Merge dev (#27)

.github/workflows/tests_slow.yml

@@ -35,7 +35,7 @@ jobs:
         pip install -r requirements-tests.txt
         pip install -r requirements-assignment.txt
         pip install -r requirements-ota.txt
-        pip install -e .[arch_sbo,botorch,trieste,tpe,rocket,egor]
+        pip install -e .[arch_sbo,botorch,trieste,tpe,rocket,egor,hebo]
         pip install jupyter ipython ipykernel
         ipython kernel install --name "python3" --user
 

.gitignore

@@ -214,3 +214,5 @@ $RECYCLE.BIN/
 
 n2.html
 site/
+
+sb_arch_opt/problems/turbofan_data

README.md

@@ -62,6 +62,10 @@ If you use SBArchOpt in your work, please cite it:
 Bussemaker, J.H., (2023). SBArchOpt: Surrogate-Based Architecture Optimization. Journal of Open Source Software, 8(89),
 5564, DOI: [10.21105/joss.05564](https://doi.org/10.21105/joss.05564)
 
+Bussemaker, J.H., et al., (2024). Surrogate-Based Optimization of System Architectures Subject to Hidden Constraints.
+In AIAA AVIATION 2024 FORUM. Las Vegas, NV, USA.
+DOI: [10.2514/6.2024-4401](https://arc.aiaa.org/doi/10.2514/6.2024-4401)
+
 ## Contributing
 
 The project is coordinated by: Jasper Bussemaker (*jasper.bussemaker at dlr.de*)

docs/algo/arch_sbo.md

@@ -1,10 +1,11 @@
 # Architecture Surrogate-Based Optimization (SBO) Algorithm
 
 ArchSBO implements a Surrogate-Based Optimization (SBO) algorithm configured for solving most types of architecture
-optimization problems. It has been developed with experience from the following work:
+optimization problems. It is presented in the following work:
 
-J.H. Bussemaker et al., "Effectiveness of Surrogate-Based Optimization Algorithms for System Architecture Optimization",
-AIAA Aviation 2021, DOI: [10.2514/6.2021-3095](https://arc.aiaa.org/doi/10.2514/6.2021-3095)
+J.H. Bussemaker et al. "Surrogate-Based Optimization of System Architectures Subject to Hidden Constraints".
+In: AIAA AVIATION 2024 FORUM. Las Vegas, NV, USA, July 2024.
+DOI: [10.2514/6.2024-4401](https://arc.aiaa.org/doi/10.2514/6.2024-4401)
 
 The algorithm uses state-of-the-art mixed-discrete hierarchical Gaussian Process (Kriging) surrogate models and ensures
 that all evaluated and selected infill points are imputed (and therefore valid).

docs/index.md

@@ -51,6 +51,10 @@ If you use SBArchOpt in your work, please cite it:
 Bussemaker, J.H., (2023). SBArchOpt: Surrogate-Based Architecture Optimization. Journal of Open Source Software, 8(89),
 5564, DOI: [10.21105/joss.05564](https://doi.org/10.21105/joss.05564)
 
+Bussemaker, J.H., et al., (2024). Surrogate-Based Optimization of System Architectures Subject to Hidden Constraints.
+In AIAA AVIATION 2024 FORUM. Las Vegas, NV, USA.
+DOI: [10.2514/6.2024-4401](https://arc.aiaa.org/doi/10.2514/6.2024-4401)
+
 ## Usage
 
 See also the tutorial(s):

sb_arch_opt/__init__.py

@@ -1 +1 @@
-__version__ = '1.5.2'
+__version__ = '1.5.3'

sb_arch_opt/algo/trieste_interface/algo.py

@@ -237,19 +237,19 @@ def _run_doe(self, n: int):
         return self.observer(self.search_space.sample(n))
 
     def get_models(self, datasets):
-        # https://secondmind-labs.github.io/trieste/1.0.0/notebooks/inequality_constraints.html#Modelling-the-two-functions
+        # https://secondmind-labs.github.io/trieste/3.3.4/notebooks/inequality_constraints.html#Modelling-the-two-functions
         search_space = self.search_space
 
         models = {}
         for tag, dataset in datasets.items():
-            # https://secondmind-labs.github.io/trieste/1.0.0/notebooks/failure_ego.html#Build-GPflow-models
+            # https://secondmind-labs.github.io/trieste/3.3.4/notebooks/failure_ego.html#Build-GPflow-models
             if tag == FAILED:
                 classifier = build_vgp_classifier(dataset, search_space, noise_free=True)
                 models[tag] = VariationalGaussianProcess(
                     classifier, BatchOptimizer(tf.optimizers.Adam(1e-3)), use_natgrads=True)
                 continue
 
-            # https://secondmind-labs.github.io/trieste/1.0.0/notebooks/expected_improvement.html#Model-the-objective-function
+            # https://secondmind-labs.github.io/trieste/3.3.4/notebooks/expected_improvement.html#Model-the-objective-function
             gpr = build_gpr(dataset, search_space, likelihood_variance=1e-7)
             models[tag] = GaussianProcessRegression(gpr, num_kernel_samples=100)
 
@@ -271,16 +271,16 @@ def get_state_path(results_folder):
     def get_acquisition_rule(self, pof=.5) -> 'AcquisitionRule':
         """
         Builds the acquisition rule based on whether the problem is single- or multi-objective and constrained or not:
-        https://secondmind-labs.github.io/trieste/1.0.0/notebooks/inequality_constraints.html#Define-the-acquisition-process
-        https://secondmind-labs.github.io/trieste/1.0.0/notebooks/multi_objective_ehvi.html#Define-the-acquisition-function
+        https://secondmind-labs.github.io/trieste/3.3.4/notebooks/inequality_constraints.html#Define-the-acquisition-process
+        https://secondmind-labs.github.io/trieste/3.3.4/notebooks/multi_objective_ehvi.html#Define-the-acquisition-function
         """
 
         if self._problem.n_eq_constr > 0:
             raise RuntimeError('Trieste currently does not support equality constraints')
 
         if self.is_constrained:
             # Reduce the PoF rules into one
-            # https://secondmind-labs.github.io/trieste/1.0.0/notebooks/inequality_constraints.html#Constrained-optimization-with-more-than-one-constraint
+            # https://secondmind-labs.github.io/trieste/3.3.4/notebooks/inequality_constraints.html#Constrained-optimization-with-more-than-one-constraint
             pof_builders = [ProbabilityOfFeasibility(threshold=pof).using(f'{CONSTR_PREFIX}{ig}')
                             for ig in range(self._problem.n_ieq_constr)]
             pof_builder = pof_builders[0] if len(pof_builders) == 1 else Product(*pof_builders)
@@ -404,10 +404,10 @@ def to_population(self, datasets: Dict[Hashable, 'Dataset']) -> Population:
 class ArchOptObserver:
     """
     The observer function that evaluates each architecture, according to the tagged observer pattern:
-    https://secondmind-labs.github.io/trieste/1.0.0/notebooks/inequality_constraints.html
+    https://secondmind-labs.github.io/trieste/3.3.4/notebooks/inequality_constraints.html
 
     Support for failed evaluations based on:
-    https://secondmind-labs.github.io/trieste/1.0.0/notebooks/failure_ego.html#Define-the-data-sets
+    https://secondmind-labs.github.io/trieste/3.3.4/notebooks/failure_ego.html#Define-the-data-sets
 
     Class needed to prevent overflow in BayesianOptimizer.__repr__
     """

sb_arch_opt/problems/turbofan_arch.py

@@ -19,9 +19,13 @@
 More information: https://github.com/jbussemaker/OpenTurbofanArchitecting
 """
 import pickle
+import shutil
 import logging
+import zipfile
+import tempfile
 import numpy as np
 from typing import *
+import urllib.request
 import concurrent.futures
 from cached_property import cached_property
 from pymoo.core.variable import Real, Integer, Choice
@@ -43,6 +47,8 @@
 
 log = logging.getLogger('sb_arch_opt.turb')
 
+DATA_URL = 'https://github.com/jbussemaker/SBArchOpt_Data/archive/refs/heads/main.zip'
+
 
 def check_dependency():
     if not HAS_OPEN_TURB_ARCH:
@@ -63,7 +69,6 @@ class OpenTurbArchProblemWrapper(HierarchyProblemBase):
     default_enable_pf_calc = False
     _robust_correct_x = False  # Bug in the bleed offtake choice
 
-    _data_folder = 'turbofan_data'
     _sub_folder = None
 
     def __init__(self, open_turb_arch_problem: 'ArchitectingProblem', n_parallel=None):
@@ -406,7 +411,7 @@ def _arch_evaluate_x_surrogate(self, x: np.ndarray):
 
     @classmethod
     def _get_data_path(cls, sub_path: str) -> str:
-        path = os.path.join(os.path.dirname(__file__), cls._data_folder, cls._sub_folder)
+        path = os.path.join(get_data_folder(), cls._sub_folder)
         os.makedirs(path, exist_ok=True)
         if sub_path is not None:
             path = os.path.join(path, sub_path)
@@ -624,6 +629,29 @@ def _arch_evaluate_x(self, x: np.ndarray):
 #         return self._arch_evaluate_x_surrogate(x)
 
 
+def get_data_folder(force_download=False):
+    """Download problem data if needed, and return path to data"""
+    cache_folder = get_cache_path()
+    folder = os.path.join(cache_folder, 'turbofan_data')
+
+    if force_download or not os.path.exists(folder):
+        if os.path.exists(folder):
+            shutil.rmtree(folder)
+
+        log.info(f'Downloading turbofan problem data from: {DATA_URL}')
+        with tempfile.TemporaryDirectory() as tmp_folder:
+            zip_file_path = os.path.join(tmp_folder, 'data.zip')
+            urllib.request.urlretrieve(DATA_URL, zip_file_path)
+
+            with zipfile.ZipFile(zip_file_path, 'r') as zip_ref:
+                repo_root_folder = zip_ref.namelist()[0][:-1]
+                zip_ref.extractall(tmp_folder)
+            shutil.move(os.path.join(tmp_folder, repo_root_folder, 'turbofan_data'), folder)
+        log.info('Turbofan problem data downloaded and available!')
+
+    return folder
+
+
 if __name__ == '__main__':
     print(SimpleTurbofanArch().pareto_front())
     # SimpleTurbofanArch().print_stats()

sb_arch_opt/tests/algo/test_hebo.py

@@ -10,9 +10,9 @@ def check_dependency():
     return pytest.mark.skipif(not HAS_HEBO, reason='HEBO dependencies not installed')
 
 
-# @pytest.mark.skipif(int(os.getenv('RUN_SLOW_TESTS', 0)) != 1, reason='Set RUN_SLOW_TESTS=1 to run slow tests')
-# def test_slow_tests():
-#     assert HAS_HEBO
+@pytest.mark.skipif(int(os.getenv('RUN_SLOW_TESTS', 0)) != 1, reason='Set RUN_SLOW_TESTS=1 to run slow tests')
+def test_slow_tests():
+    assert HAS_HEBO
 
 
 @check_dependency()

sb_arch_opt/tests/algo/test_trieste.py

@@ -6,6 +6,7 @@
 from sb_arch_opt.problems.constrained import ArchCantileveredBeam
 from sb_arch_opt.algo.trieste_interface.algo import ArchOptBayesianOptimizer
 
+
 def check_dependency():
     return pytest.mark.skipif(not HAS_TRIESTE, reason='Trieste dependencies not installed')
 

setup.py

@@ -78,9 +78,9 @@ def _get_readme():
                 'botorch~=0.8.2',
             ],
             'trieste': [
-                'trieste~=2.0.0',
-                # Until https://github.com/GPflow/GPflow/pull/2050 is merged and GPflow has been updated
-                # 'gpflow~=2.7.0', 'keras~=2.10.0', 'tensorflow-probability==0.18.0',
+                'trieste~=3.3',
+                # Until https://github.com/secondmind-labs/trieste/pull/858 is merged
+                'tensorflow<=2.16', 'tensorflow-probability<0.24',
             ],
             'tpe': [
                 'tpe==0.0.8',
@@ -97,7 +97,4 @@ def _get_readme():
         },
         python_requires='>=3.7',
         packages=find_packages(include='sb_arch_opt*'),
-        package_data={
-            'sb_arch_opt.problems': ['turbofan_data/*', 'turbofan_data/**/*'],
-        },
     )