make sure DOF bounds are cast to floats

bloptools/bayesian/acquisition/__init__.py

@@ -119,11 +119,11 @@ def get_acquisition_function(agent, acq_func_identifier="qei", return_metadata=T
         acq_func_meta = {"name": acq_func_name, "args": {"beta": beta}}
 
     elif acq_func_name == "expected_mean":
-        acq_func, _ = get_acquisition_function(agent, acq_func_identifier="ucb", beta=0, return_metadata=False)
+        acq_func = get_acquisition_function(agent, acq_func_identifier="ucb", beta=0, return_metadata=False)
         acq_func_meta = {"name": acq_func_name, "args": {}}
 
     elif acq_func_name == "monte_carlo_expected_mean":
-        acq_func, _ = get_acquisition_function(agent, acq_func_identifier="qucb", beta=0, return_metadata=False)
+        acq_func = get_acquisition_function(agent, acq_func_identifier="qucb", beta=0, return_metadata=False)
         acq_func_meta = {"name": acq_func_name, "args": {}}
 
     return (acq_func, acq_func_meta) if return_metadata else acq_func

bloptools/bayesian/agent.py

@@ -33,8 +33,6 @@
 
 MAX_TEST_INPUTS = 2**11
 
-os.environ["KMP_DUPLICATE_LIB_OK"] = "True"
-
 
 class Agent:
     def __init__(
@@ -126,8 +124,8 @@ def tell(self, new_table=None, append=True, train=True, **kwargs):
             if not train_index.sum() >= 2:
                 raise ValueError("There must be at least two valid data points per objective!")
 
-            train_inputs = torch.tensor(inputs[train_index]).double()
-            train_targets = torch.tensor(targets[train_index]).double().unsqueeze(-1)  # .unsqueeze(0)
+            train_inputs = torch.tensor(inputs[train_index], dtype=torch.double)
+            train_targets = torch.tensor(targets[train_index], dtype=torch.double).unsqueeze(-1)  # .unsqueeze(0)
 
             # for constructing the log normal noise prior
             # target_snr = 2e2
@@ -212,7 +210,7 @@ def ask(self, acq_func_identifier="qei", n=1, route=True, sequential=True, **acq
 
             candidates, _ = botorch.optim.optimize_acqf(
                 acq_function=acq_func,
-                bounds=self._active_bounds_torch,
+                bounds=self.acquisition_function_bounds,
                 q=n,
                 sequential=sequential,
                 num_restarts=NUM_RESTARTS,
@@ -392,22 +390,6 @@ def _get_objective_targets(self, i):
 
         return targets
 
-    # @property
-    # def acquisition_dofs(self):
-    #     """
-    #     Returns the acquisition DOFs, which are the DOFs to optimize over (that is, active and not read-only).
-    #     """
-    #     return self.dofs.subset(active=True, read_only=False)
-
-    # @property
-    # def acquisition_dof_limits(self):
-    #     """
-    #     Returns the acquisition limits, which are the ranges optimize over (that is, active and not read-only).
-    #     This has shape (n_acq_dofs, 2).
-    #     """
-    #     acq_dofs = self.dofs.subset(active=True, read_only=False)
-    #     return np.c_[acq_dofs.summary.lower_limit.values, acq_dofs.summary.upper_limit.values]
-
     @property
     def n_objs(self):
         """
@@ -441,17 +423,19 @@ def target_names(self):
 
     def test_inputs_grid(self, max_inputs=MAX_TEST_INPUTS):
         """
-        Returns a (n_side, ..., n_side, 1, n_active_dof) grid of test_inputs
+        Returns a (n_side, ..., n_side, 1, n_active_dof) grid of test_inputs.
+        n_side is 1 if a dof is read-only
         """
-        n_acq_dofs = len(self.dofs.subset(active=True, read_only=False))
-        n_side = int(np.power(max_inputs, n_acq_dofs**-1))
+        n_settable_acq_func_dofs = len(self.dofs.subset(active=True, read_only=False))
+        n_side_settable = int(np.power(max_inputs, n_settable_acq_func_dofs**-1))
+        n_sides = [1 if dof.read_only else n_side_settable for dof in self.dofs.subset(active=True)]
         return torch.cat(
             [
                 tensor.unsqueeze(-1)
                 for tensor in torch.meshgrid(
                     *[
-                        torch.linspace(dof.lower_limit, dof.upper_limit, n_side) if not dof.read_only else dof.readback
-                        for dof in self.dofs.subset(active=True)
+                        torch.linspace(lower_limit, upper_limit, n_side)
+                        for (lower_limit, upper_limit), n_side in zip(self.dofs.subset(active=True).limits, n_sides)
                     ],
                     indexing="ij",
                 )
@@ -463,10 +447,18 @@ def test_inputs(self, n=MAX_TEST_INPUTS):
         """
         Returns a (n, 1, n_active_dof) grid of test_inputs
         """
-        return utils.sobol_sampler(self._active_bounds_torch, n=n)
+        return utils.sobol_sampler(self.acquisition_function_bounds, n=n)
 
     @property
-    def _active_bounds_torch(self):
+    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]
+
+        return torch.tensor(np.vstack([acq_func_lower_bounds, acq_func_upper_bounds]), dtype=torch.double)
+
         return torch.tensor(
             [dof.limits if not dof.read_only else tuple(2 * [dof.readback]) for dof in self.dofs.subset(active=True)]
         ).T
@@ -644,48 +636,5 @@ def plot_validity(self, **kwargs):
         else:
             plotting._plot_valid_many_dofs(self, **kwargs)
 
-    # def plot_history(self, x_key="index", show_all_objs=False):
-    #     x = getattr(self.table, x_key).values
-
-    #     num_obj_plots = 1
-    #     if show_all_objs:
-    #         num_obj_plots = self.n_objs + 1
-
-    #     self.n_objs + 1 if self.n_objs > 1 else 1
-
-    #     hist_fig, hist_axes = plt.subplots(
-    #         num_obj_plots, 1, figsize=(6, 4 * num_obj_plots), sharex=True, constrained_layout=True, dpi=200
-    #     )
-    #     hist_axes = np.atleast_1d(hist_axes)
-
-    #     unique_strategies, acq_func_index, acq_func_inverse = np.unique(
-    #         self.table.acq_func, return_index=True, return_inverse=True
-    #     )
-
-    #     sample_colors = np.array(DEFAULT_COLOR_LIST)[acq_func_inverse]
-
-    #     if show_all_objs:
-    #         for obj_index, obj in enumerate(self.objectives):
-    #             y = self.table.loc[:, f"{obj.key}_fitness"].values
-    #             hist_axes[obj_index].scatter(x, y, c=sample_colors)
-    #             hist_axes[obj_index].plot(x, y, lw=5e-1, c="k")
-    #             hist_axes[obj_index].set_ylabel(obj.key)
-
-    #     y = self.scalarized_objectives
-
-    #     cummax_y = np.array([np.nanmax(y[: i + 1]) for i in range(len(y))])
-
-    #     hist_axes[-1].scatter(x, y, c=sample_colors)
-    #     hist_axes[-1].plot(x, y, lw=5e-1, c="k")
-
-    #     hist_axes[-1].plot(x, cummax_y, lw=5e-1, c="k", ls=":")
-
-    #     hist_axes[-1].set_ylabel("total_fitness")
-    #     hist_axes[-1].set_xlabel(x_key)
-
-    #     handles = []
-    #     for i_acq_func, acq_func in enumerate(unique_strategies):
-    #         #        i_acq_func = np.argsort(acq_func_index)[i_handle]
-    #         handles.append(Patch(color=DEFAULT_COLOR_LIST[i_acq_func], label=acq_func))
-    #     legend = hist_axes[0].legend(handles=handles, fontsize=8)
-    #     legend.set_title("acquisition function")
+    def plot_history(self, **kwargs):
+        plotting._plot_history(self, **kwargs)

bloptools/bayesian/devices.py

@@ -33,7 +33,7 @@ class DOF:
     def __init__(
         self,
         device: Signal = None,
-        limits: Tuple[numeric, numeric] = (-10.0, 10.0),
+        limits: Tuple[float, float] = (-10.0, 10.0),
         name: str = None,
         units: str = None,
         read_only: bool = None,
@@ -53,11 +53,11 @@ def __init__(
 
     @property
     def lower_limit(self):
-        return self.limits[0]
+        return float(self.limits[0])
 
     @property
     def upper_limit(self):
-        return self.limits[1]
+        return float(self.limits[1])
 
     @property
     def readback(self):

bloptools/bayesian/objective.py

@@ -6,7 +6,7 @@
 
 numeric = Union[float, int]
 
-DEFAULT_MINIMUM_SNR = 1e1
+DEFAULT_MINIMUM_SNR = 2e1
 OBJ_FIELDS = ["name", "key", "limits", "weight", "minimize", "log"]
 
 
@@ -59,7 +59,7 @@ def summary(self):
         return series
 
     def __repr__(self):
-        return self.params.__repr__()
+        return self.summary.__repr__()
 
     @property
     def has_model(self):
@@ -88,6 +88,8 @@ def summary(self):
         for attr in ["minimize", "log"]:
             summary[attr] = summary[attr].astype(bool)
 
+        return summary
+
     def __repr__(self):
         return self.summary.__repr__()
 

bloptools/bayesian/plotting.py

@@ -1,6 +1,7 @@
 import matplotlib as mpl
-import matplotlib.pyplot as plt
 import numpy as np
+from matplotlib import pyplot as plt
+from matplotlib.patches import Patch
 
 from . import acquisition
 
@@ -194,7 +195,7 @@ def _plot_acq_one_dof(agent, acq_funcs, lw=1e0, **kwargs):
         acq_func, acq_func_meta = acquisition.get_acquisition_function(agent, acq_func_identifier)
         test_acqf = acq_func(test_inputs).detach().numpy()
 
-        agent.acq_axes[iacq_func].plot(test_inputs.squeeze(), test_acqf, lw=lw, color=color)
+        agent.acq_axes[iacq_func].plot(test_inputs.squeeze(-2), test_acqf, lw=lw, color=color)
 
         agent.acq_axes[iacq_func].set_xlim(*x_dof.limits)
         agent.acq_axes[iacq_func].set_xlabel(x_dof.label)
@@ -269,7 +270,7 @@ def _plot_valid_one_dof(agent, size=16, lw=1e0):
     constraint = agent.constraint(test_inputs)[..., 0]
 
     agent.valid_ax.scatter(x_values, agent.all_objectives_valid, s=size)
-    agent.valid_ax.plot(test_inputs.squeeze(), constraint, lw=lw)
+    agent.valid_ax.plot(test_inputs.squeeze(-2), constraint, lw=lw)
     agent.valid_ax.set_xlim(*x_dof.limits)
 
 
@@ -316,45 +317,51 @@ def _plot_valid_many_dofs(agent, axes=[0, 1], shading="nearest", cmap=DEFAULT_CO
         ax.set_xlim(*x_dof.limits)
         ax.set_ylim(*y_dof.limits)
 
-    # data_ax = agent.valid_axes[0].scatter(
-    #     *agent.acquisition_inputs.values.T[:2],
-    #     c=agent.all_objectives_valid,
-    #     s=size,
-    #     vmin=0,
-    #     vmax=1,
-    #     cmap=cmap,
-    # )
-
-    # x = agent.test_inputs_grid().squeeze() if gridded else agent.test_inputs(n=MAX_TEST_INPUTS)
-    # *input_shape, input_dim = x.shape
-    # constraint = agent.classifier.probabilities(x.reshape(-1, 1, input_dim))[..., -1].reshape(input_shape)
-
-    # if gridded:
-    #     agent.valid_axes[1].pcolormesh(
-    #         x[..., 0].detach().numpy(),
-    #         x[..., 1].detach().numpy(),
-    #         constraint.detach().numpy(),
-    #         shading=shading,
-    #         cmap=cmap,
-    #         vmin=0,
-    #         vmax=1,
-    #     )
-
-    #     # agent.acq_fig.colorbar(obj_ax, ax=agent.valid_axes[iacq_func], location="bottom", aspect=32, shrink=0.8)
-
-    # else:
-    #     # agent.valid_axes.set_title(acq_func_meta["name"])
-    #     agent.valid_axes[1].scatter(
-    #         x.detach().numpy()[..., axes[0]],
-    #         x.detach().numpy()[..., axes[1]],
-    #         c=constraint.detach().numpy(),
-    #     )
-
-    # agent.valid_fig.colorbar(data_ax, ax=agent.valid_axes[:2], location="bottom", aspect=32, shrink=0.8)
-
-    # for ax in agent.valid_axes.ravel():
-    #     ax.set_xlim(*agent.dofs.subset(active=True, read_only=False)[axes[0]].limits)
-    #     ax.set_ylim(*agent.dofs.subset(active=True, read_only=False)[axes[1]].limits)
+
+def _plot_history(agent, x_key="index", show_all_objs=False):
+    x = getattr(agent.table, x_key).values
+
+    num_obj_plots = 1
+    if show_all_objs:
+        num_obj_plots = agent.n_objs + 1
+
+    agent.n_objs + 1 if agent.n_objs > 1 else 1
+
+    hist_fig, hist_axes = plt.subplots(
+        num_obj_plots, 1, figsize=(6, 4 * num_obj_plots), sharex=True, constrained_layout=True, dpi=200
+    )
+    hist_axes = np.atleast_1d(hist_axes)
+
+    unique_strategies, acq_func_index, acq_func_inverse = np.unique(
+        agent.table.acq_func, return_index=True, return_inverse=True
+    )
+
+    sample_colors = np.array(DEFAULT_COLOR_LIST)[acq_func_inverse]
+
+    if show_all_objs:
+        for obj_index, obj in enumerate(agent.objectives):
+            y = agent.table.loc[:, f"{obj.key}_fitness"].values
+            hist_axes[obj_index].scatter(x, y, c=sample_colors)
+            hist_axes[obj_index].plot(x, y, lw=5e-1, c="k")
+            hist_axes[obj_index].set_ylabel(obj.key)
+
+    y = agent.scalarized_objectives
+
+    cummax_y = np.array([np.nanmax(y[: i + 1]) for i in range(len(y))])
+
+    hist_axes[-1].scatter(x, y, c=sample_colors)
+    hist_axes[-1].plot(x, y, lw=5e-1, c="k")
+
+    hist_axes[-1].plot(x, cummax_y, lw=5e-1, c="k", ls=":")
+
+    hist_axes[-1].set_ylabel("total_fitness")
+    hist_axes[-1].set_xlabel(x_key)
+
+    handles = []
+    for i_acq_func, acq_func in enumerate(unique_strategies):
+        handles.append(Patch(color=DEFAULT_COLOR_LIST[i_acq_func], label=acq_func))
+    legend = hist_axes[0].legend(handles=handles, fontsize=8)
+    legend.set_title("acquisition function")
 
 
 def inspect_beam(agent, index, border=None):

bloptools/tests/test_passive_dofs.py

@@ -10,7 +10,7 @@ def test_passive_dofs(RE, db):
         DOF(name="x1", limits=(-5.0, 5.0)),
         DOF(name="x2", limits=(-5.0, 5.0)),
         DOF(BrownianMotion(name="brownian1"), read_only=True),
-        DOF(BrownianMotion(name="brownian1"), read_only=True),
+        DOF(BrownianMotion(name="brownian2"), read_only=True),
     ]
 
     objectives = [
@@ -28,6 +28,6 @@ def test_passive_dofs(RE, db):
 
     RE(agent.learn("qr", n=32))
 
-    agent.plot_tasks()
+    agent.plot_objectives()
     agent.plot_acquisition()
     agent.plot_validity()

bloptools/tests/test_plots.py

@@ -5,7 +5,7 @@
 def test_plots(RE, agent):
     RE(agent.learn("qr", n=32))
 
-    agent.plot_tasks()
+    agent.plot_objectives()
     agent.plot_acquisition()
     agent.plot_validity()
     agent.plot_history()