Merge pull request #202 from ICB-DCM/develop

Release 0.0.10

.travis.yml

@@ -20,11 +20,10 @@ addons:
       - libatlas-base-dev
       - lcov
       - swig3.0
+      - pandoc
 
 install:
   - mkdir -p ~/bin/ && ln -s /usr/bin/swig3.0 ~/bin/swig && export PATH=~/bin/:$PATH
-  - pip3 install --upgrade setuptools wheel pkgconfig
-  - pip3 install --upgrade jupyter # for jupyter notebooks
   - pip3 install --upgrade -r ./.travis_pip_reqs.txt
   - pip3 install .
 
@@ -33,8 +32,9 @@ script:
   - python3 -m flake8 --exclude=build,doc,example,tmp,amici_models
   - travis_wait 20 python3 -m pytest --cov=pypesto ./test/test_*
   - travis_wait 20 xvfb-run -a python3 -m pytest --cov=pypesto --cov-append ./test/visualize/test_*
+  - if [ "$TRAVIS_PULL_REQUEST" != "false" ]; then sphinx-build -W -b html -d doc/_build/html doc/ doc/_build/html; fi
+  - if [ "$TRAVIS_PULL_REQUEST" != "false" ]; then test/run_notebook.sh doc/example/; fi
   - coverage xml
-  - test/run_notebook.sh doc/example/
 
 after_success:
   - bash <(curl -s https://codecov.io/bash)

.travis_pip_reqs.txt

@@ -1,8 +1,16 @@
+setuptools
+wheel
+pkgconfig
+jupyter
 pytest
 pytest-cov
 coverage
 flake8
 dlib
+pyswarm
 gitpython
 petab>=0.0.0a16
 amici>=0.10.13
+sphinx
+nbsphinx
+sphinx_rtd_theme

doc/conf.py

@@ -104,7 +104,8 @@
 # Add any paths that contain custom static files (such as style sheets) here,
 # relative to this directory. They are copied after the builtin static files,
 # so a file named "default.css" will overwrite the builtin "default.css".
-html_static_path = ['_static']
+# html_static_path = ['_static']
+html_favicon = "logo/logo_favicon.png"
 
 # Custom sidebar templates, must be a dictionary that maps document names
 # to template names.

doc/releasenotes.rst

@@ -6,6 +6,15 @@ Release notes
 ..........
 
 
+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)
 ------------------
 

pypesto/__init__.py

@@ -21,7 +21,8 @@
                        OptimizerResult,
                        Optimizer,
                        ScipyOptimizer,
-                       DlibOptimizer)
+                       DlibOptimizer,
+                       GlobalOptimizer)
 from .profile import (parameter_profile,
                       ProfileOptions,
                       ProfilerResult)
@@ -50,6 +51,7 @@
     "Optimizer",
     "ScipyOptimizer",
     "DlibOptimizer",
+    "GlobalOptimizer",
     # profile
     "parameter_profile",
     "ProfileOptions",

pypesto/engine/__init__.py

@@ -7,13 +7,14 @@
 multistart itself is parallelized.
 """
 
-
+from .base import Engine
 from .single_core import SingleCoreEngine
 from .multi_process import MultiProcessEngine
 from .task import OptimizerTask
 
 
 __all__ = [
+    "Engine",
     "SingleCoreEngine",
     "MultiProcessEngine",
     "OptimizerTask"

pypesto/engine/base.py

@@ -1,16 +1,16 @@
 from typing import List
-from abc import abstractmethod
+import abc
 
 
-class Engine:
+class Engine(abc.ABC):
     """
     Abstract engine base class.
     """
 
     def __init__(self):
         pass
 
-    @abstractmethod
+    @abc.abstractmethod
     def execute(self, tasks: List):
         raise NotImplementedError(
             "This engine is not intended to be called.")

pypesto/objective/amici_objective.py

@@ -253,6 +253,8 @@ def _call_amici(
         if sensi_order > self.max_sensi_order:
             raise Exception("Sensitivity order not allowed.")
 
+        sensi_method = self.amici_solver.getSensitivityMethod()
+
         # prepare outputs
         nllh = 0.0
         snllh = np.zeros(self.dim)
@@ -306,14 +308,15 @@ def _call_amici(
                         snllh,
                         coefficient=-1.0
                     )
-                    # TODO: Compute the full Hessian, and check here
-                    add_sim_hess_to_opt_hess(
-                        self.x_ids,
-                        self.mapping_par_opt_to_par_sim[data_ix],
-                        rdata['FIM'],
-                        s2nllh,
-                        coefficient=-1.0
-                    )
+                    if sensi_method == 1:
+                        # TODO: Compute the full Hessian, and check here
+                        add_sim_hess_to_opt_hess(
+                            self.x_ids,
+                            self.mapping_par_opt_to_par_sim[data_ix],
+                            rdata['FIM'],
+                            s2nllh,
+                            coefficient=-1.0
+                        )
 
             elif mode == MODE_RES:
                 res = np.hstack([res, rdata['res']]) \

pypesto/objective/objective.py

@@ -17,44 +17,56 @@ class Objective:
     giving a standardized way of calling. Apart from that, it manages several
     things including fixing of parameters and history.
 
+
     Parameters
     ----------
 
     fun: callable, optional
         The objective function to be minimized. If it only computes the
         objective function value, it should be of the form
+
             ``fun(x) -> float``
+
         where x is an 1-D array with shape (n,), and n is the parameter space
         dimension.
 
     grad: callable, bool, optional
         Method for computing the gradient vector. If it is a callable,
         it should be of the form
+
             ``grad(x) -> array_like, shape (n,).``
+
         If its value is True, then fun should return the gradient as a second
         output.
 
     hess: callable, optional
         Method for computing the Hessian matrix. If it is a callable,
         it should be of the form
+
             ``hess(x) -> array, shape (n,n).``
+
         If its value is True, then fun should return the gradient as a
         second, and the Hessian as a third output, and grad should be True as
         well.
 
     hessp: callable, optional
         Method for computing the Hessian vector product, i.e.
+
             ``hessp(x, v) -> array_like, shape (n,)``
+
         computes the product H*v of the Hessian of fun at x with v.
 
     res: {callable, bool}, optional
         Method for computing residuals, i.e.
+
             ``res(x) -> array_like, shape(m,).``
 
     sres: callable, optional
         Method for computing residual sensitivities. If its is a callable,
         it should be of the form
+
             ``sres(x) -> array, shape (m,n).``
+
         If its value is True, then res should return the residual
         sensitivities as a second output.
 
@@ -74,6 +86,7 @@ class Objective:
     options: pypesto.ObjectiveOptions, optional
         Options as specified in pypesto.ObjectiveOptions.
 
+
     Attributes
     ----------
 
@@ -90,6 +103,7 @@ class Objective:
     sensitivity_orders: tuple
         Temporary variable to store requested sensitivity orders
 
+
     Notes
     -----
 
@@ -518,6 +532,7 @@ def check_grad(self,
         Compare gradient evaluation: Firstly approximate via finite
         differences, and secondly use the objective gradient.
 
+
         Parameters
         ----------
 
@@ -531,22 +546,22 @@ def check_grad(self,
             Finite differences step size. Default: 1e-5.
 
         verbosity: int
-            Level of verbosity for function output
-                0: no output
-                1: summary for all parameters
-                2: summary for individual parameters
+            Level of verbosity for function output.
+            * 0: no output,
+            * 1: summary for all parameters,
+            * 2: summary for individual parameters.
             Default: 1.
 
         mode: str
             Residual (MODE_RES) or objective function value
             (MODE_FUN, default) computation mode.
 
+
         Returns
         ----------
 
         result: pd.DataFrame
             gradient, finite difference approximations and error estimates.
-
         """
 
         if x_indices is None:

pypesto/objective/petab_import.py

@@ -80,7 +80,7 @@ def from_folder(folder,
             output_folder=output_folder,
             model_name=model_name)
 
-    def create_model(self, force_compile=False):
+    def create_model(self, force_compile=False, *args, **kwargs):
         """
         Import amici model. If necessary or force_compile is True, compile
         first.
@@ -93,10 +93,11 @@ def create_model(self, force_compile=False):
             exist yet. If True, the output folder is deleted and the model
             (re-)compiled in either case.
 
+            .. warning::
+                If `force_compile`, then an existing folder of that name will
+                be deleted.
 
-        .. warning::
-            If `force_compile`, then an existing folder of that name will be
-            deleted.
+        args, kwargs: Extra arguments passed to amici.SbmlImporter.sbml2amici
         """
         # courtesy check if target not folder
         if os.path.exists(self.output_folder) \
@@ -113,7 +114,7 @@ def create_model(self, force_compile=False):
         if self._must_compile(force_compile):
             logger.info(f"Compiling amici model to folder "
                         f"{self.output_folder}.")
-            self.compile_model()
+            self.compile_model(*args, **kwargs)
         else:
             logger.info(f"Using existing amici model in folder "
                         f"{self.output_folder}.")
@@ -156,18 +157,16 @@ def _must_compile(self, force_compile: bool):
         # no need to (re-)compile
         return False
 
-    def compile_model(self):
+    def compile_model(self, *args, **kwargs):
         """
         Compile the model. If the output folder exists already, it is first
         deleted.
-        """
 
-        # check prerequisites
-        if not petab.condition_table_is_parameter_free(
-                self.petab_problem.condition_df):
-            raise AssertionError(
-                "Parameter dependent conditions in the condition file "
-                "are not yet supported.")
+        Parameters
+        ----------
+        args, kwargs: Extra arguments passed to amici.SbmlImporter.sbml2amici
+
+        """
 
         # delete output directory
         if os.path.exists(self.output_folder):
@@ -204,7 +203,8 @@ def compile_model(self):
             observables=observables,
             constantParameters=constant_parameter_ids,
             sigmas=sigmas,
-            noise_distributions=noise_distrs
+            noise_distributions=noise_distrs,
+            *args, **kwargs
         )
 
     def create_solver(self, model=None):

pypesto/optimize/__init__.py

@@ -9,11 +9,13 @@
 from .optimizer import (OptimizerResult,
                         Optimizer,
                         ScipyOptimizer,
-                        DlibOptimizer)
+                        DlibOptimizer,
+                        GlobalOptimizer)
 
 __all__ = ["minimize",
            "OptimizeOptions",
            "OptimizerResult",
            "Optimizer",
            "ScipyOptimizer",
-           "DlibOptimizer"]
+           "DlibOptimizer",
+           "GlobalOptimizer"]

pypesto/optimize/optimize.py

@@ -82,7 +82,7 @@ def minimize(
 
     startpoint_method: {callable, False}, optional
         Method for how to choose start points. False means the optimizer does
-        not require start points
+        not require start points, e.g. 'pso' method in 'GlobalOptimizer'
 
     result: pypesto.Result
         A result object to append the optimization results to. For example,