cynet

Spatio-Temporal Interaction Network wrappers

https://github.com/zeroknowledgediscovery/cynet

Repository

Spatio-Temporal Interaction Network wrappers

README.rst

===============
cynet
===============

.. figure:: https://img.shields.io/pypi/dm/cynet.svg
   :alt: cynet PyPI Downloads
.. figure:: https://img.shields.io/pypi/v/cynet.svg
   :alt: cynet version

.. image:: http://zed.uchicago.edu/logo/logozed1.png
   :height: 400px
   :scale: 50 %
   :alt: alternate text
   :align: center

.. class:: no-web no-pdf

:Info: See  for theoretical background
:Author: ZeD@UChicago 
:Description: Implementation of the Deep Granger net inference algorithm, described in https://arxiv.org/abs/1406.6651, for learning spatio-temporal stochastic processes (*point processes*). **cynet** learns a network of generative local models, without assuming any specific model structure.

.. NOTE:: If issues arise with dependencies in python3, be sure that *tkinter* is installed

.. code-block::

    sudo apt-get install python3-tk

**Usage:**

.. code-block::

    from cynet import cynet
    from cynet.cynet import uNetworkModels as models
    from viscynet import viscynet as vcn

**cynet module includes:**
  * cynet
  * viscynet

cynet library classes:
~~~~~~~~~~~~~~~~~~~~~~
* spatioTemporal
* uNetworkModels
* simulateModels
* xgModels

Examples of Pipeline:
  You may find two examples of this pipeline in your enviroment's bin folder
  after installing the cynet package. There will also be a pdf walking through
  another extremely detailed example.

Produces detailed timeseries predictions using Deep Granger Nets.

.. image:: https://zed.uchicago.edu/img/cynetpred.png
  :align: center
  :scale: 50 %

Description of Pipeline:
  You may find two examples of this pipeline in your enviroment's bin folder
  after installing the cynet package.

  Step 1:
    Use the spatioTemporal class and its utility functions to fit and
    manipulate your data into a timeseries grid. The end outputs will be triplets:
    files that contain the rows (coordinates), the columns (dates), and the timeseries.
    The splitTS function will help generate rows of the timeseries. Generally, we
    use this to create timeseries beyond the length of the data in the triplets.
    We use the triplets to generate predictive models and then split, which have
    the longer timeseries to evaluate those models.

  Step 2:
    Run xGenESeSS on the triplets to generate predictive models. The
    xgModels class can be used to assist in this step. If running on a cluster,
    set run local to false and calling xgModels.run() will generate the shell
    commands to run xGenESeSS in a text file. Otherwise, xgModels will run
    locally using the binary installed with the package. The end result are predictive
    models. Note that example 1 starts at this point. Thus there are sample models
    provided.

  Step 3:
    To evaluate the models afterwards, use the run_pipeline utility function.
    This calls uNetworkModels and simulateModels in parallel to evaluate each model.
    simulateModels calls the cynet and flexroc binaries. Outputs will be auc, tpr,
    and fpr statistics.

    See example 2 for an example of the entire pipeline.

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