Comply to abstractmethod interface in BayesianOptimizer

Author: peanutfun

climada/test/test_util_calibrate.py

@@ -176,7 +176,7 @@ def test_single(self):
             init_points=10, n_iter=20, max_iterations=1
         )
         optimizer = BayesianOptimizer(self.input, random_state=1)
-        output = optimizer.run(controller)
+        output = optimizer.run(controller=controller)
 
         # Check result (low accuracy)
         self.assertAlmostEqual(output.params["slope"], 1.0, places=2)
@@ -210,7 +210,7 @@ def test_multiple_constrained(self):
         controller = BayesianOptimizerController.from_input(
             self.input, sampling_base=5, max_iterations=3
         )
-        output = optimizer.run(controller)
+        output = optimizer.run(controller=controller)
 
         # Check results (low accuracy)
         self.assertEqual(output.p_space.dim, 2)
@@ -246,7 +246,7 @@ def test_plots(self):
         controller = BayesianOptimizerController.from_input(
             self.input, max_iterations=1
         )
-        output = optimizer.run(controller)
+        output = optimizer.run(controller=controller)
 
         output_eval = OutputEvaluator(self.input, output)
         output_eval.impf_set.plot()

climada/util/calibrate/bayesian_optimizer.py

@@ -622,7 +622,7 @@ def _target_func(
         """Invert the cost function because BayesianOptimization maximizes the target"""
         return -self.input.cost_func(data, predicted, weights)
 
-    def run(self, controller: BayesianOptimizerController) -> BayesianOptimizerOutput:
+    def run(self, **opt_kwargs) -> BayesianOptimizerOutput:
         """Execute the optimization
 
         ``BayesianOptimization`` *maximizes* a target function. Therefore, this class
@@ -633,15 +633,25 @@ def run(self, controller: BayesianOptimizerController) -> BayesianOptimizerOutpu
         ----------
         controller : BayesianOptimizerController
             The controller instance used to set the optimization iteration parameters.
-        opt_kwargs
-            Further keyword arguments passed to ``BayesianOptimization.maximize``.
+        kwargs
+            Further keyword arguments passed to ``BayesianOptimization.maximize``. Note
+            that some arguments are also provided by
+            :py:meth:`BayesianOptimizerController.optimizer_params`.
 
         Returns
         -------
         output : BayesianOptimizerOutput
             Optimization output. :py:attr:`BayesianOptimizerOutput.p_space` stores data
             on the sampled parameter space.
         """
+        # Take the controller
+        try:
+            controller = opt_kwargs.pop("controller")
+        except KeyError as err:
+            raise RuntimeError(
+                "BayesianOptimizer.run requires 'controller' as keyword argument"
+            ) from err
+
         # Register the controller
         for event in (Events.OPTIMIZATION_STEP, Events.OPTIMIZATION_END):
             self.optimizer.subscribe(event, controller)
@@ -662,7 +672,7 @@ def run(self, controller: BayesianOptimizerController) -> BayesianOptimizerOutpu
         while controller.iterations < controller.max_iterations:
             try:
                 LOGGER.info(f"Optimization iteration: {controller.iterations}")
-                self.optimizer.maximize(**controller.optimizer_params())
+                self.optimizer.maximize(**controller.optimizer_params(), **opt_kwargs)
             except StopEarly:
                 # Start a new iteration
                 continue

climada/util/calibrate/test/test_bayesian_optimizer.py

@@ -317,7 +317,7 @@ def test_kwargs_to_impact_func_creator(self, _):
         # Call 'run'
         with patch.object(self.input, "impact_to_aligned_df") as align:
             align.return_value = (None, None)
-            self.optimizer.run(self.controller)
+            self.optimizer.run(controller=self.controller)
 
         # Check call to '_kwargs_to_impact_func_gen'
         call_args = self.input.impact_func_creator.call_args_list
@@ -341,7 +341,7 @@ def test_target_func(self, _):
         # Call 'run'
         with patch.object(self.input, "impact_to_aligned_df") as align:
             align.return_value = (None, None)
-            output = self.optimizer.run(self.controller)
+            output = self.optimizer.run(controller=self.controller)
 
         # Check target space
         npt.assert_array_equal(output.p_space.target, [-1.0, 1.0])

doc/user-guide/climada_util_calibrate.ipynb

@@ -60,7 +60,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -144,7 +144,7 @@
     "\n",
     "# Run optimization\n",
     "with log_level(\"WARNING\", \"climada.engine.impact_calc\"):\n",
-    "    output = opt.run(controller)\n",
+    "    output = opt.run(controller=controller)\n",
     "\n",
     "# Analyse results\n",
     "output.plot_p_space()\n",
@@ -2192,7 +2192,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -2231,7 +2231,7 @@
     "    # Create and run the optimizer\n",
     "    opt = BayesianOptimizer(input)\n",
     "    controller = BayesianOptimizerController.from_input(input)\n",
-    "    bayes_output = opt.run(controller)\n",
+    "    bayes_output = opt.run(controller=controller)\n",
     "    bayes_output.params  # The optimal parameters"
    ]
   },
@@ -3718,7 +3718,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3750,7 +3750,7 @@
     "    with log_level(\"INFO\", name_prefix=\"climada.util.calibrate\"):\n",
     "        opt = BayesianOptimizer(input)\n",
     "        controller = BayesianOptimizerController.from_input(input)\n",
-    "        bayes_output = opt.run(controller)\n",
+    "        bayes_output = opt.run(controller=controller)\n",
     "\n",
     "    # Evaluate output\n",
     "    output_eval = OutputEvaluator(input, bayes_output)\n",