGVector<float64_t> logPx(num_vectors);
//float64_t* logPost=SG_MALLOC(float64_t, num_vectors*m_components.vlen);

auto pb = SG_PROGRESS(range(max_iter));
while (iter<max_iter)
{
log_likelihood_prev=log_likelihood_cur;
Expand Down Expand Up @@ -181,12 +182,12 @@ float64_t CGMM::train_em(float64_t min_cov, int32_t max_iter, float64_t min_chan

if (iter>0 && log_likelihood_cur-log_likelihood_prev<min_change)
break;

pb.print_progress();
max_likelihood(alpha, min_cov);

iter++;
}

pb.complete();
return log_likelihood_cur;
}

Expand Down Expand Up @@ -218,6 +219,7 @@ float64_t CGMM::train_smem(int32_t max_iter, int32_t max_cand, float64_t min_cov
SGVector<int32_t> merge_ind(
m_components.size() * (m_components.size() - 1) / 2);

auto pb = SG_PROGRESS(range(max_iter));
while (iter<max_iter)
{
linalg::zero(logPostSum);
Expand Down Expand Up @@ -334,8 +336,9 @@ float64_t CGMM::train_smem(int32_t max_iter, int32_t max_cand, float64_t min_cov
if (!better_found)
break;
iter++;
pb.print_progress();
}

pb.complete();
return cur_likelihood;
}

Expand Down Expand Up @@ -826,4 +829,3 @@ void CGMM::register_params()
&m_coefficients, "m_coefficients", "Mixture coefficients.",
MS_NOT_AVAILABLE);
}

4 changes: 2 additions & 2 deletions src/shogun/clustering/Hierarchical.cpp
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Expand Up @@ -109,7 +109,7 @@ bool CHierarchical::train_machine(CFeatures* data)
float64_t* distances=SG_MALLOC(float64_t, num_pairs);

int32_t offs=0;
for (auto i : progress(range(0, num), *this->io))
for (auto i : SG_PROGRESS(range(0, num)))
{
for (int32_t j=i+1; j<num; j++)
{
Expand All @@ -123,7 +123,7 @@ bool CHierarchical::train_machine(CFeatures* data)
CMath::qsort_index<float64_t,pair>(distances, index, (num-1)*num/2);
//CMath::display_vector(distances, (num-1)*num/2, "dists");

auto pb = progress(range(0, num_pairs - 1), *this->io);
auto pb = SG_PROGRESS(range(0, num_pairs - 1));
int32_t k=-1;
int32_t l=0;
for (; l<num && (num-l)>=merges && k<num_pairs-1; l++)
Expand Down