Use double precision in threaded calculation of linear tree coefficients (fixes #5226) (#5368)

CMakeLists.txt

@@ -109,6 +109,7 @@ include_directories(${EIGEN_DIR})
 
 # See https://gitlab.com/libeigen/eigen/-/blob/master/COPYING.README
 add_definitions(-DEIGEN_MPL2_ONLY)
+add_definitions(-DEIGEN_DONT_PARALLELIZE)
 
 if(__BUILD_FOR_R)
     find_package(LibR REQUIRED)

R-package/configure

@@ -1713,7 +1713,7 @@ LGB_CPPFLAGS=""
 # Eigen #
 #########
 
-LGB_CPPFLAGS="${LGB_CPPFLAGS} -DEIGEN_MPL2_ONLY"
+LGB_CPPFLAGS="${LGB_CPPFLAGS} -DEIGEN_MPL2_ONLY -DEIGEN_DONT_PARALLELIZE"
 
 ###############
 # MM_PREFETCH #

R-package/configure.ac

@@ -35,7 +35,7 @@ LGB_CPPFLAGS=""
 # Eigen #
 #########
 
-LGB_CPPFLAGS="${LGB_CPPFLAGS} -DEIGEN_MPL2_ONLY"
+LGB_CPPFLAGS="${LGB_CPPFLAGS} -DEIGEN_MPL2_ONLY -DEIGEN_DONT_PARALLELIZE"
 
 ###############
 # MM_PREFETCH #

R-package/configure.win

@@ -19,7 +19,7 @@ LGB_CPPFLAGS=""
 # Eigen #
 #########
 
-LGB_CPPFLAGS="${LGB_CPPFLAGS} -DEIGEN_MPL2_ONLY"
+LGB_CPPFLAGS="${LGB_CPPFLAGS} -DEIGEN_MPL2_ONLY -DEIGEN_DONT_PARALLELIZE"
 
 ###############
 # MM_PREFETCH #

src/treelearner/linear_tree_learner.cpp

@@ -47,8 +47,8 @@ void LinearTreeLearner::InitLinear(const Dataset* train_data, const int max_leav
     // store only upper triangular half of matrix as an array, in row-major order
     // this requires (max_num_feat + 1) * (max_num_feat + 2) / 2 entries (including the constant terms of the regression)
     // we add another 8 to ensure cache lines are not shared among processors
-    XTHX_.push_back(std::vector<float>((max_num_feat + 1) * (max_num_feat + 2) / 2 + 8, 0));
-    XTg_.push_back(std::vector<float>(max_num_feat + 9, 0.0));
+    XTHX_.push_back(std::vector<double>((max_num_feat + 1) * (max_num_feat + 2) / 2 + 8, 0));
+    XTg_.push_back(std::vector<double>(max_num_feat + 9, 0.0));
   }
   XTHX_by_thread_.clear();
   XTg_by_thread_.clear();

src/treelearner/linear_tree_learner.h

@@ -118,10 +118,10 @@ class LinearTreeLearner: public SerialTreeLearner {
   /*! \brief map dataset to leaves */
   mutable std::vector<int> leaf_map_;
   /*! \brief temporary storage for calculating linear model coefficients */
-  mutable std::vector<std::vector<float>> XTHX_;
-  mutable std::vector<std::vector<float>> XTg_;
-  mutable std::vector<std::vector<std::vector<float>>> XTHX_by_thread_;
-  mutable std::vector<std::vector<std::vector<float>>> XTg_by_thread_;
+  mutable std::vector<std::vector<double>> XTHX_;
+  mutable std::vector<std::vector<double>> XTg_;
+  mutable std::vector<std::vector<std::vector<double>>> XTHX_by_thread_;
+  mutable std::vector<std::vector<std::vector<double>>> XTg_by_thread_;
 };
 
 }  // namespace LightGBM

tests/python_package_test/test_consistency.py

@@ -84,7 +84,7 @@ def test_binary_linear():
     X_test, _, X_test_fn = fd.load_dataset('.test')
     weight_train = fd.load_field('.train.weight')
     lgb_train = lgb.Dataset(X_train, y_train, params=fd.params, weight=weight_train)
-    gbm = lgb.LGBMClassifier(**fd.params, n_jobs=0)
+    gbm = lgb.LGBMClassifier(**fd.params)
     gbm.fit(X_train, y_train, sample_weight=weight_train)
     sk_pred = gbm.predict_proba(X_test)[:, 1]
     fd.train_predict_check(lgb_train, X_test, X_test_fn, sk_pred)

tests/python_package_test/test_engine.py

@@ -3046,6 +3046,26 @@ def test_interaction_constraints():
                     train_data, num_boost_round=10)
 
 
+def test_linear_trees_num_threads():
+    # check that number of threads does not affect result
+    np.random.seed(0)
+    x = np.arange(0, 1000, 0.1)
+    y = 2 * x + np.random.normal(0, 0.1, len(x))
+    x = x[:, np.newaxis]
+    lgb_train = lgb.Dataset(x, label=y)
+    params = {'verbose': -1,
+              'objective': 'regression',
+              'seed': 0,
+              'linear_tree': True,
+              'num_threads': 2}
+    est = lgb.train(params, lgb_train, num_boost_round=100)
+    pred1 = est.predict(x)
+    params["num_threads"] = 4
+    est = lgb.train(params, lgb_train, num_boost_round=100)
+    pred2 = est.predict(x)
+    np.testing.assert_allclose(pred1, pred2)
+
+
 def test_linear_trees(tmp_path):
     # check that setting linear_tree=True fits better than ordinary trees when data has linear relationship
     np.random.seed(0)

windows/LightGBM.vcxproj

@@ -101,7 +101,7 @@
   </PropertyGroup>
   <ItemDefinitionGroup>
     <ClCompile>
-      <PreprocessorDefinitions>EIGEN_MPL2_ONLY</PreprocessorDefinitions>
+      <PreprocessorDefinitions>EIGEN_MPL2_ONLY;EIGEN_DONT_PARALLELIZE</PreprocessorDefinitions>
     </ClCompile>
   </ItemDefinitionGroup>
   <ItemDefinitionGroup Condition="'$(Configuration)|$(Platform)'=='Debug_mpi|x64'">