{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# GLM: Mini-batch ADVI on hierarchical regression model\n",
    "\n",
    ":::{post} Sept 23, 2021\n",
    ":tags: generalized linear model, hierarchical model, variational inference\n",
    ":category: intermediate\n",
    ":::"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Unlike Gaussian mixture models, (hierarchical) regression models have independent variables. These variables affect the likelihood function, but are not random variables. When using mini-batch, we should take care of that."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "env: THEANO_FLAGS=device=cpu, floatX=float32, warn_float64=ignore\n",
      "Running on PyMC3 v3.11.2\n"
     ]
    }
   ],
   "source": [
    "%env THEANO_FLAGS=device=cpu, floatX=float32, warn_float64=ignore\n",
    "\n",
    "import os\n",
    "\n",
    "import arviz as az\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pymc3 as pm\n",
    "import seaborn as sns\n",
    "import theano\n",
    "import theano.tensor as tt\n",
    "\n",
    "from scipy import stats\n",
    "\n",
    "print(f\"Running on PyMC3 v{pm.__version__}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%config InlineBackend.figure_format = 'retina'\n",
    "RANDOM_SEED = 8927\n",
    "rng = np.random.default_rng(RANDOM_SEED)\n",
    "az.style.use(\"arviz-darkgrid\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>idnum</th>\n",
       "      <th>state</th>\n",
       "      <th>state2</th>\n",
       "      <th>stfips</th>\n",
       "      <th>zip</th>\n",
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       "      <th>typebldg</th>\n",
       "      <th>floor</th>\n",
       "      <th>room</th>\n",
       "      <th>...</th>\n",
       "      <th>pcterr</th>\n",
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       "      <th>dupflag</th>\n",
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       "      <th>Uppm</th>\n",
       "      <th>county_code</th>\n",
       "      <th>log_radon</th>\n",
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       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>9.6</td>\n",
       "      <td>433.316718</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>AITKIN</td>\n",
       "      <td>27001.0</td>\n",
       "      <td>0.502054</td>\n",
       "      <td>0</td>\n",
       "      <td>1.098612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>5084.0</td>\n",
       "      <td>MN</td>\n",
       "      <td>MN</td>\n",
       "      <td>27.0</td>\n",
       "      <td>56469</td>\n",
       "      <td>5.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>24.3</td>\n",
       "      <td>461.623670</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>AITKIN</td>\n",
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       "      <td>MN</td>\n",
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       "      <td>27.0</td>\n",
       "      <td>55011</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>13.8</td>\n",
       "      <td>433.316718</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>ANOKA</td>\n",
       "      <td>27003.0</td>\n",
       "      <td>0.428565</td>\n",
       "      <td>1</td>\n",
       "      <td>1.163151</td>\n",
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       "      <td>MN</td>\n",
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       "      <td>27.0</td>\n",
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       "      <td>WRIGHT</td>\n",
       "      <td>27171.0</td>\n",
       "      <td>0.913909</td>\n",
       "      <td>83</td>\n",
       "      <td>1.871802</td>\n",
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       "      <th>915</th>\n",
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       "      <td>5996.0</td>\n",
       "      <td>MN</td>\n",
       "      <td>MN</td>\n",
       "      <td>27.0</td>\n",
       "      <td>55376</td>\n",
       "      <td>5.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>171.0</td>\n",
       "      <td>WRIGHT</td>\n",
       "      <td>27171.0</td>\n",
       "      <td>0.913909</td>\n",
       "      <td>83</td>\n",
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       "      <td>WRIGHT</td>\n",
       "      <td>27171.0</td>\n",
       "      <td>0.913909</td>\n",
       "      <td>83</td>\n",
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       "      <td>MN</td>\n",
       "      <td>MN</td>\n",
       "      <td>27.0</td>\n",
       "      <td>56297</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>9.6</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>173.0</td>\n",
       "      <td>YELLOW MEDICINE</td>\n",
       "      <td>27173.0</td>\n",
       "      <td>1.426590</td>\n",
       "      <td>84</td>\n",
       "      <td>1.335001</td>\n",
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       "      <td>5999.0</td>\n",
       "      <td>MN</td>\n",
       "      <td>MN</td>\n",
       "      <td>27.0</td>\n",
       "      <td>56297</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>1214.922779</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>173.0</td>\n",
       "      <td>YELLOW MEDICINE</td>\n",
       "      <td>27173.0</td>\n",
       "      <td>1.426590</td>\n",
       "      <td>84</td>\n",
       "      <td>1.098612</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>919 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Unnamed: 0   idnum state state2  stfips    zip  region  typebldg  floor  \\\n",
       "0             0  5081.0    MN     MN    27.0  55735     5.0       1.0    1.0   \n",
       "1             1  5082.0    MN     MN    27.0  55748     5.0       1.0    0.0   \n",
       "2             2  5083.0    MN     MN    27.0  55748     5.0       1.0    0.0   \n",
       "3             3  5084.0    MN     MN    27.0  56469     5.0       1.0    0.0   \n",
       "4             4  5085.0    MN     MN    27.0  55011     3.0       1.0    0.0   \n",
       "..          ...     ...   ...    ...     ...    ...     ...       ...    ...   \n",
       "914         914  5995.0    MN     MN    27.0  55363     5.0       1.0    0.0   \n",
       "915         915  5996.0    MN     MN    27.0  55376     5.0       1.0    0.0   \n",
       "916         916  5997.0    MN     MN    27.0  55376     5.0       1.0    0.0   \n",
       "917         917  5998.0    MN     MN    27.0  56297     5.0       1.0    0.0   \n",
       "918         918  5999.0    MN     MN    27.0  56297     5.0       1.0    0.0   \n",
       "\n",
       "     room  ... pcterr        adjwt  dupflag  zipflag  cntyfips  \\\n",
       "0     3.0  ...    9.7  1146.499190      1.0      0.0       1.0   \n",
       "1     4.0  ...   14.5   471.366223      0.0      0.0       1.0   \n",
       "2     4.0  ...    9.6   433.316718      0.0      0.0       1.0   \n",
       "3     4.0  ...   24.3   461.623670      0.0      0.0       1.0   \n",
       "4     4.0  ...   13.8   433.316718      0.0      0.0       3.0   \n",
       "..    ...  ...    ...          ...      ...      ...       ...   \n",
       "914   4.0  ...    4.5  1146.499190      0.0      0.0     171.0   \n",
       "915   7.0  ...    8.3  1105.956867      0.0      0.0     171.0   \n",
       "916   4.0  ...    5.2  1214.922779      0.0      0.0     171.0   \n",
       "917   4.0  ...    9.6  1177.377355      0.0      0.0     173.0   \n",
       "918   4.0  ...    8.0  1214.922779      0.0      0.0     173.0   \n",
       "\n",
       "              county     fips      Uppm  county_code  log_radon  \n",
       "0             AITKIN  27001.0  0.502054            0   0.832909  \n",
       "1             AITKIN  27001.0  0.502054            0   0.832909  \n",
       "2             AITKIN  27001.0  0.502054            0   1.098612  \n",
       "3             AITKIN  27001.0  0.502054            0   0.095310  \n",
       "4              ANOKA  27003.0  0.428565            1   1.163151  \n",
       "..               ...      ...       ...          ...        ...  \n",
       "914           WRIGHT  27171.0  0.913909           83   1.871802  \n",
       "915           WRIGHT  27171.0  0.913909           83   1.526056  \n",
       "916           WRIGHT  27171.0  0.913909           83   1.629241  \n",
       "917  YELLOW MEDICINE  27173.0  1.426590           84   1.335001  \n",
       "918  YELLOW MEDICINE  27173.0  1.426590           84   1.098612  \n",
       "\n",
       "[919 rows x 30 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "try:\n",
    "    data = pd.read_csv(os.path.join(\"..\", \"data\", \"radon.csv\"))\n",
    "except FileNotFoundError:\n",
    "    data = pd.read_csv(pm.get_data(\"radon.csv\"))\n",
    "\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "county_idx = data[\"county_code\"].values\n",
    "floor_idx = data[\"floor\"].values\n",
    "log_radon_idx = data[\"log_radon\"].values\n",
    "\n",
    "coords = {\"counties\": data.county.unique()}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, `log_radon_idx_t` is a dependent variable, while `floor_idx_t` and `county_idx_t` determine the independent variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ada/.local/lib/python3.8/site-packages/pymc3/data.py:316: 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",
      "  self.shared = theano.shared(data[in_memory_slc])\n",
      "/home/ada/.local/lib/python3.8/site-packages/pymc3/data.py:316: 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",
      "  self.shared = theano.shared(data[in_memory_slc])\n"
     ]
    }
   ],
   "source": [
    "log_radon_idx_t = pm.Minibatch(log_radon_idx, 100)\n",
    "floor_idx_t = pm.Minibatch(floor_idx, 100)\n",
    "county_idx_t = pm.Minibatch(county_idx, 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "with pm.Model(coords=coords) as hierarchical_model:\n",
    "    # Hyperpriors for group nodes\n",
    "    mu_a = pm.Normal(\"mu_alpha\", mu=0.0, sigma=100**2)\n",
    "    sigma_a = pm.Uniform(\"sigma_alpha\", lower=0, upper=100)\n",
    "    mu_b = pm.Normal(\"mu_beta\", mu=0.0, sigma=100**2)\n",
    "    sigma_b = pm.Uniform(\"sigma_beta\", lower=0, upper=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Intercept for each county, distributed around group mean `mu_a`. Above we just set `mu` and `sd` to a fixed value while here we plug in a common group distribution for all `a` and `b` (which are vectors with the same length as the number of unique counties in our example)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "with hierarchical_model:\n",
    "\n",
    "    a = pm.Normal(\"alpha\", mu=mu_a, sigma=sigma_a, dims=\"counties\")\n",
    "    # Intercept for each county, distributed around group mean mu_a\n",
    "    b = pm.Normal(\"beta\", mu=mu_b, sigma=sigma_b, dims=\"counties\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Model prediction of radon level `a[county_idx]` translates to `a[0, 0, 0, 1, 1, ...]`, we thus link multiple household measures of a county to its coefficients."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "with hierarchical_model:\n",
    "\n",
    "    radon_est = a[county_idx_t] + b[county_idx_t] * floor_idx_t"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we specify the likelihood:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "with hierarchical_model:\n",
    "\n",
    "    # Model error\n",
    "    eps = pm.Uniform(\"eps\", lower=0, upper=100)\n",
    "\n",
    "    # Data likelihood\n",
    "    radon_like = pm.Normal(\n",
    "        \"radon_like\", mu=radon_est, sigma=eps, observed=log_radon_idx_t, total_size=len(data)\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Random variables `radon_like`, associated with `log_radon_idx_t`, should be given to the function for ADVI to denote that as observations in the likelihood term."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On the other hand, `minibatches` should include the three variables above."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then, run ADVI with mini-batch."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ada/.local/lib/python3.8/site-packages/theano/gpuarray/dnn.py:192: UserWarning: Your cuDNN version is more recent than Theano. If you encounter problems, try updating Theano or downgrading cuDNN to a version >= v5 and <= v7.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "        <style>\n",
       "            /* Turns off some styling */\n",
       "            progress {\n",
       "                /* gets rid of default border in Firefox and Opera. */\n",
       "                border: none;\n",
       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "                background-size: auto;\n",
       "            }\n",
       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "                background: #F44336;\n",
       "            }\n",
       "        </style>\n",
       "      <progress value='100000' class='' max='100000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      100.00% [100000/100000 00:17<00:00 Average Loss = 120.22]\n",
       "    </div>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Finished [100%]: Average Loss = 120.26\n"
     ]
    }
   ],
   "source": [
    "with hierarchical_model:\n",
    "    approx = pm.fit(100000, callbacks=[pm.callbacks.CheckParametersConvergence(tolerance=1e-4)])\n",
    "    idata_advi = az.from_pymc3(approx.sample(500))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check the trace of ELBO and compare the result with MCMC."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 491,
       "width": 731
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(approx.hist);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [eps, beta, alpha, sigma_beta, mu_beta, sigma_alpha, mu_alpha]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "        <style>\n",
       "            /* Turns off some styling */\n",
       "            progress {\n",
       "                /* gets rid of default border in Firefox and Opera. */\n",
       "                border: none;\n",
       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "                background-size: auto;\n",
       "            }\n",
       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "                background: #F44336;\n",
       "            }\n",
       "        </style>\n",
       "      <progress value='12000' class='' max='12000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      100.00% [12000/12000 11:00<00:00 Sampling 4 chains, 522 divergences]\n",
       "    </div>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling 4 chains for 1_000 tune and 2_000 draw iterations (4_000 + 8_000 draws total) took 661 seconds.\n",
      "There were 281 divergences after tuning. Increase `target_accept` or reparameterize.\n",
      "There were 41 divergences after tuning. Increase `target_accept` or reparameterize.\n",
      "There were 156 divergences after tuning. Increase `target_accept` or reparameterize.\n",
      "There were 44 divergences after tuning. Increase `target_accept` or reparameterize.\n",
      "The estimated number of effective samples is smaller than 200 for some parameters.\n"
     ]
    }
   ],
   "source": [
    "# Inference button (TM)!\n",
    "with pm.Model(coords=coords):\n",
    "\n",
    "    mu_a = pm.Normal(\"mu_alpha\", mu=0.0, sigma=100**2)\n",
    "    sigma_a = pm.Uniform(\"sigma_alpha\", lower=0, upper=100)\n",
    "    mu_b = pm.Normal(\"mu_beta\", mu=0.0, sigma=100**2)\n",
    "    sigma_b = pm.Uniform(\"sigma_beta\", lower=0, upper=100)\n",
    "\n",
    "    a = pm.Normal(\"alpha\", mu=mu_a, sigma=sigma_a, dims=\"counties\")\n",
    "    b = pm.Normal(\"beta\", mu=mu_b, sigma=sigma_b, dims=\"counties\")\n",
    "\n",
    "    # Model error\n",
    "    eps = pm.Uniform(\"eps\", lower=0, upper=100)\n",
    "\n",
    "    radon_est = a[county_idx] + b[county_idx] * floor_idx\n",
    "\n",
    "    radon_like = pm.Normal(\"radon_like\", mu=radon_est, sigma=eps, observed=log_radon_idx)\n",
    "\n",
    "    # essentially, this is what init='advi' does\n",
    "    step = pm.NUTS(scaling=approx.cov.eval(), is_cov=True)\n",
    "    hierarchical_trace = pm.sample(\n",
    "        2000, step, start=approx.sample()[0], progressbar=True, return_inferencedata=True\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2484x1104 with 6 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 1115,
       "width": 2495
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "az.plot_density(\n",
    "    [idata_advi, hierarchical_trace], var_names=[\"~alpha\", \"~beta\"], data_labels=[\"ADVI\", \"NUTS\"]\n",
    ");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Watermark"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Last updated: Thu Sep 23 2021\n",
      "\n",
      "Python implementation: CPython\n",
      "Python version       : 3.8.10\n",
      "IPython version      : 7.25.0\n",
      "\n",
      "xarray: 0.17.0\n",
      "\n",
      "numpy     : 1.21.0\n",
      "seaborn   : 0.11.1\n",
      "pandas    : 1.2.1\n",
      "matplotlib: 3.3.4\n",
      "theano    : 1.1.2\n",
      "pymc3     : 3.11.2\n",
      "scipy     : 1.7.1\n",
      "arviz     : 0.11.2\n",
      "\n",
      "Watermark: 2.2.0\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%load_ext watermark\n",
    "%watermark -n -u -v -iv -w -p xarray"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "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": "3.8.10"
  },
  "latex_envs": {
   "bibliofile": "biblio.bib",
   "cite_by": "apalike",
   "current_citInitial": 1,
   "eqLabelWithNumbers": true,
   "eqNumInitial": 0
  },
  "nav_menu": {},
  "toc": {
   "navigate_menu": true,
   "number_sections": true,
   "sideBar": true,
   "threshold": 6,
   "toc_cell": false,
   "toc_section_display": "block",
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}