Bug Fix

Correct bugs when attempting to allow additional features when max_cor < 1

src/feature_creation/feature_space/FeatureSpace.cpp

@@ -1019,12 +1019,14 @@ void FeatureSpace::generate_and_project(std::shared_ptr<LossFunction> loss, std:
     int worst_score_ind = std::max_element(scores_sel.begin(), scores_sel.end()) - scores_sel.begin();
     double worst_score = scores_sel[worst_score_ind];
 
+    int num_valid_feats = phi_sel.size();
     #pragma omp parallel firstprivate(worst_score, worst_score_ind, scores_sel_all)
     {
         std::shared_ptr<LossFunction> loss_copy = loss_function_util::copy(loss);
         std::vector<node_ptr> phi_sel_private(phi_sel);
         std::vector<double> scores_sel_private(scores_sel);
-        int index_base = _phi.size() + _n_sis_select * (omp_get_thread_num() + _mpi_comm->size());
+        int index_base = _phi.size() + 2 * _n_sis_select * (omp_get_thread_num() + _mpi_comm->size());
+        int num_valid_feats_private = num_valid_feats;
 
         #ifdef PARAMETERIZE
         std::shared_ptr<NLOptimizer> optimizer;
@@ -1090,7 +1092,7 @@ void FeatureSpace::generate_and_project(std::shared_ptr<LossFunction> loss, std:
             while((ii < inds.size()) && ((scores[inds[ii]] < worst_score) || (phi_sel_private.size() < _n_sis_select)))
             {
                 double cur_score = scores[inds[ii]];
-                bool valid_feat = _is_valid(
+                int valid_feat = _is_valid(
                     generated_phi[inds[ii]]->stand_value_ptr(),
                     _n_samp_train,
                     _cross_cor_max,
@@ -1099,24 +1101,26 @@ void FeatureSpace::generate_and_project(std::shared_ptr<LossFunction> loss, std:
                     node_value_arrs::N_SELECTED - _n_sis_select,
                     0
                 );
-                valid_feat = valid_feat && _is_valid_feat_list(
-                    generated_phi[inds[ii]]->stand_value_ptr(),
-                    _n_samp_train,
-                    _cross_cor_max,
-                    phi_sel_private,
-                    scores_sel_private,
-                    cur_score
-                );
-                if(valid_feat)
+                if(valid_feat > 0)
                 {
-                    if(scores_sel_private.size() == _n_sis_select)
+                    valid_feat = _is_valid_feat_list(
+                        generated_phi[inds[ii]]->stand_value_ptr(),
+                        _n_samp_train,
+                        _cross_cor_max,
+                        phi_sel_private,
+                        scores_sel_private,
+                        cur_score
+                    );
+
+                    if((valid_feat > 0) && (num_valid_feats_private >= _n_sis_select))
                     {
                         generated_phi[inds[ii]]->reindex(index_base + worst_score_ind);
                         phi_sel_private[worst_score_ind] = generated_phi[inds[ii]];
                         scores_sel_private[worst_score_ind] = cur_score;
                     }
-                    else
+                    else if(valid_feat != 0)
                     {
+                        num_valid_feats_private += valid_feat > 0;
                         generated_phi[inds[ii]]->reindex(index_base + scores_sel_private.size());
                         phi_sel_private.push_back(generated_phi[inds[ii]]);
                         scores_sel_private.push_back(cur_score);
@@ -1130,38 +1134,86 @@ void FeatureSpace::generate_and_project(std::shared_ptr<LossFunction> loss, std:
 
         #pragma omp critical
         {
-            index_base = _phi.size() + _n_sis_select * _mpi_comm->rank();
+            index_base = _phi.size() + 2 * _n_sis_select * _mpi_comm->rank();
             worst_score_ind = std::max_element(scores_sel.begin(), scores_sel.end()) - scores_sel.begin();
             for(int sc = 0; sc < scores_sel_private.size(); ++sc)
             {
-                if(
-                    ((phi_sel.size() < _n_sis_select) || (scores_sel_private[sc] < scores_sel[worst_score_ind])) &&
-                    _is_valid_feat_list(phi_sel_private[sc]->stand_value_ptr(), _n_samp_train, _cross_cor_max, phi_sel, scores_sel, scores_sel_private[sc])
-                )
+                if(scores_sel_private[sc] > scores_sel[worst_score_ind])
+                {
+                    continue;
+                }
+                int valid_feat = _is_valid_feat_list(
+                    phi_sel_private[sc]->stand_value_ptr(),
+                    _n_samp_train,
+                    _cross_cor_max,
+                    phi_sel,
+                    scores_sel,
+                    scores_sel_private[sc]
+                );
+                if((valid_feat > 0) && (num_valid_feats >= _n_sis_select))
                 {
-                    if(phi_sel.size() == _n_sis_select)
+                    scores_sel[worst_score_ind] = scores_sel_private[sc];
+                    phi_sel[worst_score_ind] = phi_sel_private[sc];
+                    if(phi_sel[worst_score_ind]->rung() == _max_rung)
                     {
-                        scores_sel[worst_score_ind] = scores_sel_private[sc];
-                        phi_sel[worst_score_ind] = phi_sel_private[sc];
-                        if(phi_sel[worst_score_ind]->rung() == _max_rung)
-                        {
-                            phi_sel[worst_score_ind]->reindex(index_base + worst_score_ind);
-                        }
+                        phi_sel[worst_score_ind]->reindex(index_base + worst_score_ind);
                     }
-                    else
+                    worst_score_ind = std::max_element(scores_sel.begin(), scores_sel.end()) - scores_sel.begin();
+                }
+                else if(valid_feat != 0)
+                {
+                    num_valid_feats += (valid_feat > 0);
+                    scores_sel.push_back(scores_sel_private[sc]);
+                    phi_sel.push_back(phi_sel_private[sc]);
+                    if(phi_sel.back()->rung() == _max_rung)
                     {
-                        scores_sel.push_back(scores_sel_private[sc]);
-                        phi_sel.push_back(phi_sel_private[sc]);
-                        if(phi_sel.back()->rung() == _max_rung)
-                        {
-                            phi_sel.back()->reindex(index_base + phi_sel.size());
-                        }
+                        phi_sel.back()->reindex(index_base + phi_sel.size());
                     }
                     worst_score_ind = std::max_element(scores_sel.begin(), scores_sel.end()) - scores_sel.begin();
                 }
             }
         }
     }
+
+    // Go through all features and remove any that were valid, but no longer are due to new better features appearing. If _cross_cor_max == 1.0 then this is not necessary
+    if(_cross_cor_max < 0.99999)
+    {
+        int index_base = _phi.size() + _n_sis_select * _mpi_comm->rank();
+        std::vector<int> inds = util_funcs::argsort(scores_sel);
+
+        std::vector<node_ptr> phi_sel_copy(phi_sel);
+        phi_sel.clear();
+
+        std::vector<double> scores_sel_copy(scores_sel);
+        scores_sel.clear();
+
+        phi_sel.push_back(phi_sel_copy[inds[0]]);
+        scores_sel.push_back(scores_sel_copy[inds[0]]);
+
+        int ii = 1;
+        while((ii < inds.size()) && (phi_sel.size() < _n_sis_select))
+        {
+            if(
+                _is_valid_feat_list(
+                    phi_sel_copy[inds[ii]]->stand_value_ptr(),
+                    _n_samp_train,
+                    _cross_cor_max,
+                    phi_sel,
+                    scores_sel,
+                    scores_sel_copy[inds[ii]]
+                ) > 0
+            )
+            {
+                scores_sel.push_back(scores_sel_copy[inds[ii]]);
+                phi_sel.push_back(phi_sel_copy[inds[ii]]);
+                if(phi_sel.back()->rung() == _max_rung)
+                {
+                    phi_sel.back()->reindex(index_base + phi_sel.size());
+                }
+            }
+            ++ii;
+        }
+    }
 }
 
 void FeatureSpace::sis(const std::vector<double>& prop)
@@ -1270,7 +1322,6 @@ void FeatureSpace::sis(std::shared_ptr<LossFunction> loss)
             phi_sel.back()->set_d_mat_ind(cur_feat + cur_feat_local);
             phi_sel.back()->set_value();
             phi_sel.back()->set_standardized_value();
-
             ++cur_feat_local;
         }
         ++ii;
@@ -1395,6 +1446,7 @@ void FeatureSpace::sis(std::shared_ptr<LossFunction> loss)
         double prefact = std::pow(-1, (_project_type != "classification"));
         for(auto& ind : inds)
         {
+
             node_value_arrs::clear_temp_reg();
             out_file_stream << std::setw(14) <<std::left << cur_feat << phi_sel[ind]->postfix_expr() << std::endl;
             sum_file_stream << std::setw(14) <<std::left << cur_feat << std::setw(24) << std::setprecision(18) << std::left << prefact * scores_sel[ind];
@@ -1413,7 +1465,13 @@ void FeatureSpace::sis(std::shared_ptr<LossFunction> loss)
 
     if(cur_feat != node_value_arrs::N_SELECTED)
     {
-        throw std::logic_error("SIS went through all features and did not select enough.");
+        throw std::logic_error(
+            "SIS went through all features and did not select enough (" +
+            std::to_string(cur_feat) +
+            " not " +
+            std::to_string(_n_sis_select) +
+            ")."
+        );
     }
 
     if(_mpi_comm->rank() == 0)

src/feature_creation/feature_space/FeatureSpace.hpp

@@ -79,7 +79,7 @@ class FeatureSpace
     const std::string _phi_out_file; //!< Filename of the file to output the feature set to
 
     std::function<bool(const double*, const int, const double, const std::vector<double>&, const double, const int, const int)> _is_valid; //!< Function used to determine of a feature is too correlated to previously selected features
-    std::function<bool(const double*, const int, const double, const std::vector<node_ptr>&, const std::vector<double>&, const double)> _is_valid_feat_list; //!< Function used to determine of a feature is too correlated to previously selected features within a given list
+    std::function<int(const double*, const int, const double, const std::vector<node_ptr>&, const std::vector<double>&, const double)> _is_valid_feat_list; //!< Function used to determine of a feature is too correlated to previously selected features within a given list
 
     std::shared_ptr<MPI_Interface> _mpi_comm; //!< the MPI communicator for the calculation
 

src/mpi_interface/MPI_Ops.cpp

@@ -72,7 +72,7 @@ std::vector<node_sc_pair> mpi_reduce_op::select_top_feats(std::vector<node_sc_pa
     int out_ind = 0;
     while((out_ind < N_SIS_SELECT) && (ff < in_vec_2.size()))
     {
-        if(in_vec_2[ff]._feat && IS_VALID(in_vec_2[ff]._feat->value_ptr(), in_vec_2[ff]._feat->n_samp(), CROSS_COR_MAX, out_vec, in_vec_2[ff]._score))
+        if(in_vec_2[ff]._feat && IS_VALID(in_vec_2[ff]._feat->stand_value_ptr(), in_vec_2[ff]._feat->n_samp(), CROSS_COR_MAX, out_vec, in_vec_2[ff]._score))
         {
             out_vec.push_back(in_vec_2[ff]);
             ++out_ind;

src/utils/compare_features.cpp

@@ -30,7 +30,7 @@ void comp_feats::set_is_valid_fxn(
     const double max_corr,
     const int n_samp,
     std::function<bool(const double*, const int, const double, const std::vector<double>&, const double, const int, const int)>& is_valid,
-    std::function<bool(const double*, const int, const double, const std::vector<node_ptr>&, const std::vector<double>&, const double)>& is_valid_feat_list
+    std::function<int(const double*, const int, const double, const std::vector<node_ptr>&, const std::vector<double>&, const double)>& is_valid_feat_list
 )
 {
     if(project_type.compare("classification") != 0)
@@ -90,11 +90,16 @@ bool comp_feats::valid_feature_against_selected_pearson_max_corr_1(
             continue;
         }
 
-        double comp_value  = 1.0 / static_cast<double>(n_samp) * std::inner_product(
-            val_ptr,
-            val_ptr + n_samp,
-            node_value_arrs::get_stand_d_matrix_ptr(dd),
-            -1.0 * base_val
+        double comp_value  = 1.0 / static_cast<double>(n_samp) * (
+            base_val -
+            std::abs(
+                std::inner_product(
+                    val_ptr,
+                    val_ptr + n_samp,
+                    node_value_arrs::get_stand_d_matrix_ptr(dd),
+                    0.0
+                )
+            )
         );
 
         if(std::abs(comp_value) < 5.0e-9)
@@ -105,7 +110,7 @@ bool comp_feats::valid_feature_against_selected_pearson_max_corr_1(
     return true;
 }
 
-bool comp_feats::valid_feature_against_selected_pearson_max_corr_1_feat_list(
+int comp_feats::valid_feature_against_selected_pearson_max_corr_1_feat_list(
     const double* val_ptr,
     const int n_samp,
     const double cross_cor_max,
@@ -123,19 +128,24 @@ bool comp_feats::valid_feature_against_selected_pearson_max_corr_1_feat_list(
             continue;
         }
 
-        double comp_value  = 1.0 / static_cast<double>(n_samp) * std::inner_product(
-            val_ptr,
-            val_ptr + n_samp,
-            selected[ff]->stand_value_ptr(true),
-            -1.0 * base_val
+        double comp_value  = 1.0 / static_cast<double>(n_samp) * (
+            base_val -
+            std::abs(
+                std::inner_product(
+                    val_ptr,
+                    val_ptr + n_samp,
+                    selected[ff]->stand_value_ptr(true),
+                    0.0
+                )
+            )
         );
 
         if(std::abs(comp_value) < 5.0e-9)
         {
-            return false;
+            return 0;
         }
     }
-    return true;
+    return 1;
 }
 
 bool comp_feats::valid_feature_against_selected_pearson_max_corr_1_mpi_op(
@@ -155,11 +165,16 @@ bool comp_feats::valid_feature_against_selected_pearson_max_corr_1_mpi_op(
             continue;
         }
 
-        double comp_value  = 1.0 / static_cast<double>(n_samp) * std::inner_product(
-            val_ptr,
-            val_ptr + n_samp,
-            feat_sc._feat->stand_value_ptr(true),
-            -1.0 * base_val
+        double comp_value  = 1.0 / static_cast<double>(n_samp) * (
+            base_val -
+            std::abs(
+                std::inner_product(
+                    val_ptr,
+                    val_ptr + n_samp,
+                    feat_sc._feat->stand_value_ptr(true),
+                    0.0
+                )
+            )
         );
 
         if(std::abs(comp_value) < 5.0e-9)
@@ -187,8 +202,6 @@ bool comp_feats::valid_feature_against_selected_pearson(
     }
 
     DGEMV_OUT.resize(end_sel - start_sel);
-    double base_val = std::inner_product(val_ptr, val_ptr + n_samp, val_ptr, 0.0) / static_cast<double>(n_samp);
-
     dgemv_(
         'N',
         DGEMV_OUT.size(),
@@ -203,13 +216,10 @@ bool comp_feats::valid_feature_against_selected_pearson(
         1
     );
 
-    double comp_value = (
-        base_val - std::abs(DGEMV_OUT[idamax_(DGEMV_OUT.size(), DGEMV_OUT.data(), 1) - 1])
-    );
-    return std::abs(comp_value) >= (1.0 - cross_cor_max + 5.0e-9);
+    return std::abs(DGEMV_OUT[idamax_(DGEMV_OUT.size(), DGEMV_OUT.data(), 1) - 1]) <= cross_cor_max;
 }
 
-bool comp_feats::valid_feature_against_selected_pearson_feat_list(
+int comp_feats::valid_feature_against_selected_pearson_feat_list(
     const double* val_ptr,
     const int n_samp,
     const double cross_cor_max,
@@ -218,20 +228,21 @@ bool comp_feats::valid_feature_against_selected_pearson_feat_list(
     const double cur_score
 )
 {
-    double base_val = std::inner_product(val_ptr, val_ptr + n_samp, val_ptr, 0.0);
-
+    int is_valid = 1;
+    double comp_value = 1.0;
     for(int ff = 0; ff < selected.size(); ++ff)
     {
-        double comp_value = 1.0 / static_cast<double>(n_samp) * std::abs(
-            std::inner_product(val_ptr, val_ptr + n_samp, selected[ff]->stand_value_ptr(true), -base_val)
+        comp_value = 1.0 / static_cast<double>(n_samp) * std::abs(
+            std::inner_product(val_ptr, val_ptr + n_samp, selected[ff]->stand_value_ptr(true), 0.0)
         );
 
-        if(std::abs(comp_value) < (1.0 - cross_cor_max + 5.0e-9))
+        if((comp_value > cross_cor_max) && (cur_score > scores_sel[ff]))
         {
-            return false;
+            return 0;
         }
+        is_valid -= 2 * (comp_value > cross_cor_max);
     }
-    return true;
+    return is_valid / std::abs(is_valid);
 }
 
 bool comp_feats::valid_feature_against_selected_pearson_mpi_op(
@@ -242,15 +253,13 @@ bool comp_feats::valid_feature_against_selected_pearson_mpi_op(
     const double cur_score
 )
 {
-    double base_val = std::inner_product(val_ptr, val_ptr + n_samp, val_ptr, 0.0);
-
     for(auto& feat_sc : out_vec)
     {
         double comp_value = 1.0 / static_cast<double>(n_samp) * std::abs(
-            std::inner_product(val_ptr, val_ptr + n_samp, feat_sc._feat->stand_value_ptr(true), -base_val)
+            std::inner_product(val_ptr, val_ptr + n_samp, feat_sc._feat->stand_value_ptr(true), 0.0)
         );
 
-        if(std::abs(comp_value) < (1.0 - cross_cor_max + 5.0e-9))
+        if(comp_value > cross_cor_max)
         {
             return false;
         }
@@ -300,7 +309,7 @@ bool comp_feats::valid_feature_against_selected_spearman_max_corr_1(
     return true;
 }
 
-bool comp_feats::valid_feature_against_selected_spearman_max_corr_1_feat_list(
+int comp_feats::valid_feature_against_selected_spearman_max_corr_1_feat_list(
     const double* val_ptr,
     const int n_samp,
     const double cross_cor_max,
@@ -334,10 +343,10 @@ bool comp_feats::valid_feature_against_selected_spearman_max_corr_1_feat_list(
         );
         if(std::abs(comp_value) < 5.0e-9)
         {
-            return false;
+            return 0;
         }
     }
-    return true;
+    return 1;
 }
 
 bool comp_feats::valid_feature_against_selected_spearman_max_corr_1_mpi_op(
@@ -390,39 +399,23 @@ bool comp_feats::valid_feature_against_selected_spearman(
     const 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;
-
+    util_funcs::rank(val_ptr, &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)
     {
-        for(int dd = start_sel; dd < end_sel; ++dd)
+        // 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);
+        double comp_value = std::abs(
+            util_funcs::r(&RANK[omp_get_thread_num() * 4 * n_samp], &RANK[(omp_get_thread_num() * 4 + 2) * n_samp], n_samp)
+        );
+        if(comp_value > cross_cor_max)
         {
-            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);
-            double comp_value = (
-                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))
-            );
-            if(std::abs(comp_value) < 1.0 -cross_cor_max + 5.0e-9)
-            {
-                is_valid = false;
-            }
+            return false;
         }
     }
-    return is_valid;
+    return true;
 }
 
-bool comp_feats::valid_feature_against_selected_spearman_feat_list(
+int comp_feats::valid_feature_against_selected_spearman_feat_list(
     const double* val_ptr,
     const int n_samp,
     const double cross_cor_max,
@@ -431,30 +424,22 @@ bool comp_feats::valid_feature_against_selected_spearman_feat_list(
     const 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)
+    util_funcs::rank(val_ptr, &RANK[(omp_get_thread_num() * 4 + 2) * n_samp], &INDEX[omp_get_thread_num() * 2 * n_samp], n_samp);
+    int is_valid = 1;
+    for(int ff = 0; ff < selected.size(); ++ff)
     {
         // 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, true), &RANK[omp_get_thread_num() * 4 * n_samp], &INDEX[omp_get_thread_num() * 2 * n_samp], n_samp);
-        double comp_value = (
-            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))
+        util_funcs::rank(selected[ff]->value_ptr(-1, true), &RANK[omp_get_thread_num() * 4 * n_samp], &INDEX[omp_get_thread_num() * 2 * n_samp], n_samp);
+        double comp_value = std::abs(
+            util_funcs::r(&RANK[omp_get_thread_num() * 4 * n_samp], &RANK[(omp_get_thread_num() * 4 + 2) * n_samp], n_samp)
         );
-        if(std::abs(comp_value) < 1.0 - cross_cor_max + 5.0e-9)
+        if((comp_value > cross_cor_max) && (cur_score > scores_sel[ff]))
         {
-            return false;
+            return 0;
         }
+        is_valid -= 2 * (std::abs(comp_value) > cross_cor_max);
     }
-    return true;
+    return is_valid / std::abs(is_valid);
 }
 
 bool comp_feats::valid_feature_against_selected_spearman_mpi_op(
@@ -465,26 +450,16 @@ bool comp_feats::valid_feature_against_selected_spearman_mpi_op(
     const 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
-        )
-    );
-
+    util_funcs::rank(val_ptr, &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(
             feat_sc._feat->value_ptr(-1, true), &RANK[omp_get_thread_num() * 4 * n_samp], &INDEX[omp_get_thread_num() * 2 * n_samp], n_samp
         );
-        double comp_value = (
-            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))
+        double comp_value = std::abs(
+            util_funcs::r(&RANK[omp_get_thread_num() * 4 * n_samp], &RANK[(omp_get_thread_num() * 4 + 2) * n_samp], n_samp)
         );
-        if(std::abs(comp_value) < 1.0 - cross_cor_max + 5.0e-9)
+        if(comp_value > cross_cor_max)
         {
             return false;
         }

src/utils/compare_features.hpp

@@ -139,7 +139,7 @@ namespace comp_feats
         const double max_corr,
         const int n_samp,
         std::function<bool(const double*, const int, const double, const std::vector<double>&, const double, const int, const int)>& is_valid,
-        std::function<bool(const double*, const int, const double, const std::vector<node_ptr>&, const std::vector<double>&, const double)>& is_valid_feat_list
+        std::function<int(const double*, const int, const double, const std::vector<node_ptr>&, const std::vector<double>&, const double)>& is_valid_feat_list
     );
 
     /**
@@ -180,9 +180,9 @@ namespace comp_feats
      * @param scores_sel The projection scores for the selected feature
      * @param cur_score The score of the current candidate feature
      *
-     * @return True if the feature is still valid
+     * @return 1 if the feature is valid, 0 if the feature is invalid, -1 if the feature is invalid but has a higher score than the conflicting features
      */
-    bool valid_feature_against_selected_pearson_max_corr_1_feat_list(
+    int valid_feature_against_selected_pearson_max_corr_1_feat_list(
         const double* val_ptr,
         const int n_samp,
         const double cross_cor_max,
@@ -243,9 +243,9 @@ namespace comp_feats
      * @param scores_sel The projection scores for the selected feature
      * @param cur_score The score of the current candidate feature
      *
-     * @return True if the feature is still valid
+     * @return 1 if the feature is valid, -1 if the feature is invalid, 0 if the feature is invalid but has a higher score than the conflicting features
      */
-    bool valid_feature_against_selected_pearson_feat_list(
+    int valid_feature_against_selected_pearson_feat_list(
         const double* val_ptr,
         const int n_samp,
         const double cross_cor_max,
@@ -306,9 +306,9 @@ namespace comp_feats
      * @param scores_sel The projection scores for the selected feature
      * @param cur_score The score of the current candidate feature
      *
-     * @return True if the feature is still valid
+     * @return 1 if the feature is valid, -1 if the feature is invalid, 0 if the feature is invalid but has a higher score than the conflicting features
      */
-    bool valid_feature_against_selected_spearman_max_corr_1_feat_list(
+    int valid_feature_against_selected_spearman_max_corr_1_feat_list(
         const double* val_ptr,
         const int n_samp,
         const double cross_cor_max,
@@ -369,9 +369,9 @@ namespace comp_feats
      * @param scores_sel The projection scores for the selected feature
      * @param cur_score The score of the current candidate feature
      *
-     * @return True if the feature is still valid
+     * @return 1 if the feature is valid, -1 if the feature is invalid, 0 if the feature is invalid but has a higher score than the conflicting features
      */
-    bool valid_feature_against_selected_spearman_feat_list(
+    int valid_feature_against_selected_spearman_feat_list(
         const double* val_ptr,
         const int n_samp,
         const double cross_cor_max,

tests/exec_test/default/sisso.json

 {
     "desc_dim": 2,
-    "n_sis_select": 1,
+    "n_sis_select": 2,
     "max_rung": 2,
     "n_residual": 1,
     "data_file": "../data.csv",