Add tests for MPI-op

Since MPI is not being used in the runners

src/feature_creation/feature_space/FeatureSpace.cpp

@@ -1061,11 +1061,11 @@ void FeatureSpace::sis(std::shared_ptr<LossFunction> loss)
     // Generate features on the fly and get the best features for this rank
     if(_n_rung_generate > 0)
     {
-#ifdef PARAMETERIZE
+        #ifdef PARAMETERIZE
         _phi.resize(_n_feat);
         _phi.insert(_phi.end(), _phi_reparam.begin() + _start_rung_reparam.back(), _phi_reparam.end());
         _scores.resize(_phi.size());
-#endif
+        #endif
         start_time = omp_get_wtime();
         phi_sel.resize(cur_feat_local);
         scores_sel.resize(cur_feat_local);

tests/googletest/utils/test_compare_features.cc

@@ -12,22 +12,26 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 #include "gtest/gtest.h"
-#include <utils/compare_features.hpp>
-#include <feature_creation/node/ModelNode.hpp>
+#include "feature_creation/node/ModelNode.hpp"
+#include "mpi_interface/MPI_Ops.hpp"
+#include "utils/compare_features.hpp"
 
 namespace {
     //test mean calculations
     TEST(CompFeats, CompFeatTest)
     {
         std::vector<double> val_1 = {1.0, 2.0, 3.0, 4.0};
-        std::vector<double> val_2 = {2.0, 2.0, 3.0, 4.0};
+        std::vector<double> val_2 = {2.0, 1.0, 3.0, 4.0};
         std::vector<double> val_3 = {2.0, 4.0, 6.0, 8.0};
         std::vector<double> target = {1.0, 3.0, 5.0, 6.0};
         std::vector<double> scores = {0.9897782665572893};
+
         std::vector<node_ptr> selected(1);
+        std::vector<node_sc_pair> mpi_op_sel(1);
 
         node_value_arrs::initialize_values_arr({4}, {0}, 1, 0, false);
         selected[0] = std::make_shared<FeatureNode>(0, "A", val_3, std::vector<double>(), Unit());
+        mpi_op_sel[0] = mpi_reduce_op::make_node_sc_pair(selected[0], scores[0]);
 
         node_value_arrs::initialize_d_matrix_arr();
         node_value_arrs::resize_d_matrix_arr(1);
@@ -36,13 +40,36 @@ namespace {
 
         EXPECT_FALSE(comp_feats::valid_feature_against_selected_pearson_max_corr_1(val_1.data(), 4, 1.0, scores, 0.9897782665572893, 1, 0));
         EXPECT_FALSE(comp_feats::valid_feature_against_selected_pearson_max_corr_1_feat_list(val_1.data(), 4, 1.0, selected, scores, 0.9897782665572893));
+        EXPECT_FALSE(comp_feats::valid_feature_against_selected_pearson_max_corr_1_mpi_op(val_1.data(), 4, 1.0, mpi_op_sel, 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_pearson_feat_list(val_1.data(), 4, 1.0, selected, scores, 0.9897782665572893));
+        EXPECT_FALSE(comp_feats::valid_feature_against_selected_pearson_mpi_op(val_1.data(), 4, 1.0, mpi_op_sel, 0.9897782665572893));
 
         EXPECT_TRUE(comp_feats::valid_feature_against_selected_pearson_max_corr_1(val_2.data(), 4, 1.0, scores, 0.9028289727756884, 1, 0));
         EXPECT_TRUE(comp_feats::valid_feature_against_selected_pearson_max_corr_1_feat_list(val_2.data(), 4, 1.0, selected, scores, 0.9028289727756884));
+        EXPECT_TRUE(comp_feats::valid_feature_against_selected_pearson_max_corr_1_mpi_op(val_2.data(), 4, 1.0, mpi_op_sel, 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_pearson_feat_list(val_2.data(), 4, 1.0, selected, scores, 0.9028289727756884));
+        EXPECT_TRUE(comp_feats::valid_feature_against_selected_pearson_mpi_op(val_2.data(), 4, 1.0, mpi_op_sel, 0.9028289727756884));
+
+        EXPECT_FALSE(comp_feats::valid_feature_against_selected_spearman_max_corr_1(val_1.data(), 4, 1.0, scores, 0.9897782665572893, 1, 0));
+        EXPECT_FALSE(comp_feats::valid_feature_against_selected_spearman_max_corr_1_feat_list(val_1.data(), 4, 1.0, selected, scores, 0.9897782665572893));
+        EXPECT_FALSE(comp_feats::valid_feature_against_selected_spearman_max_corr_1_mpi_op(val_1.data(), 4, 1.0, mpi_op_sel, 0.9897782665572893));
+
+        EXPECT_FALSE(comp_feats::valid_feature_against_selected_spearman(val_1.data(), 4, 1.0, scores, 0.9897782665572893, 1, 0));
+        EXPECT_FALSE(comp_feats::valid_feature_against_selected_spearman_feat_list(val_1.data(), 4, 1.0, selected, scores, 0.9897782665572893));
+        EXPECT_FALSE(comp_feats::valid_feature_against_selected_spearman_mpi_op(val_1.data(), 4, 1.0, mpi_op_sel, 0.9897782665572893));
+
+        EXPECT_TRUE(comp_feats::valid_feature_against_selected_spearman_max_corr_1(val_2.data(), 4, 1.0, scores, 0.9028289727756884, 1, 0));
+        EXPECT_TRUE(comp_feats::valid_feature_against_selected_spearman_max_corr_1_feat_list(val_2.data(), 4, 1.0, selected, scores, 0.9028289727756884));
+        EXPECT_TRUE(comp_feats::valid_feature_against_selected_spearman_max_corr_1_mpi_op(val_2.data(), 4, 1.0, mpi_op_sel, 0.9028289727756884));
+
+        EXPECT_TRUE(comp_feats::valid_feature_against_selected_spearman(val_2.data(), 4, 1.0, scores, 0.9028289727756884, 1, 0));
+        EXPECT_TRUE(comp_feats::valid_feature_against_selected_spearman_feat_list(val_2.data(), 4, 1.0, selected, scores, 0.9028289727756884));
+        EXPECT_TRUE(comp_feats::valid_feature_against_selected_spearman_mpi_op(val_2.data(), 4, 1.0, mpi_op_sel, 0.9028289727756884));
+
         node_value_arrs::finalize_values_arr();
     }
 }