neuropredict 0.6.3
easy and standardized predictive analysis for biomarkers, neuroimaging, and beyond
Requires Python: >=3.7


.. image:: docs/logo_neuropredict.png
    :width: 150


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**Documentation**: https://raamana.github.io/neuropredict/

News
----

-  As of ``v0.6``, **neuropredict now supports regression applications**
   i.e. predicting continuous targets (in addition to categorical
   classes), as well as allow you to **regress out covariates /
   confounds** within the nested-CV (following all the best practices).
   Utilizing this feature requires the input datasets be specified in
   the ``pyradigm`` data structures: code @ https://github.com/raamana/pyradigm,
   docs @ https://raamana.github.io/pyradigm/. Check the changelog below for more details.

Older news
----------

-  ``neuropredict`` can handle missing data now (that are encoded with
   ``numpy.NaN``). This is done respecting the cross-validation splits
   without any data leakage.

Overview
--------

On a high level,

.. image:: docs/high_level_flow.png
   :alt: roleofneuropredict


On a more detailed level,

.. image:: docs/role.png
   :alt: roleofneuropredict

-  Docs: https://raamana.github.io/neuropredict/
-  Contributors most welcome: `check ideas <CONTRIBUTING.md>`__ and the following
   `guidelines <http://contribution-guide-org.readthedocs.io>`__.
   Thanks.

Long term goals
---------------

neuropredict, the tool, is part of a broader initiative described below
to develop easy, comprehensive and standardized predictive analysis:

.. image:: docs/neuropredict_long_term_goals.jpg
   :alt: longtermgoals

Citation
--------

If ``neuropredict`` helped you in your research in one way or another,
please consider citing one or more of the following, which were
essential building blocks of neuropredict: 

 - Pradeep Reddy Raamana. (2017). neuropredict: easy machine learning and standardized predictive analysis of biomarkers (Version 0.4.5). Zenodo. http://doi.org/10.5281/zenodo.1058993 
 - Raamana et al, (2017), Python class defining a machine learning dataset ensuring key-based correspondence and maintaining integrity, Journal of Open Source Software, 2(17), 382, doi:10.21105/joss.00382

Change Log - version 0.6
--------------------------
- Major feature: Ability to predict continuous variables (regression)
- Major feature: Ability to handle confounds (regress them out, augmenting etc)
- Redesigned the internal structure for easier extensibility
- New ``CVResults`` class for easier management of a wealth of outputs generated in the Classification and Regression workflows
- API access is refreshed and easier

Change Log - version 0.5.2
--------------------------

-  Imputation of missing values
-  Additional classifiers such as ``XGBoost``, Decision Trees
-  Better internal code structure
-  Lot more tests
-  More precise tests, as we vary number of classes wildly in test
   suites
-  several bug fixes and enhancements
-  More cmd line options such as ``--print_options`` from a previous run

.. |logo| image:: docs/logo_neuropredict.png
.. |travis| image:: https://travis-ci.org/raamana/neuropredict.svg?branch=master
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.. |PyPI version| image:: https://badge.fury.io/py/neuropredict.svg
   :target: https://badge.fury.io/py/neuropredict
.. |Python versions| image:: https://img.shields.io/badge/python-3.5%2C%203.6-blue.svg
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