{
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        "author": "Federico Tomasi",
        "author_email": "federico.tomasi@dibris.unige.it",
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            "Development Status :: 4 - Beta",
            "Environment :: Console",
            "Intended Audience :: Developers",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: BSD License",
            "Natural Language :: English",
            "Operating System :: MacOS",
            "Operating System :: POSIX",
            "Operating System :: Unix",
            "Programming Language :: Python",
            "Topic :: Scientific/Engineering",
            "Topic :: Software Development"
        ],
        "description": "[![develstat](https://travis-ci.org/fdtomasi/regain.svg?branch=master)](https://travis-ci.org/fdtomasi/regain) [![covdevel](http://codecov.io/github/fdtomasi/regain/coverage.svg?branch=master)](http://codecov.io/github/fdtomasi/regain?branch=master) [![licence](https://img.shields.io/badge/licence-BSD-blue.svg)](http://opensource.org/licenses/BSD-3-Clause) [![PyPI](https://img.shields.io/pypi/v/regain.svg)](https://pypi.python.org/pypi/regain) [![Conda](https://img.shields.io/conda/v/fdtomasi/regain.svg)](https://anaconda.org/fdtomasi/regain) [![Python27](https://img.shields.io/badge/python-2.7-blue.svg)](https://badge.fury.io/py/regain) [![Python34](https://img.shields.io/badge/python-3.5-blue.svg)](https://badge.fury.io/py/regain) [![Requirements Status](https://requires.io/github/fdtomasi/regain/requirements.svg?branch=master)](https://requires.io/github/fdtomasi/regain/requirements/?branch=master)\n\n# regain\nRegularised graph inference across multiple time stamps, considering the influence of latent variables.\nIt inherits functionalities from the [scikit-learn](https://github.com/scikit-learn/scikit-learn) package.\n\n## Getting started\n### Dependencies\n`REGAIN` requires:\n- Python (>= 2.7 or >= 3.5)\n- NumPy (>= 1.8.2)\n- scikit-learn (>= 0.17)\n\nYou can install (required) dependencies by running:\n```bash\npip install -r requirements.txt\n```\n\nTo use the parameter selection via gaussian process optimisation, [skopt](https://scikit-optimize.github.io/) is required.\n\n### Installation\nThe simplest way to install regain is using pip\n```bash\npip install regain\n```\nor `conda`\n\n```bash\nconda install -c fdtomasi regain\n```\n\nIf you'd like to install from source, or want to contribute to the project (e.g. by sending pull requests via github), read on. Clone the repository in GitHub and add it to your $PYTHONPATH.\n```bash\ngit clone https://github.com/fdtomasi/regain.git\ncd regain\npython setup.py develop\n```\n\n## Quickstart\nA simple example for how to use LTGL.\n```python\nimport numpy as np\nfrom regain.covariance import LatentTimeGraphLasso\nfrom regain.datasets import make_dataset\nfrom regain.utils import error_norm_time\n\nnp.random.seed(42)\ndata = make_dataset(n_dim_lat=1, n_dim_obs=10)\nX = data.data\ntheta = data.thetas\n\nmdl = LatentTimeGraphLasso(max_iter=50).fit(X)\nprint(\"Error: %.2f\" % error_norm_time(theta, mdl.precision_))\n```\nNote that the input of `LatentTimeGraphLasso` is a three-dimensional matrix with shape `(n_times, n_samples, n_dimensions)`.\nIf you have a single time (`n_times = 1`), ensure a `X = X.reshape(1, *X.shape)` before using `LatentTimeGraphLasso`, or, alternatively, use `LatentGraphLasso`.\n\n\n## Citation\n\n`REGAIN` appeared in the following two publications.\nFor the `LatentTimeGraphLasso` please use\n\n```latex\n@inproceedings{Tomasi:2018:LVT:3219819.3220121,\n author = {Tomasi, Federico and Tozzo, Veronica and Salzo, Saverio and Verri, Alessandro},\n title = {Latent Variable Time-varying Network Inference},\n booktitle = {Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery \\&\\#38; Data Mining},\n series = {KDD '18},\n year = {2018},\n isbn = {978-1-4503-5552-0},\n location = {London, United Kingdom},\n pages = {2338--2346},\n numpages = {9},\n url = {http://doi.acm.org/10.1145/3219819.3220121},\n doi = {10.1145/3219819.3220121},\n acmid = {3220121},\n publisher = {ACM},\n address = {New York, NY, USA},\n keywords = {convex optimization, graphical models, latent variables, network inference, time-series},\n} \n```\n\nand for the `TimeGraphLassoForwardBackward` plase use\n\n```latex\n@InProceedings{pmlr-v72-tomasi18a,\n  title = \t {Forward-Backward Splitting for Time-Varying Graphical Models},\n  author = \t {Tomasi, Federico and Tozzo, Veronica and Verri, Alessandro and Salzo, Saverio},\n  booktitle = \t {Proceedings of the Ninth International Conference on Probabilistic Graphical Models},\n  pages = \t {475--486},\n  year = \t {2018},\n  editor = \t {Kratochv\\'{i}l, V\\'{a}clav and Studen\\'{y}, Milan},\n  volume = \t {72},\n  series = \t {Proceedings of Machine Learning Research},\n  address = \t {Prague, Czech Republic},\n  month = \t {11--14 Sep},\n  publisher = \t {PMLR},\n  pdf = \t {http://proceedings.mlr.press/v72/tomasi18a/tomasi18a.pdf},\n  url = \t {http://proceedings.mlr.press/v72/tomasi18a.html},\n  abstract = \t {Gaussian graphical models have received much attention in the last years, due to their flexibility and expression power. However, the optimisation of such complex models suffer from computational issues both in terms of convergence rates and memory requirements. Here, we present a forward-backward splitting (FBS) procedure for Gaussian graphical modelling of multivariate time-series which relies on recent theoretical studies ensuring convergence under mild assumptions. Our experiments show that a FBS-based implementation achieves, with very fast convergence rates, optimal results with respect to ground truth and standard methods for dynamical network inference. Optimisation algorithms which are usually exploited for network inference suffer from drawbacks when considering large sets of unknowns. Particularly for increasing data sets and model complexity, we argue for the use of fast and theoretically sound optimisation algorithms to be significant to the graphical modelling community.}\n}\n```\n\n\n",
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