use type annotations

Author: basnijholt

adaptive/learner/base_learner.py

@@ -85,7 +85,9 @@ class BaseLearner(metaclass=_RequireAttrsABCMeta):
     and returns a holoviews plot.
     """
 
-    required_attributes = ["data", "npoints", "pending_points"]
+    data: dict
+    npoints: int
+    pending_points: set
 
     def tell(self, x, y):
         """Tell the learner about a single value.

adaptive/utils.py

@@ -71,11 +71,14 @@ def decorator(method):
 
 
 class _RequireAttrsABCMeta(abc.ABCMeta):
-    required_attributes = []
-
     def __call__(self, *args, **kwargs):
         obj = super().__call__(*args, **kwargs)
-        for name in obj.required_attributes:
+        for name, type_ in obj.__annotations__.items():
             if not hasattr(obj, name):
                 raise ValueError(f"Required attribute {name} not set in __init__.")
+            elif not isinstance(getattr(obj, name), type_):
+                raise TypeError(
+                    f"The attribute '{name}' is of {type_} instead of {type(getattr(obj, name))}"
+                )
+
         return obj