feat(Optimizer): Add a basic random search

src/amltk/optimization/optimizers/random_search.py

@@ -0,0 +1,150 @@
+"""An optimizer that uses ConfigSpace for random search."""
+from __future__ import annotations
+
+from collections.abc import Iterable, Sequence
+from datetime import datetime
+from pathlib import Path
+from typing import TYPE_CHECKING, Literal, overload
+from typing_extensions import override
+
+from amltk.optimization import Metric, Optimizer, Trial
+from amltk.pipeline import Node
+from amltk.randomness import as_int, randuid
+from amltk.store import PathBucket
+
+if TYPE_CHECKING:
+    from typing_extensions import Self
+
+    from ConfigSpace import ConfigurationSpace
+
+    from amltk.types import Seed
+
+
+class RandomSearch(Optimizer[None]):
+    """An optimizer that uses ConfigSpace for random search."""
+
+    def __init__(
+        self,
+        *,
+        space: ConfigurationSpace,
+        bucket: PathBucket | None = None,
+        metrics: Metric | Sequence[Metric],
+        seed: Seed | None = None,
+    ) -> None:
+        """Initialize the optimizer.
+
+        Args:
+            space: The search space to search over.
+            bucket: The bucket given to trials generated by this optimizer.
+            metrics: The metrics to optimize. Unused for RandomSearch.
+            seed: The seed to use for the optimization.
+        """
+        metrics = metrics if isinstance(metrics, Sequence) else [metrics]
+        super().__init__(metrics=metrics, bucket=bucket)
+        seed = as_int(seed)
+        space.seed(seed)
+        self._counter = 0
+        self.seed = seed
+        self.space = space
+
+    @override
+    @classmethod
+    def create(
+        cls,
+        *,
+        space: ConfigurationSpace | Node,
+        metrics: Metric | Sequence[Metric],
+        bucket: PathBucket | str | Path | None = None,
+        seed: Seed | None = None,
+    ) -> Self:
+        """Create a random search optimizer.
+
+        Args:
+            space: The node to optimize
+            metrics: The metrics to optimize
+            bucket: The bucket to store the results in
+            seed: The seed to use for the optimization
+        """
+        seed = as_int(seed)
+        match bucket:
+            case None:
+                bucket = PathBucket(
+                    f"{cls.__name__}-{datetime.now().isoformat()}",
+                )
+            case str() | Path():
+                bucket = PathBucket(bucket)
+            case bucket:
+                bucket = bucket  # noqa: PLW0127
+
+        if isinstance(space, Node):
+            space = space.search_space(parser=cls.preferred_parser())
+
+        return cls(
+            space=space,
+            seed=seed,
+            bucket=bucket,
+            metrics=metrics,
+        )
+
+    @overload
+    def ask(self, n: int) -> Iterable[Trial[None]]:
+        ...
+
+    @overload
+    def ask(self, n: None = None) -> Trial[None]:
+        ...
+
+    @override
+    def ask(
+        self,
+        n: int | None = None,
+    ) -> Trial[None] | Iterable[Trial[None]]:
+        """Ask the optimizer for a new config.
+
+        Args:
+            n: The number of configs to ask for. If `None`, ask for a single config.
+
+
+        Returns:
+            The trial info for the new config.
+        """
+        if n is None:
+            configs = [self.space.sample_configuration()]
+        else:
+            configs = self.space.sample_configuration(n)
+
+        trials: list[Trial[None]] = []
+        for config in configs:
+            self._counter += 1
+            randuid_seed = self.seed + self._counter
+            unique_name = f"trial-{randuid(4, seed=randuid_seed)}-{self._counter}"
+            trial: Trial[None] = Trial.create(
+                name=unique_name,
+                config=dict(config),
+                info=None,
+                seed=self.seed,
+                bucket=self.bucket / unique_name,
+                metrics=self.metrics,
+            )
+            trials.append(trial)
+
+        if n is None:
+            return trials[0]
+
+        return trials
+
+    @override
+    def tell(self, report: Trial.Report[None]) -> None:
+        """Tell the optimizer about the result of a trial.
+
+        Does nothing for random search.
+
+        Args:
+            report: The report of the trial.
+        """
+
+    @override
+    @classmethod
+    def preferred_parser(cls) -> Literal["configspace"]:
+        """The preferred parser for this optimizer."""
+        return "configspace"