ahartikainen/stan_simple_reproducibility_example.ipynb

## stan_simple_reproducibility_example.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pystan\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import arviz as az"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "%load_ext autoreload"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "import arviz as az"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "az.style.use(\"arviz-whitegrid\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "N = 30\n",
    "alpha = 2\n",
    "beta = 30\n",
    "sigma = 10\n",
    "x = np.sort(np.random.rand(N)*100)\n",
    "err = np.random.randn(N) * sigma\n",
    "y = x * alpha + beta + err"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x2336a7ded88>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Stan model + fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_code = \"\"\"\n",
    "data {\n",
    "    int<lower=0> N;\n",
    "    vector[N] y;\n",
    "    vector[N] x;\n",
    "}\n",
    "parameters {\n",
    "    real alpha;\n",
    "    real beta;\n",
    "    real<lower=0> sigma;\n",
    "}\n",
    "model {\n",
    "    target += normal_lpdf(y | beta + alpha * x, sigma);\n",
    "    alpha ~ student_t(4,0,1);\n",
    "    beta ~ student_t(4,0,1);\n",
    "    sigma ~ student_t(4,0,1);\n",
    "}\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:pystan:COMPILING THE C++ CODE FOR MODEL anon_model_2c86bebbf3e65361345bad985913aa0a NOW.\n"
     ]
    }
   ],
   "source": [
    "stan_model = pystan.StanModel(model_code=model_code)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "stan_data = {\n",
    "    \"N\" : N,\n",
    "    \"x\" : x,\n",
    "    \"y\" : y,\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "fit = stan_model.sampling(iter=2000, warmup=500, data=stan_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "idata = az.from_pystan(posterior=fit, observed_data=\"y\", constant_data=[\"x\", \"N\"])"
   ]
  },
  {
   "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>mean</th>\n",
       "      <th>sd</th>\n",
       "      <th>hdi_3%</th>\n",
       "      <th>hdi_97%</th>\n",
       "      <th>mcse_mean</th>\n",
       "      <th>mcse_sd</th>\n",
       "      <th>ess_mean</th>\n",
       "      <th>ess_sd</th>\n",
       "      <th>ess_bulk</th>\n",
       "      <th>ess_tail</th>\n",
       "      <th>r_hat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alpha</th>\n",
       "      <td>2.026</td>\n",
       "      <td>0.066</td>\n",
       "      <td>1.905</td>\n",
       "      <td>2.152</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>2458.0</td>\n",
       "      <td>2427.0</td>\n",
       "      <td>2475.0</td>\n",
       "      <td>2494.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>beta</th>\n",
       "      <td>28.154</td>\n",
       "      <td>3.691</td>\n",
       "      <td>21.162</td>\n",
       "      <td>35.391</td>\n",
       "      <td>0.077</td>\n",
       "      <td>0.055</td>\n",
       "      <td>2280.0</td>\n",
       "      <td>2280.0</td>\n",
       "      <td>2326.0</td>\n",
       "      <td>2171.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sigma</th>\n",
       "      <td>10.872</td>\n",
       "      <td>1.412</td>\n",
       "      <td>8.386</td>\n",
       "      <td>13.526</td>\n",
       "      <td>0.026</td>\n",
       "      <td>0.019</td>\n",
       "      <td>2865.0</td>\n",
       "      <td>2822.0</td>\n",
       "      <td>2956.0</td>\n",
       "      <td>3085.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         mean     sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd  ess_mean  ess_sd  \\\n",
       "alpha   2.026  0.066   1.905    2.152      0.001    0.001    2458.0  2427.0   \n",
       "beta   28.154  3.691  21.162   35.391      0.077    0.055    2280.0  2280.0   \n",
       "sigma  10.872  1.412   8.386   13.526      0.026    0.019    2865.0  2822.0   \n",
       "\n",
       "       ess_bulk  ess_tail  r_hat  \n",
       "alpha    2475.0    2494.0    1.0  \n",
       "beta     2326.0    2171.0    1.0  \n",
       "sigma    2956.0    3085.0    1.0  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "az.summary(idata)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Replicate fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_stan_data(idata):\n",
    "    \"\"\"Combine data.\"\"\"\n",
    "    stan_data = {}\n",
    "    for key, item in idata.observed_data.data_vars.items():\n",
    "        stan_data[key] = item.values\n",
    "\n",
    "    for key, item in idata.constant_data.data_vars.items():\n",
    "        stan_data[key] = item.values.squeeze()\n",
    "    return stan_data\n",
    "\n",
    "def get_posterior_structure(idata, skip_main=False):\n",
    "    \"\"\"Get InferenceData structure.\"\"\"\n",
    "    group_names = {}\n",
    "    for group in idata._groups:\n",
    "        if \"sample_stats\" in group:\n",
    "            continue\n",
    "        if skip_main and group in (\"posterior\", \"prior\"):\n",
    "            continue\n",
    "        dataset = getattr(idata, group)\n",
    "        group_names[group] = list(dataset.data_vars.keys())\n",
    "    return group_names\n",
    "\n",
    "def get_model_code(idata):\n",
    "    \"\"\"Access model code.\"\"\"\n",
    "    model_code = idata.posterior.attrs[\"stan_code\"]\n",
    "    return model_code\n",
    "\n",
    "def get_chain_args_same(idata):\n",
    "    \"\"\"Get 'same' StanModel.sampling arguments.\"\"\"\n",
    "    stan_data = get_stan_data(idata)\n",
    "    chains = []\n",
    "    rename_dict = {\"random_seed\" : \"seed\", \"sampler_t\" : \"algorithm\", \"init_radius\" : \"init_r\"}\n",
    "    for chain, args in enumerate(idata.posterior.attrs[\"args\"]): \n",
    "        chain_args = {}\n",
    "        for key in [\"random_seed\", \"chain_id\", \"init\", \"init_radius\", \"iter\", \"warmup\", \"thin\", \"sampler_t\", \"control\"]:\n",
    "            if key == \"chain_id\":\n",
    "                chain_args[rename_dict.get(key, key)] = [args[key]]\n",
    "            else:\n",
    "                chain_args[rename_dict.get(key, key)] = args[key]\n",
    "        chain_args.setdefault(\"control\", {})\n",
    "        chain_args[\"control\"][rename_dict.get(\"metric\", \"metric\")] = idata.posterior.attrs[\"metric\"][chain]\n",
    "        chain_args[\"chains\"] = 1\n",
    "\n",
    "        chains.append({\"data\" : stan_data, **chain_args})\n",
    "    return chains\n",
    "\n",
    "def get_sample_init(idata, chain=None):\n",
    "    \"\"\"Get a random draw from posterior as a dictionary.\"\"\"\n",
    "    init_dict = {}\n",
    "    idx = np.random.randint(0, idata.posterior.dims[\"draw\"])\n",
    "    if chain is None:\n",
    "        chain = np.random.randint(0, idata.posterior.dims[\"chain\"])\n",
    "    for key in list(idata.posterior.data_vars):\n",
    "        item = idata.posterior.data_vars[key][chain, idx]\n",
    "        init_dict[key] = item.values.squeeze()\n",
    "    return init_dict\n",
    "\n",
    "def get_chain_args_similar(idata, stan_data=None, chains=None, seed=None):\n",
    "    \"\"\"Get 'similar' StanModel.sampling arguments.\"\"\"\n",
    "    if stan_data is None:\n",
    "        stan_data = get_stan_data(idata)\n",
    "    chains_list = []\n",
    "    rename_dict = {\"random_seed\" : \"seed\", \"sampler_t\" : \"algorithm\", \"init_radius\" : \"init_r\"}\n",
    "    nargs = len(idata.posterior.attrs[\"args\"])\n",
    "    for chain in range(chains):\n",
    "        # use chain % nargs to select some chain for the args\n",
    "        # generally, these are the same for each chain\n",
    "        args = idata.posterior.attrs[\"args\"][chain % nargs]\n",
    "        \n",
    "        # manually set seed, chain_id and disable warmup\n",
    "        chain_args = {\"seed\" : seed, \"chain_id\" : [chain], \"warmup\" : 0}\n",
    "        for key in [(\"iter\", \"warmup\"), \"thin\", \"sampler_t\"]:\n",
    "            if isinstance(key, tuple):\n",
    "                chain_args[rename_dict.get(key, key[0])] = args[key[0]] - args[key[1]]\n",
    "            else:\n",
    "                chain_args[rename_dict.get(key, key)] = args[key]\n",
    "        \n",
    "        # manually full control for metric, inv_metric, adapt_engage and stepsize\n",
    "        chain_args[\"control\"] = {}\n",
    "        chain_args[\"control\"][rename_dict.get(\"metric\", \"metric\")] = idata.posterior.attrs[\"metric\"][chain % nargs]\n",
    "        chain_args[\"control\"][rename_dict.get(\"inv_metric\", \"inv_metric\")] = {0 : idata.posterior.attrs[\"inv_metric\"][chain % nargs]}\n",
    "        chain_args[\"control\"][\"adapt_engaged\"] = False\n",
    "        chain_args[\"control\"][\"stepsize\"] = [idata.posterior.attrs[\"step_size\"][chain % nargs]]\n",
    "        \n",
    "        # sample each chain independently\n",
    "        chain_args[\"chains\"] = 1\n",
    "        \n",
    "        # get a random a draw from the previous posterior for the init\n",
    "        chain_args[\"init\"] = [get_sample_init(idata, chain % nargs)]\n",
    "        \n",
    "        chains_list.append({\"data\" : stan_data, **chain_args})\n",
    "    return chains_list\n",
    "\n",
    "from concurrent.futures import ProcessPoolExecutor, as_completed\n",
    "def run_sample(model, chains, subgroups):\n",
    "    \"\"\"Run each chain on a different process.\"\"\"\n",
    "    idata_chains = [None for _ in chains]\n",
    "    with ProcessPoolExecutor(max_workers=min(os.cpu_count(), len(chains))) as executor:\n",
    "        futures = {}\n",
    "        for idx, chain_args in enumerate(chains):\n",
    "            future = executor.submit(model.sampling, **chain_args)\n",
    "            futures[future] = idx\n",
    "        for future in as_completed(futures):\n",
    "            idx = futures[future]\n",
    "            idata_chains[idx] = az.from_pystan(posterior=future.result(), **subgroups)\n",
    "    return combine_idata(idata_chains)\n",
    "\n",
    "def combine_idata(chains):\n",
    "    \"\"\"Concat chains.\"\"\"\n",
    "    return az.concat(chains, dim=\"chain\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Replicate (with warmup --> 100 % on a same machine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_code_same = get_model_code(idata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:pystan:COMPILING THE C++ CODE FOR MODEL reproduce_model_2c86bebbf3e65361345bad985913aa0a NOW.\n"
     ]
    }
   ],
   "source": [
    "stan_model_same = pystan.StanModel(model_name=\"reproduce_model\", model_code=model_code_same)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "chain_args_same = get_chain_args_same(idata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "subgroups_same = get_posterior_structure(idata, skip_main=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "idata_same = run_sample(stan_model_same, chain_args_same, subgroups_same)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
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       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt, dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:  ()\n",
       "Data variables:\n",
       "    alpha    bool True\n",
       "    beta     bool True\n",
       "    sigma    bool True</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-538362a0-c8e7-4241-92ff-7884aa041148' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-538362a0-c8e7-4241-92ff-7884aa041148' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-74057251-fe0e-4d33-aa6a-906d51d34221' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-74057251-fe0e-4d33-aa6a-906d51d34221' class='xr-section-summary'  title='Expand/collapse section'>Coordinates: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-839524a9-8d33-440f-8314-79726a02ed1a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-839524a9-8d33-440f-8314-79726a02ed1a' class='xr-section-summary' >Data variables: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>alpha</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>bool</div><div class='xr-var-preview xr-preview'>True</div><input id='attrs-275449dc-3901-459b-a61d-c94a47f2ea9c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-275449dc-3901-459b-a61d-c94a47f2ea9c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-63b68292-09f4-4691-88b2-4e71a5750d0e' class='xr-var-data-in' type='checkbox'><label for='data-63b68292-09f4-4691-88b2-4e71a5750d0e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(True)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>beta</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>bool</div><div class='xr-var-preview xr-preview'>True</div><input id='attrs-dc64f8c2-b993-4405-a9f2-d4c50bb398ec' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-dc64f8c2-b993-4405-a9f2-d4c50bb398ec' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0ffef95d-d123-4acc-b1fe-1ecf42ee89a8' class='xr-var-data-in' type='checkbox'><label for='data-0ffef95d-d123-4acc-b1fe-1ecf42ee89a8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(True)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sigma</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>bool</div><div class='xr-var-preview xr-preview'>True</div><input id='attrs-70bdde02-f02c-4e07-a1ee-22913303f239' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-70bdde02-f02c-4e07-a1ee-22913303f239' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-45d62177-b2a2-421c-84b7-a70917353d00' class='xr-var-data-in' type='checkbox'><label for='data-45d62177-b2a2-421c-84b7-a70917353d00' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(True)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-019e126a-956a-40aa-876d-fbd44cf71a8c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-019e126a-956a-40aa-876d-fbd44cf71a8c' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  ()\n",
       "Data variables:\n",
       "    alpha    bool True\n",
       "    beta     bool True\n",
       "    sigma    bool True"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(idata.posterior == idata_same.posterior).all()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>sd</th>\n",
       "      <th>hdi_3%</th>\n",
       "      <th>hdi_97%</th>\n",
       "      <th>mcse_mean</th>\n",
       "      <th>mcse_sd</th>\n",
       "      <th>ess_mean</th>\n",
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       "      <th>ess_tail</th>\n",
       "      <th>r_hat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alpha</th>\n",
       "      <td>2.026</td>\n",
       "      <td>0.066</td>\n",
       "      <td>1.905</td>\n",
       "      <td>2.152</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>2458.0</td>\n",
       "      <td>2427.0</td>\n",
       "      <td>2475.0</td>\n",
       "      <td>2494.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>beta</th>\n",
       "      <td>28.154</td>\n",
       "      <td>3.691</td>\n",
       "      <td>21.162</td>\n",
       "      <td>35.391</td>\n",
       "      <td>0.077</td>\n",
       "      <td>0.055</td>\n",
       "      <td>2280.0</td>\n",
       "      <td>2280.0</td>\n",
       "      <td>2326.0</td>\n",
       "      <td>2171.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sigma</th>\n",
       "      <td>10.872</td>\n",
       "      <td>1.412</td>\n",
       "      <td>8.386</td>\n",
       "      <td>13.526</td>\n",
       "      <td>0.026</td>\n",
       "      <td>0.019</td>\n",
       "      <td>2865.0</td>\n",
       "      <td>2822.0</td>\n",
       "      <td>2956.0</td>\n",
       "      <td>3085.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         mean     sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd  ess_mean  ess_sd  \\\n",
       "alpha   2.026  0.066   1.905    2.152      0.001    0.001    2458.0  2427.0   \n",
       "beta   28.154  3.691  21.162   35.391      0.077    0.055    2280.0  2280.0   \n",
       "sigma  10.872  1.412   8.386   13.526      0.026    0.019    2865.0  2822.0   \n",
       "\n",
       "       ess_bulk  ess_tail  r_hat  \n",
       "alpha    2475.0    2494.0    1.0  \n",
       "beta     2326.0    2171.0    1.0  \n",
       "sigma    2956.0    3085.0    1.0  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "az.summary(idata.posterior)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>sd</th>\n",
       "      <th>hdi_3%</th>\n",
       "      <th>hdi_97%</th>\n",
       "      <th>mcse_mean</th>\n",
       "      <th>mcse_sd</th>\n",
       "      <th>ess_mean</th>\n",
       "      <th>ess_sd</th>\n",
       "      <th>ess_bulk</th>\n",
       "      <th>ess_tail</th>\n",
       "      <th>r_hat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alpha</th>\n",
       "      <td>2.026</td>\n",
       "      <td>0.066</td>\n",
       "      <td>1.905</td>\n",
       "      <td>2.152</td>\n",
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       "      <td>0.001</td>\n",
       "      <td>2458.0</td>\n",
       "      <td>2427.0</td>\n",
       "      <td>2475.0</td>\n",
       "      <td>2494.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>beta</th>\n",
       "      <td>28.154</td>\n",
       "      <td>3.691</td>\n",
       "      <td>21.162</td>\n",
       "      <td>35.391</td>\n",
       "      <td>0.077</td>\n",
       "      <td>0.055</td>\n",
       "      <td>2280.0</td>\n",
       "      <td>2280.0</td>\n",
       "      <td>2326.0</td>\n",
       "      <td>2171.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sigma</th>\n",
       "      <td>10.872</td>\n",
       "      <td>1.412</td>\n",
       "      <td>8.386</td>\n",
       "      <td>13.526</td>\n",
       "      <td>0.026</td>\n",
       "      <td>0.019</td>\n",
       "      <td>2865.0</td>\n",
       "      <td>2822.0</td>\n",
       "      <td>2956.0</td>\n",
       "      <td>3085.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         mean     sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd  ess_mean  ess_sd  \\\n",
       "alpha   2.026  0.066   1.905    2.152      0.001    0.001    2458.0  2427.0   \n",
       "beta   28.154  3.691  21.162   35.391      0.077    0.055    2280.0  2280.0   \n",
       "sigma  10.872  1.412   8.386   13.526      0.026    0.019    2865.0  2822.0   \n",
       "\n",
       "       ess_bulk  ess_tail  r_hat  \n",
       "alpha    2475.0    2494.0    1.0  \n",
       "beta     2326.0    2171.0    1.0  \n",
       "sigma    2956.0    3085.0    1.0  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "az.summary(idata_same.posterior)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2484x552 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "axes = az.plot_density([idata, idata_same], data_labels=[\"original\", \"same\"])\n",
    "for val, ax in zip([alpha, beta, sigma], axes.ravel()):\n",
    "    ax.axvline(val, color=\"k\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Replicate (without warmup --> approximate similarity up to a mcmc error)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_code_similar = get_model_code(idata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:pystan:COMPILING THE C++ CODE FOR MODEL reproduce_model_2c86bebbf3e65361345bad985913aa0a NOW.\n"
     ]
    }
   ],
   "source": [
    "stan_model_similar = pystan.StanModel(model_name=\"reproduce_model\", model_code=model_code_similar)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's run for 10 chains\n",
    "chain_args_similar = get_chain_args_similar(idata, chains=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "subgroups_similar = get_posterior_structure(idata, skip_main=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 9.78 s\n"
     ]
    }
   ],
   "source": [
    "%time idata_similar = run_sample(stan_model_similar, chain_args_similar, subgroups_similar)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:  ()\n",
       "Data variables:\n",
       "    alpha    bool False\n",
       "    beta     bool False\n",
       "    sigma    bool False</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-cb6da3f9-c5e8-430b-9504-2b68ed64cefa' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-cb6da3f9-c5e8-430b-9504-2b68ed64cefa' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-b2d0e343-5c83-4c18-8337-809afec4275f' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-b2d0e343-5c83-4c18-8337-809afec4275f' class='xr-section-summary'  title='Expand/collapse section'>Coordinates: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-d7fe43de-32ef-4303-9412-20c9a15cc687' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d7fe43de-32ef-4303-9412-20c9a15cc687' class='xr-section-summary' >Data variables: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>alpha</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>bool</div><div class='xr-var-preview xr-preview'>False</div><input id='attrs-4a14ff0d-ba76-4297-8f7a-f7fa933c253f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4a14ff0d-ba76-4297-8f7a-f7fa933c253f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fc2a7c9f-95ad-4885-a540-8857deeccaea' class='xr-var-data-in' type='checkbox'><label for='data-fc2a7c9f-95ad-4885-a540-8857deeccaea' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(False)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>beta</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>bool</div><div class='xr-var-preview xr-preview'>False</div><input id='attrs-e95b3e63-5abd-4a58-ac2f-fa1fb160e963' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e95b3e63-5abd-4a58-ac2f-fa1fb160e963' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b0a99abc-503b-43ef-b799-75b9d7580ad9' class='xr-var-data-in' type='checkbox'><label for='data-b0a99abc-503b-43ef-b799-75b9d7580ad9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(False)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sigma</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>bool</div><div class='xr-var-preview xr-preview'>False</div><input id='attrs-cf2f83b9-a619-4f8f-a00e-a43d3f933743' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cf2f83b9-a619-4f8f-a00e-a43d3f933743' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-30e69ae3-cba6-4772-ba7d-76f51a3cf5d6' class='xr-var-data-in' type='checkbox'><label for='data-30e69ae3-cba6-4772-ba7d-76f51a3cf5d6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(False)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c6eaabde-69a4-4957-a970-53576921e3ea' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-c6eaabde-69a4-4957-a970-53576921e3ea' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  ()\n",
       "Data variables:\n",
       "    alpha    bool False\n",
       "    beta     bool False\n",
       "    sigma    bool False"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(idata.posterior == idata_similar.posterior).any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
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       "      <td>2.152</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
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       "      <td>2280.0</td>\n",
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       "      <td>2956.0</td>\n",
       "      <td>3085.0</td>\n",
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       "         mean     sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd  ess_mean  ess_sd  \\\n",
       "alpha   2.026  0.066   1.905    2.152      0.001    0.001    2458.0  2427.0   \n",
       "beta   28.154  3.691  21.162   35.391      0.077    0.055    2280.0  2280.0   \n",
       "sigma  10.872  1.412   8.386   13.526      0.026    0.019    2865.0  2822.0   \n",
       "\n",
       "       ess_bulk  ess_tail  r_hat  \n",
       "alpha    2475.0    2494.0    1.0  \n",
       "beta     2326.0    2171.0    1.0  \n",
       "sigma    2956.0    3085.0    1.0  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "az.summary(idata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>mean</th>\n",
       "      <th>sd</th>\n",
       "      <th>hdi_3%</th>\n",
       "      <th>hdi_97%</th>\n",
       "      <th>mcse_mean</th>\n",
       "      <th>mcse_sd</th>\n",
       "      <th>ess_mean</th>\n",
       "      <th>ess_sd</th>\n",
       "      <th>ess_bulk</th>\n",
       "      <th>ess_tail</th>\n",
       "      <th>r_hat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alpha</th>\n",
       "      <td>2.027</td>\n",
       "      <td>0.068</td>\n",
       "      <td>1.900</td>\n",
       "      <td>2.156</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>5726.0</td>\n",
       "      <td>5689.0</td>\n",
       "      <td>5821.0</td>\n",
       "      <td>6278.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>beta</th>\n",
       "      <td>28.130</td>\n",
       "      <td>3.779</td>\n",
       "      <td>21.192</td>\n",
       "      <td>35.399</td>\n",
       "      <td>0.050</td>\n",
       "      <td>0.035</td>\n",
       "      <td>5758.0</td>\n",
       "      <td>5758.0</td>\n",
       "      <td>5875.0</td>\n",
       "      <td>6123.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sigma</th>\n",
       "      <td>10.891</td>\n",
       "      <td>1.426</td>\n",
       "      <td>8.375</td>\n",
       "      <td>13.549</td>\n",
       "      <td>0.017</td>\n",
       "      <td>0.012</td>\n",
       "      <td>7094.0</td>\n",
       "      <td>6967.0</td>\n",
       "      <td>7260.0</td>\n",
       "      <td>7252.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         mean     sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd  ess_mean  ess_sd  \\\n",
       "alpha   2.027  0.068   1.900    2.156      0.001    0.001    5726.0  5689.0   \n",
       "beta   28.130  3.779  21.192   35.399      0.050    0.035    5758.0  5758.0   \n",
       "sigma  10.891  1.426   8.375   13.549      0.017    0.012    7094.0  6967.0   \n",
       "\n",
       "       ess_bulk  ess_tail  r_hat  \n",
       "alpha    5821.0    6278.0    1.0  \n",
       "beta     5875.0    6123.0    1.0  \n",
       "sigma    7260.0    7252.0    1.0  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "az.summary(idata_similar)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2484x552 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "axes = az.plot_density([idata, idata_similar], data_labels=[\"original\", \"similar\"])\n",
    "for val, ax in zip([alpha, beta, sigma], axes.ravel()):\n",
    "    ax.axvline(val, color=\"k\")"
   ]
  }
 ],
 "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": "3.7.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}