CHANGELOG.rst

Changelog

• 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
• Pin numpy <= 1.24.0 due to GPy
• Update RTD configuration

0.8.6 (2023-03-07)

• Fix broken 0.8.5 by adding missing __init__.py to elfi/methods/bsl/

0.8.5 (2023-03-07)

• Fix the option to continue inference in model-based inference
• Move classifiers
• Fix readthedocs configuration
• Update penalty to shrinkage parameter conversion in synthetic likelihood calculation
• Update BSL pre sample methods
• Update BslSample
• Move ROMC tests
• Update to numpy 1.24
• Update readthedocs configuration
• Restrict numpy < 1.24 until codebase has been updated
• Update documentation-related files: docs, conf.py and requirements-dev.txt
• Update PULL_REQUEST_TEMPLATE.md
• Drop tests for py36 and add tests for py39 and py310
• Fix couple of minor bugs in ar1-model
• Update parent class in BOLFIRE
• Fix semiparametric synthetic likelihood with glasso/warton and add tests
• Fix plot marginals and remove plot summaries
• Fix stochastic volatility example
• Improve batch simulations in toad example
• Remove synthetic likelihood node and update BSL data collection
• Fix M/G/1 example
• Fix scratch assay example
• Add scratch assay example
• Add GP classifier for ratio estimation
• Fix multidimensional indexing in daycare example
• Add BSL method

0.8.4 (2022-06-13)

• Modify Lotka-Volterra model's priors as many methods do not support discrete random variables.
• Fix acquisition index in state plot
• Reformat summary() for Sample(ParameterInferenceResult)
• Fix linting in arch.py
• Add summary statistics to Lotka-Volterra model
• Add boolean observation_noise option to lotka_volterra
• Add parameter names as an optional input in model prior and fix the parameter order in priors used in BOLFI and BOLFIRE
• Add feature names as an optional input and make training data size a required input in BOLFIRE
• Fix the observed property in simulator nodes
• Fix default outputs in generate
• Add docstring description to ARCH-model
• Make MAP estimates calculation in BOLFIRE optional and based on log-posterior
• Use batch system to run simulations in BOLFIRE
• Use target_model.parameter_names from instead of model.parameter_names in BOLFIRE
• Extract BO results using target_model.parameter_names from instead of model.parameter_names
• Update tox.ini
• Add option to use additive acquisition cost in LCBSC
• Change sigma_proposals-input in metropolis from list to dict
• Fix is_array in utils
• Fix acq_noise_var-bug in acquisition.py. Influenced BOLFI.

0.8.3 (2022-02-17)

• Add a new inference method: BOLFIRE
• Fix the hessian approximation, visualizations and the line search algorithm in ROMC
• Add tests for all ROMC parts

0.8.2 (2021-10-13)

• Relax tightly pinned dependency on a version of dask[distributed]
• Change lotka-volterra priors to follow the given reference
• Fix README.md badges
• Fix a few small issues with CONTRIBUTING.rst
• Add Github Actions based CI workflow
• Add the skeleton of TestBench-functionality for comparing methods
• Fix a bug of plot_traces() not working if there is only 1 chain
• Fix histograms in pair_plot diagonals and improve visual outlook
• Improve axes creation and visual outlook
• Fix a bug where precomputed evidence size was not taken into account when reporting BOLFI-results
• Fix a bug where observable nodes were not colored gray when using elfi.draw
• Add plot_predicted_node_pairs in visualization.py.

0.8.0 (2021-03-29)

• Merge adaptive distance ABC-SMC and ABC-SMC functionalities
• Split DensityRatioEstimation from utils.py into separate file
• Refactor parameter_inferency.py into methodtype-wise individual files
• Rename elfi.methods.mcmc.gelman_rubin as elfi.methods.mcmc.gelman_rubin_statistic
• Refactor class ModelPrior from methods.utils to model.extensions.
• Add adaptive threshold selection method for ABC-SMC
• Modify ProgressBar-functionality
• Add constrains to ExpIntVar-acquisition so that no queries will be outside prior support
• Add ABC-SMC with adaptive distance
• Add Robust optimisation Monte Carlo method
• Fix small issues in ABC-SMC which did not work in 1-dimensional problems or with output names
• Update README.md

0.7.7 (2020-10-12)

• Update info to reflect setting python 3.6 as the default version
• Update documentation to setting python 3.6 as default
• Add dask support to elfi client options
• Add python 3.7 to travis tests and remove python 3.5 due to clash with dask
• Modify progress bar to better indicate ABC-SMC inference status
• Change networkx support from 1.X to 2.X
• Improve docstrings in elfi.methods.bo.acquisition
• Fix readthedocs-build by adding .readthedocs.yml and restricting the build to python3.5, for now

0.7.6 (2020-08-29)

• Fix incompatibility with scipy>1.5 in bo.utils.stochastic_optimization
• Minor improvements to documentation

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 metropolis sampler algorithm=metropolis for bolfi-posterior-sampling
• Add option warmup to metropolis-sampler
• Add a small test of metropolis-sampler
• Fix bug in plot_discrepancy for more than 6 parameters
• Implement plot_gp for BayesianOptimization classes for plotting discrepancies and pair-wise contours in case when we have arbitrary number of parameters
• Fix lint

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 crashing summary and plots for samples with multivariate priors
• Add progress bar for inference methods
• Add method save to Sample objects
• Add support for giving seed to generate
• Implement elfi.plot_params_vs_node for plotting parameters vs. node output

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: Lorenz model

0.7.1 (2018-04-11)

• Implemented model selection (elfi.compare_models). See API documentation.
• Fix threshold=0 in rejection sampling
• Set default batch_size to 1 in ParameterInference base class

0.7 (2017-11-30)

• 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
• 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
• 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
• Added the general Gaussian noise example model (fixed covariance)
• restrict NetworkX to versions < 2.0

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)

• Fix elfi.Prior and NoneType error #203
• Fix a bug preventing the reuse of ArrayPool data with a new inference
• Added pickling for OutputPool:s
• 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
• 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
• 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 output_names.
• methods.py renamed to parameter_inference.py
• Changes in elfi.methods.results module class names: - OptimizationResult (a new result type) - Result -> Sample - ResultSMC -> SmcSample - ResultBOLFI -> BolfiSample
• Changes in BO/BOLFI: - take advantage of priors - take advantage of seed - improved optimization scheme - bounds must be a dict
• two new toy examples added: Gaussian and the Ricker model

0.5 (2017-05-19)

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)
• methods can now be run iteratively
• persistence to .npy files
• Bayesian optimization as a separate method
• sampling in BOLFI
• MCMC sampling using the No-U-Turn-Sampler (NUTS)
• Result object for BOLFI
• virtual vectorization of external operations

See the updated notebooks and documentation for examples and details.

0.3.1 (2017-01-31)

• Clean up requirements
• Set graphviz and unqlite optional
• PyPI release (pip install elfi)

0.2.2 - 0.3

• The inference problem is now contained in an Inference Task object.
• SMC-ABC has been reimplemented.
• Results from inference are now contained in a Result object.
• Integrated basic visualization.
• Added a notebook demonstrating usage with external simulators and operations.
• Lot's of refactoring and other minor changes.