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        "description": "# sparse\\_dot\\_topn: \n\n**sparse\\_dot\\_topn** provides a fast way to performing a sparse matrix multiplication followed by top-n multiplication result selection.\n\nComparing very large feature vectors and picking the best matches, in practice often results in performing a sparse matrix multiplication followed by selecting the top-n multiplication results. In this package, we implement a customized Cython function for this purpose. When comparing our Cythonic approach to doing the same use with SciPy and NumPy functions, **our approach improves the speed by about 40% and reduces memory consumption.**\n\nThis package is made by ING Wholesale Banking Advanced Analytics team. This [blog](https://medium.com/@ingwbaa/https-medium-com-ingwbaa-boosting-selection-of-the-most-similar-entities-in-large-scale-datasets-450b3242e618) explains how we implement it.\n\n## Example\n``` python\n    import numpy as np\n    from scipy.sparse import csr_matrix\n    from scipy.sparse import rand\n    from sparse_dot_topn import awesome_cossim_topn\n\n    N = 10\n    a = rand(100, 1000000, density=0.005, format='csr')\n    b = rand(1000000, 200, density=0.005, format='csr')\n\n    # Use standard implementation\n\n    c = awesome_cossim_topn(a, b, N, 0.01)\n\n    # Use parallel implementation with 4 threads\n\n    d = awesome_cossim_topn(a, b, N, 0.01, use_threads=True, n_jobs=4)\n```\n\nYou can also find code which compares our boosting method with calling scipy+numpy function directly in example/comparison.py\n\n## Dependency and Install\nInstall `numpy` and `cython` first before installing this package. Then,\n``` sh\npip install sparse_dot_topn\n```\n\n\n## Uninstall\n``` sh\npip uninstall sparse_dot_topn\n```\n\n\n",
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