tick

tick is a Python 3 module for statistical learning, with a particular emphasis on time-dependent modelling. It is distributed under the 3-Clause BSD license, see LICENSE.txt.

The project was started in 2016 by Emmanuel Bacry, Martin Bompaire, Stéphane Gaïffas and Søren Vinther Poulsen at the Datascience initiative of École Polytechnique, France. The list of contributors is available in CONTRIBUTORS.md.

Quick description

tick is a machine learning library for Python 3. The focus is on statistical learning for time dependent systems, such as point processes. Tick features also tools for generalized linear models and a generic optimization toolbox. The core of the library is an optimization module providing model computational classes, solvers and proximal operators for regularization.

It comes also with inference and simulation tools intended for end-users who for example can easily:

Perform linear, logistic or Poisson regression
Simulate point Hawkes processes with classic or exotic kernels.
Infer Hawkes models with various assumptions on the kernels (exponential or sum of exponential kernels, linear combination of basis kernels, sparse interactions etc.)

A more comprehensive list of examples can be found at in this gallery.

Installation

Requirements

tick requires Python 3.4 or newer. Please have the required Python dependencies in your Python environment:

numpy
sciPy
numpydoc
scikit-learn
matplotlib
pandas

If you build and install tick via pip these dependencies will automatically be resolved.

If not, you can install all of these dependencies easily using:

pip install -r requirements.txt

Swig might also be necessary if precompiled binaries are not available for your distribution.

Source installations

For source installations, a C++ compiler capable of compiling C++11 source code (such as recent versions of gcc or clang) must be available. In addition, SWIG version 3.07 or newer must also be available.

Install using pip

tick is available via pip. In your local Python environment (or global, with sudo rights), do:

pip install tick

Installation may take a few minutes to build and link C++ extensions. tick should now be available.

Manual Install

It's possible to manually build and install tick via the setup.py script. To do both in one step, do:

python setup.py build install

This will build all required C++ extensions, and install the extensions in your current site-packages directory.

In-place (for developers)

If you wish to work with the source code of tick it's convenient to have the extensions installed inside the source directory. This way you will not have to install the module for each iteration of the code.

Simply do:

python setup.py build_ext --inplace

This will build all extensions, and install them directly in the source directory. To use the package outside the build directory, the build path should be added to the PYTHONPATH environment variable, as such (replace $PWD with the full path to the build directory if necessary):

export PYTHONPATH=$PYTHONPATH:$PWD

Help and Support

Documentation

Documentation is available on https://x-datainitiative.github.io/tick.
Documentation can be compiled and used locally by running make html from within the doc directory. This needs to have Sphinx installed.
Several tutorials and code-samples are available in the documentation.

Communication

To reach the developers of tick, please join our community channel on Gitter (https://gitter.im/xdata-tick).

If you've found a bug that needs attention, please raise an issue here on Github. Please try to be as precise in the bug description as possible, such that the developers and other contributors can address the issue efficiently.

Citation

If you use tick in a scientific publication, we would appreciate citations.

Developers

We welcome developers and researchers to contribute to the tick package. In order to do so, we ask that you submit pull requests that will be reviewed by the tick team before being merged into the package source.

Pull Requests

We ask that pull requests meet the following standards:

PR contains exactly 1 commit that has clear and meaningful description
All unit tests pass (Python and C++)
C++ code follows our style guide (Google style)
If new functionality is added, new unit tests must be included in the pull request

To ease this process, there is a script that automatically compiles and run all the necessary checks:

./build_test.sh

This command needs to be run from the package root directory. To run all checks, Python unittest, gtest and cpplint needs to be available. On most systems, this will suffice:

# Install cpplint
pip install cpplint

# Install gtest
git clone https://github.com/google/googletest.git
(cd googletest && mkdir -p build && cd build && cmake .. && make && make install)

Our continuous integration tool (Travis CI) will also run these checks upon submission of a pull request.

Mbompr / tick