Raw File
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sanity Check\n",
    "--\n",
    "\n",
    "Fit some approximations to a model with a *Gaussian* likelihood. Make sure they're all the same. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import GPflow\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    "import matplotlib\n",
    "matplotlib.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x10fe8b250>]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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MzIQgCNDpdGhoaFCzWyIiCjNVYdDZ2Yn169fjRz/6ETo7O/H555/jpz/96Zx2giDg6NGj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\n8wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10fd63610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.random.seed(0)\n",
    "X = np.random.rand(20,1)*10\n",
    "Y = np.sin(X) + 0.9 * np.cos(X*1.6) + np.random.randn(*X.shape)* 0.8\n",
    "Xtest = np.random.rand(10,1)*10\n",
    "plt.plot(X, Y, 'kx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "m1 = GPflow.gpr.GPR(X, Y, kern=GPflow.kernels.RBF(1))\n",
    "m2 = GPflow.vgp.VGP(X, Y, GPflow.kernels.RBF(1), likelihood=GPflow.likelihoods.Gaussian())\n",
    "m3 = GPflow.svgp.SVGP(X, Y, GPflow.kernels.RBF(1),\n",
    "                      likelihood=GPflow.likelihoods.Gaussian(),\n",
    "                      Z=X.copy(), q_diag=False)\n",
    "m3.Z.fixed = True\n",
    "m4 = GPflow.svgp.SVGP(X, Y, GPflow.kernels.RBF(1),\n",
    "                      likelihood=GPflow.likelihoods.Gaussian(),\n",
    "                      Z=X.copy(), q_diag=False, whiten=True)\n",
    "m4.Z.fixed=True\n",
    "m5 = GPflow.sgpr.SGPR(X, Y, GPflow.kernels.RBF(1), Z=X.copy())\n",
    "m5.Z.fixed = True\n",
    "m6 = GPflow.sgpr.GPRFITC(X, Y, GPflow.kernels.RBF(1), Z=X.copy())\n",
    "m6.Z.fixed = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "compiling tensorflow function...\n",
      "done\n",
      "optimization terminated, setting model state\n",
      "compiling tensorflow function...\n",
      "done\n",
      "optimization terminated, setting model state\n",
      "compiling tensorflow function...\n",
      "done\n",
      "optimization terminated, setting model state\n",
      "compiling tensorflow function...\n",
      "done\n",
      "optimization terminated, setting model state\n",
      "compiling tensorflow function...\n",
      "done\n",
      "optimization terminated, setting model state\n",
      "compiling tensorflow function...\n",
      "done\n",
      "optimization terminated, setting model state\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "      fun: 27.807510286588826\n",
       " hess_inv: <3x3 LbfgsInvHessProduct with dtype=float64>\n",
       "      jac: array([ -1.48841158e-06,   1.23604952e-06,   2.48102425e-06])\n",
       "  message: 'CONVERGENCE: NORM_OF_PROJECTED_GRADIENT_<=_PGTOL'\n",
       "     nfev: 9\n",
       "      nit: 8\n",
       "   status: 0\n",
       "  success: True\n",
       "        x: array([-0.16744768,  0.86703966, -0.1089166 ])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "m1.optimize()\n",
    "m2.optimize()\n",
    "m3.optimize()\n",
    "m4.optimize()\n",
    "m5.optimize()\n",
    "m6.optimize()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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xdmwQb7yh49FHi5z6/OXVQ7riJL276BAInI3I2TenQ2gHEoYmUNerLh1WduCz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Pdj/OADzIsWYfj3v5Gijc5RlSTCJ55IcNAggv36ET7xRNTdAuLmoikvx7B+PfrPP8f4/vvILlfT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\ngiAIwm3+Hxwm3xwPpUwrAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116e8a890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xx = np.linspace(-1, 11, 100)[:,None]\n",
    "\n",
    "\n",
    "def plot(m, color, ax):\n",
    "    mu, var = m.predict_y(xx)\n",
    "    ax.plot(xx, mu, color, lw=2)\n",
    "    ax.plot(xx, mu+ 2*np.sqrt(var), color, xx, mu-2*np.sqrt(var), color, lw=1)\n",
    "    ax.plot(X, Y, 'kx')\n",
    "\n",
    "f, ax = plt.subplots(3,2,sharex=True, sharey=True, figsize=(12,9))\n",
    "plot(m1, 'b', ax[0,0])\n",
    "plot(m2, 'r', ax[1,0])\n",
    "plot(m3, 'g', ax[0,1])\n",
    "plot(m4, 'y', ax[1,1])\n",
    "plot(m5, 'k', ax[2,0])\n",
    "plot(m6, 'c', ax[2,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table id='parms' width=100%><tr><td>Name</td><td>values</td><td>prior</td><td>constriant</td></tr><tr><td>kern.lengthscales</td><td>[ 1.21783295]</td><td>None</td><td>+ve</td></tr><tr><td>kern.variance</td><td>[ 0.61292386]</td><td>None</td><td>+ve</td></tr></table>"
      ],
      "text/plain": [
       "<GPflow.kernels.RBF at 0x10fe40990>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m1.kern"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table id='parms' width=100%><tr><td>Name</td><td>values</td><td>prior</td><td>constriant</td></tr><tr><td>kern.variance</td><td>[ 0.61300372]</td><td>None</td><td>+ve</td></tr><tr><td>kern.lengthscales</td><td>[ 1.21807525]</td><td>None</td><td>+ve</td></tr></table>"
      ],
      "text/plain": [
       "<GPflow.kernels.RBF at 0x10fe40b50>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m2.kern"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table id='parms' width=100%><tr><td>Name</td><td>values</td><td>prior</td><td>constriant</td></tr><tr><td>kern.variance</td><td>[ 0.61287153]</td><td>None</td><td>+ve</td></tr><tr><td>kern.lengthscales</td><td>[ 1.21779544]</td><td>None</td><td>+ve</td></tr></table>"
      ],
      "text/plain": [
       "<GPflow.kernels.RBF at 0x10fe32890>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m3.kern"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table id='parms' width=100%><tr><td>Name</td><td>values</td><td>prior</td><td>constriant</td></tr><tr><td>kern.lengthscales</td><td>[ 1.21779544]</td><td>None</td><td>+ve</td></tr><tr><td>kern.variance</td><td>[ 0.61287153]</td><td>None</td><td>+ve</td></tr></table>"
      ],
      "text/plain": [
       "<GPflow.kernels.RBF at 0x10fe1a5d0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m4.kern"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table id='parms' width=100%><tr><td>Name</td><td>values</td><td>prior</td><td>constriant</td></tr><tr><td>kern.variance</td><td>[ 0.61292411]</td><td>None</td><td>+ve</td></tr><tr><td>kern.lengthscales</td><td>[ 1.21783595]</td><td>None</td><td>+ve</td></tr></table>"
      ],
      "text/plain": [
       "<GPflow.kernels.RBF at 0x10ffbf790>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m5.kern"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table id='parms' width=100%><tr><td>Name</td><td>values</td><td>prior</td><td>constriant</td></tr><tr><td>kern.variance</td><td>[ 0.61292409]</td><td>None</td><td>+ve</td></tr><tr><td>kern.lengthscales</td><td>[ 1.21783288]</td><td>None</td><td>+ve</td></tr></table>"
      ],
      "text/plain": [
       "<GPflow.kernels.RBF at 0x10ffd48d0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m6.kern"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-27.80751203\n",
      "[-27.80753049]\n",
      "-27.8075204717\n",
      "-27.8075204717\n",
      "[-27.80752042]\n",
      "-27.8075102866\n"
     ]
    }
   ],
   "source": [
    "print -m1._objective(m1.get_free_state())[0]\n",
    "print -m2._objective(m2.get_free_state())[0]\n",
    "print -m3._objective(m3.get_free_state())[0]\n",
    "print -m4._objective(m4.get_free_state())[0]\n",
    "print -m5._objective(m5.get_free_state())[0]\n",
    "print -m6._objective(m6.get_free_state())[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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