11 Chapter 11/01 Ordered logistic regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING (theano.tensor.blas): Using NumPy C-API based implementation for BLAS functions.\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import gc\n",
    "import pandas as pd\n",
    "import pymc3 as pm\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy import stats\n",
    "import scipy as sp\n",
    "import numpy as np\n",
    "import theano.tensor as tt\n",
    "\n",
    "plt.style.use('seaborn-whitegrid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def summarize(alpha=0.05):\n",
    "\n",
    "    def _summarize(ser):\n",
    "        hpd_ = pm.hpd(ser, alpha=alpha)\n",
    "        return pd.Series({\n",
    "            'mean_': ser.mean(),\n",
    "            'lower': hpd_[0],\n",
    "            'upper': hpd_[1],\n",
    "        })\n",
    "\n",
    "    return _summarize\n",
    "\n",
    "\n",
    "def agg_prediction(trace_var, source_data, group_columns, aggregate_fc):\n",
    "    df = pd.DataFrame(trace_var).stack().reset_index()\n",
    "    merged = pd.merge(\n",
    "        df, source_data,\n",
    "        left_on='level_1', right_index=True\n",
    "    )\n",
    "\n",
    "    return (\n",
    "        merged.groupby(group_columns)[0].apply(aggregate_fc)\n",
    "        .unstack().sort_index().reset_index()\n",
    "    )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**11.1. Ordered categorical outcomes**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>case</th>\n",
       "      <th>response</th>\n",
       "      <th>order</th>\n",
       "      <th>id</th>\n",
       "      <th>age</th>\n",
       "      <th>male</th>\n",
       "      <th>edu</th>\n",
       "      <th>action</th>\n",
       "      <th>intention</th>\n",
       "      <th>contact</th>\n",
       "      <th>story</th>\n",
       "      <th>action2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4203</th>\n",
       "      <td>cibox</td>\n",
       "      <td>7</td>\n",
       "      <td>32</td>\n",
       "      <td>97;264</td>\n",
       "      <td>57</td>\n",
       "      <td>1</td>\n",
       "      <td>Master's Degree</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>box</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>911</th>\n",
       "      <td>fkspe</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>96;581</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>Bachelor's Degree</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>spe</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3890</th>\n",
       "      <td>ikpon</td>\n",
       "      <td>1</td>\n",
       "      <td>32</td>\n",
       "      <td>97;194</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>Some College</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>pon</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2432</th>\n",
       "      <td>cfrub</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>96;839</td>\n",
       "      <td>36</td>\n",
       "      <td>1</td>\n",
       "      <td>Master's Degree</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>rub</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3731</th>\n",
       "      <td>fkspe</td>\n",
       "      <td>3</td>\n",
       "      <td>14</td>\n",
       "      <td>97;166</td>\n",
       "      <td>46</td>\n",
       "      <td>1</td>\n",
       "      <td>Bachelor's Degree</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>spe</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       case  response  order      id  age  male                edu  action  \\\n",
       "4203  cibox         7     32  97;264   57     1    Master's Degree       0   \n",
       "911   fkspe         4      9  96;581   26     1  Bachelor's Degree       1   \n",
       "3890  ikpon         1     32  97;194   37     0       Some College       1   \n",
       "2432  cfrub         4      7  96;839   36     1    Master's Degree       0   \n",
       "3731  fkspe         3     14  97;166   46     1  Bachelor's Degree       1   \n",
       "\n",
       "      intention  contact story  action2  \n",
       "4203          1        1   box        1  \n",
       "911           0        0   spe        1  \n",
       "3890          1        0   pon        1  \n",
       "2432          0        1   rub        1  \n",
       "3731          0        0   spe        1  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('../data/Trolley.csv', sep=';')\n",
    "data.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 640x400 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "response_counts, responses = np.histogram(data.response, bins= np.linspace(1, 8, 8))\n",
    "cum_freq = response_counts.cumsum() / response_counts.sum()\n",
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(8, 5), dpi=80)\n",
    "\n",
    "ax.bar(responses[:-1], response_counts, alpha=.3)\n",
    "ax.set(ylabel='response counts', xlabel='response')\n",
    "ax.grid(None)\n",
    "\n",
    "ax2 = ax.twinx()\n",
    "ax2.plot(responses[:-1], cum_freq * 100)\n",
    "ax2.set(ylabel='cumulative ratio (%)',\n",
    "        ylim=(0, 100),\n",
    "        yticks=np.linspace(0, 100, 6)[1:])\n",
    "None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/petrushev/projects/jup_venv/lib/python3.7/site-packages/theano/tensor/subtensor.py:2197: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  rval = inputs[0].__getitem__(inputs[1:])\n",
      "/home/petrushev/projects/jup_venv/lib/python3.7/site-packages/theano/tensor/subtensor.py:2197: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  rval = inputs[0].__getitem__(inputs[1:])\n",
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "/home/petrushev/projects/jup_venv/lib/python3.7/site-packages/theano/tensor/subtensor.py:2197: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  rval = inputs[0].__getitem__(inputs[1:])\n",
      "Multiprocess sampling (2 chains in 2 jobs)\n",
      "NUTS: [cutpoints]\n",
      "Sampling 2 chains:   0%|          | 0/8000 [00:00<?, ?draws/s]/home/petrushev/projects/jup_venv/lib/python3.7/site-packages/theano/tensor/subtensor.py:2197: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  rval = inputs[0].__getitem__(inputs[1:])\n",
      "/home/petrushev/projects/jup_venv/lib/python3.7/site-packages/theano/tensor/subtensor.py:2197: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  rval = inputs[0].__getitem__(inputs[1:])\n",
      "Sampling 2 chains: 100%|██████████| 8000/8000 [00:45<00:00, 176.63draws/s]\n"
     ]
    }
   ],
   "source": [
    "with pm.Model() as m11_1:\n",
    "    cutpoints = pm.Normal(\n",
    "        'cutpoints', mu=0, sd=1.5,\n",
    "        transform=pm.distributions.transforms.ordered,\n",
    "        shape=6, testval=np.linspace(-3, 3, 6))\n",
    "\n",
    "    responses = pm.OrderedLogistic(\n",
    "        'responses', 0., cutpoints,\n",
    "        observed=data.response - 1\n",
    "    )\n",
    "\n",
    "    m11_1.name = 'm11.1'\n",
    "    m11_1.trace = pm.sample(3000, tune=1000, chains=2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pm.forestplot(m11_1.trace, varnames=['cutpoints'], rhat=False)\n",
    "None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tmp = pd.DataFrame(m11_1.trace['cutpoints']).stack().map(sp.special.expit).reset_index()\n",
    "tmp = tmp.groupby('level_1')[0].apply(summarize(0.01)).unstack()\n",
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(8, 5), dpi=80)\n",
    "\n",
    "ax.scatter(tmp.index + 1, cum_freq[:-1], label='observed')\n",
    "ax.fill_between(tmp.index+1, tmp.lower, tmp.upper, alpha=.3, label='99% hdi')\n",
    "\n",
    "ax.set(xlabel='response', ylabel='cumulative probability')\n",
    "ax.legend()\n",
    "None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/petrushev/projects/jup_venv/lib/python3.7/site-packages/theano/tensor/subtensor.py:2197: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  rval = inputs[0].__getitem__(inputs[1:])\n",
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "/home/petrushev/projects/jup_venv/lib/python3.7/site-packages/theano/tensor/subtensor.py:2197: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  rval = inputs[0].__getitem__(inputs[1:])\n",
      "Multiprocess sampling (2 chains in 2 jobs)\n",
      "NUTS: [beta, cutpoints]\n",
      "Sampling 2 chains: 100%|██████████| 8000/8000 [04:38<00:00, 14.84draws/s]\n"
     ]
    }
   ],
   "source": [
    "with pm.Model() as m11_2:\n",
    "    cutpoints = pm.Normal(\n",
    "        'cutpoints', 0., 1.5,\n",
    "        transform=pm.distributions.transforms.ordered,\n",
    "        shape=6, testval=np.linspace(-3, 3, 6))\n",
    "\n",
    "    beta = pm.Normal('beta', mu=0, sd=0.5, shape=3)\n",
    "    \n",
    "    phi = (\n",
    "        beta[0] * data.action.values\n",
    "        + beta[1] * data.intention.values\n",
    "        + beta[2] * data.contact.values\n",
    "    )\n",
    "\n",
    "    responses = pm.OrderedLogistic(\n",
    "        'responses', phi, cutpoints,\n",
    "        observed=data.response - 1\n",
    "    )\n",
    "\n",
    "    m11_2.name = 'm11.2'\n",
    "    m11_2.trace = pm.sample(3000, tune=1000, chains=2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pm.forestplot(m11_2.trace, varnames=['cutpoints', 'beta'], rhat=False)\n",
    "None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/petrushev/projects/jup_venv/lib/python3.7/site-packages/pymc3/tuning/starting.py:61: UserWarning: find_MAP should not be used to initialize the NUTS sampler, simply call pymc3.sample() and it will automatically initialize NUTS in a better way.\n",
      "  warnings.warn('find_MAP should not be used to initialize the NUTS sampler, simply call pymc3.sample() and it will automatically initialize NUTS in a better way.')\n",
      "  0%|          | 0/5000 [00:00<?, ?it/s]/home/petrushev/projects/jup_venv/lib/python3.7/site-packages/theano/tensor/subtensor.py:2197: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  rval = inputs[0].__getitem__(inputs[1:])\n",
      "logp = -18,470, ||grad|| = 5.7343: 100%|██████████| 28/28 [00:00<00:00, 174.12it/s]  \n",
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "/home/petrushev/projects/jup_venv/lib/python3.7/site-packages/theano/tensor/subtensor.py:2197: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  rval = inputs[0].__getitem__(inputs[1:])\n",
      "Multiprocess sampling (2 chains in 2 jobs)\n",
      "NUTS: [beta2, beta, cutpoints]\n",
      "Sampling 2 chains: 100%|██████████| 8000/8000 [04:32<00:00, 17.34draws/s]\n"
     ]
    }
   ],
   "source": [
    "with pm.Model() as m11_3:\n",
    "    cutpoints = pm.Normal(\n",
    "        'cutpoints', 0., 1.5,\n",
    "        transform=pm.distributions.transforms.ordered,\n",
    "        shape=6, testval=np.linspace(-3, 3, 6))\n",
    "\n",
    "    beta = pm.Normal('beta', mu=0, sd=0.5, shape=3)\n",
    "    beta2 = pm.Normal('beta2', mu=0, sd=0.5, shape=2)\n",
    "    \n",
    "    phi = (\n",
    "        beta[0] * data.action.values\n",
    "        + beta[1] * data.intention.values\n",
    "        + beta[2] * data.contact.values\n",
    "        + beta2[0] * (data.intention.values * data.action.values)\n",
    "        + beta2[1] * (data.intention.values * data.contact.values)\n",
    "    )\n",
    "\n",
    "    responses = pm.OrderedLogistic(\n",
    "        'responses', phi, cutpoints,\n",
    "        observed=data.response - 1\n",
    "    )\n",
    "\n",
    "    m11_3.name = 'm11.3'\n",
    "    start = pm.find_MAP()\n",
    "    m11_3.trace = pm.sample(3000, tune=1000, chains=2, start=start)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pm.forestplot(m11_3.trace, varnames=['cutpoints', 'beta', 'beta2'], rhat=False)\n",
    "None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tmp = pd.DataFrame(m11_3.trace['cutpoints']).stack().map(sp.special.expit).reset_index()\n",
    "tmp = tmp.groupby('level_1')[0].apply(summarize(0.11)).unstack()\n",
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(8, 5), dpi=80)\n",
    "\n",
    "ax.scatter(tmp.index + 1, cum_freq[:-1], label='observed')\n",
    "ax.plot(tmp.index + 1, tmp.mean_)\n",
    "ax.fill_between(tmp.index+1, tmp.lower, tmp.upper, alpha=.3, label='89% hdi')\n",
    "\n",
    "ax.set(xlabel='response', ylabel='cumulative probability')\n",
    "ax.legend()\n",
    "None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/petrushev/projects/jup_venv/lib/python3.7/site-packages/theano/tensor/subtensor.py:2197: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  rval = inputs[0].__getitem__(inputs[1:])\n"
     ]
    },
    {
     "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>pWAIC</th>\n",
       "      <th>dWAIC</th>\n",
       "      <th>weight</th>\n",
       "      <th>SE</th>\n",
       "      <th>dSE</th>\n",
       "      <th>var_warn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>m11.3</th>\n",
       "      <td>36929.2</td>\n",
       "      <td>10.96</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>83.04</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>m11.2</th>\n",
       "      <td>37090</td>\n",
       "      <td>9.02</td>\n",
       "      <td>160.83</td>\n",
       "      <td>0</td>\n",
       "      <td>77.54</td>\n",
       "      <td>25.01</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>m11.1</th>\n",
       "      <td>37854.4</td>\n",
       "      <td>5.99</td>\n",
       "      <td>925.23</td>\n",
       "      <td>0</td>\n",
       "      <td>59.14</td>\n",
       "      <td>61.98</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          WAIC  pWAIC   dWAIC weight     SE    dSE var_warn\n",
       "m11.3  36929.2  10.96       0      1  83.04      0        0\n",
       "m11.2    37090   9.02  160.83      0  77.54  25.01        0\n",
       "m11.1  37854.4   5.99  925.23      0  59.14  61.98        0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comparison = pm.compare({\n",
    "        m11_1: m11_1.trace,\n",
    "        m11_2: m11_2.trace,\n",
    "        m11_3: m11_3.trace,\n",
    "    },\n",
    "    method='BB-pseudo-BMA'\n",
    ")\n",
    "\n",
    "comparison"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "**Ordered categorical predictors**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.edu.unique()\n",
    "\n",
    "edus = pd.DataFrame(list(enumerate([\n",
    "    'Elementary School', 'Middle School','Some High School', \n",
    "    'High School Graduate', 'Some College', \"Bachelor's Degree\", \n",
    "    \"Master's Degree\", 'Graduate Degree'])),\n",
    "    columns=['edu_i', 'edu'])\n",
    "\n",
    "data = pd.merge(\n",
    "    data, edus, on='edu'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>case</th>\n",
       "      <th>response</th>\n",
       "      <th>order</th>\n",
       "      <th>id</th>\n",
       "      <th>age</th>\n",
       "      <th>male</th>\n",
       "      <th>edu</th>\n",
       "      <th>action</th>\n",
       "      <th>intention</th>\n",
       "      <th>contact</th>\n",
       "      <th>story</th>\n",
       "      <th>action2</th>\n",
       "      <th>edu_i</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>806</th>\n",
       "      <td>ilsha</td>\n",
       "      <td>4</td>\n",
       "      <td>21</td>\n",
       "      <td>96;741</td>\n",
       "      <td>56</td>\n",
       "      <td>0</td>\n",
       "      <td>Bachelor's Degree</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sha</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8950</th>\n",
       "      <td>fkcar</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>97;418</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate Degree</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>car</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7029</th>\n",
       "      <td>fkbur</td>\n",
       "      <td>5</td>\n",
       "      <td>22</td>\n",
       "      <td>97;439</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>Master's Degree</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>bur</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>fkcar</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>96;445</td>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "      <td>Bachelor's Degree</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>car</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1392</th>\n",
       "      <td>fkswi</td>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>97;113</td>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>Bachelor's Degree</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>swi</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       case  response  order      id  age  male                edu  action  \\\n",
       "806   ilsha         4     21  96;741   56     0  Bachelor's Degree       0   \n",
       "8950  fkcar         5      6  97;418   25     0    Graduate Degree       1   \n",
       "7029  fkbur         5     22  97;439   32     1    Master's Degree       1   \n",
       "130   fkcar         1      6  96;445   53     1  Bachelor's Degree       1   \n",
       "1392  fkswi         1     25  97;113   41     1  Bachelor's Degree       1   \n",
       "\n",
       "      intention  contact story  action2  edu_i  \n",
       "806           1        0   sha        0      5  \n",
       "8950          0        0   car        1      7  \n",
       "7029          0        0   bur        1      6  \n",
       "130           0        0   car        1      5  \n",
       "1392          0        0   swi        1      5  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>edu_i</th>\n",
       "      <th>edu</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Elementary School</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Middle School</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Some High School</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>High School Graduate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Some College</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>Bachelor's Degree</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>Master's Degree</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>Graduate Degree</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   edu_i                   edu\n",
       "0      0     Elementary School\n",
       "1      1         Middle School\n",
       "2      2      Some High School\n",
       "3      3  High School Graduate\n",
       "4      4          Some College\n",
       "5      5     Bachelor's Degree\n",
       "6      6       Master's Degree\n",
       "7      7       Graduate Degree"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "edus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/petrushev/projects/jup_venv/lib/python3.7/site-packages/theano/tensor/subtensor.py:2197: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  rval = inputs[0].__getitem__(inputs[1:])\n",
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "/home/petrushev/projects/jup_venv/lib/python3.7/site-packages/theano/tensor/subtensor.py:2197: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  rval = inputs[0].__getitem__(inputs[1:])\n",
      "Multiprocess sampling (2 chains in 2 jobs)\n",
      "NUTS: [delta_edu, beta_edu, cutpoints]\n",
      "Sampling 2 chains:   0%|          | 0/9000 [00:00<?, ?draws/s]/home/petrushev/projects/jup_venv/lib/python3.7/site-packages/theano/tensor/subtensor.py:2197: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  rval = inputs[0].__getitem__(inputs[1:])\n",
      "/home/petrushev/projects/jup_venv/lib/python3.7/site-packages/theano/tensor/subtensor.py:2197: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  rval = inputs[0].__getitem__(inputs[1:])\n",
      "Sampling 2 chains: 100%|██████████| 9000/9000 [17:36<00:00,  5.19draws/s]\n",
      "The acceptance probability does not match the target. It is 0.8895732414900395, but should be close to 0.8. Try to increase the number of tuning steps.\n"
     ]
    }
   ],
   "source": [
    "with pm.Model() as m_ord_cat:\n",
    "    n_edus = edus.shape[0]\n",
    "\n",
    "    cutpoints = pm.Normal(\n",
    "        'cutpoints', mu=0, sd=1.5,\n",
    "        transform=pm.distributions.transforms.ordered,\n",
    "        shape=6,\n",
    "        testval=np.linspace(-3, 3, 6),\n",
    "    )\n",
    "    beta_edu = pm.Normal('beta_edu', mu=0, sd=0.5)\n",
    "    delta_edu = pm.Dirichlet('delta_edu', a=np.ones(n_edus-1))\n",
    "\n",
    "    cum_delta_edu = pm.Deterministic(\n",
    "        'cum_delta_edu',\n",
    "        tt.as_tensor_variable(\n",
    "            [pm.math.sum(delta_edu[0:(end)])\n",
    "             for end in range(n_edus)]\n",
    "        )\n",
    "    )\n",
    "    phi = pm.Deterministic(\n",
    "        'phi',\n",
    "        beta_edu * cum_delta_edu[data.edu_i.values]\n",
    "    )\n",
    "\n",
    "    responses = pm.OrderedLogistic(\n",
    "        'responses', phi, cutpoints,\n",
    "        observed=(data.response.values - 1)\n",
    "    )\n",
    "\n",
    "    m_ord_cat.name = 'ordered categories'\n",
    "    m_ord_cat.trace = pm.sample(\n",
    "        3000, tune=1500, chains=2,\n",
    "    )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pm.forestplot(m_ord_cat.trace, varnames=['beta_edu', 'cum_delta_edu'], rhat=False)\n",
    "None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "/home/petrushev/projects/jup_venv/lib/python3.7/site-packages/theano/tensor/subtensor.py:2197: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  rval = inputs[0].__getitem__(inputs[1:])\n",
      "Multiprocess sampling (2 chains in 2 jobs)\n",
      "NUTS: [beta2, beta, delta_edu, beta_edu, cutpoints]\n",
      "Sampling 2 chains:   0%|          | 0/10000 [00:00<?, ?draws/s]/home/petrushev/projects/jup_venv/lib/python3.7/site-packages/theano/tensor/subtensor.py:2197: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  rval = inputs[0].__getitem__(inputs[1:])\n",
      "/home/petrushev/projects/jup_venv/lib/python3.7/site-packages/theano/tensor/subtensor.py:2197: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  rval = inputs[0].__getitem__(inputs[1:])\n",
      "Sampling 2 chains: 100%|██████████| 10000/10000 [19:47<00:00,  5.78draws/s]\n",
      "The number of effective samples is smaller than 25% for some parameters.\n"
     ]
    }
   ],
   "source": [
    "with pm.Model() as m_ord_cat_all:\n",
    "    n_edus = edus.shape[0]\n",
    "\n",
    "    cutpoints = pm.Normal(\n",
    "        'cutpoints', mu=0, sd=1.5,\n",
    "        transform=pm.distributions.transforms.ordered,\n",
    "        shape=6,\n",
    "        testval=np.linspace(-3, 3, 6),\n",
    "    )\n",
    "    beta_edu = pm.Normal('beta_edu', mu=0, sd=0.5)\n",
    "    delta_edu = pm.Dirichlet('delta_edu', a=np.ones(n_edus-1))\n",
    "\n",
    "    cum_delta_edu = pm.Deterministic(\n",
    "        'cum_delta_edu',\n",
    "        tt.as_tensor_variable(\n",
    "            [pm.math.sum(delta_edu[0:(end)])\n",
    "             for end in range(n_edus)]\n",
    "        )\n",
    "    )\n",
    "    \n",
    "    beta = pm.Normal('beta', mu=0, sd=0.5, shape=3)\n",
    "    beta2 = pm.Normal('beta2', mu=0, sd=0.5, shape=2)\n",
    "    \n",
    "    phi = pm.Deterministic(\n",
    "        'phi',\n",
    "        beta_edu * cum_delta_edu[data.edu_i.values]\n",
    "        + beta[0] * data.action.values\n",
    "        + beta[1] * data.intention.values\n",
    "        + beta[2] * data.contact.values\n",
    "        + beta2[0] * (data.intention.values * data.action.values)\n",
    "        + beta2[1] * (data.intention.values * data.contact.values)\n",
    "    )\n",
    "\n",
    "    responses = pm.OrderedLogistic(\n",
    "        'responses', phi, cutpoints,\n",
    "        observed=(data.response.values - 1)\n",
    "    )\n",
    "\n",
    "    m_ord_cat_all.name = 'all'\n",
    "    m_ord_cat_all.trace = pm.sample(\n",
    "        3000, tune=2000, chains=2,\n",
    "    )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 680x680 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(8, 8), dpi=85)\n",
    "pm.forestplot(m_ord_cat_all.trace,\n",
    "              varnames=['cutpoints', 'cum_delta_edu', 'beta_edu', 'beta', 'beta2'],\n",
    "              rhat=False)\n",
    "None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 680x680 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(8, 8), dpi=85)\n",
    "pm.forestplot(m_ord_cat.trace,\n",
    "              varnames=['delta_edu', 'cum_delta_edu', 'beta_edu'],\n",
    "              rhat=False)\n",
    "None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/petrushev/projects/jup_venv/lib/python3.7/site-packages/theano/tensor/subtensor.py:2197: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  rval = inputs[0].__getitem__(inputs[1:])\n",
      "/home/petrushev/projects/jup_venv/lib/python3.7/site-packages/theano/tensor/subtensor.py:2197: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  rval = inputs[0].__getitem__(inputs[1:])\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
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       "        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>pWAIC</th>\n",
       "      <th>dWAIC</th>\n",
       "      <th>weight</th>\n",
       "      <th>SE</th>\n",
       "      <th>dSE</th>\n",
       "      <th>var_warn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>all</th>\n",
       "      <td>36916.5</td>\n",
       "      <td>13.27</td>\n",
       "      <td>0</td>\n",
       "      <td>0.54</td>\n",
       "      <td>78.88</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>m11.3</th>\n",
       "      <td>36929.2</td>\n",
       "      <td>10.96</td>\n",
       "      <td>12.73</td>\n",
       "      <td>0.46</td>\n",
       "      <td>82.39</td>\n",
       "      <td>114.3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>m11.2</th>\n",
       "      <td>37090</td>\n",
       "      <td>9.02</td>\n",
       "      <td>173.56</td>\n",
       "      <td>0</td>\n",
       "      <td>77.96</td>\n",
       "      <td>110.87</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ordered categories</th>\n",
       "      <td>37842.4</td>\n",
       "      <td>8.02</td>\n",
       "      <td>925.97</td>\n",
       "      <td>0</td>\n",
       "      <td>57.1</td>\n",
       "      <td>61.95</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>m11.1</th>\n",
       "      <td>37854.4</td>\n",
       "      <td>5.99</td>\n",
       "      <td>937.95</td>\n",
       "      <td>0</td>\n",
       "      <td>59.18</td>\n",
       "      <td>99.57</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       WAIC  pWAIC   dWAIC weight     SE     dSE var_warn\n",
       "all                 36916.5  13.27       0   0.54  78.88       0        0\n",
       "m11.3               36929.2  10.96   12.73   0.46  82.39   114.3        0\n",
       "m11.2                 37090   9.02  173.56      0  77.96  110.87        0\n",
       "ordered categories  37842.4   8.02  925.97      0   57.1   61.95        0\n",
       "m11.1               37854.4   5.99  937.95      0  59.18   99.57        0"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comparison = pm.compare({\n",
    "        m11_1: m11_1.trace,\n",
    "        m11_2: m11_2.trace,\n",
    "        m11_3: m11_3.trace,\n",
    "        m_ord_cat: m_ord_cat.trace,\n",
    "        m_ord_cat_all: m_ord_cat_all.trace\n",
    "    },\n",
    "    method='BB-pseudo-BMA'\n",
    ")\n",
    "\n",
    "comparison"
   ]
  }
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