compareplot Bug

compareplot_bug.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pymc3 as pm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x = np.random.normal(size=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "100%|██████████| 1000/1000 [00:01<00:00, 527.34it/s]\n",
      "/opt/conda/lib/python3.6/site-packages/pymc3/step_methods/hmc/nuts.py:452: UserWarning: The acceptance probability in chain 1 does not match the target. It is 0.886927907383, but should be close to 0.8. Try to increase the number of tuning steps.\n",
      "  % (self._chain_id, mean_accept, target_accept))\n"
     ]
    }
   ],
   "source": [
    "with pm.Model() as m1:\n",
    "    mu = pm.Normal('mu', 0., 1)\n",
    "    obs = pm.Normal('obs', mu, 1., observed=x)\n",
    "    \n",
    "    m1_trace = pm.sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      " 82%|████████▏ | 824/1000 [00:00<00:00, 2057.97it/s]/opt/conda/lib/python3.6/site-packages/pymc3/step_methods/hmc/nuts.py:452: UserWarning: The acceptance probability in chain 0 does not match the target. It is 0.880967076554, but should be close to 0.8. Try to increase the number of tuning steps.\n",
      "  % (self._chain_id, mean_accept, target_accept))\n",
      "100%|██████████| 1000/1000 [00:00<00:00, 2064.08it/s]\n"
     ]
    }
   ],
   "source": [
    "with pm.Model() as m2:\n",
    "    mu = pm.Normal('mu', 0., 10.)\n",
    "    obs = pm.Normal('obs', mu, 1., observed=x)\n",
    "    \n",
    "    m2_trace = pm.sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "comp_df = pm.compare(\n",
    "    (m1_trace, m2_trace),\n",
    "    (m1, m2)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'Index' object has no attribute 'contains'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-7-58572ba8b453>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mpm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompareplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcomp_df\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pymc3/plots/compareplot.py\u001b[0m in \u001b[0;36mcompareplot\u001b[0;34m(comp_df, insample_dev, se, dse, ax, plot_kwargs)\u001b[0m\n\u001b[1;32m     52\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     53\u001b[0m     \u001b[0mic\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'WAIC'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 54\u001b[0;31m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mcomp_df\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontains\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mic\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     55\u001b[0m         \u001b[0mic\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'LOO'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     56\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'Index' object has no attribute 'contains'"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAD8CAYAAAB0IB+mAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f38dce4c128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pm.compareplot(comp_df)"
   ]
  }
 ],
 "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": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}