Merge pull request #30 from ewengillies/new_official_master

Merge Developer into Master
jakobrunge · Feb 20, 2019 · 0a1f572 · 0a1f572
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diff --git a/.gitignore b/.gitignore
@@ -0,0 +1,6 @@
+# Vim swap files
+*.swp
+*.swo
+
+# Python byte code
+*.pyc
diff --git a/.travis.yml b/.travis.yml
@@ -0,0 +1,39 @@
+language: python
+python:
+  # We don't actually use the Travis Python since we are using Conda, but this
+  # keeps it organized.
+  - "2.7"
+  - "3.5"
+  - "3.6"
+install:
+  - sudo apt-get update
+  # We do this conditionally because it saves us some downloading if the
+  # version is the same.
+  - if [[ "$TRAVIS_PYTHON_VERSION" == "2.7" ]]; then
+      wget https://repo.continuum.io/miniconda/Miniconda2-latest-Linux-x86_64.sh -O miniconda.sh;
+    else
+      wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh;
+    fi
+  - bash miniconda.sh -b -p $HOME/miniconda
+  - export PATH="$HOME/miniconda/bin:$PATH"
+  - hash -r
+  - conda config --set always_yes yes --set changeps1 no
+  - conda update -q conda
+  # Useful for debugging any issues with conda
+  - conda info -a
+
+  # Create and activate the environmenbt
+  - conda create -q -n test-environment python=$TRAVIS_PYTHON_VERSION
+  - source activate test-environment
+  # Ensure we will install all pip packages within conda
+  - conda install pip
+  # Install numpy first to avoid headaches
+  - pip install numpy
+  # Install the r_packages
+  - conda install rpy2
+  - ./install_r_packages.sh
+  # Install the packages needed for testing
+  - pip install .['test']
+script: 
+  # Run the tests
+  - pytest -v
diff --git a/README.md b/README.md
@@ -1,5 +1,5 @@
 # TIGRAMITE – Causal discovery for time series datasets
-Version 3.0 described in http://arxiv.org/abs/1702.07007
+Version 4.0 described in http://arxiv.org/abs/1702.07007v2
 
 (Python Package)
 
@@ -10,34 +10,40 @@ Version 3.0 described in http://arxiv.org/abs/1702.07007
 
 ## General Notes
 
-Tigramite is a causal time series analysis python package. It allows to efficiently reconstruct causal graphs from high-dimensional time series datasets and model the obtained causal dependencies for causal mediation and prediction analyses. Causal discovery is based on linear as well as non-parametric conditional independence tests applicable to discrete or continuously-valued time series. Also includes functions for high-quality plots of the results. Please cite the following papers depending on which method you use:
+Tigramite is a causal time series analysis python package. It allows to efficiently reconstruct causal graphs from high-dimensional time series datasets and model the obtained causal dependencies for causal mediation and prediction analyses. Causal discovery is based on linear as well as non-parametric conditional independence tests applicable to discrete or continuously-valued time series. Currently, tigramite cannot identify causal directionality for contemporaneous links which are left undirected. Also includes functions for high-quality plots of the results. Please cite the following papers depending on which method you use:
+
+- PCMCI: J. Runge et al. (2018): Detecting Causal Associations in Large Nonlinear Time Series Datasets. https://arxiv.org/abs/1702.07007v2
+- Generally: J. Runge (2018): Causal Network Reconstruction from Time Series: From Theoretical Assumptions to Practical Estimation. Chaos: An Interdisciplinary Journal of Nonlinear Science 28 (7): 075310. https://aip.scitation.org/doi/10.1063/1.5025050
+- Mediation class: J. Runge et al. (2015): Identifying causal gateways and mediators in complex spatio-temporal systems. Nature Communications, 6, 8502. http://doi.org/10.1038/ncomms9502
+- Mediation class: J. Runge (2015): Quantifying information transfer and mediation along causal pathways in complex systems. Phys. Rev. E, 92(6), 62829. http://doi.org/10.1103/PhysRevE.92.062829
+- CMIknn: J. Runge (2018): Conditional Independence Testing Based on a Nearest-Neighbor Estimator of Conditional Mutual Information. In Proceedings of the 21st International Conference on Artificial Intelligence and Statistics. http://proceedings.mlr.press/v84/runge18a.html
+
 
 ## Features
 
 - high detection power even for large-scale time series datasets
 - flexible conditional independence test statistics adapted to
   continuously-valued or discrete data, and different assumptions about
   linear or nonlinear dependencies
-- automatic hyperparameter optimization
+- automatic hyperparameter optimization for most tests
 - parallel computing script based on mpi4py
 - handling of missing values and masks
 - p-value correction and confidence interval estimation
 - causal mediation class to analyze causal pathways
 - prediction class based on sklearn models including causal feature selection
+- currently, tigramite cannot identify causal directionality for contemporaneous links which are left undirected
 
 
 ## Required python packages
 
-- numpy, tested with Version 1.10
-- scipy, tested with Version 0.17
-- sklearn, tested with Version 0.18 (optional, necessary for GPDC and GPACE tests)
-- ace python package (https://pypi.python.org/pypi/ace/0.3) OR rpy2 and R-package 'acepack' (optional, necessary for GPACE test)
-- matplotlib, tested with Version 1.5
-- networkx, tested with Version 1.10
-- basemap (only if plotting on a map is needed)
-- mpi4py (optional, necessary for using the parallelized implementation)
-- cython (optional, necessary for CMIknn and GPDC tests)
-- statsmodels, tested with Version 0.6 (optional, necessary for p-value corrections)
+- numpy>=1.10.0
+- scipy>=0.17.0
+- scikit-learn>=0.18.1   (optional, necessary for GPDC test)
+- matplotlib>=1.5.1 (optional, only for plotting)
+- networkx=1.10.0 (optional, only for plotting and mediation)
+- cython>=0.26   (optional, necessary for CMIknn and GPDC tests)
+- mpi4py>=2.0.0   (optional, necessary for using the parallelized implementation)
+- rpy2>=2.8   (optional, necessary for RCOT test)
 
 
 ## Installation
@@ -46,31 +52,25 @@ python setup.py install
 
 This will install tigramite in your path.
 
-To use just the ParCorr and CMIsymb independence tests, only numpy and scipy are required. For CMIknn, cython can optionally be used for compilation, otherwise the provided *.c file is used. GPDC also is based on cython, and additionally, sklearn is required for Gaussian Process regression.
+To use just the ParCorr and CMIsymb independence tests, only numpy and scipy are required. For other independence tests more packages are required:
 
-GPACE requires more work: Firstly, sklearn is required for Gaussian Process regression. Secondly, either the python package 'ace' or the R-package 'acepack' are required for the ACE estimator. The R-package version is much faster. 'ace' can be installed via pip install ace. 'acepack' has to be installed in R first, and can then be accessed by tigramite using the rpy2-interface. 
+- CMIknn: cython can optionally be used for compilation, otherwise the provided ``*.c'' file is used 
 
-For GPDC and GPACE we recommend to pre-compute and store the null-distribution for a wide range of expected sample sizes with the function ``generate_and_save_nulldists``. The file containing the null distributions can then be supplied to the class with the keyword null_dist_filename.
+- GPDC: also based on cython, and additionally, scikit-learn is required for Gaussian Process regression
 
+- RCOT requires more work: Firstly, rpy2 is required to access R-packages. The required R-packages can be installed with the script ``install_r_packages.sh''
 
-## User Agreement
-
-By downloading TIGRAMITE you agree with the following points: The toolbox is provided without any warranty or conditions of any kind. We assume no responsibility for errors or omissions in the results and interpretations following from application the toolbox.
 
-You commit to cite TIGRAMITE in your reports or publications if used:
-
-1. J. Runge, S. Flaxman, and D. Sejdinovic (2017): Detecting causal associations in large nonlinear time series datasets. https://arxiv.org/abs/1702.07007
-
-2. J. Runge et al. (2015): Identifying causal gateways and mediators in complex spatio-temporal systems. Nature Communications, 6, 8502. http://doi.org/10.1038/ncomms9502
+## User Agreement
 
-3. J. Runge (2015): Quantifying information transfer and mediation along causal pathways in complex systems. Phys. Rev. E, 92(6), 62829. http://doi.org/10.1103/PhysRevE.92.062829
+By downloading TIGRAMITE you agree with the following points: TIGRAMITE is provided without any warranty or conditions of any kind. We assume no responsibility for errors or omissions in the results and interpretations following from application of TIGRAMITE.
 
-4. J. Runge, J. Heitzig, V. Petoukhov, and J. Kurths (2012): Escaping the Curse of Dimensionality in Estimating Multivariate Transfer Entropy. Physical Review Letters, 108(25), 258701. http://doi.org/10.1103/PhysRevLett.108.258701
+You commit to cite above papers in your reports or publications.
 
 
 ## License
 
-Copyright (C) Jakob Runge
+Copyright (C) 2014-2019 Jakob Runge
 
 See license.txt for full text.
 

diff --git a/build_tools/conda/build.sh b/build_tools/conda/build.sh
@@ -0,0 +1,14 @@
+# NOTE: when installing, conda will include all things under $PREFIX as a part 
+# of your distributed package.  As such, the R libraries are installed to 
+# the correct library location under $PREFIX. This may not be the most robust 
+# solution, but it works for now.
+
+R_LIBS_BUILD=$PREFIX/lib/R/library
+# Install r-MASS package, which is a dependency of RCIT/RCOT
+R -e "install.packages('MASS', '$R_LIBS_BUILD', repos='https://cloud.r-project.org/')"
+# Install momentchi2, whichs is a dependency of RCIT/RCOT
+R -e "install.packages('momentchi2', '$R_LIBS_BUILD', repos = 'https://cloud.r-project.org/')"
+# Install the RCIT package
+R -e "install.packages('external_packages/RCIT', '$R_LIBS_BUILD', repos=NULL, type='source')"
+# Install tigramite
+$PYTHON setup.py install --single-version-externally-managed --record=record.txt
diff --git a/build_tools/conda/meta.yaml b/build_tools/conda/meta.yaml
@@ -0,0 +1,46 @@
+package:
+  name: tigramite
+  version: 4.0.0
+
+source:
+  path: ../..
+
+requirements:
+  build:
+    - cython
+    - numpy
+    - rpy2
+    - setuptools
+    - python
+  host:
+    - cython
+    - numpy
+    - rpy2
+    - setuptools
+    - python
+  run:
+    - python
+    - numpy 
+    - scipy
+    - six 
+    - scikit-learn>=0.18
+    - matplotlib>=1.5
+    - networkx>=1.10
+    - statsmodels
+    - rpy2
+
+test:
+  source_files:
+    - tests/*py
+  requires:
+    - nose
+    - pytest
+    - rpy2
+  commands:
+    - pytest .
+
+about:
+  home: https://github.com/jakobrunge/tigramite
+  license: GPL-3
+  license_file: license.txt
+  summary: Tigramite is a time series analysis aimed at causal discovery
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diff --git a/docs/_modules/index.html b/docs/_modules/index.html
@@ -1,33 +1,21 @@
+
 <!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN"
   "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
 
-
 <html xmlns="http://www.w3.org/1999/xhtml">
   <head>
+    <meta http-equiv="X-UA-Compatible" content="IE=Edge" />
     <meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
-
-    <title>Overview: module code &#8212; Tigramite 3.0 documentation</title>
-
+    <title>Overview: module code &#8212; Tigramite 4.0 documentation</title>
     <link rel="stylesheet" href="../_static/sphinxdoc.css" type="text/css" />
     <link rel="stylesheet" href="../_static/pygments.css" type="text/css" />
-
-    <script type="text/javascript">
-      var DOCUMENTATION_OPTIONS = {
-        URL_ROOT:    '../',
-        VERSION:     '3.0',
-        COLLAPSE_INDEX: false,
-        FILE_SUFFIX: '.html',
-        HAS_SOURCE:  true,
-        SOURCELINK_SUFFIX: '.txt'
-      };
-    </script>
+    <script type="text/javascript" src="../_static/documentation_options.js"></script>
     <script type="text/javascript" src="../_static/jquery.js"></script>
     <script type="text/javascript" src="../_static/underscore.js"></script>
     <script type="text/javascript" src="../_static/doctools.js"></script>
     <link rel="index" title="Index" href="../genindex.html" />
     <link rel="search" title="Search" href="../search.html" /> 
-  </head>
-  <body role="document">
+  </head><body>
     <div class="related" role="navigation" aria-label="related navigation">
       <h3>Navigation</h3>
       <ul>
@@ -37,19 +25,21 @@ <h3>Navigation</h3>
         <li class="right" >
           <a href="../py-modindex.html" title="Python Module Index"
              >modules</a> |</li>
-        <li class="nav-item nav-item-0"><a href="../index.html">Tigramite 3.0 documentation</a> &#187;</li> 
+        <li class="nav-item nav-item-0"><a href="../index.html">Tigramite 4.0 documentation</a> &#187;</li> 
       </ul>
     </div>
       <div class="sphinxsidebar" role="navigation" aria-label="main navigation">
         <div class="sphinxsidebarwrapper">
 <div id="searchbox" style="display: none" role="search">
   <h3>Quick search</h3>
+    <div class="searchformwrapper">
     <form class="search" action="../search.html" method="get">
-      <div><input type="text" name="q" /></div>
-      <div><input type="submit" value="Go" /></div>
+      <input type="text" name="q" />
+      <input type="submit" value="Go" />
       <input type="hidden" name="check_keywords" value="yes" />
       <input type="hidden" name="area" value="default" />
     </form>
+    </div>
 </div>
 <script type="text/javascript">$('#searchbox').show(0);</script>
         </div>
@@ -61,13 +51,12 @@ <h3>Quick search</h3>
           <div class="body" role="main">
 
   <h1>All modules for which code is available</h1>
-<ul><li><a href="tigramite/data_processing.html">tigramite.data_processing</a></li>
+<ul><li><a href="abc.html">abc</a></li>
+<li><a href="tigramite/data_processing.html">tigramite.data_processing</a></li>
 <li><a href="tigramite/independence_tests.html">tigramite.independence_tests</a></li>
-<li><a href="tigramite/linear_mediation.html">tigramite.linear_mediation</a></li>
 <li><a href="tigramite/models.html">tigramite.models</a></li>
 <li><a href="tigramite/pcmci.html">tigramite.pcmci</a></li>
 <li><a href="tigramite/plotting.html">tigramite.plotting</a></li>
-<li><a href="tigramite/prediction.html">tigramite.prediction</a></li>
 </ul>
 
           </div>
@@ -84,12 +73,12 @@ <h3>Navigation</h3>
         <li class="right" >
           <a href="../py-modindex.html" title="Python Module Index"
              >modules</a> |</li>
-        <li class="nav-item nav-item-0"><a href="../index.html">Tigramite 3.0 documentation</a> &#187;</li> 
+        <li class="nav-item nav-item-0"><a href="../index.html">Tigramite 4.0 documentation</a> &#187;</li> 
       </ul>
     </div>
     <div class="footer" role="contentinfo">
-        &#169; Copyright 2017, Jakob Runge.
-      Created using <a href="http://sphinx-doc.org/">Sphinx</a> 1.5.6.
+        &#169; Copyright 2018, Jakob Runge.
+      Created using <a href="http://sphinx-doc.org/">Sphinx</a> 1.7.4.
     </div>
   </body>
 </html>
diff --git a/docs/_sources/index.rst.txt b/docs/_sources/index.rst.txt
@@ -10,8 +10,8 @@ TIGRAMITE
 
 Tigramite is a causal time series analysis python package. It allows to efficiently reconstruct causal graphs from high-dimensional time series datasets and model the obtained causal dependencies for causal mediation and prediction analyses. Causal discovery is based on linear as well as non-parametric conditional independence tests applicable to discrete or continuously-valued time series. Also includes functions for high-quality plots of the results. Please cite the following papers depending on which method you use:
 
-1. J. Runge, S. Flaxman, D. Sejdinovic (2017): Detecting causal associations in large nonlinear time series datasets, 
-   https://arxiv.org/abs/1702.07007
+1. J. Runge et al. (2018): Detecting Causal Associations in Large Nonlinear Time Series Datasets.
+   https://arxiv.org/abs/1702.07007v2
 
 2. J. Runge et al. (2015): Identifying causal gateways and mediators in complex spatio-temporal systems. 
    Nature Communications, 6, 8502. 
@@ -21,9 +21,13 @@ Tigramite is a causal time series analysis python package. It allows to efficien
    Phys. Rev. E, 92(6), 62829. 
    http://doi.org/10.1103/PhysRevE.92.062829
 
-4. J. Runge, J. Heitzig, V. Petoukhov, and J. Kurths (2012): Escaping the Curse of Dimensionality in Estimating Multivariate Transfer Entropy. 
-   Physical Review Letters, 108(25), 258701. 
-   http://doi.org/10.1103/PhysRevLett.108.258701
+4. J. Runge (2018): Conditional Independence Testing Based on a Nearest-Neighbor Estimator of Conditional Mutual Information.
+   In Proceedings of the 21st International Conference on Artificial Intelligence and Statistics.
+   http://proceedings.mlr.press/v84/runge18a.html
+
+5. J. Runge (2018): Causal Network Reconstruction from Time Series: From Theoretical Assumptions to Practical Estimation.
+   Chaos: An Interdisciplinary Journal of Nonlinear Science 28 (7): 075310.   
+   https://aip.scitation.org/doi/10.1063/1.5025050
 
 
 .. toctree::
@@ -35,8 +39,6 @@ Tigramite is a causal time series analysis python package. It allows to efficien
    tigramite.pcmci.PCMCI
    tigramite.independence_tests.CondIndTest
    tigramite.independence_tests.ParCorr
-   tigramite.independence_tests.GP
-   tigramite.independence_tests.GPACE
    tigramite.independence_tests.GPDC
    tigramite.independence_tests.CMIknn
    tigramite.independence_tests.CMIsymb  
@@ -67,12 +69,6 @@ Test statistics:
 .. autoclass:: tigramite.independence_tests.ParCorr
    :members:
 
-.. autoclass:: tigramite.independence_tests.GP
-   :members:
-
-.. autoclass:: tigramite.independence_tests.GPACE
-   :members:
-
 .. autoclass:: tigramite.independence_tests.GPDC
    :members: