parafac-als.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tensor Decompositions with NumPy and Dask.array\n",
    "===========================\n",
    "\n",
    "We implement a very simple algorithm to compute the PARAFAC/CANDECOMP tensor decomposition using alternating least squares.\n",
    "\n",
    "We follow section 4.1 from this paper: https://www.cs.cmu.edu/~pmuthuku/mlsp_page/lectures/Parafac.pdf\n",
    "\n",
    "[PARAFAC/CANDECOMP](https://en.wikipedia.org/wiki/Tensor_rank_decomposition) is an array decomposition that generalizes SVD/PCA to higher dimensions.  It is commonly used when trying to find low-rank structure in observational data in multiple dimensions.  \n",
    "\n",
    "<img src=\"https://upload.wikimedia.org/wikipedia/commons/5/52/Collaborative_filtering.gif\" \n",
    "     align=\"right\"\n",
    "     width=\"40%\">\n",
    "\n",
    "In a typical two dimensional setting we might call this collaborative filtering, and is an approach used by companies like Amazon and Netflix to help recommend products to users based on the habits of similar users.  However nothing about this problem is specifically restricted to two dimensions.  We might consider the following applications:\n",
    "\n",
    "1.  What customers bought what products at what times of day or year\n",
    "2.  Which computers talked to which other computers over which ports under which user\n",
    "3.  Outcomes of treatments affecting patients with which dieseases\n",
    "\n",
    "When we look around for algorithms to generalize SVD/PCA to multiple dimensions we come across a family of algorithms called tensor decompositions.  This notebook implements the simplest such algorithm in a common and simple way using NumPy and then scales it out naively using Dask arrays."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Alternating Least Squares"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from functools import reduce\n",
    "import operator\n",
    "import dask\n",
    "\n",
    "\n",
    "def outer_product(*Xs):\n",
    "    \"\"\" Outer/tensor product of multiple 2d arrays\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    *Xs: arrays of size (k, n)\n",
    "        All inputs must have the same first dimension but may have varying\n",
    "        second dimensions\n",
    "    \"\"\"\n",
    "    n = len(Xs)\n",
    "    indexes = [(slice(None, None),) +\n",
    "               (None,) * i +\n",
    "               (slice(None, None),) +\n",
    "               (None,) * (n - i - 1) for i in range(len(Xs))]\n",
    "    Ys = [X[ind] for X, ind in zip(Xs, indexes)]\n",
    "    Ys = sorted(Ys, key=lambda y: y.nbytes)  # smaller outer products first\n",
    "    return reduce(operator.mul, Ys)\n",
    "\n",
    "\n",
    "def parafac_als(X, n_factors, n_iter=100):\n",
    "    \"\"\" Parafac tensor decomposition\n",
    "\n",
    "    This implements the basic algorithm in section 4.1 of this paper\n",
    "\n",
    "        https://www.cs.cmu.edu/~pmuthuku/mlsp_page/lectures/Parafac.pdf\n",
    "    \"\"\"\n",
    "    # Randomly initialize factors\n",
    "    factors = [np.random.random((n_factors, X.shape[i])) for i in range(0, X.ndim)]\n",
    "    \n",
    "    # Solve\n",
    "    for itr in range(n_iter):\n",
    "        for i in range(X.ndim):\n",
    "            not_i = tuple(j for j in range(X.ndim) if j != i)\n",
    "            Xp = X.transpose((i,) + not_i)\n",
    "            Xp = Xp.reshape((Xp.shape[0], np.prod(Xp.shape[1:])))\n",
    "            Z = outer_product(*[factors[j] for j in not_i])\n",
    "            Z = Z.reshape((Z.shape[0], np.prod(Z.shape[1:])))\n",
    "\n",
    "            factor, residuals, rank, s = np.linalg.lstsq(Z.T, Xp.T)\n",
    "            factors[i] = factor\n",
    "    return factors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Demonstrate accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[[1, 1, 0, 1, 1],\n",
       "         [1, 1, 1, 2, 1]],\n",
       "\n",
       "        [[1, 1, 0, 1, 1],\n",
       "         [1, 1, 0, 1, 1]]],\n",
       "\n",
       "\n",
       "       [[[0, 0, 0, 0, 0],\n",
       "         [0, 0, 1, 1, 0]],\n",
       "\n",
       "        [[0, 0, 0, 0, 0],\n",
       "         [0, 0, 0, 0, 0]]],\n",
       "\n",
       "\n",
       "       [[[1, 1, 0, 1, 1],\n",
       "         [1, 1, 1, 2, 1]],\n",
       "\n",
       "        [[1, 1, 0, 1, 1],\n",
       "         [1, 1, 0, 1, 1]]],\n",
       "\n",
       "\n",
       "       [[[0, 0, 0, 0, 0],\n",
       "         [0, 0, 0, 0, 0]],\n",
       "\n",
       "        [[0, 0, 0, 0, 0],\n",
       "         [0, 0, 0, 0, 0]]],\n",
       "\n",
       "\n",
       "       [[[1, 1, 0, 1, 1],\n",
       "         [1, 1, 0, 1, 1]],\n",
       "\n",
       "        [[1, 1, 0, 1, 1],\n",
       "         [1, 1, 0, 1, 1]]],\n",
       "\n",
       "\n",
       "       [[[0, 0, 0, 0, 0],\n",
       "         [0, 0, 0, 0, 0]],\n",
       "\n",
       "        [[0, 0, 0, 0, 0],\n",
       "         [0, 0, 0, 0, 0]]]])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "true_factors = [[[1, 1, 1, 0, 0, 0],\n",
    "                 [1, 0, 1, 0, 1, 0]],\n",
    "                [[1, 0],\n",
    "                 [1, 1]],\n",
    "                [[0, 1],\n",
    "                 [1, 1]],\n",
    "                [[0, 0, 1, 1, 0],\n",
    "                 [1, 1, 0, 1, 1]]]\n",
    "true_factors = [np.array(x).reshape((2, len(x[0]))) for x in true_factors]\n",
    "\n",
    "X = outer_product(*true_factors).sum(axis=0)\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[[  1.00001470e+00,   1.00001470e+00,  -1.14710333e-04,\n",
       "            9.99899985e-01,   1.00001470e+00],\n",
       "         [  1.00004331e+00,   1.00004331e+00,   1.00028312e+00,\n",
       "            2.00032642e+00,   1.00004331e+00]],\n",
       "\n",
       "        [[  9.99849453e-01,   9.99849453e-01,  -5.35488732e-04,\n",
       "            9.99313964e-01,   9.99849453e-01],\n",
       "         [  1.00004716e+00,   1.00004716e+00,   1.26153790e-04,\n",
       "            1.00017331e+00,   1.00004716e+00]]],\n",
       "\n",
       "\n",
       "       [[[  2.49442423e-04,   2.49442423e-04,   9.35537112e-04,\n",
       "            1.18497953e-03,   2.49442423e-04],\n",
       "         [ -8.92432553e-05,  -8.92432553e-05,   9.99421862e-01,\n",
       "            9.99332618e-01,  -8.92432553e-05]],\n",
       "\n",
       "        [[ -4.84114017e-04,  -4.84114017e-04,  -2.04090234e-03,\n",
       "           -2.52501636e-03,  -4.84114017e-04],\n",
       "         [  1.88343989e-04,   1.88343989e-04,   9.63345064e-04,\n",
       "            1.15168905e-03,   1.88343989e-04]]],\n",
       "\n",
       "\n",
       "       [[[  1.00001470e+00,   1.00001470e+00,  -1.14710333e-04,\n",
       "            9.99899985e-01,   1.00001470e+00],\n",
       "         [  1.00004331e+00,   1.00004331e+00,   1.00028312e+00,\n",
       "            2.00032642e+00,   1.00004331e+00]],\n",
       "\n",
       "        [[  9.99849453e-01,   9.99849453e-01,  -5.35488732e-04,\n",
       "            9.99313964e-01,   9.99849453e-01],\n",
       "         [  1.00004716e+00,   1.00004716e+00,   1.26153790e-04,\n",
       "            1.00017331e+00,   1.00004716e+00]]],\n",
       "\n",
       "\n",
       "       [[[  0.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
       "            0.00000000e+00,   0.00000000e+00],\n",
       "         [  0.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
       "            0.00000000e+00,   0.00000000e+00]],\n",
       "\n",
       "        [[  0.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
       "            0.00000000e+00,   0.00000000e+00],\n",
       "         [  0.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
       "            0.00000000e+00,   0.00000000e+00]]],\n",
       "\n",
       "\n",
       "       [[[  9.99765253e-01,   9.99765253e-01,  -1.05024745e-03,\n",
       "            9.98715005e-01,   9.99765253e-01],\n",
       "         [  1.00013255e+00,   1.00013255e+00,   8.61254208e-04,\n",
       "            1.00099380e+00,   1.00013255e+00]],\n",
       "\n",
       "        [[  1.00033357e+00,   1.00033357e+00,   1.50541361e-03,\n",
       "            1.00183898e+00,   1.00033357e+00],\n",
       "         [  9.99858812e-01,   9.99858812e-01,  -8.37191274e-04,\n",
       "            9.99021621e-01,   9.99858812e-01]]],\n",
       "\n",
       "\n",
       "       [[[  0.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
       "            0.00000000e+00,   0.00000000e+00],\n",
       "         [  0.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
       "            0.00000000e+00,   0.00000000e+00]],\n",
       "\n",
       "        [[  0.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
       "            0.00000000e+00,   0.00000000e+00],\n",
       "         [  0.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
       "            0.00000000e+00,   0.00000000e+00]]]])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "factors = parafac_als(X, 3, 10)\n",
    "\n",
    "computed = outer_product(*factors).sum(axis=0)\n",
    "computed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[[ 0.,  0., -0.,  0.,  0.],\n",
       "         [ 0.,  0.,  0.,  0.,  0.]],\n",
       "\n",
       "        [[ 0.,  0., -0.,  0.,  0.],\n",
       "         [ 0.,  0.,  0.,  0.,  0.]]],\n",
       "\n",
       "\n",
       "       [[[ 0.,  0.,  0.,  0.,  0.],\n",
       "         [-0., -0.,  0.,  0., -0.]],\n",
       "\n",
       "        [[-0., -0., -0., -0., -0.],\n",
       "         [ 0.,  0.,  0.,  0.,  0.]]],\n",
       "\n",
       "\n",
       "       [[[ 0.,  0., -0.,  0.,  0.],\n",
       "         [ 0.,  0.,  0.,  0.,  0.]],\n",
       "\n",
       "        [[ 0.,  0., -0.,  0.,  0.],\n",
       "         [ 0.,  0.,  0.,  0.,  0.]]],\n",
       "\n",
       "\n",
       "       [[[ 0.,  0.,  0.,  0.,  0.],\n",
       "         [ 0.,  0.,  0.,  0.,  0.]],\n",
       "\n",
       "        [[ 0.,  0.,  0.,  0.,  0.],\n",
       "         [ 0.,  0.,  0.,  0.,  0.]]],\n",
       "\n",
       "\n",
       "       [[[ 0.,  0., -0.,  0.,  0.],\n",
       "         [ 0.,  0.,  0.,  0.,  0.]],\n",
       "\n",
       "        [[ 0.,  0.,  0.,  0.,  0.],\n",
       "         [ 0.,  0., -0.,  0.,  0.]]],\n",
       "\n",
       "\n",
       "       [[[ 0.,  0.,  0.,  0.,  0.],\n",
       "         [ 0.,  0.,  0.,  0.,  0.]],\n",
       "\n",
       "        [[ 0.,  0.,  0.,  0.,  0.],\n",
       "         [ 0.,  0.,  0.,  0.,  0.]]]])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "computed.round(0) - X"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scale with Dask.array\n",
    "\n",
    "The ALS algorithm above is largely reshapings, scalar multiplication, matrix multiplication, and least squares computations (A QR decomposition followed by a triangular solve) all of which Dask.array can do easily.  Here we adapt our code from before to work on dask.arrays.  We include the original unchanged lines to show how easy it is to adapt."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import dask.array as da\n",
    "\n",
    "def parafac_als(X, n_factors, n_iter=100):\n",
    "    \"\"\" Parafac tensor decomposition\n",
    "\n",
    "    This implements the basic algorithm in section 4.1 of this paper\n",
    "\n",
    "        https://www.cs.cmu.edu/~pmuthuku/mlsp_page/lectures/Parafac.pdf\n",
    "    \"\"\"\n",
    "    # Randomly initialize factors\n",
    "    # factors = [np.random.random((n_factors, X.shape[i])) for i in range(0, X.ndim)]\n",
    "    factors = [da.random.random((n_factors, X.shape[i]), \n",
    "                                chunks=(None, X.chunks[i])) \n",
    "               for i in range(0, X.ndim)]\n",
    "    \n",
    "    # Solve\n",
    "    for itr in range(n_iter):\n",
    "        for i in range(X.ndim):\n",
    "            not_i = tuple(j for j in range(X.ndim) if j != i)\n",
    "            Xp = X.transpose((i,) + not_i)\n",
    "            Xp = Xp.reshape((Xp.shape[0], np.prod(Xp.shape[1:])))\n",
    "            Z = outer_product(*[factors[j] for j in not_i])\n",
    "            Z = Z.reshape((Z.shape[0], np.prod(Z.shape[1:])))\n",
    "\n",
    "            # factor, residuals, rank, s = np.linalg.lstsq(Z.T, Xp.T)\n",
    "            factor, residuals, rank, s = da.linalg.lstsq(Z.T, Xp.T)\n",
    "            factors[i] = factor\n",
    "    return factors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualize result\n",
    "\n",
    "To show the complexity of the algorithm we visualize a single round of chunked ALS as a task graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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k7De8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gtrYW9fX18Pv9SE1NRVlZmQiQ3759O8rKyuJuED6ekZERXHnllQCAQ4cOLdlga7p8s+R7770X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\nYPz8+dSnPoV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