Added callback function support for adam-amsgrad optimizer. (#869)

* Added callback functionality to ADAM optimiser

* Added unittest for callback function

qiskit_machine_learning/optimizers/adam_amsgrad.py

@@ -21,6 +21,8 @@
 import numpy as np
 from .optimizer import Optimizer, OptimizerSupportLevel, OptimizerResult, POINT
 
+CALLBACK = Callable[[int, POINT, float], None]
+
 # pylint: disable=invalid-name
 
 
@@ -69,6 +71,7 @@ def __init__(
         eps: float = 1e-10,
         amsgrad: bool = False,
         snapshot_dir: str | None = None,
+        callback: CALLBACK | None = None,
     ) -> None:
         """
         Args:
@@ -83,8 +86,11 @@ def __init__(
             amsgrad: True to use AMSGRAD, False if not
             snapshot_dir: If not None save the optimizer's parameter
                 after every step to the given directory
+            callback: A callback function passed information in each iteration step.
+                The information is, in this order: current time step, the parameters, the function value.
         """
         super().__init__()
+        self.callback = callback
         for k, v in list(locals().items()):
             if k in self._OPTIONS:
                 self._options[k] = v
@@ -233,6 +239,9 @@ def minimize(
             if self._snapshot_dir is not None:
                 self.save_params(self._snapshot_dir)
 
+            if self.callback is not None:
+                self.callback(self._t, params_new, fun(params_new))
+
             # check termination
             if np.linalg.norm(params - params_new) < self._tol:
                 break

releasenotes/notes/add-adam_amsgrad_callback-e8e1374d72688da4.yaml

@@ -0,0 +1,20 @@
+---
+features:
+  - |
+    The :class:`~qiskit_machine_learning.optimizers.ADAM` class now supports a callback function. 
+    This feature allows users to pass a custom callback function that will be called with
+    information at each iteration step during the optimization process. 
+    The information passed to the callback includes the current time step, the parameters, 
+    and the function value. The callback function should be of the type 
+    `Callable[[int, Union[float, np.ndarray], float], None]`. 
+
+    Example of a callback function:
+
+    .. code-block:: python
+    
+        def callback(iteration:int, weights:np.ndarray, loss:float):
+          ...
+          acc = calculate_accuracy(weights)
+          print(acc)
+          print(loss)
+          ...

test/optimizers/test_adam_amsgrad.py

@@ -79,6 +79,24 @@ def test_settings(self):
         self.assertEqual(settings["amsgrad"], False)
         self.assertEqual(settings["snapshot_dir"], None)
 
+    def test_callback(self):
+        """Test using the callback."""
+
+        history = {"ite": [], "weights": [], "fvals": []}
+
+        def callback(n_t, weight, fval):
+            history["ite"].append(n_t)
+            history["weights"].append(weight)
+            history["fvals"].append(fval)
+
+        adam = ADAM(maxiter=100, tol=1e-6, lr=1e-1, callback=callback)
+        adam.minimize(self.quadratic_objective, self.initial_point)
+
+        expected_types = [int, np.ndarray, float]
+        for i, (key, values) in enumerate(history.items()):
+            self.assertTrue(all(isinstance(value, expected_types[i]) for value in values))
+            self.assertEqual(len(history[key]), 100)
+
 
 if __name__ == "__main__":
     unittest.main()