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        "author": "Roman Feldbauer",
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            "License :: OSI Approved :: BSD License",
            "Operating System :: MacOS :: MacOS X",
            "Operating System :: Microsoft :: Windows",
            "Operating System :: POSIX :: Linux",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.7",
            "Programming Language :: Python :: 3.8",
            "Topic :: Scientific/Engineering",
            "Topic :: Scientific/Engineering :: Artificial Intelligence"
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        "description": "[![PyPI](https://img.shields.io/pypi/v/scikit-hubness.svg)](\nhttps://pypi.org/project/scikit-hubness)\n[![Docs](https://readthedocs.org/projects/scikit-hubness/badge/?version=latest)](\nhttps://scikit-hubness.readthedocs.io/en/latest/?badge=latest)\n[![TravisCI](https://travis-ci.com/VarIr/scikit-hubness.svg?branch=master)](\nhttps://travis-ci.com/VarIr/scikit-hubness)\n[![Coverage](https://codecov.io/gh/VarIr/scikit-hubness/branch/master/graph/badge.svg?branch=master)](\nhttps://codecov.io/gh/VarIr/scikit-hubness)\n[![AppVeyorCI](https://ci.appveyor.com/api/projects/status/85bs46irwcwwbvyt/branch/master?svg=true)](\nhttps://ci.appveyor.com/project/VarIr/hubness/branch/master)\n[![Quality](https://img.shields.io/lgtm/grade/python/g/VarIr/scikit-hubness.svg?logo=lgtm&logoWidth=18)](\nhttps://lgtm.com/projects/g/VarIr/scikit-hubness/context:python)\n[![License](https://img.shields.io/github/license/VarIr/scikit-hubness.svg)](\nhttps://github.com/VarIr/scikit-hubness/blob/master/LICENSE.txt)\n[![DOI](https://zenodo.org/badge/193863864.svg)](\nhttps://zenodo.org/badge/latestdoi/193863864)\n[![arXiv](https://img.shields.io/badge/cs.LG-arXiv%3A1912.00706-B31B1B)](\nhttps://arxiv.org/abs/1912.00706)\n[![status](https://joss.theoj.org/papers/b9b56c7c109ff2a8a0c7c216cb3f8c39/status.svg)](\nhttps://joss.theoj.org/papers/b9b56c7c109ff2a8a0c7c216cb3f8c39)\n\n# scikit-hubness\n\n`scikit-hubness` comprises tools for the analysis and\nreduction of hubness in high-dimensional data.\nHubness is an aspect of the _curse of dimensionality_\nand is detrimental to many machine learning and data mining tasks.\n\nThe `skhubness.analysis` and `skhubness.reduction` packages allow to\n\n- analyze, whether your data sets show hubness\n- reduce hubness via a variety of different techniques \n- perform downstream analysis (performance assessment) with `scikit-learn`\n  due to compatible data structures\n\nThe `skhubness.neighbors` package acts as a drop-in replacement for `sklearn.neighbors`.\nIn addition to the functionality inherited from `scikit-learn`,\nit also features\n- _approximate nearest neighbor_ search\n- hubness reduction\n- and combinations,\n\nwhich allows for fast hubness-reduced neighbor search in large datasets\n(tested with >1M objects).\n\nWe follow the API conventions and code style of `scikit-learn`.\n\n## Installation\n\n\nMake sure you have a working Python3 environment (at least 3.7).\n\nUse pip to install the latest stable version of `scikit-hubness` from PyPI:\n\n```bash\npip install scikit-hubness\n```\n\nDependencies are installed automatically, if necessary.\n`scikit-hubness` requires `numpy`, `scipy` and `scikit-learn`.\nApproximate nearest neighbor search and approximate hubness reduction\nadditionally requires at least one of the following packges:\n* [`nmslib`](https://github.com/nmslib/nmslib)\n    for hierachical navigable small-world graphs ('hnsw')\n* [`ngtpy`](https://github.com/yahoojapan/NGT/)\n    for nearest neighbor graphs ('nng'), and variants (ANNG, ONNG)\n* [`puffinn`](https://github.com/puffinn/puffinn)\n    for locality-sensitive hashing ('lsh')\n* [`falconn`](https://github.com/FALCONN-LIB/FALCONN)\n    for alternative LSH ('falconn_lsh') , or\n* [`annoy`](https://github.com/spotify/annoy)\n    for random projection forests ('rptree').\n\nSome modules require `tqdm` or `joblib`. All these packages are available\nfrom open repositories, such as [PyPI](https://pypi.org).\n\nFor more details and alternatives, please see the [Installation instructions](\nhttp://scikit-hubness.readthedocs.io/en/latest/user_guide/installation.html).\n\n## Documentation\n\nDocumentation is available online: \nhttp://scikit-hubness.readthedocs.io/en/latest/index.html\n\n\n## What's new\n\nSee the [changelog](docs/changelog.md) to find what's new in the latest package version.\n\n\n## Quickstart\n\nUsers of `scikit-hubness` may want to \n\n1. analyse, whether their data show hubness\n2. reduce hubness\n3. perform learning (classification, regression, ...)\n\nThe following example shows all these steps for an example dataset\nfrom the text domain (dexter). (Please make sure you have installed `scikit-hubness`).\n\n```python\n# load the example dataset 'dexter'\nfrom skhubness.data import load_dexter\nX, y = load_dexter()\n\n# dexter is embedded in a high-dimensional space,\n# and could, thus, be prone to hubness\nprint(f'X.shape = {X.shape}, y.shape={y.shape}')\n\n# assess the actual degree of hubness in dexter\nfrom skhubness import Hubness\nhub = Hubness(k=10, metric='cosine')\nhub.fit(X)\nk_skew = hub.score()\nprint(f'Skewness = {k_skew:.3f}')\n\n# additional hubness indices are available, for example:\nprint(f'Robin hood index: {hub.robinhood_index:.3f}')\nprint(f'Antihub occurrence: {hub.antihub_occurrence:.3f}')\nprint(f'Hub occurrence: {hub.hub_occurrence:.3f}')\n\n# There is considerable hubness in dexter.\n# Let's see, whether hubness reduction can improve\n# kNN classification performance \nfrom sklearn.model_selection import cross_val_score\nfrom skhubness.neighbors import KNeighborsClassifier\n\n# vanilla kNN\nknn_standard = KNeighborsClassifier(n_neighbors=5,\n                                    metric='cosine')\nacc_standard = cross_val_score(knn_standard, X, y, cv=5)\n\n# kNN with hubness reduction (mutual proximity)\nknn_mp = KNeighborsClassifier(n_neighbors=5,\n                              metric='cosine',\n                              hubness='mutual_proximity')\nacc_mp = cross_val_score(knn_mp, X, y, cv=5)\n\nprint(f'Accuracy (vanilla kNN): {acc_standard.mean():.3f}')\nprint(f'Accuracy (kNN with hubness reduction): {acc_mp.mean():.3f}')\n\n# Accuracy was considerably improved by mutual proximity.\n# Did it actually reduce hubness?\nhub_mp = Hubness(k=10, metric='cosine',\n                 hubness='mutual_proximity')\nhub_mp.fit(X)\nk_skew_mp = hub_mp.score()\nprint(f'Skewness after MP: {k_skew_mp:.3f} '\n      f'(reduction of {k_skew - k_skew_mp:.3f})')\nprint(f'Robin hood: {hub_mp.robinhood_index:.3f} '\n      f'(reduction of {hub.robinhood_index - hub_mp.robinhood_index:.3f})')\n\n# The neighbor graph can also be created directly,\n# with or without hubness reduction\nfrom skhubness.neighbors import kneighbors_graph\nneighbor_graph = kneighbors_graph(X, n_neighbors=5, hubness='mutual_proximity')\n```\n\nCheck the [User Guide](http://scikit-hubness.readthedocs.io/en/latest/user_guide.html)\nfor additional example usage. \n\n\n## Development\n\nThe developers of `scikit-hubness` welcome all kinds of contributions!\nGet in touch with us if you have comments,\nwould like to see an additional feature implemented,\nwould like to contribute code or have any other kind of issue.\nDon't hesitate to file an [issue](https://github.com/VarIr/scikit-hubness/issues)\nhere on GitHub.\n\nFor more information about contributing, please have a look at the\n[contributors guidelines](CONTRIBUTING.rst).\n\n    (c) 2018-2019, Roman Feldbauer\n    Austrian Research Institute for Artificial Intelligence (OFAI) and\n    University of Vienna, Division of Computational Systems Biology (CUBE)\n    Contact: <roman.feldbauer@univie.ac.at>\n\n## Citation\n\nA software publication paper is currently in preparation. Until then,\nif you use `scikit-hubness` in your scientific publication, please cite:\n\n    @INPROCEEDINGS{8588814,\n    author={R. {Feldbauer} and M. {Leodolter} and C. {Plant} and A. {Flexer}},\n    booktitle={2018 IEEE International Conference on Big Knowledge (ICBK)},\n    title={Fast Approximate Hubness Reduction for Large High-Dimensional Data},\n    year={2018},\n    volume={},\n    number={},\n    pages={358-367},\n    keywords={computational complexity;data analysis;data mining;mobile computing;public domain software;software packages;mobile device;open source software package;high-dimensional data mining;fast approximate hubness reduction;massive mobility data;linear complexity;quadratic algorithmic complexity;dimensionality curse;Complexity theory;Indexes;Estimation;Data mining;Approximation algorithms;Time measurement;curse of dimensionality;high-dimensional data mining;hubness;linear complexity;interpretability;smartphones;transport mode detection},\n    doi={10.1109/ICBK.2018.00055},\n    ISSN={},\n    month={Nov},}\n\nThe technical report `Fast approximate hubness reduction for large high-dimensional data`\nis available at [OFAI](http://www.ofai.at/cgi-bin/tr-online?number+2018-02).\n\n### Additional reading\n\n`Local and Global Scaling Reduce Hubs in Space`, Journal of Machine Learning Research 2012,\n[Link](http://www.jmlr.org/papers/v13/schnitzer12a.html).\n\n`A comprehensive empirical comparison of hubness reduction in high-dimensional spaces`,\nKnowledge and Information Systems 2018, [DOI](https://doi.org/10.1007/s10115-018-1205-y).\n\nLicense\n-------\n`scikit-hubness` is licensed under the terms of the BSD-3-Clause [license](LICENSE.txt).\nThe `skhubness.neighbors` package was modified from `sklearn.neighbors`,\ndistributed under the same [license](external/SCIKIT_LEARN_LICENSE.txt).\nUsers can, therefore, safely use `scikit-hubness` in the same way they\nuse `scikit-learn`.\n\n\n------------------------------------------------------------------------------\nNote:\nIndividual files contain the following tag instead of the full license text.\n\n        SPDX-License-Identifier: BSD-3-Clause\n\nThis enables machine processing of license information based on the SPDX\nLicense Identifiers that are here available: https://spdx.org/licenses/\n\nAcknowledgements\n----------------\nSeveral parts of `scikit-hubness` adapt code from `scikit-learn`.\nWe thank all the authors and contributors of this project\nfor the tremendous work they have done.\n\nPyVmMonitor is being used to support the development of this free open source \nsoftware package. For more information go to http://www.pyvmmonitor.com\n\n\n",
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