generalize trees in PartitionTwoDimensionalBoosting

README.md

@@ -650,6 +650,7 @@ We also build on top of many great packages. Please check them out!
 - [Differentially Private Gradient Boosting on Linear Learners for Tabular Data Analysis](https://assets.amazon.science/fa/3a/a62ba73f4bbda1d880b678c39193/differentially-private-gradient-boosting-on-linear-learners-for-tabular-data-analysis.pdf)
 - [Differentially private and explainable boosting machine with enhanced utility](https://www.sciencedirect.com/science/article/abs/pii/S0925231224011950)
 - [Concrete compressive strength prediction using an explainable boosting machine model](https://www.sciencedirect.com/science/article/pii/S2214509523000244/pdfft?md5=171c275b6bcae8897cef03d931e908e2&pid=1-s2.0-S2214509523000244-main.pdf)
+- [Proxy endpoints - bridging clinical trials and real world data](https://pdf.sciencedirectassets.com/272371/1-s2.0-S1532046424X00064/1-s2.0-S1532046424001412/main.pdf?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEIn%2F%2F%2F%2F%2F%2F%2F%2F%2F%2FwEaCXVzLWVhc3QtMSJGMEQCIBYgAN6aOVrDnvQ1932tPndUyJ0Dm1nHdMVLiekPVduQAiAzbYe7W%2Bd6Dj8ee42ZeZnQxJwEjEjuGdiUEPx0a2G43SqyBQgSEAUaDDA1OTAwMzU0Njg2NSIMyMkCUNFeDTCUCppMKo8FiVShykb8phR%2F8aWUGE9gfnE5y7X3Jj1ZA2CVldH13T67s536bdTBhjIMF18rV0YP9iMi6B5aGr%2F286ovIJl332fxZ6iQNBIOPTm8kXQDUqvZbknYldiZqUPs69kuC%2FcKnJd1BWnv2SEZwbRuX94rWnRDPDaSoJx%2FVS6o4qsbFjp9%2BMYZr%2BvJzWHKrXAI4W%2Fh9%2BsIa0yvlac3IMWzAeD23HzDNmF0nqjJ6BSZzmDNW4HRIGBTrTUTO40TzQzhaOY7wyGA0Zv8SpWIULI%2FrY8z8EOX%2FU6OhqgyIMKv%2FSx3rUpMi5CrC1WcpnL97j%2FDAijNi4vMfG1b%2BBQIFRu2EmUky76k4w3FYxkCpYj4n4mk9H%2B%2Bc9C%2BdjKjUiayi%2FisIZUD7ISNhQ9oov0kXI1IVTCGKKQC9jqHOvdiA8YbVuMdEzy1Lkx%2B1kiEo79qvSlpTe2BtWAOm2Iequ01XoaMv%2FQb4ajhWKKSkTafzDAxc58aayP1YH49UzQ68Me7ecdHpx3JUHyYnxJGQ82wRpPkfZJA5wCmOUVI%2FBLuwFJyczG0LpALN5IpIqZz%2B8DvDR0xjRoN49dVwhrTSQ9BesvXbi2LKVm1ptacaaKqyx0PwLjQYKOd%2BPI3zCvRxEiM3IKSNFRLsUTyPNEE4E8pMFNxfyEX59yvTQrHwM62P7hvxHs%2BY6CxUGZTKBQwDAgxttJmiO%2BvjCRbTBXZg1WrQdXCkxntBXb15Mnqxo4lyPzUUkLdLAFK%2BLSwzBIcvSw2qG81Y8qhWmBgBT9vfAoSrjxsILFrB3nnz7u9XNNpRxb5Z9NuNG92%2Fpd%2F%2F5VespMY8Q0iwsNqazZ4M4H8UB34JgtrUEY27WrIsDWzLR%2FAYAxU%2BZHrFzCrsae5BjqyASqDBsNqjEkho%2FbuQDT%2F0vGx%2BgAqrksvVX0GrzNgvqnuPyvw6%2F%2B40ZJP5EA4axfltOYb2tNjd18Ngy2A3cd6J57v1G7wYyuSFIUfHGN5LA8BXK7p0x1mNcwN3pKHtAf260gjpsWMG7anvpK%2F3YupTz498C1lAmurJD%2BLN41lq05wBr403cchE41yzqAKHVKVpNq9s6oGHJmq0KJRvk%2FfjZr8oLhod5gtrwLKvLGqULf50L0%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20241105T092058Z&X-Amz-SignedHeaders=host&X-Amz-Expires=300&X-Amz-Credential=ASIAQ3PHCVTYUKUJCDYI%2F20241105%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=15f8e40964e2c750ae15e43aa8e7f7c76eef6a76b792e41434d14bed42b31432&hash=d4a3e49b29443e5eea9e5a44c0dc11b3f30b21addbe6d6d20d523c68db23cd23&host=68042c943591013ac2b2430a89b270f6af2c76d8dfd086a07176afe7c76c2c61&pii=S1532046424001412&tid=spdf-4fbabbd8-becb-4526-98d3-c7517914e457&sid=8ab2a095350fc74edc4b8765ecd8c0260edcgxrqa&type=client&tsoh=d3d3LnNjaWVuY2VkaXJlY3QuY29t&ua=0f165f0b050207505b0151&rr=8ddbc55a0d60a380&cc=us)
 - [Machine Learning Model Reveals Determinators for Admission to Acute Mental Health Wards From Emergency Department Presentations](https://onlinelibrary.wiley.com/doi/epdf/10.1111/inm.13402)
 - [Proposing an inherently interpretable machine learning model for shear strength prediction of reinforced concrete beams with stirrups](https://pdf.sciencedirectassets.com/287527/1-s2.0-S2214509523X00035/1-s2.0-S2214509524005011/main.pdf?X-Amz-Security-Token=IQoJb3JpZ2luX2VjECUaCXVzLWVhc3QtMSJGMEQCIB0r0KsYBZufOjbCVtUtozwn1QKMdLt2tbbfhuJKjWlXAiB5Dfr7p0yyj%2FSfypTLmjPL8WbjGAB3tRACFjyyqQbbfiq8BQiu%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F8BEAUaDDA1OTAwMzU0Njg2NSIMqBpZ2HmN91c%2BJPqpKpAFZtvqQjCScZa4FN%2FeubsPzOk5c%2B58LliO4Zr%2Bn1pm3vtW4I9I1vA29pkhT5was1N3ccPPIm2jNLwJ%2FHiZej7A2SmFv13Ro3sTvhqG%2F6A9Xx70Nx9jOlDPJUmCypKadKp0FGfuhZQuxeN0b%2F1QUUQZG4RpxC%2FXorRRHmb%2FrXcOWBwu4PmLZAkWmTKpncjDI7oj8eh8yBe6%2FA3JkJ14ZyBgR7JnPzR2ZqMdIhvlKoyMn6EnL1Azq2y3qwEMdzSCvz3wH3sT4pClc2vPs6ruQS4CdT3E7BHrf42Q0VnUXWjuy7gt9iRr0vaWR3tD%2FxyrrEKw7XuMHO9L4rQ4Pfn1dhGZ2J8H5ocwJGSh13U5fY6noyaTNViqvHx1oHNMWL03QpkJxmUxYquBWepcDjxEc32V6eGF7Ecm8Vij3s20wdRNcHqxGFKlUCgph48CKUA79iwSGQCkWQh7bq%2FTtowTbSPud7l8xeG1MvfIVy%2B6yzrjqygvPBQs3qkvdoWUrKXe57bhr2jEkKlSdYyp2TJMD6yoYRdTPyFx5xb0KgIt6KQTPmfbqYXkd3FFz3uc0HmWC5NQz6qP9UzNcBhcK8dXo3Dw042pl0HLO1njFaa%2BBfbT89VUVUIqjrAcmHweIl1v7Eyldzr%2BGBXIlsxPO3gPzyPLF2LTggc6dA%2Bswxmgmkv%2B7n5pU5%2F5sxvEhemb%2Fqu%2B8d47O%2Bn6RH8fL4eLGGL2d0dvFvyE7gEwt%2BaU9HsIN0IHqyH5VmaTF5zaKy%2Fn%2BhkF8yGpe5Hq5yNOUGrfQgfyFn4Kqd%2FTVajxIFzk8DEY%2F%2FFtyGJ%2B8BrHV4P%2FYs8R4XcBzPQtyrTuUC1CGmF01Tc2gnnEo4pVPaIjfBk9B%2BXVMc3Mu4Ywy4L%2BsgY6sgFK3hFIXjIfoVjqrIlBvsGYaFiZB1bVKBVy3DRiBgozzYmIVhipN%2FS%2BPok1oETqvYVvLqEVkGcb5W7nUIK16lFgjwDq6ePuxdqSafgOw5jVQroNsDCPRz8B%2F4fg7kv6gs4R9SX7gCaQ2V7L6NxqJDUUqsCMtIYq05Qx43dGByqLoVEz9USpRBmTLQwpGvOmUaGNNwTsCwmt5gRP8UX3CnkwI%2FydxmhrXLEdaUIFVwJbIor9&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20240604T221639Z&X-Amz-SignedHeaders=host&X-Amz-Expires=300&X-Amz-Credential=ASIAQ3PHCVTY4E2DAHPF%2F20240604%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=eece32da8855b55208baecc0ce041e79aa03be1c292b58c67ce0215de36cbdb4&hash=46dd1da122f4cea242c6444a811fb16dde5cb8465e88552ac3eaeee97b975e9b&host=68042c943591013ac2b2430a89b270f6af2c76d8dfd086a07176afe7c76c2c61&pii=S2214509524005011&tid=spdf-45c1c4d1-dd97-4c0d-a04f-c30843a79e78&sid=1fea53ed2d5cf1443e4a7c4-33f4bf6475e1gxrqa&type=client&tsoh=d3d3LnNjaWVuY2VkaXJlY3QuY29t&ua=0f155c5f060d565b01055d&rr=88eb49dd2a5f7688&cc=us)
 - [Using explainable machine learning and fitbit data to investigate predictors of adolescent obesity](https://www.nature.com/articles/s41598-024-60811-2)

shared/libebm/BoosterShell.cpp

@@ -45,6 +45,7 @@ void BoosterShell::Free(BoosterShell* const pBoosterShell) {
       AlignedFree(pBoosterShell->m_aMulticlassMidwayTemp);
       AlignedFree(pBoosterShell->m_aSplitPositionsTemp);
       AlignedFree(pBoosterShell->m_aTreeNodesTemp);
+      AlignedFree(pBoosterShell->m_aTemp1);
       BoosterCore::Free(pBoosterShell->m_pBoosterCore);
 
       // before we free our memory, indicate it was freed so if our higher level language attempts to use it we have
@@ -162,6 +163,7 @@ ErrorEbm BoosterShell::FillAllocations() {
          if(nullptr == m_aTreeNodesTemp) {
             goto failed_allocation;
          }
+         m_cTreeNodesTempBytes = m_pBoosterCore->GetCountBytesTreeNodes();
       }
    }
 

shared/libebm/BoosterShell.hpp

@@ -46,7 +46,12 @@ class BoosterShell final {
    // right?
    void* m_aMulticlassMidwayTemp;
 
+   size_t m_cTemp1Bytes;
+   void* m_aTemp1;
+
+   size_t m_cTreeNodesTempBytes;
    void* m_aTreeNodesTemp;
+
    void* m_aSplitPositionsTemp;
 
 #ifndef NDEBUG
@@ -70,6 +75,11 @@ class BoosterShell final {
       m_aBoostingFastBinsTemp = nullptr;
       m_aBoostingMainBins = nullptr;
       m_aMulticlassMidwayTemp = nullptr;
+
+      m_cTemp1Bytes = 0;
+      m_aTemp1 = nullptr;
+
+      m_cTreeNodesTempBytes = 0;
       m_aTreeNodesTemp = nullptr;
       m_aSplitPositionsTemp = nullptr;
    }
@@ -126,6 +136,17 @@ class BoosterShell final {
       return static_cast<TreeNode<bHessian, cCompilerScores>*>(m_aTreeNodesTemp);
    }
 
+   INLINE_ALWAYS void* GetTreeNodeMultiTemp() { return m_aTreeNodesTemp; }
+
+   INLINE_ALWAYS ErrorEbm ReserveTreeNodesTemp(const size_t cBytes) {
+      return AlignedGrow(static_cast<void**>(&m_aTreeNodesTemp), &m_cTreeNodesTempBytes, cBytes, EBM_FALSE);
+   }
+
+   INLINE_ALWAYS void* GetTemp1() { return m_aTemp1; }
+   INLINE_ALWAYS ErrorEbm ReserveTemp1(const size_t cBytes) {
+      return AlignedGrow(static_cast<void**>(&m_aTemp1), &m_cTemp1Bytes, cBytes, EBM_FALSE);
+   }
+
    template<bool bHessian, size_t cCompilerScores = 1>
    INLINE_ALWAYS SplitPosition<bHessian, cCompilerScores>* GetSplitPositionsTemp() {
       return static_cast<SplitPosition<bHessian, cCompilerScores>*>(m_aSplitPositionsTemp);