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上传时间: 2018-11-29
详细说明： ## A C++11 implementation of the B-Tree part of "The Case for Learned Index Structures" A research **proof of concept** that implements the B-Tree section of [The Case for Learned Index Structures](https://arxiv.org/pdf/1712.01208.pdf) paper in C++. The general design is to have a single lookup structure that you can parameterize with a KeyType and a ValueType, and an overflow list that keeps new inserts until you retrain. There is a value in the constructor of the RMI that triggers a retrain when the overflow array reaches a certain size. The basic API: ```c++ // [first/second]StageParams are network parameters int maxAllowedError = 256; int maxBufferBeforeRetrain = 10001; auto modelIndex = RecursiveModelIndex recursiveModelIndex(firstStageParams, secondStageParams, maxAllowedError, maxBufferBeforeRetrain); for (int ii = 0; ii < 10000; ++ii) { modelIndex.insert(ii, ii * 2); } // Since we still have one more insert before retraining, retrain before searching... modelIndex.train(); auto result = modelIndex.find(5); if (result) { std::cout << "Yay! We got: " << result.get().first << ", " << result.get().second << std::endl; } else { std::cout << "Value not found." << std::endl; // This shouldn't happen in the above usage... } ``` See [src/main.cpp](src/main.cpp) for a usage example where it stores scaled log normal data. ### Dependencies - [nn_cpp](https://github.com/bcaine/nn_cpp) - Eigen based minimalistic C++ Neural Network library - [cpp-btree](https://code.google.com/archive/p/cpp-btree/) - A fast C++ implementation of a B+ Tree ### TODO: - Lots of code cleanup - Profiling of where the slowdowns are. On small tests, the cpp_btree lib beats it by 10-100x - Eigen::TensorFixed in nn_cpp would definitely help - Increasing dataset size may lead to more of an advantage to the RMI - Being much, much more efficient with memory and conversions (lots of casting) - Very non-linear data the second stage tends to break down or stop performing on. - A non-trivial amount of our second stage "dies" in the sense that we don't use it for predictions. - The larger the dataset, or the more second stage nodes, the more likely this is. Bug somewhere? - Experimenting/tuning of training parameters - Still more learning rate sensitive than I'd like - Checking, and failing if there are non-integer keys - Tests on the actual RMI code (instead of using tests for experiments) - Move retrain to non-blocking thread - Logging ...展开详情收缩
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