save execution time directly on the futures inside the runners

Previously we were wrapping the learner function and doing the
timing there. This makes things more complicated in the case that
we are learning an 'async def' function.

Author: jbweston

adaptive/runner.py

@@ -11,7 +11,6 @@
 import abc
 
 from .notebook_integration import live_plot, live_info, in_ipynb
-from .utils import timed
 
 try:
     import ipyparallel
@@ -105,8 +104,7 @@ class BaseRunner(metaclass=abc.ABCMeta):
     Methods
     -------
     overhead : callable
-        The overhead in percent of using Adaptive. This includes the
-        overhead of the executor. Essentially, this is
+        The overhead in percent of using Adaptive. Essentially, this is
         ``100 * (1 - total_elapsed_function_time / self.elapsed_time())``.
 
     """
@@ -130,8 +128,7 @@ def __init__(self, learner, goal, *,
         self.learner = learner
         self.log = [] if log else None
 
-        # Function timing
-        self.function = functools.partial(timed, self.learner.function)
+        # Timing
         self.start_time = time.time()
         self.end_time = None
         self._elapsed_function_time = 0
@@ -190,7 +187,8 @@ def _process_futures(self, done_futs):
         for fut in done_futs:
             x = self.pending_points.pop(fut)
             try:
-                y, t = fut.result()
+                y = fut.result()
+                t = time.time() - fut.start_time  # total execution time
             except Exception as e:
                 self.tracebacks[x] = traceback.format_exc()
                 self.to_retry[x] = self.to_retry.get(x, 0) + 1
@@ -218,7 +216,9 @@ def _get_futures(self):
         points, _ = self._ask(n_new_tasks)
 
         for x in points:
-            self.pending_points[self._submit(x)] = x
+            fut = self._submit(x)
+            fut.start_time = time.time()  # so we can measure execution time
+            self.pending_points[fut] = x
 
         # Collect and results and add them to the learner
         futures = list(self.pending_points.keys())
@@ -332,7 +332,7 @@ def __init__(self, learner, goal, *,
         self._run()
 
     def _submit(self, x):
-        return self.executor.submit(self.function, x)
+        return self.executor.submit(self.learner.function, x)
 
     def _run(self):
         first_completed = concurrent.FIRST_COMPLETED
@@ -471,9 +471,9 @@ def goal(_):
     def _submit(self, x):
         ioloop = self.ioloop
         if inspect.iscoroutinefunction(self.learner.function):
-            return ioloop.create_task(self.function(x))
+            return ioloop.create_task(self.learner.function(x))
         else:
-            return ioloop.run_in_executor(self.executor, self.function, x)
+            return ioloop.run_in_executor(self.executor, self.learner.function, x)
 
     def status(self):
         """Return the runner status as a string.

adaptive/utils.py

@@ -8,12 +8,6 @@
 import time
 
 
-def timed(f, *args, **kwargs):
-    t_start = time.time()
-    result = f(*args, **kwargs)
-    return result, time.time() - t_start
-
-
 def named_product(**items):
     names = items.keys()
     vals = items.values()