V088

.github/workflows/pytest.yml

@@ -15,7 +15,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        python-version: ['3.7', '3.8', '3.9', '3.10']
+        python-version: ['3.9', '3.10', '3.11', '3.12']
 
     steps:
     - uses: actions/checkout@v2

CHANGELOG.rst

@@ -1,6 +1,23 @@
 Changelog
 =========
 
+0.8.8 (2026-10-08)
+------------------
+- Enable `numpy >= 2.0` 
+- Add RobustGPyRegression model that can handle non-finite output values
+- Update surrogate model initialisation to use all initial evidence
+- Update BOLFI and BOLFIRE to use a shared sample class that returns individual chains in the arviz inference data
+- Use kernel copy to avoid pickle issue and allow BOLFI parallelisation with non-default kernel
+- Restrict matplotlib version < 3.9 for compatibility with GPy
+- Add option to use additive or multiplicative adjustment in any acquisition method
+- Add `arziv`-mocking to rtd-setup
+- Add convenience method for obtaining elfi samples as `InferenceData`` to be used with `arviz`
+- Improve `randmaxvar` batch acquisitions and initialisation by enabling sampling from prior
+- Drop official Python support for 3.7 and 3.8 as GPy is not officially supported for these versions
+- Enable using `maxiter` in `bo.utils.minimize`
+- Fix surrogate model copy operation
+- Fix typo in requirements.txt
+
 0.8.7 (2023-09-21)
 ------------------
 - Update available methods list

CONTRIBUTING.rst

@@ -72,16 +72,16 @@ Ready to contribute? Here's how to set up `ELFI` for local development.
 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. Currently supported Python versions
-   are 3.7, 3.8, 3.9, 3.10::
+   are 3.9, 3.10, 3.11, 3.12::
 
    $ conda -V
    $ python -V
 
 4. Install your local copy and the development requirements into a conda
-   environment. You may need to replace "3.7" in the first line with the python
+   environment. You may need to replace "3.9" in the first line with the python
    version printed in the previous step::
 
-    $ conda create -n elfi python=3.7 numpy
+    $ conda create -n elfi python=3.9 numpy
     $ source activate elfi
     $ cd elfi
     $ make dev
@@ -136,7 +136,7 @@ Before you submit a pull request, check that it meets these guidelines:
 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.md.
-3. The pull request should work for Python 3.7 and later. Check
+3. The pull request should work for Python 3.9 and later. Check
    https://github.com/elfi-dev/elfi/actions/workflows/pytest.yml
    and make sure that the tests pass for all supported Python versions.
 

README.md

@@ -1,4 +1,4 @@
-**Version 0.8.7 released!** See the [CHANGELOG](CHANGELOG.rst) and [notebooks](https://github.com/elfi-dev/notebooks).
+**Version 0.8.8 released!** See the [CHANGELOG](CHANGELOG.rst) and [notebooks](https://github.com/elfi-dev/notebooks).
 
 <img src="https://raw.githubusercontent.com/elfi-dev/elfi/dev/docs/logos/elfi_logo_text_nobg.png" width="200" />
 
@@ -44,7 +44,7 @@ is preferable.
 Installation with pip
 ---------------------
 
-ELFI requires Python 3.7 or greater. You can install ELFI by typing in your terminal:
+ELFI requires Python 3.9 or greater. You can install ELFI by typing in your terminal:
 
 ```
 pip install elfi
@@ -95,7 +95,7 @@ with your default Python environment and can easily use different versions of Py
 in different projects. You can create a virtual environment for ELFI using anaconda with:
 
 ```
-conda create -n elfi python=3.7 numpy
+conda create -n elfi python=3.9 numpy
 source activate elfi
 pip install elfi
 ```
@@ -125,7 +125,7 @@ Resolving these may sometimes go wrong:
 - If you receive an error about `yaml.load`, install `pyyaml`.
 - On OS X with Anaconda virtual environment say `conda install python.app` and then use
 `pythonw` instead of `python`.
-- Note that ELFI requires Python 3.7 or greater so try `pip3 install elfi`.
+- Note that ELFI requires Python 3.9 or greater so try `pip3 install elfi`.
 - Make sure your Python installation meets the versions listed in `requirements.txt`.
 
 

docs/api.rst

@@ -65,7 +65,7 @@ Below is a list of inference methods included in ELFI.
    OptimizationResult
    Sample
    SmcSample
-   BolfiSample
+   McmcSample
 
 
 **Post-processing**
@@ -244,7 +244,7 @@ Inference API classes
    :members:
    :inherited-members:
 
-.. autoclass:: BolfiSample
+.. autoclass:: McmcSample
    :members:
    :inherited-members:
 

docs/conf.py

@@ -23,6 +23,10 @@ class Mock(MagicMock):
     def __getattr__(cls, name):
         return MagicMock()
 
+extensions = [
+    'sphinx_rtd_theme',
+]
+
 on_RTD = os.environ.get('READTHEDOCS', None) == 'True'
 if on_RTD:
     MOCK_MODULES = [
@@ -33,7 +37,7 @@ def __getattr__(cls, name):
         'scipy.sparse', 'scipy.special', 'matplotlib.pyplot', 'numpy.random', 'networkx',
         'ipyparallel', 'numpy.lib', 'numpy.lib.format', 'sklearn.linear_model',
         'sklearn.pipeline', 'sklearn.preprocessing', 'numdifftools', 'GPy.kern', 'GPy.models',
-        'sklearn.covariance', 'sklearn.exceptions'
+        'sklearn.covariance', 'sklearn.exceptions', 'arviz'
     ]
     sys.modules.update((mod_name, Mock()) for mod_name in MOCK_MODULES)
 

docs/installation.rst

@@ -3,7 +3,7 @@
 Installation
 ============
 
-ELFI requires Python 3.7 or greater (see below how to install). To install ELFI, simply
+ELFI requires Python 3.9 or greater (see below how to install). To install ELFI, simply
 type in your terminal:
 
 .. code-block:: console
@@ -18,16 +18,16 @@ process.
 .. _Python installation guide: http://docs.python-guide.org/en/latest/starting/installation/
 
 
-Installing Python 3.7
+Installing Python 3.9
 ---------------------
 
 If you are new to Python, perhaps the simplest way to install it is with Anaconda_ that
-manages different Python versions. After installing Anaconda, you can create a Python 3.7.
+manages different Python versions. After installing Anaconda, you can create a Python 3.9.
 environment with ELFI:
 
 .. code-block:: console
 
-    conda create -n elfi python=3.7 numpy
+    conda create -n elfi python=3.9 numpy
     source activate elfi
     pip install elfi
 
@@ -51,7 +51,7 @@ Resolving these may sometimes go wrong:
 * If you receive an error about missing ``numpy``, please install it first.
 * If you receive an error about `yaml.load`, install ``pyyaml``.
 * On OS X with Anaconda virtual environment say `conda install python.app` and then use `pythonw` instead of `python`.
-* Note that ELFI requires Python 3.7 or greater
+* Note that ELFI requires Python 3.9 or greater
 * In some environments ``pip`` refers to Python 2.x, and you have to use ``pip3`` to use the Python 3.x version
 * Make sure your Python installation meets the versions listed in requirements_.
 

docs/quickstart.rst

@@ -3,7 +3,7 @@ Quickstart
 
 First ensure you have
 `installed <http://elfi.readthedocs.io/en/stable/installation.html>`__
-Python 3.7 (or greater) and ELFI. After installation you can start using
+Python 3.5 (or greater) and ELFI. After installation you can start using
 ELFI:
 
 .. code:: ipython3

docs/usage/BOLFI.rst

@@ -92,7 +92,7 @@ surface is updated after each batch (especially so if the noise is 0!).
 .. code:: ipython3
 
     bolfi = elfi.BOLFI(log_d, batch_size=1, initial_evidence=20, update_interval=10, 
-                       bounds={'t1':(-2, 2), 't2':(-1, 1)}, acq_noise_var=[0.1, 0.1], seed=seed)
+                       bounds={'t1':(-2, 2), 't2':(-1, 1)}, acq_noise_var=0.1, seed=seed)
 
 Sometimes you may have some samples readily available. You could then
 initialize the GP model with a dictionary of previous results by giving

docs/usage/external.rst

@@ -9,9 +9,9 @@ If your simulator or other operations are implemented in a programming
 language other than Python, you can still use ELFI. This notebook
 briefly demonstrates how to do this in three common scenarios:
 
--  External executable (written e.g. in C++ or a shell script)
--  R function
--  MATLAB function
+- External executable (written e.g. in C++ or a shell script)
+- R function
+- MATLAB function
 
 Let’s begin by importing some libraries that we will be using:
 
@@ -115,14 +115,14 @@ represents a “mutation” of the disease and the count describes how many
 are currently infected by that mutation. There are three rates and the
 population size:
 
--  :math:`\alpha` - (birth rate) the rate at which any infectious host
-   transmits the disease.
--  :math:`\delta` - (death rate) the rate at which any existing
-   infectious hosts either recovers or dies.
--  :math:`\tau` - (mutation rate) the rate at which any infectious host
-   develops a new unseen mutation of the disease within themselves.
--  :math:`N` - (population size) the size of the simulated infectious
-   population
+- :math:`\alpha` - (birth rate) the rate at which any infectious host
+  transmits the disease.
+- :math:`\delta` - (death rate) the rate at which any existing
+  infectious hosts either recovers or dies.
+- :math:`\tau` - (mutation rate) the rate at which any infectious host
+  develops a new unseen mutation of the disease within themselves.
+- :math:`N` - (population size) the size of the simulated infectious
+  population
 
 It is assumed that the susceptible population is infinite, the hosts
 carry only one mutation of the disease and transmit that mutation
@@ -620,10 +620,10 @@ insignificant.
 References
 ~~~~~~~~~~
 
--  [1] Jarno Lintusaari, Michael U. Gutmann, Ritabrata Dutta, Samuel
-   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
-   to estimate tuberculosis transmission parameters from genotype data.”
-   Genetics 173.3 (2006): 1511-1520.
+- [1] Jarno Lintusaari, Michael U. Gutmann, Ritabrata Dutta, Samuel
+  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 to
+  estimate tuberculosis transmission parameters from genotype data.”
+  Genetics 173.3 (2006): 1511-1520.

docs/usage/parallelization.rst

@@ -8,13 +8,13 @@ Parallelization
 Behind the scenes, ELFI can automatically parallelize the computational
 inference via different clients. Currently ELFI includes three clients:
 
--  ``elfi.clients.native`` (activated by default): does not parallelize
-   but makes it easy to test and debug your code.
--  ``elfi.clients.multiprocessing``: basic local parallelization using
-   Python’s built-in multiprocessing library
--  ``elfi.clients.ipyparallel``:
-   `ipyparallel <http://ipyparallel.readthedocs.io/>`__ based client
-   that can parallelize from multiple cores up to a distributed cluster.
+- ``elfi.clients.native`` (activated by default): does not parallelize
+  but makes it easy to test and debug your code.
+- ``elfi.clients.multiprocessing``: basic local parallelization using
+  Python’s built-in multiprocessing library
+- ``elfi.clients.ipyparallel``:
+  `ipyparallel <http://ipyparallel.readthedocs.io/>`__ based client that
+  can parallelize from multiple cores up to a distributed cluster.
 
 A client is activated by giving the name of the client to
 ``elfi.set_client``.
@@ -157,7 +157,7 @@ start a local cluster to the background using 4 CPU cores:
 
 .. code:: ipython3
 
-    !ipcluster start -n 4 --daemonize
+    !ipcluster start -n 4 --daemon
 
     # This is here just to ensure that ipcluster has enough time to start properly before continuing
     import time

docs/usage/tutorial.rst

@@ -143,16 +143,16 @@ 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. 
 
--  `Lintusaari, J., Gutmann, M. U., Dutta, R., Kaski, S., and Corander,
-   J. (2016). Fundamentals and recent developments in approximate
-   Bayesian computation. Systematic Biology, doi:
-   10.1093/sysbio/syw077. <http://sysbio.oxfordjournals.org/content/early/2016/09/07/sysbio.syw077.full.pdf>`__
+- `Lintusaari, J., Gutmann, M. U., Dutta, R., Kaski, S., and Corander,
+  J. (2016). Fundamentals and recent developments in approximate
+  Bayesian computation. Systematic Biology, doi:
+  10.1093/sysbio/syw077. <http://sysbio.oxfordjournals.org/content/early/2016/09/07/sysbio.syw077.full.pdf>`__
 
--  `Marin, J.-M., Pudlo, P., Robert, C. P., and Ryder, R. J. (2012).
-   Approximate Bayesian computational methods. Statistics and Computing,
-   22(6):1167–1180. <http://link.springer.com/article/10.1007/s11222-011-9288-2>`__
+- `Marin, J.-M., Pudlo, P., Robert, C. P., and Ryder, R. J. (2012).
+  Approximate Bayesian computational methods. Statistics and Computing,
+  22(6):1167–1180. <http://link.springer.com/article/10.1007/s11222-011-9288-2>`__
 
--  https://en.wikipedia.org/wiki/Approximate_Bayesian_computation
+- https://en.wikipedia.org/wiki/Approximate_Bayesian_computation
 
 Defining the model
 ------------------

elfi/__init__.py

@@ -26,10 +26,10 @@
 from elfi.visualization.visualization import nx_draw as draw
 from elfi.visualization.visualization import plot_params_vs_node
 from elfi.visualization.visualization import plot_predicted_summaries
-from elfi.methods.bo.gpy_regression import GPyRegression
+from elfi.methods.bo.gpy_regression import GPyRegression, RobustGPyRegression
 
 __author__ = 'ELFI authors'
 __email__ = 'elfi-support@hiit.fi'
 
 # make sure __version_ is on the last non-empty line (read by setup.py)
-__version__ = '0.8.7'
+__version__ = '0.8.8'

elfi/examples/bdm.py

@@ -25,10 +25,11 @@ def prepare_inputs(*inputs, **kwinputs):
     """
     alpha, delta, tau, N = inputs
     meta = kwinputs['meta']
-
     # Organize the parameters to an array. The broadcasting works nicely with constant
     # arguments.
-    param_array = np.row_stack(np.broadcast(alpha, delta, tau, N))
+    param_array = np.vstack(
+        [(a, d, t, N) for (a, d, t, N) in np.broadcast(alpha, delta, tau, N)]
+        )
 
     # Prepare a unique filename for parallel settings
     filename = '{model_name}_{batch_index}_{submission_index}.txt'.format(**meta)
@@ -37,7 +38,6 @@ def prepare_inputs(*inputs, **kwinputs):
     # Add the filenames to kwinputs
     kwinputs['filename'] = filename
     kwinputs['output_filename'] = filename[:-4] + '_out.txt'
-
     # Return new inputs that the command will receive
     return inputs, kwinputs
 
@@ -71,7 +71,7 @@ def process_result(completed_process, *inputs, **kwinputs):
 def T1(clusters):
     """Summary statistic for BDM."""
     clusters = np.atleast_2d(clusters)
-    return np.sum(clusters > 0, 1) / np.sum(clusters, 1)
+    return np.sum(clusters > 0, axis=1) / np.sum(clusters, axis=1)
 
 
 def T2(clusters, n=20):

elfi/examples/bignk.py

@@ -83,7 +83,7 @@ def BiGNK(A1, A2, B1, B2, g1, g2, k1, k2, rho, c=.8, n_obs=150, batch_size=1, ra
     z_batches = []
     for i in range(batch_size):
         # Initialising a separate covariance matrix for each batch.
-        matrix_cov = np.array([[1, float(rho[i])], [float(rho[i]), 1]])
+        matrix_cov = np.array([[1, rho[i, 0]], [rho[i, 0], 1]])
 
         z_batch = ss.multivariate_normal.rvs(cov=matrix_cov, size=n_obs, random_state=random_state)
         z_batches.append(z_batch)