fix(bo): Switch to using id_generator

neps/optimizers/bayesian_optimization.py

@@ -1,5 +1,6 @@
 from __future__ import annotations
 
+import itertools
 import math
 from collections.abc import Mapping
 from dataclasses import dataclass
@@ -89,9 +90,7 @@ def __call__(
 
         n_to_sample = 1 if n is None else n
         n_sampled = len(trials)
-        config_ids = iter(
-            str(i + 1) for i in range(n_sampled, n_sampled + n_to_sample + 1)
-        )
+        id_generator = iter(str(i) for i in itertools.count(n_sampled + 1))
 
         # If the amount of configs evaluated is less than the initial design
         # requirement, keep drawing from initial design
@@ -103,6 +102,8 @@ def __call__(
         sampled_configs: list[SampledConfig] = []
 
         if n_evaluated < self.n_initial_design:
+            # For reproducibility, we need to ensure we do the same sample of all
+            # configs each time.
             design_samples = make_initial_design(
                 parameters=parameters,
                 encoder=self.encoder,
@@ -111,13 +112,21 @@ def __call__(
                 seed=None,  # TODO: Seeding, however we need to avoid repeating configs
                 sample_size=self.n_initial_design,
             )
+
+            # Then take the subset we actually need
+            design_samples = design_samples[n_evaluated:]
             for sample in design_samples:
                 sample.update(self.space.constants)
 
             sampled_configs.extend(
                 [
                     SampledConfig(id=config_id, config=config)
-                    for config_id, config in zip(config_ids, design_samples, strict=True)
+                    for config_id, config in zip(
+                        id_generator,
+                        design_samples,
+                        # NOTE: We use a generator for the ids so no need for strict
+                        strict=False,
+                    )
                 ]
             )
 
@@ -187,7 +196,12 @@ def __call__(
         sampled_configs.extend(
             [
                 SampledConfig(id=config_id, config=config)
-                for config_id, config in zip(config_ids, configs, strict=True)
+                for config_id, config in zip(
+                    id_generator,
+                    configs,
+                    # NOTE: We use a generator for the ids so no need for strict
+                    strict=False,
+                )
             ]
         )
         return sampled_configs[0] if n is None else sampled_configs