Add loss_goal, npoints_goal, and an auto_goal function and use it in the runners

.pre-commit-config.yaml

@@ -21,7 +21,7 @@ repos:
     rev: 5.10.1
     hooks:
       - id: isort
-  - repo: https://gitlab.com/pycqa/flake8
+  - repo: https://github.com/pycqa/flake8
     rev: 3.9.2
     hooks:
       - id: flake8

README.md

@@ -75,7 +75,7 @@ def peak(x, a=0.01):
 
 
 learner = Learner1D(peak, bounds=(-1, 1))
-runner = Runner(learner, goal=lambda l: l.loss() < 0.01)
+runner = Runner(learner, loss_goal=0.01)
 runner.live_info()
 runner.live_plot()
 ```

adaptive/__init__.py

@@ -1,6 +1,5 @@
 from contextlib import suppress
 
-from adaptive import learner, runner, utils
 from adaptive._version import __version__
 from adaptive.learner import (
     AverageLearner,
@@ -22,6 +21,8 @@
 )
 from adaptive.runner import AsyncRunner, BlockingRunner, Runner
 
+from adaptive import learner, runner, utils  # isort:skip
+
 __all__ = [
     "learner",
     "runner",

adaptive/learner/data_saver.py

@@ -20,7 +20,7 @@ def _to_key(x):
     return tuple(x.values) if x.values.size > 1 else x.item()
 
 
-class DataSaver:
+class DataSaver(BaseLearner):
     """Save extra data associated with the values that need to be learned.
 
     Parameters
@@ -50,6 +50,18 @@ def new(self) -> DataSaver:
         """Return a new `DataSaver` with the same `arg_picker` and `learner`."""
         return DataSaver(self.learner.new(), self.arg_picker)
 
+    @copy_docstring_from(BaseLearner.ask)
+    def ask(self, *args, **kwargs):
+        return self.learner.ask(*args, **kwargs)
+
+    @copy_docstring_from(BaseLearner.loss)
+    def loss(self, *args, **kwargs):
+        return self.learner.loss(*args, **kwargs)
+
+    @copy_docstring_from(BaseLearner.remove_unfinished)
+    def remove_unfinished(self, *args, **kwargs):
+        return self.learner.remove_unfinished(*args, **kwargs)
+
     def __getattr__(self, attr: str) -> Any:
         return getattr(self.learner, attr)