{"id":20107193,"url":"https://github.com/maddosaurus/mlt","last_synced_at":"2026-05-07T03:36:17.504Z","repository":{"id":43844671,"uuid":"164012428","full_name":"Maddosaurus/MLT","owner":"Maddosaurus","description":"The Machine Learning 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returned=1 errno=0 peeraddr=140.82.121.6:443 state=error: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["anomaly-detection","framework","keras","machine-learning","neural-network","pyod","python3","scikit-learn","tensorflow"],"created_at":"2024-11-13T17:56:02.368Z","updated_at":"2026-05-07T03:36:17.487Z","avatar_url":"https://github.com/Maddosaurus.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"\u003cp align=\"center\"\u003e\n    \u003ca href='https://mlt.readthedocs.io/en/latest/?badge=latest'\u003e\n        \u003cimg src='https://readthedocs.org/projects/mlt/badge/?version=latest' alt='Documentation Status' /\u003e\n    \u003c/a\u003e\n    \u003ca href='https://travis-ci.com/Maddosaurus/MLT'\u003e\n        \u003cimg src='https://img.shields.io/travis/com/Maddosaurus/MLT.svg' alt='TravisCI Status' /\u003e\n    \u003c/a\u003e\n    \u003ca href=\"https://codecov.io/gh/Maddosaurus/MLT\"\u003e\n        \u003cimg src=\"https://codecov.io/gh/Maddosaurus/MLT/branch/master/graph/badge.svg\" alt='Code Coverage'/\u003e\n\u003c/a\u003e\n\u003c/p\u003e\n\n\u003cp align=\"center\"\u003e\n    \u003ca href=\"https://github.com/Maddosaurus/MLT/graphs/commit-activity\"\u003e\n        \u003cimg src=\"https://img.shields.io/badge/maintained-yes-brightgreen.svg\" alt=\"Maintenance Status\"\u003e\n    \u003c/a\u003e\n    \u003ca href=\"https://github.com/Maddosaurus/MLT/releases\"\u003e\n        \u003cimg src=\"https://img.shields.io/badge/version-1.0--prerelease-red.svg\" alt=\"Version\"\u003e\n    \u003c/a\u003e\n    \u003ca href=\"https://github.com/Maddosaurus/MLT/pulls\"\u003e\n        \u003cimg src=\"https://img.shields.io/badge/contributions-welcome-brightgreen.svg?style=flat\" alt=\"Contributions\"\u003e\n    \u003c/a\u003e\n\u003c/p\u003e\n\n\n# The MachineLearning Testbench\nThis piece of software prepares different datasets and ML algorithms as well as implementations and saves the qualitative benchmark results.  \nIt emerged as part of a CompSci Masters' Thesis at the University of Applied Sciences and Arts Dortmund.  \n\n**It is currently in a state of prerelease and still subject to changes!**  \n**A stable release can be expected around 03/2019**\n\n## Getting Started\nHave a look at the [Getting Started](https://mlt.readthedocs.io/en/latest/gettingstarted.html) section in the documentation for a detailed guide.  \nHere is a minimal working example to check your installation:\n```bash\ngit clone https://github.com/Maddosaurus/MLT\ncd MLT\npipenv install\ncd MLT/datasets\ngit clone https://github.com/defcom17/NSL_KDD NSL_KDD\ncd ..\npython run.py --pnsl\npython run.py --single --nsl --xgb 10 10 0.1\n```\nUpon completion, you should be able to find infos for the test run in your console as well as in the subfolder `results`.\n\n## Requirements\n- Python 3.6+\n- CUDA 9.1 (optional)\n- tensorflow-gpu (optional)\n\nIf you plan on using GPU-accelerated learning (strongly recommended), please set up CUDA 9.1 on your system. The current version of Tensorflow relies on CUDA 9.1 (not 10!). Please refer to the [Tensorflow Install How To](https://www.tensorflow.org/install/gpu) for up to date install instructions!  \nIf you are interested in using the GPU-accelerated deep learning potion, make sure to replace `tensorflow` with `tensorflow-gpu` in your installation.\nThe use of a virtual environment is strongly advised!  \nAll package requirements can be installed via `pipenv install` (add `--dev` for development dependencies).\n\nBesides these, you will need copies of the *NSL-KDD* and *CICIDS2017* datasets stored in the subfolder `datasets` (`/NSL_KDD` and `/CICIDS2017pub`). The CICIDS2017 dataset can be downloaded at the [University of New Brunswick](http://www.unb.ca/cic/datasets/index.html), while NSL-KDD can be obtained [on GitHub](https://github.com/defcom17/NSL_KDD). Additional datasets can be included analogous to these.  \n\n## Documentation\nThe current documentation can be found at [readthedocs.io](https://mlt.readthedocs.io/en/latest/).  \nIf you're intersted in manually building the API documentation, run `make html` in the `docroot` folder. This command will generate the full sphinx-doc for the project.\nYou can view a local copy of the docs by running `cd docroot/_build/html \u0026\u0026 python -m http.server` from the project root.\n\n\n## Workflow\nThe general workflow is:  \n1. Dataset Preparation (sanitize and pickle)\n2. Algorithm definition\n3. Feature Selection, optional CV spits and Normalization/Scaling\n4. Algorithm Training\n5. Result Collection and Evaluation\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmaddosaurus%2Fmlt","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmaddosaurus%2Fmlt","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmaddosaurus%2Fmlt/lists"}