Merge pull request #1132 from ICB-DCM/develop

Release 0.3.3

CHANGELOG.rst

@@ -6,6 +6,25 @@ Release notes
 ..........
 
 
+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)
 -------------------
 

README.md

@@ -59,8 +59,29 @@ 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
+
+**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).
+
+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):
+
+<p align="center">
+  <img src="https://raw.githubusercontent.com/ICB-DCM/pyPESTO/master/doc/logo/logo.png" height="75" alt="AMICI Logo">
+</p>
+
 ## References
 
-[**PESTO**](https://github.com/ICB-DCM/PESTO/):
-Parameter estimation toolbox for MATLAB. Development is discontinued, but PESTO
-comes with additional features waiting to be ported to pyPESTO.
+pyPESTO supersedes [**PESTO**](https://github.com/ICB-DCM/PESTO/) a parameter estimation
+toolbox for MATLAB, whose development is discontinued.

doc/example/conversion_reaction/PEtabJl_module.jl

@@ -5,15 +5,15 @@ using Sundials
 using PEtab
 
 pathYaml = "/Users/pauljonasjost/Documents/GitHub_Folders/pyPESTO/test/julia/../../doc/example/conversion_reaction/conversion_reaction.yaml"
-petabModel = readPEtabModel(pathYaml, verbose=true)
+petabModel = PEtabModel(pathYaml, verbose=true)
 
-# A full list of options for createPEtabODEProblem can be found at https://sebapersson.github.io/PEtab.jl/dev/API_choosen/#PEtab.setupPEtabODEProblem
-petabProblem = createPEtabODEProblem(
+# A full list of options for PEtabODEProblem can be found at https://sebapersson.github.io/PEtab.jl/stable/API_choosen/
+petabProblem = PEtabODEProblem(
 	petabModel,
-	odeSolverOptions=ODESolverOptions(Rodas5P(), abstol=1e-08, reltol=1e-08, maxiters=Int64(1e4)),
-	gradientMethod=:ForwardDiff,
-	hessianMethod=:ForwardDiff,
-	sparseJacobian=nothing,
+	ode_solver=ODESolver(Rodas5P(), abstol=1e-08, reltol=1e-08, maxiters=Int64(1e4)),
+	gradient_method=:ForwardDiff,
+	hessian_method=:ForwardDiff,
+	sparse_jacobian=nothing,
 	verbose=true
 )
 

doc/how_to_cite.rst

@@ -0,0 +1,28 @@
+How to cite pyPESTO
+===================
+
+**Citeable DOI for the latest pyPESTO release:**
+
+.. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.2553546.svg
+   :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>`_.
+
+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>`_:
+
+.. image:: https://raw.githubusercontent.com/ICB-DCM/pyPESTO/master/doc/logo/logo.png
+   :target: https://raw.githubusercontent.com/ICB-DCM/pyPESTO/master/doc/logo/logo.png
+   :height: 75
+   :alt: pyPESTO LOGO

doc/index.rst

@@ -45,6 +45,7 @@ pyPESTO - Parameter EStimation TOolbox for python
    references
    contact
    license
+   how_to_cite
    logo
 
 

doc/references.rst

@@ -1,7 +1,7 @@
-Publications using pypesto
+Publications using pyPESTO
 ==========================
 
-pypesto was used in the following publications:
+pyPESTO was used in the following publications:
 
 .. bibliography:: using_pypesto.bib
    :list: enumerated

pypesto/C.py

@@ -35,6 +35,9 @@
 ENSEMBLE_TYPE = 'ensemble_type'
 PREDICTIONS = 'predictions'
 
+SIMULTANEOUS = 'simultaneous'
+POINTWISE = 'pointwise'
+
 LOWER_BOUND = 'lower_bound'
 UPPER_BOUND = 'upper_bound'
 PREEQUILIBRATION_CONDITION_ID = 'preequilibrationConditionId'

pypesto/ensemble/ensemble.py

@@ -29,12 +29,14 @@
     OUTPUT,
     OUTPUT_SENSI,
     PERCENTILE,
+    POINTWISE,
     PREDICTION_ARRAYS,
     PREDICTION_ID,
     PREDICTION_RESULTS,
     PREDICTION_SUMMARY,
     PREDICTIONS,
     PREDICTOR,
+    SIMULTANEOUS,
     STANDARD_DEVIATION,
     SUMMARY,
     TIMEPOINTS,
@@ -668,7 +670,7 @@ def from_optimization_endpoints(
             # did not reach maximum size and the next value is still
             # lower than the cutoff value
             if start['fval'] <= abs_cutoff and len(x_vectors) < max_size:
-                x_vectors.append(start['x'])
+                x_vectors.append(start['x'][result.problem.x_free_indices])
 
                 # the vector tag will be a -1 to indicate it is the last step
                 vector_tags.append((int(start['id']), -1))
@@ -1204,7 +1206,11 @@ def get_percentile_label(percentile: Union[float, int, str]) -> str:
     return f'{PERCENTILE} {percentile}'
 
 
-def calculate_cutoff(result: Result, percentile: float = 0.95):
+def calculate_cutoff(
+    result: Result,
+    percentile: float = 0.95,
+    cr_option: str = SIMULTANEOUS,
+):
     """
     Calculate the cutoff of the ensemble.
 
@@ -1220,6 +1226,10 @@ def calculate_cutoff(result: Result, percentile: float = 0.95):
         The percentile of the chi^2 distribution. Between 0 and 100.
         Higher values will result in a more lax cutoff. If the value is greater
         than 100, the cutoff will be returned as np.inf.
+    cr_option:
+        The type of confidence region, which determines the degree of freedom of
+        the chi^2 distribution for the cutoff value. It can take 'simultaneous' or
+        'pointwise'.
 
     Returns
     -------
@@ -1230,10 +1240,19 @@ def calculate_cutoff(result: Result, percentile: float = 0.95):
             f"percentile={percentile} is too large. Choose "
             f"0<=percentile<=100."
         )
+    if cr_option not in [SIMULTANEOUS, POINTWISE]:
+        raise ValueError(
+            "Confidence region must be either simultaneous or pointwise."
+        )
+
     # optimal point as base:
     fval_opt = result.optimize_result[0].fval
-    # degrees of freedom is equal to the number of parameters
-    df = result.problem.dim
-    range = chi2.ppf(q=percentile / 100, df=df)
+    if cr_option == SIMULTANEOUS:
+        # degrees of freedom is equal to the number of parameters
+        df = result.problem.dim
+    elif cr_option == POINTWISE:
+        # degrees of freedom is equal to 1
+        df = 1
 
+    range = chi2.ppf(q=percentile / 100, df=df)
     return fval_opt + range

pypesto/objective/julia/petabJl.py

@@ -61,10 +61,10 @@ def __init__(
         self.petab_jl_problem = petab_jl_problem
 
         # get functions
-        fun = self.petab_jl_problem.computeCost
-        grad = self.petab_jl_problem.computeGradient
-        hess = self.petab_jl_problem.computeHessian
-        x_names = np.asarray(self.petab_jl_problem.θ_estNames)
+        fun = self.petab_jl_problem.compute_cost
+        grad = self.petab_jl_problem.compute_gradient
+        hess = self.petab_jl_problem.compute_hessian
+        x_names = np.asarray(self.petab_jl_problem.θ_names)
 
         # call the super super super constructor
         super(JuliaObjective, self).__init__(
@@ -102,10 +102,10 @@ def __setstate__(self, state):
         self.petab_jl_problem = petab_jl_problem
 
         # get functions
-        fun = self.petab_jl_problem.computeCost
-        grad = self.petab_jl_problem.computeGradient
-        hess = self.petab_jl_problem.computeHessian
-        x_names = np.asarray(self.petab_jl_problem.θ_estNames)
+        fun = self.petab_jl_problem.compute_cost
+        grad = self.petab_jl_problem.compute_gradient
+        hess = self.petab_jl_problem.compute_hessian
+        x_names = np.asarray(self.petab_jl_problem.θ_names)
 
         # call the super super constructor
         super(JuliaObjective, self).__init__(fun, grad, hess, x_names)