477 add progress hook to optimize

docs/source/interfaces.ipynb

@@ -23,6 +23,7 @@
     "import os\n",
     "import pyciemss\n",
     "import torch\n",
+    "from tqdm import tqdm\n",
     "import pandas as pd\n",
     "import numpy as np\n",
     "from typing import Dict, List, Callable\n",
@@ -2523,7 +2524,10 @@
     "end_time = 40.0\n",
     "logging_step_size = 1.0\n",
     "observed_params = [\"I_state\"]\n",
-    "intervened_params = [\"p_cbeta\"]"
+    "intervened_params = [\"p_cbeta\"]\n",
+    "\n",
+    "maxiter=5\n",
+    "maxfeval=10"
    ]
   },
   {
@@ -2535,11 +2539,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "C:\\Users\\Anirban\\Documents\\GitHub\\pyciemss\\pyciemss\\interfaces.py:870: UserWarning: risk_bound is not a List. Forcing it to be a list.\n",
-      "  warnings.warn(\"risk_bound is not a List. Forcing it to be a list.\")\n",
-      "C:\\Users\\Anirban\\Documents\\GitHub\\pyciemss\\pyciemss\\interfaces.py:873: UserWarning: qoi is not a List. Forcing it to be a list.\n",
-      "  warnings.warn(\"qoi is not a List. Forcing it to be a list.\")\n",
-      " 40%|████      | 8/20 [00:04<00:07,  1.62it/s]"
+      " 75%|████████████████████████████████████            | 45/60 [00:06<00:01,  9.23it/s]"
      ]
     },
     {
@@ -2551,9 +2551,9 @@
       "                    success: True\n",
       "                        fun: 0.14687910929135808\n",
       "                          x: [ 2.032e-01]\n",
-      "                        nit: 1\n",
-      "      minimization_failures: 0\n",
-      "                       nfev: 8\n",
+      "                        nit: 5\n",
+      "      minimization_failures: 4\n",
+      "                       nfev: 46\n",
       " lowest_optimization_result: message: Optimization terminated successfully.\n",
       "                             success: True\n",
       "                              status: 1\n",
@@ -2562,13 +2562,6 @@
       "                                nfev: 8\n",
       "                               maxcv: 0.0}\n"
      ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
     }
    ],
    "source": [
@@ -2586,6 +2579,14 @@
     "    param_value=[lambda x: torch.tensor(x)],\n",
     "    start_time=intervention_time,\n",
     ")\n",
+    "\n",
+    "# Progress bar\n",
+    "pbar = tqdm(total=maxfeval * (maxiter + 1))\n",
+    "\n",
+    "# update_progress(coordinate, function_min, accept)\n",
+    "def update_progress(xk):\n",
+    "    pbar.update(1)\n",
+    "\n",
     "opt_result = pyciemss.optimize(\n",
     "    model3,\n",
     "    end_time,\n",
@@ -2598,10 +2599,11 @@
     "    bounds_interventions=bounds_interventions,\n",
     "    start_time=0.0,\n",
     "    n_samples_ouu=int(1e2),\n",
-    "    maxiter=0,\n",
-    "    maxfeval=20,\n",
+    "    maxiter=maxiter,\n",
+    "    maxfeval=maxfeval,\n",
     "    solver_method=\"euler\",\n",
-    "    solver_options={\"step_size\": logging_step_size / 2},\n",
+    "    solver_options={\"step_size\": logging_step_size/2},\n",
+    "    progress_hook=update_progress,\n",
     ")\n",
     "print(f\"Optimal policy:\", opt_result[\"policy\"])\n",
     "print(opt_result)"
@@ -3339,7 +3341,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.13"
+   "version": "3.10.9"
   }
  },
  "nbformat": 4,

pyciemss/interfaces.py

@@ -792,6 +792,7 @@ def optimize(
     maxfeval: int = 25,
     verbose: bool = False,
     roundup_decimal: int = 4,
+    progress_hook: Callable[[torch.Tensor], None] = lambda x: None,
 ) -> Dict[str, Any]:
     r"""
     Load a model from a file, compile it into a probabilistic program, and optimize under uncertainty with risk-based
@@ -858,6 +859,10 @@ def optimize(
             - Whether to print out the optimization under uncertainty progress.
         roundup_decimal: int
             - Number of significant digits for the optimal policy.
+        progress_hook: progress_hook: Callable[[torch.Tensor], None],
+            - A callback function that takes in the current parameter vector as a tensor.
+                If the function returns StopIteration, the minimization will terminate.
+            - This can be used to implement custom progress bars and/or early stopping criteria.
 
     Returns:
         result: Dict[str, Any]
@@ -946,6 +951,7 @@ def objfun_penalty(x):
             maxiter=maxiter,
             maxfeval=maxfeval,
             u_bounds=bounds_np,
+            progress_hook=progress_hook,
         ).solve()
 
         # Rounding up to given number of decimal places

pyciemss/ouu/ouu.py

@@ -9,7 +9,6 @@
 from chirho.dynamical.handlers.solver import TorchDiffEq
 from chirho.interventional.ops import Intervention
 from scipy.optimize import basinhopping
-from tqdm import tqdm
 
 from pyciemss.integration_utils.intervention_builder import (
     combine_static_parameter_interventions,
@@ -181,34 +180,34 @@ def __init__(
         maxfeval: int = 100,
         maxiter: int = 100,
         u_bounds: np.ndarray = np.atleast_2d([[0], [1]]),
+        progress_hook: Callable[
+            [torch.Tensor], None
+        ] = lambda x: None,  # update_progress
     ):
         self.x0 = np.squeeze(np.array([x0]))
         self.objfun = objfun
         self.constraints = constraints
         self.maxiter = maxiter
         self.maxfeval = maxfeval
         self.u_bounds = u_bounds
+        self.progress_hook = progress_hook
+        # 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=update_progress,
             options={
                 "rhobeg": 0.1
                 * np.linalg.norm(self.u_bounds[1, :] - self.u_bounds[0, :]),
                 "disp": False,
                 "maxiter": self.maxfeval,
                 "catol": 1e-5,
             },
+            callback=self.progress_hook,
         )
         take_step = RandomDisplacementBounds(self.u_bounds[0, :], self.u_bounds[1, :])
 

tests/test_interfaces.py

@@ -563,14 +563,27 @@ def test_output_format(
 def test_optimize(model_fixture, start_time, end_time, num_samples):
     logging_step_size = 1.0
     model_url = model_fixture.url
+
+    class TestProgressHook:
+        def __init__(self):
+            self.result_x = []
+
+        def __call__(self, x):
+            # Log the iteration number
+            self.result_x.append(x)
+            print(f"Result: {self.result_x}")
+
+    progress_hook = TestProgressHook()
+
     optimize_kwargs = {
         **model_fixture.optimize_kwargs,
         "solver_method": "euler",
         "solver_options": {"step_size": 0.1},
         "start_time": start_time,
-        "n_samples_ouu": int(5),
+        "n_samples_ouu": int(2),
         "maxiter": 1,
         "maxfeval": 2,
+        "progress_hook": progress_hook,
     }
     bounds_interventions = optimize_kwargs["bounds_interventions"]
     opt_result = optimize(
@@ -622,6 +635,10 @@ def test_optimize(model_fixture, start_time, end_time, num_samples):
         intervened_result_subset, start_time, end_time, logging_step_size, num_samples
     )
 
+    assert len(progress_hook.result_x) <= (
+        (optimize_kwargs["maxfeval"] + 1) * (optimize_kwargs["maxiter"] + 1)
+    )
+
 
 @pytest.mark.parametrize("model_fixture", MODELS)
 @pytest.mark.parametrize("bad_num_iterations", NON_POS_INTS)