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@AustinRochford
Created September 27, 2016 15:29
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Dependent Dirichlet Process Regression
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from matplotlib import pyplot as plt\n",
"import numpy as np\n",
"import pandas as pd\n",
"import pymc3 as pm\n",
"from pymc3.distributions import draw_values\n",
"from pymc3.distributions.dist_math import bound\n",
"from pymc3.math import logsumexp\n",
"import scipy as sp\n",
"import seaborn as sns\n",
"from theano import shared\n",
"from theano import tensor as tt"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"SEED = 81882 # from random.org"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"blue, *_ = sns.color_palette()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"df = (pd.read_csv('http://www.stat.cmu.edu/~larry/all-of-nonpar/=data/lidar.dat',\n",
" sep=' *', engine='python')\n",
" .assign(std_range=lambda df: (df.range - df.range.mean()) / df.range.std(),\n",
" std_logratio=lambda df: (df.logratio - df.logratio.mean()) / df.logratio.std()))"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>range</th>\n",
" <th>logratio</th>\n",
" <th>std_logratio</th>\n",
" <th>std_range</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>390</td>\n",
" <td>-0.050356</td>\n",
" <td>0.852467</td>\n",
" <td>-1.717725</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>391</td>\n",
" <td>-0.060097</td>\n",
" <td>0.817981</td>\n",
" <td>-1.707299</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>393</td>\n",
" <td>-0.041901</td>\n",
" <td>0.882398</td>\n",
" <td>-1.686447</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>394</td>\n",
" <td>-0.050985</td>\n",
" <td>0.850240</td>\n",
" <td>-1.676020</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>396</td>\n",
" <td>-0.059913</td>\n",
" <td>0.818631</td>\n",
" <td>-1.655168</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" range logratio std_logratio std_range\n",
"0 390 -0.050356 0.852467 -1.717725\n",
"1 391 -0.060097 0.817981 -1.707299\n",
"2 393 -0.041901 0.882398 -1.686447\n",
"3 394 -0.050985 0.850240 -1.676020\n",
"4 396 -0.059913 0.818631 -1.655168"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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1bW9EFHoM6DL8/bGV+77iSMrP53iP+LzX4jtPvYdLcgLXWPcXePXMDJz77hxaTzTCMSoR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\nAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f61cc7b0c18>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(figsize=(8, 6))\n",
"\n",
"ax.scatter(df.std_range, df.std_logratio,\n",
" c=blue);"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"N = df.shape[0]\n",
"K = 15\n",
"\n",
"x = df.std_range.values[:, np.newaxis]\n",
"y = df.std_logratio.values[:, np.newaxis]"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def normal_mixture_rvs(*args, **kwargs):\n",
" w = kwargs['w']\n",
" mu = kwargs['mu']\n",
" tau = kwargs['tau']\n",
" \n",
" size = kwargs['size']\n",
" \n",
" component = np.array([np.random.choice(w.shape[1], size=size, p=w_ / w_.sum())\n",
" for w_ in w])\n",
" \n",
" return sp.stats.norm.rvs(mu[np.arange(w.shape[0]), component], tau[component]**-0.5)\n",
"\n",
"class NormalMixture(pm.distributions.Continuous):\n",
" def __init__(self, w, mu, tau, *args, **kwargs):\n",
" super(NormalMixture, self).__init__(*args, **kwargs)\n",
" \n",
" self.w = w\n",
" self.mu = mu\n",
" self.tau = tau\n",
" \n",
" self.mean = (w * mu).sum()\n",
" \n",
" def random(self, point=None, size=None, repeat=None):\n",
" w, mu, tau = draw_values([self.w, self.mu, self.tau], point=point)\n",
" \n",
" return normal_mixture_rvs(w=w, mu=mu, tau=tau, size=size)\n",
" \n",
" def logp(self, value):\n",
" w = self.w\n",
" mu = self.mu\n",
" tau = self.tau\n",
" \n",
" return bound(logsumexp(tt.log(w) + (-tau * (value - mu)**2 + tt.log(tau / np.pi / 2.)) / 2.,\n",
" axis=1).sum(),\n",
" tau >=0, w >= 0, w <= 1)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"x_shared = shared(x, broadcastable=(False, True))"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Applied log-transform to tau and added transformed tau_log_ to model.\n"
]
}
],
"source": [
"with pm.Model() as model:\n",
" alpha = pm.Normal('alpha', 0., 1., shape=K)\n",
" beta = pm.Normal('beta', 0., 1., shape=K)\n",
"\n",
" v = pm.Deterministic('v', tt.nnet.sigmoid(alpha + beta * x_shared))\n",
" w = pm.Deterministic('w', v * tt.concatenate([tt.ones_like(x_shared),\n",
" tt.extra_ops.cumprod(1 - v, axis=1)[:, :-1]],\n",
" axis=1))\n",
" \n",
" intercept = pm.Normal('intercept', 0., 1e-2, shape=K)\n",
" slope = pm.Normal('slope', 0., 1e-2, shape=K)\n",
" tau = pm.Gamma('tau', 1., 1., shape=K)\n",
" ys = pm.Deterministic('ys', intercept + slope * x_shared)\n",
" \n",
" y_obs = NormalMixture('y_obs', w, ys, tau, observed=y)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" [-----------------100%-----------------] 50000 of 50000 complete in 148.3 sec"
]
}
],
"source": [
"SAMPLES = 50000\n",
"BURN = 25000\n",
"THIN = 25\n",
"\n",
"with model:\n",
" step = pm.Metropolis()\n",
" trace_ = pm.sample(SAMPLES, step, random_seed=SEED)\n",
"\n",
"trace = trace_[BURN::THIN]"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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U7D1+O49dYvyM377jn86ZtK13HDsnz5Om3Cw3y7AAAAmFkpZkWZYq/DnqPD2k/qFR03EA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\naQAAEhQlDQBAgqKkAQBIUJQ0AAAJ6v8DS1Ir01Qmm1AAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f61b9b20f60>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(figsize=(8, 6))\n",
"\n",
"ax.plot(1 - trace['w'].cumsum(axis=-1).mean(axis=1).mean(axis=0).T);"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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NZlv2UIiIIqNVkOaZdFe3ZWN857Iqpb91m4zwXJqIzKVVkC6zJGigGHMnLNXS\n32Q2GSEiiotWQbpad6V3YFJFKeYgpVr6W3cngUGaiMylVZAuK9CBSRWFmFfSqh01lFh1jIgSQJsg\nLTowyc7RVUV+OIt0yortTFa19DdZTUaIiOKkTZBuND147U7wj3PSzecKx1d1rKJY+lvcZ/JERDJo\nE6TLNTU6MKmk4NioxJQrXFEs/S04k+Z2NxEZTJsgXVGkJKVKSo6NltdBo+lF/l6qnUmLJiNcSROR\nyfQJ0ky/WibOLV+R/lZSZCUNzP8uxN1Tm4goTvoE6boaHZhUEmeusEh/GxnKRP5e/ZLRZISIKE7a\nBWlud3eJzyKOblCVunrpb2InYWa2JXkkRETR6CtInzx5Ert27cLOnTtx9OjRFV/3ne98B9dffz1+\n9rOfhTZAgdvdy4nPIuq+yr7vo1xTL/1N3PQv8/IYERmqZ5DudDo4fPgwjh07hmeffRZTU1M4ffr0\nstfV63V8+9vfxk033RTJQIOLS4oFCpmCIBXxmbSq6W8lpmERkeF6BulTp05h27Zt2Lp1K7LZLHbv\n3o3jx48ve93Xv/51fPKTn0Q2G03JTpU6MKkirk5YZcUKmQgFpmERkeF6Bunp6Wls3rw5+Hp8fBwX\nLlxY8pqXXnoJ58+fxy233BL+CBeo1IFJFcF2d8Rn0qqlXwksaEJEpusZpHsVyvB9H1/+8pdx3333\n9f09q6VaByZViFzhqLe7Vas2JpRYGpSIDNczn2bTpk04d+5c8PX09DQ2btwYfF2v1/Hzn/8cH/nI\nR+D7Pl5//XX80R/9ER5++GH82q/92lV/9tjYaF+DvPhmAwCw8Rqn7+/RQRhzWV8YwsysG+nn0sY0\nAOC6LaVQ32etP8senn9omGt1tPu90G28YeP8OX/qT88gPTExgTNnzuDs2bMYGxvD1NQUjhw5Evx9\nPp/HD3/4w+Drj3zkI7j//vtxww039Hzzixdn+hrky69VAQBDmVTf36O6sbHRUOaSH8rg7MUaXjtf\nQSYdTUbd2en5cVrtdmiffxjz7/g+0ikLFy7Vtfq9COu/va44f84/qfMf5OGkZ5BOp9M4dOgQDhw4\nAN/3sX//fmzfvh0PPfQQJiYmsGPHjiWvtywr9O1u1TowqUR8JtW6i/WFoUjeI0h/U+zzj7vJCBFR\n3PoqHzU5OYnJycklf3bw4MErvvaxxx5b+6guU1b0TFQFi1s2RhakFb04Bszf8D73eh2+78NSqNAK\nEVEYtKgPmq0gAAAXKElEQVQ4VlWsA5NK4kjDUjn9rdtkpC17KEREodMiSJcVXsnJFrRsjDANS+X0\nt24aFnOlicg8WgRplgRdWdS5wqqnvxVjbDJCRBQ3LYK06MDkDEdTzUxnpYiDVDVobKLmUUMxxiYj\nRERx0yJIl2vqdWBSRSHilXS3RaiqK+l4mowQEcmgfJD2fR+VunodmFSRH84inbIiq1+tevqbuEwY\nddU1IiIZlA/SqnZgUkWQKxxRkBLbyCVFP//g4hxX0kRkIOWDtOjApOp2qwpEkA67iAzQ3UYuqLqS\njqnJCBGRDMoH6UpwcUnNIKGCbq6wF/rPFtvIJUUfkuJqMkJEJIP6QZrpVz1FmYbVLQmq5nY3MP+7\nwe1uIjKR+kG6zmpjvRQiTMMK0t+G+qogK0Upb6PWaMFrd2QPhYgoVPoEaa6kV1SKMFe4vFBtTOW6\n2AWn22SEiMgk6gdpbnf3FFWusEh/U/2zF4VWorrhTkQki/pBuq52nq4KRHpa2JenZjVJf2MaFhGZ\nSosgrWoHJlVE1QlL9UImAptsEJGp1A/SCndgUkVUucK63Adgkw0iMpXSQVr1DkyqiCpXWIf0K2Bx\nu04GaSIyi9JBusr0q75FkSuszUo64iYjRESyKB2kdQkSKig64ecK63Im7UTcZISISBa1g7QmQUIF\n4jMKM1dYXMRS/SEp6iYjRESyKB2ky5oECRUEl6dCDdJiJ0P944aiY6Nci6bJCBGRLEoH6WqNZ9L9\nKkWQhlWpuXCGMshmlP41ATAfpL12NE1GiIhkUfpf3zLPpPtWcMLPFa7UXW1ahBZZdYyIDKR0kBYX\ngUpcSfcU9g1nkf6my2cvHuTKzJUmIoMoHaRFB6YRhTswqaIUckGPqma7GCVWHSMiAykdpMu1+e3W\nlMIdmFRRCHklrVvN9CjbdRIRyaJskBYdmEqaBAnZ8iHnCpeD7mOabHezoAkRGUjZIN3QpAOTKsLO\nFdatkEyJnbCIyEDKBmlxAUiX28UqEEE6jFxh3QrJsBMWEZlI2SAtVnLc7u5fybHR8sLJFdZtJZ3N\npDGSy3C7m4iMom6QrrHa2GqFeS6rSwesxYr58JuMEBHJpG6QZgesVRM3nMPIFa4spL85GqW/RdFk\nhIhIJvWDNFfSfQszV7hSc1HM27A0Sn8TD3RhNhkhIpJJ3SDN7e5VE59VdY0r6fn0t6Z2N+uLDtOw\niMgs6gZpzYppqEAE1fIag9Rs04PX9rV7QCpG0GSEiEgmpYP0SC6DbCYteyjaCCtIie/X7WZ9UL+b\naVhEZAh1g/TCmSj1L9juXmOQEkcNuuWoi52EtW73ExGpQskgLTow6bbdKpudTWM4l1nzdreuN+vF\nQ91a509EpAolg3RV0yChgqKz9lxhkcJV0uwhKbg4FlL9ciIi2ZQM0ky/GlwYucLiIamg2XGDs9Bk\nhClYRGQKNYO0ZnWjVSI+s7UEKnHxqqRZCpZoMhJGMRciIhUoGaRFkOBKevXE5am15ApXNG5uUsqH\n12SEiEg2JYN0tcYz6UGVQkjDqtZdOEMZZDNK/npcVdHJwWt3MBtCkxEiItmU/Fe4zDPpgRWctZcG\nLdea2j4gFdhXmogMomSQFrdzS5oGCpnWWtCk5XVQn/O0fUAqhdgJjIhINiWDdHWhA9OIRh2YVFFa\n45l0VfNyrEzDIiKTKBmkyzUXBcdGSqMOTKoorHElqXv6m9im50qaiEygXJCe78Dkalc3WhX5hVzh\nQVeSleBmvZ5HDUWeSRORQZQL0o2mB6/d0TZIyCZyhQdeSWueo14M4eIcEZEqlAvSZY1zdFUhgvQg\nucL6b3fz4hgRmUO5IC3+ceV29+BKjo2W10FjgFxhsU2uawpWNpPGSC7D7W4iMoJ6QbrGamNrtZbV\npO4raWB+/lxJE5EJ1AvS7IC1ZoWF8/xBalhX6i4yaQuOxulvYTQZISJSgbpBWuOVnGzdgh6rvzxV\nqTVRdGxYGqe/iQc8dsMiIt2pF6S53b1m4rOrrnIlLdLfCprfrBfzZzcsItKdekFa84pXKhAryfIq\nV5L1OQ9e29f+0l5xDTsJREQqUS9I11yM5DLIZtKyh6KtQQt6mHLUsNbSqEREqlAvSNddrqLXaNCC\nHlXN06+EQgjtOomIVNBXkD558iR27dqFnTt34ujRo8v+/tFHH8Xu3buxd+9efPzjH8drr7020GC8\ndge1Rkv7lZxsdjaN4Vxm1StJU1qEdh9SGKSJSG89g3Sn08Hhw4dx7NgxPPvss5iamsLp06eXvOaG\nG27Ak08+iaeffhp33HEHHnzwwYEGU2X6VWiKjr367e6aGUFatDhlJywi0l3PIH3q1Cls27YNW7du\nRTabxe7du3H8+PElr/nN3/xN5HLz/zDedNNNmJ6eHmgwppyJqmCQXOGguYbmD0nOUGa+yQhX0kSk\nuZ5Benp6Gps3bw6+Hh8fx4ULF1Z8/RNPPIHJycmBBqN7cweViM9wNbnCpjwkWZY1X3WMZ9JEpLme\nQXo1TRqefvpp/OxnP8MnPvGJgQZTrjNHOizFAW44VwxqblJ0bFTqzYGajBARqaJn7cdNmzbh3Llz\nwdfT09PYuHHjste98MILOHr0KL797W8jm8329eZjY6NLvvb8+SpX27aWlv2diaKc49bxhZ+dTvf9\nPrU5D6MjWWzZXIxsXItFOf+xdQ5efm0GI/kh5EfUe+hIwu/31XD+nD/1p2eQnpiYwJkzZ3D27FmM\njY1hamoKR44cWfKaF198EV/84hdx7NgxrFu3ru83v3hxZsnX5y7Mf+177WV/Z5qxsdFI55jG/Ary\nzLky3rLR6et7LlUaKOZzsXz2Uc9/2J7fJPr5q5ewdUN/849L1HNXHefP+Sd1/oM8nPQM0ul0GocO\nHcKBAwfg+z7279+P7du346GHHsLExAR27NiBr371q2g0Gvj0pz8N3/exZcsW/PVf//WqByNu45Y0\nv7ikguIqc4VbXgf1OQ+/Mm7GE263NGpTuSBNRNSvvlodTU5OLrsMdvDgweB/f/Ob3wxlMNW6i3TK\nwojGHZhUsdqqW1XDyrGKG+q84U1EOlOq4li55qLg2Ehp3IFJFYVV9pQWrytp3lxDYJMNIjKBMkFa\ndGDSvbmDKvLD2flc4T4LeojXmXCzGxgsBY2ISDXKBOlG04PX7gSpQ7Q2KctCwbFXvZI2ZrtbrKTZ\nCYuINKZMkC6zkEnoCo6Ncs3tK1e4LC7tmbKSHrATGBGRSpQJ0qZUu1JJybHhtTtoNL2erxXbwgVD\nbtZnM2k4QxludxOR1tQJ0jVWGwtbcRWXx4KLYwbtZMzvJHC7m4j0pU6QZges0BUWzvf7ueFcrrnI\npC2M5MxJfyvlc6jPeWh5/TcZISJSiXpBmivp0JSClXTv1WS13kTRsWEZlP4WFDThljcRaUqdIM3t\n7tB1q25dPUiJ9DfTdjFEOhkLmhCRrtQJ0oalAKlABN1yjyBVn/PgtX3jHpBEedl+c8WJiFSjTpCu\nuRjJZZDNpGUPxRj9piGZeh+gyJU0EWlOnSBdd7mKDlk3SF19JWnqUcNqbrcTEalIiSDttTuoNVrG\nBQnZ7Gwaw7lMzyBl6lFDdyeB291EpCclgnTV0O1WFRQdu/d2d83Mm/XshEVEulMiSDP9KjpFx0at\n0YLXXjlXWGyHm1Y33RnKIJ2y2AmLiLSlRpBm3e7I9NMNysRqYwBgWRaKeRtVNtkgIk0pEaRFpyJT\nehmrRKyOr7blKx6STGlTuVjRyaFS76/JCBGRapQI0qLYRsGwlZwKgqpjV9nyrdRd5IezyKSV+HUI\nVdGx4bV91Od6NxkhIlKNEv8ql3kmHZlCH2lYlVrT2M+eaVhEpDMlgrRIkSnxdnfoij1W0i2vg/qc\nZ+RWN8A0LCLSmxJBulp3kU5ZGBkypwOTKko9zqSrhl4aE5iGRUQ6UyJIl2suCo6NlEEdmFRR6LHd\nWzY0/Uoo9VkalYhIRdKDtOjAZOpKTrb8cBbplLXidm/V8PS3wiradRIRqUZ6kG40PXjtjrErOdlS\nloWCY19lJW32pT022SAinUkP0mXDV3IqKDg2yrUr5wqb2lxDCPLEud1NRBqSHqRZEjR6JceG1+6g\n0VyeK2x63fRsJgVnqHeTESIiFckP0oav5FRwtVzhJOxkFPM5pmARkZbkB2nDV3IqKCxs+V6p0USl\n7iKTTmEkZ276W9GxUZ/z0PJWbjJCRKQidYI0V9KRKV3lhnOlPl9tzDI4/a2fJiNERCqSH6TFdrfB\n262yFVfIFfZ9H5Waa/xnL+ZfZhoWEWlGfpDmSjpyK1Xdqs95aHd84z97ccO7yhveRKQZ+UG65mIk\nl0E2k5Y9FGOttJLu7mKYfR+ATTaISFfyg3Td/O1W2YordMISQatk/Ep6YbubN7yJSDNSg7TX7qDW\naBm/3SqbnU1jOLc8V7iSkD7eYqeAF8eISDdSg7TphTRUUnTs5dvdCbkP0F1JM0gTkV6kBumkBAkV\nlPI2ao0WvHY3V1hsf5vex9sZyiCTtngmTUTakRukE1DtShUFZ3mucPD5G/6QZFnW/E4CU7CISDNS\ng7TIWy2xA1bkgkYTi4P0wv8uGB6kgfmqa5UVmowQEalK7pl0Qi4uqSCoOrboXLZcayI/nEUmLf2S\nf+RKeRvtjo/63PImI0REqpK8kk7GdqsKCldIw6rW3cR89t1ccW55E5E+JJ9JJ+PikgqKl62kW14b\n9TkvMfcBug8pvDxGRPqQnoKVTlkYGTK3A5MqSpedSSftZr14ELw8DY2ISGVyt7trLgqOjZTBHZhU\nIc79RdWtpLUILXIlTUQakhakfd9Hpe4GF5ooWvnhLNIpK0jBSkr6ldBtMsIzaSLSh7QgXZ/z4LU7\nQWoQRStlWSg49vLt7oQ8JK3UZISISGXSgvSb1TkAyQkSKig4NsoLucJBB6yEPCTx4hgR6UhekJ5Z\nCNIJ2W5VQcmx4bU7aDS9xF0cy2ZScIYy7IRFRFqRFqQvVcVKLhlBQgWL+yqLbd8k3Qko5nPshEVE\nWpEWpMtiJZ2Q28UqKCxsbZdrLir1JjLpFIZzyUl/Kzo26nMeWl6n94uJiBQg8UyaK+m4BaVB683g\nZr2VoPS3Yn551TUiIpXJ2+6e4cWxuAV9lWfmt7uT9oDEXGki0o287W6upGMnjhbOvVFHu+MnovvV\nYkEnMKZhEZEmpK6kR3IZZDNpWUNIHPFA9IvpGoDk1UxffHGOiEgHUs+kudUdLxGkz75eW/J1UpTY\nCYuINCMtSM/MJu9MVDY7m8ZwLgOv7QNI3n2AQn5pkxEiItVJbbDB9Kv4LX4wSkq1MaGUZ2lQItKL\n3CDNlXTsFhcvSdpKeiSXQSZtMQWLiLQheSWdrCChgsKSlXSyPn/LslBc1GSEiEh1fQXpkydPYteu\nXdi5cyeOHj267O9d18VnPvMZ3HHHHfjABz6Ac+fO9fXmpYRtt6pg8RZ30lKwgPkjlspCkxEiItX1\nDNKdTgeHDx/GsWPH8Oyzz2JqagqnT59e8ponnngCxWIR3/3ud/Gxj30MX/3qV/t68wJX0rET2935\n4SwyaakbKVIUHRvtjo/6nCd7KEREPfX8V/rUqVPYtm0btm7dimw2i927d+P48eNLXnP8+HHs27cP\nALBz50788Ic/7OvNk7bdqgKxek7qUYO4rMg0LCLSQc/uCtPT09i8eXPw9fj4OH76058uec2FCxew\nadMmAEA6nUahUEC5XEapVLrqz05aMQ0ViOCc1AckMe8z0zVks/OFdBZXL7cu+x/Wor+9Upnzy2uf\nL/7SuuwPxdfZoSaqs3LPxWVWbLdrTcxInr9MubqLWqMlexjSJHn+YwN8T88g3c/Z3eWv8X2/Z+OG\nTNrCyFByOjCpYt3Cg1FSH5DEdv//efZFySMhoqR55mt7V/09PaPkpk2bllwEm56exsaNG5e95vz5\n8xgfH0e73UatVkOxWLzqz33qwf+16sGaZmxsVMp7DvKLEgUZ899/+/XYf/v1sb8vEdEgep5JT0xM\n4MyZMzh79ixc18XU1BRuu+22Ja/ZsWMHnnrqKQDAd77zHfzWb/1WNKMlIiJKEMvvYz/75MmT+NKX\nvgTf97F//3586lOfwkMPPYSJiQns2LEDruvic5/7HF566SWUSiUcOXIE1157bRzjJyIiMlZfQZqI\niIjil7xEWSIiIk0wSBMRESmKQZqIiEhRUoJ0r1rgpjp//jw++tGP4s4778SePXvw2GOPyR6SFJ1O\nB/v27cMf/uEfyh5K7GZmZnDw4EG8973vxe7du/GTn/xE9pBi9eijj+L3fu/3sGfPHtx7771wXbOL\nmjzwwAO4+eabsWfPnuDPKpUKDhw4gJ07d+ITn/gEZmZmJI4wOlea+4MPPoj3vve92Lt3L+655x7U\najWJI4zWleYvHDt2DNdffz3K5XLPnxN7kO6nFrip0uk07r//fjz33HP4u7/7Ozz++OOJmftijz32\nGLZv3y57GFJ86Utfwi233IJ/+qd/wtNPP52oz2F6ehrf+ta38OSTT+KZZ55Bu93Gc889J3tYkXrf\n+96HY8eOLfmzo0eP4rd/+7fxz//8z3jXu96FRx55RNLoonWluf/O7/wOpqam8PTTT2Pbtm3Gzh24\n8vyB+cXaCy+8gC1btvT1c2IP0v3UAjfV2NgY3v72twMAHMfB9u3bceHCBcmjitf58+dx4sQJvP/9\n75c9lNjVajX8+7//O+6++24AQCaTQT6flzyqeHU6HTQaDXieh7m5uWWFkUzzzne+E4VCYcmfLe51\nsG/fPvzLv/yLjKFF7kpzv/nmm5FKzYedm266CefPn5cxtFhcaf4A8OUvfxmf//zn+/45sQfpK9UC\nT1qgAoBf/vKX+O///m+84x3vkD2UWIlf0F5lY030y1/+EuvWrcP999+Pffv24dChQ5ibm5M9rNiM\nj4/j4x//OG699VZMTk5idHQUN998s+xhxe7SpUvYsGEDgPkH9zfffFPyiOR44oknMDk5KXsYsXr+\n+eexefNmvO1tb+v7e2IP0kzLBur1Og4ePIgHHngAjuPIHk5svve972HDhg14+9vfnsjfA8/z8OKL\nL+JDH/oQnnrqKQwNDSXqTka1WsXx48fxr//6r/j+97+P2dlZPPPMM7KHRRI8/PDDyGazVzyvNdXc\n3Bz+5m/+Bvfcc0/wZ/38Oxh7kO6nFrjJPM/DwYMHsXfvXrznPe+RPZxY/fjHP8bzzz+P2267Dffe\ney9+9KMfrWrbR3ebNm3Cpk2bMDExAWC+reuLLyan0ccLL7yA6667DqVSCel0Grfffjv+8z//U/aw\nYnfNNdfg9ddfBwBcvHgR69evlzyieD311FM4ceIEvva1r8keSqxEee29e/fi3e9+N6anp3H33Xfj\njTfeuOr3xR6k+6kFbrIHHngAv/qrv4qPfexjsocSuz/5kz/B9773PRw/fhxHjhzBu971Ljz44IOy\nhxWbDRs2YPPmzXj55ZcBAP/2b/+WqItjW7ZswU9+8hM0m034vp+Y+V++Wnr3u9+NJ598EsB8wDL5\n37/L537y5El84xvfwMMPPwzbNr9d7uL5v/Wtb8UPfvADHD9+HM8//zzGx8fx1FNP4Zprrrnqz4i9\nV2Q6ncahQ4dw4MCBoBZ4Ev6PCgD/8R//gWeeeQZvfetbcdddd8GyLHzmM59J3LlMkn3hC1/AZz/7\nWXieh+uuuw5f+cpXZA8pNu94xzuwc+dO3HXXXchkMrjhhhvw+7//+7KHFSmxY1Qul3Hrrbfinnvu\nwac+9Sl8+tOfxj/+4z9iy5Yt+PrXvy57mJG40twfeeQRtFotHDhwAABw44034s///M/lDjQiV5q/\nuDQKzPei72e7m7W7iYiIFMWKY0RERIpikCYiIlIUgzQREZGiGKSJiIgUxSBNRESkKAZpIiIiRTFI\nExERKYpBmoiISFH/H0s47MwsQRmUAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f60c986c550>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(figsize=(8, 6))\n",
"\n",
"ax.plot(trace['w'].max(axis=1).mean(axis=0));"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"plot_x = np.linspace(df.std_range.min() - 0.05, df.std_range.max() + 0.05, 100)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"x_shared.set_value(plot_x[:, np.newaxis])"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"PPC_SAMPLES = 10000\n",
"\n",
"with model:\n",
" ppc_trace = pm.sample_ppc(trace, PPC_SAMPLES)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"low, high = np.percentile(ppc_trace['y_obs'], [2.5, 97.5], axis=0)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"data": {
"image/png": 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8scgiqF5qqsK3X9yFsbExjI6OYmxsFMPDw+A4jvfz27lzN+rqalFcfNQioNsK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\n5GLojT4AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f60c74635c0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(figsize=(8, 6))\n",
"\n",
"ax.fill_between(plot_x, low, high, color='k', alpha=0.35);\n",
"ax.plot(plot_x, ppc_trace['y_obs'].mean(axis=0), c='k');\n",
"\n",
"ax.scatter(df.std_range, df.std_logratio,\n",
" c=blue, zorder=10);\n",
"\n",
"ax.set_xlim(-2, 2);"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Iteration 0 [0%]: ELBO = -1307888.23\n",
"Iteration 4500 [10%]: Average ELBO = -1126288.4\n",
"Iteration 9000 [20%]: Average ELBO = -721150.08\n",
"Iteration 13500 [30%]: Average ELBO = -524198.24\n",
"Iteration 18000 [40%]: Average ELBO = -406682.38\n",
"Iteration 22500 [50%]: Average ELBO = -108289.52\n",
"Iteration 27000 [60%]: Average ELBO = 57216.47\n",
"Iteration 31500 [70%]: Average ELBO = 108267.19\n",
"Iteration 36000 [80%]: Average ELBO = 144772.1\n",
"Iteration 40500 [90%]: Average ELBO = 167220.67\n",
"Finished [100%]: Average ELBO = 184379.01\n",
"CPU times: user 21.2 s, sys: 0 ns, total: 21.2 s\n",
"Wall time: 34.5 s\n"
]
}
],
"source": [
"%%time\n",
"x_shared.set_value(x)\n",
"\n",
"with model:\n",
" advi_fit = pm.advi(n=45000, random_seed=SEED)"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"with model:\n",
" advi_trace = pm.sample_vp(advi_fit, 1000, random_seed=SEED)\n",
"\n",
"x_shared.set_value(plot_x[:, np.newaxis])\n",
"\n",
"with model:\n",
" advi_ppc_trace = pm.sample_ppc(advi_trace, PPC_SAMPLES)"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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ddjB/EeKw69EFE5WeHKV/FJ/Um5sO9fn/GQAAX6XXM+nk5GRVVFT0PHa5XEpMTPzS42+/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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f60b65f85f8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(figsize=(8, 6))\n",
"\n",
"ax.plot(1 - advi_trace['w'].cumsum(axis=-1).mean(axis=1).mean(axis=0).T);"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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scgCgUwhphAVHUrSmje2n49WNepsGJwB6CUIaYeOW8QMVG2XT65uOqqa+2exy\nAMArQhph43SDk0FqaGrVqxuPmF0OAHhFSCOsTB3TV46kaK3fWqwyGpwACHKENMKKzWrR3MnZanN7\n9NK6A2aXAwAXRUgj7IwdmqZL+yVq6/5KfVZ0wuxyAOCCCGmEHcMwdOe00w1OXig4IDcNTgAEKUIa\nYWmQM0FXj0jXUVeNPthdZnY5AHBehDTC1m2TLpHNatErGw6pqYUGJwCCDyGNsJWaGK0bruivEzVN\neuujIrPLAYCvIKQR1vKuyVJ8jF1vfFCkU7VNZpcDAOfoVEgXFhbqxhtv1PTp07V8+fKvPL9y5Url\n5eVp5syZuueee1RaWurzQgF/iI60ada1g9TU0qY17x42uxwAOIfXkHa73Xr44Ye1YsUKvf7668rP\nz9fBgwfPOWbEiBFavXq11q5dqxtuuEGPPfaY3woGfG1ibqacKTF6d0eJjlXUml0OAHTwGtI7duxQ\nVlaW+vbtK7vdrry8PBUUFJxzzJVXXqnIyEhJUm5urlwul3+qBfzAarFo3pTB8nikVe/Q4ARA8PAa\n0i6XS06ns+Nxenq6yssvfE/el19+WRMnTvRNdUCAXJ6douFZydp1uEq7Dh03uxwAkNSJkPZ0odHD\n2rVrtXv3bn3729/uUVFAoBmGoTumDpYh6cV1B+R20+AEgPls3g7IyMhQSUlJx2OXyyWHw/GV4zZt\n2qTly5fr2Wefld1u79SHp6XFd6HU0MP4g2v8aWnxmnbFAL39cZG2Ha7S9KsH+vWzwhnjZ/zoHK8h\nnZOTo6KiIhUXFystLU35+flaunTpOcfs2bNHDz30kFasWKHk5OROf3hFRU3XKw4RaWnxjD8Ix3/T\nlf1VuO2Ynn5jr4b3S1R0pNe/Il0WrGMPFMbP+MN1/N35cuL1dLfVatWSJUu0YMEC3XLLLcrLy1N2\ndraWLVumdevWSZJ+85vfqKGhQT/4wQ80a9Ysfe973+t69UAQSI6P1I1XDlB1XbP+9iENTgCYy/B0\n5aKzj4XrtykpvL9NSsE9/qbmNv1s+ftqaGzVI/derT4JUT59/2AeeyAwfsYfruP3y0waCDeREVbd\nNvESNbe6tbrwkNnlAAhjhDRwHhNGOtXfEadNu8p0tCw8v/UDMB8hDZyHxXJ6S5YkvfjO/i5tRQQA\nXyGkgQsYMbCPLs9O0adFJ7XtQKXZ5QAIQ4Q0cBHzpgyWxTC0at1Btba5zS4HQJghpIGLyEyN1aTc\nTLmq6rWRmnBtAAASHklEQVR+a7HZ5QAIM4Q04MXMawcpKsKqVzceUX1ji9nlAAgjhDTgRUJshPKu\nyVJtQ4te33TU7HIAhBFCGuiEG67or5SESL29+XNVnGwwuxwAYYKQBjrBbrNqzqRstbZ59PL6g2aX\nAyBMENJAJ105Il2DnPH6+NNyHSg+ZXY5AMIAIQ10ksUwdMfUSyVJLxbQ4ASA/xHSQBcM6Z+ksUPT\ndLCkWh9/Wm52OQBCHCENdNHtk7NltRh6ef1BtbT2rgYnHo9HzS1tZpcBoJMIaaCLHMkxmja2nypP\nNapg8zGzy+m06rpm/fbFbfrBsvf06dETZpcDoBMIaaAbbhk/ULFRNr226Yhq6pvNLserA8dO6Rcr\nP9aeIyfU1NKmP6zeoSIXd/cCgh0hDXRDXLRdMyYMUkNTq17deMTsci7I4/HoH598rl//ZYtO1jZp\n7uRsfefWy9TY1Kalq7ar/ES92SUCuAhCGuimqWP6ypEcrfVbi1VWFXxh19jcqidf3a3n396v2Cib\nfnznaN18dZauGpGuu64fouq6Zi19cbtO1QX/mQAgXBHSQDfZrBbdPjlbbW6PXlp3wOxyzlF6vE4P\n//kTfbS3XIP7Juqhe67U8Kzkjuenje2nW8YPVPnJBj2+apsamlpNrBbAhRDSQA+MGZKmIf0StXV/\npT4rCo7FWB/tdemXf/5Epcfrdd24fnrgrtFKjo/8ynGzvzZIk3IzVeSq1R9e2aGWVlZ9A8GGkAZ6\nwDAM3THtdIOTFwoOyG1ig5PWNreef3u//rh2t+SRvjvzMt113RDZrOf/a24YhubfMFRjhqTp06KT\nWv7aHrndNGgBggkhDfTQIGeCrh6RrqOuGr2/q8yUGk7UNOmx57fqH598LmdKjJZ8a5yuHJ7u9XUW\ni6Hv3DpCQ/snafNnFXr2H/vopAYEEUIa8IHbJl0im9Wi1YWH1BTgZiF7j57QL/70kQ4cO6Urhzv0\n87vHKTM1ttOvt9usWjjncvV3xGn91mKtfe+wH6sF0BWENOADqYnRuuGK/jpR06S3PioKyGd6PB79\n7YOj+s8XtqqusVVfn3apvnPrZYqOtHX5vWKibLpv3iilJkbp1Y1HtG5L72nSAoQyQhrwkbxrshQf\nY9cbHxTpVG2TXz+rvrFV/3f1Tr20/qASYyP0wF2jdf0V/WUYRrffMykuUvffmauEGLuefWsfvcmB\nIEBIAz4SHWnTrGsHqamlTWve9d8p48/La/XLP3+srfsrNWxAkh6650pd2i/JJ++dnhyj++blKjLC\nqv9+bbf2HqnyyfsC6B5CGvChibmZcqbE6N0dJTpWUevz99+4s1S/evoTlZ9o0M1XZ+n+O3OVGBvh\n08/IyojXwttyJEnLVu/U0TLahwJmIaQBH7JaLLpj6mB5PNKqd3zX4KSl1a2n//6ZVuTvldVqaOFt\nOZo7OVtWi3/+Cg8f2Ef3zrhMzc1tWrpqm1xB2FENCAeENOBjOZekaMTAZO06XKVdh473+P0qTzXo\n0Wc3a/3WYvVLi9P/+acrNHpImg8qvbhxwxz65g1DVFPfot++uE0n/XydHcBXEdKAjxmGoXlTBsuQ\n9OK6Az1qELLz0HH94k8f60hZjcaPzND/vnus0pNjfFesF1PG9NOtEwaq8lSjlr64XfWNLQH7bACE\nNOAXA9LjNeFyp4or6vTujpIuv97t8Wjte4f1u1Xb1dTSprtvHKpv5w1XpN3qh2ovbua1gzR5dF8d\nq6jVsld2qjnA+8CBcEZIA34y+2uXKMJu0Zp3D3fpBha1DS363Uvbtfa9w+qTEKUHvzlWk3P79mh7\nVU8YhqFvXj9E44amad/nJ/Xkq7vV5nabUgsQbghpwE+S4yN101VZqq5r1t8+7FyDk8Ol1frFnz7S\nrkNVGnlJHz10zxUa5Ezwc6XeWSyG/mXGZRo2IElb91fqmb9/RvtQIAAIacCPbrxygJLiIvTWR0Wq\nqm684HEej0frtxbr0Wc3q6q6SbOuHaQf3j5KcdH2AFZ7cXabRQvnXK4B6XEq3F6qNe8eMrskIOQR\n0oAfRUZYNXviJWpudWt14flDramlTSvy9+rpv3+mSLtV980bpVuvHSSLSae3LyY60qb75uXKkRSt\n1zcd1duffG52SUBII6QBP5sw0qn+jjht2lX2lcYgrqp6/erpzdq0q0yDnPF66J4rNPKSFJMq7ZzE\n2Aj96M5cJcRG6Pm39+vDPS6zSwJCFiEN+JnFYuiOqYMlSS++s7/jWu7WfRX65Z8/1rGKWk0e3Vc/\n+8ZYpSZGm1lqpzmSovWjeaMUFWnVU6/v0e7DtA8F/IGQBgJgxMA+ujw7RZ8WndT7O0v10voD+sPq\nnWpr8+jbecN19/Shstt611/HAenxWnjb5TIMQ/939U4dLq02uyQg5PSufxWAXmzelMGyGIZ+/fTH\n+tsHRXIkR+t/3z1OE3KcZpfWbcOykvWdW0eoubVNj6/artLjdWaXBIQUQhoIkMzUWE0enSm3Rxp9\naar+z7euUH9HnNll9djYoQ7Nnz5UtQ0tWvridp2ooX0o4Ctdvzs8gG77+nWXKu9r2UqKsprWnMQf\nJuf2VXVds/767mEtXbVNP/vGGMVGBc/2MaC3YiYNBJDVYtGQAckhFdDtZowfqKlj+qq4ok7LXt5B\n+1DABwhpAD5hGIbuum6Irhjm0P5jp/THtbQPBXqKkAbgMxaLoX++ZYSGZyVr24FK/flN2ocCPUFI\nA/Apu82i79+Wo6yMeL23o1SvbKB9KNBdhDQAn4uOtOm+20cpPTlab3xwVG99TPtQoDsIaQB+kRAb\noR/dkavE2Ai9ULBf7+8uM7skoNchpAH4TVpStH50R66iI236n/y92nnouNklAb0KIQ3Ar/o74rRo\nTo4Mw9D/W7NTnx2lzzfQWYQ0AL8bOiBZ/zrzMrW0urX4vzZq+au7tftwldxuVn4DF0PHMQABMXpI\nmr47c6TWvndYH+xx6YM9LiXHR2r8yAyNH5khZ0qs2SUCQYeQBhAwVwxz6KZrL9EH24q1cVepPtrr\nUv77R5X//lFlZyZofI5TVw530FIUOIOQBhBQhmFocL9EDe6XqK9Pu1Rb9ldo084y7T5SpYMl1Xr+\n7f0afWmqJuQ4ddmgZFktXJVD+CKkAZgmwm7V1SMydPWIDJ2oadL7u8u0cWepPv60XB9/Wq7E2Ahd\nMzJDE0ZmqG9a779jGNBVhDSAoJAcH6mbr87STVcN0OHSGm3cWaoP97j05odFevPDIg3MiNeEHKeu\nGpGuuGhOhyM8ENIAgophGLokM0GXZCbozmmDte3AcW3cWapdh6p0pGyfXijYr9xLUzVhpFMjL+kj\nm5XT4QhdhDSAoGW3WXXFMIeuGObQqdomvb/bpY27SrX5swpt/qxCCTF2XX1ZhibkONXfwelwhB5C\nGkCvkBgXqRuvGqDpV/ZXkatW7505Hf7Wx5/rrY8/1wBH3OnT4ZelKyEmwuxyAZ/oVEgXFhbqkUce\nkcfj0Zw5c3Tvvfee83xzc7N++tOfavfu3UpOTtbjjz+uzMxMvxQMILwZhqGsjHhlZcTrjqmDtf3M\n6fCdh47r+YL9WrXugC7PTtH4kU6NGpzC6XD0al5D2u126+GHH9bKlSvlcDg0d+5cTZs2TdnZ2R3H\nvPzyy0pMTNRbb72lN954Q7/5zW/0+OOP+7VwALBZLRo7NE1jh6apuq5ZH+xxadPOUm3dX6mt+ysV\nF23XVSPSdW2OUwPS42QYhtklA13iNaR37NihrKws9e3bV5KUl5engoKCc0K6oKBAixYtkiRNnz5d\nv/zlL/1ULgCcX0JshG64or9uuKK/ilw12rSrTO/vLlPB5mMq2HxMfdNiNWGkU5cN6qMIu0URNqvs\nNosibBbZbBZZCHAEIa8h7XK55HQ6Ox6np6dr586d5xxTXl6ujIwMSZLValVCQoJOnjyppKQkH5cL\nAN4NSI/XgPR4zZ2crV2HqrRxZ6m2HajUqnUHpHXnf43Nejqw7Wf+F2G3nv217Wyotz+2fyHk7XYv\nz3f83CprpF0na5vOW8MFvyZc5AvEhV/TxeMDJKK2STX1zaZ9vplnU9K68RqvIe3xeG+A/+VjPB4P\np5UAmM5mtSj30lTlXpqq2oYWfbjHpZLjdWppcaulza3mlja1tLrV3OpWS6tbLa1tHb+urmtWc2ub\nWlrc4jYg8IXXfjuzy6/xGtIZGRkqKSnpeOxyueRwOL5yTFlZmdLT09XW1qba2lolJiZ6/fC0tPgu\nFxxKGH/4jj+cxy6ZM/40SYMG9An45wI94XXZY05OjoqKilRcXKzm5mbl5+dr2rRp5xwzZcoUrVmz\nRpL05ptv6uqrr/ZPtQAAhBHD04nz2YWFhfrVr34lj8ejuXPn6t5779WyZcuUk5OjKVOmqLm5WT/5\nyU+0d+9eJSUlaenSperXr18g6gcAIGR1KqQBAEDgscsfAIAgRUgDABCkCGkAAIKUKSFdWFioG2+8\nUdOnT9fy5cvNKMEUZWVluvvuu3XzzTdrxowZevrpp80uyRRut1uzZ8/Wd7/7XbNLCbiamhotWrRI\nN910k/Ly8rR9+3azSwqolStX6pZbbtGMGTN0//33q7nZvKYWgbB48WKNHz9eM2bM6PjZqVOntGDB\nAk2fPl3f/va3VVNTY2KF/nO+sT/22GO66aabNHPmTC1cuFC1tbUmVuhf5xt/uxUrVmjYsGE6efKk\n1/cJeEi39wJfsWKFXn/9deXn5+vgwYOBLsMUVqtVDz74oN544w298MILeu6558Jm7F/09NNPn9NW\nNpz86le/0qRJk/S3v/1Na9euDav/H1wul5555hmtXr1ar732mtra2vTGG2+YXZZf3XbbbVqxYsU5\nP1u+fLmuueYa/f3vf9dVV12lJ5980qTq/Ot8Y7/22muVn5+vtWvXKisrK2THLp1//NLpydqmTZs6\nfROqgIf0F3uB2+32jl7g4SAtLU3Dhw+XJMXGxio7O1vl5eUmVxVYZWVl2rBhg26//XazSwm42tpa\nffLJJ5ozZ44kyWazKS4uvO6B7Ha71dDQoNbWVjU2Nn6lMVKoGTdunBISEs75WUFBgWbPni1Jmj17\ntt5++20zSvO78419/PjxslhOx05ubq7KysrMKC0gzjd+SXrkkUf0wAMPdPp9Ah7S5+sFHm5BJUnH\njh3Tp59+qssvv9zsUgKq/Q9oOLaNPXbsmJKTk/Xggw9q9uzZWrJkiRobG80uK2DS09N1zz33aPLk\nyZo4caLi4+M1fvx4s8sKuKqqKqWmpko6/cX9xIkTJldkjpdfflkTJ040u4yAeuedd+R0OjV06NBO\nvybgIc22bKmurk6LFi3S4sWLFRsba3Y5AbN+/XqlpqZq+PDhYfnnoLW1VXv27NFdd92lNWvWKCoq\nKqzWZFRXV6ugoEDr1q3Tu+++q/r6er322mtmlwUTPPHEE7Lb7ee9XhuqGhsb9cc//lELFy7s+Fln\n/h0MeEh3phd4KGttbdWiRYs0c+ZMXXfddWaXE1BbtmzRO++8o2nTpun+++/Xhx9+2KXTPr1dRkaG\nMjIylJOTI+n0bV337NljclWBs2nTJvXv319JSUmyWq26/vrrtXXrVrPLCriUlBRVVlZKkioqKtSn\nT3j1E1+zZo02bNig3/72t2aXElDt7bVnzpypqVOnyuVyac6cOTp+/PhFXxfwkO5ML/BQtnjxYg0e\nPFjf+ta3zC4l4H70ox9p/fr1Kigo0NKlS3XVVVfpscceM7usgElNTZXT6dThw4clSR988EFYLRzL\nzMzU9u3b1dTUJI/HEzbj//JsaerUqVq9erWk04EVyv/+fXnshYWFeuqpp/TEE08oIiLCpKoC54vj\nHzJkiDZu3KiCggK98847Sk9P15o1a5SSknLR9/B6Fyxfs1qtWrJkiRYsWNDRCzwc/qJK0ubNm/Xa\na69pyJAhmjVrlgzD0H333Rd212XC2c9//nP9+Mc/Vmtrq/r3769HH33U7JIC5vLLL9f06dM1a9Ys\n2Ww2jRgxQvPmzTO7LL9qP2N08uRJTZ48WQsXLtS9996rH/zgB3rllVeUmZmp3//+92aX6RfnG/uT\nTz6plpYWLViwQJI0atQo/du//Zu5hfrJ+cbfvmhUOn1f686c7qZ3NwAAQYqOYwAABClCGgCAIEVI\nAwAQpAhpAACCFCENAECQIqQBAAhShDQAAEGKkAYAIEj9f7uWA0lODXXWAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f60b7277a58>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(figsize=(8, 6))\n",
"\n",
"ax.plot(advi_trace['w'].max(axis=1).mean(axis=0));"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"advi_low, advi_high = np.percentile(advi_ppc_trace['y_obs'], [2.5, 97.5], axis=0)"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"data": {
"image/png": 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wSMjLWzHp/aJFrKxKFyv1AGKvLhS+6VvFd4ixeRRjc2zG5vLyi2ht7URx8fMR29JmJsRK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dAAAV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\nRIrwP2qvdEkenBwcAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f60b6581438>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, (mcmc_ax, advi_ax) = plt.subplots(ncols=2, sharex=True, sharey=True, figsize=(16, 6))\n",
"\n",
"mcmc_ax.fill_between(plot_x, low, high, color='k', alpha=0.35);\n",
"mcmc_ax.plot(plot_x, ppc_trace['y_obs'].mean(axis=0), c='k');\n",
"\n",
"mcmc_ax.scatter(df.std_range, df.std_logratio,\n",
" c=blue);\n",
"\n",
"mcmc_ax.set_xlim(-2, 2);\n",
"\n",
"advi_ax.fill_between(plot_x, advi_low, advi_high, color='k', alpha=0.35);\n",
"advi_ax.plot(plot_x, advi_ppc_trace['y_obs'].mean(axis=0), c='k');\n",
"\n",
"advi_ax.scatter(df.std_range, df.std_logratio,\n",
" c=blue);\n",
"\n",
"advi_ax.set_xlim(-2, 2);"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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Wd7Jqz7gzH4ZJuqI7ncODmJp2qcfGJiIi3qUZE2l3ncODsFD3zJpoWzDzJ19D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\nm/EvaE6n5Jp2uiUAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f60b65bccf8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(figsize=(8, 6))\n",
"\n",
"ax.plot(advi_fit.elbo_vals);\n",
"\n",
"ax.set_xlabel('ADVI Iteration');\n",
"ax.set_ylabel('ELBO');"
]
}
],
"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": "3.5.1"
},
"widgets": {
"state": {},
"version": "1.1.2"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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