testing (wip)

Author: nataliarodriguez-uc

src/hiopbbpy/opt/BODemoNLP.ipynb

@@ -4069,85 +4069,6 @@
    "source": [
     "#### Testing Values of Kernels and Bounds with SMT"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Lower kernel bounds (kL): [0.52729242 0.91393119 0.52729242]\n",
-      "Upper kernel bounds (kU): [0.96078944 1.         0.96078944]\n",
-      "\n",
-      "Kernel matrix from BnB:\n",
-      "[[1.         0.77880078 0.36787944]\n",
-      " [0.77880078 1.         0.77880078]\n",
-      " [0.36787944 0.77880078 1.        ]]\n"
-     ]
-    },
-    {
-     "ename": "ValueError",
-     "evalue": "cannot reshape array of size 2 into shape (1,3)",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[19], line 43\u001b[0m\n\u001b[1;32m     40\u001b[0m \u001b[38;5;66;03m# === Kernel matrix from SMT ===\u001b[39;00m\n\u001b[1;32m     41\u001b[0m \u001b[38;5;66;03m# SMT uses r = ||(x_i - x_j)/theta|| = Euclidean norm\u001b[39;00m\n\u001b[1;32m     42\u001b[0m r \u001b[38;5;241m=\u001b[39m cdist(x \u001b[38;5;241m/\u001b[39m theta, x \u001b[38;5;241m/\u001b[39m theta, metric\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124meuclidean\u001b[39m\u001b[38;5;124m'\u001b[39m)  \u001b[38;5;66;03m# scaled distance\u001b[39;00m\n\u001b[0;32m---> 43\u001b[0m K_smt \u001b[38;5;241m=\u001b[39m \u001b[43mgp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msurrogatesmt\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcorr\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__call__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mr\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     44\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mKernel matrix from SMT:\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m     45\u001b[0m \u001b[38;5;28mprint\u001b[39m(K_smt)\n",
-      "File \u001b[0;32m~/Documents/Project/hiop/.venv/lib/python3.9/site-packages/smt/kernels/kernels.py:56\u001b[0m, in \u001b[0;36mPowExp.__call__\u001b[0;34m(self, d, grad_ind, hess_ind, derivative_params)\u001b[0m\n\u001b[1;32m     52\u001b[0m i, nb_limit \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m, \u001b[38;5;28mint\u001b[39m(\u001b[38;5;241m1e4\u001b[39m)\n\u001b[1;32m     53\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m i \u001b[38;5;241m*\u001b[39m nb_limit \u001b[38;5;241m<\u001b[39m\u001b[38;5;241m=\u001b[39m d\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m]:\n\u001b[1;32m     54\u001b[0m     r[i \u001b[38;5;241m*\u001b[39m nb_limit : (i \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m1\u001b[39m) \u001b[38;5;241m*\u001b[39m nb_limit, \u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mexp(\n\u001b[1;32m     55\u001b[0m         \u001b[38;5;241m-\u001b[39mnp\u001b[38;5;241m.\u001b[39msum(\n\u001b[0;32m---> 56\u001b[0m             \u001b[43mtheta\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mreshape\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mn_components\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     57\u001b[0m             \u001b[38;5;241m*\u001b[39m d[i \u001b[38;5;241m*\u001b[39m nb_limit : (i \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m1\u001b[39m) \u001b[38;5;241m*\u001b[39m nb_limit, :],\n\u001b[1;32m     58\u001b[0m             axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m,\n\u001b[1;32m     59\u001b[0m         )\n\u001b[1;32m     60\u001b[0m     )\n\u001b[1;32m     61\u001b[0m     i \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[1;32m     63\u001b[0m i \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n",
-      "\u001b[0;31mValueError\u001b[0m: cannot reshape array of size 2 into shape (1,3)"
-     ]
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "from scipy.spatial.distance import cdist      # Replace with correct import\n",
-    "\n",
-    "# === Example data ===\n",
-    "x = np.array([\n",
-    "    [0.0, 0.0],\n",
-    "    [0.5, 0.5],\n",
-    "    [1.0, 1.0]\n",
-    "])\n",
-    "l = np.array([0.2, 0.2])\n",
-    "u = np.array([0.8, 0.8])\n",
-    "\n",
-    "# === Train SMT GP ===\n",
-    "theta = np.array([1.0, 1.0])\n",
-    "gp = smtKRG(theta=theta, xlimits=np.array([[0,1],[0,1]]), ndim=2, corr=\"pow_exp\")\n",
-    "y = np.linalg.norm(x, axis=1).reshape(-1,1)  # dummy target\n",
-    "gp.train(x, y)\n",
-    "\n",
-    "# === Instantiate BnB object ===\n",
-    "bnb = BnBAlgorithmBase()\n",
-    "bnb.theta = theta  # match SMT\n",
-    "bnb.set_kernel(\"pow_exp\")\n",
-    "\n",
-    "# === Kernel bounds ===\n",
-    "kL, kU = bnb.ker_bounds(x, l, u)\n",
-    "print(\"Lower kernel bounds (kL):\", kL)\n",
-    "print(\"Upper kernel bounds (kU):\", kU)\n",
-    "\n",
-    "# === Manual kernel matrix using BnB kernel ===\n",
-    "n = len(x)\n",
-    "K_manual = np.zeros((n, n))\n",
-    "for i in range(n):\n",
-    "    for j in range(n):\n",
-    "        d = np.sum(((x[i] - x[j]) / bnb.theta) ** 2)  # scaled squared distance\n",
-    "        K_manual[i, j] = bnb.kernel_func(d)\n",
-    "\n",
-    "print(\"\\nKernel matrix from BnB:\")\n",
-    "print(K_manual)\n",
-    "\n",
-    "# === Kernel matrix from SMT ===\n",
-    "# SMT uses r = ||(x_i - x_j)/theta|| = Euclidean norm\n",
-    "r = cdist(x / theta, x / theta, metric='euclidean')  # scaled distance\n",
-    "K_smt = gp.surrogatesmt.corr.__call__(r)\n",
-    "print(\"\\nKernel matrix from SMT:\")\n",
-    "print(K_smt)\n"
-   ]
   }
  ],
  "metadata": {