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https://raw.githubusercontent.com/scikit-learn-contrib/hdbscan/master/README.rst\n\n.. image:: https://img.shields.io/pypi/l/colin-mico.svg\n    :target: https://github.com/jupiters1117/mico/master/LICENSE\n    :alt: License\n\nMICO: Mutual Information and Conic Optimization for feature selection\n---------------------------------------------------------------------\n\n**MICO** is a Python package that implements a conic optimization based feature selection method with mutual information (MI) measure [1]_. The idea behind the approach is to measure the features’relevance and redundancy using MI, and then formulate a feature selection problem as a pure-binary quadratic optimization problem, which can be heuristically solved by an efficient randomization algorithm via semidefinite programming [2]_. Optimization software **Colin** [6]_ is used for solving the underlying conic optimization problems.\n\nThis package\n\n- implements three methods for feature selections:\n\n  + **MICO** : Conic Optimization approach\n  + **MIFS** : Forward Selection approach\n  + **MIBS** : Backward Selection approach\n\n- supports three different MI measures:\n\n  + **JMI** : Joint Mutual Information [3]_\n  + **JMIM** : Joint Mutual Information Maximisation [4]_\n  + **MRMR** : Max-Relevance Min-Redundancy [5]_\n\n- generates feature importance scores for all selected features.\n- provides scikit-learn compatible APIs.\n\n\nInstallation\n------------\n\n1. Download **Colin** distribution from http://www.colinopt.org/downloads.php and unpack it into a chosen directory (`\u003cCLNHOME\u003e`).\n   Then install **Colin** package:\n\n.. code-block:: bash\n\n    cd \u003cCLNHOME\u003e/python\n    pip install -r requirements.txt\n    python setup.py install\n\n2. To install **MICO** package, use:\n\n.. code-block:: bash\n\n    pip install -r requirements.txt\n    python setup.py install\n\nor\n\n.. code-block:: bash\n\n    pip install colin-mico\n\nTo install the development version, you may use:\n\n.. code-block:: bash\n\n    pip install --upgrade git+https://github.com/jupiters1117/mico\n\n\nUsage\n-----\n\nThis package provides scikit-learn compatible APIs:\n\n* ``fit(X, y)``\n* ``transform(X)``\n* ``fit_transform(X, y)``\n\n\nExamples\n--------\n\nThe following example illustrates the use of the package:\n\n.. code-block:: python\n\n    import pandas as pd\n    from sklearn.datasets import load_breast_cancer\n\n    # Prepare data.\n    data = load_breast_cancer()\n    y = data.target\n    X = pd.DataFrame(data.data, columns=data.feature_names)\n\n    # Perform feature selection.\n    mico = MutualInformationConicOptimization(verbose=1, categorical=True)\n    mico.fit(X, y)\n\n    # Populate selected features.\n    print(\"Selected features: {}\".format(mico.get_support()))\n\n    # Populate feature importance scores.\n    print(\"Feature importance scores: {}\".format(mico.feature_importances_))\n\n    # Call transform() on X.\n    X_transformed = mico.transform(X)\n\n\nDocumentation\n-------------\n\nUser guide, examples, and API are available `here \u003chttps://jupiters1117.github.io/mico/\u003e`_.\n\n\nReferences\n----------\n\n.. [1] T Naghibi, S Hoffmann and B Pfister, \"A semidefinite programming based search strategy for feature selection with mutual information measure\", IEEE Transactions on Pattern Analysis and Machine Intelligence, 37(8), pp. 1529--1541, 2015. [`Pre-print \u003chttps://arxiv.org/pdf/1409.7384.pdf\u003e`_]\n.. [2] M Goemans and D Williamson, \"Improved approximation algorithms for maximum cut and satisfiability problems using semidefinite programming\", J. ACM, 42(6), pp. 1115--1145, 1995 [`Pre-print \u003chttp://www-math.mit.edu/~goemans/PAPERS/maxcut-jacm.pdf\u003e`_]\n.. [3] H Yang and J Moody, \"Data Visualization and Feature Selection: New Algorithms for Nongaussian Data\", NIPS 1999. [`Pre-print \u003chttps://papers.nips.cc/paper/1779-data-visualization-and-feature-selection-new-algorithms-for-nongaussian-data.pdf\u003e`_]\n.. [4] M Bennasar, Y Hicks, abd R Setchi, \"Feature selection using Joint Mutual Information Maximisation\", Expert Systems with Applications, 42(22), pp. 8520--8532, 2015 [`pre-print \u003chttps://core.ac.uk/download/pdf/82448198.pdf\u003e`_]\n.. [5] H Peng, F Long, and C Ding, \"Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy\", IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(8), pp. 1226--1238, 2005. [`Pre-print \u003chttp://ranger.uta.edu/~chqding/papers/mRMR_PAMI.pdf\u003e`_]\n.. [6] Colin: Conic-form Linear Optimizer (www.colinopt.org).\n\n\nCredits\n-------\n\n- KuoLing Huang, 2019-presents\n\n\nLicensing\n---------\n\n**MICO** is 3-clause BSD licensed.\n\n\nNote\n----\n\n**MICO** is heavily inspired from `MIFS: Parallelized Mutual Information based Feature Selection module \u003chttps://github.com/danielhomola/mifs\u003e`_ by Daniel Homola.\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjupiters1117%2Fmico","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fjupiters1117%2Fmico","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjupiters1117%2Fmico/lists"}