diff --git a/CMakeLists.txt b/CMakeLists.txt
index 7097d58f1f4cbb1e00771fbb4baa9002334ff0b0..82e4fabc20801f431f60f6264536b63ac44e827a 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -301,16 +301,16 @@ else(EXTERNAL_BOOST)
endif()
endif()
# Check BLAS/LAPACK
-if(CMAKE_MINOR_VERSION GREATER_EQUAL 14)
- set(BLA_VENDOR Intel10_64lp)
-else()
- set(BLA_VENDOR Intel)
-endif()
+set(BLA_VENDOR Intel10_64lp)
find_package(LAPACK)
if(NOT LAPACK_FOUND)
- set(BLA_VENDOR All)
- find_package(LAPACK REQUIRED)
+ set(BLA_VENDOR Intel)
+ find_package(LAPACK)
+ if(NOT LAPACK_FOUND)
+ set(BLA_VENDOR All)
+ find_package(LAPACK REQUIRED)
+ endif()
endif()
list(GET LAPACK_LIBRARIES 0 LAPACK_LIBRARY)
diff --git a/src/classification/ConvexHull1D.hpp b/src/classification/ConvexHull1D.hpp
index c3294940b5768a6c0ba9bdfecec8f5500dafc647..d932a7bc9a7ff598e82604e4f05d02069680d279 100644
--- a/src/classification/ConvexHull1D.hpp
+++ b/src/classification/ConvexHull1D.hpp
@@ -67,7 +67,7 @@ public:
* @param class_szs number of elements in each class
* @returns The projection score for the particular feature
*/
- double overlap_1d(double* value, double width = 1e-5);
+ double overlap_1d(double* value, double width = 0.0);
};
diff --git a/src/descriptor_identifier/SISSO_DI/SISSOClassifier.cpp b/src/descriptor_identifier/SISSO_DI/SISSOClassifier.cpp
index 289a317cd50212c695ce03853b6e52263c785b17..172fa37d5600ca4e8bdaadae92b5bd00b2998cc9 100644
--- a/src/descriptor_identifier/SISSO_DI/SISSOClassifier.cpp
+++ b/src/descriptor_identifier/SISSO_DI/SISSOClassifier.cpp
@@ -231,7 +231,6 @@ void SISSOClassifier::l0_norm(std::vector<double>& prop, int n_dim)
}
}
-
std::vector<int> all_min_n_convex_overlap(_mpi_comm->size() * n_get_models);
std::vector<double> all_min_svm_score(_mpi_comm->size() * n_get_models);
std::vector<double> all_min_svm_margin(_mpi_comm->size() * n_get_models);
diff --git a/src/feature_creation/feature_space/FeatureSpace.cpp b/src/feature_creation/feature_space/FeatureSpace.cpp
index 1d20a41fa0d90a030d2d96568100ad9572a269bc..f75ae553b063b6ac5456ff61cd19a58ee7f45a51 100644
--- a/src/feature_creation/feature_space/FeatureSpace.cpp
+++ b/src/feature_creation/feature_space/FeatureSpace.cpp
@@ -103,55 +103,37 @@ void FeatureSpace::initialize_fs()
else if(_max_phi - _n_rung_generate < _n_rung_store)
throw std::logic_error("Requesting to store more rungs than what can be pre-generated.");
- if(_mpi_comm->rank() == 0)
- {
- std::ofstream out_file_stream = std::ofstream();
- std::ofstream sum_file_stream = std::ofstream();
- out_file_stream.open(_feature_space_file);
- sum_file_stream.open(_feature_space_summary_file);
- out_file_stream << std::setw(14) <<std::left << "# FEAT_ID" << "Feature Postfix Expression (RPN)" << std::endl;
- sum_file_stream << std::setw(14) <<std::left << "# FEAT_ID" << std::setw(24) << std::left << "Score" << "Feature Expression" << std::endl;
- out_file_stream.close();
- sum_file_stream.close();
- }
-
- if(_project_type.compare("regression") == 0)
- {
- _project = project_funcs::project_r2;
- _project_no_omp = project_funcs::project_r2_no_omp;
- }
- else if(_project_type.compare("classification") == 0)
+ node_value_arrs::set_task_sz_train(_task_sizes);
+ int n_max_ops = 0;
+ for(int rr = 0; rr < _max_phi - _n_rung_store; ++rr)
+ n_max_ops += std::pow(2, rr);
+ if((n_max_ops > _phi_0.size()) && (_n_rung_store == 0))
{
- _project = project_funcs::project_classify;
- _project_no_omp = project_funcs::project_classify_no_omp;
+ std::cerr << "WARNING: Setting _n_rung_store to 1 to prevent possible overwrite issues" << std::endl;
+ ++_n_rung_store;
+ _n_rung_generate -= (_n_rung_generate == 1) && (_n_rung_store + _n_rung_generate > _max_phi);
}
- else if(_project_type.compare("log_regression") == 0)
- {
- if(_task_sizes.size() > 1)
- throw std::logic_error("Log Regression can not be done using multiple tasks.");
- if(*std::min_element(_prop.begin(), _prop.end()) < 0.0)
- throw std::logic_error("Log Regression can not be if the property is negative.");
+ initialize_fs_output_files();
+ project_funcs::set_project_fxn(project_type, _task_sizes.size(), _project, _project_no_omp);
+ comp_feats::set_is_valid_fxn(project_type, _cross_cor_max, _n_samp, _is_valid, _is_valid_feat_list);
+ set_op_lists();
- _project = project_funcs::project_log_r2;
- _project_no_omp = project_funcs::project_log_r2_no_omp;
- }
- else
- throw std::logic_error("Wrong projection type passed to FeatureSpace constructor.");
+ double start = omp_get_wtime();
+ generate_feature_space(prop);
+ _mpi_comm->barrier();
+ double duration = omp_get_wtime() - start;
+ if(_mpi_comm->rank() == 0)
+ std::cout << "time to generate feat sapce: " << duration << " s" << std::endl;
- if(_cross_cor_max < 0.99999)
- {
- _is_valid = comp_feats::valid_feature_against_selected;
- _is_valid_feat_list = comp_feats::valid_feature_against_selected_feat_list;
- }
- else
- {
- _is_valid = comp_feats::valid_feature_against_selected_max_corr_1;
- _is_valid_feat_list = comp_feats::valid_feature_against_selected_max_corr_1_feat_list;
- }
+ mpi_reduce_op::set_op(_project_type, _cross_cor_max, _n_sis_select);
- mpi_reduce_op::set_op(_cross_cor_max, _n_sis_select);
+ _scores.reserve(_phi.size());
+ _scores.resize(_phi.size());
+}
+void FeatureSpace::set_op_lists()
+{
for(auto & op : _allowed_ops)
{
if((op.compare("add") == 0) || (op.compare("mult") == 0) || (op.compare("abs_diff") == 0) || (op.compare("sub") == 0))
@@ -173,17 +155,21 @@ void FeatureSpace::initialize_fs()
_un_param_operators.push_back(allowed_op_maps::unary_param_operator_map[op]);
}
#endif
+}
- _mpi_comm->barrier();
- double start = omp_get_wtime();
- generate_feature_space();
- _mpi_comm->barrier();
- double duration = omp_get_wtime() - start;
+void FeatureSpace::initialize_fs_output_files()
+{
if(_mpi_comm->rank() == 0)
- std::cout << "time to generate feat sapce: " << duration << " s" << std::endl;
-
- _scores.reserve(_phi.size());
- _scores.resize(_phi.size());
+ {
+ std::ofstream out_file_stream = std::ofstream();
+ std::ofstream sum_file_stream = std::ofstream();
+ out_file_stream.open(_feature_space_file);
+ sum_file_stream.open(_feature_space_summary_file);
+ out_file_stream << std::setw(14) <<std::left << "# FEAT_ID" << "Feature Postfix Expression (RPN)" << std::endl;
+ sum_file_stream << std::setw(14) <<std::left << "# FEAT_ID" << std::setw(24) << std::left << "Score" << "Feature Expression" << std::endl;
+ out_file_stream.close();
+ sum_file_stream.close();
+ }
}
#ifdef PARAMETERIZE
@@ -595,7 +581,7 @@ void FeatureSpace::project_generated(double* prop, int size, std::vector<node_pt
#pragma omp for schedule(dynamic)
for(auto feat = _phi.begin() + _start_gen.back() + _mpi_comm->rank(); feat < _phi.end(); feat += _mpi_comm->size())
{
- unsigned long int feat_ind = _phi.size() + _n_sis_select * omp_get_num_threads();
+ unsigned long int feat_ind = node_value_arrs::N_STORE_FEATURES + _n_sis_select * (omp_get_num_threads() + _mpi_comm->size());
node_value_arrs::clear_temp_reg_thread();
std::vector<node_ptr> generated_phi;
@@ -620,9 +606,7 @@ void FeatureSpace::project_generated(double* prop, int size, std::vector<node_pt
int ii = 0;
while((ii < inds.size()) && (scores[inds[ii]] < -1.0))
- {
++ii;
- }
while((ii < inds.size()) && ((scores[inds[ii]] < worst_score) || (phi_sel_private.size() < _n_sis_select)))
{
@@ -631,16 +615,15 @@ void FeatureSpace::project_generated(double* prop, int size, std::vector<node_pt
{
if(scores_sel_private.size() == _n_sis_select)
{
- phi_sel_private[worst_score_ind]->set_selected(false);
- phi_sel_private[worst_score_ind]->set_d_mat_ind(-1);
-
- generated_phi[inds[ii]]->reindex(_phi.size() + worst_score_ind + _n_sis_select * omp_get_thread_num());
+ generated_phi[inds[ii]]->reindex(node_value_arrs::N_STORE_FEATURES + worst_score_ind + _n_sis_select * (omp_get_thread_num() + _mpi_comm->size()));
+ generated_phi[inds[ii]]->set_value();
phi_sel_private[worst_score_ind] = generated_phi[inds[ii]];
scores_sel_private[worst_score_ind] = cur_score;
}
else
{
- generated_phi[inds[ii]]->reindex(_phi.size() + scores_sel_private.size() + _n_sis_select * omp_get_thread_num());
+ generated_phi[inds[ii]]->reindex(node_value_arrs::N_STORE_FEATURES + scores_sel_private.size() + _n_sis_select * (omp_get_thread_num() + _mpi_comm->size()));
+ generated_phi[inds[ii]]->set_value();
phi_sel_private.push_back(generated_phi[inds[ii]]);
scores_sel_private.push_back(cur_score);
}
@@ -658,14 +641,19 @@ void FeatureSpace::project_generated(double* prop, int size, std::vector<node_pt
{
if(((phi_sel.size() < _n_sis_select) || (scores_sel_private[sc] < scores_sel[worst_score_ind])) && _is_valid_feat_list(phi_sel_private[sc]->value_ptr(0), _n_samp, _cross_cor_max, phi_sel, scores_sel, scores_sel_private[sc]))
{
+
if(phi_sel.size() == _n_sis_select)
{
scores_sel[worst_score_ind] = scores_sel_private[sc];
+ phi_sel_private[sc]->reindex(node_value_arrs::N_STORE_FEATURES + worst_score_ind + _n_sis_select * _mpi_comm->rank());
+ phi_sel_private[sc]->set_value();
phi_sel[worst_score_ind] = phi_sel_private[sc];
}
else
{
scores_sel.push_back(scores_sel_private[sc]);
+ phi_sel_private[sc]->reindex(node_value_arrs::N_STORE_FEATURES + phi_sel.size() + _n_sis_select * _mpi_comm->rank());
+ phi_sel_private[sc]->set_value();
phi_sel.push_back(phi_sel_private[sc]);
}
worst_score_ind = std::max_element(scores_sel.begin(), scores_sel.end()) - scores_sel.begin();
@@ -687,7 +675,7 @@ void FeatureSpace::sis(std::vector<double>& prop)
sum_file_stream.open(_feature_space_summary_file, std::ios::app);
std::vector<node_ptr> phi_sel;
- std::vector<double> scores_sel(_n_sis_select, 0.0);
+ std::vector<double> scores_sel(_n_sis_select, std::numeric_limits<double>::infinity());
phi_sel.reserve(_n_sis_select);
@@ -741,30 +729,23 @@ void FeatureSpace::sis(std::vector<double>& prop)
std::cout << "Time to get best features on rank : " << omp_get_wtime() - start << " s" << std::endl;
start = omp_get_wtime();
+
+ for(auto& feat : phi_sel)
+ {
+ feat->set_selected(false);
+ feat->set_d_mat_ind(-1);
+ }
+
if(_n_rung_generate > 0)
{
phi_sel.resize(cur_feat_local);
- scores_sel.resize(cur_feat_local);
-
project_generated(prop.data(), prop.size(), phi_sel, scores_sel);
- node_value_arrs::clear_temp_reg();
- node_value_arrs::clear_temp_test_reg();
- for(auto& feat : _phi)
- {
- feat->set_selected(false);
- feat->set_d_mat_ind(-1);
- }
_mpi_comm->barrier();
if(_mpi_comm->rank() == 0)
std::cout << "Projection time for features generated on the fly: " << omp_get_wtime() - start << " s" << std::endl;
}
- for(auto& feat : phi_sel)
- {
- feat->set_selected(false);
- feat->set_d_mat_ind(-1);
- }
std::fill_n(&scores_sel_all[cur_feat], _n_sis_select, 0.0);
// If we are only on one process then phi_sel are the selected features
start = omp_get_wtime();
@@ -776,7 +757,6 @@ void FeatureSpace::sis(std::vector<double>& prop)
local_sel.push_back(mpi_reduce_op::make_node_sc_pair(phi_sel[ff], scores_sel[ff]));
std::vector<node_sc_pair> selected(_n_sis_select);
-
mpi::all_reduce(
*_mpi_comm,
local_sel,
diff --git a/src/feature_creation/feature_space/FeatureSpace.hpp b/src/feature_creation/feature_space/FeatureSpace.hpp
index 76b81ba6239abedb358cff1357cfd1457fede952..cf55c80b3c66c76343d1e6f3dbcc26285fe70461 100644
--- a/src/feature_creation/feature_space/FeatureSpace.hpp
+++ b/src/feature_creation/feature_space/FeatureSpace.hpp
@@ -130,6 +130,15 @@ public:
*/
void initialize_fs();
+ /**
+ * @brief Uses _allowed_ops to set the operator lists
+ */
+ void set_op_lists();
+
+ /**
+ * @brief Initializes the output files for SIS
+ */
+ void initialize_fs_output_files();
/**
* @brief Generate the full feature set from the allowed operators and initial feature set
* @details populates phi with all features from an initial set and the allowed operators
diff --git a/src/feature_creation/node/FeatureNode.cpp b/src/feature_creation/node/FeatureNode.cpp
index de7c9b37e6deee36da3f5a43fcfb26169a96ac11..1d3b293bf003fb8413456e2a1f1ab84992f23e0c 100644
--- a/src/feature_creation/node/FeatureNode.cpp
+++ b/src/feature_creation/node/FeatureNode.cpp
@@ -21,6 +21,21 @@ FeatureNode::FeatureNode(unsigned long int feat_ind, std::string expr, std::vect
FeatureNode::~FeatureNode()
{}
+bool FeatureNode::is_const()
+{
+ bool is_c = false;
+ int pos = 0;
+
+ double* val_ptr = value_ptr();
+ for(auto& sz : node_value_arrs::TASK_SZ_TRAIN)
+ {
+ double mean = util_funcs::mean(val_ptr + pos, sz);
+ is_c = is_c || std::all_of(val_ptr + pos, val_ptr + pos + sz, [&mean](double d){return std::abs(d - mean) < 1e-12;});
+ pos += sz;
+ }
+ return is_c;
+}
+
void FeatureNode::update_add_sub_leaves(std::map<std::string, int>& add_sub_leaves, int pl_mn, int& expected_abs_tot)
{
if(add_sub_leaves.count(_expr) > 0)
diff --git a/src/feature_creation/node/FeatureNode.hpp b/src/feature_creation/node/FeatureNode.hpp
index efdddbdc6b6f7d061dd55176d468c3be94f567a6..83a71e749ebac129f2efbbab39f671cea5e41106 100644
--- a/src/feature_creation/node/FeatureNode.hpp
+++ b/src/feature_creation/node/FeatureNode.hpp
@@ -191,11 +191,7 @@ public:
/**
* @brief Check if feature is constant
*/
- inline bool is_const()
- {
- double mean = util_funcs::mean(value_ptr(), _n_samp);
- return std::all_of(value_ptr(), value_ptr() + _n_samp, [&mean](double d){return std::abs(d - mean) < 1e-12;});
- }
+ bool is_const();
/**
* @brief Returns the type of node this is
diff --git a/src/feature_creation/node/operator_nodes/OperatorNode.hpp b/src/feature_creation/node/operator_nodes/OperatorNode.hpp
index 3dab50697b991c7325e7accbd2f867567a0c41f4..93b18863b3a782417cfdfba8a7382182428f9792 100644
--- a/src/feature_creation/node/operator_nodes/OperatorNode.hpp
+++ b/src/feature_creation/node/operator_nodes/OperatorNode.hpp
@@ -197,7 +197,9 @@ public:
offset = (offset == -1) ? rung() : offset;
if((rung() > node_value_arrs::N_RUNGS_STORED) && (node_value_arrs::temp_storage_reg(_arr_ind, offset) != _feat_ind))
+ {
set_value(offset);
+ }
return node_value_arrs::get_value_ptr(_arr_ind, _feat_ind, offset);
}
@@ -214,7 +216,9 @@ public:
{
offset = (offset == -1) ? rung() : offset;
if((rung() > node_value_arrs::N_RUNGS_STORED) && (node_value_arrs::temp_storage_test_reg(_arr_ind, offset) != _feat_ind))
+ {
set_test_value(offset);
+ }
return node_value_arrs::get_test_value_ptr(_arr_ind, _feat_ind, offset);
}
@@ -223,15 +227,28 @@ public:
/**
* @brief Check if the feature contains NaN
*/
- inline bool is_nan(){return std::any_of(value_ptr(), value_ptr() + _n_samp, [](double d){return !std::isfinite(d);});}
+ inline bool is_nan()
+ {
+ double* val_ptr = value_ptr();
+ return std::any_of(val_ptr, val_ptr + _n_samp, [](double d){return !std::isfinite(d);});
+ }
// DocString: op_node_is_const
/**
* @brief Check if feature is constant
*/
- inline bool is_const()
+ bool is_const()
{
- return util_funcs::stand_dev(value_ptr(), _n_samp) < 1.0e-13;
+ double* val_ptr = value_ptr();
+
+ bool is_c = false;//util_funcs::stand_dev(val_ptr, _n_samp) < 1.0e-13;
+ int pos = 0;
+ for(auto& sz : node_value_arrs::TASK_SZ_TRAIN)
+ {
+ is_c = is_c || (util_funcs::stand_dev(val_ptr + pos, sz) < 1.0e-13);
+ pos += sz;
+ }
+ return is_c;
}
// DocString: op_node_rung
diff --git a/src/feature_creation/node/operator_nodes/allowed_operator_nodes/abs/absolute_value.cpp b/src/feature_creation/node/operator_nodes/allowed_operator_nodes/abs/absolute_value.cpp
index a9e4339da5ead059377093032a960066da1bd572..52241255ed421ca40626ed553adf5ac5ccd74491 100644
--- a/src/feature_creation/node/operator_nodes/allowed_operator_nodes/abs/absolute_value.cpp
+++ b/src/feature_creation/node/operator_nodes/allowed_operator_nodes/abs/absolute_value.cpp
@@ -12,13 +12,13 @@ void generateAbsNode(std::vector<node_ptr>& feat_list, node_ptr feat, unsigned l
if(*std::min_element(val_ptr, val_ptr + feat->n_samp()) > 0.0)
return;
- val_ptr = node_value_arrs::get_value_ptr(feat_ind, feat_ind, offset);
- allowed_op_funcs::abs(feat->n_samp(), feat->value_ptr(offset + 2), 1.0, 0.0, val_ptr);
+ node_ptr new_feat = std::make_shared<AbsNode>(feat, feat_ind);
+ val_ptr = new_feat->value_ptr();
- if((util_funcs::stand_dev(val_ptr, feat->n_samp()) < 1.0e-13) || std::any_of(val_ptr, val_ptr + feat->n_samp(), [&u_bound](double d){return !std::isfinite(d) || (std::abs(d) > u_bound);}) || (util_funcs::max_abs_val<double>(val_ptr, feat->n_samp()) < l_bound))
+ if(new_feat->is_const() || std::any_of(val_ptr, val_ptr + new_feat->n_samp(), [&u_bound](double d){return !std::isfinite(d) || (std::abs(d) > u_bound);}) || (util_funcs::max_abs_val<double>(val_ptr, new_feat->n_samp()) < l_bound))
return;
- feat_list.push_back(std::make_shared<AbsNode>(feat, feat_ind));
+ feat_list.push_back(new_feat);
}
AbsNode::AbsNode()
diff --git a/src/feature_creation/node/operator_nodes/allowed_operator_nodes/abs_diff/absolute_difference.cpp b/src/feature_creation/node/operator_nodes/allowed_operator_nodes/abs_diff/absolute_difference.cpp
index db41051b3419de2692a4a67e2eb98634be85284d..7422e108fb2a832757e130eabbe059b4fc577330 100644
--- a/src/feature_creation/node/operator_nodes/allowed_operator_nodes/abs_diff/absolute_difference.cpp
+++ b/src/feature_creation/node/operator_nodes/allowed_operator_nodes/abs_diff/absolute_difference.cpp
@@ -15,24 +15,21 @@ void generateAbsDiffNode(std::vector<node_ptr>& feat_list, node_ptr feat_1, node
return;
int add_sub_tot_first = std::abs(add_sub_leaves.begin()->second);
-
if((std::abs(add_sub_tot_first) > 1) && std::all_of(add_sub_leaves.begin(), add_sub_leaves.end(), [&add_sub_tot_first](auto el){return std::abs(el.second) == add_sub_tot_first;}))
return;
-
- int offset = std::max(feat_1->rung(), feat_2->rung()) + 1;
- double* val_ptr = node_value_arrs::get_value_ptr(feat_ind, feat_ind, offset);
-
- allowed_op_funcs::sub(feat_1->n_samp(), feat_1->value_ptr(offset + 2), feat_2->value_ptr(offset + 1), 1.0, 0.0, val_ptr);
- if(*std::min_element(val_ptr, val_ptr + feat_1->n_samp()) > l_bound)
+ node_ptr new_feat = std::make_shared<SubNode>(feat_1, feat_2, feat_ind);
+ double* val_ptr = new_feat->value_ptr();
+ if(*std::min_element(val_ptr, val_ptr + new_feat->n_samp()) > l_bound)
return;
- allowed_op_funcs::abs_diff(feat_1->n_samp(), feat_1->value_ptr(offset + 2), feat_2->value_ptr(offset + 1), 1.0, 0.0, val_ptr);
+ new_feat = std::make_shared<AbsDiffNode>(feat_1, feat_2, feat_ind);
+ new_feat->set_value();
- if((util_funcs::stand_dev(val_ptr, feat_1->n_samp()) < 1.0e-13) || std::any_of(val_ptr, val_ptr + feat_1->n_samp(), [&u_bound](double d){return !std::isfinite(d) || (std::abs(d) > u_bound);}) || (util_funcs::max_abs_val<double>(val_ptr, feat_1->n_samp()) < l_bound))
+ if(new_feat->is_const() || std::any_of(val_ptr, val_ptr + new_feat->n_samp(), [&u_bound](double d){return !std::isfinite(d) || (std::abs(d) > u_bound);}) || (util_funcs::max_abs_val<double>(val_ptr, new_feat->n_samp()) < l_bound))
return;
- feat_list.push_back(std::make_shared<AbsDiffNode>(feat_1, feat_2, feat_ind));
+ feat_list.push_back(new_feat);
}
AbsDiffNode::AbsDiffNode()
diff --git a/src/feature_creation/node/operator_nodes/allowed_operator_nodes/add/add.cpp b/src/feature_creation/node/operator_nodes/allowed_operator_nodes/add/add.cpp
index b01a02c1d28b9045df684548e56fdf9fe3f83372..ae6736434743042d5938b3f8bb10972be8ed9e89 100644
--- a/src/feature_creation/node/operator_nodes/allowed_operator_nodes/add/add.cpp
+++ b/src/feature_creation/node/operator_nodes/allowed_operator_nodes/add/add.cpp
@@ -18,14 +18,12 @@ void generateAddNode(std::vector<node_ptr>& feat_list, node_ptr feat_1, node_ptr
if((std::abs(add_sub_tot_first) > 1) && std::all_of(add_sub_leaves.begin(), add_sub_leaves.end(), [&add_sub_tot_first](auto el){return std::abs(el.second) == add_sub_tot_first;}))
return;
- int offset = feat_1->rung() + 1;
- double* val_ptr = node_value_arrs::get_value_ptr(feat_ind, feat_ind, offset);
- allowed_op_funcs::add(feat_1->n_samp(), feat_1->value_ptr(offset + 2), feat_2->value_ptr(offset + 1), 1.0, 0.0, val_ptr);
+ node_ptr new_feat = std::make_shared<AddNode>(feat_1, feat_2, feat_ind);
+ double* val_ptr = new_feat->value_ptr();
+ if(new_feat->is_const() || std::any_of(val_ptr, val_ptr + new_feat->n_samp(), [&u_bound](double d){return !std::isfinite(d) || (std::abs(d) > u_bound);}) || (util_funcs::max_abs_val<double>(val_ptr, new_feat->n_samp()) < l_bound))
+ return;
- if((util_funcs::stand_dev(val_ptr, feat_1->n_samp()) < 1.0e-13) || std::any_of(val_ptr, val_ptr + feat_1->n_samp(), [&u_bound](double d){return !std::isfinite(d) || (std::abs(d) > u_bound);}) || (util_funcs::max_abs_val<double>(val_ptr, feat_1->n_samp()) < l_bound))
- return;
-
- feat_list.push_back(std::make_shared<AddNode>(feat_1, feat_2, feat_ind));
+ feat_list.push_back(new_feat);
}
AddNode::AddNode()
diff --git a/src/feature_creation/node/operator_nodes/allowed_operator_nodes/cb/cube.cpp b/src/feature_creation/node/operator_nodes/allowed_operator_nodes/cb/cube.cpp
index 88b636c40ae53dff6b4f5c62ed7fe69c4e22f1da..2c3396fd68670b2e40942098b8e4f9904c788ca4 100644
--- a/src/feature_creation/node/operator_nodes/allowed_operator_nodes/cb/cube.cpp
+++ b/src/feature_creation/node/operator_nodes/allowed_operator_nodes/cb/cube.cpp
@@ -6,14 +6,14 @@ void generateCbNode(std::vector<node_ptr>& feat_list, node_ptr feat, unsigned lo
if((feat->type() == NODE_TYPE::CBRT) || (feat->type() == NODE_TYPE::SQ) || (feat->type() == NODE_TYPE::INV))
return;
- int offset = feat->rung() + 1;
- double* val_ptr = node_value_arrs::get_value_ptr(feat_ind, feat_ind, offset);
- allowed_op_funcs::cb(feat->n_samp(), feat->value_ptr(offset + 2), 1.0, 0.0, val_ptr);
+ node_ptr new_feat = std::make_shared<CbNode>(feat, feat_ind);
+ double* val_ptr = new_feat->value_ptr();
- if((util_funcs::stand_dev(val_ptr, feat->n_samp()) < 1.0e-13) || std::any_of(val_ptr, val_ptr + feat->n_samp(), [&u_bound](double d){return !std::isfinite(d) || (std::abs(d) > u_bound);}) || (util_funcs::max_abs_val<double>(val_ptr, feat->n_samp()) < l_bound))
+ // No is_const check since cube function can only be constant if feat is constant
+ if(std::any_of(val_ptr, val_ptr + new_feat->n_samp(), [&u_bound](double d){return !std::isfinite(d) || (std::abs(d) > u_bound);}) || (util_funcs::max_abs_val<double>(val_ptr, new_feat->n_samp()) < l_bound))
return;
- feat_list.push_back(std::make_shared<CbNode>(feat, feat_ind));
+ feat_list.push_back(new_feat);
}
CbNode::CbNode()
@@ -30,7 +30,9 @@ CbNode::CbNode(node_ptr feat, unsigned long int feat_ind, double l_bound, double
throw InvalidFeatureException();
set_value();
- if(is_nan() || is_const() || (util_funcs::max_abs_val<double>(value_ptr(), _n_samp) > u_bound) || (util_funcs::max_abs_val<double>(value_ptr(), _n_samp) < l_bound))
+
+ // No is_const check since cube function can only be constant if feat is constant
+ if(is_nan() || (util_funcs::max_abs_val<double>(value_ptr(), _n_samp) > u_bound) || (util_funcs::max_abs_val<double>(value_ptr(), _n_samp) < l_bound))
throw InvalidFeatureException();
}
diff --git a/src/feature_creation/node/operator_nodes/allowed_operator_nodes/cb/parameterized_cube.cpp b/src/feature_creation/node/operator_nodes/allowed_operator_nodes/cb/parameterized_cube.cpp
index 14befdb4aa0f09434b10302d7b18f86bcde3c34e..2792a7ab70deccbc199de3920f440327fc93abcd 100644
--- a/src/feature_creation/node/operator_nodes/allowed_operator_nodes/cb/parameterized_cube.cpp
+++ b/src/feature_creation/node/operator_nodes/allowed_operator_nodes/cb/parameterized_cube.cpp
@@ -10,7 +10,8 @@ void generateCbParamNode(std::vector<node_ptr>& feat_list, node_ptr feat, unsign
return;
new_feat->set_value();
- if(new_feat->is_nan() || new_feat->is_const() || (util_funcs::max_abs_val<double>(new_feat->value_ptr(), new_feat->n_samp()) > u_bound) || (util_funcs::max_abs_val<double>(new_feat->value_ptr(), new_feat->n_samp()) < l_bound))
+ // No is_const check since cube function can only be constant if feat is constant
+ if(new_feat->is_nan() || (util_funcs::max_abs_val<double>(new_feat->value_ptr(), new_feat->n_samp()) > u_bound) || (util_funcs::max_abs_val<double>(new_feat->value_ptr(), new_feat->n_samp()) < l_bound))
return;
feat_list.push_back(new_feat);
@@ -26,7 +27,8 @@ CbParamNode::CbParamNode(node_ptr feat, unsigned long int feat_ind, double l_bou
get_parameters(optimizer);
set_value();
- if((std::abs(_params[0]) <= 1e-10) || is_nan() || is_const() || (util_funcs::max_abs_val<double>(value_ptr(), _n_samp) > u_bound) || (util_funcs::max_abs_val<double>(value_ptr(), _n_samp) < l_bound))
+ // No is_const check since cube function can only be constant if feat is constant
+ if((std::abs(_params[0]) <= 1e-10) || is_nan() || (util_funcs::max_abs_val<double>(value_ptr(), _n_samp) > u_bound) || (util_funcs::max_abs_val<double>(value_ptr(), _n_samp) < l_bound))
throw InvalidFeatureException();
}
diff --git a/src/feature_creation/node/operator_nodes/allowed_operator_nodes/cbrt/cube_root.cpp b/src/feature_creation/node/operator_nodes/allowed_operator_nodes/cbrt/cube_root.cpp
index 5d5d14b5ba3903cd6c41620785d7fec386330996..3b6e5ac8d740123bd412e2a88e2621476eda0012 100644
--- a/src/feature_creation/node/operator_nodes/allowed_operator_nodes/cbrt/cube_root.cpp
+++ b/src/feature_creation/node/operator_nodes/allowed_operator_nodes/cbrt/cube_root.cpp
@@ -6,19 +6,14 @@ void generateCbrtNode(std::vector<node_ptr>& feat_list, node_ptr feat, unsigned
if((feat->type() == NODE_TYPE::CB) || (feat->type() == NODE_TYPE::SQ) || (feat->type() == NODE_TYPE::SIX_POW) || (feat->type() == NODE_TYPE::INV))
return;
- int offset = feat->rung() + 1;
+ node_ptr new_feat = std::make_shared<CbrtNode>(feat, feat_ind);
+ double* val_ptr = new_feat->value_ptr();
- double* val_ptr = feat->value_ptr(offset + 2);
- if(*std::min_element(val_ptr, val_ptr + feat->n_samp()) < 0.0)
+ // No is_const check since cube function can only be constant if feat is constant
+ if(std::any_of(val_ptr, val_ptr + new_feat->n_samp(), [&u_bound](double d){return !std::isfinite(d) || (std::abs(d) > u_bound);}) || (util_funcs::max_abs_val<double>(val_ptr, new_feat->n_samp()) < l_bound))
return;
- val_ptr = node_value_arrs::get_value_ptr(feat_ind, feat_ind, offset);
- allowed_op_funcs::cbrt(feat->n_samp(), feat->value_ptr(offset + 2), 1.0, 0.0, val_ptr);
-
- if((util_funcs::stand_dev(val_ptr, feat->n_samp()) < 1.0e-13) || std::any_of(val_ptr, val_ptr + feat->n_samp(), [&u_bound](double d){return !std::isfinite(d) || (std::abs(d) > u_bound);}) || (util_funcs::max_abs_val<double>(val_ptr, feat->n_samp()) < l_bound))
- return;
-
- feat_list.push_back(std::make_shared<CbrtNode>(feat, feat_ind));
+ feat_list.push_back(new_feat);
}
CbrtNode::CbrtNode()
@@ -39,9 +34,10 @@ CbrtNode::CbrtNode(node_ptr feat, unsigned long int feat_ind, double l_bound, do
throw InvalidFeatureException();
set_value();
- if(is_nan() || is_const() || (util_funcs::max_abs_val<double>(value_ptr(), _n_samp) > u_bound) || (util_funcs::max_abs_val<double>(value_ptr(), _n_samp) < l_bound))
- throw InvalidFeatureException();
+ // No is_const check since cube function can only be constant if feat is constant
+ if(is_nan() || (util_funcs::max_abs_val<double>(value_ptr(), _n_samp) > u_bound) || (util_funcs::max_abs_val<double>(value_ptr(), _n_samp) < l_bound))
+ throw InvalidFeatureException();
}
void CbrtNode::update_add_sub_leaves(std::map<std::string, int>& add_sub_leaves, int pl_mn, int& expected_abs_tot)
diff --git a/src/feature_creation/node/operator_nodes/allowed_operator_nodes/cbrt/parameterized_cube_root.cpp b/src/feature_creation/node/operator_nodes/allowed_operator_nodes/cbrt/parameterized_cube_root.cpp
index ab9b657bde1e52c1f7772c4f55a4e392c71f17fa..ee913d0758b0dfca33639964a5f2ffca5e697f56 100644
--- a/src/feature_creation/node/operator_nodes/allowed_operator_nodes/cbrt/parameterized_cube_root.cpp
+++ b/src/feature_creation/node/operator_nodes/allowed_operator_nodes/cbrt/parameterized_cube_root.cpp
@@ -10,7 +10,8 @@ void generateCbrtParamNode(std::vector<node_ptr>& feat_list, node_ptr feat, unsi
return;
new_feat->set_value();
- if(new_feat->is_nan() || new_feat->is_const() || (util_funcs::max_abs_val<double>(new_feat->value_ptr(), new_feat->n_samp()) > u_bound) || (util_funcs::max_abs_val<double>(new_feat->value_ptr(), new_feat->n_samp()) < l_bound))
+ // No is_const check since cube function can only be constant if feat is constant
+ if(new_feat->is_nan() || (util_funcs::max_abs_val<double>(new_feat->value_ptr(), new_feat->n_samp()) > u_bound) || (util_funcs::max_abs_val<double>(new_feat->value_ptr(), new_feat->n_samp()) < l_bound))
return;
feat_list.push_back(new_feat);
@@ -26,7 +27,8 @@ CbrtParamNode::CbrtParamNode(node_ptr feat, unsigned long int feat_ind, double l
_params.resize(n_params(), 0.0);
get_parameters(optimizer);
- if((std::abs(_params[0]) <= 1e-10) || is_nan() || is_const() || (util_funcs::max_abs_val<double>(value_ptr(), _n_samp) > u_bound) || (util_funcs::max_abs_val<double>(value_ptr(), _n_samp) < l_bound))
+ // No is_const check since cube function can only be constant if feat is constant
+ if((std::abs(_params[0]) <= 1e-10) || is_nan() || (util_funcs::max_abs_val<double>(value_ptr(), _n_samp) > u_bound) || (util_funcs::max_abs_val<double>(value_ptr(), _n_samp) < l_bound))
throw InvalidFeatureException();
}
diff --git a/src/feature_creation/node/operator_nodes/allowed_operator_nodes/cos/cos.cpp b/src/feature_creation/node/operator_nodes/allowed_operator_nodes/cos/cos.cpp
index afca45d4da890fb47c3caaf6d36f25a48bfb4c0d..b6462cf08c4e4ad1feba553b336dbb091735fc8d 100644
--- a/src/feature_creation/node/operator_nodes/allowed_operator_nodes/cos/cos.cpp
+++ b/src/feature_creation/node/operator_nodes/allowed_operator_nodes/cos/cos.cpp
@@ -6,13 +6,13 @@ void generateCosNode(std::vector<node_ptr>& feat_list, node_ptr feat, unsigned l
if(feat->unit() != Unit() || (feat->type() == NODE_TYPE::SIN) || (feat->type() == NODE_TYPE::COS))
return;
- int offset = feat->rung() + 1;
- double* val_ptr = node_value_arrs::get_value_ptr(feat_ind, feat_ind, offset);
- allowed_op_funcs::cos(feat->n_samp(), feat->value_ptr(offset + 2), 1.0, 0.0, val_ptr);
+ node_ptr new_feat = std::make_shared<CosNode>(feat, feat_ind);
+ double* val_ptr = new_feat->value_ptr();
- if((util_funcs::stand_dev(val_ptr, feat->n_samp()) < 1.0e-13) || std::any_of(val_ptr, val_ptr + feat->n_samp(), [&u_bound](double d){return !std::isfinite(d) || (std::abs(d) > u_bound);}) || (util_funcs::max_abs_val<double>(val_ptr, feat->n_samp()) < l_bound))
+ if(new_feat->is_const() || std::any_of(val_ptr, val_ptr + new_feat->n_samp(), [&u_bound](double d){return !std::isfinite(d) || (std::abs(d) > u_bound);}) || (util_funcs::max_abs_val<double>(val_ptr, new_feat->n_samp()) < l_bound))
return;
- feat_list.push_back(std::make_shared<CosNode>(feat, feat_ind));
+
+ feat_list.push_back(new_feat);
}
CosNode::CosNode()
diff --git a/src/feature_creation/node/operator_nodes/allowed_operator_nodes/div/divide.cpp b/src/feature_creation/node/operator_nodes/allowed_operator_nodes/div/divide.cpp
index e26009893c74827b8d94ee766d88b2fca7f1575b..8dc797aeeea644ea5d883a1bc39c5915df028146 100644
--- a/src/feature_creation/node/operator_nodes/allowed_operator_nodes/div/divide.cpp
+++ b/src/feature_creation/node/operator_nodes/allowed_operator_nodes/div/divide.cpp
@@ -14,14 +14,16 @@ void generateDivNode(std::vector<node_ptr>& feat_list, node_ptr feat_1, node_ptr
if((div_mult_leaves.size() < 2) || (std::abs(std::accumulate(div_mult_leaves.begin(), div_mult_leaves.end(), -1.0*expected_abs_tot, [](double tot, auto el){return tot + std::abs(el.second);})) > 1e-12))
return;
- int offset = std::max(feat_1->rung(), feat_2->rung()) + 1;
- double* val_ptr = node_value_arrs::get_value_ptr(feat_ind, feat_ind, offset);
- allowed_op_funcs::div(feat_1->n_samp(), feat_1->value_ptr(offset + 2), feat_2->value_ptr(offset + 1), 1.0, 0.0, val_ptr);
+ double div_mult_tot_first = div_mult_leaves.begin()->second;
+ if((std::abs(div_mult_tot_first) != 1.0) && std::all_of(div_mult_leaves.begin(), div_mult_leaves.end(), [&div_mult_tot_first](auto el){return el.second == div_mult_tot_first;}))
+ return;
- if((util_funcs::stand_dev(val_ptr, feat_1->n_samp()) < 1.0e-13) || std::any_of(val_ptr, val_ptr + feat_1->n_samp(), [&u_bound](double d){return !std::isfinite(d) || (std::abs(d) > u_bound);}) || (util_funcs::max_abs_val<double>(val_ptr, feat_1->n_samp()) < l_bound))
+ node_ptr new_feat = std::make_shared<DivNode>(feat_1, feat_2, feat_ind);
+ double* val_ptr = new_feat->value_ptr();
+ if(new_feat->is_const() || std::any_of(val_ptr, val_ptr + new_feat->n_samp(), [&u_bound](double d){return !std::isfinite(d) || (std::abs(d) > u_bound);}) || (util_funcs::max_abs_val<double>(val_ptr, new_feat->n_samp()) < l_bound))
return;
- feat_list.push_back(std::make_shared<DivNode>(feat_1, feat_2, feat_ind));
+ feat_list.push_back(new_feat);
}
DivNode::DivNode()
diff --git a/src/feature_creation/node/operator_nodes/allowed_operator_nodes/exp/exponential.cpp b/src/feature_creation/node/operator_nodes/allowed_operator_nodes/exp/exponential.cpp
index d765d807f950297f34aac9936daeb88852b5fa4c..f47902ec5452d983863b16c722899e3ca63023f6 100644
--- a/src/feature_creation/node/operator_nodes/allowed_operator_nodes/exp/exponential.cpp
+++ b/src/feature_creation/node/operator_nodes/allowed_operator_nodes/exp/exponential.cpp
@@ -6,14 +6,13 @@ void generateExpNode(std::vector<node_ptr>& feat_list, node_ptr feat, unsigned l
if((feat->unit() != Unit()) || (feat->type() == NODE_TYPE::NEG_EXP) || (feat->type() == NODE_TYPE::EXP) || (feat->type() == NODE_TYPE::ADD) || (feat->type() == NODE_TYPE::SUB) || (feat->type() == NODE_TYPE::LOG))
return;
- int offset = feat->rung() + 1;
- double* val_ptr = node_value_arrs::get_value_ptr(feat_ind, feat_ind, offset);
- allowed_op_funcs::exp(feat->n_samp(), feat->value_ptr(offset + 2), 1.0, 0.0, val_ptr);
+ node_ptr new_feat = std::make_shared<ExpNode>(feat, feat_ind);
+ double* val_ptr = new_feat->value_ptr();
- if(std::any_of(val_ptr, val_ptr + feat->n_samp(), [&u_bound](double d){return !std::isfinite(d) || (std::abs(d) > u_bound);}) || (util_funcs::max_abs_val<double>(val_ptr, feat->n_samp()) < l_bound))
- return;
-
- feat_list.push_back(std::make_shared<ExpNode>(feat, feat_ind));
+ // No is_const check since cube function can only be constant if feat is constant
+ if(std::any_of(val_ptr, val_ptr + new_feat->n_samp(), [&u_bound](double d){return !std::isfinite(d) || (std::abs(d) > u_bound);}) || (util_funcs::max_abs_val<double>(val_ptr, new_feat->n_samp()) < l_bound))
+ return;
+ feat_list.push_back(new_feat);
}
ExpNode::ExpNode()
@@ -33,6 +32,8 @@ ExpNode::ExpNode(node_ptr feat, unsigned long int feat_ind, double l_bound, doub
throw InvalidFeatureException();
set_value();
+
+ // No is_const check since cube function can only be constant if feat is constant
if(is_nan() || (util_funcs::max_abs_val<double>(value_ptr(), _n_samp) > u_bound) || (util_funcs::max_abs_val<double>(value_ptr(), _n_samp) < l_bound))
throw InvalidFeatureException();
diff --git a/src/feature_creation/node/operator_nodes/allowed_operator_nodes/exp/parameterized_exponential.cpp b/src/feature_creation/node/operator_nodes/allowed_operator_nodes/exp/parameterized_exponential.cpp
index a9c3046c109a7820cddc027e7428d13b91dbebe6..b98c89b03d98b39bfa60b1523f92cf6a8f66b942 100644
--- a/src/feature_creation/node/operator_nodes/allowed_operator_nodes/exp/parameterized_exponential.cpp
+++ b/src/feature_creation/node/operator_nodes/allowed_operator_nodes/exp/parameterized_exponential.cpp
@@ -14,7 +14,8 @@ void generateExpParamNode(std::vector<node_ptr>& feat_list, node_ptr feat, unsig
return;
new_feat->set_value();
- if(new_feat->is_nan() || new_feat->is_const() || (util_funcs::max_abs_val<double>(new_feat->value_ptr(), new_feat->n_samp()) > u_bound) || (util_funcs::max_abs_val<double>(new_feat->value_ptr(), new_feat->n_samp()) < l_bound))
+ // No is_const check since cube function can only be constant if feat is constant
+ if(new_feat->is_nan() || (util_funcs::max_abs_val<double>(new_feat->value_ptr(), new_feat->n_samp()) > u_bound) || (util_funcs::max_abs_val<double>(new_feat->value_ptr(), new_feat->n_samp()) < l_bound))
return;
feat_list.push_back(new_feat);
@@ -32,7 +33,8 @@ ExpParamNode::ExpParamNode(node_ptr feat, unsigned long int feat_ind, double l_b
_params.resize(n_params(), 0.0);
get_parameters(optimizer);
- if((std::abs(_params[0]) <= 1e-10) || is_nan() || is_const() || (util_funcs::max_abs_val<double>(value_ptr(), _n_samp) > u_bound) || (util_funcs::max_abs_val<double>(value_ptr(), _n_samp) < l_bound))
+ // No is_const check since cube function can only be constant if feat is constant
+ if((std::abs(_params[0]) <= 1e-10) || is_nan() || (util_funcs::max_abs_val<double>(value_ptr(), _n_samp) > u_bound) || (util_funcs::max_abs_val<double>(value_ptr(), _n_samp) < l_bound))
throw InvalidFeatureException();
}
diff --git a/src/feature_creation/node/operator_nodes/allowed_operator_nodes/inv/inverse.cpp b/src/feature_creation/node/operator_nodes/allowed_operator_nodes/inv/inverse.cpp
index e5832e534c99358383b1dd118fbf7a6b642a7020..0b8cd16850f09d4ec60765e15cc2d4273182650b 100644
--- a/src/feature_creation/node/operator_nodes/allowed_operator_nodes/inv/inverse.cpp
+++ b/src/feature_creation/node/operator_nodes/allowed_operator_nodes/inv/inverse.cpp
@@ -7,14 +7,14 @@ void generateInvNode(std::vector<node_ptr>& feat_list, node_ptr feat, unsigned l
if((feat->type() == NODE_TYPE::DIV) || (feat->type() == NODE_TYPE::EXP) || (feat->type() == NODE_TYPE::NEG_EXP) || (feat->type() == NODE_TYPE::INV))
return;
- int offset = feat->rung() + 1;
- double* val_ptr = node_value_arrs::get_value_ptr(feat_ind, feat_ind, offset);
- allowed_op_funcs::inv(feat->n_samp(), feat->value_ptr(offset + 2), 1.0, 0.0, val_ptr);
+ node_ptr new_feat = std::make_shared<InvNode>(feat, feat_ind);
+ double* val_ptr = new_feat->value_ptr();
- if((util_funcs::stand_dev(val_ptr, feat->n_samp()) < 1.0e-13) || std::any_of(val_ptr, val_ptr + feat->n_samp(), [&u_bound](double d){return !std::isfinite(d) || (std::abs(d) > u_bound);}) || (util_funcs::max_abs_val<double>(val_ptr, feat->n_samp()) < l_bound))
- return;
+ // No is_const check since cube function can only be constant if feat is constant
+ if(std::any_of(val_ptr, val_ptr + new_feat->n_samp(), [&u_bound](double d){return !std::isfinite(d) || (std::abs(d) > u_bound);}) || (util_funcs::max_abs_val<double>(val_ptr, new_feat->n_samp()) < l_bound))
+ return;
- feat_list.push_back(std::make_shared<InvNode>(feat, feat_ind));
+ feat_list.push_back(new_feat);
}
InvNode::InvNode()
@@ -31,7 +31,9 @@ InvNode::InvNode(node_ptr feat, unsigned long int feat_ind, double l_bound, doub
throw InvalidFeatureException();
set_value();
- if(is_nan() || is_const() || (util_funcs::max_abs_val<double>(value_ptr(), _n_samp) > u_bound) || (util_funcs::max_abs_val<double>(value_ptr(), _n_samp) < l_bound))
+
+ // No is_const check since cube function can only be constant if feat is constant
+ if(is_nan() || (util_funcs::max_abs_val<double>(value_ptr(), _n_samp) > u_bound) || (util_funcs::max_abs_val<double>(value_ptr(), _n_samp) < l_bound))
throw InvalidFeatureException();
}
diff --git a/src/feature_creation/node/operator_nodes/allowed_operator_nodes/inv/parameterized_inverse.cpp b/src/feature_creation/node/operator_nodes/allowed_operator_nodes/inv/parameterized_inverse.cpp
index 51672fac9dfdcfa7e06b8ea1d93092e0a3909396..de97c39a40dbed4f9d7ad39e3a1907a4c84b5753 100644
--- a/src/feature_creation/node/operator_nodes/allowed_operator_nodes/inv/parameterized_inverse.cpp
+++ b/src/feature_creation/node/operator_nodes/allowed_operator_nodes/inv/parameterized_inverse.cpp
@@ -14,7 +14,8 @@ void generateInvParamNode(std::vector<node_ptr>& feat_list, node_ptr feat, unsig
return;
new_feat->set_value();
- if(new_feat->is_nan() || new_feat->is_const() || (util_funcs::max_abs_val<double>(new_feat->value_ptr(), new_feat->n_samp()) > u_bound) || (util_funcs::max_abs_val<double>(new_feat->value_ptr(), new_feat->n_samp()) < l_bound))
+ // No is_const check since cube function can only be constant if feat is constant
+ if(new_feat->is_nan() || (util_funcs::max_abs_val<double>(new_feat->value_ptr(), new_feat->n_samp()) > u_bound) || (util_funcs::max_abs_val<double>(new_feat->value_ptr(), new_feat->n_samp()) < l_bound))
return;
feat_list.push_back(new_feat);
@@ -32,7 +33,8 @@ InvParamNode::InvParamNode(node_ptr feat, unsigned long int feat_ind, double l_b
_params.resize(n_params(), 0.0);
get_parameters(optimizer);
- if((std::abs(_params[0]) <= 1e-10) || is_nan() || is_const() || (util_funcs::max_abs_val<double>(value_ptr(), _n_samp) > u_bound) || (util_funcs::max_abs_val<double>(value_ptr(), _n_samp) < l_bound))
+ // No is_const check since cube function can only be constant if feat is constant
+ if((std::abs(_params[0]) <= 1e-10) || is_nan() || (util_funcs::max_abs_val<double>(value_ptr(), _n_samp) > u_bound) || (util_funcs::max_abs_val<double>(value_ptr(), _n_samp) < l_bound))
throw InvalidFeatureException();
}
diff --git a/src/feature_creation/node/operator_nodes/allowed_operator_nodes/log/log.cpp b/src/feature_creation/node/operator_nodes/allowed_operator_nodes/log/log.cpp
index 95e7f1ece42ac44a645f01118d9f3ff8c76a1a9d..454df9221929ab2cf6d4963bde72e13b1b8c8ae5 100644
--- a/src/feature_creation/node/operator_nodes/allowed_operator_nodes/log/log.cpp
+++ b/src/feature_creation/node/operator_nodes/allowed_operator_nodes/log/log.cpp
@@ -6,14 +6,14 @@ void generateLogNode(std::vector<node_ptr>& feat_list, node_ptr feat, unsigned l
if(feat->unit() != Unit() || (feat->type() == NODE_TYPE::NEG_EXP) || (feat->type() == NODE_TYPE::EXP) || (feat->type() == NODE_TYPE::DIV) || (feat->type() == NODE_TYPE::INV) || (feat->type() == NODE_TYPE::MULT) || (feat->type() == NODE_TYPE::LOG) || (feat->type() == NODE_TYPE::SIX_POW) || (feat->type() == NODE_TYPE::CB) || (feat->type() == NODE_TYPE::SQ) || (feat->type() == NODE_TYPE::CBRT) || (feat->type() == NODE_TYPE::SQRT))
return;
- int offset = feat->rung() + 1;
- double* val_ptr = node_value_arrs::get_value_ptr(feat_ind, feat_ind, offset);
- allowed_op_funcs::log(feat->n_samp(), feat->value_ptr(offset + 2), 1.0, 0.0, val_ptr);
+ node_ptr new_feat = std::make_shared<LogNode>(feat, feat_ind);
+ double* val_ptr = new_feat->value_ptr();
- if((util_funcs::stand_dev(val_ptr, feat->n_samp()) < 1.0e-13) || std::any_of(val_ptr, val_ptr + feat->n_samp(), [&u_bound](double d){return !std::isfinite(d) || (std::abs(d) > u_bound);}) || (util_funcs::max_abs_val<double>(val_ptr, feat->n_samp()) < l_bound))
+ // No is_const check since cube function can only be constant if feat is constant
+ if(std::any_of(val_ptr, val_ptr + new_feat->n_samp(), [&u_bound](double d){return !std::isfinite(d) || (std::abs(d) > u_bound);}) || (util_funcs::max_abs_val<double>(val_ptr, new_feat->n_samp()) < l_bound))
return;
- feat_list.push_back(std::make_shared<LogNode>(feat, feat_ind));
+ feat_list.push_back(new_feat);
}
LogNode::LogNode()
@@ -33,7 +33,9 @@ LogNode::LogNode(node_ptr feat, unsigned long int feat_ind, double l_bound, doub
throw InvalidFeatureException();
set_value();
- if(is_nan() || is_const() || (util_funcs::max_abs_val<double>(value_ptr(), _n_samp) > u_bound) || (util_funcs::max_abs_val<double>(value_ptr(), _n_samp) < l_bound))
+
+ // No is_const check since cube function can only be constant if feat is constant
+ if(is_nan() || (util_funcs::max_abs_val<double>(value_ptr(), _n_samp) > u_bound) || (util_funcs::max_abs_val<double>(value_ptr(), _n_samp) < l_bound))
throw InvalidFeatureException();
set_test_value();
diff --git a/src/feature_creation/node/operator_nodes/allowed_operator_nodes/log/parameterized_log.cpp b/src/feature_creation/node/operator_nodes/allowed_operator_nodes/log/parameterized_log.cpp
index 464ccb303d19d06245f0e590ef320cec81507e83..35770632bc12c9201dad811b05d8f316847a2235 100644
--- a/src/feature_creation/node/operator_nodes/allowed_operator_nodes/log/parameterized_log.cpp
+++ b/src/feature_creation/node/operator_nodes/allowed_operator_nodes/log/parameterized_log.cpp
@@ -14,7 +14,8 @@ void generateLogParamNode(std::vector<node_ptr>& feat_list, node_ptr feat, unsig
return;
new_feat->set_value();
- if(new_feat->is_nan() || new_feat->is_const() || (util_funcs::max_abs_val<double>(new_feat->value_ptr(), new_feat->n_samp()) > u_bound) || (util_funcs::max_abs_val<double>(new_feat->value_ptr(), new_feat->n_samp()) < l_bound))
+ // No is_const check since cube function can only be constant if feat is constant
+ if(new_feat->is_nan() || (util_funcs::max_abs_val<double>(new_feat->value_ptr(), new_feat->n_samp()) > u_bound) || (util_funcs::max_abs_val<double>(new_feat->value_ptr(), new_feat->n_samp()) < l_bound))
return;
feat_list.push_back(new_feat);
@@ -32,7 +33,8 @@ LogParamNode::LogParamNode(node_ptr feat, unsigned long int feat_ind, double l_b
_params.resize(n_params(), 0.0);
get_parameters(optimizer);
- if((std::abs(_params[0]) <= 1e-10) || is_nan() || is_const() || (util_funcs::max_abs_val<double>(value_ptr(), _n_samp) > u_bound) || (util_funcs::max_abs_val<double>(value_ptr(), _n_samp) < l_bound))
+ // No is_const check since cube function can only be constant if feat is constant
+ if((std::abs(_params[0]) <= 1e-10) || is_nan() || (util_funcs::max_abs_val<double>(value_ptr(), _n_samp) > u_bound) || (util_funcs::max_abs_val<double>(value_ptr(), _n_samp) < l_bound))
throw InvalidFeatureException();
}
diff --git a/src/feature_creation/node/operator_nodes/allowed_operator_nodes/mult/multiply.cpp b/src/feature_creation/node/operator_nodes/allowed_operator_nodes/mult/multiply.cpp
index 0d003fe22f3e376beefcc2be94ee339e4f711e97..0a7ad6edcd5010ec64c73b37d06a2e0cf86b4af0 100644
--- a/src/feature_creation/node/operator_nodes/allowed_operator_nodes/mult/multiply.cpp
+++ b/src/feature_creation/node/operator_nodes/allowed_operator_nodes/mult/multiply.cpp
@@ -19,14 +19,12 @@ void generateMultNode(std::vector<node_ptr>& feat_list, node_ptr feat_1, node_pt
if((std::abs(div_mult_tot_first) - 1.0 > 1e-12) && std::all_of(div_mult_leaves.begin(), div_mult_leaves.end(), [&div_mult_tot_first](auto el){return std::abs(el.second) == div_mult_tot_first;}))
return;
- int offset = std::max(feat_1->rung(), feat_2->rung()) + 1;
- double* val_ptr = node_value_arrs::get_value_ptr(feat_ind, feat_ind, offset);
- allowed_op_funcs::mult(feat_1->n_samp(), feat_1->value_ptr(offset + 2), feat_2->value_ptr(offset + 1), 1.0, 0.0, val_ptr);
-
- if((util_funcs::stand_dev(val_ptr, feat_1->n_samp()) < 1.0e-13) || std::any_of(val_ptr, val_ptr + feat_1->n_samp(), [&u_bound](double d){return !std::isfinite(d) || (std::abs(d) > u_bound);}) || (util_funcs::max_abs_val<double>(val_ptr, feat_1->n_samp()) < l_bound))
+ node_ptr new_feat = std::make_shared<MultNode>(feat_1, feat_2, feat_ind);
+ double* val_ptr = new_feat->value_ptr();
+ if(new_feat->is_const() || std::any_of(val_ptr, val_ptr + new_feat->n_samp(), [&u_bound](double d){return !std::isfinite(d) || (std::abs(d) > u_bound);}) || (util_funcs::max_abs_val<double>(val_ptr, new_feat->n_samp()) < l_bound))
return;
- feat_list.push_back(std::make_shared<MultNode>(feat_1, feat_2, feat_ind));
+ feat_list.push_back(new_feat);
}
MultNode::MultNode()
diff --git a/src/feature_creation/node/operator_nodes/allowed_operator_nodes/neg_exp/negative_exponential.cpp b/src/feature_creation/node/operator_nodes/allowed_operator_nodes/neg_exp/negative_exponential.cpp
index 5cccab349faccfd3ffe3b62ed41edb4a0b4ca342..f966de42c32f821ef5cd8717b84da97877777dbd 100644
--- a/src/feature_creation/node/operator_nodes/allowed_operator_nodes/neg_exp/negative_exponential.cpp
+++ b/src/feature_creation/node/operator_nodes/allowed_operator_nodes/neg_exp/negative_exponential.cpp
@@ -6,14 +6,14 @@ void generateNegExpNode(std::vector<node_ptr>& feat_list, node_ptr feat, unsigne
if(feat->unit() != Unit() || (feat->type() == NODE_TYPE::NEG_EXP) || (feat->type() == NODE_TYPE::EXP) || (feat->type() == NODE_TYPE::ADD) || (feat->type() == NODE_TYPE::SUB) || (feat->type() == NODE_TYPE::LOG))
return;
- int offset = feat->rung() + 1;
- double* val_ptr = node_value_arrs::get_value_ptr(feat_ind, feat_ind, offset);
- allowed_op_funcs::neg_exp(feat->n_samp(), feat->value_ptr(offset + 2), 1.0, 0.0, val_ptr);
+ node_ptr new_feat = std::make_shared<NegExpNode>(feat, feat_ind);
+ double* val_ptr = new_feat->value_ptr();
- if((util_funcs::stand_dev(val_ptr, feat->n_samp()) < 1.0e-13) || std::any_of(val_ptr, val_ptr + feat->n_samp(), [&u_bound](double d){return !std::isfinite(d) || (std::abs(d) > u_bound);}) || (util_funcs::max_abs_val<double>(val_ptr, feat->n_samp()) < l_bound))
+ // No is_const check since cube function can only be constant if feat is constant
+ if(std::any_of(val_ptr, val_ptr + new_feat->n_samp(), [&u_bound](double d){return !std::isfinite(d) || (std::abs(d) > u_bound);}) || (util_funcs::max_abs_val<double>(val_ptr, new_feat->n_samp()) < l_bound))
return;
- feat_list.push_back(std::make_shared<NegExpNode>(feat, feat_ind));
+ feat_list.push_back(new_feat);
}
NegExpNode::NegExpNode()
@@ -33,7 +33,9 @@ NegExpNode::NegExpNode(node_ptr feat, unsigned long int feat_ind, double l_bound
throw InvalidFeatureException();
set_value();
- if(is_nan() || is_const() || (util_funcs::max_abs_val<double>(value_ptr(), _n_samp) > u_bound) || (util_funcs::max_abs_val<double>(value_ptr(), _n_samp) < l_bound))
+
+ // No is_const check since cube function can only be constant if feat is constant
+ if(is_nan() || (util_funcs::max_abs_val<double>(value_ptr(), _n_samp) > u_bound) || (util_funcs::max_abs_val<double>(value_ptr(), _n_samp) < l_bound))
throw InvalidFeatureException();
}
diff --git a/src/feature_creation/node/operator_nodes/allowed_operator_nodes/neg_exp/parameterized_negative_exponential.cpp b/src/feature_creation/node/operator_nodes/allowed_operator_nodes/neg_exp/parameterized_negative_exponential.cpp
index b75078864edea758ac6800ca8d89e99aad444fec..dbf3334b8380bcc051d3fd854cb4d82b9e25cbae 100644
--- a/src/feature_creation/node/operator_nodes/allowed_operator_nodes/neg_exp/parameterized_negative_exponential.cpp
+++ b/src/feature_creation/node/operator_nodes/allowed_operator_nodes/neg_exp/parameterized_negative_exponential.cpp
@@ -14,7 +14,8 @@ void generateNegExpParamNode(std::vector<node_ptr>& feat_list, node_ptr feat, un
return;
new_feat->set_value();
- if(new_feat->is_nan() || new_feat->is_const() || (util_funcs::max_abs_val<double>(new_feat->value_ptr(), new_feat->n_samp()) > u_bound) || (util_funcs::max_abs_val<double>(new_feat->value_ptr(), new_feat->n_samp()) < l_bound))
+ // No is_const check since cube function can only be constant if feat is constant
+ if(new_feat->is_nan() || (util_funcs::max_abs_val<double>(new_feat->value_ptr(), new_feat->n_samp()) > u_bound) || (util_funcs::max_abs_val<double>(new_feat->value_ptr(), new_feat->n_samp()) < l_bound))
return;
feat_list.push_back(new_feat);
@@ -32,7 +33,8 @@ NegExpParamNode::NegExpParamNode(node_ptr feat, unsigned long int feat_ind, doub
_params.resize(n_params(), 0.0);
get_parameters(optimizer);
- if((std::abs(_params[0]) <= 1e-10) || is_nan() || is_const() || (util_funcs::max_abs_val<double>(value_ptr(), _n_samp) > u_bound) || (util_funcs::max_abs_val<double>(value_ptr(), _n_samp) < l_bound))
+ // No is_const check since cube function can only be constant if feat is constant
+ if((std::abs(_params[0]) <= 1e-10) || is_nan() || (util_funcs::max_abs_val<double>(value_ptr(), _n_samp) > u_bound) || (util_funcs::max_abs_val<double>(value_ptr(), _n_samp) < l_bound))
throw InvalidFeatureException();
}
diff --git a/src/feature_creation/node/operator_nodes/allowed_operator_nodes/sin/sin.cpp b/src/feature_creation/node/operator_nodes/allowed_operator_nodes/sin/sin.cpp
index 95ae383ac0fa69bd2826ca7ef21a39a7c1e654ff..8e242afb968d3f25d3027ca6fea77fa80c0d9c1e 100644
--- a/src/feature_creation/node/operator_nodes/allowed_operator_nodes/sin/sin.cpp
+++ b/src/feature_creation/node/operator_nodes/allowed_operator_nodes/sin/sin.cpp
@@ -6,14 +6,13 @@ void generateSinNode(std::vector<node_ptr>& feat_list, node_ptr feat, unsigned l
if(feat->unit() != Unit() || (feat->type() == NODE_TYPE::SIN) || (feat->type() == NODE_TYPE::COS))
return;
- int offset = feat->rung() + 1;
- double* val_ptr = node_value_arrs::get_value_ptr(feat_ind, feat_ind, offset);
- allowed_op_funcs::sin(feat->n_samp(), feat->value_ptr(offset + 2), 1.0, 0.0, val_ptr);
+ node_ptr new_feat = std::make_shared<SinNode>(feat, feat_ind);
+ double* val_ptr = new_feat->value_ptr();
- if((util_funcs::stand_dev(val_ptr, feat->n_samp()) < 1.0e-13) || std::any_of(val_ptr, val_ptr + feat->n_samp(), [&u_bound](double d){return !std::isfinite(d) || (std::abs(d) > u_bound);}) || (util_funcs::max_abs_val<double>(val_ptr, feat->n_samp()) < l_bound))
- return;
+ if(new_feat->is_const() || std::any_of(val_ptr, val_ptr + new_feat->n_samp(), [&u_bound](double d){return !std::isfinite(d) || (std::abs(d) > u_bound);}) || (util_funcs::max_abs_val<double>(val_ptr, new_feat->n_samp()) < l_bound))
+ return;
- feat_list.push_back(std::make_shared<SinNode>(feat, feat_ind));
+ feat_list.push_back(new_feat);
}
SinNode::SinNode()
diff --git a/src/feature_creation/node/operator_nodes/allowed_operator_nodes/six_pow/sixth_power.cpp b/src/feature_creation/node/operator_nodes/allowed_operator_nodes/six_pow/sixth_power.cpp
index 06470e3dfb5967e136c2e744f2d93802a1b3f964..5e579ad4566f6104bb3823e871eb7c0f95c66c37 100644
--- a/src/feature_creation/node/operator_nodes/allowed_operator_nodes/six_pow/sixth_power.cpp
+++ b/src/feature_creation/node/operator_nodes/allowed_operator_nodes/six_pow/sixth_power.cpp
@@ -6,14 +6,13 @@ void generateSixPowNode(std::vector<node_ptr>& feat_list, node_ptr feat, unsigne
if((feat->type() == NODE_TYPE::CBRT) || (feat->type() == NODE_TYPE::SQRT) || (feat->type() == NODE_TYPE::SQ) || (feat->type() == NODE_TYPE::CB) || (feat->type() == NODE_TYPE::INV))
return;
- int offset = feat->rung() + 1;
- double* val_ptr = node_value_arrs::get_value_ptr(feat_ind, feat_ind, offset);
- allowed_op_funcs::sixth_pow(feat->n_samp(), feat->value_ptr(offset + 2), 1.0, 0.0, val_ptr);
+ node_ptr new_feat = std::make_shared<SixPowNode>(feat, feat_ind);
+ double* val_ptr = new_feat->value_ptr();
- if((util_funcs::stand_dev(val_ptr, feat->n_samp()) < 1.0e-13) || std::any_of(val_ptr, val_ptr + feat->n_samp(), [&u_bound](double d){return (!std::isfinite(d)) || (std::abs(d) > u_bound);}) || (util_funcs::max_abs_val<double>(val_ptr, feat->n_samp()) < l_bound))
+ if(new_feat->is_const() || std::any_of(val_ptr, val_ptr + new_feat->n_samp(), [&u_bound](double d){return (!std::isfinite(d)) || (std::abs(d) > u_bound);}) || (util_funcs::max_abs_val<double>(val_ptr, new_feat->n_samp()) < l_bound))
return;
- feat_list.push_back(std::make_shared<SixPowNode>(feat, feat_ind));
+ feat_list.push_back(new_feat);
}
SixPowNode::SixPowNode()
diff --git a/src/feature_creation/node/operator_nodes/allowed_operator_nodes/sq/square.cpp b/src/feature_creation/node/operator_nodes/allowed_operator_nodes/sq/square.cpp
index c7bb5616e741e33cd16b7fea4934f9b3a934c67d..9ece13f7541bfb761cee877a6da33881678e6274 100644
--- a/src/feature_creation/node/operator_nodes/allowed_operator_nodes/sq/square.cpp
+++ b/src/feature_creation/node/operator_nodes/allowed_operator_nodes/sq/square.cpp
@@ -6,14 +6,13 @@ void generateSqNode(std::vector<node_ptr>& feat_list, node_ptr feat, unsigned lo
if((feat->type() == NODE_TYPE::SQRT) || (feat->type() == NODE_TYPE::INV))
return;
- int offset = feat->rung() + 1;
- double* val_ptr = node_value_arrs::get_value_ptr(feat_ind, feat_ind, offset);
- allowed_op_funcs::sq(feat->n_samp(), feat->value_ptr(offset + 2), 1.0, 0.0, val_ptr);
+ node_ptr new_feat = std::make_shared<SqNode>(feat, feat_ind);
+ double* val_ptr = new_feat->value_ptr();
- if((util_funcs::stand_dev(val_ptr, feat->n_samp()) < 1.0e-13) || std::any_of(val_ptr, val_ptr + feat->n_samp(), [&u_bound](double d){return !std::isfinite(d) || (std::abs(d) > u_bound);}) || (util_funcs::max_abs_val<double>(val_ptr, feat->n_samp()) < l_bound))
+ if(new_feat->is_const() || std::any_of(val_ptr, val_ptr + new_feat->n_samp(), [&u_bound](double d){return !std::isfinite(d) || (std::abs(d) > u_bound);}) || (util_funcs::max_abs_val<double>(val_ptr, new_feat->n_samp()) < l_bound))
return;
- feat_list.push_back(std::make_shared<SqNode>(feat, feat_ind));
+ feat_list.push_back(new_feat);
}
SqNode::SqNode()
diff --git a/src/feature_creation/node/operator_nodes/allowed_operator_nodes/sqrt/parameterized_square_root.cpp b/src/feature_creation/node/operator_nodes/allowed_operator_nodes/sqrt/parameterized_square_root.cpp
index 4253636afb2c1184fde7ee22b705316345c2315c..99f29da3779dbfbcae5aded3a06207628e1ec8ca 100644
--- a/src/feature_creation/node/operator_nodes/allowed_operator_nodes/sqrt/parameterized_square_root.cpp
+++ b/src/feature_creation/node/operator_nodes/allowed_operator_nodes/sqrt/parameterized_square_root.cpp
@@ -11,7 +11,8 @@ void generateSqrtParamNode(std::vector<node_ptr>& feat_list, node_ptr feat, unsi
return;
new_feat->set_value();
- if(new_feat->is_nan() || new_feat->is_const() || (util_funcs::max_abs_val<double>(new_feat->value_ptr(), new_feat->n_samp()) > u_bound) || (util_funcs::max_abs_val<double>(new_feat->value_ptr(), new_feat->n_samp()) < l_bound))
+ // No is_const check since cube function can only be constant if feat is constant
+ if(new_feat->is_nan() || (util_funcs::max_abs_val<double>(new_feat->value_ptr(), new_feat->n_samp()) > u_bound) || (util_funcs::max_abs_val<double>(new_feat->value_ptr(), new_feat->n_samp()) < l_bound))
return;
feat_list.push_back(new_feat);
@@ -27,7 +28,8 @@ SqrtParamNode::SqrtParamNode(node_ptr feat, unsigned long int feat_ind, double l
_params.resize(n_params(), 0.0);
get_parameters(optimizer);
- if((std::abs(_params[0]) <= 1e-10) || is_nan() || is_const() || (util_funcs::max_abs_val<double>(value_ptr(), _n_samp) > u_bound) || (util_funcs::max_abs_val<double>(value_ptr(), _n_samp) < l_bound))
+ // No is_const check since cube function can only be constant if feat is constant
+ if((std::abs(_params[0]) <= 1e-10) || is_nan() || (util_funcs::max_abs_val<double>(value_ptr(), _n_samp) > u_bound) || (util_funcs::max_abs_val<double>(value_ptr(), _n_samp) < l_bound))
throw InvalidFeatureException();
}
diff --git a/src/feature_creation/node/operator_nodes/allowed_operator_nodes/sqrt/square_root.cpp b/src/feature_creation/node/operator_nodes/allowed_operator_nodes/sqrt/square_root.cpp
index d7da0473a7b43279b21b21bfbb4543ae6c2ce716..562bc6698a4126dca80748dfd4a2ee3aeb31322b 100644
--- a/src/feature_creation/node/operator_nodes/allowed_operator_nodes/sqrt/square_root.cpp
+++ b/src/feature_creation/node/operator_nodes/allowed_operator_nodes/sqrt/square_root.cpp
@@ -6,14 +6,13 @@ void generateSqrtNode(std::vector<node_ptr>& feat_list, node_ptr feat, unsigned
if((feat->type() == NODE_TYPE::SQ) || (feat->type() == NODE_TYPE::CB) || (feat->type() == NODE_TYPE::SIX_POW) || (feat->type() == NODE_TYPE::CBRT) || (feat->type() == NODE_TYPE::INV))
return;
- int offset = feat->rung() + 1;
- double* val_ptr = node_value_arrs::get_value_ptr(feat_ind, feat_ind, offset);
- allowed_op_funcs::sqrt(feat->n_samp(), feat->value_ptr(offset + 2), 1.0, 0.0, val_ptr);
+ node_ptr new_feat = std::make_shared<SqrtNode>(feat, feat_ind);
+ double* val_ptr = new_feat->value_ptr();
- if((util_funcs::stand_dev(val_ptr, feat->n_samp()) < 1.0e-13) || std::any_of(val_ptr, val_ptr + feat->n_samp(), [&u_bound](double d){return !std::isfinite(d) || (std::abs(d) > u_bound);}) || (util_funcs::max_abs_val<double>(val_ptr, feat->n_samp()) < l_bound))
+ if(std::any_of(val_ptr, val_ptr + new_feat->n_samp(), [&u_bound](double d){return !std::isfinite(d) || (std::abs(d) > u_bound);}) || (util_funcs::max_abs_val<double>(val_ptr, new_feat->n_samp()) < l_bound))
return;
- feat_list.push_back(std::make_shared<SqrtNode>(feat, feat_ind));
+ feat_list.push_back(new_feat);
}
SqrtNode::SqrtNode()
diff --git a/src/feature_creation/node/operator_nodes/allowed_operator_nodes/sub/subtract.cpp b/src/feature_creation/node/operator_nodes/allowed_operator_nodes/sub/subtract.cpp
index 3d9e8c970caf49d4e2fb9fd4e85238de82381d50..61cdc8495bb4cac28ffce68f1d41bca9bedfec30 100644
--- a/src/feature_creation/node/operator_nodes/allowed_operator_nodes/sub/subtract.cpp
+++ b/src/feature_creation/node/operator_nodes/allowed_operator_nodes/sub/subtract.cpp
@@ -18,14 +18,13 @@ void generateSubNode(std::vector<node_ptr>& feat_list, node_ptr feat_1, node_ptr
if((std::abs(add_sub_tot_first) > 1) && std::all_of(add_sub_leaves.begin(), add_sub_leaves.end(), [&add_sub_tot_first](auto el){return std::abs(el.second) == add_sub_tot_first;}))
return;
- int offset = std::max(feat_1->rung(), feat_2->rung()) + 1;
- double* val_ptr = node_value_arrs::get_value_ptr(feat_ind, feat_ind, offset);
- allowed_op_funcs::sub(feat_1->n_samp(), feat_1->value_ptr(offset + 2), feat_2->value_ptr(offset + 1), 1.0, 0.0, val_ptr);
+ node_ptr new_feat = std::make_shared<SubNode>(feat_1, feat_2, feat_ind);
+ double* val_ptr = new_feat->value_ptr();
- if((util_funcs::stand_dev(val_ptr, feat_1->n_samp()) < 1.0e-13) || std::any_of(val_ptr, val_ptr + feat_1->n_samp(), [&u_bound](double d){return !std::isfinite(d) || (std::abs(d) > u_bound);}) || (util_funcs::max_abs_val<double>(val_ptr, feat_1->n_samp()) < l_bound))
- return;
+ if(new_feat->is_const() || std::any_of(val_ptr, val_ptr + new_feat->n_samp(), [&u_bound](double d){return !std::isfinite(d) || (std::abs(d) > u_bound);}) || (util_funcs::max_abs_val<double>(val_ptr, new_feat->n_samp()) < l_bound))
+ return;
- feat_list.push_back(std::make_shared<SubNode>(feat_1, feat_2, feat_ind));
+ feat_list.push_back(new_feat);
}
SubNode::SubNode()
diff --git a/src/feature_creation/node/value_storage/nodes_value_containers.cpp b/src/feature_creation/node/value_storage/nodes_value_containers.cpp
index e5a343a18ca931188af7248ecfacc0fa16e011ff..3bd1c88e6228b59cff52bd9d61ded90a60b7565b 100644
--- a/src/feature_creation/node/value_storage/nodes_value_containers.cpp
+++ b/src/feature_creation/node/value_storage/nodes_value_containers.cpp
@@ -11,14 +11,23 @@ int node_value_arrs::MAX_N_THREADS = omp_get_max_threads();
std::vector<int> node_value_arrs::TEMP_STORAGE_REG;
std::vector<int> node_value_arrs::TEMP_STORAGE_TEST_REG;
+std::vector<int> node_value_arrs::TASK_SZ_TRAIN;
+std::vector<int> node_value_arrs::TASK_SZ_TEST;
+
std::vector<double> node_value_arrs::D_MATRIX;
std::vector<double> node_value_arrs::VALUES_ARR;
std::vector<double> node_value_arrs::TEST_VALUES_ARR;
std::vector<double> node_value_arrs::TEMP_STORAGE_ARR;
std::vector<double> node_value_arrs::TEMP_STORAGE_TEST_ARR;
-void node_value_arrs::initialize_values_arr(int n_samples, int n_samples_test, int n_primary_feat)
+void node_value_arrs::initialize_values_arr(int n_samples, int n_samples_test, int n_primary_feat, bool set_task_sz)
{
+ if(set_task_sz)
+ TASK_SZ_TRAIN = {n_samples};
+
+ if(set_task_sz)
+ TASK_SZ_TEST = {n_samples_test};
+
N_SAMPLES = n_samples;
N_SAMPLES_TEST = n_samples_test;
N_RUNGS_STORED = 0;
@@ -34,6 +43,33 @@ void node_value_arrs::initialize_values_arr(int n_samples, int n_samples_test, i
TEMP_STORAGE_TEST_REG = std::vector<int>(MAX_N_THREADS * (3 * N_STORE_FEATURES + 1), -1);
}
+void node_value_arrs::initialize_values_arr(std::vector<int> task_sz_train, std::vector<int> task_sz_test, int n_primary_feat)
+{
+ TASK_SZ_TRAIN = task_sz_train;
+ TASK_SZ_TEST = task_sz_test;
+
+ initialize_values_arr(
+ std::accumulate(task_sz_train.begin(), task_sz_train.end(), 0),
+ std::accumulate(task_sz_test.begin(), task_sz_test.end(), 0),
+ n_primary_feat,
+ false
+ );
+}
+
+void node_value_arrs::set_task_sz_train(std::vector<int> task_sz_train)
+{
+ if(std::accumulate(task_sz_train.begin(), task_sz_train.end(), 0) != N_SAMPLES)
+ throw std::logic_error("The total number of samples has changed, task_sz_train is wrong.");
+ TASK_SZ_TRAIN = task_sz_train;
+}
+
+void node_value_arrs::set_task_sz_test(std::vector<int> task_sz_test)
+{
+ if(std::accumulate(task_sz_test.begin(), task_sz_test.end(), 0) != N_SAMPLES_TEST)
+ throw std::logic_error("The total number of test samples has changed, task_sz_test is wrong.");
+ TASK_SZ_TEST = task_sz_test;
+}
+
void node_value_arrs::resize_values_arr(int n_dims, int n_feat, bool use_temp)
{
N_RUNGS_STORED = n_dims;
diff --git a/src/feature_creation/node/value_storage/nodes_value_containers.hpp b/src/feature_creation/node/value_storage/nodes_value_containers.hpp
index b16ed2b9aa9ae96a7933680016c752014b807370..1e73ab5f094394fd506539a76e05de7e2b7a238f 100644
--- a/src/feature_creation/node/value_storage/nodes_value_containers.hpp
+++ b/src/feature_creation/node/value_storage/nodes_value_containers.hpp
@@ -12,6 +12,7 @@
#include <algorithm>
#include <memory>
+#include <numeric>
#include <vector>
#include <omp.h>
@@ -28,12 +29,27 @@ namespace node_value_arrs
extern std::vector<int> TEMP_STORAGE_REG; //!< Register to see which feature is stored in each slot for the training data
extern std::vector<int> TEMP_STORAGE_TEST_REG; //!< Register to see which feature is stored in each slot for the test data
+ extern std::vector<int> TASK_SZ_TRAIN; //!< Number of training samples per task
+ extern std::vector<int> TASK_SZ_TEST; //!< Number of test sample per task
+
extern int N_SELECTED; //!< Number of features selected
extern int N_SAMPLES; //!< Number of training samples for each feature
extern int N_SAMPLES_TEST; //!< Number of test samples for each feature
extern int N_STORE_FEATURES; //!< Number of features with stored values
extern int N_RUNGS_STORED; //!< Number of rungs with values stored
extern int MAX_N_THREADS; //!< Get the maximum number of threads possible
+
+ /**
+ * @brief Initialize the node value arrays
+ * @details Using the size of the initial feature space constructor the storage arrays
+ *
+ * @param n_samples Number of training samples for each feature
+ * @param n_samples_test Number of test samples for each feature
+ * @param n_primary_feat Number of primary features
+ * @param set_test_task_sz If True reset the task_sz vectors
+ */
+ void initialize_values_arr(int n_samples, int n_samples_test, int n_primary_feat, bool et_task_sz);
+
/**
* @brief Initialize the node value arrays
* @details Using the size of the initial feature space constructor the storage arrays
@@ -42,7 +58,20 @@ namespace node_value_arrs
* @param n_samples_test Number of test samples for each feature
* @param n_primary_feat Number of primary features
*/
- void initialize_values_arr(int n_samples, int n_samples_test, int n_primary_feat);
+ inline void initialize_values_arr(int n_samples, int n_samples_test, int n_primary_feat)
+ {
+ initialize_values_arr(n_samples, n_samples_test, n_primary_feat, true);
+ }
+
+ /**
+ * @brief Initialize the node value arrays
+ * @details Using the size of the initial feature space constructor the storage arrays
+ *
+ * @param task_sz_train Number of training samples per task
+ * @param task_sz_test Number of test sample per task
+ * @param n_primary_feat Number of primary features
+ */
+ void initialize_values_arr(std::vector<int> task_sz_train, std::vector<int> task_sz_test, int n_primary_feat);
/**
* @brief Resize the node value arrays
@@ -52,7 +81,7 @@ namespace node_value_arrs
* @param n_feat number of features to store
* @param use_temp If true keep the temporary_storage
*/
- void resize_values_arr(int n_dims, int n_feat, bool use_temp);
+ void resize_values_arr(int n_dims, unsigned long int n_feat, bool use_temp);
/**
* @brief Initialize the descriptor matrix
@@ -69,6 +98,20 @@ namespace node_value_arrs
*/
void resize_d_matrix_arr(int n_select);
+ /**
+ * @brief Reset the global TASK_SZ_TRAIN vector
+ *
+ * @param task_sz_train the new task_sz train
+ */
+ void set_task_sz_train(std::vector<int> task_sz_train);
+
+ /**
+ * @brief Reset the global TASK_SZ_TEST vector
+ *
+ * @param task_sz_train the new test_sz train
+ */
+ void set_task_sz_test(std::vector<int> task_sz_test);
+
/**
* @brief Get a reference slot/feature register of the training data
*
diff --git a/src/mpi_interface/MPI_ops.cpp b/src/mpi_interface/MPI_ops.cpp
index 5502c9d9b5a33147f9703ff7d3afccc2484aef48..00c3cbf39980f66b9d243f58280dcdbc3dbd9ac2 100644
--- a/src/mpi_interface/MPI_ops.cpp
+++ b/src/mpi_interface/MPI_ops.cpp
@@ -5,15 +5,25 @@ std::function<bool(double*, int, double, std::vector<node_sc_pair>&, double)> mp
double mpi_reduce_op::CROSS_COR_MAX;
int mpi_reduce_op::N_SIS_SELECT;
-void mpi_reduce_op::set_op(double cross_cor_max, int n_sis_select)
+void mpi_reduce_op::set_op(std::string project_type, double cross_cor_max, int n_sis_select)
{
// MPI_Op_create(*select_top_feats, 1, &top_feats)
N_SIS_SELECT = n_sis_select;
CROSS_COR_MAX = cross_cor_max;
- if(CROSS_COR_MAX < 0.99999)
- IS_VALID = comp_feats::valid_feature_against_selected_feat_sc_list;
+ if(project_type.compare("classification") == 0)
+ {
+ if(CROSS_COR_MAX < 0.99999)
+ IS_VALID = comp_feats::valid_feature_against_spearman_selected_feat;
+ else
+ IS_VALID = comp_feats::valid_feature_against_spearman_selected_max_corr_1;
+ }
else
- IS_VALID = comp_feats::valid_feature_against_selected_max_corr_1_feat_sc_list;
+ {
+ if(CROSS_COR_MAX < 0.99999)
+ IS_VALID = comp_feats::valid_feature_against_pearson_selected_feat;
+ else
+ IS_VALID = comp_feats::valid_feature_against_pearson_selected_max_corr_1;
+ }
}
std::vector<node_sc_pair> mpi_reduce_op::select_top_feats(std::vector<node_sc_pair> in_vec_1, std::vector<node_sc_pair> in_vec_2)
diff --git a/src/mpi_interface/MPI_ops.hpp b/src/mpi_interface/MPI_ops.hpp
index 7ed0410da62d5987be962b0818829f509cedd440..2875213f2550d95b40c556f088b609a48ac66a1e 100644
--- a/src/mpi_interface/MPI_ops.hpp
+++ b/src/mpi_interface/MPI_ops.hpp
@@ -26,7 +26,7 @@ namespace mpi_reduce_op
std::vector<node_sc_pair> select_top_feats(std::vector<node_sc_pair> in_vec_1, std::vector<node_sc_pair> in_vec_2);
- void set_op(double cross_cor_max, int n_sis_select);
+ void set_op(std::string project_type, double cross_cor_max, int n_sis_select);
}
#endif
diff --git a/src/python/bindings_docstring_keyed.cpp b/src/python/bindings_docstring_keyed.cpp
index 2666215f5d04841b1ee982429b58a2be74c879e4..675eeac52953b556448fe528708791ae43459640 100644
--- a/src/python/bindings_docstring_keyed.cpp
+++ b/src/python/bindings_docstring_keyed.cpp
@@ -37,8 +37,10 @@ void sisso::register_all()
sisso::feature_creation::node::registerSqrtNode();
sisso::feature_creation::node::registerSixPowNode();
+ void (*init_val_ar)(int, int, int) = &node_value_arrs::initialize_values_arr;
+
def("phi_selected_from_file", &str2node::phi_selected_from_file_py);
- def("initialize_values_arr", &node_value_arrs::initialize_values_arr);
+ def("initialize_values_arr", init_val_ar);
def("initialize_d_matrix_arr", &node_value_arrs::initialize_d_matrix_arr);
diff --git a/src/python/feature_creation/FeatureSpace.cpp b/src/python/feature_creation/FeatureSpace.cpp
index f68e6f2d4452de2ecf64f6a042111afeda6f6180..88baa033496624b2c4139630a2e08f0b8e952027 100644
--- a/src/python/feature_creation/FeatureSpace.cpp
+++ b/src/python/feature_creation/FeatureSpace.cpp
@@ -83,7 +83,6 @@ FeatureSpace::FeatureSpace(
{
initialize_fs();
}
-
FeatureSpace::FeatureSpace(
std::string feature_file,
py::list phi_0,
@@ -111,49 +110,9 @@ FeatureSpace::FeatureSpace(
_n_samp(_phi_0[0]->n_samp()),
_max_param_depth(-1)
{
- if(_project_type.compare("regression") == 0)
- {
- _project = project_funcs::project_r2;
- _project_no_omp = project_funcs::project_r2_no_omp;
- }
- else if(_project_type.compare("classification") == 0)
- {
- _project = project_funcs::project_classify;
- _project_no_omp = project_funcs::project_classify_no_omp;
- }
- else if(_project_type.compare("log_regression") == 0)
- {
- if(_task_sizes.size() > 1)
- throw std::logic_error("Log Regression can not be done using multiple tasks.");
- _project = project_funcs::project_log_r2;
- _project_no_omp = project_funcs::project_log_r2_no_omp;
- }
- else
- {
- throw std::logic_error("Wrong projection type passed to FeatureSpace constructor.");
- }
-
- if(_cross_cor_max < 0.99999)
- {
- _is_valid = comp_feats::valid_feature_against_selected;
- _is_valid_feat_list = comp_feats::valid_feature_against_selected_feat_list;
- }
- else
- {
- _is_valid = comp_feats::valid_feature_against_selected_max_corr_1;
- _is_valid_feat_list = comp_feats::valid_feature_against_selected_max_corr_1_feat_list;
- }
-
- if(_cross_cor_max < 0.99999)
- {
- _is_valid = comp_feats::valid_feature_against_selected;
- _is_valid_feat_list = comp_feats::valid_feature_against_selected_feat_list;
- }
- else
- {
- _is_valid = comp_feats::valid_feature_against_selected_max_corr_1;
- _is_valid_feat_list = comp_feats::valid_feature_against_selected_max_corr_1_feat_list;
- }
+ project_funcs::set_project_fxn(project_type, _task_sizes.size(), _project, _project_no_omp);
+ comp_feats::set_is_valid_fxn(project_type, _cross_cor_max, _n_samp, _is_valid, _is_valid_feat_list);
+ mpi_reduce_op::set_op(_project_type, _cross_cor_max, _n_sis_select);
std::vector<node_ptr> phi_temp = str2node::phi_from_file(feature_file, _phi_0);
@@ -243,49 +202,9 @@ FeatureSpace::FeatureSpace(
_n_samp(_phi_0[0]->n_samp()),
_max_param_depth(-1)
{
- if(project_type.compare("regression") == 0)
- {
- _project = project_funcs::project_r2;
- _project_no_omp = project_funcs::project_r2_no_omp;
- }
- else if(project_type.compare("classification") == 0)
- {
- _project = project_funcs::project_classify;
- _project_no_omp = project_funcs::project_classify_no_omp;
- }
- else if(project_type.compare("log_regression") == 0)
- {
- if(_task_sizes.size() > 1)
- throw std::logic_error("Log Regression can not be done using multiple tasks.");
- _project = project_funcs::project_log_r2;
- _project_no_omp = project_funcs::project_log_r2_no_omp;
- }
- else
- {
- throw std::logic_error("Wrong projection type passed to FeatureSpace constructor.");
- }
-
- if(_cross_cor_max < 0.99999)
- {
- _is_valid = comp_feats::valid_feature_against_selected;
- _is_valid_feat_list = comp_feats::valid_feature_against_selected_feat_list;
- }
- else
- {
- _is_valid = comp_feats::valid_feature_against_selected_max_corr_1;
- _is_valid_feat_list = comp_feats::valid_feature_against_selected_max_corr_1_feat_list;
- }
-
- if(_cross_cor_max < 0.99999)
- {
- _is_valid = comp_feats::valid_feature_against_selected;
- _is_valid_feat_list = comp_feats::valid_feature_against_selected_feat_list;
- }
- else
- {
- _is_valid = comp_feats::valid_feature_against_selected_max_corr_1;
- _is_valid_feat_list = comp_feats::valid_feature_against_selected_max_corr_1_feat_list;
- }
+ project_funcs::set_project_fxn(project_type, _task_sizes.size(), _project, _project_no_omp);
+ comp_feats::set_is_valid_fxn(project_type, _cross_cor_max, _n_samp, _is_valid, _is_valid_feat_list);
+ mpi_reduce_op::set_op(_project_type, _cross_cor_max, _n_sis_select);
std::vector<node_ptr> phi_temp = str2node::phi_from_file(feature_file, _phi_0);
@@ -349,7 +268,6 @@ FeatureSpace::FeatureSpace(
_scores.resize(_n_feat);
}
-
py::list FeatureSpace::phi0_py()
{
py::list feat_lst;
diff --git a/src/utils/compare_features.cpp b/src/utils/compare_features.cpp
index 0f2cce1444971511a4a8b60d1486a7de304b7a92..1475bc2a177ae74f21c9f38f3304415f97282f6a 100644
--- a/src/utils/compare_features.cpp
+++ b/src/utils/compare_features.cpp
@@ -1,7 +1,50 @@
#include <utils/compare_features.hpp>
std::vector<double> comp_feats::CORR_CHECK;
-bool comp_feats::valid_feature_against_selected_max_corr_1(double* val_ptr, int n_samp, double cross_cor_max, std::vector<double>& scores_sel, double cur_score, int end_sel, int start_sel)
+std::vector<double> comp_feats::RANK;
+std::vector<int> comp_feats::INDEX;
+
+void comp_feats::set_is_valid_fxn(
+ const std::string project_type,
+ const double max_corr,
+ const int n_samp,
+ std::function<bool(double*, int, double, std::vector<double>&, double, int, int)>& is_valid,
+ std::function<bool(double*, int, double, std::vector<node_ptr>&, std::vector<double>&, double)>& is_valid_feat_list
+)
+{
+ if(project_type.compare("classification") != 0)
+ {
+ if(max_corr < 0.99999)
+ {
+ is_valid = valid_feature_against_selected_pearson;
+ is_valid_feat_list = valid_feature_against_selected_pearson;
+ }
+ else
+ {
+ is_valid = valid_feature_against_selected_pearson_max_corr_1;
+ is_valid_feat_list = valid_feature_against_selected_pearson_max_corr_1;
+ }
+ }
+ else
+ {
+ // Resize the rank and index to fit all of the data for each thread with padding
+ RANK.resize(4 * omp_get_max_threads() * n_samp);
+ INDEX.resize(2 * omp_get_max_threads() * n_samp);
+ if(max_corr < 0.99999)
+ {
+ is_valid = valid_feature_against_selected_spearman;
+ is_valid_feat_list = valid_feature_against_selected_spearman;
+ }
+ else
+ {
+ is_valid = valid_feature_against_selected_spearman_max_corr_1;
+ is_valid_feat_list = valid_feature_against_selected_spearman_max_corr_1;
+ }
+ }
+}
+
+
+bool comp_feats::valid_feature_against_selected_pearson_max_corr_1(double* val_ptr, int n_samp, double cross_cor_max, std::vector<double>& scores_sel, double cur_score, int end_sel, int start_sel)
{
double base_val = util_funcs::r(val_ptr, val_ptr, n_samp);
@@ -16,7 +59,7 @@ bool comp_feats::valid_feature_against_selected_max_corr_1(double* val_ptr, int
return true;
}
-bool comp_feats::valid_feature_against_selected_max_corr_1_feat_list(double* val_ptr, int n_samp, double cross_cor_max, std::vector<node_ptr>& selected, std::vector<double>& scores_sel, double cur_score)
+bool comp_feats::valid_feature_against_selected_pearson_max_corr_1(double* val_ptr, int n_samp, double cross_cor_max, std::vector<node_ptr>& selected, std::vector<double>& scores_sel, double cur_score)
{
double base_val = util_funcs::r(val_ptr, val_ptr, n_samp);
@@ -31,8 +74,23 @@ bool comp_feats::valid_feature_against_selected_max_corr_1_feat_list(double* val
return true;
}
+bool comp_feats::valid_feature_against_selected_pearson_max_corr_1(double* val_ptr, int n_samp, double cross_cor_max, std::vector<node_sc_pair>& out_vec, double cur_score)
+{
+ double base_val = util_funcs::r(val_ptr, val_ptr, n_samp);
+
+ for(auto& feat_sc : out_vec)
+ {
+ if(abs(cur_score - std::get<1>(feat_sc)) > 1e-5)
+ continue;
+
+ if((base_val - std::abs(util_funcs::r(std::get<0>(feat_sc)->value_ptr(1), val_ptr, n_samp))) < 1e-9)
+ return false;
+ }
+ return true;
+}
-bool comp_feats::valid_feature_against_selected(double* val_ptr, int n_samp, double cross_cor_max, std::vector<double>& scores_sel, double cur_score, int end_sel, int start_sel)
+
+bool comp_feats::valid_feature_against_selected_pearson(double* val_ptr, int n_samp, double cross_cor_max, std::vector<double>& scores_sel, double cur_score, int end_sel, int start_sel)
{
double base_val = util_funcs::r(val_ptr, val_ptr, n_samp);
volatile bool is_valid = true;
@@ -49,7 +107,7 @@ bool comp_feats::valid_feature_against_selected(double* val_ptr, int n_samp, dou
return is_valid;
}
-bool comp_feats::valid_feature_against_selected_feat_list(double* val_ptr, int n_samp, double cross_cor_max, std::vector<node_ptr>& selected, std::vector<double>& scores_sel, double cur_score)
+bool comp_feats::valid_feature_against_selected_pearson(double* val_ptr, int n_samp, double cross_cor_max, std::vector<node_ptr>& selected, std::vector<double>& scores_sel, double cur_score)
{
double base_val = util_funcs::r(val_ptr, val_ptr, n_samp);
@@ -61,28 +119,110 @@ bool comp_feats::valid_feature_against_selected_feat_list(double* val_ptr, int n
return true;
}
-bool comp_feats::valid_feature_against_selected_max_corr_1_feat_sc_list(double* val_ptr, int n_samp, double cross_cor_max, std::vector<node_sc_pair>& out_vec, double cur_score)
+bool comp_feats::valid_feature_against_selected_pearson(double* val_ptr, int n_samp, double cross_cor_max, std::vector<node_sc_pair>& out_vec, double cur_score)
{
double base_val = util_funcs::r(val_ptr, val_ptr, n_samp);
for(auto& feat_sc : out_vec)
{
- if(abs(cur_score - std::get<1>(feat_sc)) > 1e-5)
+ if((base_val - std::abs(util_funcs::r(std::get<0>(feat_sc)->value_ptr(1), val_ptr, n_samp))) < (1.0 - cross_cor_max + 1e-10))
+ return false;
+ }
+ return true;
+}
+
+
+bool comp_feats::valid_feature_against_selected_spearman_max_corr_1(double* val_ptr, int n_samp, double cross_cor_max, std::vector<double>& scores_sel, double cur_score, int end_sel, int start_sel)
+{
+ double base_val = std::abs(util_funcs::spearman_r(val_ptr, val_ptr, &RANK[omp_get_thread_num() * 4 * n_samp], &RANK[(omp_get_thread_num() * 4 + 2) * n_samp], &INDEX[omp_get_thread_num() * 2 * n_samp], n_samp));
+
+ for(int dd = start_sel; dd < end_sel; ++dd)
+ {
+ if(abs(std::floor(cur_score) - std::floor(scores_sel[dd])) > 1e-5)
continue;
- if((base_val - std::abs(util_funcs::r(std::get<0>(feat_sc)->value_ptr(1), val_ptr, n_samp))) < 1e-9)
+ // Rank the new variable and take the Pearson correlation of the rank variables (val_ptr rank still in &RANK[(omp_get_thread_num() * 4 + 2) * n_samp])
+ util_funcs::rank(node_value_arrs::get_d_matrix_ptr(dd), &RANK[omp_get_thread_num() * 4 * n_samp], &INDEX[omp_get_thread_num() * 2 * n_samp], n_samp);
+ if((base_val - std::abs(util_funcs::r(&RANK[omp_get_thread_num() * 4 * n_samp], &RANK[(omp_get_thread_num() * 4 + 2) * n_samp], n_samp))) < 1e-9)
return false;
}
return true;
}
-bool comp_feats::valid_feature_against_selected_feat_sc_list(double* val_ptr, int n_samp, double cross_cor_max, std::vector<node_sc_pair>& out_vec, double cur_score)
+bool comp_feats::valid_feature_against_selected_spearman_max_corr_1(double* val_ptr, int n_samp, double cross_cor_max, std::vector<node_ptr>& selected, std::vector<double>& scores_sel, double cur_score)
{
- double base_val = util_funcs::r(val_ptr, val_ptr, n_samp);
+ double base_val = std::abs(util_funcs::spearman_r(val_ptr, val_ptr, &RANK[omp_get_thread_num() * 4 * n_samp], &RANK[(omp_get_thread_num() * 4 + 2) * n_samp], &INDEX[omp_get_thread_num() * 2 * n_samp], n_samp));
+
+ for(int ff = 0; ff < selected.size(); ++ff)
+ {
+ if(abs(std::floor(cur_score) - std::floor(scores_sel[ff])) > 1e-5)
+ continue;
+
+ // Rank the new variable and take the Pearson correlation of the rank variables (val_ptr rank still in &RANK[(omp_get_thread_num() * 4 + 2) * n_samp])
+ util_funcs::rank(selected[ff]->value_ptr(1), &RANK[omp_get_thread_num() * 4 * n_samp], &INDEX[omp_get_thread_num() * 2 * n_samp], n_samp);
+ if((base_val - std::abs(util_funcs::r(&RANK[omp_get_thread_num() * 4 * n_samp], &RANK[(omp_get_thread_num() * 4 + 2) * n_samp], n_samp))) < 1e-9)
+ return false;
+ }
+ return true;
+}
+
+bool comp_feats::valid_feature_against_selected_spearman_max_corr_1(double* val_ptr, int n_samp, double cross_cor_max, std::vector<node_sc_pair>& out_vec, double cur_score)
+{
+ double base_val = std::abs(util_funcs::spearman_r(val_ptr, val_ptr, &RANK[omp_get_thread_num() * 4 * n_samp], &RANK[(omp_get_thread_num() * 4 + 2) * n_samp], &INDEX[omp_get_thread_num() * 2 * n_samp], n_samp));
for(auto& feat_sc : out_vec)
{
- if((base_val - std::abs(util_funcs::r(std::get<0>(feat_sc)->value_ptr(1), val_ptr, n_samp))) < (1.0 - cross_cor_max + 1e-10))
+ if(abs(std::floor(cur_score) - std::floor(std::get<1>(feat_sc))) > 1e-5)
+ continue;
+
+ util_funcs::rank(std::get<0>(feat_sc)->value_ptr(1), &RANK[omp_get_thread_num() * 4 * n_samp], &INDEX[omp_get_thread_num() * 2 * n_samp], n_samp);
+ if((base_val - std::abs(util_funcs::r(&RANK[omp_get_thread_num() * 4 * n_samp], &RANK[(omp_get_thread_num() * 4 + 2) * n_samp], n_samp))) < cross_cor_max + 1e-9)
+ return false;
+ }
+ return true;
+}
+
+bool comp_feats::valid_feature_against_selected_spearman(double* val_ptr, int n_samp, double cross_cor_max, std::vector<double>& scores_sel, double cur_score, int end_sel, int start_sel)
+{
+ double base_val = std::abs(util_funcs::spearman_r(val_ptr, val_ptr, &RANK[omp_get_thread_num() * 4 * n_samp], &RANK[(omp_get_thread_num() * 4 + 2) * n_samp], &INDEX[omp_get_thread_num() * 2 * n_samp], n_samp));
+ volatile bool is_valid = true;
+
+ #pragma omp parallel for schedule(dynamic)
+ for(int dd = start_sel; dd < end_sel; ++dd)
+ {
+ if(!is_valid)
+ continue;
+
+ // Rank the new variable and take the Pearson correlation of the rank variables (val_ptr rank still in &RANK[(omp_get_thread_num() * 4 + 2) * n_samp])
+ util_funcs::rank(node_value_arrs::get_d_matrix_ptr(dd), &RANK[omp_get_thread_num() * 4 * n_samp], &INDEX[omp_get_thread_num() * 2 * n_samp], n_samp);
+ if((base_val - std::abs(util_funcs::r(&RANK[omp_get_thread_num() * 4 * n_samp], &RANK[(omp_get_thread_num() * 4 + 2) * n_samp], n_samp))) < cross_cor_max + 1e-9)
+ is_valid = false;
+ }
+ return is_valid;
+}
+
+bool comp_feats::valid_feature_against_selected_spearman(double* val_ptr, int n_samp, double cross_cor_max, std::vector<node_ptr>& selected, std::vector<double>& scores_sel, double cur_score)
+{
+ double base_val = std::abs(util_funcs::spearman_r(val_ptr, val_ptr, &RANK[omp_get_thread_num() * 4 * n_samp], &RANK[(omp_get_thread_num() * 4 + 2) * n_samp], &INDEX[omp_get_thread_num() * 2 * n_samp], n_samp));
+
+ for(auto& feat : selected)
+ {
+ // Rank the new variable and take the Pearson correlation of the rank variables (val_ptr rank still in &RANK[(omp_get_thread_num() * 4 + 2) * n_samp])
+ util_funcs::rank(feat->value_ptr(1), &RANK[omp_get_thread_num() * 4 * n_samp], &INDEX[omp_get_thread_num() * 2 * n_samp], n_samp);
+ if((base_val - std::abs(util_funcs::r(&RANK[omp_get_thread_num() * 4 * n_samp], &RANK[(omp_get_thread_num() * 4 + 2) * n_samp], n_samp))) < cross_cor_max + 1e-9)
+ return false;
+ }
+ return true;
+}
+
+bool comp_feats::valid_feature_against_selected_spearman(double* val_ptr, int n_samp, double cross_cor_max, std::vector<node_sc_pair>& out_vec, double cur_score)
+{
+ double base_val = std::abs(util_funcs::spearman_r(val_ptr, val_ptr, &RANK[omp_get_thread_num() * 4 * n_samp], &RANK[(omp_get_thread_num() * 4 + 2) * n_samp], &INDEX[omp_get_thread_num() * 2 * n_samp], n_samp));
+
+ for(auto& feat_sc : out_vec)
+ {
+ util_funcs::rank(std::get<0>(feat_sc)->value_ptr(1), &RANK[omp_get_thread_num() * 4 * n_samp], &INDEX[omp_get_thread_num() * 2 * n_samp], n_samp);
+ if((base_val - std::abs(util_funcs::r(&RANK[omp_get_thread_num() * 4 * n_samp], &RANK[(omp_get_thread_num() * 4 + 2) * n_samp], n_samp))) < cross_cor_max + 1e-9)
return false;
}
return true;
diff --git a/src/utils/compare_features.hpp b/src/utils/compare_features.hpp
index d8963e2bb73f3cb1ba244e6166cf646e152944b4..0badade62e81b40cc32c5698ac26a7e2e6e666a1 100644
--- a/src/utils/compare_features.hpp
+++ b/src/utils/compare_features.hpp
@@ -16,8 +16,28 @@ typedef std::tuple<node_ptr, double> node_sc_pair;
namespace comp_feats
{
extern std::vector<double> CORR_CHECK; //!< vector for storing cross_corelation values
+ extern std::vector<double> RANK; //!< Global variable used to store the rank variables for Spearman correlation
+ extern std::vector<int> INDEX; //!< Global variable used to store the sorting indexes for Spearman correlation
+
+ /**
+ * @brief Set the is_valid and is_valid_feat_list functions for SIS
+ *
+ * @param project_type Type of projection to perform
+ * @param max_corr Maximum cross-correlation value
+ @param n_samp number of samples in the training set
+ * @param is_valid The _is_valid function from the feature_space
+ * @param is_valid_feat_list The _is_valid_feat_list function from the feature_space
+ */
+ void set_is_valid_fxn(
+ const std::string project_type,
+ const double max_corr,
+ const int n_samp,
+ std::function<bool(double*, int, double, std::vector<double>&, double, int, int)>& is_valid,
+ std::function<bool(double*, int, double, std::vector<node_ptr>&, std::vector<double>&, double)>& is_valid_feat_list
+ );
+
/**
- * @brief Checks the feature to see if it is still valid against previously selected features
+ * @brief Checks the feature to see if it is still valid against previously selected features (using the Pearson correlation coefficient with a maximum cross-correlation of 1.0)
*
* @param val_ptr pointer to value array of the current feature
* @param scores_sel the scores of the previously selected features
@@ -27,10 +47,10 @@ namespace comp_feats
*
* @return True if the feature is still valid
*/
- bool valid_feature_against_selected_max_corr_1(double* val_ptr, int n_samp, double cross_cor_max, std::vector<double>& scores_sel, double cur_score, int end_sel, int start_sel = 0);
+ bool valid_feature_against_selected_pearson_max_corr_1(double* val_ptr, int n_samp, double cross_cor_max, std::vector<double>& scores_sel, double cur_score, int end_sel, int start_sel = 0);
/**
- * @brief Checks the feature to see if it is still valid against previously selected features
+ * @brief Checks the feature to see if it is still valid against previously selected features (using the Pearson correlation coefficient with a maximum cross-correlation of 1.0)
*
* @param val_ptr pointer to value array of the current feature
* @param selected list of previously selected features
@@ -39,10 +59,24 @@ namespace comp_feats
*
* @return True if the feature is still valid
*/
- bool valid_feature_against_selected_max_corr_1_feat_list(double* val_ptr, int n_samp, double cross_cor_max, std::vector<node_ptr>& selected, std::vector<double>& scores_sel, double cur_score);
+ bool valid_feature_against_selected_pearson_max_corr_1(double* val_ptr, int n_samp, double cross_cor_max, std::vector<node_ptr>& selected, std::vector<double>& scores_sel, double cur_score);
+
+ /**
+ * @brief Checks the feature to see if it is still valid against previously selected features (using the Pearson correlation coefficient with a maximum cross-correlation of 1.0)
+ *
+ * @param val_ptr pointer to value array of the current feature
+ * @param n_samp number of samples per feature
+ * @param cross_cor_max maximum cross correlation for the featues
+ * @param start pointer to the first feature to compare against
+ * @param end pointer to the final feature to compare against
+ * @param cur_score The score of the current candidate feature
+ *
+ * @return True if the feature is still valid
+ */
+ bool valid_feature_against_selected_pearson_max_corr_1(double* val_ptr, int n_samp, double cross_cor_max, std::vector<node_sc_pair>& out_vec, double cur_score);
/**
- * @brief Checks the feature to see if it is still valid against previously selected features
+ * @brief Checks the feature to see if it is still valid against previously selected features (using the Pearson correlation coefficient)
*
* @param val_ptr pointer to value array of the current feature
* @param scores_sel the scores of the previously selected features (not used, but passed to keep consistency)
@@ -52,10 +86,10 @@ namespace comp_feats
*
* @return True if the feature is still valid
*/
- bool valid_feature_against_selected(double* val_ptr, int n_samp, double cross_cor_max, std::vector<double>& scores_sel, double cur_score, int end_sel, int start_sel = 0);
+ bool valid_feature_against_selected_pearson(double* val_ptr, int n_samp, double cross_cor_max, std::vector<double>& scores_sel, double cur_score, int end_sel, int start_sel = 0);
/**
- * @brief Checks the feature to see if it is still valid against previously selected features
+ * @brief Checks the feature to see if it is still valid against previously selected features (using the Pearson correlation coefficient)
*
* @param val_ptr pointer to value array of the current feature
* @param selected list of previously selected features
@@ -64,37 +98,99 @@ namespace comp_feats
*
* @return True if the feature is still valid
*/
- bool valid_feature_against_selected_feat_list(double* val_ptr, int n_samp, double cross_cor_max, std::vector<node_ptr>& selected, std::vector<double>& scores_sel, double cur_score);
+ bool valid_feature_against_selected_pearson(double* val_ptr, int n_samp, double cross_cor_max, std::vector<node_ptr>& selected, std::vector<double>& scores_sel, double cur_score);
/**
- * @brief Checks the feature to see if it is still valid against previously selected features
+ * @brief Checks the feature to see if it is still valid against previously selected features (using the Pearson correlation coefficient)
*
* @param val_ptr pointer to value array of the current feature
* @param n_samp number of samples per feature
* @param cross_cor_max maximum cross correlation for the featues
* @param start pointer to the first feature to compare against
* @param end pointer to the final feature to compare against
+ * @param cur_score The score of the current candidate feature (not used, but passed to keep consistency)
+ *
+ * @return True if the feature is still valid
+ */
+ bool valid_feature_against_selected_pearson(double* val_ptr, int n_samp, double cross_cor_max, std::vector<node_sc_pair>& out_vec, double cur_score);
+
+ /**
+ * @brief Checks the feature to see if it is still valid against previously selected features (using the rank correlation coefficient with a maximum cross-correlation of 1.0)
+ *
+ * @param val_ptr pointer to value array of the current feature
+ * @param scores_sel the scores of the previously selected features
* @param cur_score The score of the current candidate feature
+ * @param end_sel index of the feature to stop checking
+ * @param start_sel index of the feature to stop checking
*
* @return True if the feature is still valid
*/
- bool valid_feature_against_selected_max_corr_1_feat_sc_list(double* val_ptr, int n_samp, double cross_cor_max, std::vector<node_sc_pair>& out_vec, double cur_score);
+ bool valid_feature_against_selected_spearman_max_corr_1(double* val_ptr, int n_samp, double cross_cor_max, std::vector<double>& scores_sel, double cur_score, int end_sel, int start_sel = 0);
/**
- * @brief Checks the feature to see if it is still valid against previously selected features
+ * @brief Checks the feature to see if it is still valid against previously selected features (using the rank correlation coefficient with a maximum cross-correlation of 1.0)
+ *
+ * @param val_ptr pointer to value array of the current feature
+ * @param selected list of previously selected features
+ * @param scores_sel the scores of the previously selected features
+ * @param cur_score The score of the current candidate feature
+ *
+ * @return True if the feature is still valid
+ */
+ bool valid_feature_against_selected_spearman_max_corr_1(double* val_ptr, int n_samp, double cross_cor_max, std::vector<node_ptr>& selected, std::vector<double>& scores_sel, double cur_score);
+
+ /**
+ * @brief Checks the feature to see if it is still valid against previously selected features (using the rank correlation coefficient with a maximum cross-correlation of 1.0)
*
* @param val_ptr pointer to value array of the current feature
* @param n_samp number of samples per feature
* @param cross_cor_max maximum cross correlation for the featues
* @param start pointer to the first feature to compare against
* @param end pointer to the final feature to compare against
+ * @param cur_score The score of the current candidate feature
+ *
+ * @return True if the feature is still valid
+ */
+ bool valid_feature_against_selected_spearman_max_corr_1(double* val_ptr, int n_samp, double cross_cor_max, std::vector<node_sc_pair>& out_vec, double cur_score);
+
+ /**
+ * @brief Checks the feature to see if it is still valid against previously selected features (using the rank correlation coefficient)
+ *
+ * @param val_ptr pointer to value array of the current feature
+ * @param scores_sel the scores of the previously selected features (not used, but passed to keep consistency)
* @param cur_score The score of the current candidate feature (not used, but passed to keep consistency)
+ * @param end_sel index of the feature to stop checking
+ * @param start_sel index of the feature to stop checking
*
* @return True if the feature is still valid
*/
- bool valid_feature_against_selected_feat_sc_list(double* val_ptr, int n_samp, double cross_cor_max, std::vector<node_sc_pair>& out_vec, double cur_score);
+ bool valid_feature_against_selected_spearman(double* val_ptr, int n_samp, double cross_cor_max, std::vector<double>& scores_sel, double cur_score, int end_sel, int start_sel = 0);
+ /**
+ * @brief Checks the feature to see if it is still valid against previously selected features (using the rank correlation coefficient)
+ *
+ * @param val_ptr pointer to value array of the current feature
+ * @param selected list of previously selected features
+ * @param scores_sel the scores of the previously selected features (not used, but passed to keep consistency)
+ * @param cur_score The score of the current candidate feature (not used, but passed to keep consistency)
+ *
+ * @return True if the feature is still valid
+ */
+ bool valid_feature_against_selected_spearman(double* val_ptr, int n_samp, double cross_cor_max, std::vector<node_ptr>& selected, std::vector<double>& scores_sel, double cur_score);
+ /**
+ * @brief Checks the feature to see if it is still valid against previously selected features (using the rank correlation coefficient)
+ *
+ * @param val_ptr pointer to value array of the current feature
+ * @param n_samp number of samples per feature
+ * @param cross_cor_max maximum cross correlation for the featues
+ * @param start pointer to the first feature to compare against
+ * @param end pointer to the final feature to compare against
+ * @param cur_score The score of the current candidate feature (not used, but passed to keep consistency)
+ *
+ * @return True if the feature is still valid
+ */
+ bool valid_feature_against_selected_spearman(double* val_ptr, int n_samp, double cross_cor_max, std::vector<node_sc_pair>& out_vec, double cur_score);
}
#endif
diff --git a/src/utils/math_funcs.cpp b/src/utils/math_funcs.cpp
index c8e02a275b1cdaeadf5e70c9926e2cd109eae985..4d4b231b99858d3a477379bbcf5e1f0eba73fd6e 100644
--- a/src/utils/math_funcs.cpp
+++ b/src/utils/math_funcs.cpp
@@ -118,3 +118,53 @@ std::vector<int> util_funcs::argsort(std::vector<int>& vec)
return index;
}
+
+void util_funcs::rank(double* a, double* rank, int* index, int size)
+{
+ std::iota(index, index + size, 0);
+ std::sort(index, index + size, [a](int i1, int i2){return a[i1] < a[i2];});
+ int ii = 1;
+ int ii_start = 0;
+ while(ii_start < size)
+ {
+ // while((ii < size) && (round(a[index[ii]], 10) == round(a[index[ii - 1]], 10)))
+ while((ii < size) && (std::abs(a[index[ii]] - a[index[ii - 1]]) < 1e-10 * std::pow(10.0, std::floor(std::log10(a[index[ii]])))))
+ ++ii;
+
+ for(int jj = ii_start; jj < ii; ++jj)
+ rank[index[jj]] = static_cast<double>(ii + ii_start - 1) / 2.0 + 1.0;
+ ii_start = ii;
+ ++ii;
+ }
+}
+
+double util_funcs::spearman_r(double* a, double* b, double* rank_a, double* rank_b, int* index, int size)
+{
+ rank(a, rank_a, index, size);
+ rank(b, rank_b, index, size);
+ return r(rank_a, rank_b, size);
+}
+
+double util_funcs::spearman_r(double* a, double* b, double* rank_a, double* rank_b, int* index, std::vector<int>& sizes)
+{
+ double result = 0.0;
+ int pos = 0;
+ for(auto& sz : sizes)
+ {
+ result += spearman_r(a + pos, b + pos, rank_a, rank_b, index, sz);
+ pos += sz;
+ }
+ return result / static_cast<double>(sizes.size());
+}
+
+double util_funcs::spearman_r(double* a, double* b, double* rank_a, double* rank_b, int* index, int* sz, int n_tasks)
+{
+ double result = 0.0;
+ int pos = 0;
+ for(int nt = 0; nt < n_tasks; ++nt)
+ {
+ result += spearman_r(a + pos, b + pos, rank_a, rank_b, index, sz[nt]);
+ pos += sz[nt];
+ }
+ return result / static_cast<double>(n_tasks);
+}
diff --git a/src/utils/math_funcs.hpp b/src/utils/math_funcs.hpp
index 9811767c733d14c83fe1f11d684adc706a65e7da..2368121eeb4611da7ecefd0b4d24809d116c5f00 100644
--- a/src/utils/math_funcs.hpp
+++ b/src/utils/math_funcs.hpp
@@ -21,6 +21,12 @@ namespace util_funcs
template<typename T>
inline T sign(T number){return (number > T(0)) - (number < T(0));};
+ inline double round(double num, int n)
+ {
+ double exp = std::floor(std::log10(num));
+ return std::pow(10.0, exp - static_cast<double>(n)) * std::round(num * std::pow(10.0, static_cast<double>(n) - exp));
+ }
+
/**
* @brief Find the mean of of a vector
*
@@ -197,6 +203,7 @@ namespace util_funcs
*
* @param a the pointer to the head of the first vector
* @param b the pointer to the head of the second vector
+ * @param log_a the pointer to the head of the vector used to store the log_transformed a value
* @param size the size of the vector
* @return The Coefficient of Determination
*/
@@ -207,6 +214,7 @@ namespace util_funcs
*
* @param a the pointer to the head of the first vector
* @param b the pointer to the head of the second vector
+ * @param log_a the pointer to the head of the vector used to store the log_transformed a value
* @param sizes the sizes of the tasks to calculate the correlation on
* @return The average Coefficient of Determination
*/
@@ -217,12 +225,63 @@ namespace util_funcs
*
* @param a the pointer to the head of the first vector
* @param b the pointer to the head of the second vector
+ * @param log_a the pointer to the head of the vector used to store the log_transformed a value
* @param sz the start of vector that describes the sizes of the tasks to calculate the correlation on
* @param n_tasks number of tasks to average over
* @return The average Coefficient of Determination
*/
double log_r2(double* a, double* b, double* log_a, const int* sz, int n_tasks);
+ /**
+ * @brief Gets the rank variables for a vector
+ *
+ * @param a pointer to the head of the vector to find the rank of
+ * @param rank pointer to the head of the vector to store the resulting ranks
+ * @param index pointer to vector used store the sorted indexes
+ * @param sz The size of the vector
+ */
+ void rank(double* a, double* rank, int* index, int size);
+
+ /**
+ * @brief Calculate the Spearman's rank correlation coefficient between two vectors
+ *
+ * @param a the pointer to the head of the first vector
+ * @param b the pointer to the head of the second vector
+ * @param rank_a the pointer to the head of the vector used to rank of a
+ * @param rank_b the pointer to the head of the vector used to rank of b
+ * @param index the pointer used to store the sorted indexes
+ * @param size the size of the vector
+ * @return The Coefficient of Determination
+ */
+ double spearman_r(double* a, double* b, double* rank_a, double* rank_b, int* index, int size);
+
+ /**
+ * @brief Calculate the average Spearman's rank correlation coefficient between two vectors
+ *
+ * @param a the pointer to the head of the first vector
+ * @param b the pointer to the head of the second vector
+ * @param rank_a the pointer to the head of the vector used to rank of a
+ * @param rank_b the pointer to the head of the vector used to rank of b
+ * @param index the pointer used to store the sorted indexes
+ * @param sizes the sizes of the tasks to calculate the correlation on
+ * @return The average Coefficient of Determination
+ */
+ double spearman_r(double* a, double* b, double* rank_a, double* rank_b, int* index, std::vector<int>& sizes);
+
+ /**
+ * @brief Calculate the average Spearman's rank correlation coefficient between two vectors
+ *
+ * @param a the pointer to the head of the first vector
+ * @param b the pointer to the head of the second vector
+ * @param rank_a the pointer to the head of the vector used to rank of a
+ * @param rank_b the pointer to the head of the vector used to rank of b
+ * @param index the pointer used to store the sorted indexes
+ * @param sz the start of vector that describes the sizes of the tasks to calculate the correlation on
+ * @param n_tasks number of tasks to average over
+ * @return The average Coefficient of Determination
+ */
+ double spearman_r(double* a, double* b, double* rank_a, double* rank_b, int* index, int* sz, int n_tasks);
+
/**
* @brief Sort a vector and return the indexes of the unsorted array that corresponds to the sorted one
*
diff --git a/src/utils/project.cpp b/src/utils/project.cpp
index 048b5c5b23913fcd67d9c36a7608944196ed1d7f..8efa8739001702adcd0d2ed23f312d0d19a8641c 100644
--- a/src/utils/project.cpp
+++ b/src/utils/project.cpp
@@ -1,5 +1,34 @@
#include <utils/project.hpp>
+void project_funcs::set_project_fxn(
+ const std::string project_type,
+ const int n_task,
+ std::function<void(double*, double*, std::vector<node_ptr>&, std::vector<int>&, int)>& project,
+ std::function<void(double*, double*, std::vector<node_ptr>&, std::vector<int>&, int)>& project_no_omp
+)
+{
+ if(project_type.compare("regression") == 0)
+ {
+ project = project_r2;
+ project_no_omp = project_r2_no_omp;
+ }
+ else if(project_type.compare("classification") == 0)
+ {
+ project = project_classify;
+ project_no_omp = project_classify_no_omp;
+ }
+ else if(project_type.compare("log_regression") == 0)
+ {
+ if(n_task > 1)
+ throw std::logic_error("Log Regression can not be done using multiple tasks.");
+ project = project_log_r2;
+ project_no_omp = project_log_r2_no_omp;
+ }
+ else
+ throw std::logic_error("Wrong projection type passed to FeatureSpace constructor.");
+
+}
+
void project_funcs::project_r(double* prop, double* scores, std::vector<node_ptr>& phi, const std::vector<int>& sizes, int n_prop)
{
int n_samp = std::accumulate(sizes.begin(), sizes.end(), 0);
@@ -83,7 +112,6 @@ void project_funcs::project_classify(double* prop, double* scores, std::vector<n
#pragma omp barrier
}
std::transform(scores + start, scores + end, scores + start, [](double score){return std::isnan(score) ? std::numeric_limits<double>::infinity() : score;});
-
}
}
diff --git a/src/utils/project.hpp b/src/utils/project.hpp
index afa5038d7e966545f3114f7d64e830f053591b71..24f7afc922cd442ab6e2904d1c2f6b914028bbc5 100644
--- a/src/utils/project.hpp
+++ b/src/utils/project.hpp
@@ -17,6 +17,20 @@
namespace project_funcs
{
+ /**
+ * @brief Set the project functions for SIS
+ *
+ * @param project_type Type of projection to perform
+ * @param poroject The _poroject function from the feature_space
+ * @param project_no_omp The _project_no_omp function from the feature_space
+ */
+ void set_project_fxn(
+ const std::string project_type,
+ const int n_task,
+ std::function<void(double*, double*, std::vector<node_ptr>&, std::vector<int>&, int)>& project,
+ std::function<void(double*, double*, std::vector<node_ptr>&, std::vector<int>&, int)>& project_no_omp
+ );
+
/**
* @brief Calculate the projection scores of a set of features to a vector via Pearson correlation
*
diff --git a/tests/exec_test/classification/data.csv b/tests/exec_test/classification/data.csv
index 6c0ead3c18944a69d6573acb817c7ae51f9f76f8..711bcdb56da1d6e97e6645b854912d9574b7d4f8 100644
--- a/tests/exec_test/classification/data.csv
+++ b/tests/exec_test/classification/data.csv
@@ -1,101 +1,101 @@
index,prop,A,B,C,D,E,F,G,H,I,J
-0,1.0,0.01,-0.01,10.0,10.0,-0.49282517909827384,0.17302597769416206,0.598942935224295,-0.2987544751968252,-0.5812549090102688,-0.11065649421055634
-1,1.0,-1.8944281037421362,-1.3199613439800735,0.9557138968762431,0.4781172014274879,0.7775861911003294,0.6553697167785566,0.17491417142796584,-0.2884988775306041,-0.04531653614948916,-0.6065861937524113
-2,1.0,-1.4746015071142384,-1.2261496452343335,0.33014029248479626,-0.38950549868991224,0.13893526582480842,-0.8718692821675553,0.3747246204870105,0.1641859118951301,0.29356070144371693,0.28560894022002103
-3,1.0,-1.3021341433673468,-1.8262126241881185,-0.71381302228685,0.9687695850076805,0.6831481792028635,0.7991250925387956,0.30947917352650367,0.7280520310034677,0.7254955809948858,-0.6765763028042482
-4,1.0,-1.7393863226933401,-1.583498665054882,-0.06778341709581581,0.22998854989132322,0.5824275980446467,0.2619471491848253,-0.31573435079735024,0.6117812214426803,-0.18705821696723812,0.8717643476903345
-5,1.0,-1.5666089663239755,-1.058618149021826,-0.21280098230276434,0.9154597761466068,-0.2634655525918126,-0.6661264959880135,-0.19502899649000716,-0.23717005768011568,-0.9333588585968833,-0.19726273171241027
-6,1.0,-1.5534087615389538,-1.2520923128583763,0.9725857879017872,-0.8725028871856755,0.7291109108144516,0.26524478721086564,-0.7269009736244325,0.2486261701079393,0.8090043968802652,-0.27849406478047856
-7,1.0,-1.5462532513644658,-1.8123888845081852,0.4316458762210631,-0.5952232732793832,0.9535570697581124,-0.22291521912156265,0.25767093907617356,0.7289278790983178,-0.5797830554176866,-0.17996031942809454
-8,1.0,-1.1273555452403516,-1.6926149744472814,0.8270911990526928,0.20878148291075949,-0.5733171873615286,-0.004887589213519616,0.3419187160346375,-0.2929016713562016,-0.34531700628784034,0.7669205476300731
-9,1.0,-1.3536783481588408,-1.381410564729617,0.023965203621815423,-0.8054125694184839,0.8068101397184948,-0.8065762631278186,-0.3927997785617203,-0.4638193637740795,0.16095248005356044,-0.5534197471316085
-10,1.0,-1.1785315188879562,-1.2770582929850374,0.916015229666356,0.027594664515699696,-0.8295070079776354,-0.7000636893272012,-0.7156014565887143,-0.4397991651435269,-0.4872412204948866,0.24527926705612058
-11,1.0,-1.1754704976687549,-1.0561328124666542,0.4045577188977567,0.17599736106236108,0.22738373082247842,0.7742546525779774,-0.6160809969176364,-0.948639007451084,0.9540764333752245,-0.49710200117233927
-12,1.0,-1.6727791503394291,-1.8619023988358836,0.9067088448860641,-0.5927370303736976,0.8852298038909494,0.8220692972419072,0.20436748556299245,0.24610324883504986,-0.079476866422163,-0.24400699558343364
-13,1.0,-1.9632616543888368,-1.316804580896929,-0.2845703941884137,-0.19868606157423807,-0.1688976095411121,-0.9293925911269099,0.26589905967191263,-0.8287276425847814,-0.42745309747416815,-0.7383680916088831
-14,1.0,-1.7949776980848098,-1.1394821735708154,0.011556817105956574,0.49940831475722924,0.25316342477447806,0.017645446880421023,0.40173516709526447,-0.6502876172985015,-0.262217482830833,-0.5101021201305884
-15,1.0,-1.079571205362615,-1.9324595507799076,-0.5201611742812009,-0.3924784593473962,0.14749583956586831,0.7808796064740751,-0.28104468779917324,-0.14803690813578552,-0.2088104722240931,0.2789619297181276
-16,1.0,-1.5255514503750653,-1.7245520919673556,0.562803219191695,0.3236760616369958,0.49073713641037187,-0.9547619269949603,0.028603504036769145,-0.2462952196505066,-0.29773629359773923,-0.6324738309576534
-17,1.0,-1.2914230950731502,-1.950696152621199,0.8596694610541045,0.4857728192540889,0.26888359882500934,0.253553321699552,-0.04574308756939516,0.6679340327824896,0.3085919639199468,0.7710843014640274
-18,1.0,-1.234047871210012,-1.6817351928784738,-0.11887110046241278,0.15900093776813162,0.29854288417560015,-0.20382920533253834,-0.6379456952513525,0.6580496908109092,0.9495295891340081,-0.577812553880056
-19,1.0,-1.1351305002955077,-1.3119036669604036,0.7403741109304496,0.558782660077505,-0.0960521263545826,0.5291198173605371,0.37219060477014443,0.6886564662536832,-0.8194331653155049,-0.12814415930810963
-20,1.0,-0.01,0.01,-10.0,-10.0,0.7852373497328908,-0.38721773049540054,-0.9424092188994484,0.16080657729767522,-0.7239699836619722,-0.4526501344158229
-21,1.0,1.1507658618080976,1.726050539272399,-0.9229073425132097,0.46575138421963236,-0.81727500527083,-0.18247264092662796,0.8875260706203563,0.11159288397848788,0.3864350788801618,-0.44001721122127235
-22,1.0,1.9038976822470102,1.7188075931659075,0.2803397954645097,0.379365407838544,0.6348430081926235,0.37175391878083874,-0.6116533053698634,0.7325679278741848,0.85803611350317,-0.5779734417084115
-23,1.0,1.777519764523806,1.2869705037057755,0.22285089894507704,-0.4240570888282873,-0.27619426781835954,0.6169416676806943,-0.696779972923147,0.23612770730498034,0.7607058897805274,0.34004139732032956
-24,1.0,1.6531432749387367,1.162828102113115,-0.3395011973829445,0.8985295913658116,-0.8815382282315818,0.09072882666430049,-0.8583958707809345,0.9072723315158959,0.16053173561906742,0.6789118117688409
-25,1.0,1.309551805582044,1.3682775573764787,-0.4445767545785626,-0.7887117451257197,0.2466257730701833,-0.6634740188183126,-0.4463555520604636,0.7503127731346337,-0.9895952297013597,-0.1501201098407059
-26,1.0,1.4492443117189315,1.4032886458116898,-0.38867957733440184,-0.7081934507919516,0.8503100848003078,-0.7672563385316118,0.37050931732919423,0.38735492101575075,0.5331603211649865,0.14939021245513073
-27,1.0,1.613625013917531,1.054483144145669,-0.35964468015596895,0.6825554041477278,-0.5319540093654402,0.9341016895908625,-0.7360028401883201,-0.2996029145405299,0.3511255963558182,-0.1878428846692788
-28,1.0,1.0243392720597995,1.9105960212113278,-0.24940915747071712,-0.13707002438664384,-0.707128531134104,0.9449320492342947,0.43123336605298723,0.44954399095926245,0.9129019322800267,0.7739461096382698
-29,1.0,1.9944467859460666,1.6720498444130638,0.9353026424804634,0.8337209665238072,0.25416795671734294,-0.007922712021390721,-0.11477629537676681,-0.2760428960022421,-0.8130984031254187,0.3419220522124746
-30,1.0,1.4011033028792645,1.109011516195995,0.4257167722550872,0.5441748037327634,0.492863854358204,-0.04758979171716571,-0.7438407906336721,0.5252894890604114,0.8296117155449363,0.01519322184552152
-31,1.0,1.9499509062547116,1.0572741079996884,0.12665368551441047,0.469705238170149,0.8744362482730081,-0.7595711754681347,0.31023073561926484,-0.8034208437448502,-0.4624310824864766,-0.40716588675912946
-32,1.0,1.472646250429945,1.1891364327906537,-0.7313930180310388,0.46110222460300854,-0.8845283918853222,-0.41989394484079834,0.6475182143890668,0.6881263264084854,0.7546563716916033,0.1168819230678162
-33,1.0,1.4590167720750702,1.1702436403729433,-0.5274334249471311,-0.5987746978086463,0.11319779160167642,-0.50528865259863,0.11757211428893855,0.590400320594326,-0.15515938676912566,0.3548279774131968
-34,1.0,1.3204274424304074,1.198019529303835,-0.8186266224051648,-0.0290085645105993,0.9523159683784683,0.8174957842139241,0.18222455484504252,-0.013773043646529981,-0.2627319529358798,-0.8595305628086736
-35,1.0,1.8813823797628926,1.0367008183967852,0.3052186880166259,-0.06088564766002724,-0.38790344660551357,-0.10806404273546488,-0.9629804050096822,-0.42428960420351114,-0.2534422930772855,0.30963736841129674
-36,1.0,1.9986688782460984,1.3690925712861786,0.5431284474003899,0.5913724730408367,-0.8353670866934573,0.19831525342250655,-0.18143473978380187,0.6364291057549478,0.42062864699233105,0.9901223646646209
-37,1.0,1.504558184990442,1.190949743496727,-0.6532636073327616,0.35935945086837595,0.30131719114182043,0.6495817943565889,0.9422689556330865,0.8846598944893771,-0.4731712393443981,0.039635066570717026
-38,1.0,1.008333615471538,1.981506300008274,-0.8123524571767606,0.2197661015909831,-0.6502106779028902,0.4236216902915564,-0.5886509927579104,0.061487886019890414,-0.2377374740160869,0.6412843473808252
-39,1.0,1.6017918572461922,1.1250859962714062,-0.8298193869407411,-0.3451046875738015,0.48516607054511907,-0.2588397274480556,-0.9206152083268813,0.27549821587142675,-0.629075534110342,-0.6425278879606868
-40,0.0,0.01,0.01,10.0,-10.0,0.04151985651136059,0.2330346162909498,-0.49723324619118703,-0.023544587617094992,-0.41854083777000306,-0.5502339327925116
-41,0.0,-1.0914757437035456,1.704187017012845,-0.4803164747027948,-0.7537847103406319,-0.6132342356169982,0.16795557366247427,0.45563663131504173,-0.3802276359532064,0.48021383007368956,-0.45367492988510794
-42,0.0,-1.9425392252914977,1.5931139414465358,0.31009805091338705,-0.835007082906627,0.40758014085085303,0.5569242475965532,-0.38861660463934644,0.6021510475141199,-0.9843221980987535,-0.9963328889833365
-43,0.0,-1.403024210449149,1.0504137974303827,-0.8984564534469641,-0.7970299242453491,0.47491891024478017,0.48019322053841695,-0.7508561635586863,-0.043960372032017636,-0.24265139180566164,-0.10923906105400616
-44,0.0,-1.45810616907354,1.084683264970634,0.5713295229340183,-0.6773798263796229,0.09839698468883196,-0.9615991701040352,-0.7539225919221575,0.3614358912575588,-0.6380304554939824,0.40434902484390767
-45,0.0,-1.6042143290163837,1.5773097324751753,0.40243320555526796,-0.06801187450077983,-0.3730896611520318,0.23970878487105018,0.41645110664336094,-0.5059916627143299,-0.8866903480674095,0.3036452361644302
-46,0.0,-1.5486866135010242,1.3288318457670756,-0.17492524550976607,0.05033039145153584,-0.38867679574193215,-0.7230760497855304,0.6460761077249644,-0.0010558990958802195,0.4919287207437726,-0.647995101369186
-47,0.0,-1.8920756792534963,1.7657625846115277,0.28941076121752496,0.4856651089687205,0.33868477386080054,0.3743195814396485,-0.10547901462716669,0.004520417892417861,0.2228622619759395,0.23538363683763985
-48,0.0,-1.5144292231365304,1.6984040931515498,-0.19904456301740736,-0.8558041127811826,0.9475720005649064,0.6549395628101518,0.802084131057488,0.010033694468233323,0.44976636625057376,0.11997413461843287
-49,0.0,-1.334691441718412,1.8012484689328736,-0.34672956898995055,-0.614828863660672,0.5781503720015266,-0.6973564899083871,-0.27249617742754695,-0.3266795053631859,0.40318590749462313,0.6598349869723568
-50,0.0,-1.392160865916832,1.96030807097305,-0.4709187754332349,-0.16596517376787534,-0.11837327580213919,-0.8046715422993092,-0.27309628387497664,0.9646762900347077,-0.2407860162851745,0.7810927507182175
-51,0.0,-1.1081877449652693,1.1321805921252017,-0.5463150777240524,-0.26339780806113056,0.0734161121606478,-0.5615845135833513,-0.003812545601593831,-0.06790170865979794,-0.7973376248924131,-0.5024942886762793
-52,0.0,-1.1273342237834545,1.222900933909083,-0.23961815168048672,0.2812826831120643,-0.12225333824316409,-0.4163409124224706,-0.3029448237633121,0.9506971678575753,0.08477434826975472,0.24564363747814055
-53,0.0,-1.5450458531844702,1.4646555655585867,0.6724652616073976,0.9636771128762993,-0.7328669447410141,0.26987900702231227,-0.7341217639847928,-0.1847500436486904,0.49478360423045675,-0.5634696889084065
-54,0.0,-1.6972898977881243,1.9342793806461098,0.916674666213795,0.7441006696135166,-0.5363256808793411,0.7453493138967058,-0.6084949711216283,-0.036147807131094334,0.7300972119817077,-0.9860206879212554
-55,0.0,-1.467166856883277,1.9195073363935855,-0.04001237513761069,0.2482575243891476,-0.795936343325832,-0.755933622220192,0.6649430625674231,-0.5608250699419657,-0.987328335835364,0.009181823833889657
-56,0.0,-1.507858084142104,1.11065681931139,-0.7565527152681395,-0.43396597947565385,-0.9258208007633866,0.6212043805382641,-0.7253554358023508,-0.08719504527829058,0.5000400077995837,-0.3510240708674768
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-59,0.0,-1.7032511687316396,1.561738983983668,0.937331444475048,-0.18914659666867584,0.7267575281390293,0.571196020214809,0.1504784966595285,0.7163709047538911,0.6459479363917942,-0.09651249984138066
-60,0.0,-0.01,-0.01,-10.0,10.0,0.3037482340767377,0.0946840691842421,0.8466519087621067,0.5057109910970319,-0.6648466204250763,-0.7229347856701709
-61,0.0,1.517475034607442,-1.5797683396912157,-0.31385345647165575,-0.6706416904370422,0.33748118903604074,-0.6950596675808771,0.38251266476664836,-0.7540512945658595,-0.5409128937716641,-0.15273659248128935
-62,0.0,1.3672941639996612,-1.5494260699524456,0.7462797650357975,0.3206679093982656,0.0757122783161257,0.5570890283268028,-0.31445996245727414,-0.09117939535299069,-0.7125726183527377,-0.8625237702649187
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diff --git a/tests/googletest/descriptor_identification/sisso_regressor/test_sisso_classifier.cc b/tests/googletest/descriptor_identification/sisso_regressor/test_sisso_classifier.cc
index c1ce780eea46d81d00a7fe9fc23fcc1298513293..1e4ac804e2c89e0cc719cefa4c7d1b8a4154e9ed 100644
--- a/tests/googletest/descriptor_identification/sisso_regressor/test_sisso_classifier.cc
+++ b/tests/googletest/descriptor_identification/sisso_regressor/test_sisso_classifier.cc
@@ -52,11 +52,15 @@ namespace
value_2[ii] = distribution_12_neg(generator);
}
- value_1[0] = 0.40;
- value_1[40] = 0.40;
+ value_1[0] = 0.45;
+ value_1[1] = 0.40;
+ value_1[40] = 0.45;
+ value_1[41] = 0.40;
- value_2[0] = -0.40;
- value_2[40] = 0.40;
+ value_2[0] = -0.45;
+ value_2[1] = -0.40;
+ value_2[40] = 0.45;
+ value_2[41] = 0.40;
for(int ii = 0; ii < 5; ++ii)
{
@@ -82,11 +86,11 @@ namespace
test_value_2[ii] = distribution_12_neg(generator);
}
- test_value_1[0] = 0.40;
- test_value_1[10] = 0.40;
+ test_value_1[0] = 0.45;
+ test_value_1[10] = 0.45;
- test_value_2[0] = -0.40;
- test_value_2[10] = 0.40;
+ test_value_2[0] = -0.45;
+ test_value_2[10] = 0.45;
node_ptr feat_1 = std::make_shared<FeatureNode>(0, "A", value_1, test_value_1, Unit("m"));
node_ptr feat_2 = std::make_shared<FeatureNode>(1, "B", value_2, test_value_2, Unit("m"));
@@ -163,13 +167,13 @@ namespace
EXPECT_EQ(sisso.models().size(), 2);
EXPECT_EQ(sisso.models()[0].size(), 3);
- EXPECT_EQ(sisso.models()[0][0].n_convex_overlap_train(), 2);
- EXPECT_EQ(sisso.models().back()[0].n_convex_overlap_train(), 0);
-
+ EXPECT_EQ(sisso.models()[0][0].n_convex_overlap_train(), 4);
EXPECT_EQ(sisso.models()[0][0].n_convex_overlap_test(), 2);
+
+ EXPECT_EQ(sisso.models().back()[0].n_convex_overlap_train(), 0);
EXPECT_EQ(sisso.models().back()[0].n_convex_overlap_test(), 0);
- EXPECT_EQ(sisso.models()[0][0].n_svm_misclassified_train(), 1);
+ EXPECT_EQ(sisso.models()[0][0].n_svm_misclassified_train(), 2);
EXPECT_EQ(sisso.models()[0][0].n_svm_misclassified_test(), 1);
EXPECT_EQ(sisso.models().back()[0].n_svm_misclassified_train(), 0);
diff --git a/tests/googletest/utils/test_compare_features.cc b/tests/googletest/utils/test_compare_features.cc
index 22d92c2eaeefd0effd82b3e464c2dd20ccd978e2..831c4a156ac5119ec7143abea957eeb3d4aba403 100644
--- a/tests/googletest/utils/test_compare_features.cc
+++ b/tests/googletest/utils/test_compare_features.cc
@@ -21,14 +21,14 @@ namespace {
std::copy_n(val_3.data(), val_3.size(), node_value_arrs::get_d_matrix_ptr(0));
- EXPECT_FALSE(comp_feats::valid_feature_against_selected_max_corr_1(val_1.data(), 4, 1.0, scores, 0.9897782665572893, 1, 0));
- EXPECT_FALSE(comp_feats::valid_feature_against_selected_max_corr_1_feat_list(val_1.data(), 4, 1.0, selected, scores, 0.9897782665572893));
- EXPECT_FALSE(comp_feats::valid_feature_against_selected(val_1.data(), 4, 1.0, scores, 0.9897782665572893, 1, 0));
- EXPECT_FALSE(comp_feats::valid_feature_against_selected_feat_list(val_1.data(), 4, 1.0, selected, scores, 0.9897782665572893));
+ EXPECT_FALSE(comp_feats::valid_feature_against_selected_max_corr_1_pearson(val_1.data(), 4, 1.0, scores, 0.9897782665572893, 1, 0));
+ EXPECT_FALSE(comp_feats::valid_feature_against_selected_max_corr_1_feat_list_pearson(val_1.data(), 4, 1.0, selected, scores, 0.9897782665572893));
+ EXPECT_FALSE(comp_feats::valid_feature_against_selected_pearson(val_1.data(), 4, 1.0, scores, 0.9897782665572893, 1, 0));
+ EXPECT_FALSE(comp_feats::valid_feature_against_selected_feat_list_pearson(val_1.data(), 4, 1.0, selected, scores, 0.9897782665572893));
- EXPECT_TRUE(comp_feats::valid_feature_against_selected_max_corr_1(val_2.data(), 4, 1.0, scores, 0.9028289727756884, 1, 0));
- EXPECT_TRUE(comp_feats::valid_feature_against_selected_max_corr_1_feat_list(val_2.data(), 4, 1.0, selected, scores, 0.9028289727756884));
- EXPECT_TRUE(comp_feats::valid_feature_against_selected(val_2.data(), 4, 1.0, scores, 0.9028289727756884, 1, 0));
- EXPECT_TRUE(comp_feats::valid_feature_against_selected_feat_list(val_2.data(), 4, 1.0, selected, scores, 0.9028289727756884));
+ EXPECT_TRUE(comp_feats::valid_feature_against_selected_max_corr_1_pearson(val_2.data(), 4, 1.0, scores, 0.9028289727756884, 1, 0));
+ EXPECT_TRUE(comp_feats::valid_feature_against_selected_max_corr_1_feat_list_pearson(val_2.data(), 4, 1.0, selected, scores, 0.9028289727756884));
+ EXPECT_TRUE(comp_feats::valid_feature_against_selected_pearson(val_2.data(), 4, 1.0, scores, 0.9028289727756884, 1, 0));
+ EXPECT_TRUE(comp_feats::valid_feature_against_selected_feat_list_pearson(val_2.data(), 4, 1.0, selected, scores, 0.9028289727756884));
}
}