Release 0.4.1

Merge pull request #1235 from ICB-DCM/develop

.github/CODEOWNERS

@@ -3,10 +3,13 @@
 # default owners
 * @ICB-DCM/pypesto-maintainers
 
+/doc/example/censored.ipynb @Doresic
 /doc/example/hdf5_storage.ipynb @PaulJonasJost
 /doc/example/hierarchical.ipynb @dilpath @dweindl
 /doc/example/julia.ipynb @PaulJonasJost
 /doc/example/model_selection.ipynb @dilpath
+/doc/example/nonlinear_monotone.ipynb @Doresic
+/doc/example/ordinal.ipynb @Doresic
 /doc/example/petab_import.ipynb @dweindl @FFroehlich
 /doc/example/sampler_study.ipynb @dilpath
 /doc/example/sampling_diagnostics.ipynb @dilpath
@@ -16,8 +19,9 @@
 /pypesto/engine/ @PaulJonasJost
 /pypesto/engine/mpi_pool.py @PaulJonasJost
 /pypesto/ensemble/ @dilpath @PaulJonasJost
-/pypesto/hierarchical/ @dweindl @doresic
-/pypesto/hierarchical/optimal_scaling_approach/ @doresic
+/pypesto/hierarchical/ @dweindl @Doresic
+/pypesto/hierarchical/optimal_scaling_approach/ @Doresic
+/pypesto/hierarchical/spline_approximation/ @Doresic
 /pypesto/history/ @PaulJonasJost
 /pypesto/objective/ @PaulJonasJost
 /pypesto/objective/amici/ @dweindl @FFroehlich
@@ -33,7 +37,7 @@
 /pypesto/select/ @dilpath
 /pypesto/startpoint/ @PaulJonasJost
 /pypesto/store/ @PaulJonasJost
-/pypesto/visualize/@stephanmg @m-philipps
+/pypesto/visualize/ @stephanmg @m-philipps
 /test/base/ @PaulJonasJost @m-philipps
 /test/doc/ @PaulJonasJost
 /test/hierarchical/ @dweindl @doresic

.github/workflows/ci.yml

@@ -171,6 +171,11 @@ jobs:
       matrix:
         python-version: ['3.9', '3.11']
 
+    # needed to allow julia-actions/cache to delete old caches that it has created
+    permissions:
+      actions: write
+      contents: read
+
     steps:
     - name: Check out repository
       uses: actions/checkout@v3
@@ -180,25 +185,27 @@ jobs:
       with:
         python-version: ${{ matrix.python-version }}
 
-    - name: Install julia
-      uses: julia-actions/setup-julia@v1
-      with:
-        version: 1.9
-
-    - name: Cache
+    - name: Cache tox and cache
       uses: actions/cache@v3
       with:
         path: |
           ~/.cache
           .tox/
-          ~/.julia/artifacts
         key: ${{ runner.os }}-${{ matrix.python-version }}-ci-${{ github.job }}
 
+    - name: Install julia
+      uses: julia-actions/setup-julia@v1
+      with:
+        version: 1.9
+
+    - name: Cache Julia
+      uses: julia-actions/cache@v1
+
     - name: Install dependencies
       run: .github/workflows/install_deps.sh
 
     - name: Install PEtabJL dependencies
-      run: julia -e 'using Pkg; Pkg.add("PEtab"); Pkg.add("OrdinaryDiffEq"), Pkg.add("Sundials")'
+      run: julia -e 'using Pkg; Pkg.add("PEtab"); Pkg.add("OrdinaryDiffEq"); Pkg.add("Sundials")'
 
     - name: Run tests
       timeout-minutes: 25

CHANGELOG.rst

@@ -6,6 +6,28 @@ Release notes
 ..........
 
 
+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)
 -------------------
 

README.md

@@ -28,9 +28,9 @@ pyPESTO features include:
 * Parameter estimation with ordinal data as described in
   [Schmiester et al. (2020)](https://doi.org/10.1007/s00285-020-01522-w) and
   [Schmiester et al. (2021)](https://doi.org/10.1093/bioinformatics/btab512).
-  ([example](https://github.com/ICB-DCM/pyPESTO/blob/master/doc/example/example_ordinal.ipynb))
-* Parameter estimation with censored data. ([example](https://github.com/ICB-DCM/pyPESTO/blob/master/doc/example/example_censored.ipynb))
-* Parameter estimation with nonlinear-monotone data. ([example](https://github.com/ICB-DCM/pyPESTO/blob/master/doc/example/example_nonlinear_monotone.ipynb))
+  ([example](https://github.com/ICB-DCM/pyPESTO/blob/master/doc/example/ordinal.ipynb))
+* Parameter estimation with censored data. ([example](https://github.com/ICB-DCM/pyPESTO/blob/master/doc/example/censored.ipynb))
+* Parameter estimation with nonlinear-monotone data. ([example](https://github.com/ICB-DCM/pyPESTO/blob/master/doc/example/nonlinear_monotone.ipynb))
 
 ## Quick install
 
@@ -59,20 +59,17 @@ We are happy about any contributions. For more information on how to contribute
 to pyPESTO check out
 <https://pypesto.readthedocs.io/en/latest/contribute.html>
 
-## Publications
+## How to Cite
 
 **Citeable DOI for the latest pyPESTO release:**
 [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.2553546.svg)](https://doi.org/10.5281/zenodo.2553546)
 
-There is a list of [publications using pyPESTO](https://pypesto.readthedocs.io/en/latest/references.html).
-If you used pyPESTO in your work, we are happy to include
-your project, please let us know via a GitHub issue.
-
 When using pyPESTO in your project, please cite
 * Schälte, Y., Fröhlich, F., Jost, P. J., Vanhoefer, J., Pathirana, D., Stapor, P.,
   Lakrisenko, P., Wang, D., Raimúndez, E., Merkt, S., Schmiester, L., Städter, P.,
   Grein, S., Dudkin, E., Doresic, D., Weindl, D., & Hasenauer, J. (2023). pyPESTO: A
-  modular and scalable tool for parameter estimation for dynamic models [(arXiv:2305.01821)](https://doi.org/10.48550/arXiv.2305.01821).
+  modular and scalable tool for parameter estimation for dynamic models,
+  Bioinformatics, 2023, btad711, [doi:10.1093/bioinformatics/btad711](https://doi.org/10.1093/bioinformatics/btad711)
 
 When presenting work that employs pyPESTO, feel free to use one of the icons in
 [doc/logo/](https://github.com/ICB-DCM/pyPESTO/tree/main/doc/logo):
@@ -81,6 +78,10 @@ When presenting work that employs pyPESTO, feel free to use one of the icons in
   <img src="https://raw.githubusercontent.com/ICB-DCM/pyPESTO/master/doc/logo/logo.png" height="75" alt="AMICI Logo">
 </p>
 
+There is a list of [publications using pyPESTO](https://pypesto.readthedocs.io/en/latest/references.html).
+If you used pyPESTO in your work, we are happy to include
+your project, please let us know via a GitHub issue.
+
 ## References
 
 pyPESTO supersedes [**PESTO**](https://github.com/ICB-DCM/PESTO/) a parameter estimation

doc/conf.py

@@ -71,6 +71,10 @@
     'autodoc_inherit_docstrings': True,
 }
 autodoc_mock_imports = ["amici"]
+autodoc_class_signature = "separated"
+
+# napoleon options
+napoleon_use_rtype = False
 
 # links for intersphinx
 intersphinx_mapping = {
@@ -90,7 +94,7 @@
 typehints_document_rtype = True
 autodoc_typehints = "description"
 
-bibtex_bibfiles = ["using_pypesto.bib"]
+bibtex_bibfiles = ["using_pypesto.bib", "references.bib"]
 
 # Add any paths that contain templates here, relative to this directory.
 templates_path = ['_templates']

doc/example.rst

@@ -52,9 +52,9 @@ Algorithms and features
    example/model_selection.ipynb
    example/julia.ipynb
    example/hierarchical.ipynb
-   example/example_ordinal.ipynb
-   example/example_censored.ipynb
-   example/example_nonlinear_monotone.ipynb
+   example/ordinal.ipynb
+   example/censored.ipynb
+   example/nonlinear_monotone.ipynb
 
 Application examples
 --------------------

doc/example/example_censored.ipynb → doc/example/censored.ipynb

@@ -495,7 +495,7 @@
     "For censored measurements, the `measurement` column will be ignored. For the `Ybar` observable we didn't specify a measurement type, so those will be used as quantitative.\n",
     "\n",
     "#### Note on inclusion of additional data types:\n",
-    "It is possible to include observables with different types of data to the same `petab_problem`. Refer to the notebooks on using [nonlinear-monotone data](example_nonlinear_monotone.ipynb) and [ordinal data](example_ordinal.ipynb) for details on integration of other data types. Additionally, as shown in this example, if the `measurementType` column is left empty for all measurements of an observable, the observable will be treated as quantitative."
+    "It is possible to include observables with different types of data to the same `petab_problem`. Refer to the notebooks on using [nonlinear-monotone data](nonlinear_monotone.ipynb) and [ordinal data](ordinal.ipynb) for details on integration of other data types. Additionally, as shown in this example, if the `measurementType` column is left empty for all measurements of an observable, the observable will be treated as quantitative."
    ]
   },
   {

doc/example/example_nonlinear_monotone.ipynb → doc/example/nonlinear_monotone.ipynb

@@ -606,7 +606,7 @@
    "metadata": {},
    "source": [
     "#### Note on inclusion of additional data types:\n",
-    "It is possible to include observables with different types of data to the same `petab_problem`. Refer to the notebooks on using [ordinal data](example_ordinal.ipynb) and [censored data](example_censored.ipynb) for details on integration of other data types. If the `measurementType` column is left empty for all measurements of an observable, the observable will be treated as quantitative."
+    "It is possible to include observables with different types of data to the same `petab_problem`. Refer to the notebooks on using [ordinal data](ordinal.ipynb) and [censored data](censored.ipynb) for details on integration of other data types. If the `measurementType` column is left empty for all measurements of an observable, the observable will be treated as quantitative."
    ]
   },
   {

doc/example/example_ordinal.ipynb → doc/example/ordinal.ipynb

@@ -481,7 +481,7 @@
     "Measurements with a larger category number will be constrained to be higher in the ordering. Multiple measurements can be assigned to the same category. If this is done, these measurements will be treated as indistinguishable. \n",
     "\n",
     "#### Note on inclusion of additional data types:\n",
-    "It is possible to include observables with different types of data to the same `petab_problem`. Refer to the notebooks on using [nonlinear-monotone data](example_nonlinear_monotone.ipynb) and [censored data](example_censored.ipynb) for details on integration of other data types. If the `measurementType` column is left empty for all measurements of an observable, the observable will be treated as quantitative."
+    "It is possible to include observables with different types of data to the same `petab_problem`. Refer to the notebooks on using [nonlinear-monotone data](nonlinear_monotone.ipynb) and [censored data](censored.ipynb) for details on integration of other data types. If the `measurementType` column is left empty for all measurements of an observable, the observable will be treated as quantitative."
    ]
   },
   {

doc/how_to_cite.rst

@@ -7,17 +7,13 @@ How to cite pyPESTO
    :target: https://doi.org/10.5281/zenodo.2553546
    :alt: pyPESTO release DOI
 
-
-There is a list of `publications using pyPESTO <https://pypesto.readthedocs.io/en/latest/references.html>`_.
-If you used pyPESTO in your work, we are happy to include
-your project, please let us know via a GitHub issue.
-
 When using pyPESTO in your project, please cite
 
 - Schälte, Y., Fröhlich, F., Jost, P. J., Vanhoefer, J., Pathirana, D., Stapor, P.,
   Lakrisenko, P., Wang, D., Raimúndez, E., Merkt, S., Schmiester, L., Städter, P.,
   Grein, S., Dudkin, E., Doresic, D., Weindl, D., & Hasenauer, J. (2023). pyPESTO: A
-  modular and scalable tool for parameter estimation for dynamic models `arXiv:2305.01821 <https://doi.org/10.48550/arXiv.2305.01821>`_.
+  modular and scalable tool for parameter estimation for dynamic models,
+  Bioinformatics, 2023;, btad711, https://doi.org/10.1093/bioinformatics/btad711
 
 When presenting work that employs pyPESTO, feel free to use one of the icons in
 `doc/logo/ <https://github.com/ICB-DCM/pyPESTO/tree/main/doc/logo>`_:
@@ -26,3 +22,7 @@ When presenting work that employs pyPESTO, feel free to use one of the icons in
    :target: https://raw.githubusercontent.com/ICB-DCM/pyPESTO/master/doc/logo/logo.png
    :height: 75
    :alt: pyPESTO LOGO
+
+There is a list of `publications using pyPESTO <https://pypesto.readthedocs.io/en/latest/references.html>`_.
+If you used pyPESTO in your work, we are happy to include
+your project, please let us know via a GitHub issue.

doc/references.bib

@@ -0,0 +1,67 @@
+
+@Article{EgeaBal2009,
+  author           = {Egea, Jose A. and Balsa-Canto, Eva and García, María-Sonia G. and Banga, Julio R.},
+  journal          = {Industrial & Engineering Chemistry Research},
+  title            = {Dynamic Optimization of Nonlinear Processes with an Enhanced Scatter Search Method},
+  year             = {2009},
+  issn             = {1520-5045},
+  month            = apr,
+  number           = {9},
+  pages            = {4388--4401},
+  volume           = {48},
+  creationdate     = {2023-11-21T15:56:38},
+  doi              = {10.1021/ie801717t},
+  modificationdate = {2023-11-21T16:27:59},
+  publisher        = {American Chemical Society (ACS)},
+}
+
+@Article{EgeaMar2010,
+  author           = {Jose A. Egea and Rafael Martí and Julio R. Banga},
+  journal          = {Computers & Operations Research},
+  title            = {An evolutionary method for complex-process optimization},
+  year             = {2010},
+  issn             = {0305-0548},
+  number           = {2},
+  pages            = {315-324},
+  volume           = {37},
+  abstract         = {In this paper we present a new evolutionary method for complex-process optimization. It is partially based on the principles of the scatter search methodology, but it makes use of innovative strategies to be more effective in the context of complex-process optimization using a small number of tuning parameters. In particular, we introduce a new combination method based on path relinking, which considers a broader area around the population members than previous combination methods. We also use a population-update method which improves the balance between intensification and diversification. New strategies to intensify the search and to escape from suboptimal solutions are also presented. The application of the proposed evolutionary algorithm to different sets of both state-of-the-art continuous global optimization and complex-process optimization problems reveals that it is robust and efficient for the type of problems intended to solve, outperforming the results obtained with other methods found in the literature.},
+  creationdate     = {2023-11-21T15:57:20},
+  doi              = {10.1016/j.cor.2009.05.003},
+  keywords         = {Evolutionary algorithms, Complex-process optimization, Continuous optimization, Global optimization, Metaheuristics},
+  modificationdate = {2023-11-21T15:57:20},
+  url              = {https://www.sciencedirect.com/science/article/pii/S0305054809001440},
+}
+
+
+@Article{VillaverdeEge2012,
+  author           = {Villaverde, Alejandro F and Egea, Jose A and Banga, Julio R},
+  journal          = {BMC Systems Biology},
+  title            = {A cooperative strategy for parameter estimation in large scale systems biology models},
+  year             = {2012},
+  issn             = {1752-0509},
+  month            = jun,
+  number           = {1},
+  volume           = {6},
+  creationdate     = {2023-11-21T15:57:46},
+  doi              = {10.1186/1752-0509-6-75},
+  modificationdate = {2023-11-21T15:57:46},
+  publisher        = {Springer Science and Business Media LLC},
+}
+
+
+@Article{PenasGon2017,
+  author           = {Penas, David R. and González, Patricia and Egea, Jose A. and Doallo, Ramón and Banga, Julio R.},
+  journal          = {BMC Bioinformatics},
+  title            = {Parameter estimation in large-scale systems biology models: a parallel and self-adaptive cooperative strategy},
+  year             = {2017},
+  issn             = {1471-2105},
+  month            = jan,
+  number           = {1},
+  volume           = {18},
+  creationdate     = {2023-11-21T15:57:58},
+  doi              = {10.1186/s12859-016-1452-4},
+  modificationdate = {2023-11-21T15:57:58},
+  publisher        = {Springer Science and Business Media LLC},
+}
+
+@Comment{jabref-meta: databaseType:bibtex;}

pypesto/engine/base.py

@@ -1,6 +1,6 @@
 """Abstract engine base class."""
 import abc
-from typing import Any, List
+from typing import Any
 
 from .task import Task
 
@@ -13,15 +13,15 @@ def __init__(self):
 
     @abc.abstractmethod
     def execute(
-        self, tasks: List[Task], progress_bar: bool = True
-    ) -> List[Any]:
+        self, tasks: list[Task], progress_bar: bool = True
+    ) -> list[Any]:
         """Execute tasks.
 
         Parameters
         ----------
         tasks:
             List of tasks to execute.
         progress_bar:
-            Whether to display a progress bar.
+            Whether to display a progress bar. Defaults to ``True``.
         """
         raise NotImplementedError("This engine is not intended to be called.")

pypesto/engine/mpi_pool.py

@@ -1,6 +1,6 @@
 """Engines with multi-node parallelization."""
 import logging
-from typing import Any, List
+from typing import Any
 
 import cloudpickle as pickle
 from mpi4py import MPI
@@ -32,17 +32,21 @@ def __init__(self):
         super().__init__()
 
     def execute(
-        self, tasks: List[Task], progress_bar: bool = True
-    ) -> List[Any]:
+        self, tasks: list[Task], progress_bar: bool = True
+    ) -> list[Any]:
         """
         Pickle tasks and distribute work to workers.
 
         Parameters
         ----------
         tasks:
-            List of tasks to execute.
+            List of :class:`pypesto.engine.Task` to execute.
         progress_bar:
-            Whether to display a progress bar.
+            Whether to display a progress bar. Defaults to ``True``.
+
+        Returns
+        -------
+        A list of results.
         """
         pickled_tasks = [pickle.dumps(task) for task in tasks]