Add classes for DOFs and objectives

bloptools/bayesian/agent.py

@@ -136,7 +136,7 @@ def tell(self, new_table=None, append=True, train=True, **kwargs):
 
             likelihood = gpytorch.likelihoods.GaussianLikelihood(
                 noise_constraint=gpytorch.constraints.Interval(
-                    torch.tensor(1e-4).square(),
+                    torch.tensor(1e-2).square(),
                     torch.tensor(1 / obj.min_snr).square(),
                 ),
                 # noise_prior=gpytorch.priors.torch_priors.LogNormalPrior(loc=loc, scale=scale),
@@ -293,6 +293,9 @@ def acquire(self, acquisition_inputs):
             #     for obj in self.objectives:
             #         products.loc[index, objective["key"]] = getattr(entry, objective["key"])
 
+        except KeyboardInterrupt:
+            raise KeyboardInterrupt()
+
         except Exception as error:
             if not self.allow_acquisition_errors:
                 raise error
@@ -334,11 +337,12 @@ def learn(
 
         if acq_func is not None:
             for i in range(iterations):
-                x, acq_func_meta = self.ask(n=n, acq_func_identifier=acq_func, **kwargs)
-
-                new_table = yield from self.acquire(x)
-                new_table.loc[:, "acq_func"] = acq_func_meta["name"]
-                self.tell(new_table=new_table, train=train)
+                print(f"running iteration {i + 1} / {iterations}")
+                for single_acq_func in np.atleast_1d(acq_func):
+                    x, acq_func_meta = self.ask(n=n, acq_func_identifier=single_acq_func, **kwargs)
+                    new_table = yield from self.acquire(x)
+                    new_table.loc[:, "acq_func"] = acq_func_meta["name"]
+                    self.tell(new_table=new_table, train=train)
 
         self.initialized = True
 
@@ -357,12 +361,12 @@ 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:
-                offset = 0.25 * np.ptp(dof.limits) * np.random.uniform(low=-1, high=1)
-                dof.limits = (cache_limits[dof.name] + offset, cache_limits[dof.name] + offset)
+            # for dof in self.dofs:
+            #     offset = 0.25 * np.ptp(dof.limits) * np.random.uniform(low=-1, high=1)
+            #     dof.limits = (cache_limits[dof.name][0] + offset, cache_limits[dof.name][1] + offset)
 
             self.reset()
 

bloptools/bayesian/devices.py

@@ -51,6 +51,8 @@ def __init__(
         self.active = active
         self.latent_group = latent_group if latent_group is not None else str(uuid.uuid4())
 
+        self.device.kind = "hinted"
+
     @property
     def lower_limit(self):
         return float(self.limits[0])

docs/source/tutorials/passive-dofs.ipynb

@@ -38,43 +38,42 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from bloptools.utils import prepare_re_env\n",
-    "\n",
-    "%run -i $prepare_re_env.__file__ --db-type=temp\n",
-    "import pytest\n",
-    "\n",
     "from bloptools.utils import functions\n",
     "from bloptools.bayesian import DOF, Agent, BrownianMotion, Objective\n",
     "\n",
     "\n",
-    "@pytest.mark.test_func\n",
-    "def test_passive_dofs(RE, db):\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(BrownianMotion(name=\"brownian1\"), read_only=True),\n",
-    "        DOF(BrownianMotion(name=\"brownian2\"), read_only=True, active=False),\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(BrownianMotion(name=\"brownian1\"), read_only=True),\n",
+    "    DOF(BrownianMotion(name=\"brownian2\"), read_only=True, active=False),\n",
+    "]\n",
     "\n",
-    "    objectives = [\n",
-    "        Objective(key=\"himmelblau\", minimize=True),\n",
-    "    ]\n",
+    "objectives = [\n",
+    "    Objective(key=\"himmelblau\", minimize=True),\n",
+    "]\n",
     "\n",
-    "    agent = Agent(\n",
-    "        dofs=dofs,\n",
-    "        objectives=objectives,\n",
-    "        digestion=functions.constrained_himmelblau_digestion,\n",
-    "        db=db,\n",
-    "        verbose=True,\n",
-    "        tolerate_acquisition_errors=False,\n",
-    "    )\n",
+    "agent = Agent(\n",
+    "    dofs=dofs,\n",
+    "    objectives=objectives,\n",
+    "    digestion=functions.constrained_himmelblau_digestion,\n",
+    "    db=db,\n",
+    "    verbose=True,\n",
+    "    tolerate_acquisition_errors=False,\n",
+    ")\n",
     "\n",
-    "    RE(agent.learn(\"qr\", n=32))\n",
-    "\n",
-    "    agent.plot_objectives()\n",
-    "    agent.plot_acquisition()\n",
-    "    agent.plot_validity()"
+    "RE(agent.learn(\"qr\", n=16))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "49127601",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "agent.dofs"
    ]
   },
   {
@@ -86,6 +85,14 @@
    "source": [
     "agent.plot_objectives()"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4d856a7d",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -104,7 +111,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.4"
+   "version": "3.10.12"
   },
   "vscode": {
    "interpreter": {

requirements-dev.txt

@@ -2,11 +2,14 @@
 # the documentation) but not necessarily required for _using_ it.
 black
 pytest-codecov
+chardet
 coverage
 flake8
 furo
 isort
+markupsafe
 nbstripout
+numpydoc
 pre-commit
 pre-commit-hooks
 pytest

requirements.txt

@@ -3,6 +3,7 @@ botorch
 databroker
 gpytorch
 h5py
+IPython
 matplotlib
 numpy
 ophyd