Merge branch 'develop' into fix_1448_plist

.github/workflows/ci.yml

@@ -199,17 +199,14 @@ jobs:
     - name: Install julia
       uses: julia-actions/setup-julia@v2
       with:
-        version: 1.9
-
-    - name: Cache Julia
-      uses: julia-actions/cache@v2
+        version: 1.11
 
     - name: Install dependencies
       run: .github/workflows/install_deps.sh
 
     - name: Install PEtabJL dependencies
       run: >
-        julia -e 'using Pkg; Pkg.add(Pkg.PackageSpec(;name="PEtab", version="2.5.0"));
+        julia -e 'using Pkg; Pkg.add("PEtab");
         Pkg.add("OrdinaryDiffEq"); Pkg.add("Sundials")'
 
     - name: Run tests

doc/using_pypesto.bib

@@ -293,16 +293,18 @@ @Misc{LakrisenkoPat2024
   primaryclass     = {q-bio.QM},
 }
 
-@Misc{PhilippsKoe2024,
+@Article{PhilippsKoe2024,
   author           = {Maren Philipps and Antonia Körner and Jakob Vanhoefer and Dilan Pathirana and Jan Hasenauer},
   title            = {Non-Negative Universal Differential Equations With Applications in Systems Biology},
   year             = {2024},
-  archiveprefix    = {arXiv},
-  creationdate     = {2024-06-28T08:40:06},
-  eprint           = {2406.14246},
-  modificationdate = {2024-06-28T08:40:06},
-  primaryclass     = {q-bio.QM},
-  url              = {https://arxiv.org/abs/2406.14246},
+  journal          = {IFAC-PapersOnLine},
+  volume           = {58},
+  number           = {23},
+  pages            = {25-30},
+  issn             = {2405-8963},
+  doi              = {https://doi.org/10.1016/j.ifacol.2024.10.005},
+  url              = {https://www.sciencedirect.com/science/article/pii/S2405896324017518},
+  abstract         = {Universal differential equations (UDEs) leverage the respective advantages of mechanistic models and artificial neural networks and combine them into one dynamic model. However, these hybrid models can suffer from unrealistic solutions, such as negative values for biochemical quantities. We present non-negative UDE (nUDEs), a constrained UDE variant that guarantees non-negative values. Furthermore, we explore regularisation techniques to improve generalisation and interpretability of UDEs.}
 }
 
 @Article{SchmiesterBra2024,

pypesto/objective/julia/petabJl.py

@@ -62,10 +62,10 @@ def __init__(
         self.petab_jl_problem = petab_jl_problem
 
         # get functions
-        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)
+        fun = self.petab_jl_problem.nllh
+        grad = self.petab_jl_problem.grad
+        hess = self.petab_jl_problem.hess
+        x_names = np.asarray(self.petab_jl_problem.xnames)
 
         # call the super super super constructor
         super(JuliaObjective, self).__init__(
@@ -105,10 +105,10 @@ def __setstate__(self, state):
         self.petab_jl_problem = petab_jl_problem
 
         # get functions
-        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)
+        fun = self.petab_jl_problem.nllh
+        grad = self.petab_jl_problem.grad
+        hess = self.petab_jl_problem.hess
+        x_names = np.asarray(self.petab_jl_problem.xnames)
 
         # call the super super constructor
         super(JuliaObjective, self).__init__(fun, grad, hess, x_names)

pypesto/objective/julia/petab_jl_importer.py

@@ -309,7 +309,7 @@ def _write_julia_file(
         f"found at {link_to_options}\n"
         f"petabProblem = PEtabODEProblem(\n\t"
         f"petabModel,\n\t"
-        f"ode_solver=ODESolver({odeSolvOpt_str}),\n\t"
+        f"odesolver=ODESolver({odeSolvOpt_str}),\n\t"
         f"gradient_method=:{options['gradient_method']},\n\t"
         f"hessian_method=:{options['hessian_method']},\n\t"
         f"sparse_jacobian={options['sparse_jacobian']},\n\t"

pypesto/profile/options.py

@@ -32,9 +32,9 @@ class ProfileOptions(dict):
     reg_order:
         Maximum degree of regression polynomial used in regression based
         adaptive profile points proposal.
-    magic_factor_obj_value:
-        There is this magic factor in the old profiling code which slows down
-        profiling at small ratios (must be >= 0 and < 1).
+    adaptive_target_scaling_factor:
+        The scaling factor of the next_obj_target in next guess generation.
+        Larger values result in larger next_guess step size (must be > 1).
     whole_path:
         Whether to profile the whole bounds or only till we get below the
         ratio.
@@ -44,13 +44,13 @@ def __init__(
         self,
         default_step_size: float = 0.01,
         min_step_size: float = 0.001,
-        max_step_size: float = 1.0,
+        max_step_size: float = 0.1,
         step_size_factor: float = 1.25,
         delta_ratio_max: float = 0.1,
         ratio_min: float = 0.145,
         reg_points: int = 10,
         reg_order: int = 4,
-        magic_factor_obj_value: float = 0.5,
+        adaptive_target_scaling_factor: float = 1.5,
         whole_path: bool = False,
     ):
         super().__init__()
@@ -63,7 +63,7 @@ def __init__(
         self.delta_ratio_max = delta_ratio_max
         self.reg_points = reg_points
         self.reg_order = reg_order
-        self.magic_factor_obj_value = magic_factor_obj_value
+        self.adaptive_target_scaling_factor = adaptive_target_scaling_factor
         self.whole_path = whole_path
 
         self.validate()
@@ -112,5 +112,5 @@ def validate(self):
         if self.default_step_size < self.min_step_size:
             raise ValueError("default_step_size must be >= min_step_size.")
 
-        if self.magic_factor_obj_value < 0 or self.magic_factor_obj_value >= 1:
-            raise ValueError("magic_factor_obj_value must be >= 0 and < 1.")
+        if self.adaptive_target_scaling_factor < 1:
+            raise ValueError("adaptive_target_scaling_factor must be > 1.")

pypesto/profile/profile.py

@@ -24,7 +24,7 @@ def parameter_profile(
     profile_index: Iterable[int] = None,
     profile_list: int = None,
     result_index: int = 0,
-    next_guess_method: Union[Callable, str] = "adaptive_step_regression",
+    next_guess_method: Union[Callable, str] = "adaptive_step_order_1",
     profile_options: ProfileOptions = None,
     progress_bar: bool = None,
     filename: Union[str, Callable, None] = None,
@@ -93,7 +93,9 @@ def parameter_profile(
     profile_options = ProfileOptions.create_instance(profile_options)
     profile_options.validate()
 
-    # create a function handle that will be called later to get the next point
+    # Create a function handle that will be called later to get the next point.
+    # This function will be used to generate the initial points of optimization
+    # steps in profiling in `walk_along_profile.py`
     if isinstance(next_guess_method, str):
 
         def create_next_guess(
@@ -104,6 +106,8 @@ def create_next_guess(
             current_profile_,
             problem_,
             global_opt_,
+            min_step_increase_factor_,
+            max_step_reduce_factor_,
         ):
             return next_guess(
                 x,
@@ -114,6 +118,8 @@ def create_next_guess(
                 current_profile_,
                 problem_,
                 global_opt_,
+                min_step_increase_factor_,
+                max_step_reduce_factor_,
             )
 
     elif callable(next_guess_method):