README.txt

DeepSurv
========

DeepSurv implements a deep learning generalization of the Cox proportional hazards model using Theano and Lasagne. 

DeepSurv has an advantage over traditional Cox regression because it does not require an *a priori* selection of covariates, but learns them adaptively. 

DeepSurv can be used in numerous survival analysis applications. One medical application is provided, recommend_treatment, which provides treatment recommendations for a set of patient observations. 

Installation:
-------------

Dependencies:
^^^^^^^^^^^^^

Theano, Lasagne, lifelines, matplotlib (for visualization) and all of their respective dependencies. 

Installing:
^^^^^^^^^^^

You can install *DeepSurv* using

::

	pip install deepsurv

from the command line.

Running the tests:
^^^^^^^^^^^^^^^^^^
After installing, you can optionally run the test suite with

::

	py.test

from the command line while in the module main directory.

Training a Network:
-------------------

Training DeepSurv can be done in a few lines. 
First, all you need to do is prepare the datasets to have the following keys:

::

	{ 
		'x': (n,d) observations (dtype = float32), 
	 	't': (n) event times (dtype = float32),
	 	'e': (n) event indicators (dtype = int32)
	}

Then prepare a dictionary of hyper-parameters. And it takes only two lines to train a network:

::

	network = deepsurv.DeepSurv(**hyperparams)
	log = network.train(train_data, valid_data, n_epochs=500)

You can then evaluate its success on testing data:

::

	network.get_concordance_index(**test_data)
	>> 0.62269622730138632

If you have matplotlib installed, you can visualize the training and validation curves after training the network:

::

	deepsurv.plot_log(log)



License:
-------- 

MIT License

Copyright (c) 2016, Jared Katzman

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
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The above copyright notice and this permission notice shall be included in all
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