EarlyStopping based no improvement interval (#1560)

* adding no_improvement_stopper callback and test for it.
Early stop when no loss reduce during tolerance_window asks

tested python3 -m pytest test_callbacks.py

example of using :
'''
    optimizer.register_callback("ask", ng.callbacks.EarlyStopping.no_improvement_stopper(no_imp_window))  # should not get triggered
'''

* comment fix

* english fix

* reformated with black and fix typing in test

* add typing spect

nevergrad/optimization/callbacks.py

@@ -347,6 +347,11 @@ def timer(cls, max_duration: float) -> "EarlyStopping":
         """Early stop when max_duration seconds has been reached (from the first ask)"""
         return cls(_DurationCriterion(max_duration))
 
+    @classmethod
+    def no_improvement_stopper(cls, tolerance_window: int) -> "EarlyStopping":
+        """Early stop when loss didn't reduce during tolerance_window asks"""
+        return cls(_LossImprovementToleranceCriterion(tolerance_window))
+
 
 class _DurationCriterion:
     def __init__(self, max_duration: float) -> None:
@@ -357,3 +362,25 @@ def __call__(self, optimizer: base.Optimizer) -> bool:
         if np.isinf(self._start):
             self._start = time.time()
         return time.time() > self._start + self._max_duration
+
+
+class _LossImprovementToleranceCriterion:
+    def __init__(self, tolerance_window: int) -> None:
+        self._tolerance_window: int = tolerance_window
+        self._best_value: tp.Optional[np.ndarray] = None
+        self._tolerance_count: int = 0
+
+    def __call__(self, optimizer: base.Optimizer) -> bool:
+        best_param = optimizer.provide_recommendation()
+        if best_param is None or (best_param.loss is None and best_param._losses is None):
+            return False
+        best_last_losses = best_param.losses
+        if self._best_value is None:
+            self._best_value = best_last_losses
+            return False
+        if self._best_value <= best_last_losses:
+            self._tolerance_count += 1
+        else:
+            self._tolerance_count = 0
+            self._best_value = best_last_losses
+        return self._tolerance_count > self._tolerance_window

nevergrad/optimization/test_callbacks.py

@@ -105,12 +105,16 @@ def test_progressbar_dump(tmp_path: Path) -> None:
 
 
 class _EarlyStoppingTestee:
-    def __init__(self) -> None:
+    def __init__(self, val=None, multi=False) -> None:
         self.num_calls = 0
+        self.val = val
+        self.multi = False
 
-    def __call__(self, *args, **kwds) -> float:
+    def __call__(self, *args, **kwds) -> tp.Union[float, tp.Tuple]:
         self.num_calls += 1
-        return np.random.rand()
+        if self.val is not None:
+            return self.val if not self.multi else (self.val, self.val)
+        return np.random.rand() if not self.multi else (np.random.rand(), np.random.rand())
 
 
 def test_early_stopping() -> None:
@@ -127,6 +131,26 @@ def test_early_stopping() -> None:
     assert optimizer.current_bests["minimum"].mean < 12
     assert optimizer.recommend().loss < 12  # type: ignore
 
+    # test for no improvement
+    func = _EarlyStoppingTestee(5)
+    optimizer = optimizerlib.OnePlusOne(parametrization=instrum, budget=100)
+    no_imp_window = 7
+    optimizer.register_callback(
+        "ask", ng.callbacks.EarlyStopping.no_improvement_stopper(no_imp_window)
+    )  # should get triggered
+    optimizer.minimize(func, verbosity=2)
+    assert func.num_calls == no_imp_window + 2
+
+    # test for no improvement multi objective
+    func = _EarlyStoppingTestee(5, multi=True)
+    optimizer = optimizerlib.OnePlusOne(parametrization=instrum, budget=100)
+    no_imp_window = 5
+    optimizer.register_callback(
+        "ask", ng.callbacks.EarlyStopping.no_improvement_stopper(no_imp_window)
+    )  # should get triggered
+    optimizer.minimize(func, verbosity=2)
+    assert func.num_calls == no_imp_window + 2
+
 
 def test_duration_criterion() -> None:
     optim = optimizerlib.OnePlusOne(2, budget=100)