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        "author": "source{d}",
        "author_email": "machine-learning@sourced.tech",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 3 - Alpha",
            "Environment :: Console",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: Apache Software License",
            "Operating System :: POSIX",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7",
            "Topic :: Software Development :: Libraries"
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        "description": "# MLonCode research playground [![PyPI](https://img.shields.io/pypi/v/sourced-ml.svg)](https://pypi.python.org/pypi/sourced-ml) [![Build Status](https://travis-ci.org/src-d/ml.svg)](https://travis-ci.org/src-d/ml) [![Docker Build Status](https://img.shields.io/docker/build/srcd/ml.svg)](https://hub.docker.com/r/srcd/ml) [![codecov](https://codecov.io/github/src-d/ml/coverage.svg)](https://codecov.io/gh/src-d/ml)\n\nThis project is the foundation for [MLonCode](https://github.com/src-d/awesome-machine-learning-on-source-code) research and development. It abstracts feature extraction and training models, thus allowing to focus on the higher level tasks.\n\nCurrently, the following models are implemented:\n\n* BOW - weighted bag of x, where x is many different extracted feature types.\n* id2vec, source code identifier embeddings.\n* docfreq, feature document frequencies \\(part of TF-IDF\\).\n* topic modeling over source code identifiers.\n\nIt is written in Python3 and has been tested on Linux and macOS. source{d} ml is tightly coupled with [source{d} engine](https://engine.sourced.tech) and delegates all the feature extraction parallelization to it.\n\nHere is the list of proof-of-concept projects which are built using sourced.ml:\n\n* [vecino](https://github.com/src-d/vecino) - finding similar repositories.\n* [tmsc](https://github.com/src-d/tmsc) - listing topics of a repository.\n* [snippet-ranger](https://github.com/src-d/snippet-ranger) - topic modeling of source code snippets.\n* [apollo](https://github.com/src-d/apollo) - source code deduplication at scale.\n\n## Installation\n\nWhether you wish to include Spark in your installation or would rather use an existing\ninstallation, to use `sourced-ml` you will need to have some native libraries installed,\ne.g. on Ubuntu you must first run: `apt install libxml2-dev libsnappy-dev`. [Tensorflow](https://tensorflow.org)\nis also a requirement - we support both the CPU and GPU  version. \nIn order to select which version you want, modify the package name in the next section\nto either `sourced-ml[tf]` or `sourced-ml[tf-gpu]` depending on your choice.\n**If you don't, neither version will be installed.**\n\n### With Apache Spark included\n\n```text\npip3 install sourced-ml\n```\n\n### Use existing Apache Spark\n\nIf you already have Apache Spark installed and configured on your environment at `$APACHE_SPARK` you can re-use it and avoid downloading 200Mb through [pip \"editable installs\"](https://pip.pypa.io/en/stable/reference/pip_install/#editable-installs) by\n\n```text\npip3 install -e \"$SPARK_HOME/python\"\npip3 install sourced-ml\n```\n\nIn both cases, you will need to have some native libraries installed. E.g., \non Ubuntu `apt install libxml2-dev libsnappy-dev`. Some parts require [Tensorflow](https://tensorflow.org).\n\n## Usage\n\nThis project exposes two interfaces: API and command line. The command line is\n\n```text\nsrcml --help\n```\n\n## Docker image\n\n```text\ndocker run -it --rm srcd/ml --help\n```\n\nIf this first command fails with\n\n```text\nCannot connect to the Docker daemon. Is the docker daemon running on this host?\n```\n\nAnd you are sure that the daemon is running, then you need to add your user to `docker` group: refer to the [documentation](https://docs.docker.com/engine/installation/linux/linux-postinstall/#manage-docker-as-a-non-root-user).\n\n## Contributions\n\n...are welcome! See [CONTRIBUTING](contributing.md) and [CODE\\_OF\\_CONDUCT.md](code_of_conduct.md).\n\n## License\n\n[Apache 2.0](license.md)\n\n## Algorithms\n\n#### Identifier embeddings\n\nWe build the source code identifier co-occurrence matrix for every repository.\n\n1. Read Git repositories.\n2. Classify files using [enry](https://github.com/src-d/enry).\n3. Extract [UAST](https://doc.bblf.sh/uast/specification.html) from each supported file.\n4. [Split and stem](https://github.com/src-d/ml/tree/d1f13d079f57caa6338bb7eb8acb9062e011eda9/sourced/ml/algorithms/token_parser.py) all the identifiers in each tree.\n5. [Traverse UAST](https://github.com/src-d/ml/tree/d1f13d079f57caa6338bb7eb8acb9062e011eda9/sourced/ml/transformers/coocc.py), collapse all non-identifier paths and record all\n\n   identifiers on the same level as co-occurring. Besides, connect them with their immediate parents.\n\n6. Write the global co-occurrence matrix.\n7. Train the embeddings using [Swivel](https://github.com/src-d/ml/tree/d1f13d079f57caa6338bb7eb8acb9062e011eda9/sourced/ml/algorithms/swivel.py) \\(requires Tensorflow\\). Interactively view\n\n   the intermediate results in Tensorboard using `--logs`.\n\n8. Write the identifier embeddings model.\n\n1-5 is performed with `repos2coocc` command, 6 with `id2vec_preproc`, 7 with `id2vec_train`, 8 with `id2vec_postproc`.\n\n#### Weighted Bag of X\n\nWe represent every repository as a weighted bag-of-vectors, provided by we've got document frequencies \\(\"docfreq\"\\) and identifier embeddings \\(\"id2vec\"\\).\n\n1. Clone or read the repository from disk.\n2. Classify files using [enry](https://github.com/src-d/enry).\n3. Extract [UAST](https://doc.bblf.sh/uast/specification.html) from each supported file.\n4. Extract various features from each tree, e.g. identifiers, literals or node2vec-like structural fingerprints.\n5. Group by repository, file or function.\n6. Set the weight of each such feature according to TF-IDF.\n7. Write the BOW model.\n\n1-7 are performed with `repos2bow` command.\n\n#### Topic modeling\n\nSee [here](doc/topic_modeling.md).\n\n## Glossary\n\nSee [here](GLOSSARY.md).\n\n\n",
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