Skip to content
Snippets Groups Projects
Commit 9d933d79 authored by Thomas Purcell's avatar Thomas Purcell
Browse files

Classification runs but running with different numbers of MPI ranks leads to different results 

Debug tomorrow
parent c348ec24
No related branches found
No related tags found
No related merge requests found
Show whitespace changes
Inline Side-by-side
Showing
with 180 additions and 86 deletions
src/classification/utils.cpp
+ 14
− 1
View file @ 9d933d79
...@@ -92,3 +92,16 @@ double convex_hull::overlap_1d(double* value, double width) ...@@ -92,3 +92,16 @@ double convex_hull::overlap_1d(double* value, double width)
} }
return util_funcs::mean(TASK_SCORES); return util_funcs::mean(TASK_SCORES);
} }
// int lp_utils::pt_in_convex_hull(ClpSimplex& lp_simplex, std::vector<int>& inds, double* row_lower, double* row_upper, int samp_ind)
// {
// std::transform(inds.begin(), inds.end(), row_lower, [&samp_ind](int ind){return prop_sorted_d_mat::get_sorted_d_matrix_el(ind, samp_ind);});
// std::transform(inds.begin(), inds.end(), row_upper, [&samp_ind](int ind){return prop_sorted_d_mat::get_sorted_d_matrix_el(ind, samp_ind);});
// lp_simplex.chgRowLower(row_lower);
// lp_simplex.chgRowUpper(row_upper);
// lp_simplex.primal();
// return lp_simplex.primalFeasible();
// }
src/classification/utils.hpp
+ 6
− 0
View file @ 9d933d79
...@@ -10,6 +10,11 @@ ...@@ -10,6 +10,11 @@
#include <utils/math_funcs.hpp> #include <utils/math_funcs.hpp>
#include <iomanip> #include <iomanip>
#include <coin/ClpSimplex.hpp>
#include <classification/prop_sorted_d_mat.hpp>
namespace convex_hull namespace convex_hull
{ {
extern std::vector<double> SORTED_VALUE; //!< The value sorted based on the property extern std::vector<double> SORTED_VALUE; //!< The value sorted based on the property
...@@ -39,4 +44,5 @@ namespace convex_hull ...@@ -39,4 +44,5 @@ namespace convex_hull
double overlap_1d(double* value, double width = 1e-10); double overlap_1d(double* value, double width = 1e-10);
} }
#endif #endif
\ No newline at end of file
src/descriptor_identifier/Model/ModelClassifier.cpp
+ 17
− 32
View file @ 9d933d79
...@@ -16,6 +16,7 @@ ModelClassifier::ModelClassifier(Unit prop_unit, std::vector<double> prop_train, ...@@ -16,6 +16,7 @@ ModelClassifier::ModelClassifier(Unit prop_unit, std::vector<double> prop_train,
int n_row = _n_dim + 1; int n_row = _n_dim + 1;
ClpSimplex lp_simplex; //!< Model used to find if a point is inside the convex hull ClpSimplex lp_simplex; //!< Model used to find if a point is inside the convex hull
lp_simplex.setLogLevel(0); lp_simplex.setLogLevel(0);
lp_simplex.setMoreSpecialOptions(2);
std::vector<std::vector<int>> inds_check; std::vector<std::vector<int>> inds_check;
std::vector<double> lp_column_lower(_n_samp_train + 1, 0.0); std::vector<double> lp_column_lower(_n_samp_train + 1, 0.0);
...@@ -76,13 +77,8 @@ ModelClassifier::ModelClassifier(Unit prop_unit, std::vector<double> prop_train, ...@@ -76,13 +77,8 @@ ModelClassifier::ModelClassifier(Unit prop_unit, std::vector<double> prop_train,
int n_col = inds_class_train[c1].size(); int n_col = inds_class_train[c1].size();
for(int dd = 0; dd < _n_dim; ++dd) for(int dd = 0; dd < _n_dim; ++dd)
{
for(int ii = 0; ii < n_col; ++ii) for(int ii = 0; ii < n_col; ++ii)
{
lp_elements[ii * n_row + dd] = _feats[dd]->value_ptr()[inds_class_train[c1][ii]]; lp_elements[ii * n_row + dd] = _feats[dd]->value_ptr()[inds_class_train[c1][ii]];
// std::cout << inds_class_train[c1][ii] << ',' << lp_elements[ii * n_row + dd] << ',' << lp_elements[ii * n_row + dd + 1] << std::endl;
}
}
for(int c2 = 0; c2 < inds_class_train.size(); ++c2) for(int c2 = 0; c2 < inds_class_train.size(); ++c2)
{ {
...@@ -134,7 +130,6 @@ ModelClassifier::ModelClassifier(Unit prop_unit, std::vector<double> prop_train, ...@@ -134,7 +130,6 @@ ModelClassifier::ModelClassifier(Unit prop_unit, std::vector<double> prop_train,
} }
// Setup ML Shark data structures for SVM training // Setup ML Shark data structures for SVM training
std::cout << "setup ml" << std::endl;
std::vector<shark::LabeledData<shark::RealVector, unsigned int>> training(_task_sizes_train.size()); std::vector<shark::LabeledData<shark::RealVector, unsigned int>> training(_task_sizes_train.size());
std::vector<shark::LabeledData<shark::RealVector, unsigned int>> test(_task_sizes_test.size()); std::vector<shark::LabeledData<shark::RealVector, unsigned int>> test(_task_sizes_test.size());
std::vector<unsigned int> prop_to_copy(_prop_train.size()); std::vector<unsigned int> prop_to_copy(_prop_train.size());
...@@ -189,6 +184,15 @@ ModelClassifier::ModelClassifier(Unit prop_unit, std::vector<double> prop_train, ...@@ -189,6 +184,15 @@ ModelClassifier::ModelClassifier(Unit prop_unit, std::vector<double> prop_train,
n_els += shark::batchSize(batch); n_els += shark::batchSize(batch);
n_copied += shark::batchSize(batch); n_copied += shark::batchSize(batch);
} }
n_copied = 0;
for(auto& batch : data.batches())
{
for(int ff = 0; ff < _n_dim; ++ff)
dcopy_(shark::batchSize(batch), _feats[ff]->svm_test_value_ptr() + n_copied, 1, &batch(0, 0) + ff, _n_dim);
n_copied += shark::batchSize(batch);
}
test[tt] = shark::LabeledData<shark::RealVector, unsigned int>(data, labels); test[tt] = shark::LabeledData<shark::RealVector, unsigned int>(data, labels);
} }
...@@ -205,11 +209,10 @@ ModelClassifier::ModelClassifier(Unit prop_unit, std::vector<double> prop_train, ...@@ -205,11 +209,10 @@ ModelClassifier::ModelClassifier(Unit prop_unit, std::vector<double> prop_train,
int ii_test = 0; int ii_test = 0;
for(int tt = 0; tt < _task_sizes_train.size(); ++tt) for(int tt = 0; tt < _task_sizes_train.size(); ++tt)
{ {
std::cout << "perform svm" << std::endl;
trainer.stoppingCondition().maxSeconds = 0.001; trainer.stoppingCondition().maxSeconds = 0.001;
trainer.stoppingCondition().maxIterations = 1000;
trainer.stoppingCondition().minAccuracy = 0.01;
trainer.train(model, training[tt]); trainer.train(model, training[tt]);
std::cout << "process svm" << std::endl;
if(_fix_intercept) if(_fix_intercept)
{ {
_coefs.push_back(std::vector<double>(_n_dim, 0.0)); _coefs.push_back(std::vector<double>(_n_dim, 0.0));
...@@ -222,34 +225,16 @@ ModelClassifier::ModelClassifier(Unit prop_unit, std::vector<double> prop_train, ...@@ -222,34 +225,16 @@ ModelClassifier::ModelClassifier(Unit prop_unit, std::vector<double> prop_train,
_coefs.push_back(std::vector<double>(_n_dim + 1, 0.0)); _coefs.push_back(std::vector<double>(_n_dim + 1, 0.0));
std::copy_n(model.parameterVector().begin(), _n_dim + 1, _coefs.back().begin()); std::copy_n(model.parameterVector().begin(), _n_dim + 1, _coefs.back().begin());
for(int cc = 0; cc < _n_dim; ++cc) for(int cc = 0; cc < _n_dim; ++cc)
{
_coefs.back()[cc] = _feats[cc]->remap_w_svm(_coefs.back()[cc]); _coefs.back()[cc] = _feats[cc]->remap_w_svm(_coefs.back()[cc]);
_coefs.back()[_n_dim] = _feats.back()->remap_b_svm(_coefs.back()[_n_dim]); _coefs.back()[_n_dim] -= _feats[cc]->remap_b_svm(_coefs.back()[cc]);
}
}
// output = model(training[tt].inputs()); }
// for(auto element: output.elements()) }
// {
// _prop_train_est[ii_train] = static_cast<double>(element);
// ++ii_train;
// }
// if(_task_sizes_test[tt] == 0)
// continue;
// output = model(test[tt].inputs());
// for(auto element: output.elements())
// {
// _prop_test_est[ii_test] = static_cast<double>(element);
// ++ii_test;
// }
}
std::cout << "set error" << std::endl;
std::transform(_prop_train_est.begin(), _prop_train_est.end(), _prop_train.begin(), _train_error.begin(), [](double est, double real){return (std::abs(est - real) < 1e-10) ? -1 : real;}); std::transform(_prop_train_est.begin(), _prop_train_est.end(), _prop_train.begin(), _train_error.begin(), [](double est, double real){return (std::abs(est - real) < 1e-10) ? -1 : real;});
std::transform(_prop_test_est.begin(), _prop_test_est.end(), _prop_test.begin(), _test_error.begin(), [](double est, double real){return (std::abs(est - real) < 1e-10) ? -1 : real;}); std::transform(_prop_test_est.begin(), _prop_test_est.end(), _prop_test.begin(), _test_error.begin(), [](double est, double real){return (std::abs(est - real) < 1e-10) ? -1 : real;});
std::cout << *std::min_element(_train_error.begin(), _train_error.end()) << std::endl;
std::cout << *std::max_element(_train_error.begin(), _train_error.end()) << std::endl;
std::cout << std::count_if(_train_error.begin(), _train_error.end(), [](double te){return te >= 0;}) << std::endl;
std::cout << "out" << std::endl;
} }
ModelClassifier::ModelClassifier(std::string train_file) ModelClassifier::ModelClassifier(std::string train_file)
......
src/descriptor_identifier/Model/ModelClassifier.hpp
+ 3
− 1
View file @ 9d933d79
...@@ -18,9 +18,11 @@ ...@@ -18,9 +18,11 @@
#include<fstream> #include<fstream>
#include<iostream> #include<iostream>
#include <shark/Algorithms/Trainers/CSvmTrainer.h>
#include <shark/Algorithms/Trainers/NormalizeComponentsUnitVariance.h>
#include <shark/Data/Dataset.h> #include <shark/Data/Dataset.h>
#include <shark/Models/Normalizer.h>
#include <shark/ObjectiveFunctions/Loss/ZeroOneLoss.h> #include <shark/ObjectiveFunctions/Loss/ZeroOneLoss.h>
#include <shark/Algorithms/Trainers/CSvmTrainer.h>
#include <coin/ClpSimplex.hpp> #include <coin/ClpSimplex.hpp>
#include <descriptor_identifier/Model/Model.hpp> #include <descriptor_identifier/Model/Model.hpp>
......
src/descriptor_identifier/SISSO_DI/SISSOClassifier.cpp
+ 38
− 30
View file @ 9d933d79
...@@ -15,8 +15,8 @@ SISSOClassifier::SISSOClassifier( ...@@ -15,8 +15,8 @@ SISSOClassifier::SISSOClassifier(
): ):
SISSO_DI(feat_space, prop_unit, prop, prop_test, task_sizes_train, task_sizes_test, leave_out_inds, n_dim, n_residual, n_models_store, fix_intercept), SISSO_DI(feat_space, prop_unit, prop, prop_test, task_sizes_train, task_sizes_test, leave_out_inds, n_dim, n_residual, n_models_store, fix_intercept),
_lp_elements((n_dim + 1) * _n_samp, 0.0), _lp_elements((n_dim + 1) * _n_samp, 0.0),
_lp_row_lower(_n_dim + 1, 1.0), _lp_row_lower((n_dim + 1) * _n_samp, 1.0),
_lp_row_upper(_n_dim + 1, 1.0), _lp_row_upper((n_dim + 1) * _n_samp, 1.0),
_lp_objective(_n_samp, 0.0), _lp_objective(_n_samp, 0.0),
_lp_column_lower(_n_samp, 0.0), _lp_column_lower(_n_samp, 0.0),
_lp_column_upper(_n_samp, COIN_DBL_MAX), _lp_column_upper(_n_samp, COIN_DBL_MAX),
...@@ -37,10 +37,8 @@ SISSOClassifier::SISSOClassifier( ...@@ -37,10 +37,8 @@ SISSOClassifier::SISSOClassifier(
if(std::none_of(prop.begin(), prop.end(), [&pt](double pp){return std::abs(pp - pt) < 1e-10;})) if(std::none_of(prop.begin(), prop.end(), [&pt](double pp){return std::abs(pp - pt) < 1e-10;}))
throw std::logic_error("A class in the property vector (test set) is not in the training set."); throw std::logic_error("A class in the property vector (test set) is not in the training set.");
_lp_row_lower[_n_dim] -= _width;
_lp_row_upper[_n_dim] += _width;
_lp_simplex.setLogLevel(0); _lp_simplex.setLogLevel(0);
_lp_simplex.setMoreSpecialOptions(8192 + 64 + 2);
_trainer.stoppingCondition().maxSeconds = 0.001; _trainer.stoppingCondition().maxSeconds = 0.001;
std::vector<int> inds = util_funcs::argsort(prop); std::vector<int> inds = util_funcs::argsort(prop);
...@@ -87,7 +85,10 @@ SISSOClassifier::SISSOClassifier( ...@@ -87,7 +85,10 @@ SISSOClassifier::SISSOClassifier(
std::iota(inds.begin(), inds.end(), task_start); std::iota(inds.begin(), inds.end(), task_start);
for(int cc = 0; cc < _n_class; ++cc) for(int cc = 0; cc < _n_class; ++cc)
_inds_check.push_back(inds); {
_class_task_check.push_back(std::vector<int>(_n_class));
std::iota(_class_task_check.back().begin(), _class_task_check.back().end(), _n_class * tt);
}
util_funcs::argsort(inds.data(), inds.data() + inds.size(), &_prop[task_start]); util_funcs::argsort(inds.data(), inds.data() + inds.size(), &_prop[task_start]);
_sample_inds_to_sorted_dmat_inds[inds[0]] = task_start; _sample_inds_to_sorted_dmat_inds[inds[0]] = task_start;
...@@ -98,13 +99,13 @@ SISSOClassifier::SISSOClassifier( ...@@ -98,13 +99,13 @@ SISSOClassifier::SISSOClassifier(
_sample_inds_to_sorted_dmat_inds[inds[ii]] = ii + task_start; _sample_inds_to_sorted_dmat_inds[inds[ii]] = ii + task_start;
if(_prop[inds[ii]] != _prop[inds[ii - 1]]) if(_prop[inds[ii]] != _prop[inds[ii - 1]])
{ {
_inds_check[tt * _n_class + cc].erase(_inds_check[tt * _n_class + cc].begin() + cls_start, _inds_check[tt * _n_class + cc].begin() + ii); _class_task_check[tt * _n_class + cc].erase(_class_task_check[tt * _n_class + cc].begin() + cc);
n_samp_per_class.push_back(ii - cls_start); n_samp_per_class.push_back(ii - cls_start);
cls_start = ii; cls_start = ii;
++cc; ++cc;
} }
} }
_inds_check[tt * _n_class + cc].erase(_inds_check[tt * _n_class + cc].begin() + cls_start, _inds_check[tt * _n_class + cc].end()); _class_task_check[tt * _n_class + cc].erase(_class_task_check[tt * _n_class + cc].begin() + cc);
n_samp_per_class.push_back(inds.size() - cls_start); n_samp_per_class.push_back(inds.size() - cls_start);
if(n_samp_per_class.size() != (tt + 1) * _n_class) if(n_samp_per_class.size() != (tt + 1) * _n_class)
...@@ -158,10 +159,11 @@ void SISSOClassifier::set_data(std::vector<int>& inds, int start, int task) ...@@ -158,10 +159,11 @@ void SISSOClassifier::set_data(std::vector<int>& inds, int start, int task)
void SISSOClassifier::svm(std::vector<int>& inds, double* coefs, int start, int task) void SISSOClassifier::svm(std::vector<int>& inds, double* coefs, int start, int task)
{ {
_trainer.stoppingCondition().maxSeconds = 0.001; _trainer.stoppingCondition().maxSeconds = 0.001;
_trainer.stoppingCondition().maxIterations = 1000;
_trainer.stoppingCondition().minAccuracy = 0.01;
// _trainer.stoppingCondition().maxIterations = 100; // _trainer.stoppingCondition().maxIterations = 100;
set_data(inds, start, task); set_data(inds, start, task);
std::cout << _training[0].inputs().elements()[0] << std::endl;
_trainer.train(_model, _training[task]); _trainer.train(_model, _training[task]);
std::copy_n(_model.parameterVector().begin(), inds.size() + (!_fix_intercept), coefs); std::copy_n(_model.parameterVector().begin(), inds.size() + (!_fix_intercept), coefs);
if(!_fix_intercept) if(!_fix_intercept)
...@@ -243,6 +245,17 @@ void SISSOClassifier::set_lp_data(int task, int cls) ...@@ -243,6 +245,17 @@ void SISSOClassifier::set_lp_data(int task, int cls)
int n_dim = _cur_inds.size(); int n_dim = _cur_inds.size();
for(int ii = 0; ii < n_dim; ++ii) for(int ii = 0; ii < n_dim; ++ii)
dcopy_(_lp_n_col, prop_sorted_d_mat::access_sorted_d_matrix(_cur_inds[ii], task, cls), 1, &_lp_elements[ii], _lp_n_row); dcopy_(_lp_n_col, prop_sorted_d_mat::access_sorted_d_matrix(_cur_inds[ii], task, cls), 1, &_lp_elements[ii], _lp_n_row);
int n_copied = 0;
for(auto& cls_task_ind : _class_task_check[task * _n_class + cls])
{
for(int ii = 0; ii < _cur_inds.size(); ++ii)
{
dcopy_(prop_sorted_d_mat::N_SAMPLES_PER_CLASS[cls_task_ind], prop_sorted_d_mat::access_sorted_d_matrix(_cur_inds[ii], task, cls_task_ind % _n_class), 1, &_lp_row_lower[n_copied + ii], _lp_n_row);
dcopy_(prop_sorted_d_mat::N_SAMPLES_PER_CLASS[cls_task_ind], prop_sorted_d_mat::access_sorted_d_matrix(_cur_inds[ii], task, cls_task_ind % _n_class), 1, &_lp_row_upper[n_copied + ii], _lp_n_row);
}
n_copied += prop_sorted_d_mat::N_SAMPLES_PER_CLASS[cls_task_ind] * _cur_inds.size();
}
_sz_cls_task_check = n_copied / _cur_inds.size();
_lp_simplex.loadProblem( _lp_simplex.loadProblem(
_lp_n_col, _lp_n_col,
...@@ -258,17 +271,19 @@ void SISSOClassifier::set_lp_data(int task, int cls) ...@@ -258,17 +271,19 @@ void SISSOClassifier::set_lp_data(int task, int cls)
); );
} }
int SISSOClassifier::pt_in_convex_hull_lp(int samp_ind) int SISSOClassifier::pt_in_convex_hull_lp(int cls_task_ind)
{ {
std::transform(_cur_inds.begin(), _cur_inds.end(), _lp_row_upper.begin(), [&samp_ind](int ind){return prop_sorted_d_mat::get_sorted_d_matrix_el(ind, samp_ind);}); int n_overlap = 0;
std::transform(_cur_inds.begin(), _cur_inds.end(), _lp_row_lower.begin(), [&samp_ind](int ind){return prop_sorted_d_mat::get_sorted_d_matrix_el(ind, samp_ind);}); for(int ii = 0; ii < _sz_cls_task_check; ++ii)
{
_lp_simplex.chgRowLower(_lp_row_lower.data()); _lp_simplex.chgRowLower(_lp_row_lower.data() + ii * _lp_n_row);
_lp_simplex.chgRowUpper(_lp_row_upper.data()); _lp_simplex.chgRowUpper(_lp_row_upper.data() + ii * _lp_n_row);
_lp_simplex.primal(); _lp_simplex.dual();
n_overlap += _lp_simplex.primalFeasible();
}
return _lp_simplex.primalFeasible(); return n_overlap;
} }
int SISSOClassifier::get_n_overlap_lp() int SISSOClassifier::get_n_overlap_lp()
...@@ -280,8 +295,9 @@ int SISSOClassifier::get_n_overlap_lp() ...@@ -280,8 +295,9 @@ int SISSOClassifier::get_n_overlap_lp()
{ {
_lp_n_col = prop_sorted_d_mat::get_class_size(tt, cc); _lp_n_col = prop_sorted_d_mat::get_class_size(tt, cc);
set_lp_data(tt, cc); set_lp_data(tt, cc);
for(auto& ind : _inds_check[tt * _n_class + cc]) // n_overlap += std::accumulate(_inds_check[tt * _n_class + cc].begin(), _inds_check[tt * _n_class + cc].end(), 0, [this](int tot, int samp_ind){return tot + pt_in_convex_hull_lp(samp_ind);});
n_overlap += pt_in_convex_hull_lp(ind); for(auto& cls_task_ind : _class_task_check[tt * _n_class + cc])
n_overlap += pt_in_convex_hull_lp(cls_task_ind);
} }
} }
return n_overlap; return n_overlap;
...@@ -294,6 +310,8 @@ void SISSOClassifier::l0_norm(std::vector<double>& prop, int n_dim) ...@@ -294,6 +310,8 @@ void SISSOClassifier::l0_norm(std::vector<double>& prop, int n_dim)
_lp_n_row = n_dim + 1; _lp_n_row = n_dim + 1;
std::fill_n(_lp_elements.begin(), _lp_elements.size(), 1.0); std::fill_n(_lp_elements.begin(), _lp_elements.size(), 1.0);
std::fill_n(_lp_row_lower.begin(), _lp_row_lower.size(), 1.0 - _width);
std::fill_n(_lp_row_upper.begin(), _lp_row_upper.size(), 1.0 + _width);
std::iota(_lp_column_start.begin(), _lp_column_start.end(), 0); std::iota(_lp_column_start.begin(), _lp_column_start.end(), 0);
std::transform(_lp_column_start.begin(), _lp_column_start.end(), _lp_column_start.begin(), [&n_dim](int ii){return (n_dim + 1) * ii;}); std::transform(_lp_column_start.begin(), _lp_column_start.end(), _lp_column_start.begin(), [&n_dim](int ii){return (n_dim + 1) * ii;});
...@@ -332,10 +350,6 @@ void SISSOClassifier::l0_norm(std::vector<double>& prop, int n_dim) ...@@ -332,10 +350,6 @@ void SISSOClassifier::l0_norm(std::vector<double>& prop, int n_dim)
} }
} while(util_funcs::iterate(_cur_inds, _cur_inds.size(), _mpi_comm->size())); } while(util_funcs::iterate(_cur_inds, _cur_inds.size(), _mpi_comm->size()));
for(int nn = 0; nn < min_n_convex_overlap.size(); ++nn)
std::cout << nn << '\t' << min_n_convex_overlap[nn] << '\t' << min_inds[nn].size() << std::endl;
std::vector<int> inds_min(n_get_models * n_dim, -1); std::vector<int> inds_min(n_get_models * n_dim, -1);
if(min_inds[max_error_ind].size() > 1) if(min_inds[max_error_ind].size() > 1)
{ {
...@@ -353,7 +367,7 @@ void SISSOClassifier::l0_norm(std::vector<double>& prop, int n_dim) ...@@ -353,7 +367,7 @@ void SISSOClassifier::l0_norm(std::vector<double>& prop, int n_dim)
} }
test_inds = find_min_svm(test_inds, n_keep); test_inds = find_min_svm(test_inds, n_keep);
for(int nn = 0; nn < n_keep; ++nn) for(int nn = 0; nn < n_keep; ++nn)
min_inds[inds_change[nn]] = {test_inds[inds_change[nn]]}; min_inds[inds_change[nn]] = {test_inds[nn]};
} }
for(int ii = 0; ii < n_get_models; ++ii) for(int ii = 0; ii < n_get_models; ++ii)
std::copy_n(min_inds[ii][0].begin(), min_inds[ii][0].size(), &inds_min[n_dim * ii]); std::copy_n(min_inds[ii][0].begin(), min_inds[ii][0].size(), &inds_min[n_dim * ii]);
...@@ -396,11 +410,9 @@ void SISSOClassifier::l0_norm(std::vector<double>& prop, int n_dim) ...@@ -396,11 +410,9 @@ void SISSOClassifier::l0_norm(std::vector<double>& prop, int n_dim)
min_nodes[ii] = std::make_shared<ModelNode>(_feat_space->phi_selected()[index]->arr_ind(), _feat_space->phi_selected()[index]->rung(), _feat_space->phi_selected()[index]->expr(), _feat_space->phi_selected()[index]->postfix_expr(), _feat_space->phi_selected()[index]->value(), _feat_space->phi_selected()[index]->test_value(), _feat_space->phi_selected()[index]->unit()); min_nodes[ii] = std::make_shared<ModelNode>(_feat_space->phi_selected()[index]->arr_ind(), _feat_space->phi_selected()[index]->rung(), _feat_space->phi_selected()[index]->expr(), _feat_space->phi_selected()[index]->postfix_expr(), _feat_space->phi_selected()[index]->value(), _feat_space->phi_selected()[index]->test_value(), _feat_space->phi_selected()[index]->unit());
} }
models.push_back(ModelClassifier(_prop_unit, _prop, _prop_test, min_nodes, _task_sizes_train, _task_sizes_test, _fix_intercept)); models.push_back(ModelClassifier(_prop_unit, _prop, _prop_test, min_nodes, _task_sizes_train, _task_sizes_test, _fix_intercept));
std::cout << models.back().n_svm_misclassified_train() << std::endl;
} }
_models.push_back(models); _models.push_back(models);
std::cout << _models.back().back().n_svm_misclassified_train() << std::endl;
} }
void SISSOClassifier::fit() void SISSOClassifier::fit()
...@@ -462,19 +474,15 @@ void SISSOClassifier::fit() ...@@ -462,19 +474,15 @@ void SISSOClassifier::fit()
if(_mpi_comm->rank() == 0) if(_mpi_comm->rank() == 0)
{ {
std::cout << "Time for l0-norm: " << duration << std::endl; std::cout << "Time for l0-norm: " << duration << std::endl;
std::cout << _models.back().back().n_svm_misclassified_train() << std::endl;
for(int rr = 0; rr < _n_models_store; ++rr) for(int rr = 0; rr < _n_models_store; ++rr)
{ {
_models.back()[rr].to_file("models/train_dim_" + std::to_string(dd) + "_model_" + std::to_string(rr) + ".dat"); _models.back()[rr].to_file("models/train_dim_" + std::to_string(dd) + "_model_" + std::to_string(rr) + ".dat");
if(_leave_out_inds.size() > 0) if(_leave_out_inds.size() > 0)
_models.back()[rr].to_file("models/test_dim_" + std::to_string(dd) + "_model_" + std::to_string(rr) + ".dat", false, _leave_out_inds); _models.back()[rr].to_file("models/test_dim_" + std::to_string(dd) + "_model_" + std::to_string(rr) + ".dat", false, _leave_out_inds);
} }
std::cout << _models.back().back().n_svm_misclassified_train() << std::endl;
} }
for(int rr = 0; rr < _n_residual; ++rr) for(int rr = 0; rr < _n_residual; ++rr)
_models.back()[rr].copy_error(&residual[rr * _n_samp]); _models.back()[rr].copy_error(&residual[rr * _n_samp]);
std::cout << _models.back().back().n_svm_misclassified_train() << std::endl;
} }
} }
src/descriptor_identifier/SISSO_DI/SISSOClassifier.hpp
+ 2
− 1
View file @ 9d933d79
...@@ -37,7 +37,7 @@ protected: ...@@ -37,7 +37,7 @@ protected:
ClpSimplex _lp_simplex; //!< Model used to find if a point is inside the convex hull ClpSimplex _lp_simplex; //!< Model used to find if a point is inside the convex hull
std::vector<std::vector<ModelClassifier>> _models; //!< List of models std::vector<std::vector<ModelClassifier>> _models; //!< List of models
std::vector<std::vector<int>> _inds_check; std::vector<std::vector<int>> _class_task_check;
std::vector<double> _lp_elements; std::vector<double> _lp_elements;
std::vector<double> _lp_row_lower; std::vector<double> _lp_row_lower;
...@@ -66,6 +66,7 @@ protected: ...@@ -66,6 +66,7 @@ protected:
double _c; double _c;
double _width; double _width;
int _sz_cls_task_check;
int _n_class; int _n_class;
int _lp_n_col; int _lp_n_col;
int _lp_n_row; int _lp_n_row;
......
src/feature_creation/node/ModelNode.cpp
+ 6
− 6
View file @ 9d933d79
...@@ -12,14 +12,14 @@ ModelNode::ModelNode(int feat_ind, int rung, std::string expr, std::string post_ ...@@ -12,14 +12,14 @@ ModelNode::ModelNode(int feat_ind, int rung, std::string expr, std::string post_
_w_remap_svm(1.0), _w_remap_svm(1.0),
_rung(rung) _rung(rung)
{ {
double w_remap_svm_temp = 2.0 / (*std::max_element(_value.begin(), _value.end()) - *std::min_element(_value.begin(), _value.end())); double w_remap_svm_temp = 1.0 / (*std::max_element(_value.begin(), _value.end()) - *std::min_element(_value.begin(), _value.end()));
double b_remap_svm_temp = w_remap_svm_temp * (*std::max_element(_value.begin(), _value.end()) + *std::min_element(_value.begin(), _value.end())) / 2.0; double b_remap_svm_temp = *std::min_element(_value.begin(), _value.end());
_w_remap_svm = w_remap_svm_temp; _w_remap_svm = w_remap_svm_temp;
_b_remap_svm = b_remap_svm_temp; _b_remap_svm = b_remap_svm_temp;
std::transform(_value.begin(), _value.end(), _value_svm.begin(), [w_remap_svm_temp, b_remap_svm_temp](double val){return val * w_remap_svm_temp - b_remap_svm_temp;}); std::transform(_value.begin(), _value.end(), _value_svm.begin(), [w_remap_svm_temp, b_remap_svm_temp](double val){return (val - b_remap_svm_temp) * w_remap_svm_temp;});
std::transform(_test_value.begin(), _test_value.end(), _test_value_svm.begin(), [w_remap_svm_temp, b_remap_svm_temp](double val){return val * w_remap_svm_temp - b_remap_svm_temp;}); std::transform(_test_value.begin(), _test_value.end(), _test_value_svm.begin(), [w_remap_svm_temp, b_remap_svm_temp](double val){return (val - b_remap_svm_temp) * w_remap_svm_temp;});
} }
ModelNode::~ModelNode() ModelNode::~ModelNode()
......
src/feature_creation/node/ModelNode.hpp
+ 6
− 2
View file @ 9d933d79
...@@ -100,9 +100,13 @@ public: ...@@ -100,9 +100,13 @@ public:
inline double* svm_value_ptr(){return _value_svm.data();} inline double* svm_value_ptr(){return _value_svm.data();}
inline double remap_b_svm(double b){return b + _b_remap_svm;} inline std::vector<double> svm_test_value(){return _test_value_svm;}
inline double remap_w_svm(double w){return w / _w_remap_svm;} inline double* svm_test_value_ptr(){return _test_value_svm.data();}
inline double remap_b_svm(double w){return w * _b_remap_svm;}
inline double remap_w_svm(double w){return w * _w_remap_svm;}
// DocString: model_node_set_value // DocString: model_node_set_value
/** /**
......
src/main.cpp
+ 0
− 1
View file @ 9d933d79
...@@ -64,7 +64,6 @@ int main(int argc, char const *argv[]) ...@@ -64,7 +64,6 @@ int main(int argc, char const *argv[])
{ {
for(int ii = 0; ii < sisso.models().size(); ++ii) for(int ii = 0; ii < sisso.models().size(); ++ii)
{ {
std::cout << sisso.models()[ii][0].n_convex_overlap_train() << '\t' << sisso.models()[ii][0].n_svm_misclassified_train() << '\t' << sisso.models()[ii][0].n_samp_train() << '\t' << static_cast<double>(sisso.models()[ii][0].n_convex_overlap_train()) / static_cast<double>(sisso.models()[ii][0].n_samp_train()) << std::endl;
std::cout << "Train Error: " << sisso.models()[ii][0].percent_train_error(); std::cout << "Train Error: " << sisso.models()[ii][0].percent_train_error();
if(IP._prop_test.size() > 0) if(IP._prop_test.size() > 0)
std::cout << "; Test Error: " << sisso.models()[ii][0].percent_test_error() << std::endl; std::cout << "; Test Error: " << sisso.models()[ii][0].percent_test_error() << std::endl;
......
src/python/descriptor_identifier/SISSOClassifier.cpp
+ 88
− 12
View file @ 9d933d79
...@@ -14,6 +14,14 @@ SISSOClassifier::SISSOClassifier( ...@@ -14,6 +14,14 @@ SISSOClassifier::SISSOClassifier(
bool fix_intercept bool fix_intercept
) : ) :
SISSO_DI(feat_space, prop_unit, prop, prop_test, task_sizes_train, task_sizes_test, leave_out_inds, n_dim, n_residual, n_models_store, fix_intercept), SISSO_DI(feat_space, prop_unit, prop, prop_test, task_sizes_train, task_sizes_test, leave_out_inds, n_dim, n_residual, n_models_store, fix_intercept),
_lp_elements((n_dim + 1) * _n_samp, 0.0),
_lp_row_lower((n_dim + 1) * _n_samp, 1.0),
_lp_row_upper((n_dim + 1) * _n_samp, 1.0),
_lp_objective(_n_samp, 0.0),
_lp_column_lower(_n_samp, 0.0),
_lp_column_upper(_n_samp, COIN_DBL_MAX),
_lp_column_start(_n_samp, 0),
_lp_row((n_dim + 1) * _n_samp, 0),
_training(_task_sizes_train.size()), _training(_task_sizes_train.size()),
_int_prop(_prop.size()), _int_prop(_prop.size()),
_int_prop_test(_prop_test.size()), _int_prop_test(_prop_test.size()),
...@@ -24,7 +32,31 @@ SISSOClassifier::SISSOClassifier( ...@@ -24,7 +32,31 @@ SISSOClassifier::SISSOClassifier(
std::vector<double> prop_vec = python_conv_utils::from_ndarray<double>(prop); std::vector<double> prop_vec = python_conv_utils::from_ndarray<double>(prop);
std::vector<double> prop_test_vec = python_conv_utils::from_ndarray<double>(prop_test); std::vector<double> prop_test_vec = python_conv_utils::from_ndarray<double>(prop_test);
_trainer.stoppingCondition().maxSeconds = 0.01; for(auto& pt : prop_test_vec)
if(std::none_of(prop_vec.begin(), prop_vec.end(), [&pt](double pp){return std::abs(pp - pt) < 1e-10;}))
throw std::logic_error("A class in the property vector (test set) is not in the training set.");
_lp_simplex.setLogLevel(0);
_lp_simplex.setMoreSpecialOptions(8192 + 64 + 2);
_trainer.stoppingCondition().maxSeconds = 0.001;
std::vector<int> inds = util_funcs::argsort(prop_vec);
std::map<double, double> class_map;
double class_num = 0;
prop_vec[inds[0]] = 0;
class_map[inds[0]] = 0;
for(int ii = 1; ii < inds.size(); ++ii)
{
if(_prop[inds[ii]] != _prop[inds[ii-1]])
{
++class_num;
++_n_class;
class_map[_prop[inds[ii]]] = class_num;
}
prop_vec[inds[ii]] = class_num;
}
if(_n_class > 2)
_trainer.setMcSvmType(shark::McSvm::WW);
double min_prop = *std::min_element(prop_vec.begin(), prop_vec.end()); double min_prop = *std::min_element(prop_vec.begin(), prop_vec.end());
if(min_prop < 0.0) if(min_prop < 0.0)
...@@ -57,16 +89,20 @@ SISSOClassifier::SISSOClassifier( ...@@ -57,16 +89,20 @@ SISSOClassifier::SISSOClassifier(
std::vector<int> n_samp_per_class; std::vector<int> n_samp_per_class;
int task_start; int task_start;
for(int tt = 0; tt < _n_task; ++tt) for(int tt = 0; tt < _n_task; ++tt)
{ {
std::vector<int> inds(_task_sizes_train[tt]); inds = std::vector<int>(_task_sizes_train[tt]);
std::iota(inds.begin(), inds.end(), task_start); std::iota(inds.begin(), inds.end(), task_start);
for(int cc = 0; cc < _n_class; ++cc) for(int cc = 0; cc < _n_class; ++cc)
_inds_check.push_back(inds); {
_class_task_check.push_back(std::vector<int>(_n_class));
std::iota(_class_task_check.back().begin(), _class_task_check.back().end(), _n_class * tt);
}
util_funcs::argsort(inds.data(), inds.data() + inds.size(), &_prop[task_start]); util_funcs::argsort(inds.data(), inds.data() + inds.size(), &_prop[task_start]);
_sample_inds_to_sorted_dmat_inds[0] = task_start; _sample_inds_to_sorted_dmat_inds[inds[0]] = task_start;
int cls_start = 0; int cls_start = 0;
int cc = 0; int cc = 0;
for(int ii = 1; ii < inds.size(); ++ii) for(int ii = 1; ii < inds.size(); ++ii)
...@@ -74,12 +110,14 @@ SISSOClassifier::SISSOClassifier( ...@@ -74,12 +110,14 @@ SISSOClassifier::SISSOClassifier(
_sample_inds_to_sorted_dmat_inds[inds[ii]] = ii + task_start; _sample_inds_to_sorted_dmat_inds[inds[ii]] = ii + task_start;
if(_prop[inds[ii]] != _prop[inds[ii - 1]]) if(_prop[inds[ii]] != _prop[inds[ii - 1]])
{ {
_inds_check[tt * _n_class + cc].erase(_inds_check[tt * _n_class + cc].begin() + cls_start, _inds_check[tt * _n_class + cc].begin() + ii); _class_task_check[tt * _n_class + cc].erase(_class_task_check[tt * _n_class + cc].begin() + cc);
n_samp_per_class.push_back(ii - cls_start); n_samp_per_class.push_back(ii - cls_start);
cls_start = ii; cls_start = ii;
++cc; ++cc;
} }
} }
_class_task_check[tt * _n_class + cc].erase(_class_task_check[tt * _n_class + cc].begin() + cc);
n_samp_per_class.push_back(inds.size() - cls_start);
if(n_samp_per_class.size() != (tt + 1) * _n_class) if(n_samp_per_class.size() != (tt + 1) * _n_class)
throw std::logic_error("A class is not represented in task " + std::to_string(tt) + "."); throw std::logic_error("A class is not represented in task " + std::to_string(tt) + ".");
...@@ -103,6 +141,14 @@ SISSOClassifier::SISSOClassifier( ...@@ -103,6 +141,14 @@ SISSOClassifier::SISSOClassifier(
bool fix_intercept bool fix_intercept
) : ) :
SISSO_DI(feat_space, prop_unit, prop, prop_test, task_sizes_train, task_sizes_test, leave_out_inds, n_dim, n_residual, n_models_store, fix_intercept), SISSO_DI(feat_space, prop_unit, prop, prop_test, task_sizes_train, task_sizes_test, leave_out_inds, n_dim, n_residual, n_models_store, fix_intercept),
_lp_elements((n_dim + 1) * _n_samp, 0.0),
_lp_row_lower((n_dim + 1) * _n_samp, 1.0),
_lp_row_upper((n_dim + 1) * _n_samp, 1.0),
_lp_objective(_n_samp, 0.0),
_lp_column_lower(_n_samp, 0.0),
_lp_column_upper(_n_samp, COIN_DBL_MAX),
_lp_column_start(_n_samp, 0),
_lp_row((n_dim + 1) * _n_samp, 0),
_training(_task_sizes_train.size()), _training(_task_sizes_train.size()),
_int_prop(_prop.size()), _int_prop(_prop.size()),
_int_prop_test(_prop_test.size()), _int_prop_test(_prop_test.size()),
...@@ -113,7 +159,31 @@ SISSOClassifier::SISSOClassifier( ...@@ -113,7 +159,31 @@ SISSOClassifier::SISSOClassifier(
std::vector<double> prop_vec = python_conv_utils::from_list<double>(prop); std::vector<double> prop_vec = python_conv_utils::from_list<double>(prop);
std::vector<double> prop_test_vec = python_conv_utils::from_list<double>(prop_test); std::vector<double> prop_test_vec = python_conv_utils::from_list<double>(prop_test);
_trainer.stoppingCondition().maxSeconds = 0.01; for(auto& pt : prop_test_vec)
if(std::none_of(prop_vec.begin(), prop_vec.end(), [&pt](double pp){return std::abs(pp - pt) < 1e-10;}))
throw std::logic_error("A class in the property vector (test set) is not in the training set.");
_lp_simplex.setLogLevel(0);
_lp_simplex.setMoreSpecialOptions(8192 + 64 + 2);
_trainer.stoppingCondition().maxSeconds = 0.001;
std::vector<int> inds = util_funcs::argsort(prop_vec);
std::map<double, double> class_map;
double class_num = 0;
prop_vec[inds[0]] = 0;
class_map[inds[0]] = 0;
for(int ii = 1; ii < inds.size(); ++ii)
{
if(_prop[inds[ii]] != _prop[inds[ii-1]])
{
++class_num;
++_n_class;
class_map[_prop[inds[ii]]] = class_num;
}
prop_vec[inds[ii]] = class_num;
}
if(_n_class > 2)
_trainer.setMcSvmType(shark::McSvm::WW);
double min_prop = *std::min_element(prop_vec.begin(), prop_vec.end()); double min_prop = *std::min_element(prop_vec.begin(), prop_vec.end());
if(min_prop < 0.0) if(min_prop < 0.0)
...@@ -143,16 +213,20 @@ SISSOClassifier::SISSOClassifier( ...@@ -143,16 +213,20 @@ SISSOClassifier::SISSOClassifier(
std::vector<int> n_samp_per_class; std::vector<int> n_samp_per_class;
int task_start; int task_start;
for(int tt = 0; tt < _n_task; ++tt) for(int tt = 0; tt < _n_task; ++tt)
{ {
std::vector<int> inds(_task_sizes_train[tt]); inds = std::vector<int>(_task_sizes_train[tt]);
std::iota(inds.begin(), inds.end(), task_start); std::iota(inds.begin(), inds.end(), task_start);
for(int cc = 0; cc < _n_class; ++cc) for(int cc = 0; cc < _n_class; ++cc)
_inds_check.push_back(inds); {
_class_task_check.push_back(std::vector<int>(_n_class));
std::iota(_class_task_check.back().begin(), _class_task_check.back().end(), _n_class * tt);
}
util_funcs::argsort(inds.data(), inds.data() + inds.size(), &_prop[task_start]); util_funcs::argsort(inds.data(), inds.data() + inds.size(), &_prop[task_start]);
_sample_inds_to_sorted_dmat_inds[0] = task_start; _sample_inds_to_sorted_dmat_inds[inds[0]] = task_start;
int cls_start = 0; int cls_start = 0;
int cc = 0; int cc = 0;
for(int ii = 1; ii < inds.size(); ++ii) for(int ii = 1; ii < inds.size(); ++ii)
...@@ -160,12 +234,14 @@ SISSOClassifier::SISSOClassifier( ...@@ -160,12 +234,14 @@ SISSOClassifier::SISSOClassifier(
_sample_inds_to_sorted_dmat_inds[inds[ii]] = ii + task_start; _sample_inds_to_sorted_dmat_inds[inds[ii]] = ii + task_start;
if(_prop[inds[ii]] != _prop[inds[ii - 1]]) if(_prop[inds[ii]] != _prop[inds[ii - 1]])
{ {
_inds_check[tt * _n_class + cc].erase(_inds_check[tt * _n_class + cc].begin() + cls_start, _inds_check[tt * _n_class + cc].begin() + ii); _class_task_check[tt * _n_class + cc].erase(_class_task_check[tt * _n_class + cc].begin() + cc);
n_samp_per_class.push_back(ii - cls_start); n_samp_per_class.push_back(ii - cls_start);
cls_start = ii; cls_start = ii;
++cc; ++cc;
} }
} }
_class_task_check[tt * _n_class + cc].erase(_class_task_check[tt * _n_class + cc].begin() + cc);
n_samp_per_class.push_back(inds.size() - cls_start);
if(n_samp_per_class.size() != (tt + 1) * _n_class) if(n_samp_per_class.size() != (tt + 1) * _n_class)
throw std::logic_error("A class is not represented in task " + std::to_string(tt) + "."); throw std::logic_error("A class is not represented in task " + std::to_string(tt) + ".");
......
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Please register or to comment