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        "author": "Krzysztof Chalupka",
        "author_email": "janchatko@gmail.com",
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        "classifiers": [
            "Development Status :: 3 - Alpha",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.3",
            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3.5",
            "Topic :: Scientific/Engineering :: Artificial Intelligence"
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        "description": ".. image:: https://img.shields.io/badge/License-MIT-yellow.svg\n    :target: https://opensource.org/licenses/MIT\n    :alt: License\n\n*A Decision Tree (Conditional) Independence Test (DTIT).*\n\nIntroduction\n-----------\nLet *x, y, z* be random variables. Then deciding whether *P(y | x, z) = P(y | z)* \ncan be difficult, especially if the variables are continuous. This package \nimplements a simple yet efficient and effective conditional independence test,\ndescribed in [link to arXiv when we write it up!]. Important features that differentiate\nthis test from competition:\n\n* It is fast. Worst-case speed scales as O(n_data * log(n_data) * dim), where dim is max(x_dim + z_dim, y_dim). However, amortized speed is O(n_data * log(n_data) * log(dim)).\n\n* It applies to cases where some of x, y, z are continuous and some are discrete, or categorical (one-hot-encoded).\n\n* It is very simple to understand and modify.\n\n* It can be used for unconditional independence testing with almost no changes to the procedure.\n\nWe have applied this test to tens of thousands of samples of thousand-dimensional datapoints in seconds. For smaller dimensionalities and sample sizes, it takes a fraction of a second. The algorithm is described in [arXiv link coming], where we also provide detailed experimental results and comparison with other methods. However for now, you should be able to just look through the code to understand what's going on -- it's only 90 lines of Python, including detailed comments!\n\nUsage\n-----\nBasic usage is simple, and the default settings should work in most cases. To perform an *unconditional test*, use dtit.test(x, y):\n\n.. code:: python\n\n  import numpy as np\n  from dtit import dtit\n\n  x = np.random.rand(1000, 1)\n  y = np.random.randn(1000, 1)\n\n  pval_i = dtit.test(x, y) # p-value should be uniform on [0, 1].\n  pval_d = dtit.test(x, x + y) # p-value should be very small.\n\nTo perform a conditional test, just add the third variable z to the inputs:\n\n.. code:: python\n\n  import numpy as np\n  from dtit import dtit\n\n  # Generate some data such that x is indpendent of y given z.\n  n_samples = 1000\n  z = np.random.dirichlet(alpha=np.ones(2), size=n_samples)\n  x = np.vstack([np.random.multinomial(20, p) for p in z]).astype(float)\n  y = np.vstack([np.random.multinomial(20, p) for p in z]).astype(float)\n\n  # Check that x and y are dependent (p-value should be uniform on [0, 1]).\n  pval_d = dtit.test(x, y)\n  # Check that z d-separates x and y (the p-value should be small).\n  pval_i = dtit.test(x, y, z)\n\nInstallation\n-----------\npip install dtit\n\n\nRequirements\n------------\nTested with Python 3.6 and\n\n    * numpy >= 1.12\n    * scikit-learn >= 0.18.1\n    * scipy >= 0.16.1\n\n.. _pip: http://www.pip-installer.org/en/latest/\n\n\n",
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        "keywords": "machine learning statistics decision trees",
        "license": "MIT",
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