Update build_gp_surrogates notebook for vocs

Author: shuds13

doc/source/user_guide/advanced_usage/build_gp_surrogates.ipynb

@@ -11,7 +11,7 @@
     "\n",
     "The :class:`~optimas.diagnostics.ExplorationDiagnostics` class\n",
     "provides a simple way of fitting a Gaussian process (GP) model to any of the\n",
-    "objectives or analyzed parameters of an ``optimas``\n",
+    "objectives or observables of an ``optimas``\n",
     ":class:`~optimas.explorations.Exploration`, independently of which generator\n",
     "was used. This is useful to get a better understanding of the underlying\n",
     "function, make predictions, etc.\n",
@@ -29,7 +29,7 @@
     "\n",
     "The following cell sets up and runs an optimization with two input parameters\n",
     "``x1`` and ``x2``, two objectives ``f1`` and ``f2``, and one additional\n",
-    "analyzed parameter ``p1``.\n",
+    "observable ``p1``.\n",
     "At each evaluation, the ``eval_func_sf_moo`` function is run,\n",
     "which assigns a value to each outcome parameter according to the analytical\n",
     "formulas\n",
@@ -55,9 +55,9 @@
    "source": [
     "import numpy as np\n",
     "from optimas.explorations import Exploration\n",
-    "from optimas.core import VaryingParameter, Objective, Parameter\n",
     "from optimas.generators import RandomSamplingGenerator\n",
     "from optimas.evaluators import FunctionEvaluator\n",
+    "from gest_api.vocs import VOCS\n",
     "\n",
     "\n",
     "def eval_func_sf_moo(input_params, output_params):\n",
@@ -70,17 +70,20 @@
     "    output_params[\"p1\"] = np.sin(x1) + np.cos(x2)\n",
     "\n",
     "\n",
-    "var1 = VaryingParameter(\"x1\", 0.0, 5.0)\n",
-    "var2 = VaryingParameter(\"x2\", -5.0, 5.0)\n",
-    "par1 = Parameter(\"p1\")\n",
-    "obj1 = Objective(\"f1\", minimize=True)\n",
-    "obj2 = Objective(\"f2\", minimize=False)\n",
-    "\n",
-    "gen = RandomSamplingGenerator(\n",
-    "    varying_parameters=[var1, var2],\n",
-    "    objectives=[obj1, obj2],\n",
-    "    analyzed_parameters=[par1],\n",
+    "# Create VOCS object defining variables, objectives, and observables.\n",
+    "vocs = VOCS(\n",
+    "    variables={\n",
+    "        \"x1\": [0.0, 5.0],\n",
+    "        \"x2\": [-5.0, 5.0],\n",
+    "    },\n",
+    "    objectives={\n",
+    "        \"f1\": \"MINIMIZE\",\n",
+    "        \"f2\": \"MAXIMIZE\",\n",
+    "    },\n",
+    "    observables=[\"p1\"],\n",
     ")\n",
+    "\n",
+    "gen = RandomSamplingGenerator(vocs=vocs)\n",
     "ev = FunctionEvaluator(function=eval_func_sf_moo)\n",
     "exploration = Exploration(\n",
     "    generator=gen,\n",
@@ -125,18 +128,18 @@
     "raw_mimetype": "text/restructuredtext"
    },
    "source": [
-    "Building a GP model of each objective and analyzed parameter\n",
-    "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+    "Building a GP model of each objective and observable\n",
+    "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
     "\n",
     "To build a GP model, simply call\n",
     ":meth:`~optimas.diagnostics.Exploration.build_gp_model` on the diagnostics,\n",
     "indicating the name of the variable to which the model should be fitted.\n",
-    "This variable can be any ``objective`` or ``analyzed_parameter`` of the\n",
+    "This variable can be any ``objective`` or ``observable`` of the\n",
     "optimization.\n",
     "\n",
-    "Note that when building a surrogate model of an analyzed parameter, it is\n",
+    "Note that when building a surrogate model of an observable, it is\n",
     "required to provide a value to the ``minimize`` argument. This parameter\n",
-    "should therefore be ``True`` if lower values of the analyzed parameter are\n",
+    "should therefore be ``True`` if lower values of the observable are\n",
     "better than higher values. This information is necessary, e.g., for determining\n",
     "the best point in the model."
    ]
@@ -147,7 +150,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Build one model for each objective and analyzed parameter.\n",
+    "# Build one model for each objective and observable.\n",
     "f1_model = diags.build_gp_model(\"f1\")\n",
     "f2_model = diags.build_gp_model(\"f2\")\n",
     "p1_model = diags.build_gp_model(\"p1\", minimize=False)"