Add type-hints to adaptive/learner/skopt_learner.py

Author: basnijholt

adaptive/learner/skopt_learner.py

@@ -1,6 +1,7 @@
 from __future__ import annotations
 
 import collections
+from typing import Callable
 
 import numpy as np
 from skopt import Optimizer
@@ -25,8 +26,8 @@ class SKOptLearner(Optimizer, BaseLearner):
         Arguments to pass to ``skopt.Optimizer``.
     """
 
-    def __init__(self, function, **kwargs):
-        self.function = function
+    def __init__(self, function: Callable, **kwargs) -> None:
+        self.function = function  # type: ignore
         self.pending_points = set()
         self.data = collections.OrderedDict()
         self._kwargs = kwargs
@@ -36,7 +37,7 @@ def new(self) -> SKOptLearner:
         """Return a new `~adaptive.SKOptLearner` without the data."""
         return SKOptLearner(self.function, **self._kwargs)
 
-    def tell(self, x, y, fit=True):
+    def tell(self, x: float | list[float], y: float, fit: bool = True) -> None:
         if isinstance(x, collections.abc.Iterable):
             self.pending_points.discard(tuple(x))
             self.data[tuple(x)] = y
@@ -55,7 +56,7 @@ def remove_unfinished(self):
         pass
 
     @cache_latest
-    def loss(self, real=True):
+    def loss(self, real: bool = True) -> float:
         if not self.models:
             return np.inf
         else:
@@ -65,7 +66,12 @@ def loss(self, real=True):
             # estimator of loss, but it is the cheapest.
             return 1 - model.score(self.Xi, self.yi)
 
-    def ask(self, n, tell_pending=True):
+    def ask(
+        self, n: int, tell_pending: bool = True
+    ) -> (
+        tuple[list[float], list[float]]
+        | tuple[list[list[float]], list[float]]  # XXX: this indicates a bug!
+    ):
         if not tell_pending:
             raise NotImplementedError(
                 "Asking points is an irreversible "
@@ -79,7 +85,7 @@ def ask(self, n, tell_pending=True):
             return [p[0] for p in points], [self.loss() / n] * n
 
     @property
-    def npoints(self):
+    def npoints(self) -> int:
         """Number of evaluated points."""
         return len(self.Xi)