Metadata-Version: 1.1
Name: dtit
Version: 1.0.0
Summary: A decision-tree based conditional independence test
Home-page: https://github.com/kjchalup/dtit
Author: Krzysztof Chalupka
Author-email: kjchalup@caltech.edu
License: MIT
Description: .. image:: https://img.shields.io/badge/License-MIT-yellow.svg
            :target: https://opensource.org/licenses/MIT
            :alt: License
        
        *A Decision Tree (Conditional) Independence Test (DTIT).*
        
        Introduction
        -----------
        Let *x, y, z* be random variables. Then deciding whether *P(y | x, z) = P(y | z)* 
        can be difficult, especially if the variables are continuous. This package 
        implements a simple yet efficient and effective conditional independence test,
        described in [link to arXiv when we write it up!]. Important features that differentiate
        this test from competition:
        
        * 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)).
        
        * It applies to cases where some of x, y, z are continuous and some are discrete, or categorical (one-hot-encoded).
        
        * It is very simple to understand and modify.
        
        We 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!
        
        Usage
        -----
        Basic usage is simple:
         
        .. code:: python
        
          import numpy as np
          import dtit
          # Generate some data such that x is indpendent of y given z.
          n_samples = 300
          z = np.random.dirichlet(alpha=np.ones(2), size=n_samples)
          x = np.vstack([np.random.multinomial(20, p) for p in z])
          y = np.vstack([np.random.multinomial(20, p) for p in z])
          
          # Run the conditional independence test.
          pval = dtit.test(x, y, z)
        
        Here, we created discrete variables *x* and *y*, d-separated by a "common cause"
        *z*. The null hypothesis is that *x* is independent of *y* given *z*. Since in this 
        case the variables are independent given *z*, pval should be distributed uniformly on [0, 1].
        
        Requirements
        ------------
        To use the nn methods:
            * numpy >= 1.12
            * scikit-learn >= 0.18.1
            * scipy >= 0.16.1
        
        .. _pip: http://www.pip-installer.org/en/latest/
        
Keywords: machine learning statistics decision trees
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
