CHANGELOG.rst

Release notes

0.5 series

0.5.4 (2024-10-19)

• Breaking Changes

  • Remove Aesara support (#1453, #1455)

• General

  • CI improvements (#1436, #1437, #1438, #1439, #1440, #1443, #1473, #1484, #1486, #1490, #1485)
  • Update references/documentation (#1404, #1456, #1474, #1479, #1483, #1470, #1498)

• Profile

  • Improve Profiling Code (#1447)

• Visualize

  • allow log and/or linear scale for visualization (#1435)
  • More informative error message for start indices. (#1472)

• Optimization

  • SacessOptimizer: Fix acceptance threshold for objective improvement (#1457)
  • SacessOptimizer: expose more hyperparameters + minor fixes (#1459, #1476)
  • SacessOptimizer, ESSOptimizer: Bound-normalize parameters for proximity check (#1462)
  • ESSOptimizer: Fix bug in recombination and go-beyond (#1477, #1480)

• Objective

  • FD-objective correctly working with fixed parameters (#1446)
  • Petab Importer reforge (#1442, #1502)
  • Use cloudpickle for serializing NegLogParameterPriors (#1467)
  • Update PEtab.jl integration to match version 3.X (#1489)

• Sampling

  • Bayes Factor Tutorial (#1444)

• Ensemble

  • Added HPD calculation to ensemble (#1431)

0.5.3 (2024-08-01)

• General

  • Notebook on history usage and comparison of multiple results. (#1389)
  • GHA/test improvements (#1423, #1408, #1430)
  • Numpy 2.0 compatibility (#1420, #1433)

• PEtab

  • Issue a warning if a fixed parameter has a prior defined (#1413)
  • Update to libpetab 0.4.0 (#1422)

• Optimize

  • Added a Result object with lazy hdf5 loading (#1421)

• RoadRunner

  • Roadrunner handling of petab issue 0019 (#1419)
  • Disentangle amici and roadrunner (#1429)

• Amici

  • Require return_dict in ObjectiveBase.call_unprocessed (fixes AMICI posterior RData) (#1424)

• Hierarchical

  • Visualize: visualization of estimated observable mapping (#1409)
  • Hierarchical: avoid recomputing inner parameters if simulation failed (#1426)

• Visualization

  • Fixing Aggregated Objective Visualisations (#1411)

0.5.2 (2024-05-27)

• New Feature: Variational inference with PyMC (#1306)

• PEtab

  • Import of petab independent of amici (#1355)

• Problem

  • Added option to sample startpoints for a problem, from the problem directly. (#1364)
  • More detailed defaults for problem.get_full_vector (#1393)
  • Save pypesto and python version to the problem. (#1382)

• Objective

  • Fix calling priors in sampling with fixed parameters (#1378)
  • Fix JaxObjective (#1400)

• Optimize

  • ESS optimizers: suppress divide-by-zero warnings; report n_eval (#1380)
  • SacessOptimizer: collect worker stats (#1381)
  • Add load method to Hdf5AmiciHistory (#1370)

• Hierarchical

  • Relative: fix log of zero for default 0 sigma values (#1377)

• Sample

  • Fix pypesto.sample.geweke_test.spectrum for nfft<=3 (#1388)

• Visualize

  • Handle correlation plot with nans (#1365)

• General

  • Remove scipy requirement from pypesto[pymc] (#1376)
  • Require and test python >=3.10 according to NEP 29 (#1379)
  • Fix various warnings (#1384)
  • Small changes to GHA actions and tests (#1386, #1387, #1402, #1385)
  • Improve Documentation (#1394, #1391, #1399, #1292, #1390)

0.5.0 (2024-04-10)

• General

  • Include pymc in the documentation. (#1305)
  • Ruff Codechecks (#1307)
  • Support RoadRunner as simulator for PEtab problems (#1336, #1347, #1348, #1363)

• Hierarchical

  • Semiquant: Fix spline knot initialization (#1313, #1323)
  • Semiquant: Add spline knots to the optimization result (#1314)
  • Semiquant: fix inner opt tolerance (#1330)
  • Relative: Fix return of relative calculator if sim fails (#1315)
  • Relative: Hierarchical optimization: fix unnecessary simulation (#1327)
  • Relative: Fix return of inner parameters on objective call (#1333)

• Optimize

  • Support ipopt with gradient approximation (#1310)
  • Deprecate CmaesOptimizer in favor of CmaOptimizer (#1311)
  • ESSOptimizer: Respect local_n2 in case of failed initial local search (#1328)
  • Remove CESSOptimizer (#1320)
  • SacessOptimizer: use 'spawn' start method for multiprocessing (#1353)

• PEtab

  • Fix unwanted amici model recompilation in PEtab importer (#1319)

• Sample

  • Adding Thermodynamic Integration (#1326, #1361)
  • Dynesty warnings added (#1324)
  • Dynesty: method to save raw results (#1331)

• Ensembles

  • Ensembles: don't expect OptimizerResult.id to be convertible to int (#1351)

• Misc

  • Updated Code to match dependency updates (#1316, #1344, #1346, #1345)
  • Ignore code formatting in git blame (#1317)
  • Updated deployment method (#1341, #1371, #1373)
  • add pyupgrade to codechecks (#1352)
  • Temporarily require scipy<1.13.0 for pypesto[pymc] (#1360)

0.4 series

0.4.2 (2024-01-30)

• General

  • Stabilize tests (#1240, #1254, #1300, #1302, #1303)
  • Update type annotations and documentations (#1239, #1248, #1255, #1258, #1251, #1268, #1275)
  • GHA/Codeowner changes (#1260, #1261, #1259, #1262, #1285)
  • Update utility functions (#1243)
  • Refactor progress bars (#1272)
  • Clear Notebook output(#1246, #1277, #1274, #1271, #1276, #1278)

• Optimize

  • (Sac)ESSOptimizer: History of best objective values (#1212)
  • Fix missing fixed parameters in scatter search results (#1265)
  • Fix TypeError in pypesto.result.optimize.OptimizerResult.summary if x0 is None (#1266)
  • ESSOptimizer: Include results for local searches in OptimizeResult (#1270)

• New Feature: Spline Approximation (#1222)

• Select

  • Allow for hierarchical problems (#1241)
  • custom minimize method (#1264)
  • Set estimated parameters in petab_select.Models (#1287)

• Hierarchical

  • Log space startpoint sampling (#1242)
  • Support for box constraints on offset and scaling parameters (#1238)
  • restructuring and add relative to InnerCalculatorCollector (#1245)
  • Semiquantitative: Robust regularization calculation (#1297)

• History

  • Support pathlib.Path for result/history files (#1247)
  • Extended Amici history (#1263)

• Visualize

  • Fix time trajectories for hierarchical problems (#1213)
  • Fix hierarchical parameter plotting for all optimizers (#1244)
  • Sacess history plot (#1250)

• Objective

  • Fix PEtab.jl version to before 2.5.0 (temporarily) (#1256)

• PEtab

  • Enable Importer passing verbose to create_model (#1269)
  • PetabImporter: version-specific amici model directories (#1283)

• Problem

  • Problem: add inner problem names, bounds and hierarchical flag (#1282)
  • Use warnings.warn instead of logging.warn when loading Problem from HDF5 without an Objective (#1253)

• Ensemble

  • EnsemblePrediction: remove "no predictor" warning (#1293)

0.4.1 (2023-12-05)

• General

  • Documentation (#1214, #1227, #1223, #1230, #1229)
  • Update code to avoid deprecations and warnings (#1217, #1219)
  • Updated codeownership (#1232, #1233)
  • Update Citation (#1221)
  • Improved Testing (#1218, #1216, #1231)

• History:

  • Enable converting MemoryHistory to Hdf5History (#1211)

• Profile:

  • Code simplification and other clean up (#1225)
  • Fix incorrect indexing in pypesto.profile.profile_next_guess.get_reg_polynomial (#1226)

• Optimize

  • Warnings for scipy together with laplace prior (#1228)

• Visualization:

  • Skip the history trace, if trace is empty. Occurs for infinite initial values. (#1234)

• Ensemble

  • Fix Ensemble.from_optimization_endpoints (#1237)

0.4.0 (2023-11-22)

• General

  • Documentation (#1140, #1146, #1152, #1149, #1192)
  • Updated Jupyter Notebooks (#1141)
  • Update code to avoid deprecations/warnings (#1158, #1184)
  • Updated maintainers and codeownership (#1171, #1170)
  • Improve tests and GHA (#1178, #1185, #1188, #1190, #1193, #1199, #1198, #1197, #1208)

• Profile:

  • Fix problem overwrite of profiling (#1153)
  • Add warning, trying to profile fixed parameter (#1155)
  • ProfileOptions: add some basic integrity checking (#1163)
  • Fix pypesto.profile.parameter_profile incorrectly assuming symmetric bounds (#1166)
  • Improve pypesto/profile/profile_next_guess.py (#1167)
  • Parameter profile: retry optimization in case of failure (#1168)
  • Fix incorrect types in pypesto.result.profile.ProfilerResult (#1210)

• Problem:

  • Add/forward startpoint_kwargs in PetabImporter.create_problem (#1135)
  • Support valid AMICI noise distributions that are invalid in PEtab (#1157)
  • Fix startpoint sampling for PEtab-derived problems with fixed parameters (#1169)

• Optimize

  • Log traceback in case of exceptions during optimizations (#1156)
  • Saccess optimizer improvements (#1177, #1187, #1194, #1195, #1201, #1202, #1204)
  • ESS optimizer improvements (#1176, #1181, #1182)
  • Fix check for allow_failed_starts (#1180)
  • Handle message and exitflag in histories (#1203)
  • Fix indexing error for 0-dimensional HDF5 datasets (#1206)

• Hierarchical:

  • Fix HierarchicalAmiciCalculator.__call__ not setting 'hess' in result (#1161)

• Visualization:

  • Fix legend argument checking for waterfall/parameter/history plots (#1139)
  • Fix waterfall start indices for multiple results (#1200)

0.3 series

0.3.3 (2023-10-19)

• Visualize:

  • Get optimization result by id (#1116)

• Storage:

  • allow "{id}" in history storage filename (#1118)

• Objective:

  • adjusted PEtab.jl syntax to new release (#1128, #1131)
  • Documentation on PEtab importer updated (#1126)

• Ensembles

  • Additional option for cutoff calculation (#1124)
  • Ensembles from optimization endpoints now only takes free parameters (#1130)

• General

  • Added How to Cite (#1125)
  • Additional summary option (#1134)
  • Speed up base tests (#1127)

0.3.2 (2023-10-02)

• Visualize:

  • Restrict fval magnitude in waterfall with order_by_id (#1090)
  • Hierarchical parameter plot fix (#1106)
  • Fix y-limits on waterfall (#1109)

• Sampling:

  • Use cloudpickle for pickling dynesty sampler (#1094)

• Optimize

  • Small fix on hierarchical initialise (#1095)
  • Fix startpoint sampling for hierarchical optimization (#1105)
  • SacessOptimizer: retry reading, delay deleting (#1110)
  • SacessOptimizer: Fix logging with multiprocessing (#1112)
  • SacessOptimizer: tmpdir option (#1115)

• Storage:

  • fix storage (#1099)

• Examples

  • Notebook on differences (#1098)

• Problem

  • Add startpoint_method to Problem (#1093)

• General

  • Added new entry to bib (#1100)
  • PetabJL integration (#1089)
  • Other platform tests (#1113)
  • Dokumentation fixes (#1120)
  • Updated CODEOWNER (#1123)

0.3.1 (2023-06-22)

• Visualize:

  • Parameter plot w/ hier. pars, noise estimation for splines (#1061)

• Sampling:

  • AdaptiveMetropolis failure fix for bounded priors (#1065)

• Ensembles

  • Speed up Ensemble from History (#1063)

• PEtab support:

  • Support for petab 0.2.x (#1073)
  • Remove PetabImporterPysb #1082)

• Objective

  • AggregatedObjective: objective-specific kwargs for call_unprocessed (#1068)

• Select

  • Use predecessor stored in file (#1059)
  • support petab-select version 0.1.8 (#1070)

• Examples

  • Synthetic data: update for libpetab-python v0.2.0 (#1060)
  • Fix error in sampling_diagnostics which led to test failure(#1092)

• General

  • Test fixes (#1064)
  • Fix numpy DeprecationWarnings (#1076)
  • GHA: Fix deprecation warnings (#1075)
  • Fixed bug on existing file and no overwrite (#1046)
  • Fix error in bound checking (#1081)

0.3.0 (2023-05-02)

New functionalities compared to 0.2.0:

• New supported data types for parameter estimation:

  • ordinal data
  • censored data
  • unbounded parameter optimization

• New optimization approaches:

  • Hierarchical optimization
  • Spline approximation

• New optimizers: CMA-ES, Enhanced Scatter Search, Fides, NLopt, SACESS, SciPy Differential Evolution
• New samplers: Emcee, Dynesty, Pymc v4
• New Objectives: Aesara objective, Julia objective, Jax objective
• Ensemble analysis
• Model selection
• Predictions
• Hdf5 Storage

Not supported functionalities and versions compared to 0.2.0:

• Removed Python 3.8 and older support
• Pymc (v3)
• Removed Theano objective
• Changed parameter indexing from boolean to int in profiling routines

0.2 series

0.2.17 (2023-05-02)

• Optimize:

  • Parameter estimation from ordinal data (#971)
  • Parameter estimation from nonlinear-monotone data using spline approximation (#1028)
  • Parameter estimation using censored data (#1041)
  • Fix optimizer start point handling. (#1027)
  • Add option to summary to print full or reduced vectors. (#1040, #1045)

• Sampling:

  • Dynesty sampler parallelization: changed the nested loglikelihood function to a class method (#1037)
  • Dynesty sampler docs (#1039)

• Engine

  • Allow custom multiprocessing context (#1032)

• General

  • Updated example notebooks (#1050, #1026, #1051, #1056)
  • Refactor docs (#1052)
  • Update Dockerfile (#1034)
  • proper bound handling for x_guesses (#1029)
  • Updated to flake8 standards (#1042, #1049)
  • Removed Python 3.8 support according to NEP29 (#1056)

0.2.16 (2023-02-23)

• Optimize:

  • sacess optimizer (#988, #997)
  • Warn only once if using ineffiecient objective settings (#996)
  • Hierarchical Optimization (#1006)
  • Fix cma documentation (#987)

• Petab

  • Improvement to create_startpoint_method() (#1018)

• Sampling:

  • Dynesty sampler (#1002)
  • Fix test/sample/test_sample.py::test_samples_cis failures (#1004)

• Visualization:

  • Fix misuse of start indices in waterfall plot (#1000)
  • Fix large function values in clustering for visualizations (#999)
  • parameter correlation diverging color scheme (#1009)
  • Optimization Parameter scatter plot (#1015)

• Profiling:

  • added option to profile the whole parameter bounds. (#1014)

• General

  • Add CODEOWNERS (#1001)
  • Add list of publications using pypesto (#1008)
  • allow passing results to __init__ of pypesto.Result (#998)
  • Updated flake8 to ignore Error B028 from bugbear until support for python 3.8 runs out. (#1005)
  • black update (#1010)
  • Doc typo fixes (#995)
  • Doc: Install amici on RTD (#1016)
  • Add getting_started notebook (#1023)
  • remove alernative formats build (#1022)

0.2.15 (2022-12-21)

• Optimize:

  • Add an Enhanced Scatter Search optimizer (#941, #972)
  • Cooperative enhanced scatter search (#954)
  • Hierarchical optimization (#952, #975 )
  • Allow scipy optimizer to use fun with integrated grad (#979)

• Sampling:

  • Remove fixed parameters from pymc sampling (#951)
  • emcee sampler: initialize walkers near optimum (#961)
  • dynesty Sampler (#963)
  • Fix pymc>=5 aesara/pytensor issues (#983)

• Visualization:

  • Multi-result waterfall plot (#966)
  • Model fit visualization: use problem.objective to simulate, instead of AMICI directly (#969)
  • Unfix matplotlib version (#977)
  • Plot measurements in sampling_prediction_trajectories (#976)

• Objective definition:

  • Support for jax objectives (#986)

• General

  • Fix license_file SetuptoolsDeprecationWarning (#965)
  • Remove benchmark-models-petab requirement (#964)
  • Github Actions(#958, #989 )
  • Fix typehint for problem.x_priors_defs (#962)
  • Fix tox4-related issues (#981)
  • Fix AMICI deprecation warning (#956)
  • Add pypesto.visualize.model_fit to API doc (#991)
  • Exclude numpy==1.24.0 (#993)

0.2.14 (2022-10-25)

• Ensembles:

  • Save and load weights and sigmay (#876)
  • Define relative cutoff (#855)

• PEtab:

  • Pass problem kwargs via petab importer (#874)
  • Use benchmark-models-petab instead of manual download (#915)
  • Use fake RData in in prediction_to_petab_measurement_df (#925)

• Optimize:

  • Fides: Include message according to exitflag (#878)

• Sampling:

  • Added Pymc v4 Sampler (#818, #944, #948)

• Visualization:

  • Fix waterfall plot limits for non-offsetted log-plots (#891)
  • Plot unflattened model fit from flattened PEtab problems (#914)
  • Added the offset value to waterfall plot for better intuitive understanding (#910, #945)
  • Visualize parameter correlation (#888)

• History and storage:

  • Fix history-result reconstruction mismatch (#902)
  • Move history to own module (#903)
  • Remove chi2, schi2 except for history convenience function (#904)
  • Clean up history hierarchy (#908)
  • Fix read_result with history (#907)
  • Improve hdf5 history file lock (#909, #921)
  • Fix message in check_overwrite (#894)
  • Deactivate automatic saving (#930, #932)
  • Allow problem=None in read_result_from_file (#936)
  • Remove superfluous get_or_create_group (#937)
  • Extract read_history_from_file from read_result_from_file (#939)
  • Select: use model ID in save postprocessor filename, by default (#943)

• Select:

  • Clean up use of minimize_options in model problem (#918)
  • User-supplied method to produce pyPESTO problem (#884)
  • Report, and binary model ID post-processors (#900)
  • Move method.py functionalities to ui.py in petab_select (#919)

• Objective and Result:

  • Julia objective (#927)
  • Fix set of keys to aggregate results in aggregated objective (#883)
  • Nicer OptimizeResult.summary (#895, #916, #935, #942, )
  • Fix disjoint IDs check in OptimizerResult.append (#922)
  • Fix OptimizeResult pickling (#953)

• General:

  • Remove version from CITATION.cff (#887)
  • Fix CI and docs (#892, #893)
  • Literal typehints for mode (#899)
  • Fix pandas deprecation warning (#896)
  • Document NEP 29 (time-window based python support) (#905)
  • Fix get_for_key deprecation warning (#906)
  • Fix multiple warnings from existing AMICI model (#912)
  • Fix warning from AMICI fixed overrides (#912)
  • Fix flaky test CRFunModeHistoryTest.test_trace_all (#917)
  • Fix novel B024 ABC without abstract methods (#923)
  • Improve API docs and add overview notebook (#911)
  • Fix typos (#926)
  • Fix julia tests (#929, #933)
  • Fix flaky test_mpipoolengine (#938)
  • More informative test IDs in test_optimize (#940)
  • Speed-up import via lazy imports (#946)

0.2.13 (2022-05-24)

• Ensembles:

  • Added standard deviation to ensemble prediction plots (#853)

• Storage

  • Distinguish between scalar and vector values in Hdf5History._get_hdf5_entries (#856)
  • Fix hdf5 history overwrite (#861)
  • Updated optimization storage format. Made attributes explicit. (#863)
  • Added problem to result from read_results_from_file (#862)

• General

  • Various additions to Optimize(r)Result summary method (#859, #865, #866, #867)
  • Fixed optimizer history fval offset (#834)
  • Updated the profile, minimize, sample and added overwrite as argument. (#864)
  • Fixed y-labels in pypesto.visualize.optimizer_history (#869)
  • Created show_bounds, to display proper sampling scatter plots. (#868)
  • Enabled saving messages and exit flags in hdf5 history in case of finished run (#873)
  • Select: use objective function evaluation time as optimization time for models with no estimated parameters (#872)
  • removed checking for equality and checking for np.allclose in test_aesara (#877)

0.2.12 (2022-04-11)

• AMICI:

  • Update to renamed steady state sensitivity modes (#843)
  • Set amici.Solver.setReturnDataReportingMode (#835)
  • Optimize pypesto/objective/amici_util.py::par_index_slices (#845)
  • Remove Solver.getPreequilibration (#830)
  • fix n_res size for error output with parameter dependent sigma (#812)
  • PetabImporter: Auto-regenerate AMICI models in case of version mismatch (#848)

• Pymc3

  • Disable Pymc3 Sampler tests (#831)

• Visualizations:

  • Waterfall zoom (#808)
  • Reverse opacities of colors in prediction trajectories plots(#838)
  • Model fit plots (#850)

• OptimizeResult:

  • Summary method (#816)
  • Append method for OptimizeResult (#815)
  • added __getattr__ function to OptimizeResult (#802)

• General:

  • disable progress bar in tests (#799)
  • Make Fides work with objectives, that do not have a hessian (#807)
  • removed ftol in favor of tol (#803)
  • Fix pyPESTO Select test; Update to stable black version (#810)
  • Fix id assignment in case of large number of starts (#825)
  • Temporarily fix jinja2 version (#826)
  • Upgrade black to be compatible with latest click (#829)
  • Fix wrong link in doc/example/hdf5_storage.ipynb (#827)
  • Mark test/base/test_prior.py::test_mode as flaky (#833)
  • Custom methods for autosave filenames (#822)
  • fix saving ensemble predictions to hdf5 (#840)
  • Upgrade nbQA to 1.3.1 (#846)
  • Replaced constantParameters with constant_parameters in notebook (#852)

0.2.11 (2022-01-11)

• Model selection (#397):

  • Automated model selection with forward/backward/brute force methods and AIC/AICc/BIC criteria
  • Much functionality (methods, criteria, model space, problem specification) via PEtab Select <https://github.com/PEtab-dev/petab_select>
  • Plotting routines
  • Example notebook <https://github.com/ICB-DCM/pyPESTO/blob/main/doc/example/model_selection.ipynb>
  • Model calibration postprocessors
  • Select first model that improves on predecessor model
  • Use previous MLE as startpoint
  • Tests

• AMICI:

  • Maintain model settings when pickling for multiprocessing (#747)

• General:

  • Apply nbqa black and isort to auto-format all notebooks via pre-commit hook (#794)
  • Apply black formatting via pre-commit hook (#796)
  • Require Python >= 3.8 (#795)
  • Fix various warnings (#778)
  • Minor fixes (#792)

0.2.10 (2022-01-06)

• AMICI:

  • Make AMICI objective report only what is being asked for (#777)

• Optimization:

  • (Breaking) Refactor startpoint generation with clear assignments; allow checking gradients (#769)
  • (Breaking) Prioritize history vs optimize result (#775)

• Storage:

  • Fix loading empty history and result generation from multiple histories (#764)
  • Fix autosave function for single-core (#770)
  • Fix potential autosave overwriting and typehints (#772)
  • Allow loading of partial results from history file (#783)

• CI:

  • Compile AMICI models without gradients in test suite (#774)

• General:

  • (Breaking) Create result sub-module; shift storage+result related functionality (#784)
  • Fix finite difference constant mode (#786)
  • Refactor ensemble module (#788)
  • Introduce general C constants file (#788)
  • Apply isort for automatic imports formatting (#785)
  • Reduce run log output (#789)
  • Various minor fixes (#765, #766, #768, #771)

0.2.9 (2021-11-03)

• General:

  • Automatically save results (#749)
  • Update all docstrings to numpy standard (#750)
  • Add Google Colab and nbviewer links to all notebooks for online execution (#758)
  • Option to not save hess and sres in result (#760)
  • Set minimum supported python version to 3.7 (#755)

• Visualization:

  • Parameterize start index in optimized model fit (#744)

0.2.8 (2021-10-28)

• 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)

0.2.7 (2021-07-30)

• 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)

0.2.6 (2021-05-17)

• 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)

0.2.5 (2021-05-04)

• Objectives:

  • New Aesara objectve (#623, #629, #635)

• 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)

0.2.4 (2021-03-12)

• 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)

0.2.3 (2021-01-18)

• New optimizers:

  • FIDES (#506, #503 # 500)
  • NLopt (#493)

• 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)

0.2.2 (2020-10-05)

• 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)

0.2.1 (2020-09-07)

• 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 import, error handling) (#448, #452, #454)

• Improved sampling diagnostics (e.g. effective samples size) (#426)
• Improvements and bug fixes in parameter plots (#425)

0.2.0 (2020-06-17)

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)

0.1 series

0.1.0 (2020-06-17)

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)

0.0 series

0.0.13 (2020-05-03)

• Tidy up and speed up tests (#265 and others).
• Basic self-implemented Adaptive Metropolis and Adaptive Parallel Tempering sampling routines (#268).
• Fix namespace sample -> sampling (#275).
• Fix covariance matrix regularization (#275).
• Fix circular dependency PetabImporter - PetabAmiciObjective via AmiciObjectBuilder, PetabAmiciObjective becomes obsolete (#274).
• Define AmiciCalculator to separate the AMICI call logic (required for hierarchical optimization) (#277).
• Define initialize function for resetting steady states in AmiciObjective (#281).
• Fix scipy least squares options (#283).
• Allow failed starts by default (#280).
• Always copy parameter vector in objective to avoid side effects (#291).
• Add Dockerfile (#288).
• Fix header names in CSV history (#299).

Documentation:

• Use imported members in autodoc (#270).
• Enable python syntax highlighting in notebooks (#271).

0.0.12 (2020-04-06)

• Add typehints to global functions and classes.
• Add PetabImporter.rdatas_to_simulation_df function (all #235).
• Adapt y scale in waterfall plot if convergence was too good (#236).
• Clarify that Objective is of type negative log-posterior, for minimization (#243).
• Tidy up AmiciObjective.parameter_mapping as implemented in AMICI now (#247).
• Add MultiThreadEngine implementing multi-threading aside the MultiProcessEngine implementing multi-processing (#254).
• Fix copying and pickling of AmiciObjective (#252, #257).
• Remove circular dependence history-objective (#254).
• Fix problem of visualizing results with failed starts (#249).
• Rework history: make thread-safe, use factory methods, make context-specific (#256).
• Improve PEtab usage example (#258).
• Define history base contract, enabling different backends (#260).
• Store optimization results to HDF5 (#261).
• Simplify tests (#263).

Breaking changes:

• HistoryOptions passed to pypesto.minimize instead of Objective (#256).
• GlobalOptimizer renamed to PyswarmOptimizer (#235).

0.0.11 (2020-03-17)

• Rewrite AmiciObjective and PetabAmiciObjective simulation routine to directly use amici.petab_objective routines (#209, #219, #225).
• Implement petab test suite checks (#228).
• Various error fixes, in particular regarding PEtab and visualization.
• Improve trace structure.
• Fix conversion between fval and chi2, fix FIM (all #223).

0.0.10 (2019-12-04)

• Only compute FIM when sensitivities are available (#194).
• Fix documentation build (#197).
• Add support for pyswarm optimizer (#198).
• Run travis tests for documentation and notebooks only on pull requests (#199).

0.0.9 (2019-10-11)

• Update to AMICI 0.10.13, fix API changes (#185).
• Start using PEtab import from AMICI to be able to import constant species (#184, #185)
• Require PEtab>=0.0.0a16 (#183)

0.0.8 (2019-09-01)

• Add logo (#178).
• Fix petab API changes (#179).
• Some minor bugfixes (#168).

0.0.7 (2019-03-21)

• Support noise models in Petab and Amici.
• Minor Petab update bug fixes.

0.0.6 (2019-03-13)

• Several minor error fixes, in particular on tests and steady state.

0.0.5 (2019-03-11)

• Introduce AggregatedObjective to use multiple objectives at once.
• Estimate steady state in AmiciObjective.
• Check amici model build version in PetabImporter.
• Use Amici multithreading in AmiciObjective.
• Allow to sort multistarts by initial value.
• Show usage of visualization routines in notebooks.
• Various fixes, in particular to visualization.

0.0.4 (2019-02-25)

• Implement multi process parallelization engine for optimization.
• Introduce PrePostProcessor to more reliably handle pre- and post-processing.
• Fix problems with simulating for multiple conditions.
• Add more visualization routines and options for those (colors, reference points, plotting of lists of result obejcts)

0.0.3 (2019-01-30)

• Import amici models and the petab data format automatically using pypesto.PetabImporter.
• Basic profiling routines.

0.0.2 (2018-10-18)

• Fix parameter values
• Record trace of function values
• Amici objective to directly handle amici models

0.0.1 (2018-07-25)

• Basic framework and implementation of the optimization