Metadata-Version: 2.1
Name: regain
Version: 0.1.7
Summary: REGAIN (Regularised Graph Inference)
Home-page: https://github.com/fdtomasi/regain
Author: Federico Tomasi
Author-email: federico.tomasi@dibris.unige.it
Maintainer: Federico Tomasi
Maintainer-email: federico.tomasi@dibris.unige.it
License: FreeBSD
Download-URL: https://github.com/fdtomasi/regain/archive/v0.1.7.tar.gz
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        # regain
        Regularised graph inference across multiple time stamps, considering the influence of latent variables.
        It inherits functionalities from the [scikit-learn](https://github.com/scikit-learn/scikit-learn) package.
        
        ## Getting started
        ### Dependencies
        `REGAIN` requires:
        - Python (>= 2.7 or >= 3.5)
        - NumPy (>= 1.8.2)
        - scikit-learn (>= 0.17)
        
        You can install (required) dependencies by running:
        ```bash
        pip install -r requirements.txt
        ```
        
        To use the parameter selection via gaussian process optimisation, [skopt](https://scikit-optimize.github.io/) is required.
        
        ### Installation
        The simplest way to install regain is using pip
        ```bash
        pip install regain
        ```
        or `conda`
        
        ```bash
        conda install -c fdtomasi regain
        ```
        
        If 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.
        ```bash
        git clone https://github.com/fdtomasi/regain.git
        cd regain
        python setup.py develop
        ```
        
        ## Quickstart
        A simple example for how to use LTGL.
        ```python
        import numpy as np
        from regain.covariance import LatentTimeGraphLasso
        from regain.datasets import make_dataset
        from regain.utils import error_norm_time
        
        np.random.seed(42)
        data = make_dataset(n_dim_lat=1, n_dim_obs=10)
        X = data.data
        theta = data.thetas
        
        mdl = LatentTimeGraphLasso(max_iter=50).fit(X)
        print("Error: %.2f" % error_norm_time(theta, mdl.precision_))
        ```
        Note that the input of `LatentTimeGraphLasso` is a three-dimensional matrix with shape `(n_times, n_samples, n_dimensions)`.
        If you have a single time (`n_times = 1`), ensure a `X = X.reshape(1, *X.shape)` before using `LatentTimeGraphLasso`, or, alternatively, use `LatentGraphLasso`.
        
        
        ## Citation
        
        `REGAIN` appeared in the following two publications.
        For the `LatentTimeGraphLasso` please use
        
        ```latex
        @inproceedings{Tomasi:2018:LVT:3219819.3220121,
         author = {Tomasi, Federico and Tozzo, Veronica and Salzo, Saverio and Verri, Alessandro},
         title = {Latent Variable Time-varying Network Inference},
         booktitle = {Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery \&\#38; Data Mining},
         series = {KDD '18},
         year = {2018},
         isbn = {978-1-4503-5552-0},
         location = {London, United Kingdom},
         pages = {2338--2346},
         numpages = {9},
         url = {http://doi.acm.org/10.1145/3219819.3220121},
         doi = {10.1145/3219819.3220121},
         acmid = {3220121},
         publisher = {ACM},
         address = {New York, NY, USA},
         keywords = {convex optimization, graphical models, latent variables, network inference, time-series},
        } 
        ```
        
        and for the `TimeGraphLassoForwardBackward` plase use
        
        ```latex
        @InProceedings{pmlr-v72-tomasi18a,
          title = 	 {Forward-Backward Splitting for Time-Varying Graphical Models},
          author = 	 {Tomasi, Federico and Tozzo, Veronica and Verri, Alessandro and Salzo, Saverio},
          booktitle = 	 {Proceedings of the Ninth International Conference on Probabilistic Graphical Models},
          pages = 	 {475--486},
          year = 	 {2018},
          editor = 	 {Kratochv\'{i}l, V\'{a}clav and Studen\'{y}, Milan},
          volume = 	 {72},
          series = 	 {Proceedings of Machine Learning Research},
          address = 	 {Prague, Czech Republic},
          month = 	 {11--14 Sep},
          publisher = 	 {PMLR},
          pdf = 	 {http://proceedings.mlr.press/v72/tomasi18a/tomasi18a.pdf},
          url = 	 {http://proceedings.mlr.press/v72/tomasi18a.html},
          abstract = 	 {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.}
        }
        ```
        
Keywords: graph inference,latent variables
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Environment :: Console
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: Programming Language :: Python
Classifier: License :: OSI Approved :: BSD License
Classifier: Topic :: Software Development
Classifier: Topic :: Scientific/Engineering
Classifier: Natural Language :: English
Classifier: Operating System :: POSIX
Classifier: Operating System :: Unix
Classifier: Operating System :: MacOS
Classifier: Programming Language :: Python
Requires: numpy (>=1.11)
Requires: scipy (>=0.16.1,>=1.0)
Requires: sklearn (>=0.17)
Requires: six
Description-Content-Type: text/markdown
