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        "author": "Kevin Heinicke <kevin@kehei.de>",
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            "Development Status :: 3 - Alpha",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python :: 3.6",
            "Topic :: Scientific/Engineering :: Artificial Intelligence",
            "Topic :: Scientific/Engineering :: Physics",
            "Topic :: Software Development :: Code Generators"
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        "description": "# Generate C++ representations of boosted decision trees\n\nThis project tries to provide a generic functionality to transpile trained BDTs\ninto minimal, efficient C++ functions to evaluate single vectors of features.\n\nWhile many frameworks exist to train, evaluate and store BDTs, its often hard\nto use the results in a productive manner.\n\n## Installation\n\nSo far there is only python3 support. Run\n```\npip install bdt2cpp\n```\nto install the latest tagged version or\n```\npip install git+https://github.com/bixel/bdt2cpp.git\n```\nfor the current master version.\n\n### lxplus\n\nIf you want to use bdt2cpp on CERNs lxplus machines, you need to get hold of\nminimum python3.6. According to [CERNs Service Article\nKB0000730](https://cern.service-now.com/service-portal/article.do?n=KB0000730),\none way to install the tool is:\n\n```sh\n# On lxplus\nscl enable rh-python36\n\n# this will install bdt2cpp to your `~/.local/` directory\npip install --user bdt2cpp\n```\n\n## Usage\n\nTo generate a minimal Makefile together with the C++ code inside a `build/`\ndirectory from a given XGBoost dump or TMVA `.xml` file, simply run\n```\nbdt2cpp my-bdt-dump.xgb\n```\nYou will find the corresponding files within the `build/` directory and if you\nhave installed `clang`, you can simply\n```\ncd build\nmake\n```\n\n*Note for CERN Users*: Currently, the Makefile uses clang as the default\ncompiler. You might need to adjust that in the generated file (inside the\n`build/` directory)\n\nThe generated executable is essentially a very minimal placeholder, if you had\n3 input features you could quickly cross-check the predictions against the\noriginal training framework:\n```\ncd build\n./main 1 2 3\n```\nshould give the same output as received within the training framework if a\nfeature vector `f = (1, 2, 3)` is evaluated.\n\n\nTo see the complete list of features with some explanations, run\n```\nbdt2cpp -h\n```\n\n\n",
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