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    "info": {
        "author": "Olivier Grisel",
        "author_email": "olivier.grisel@ensta.org",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 3 - Alpha",
            "Environment :: Console",
            "Intended Audience :: Developers",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7",
            "Topic :: Scientific/Engineering"
        ],
        "description": "# pygbm [![Build Status](https://travis-ci.org/ogrisel/pygbm.svg?branch=master)](https://travis-ci.org/ogrisel/pygbm) [![codecov](https://codecov.io/gh/ogrisel/pygbm/branch/master/graph/badge.svg)](https://codecov.io/gh/ogrisel/pygbm) [![python versions](https://img.shields.io/badge/python-3.6+-blue.svg)](https://github.com/ogrisel/pygbm)\n\n\n\nExperimental Gradient Boosting Machines in Python.\n\nThe goal of this project is to evaluate whether it's possible to implement a\npure Python yet efficient version histogram-binning of Gradient Boosting\nTrees (possibly with all the LightGBM optimizations) while staying in pure\nPython 3.6+ using the [numba](http://numba.pydata.org/) jit compiler.\n\npygbm provides a set of scikit-learn compatible estimator classes that\nshould play well with the scikit-learn `Pipeline` and model selection tools\n(grid search and randomized hyperparameter search).\n\nLonger term plans include integration with dask and dask-ml for\nout-of-core and distributed fitting on a cluster.\n\n## Installation\n\nThe project is available on PyPI and can be installed with `pip`:\n\n    pip install pygbm\n\nYou'll need Python 3.6 at least.\n\n## Documentation\n\nThe API documentation is available at:\n\nhttps://pygbm.readthedocs.io/\n\nYou might also want to have a look at the `examples/` folder of this repo.\n\n## Status\n\nThe project is experimental. The API is subject to change without deprecation notice. Use at your own risk.\n\nWe welcome any feedback in the github issue tracker:\n\nhttps://github.com/ogrisel/pygbm/issues\n\n## Running the development version\n\nUse pip to install in \"editable\" mode:\n\n    git clone https://github.com/ogrisel/pygbm.git\n    cd pygbm\n    pip install -r requirements.txt\n    pip install --editable .\n\nRun the tests with pytest:\n\n    pip install -r requirements.txt\n    pytest\n\n## Benchmarking\n\nThe `benchmarks` folder contains some scripts to evaluate the computation\nperformance of various parts of pygbm. Keep in mind that numba's JIT\ncompilation [takes\ntime](http://numba.pydata.org/numba-doc/latest/user/5minguide.html#how-to-measure-the-performance-of-numba)!\n\n### Profiling\n\nTo profile the benchmarks, you can use\n[snakeviz](https://jiffyclub.github.io/snakeviz/) to get an interactive\nHTML report:\n\n    pip install snakeviz\n    python -m cProfile -o bench_higgs_boson.prof benchmarks/bench_higgs_boson.py\n    snakeviz bench_higgs_boson.prof\n\n### Debugging numba type inference\n\nTo introspect the results of type inference steps in the numba sections\ncalled by a given benchmarking script:\n\n    numba --annotate-html bench_higgs_boson.html benchmarks/bench_higgs_boson.py\n\nIn particular it is interesting to check that the numerical variables in\nthe hot loops highlighted by the snakeviz profiling report have the\nexpected precision level (e.g. `float32` for loss computation, `uint8`\nfor binned feature values, ...).\n\n### Impact of thread-based parallelism\n\nSome benchmarks can call numba functions that leverage the built-in\nthread-based parallelism with `@njit(parallel=True)` and `prange` loops.\nOn a multicore machine you can evaluate how the thread-based parallelism\nscales by explicitly setting the `NUMBA_NUM_THREAD` environment\nvariable. For instance try:\n\n    NUMBA_NUM_THREADS=1 python benchmarks/bench_binning.py\n\nvs:\n\n    NUMBA_NUM_THREADS=4 python benchmarks/bench_binning.py\n\n\n## Acknowledgements\n\nThe work from Nicolas Hug is supported by the National Science Foundation\nunder Grant No. 1740305 and by DARPA under Grant No. DARPA-BAA-16-51\n\n\n",
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