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        "author": "Iain Carmichael",
        "author_email": "idc9@cornell.edu",
        "bugtrack_url": null,
        "classifiers": [
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: Implementation :: PyPy"
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        "description": "\njive\n----\n\n**author**: `Iain Carmichael`_\n\nAdditional documentation, examples and code revisions are coming soon.\nFor questions, issues or feature requests please reach out to Iain:\niain@unc.edu.\n\nOverview\n========\n\n**jive** is a data analysis package for high-dimensional, multi-block\n(or multi-view) data. The multi-block data setting means two or more data\nmatrices with a fixed set of observations (e.g. patients) and multiple\nsets of features (e.g. clinical features and gene expression data).\n\n\nThe primary algorithm in this package is Angle based Joint and\nIndividual Variation Explained (AJIVE) which is a data integration/feature\nextraction algorithm. AJIVE finds joint modes of variation which are\ncommon to all K data blocks as well as modes of *individual* variation which\nare specific to each block. For a detailed discussion of AJIVE see\n`Angle-Based Joint and Individual Variation Explained`_. An R version of\nAJIVE can be found `here`_.\n\nInstallation\n============\nTo install use pip:\n\n::\n\n    pip install jive\n\n\nOr clone the repo:\n\n::\n\n    git clone https://github.com/idc9/py_jive.git\n    python setup.py install\n\njive is currently available for python 3\n\nExample\n=======\n\n.. code:: python\n\n    from jive.AJIVE import AJIVE\n    from jive.PCA import PCA\n    from jive.ajive_fig2 import generate_data_ajive_fig2\n    from jive.viz.block_visualization import data_block_heatmaps, jive_full_estimate_heatmaps\n    import matplotlib.pyplot as plt\n    # %matplotlib inline\n\n    X, Y = generate_data_ajive_fig2()\n    data_block_heatmaps([X, Y])\n\n.. image:: doc/figures/data_heatmaps.png\n\n.. code:: python\n\n    # determine initial signal ranks by inspecting scree plots\n    plt.figure(figsize=[10, 5])\n    plt.subplot(1, 2, 1)\n    PCA().fit(X).plot_scree()\n    plt.subplot(1, 2, 2)\n    PCA().fit(Y).plot_scree()\n\n.. image:: doc/figures/scree_plots.png\n\n.. code:: python\n\n    ajive = AJIVE(init_signal_ranks={'x': 2, 'y': 3})\n    ajive.fit(blocks={'x': X, 'y': Y})\n\n    plt.figure(figsize=[10, 20])\n    jive_full_estimate_heatmaps(ajive.get_full_block_estimates(),\n                                blocks={'x': X, 'y': Y})\n\n.. image:: doc/figures/jive_estimate_heatmaps.png\n\n.. code:: python\n\n    ajive.plot_joint_diagnostic()\n\n.. image:: doc/figures/jive_diagnostic.png\n\n\nHelp and Support\n================\n\nAdditional documentation, examples and code revisions are coming soon.\nFor questions, issues or feature requests please reach out to Iain:\niain@unc.edu.\n\nDocumentation\n^^^^^^^^^^^^^\n\nThe source code is located on github:\n`https://github.com/idc9/py\\_jive`_. Currently the best math reference\nis the `AJIVE paper`_.\n\nTesting\n^^^^^^^\n\nTesting is done using `nose`_.\n\nContributing\n^^^^^^^^^^^^\n\nWe welcome contributions to make this a stronger package: data examples,\nbug fixes, spelling errors, new features, etc.\n\nCitation\n^^^^^^^^\n\nA `Journal of Statistical Software`_ paper is coming soon.\n\n.. _Iain Carmichael: https://idc9.github.io/\n.. _Angle-Based Joint and Individual Variation Explained: https://arxiv.org/pdf/1704.02060.pdf\n.. _here: https://github.com/idc9/r_jive\n.. _these example notebooks: doc/example_notebooks/\n.. _`https://github.com/idc9/py\\_jive`: https://github.com/idc9/r_jive\n.. _AJIVE paper: https://arxiv.org/pdf/1704.02060.pdf\n.. _nose: http://nose.readthedocs.io/en/latest/\n.. _Journal of Statistical Software: https://www.jstatsoft.org/index\n\n\n",
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