Merge pull request #317 from elfi-dev/dev

Release v. 0.7.5

- Improved the appearance of figures produced by `plot_gp` and added the option
  to draw true parameter indicators on the subplots using the optional input
  `true_params`
- Modified DCC model by taking into account that subject can't infect herself
- Added ability to set minimizer constrains for BOLFI
- Enable bolfi.fit using only pre-generated initial evidence points
- Fixed a bug causing random seed number to be deterministic
- Updated requirements-dev.txt with pytest>=4.4
- Minor changes to documentation and refactoring
- Added `make test-notslow` alternative

Author: hpesonen

.github/PULL_REQUEST_TEMPLATE.md

@@ -2,8 +2,16 @@
 Please provide a short summary here
 
 #### Please make sure
-- You have updated the CHANGELOG.rst
-- You have updated the documentation (if applicable)
+
+- [ ] You have updated the CHANGELOG.rst
+- [ ] You have provided a short summary of your changes (see previous section)
+- [ ] You have listed the copyright holder for the work you are submitting (see next section)
+
+If your contribution adds, removes or somehow changes the functional behavior of the package, please check that
+
+- [ ] You have included or updated all the relevant documentation
+- [ ] The proposed changes pass all unit tests (check step 6 of CONTRIBUTING.rst for details)
+- [ ] You have added appropriate unit tests to ensure the new features behave as expected
 
 #### Copyright and Licensing
 

CHANGELOG.rst

@@ -1,11 +1,24 @@
 Changelog
 =========
 
+0.7.5 (2019-12-18)
+------------------
+- Improved the appearance of figures produced by `plot_gp` and added the option
+  to draw true parameter indicators on the subplots using the optional input
+  `true_params`
+- Modified DCC model by taking into account that subject can't infect herself
+- Added ability to set minimizer constrains for BOLFI
+- Enable bolfi.fit using only pre-generated initial evidence points
+- Fixed a bug causing random seed number to be deterministic
+- Updated requirements-dev.txt with pytest>=4.4
+- Minor changes to documentation and refactoring
+- Added `make test-notslow` alternative
 
 0.7.4 (2019-03-07)
 ------------------
 - Add sampler option `algorithm` for bolfi-posterior-sampling
-- Add a check whether the option given for `algorithm` is one if the implemented samplers
+- Add a check whether the option given for `algorithm` is one if the
+  implemented samplers
 - Add metropolis sampler `algorithm=metropolis` for bolfi-posterior-sampling
 - Add option `warmup` to metropolis-sampler
 - Add a small test of metropolis-sampler
@@ -17,7 +30,8 @@ Changelog
 0.7.3 (2018-08-30)
 ------------------
 - Fix bug in plot_pairs which crashes in case of 1 parameter
-- Fix bug in plot_marginals which outputs empty plots in case where we have parameter more than 5
+- Fix bug in plot_marginals which outputs empty plots in case where we have
+  parameter more than 5
 - Fix crashing summary and plots for samples with multivariate priors
 - Add progress bar for inference methods
 - Add method save to Sample objects
@@ -27,7 +41,8 @@ Changelog
 0.7.2 (2018-06-20)
 ------------------
 - Added support for kwargs in elfi.set_client
-- Added new example: inference of transmission dynamics of bacteria in daycare centers
+- Added new example: inference of transmission dynamics of bacteria in daycare
+  centers
 - Added new example: Lorenz model
 
 0.7.1 (2018-04-11)
@@ -40,19 +55,22 @@ Changelog
 ----------------
 - Added new example: the stochastic Lotka-Volterra model
 - Fix methods.bo.utils.minimize to be strictly within bounds
-- Implemented the Two Stage Procedure, a method of summary-statistics diagnostics
+- Implemented the Two Stage Procedure, a method of summary-statistics
+  diagnostics
 - Added the MaxVar acquisition method
 - Added the RandMaxVar acquisition method
 - Added the ExpIntVar acquisition method
-- Implemented the Two Stage Procedure, a method of summary-statistics diagnostics
+- Implemented the Two Stage Procedure, a method of summary-statistics
+  diagnostics
 - Added new example: the stochastic Lotka-Volterra model
 - Fix methods.bo.utils.minimize to be strictly within bounds
 - Fix elfi.Distance to support scipy 1.0.0
 
 0.6.3 (2017-09-28)
 ------------------
 
-- Further performance improvements for rerunning inference using stored data via caches
+- Further performance improvements for rerunning inference using stored data
+  via caches
 - Added the general Gaussian noise example model (fixed covariance)
 - restrict NetworkX to versions < 2.0
 
@@ -74,18 +92,21 @@ Changelog
 - Added OutputPool.open to read a closed pool from disk
 - Refactored Sample and SmcSample classes
 - Added elfi.new_model method
-- Made elfi.set_client method to accept clients as strings for easier client switching
+- Made elfi.set_client method to accept clients as strings for easier client
+  switching
 - Fixed a bug in NpyArray that would lead to an inconsistent state if multiple
   simultaneous instances were opened.
 - Added the ability to move the pool data folder
 - Sample.summary is now a method instead of a property
-- SmcSample methods takes the keyword argument 'all' to show results of all populations
+- SmcSample methods takes the keyword argument 'all' to show results of all
+  populations
 - Added a section about iterative advancing to documentation
 
 0.6 (2017-07-03)
 ----------------
 
-- Changed some of the internal variable names in methods.py. Most notable outputs is now
+- Changed some of the internal variable names in methods.py. Most notable
+  outputs is now
   output_names.
 - methods.py renamed to parameter_inference.py
 - Changes in elfi.methods.results module class names:
@@ -108,7 +129,8 @@ Major update, a lot of code base rewritten.
 Most important changes:
 
 - revised syntax for model definition (esp. naming)
-- scheduler-independent parallelization interface (currently supports native & ipyparallel)
+- scheduler-independent parallelization interface (currently supports native &
+  ipyparallel)
 - methods can now be run iteratively
 - persistence to .npy files
 - Bayesian optimization as a separate method

CONTRIBUTING.rst

@@ -54,61 +54,76 @@ If you are proposing a feature:
 * Remember that this is a volunteer-driven project, and that contributions
   are welcome :)
 
+
 Get Started!
 ------------
 
+`ELFI` is a project with dozens of collaborators, so organization is key to making our contributions effective and avoid reword. Thus, in addition to the recommendations below we strongly recommend reading our `Wiki <https://github.com/elfi-dev/elfi/wiki>`_ to see what is the suggested git workflow procedure for your type of contribution.
+
 Ready to contribute? Here's how to set up `ELFI` for local development.
 
 1. Fork the `elfi` repo on GitHub.
 2. Clone your fork locally::
 
     $ git clone git@github.com:your_name_here/elfi.git
 
-3. Install your local copy and the development requirements into a conda environment::
+3. Make sure you have `Python 3 <https://www.python.org/>`_ and
+   `Anaconda Distribution <https://www.anaconda.com/>`_ installed on your
+   machine. Check your conda and Python versions::
+
+   $ conda -V
+   $ python -V
+
+4. Install your local copy and the development requirements into a conda
+   environment. You may need to replace "3.5" in the first line with the python
+   version printed in the previous step::
 
     $ conda create -n elfi python=3.5 numpy
     $ source activate elfi
     $ cd elfi
     $ make dev
 
-4. Create a branch for local development::
+5. Create a branch for local development::
 
     $ git checkout -b name-of-your-bugfix-or-feature
 
    Now you can make your changes locally.
-   
-5. Follow the `Style Guidelines`_
 
-6. When you're done making changes, check that your changes pass flake8 and the tests::
+6. Follow the `Style Guidelines`_
+
+7. When you're done making changes, check that your changes pass flake8 and the tests::
 
     $ make lint
     $ make test
 
+   You may run `make test-notslow` instead of `make test` *as long as your proposed changes are unrelated to BOLFI*.
+
    Also make sure that the docstrings of your code are formatted properly::
 
     $ make docs
 
-7. Commit your changes and push your branch to GitHub::
+8. Commit your changes and push your branch to GitHub::
 
     $ git add .
     $ git commit -m "Your detailed description of your changes."
     $ git push origin name-of-your-bugfix-or-feature
 
-8. Submit a pull request through the GitHub website.
+9. Submit a pull request through the GitHub website.
 
 Style Guidelines
 ----------------
 
 The Python code in ELFI mostly follows `PEP8 <http://pep8.org/>`_, which is considered the de-facto code style guide for Python. Lines should not exceed 100 characters.
-   
+
 Docstrings follow the `NumPy style <http://sphinxcontrib-napoleon.readthedocs.io/en/latest/example_numpy.html>`_.
 
 Pull Request Guidelines
 -----------------------
 
 Before you submit a pull request, check that it meets these guidelines:
 
-1. The pull request should include tests that will be run automatically using Travis-CI.
+1. The pull request should include tests that will be run automatically using
+   Travis-CI.
 2. If the pull request adds functionality, the docs should be updated. Put
    your new functionality into a function with a docstring, and add the
    feature to the list in README.rst.

Makefile

@@ -52,13 +52,16 @@ lint: ## check style with flake8
 	flake8 elfi tests
 
 test: ## run tests quickly with the default Python
-	PYTHONPATH=$$PYTHONPATH:. py.test --reruns 1
+	PYTHONPATH=$$PYTHONPATH:. pytest --reruns 1
+
+test-notslow: ## skips tests marked as slowtest
+	PYTHONPATH=$$PYTHONPATH:. pytest -m "not slowtest"
 
 test-all: ## run tests on every Python version with tox
 	tox
 
 coverage: ## check code coverage quickly with the default Python
-	py.test --cov=elfi
+	pytest --cov=elfi
 	coverage report -m
 	coverage html
 	$(BROWSER) htmlcov/index.html

README.md

@@ -1,4 +1,4 @@
-**Version 0.7.4 released!** See the CHANGELOG and [notebooks](https://github.com/elfi-dev/notebooks).
+**Version 0.7.5 released!** See the CHANGELOG and [notebooks](https://github.com/elfi-dev/notebooks).
 
 **NOTE:** For the time being NetworkX 2 is incompatible with ELFI.
 

docs/quickstart.rst

@@ -1,4 +1,3 @@
-
 Quickstart
 ==========
 

docs/usage/BOLFI.rst

@@ -1,4 +1,3 @@
-
 This tutorial is generated from a `Jupyter <http://jupyter.org/>`__
 notebook that can be found
 `here <https://github.com/elfi-dev/notebooks>`__.

docs/usage/external.rst

@@ -1,4 +1,3 @@
-
 This tutorial is generated from a `Jupyter <http://jupyter.org/>`__
 notebook that can be found
 `here <https://github.com/elfi-dev/notebooks>`__.
@@ -128,7 +127,7 @@ population size:
 It is assumed that the susceptible population is infinite, the hosts
 carry only one mutation of the disease and transmit that mutation
 onward. A more accurate description of the model can be found from the
-original paper or e.g. `Lintusaari at al
+original paper or e.g. `Lintusaari at al
 2016 <https://doi.org/10.1093/sysbio/syw077>`__ *[1]*.
 
 .. For documentation
@@ -625,6 +624,6 @@ References
    Kaski, Jukka Corander; Fundamentals and Recent Developments in
    Approximate Bayesian Computation. Syst Biol 2017; 66 (1): e66-e82.
    doi: 10.1093/sysbio/syw077
--  [2] Tanaka, Mark M., et al. “Using approximate Bayesian computation
+-  [2] Tanaka, Mark M., et al. “Using approximate Bayesian computation
    to estimate tuberculosis transmission parameters from genotype data.”
    Genetics 173.3 (2006): 1511-1520.

docs/usage/parallelization.rst

@@ -1,4 +1,3 @@
-
 This tutorial is generated from a `Jupyter <http://jupyter.org/>`__
 notebook that can be found
 `here <https://github.com/elfi-dev/notebooks>`__.
@@ -301,3 +300,4 @@ Remember to stop the ipcluster when done
 
     2018-04-24 19:14:56.997 [IPClusterStop] Stopping cluster [pid=39639] with [signal=<Signals.SIGINT: 2>]
 
+

docs/usage/tutorial.rst

@@ -1,4 +1,3 @@
-
 This tutorial is generated from a `Jupyter <http://jupyter.org/>`__
 notebook that can be found
 `here <https://github.com/elfi-dev/notebooks>`__.
@@ -95,8 +94,8 @@ scalars as they are first converted to vectors.
 .. Important:: In order to guarantee a consistent state of pseudo-random number generation, the simulator must have `random_state` as a keyword argument for reading in a `numpy.RandomState` object.
 
 Let’s now use this simulator to create toy observations. We will use
-parameter values :math:`\theta_1=0.6, \theta_2=0.2` as in `Marin et al.
-(2012) <http://link.springer.com/article/10.1007/s11222-011-9288-2>`__
+parameter values :math:`\theta_1=0.6, \theta_2=0.2` as in `Marin et
+al. (2012) <http://link.springer.com/article/10.1007/s11222-011-9288-2>`__
 and then try to infer these parameter values back based on the toy
 observed data alone.
 
@@ -142,7 +141,7 @@ have been produced by similar parameters. Looking at the picture above,
 in essence we would keep simulating until we have found enough sequences
 that are similar to the observed sequence. Although the idea may appear
 inapplicable for the task at hand, you will soon see that it does work.
-For more information about ABC, please see e.g.
+For more information about ABC, please see e.g. 
 
 -  `Lintusaari, J., Gutmann, M. U., Dutta, R., Kaski, S., and Corander,
    J. (2016). Fundamentals and recent developments in approximate
@@ -261,8 +260,8 @@ Modifying the model
 -------------------
 
 Although the above definition is perfectly valid, let’s use the same
-priors as in `Marin et al.
-(2012) <http://link.springer.com/article/10.1007/s11222-011-9288-2>`__
+priors as in `Marin et
+al. (2012) <http://link.springer.com/article/10.1007/s11222-011-9288-2>`__
 that guarantee that the problem will be identifiable (loosely speaking,
 the likelihood willl have just one mode). Marin et al. used priors for
 which :math:`-2<\theta_1<2` with :math:`\theta_1+\theta_2>-1` and
@@ -532,12 +531,12 @@ applicable.
 Storing simulated data
 ----------------------
 
-As the samples are already in numpy arrays, you can just say e.g.
-``np.save('t1_data.npy', result.samples['t1'])`` to save them. However,
-ELFI provides some additional functionality. You may define a *pool* for
-storing all outputs of any node in the model (not just the accepted
-samples). Let’s save all outputs for ``t1``, ``t2``, ``S1`` and ``S2``
-in our model:
+As the samples are already in numpy arrays, you can just say
+e.g. ``np.save('t1_data.npy', result.samples['t1'])`` to save them.
+However, ELFI provides some additional functionality. You may define a
+*pool* for storing all outputs of any node in the model (not just the
+accepted samples). Let’s save all outputs for ``t1``, ``t2``, ``S1`` and
+``S2`` in our model:
 
 .. code:: ipython3
 
@@ -758,9 +757,9 @@ Often “pairwise relationships” are more informative:
 .. image:: http://research.cs.aalto.fi/pml/software/elfi/docs/0.6.2/usage/tutorial_files/tutorial_79_0.png
 
 
-Note that if working in a non-interactive environment, you can use e.g.
-``plt.savefig('pairs.png')`` after an ELFI plotting command to save the
-current figure to disk.
+Note that if working in a non-interactive environment, you can use
+e.g. ``plt.savefig('pairs.png')`` after an ELFI plotting command to save
+the current figure to disk.
 
 Sequential Monte Carlo ABC
 --------------------------
@@ -1013,3 +1012,4 @@ practices with ELFI.
 
 That’s it! See the other documentation for more advanced topics on
 e.g. BOLFI, external simulators and parallelization.
+

elfi/__init__.py

@@ -26,4 +26,4 @@
 __email__ = 'elfi-support@hiit.fi'
 
 # make sure __version_ is on the last non-empty line (read by setup.py)
-__version__ = '0.7.4'
+__version__ = '0.7.5'