docs/tutorials/active_subspaces.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Active Subspaces\n",
    "\n",
    "Sometimes, the behavior of an $N$-dimensional model $f$ can be explained best by a *linear reparameterization* of its inputs variables, i.e. we can write $f(\\mathbf{x}) = g(\\mathbf{y}) = g(\\mathbf{M} \\cdot \\mathbf{x})$ where $\\mathbf{M}$ has size $M \\times N$ and $M < N$. When this happens, we say that $f$ admits an $M$-dimensional *active subspace* with basis given by $\\mathbf{M}$'s rows. Those basis vectors are the main directions of variance of the function $f$.\n",
    "\n",
    "The main directions are the eigenvectors of the matrix\n",
    "\n",
    "$\\mathbb{E}[\\nabla f^T \\cdot \\nabla f] = \\begin{pmatrix}\n",
    "\\mathbb{E}[f_{x_1} \\cdot f_{x_1}] & \\dots & \\mathbb{E}[f_{x_1} \\cdot f_{x_N}] \\\\\n",
    "\\dots & \\dots & \\dots \\\\\n",
    "\\mathbb{E}[f_{x_N} \\cdot f_{x_1}] & \\dots & \\mathbb{E}[f_{x_N} \\cdot f_{x_N}]\n",
    "\\end{pmatrix}$\n",
    "\n",
    "whereas the eigenvalues reveal the subspace's dimensionality --that is, a large gap between the $M$-th and $(M+1)$-th eigenvalue indicates that an $M$-dimensional active subspace is present.\n",
    "\n",
    "The necessary expected values are easy to compute from a tensor decomposition: they are just dot products between tensors. We will show a small demonstration of that in this notebook using a 4D function.\n",
    "\n",
    "Reference: see e.g. [\"Discovering an Active Subspace in a Single-Diode Solar Cell Model\", P. Constantine et al. (2015)](https://arxiv.org/abs/1406.7607)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tntorch as tn\n",
    "import torch\n",
    "torch.set_default_dtype(torch.float64)\n",
    "\n",
    "def f(X):\n",
    "    return X[:, 0] * X[:, 1] + X[:, 2]\n",
    "\n",
    "ticks = 64\n",
    "P = 100\n",
    "N = 4\n",
    "\n",
    "X = torch.rand((P, N))\n",
    "X *= (ticks-1)\n",
    "X = torch.round(X)\n",
    "y = f(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will fit this function `f` using a low-degree expansion in terms of [Legendre polynomials](pce.ipynb)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iter: 0       | loss:   0.999753 | total time:    0.0681\n",
      "iter: 500     | loss:   0.976744 | total time:    0.5568\n",
      "iter: 1000    | loss:   0.748542 | total time:    1.0160\n",
      "iter: 1500    | loss:   0.136286 | total time:    1.4746\n",
      "iter: 2000    | loss:   0.008914 | total time:    1.9377\n",
      "iter: 2500    | loss:   0.008340 | total time:    2.3975\n",
      "iter: 3000    | loss:   0.007649 | total time:    2.8598\n",
      "iter: 3500    | loss:   0.006894 | total time:    3.3183\n",
      "iter: 4000    | loss:   0.006212 | total time:    3.7835\n",
      "iter: 4203    | loss:   0.006041 | total time:    3.9697 <- converged (tol=0.0001)\n"
     ]
    }
   ],
   "source": [
    "t = tn.rand([ticks]*N, ranks_tt=2, ranks_tucker=2, requires_grad=True)\n",
    "t.set_factors('legendre')\n",
    "\n",
    "def loss(t):\n",
    "    return torch.norm(t[X].torch()-y) / torch.norm(y)\n",
    "tn.optimize(t, loss)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "eigvals, eigvecs = tn.active_subspace(t, bounds=None)\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "plt.figure()\n",
    "plt.bar(range(N), eigvals.detach().numpy())\n",
    "plt.title('Eigenvalues')\n",
    "plt.xlabel('Component')\n",
    "plt.ylabel('Magnitude')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In view of those eigenvalues, we can conclude that the learned model can be written (almost) perfectly in terms of 2 linearly reparameterized variables."
   ]
  }
 ],
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