{ "info": { "author": "Will Rowe", "author_email": "will.rowe@stfc.ac.uk", "bugtrack_url": null, "classifiers": [], "description": "
\n \"banner-logo\"\n
\n \"travis\"\n Documentation Status\n \"License\"\n \"DOI\"\n
\n\n***\n\n```\nBANNER is still under development - features and improvements are being added, so please check back soon.\n```\n\n***\n\n## Overview\n\n`BANNER` is a tool that lives inside [HULK](https://github.com/will-rowe/hulk) and aims to make sense of **hulk sketches**. At the moment, it trains a [Random Forest Classifier](http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html) using a set of labelled **hulk sketches**. It can then use this model to predict the label of microbiomes as they are sketches by ``HULK``.\n\nFor example, you could train `BANNER` using a set of microbiomes from patients that either have or haven't received antibiotic treatment. You can then use `BANNER` to predict whether a new microbiome sample exhibits signs of antibiotic dysbiosis. I will post more information and examples soon...\n\n## Installation\n\n### Bioconda\n\n```\nconda install banner\n```\n\n> note: if using Conda make sure you have added the [Bioconda](https://bioconda.github.io/) channel first\n\n#### Pip\n\n```\npip install banner\n```\n\n## Quick Start\n\n`BANNER` is called by typing **banner**, followed by the subcommand you wish to run. There are two main subcommands: **train** and **predict**. This quick start will show you how to get things running but it is recommended to follow the [HULK documentation](http://hulk-documentation.readthedocs.io/en/latest/?badge=latest).\n\n```bash\n# Train a random forest classifier\nbanner train -m hulk-banner-matrix.csv -o banner.rfc\n\n# Predict the label for a hulk sketch\nhulk sketch -f mystery-sample.fastq --stream -p 8 | banner predict -m banner.rfc\n```\n\n\n##\u00a0Notes\n\n* only supports 2 labels at the moment\n\n* there is very limited checking and not many unit tests...\n\n\n", "description_content_type": "", "docs_url": null, "download_url": "", "downloads": { "last_day": -1, "last_month": -1, "last_week": -1 }, "home_page": "http://will-rowe.github.io/", "keywords": "", "license": "MIT", "maintainer": "", "maintainer_email": "", "name": "banner", "package_url": "https://pypi.org/project/banner/", "platform": "", "project_url": "https://pypi.org/project/banner/", "project_urls": { "Homepage": "http://will-rowe.github.io/" }, "release_url": "https://pypi.org/project/banner/0.0.2/", "requires_dist": null, "requires_python": "", "summary": "banner is a tool for predicting microbiome labels based on hulk sketches", "version": "0.0.2" }, "last_serial": 4225960, "releases": { "0.0.1": [ { "comment_text": "", "digests": { "md5": "5748112ce6887c46a16bce3c5decd65d", "sha256": "b088c7aed58cc8bd24023cc0fa6726ad8f5ed5f88ffa5d65a3b8406e8639bbe5" }, "downloads": -1, "filename": "banner-0.0.1-py3-none-any.whl", "has_sig": false, "md5_digest": "5748112ce6887c46a16bce3c5decd65d", "packagetype": "bdist_wheel", "python_version": "py3", "requires_python": null, "size": 6114, "upload_time": "2018-08-13T21:26:19", "url": "https://files.pythonhosted.org/packages/b6/f2/7d44cda9c38aca79b629f75582921a98344d07b3bff888ef9bd00900175c/banner-0.0.1-py3-none-any.whl" }, { "comment_text": "", "digests": { "md5": "b7b740be9023f8f00acaa8c439756b2b", "sha256": "0588311b3134848f6ef108701f726740f0eb2e6c4eb5c85530eee62603ad5578" }, "downloads": -1, "filename": "banner-0.0.1.tar.gz", "has_sig": false, "md5_digest": "b7b740be9023f8f00acaa8c439756b2b", "packagetype": "sdist", "python_version": "source", "requires_python": null, "size": 4923, "upload_time": "2018-08-13T21:26:20", "url": "https://files.pythonhosted.org/packages/ca/ac/42945db940afc21f3513dbbd950c7f8560ada4a630fa34207a90517014a0/banner-0.0.1.tar.gz" } ], "0.0.2": [ { "comment_text": "", "digests": { "md5": "45b787dd04b4cfda1bea37c8d697cb90", "sha256": "3a850eca7c28e8f93a250173620232f3dbfab9a56d0e2946ecc4a8f6041d8d86" }, "downloads": -1, "filename": "banner-0.0.2-py3-none-any.whl", "has_sig": false, "md5_digest": "45b787dd04b4cfda1bea37c8d697cb90", "packagetype": "bdist_wheel", "python_version": "py3", "requires_python": null, "size": 6280, "upload_time": "2018-08-31T10:27:19", "url": "https://files.pythonhosted.org/packages/d5/92/435c41ccfc91a950d03a0fe80fc25ee76f5da74fb9e27b8606956d58f5b9/banner-0.0.2-py3-none-any.whl" }, { "comment_text": "", "digests": { "md5": "29913b16a1a3b414e9900b014e28bf80", "sha256": "421050ab55d7006278df5ac3578ac6e08daddb6f0a115d584075076b2972484b" }, "downloads": -1, "filename": "banner-0.0.2.tar.gz", "has_sig": false, "md5_digest": "29913b16a1a3b414e9900b014e28bf80", "packagetype": "sdist", "python_version": "source", "requires_python": null, "size": 5073, "upload_time": "2018-08-31T10:27:20", "url": "https://files.pythonhosted.org/packages/89/a1/ca9cb7ac38a3ade6f7cc3996ae49420cebce7f420698a2012bdfd19498b6/banner-0.0.2.tar.gz" } ] }, "urls": [ { "comment_text": "", "digests": { "md5": "45b787dd04b4cfda1bea37c8d697cb90", "sha256": "3a850eca7c28e8f93a250173620232f3dbfab9a56d0e2946ecc4a8f6041d8d86" }, "downloads": -1, "filename": "banner-0.0.2-py3-none-any.whl", "has_sig": false, "md5_digest": "45b787dd04b4cfda1bea37c8d697cb90", "packagetype": "bdist_wheel", "python_version": "py3", "requires_python": null, "size": 6280, "upload_time": "2018-08-31T10:27:19", "url": "https://files.pythonhosted.org/packages/d5/92/435c41ccfc91a950d03a0fe80fc25ee76f5da74fb9e27b8606956d58f5b9/banner-0.0.2-py3-none-any.whl" }, { "comment_text": "", "digests": { "md5": "29913b16a1a3b414e9900b014e28bf80", "sha256": "421050ab55d7006278df5ac3578ac6e08daddb6f0a115d584075076b2972484b" }, "downloads": -1, "filename": "banner-0.0.2.tar.gz", "has_sig": false, "md5_digest": "29913b16a1a3b414e9900b014e28bf80", "packagetype": "sdist", "python_version": "source", "requires_python": null, "size": 5073, "upload_time": "2018-08-31T10:27:20", "url": "https://files.pythonhosted.org/packages/89/a1/ca9cb7ac38a3ade6f7cc3996ae49420cebce7f420698a2012bdfd19498b6/banner-0.0.2.tar.gz" } ] }