simple_compare_example.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pymc3 as pm\n",
    "import arviz as az\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(42)\n",
    "x = np.random.randn(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "with pm.Model() as m1:\n",
    "    m = pm.Normal('m', 0, 1)\n",
    "    l = pm.Normal('l', m, observed=x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "with pm.Model() as m2:\n",
    "    m = pm.Normal('m', 0, 1.1)\n",
    "    l = pm.Normal('l', m, observed=x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [m]\n",
      "Sampling 4 chains: 100%|██████████| 4000/4000 [00:00<00:00, 7948.32draws/s]\n"
     ]
    }
   ],
   "source": [
    "with m1:\n",
    "    tr1 = pm.sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [m]\n",
      "Sampling 4 chains: 100%|██████████| 4000/4000 [00:00<00:00, 7828.57draws/s]\n"
     ]
    }
   ],
   "source": [
    "with m2:\n",
    "    tr2 = pm.sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>waic</th>\n",
       "      <th>p_waic</th>\n",
       "      <th>d_waic</th>\n",
       "      <th>weight</th>\n",
       "      <th>se</th>\n",
       "      <th>dse</th>\n",
       "      <th>warning</th>\n",
       "      <th>waic_scale</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>model1</th>\n",
       "      <td>267.215</td>\n",
       "      <td>0.797262</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>11.1965</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>deviance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model2</th>\n",
       "      <td>267.219</td>\n",
       "      <td>0.79825</td>\n",
       "      <td>0.0040196</td>\n",
       "      <td>2.22045e-16</td>\n",
       "      <td>11.1885</td>\n",
       "      <td>0.0648026</td>\n",
       "      <td>0</td>\n",
       "      <td>deviance</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           waic    p_waic     d_waic       weight       se        dse warning  \\\n",
       "model1  267.215  0.797262          0            1  11.1965          0       0   \n",
       "model2  267.219   0.79825  0.0040196  2.22045e-16  11.1885  0.0648026       0   \n",
       "\n",
       "       waic_scale  \n",
       "model1   deviance  \n",
       "model2   deviance  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "az.compare({'model1': tr1, 'model2': tr2})"
   ]
  }
 ],
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