Merge branch '0.6.2'

CHANGELOG.rst

@@ -1,5 +1,14 @@
 Changelog
-==========
+=========
+
+0.6.2 (2017-09-06)
+------------------
+
+- Easier saving and loading of ElfiModel
+- Renamed elfi.set_current_model to elfi.set_default_model
+- Renamed elfi.get_current_model to elfi.get_default_model
+- Improved performance when rerunning inference using stored data
+- Change SMC to use ModelPrior, use to immediately reject invalid proposals
 
 0.6.1 (2017-07-21)
 ------------------

CONTRIBUTING.rst

@@ -64,11 +64,11 @@ Ready to contribute? Here's how to set up `ELFI` for local development.
 
     $ git clone git@github.com:your_name_here/elfi.git
 
-3. Install your local copy and the development requirements into a virtualenv. Assuming you have virtualenvwrapper installed, this is how you set up your fork for local development. Due to a bug in the pip installation of GPy numpy needs to be installed manually.::
+3. Install your local copy and the development requirements into a conda environment::
 
-    $ mkvirtualenv elfi
-    $ cd elfi/
-    $ pip install numpy
+    $ conda create -n elfi python=3.5 numpy
+    $ source activate elfi
+    $ cd elfi
     $ make dev
 
 4. Create a branch for local development::
@@ -84,7 +84,7 @@ Ready to contribute? Here's how to set up `ELFI` for local development.
     $ make lint
     $ make test
 
-  Also make sure that the docstrings of your code are formatted properly::
+   Also make sure that the docstrings of your code are formatted properly::
 
     $ make docs
 
@@ -99,26 +99,20 @@ Ready to contribute? Here's how to set up `ELFI` for local development.
 Style Guidelines
 ----------------
 
-The projects follows the `Khan Academy Style Guide <https://github.com/Khan/style-guides/blob/master/style/python.md>`_. Except that we use numpy style docstrings instead of Google style docstrings.
+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.
 
-See `this example <http://sphinxcontrib-napoleon.readthedocs.io/en/latest/example_numpy.html>`_ for how to format the docstrings.
+Docstrings follow the `NumPy style <http://sphinxcontrib-napoleon.readthedocs.io/en/latest/example_numpy.html>`_.
 
-Additional Style Guidelines
-~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-- Use the ``.format()`` string method instead of the old percent operator. For more information see `PyFormat <https://pyformat.info/>`_.
-- Use the type hinting syntax suggested `here <https://www.jetbrains.com/help/pycharm/2016.1/type-hinting-in-pycharm.html>`_ in the docstrings.
-
 Pull Request Guidelines
 -----------------------
 
 Before you submit a pull request, check that it meets these guidelines:
 
-1. The pull request should include tests.
+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.
-3. The pull request should work for Python 2.7,  3.5 and later. Check
+3. The pull request should work for Python 3.5 and later. Check
    https://travis-ci.org/elfi-dev/elfi/pull_requests
    and make sure that the tests pass for all supported Python versions.
 

Makefile

@@ -69,7 +69,7 @@ docs: ## generate Sphinx HTML documentation, including API docs
 	$(MAKE) -C docs html
 	# $(BROWSER) docs/_build/html/index.html
 
-CONTENT_URL := http://research.cs.aalto.fi/pml/software/elfi/docs/0.6.1/
+CONTENT_URL := http://research.cs.aalto.fi/pml/software/elfi/docs/0.6.2/
 
 notebook-docs: ## Conver notebooks to rst docs. Assumes you have them in `notebooks` directory.
 	jupyter nbconvert --to rst ../notebooks/quickstart.ipynb --output-dir docs

README.md

@@ -1,4 +1,4 @@
-**Version 0.6.1 released!** See the CHANGELOG and [notebooks](https://github.com/elfi-dev/notebooks).
+**Version 0.6.2 released!** See the CHANGELOG and [notebooks](https://github.com/elfi-dev/notebooks).
 
 ELFI - Engine for Likelihood-Free Inference
 ===========================================

docs/api.rst

@@ -32,8 +32,9 @@ Below is the API for creating generative models.
 
 .. autosummary::
    elfi.new_model
-   elfi.get_current_model
-   elfi.set_current_model
+   elfi.load_model
+   elfi.get_default_model
+   elfi.set_default_model
 
 .. currentmodule:: elfi.visualization.visualization
 

docs/conf.py

@@ -13,33 +13,34 @@
 # All configuration values have a default; values that are commented out
 # serve to show the default.
 
-import sys
 import os
-
+import sys
 # http://docs.readthedocs.io/en/latest/faq.html#i-get-import-errors-on-libraries-that-depend-on-c-modules
 from unittest.mock import MagicMock
 
 
 class Mock(MagicMock):
     @classmethod
     def __getattr__(cls, name):
-            return MagicMock()
+        return MagicMock()
+
 
 on_RTD = os.environ.get('READTHEDOCS', None) == 'True'
 if on_RTD:
-    MOCK_MODULES = ['pygtk', 'gtk', 'gobject', 'argparse', 'numpy', 'pandas', 'scipy',
-                    'unqlite', 'dask', 'distributed', 'distributed.client', 'graphviz', 'matplotlib',
-                    'sobol_seq', 'GPy', 'dask.delayed', 'scipy.optimize', 'scipy.stats',
-                    'scipy.spatial', 'scipy.sparse', 'matplotlib.pyplot', 'numpy.random', 'networkx',
-                    'ipyparallel', 'numpy.lib', 'numpy.lib.format', 'sklearn.linear_model']
+    MOCK_MODULES = [
+        'pygtk', 'gtk', 'gobject', 'argparse', 'numpy', 'pandas', 'scipy', 'unqlite', 'dask',
+        'distributed', 'distributed.client', 'graphviz', 'matplotlib', 'sobol_seq', 'GPy',
+        'dask.delayed', 'scipy.optimize', 'scipy.stats', 'scipy.spatial', 'scipy.sparse',
+        'matplotlib.pyplot', 'numpy.random', 'networkx', 'ipyparallel', 'numpy.lib',
+        'numpy.lib.format', 'sklearn.linear_model'
+    ]
     sys.modules.update((mod_name, Mock()) for mod_name in MOCK_MODULES)
 
     html_theme = 'default'
 
 else:
     html_theme = 'sphinx_rtd_theme'
 
-
 # https://github.com/rtfd/readthedocs.org/issues/1139
 """
 def run_apidoc(_):
@@ -179,7 +180,6 @@ def setup(app):
 # If true, `todo` and `todoList` produce output, else they produce nothing.
 todo_include_todos = False
 
-
 # -- Options for HTML output ----------------------------------------------
 
 # The theme to use for HTML and HTML Help pages.  See the documentation for

docs/quickstart.rst

@@ -7,15 +7,15 @@ First ensure you have
 Python 3.5 (or greater) and ELFI. After installation you can start using
 ELFI:
 
-.. code:: python
+.. code:: ipython3
 
     import elfi
 
 ELFI includes an easy to use generative modeling syntax, where the
 generative model is specified as a directed acyclic graph (DAG). Let’s
 create two prior nodes:
 
-.. code:: python
+.. code:: ipython3
 
     mu = elfi.Prior('uniform', -2, 4)
     sigma = elfi.Prior('uniform', 1, 4)
@@ -30,7 +30,7 @@ summary statistics for the data. As an example, lets define the
 simulator as 30 draws from a Gaussian distribution with a given mean and
 standard deviation. Let's use mean and variance as our summaries:
 
-.. code:: python
+.. code:: ipython3
 
     import scipy.stats as ss
     import numpy as np
@@ -48,7 +48,7 @@ standard deviation. Let's use mean and variance as our summaries:
 Let’s now assume we have some observed data ``y0`` (here we just create
 some with the simulator):
 
-.. code:: python
+.. code:: ipython3
 
     # Set the generating parameters that we will try to infer
     mean0 = 1
@@ -73,7 +73,7 @@ Now we have all the components needed. Let’s complete our model by
 adding the simulator, the observed data, summaries and a distance to our
 model:
 
-.. code:: python
+.. code:: ipython3
 
     # Add the simulator node and observed data to the model
     sim = elfi.Simulator(simulator, mu, sigma, observed=y0)
@@ -89,15 +89,15 @@ model:
 If you have ``graphviz`` installed to your system, you can also
 visualize the model:
 
-.. code:: python
+.. code:: ipython3
 
     # Plot the complete model (requires graphviz)
     elfi.draw(d)
 
 
 
 
-.. image:: http://research.cs.aalto.fi/pml/software/elfi/docs/0.6.1/quickstart_files/quickstart_11_0.svg
+.. image:: http://research.cs.aalto.fi/pml/software/elfi/docs/0.6.2/quickstart_files/quickstart_11_0.svg
 
 
 
@@ -108,7 +108,7 @@ We can try to infer the true generating parameters ``mean0`` and
 Rejection sampling and sample 1000 samples from the approximate
 posterior using threshold value 0.5:
 
-.. code:: python
+.. code:: ipython3
 
     rej = elfi.Rejection(d, batch_size=10000, seed=30052017)
     res = rej.sample(1000, threshold=.5)
@@ -127,13 +127,13 @@ posterior using threshold value 0.5:
 
 Let's plot also the marginal distributions for the parameters:
 
-.. code:: python
+.. code:: ipython3
 
     import matplotlib.pyplot as plt
     res.plot_marginals()
     plt.show()
 
 
 
-.. image:: http://research.cs.aalto.fi/pml/software/elfi/docs/0.6.1/quickstart_files/quickstart_16_0.png
+.. image:: http://research.cs.aalto.fi/pml/software/elfi/docs/0.6.2/quickstart_files/quickstart_16_0.png