Updated Notebooks

Notebooks/Trieste_GP_opt.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "25d01f84-863b-4238-964b-e24425bb8107",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import tensorflow as tf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "242c5eb2-2531-481d-8c21-0b0178ac2db5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.random.seed(1793)\n",
+    "tf.random.set_seed(1793)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "49fa101c-15a9-47be-b254-6cbde6dd04e9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import trieste\n",
+    "from trieste.utils.objectives import branin\n",
+    "\n",
+    "search_space = trieste.space.Box([0, 0], [1, 1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "050686af-8c93-4996-b852-c925d7cc7e98",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def new_branin(data):\n",
+    "    print(data.shape)\n",
+    "    return branin(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "cd44cddb-5090-4e4f-b651-f7d57f145b70",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(5, 2)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from trieste.acquisition.rule import OBJECTIVE\n",
+    "\n",
+    "observer = trieste.utils.objectives.mk_observer(new_branin, OBJECTIVE)\n",
+    "\n",
+    "num_initial_points = 5\n",
+    "initial_query_points = search_space.sample(num_initial_points)\n",
+    "initial_data = observer(initial_query_points)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "696dfd67-0e37-42f0-af5b-64937bffc2aa",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Building a model\n"
+     ]
+    }
+   ],
+   "source": [
+    "import gpflow\n",
+    "\n",
+    "def build_model(data):\n",
+    "    print(\"Building a model\")\n",
+    "    variance = tf.math.reduce_variance(data.observations)\n",
+    "    kernel = gpflow.kernels.Matern52(variance=variance, lengthscales=[0.2, 0.2])\n",
+    "    gpr = gpflow.models.GPR(data.astuple(), kernel, noise_variance=1e-5)\n",
+    "    gpflow.set_trainable(gpr.likelihood, False)\n",
+    "\n",
+    "    return {OBJECTIVE: {\n",
+    "        \"model\": gpr,\n",
+    "        \"optimizer\": gpflow.optimizers.Scipy(),\n",
+    "        \"optimizer_args\": {\n",
+    "            \"minimize_args\": {\"options\": dict(maxiter=100)},\n",
+    "        },\n",
+    "    }}\n",
+    "\n",
+    "model = build_model(initial_data[OBJECTIVE])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "5fa0dabe-9930-45bc-86cf-843e1dbe10a1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(1, 2)\n",
+      "(1, 2)\n",
+      "(1, 2)\n",
+      "(1, 2)\n",
+      "(1, 2)\n",
+      "(1, 2)\n",
+      "(1, 2)\n",
+      "(1, 2)\n",
+      "(1, 2)\n",
+      "(1, 2)\n",
+      "(1, 2)\n",
+      "(1, 2)\n",
+      "(1, 2)\n",
+      "(1, 2)\n",
+      "(1, 2)\n",
+      "(1, 2)\n",
+      "(1, 2)\n",
+      "(1, 2)\n",
+      "(1, 2)\n",
+      "(1, 2)\n",
+      "(1, 2)\n",
+      "(1, 2)\n",
+      "(1, 2)\n",
+      "(1, 2)\n",
+      "(1, 2)\n",
+      "Optimization completed without errors\n"
+     ]
+    }
+   ],
+   "source": [
+    "bo = trieste.bayesian_optimizer.BayesianOptimizer(observer, search_space)\n",
+    "\n",
+    "result = bo.optimize(25, initial_data, model)\n",
+    "dataset = result.try_get_final_datasets()[OBJECTIVE]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "7d1bcebb-8ac5-4b60-9589-922c2a5d5429",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "query point: [0.54162649 0.14935401]\n",
+      "observation: [0.40162417]\n"
+     ]
+    }
+   ],
+   "source": [
+    "query_points = dataset.query_points.numpy()\n",
+    "observations = dataset.observations.numpy()\n",
+    "\n",
+    "arg_min_idx = tf.squeeze(tf.argmin(observations, axis=0))\n",
+    "\n",
+    "print(f\"query point: {query_points[arg_min_idx, :]}\")\n",
+    "print(f\"observation: {observations[arg_min_idx, :]}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "297fc50a-f755-40f1-8c9f-aaed111951cf",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<tf.Tensor: shape=(5, 1), dtype=float64, numpy=\n",
+       "array([[ 52.74206955],\n",
+       "       [ 20.91833473],\n",
+       "       [110.23393455],\n",
+       "       [225.05283879],\n",
+       "       [ 52.49971937]])>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "initial_data[OBJECTIVE].observations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "624720e0-5600-4a74-a2ce-6d09e248c069",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}