Indexing vs Design Matrix for PyMC Model

Indexing vs Design Matrix.ipynb

{
  "cells": [
    {
      "metadata": {
        "toc": true
      },
      "cell_type": "markdown",
      "source": "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#Fake-Data,-One-Level\" data-toc-modified-id=\"Fake-Data,-One-Level-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>Fake Data, One Level</a></span></li><li><span><a href=\"#Models\" data-toc-modified-id=\"Models-2\"><span class=\"toc-item-num\">2&nbsp;&nbsp;</span>Models</a></span><ul class=\"toc-item\"><li><span><a href=\"#Model-1,-Coords\" data-toc-modified-id=\"Model-1,-Coords-2.1\"><span class=\"toc-item-num\">2.1&nbsp;&nbsp;</span>Model 1, Coords</a></span></li><li><span><a href=\"#Model-2:-Design-Matrix\" data-toc-modified-id=\"Model-2:-Design-Matrix-2.2\"><span class=\"toc-item-num\">2.2&nbsp;&nbsp;</span>Model 2: Design Matrix</a></span></li><li><span><a href=\"#Test-Data\" data-toc-modified-id=\"Test-Data-2.3\"><span class=\"toc-item-num\">2.3&nbsp;&nbsp;</span>Test Data</a></span><ul class=\"toc-item\"><li><span><a href=\"#set_data()-api\" data-toc-modified-id=\"set_data()-api-2.3.1\"><span class=\"toc-item-num\">2.3.1&nbsp;&nbsp;</span>set_data() api</a></span></li></ul></li><li><span><a href=\"#DIY-approach\" data-toc-modified-id=\"DIY-approach-2.4\"><span class=\"toc-item-num\">2.4&nbsp;&nbsp;</span>DIY approach</a></span></li></ul></li></ul></div>"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "import numpy as np\nimport pymc as pm\nimport arviz as az\nimport pandas as pd\nfrom scipy import stats\n\nimport matplotlib.pyplot as plt",
      "execution_count": 1,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "rng = np.random.default_rng(69420)",
      "execution_count": 2,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "# Fake Data, One Level"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "n_sessions = 5\nN = 10\nsession_idx = np.arange(n_sessions).repeat(N).astype('int')\n\nvelocity_data = rng.normal(loc=0, scale=1, size=session_idx.shape[0])\ndf = pd.DataFrame(np.c_[session_idx, velocity_data], columns=['session', 'velocity'])\n\nlinear_slope = stats.truncnorm(loc=-1, scale=1, a=-np.inf, b=0).rvs(n_sessions)\nquad_slope = stats.norm().rvs(n_sessions)\n\nshared_intercept = rng.normal()\nobs_intercept = rng.normal(loc=shared_intercept, size=n_sessions)\n\nsigma = stats.halfnorm().rvs()\nload_mu = obs_intercept[session_idx] + linear_slope[session_idx] * velocity_data +  quad_slope[session_idx] * velocity_data ** 2\ntrue_load = stats.norm(loc=load_mu, scale=sigma).rvs()\n\ndf['load'] = true_load",
      "execution_count": 3,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df_test = df.loc[(df.index % 8 == 0) | (df.index % 9 == 0) & (df.index != 0)]\ndf_train = df.loc[~df.index.isin(df_test.index)]\n\nsession_idx_train = df_train.session.values.astype(int)",
      "execution_count": 4,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "# Models"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "## Model 1, Coords"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "coords = {'session': pd.factorize(session_idx_train)[-1],\n          'observation':df_train.index.values}\n\nwith pm.Model(coords=coords) as model:\n    velocity_data = pm.Data('X_data', df_train.velocity.values, mutable=True)\n    load_data = pm.Data('load', df_train.load.values, mutable=True)\n    \n    intercept_global = pm.Normal(name = 'intercept_global')\n    slope_global = pm.Normal(name='slope_global')\n    curve_global = pm.Normal(name='curve_global')\n    \n    session_intercept_sigma = pm.HalfNormal(name = 'session_intercept_sigma')\n    session_intercept_offset = pm.Normal(name='sigma_intercept_offset', dims=['session'])\n    intercept_session = pm.Deterministic('intercept_session', intercept_global + session_intercept_sigma * session_intercept_offset, dims=['session'])\n    \n    session_slope_sigma = pm.HalfNormal(name = 'session_slope_sigma')\n    session_slope_offset = pm.Normal(name='sigma_slope_offset', dims=['session'])\n    slope_session = pm.Deterministic('slope_session', slope_global + session_slope_sigma * session_slope_offset, dims=['session'])\n\n    session_curve_sigma = pm.HalfNormal(name = 'session_curve_sigma')\n    session_curve_offset = pm.Normal(name='sigma_curve_offset', dims=['session'])\n    curve_session = pm.Deterministic('curve_session', curve_global + session_curve_sigma * session_curve_offset, dims=['session'])\n    \n    intercept = intercept_session[session_idx_train]\n    slope = slope_session[session_idx_train]\n    curve = curve_session[session_idx_train]\n    \n    load_mu = pm.Deterministic(name = 'load_mu',\n                               var = intercept - slope * velocity_data - curve*velocity_data**2,\n                               dims = 'observation'\n                               )\n    \n    error = pm.HalfNormal('error')\n    # Likelihood\n    load_likelihood = pm.Normal(name = 'load_estimate',\n                                mu = load_mu,\n                                sigma = error,\n                                observed = load_data,\n                                dims = 'observation'\n                                )\n    \n    idata = pm.sample(init='jitter+adapt_diag_grad', target_accept=0.9)",
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": "Auto-assigning NUTS sampler...\nInitializing NUTS using jitter+adapt_diag_grad...\nMultiprocess sampling (4 chains in 4 jobs)\nNUTS: [intercept_global, slope_global, curve_global, session_intercept_sigma, sigma_intercept_offset, session_slope_sigma, sigma_slope_offset, session_curve_sigma, sigma_curve_offset, error]\n",
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<IPython.core.display.HTML object>",
            "text/html": "\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:not([value]), progress:not([value])::-webkit-progress-bar {\n        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n    }\n    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n        background: #F44336;\n    }\n</style>\n"
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        {
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          "data": {
            "text/plain": "<IPython.core.display.HTML object>",
            "text/html": "\n    <div>\n      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n      100.00% [8000/8000 00:33&lt;00:00 Sampling 4 chains, 0 divergences]\n    </div>\n    "
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "text": "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 52 seconds.\n",
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "summary_coords = az.summary(idata, var_names = ['~load_mu'])\nsummary_coords",
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 6,
          "data": {
            "text/plain": "                            mean     sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd  \\\nintercept_global           0.184  0.424  -0.660    0.956      0.010    0.007   \nslope_global               1.170  0.340   0.482    1.752      0.008    0.005   \ncurve_global              -0.213  0.370  -0.875    0.520      0.010    0.007   \nsigma_intercept_offset[0]  1.693  0.651   0.548    2.946      0.013    0.009   \nsigma_intercept_offset[1] -0.413  0.540  -1.378    0.660      0.011    0.008   \nsigma_intercept_offset[2] -0.363  0.539  -1.435    0.591      0.011    0.008   \nsigma_intercept_offset[3] -0.553  0.560  -1.592    0.488      0.011    0.008   \nsigma_intercept_offset[4] -0.226  0.530  -1.221    0.728      0.011    0.008   \nsigma_slope_offset[0]     -0.196  0.885  -1.867    1.462      0.013    0.014   \nsigma_slope_offset[1]      0.198  0.748  -1.299    1.525      0.013    0.012   \nsigma_slope_offset[2]     -0.562  0.806  -2.016    1.016      0.014    0.012   \nsigma_slope_offset[3]      0.792  0.747  -0.660    2.193      0.015    0.011   \nsigma_slope_offset[4]      0.292  0.865  -1.441    1.854      0.013    0.014   \nsigma_curve_offset[0]     -0.206  0.598  -1.332    0.948      0.013    0.009   \nsigma_curve_offset[1]      1.021  0.673  -0.202    2.284      0.018    0.013   \nsigma_curve_offset[2]     -0.775  0.643  -1.952    0.458      0.013    0.009   \nsigma_curve_offset[3]     -0.679  0.594  -1.782    0.418      0.014    0.010   \nsigma_curve_offset[4]      0.449  0.891  -1.200    2.110      0.012    0.012   \nsession_intercept_sigma    0.982  0.361   0.396    1.644      0.008    0.005   \nsession_slope_sigma        0.513  0.345   0.000    1.094      0.008    0.006   \nsession_curve_sigma        0.714  0.334   0.219    1.302      0.008    0.006   \nerror                      0.731  0.106   0.550    0.936      0.002    0.001   \nintercept_session[0]       1.720  0.369   1.046    2.423      0.006    0.004   \nintercept_session[1]      -0.178  0.311  -0.752    0.407      0.004    0.004   \nintercept_session[2]      -0.136  0.351  -0.807    0.507      0.006    0.004   \nintercept_session[3]      -0.306  0.350  -0.927    0.374      0.005    0.004   \nintercept_session[4]      -0.007  0.316  -0.619    0.561      0.005    0.004   \nslope_session[0]           1.056  0.539  -0.052    1.987      0.010    0.007   \nslope_session[1]           1.306  0.266   0.829    1.826      0.004    0.003   \nslope_session[2]           0.900  0.401   0.177    1.614      0.008    0.006   \nslope_session[3]           1.588  0.264   1.121    2.094      0.005    0.003   \nslope_session[4]           1.367  0.422   0.521    2.157      0.006    0.004   \ncurve_session[0]          -0.356  0.300  -0.930    0.204      0.005    0.004   \ncurve_session[1]           0.400  0.142   0.122    0.658      0.002    0.001   \ncurve_session[2]          -0.729  0.360  -1.383   -0.041      0.007    0.005   \ncurve_session[3]          -0.639  0.196  -1.001   -0.271      0.003    0.002   \ncurve_session[4]           0.118  0.675  -1.150    1.393      0.011    0.009   \n\n                           ess_bulk  ess_tail  r_hat  \nintercept_global             1976.0    2334.0    1.0  \nslope_global                 2183.0    1959.0    1.0  \ncurve_global                 1369.0    1594.0    1.0  \nsigma_intercept_offset[0]    2635.0    2899.0    1.0  \nsigma_intercept_offset[1]    2488.0    2776.0    1.0  \nsigma_intercept_offset[2]    2441.0    2784.0    1.0  \nsigma_intercept_offset[3]    2424.0    2293.0    1.0  \nsigma_intercept_offset[4]    2434.0    3017.0    1.0  \nsigma_slope_offset[0]        4450.0    2991.0    1.0  \nsigma_slope_offset[1]        3244.0    3040.0    1.0  \nsigma_slope_offset[2]        3502.0    2276.0    1.0  \nsigma_slope_offset[3]        2538.0    2193.0    1.0  \nsigma_slope_offset[4]        4734.0    2556.0    1.0  \nsigma_curve_offset[0]        2195.0    2512.0    1.0  \nsigma_curve_offset[1]        1331.0    1680.0    1.0  \nsigma_curve_offset[2]        2586.0    2676.0    1.0  \nsigma_curve_offset[3]        1797.0    2200.0    1.0  \nsigma_curve_offset[4]        5690.0    3296.0    1.0  \nsession_intercept_sigma      2151.0    2458.0    1.0  \nsession_slope_sigma          1344.0    1062.0    1.0  \nsession_curve_sigma          1706.0    2280.0    1.0  \nerror                        3081.0    2560.0    1.0  \nintercept_session[0]         3633.0    2972.0    1.0  \nintercept_session[1]         5315.0    3458.0    1.0  \nintercept_session[2]         3898.0    3400.0    1.0  \nintercept_session[3]         4353.0    3193.0    1.0  \nintercept_session[4]         4779.0    3859.0    1.0  \nslope_session[0]             2903.0    2941.0    1.0  \nslope_session[1]             5014.0    3512.0    1.0  \nslope_session[2]             2495.0    3187.0    1.0  \nslope_session[3]             2969.0    2881.0    1.0  \nslope_session[4]             4880.0    3709.0    1.0  \ncurve_session[0]             3062.0    3089.0    1.0  \ncurve_session[1]             5106.0    3464.0    1.0  \ncurve_session[2]             2910.0    2792.0    1.0  \ncurve_session[3]             3723.0    2891.0    1.0  \ncurve_session[4]             3719.0    3374.0    1.0  ",
            "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_bulk</th>\n      <th>ess_tail</th>\n      <th>r_hat</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>intercept_global</th>\n      <td>0.184</td>\n      <td>0.424</td>\n      <td>-0.660</td>\n      <td>0.956</td>\n      <td>0.010</td>\n      <td>0.007</td>\n      <td>1976.0</td>\n      <td>2334.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>slope_global</th>\n      <td>1.170</td>\n      <td>0.340</td>\n      <td>0.482</td>\n      <td>1.752</td>\n      <td>0.008</td>\n      <td>0.005</td>\n      <td>2183.0</td>\n      <td>1959.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>curve_global</th>\n      <td>-0.213</td>\n      <td>0.370</td>\n      <td>-0.875</td>\n      <td>0.520</td>\n      <td>0.010</td>\n      <td>0.007</td>\n      <td>1369.0</td>\n      <td>1594.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>sigma_intercept_offset[0]</th>\n      <td>1.693</td>\n      <td>0.651</td>\n      <td>0.548</td>\n      <td>2.946</td>\n      <td>0.013</td>\n      <td>0.009</td>\n      <td>2635.0</td>\n      <td>2899.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>sigma_intercept_offset[1]</th>\n      <td>-0.413</td>\n      <td>0.540</td>\n      <td>-1.378</td>\n      <td>0.660</td>\n      <td>0.011</td>\n      <td>0.008</td>\n      <td>2488.0</td>\n      <td>2776.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>sigma_intercept_offset[2]</th>\n      <td>-0.363</td>\n      <td>0.539</td>\n      <td>-1.435</td>\n      <td>0.591</td>\n      <td>0.011</td>\n      <td>0.008</td>\n      <td>2441.0</td>\n      <td>2784.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>sigma_intercept_offset[3]</th>\n      <td>-0.553</td>\n      <td>0.560</td>\n      <td>-1.592</td>\n      <td>0.488</td>\n      <td>0.011</td>\n      <td>0.008</td>\n      <td>2424.0</td>\n      <td>2293.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>sigma_intercept_offset[4]</th>\n      <td>-0.226</td>\n      <td>0.530</td>\n      <td>-1.221</td>\n      <td>0.728</td>\n      <td>0.011</td>\n      <td>0.008</td>\n      <td>2434.0</td>\n      <td>3017.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>sigma_slope_offset[0]</th>\n      <td>-0.196</td>\n      <td>0.885</td>\n      <td>-1.867</td>\n      <td>1.462</td>\n      <td>0.013</td>\n      <td>0.014</td>\n      <td>4450.0</td>\n      <td>2991.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>sigma_slope_offset[1]</th>\n      <td>0.198</td>\n      <td>0.748</td>\n      <td>-1.299</td>\n      <td>1.525</td>\n      <td>0.013</td>\n      <td>0.012</td>\n      <td>3244.0</td>\n      <td>3040.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>sigma_slope_offset[2]</th>\n      <td>-0.562</td>\n      <td>0.806</td>\n      <td>-2.016</td>\n      <td>1.016</td>\n      <td>0.014</td>\n      <td>0.012</td>\n      <td>3502.0</td>\n      <td>2276.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>sigma_slope_offset[3]</th>\n      <td>0.792</td>\n      <td>0.747</td>\n      <td>-0.660</td>\n      <td>2.193</td>\n      <td>0.015</td>\n      <td>0.011</td>\n      <td>2538.0</td>\n      <td>2193.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>sigma_slope_offset[4]</th>\n      <td>0.292</td>\n      <td>0.865</td>\n      <td>-1.441</td>\n      <td>1.854</td>\n      <td>0.013</td>\n      <td>0.014</td>\n      <td>4734.0</td>\n      <td>2556.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>sigma_curve_offset[0]</th>\n      <td>-0.206</td>\n      <td>0.598</td>\n      <td>-1.332</td>\n      <td>0.948</td>\n      <td>0.013</td>\n      <td>0.009</td>\n      <td>2195.0</td>\n      <td>2512.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>sigma_curve_offset[1]</th>\n      <td>1.021</td>\n      <td>0.673</td>\n      <td>-0.202</td>\n      <td>2.284</td>\n      <td>0.018</td>\n      <td>0.013</td>\n      <td>1331.0</td>\n      <td>1680.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>sigma_curve_offset[2]</th>\n      <td>-0.775</td>\n      <td>0.643</td>\n      <td>-1.952</td>\n      <td>0.458</td>\n      <td>0.013</td>\n      <td>0.009</td>\n      <td>2586.0</td>\n      <td>2676.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>sigma_curve_offset[3]</th>\n      <td>-0.679</td>\n      <td>0.594</td>\n      <td>-1.782</td>\n      <td>0.418</td>\n      <td>0.014</td>\n      <td>0.010</td>\n      <td>1797.0</td>\n      <td>2200.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>sigma_curve_offset[4]</th>\n      <td>0.449</td>\n      <td>0.891</td>\n      <td>-1.200</td>\n      <td>2.110</td>\n      <td>0.012</td>\n      <td>0.012</td>\n      <td>5690.0</td>\n      <td>3296.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>session_intercept_sigma</th>\n      <td>0.982</td>\n      <td>0.361</td>\n      <td>0.396</td>\n      <td>1.644</td>\n      <td>0.008</td>\n      <td>0.005</td>\n      <td>2151.0</td>\n      <td>2458.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>session_slope_sigma</th>\n      <td>0.513</td>\n      <td>0.345</td>\n      <td>0.000</td>\n      <td>1.094</td>\n      <td>0.008</td>\n      <td>0.006</td>\n      <td>1344.0</td>\n      <td>1062.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>session_curve_sigma</th>\n      <td>0.714</td>\n      <td>0.334</td>\n      <td>0.219</td>\n      <td>1.302</td>\n      <td>0.008</td>\n      <td>0.006</td>\n      <td>1706.0</td>\n      <td>2280.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>error</th>\n      <td>0.731</td>\n      <td>0.106</td>\n      <td>0.550</td>\n      <td>0.936</td>\n      <td>0.002</td>\n      <td>0.001</td>\n      <td>3081.0</td>\n      <td>2560.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>intercept_session[0]</th>\n      <td>1.720</td>\n      <td>0.369</td>\n      <td>1.046</td>\n      <td>2.423</td>\n      <td>0.006</td>\n      <td>0.004</td>\n      <td>3633.0</td>\n      <td>2972.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>intercept_session[1]</th>\n      <td>-0.178</td>\n      <td>0.311</td>\n      <td>-0.752</td>\n      <td>0.407</td>\n      <td>0.004</td>\n      <td>0.004</td>\n      <td>5315.0</td>\n      <td>3458.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>intercept_session[2]</th>\n      <td>-0.136</td>\n      <td>0.351</td>\n      <td>-0.807</td>\n      <td>0.507</td>\n      <td>0.006</td>\n      <td>0.004</td>\n      <td>3898.0</td>\n      <td>3400.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>intercept_session[3]</th>\n      <td>-0.306</td>\n      <td>0.350</td>\n      <td>-0.927</td>\n      <td>0.374</td>\n      <td>0.005</td>\n      <td>0.004</td>\n      <td>4353.0</td>\n      <td>3193.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>intercept_session[4]</th>\n      <td>-0.007</td>\n      <td>0.316</td>\n      <td>-0.619</td>\n      <td>0.561</td>\n      <td>0.005</td>\n      <td>0.004</td>\n      <td>4779.0</td>\n      <td>3859.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>slope_session[0]</th>\n      <td>1.056</td>\n      <td>0.539</td>\n      <td>-0.052</td>\n      <td>1.987</td>\n      <td>0.010</td>\n      <td>0.007</td>\n      <td>2903.0</td>\n      <td>2941.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>slope_session[1]</th>\n      <td>1.306</td>\n      <td>0.266</td>\n      <td>0.829</td>\n      <td>1.826</td>\n      <td>0.004</td>\n      <td>0.003</td>\n      <td>5014.0</td>\n      <td>3512.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>slope_session[2]</th>\n      <td>0.900</td>\n      <td>0.401</td>\n      <td>0.177</td>\n      <td>1.614</td>\n      <td>0.008</td>\n      <td>0.006</td>\n      <td>2495.0</td>\n      <td>3187.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>slope_session[3]</th>\n      <td>1.588</td>\n      <td>0.264</td>\n      <td>1.121</td>\n      <td>2.094</td>\n      <td>0.005</td>\n      <td>0.003</td>\n      <td>2969.0</td>\n      <td>2881.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>slope_session[4]</th>\n      <td>1.367</td>\n      <td>0.422</td>\n      <td>0.521</td>\n      <td>2.157</td>\n      <td>0.006</td>\n      <td>0.004</td>\n      <td>4880.0</td>\n      <td>3709.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>curve_session[0]</th>\n      <td>-0.356</td>\n      <td>0.300</td>\n      <td>-0.930</td>\n      <td>0.204</td>\n      <td>0.005</td>\n      <td>0.004</td>\n      <td>3062.0</td>\n      <td>3089.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>curve_session[1]</th>\n      <td>0.400</td>\n      <td>0.142</td>\n      <td>0.122</td>\n      <td>0.658</td>\n      <td>0.002</td>\n      <td>0.001</td>\n      <td>5106.0</td>\n      <td>3464.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>curve_session[2]</th>\n      <td>-0.729</td>\n      <td>0.360</td>\n      <td>-1.383</td>\n      <td>-0.041</td>\n      <td>0.007</td>\n      <td>0.005</td>\n      <td>2910.0</td>\n      <td>2792.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>curve_session[3]</th>\n      <td>-0.639</td>\n      <td>0.196</td>\n      <td>-1.001</td>\n      <td>-0.271</td>\n      <td>0.003</td>\n      <td>0.002</td>\n      <td>3723.0</td>\n      <td>2891.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>curve_session[4]</th>\n      <td>0.118</td>\n      <td>0.675</td>\n      <td>-1.150</td>\n      <td>1.393</td>\n      <td>0.011</td>\n      <td>0.009</td>\n      <td>3719.0</td>\n      <td>3374.0</td>\n      <td>1.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "axes = az.plot_posterior(idata, var_names=['intercept_session'])\nfor axis, true_value in zip(axes.ravel(), obs_intercept):\n    axis.axvline(true_value, ls='--', color='tab:red')\nplt.show()",
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 2208x1104 with 6 Axes>",
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          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "## Model 2: Design Matrix"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "print(df.sample(5).to_string())",
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": "    session  velocity      load\n23      2.0  1.761553  0.092207\n43      4.0 -0.017370 -1.072027\n48      4.0 -1.104879  3.552773\n4       0.0  2.084761  1.519246\n19      1.0  1.168008 -1.991443\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "X = pd.get_dummies(df_train['session'].apply(int), prefix='session')\nX_trend = (X * df_train.velocity.values[:, None]).rename(columns=lambda x: x.replace('session', 'trend_session'))\nX_curve = (X * df_train.velocity.values[:, None] ** 2).rename(columns=lambda x: x.replace('session', 'curve_session'))\n\nfrom functools import reduce\nX = reduce(lambda left, right: left.join(right), [X, X_trend, X_curve])",
      "execution_count": 9,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "print(X.head(10).to_string())",
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": "    session_0  session_1  session_2  session_3  session_4  trend_session_0  trend_session_1  trend_session_2  trend_session_3  trend_session_4  curve_session_0  curve_session_1  curve_session_2  curve_session_3  curve_session_4\n1           1          0          0          0          0         0.310387         0.000000              0.0              0.0              0.0         0.096340         0.000000              0.0              0.0              0.0\n2           1          0          0          0          0         0.867316         0.000000              0.0              0.0              0.0         0.752237         0.000000              0.0              0.0              0.0\n3           1          0          0          0          0         1.530482         0.000000              0.0              0.0              0.0         2.342377         0.000000              0.0              0.0              0.0\n4           1          0          0          0          0         2.084761         0.000000              0.0              0.0              0.0         4.346229         0.000000              0.0              0.0              0.0\n5           1          0          0          0          0         0.099846         0.000000              0.0              0.0              0.0         0.009969         0.000000              0.0              0.0              0.0\n6           1          0          0          0          0        -0.126080        -0.000000             -0.0             -0.0             -0.0         0.015896         0.000000              0.0              0.0              0.0\n7           1          0          0          0          0        -0.008299        -0.000000             -0.0             -0.0             -0.0         0.000069         0.000000              0.0              0.0              0.0\n10          0          1          0          0          0        -0.000000        -0.361010             -0.0             -0.0             -0.0         0.000000         0.130328              0.0              0.0              0.0\n11          0          1          0          0          0        -0.000000        -0.623473             -0.0             -0.0             -0.0         0.000000         0.388719              0.0              0.0              0.0\n12          0          1          0          0          0         0.000000         0.494264              0.0              0.0              0.0         0.000000         0.244297              0.0              0.0              0.0\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "coords = {'session': pd.factorize(session_idx)[-1]}\n\nwith pm.Model(coords=coords) as model2:\n    X_data = pm.Data('X_data', X.values, mutable=True)\n    load_data = pm.Data('load', df_train.load.values, mutable=True)\n    \n    intercept_global = pm.Normal(name = 'intercept_global')\n    slope_global = pm.Normal(name='slope_global')\n    curve_global = pm.Normal(name='curve_global')\n    \n    session_intercept_sigma = pm.HalfNormal(name = 'session_intercept_sigma')\n    session_intercept_offset = pm.Normal(name='sigma_intercept_offset', dims=['session'])\n    intercept_session = pm.Deterministic('intercept_session', intercept_global + session_intercept_sigma * session_intercept_offset, dims=['session'])\n    \n    session_slope_sigma = pm.HalfNormal(name = 'session_slope_sigma')\n    session_slope_offset = pm.Normal(name='sigma_slope_offset', dims=['session'])\n    slope_session = pm.Deterministic('slope_session', slope_global + session_slope_sigma * session_slope_offset, dims=['session'])\n\n    session_curve_sigma = pm.HalfNormal(name = 'session_curve_sigma')\n    session_curve_offset = pm.Normal(name='sigma_curve_offset', dims=['session'])\n    curve_session = pm.Deterministic('curve_session', curve_global + session_curve_sigma * session_curve_offset, dims=['session'])\n    \n    beta = pm.Deterministic('beta', pm.math.concatenate([intercept_session, slope_session, curve_session]))\n    \n    load_mu = pm.Deterministic(name = 'load_mu',\n                               var = X_data @ beta)\n    \n    error = pm.HalfNormal('error')\n    # Likelihood\n    load_likelihood = pm.Normal(name = 'load_estimate',\n                                mu = load_mu,\n                                sigma = error,\n                                observed = load_data)\n    \n    idata2 = pm.sample(init='jitter+adapt_diag_grad', target_accept=0.95)",
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": "Auto-assigning NUTS sampler...\nInitializing NUTS using jitter+adapt_diag_grad...\nMultiprocess sampling (4 chains in 4 jobs)\nNUTS: [intercept_global, slope_global, curve_global, session_intercept_sigma, sigma_intercept_offset, session_slope_sigma, sigma_slope_offset, session_curve_sigma, sigma_curve_offset, error]\n",
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<IPython.core.display.HTML object>",
            "text/html": "\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:not([value]), progress:not([value])::-webkit-progress-bar {\n        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n    }\n    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n        background: #F44336;\n    }\n</style>\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<IPython.core.display.HTML object>",
            "text/html": "\n    <div>\n      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n      100.00% [8000/8000 00:35&lt;00:00 Sampling 4 chains, 0 divergences]\n    </div>\n    "
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "text": "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 55 seconds.\n",
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "axes = az.plot_posterior(idata2, var_names=['intercept_session'])\nfor axis, true_value in zip(axes.ravel(), obs_intercept):\n    axis.axvline(true_value, ls='--', color='tab:red')\nplt.show()",
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 2208x1104 with 6 Axes>",
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "summary_design = az.summary(idata2, var_names=['~load_mu'])\nsummary_design",
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 13,
          "data": {
            "text/plain": "                            mean     sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd  \\\nintercept_global           0.185  0.445  -0.638    1.044      0.010    0.008   \nslope_global              -1.165  0.342  -1.737   -0.449      0.008    0.006   \ncurve_global               0.215  0.370  -0.481    0.907      0.011    0.008   \nsigma_intercept_offset[0]  1.664  0.676   0.493    2.956      0.014    0.010   \nsigma_intercept_offset[1] -0.397  0.526  -1.459    0.525      0.011    0.008   \nsigma_intercept_offset[2] -0.355  0.543  -1.296    0.721      0.011    0.008   \nsigma_intercept_offset[3] -0.539  0.549  -1.632    0.446      0.011    0.008   \nsigma_intercept_offset[4] -0.205  0.528  -1.228    0.806      0.011    0.008   \nsigma_slope_offset[0]      0.178  0.881  -1.345    1.931      0.014    0.013   \nsigma_slope_offset[1]     -0.203  0.751  -1.626    1.195      0.012    0.011   \nsigma_slope_offset[2]      0.557  0.803  -0.922    2.062      0.014    0.011   \nsigma_slope_offset[3]     -0.793  0.740  -2.222    0.543      0.014    0.010   \nsigma_slope_offset[4]     -0.308  0.851  -1.938    1.318      0.011    0.014   \nsigma_curve_offset[0]      0.203  0.602  -0.908    1.368      0.014    0.010   \nsigma_curve_offset[1]     -0.998  0.654  -2.305    0.135      0.018    0.013   \nsigma_curve_offset[2]      0.761  0.654  -0.479    2.004      0.015    0.011   \nsigma_curve_offset[3]      0.670  0.597  -0.371    1.859      0.015    0.010   \nsigma_curve_offset[4]     -0.446  0.861  -2.055    1.215      0.012    0.013   \nsession_intercept_sigma    1.012  0.371   0.431    1.728      0.008    0.006   \nsession_slope_sigma        0.516  0.356   0.000    1.126      0.008    0.006   \nsession_curve_sigma        0.719  0.313   0.237    1.298      0.008    0.006   \nerror                      0.730  0.106   0.542    0.923      0.002    0.001   \nintercept_session[0]       1.725  0.368   1.003    2.386      0.006    0.004   \nintercept_session[1]      -0.181  0.305  -0.745    0.415      0.005    0.004   \nintercept_session[2]      -0.146  0.356  -0.758    0.537      0.006    0.005   \nintercept_session[3]      -0.316  0.351  -0.993    0.310      0.005    0.004   \nintercept_session[4]      -0.003  0.316  -0.587    0.583      0.005    0.004   \nslope_session[0]          -1.061  0.534  -1.855    0.149      0.010    0.007   \nslope_session[1]          -1.298  0.276  -1.805   -0.749      0.004    0.003   \nslope_session[2]          -0.901  0.404  -1.621   -0.161      0.009    0.006   \nslope_session[3]          -1.587  0.261  -2.082   -1.107      0.005    0.003   \nslope_session[4]          -1.367  0.423  -2.223   -0.594      0.006    0.004   \ncurve_session[0]           0.358  0.300  -0.242    0.879      0.006    0.004   \ncurve_session[1]          -0.398  0.143  -0.680   -0.139      0.002    0.002   \ncurve_session[2]           0.732  0.361   0.080    1.412      0.007    0.005   \ncurve_session[3]           0.641  0.196   0.242    0.976      0.003    0.002   \ncurve_session[4]          -0.123  0.676  -1.472    1.076      0.012    0.010   \nbeta[0]                    1.725  0.368   1.003    2.386      0.006    0.004   \nbeta[1]                   -0.181  0.305  -0.745    0.415      0.005    0.004   \nbeta[2]                   -0.146  0.356  -0.758    0.537      0.006    0.005   \nbeta[3]                   -0.316  0.351  -0.993    0.310      0.005    0.004   \nbeta[4]                   -0.003  0.316  -0.587    0.583      0.005    0.004   \nbeta[5]                   -1.061  0.534  -1.855    0.149      0.010    0.007   \nbeta[6]                   -1.298  0.276  -1.805   -0.749      0.004    0.003   \nbeta[7]                   -0.901  0.404  -1.621   -0.161      0.009    0.006   \nbeta[8]                   -1.587  0.261  -2.082   -1.107      0.005    0.003   \nbeta[9]                   -1.367  0.423  -2.223   -0.594      0.006    0.004   \nbeta[10]                   0.358  0.300  -0.242    0.879      0.006    0.004   \nbeta[11]                  -0.398  0.143  -0.680   -0.139      0.002    0.002   \nbeta[12]                   0.732  0.361   0.080    1.412      0.007    0.005   \nbeta[13]                   0.641  0.196   0.242    0.976      0.003    0.002   \nbeta[14]                  -0.123  0.676  -1.472    1.076      0.012    0.010   \n\n                           ess_bulk  ess_tail  r_hat  \nintercept_global             1835.0    2221.0    1.0  \nslope_global                 1950.0    2105.0    1.0  \ncurve_global                 1232.0    1486.0    1.0  \nsigma_intercept_offset[0]    2229.0    2356.0    1.0  \nsigma_intercept_offset[1]    2291.0    2981.0    1.0  \nsigma_intercept_offset[2]    2236.0    2810.0    1.0  \nsigma_intercept_offset[3]    2370.0    2388.0    1.0  \nsigma_intercept_offset[4]    2497.0    2557.0    1.0  \nsigma_slope_offset[0]        4078.0    2610.0    1.0  \nsigma_slope_offset[1]        3745.0    2684.0    1.0  \nsigma_slope_offset[2]        3228.0    2469.0    1.0  \nsigma_slope_offset[3]        2746.0    2250.0    1.0  \nsigma_slope_offset[4]        5462.0    3094.0    1.0  \nsigma_curve_offset[0]        1856.0    2503.0    1.0  \nsigma_curve_offset[1]        1347.0    1730.0    1.0  \nsigma_curve_offset[2]        1839.0    2096.0    1.0  \nsigma_curve_offset[3]        1596.0    1937.0    1.0  \nsigma_curve_offset[4]        5399.0    2670.0    1.0  \nsession_intercept_sigma      2028.0    2486.0    1.0  \nsession_slope_sigma          1418.0    1394.0    1.0  \nsession_curve_sigma          1612.0    1930.0    1.0  \nerror                        3085.0    2982.0    1.0  \nintercept_session[0]         4223.0    2980.0    1.0  \nintercept_session[1]         4567.0    3614.0    1.0  \nintercept_session[2]         3090.0    3219.0    1.0  \nintercept_session[3]         4294.0    3332.0    1.0  \nintercept_session[4]         4735.0    3160.0    1.0  \nslope_session[0]             2747.0    3264.0    1.0  \nslope_session[1]             4621.0    3075.0    1.0  \nslope_session[2]             2076.0    3048.0    1.0  \nslope_session[3]             3070.0    2905.0    1.0  \nslope_session[4]             5056.0    3686.0    1.0  \ncurve_session[0]             3006.0    2864.0    1.0  \ncurve_session[1]             4384.0    3345.0    1.0  \ncurve_session[2]             2471.0    2877.0    1.0  \ncurve_session[3]             3830.0    3340.0    1.0  \ncurve_session[4]             3349.0    2728.0    1.0  \nbeta[0]                      4223.0    2980.0    1.0  \nbeta[1]                      4567.0    3614.0    1.0  \nbeta[2]                      3090.0    3219.0    1.0  \nbeta[3]                      4294.0    3332.0    1.0  \nbeta[4]                      4735.0    3160.0    1.0  \nbeta[5]                      2747.0    3264.0    1.0  \nbeta[6]                      4621.0    3075.0    1.0  \nbeta[7]                      2076.0    3048.0    1.0  \nbeta[8]                      3070.0    2905.0    1.0  \nbeta[9]                      5056.0    3686.0    1.0  \nbeta[10]                     3006.0    2864.0    1.0  \nbeta[11]                     4384.0    3345.0    1.0  \nbeta[12]                     2471.0    2877.0    1.0  \nbeta[13]                     3830.0    3340.0    1.0  \nbeta[14]                     3349.0    2728.0    1.0  ",
            "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_bulk</th>\n      <th>ess_tail</th>\n      <th>r_hat</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>intercept_global</th>\n      <td>0.185</td>\n      <td>0.445</td>\n      <td>-0.638</td>\n      <td>1.044</td>\n      <td>0.010</td>\n      <td>0.008</td>\n      <td>1835.0</td>\n      <td>2221.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>slope_global</th>\n      <td>-1.165</td>\n      <td>0.342</td>\n      <td>-1.737</td>\n      <td>-0.449</td>\n      <td>0.008</td>\n      <td>0.006</td>\n      <td>1950.0</td>\n      <td>2105.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>curve_global</th>\n      <td>0.215</td>\n      <td>0.370</td>\n      <td>-0.481</td>\n      <td>0.907</td>\n      <td>0.011</td>\n      <td>0.008</td>\n      <td>1232.0</td>\n      <td>1486.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>sigma_intercept_offset[0]</th>\n      <td>1.664</td>\n      <td>0.676</td>\n      <td>0.493</td>\n      <td>2.956</td>\n      <td>0.014</td>\n      <td>0.010</td>\n      <td>2229.0</td>\n      <td>2356.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>sigma_intercept_offset[1]</th>\n      <td>-0.397</td>\n      <td>0.526</td>\n      <td>-1.459</td>\n      <td>0.525</td>\n      <td>0.011</td>\n      <td>0.008</td>\n      <td>2291.0</td>\n      <td>2981.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>sigma_intercept_offset[2]</th>\n      <td>-0.355</td>\n      <td>0.543</td>\n      <td>-1.296</td>\n      <td>0.721</td>\n      <td>0.011</td>\n      <td>0.008</td>\n      <td>2236.0</td>\n      <td>2810.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>sigma_intercept_offset[3]</th>\n      <td>-0.539</td>\n      <td>0.549</td>\n      <td>-1.632</td>\n      <td>0.446</td>\n      <td>0.011</td>\n      <td>0.008</td>\n      <td>2370.0</td>\n      <td>2388.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>sigma_intercept_offset[4]</th>\n      <td>-0.205</td>\n      <td>0.528</td>\n      <td>-1.228</td>\n      <td>0.806</td>\n      <td>0.011</td>\n      <td>0.008</td>\n      <td>2497.0</td>\n      <td>2557.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>sigma_slope_offset[0]</th>\n      <td>0.178</td>\n      <td>0.881</td>\n      <td>-1.345</td>\n      <td>1.931</td>\n      <td>0.014</td>\n      <td>0.013</td>\n      <td>4078.0</td>\n      <td>2610.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>sigma_slope_offset[1]</th>\n      <td>-0.203</td>\n      <td>0.751</td>\n      <td>-1.626</td>\n      <td>1.195</td>\n      <td>0.012</td>\n      <td>0.011</td>\n      <td>3745.0</td>\n      <td>2684.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>sigma_slope_offset[2]</th>\n      <td>0.557</td>\n      <td>0.803</td>\n      <td>-0.922</td>\n      <td>2.062</td>\n      <td>0.014</td>\n      <td>0.011</td>\n      <td>3228.0</td>\n      <td>2469.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>sigma_slope_offset[3]</th>\n      <td>-0.793</td>\n      <td>0.740</td>\n      <td>-2.222</td>\n      <td>0.543</td>\n      <td>0.014</td>\n      <td>0.010</td>\n      <td>2746.0</td>\n      <td>2250.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>sigma_slope_offset[4]</th>\n      <td>-0.308</td>\n      <td>0.851</td>\n      <td>-1.938</td>\n      <td>1.318</td>\n      <td>0.011</td>\n      <td>0.014</td>\n      <td>5462.0</td>\n      <td>3094.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>sigma_curve_offset[0]</th>\n      <td>0.203</td>\n      <td>0.602</td>\n      <td>-0.908</td>\n      <td>1.368</td>\n      <td>0.014</td>\n      <td>0.010</td>\n      <td>1856.0</td>\n      <td>2503.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>sigma_curve_offset[1]</th>\n      <td>-0.998</td>\n      <td>0.654</td>\n      <td>-2.305</td>\n      <td>0.135</td>\n      <td>0.018</td>\n      <td>0.013</td>\n      <td>1347.0</td>\n      <td>1730.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>sigma_curve_offset[2]</th>\n      <td>0.761</td>\n      <td>0.654</td>\n      <td>-0.479</td>\n      <td>2.004</td>\n      <td>0.015</td>\n      <td>0.011</td>\n      <td>1839.0</td>\n      <td>2096.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>sigma_curve_offset[3]</th>\n      <td>0.670</td>\n      <td>0.597</td>\n      <td>-0.371</td>\n      <td>1.859</td>\n      <td>0.015</td>\n      <td>0.010</td>\n      <td>1596.0</td>\n      <td>1937.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>sigma_curve_offset[4]</th>\n      <td>-0.446</td>\n      <td>0.861</td>\n      <td>-2.055</td>\n      <td>1.215</td>\n      <td>0.012</td>\n      <td>0.013</td>\n      <td>5399.0</td>\n      <td>2670.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>session_intercept_sigma</th>\n      <td>1.012</td>\n      <td>0.371</td>\n      <td>0.431</td>\n      <td>1.728</td>\n      <td>0.008</td>\n      <td>0.006</td>\n      <td>2028.0</td>\n      <td>2486.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>session_slope_sigma</th>\n      <td>0.516</td>\n      <td>0.356</td>\n      <td>0.000</td>\n      <td>1.126</td>\n      <td>0.008</td>\n      <td>0.006</td>\n      <td>1418.0</td>\n      <td>1394.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>session_curve_sigma</th>\n      <td>0.719</td>\n      <td>0.313</td>\n      <td>0.237</td>\n      <td>1.298</td>\n      <td>0.008</td>\n      <td>0.006</td>\n      <td>1612.0</td>\n      <td>1930.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>error</th>\n      <td>0.730</td>\n      <td>0.106</td>\n      <td>0.542</td>\n      <td>0.923</td>\n      <td>0.002</td>\n      <td>0.001</td>\n      <td>3085.0</td>\n      <td>2982.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>intercept_session[0]</th>\n      <td>1.725</td>\n      <td>0.368</td>\n      <td>1.003</td>\n      <td>2.386</td>\n      <td>0.006</td>\n      <td>0.004</td>\n      <td>4223.0</td>\n      <td>2980.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>intercept_session[1]</th>\n      <td>-0.181</td>\n      <td>0.305</td>\n      <td>-0.745</td>\n      <td>0.415</td>\n      <td>0.005</td>\n      <td>0.004</td>\n      <td>4567.0</td>\n      <td>3614.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>intercept_session[2]</th>\n      <td>-0.146</td>\n      <td>0.356</td>\n      <td>-0.758</td>\n      <td>0.537</td>\n      <td>0.006</td>\n      <td>0.005</td>\n      <td>3090.0</td>\n      <td>3219.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>intercept_session[3]</th>\n      <td>-0.316</td>\n      <td>0.351</td>\n      <td>-0.993</td>\n      <td>0.310</td>\n      <td>0.005</td>\n      <td>0.004</td>\n      <td>4294.0</td>\n      <td>3332.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>intercept_session[4]</th>\n      <td>-0.003</td>\n      <td>0.316</td>\n      <td>-0.587</td>\n      <td>0.583</td>\n      <td>0.005</td>\n      <td>0.004</td>\n      <td>4735.0</td>\n      <td>3160.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>slope_session[0]</th>\n      <td>-1.061</td>\n      <td>0.534</td>\n      <td>-1.855</td>\n      <td>0.149</td>\n      <td>0.010</td>\n      <td>0.007</td>\n      <td>2747.0</td>\n      <td>3264.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>slope_session[1]</th>\n      <td>-1.298</td>\n      <td>0.276</td>\n      <td>-1.805</td>\n      <td>-0.749</td>\n      <td>0.004</td>\n      <td>0.003</td>\n      <td>4621.0</td>\n      <td>3075.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>slope_session[2]</th>\n      <td>-0.901</td>\n      <td>0.404</td>\n      <td>-1.621</td>\n      <td>-0.161</td>\n      <td>0.009</td>\n      <td>0.006</td>\n      <td>2076.0</td>\n      <td>3048.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>slope_session[3]</th>\n      <td>-1.587</td>\n      <td>0.261</td>\n      <td>-2.082</td>\n      <td>-1.107</td>\n      <td>0.005</td>\n      <td>0.003</td>\n      <td>3070.0</td>\n      <td>2905.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>slope_session[4]</th>\n      <td>-1.367</td>\n      <td>0.423</td>\n      <td>-2.223</td>\n      <td>-0.594</td>\n      <td>0.006</td>\n      <td>0.004</td>\n      <td>5056.0</td>\n      <td>3686.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>curve_session[0]</th>\n      <td>0.358</td>\n      <td>0.300</td>\n      <td>-0.242</td>\n      <td>0.879</td>\n      <td>0.006</td>\n      <td>0.004</td>\n      <td>3006.0</td>\n      <td>2864.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>curve_session[1]</th>\n      <td>-0.398</td>\n      <td>0.143</td>\n      <td>-0.680</td>\n      <td>-0.139</td>\n      <td>0.002</td>\n      <td>0.002</td>\n      <td>4384.0</td>\n      <td>3345.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>curve_session[2]</th>\n      <td>0.732</td>\n      <td>0.361</td>\n      <td>0.080</td>\n      <td>1.412</td>\n      <td>0.007</td>\n      <td>0.005</td>\n      <td>2471.0</td>\n      <td>2877.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>curve_session[3]</th>\n      <td>0.641</td>\n      <td>0.196</td>\n      <td>0.242</td>\n      <td>0.976</td>\n      <td>0.003</td>\n      <td>0.002</td>\n      <td>3830.0</td>\n      <td>3340.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>curve_session[4]</th>\n      <td>-0.123</td>\n      <td>0.676</td>\n      <td>-1.472</td>\n      <td>1.076</td>\n      <td>0.012</td>\n      <td>0.010</td>\n      <td>3349.0</td>\n      <td>2728.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>beta[0]</th>\n      <td>1.725</td>\n      <td>0.368</td>\n      <td>1.003</td>\n      <td>2.386</td>\n      <td>0.006</td>\n      <td>0.004</td>\n      <td>4223.0</td>\n      <td>2980.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>beta[1]</th>\n      <td>-0.181</td>\n      <td>0.305</td>\n      <td>-0.745</td>\n      <td>0.415</td>\n      <td>0.005</td>\n      <td>0.004</td>\n      <td>4567.0</td>\n      <td>3614.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>beta[2]</th>\n      <td>-0.146</td>\n      <td>0.356</td>\n      <td>-0.758</td>\n      <td>0.537</td>\n      <td>0.006</td>\n      <td>0.005</td>\n      <td>3090.0</td>\n      <td>3219.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>beta[3]</th>\n      <td>-0.316</td>\n      <td>0.351</td>\n      <td>-0.993</td>\n      <td>0.310</td>\n      <td>0.005</td>\n      <td>0.004</td>\n      <td>4294.0</td>\n      <td>3332.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>beta[4]</th>\n      <td>-0.003</td>\n      <td>0.316</td>\n      <td>-0.587</td>\n      <td>0.583</td>\n      <td>0.005</td>\n      <td>0.004</td>\n      <td>4735.0</td>\n      <td>3160.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>beta[5]</th>\n      <td>-1.061</td>\n      <td>0.534</td>\n      <td>-1.855</td>\n      <td>0.149</td>\n      <td>0.010</td>\n      <td>0.007</td>\n      <td>2747.0</td>\n      <td>3264.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>beta[6]</th>\n      <td>-1.298</td>\n      <td>0.276</td>\n      <td>-1.805</td>\n      <td>-0.749</td>\n      <td>0.004</td>\n      <td>0.003</td>\n      <td>4621.0</td>\n      <td>3075.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>beta[7]</th>\n      <td>-0.901</td>\n      <td>0.404</td>\n      <td>-1.621</td>\n      <td>-0.161</td>\n      <td>0.009</td>\n      <td>0.006</td>\n      <td>2076.0</td>\n      <td>3048.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>beta[8]</th>\n      <td>-1.587</td>\n      <td>0.261</td>\n      <td>-2.082</td>\n      <td>-1.107</td>\n      <td>0.005</td>\n      <td>0.003</td>\n      <td>3070.0</td>\n      <td>2905.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>beta[9]</th>\n      <td>-1.367</td>\n      <td>0.423</td>\n      <td>-2.223</td>\n      <td>-0.594</td>\n      <td>0.006</td>\n      <td>0.004</td>\n      <td>5056.0</td>\n      <td>3686.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>beta[10]</th>\n      <td>0.358</td>\n      <td>0.300</td>\n      <td>-0.242</td>\n      <td>0.879</td>\n      <td>0.006</td>\n      <td>0.004</td>\n      <td>3006.0</td>\n      <td>2864.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>beta[11]</th>\n      <td>-0.398</td>\n      <td>0.143</td>\n      <td>-0.680</td>\n      <td>-0.139</td>\n      <td>0.002</td>\n      <td>0.002</td>\n      <td>4384.0</td>\n      <td>3345.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>beta[12]</th>\n      <td>0.732</td>\n      <td>0.361</td>\n      <td>0.080</td>\n      <td>1.412</td>\n      <td>0.007</td>\n      <td>0.005</td>\n      <td>2471.0</td>\n      <td>2877.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>beta[13]</th>\n      <td>0.641</td>\n      <td>0.196</td>\n      <td>0.242</td>\n      <td>0.976</td>\n      <td>0.003</td>\n      <td>0.002</td>\n      <td>3830.0</td>\n      <td>3340.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>beta[14]</th>\n      <td>-0.123</td>\n      <td>0.676</td>\n      <td>-1.472</td>\n      <td>1.076</td>\n      <td>0.012</td>\n      <td>0.010</td>\n      <td>3349.0</td>\n      <td>2728.0</td>\n      <td>1.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "summary_coords['mean'].to_frame().rename(columns={'mean':'mean_coords'}).join(summary_design['mean'].to_frame().rename({'mean':'mean_design'}))",
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 14,
          "data": {
            "text/plain": "                           mean_coords   mean\nintercept_global                 0.184  0.185\nslope_global                     1.170 -1.165\ncurve_global                    -0.213  0.215\nsigma_intercept_offset[0]        1.693  1.664\nsigma_intercept_offset[1]       -0.413 -0.397\nsigma_intercept_offset[2]       -0.363 -0.355\nsigma_intercept_offset[3]       -0.553 -0.539\nsigma_intercept_offset[4]       -0.226 -0.205\nsigma_slope_offset[0]           -0.196  0.178\nsigma_slope_offset[1]            0.198 -0.203\nsigma_slope_offset[2]           -0.562  0.557\nsigma_slope_offset[3]            0.792 -0.793\nsigma_slope_offset[4]            0.292 -0.308\nsigma_curve_offset[0]           -0.206  0.203\nsigma_curve_offset[1]            1.021 -0.998\nsigma_curve_offset[2]           -0.775  0.761\nsigma_curve_offset[3]           -0.679  0.670\nsigma_curve_offset[4]            0.449 -0.446\nsession_intercept_sigma          0.982  1.012\nsession_slope_sigma              0.513  0.516\nsession_curve_sigma              0.714  0.719\nerror                            0.731  0.730\nintercept_session[0]             1.720  1.725\nintercept_session[1]            -0.178 -0.181\nintercept_session[2]            -0.136 -0.146\nintercept_session[3]            -0.306 -0.316\nintercept_session[4]            -0.007 -0.003\nslope_session[0]                 1.056 -1.061\nslope_session[1]                 1.306 -1.298\nslope_session[2]                 0.900 -0.901\nslope_session[3]                 1.588 -1.587\nslope_session[4]                 1.367 -1.367\ncurve_session[0]                -0.356  0.358\ncurve_session[1]                 0.400 -0.398\ncurve_session[2]                -0.729  0.732\ncurve_session[3]                -0.639  0.641\ncurve_session[4]                 0.118 -0.123",
            "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_coords</th>\n      <th>mean</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>intercept_global</th>\n      <td>0.184</td>\n      <td>0.185</td>\n    </tr>\n    <tr>\n      <th>slope_global</th>\n      <td>1.170</td>\n      <td>-1.165</td>\n    </tr>\n    <tr>\n      <th>curve_global</th>\n      <td>-0.213</td>\n      <td>0.215</td>\n    </tr>\n    <tr>\n      <th>sigma_intercept_offset[0]</th>\n      <td>1.693</td>\n      <td>1.664</td>\n    </tr>\n    <tr>\n      <th>sigma_intercept_offset[1]</th>\n      <td>-0.413</td>\n      <td>-0.397</td>\n    </tr>\n    <tr>\n      <th>sigma_intercept_offset[2]</th>\n      <td>-0.363</td>\n      <td>-0.355</td>\n    </tr>\n    <tr>\n      <th>sigma_intercept_offset[3]</th>\n      <td>-0.553</td>\n      <td>-0.539</td>\n    </tr>\n    <tr>\n      <th>sigma_intercept_offset[4]</th>\n      <td>-0.226</td>\n      <td>-0.205</td>\n    </tr>\n    <tr>\n      <th>sigma_slope_offset[0]</th>\n      <td>-0.196</td>\n      <td>0.178</td>\n    </tr>\n    <tr>\n      <th>sigma_slope_offset[1]</th>\n      <td>0.198</td>\n      <td>-0.203</td>\n    </tr>\n    <tr>\n      <th>sigma_slope_offset[2]</th>\n      <td>-0.562</td>\n      <td>0.557</td>\n    </tr>\n    <tr>\n      <th>sigma_slope_offset[3]</th>\n      <td>0.792</td>\n      <td>-0.793</td>\n    </tr>\n    <tr>\n      <th>sigma_slope_offset[4]</th>\n      <td>0.292</td>\n      <td>-0.308</td>\n    </tr>\n    <tr>\n      <th>sigma_curve_offset[0]</th>\n      <td>-0.206</td>\n      <td>0.203</td>\n    </tr>\n    <tr>\n      <th>sigma_curve_offset[1]</th>\n      <td>1.021</td>\n      <td>-0.998</td>\n    </tr>\n    <tr>\n      <th>sigma_curve_offset[2]</th>\n      <td>-0.775</td>\n      <td>0.761</td>\n    </tr>\n    <tr>\n      <th>sigma_curve_offset[3]</th>\n      <td>-0.679</td>\n      <td>0.670</td>\n    </tr>\n    <tr>\n      <th>sigma_curve_offset[4]</th>\n      <td>0.449</td>\n      <td>-0.446</td>\n    </tr>\n    <tr>\n      <th>session_intercept_sigma</th>\n      <td>0.982</td>\n      <td>1.012</td>\n    </tr>\n    <tr>\n      <th>session_slope_sigma</th>\n      <td>0.513</td>\n      <td>0.516</td>\n    </tr>\n    <tr>\n      <th>session_curve_sigma</th>\n      <td>0.714</td>\n      <td>0.719</td>\n    </tr>\n    <tr>\n      <th>error</th>\n      <td>0.731</td>\n      <td>0.730</td>\n    </tr>\n    <tr>\n      <th>intercept_session[0]</th>\n      <td>1.720</td>\n      <td>1.725</td>\n    </tr>\n    <tr>\n      <th>intercept_session[1]</th>\n      <td>-0.178</td>\n      <td>-0.181</td>\n    </tr>\n    <tr>\n      <th>intercept_session[2]</th>\n      <td>-0.136</td>\n      <td>-0.146</td>\n    </tr>\n    <tr>\n      <th>intercept_session[3]</th>\n      <td>-0.306</td>\n      <td>-0.316</td>\n    </tr>\n    <tr>\n      <th>intercept_session[4]</th>\n      <td>-0.007</td>\n      <td>-0.003</td>\n    </tr>\n    <tr>\n      <th>slope_session[0]</th>\n      <td>1.056</td>\n      <td>-1.061</td>\n    </tr>\n    <tr>\n      <th>slope_session[1]</th>\n      <td>1.306</td>\n      <td>-1.298</td>\n    </tr>\n    <tr>\n      <th>slope_session[2]</th>\n      <td>0.900</td>\n      <td>-0.901</td>\n    </tr>\n    <tr>\n      <th>slope_session[3]</th>\n      <td>1.588</td>\n      <td>-1.587</td>\n    </tr>\n    <tr>\n      <th>slope_session[4]</th>\n      <td>1.367</td>\n      <td>-1.367</td>\n    </tr>\n    <tr>\n      <th>curve_session[0]</th>\n      <td>-0.356</td>\n      <td>0.358</td>\n    </tr>\n    <tr>\n      <th>curve_session[1]</th>\n      <td>0.400</td>\n      <td>-0.398</td>\n    </tr>\n    <tr>\n      <th>curve_session[2]</th>\n      <td>-0.729</td>\n      <td>0.732</td>\n    </tr>\n    <tr>\n      <th>curve_session[3]</th>\n      <td>-0.639</td>\n      <td>0.641</td>\n    </tr>\n    <tr>\n      <th>curve_session[4]</th>\n      <td>0.118</td>\n      <td>-0.123</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "with model2:\n    ppc = pm.sample_posterior_predictive(idata2)\npost = az.extract_dataset(ppc, 'posterior_predictive')",
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<IPython.core.display.HTML object>",
            "text/html": "\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:not([value]), progress:not([value])::-webkit-progress-bar {\n        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n    }\n    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n        background: #F44336;\n    }\n</style>\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<IPython.core.display.HTML object>",
            "text/html": "\n    <div>\n      <progress value='4000' class='' max='4000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n      100.00% [4000/4000 00:00&lt;00:00]\n    </div>\n    "
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "fig, ax = plt.subplots(2, 3, figsize=(14, 6), dpi=100)\nfor i, axis in enumerate(fig.axes):\n    if i >= n_sessions:\n        axis.set_visible(False)\n        continue\n        \n    session_mask = df_train.index[df_train.session == i]\n    session_imask = np.arange(df_train.shape[0])[df_train.session == i]\n    \n    data = df_train.loc[session_mask]\n    data_hat = post.load_estimate.sel(load_estimate_dim_0 = session_imask)\n    \n    q05, q50, q95 = data_hat.quantile([0.05, 0.5, 0.95], dim=['sample'])\n    \n    axis.scatter(data.velocity, data.load)\n    x_vals = data.velocity.values\n    sorted_idx = data.velocity.argsort().values\n    \n    axis.plot(x_vals[sorted_idx], q50.values[sorted_idx])\n    xmin, xmax = axis.get_xlim()\n    axis.fill_between(x_vals[sorted_idx], y1=q05[sorted_idx], y2=q95[sorted_idx], color='tab:orange', alpha=0.25)\n    axis.set(xlabel='velocity', ylabel='load', title=f'Session {i}')\n    \nfig.tight_layout()\nplt.show()",
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 1400x600 with 6 Axes>",
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "## Test Data"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "X_test = pd.get_dummies(df_test['session'].apply(int), prefix='session')\nX_test_trend = (X_test * df_test.velocity.values[:, None]).rename(columns=lambda x: x.replace('session', 'trend_session'))\nX_test_curve = (X_test * df_test.velocity.values[:, None] ** 2).rename(columns=lambda x: x.replace('session', 'curve_session'))\n\nfrom functools import reduce\nX_test = reduce(lambda left, right: left.join(right), [X_test, X_test_trend, X_test_curve])",
      "execution_count": 17,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "### set_data() api"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# new data\nwith model2:\n    model2.set_data('X_data', X_test)\n    model2.set_data('load', df_test.load.values)\n    \n    ppc_test = pm.sample_posterior_predictive(idata2)\n    post_test = az.extract_dataset(ppc_test, 'posterior_predictive')",
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<IPython.core.display.HTML object>",
            "text/html": "\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:not([value]), progress:not([value])::-webkit-progress-bar {\n        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n    }\n    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n        background: #F44336;\n    }\n</style>\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<IPython.core.display.HTML object>",
            "text/html": "\n    <div>\n      <progress value='4000' class='' max='4000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n      100.00% [4000/4000 00:00&lt;00:00]\n    </div>\n    "
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "fig, ax = plt.subplots(2, 3, figsize=(14, 6), dpi=100)\nfor i, axis in enumerate(fig.axes):\n    if i >= n_sessions:\n        axis.set_visible(False)\n        continue\n        \n    session_mask = df_test.index[df_test.session == i]\n    session_imask = np.arange(df_test.shape[0])[(df_test.session == i).values]\n    \n    data = df_test.loc[session_mask]\n    data_hat = post_test.load_estimate.sel(load_estimate_dim_0 = session_imask)\n    \n    q05, q50, q95 = data_hat.quantile([0.05, 0.5, 0.95], dim=['sample'])\n    \n    axis.scatter(data.velocity, data.load)\n    x_vals = data.velocity.values\n    sorted_idx = data.velocity.argsort().values\n    \n    axis.plot(x_vals[sorted_idx], q50.values[sorted_idx])\n    xmin, xmax = axis.get_xlim()\n    axis.fill_between(x_vals[sorted_idx], y1=q05[sorted_idx], y2=q95[sorted_idx], color='tab:orange', alpha=0.25)\n    axis.set(xlabel='velocity', ylabel='load', title=f'Session {i}')\n    \nfig.tight_layout()\nplt.show()",
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 1400x600 with 6 Axes>",
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "## DIY approach"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "trace = az.extract_dataset(idata2, 'posterior')\nbeta_posterior = trace.beta.values\nerror_posterior = trace.error.values\nload_mu_hat = X_test @ beta_posterior\nload_hat_test = pd.DataFrame(rng.normal(loc=load_mu_hat, scale=error_posterior),\n                             index=load_mu_hat.index,\n                             columns=load_mu_hat.columns)",
      "execution_count": 20,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "fig, ax = plt.subplots(2, 3, figsize=(14, 6), dpi=100)\nfor i, axis in enumerate(fig.axes):\n    if i >= n_sessions:\n        axis.set_visible(False)\n        continue\n        \n    session_mask = df_test.index[df_test.session == i]\n    \n    data = df_test.loc[session_mask]\n    data_hat = load_hat_test.loc[session_mask]\n    \n    q05, q50, q95 = data_hat.quantile([0.05, 0.5, 0.95], axis=1).values\n    \n    axis.scatter(data.velocity, data.load)\n    x_vals = data.velocity.values\n    sorted_idx = data.velocity.argsort().values\n    \n    axis.plot(x_vals[sorted_idx], q50[sorted_idx])\n    xmin, xmax = axis.get_xlim()\n    axis.fill_between(x_vals[sorted_idx], y1=q05[sorted_idx], y2=q95[sorted_idx], color='tab:orange', alpha=0.25)\n    axis.set(xlabel='velocity', ylabel='load', title=f'Session {i}')\n    \nfig.tight_layout()\nplt.show()",
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 1400x600 with 6 Axes>",
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3 (ipykernel)",
      "language": "python"
    },
    "toc": {
      "nav_menu": {},
      "number_sections": true,
      "sideBar": true,
      "skip_h1_title": false,
      "base_numbering": 1,
      "title_cell": "Table of Contents",
      "title_sidebar": "Contents",
      "toc_cell": true,
      "toc_position": {},
      "toc_section_display": true,
      "toc_window_display": false
    },
    "language_info": {
      "name": "python",
      "version": "3.10.6",
      "mimetype": "text/x-python",
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "pygments_lexer": "ipython3",
      "nbconvert_exporter": "python",
      "file_extension": ".py"
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