Releases: ICB-DCM/pyPESTO
Releases · ICB-DCM/pyPESTO
pyPESTO 0.2.8
-
PEtab:
- Use correct measurement column name in
rdatas_to_simulation_df
(#721)
- Visualize optimized model fit via PEtab problem (#725)
- Un-ignore observable scaling tests (#742)
- New function to plot model trajectory with custom time points (#739)
-
Optimization:
- OOD Refactor startpoint generation (#732)
- Update to fides 0.6.0 (#733)
- Correctly report FVAL vs CHI2 values in fides (#741)
-
Ensemble:
- Option for using weighted ensemble means (#702)
- Default names and bounds for
Ensemble.from_sample
(#730)
-
Storage:
- Load optimization result from HDF5 history (#726)
-
General:
- Enable use of priors with least squares optimizers (#745)
- Add temporary CITATION.cff file (#734)
- Regular scheduled CI runs (#754)
- Allow to not copy objective in problem (#756)
-
Fixes:
- Fix non-exported visualization in notebook (#729)
- Mark some more tests as flaky (#704)
- Fix minor data type and OOD issues in parameter and waterfall plots
(#731)
pyPESTO 0.2.7
-
Finite Differences:
- Adaptive finite differences (#671)
- Add helper function for checking gradients of objectives (#690)
- Small bug fixes (#711, #714)
-
Storage:
- Store representation of the objective (#669)
- Minor fixes in HDF5 history (#679)
- HDF5 reader for ensemble predictions (#681)
- Update storage demo jupyter notebook (#699)
- Option to trim trace to be monotonically decreasing (#705)
-
General:
- Improved tests and bug fixes of validation intervals (#676, #685)
- Add input file validation via PEtab linter for PEtab import (#678)
- Remove default values from docstring (#680)
- Minor fixes/improvements of ensembles (#687, #688)
- Fix sorting of optimization values including
NaN
values (#691)
- Specify axis limits for plotting (#693)
- Minor fixes in visualization (#696)
- Add installation option
all_optimizers
(#695)
- Improve installation documentation (#689)
- Update
pysb
and BNG
version on GitHub Actions (#697)
- Bug fix in steady state guesses (#715)
pyPESTO 0.2.6
-
Objective:
- Basic finite differences (#666)
- Fix factor 2 in res/fval values (#619)
-
Optimization:
- Sort optimization results when appending (#668)
- Read optimizer result from HDF5 (previously only CSV) (#663)
-
Storage:
- Load ensemble from HDF5 (#640)
-
CI:
- Add flake8 checks as pre-commit hook (#662)
- Add efficient biological conversion reaction test model (#619)
-
General:
- No automatic import of the predict module (#657)
- Assert unique problem parameter names (#665)
- Load ensemble from optimization result with and without history usage
(#640)
- Calculate validation profile significance (#658)
- Set pypesto screen logger to "INFO" by default (#667)
-
Minor fixes:
- Fix axis variable overwriting in
visualize.sampling_parameter_traces
(#665)
pyPESTO 0.2.5
-
Objectives:
-
Sampling:
- New Emcee sampler (#606)
- Fix compatibility to new Theano version (#650)
-
Storage:
- Improve hdf5 storage documentation (#612)
- Hdf5 history for MultiProcessEngine (#650)
- Minor fixes (#637, #638, #645, #649)
-
Visualization:
- Fix bounds of parameter plots (#601)
- Fix waterfall plots with multiple results (#611)
-
CI:
- Move CI tests on GitHub Actions to python 3.9 (#598)
- Add issue template (#604)
- Update BionetGen Link (#630)
- Introduce project.toml (#634)
-
General:
- Introduce progress bar for optimization, profiles and ensembles (#641)
- Extend gradient checking functionality (#644)
-
Minor fixes:
- Fix installation of ipopt (#599)
- Fix Zenodo link (#601)
- Fix duplicates in documentation (#603)
- Fix least squares optimizers (#617 #631 #632)
- Fix trust region options (#616)
- Fix slicing for new AMICI release (#621)
- Refactor and document latin hypercube sampling (#647)
- Fix missing SBML name in PEtab import (#648)
pyPESTO 0.2.4
-
Ensembles/Sampling:
- General ensemble analysis, visualization, storage (#557, #565, #568)
- Calculate and plot MCMC parameter and prediction CIs via ensemble
definition, parallelize ensemble predictions (#490)
-
Optimization:
- New optimizer: SciPy Differential Evolution (#543)
- Set fides default to hybrid (#578)
-
AMICI:
- Make
guess_steadystate
less restrictive (#561) and have a more
intuitive default behavior (#562, #582)
- Customize time points (#490)
-
Storage:
- Save HDF5 history with SingleCoreEngine (#564)
- Add read/write function for whole results (#589)
-
Engines:
- MPI based distributed parallelization (#542)
-
Visualization:
- Speed up waterfall plots by resizing scales only once (#577)
- Change waterfall default offset to 1 - minimum (#593)
-
CI:
- Move GHA CI tests to pull request level for better cooperability (#574)
- Streamline test environments using tox and pre-commit hooks (#579)
- Test profile and sampling storage (#585)
- Update for Ubuntu 20.04, add rerun on failure (#587)
-
Minor fixes (release notes #558, nlop tests #559, close files #495,
visualization #554, deployment #560, flakiness #570,
aggregated deepcopy #572, respect user-provided offsets #576,
update to SWIG 4 #591, check overwrite in profile writing #566)
pyPESTO 0.2.3
-
New Optimizers:
-
Extended PEtab support:
- PySB import (#437)
- Support of PEtab's initializationPriors (#535)
- Support of prior parameterScale{Normal,Laplace} (#520)
- Example notebook for synthetic data generation (#482)
-
General new and improved functionality:
- Predictions (#544)
- Move tests to GitHub Actions (#524)
- Parallelize profile calculation (#532)
- Save
x_guesses
in pypesto.problem
(#494)
- Improved finite difference gradients (#464)
- Support of unconstrained optimization (#519)
- Additional NaN check for fval, grad and hessian (#521)
- Add sanity checks for optimizer bounds (#516)
-
Improvements in storage:
- Fix hdf5 export of optimizer history (#536)
- Fix reading
x_names
from hdf5 history (#528)
- Storage does not save empty arrays (#489)
- hdf5 storage sampling (#546)
- hdf5 storage parameter profiles (#546)
-
Improvements in the visualization routines:
- Plot parameter values as histogram (#485)
- Fix y axis limits in waterfall plots (#503)
- Fix color scheme in visualization (#498)
- Improved visualization of optimization results (#486)
-
Several small bug fixes (#547, #541, #538, #533, #512, #508)
pyPESTO 0.2.2
- New optimizer: CMA-ES (#457)
- New plot: Optimizer convergence summary (#446)
- Fixes in visualization:
- Type checks for reference points (#460)
- y_limits in waterfall plots with multiple results (#475)
- Support of new amici release (#469)
- Multiple fixes in optimization code:
- Remove unused argument for dlib optimizer (#466)
- Add check for installation of ipopt (#470)
- Add maxiter as default option of dlib (#474)
- Numpy based subindexing in amici_util (#462)
- Check amici/PEtab installation (#477)
pyPESTO 0.2.1
- Example Notebook for prior functionality (#438)
- Changed parameter indexing in profiling routines (#419)
- Basic sanity checking for parameter fixing (#420)
- Bug fixes in:
- Displaying of multi start optimization (#430)
- AMICI error output (#428)
- Axes scaling/limits in waterfall plots (#441)
- Priors (PEtab impor, error handling) (#448, #452, #454)
- Improved sampling diagnostics (e.g. effective samples size) (#426)
- Improvements and bug fixes in parameter plots (#425)
pyPESTO 0.2.0
Major
- Modularize import, to import optimization, sampling and profiling separately (#413)
Minor
- Bug fixes in
- sampling (#412)
- visualization (#405)
- PEtab import (#403)
- Hessian computation (#390)
- Improve hdf5 error output (#409)
- Outlaw large new files in GitHub commits (#388)
pyPESTO 0.1.0
Objective
- Write solver settings to stream to enable serialization for distributed
systems (#308)
- Refactor objective function (#347)
- Removes necessity for all of the nasty binding/undbinding in AmiciObjective
- Substantially reduces the complexity of the AggregatedObjective class
- Aggregation of functions with inconsistent sensi_order/mode support
- Introduce ObjectiveBase as an abstract Objective class
- Introduce FunctionObjective for objectives from functions
- Implement priors with gradients, integrate with PEtab (#357)
- Fix minus sign in AmiciObjective.get_error_output (#361)
- Implement a prior class, derivatives for standard models, interface with
PEtab (#357)
- Use
amici.import_model_module
to resolve module loading failure (#384)
Problem
- Tidy up problem vectors using properties (#393)
Optimization
- Interface IpOpt optimizer (#373)
Profiles
- Tidy up profiles (#356)
- Refactor profiles; add locally approximated profiles (#369)
- Fix profiling and visualization with fixed parameters (#393)
Sampling
- Geweke test for sampling convergence (#339)
- Implement basic Pymc3 sampler (#351)
- Make theano for pymc3 an optional dependency (allows using pypesto without
pymc3) (#356)
- Progress bar for MCMC sampling (#366)
- Fix Geweke test crash for small sample sizes (#376)
- In parallel tempering, allow to only temperate the likelihood, not the prior
(#396)
History and storage
- Allow storing results in a pre-filled hdf5 file (#290)
- Various fixes of the history (reduced vs. full parameters, read-in from file,
chi2 values) (#315)
- Fix proper dimensions in result for failed start (#317)
- Create required directories before creating hdf5 file (#326)
- Improve storage and docs documentation (#328)
- Fix storing x_free_indices in hdf5 result (#334)
- Fix problem hdf5 return format (#336)
- Implement partial trace extraction, simplify History API (#337)
- Save really all attributes of a Problem to hdf5 (#342)
Visualization
- Customizable xLabels and tight layout for profile plots (#331)
- Fix non-positive bottom ylim on a log-scale axis in waterfall plots (#348)
- Fix "palette list has the wrong number of colors" in sampling plots (#372)
- Allow to plot multiple profiles from one result (#399)
Logging
- Allow easier specification of only logging for submodules (#398)
Tests
- Speed up travis build (#329)
- Update travis test system to latest ubuntu and python 3.8 (#330)
- Additional code quality checks, minor simplifications (#395)