fixed notebooks

blop/__init__.py

@@ -1,6 +1,6 @@
 from . import utils  # noqa F401
 from ._version import get_versions
-from .agent import Agent # noqa F401
+from .agent import Agent  # noqa F401
 from .dofs import DOF  # noqa F401
 from .objectives import Objective  # noqa F401
 

blop/agent.py

@@ -23,12 +23,12 @@
 from ophyd import Signal
 
 from . import utils
-from .dofs import DOF, DOFList
-from .objectives import Objective, ObjectiveList
 from .bayesian import acquisition, models, plotting
+from .bayesian.transforms import TargetingPosteriorTransform
 from .digestion import default_digestion_function
+from .dofs import DOF, DOFList
+from .objectives import Objective, ObjectiveList
 from .plans import default_acquisition_plan
-from .bayesian.transforms import TargetingPosteriorTransform
 
 warnings.filterwarnings("ignore", category=botorch.exceptions.warnings.InputDataWarning)
 
@@ -37,19 +37,17 @@
 MAX_TEST_INPUTS = 2**11
 
 
-
 def _validate_dofs_and_objs(dofs: DOFList, objs: ObjectiveList):
-
     if len(dofs) == 0:
-        raise ValueError(f"You must supply at least one DOF.")
+        raise ValueError("You must supply at least one DOF.")
 
     if len(objs) == 0:
-        raise ValueError(f"You must supply at least one objective.")
+        raise ValueError("You must supply at least one objective.")
 
     for obj in objs:
         for latent_group in obj.latent_groups:
             for dof_name in latent_group:
-                if not dof_name in dofs.names:
+                if dof_name not in dofs.names:
                     raise ValueError(f"DOF name '{dof_name}' in latent group for objective '{obj.name}' does not exist.")
 
 
@@ -144,13 +142,11 @@ def __iter__(self):
             yield self.dofs[index]
 
     def __getattr__(self, attr):
-
         acq_func_name = acquisition.parse_acq_func_identifier(attr)
         if acq_func_name is not None:
             return self.get_acquisition_function(identifier=acq_func_name)
-
-        raise AttributeError(f"DOFList object has no attribute named '{attr}'.")
 
+        raise AttributeError(f"DOFList object has no attribute named '{attr}'.")
 
     def view(self, item: str = "mean", cmap: str = "turbo", max_inputs: int = MAX_TEST_INPUTS):
         """
@@ -513,7 +509,7 @@ def _construct_model(self, obj, skew_dims=None):
         return model
 
     def _construct_classifier(self, skew_dims=None):
-        skew_dims = [tuple([i]) for i in range(len(self.dofs))]
+        skew_dims = [tuple([i]) for i in range(len(self.dofs.subset(active=True)))]
 
         train_inputs = self.train_inputs(active=True)
         trusted = ~torch.isnan(train_inputs).any(axis=1)
@@ -654,7 +650,6 @@ def acquisition_function_bounds(self):
         acq_func_upper_bounds = np.where(self.dofs.read_only, self.dofs.readback, self.dofs.search_upper_bounds)
 
         return torch.tensor(np.vstack([acq_func_lower_bounds, acq_func_upper_bounds]), dtype=torch.double)
-
 
     def latent_dim_tuples(self, obj_index=None):
         """
@@ -663,8 +658,8 @@ def latent_dim_tuples(self, obj_index=None):
         """
 
         if obj_index is None:
-            return {obj.name:self.latent_dim_tuples(obj_index=obj.name) for obj in self.objectives}
-        
+            return {obj.name: self.latent_dim_tuples(obj_index=obj.name) for obj in self.objectives}
+
         obj = self.objectives[obj_index]
 
         latent_group_index = {}
@@ -673,7 +668,7 @@ def latent_dim_tuples(self, obj_index=None):
             for group_index, latent_group in enumerate(obj.latent_groups):
                 if dof.name in latent_group:
                     latent_group_index[dof.name] = group_index
-                    
+
         u, uinv = np.unique(list(latent_group_index.values()), return_inverse=True)
         return [tuple(np.where(uinv == i)[0]) for i in range(len(u))]
 

blop/bayesian/acquisition/__init__.py

@@ -23,6 +23,7 @@ def parse_acq_func_identifier(identifier):
             return acq_func_name
     return None
 
+
 def get_acquisition_function(agent, identifier="qei", return_metadata=True, verbose=False, **acq_func_kwargs):
     """Generates an acquisition function from a supplied identifier. A list of acquisition functions and
     their identifiers can be found at `agent.all_acq_funcs`.
@@ -31,7 +32,7 @@ def get_acquisition_function(agent, identifier="qei", return_metadata=True, verb
     acq_func_name = parse_acq_func_identifier(identifier)
     if acq_func_name is None:
         raise ValueError(f'Unrecognized acquisition function identifier "{identifier}".')
-    
+
     acq_func_config = config["upper_confidence_bound"]
 
     if config[acq_func_name]["multitask_only"] and (len(agent.objectives) == 1):

blop/bayesian/kernels.py

@@ -80,7 +80,7 @@ def __init__(
             )
 
         if self.n_skew_entries > 0:
-            skew_entries_constraint = gpytorch.constraints.Interval(-2*np.pi, 2*np.pi)
+            skew_entries_constraint = gpytorch.constraints.Interval(-2 * np.pi, 2 * np.pi)
             skew_entries_initial = torch.zeros((self.num_outputs, self.n_skew_entries), dtype=torch.float64)
             self.register_parameter(name="raw_skew_entries", parameter=torch.nn.Parameter(skew_entries_initial))
             self.register_constraint(param_name="raw_skew_entries", constraint=skew_entries_constraint)

blop/bayesian/plotting.py

@@ -87,7 +87,6 @@ def _plot_objs_many_dofs(agent, axes=(0, 1), shading="nearest", cmap=DEFAULT_COL
     test_y = test_inputs[..., 0, axes[1]].detach().squeeze().numpy()
 
     for obj_index, obj in enumerate(agent.objectives):
-
         targets = agent.train_targets(obj.name).squeeze(-1).numpy()
 
         obj_vmin, obj_vmax = np.nanpercentile(targets, q=[1, 99])

blop/dofs.py

@@ -101,7 +101,7 @@ def search_lower_bound(self):
     @property
     def search_upper_bound(self):
         return float(self.search_bounds[1])
-    
+
     @property
     def trust_lower_bound(self):
         return float(self.trust_bounds[0])

blop/objectives.py

@@ -1,6 +1,6 @@
 from collections.abc import Sequence
 from dataclasses import dataclass, field
-from typing import Tuple, List, Union
+from typing import List, Tuple, Union
 
 import numpy as np
 import pandas as pd
@@ -134,7 +134,7 @@ def __getattr__(self, attr):
         if attr in self.names:
             return self.__getitem__(attr)
         else:
-            raise AttributeError(f'No attribute named {attr}')
+            raise AttributeError(f"No attribute named {attr}")
 
     def __getitem__(self, i):
         if type(i) is int:

docs/source/tutorials/himmelblau.ipynb

@@ -69,11 +69,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from blop.bayesian import DOF\n",
+    "from blop import DOF\n",
     "\n",
     "dofs = [\n",
-    "    DOF(name=\"x1\", limits=(-6, 6)),\n",
-    "    DOF(name=\"x2\", limits=(-6, 6)),\n",
+    "    DOF(name=\"x1\", search_bounds=(-6, 6)),\n",
+    "    DOF(name=\"x2\", search_bounds=(-6, 6)),\n",
     "]"
    ]
   },
@@ -92,7 +92,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from blop.bayesian import Objective\n",
+    "from blop import Objective\n",
     "\n",
     "objectives = [Objective(name=\"himmelblau\", description=\"Himmeblau's function\", target=\"min\")]"
    ]
@@ -140,8 +140,7 @@
    },
    "outputs": [],
    "source": [
-    "from blop.bayesian import Agent\n",
-    "\n",
+    "from blop import Agent\n",
     "\n",
     "agent = Agent(\n",
     "    dofs=dofs,\n",

docs/source/tutorials/hyperparameters.ipynb

@@ -72,11 +72,11 @@
     "\n",
     "%run -i $prepare_re_env.__file__ --db-type=temp\n",
     "\n",
-    "from blop.bayesian import DOF, Objective, Agent\n",
+    "from blop import DOF, Objective, Agent\n",
     "\n",
     "dofs = [\n",
-    "    DOF(name=\"x1\", limits=(-6, 6)),\n",
-    "    DOF(name=\"x2\", limits=(-6, 6)),\n",
+    "    DOF(name=\"x1\", search_bounds=(-6, 6)),\n",
+    "    DOF(name=\"x2\", search_bounds=(-6, 6)),\n",
     "]\n",
     "\n",
     "objectives = [\n",
@@ -127,6 +127,16 @@
     "RE(agent.learn(\"qei\", n=4, iterations=4))\n",
     "agent.plot_objectives()"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b70eaf9b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "agent.best"
+   ]
   }
  ],
  "metadata": {
@@ -145,7 +155,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.5"
+   "version": "3.10.0"
   },
   "vscode": {
    "interpreter": {

docs/source/tutorials/passive-dofs.ipynb

@@ -39,13 +39,14 @@
    "outputs": [],
    "source": [
     "from blop.utils import functions\n",
-    "from blop.bayesian import DOF, Agent, BrownianMotion, Objective\n",
+    "from blop import DOF, Agent, Objective\n",
+    "from blop.dofs import BrownianMotion\n",
     "\n",
     "\n",
     "dofs = [\n",
-    "    DOF(name=\"x1\", limits=(-5.0, 5.0)),\n",
-    "    DOF(name=\"x2\", limits=(-5.0, 5.0)),\n",
-    "    DOF(name=\"x3\", limits=(-5.0, 5.0), active=False),\n",
+    "    DOF(name=\"x1\", search_bounds=(-5.0, 5.0)),\n",
+    "    DOF(name=\"x2\", search_bounds=(-5.0, 5.0)),\n",
+    "    DOF(name=\"x3\", search_bounds=(-5.0, 5.0), active=False),\n",
     "    DOF(device=BrownianMotion(name=\"brownian1\"), read_only=True),\n",
     "    DOF(device=BrownianMotion(name=\"brownian2\"), read_only=True, active=False),\n",
     "]\n",
@@ -75,6 +76,16 @@
    "source": [
     "agent.plot_objectives()"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "312610b1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "agent.latent_dim_tuples()"
+   ]
   }
  ],
  "metadata": {
@@ -93,7 +104,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.5"
+   "version": "3.10.0"
   },
   "vscode": {
    "interpreter": {