work at ATF on Oct 12

bloptools/bayesian/acquisition/__init__.py

@@ -30,7 +30,7 @@ def parse_acq_func(acq_func_identifier):
     return acq_func_name
 
 
-def get_acquisition_function(agent, acq_func_identifier="qei", return_metadata=True, **acq_func_kwargs):
+def get_acquisition_function(agent, acq_func_identifier="qei", return_metadata=True, verbose=False, **acq_func_kwargs):
     """
     Generates an acquisition function from a supplied identifier.
     """

bloptools/bayesian/acquisition/config.yml

@@ -64,6 +64,14 @@ monte_carlo_probability_of_improvement:
   pretty_name: Monte Carlo probability of improvement
   type: monte_carlo
 
+random:
+  description: A uniform random sampel of the parameters.
+  identifiers:
+  - r
+  multitask_only: false
+  pretty_name: Random
+  type: none
+
 quasi-random:
   description: Sobol-sampled quasi-random points.
   identifiers:
@@ -75,7 +83,8 @@ quasi-random:
 grid:
   description: A grid scan over the parameters.
   identifiers:
-  - grid_scan
+  - g
+  - gs
   multitask_only: false
   pretty_name: Grid scan
   type: none

bloptools/bayesian/agent.py

@@ -104,6 +104,8 @@ def __init__(
     def tell(self, new_table=None, append=True, train=True, **kwargs):
         """
         Inform the agent about new inputs and targets for the model.
+
+        If run with no arguments, it will just reconstruct all the models. 
         """
 
         new_table = pd.DataFrame() if new_table is None else new_table
@@ -208,7 +210,7 @@ def ask(self, acq_func_identifier="qei", n=1, route=True, sequential=True, **acq
             NUM_RESTARTS = 8
             RAW_SAMPLES = 1024
 
-            candidates, _ = botorch.optim.optimize_acqf(
+            candidates, acqf_objective = botorch.optim.optimize_acqf(
                 acq_function=acq_func,
                 bounds=self.acquisition_function_bounds,
                 q=n,
@@ -226,6 +228,13 @@ def ask(self, acq_func_identifier="qei", n=1, route=True, sequential=True, **acq
             acq_func_meta["read_only_values"] = read_only_X
 
         else:
+
+            acqf_objective = None
+
+            if acq_func_name == "random":
+                acquisition_X = torch.rand()
+                acq_func_meta = {"name": "random", "args": {}}
+
             if acq_func_name == "quasi-random":
                 acquisition_X = self._subset_inputs_sampler(n=n, active=True, read_only=False).squeeze(1).numpy()
                 acq_func_meta = {"name": "quasi-random", "args": {}}
@@ -241,7 +250,7 @@ def ask(self, acq_func_identifier="qei", n=1, route=True, sequential=True, **acq
         acq_func_meta["duration"] = duration = ttime.monotonic() - start_time
 
         if self.verbose:
-            print(f"found points {acquisition_X} in {duration:.01f} seconds")
+            print(f"found points {acquisition_X} with acqf {acq_func_meta['name']} in {duration:.01f} seconds (obj = {acqf_objective})")
 
         if route and n > 1:
             routing_index = utils.route(self.dofs.subset(active=True, read_only=False).readback, acquisition_X)
@@ -257,7 +266,7 @@ def acquire(self, acquisition_inputs):
         """
         try:
             acquisition_devices = self.dofs.subset(active=True, read_only=False).devices
-            read_only_devices = self.dofs.subset(active=True, read_only=True).devices
+            #read_only_devices = self.dofs.subset(active=True, read_only=True).devices
 
             # the acquisition plan always takes as arguments:
             # (things to move, where to move them, things to trigger once you get there)
@@ -266,7 +275,7 @@ def acquire(self, acquisition_inputs):
             uid = yield from self.acquisition_plan(
                 acquisition_devices,
                 acquisition_inputs.astype(float),
-                [*self.dets, *read_only_devices],
+                [*self.dets, *self.dofs.devices],
                 delay=self.trigger_delay,
             )
 
@@ -341,11 +350,12 @@ def reset(self):
     def benchmark(
         self, output_dir="./", runs=16, n_init=64, learning_kwargs_list=[{"acq_func": "qei", "n": 4, "iterations": 16}]
     ):
-        cache_limits = {dof["name"]: dof["limits"] for dof in self.dofs}
+        cache_limits = {dof.name:dof.limits for dof in self.dofs}
 
         for run in range(runs):
             for dof in self.dofs:
-                dof["limits"] = cache_limits[dof["name"]] + 0.25 * np.ptp(dof["limits"]) * np.random.uniform(low=-1, high=1)
+                offset = 0.25 * np.ptp(dof.limits) * np.random.uniform(low=-1, high=1)
+                dof.limits = (dof.limits[0] + offset, dof.limits[1] + offset)
 
             self.reset()
 
@@ -367,7 +377,7 @@ def model(self):
 
     @property
     def objective_weights_torch(self):
-        return torch.tensor(self.objectives.weights, dtype=torch.float)
+        return torch.tensor(self.objectives.weights, dtype=torch.double)
 
     def _get_objective_targets(self, i):
         """
@@ -454,8 +464,10 @@ def acquisition_function_bounds(self):
         """
         Returns a (2, n_active_dof) array of bounds for the acquisition function
         """
-        acq_func_lower_bounds = [dof.lower_limit if not dof.read_only else dof.readback for dof in self.dofs]
-        acq_func_upper_bounds = [dof.upper_limit if not dof.read_only else dof.readback for dof in self.dofs]
+        active_dofs = self.dofs.subset(active=True)
+
+        acq_func_lower_bounds = [dof.lower_limit if not dof.read_only else dof.readback for dof in active_dofs]
+        acq_func_upper_bounds = [dof.upper_limit if not dof.read_only else dof.readback for dof in active_dofs]
 
         return torch.tensor(np.vstack([acq_func_lower_bounds, acq_func_upper_bounds]), dtype=torch.double)
 
@@ -598,20 +610,23 @@ def inputs(self):
     def active_inputs(self):
         return self.table.loc[:, self.dofs.subset(active=True).device_names].astype(float)
 
-    # @property
-    # def acquisition_inputs(self):
-    #     return self.table.loc[:, self.dofs.subset(active=True, read_only=False).names].astype(float)
+    @property
+    def acquisition_inputs(self):
+        return self.table.loc[:, self.dofs.subset(active=True, read_only=False).names].astype(float)
 
-    # @property
-    # def best_inputs(self):
-    #     return self.acquisition_inputs.values[np.nanargmax(self.scalarized_objectives)]
-
-    # def go_to(self, acquisition_inputs):
-    #     args = []
-    #     for dof, value in zip(self.dofs.subset(read_only=False), np.atleast_1d(acquisition_inputs).T):
-    #         args.append(dof.device)
-    #         args.append(value)
-    #     yield from bps.mv(*args)
+    @property
+    def best_inputs(self):
+        """
+        Returns a value for each currently active and non-read-only degree of freedom
+        """
+        return self.table.loc[np.nanargmax(self.scalarized_objectives), self.dofs.subset(active=True, read_only=False).names]
+
+    def go_to(self, positions):
+        args = []
+        for dof, value in zip(self.dofs.subset(active=True, read_only=False), np.atleast_1d(positions)):
+            args.append(dof.device)
+            args.append(value)
+        yield from bps.mv(*args)
 
     def go_to_best(self):
         yield from self.go_to(self.best_inputs)

bloptools/bayesian/objective.py

@@ -7,7 +7,7 @@
 numeric = Union[float, int]
 
 DEFAULT_MINIMUM_SNR = 2e1
-OBJ_FIELDS = ["name", "key", "limits", "weight", "minimize", "log"]
+OBJ_FIELDS = ["name", "key", "limits", "weight", "minimize", "log", "noise"]
 
 
 class DuplicateKeyError(ValueError):
@@ -62,8 +62,8 @@ def __repr__(self):
         return self.summary.__repr__()
 
     @property
-    def has_model(self):
-        return hasattr(self, "model")
+    def noise(self):
+        return self.model.likelihood.noise.item() if hasattr(self, "model") else None
 
 
 class ObjectiveList(Sequence):

bloptools/bayesian/plotting.py

@@ -23,7 +23,7 @@ def _plot_objs_one_dof(agent, size=16, lw=1e0):
 
     agent.obj_axes = np.atleast_1d(agent.obj_axes)
 
-    x_dof = agent.dofs[0]
+    x_dof = agent.dofs.subset(active=True)[0]
     x_values = agent.table.loc[:, x_dof.device.name].values
 
     for obj_index, obj in enumerate(agent.objectives):
@@ -68,8 +68,8 @@ def _plot_objs_many_dofs(agent, axes=[0, 1], shading="nearest", cmap=DEFAULT_COL
     )
 
     agent.obj_axes = np.atleast_2d(agent.obj_axes)
-
-    x_dof, y_dof = agent.dofs[axes[0]], agent.dofs[axes[1]]
+    
+    x_dof, y_dof = agent.dofs.subset(active=True)[axes[0]], agent.dofs.subset(active=True)[axes[1]]
     x_values = agent.table.loc[:, x_dof.device.name].values
     y_values = agent.table.loc[:, y_dof.device.name].values
 
@@ -185,7 +185,7 @@ def _plot_acq_one_dof(agent, acq_funcs, lw=1e0, **kwargs):
     )
 
     agent.acq_axes = np.atleast_1d(agent.acq_axes)
-    x_dof = agent.dofs[0]
+    x_dof = agent.dofs.subset(active=True)[0]
 
     test_inputs = agent.test_inputs_grid()
 
@@ -219,7 +219,7 @@ def _plot_acq_many_dofs(
 
     agent.acq_axes = np.atleast_1d(agent.acq_axes)
 
-    x_dof, y_dof = agent.dofs[axes[0]], agent.dofs[axes[1]]
+    x_dof, y_dof = agent.dofs.subset(active=True)[axes[0]], agent.dofs.subset(active=True)[axes[1]]
 
     # test_inputs has shape (..., 1, n_active_dofs)
     test_inputs = agent.test_inputs_grid() if gridded else agent.test_inputs(n=1024)
@@ -263,7 +263,7 @@ def _plot_acq_many_dofs(
 def _plot_valid_one_dof(agent, size=16, lw=1e0):
     agent.valid_fig, agent.valid_ax = plt.subplots(1, 1, figsize=(6, 4 * agent.n_objs), constrained_layout=True)
 
-    x_dof = agent.dofs[0]
+    x_dof = agent.dofs.subset(active=True)[0]
     x_values = agent.table.loc[:, x_dof.device.name].values
 
     test_inputs = agent.test_inputs_grid()
@@ -280,7 +280,7 @@ def _plot_valid_many_dofs(agent, axes=[0, 1], shading="nearest", cmap=DEFAULT_CO
     if gridded is None:
         gridded = len(agent.dofs.subset(active=True, read_only=False)) == 2
 
-    x_dof, y_dof = agent.dofs[axes[0]], agent.dofs[axes[1]]
+    x_dof, y_dof = agent.dofs.subset(active=True)[axes[0]], agent.dofs.subset(active=True)[axes[1]]
 
     # test_inputs has shape (..., 1, n_active_dofs)
     test_inputs = agent.test_inputs_grid() if gridded else agent.test_inputs(n=1024)