Merge branch 'main' into i_optimality

.github/workflows/lint.yaml

@@ -19,7 +19,7 @@ jobs:
       - name: Set up Python
         uses: actions/setup-python@v5
         with:
-          python-version: "3.9"
+          python-version: "3.10"
 
       - name: Set up uv
         uses: astral-sh/setup-uv@v2

.github/workflows/test.yaml

@@ -17,7 +17,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: ['3.9', '3.10', '3.11' ]
+        python-version: ['3.10', '3.11', '3.12', '3.13']
     steps:
       - name: Check out repo
         uses: actions/checkout@v4
@@ -42,7 +42,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: [ '3.9', '3.11' ]
+        python-version: [ '3.10', '3.12' ]
     steps:
       - name: Check out repo
         uses: actions/checkout@v4
@@ -72,7 +72,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: [ '3.10' ]
+        python-version: [ '3.12' ]
     steps:
       - name: Check out repo
         uses: actions/checkout@v4
@@ -106,7 +106,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: [ '3.10' ]
+        python-version: [ '3.12' ]
     steps:
       - name: Check out repo
         uses: actions/checkout@v4

.gitignore

@@ -118,6 +118,9 @@ venv.bak/
 .vscode/
 .devcontainer
 
+# pycharm project settings
+.idea/
+
 # Rope project settings
 .ropeproject
 

bofire/benchmarks/detergent.py

@@ -57,11 +57,11 @@ def __init__(self):
 
         self._domain = Domain.from_lists(
             inputs=[
-                ContinuousInput(key="x1", bounds=(0.0, 0.2)),
-                ContinuousInput(key="x2", bounds=(0.0, 0.3)),
-                ContinuousInput(key="x3", bounds=(0.02, 0.2)),
-                ContinuousInput(key="x4", bounds=(0.0, 0.06)),
-                ContinuousInput(key="x5", bounds=(0.0, 0.04)),
+                ContinuousInput(key="x1", bounds=[0.0, 0.2]),
+                ContinuousInput(key="x2", bounds=[0.0, 0.3]),
+                ContinuousInput(key="x3", bounds=[0.02, 0.2]),
+                ContinuousInput(key="x4", bounds=[0.0, 0.06]),
+                ContinuousInput(key="x5", bounds=[0.0, 0.04]),
             ],
             outputs=[ContinuousOutput(key=f"y{i+1}") for i in range(5)],
             constraints=[

bofire/benchmarks/hyperopt.py

@@ -1,6 +1,7 @@
 from typing import Optional
 
 import pandas as pd
+from tqdm import tqdm
 
 import bofire.surrogates.api as surrogates
 from bofire.benchmarks.benchmark import Benchmark
@@ -15,6 +16,7 @@ def __init__(
         training_data: pd.DataFrame,
         folds: int,
         random_state: Optional[int] = None,
+        show_progress_bar: bool = False,
     ) -> None:
         super().__init__()
         if surrogate_data.hyperconfig is None:
@@ -24,6 +26,7 @@ def __init__(
         self.folds = folds
         self.results = None
         self.random_state = random_state
+        self.show_progress_bar = show_progress_bar
 
     @property
     def domain(self) -> Domain:
@@ -34,7 +37,11 @@ def target_metric(self):
         return self.surrogate_data.hyperconfig.target_metric  # type: ignore
 
     def _f(self, candidates: pd.DataFrame) -> pd.DataFrame:
-        for i, candidate in candidates.iterrows():
+        for i, candidate in tqdm(
+            candidates.iterrows(),
+            total=candidates.shape[0],
+            disable=self.show_progress_bar is False,
+        ):
             self.surrogate_data.update_hyperparameters(candidate)
             surrogate = surrogates.map(self.surrogate_data)
             _, cv_test, _ = surrogate.cross_validate(  # type: ignore

bofire/benchmarks/multi.py

@@ -54,7 +54,7 @@ def __init__(self, dim: PositiveInt, num_objectives: PositiveInt = 2, **kwargs):
 
         inputs = []
         for i in range(self.dim):
-            inputs.append(ContinuousInput(key="x_%i" % (i), bounds=(0, 1)))
+            inputs.append(ContinuousInput(key="x_%i" % (i), bounds=[0, 1]))
         outputs = []
         self.k = self.dim - self.num_objectives + 1
         for i in range(self.num_objectives):
@@ -140,8 +140,8 @@ def __init__(self, constraints: bool = True, **kwargs):
         self._domain = Domain(
             inputs=Inputs(
                 features=[
-                    ContinuousInput(key="x1", bounds=(0, 5)),
-                    ContinuousInput(key="x2", bounds=(0, 3)),
+                    ContinuousInput(key="x1", bounds=[0, 5]),
+                    ContinuousInput(key="x2", bounds=[0, 3]),
                 ],
             ),
             outputs=Outputs(
@@ -187,8 +187,8 @@ def __init__(self, **kwargs):
         self._domain = Domain(
             inputs=Inputs(
                 features=[
-                    ContinuousInput(key="x1", bounds=(0, math.pi)),
-                    ContinuousInput(key="x2", bounds=(0, math.pi)),
+                    ContinuousInput(key="x1", bounds=[0, math.pi]),
+                    ContinuousInput(key="x2", bounds=[0, math.pi]),
                 ],
             ),
             outputs=Outputs(
@@ -293,13 +293,13 @@ def __init__(self, C_i: Optional[np.ndarray] = None, **kwargs):
         # Decision variables
         # "residence time in minutes"
         inputs = [
-            ContinuousInput(key="tau", bounds=(0.5, 2)),
+            ContinuousInput(key="tau", bounds=[0.5, 2]),
             # "equivalents of pyrrolidine"
-            ContinuousInput(key="equiv_pldn", bounds=(1, 5)),
+            ContinuousInput(key="equiv_pldn", bounds=[1, 5]),
             # "concentration of 2,4 dinitrofluorobenenze at reactor inlet (after mixing) in M"
-            ContinuousInput(key="conc_dfnb", bounds=(0.1, 0.5)),
+            ContinuousInput(key="conc_dfnb", bounds=[0.1, 0.5]),
             # "Reactor temperature in degrees celsius"
-            ContinuousInput(key="temperature", bounds=(30, 120)),
+            ContinuousInput(key="temperature", bounds=[30, 120]),
         ]
         # Objectives
         # "space time yield (kg/m^3/h)"
@@ -442,7 +442,7 @@ def __init__(self, n_inputs=30, **kwargs):
         super().__init__(**kwargs)
         self.n_inputs = n_inputs
         inputs = [
-            ContinuousInput(key=f"x{i+1}", bounds=(0, 1)) for i in range(n_inputs)
+            ContinuousInput(key=f"x{i+1}", bounds=[0, 1]) for i in range(n_inputs)
         ]
         inputs = Inputs(features=inputs)
         outputs = [
@@ -550,11 +550,11 @@ def __init__(
                 ],
             ),
             # "base equivalents"
-            ContinuousInput(key="base_eq", bounds=(1, 2.5)),
+            ContinuousInput(key="base_eq", bounds=[1, 2.5]),
             # "Reactor temperature in degrees celsius"
-            ContinuousInput(key="temperature", bounds=(30, 100)),
+            ContinuousInput(key="temperature", bounds=[30, 100]),
             # "residence time in seconds (s)"
-            ContinuousInput(key="t_res", bounds=(60, 1800)),
+            ContinuousInput(key="t_res", bounds=[60, 1800]),
         ]
 
         input_preprocessing_specs = {
@@ -566,11 +566,11 @@ def __init__(
         outputs = [
             ContinuousOutput(
                 key="yield",
-                objective=MaximizeObjective(w=1.0, bounds=(0.0, 1.0)),
+                objective=MaximizeObjective(w=1.0, bounds=[0.0, 1.0]),
             ),
             ContinuousOutput(
                 key="cost",
-                objective=MinimizeObjective(w=1.0, bounds=(0.0, 1.0)),
+                objective=MinimizeObjective(w=1.0, bounds=[0.0, 1.0]),
             ),
         ]
         self.ref_point = {"yield": 0.0, "cost": 1.0}

bofire/benchmarks/single.py

@@ -110,7 +110,7 @@ def __init__(
         # continuous input features
         for d in range(self.dim):
             input_feature_list.append(
-                ContinuousInput(key=f"x_{d+1}", bounds=(self.lower, self.upper)),
+                ContinuousInput(key=f"x_{d+1}", bounds=[self.lower, self.upper]),
             )
 
         # Objective
@@ -179,7 +179,7 @@ def __init__(self, dim: int = 6, allowed_k: Optional[int] = None, **kwargs) -> N
         self._domain = Domain(
             inputs=Inputs(
                 features=[
-                    ContinuousInput(key=f"x_{i}", bounds=(0, 1)) for i in range(dim)
+                    ContinuousInput(key=f"x_{i}", bounds=[0, 1]) for i in range(dim)
                 ],
             ),
             outputs=Outputs(
@@ -235,7 +235,7 @@ def __init__(self, dim: int = 6, allowed_k: Optional[int] = None, **kwargs) -> N
         self._domain = Domain(
             inputs=Inputs(
                 features=[
-                    ContinuousInput(key=f"x_{i}", bounds=(0, 1)) for i in range(dim)
+                    ContinuousInput(key=f"x_{i}", bounds=[0, 1]) for i in range(dim)
                 ]
             ),
             outputs=Outputs(
@@ -291,27 +291,23 @@ def __init__(self, locality_factor: Optional[float] = None, **kwargs) -> None:
                 features=[
                     ContinuousInput(
                         key="x_1",
-                        bounds=(-5.0, 10),
-                        local_relative_bounds=(
-                            (
-                                0.5 * locality_factor,
-                                0.5 * locality_factor,
-                            )
-                            if locality_factor is not None
-                            else None
-                        ),
+                        bounds=[-5.0, 10],
+                        local_relative_bounds=[
+                            0.5 * locality_factor,
+                            0.5 * locality_factor,
+                        ]
+                        if locality_factor is not None
+                        else None,
                     ),
                     ContinuousInput(
                         key="x_2",
-                        bounds=(0.0, 15.0),
-                        local_relative_bounds=(
-                            (
-                                1.5 * locality_factor,
-                                1.5 * locality_factor,
-                            )
-                            if locality_factor is not None
-                            else None
-                        ),
+                        bounds=[0.0, 15.0],
+                        local_relative_bounds=[
+                            1.5 * locality_factor,
+                            1.5 * locality_factor,
+                        ]
+                        if locality_factor is not None
+                        else None,
                     ),
                 ],
             ),
@@ -355,7 +351,7 @@ def __init__(self, **kwargs) -> None:
         self._domain = Domain(
             inputs=Inputs(
                 features=[
-                    ContinuousInput(key=f"x_{i+1:02d}", bounds=(0, 1))
+                    ContinuousInput(key=f"x_{i+1:02d}", bounds=[0, 1])
                     for i in range(30)
                 ],
             ),
@@ -398,8 +394,8 @@ def __init__(self, use_constraints: bool = False, **kwargs):
         self.use_constraints = use_constraints
         inputs = []
 
-        inputs.append(ContinuousInput(key="x_1", bounds=(-6, 6)))
-        inputs.append(ContinuousInput(key="x_2", bounds=(-6, 6)))
+        inputs.append(ContinuousInput(key="x_1", bounds=[-6, 6]))
+        inputs.append(ContinuousInput(key="x_2", bounds=[-6, 6]))
 
         objective = MinimizeObjective(w=1.0)
         output_feature = ContinuousOutput(key="y", objective=objective)
@@ -472,8 +468,8 @@ def __init__(self, use_constraints: bool = False, **kwargs):
         inputs = []
 
         inputs.append(TaskInput(key="task_id", categories=["task_1", "task_2"]))
-        inputs.append(ContinuousInput(key="x_1", bounds=(-6, 6)))
-        inputs.append(ContinuousInput(key="x_2", bounds=(-6, 6)))
+        inputs.append(ContinuousInput(key="x_1", bounds=[-6, 6]))
+        inputs.append(ContinuousInput(key="x_2", bounds=[-6, 6]))
 
         objective = MinimizeObjective(w=1.0)
         output_feature = ContinuousOutput(key="y", objective=objective)
@@ -645,7 +641,7 @@ def __init__(
         self._domain = Domain(
             inputs=Inputs(
                 features=[
-                    ContinuousInput(key=f"x_{i}", bounds=(0, 1))
+                    ContinuousInput(key=f"x_{i}", bounds=[0, 1])
                     for i in range(self.dim)
                 ],
             ),

bofire/data_models/domain/features.py

@@ -622,6 +622,7 @@ def _validate_transform_specs(
                     raise ValueError(
                         f"Forbidden transform type for feature with key {key}",
                     )
+
         return specs
 
     def get_bounds(

bofire/data_models/features/continuous.py

@@ -27,7 +27,7 @@ class ContinuousInput(NumericalInput):
 
     bounds: Bounds
     local_relative_bounds: Optional[
-        Tuple[Annotated[float, Field(gt=0)], Annotated[float, Field(gt=0)]]
+        Annotated[List[Annotated[float, Field(gt=0)]], Field(min_items=2, max_items=2)]  # type: ignore
     ] = None
     stepsize: Optional[float] = None
 

bofire/data_models/kernels/aggregation.py

@@ -3,13 +3,13 @@
 
 from bofire.data_models.kernels.categorical import HammingDistanceKernel
 from bofire.data_models.kernels.continuous import LinearKernel, MaternKernel, RBFKernel
-from bofire.data_models.kernels.kernel import Kernel
+from bofire.data_models.kernels.kernel import AggregationKernel
 from bofire.data_models.kernels.molecular import TanimotoKernel
 from bofire.data_models.kernels.shape import WassersteinKernel
 from bofire.data_models.priors.api import AnyGeneralPrior
 
 
-class AdditiveKernel(Kernel):
+class AdditiveKernel(AggregationKernel):
     type: Literal["AdditiveKernel"] = "AdditiveKernel"
     kernels: Sequence[
         Union[
@@ -26,7 +26,7 @@ class AdditiveKernel(Kernel):
     type: Literal["AdditiveKernel"] = "AdditiveKernel"
 
 
-class MultiplicativeKernel(Kernel):
+class MultiplicativeKernel(AggregationKernel):
     type: Literal["MultiplicativeKernel"] = "MultiplicativeKernel"
     kernels: Sequence[
         Union[
@@ -42,7 +42,7 @@ class MultiplicativeKernel(Kernel):
     ]
 
 
-class ScaleKernel(Kernel):
+class ScaleKernel(AggregationKernel):
     type: Literal["ScaleKernel"] = "ScaleKernel"
     base_kernel: Union[
         RBFKernel,

bofire/data_models/kernels/api.py

@@ -17,12 +17,23 @@
     PolynomialKernel,
     RBFKernel,
 )
-from bofire.data_models.kernels.kernel import Kernel
+from bofire.data_models.kernels.kernel import (
+    AggregationKernel,
+    FeatureSpecificKernel,
+    Kernel,
+)
 from bofire.data_models.kernels.molecular import MolecularKernel, TanimotoKernel
 from bofire.data_models.kernels.shape import WassersteinKernel
 
 
-AbstractKernel = Union[Kernel, CategoricalKernel, ContinuousKernel, MolecularKernel]
+AbstractKernel = Union[
+    Kernel,
+    CategoricalKernel,
+    ContinuousKernel,
+    MolecularKernel,
+    FeatureSpecificKernel,
+    AggregationKernel,
+]
 
 AnyContinuousKernel = Union[
     MaternKernel,

bofire/data_models/kernels/categorical.py

@@ -1,9 +1,9 @@
 from typing import Literal
 
-from bofire.data_models.kernels.kernel import Kernel
+from bofire.data_models.kernels.kernel import FeatureSpecificKernel
 
 
-class CategoricalKernel(Kernel):
+class CategoricalKernel(FeatureSpecificKernel):
     pass