Merge: Integrate Hook Examples (#331)

Integrating the dev branch dealing with the new hooks mechanic and
corresponding examples.
Includes #322 #287

Author: Scienfitz

CHANGELOG.md

@@ -48,6 +48,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
   `DiscreteExcludeConstraint`, `DiscreteLinkedParametersConstraint` and 
   `DiscreteNoLabelDuplicatesConstraint`
 - Discrete search space Cartesian product can be created lazily via Polars
+- Examples demonstrating the `register_hooks` utility: basic registration mechanism,
+  monitoring the probability of improvement, and automatic campaign stopping
 
 ### Changed
 - Passing an `Objective` to `Campaign` is now optional

baybe/simulation/scenarios.py

@@ -31,6 +31,7 @@ def simulate_scenarios(
     initial_data: list[pd.DataFrame] | None = None,
     groupby: list[str] | None = None,
     n_mc_iterations: int = 1,
+    random_seed: int | None = None,
     impute_mode: Literal[
         "error", "worst", "best", "mean", "random", "ignore"
     ] = "error",
@@ -52,6 +53,9 @@ def simulate_scenarios(
             A separate simulation will be conducted for each partition, with the search
             restricted to that partition.
         n_mc_iterations: The number of Monte Carlo simulations to be used.
+        random_seed: An optional integer specifying the random seed for the first Monte
+            Carlo run. Each subsequent runs will increase this value by 1. If omitted,
+            the current random seed is used.
         impute_mode: See :func:`baybe.simulation.core.simulate_experiment`.
         noise_percent: See :func:`baybe.simulation.core.simulate_experiment`.
 
@@ -64,7 +68,10 @@ def simulate_scenarios(
         function:
 
         * ``Scenario``: Specifies the scenario identifier of the respective simulation.
-        * ``Random_Seed``: Specifies the random seed used for the respective simulation.
+        * ``Monte_Carlo_Run``: Specifies the Monte Carlo repetition of the
+          respective simulation.
+        * Optional, if ``random_seed`` is provided: A column ``Random_Seed`` that
+          specifies the random seed used for the respective simulation.
         * Optional, if ``initial_data`` is provided: A column ``Initial_Data`` that
           specifies the index of the initial data set used for the respective
           simulation.
@@ -90,24 +97,26 @@ def make_xyzpy_callable(result_variable: str) -> Callable:
         @xyzpy.label(var_names=[result_variable])
         def simulate(
             Scenario: str,
-            Random_Seed=None,
+            Monte_Carlo_Run: int,
             Initial_Data=None,
         ):
             """Callable for xyzpy simulation."""
             data = None if initial_data is None else initial_data[Initial_Data]
-            return SimulationResult(
-                _simulate_groupby(
-                    scenarios[Scenario],
-                    lookup,
-                    batch_size=batch_size,
-                    n_doe_iterations=n_doe_iterations,
-                    initial_data=data,
-                    groupby=groupby,
-                    random_seed=Random_Seed,
-                    impute_mode=impute_mode,
-                    noise_percent=noise_percent,
-                )
+            seed = None if random_seed is None else Monte_Carlo_Run + _DEFAULT_SEED
+            result = _simulate_groupby(
+                scenarios[Scenario],
+                lookup,
+                batch_size=batch_size,
+                n_doe_iterations=n_doe_iterations,
+                initial_data=data,
+                groupby=groupby,
+                random_seed=seed,
+                impute_mode=impute_mode,
+                noise_percent=noise_percent,
             )
+            if random_seed is not None:
+                result["Random_Seed"] = seed
+            return SimulationResult(result)
 
         return simulate
 
@@ -130,7 +139,7 @@ def unpack_simulation_results(array: DataArray) -> pd.DataFrame:
 
     # Collect the settings to be simulated
     combos = {"Scenario": scenarios.keys()}
-    combos["Random_Seed"] = range(_DEFAULT_SEED, _DEFAULT_SEED + n_mc_iterations)
+    combos["Monte_Carlo_Run"] = range(n_mc_iterations)
     if initial_data:
         combos["Initial_Data"] = range(len(initial_data))
 

baybe/utils/botorch_wrapper.py

@@ -1,7 +1,7 @@
 """A wrapper class for synthetic BoTorch test functions."""
 
+import torch
 from botorch.test_functions import SyntheticTestFunction
-from torch import Tensor
 
 
 def botorch_function_wrapper(test_function: SyntheticTestFunction):
@@ -19,7 +19,7 @@ def botorch_function_wrapper(test_function: SyntheticTestFunction):
 
     def wrapper(*x: float) -> float:
         # Cast the provided list of floats to a tensor.
-        x_tensor = Tensor(x)
+        x_tensor = torch.tensor(x)
         result = test_function.forward(x_tensor)
         # We do not need to return a tuple here.
         return float(result)

baybe/utils/plotting.py

@@ -10,10 +10,11 @@
 import matplotlib.pyplot as plt
 from matplotlib.axes import Axes
 from matplotlib.figure import Figure
+from mpl_toolkits.mplot3d import Axes3D
 
 
 def create_example_plots(
-    ax: Axes,
+    ax: Axes | Axes3D,
     base_name: str,
     path: Path = Path("."),
 ) -> None:
@@ -31,6 +32,9 @@ def create_example_plots(
         ax: The Axes object containing the figure that should be plotted.
         base_name: The base name that is used for naming the output files.
         path: Optional path to the directory in which the plots should be saved.
+
+    Returns:
+        The ``Figure`` containing ``ax``
     """
     # Check whether we immediately return due to just running a SMOKE_TEST
     if "SMOKE_TEST" in os.environ:
@@ -96,6 +100,9 @@ def create_example_plots(
         ax.xaxis.label.set_fontsize(fontsize)
         ax.yaxis.label.set_color(color)
         ax.yaxis.label.set_fontsize(fontsize)
+        if isinstance(ax, Axes3D):
+            ax.zaxis.label.set_color(color)
+            ax.zaxis.label.set_fontsize(fontsize)
 
         # Adjust the size of the ax
         # mypy thinks that ax.figure might become None, hence the explicit ignore
@@ -105,18 +112,22 @@ def create_example_plots(
             warnings.warn("Could not adjust size of plot due to it not being a Figure.")
 
         # Adjust the labels
-        for label in ax.get_xticklabels() + ax.get_yticklabels():
+        ticklabels = ax.get_xticklabels() + ax.get_yticklabels()
+        if isinstance(ax, Axes3D):
+            ticklabels += ax.get_zticklabels()
+        for label in ticklabels:
             label.set_color(color)
             label.set_fontsize(fontsize)
 
-        # Adjust the legend
+        # Adjust the legend if it exists
         legend = ax.get_legend()
-        legend.get_frame().set_alpha(framealpha)
-        legend.get_title().set_color(color)
-        legend.get_title().set_fontsize(fontsize)
-        for text in legend.get_texts():
-            text.set_fontsize(fontsize)
-            text.set_color(color)
+        if legend:
+            legend.get_frame().set_alpha(framealpha)
+            legend.get_title().set_color(color)
+            legend.get_title().set_fontsize(fontsize)
+            for text in legend.get_texts():
+                text.set_fontsize(fontsize)
+                text.set_color(color)
 
         output_path = Path(path, f"{base_name}_{theme_name}.svg")
         # mypy thinks that ax.figure might become None, hence the explicit ignore
@@ -128,4 +139,4 @@ def create_example_plots(
             )
         else:
             warnings.warn("Plots could not be saved.")
-        plt.close()
+    plt.close()