moving callback for progress_hook

docs/source/interfaces.ipynb

@@ -16,7 +16,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2514,7 +2514,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2542,15 +2542,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      " 46%|██████████████████████▏                         | 37/80 [00:41<00:47,  1.11s/it]\n",
-      " 45%|█████████████████████▋                          | 38/84 [00:04<00:04,  9.46it/s]"
+      "100%|███████████████████████████████████████████████████| 4/4 [00:16<00:00,  4.16s/it]\n",
+      "100%|███████████████████████████████████████████████████| 4/4 [00:03<00:00,  1.08it/s]"
      ]
     },
     {
@@ -2564,7 +2564,7 @@
       "                          x: [ 1.389e-01]\n",
       "                        nit: 3\n",
       "      minimization_failures: 3\n",
-      "                       nfev: 38\n",
+      "                       nfev: 30\n",
       " lowest_optimization_result: message: Optimization terminated successfully.\n",
       "                             success: True\n",
       "                              status: 1\n",
@@ -2591,10 +2591,11 @@
     "    start_time = intervention_time,\n",
     ")\n",
     "\n",
+    "# Progress bar\n",
+    "pbar = tqdm(total=maxiter + 1)\n",
     "\n",
-    "pbar = tqdm(total=(maxfeval + 1) * (maxiter + 1))\n",
-    "\n",
-    "def update_progress(xk):\n",
+    "# update_progress(coordinate, function_min, accept)\n",
+    "def update_progress(xk, xf, xa):\n",
     "    pbar.update(1)\n",
     "\n",
     "opt_result = pyciemss.optimize(\n",

pyciemss/interfaces.py

@@ -791,7 +791,7 @@ def optimize(
     maxfeval: int = 25,
     verbose: bool = False,
     roundup_decimal: int = 4,
-    progress_hook: Callable = lambda i, feval: None,
+    progress_hook: Callable[[np.array, float, bool], bool] = lambda x, f, accept: False,
 ) -> Dict[str, Any]:
     r"""
     Load a model from a file, compile it into a probabilistic program, and optimize under uncertainty with risk-based
@@ -856,6 +856,9 @@ def optimize(
             - Whether to print out the optimization under uncertainty progress.
         roundup_decimal: int
             - Number of significant digits for the optimal policy.
+        progress_hook: Callable[[np.array, float, bool], bool],
+            - A callback function that takes in the current coordinate(s) as an np.array, the minimal function evaluation at the coordinate(s), and whether or not that minimal value was accepted. When the function returns True, the basinhopping routine will stop.
+            - This can be used to implement custom progress bars and/or early stopping criteria.
 
     Returns:
         result: Dict[str, Any]

pyciemss/ouu/ouu.py

@@ -19,8 +19,6 @@
 )
 from pyciemss.ouu.risk_measures import alpha_superquantile
 
-# from tqdm import tqdm
-
 
 class RandomDisplacementBounds:
     """
@@ -187,7 +185,7 @@ def __init__(
         maxfeval: int = 100,
         maxiter: int = 100,
         u_bounds: np.ndarray = np.atleast_2d([[0], [1]]),
-        progress_hook: Callable = lambda i: None,
+        progress_hook: Callable[[np.array, float, bool], bool] = lambda x, f, accept: False,
     ):
         self.x0 = np.squeeze(np.array([x0]))
         self.objfun = objfun
@@ -203,18 +201,12 @@ def __init__(
         # self.kwargs = kwargs
 
     def solve(self):
-        #     pbar = tqdm(total=self.maxfeval * (self.maxiter + 1))
-
-        #     def update_progress(xk):
-        #         pbar.update(1)
-
         # wrapper around SciPy optimizer(s)
         # rhobeg is set to 10% of longest euclidean distance
         minimizer_kwargs = dict(
             constraints=self.constraints,
             method="COBYLA",
             tol=1e-5,
-            callback=self.progress_hook,
             options={
                 "rhobeg": 0.1
                 * np.linalg.norm(self.u_bounds[1, :] - self.u_bounds[0, :]),
@@ -234,6 +226,7 @@ def solve(self):
             niter=self.maxiter,
             minimizer_kwargs=minimizer_kwargs,
             take_step=take_step,
+            callback=self.progress_hook,
             interval=2,
             disp=False,
         )

tests/test_interfaces.py

@@ -565,13 +565,16 @@ def test_optimize(model_fixture, start_time, end_time, num_samples):
 
     class TestProgressHook:
         def __init__(self):
-            self.iterations = []
-            # self.function_evals = []
+            self.coordinates = []
+            self.function_min = []
+            self.accept = []
 
-        def __call__(self, iteration):
+        def __call__(self, x, f, accept):
             # Log the iteration number
-            self.iterations.append(iteration)
-            # self.function_evals.append(feval)
+            self.coordinates.append(x)
+            self.function_min.append(f)
+            self.accept.append(accept)
+            print(f"Coordinate(s): {x}, function min: {f}, accept: {accept}")
 
     progress_hook = TestProgressHook()
 
@@ -631,10 +634,9 @@ def __call__(self, iteration):
         intervened_result_subset, start_time, end_time, logging_step_size, num_samples
     )
 
-    assert len(progress_hook.iterations) == (optimize_kwargs["maxfeval"] + 1) * (
+    assert len(progress_hook.coordinates) == (
         optimize_kwargs["maxiter"] + 1
     )
-    # assert len(progress_hook.function_evals) == optimize_kwargs["maxfeval"]
 
 
 @pytest.mark.parametrize("model_fixture", MODELS)