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utkarsh-maheshwari/Regression_Example.ipynb

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Regression_Example.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "certain-press",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"WARNING (theano.tensor.blas): Using NumPy C-API based implementation for BLAS functions.\n"
]
}
],
"source": [
"import pymc3 as pm\n",
"import numpy as np\n",
"import pandas as pd\n",
"import arviz as az\n",
"import matplotlib.pyplot as plt\n",
"import xarray as xr"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "black-stopping",
"metadata": {
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"outputs": [
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" <th></th>\n",
" <th>kid_score</th>\n",
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"text/plain": [
" kid_score mom_hs mom_iq mom_work mom_age\n",
"0 65 1 121.117529 4 27\n",
"1 98 1 89.361882 4 25\n",
"2 85 1 115.443165 4 27\n",
"3 83 1 99.449639 3 25\n",
"4 115 1 92.745710 4 27"
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"execution_count": 2,
"metadata": {},
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"source": [
"data = pd.read_csv(\"kidiq.csv\")\n",
"data.head()"
]
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"cell_type": "code",
"execution_count": 3,
"id": "purple-approval",
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"name": "stderr",
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"/home/utkarsh/.local/lib/python3.8/site-packages/pymc3/sampling.py:465: FutureWarning: In an upcoming release, pm.sample will return an `arviz.InferenceData` object instead of a `MultiTrace` by default. You can pass return_inferencedata=True or return_inferencedata=False to be safe and silence this warning.\n",
" warnings.warn(\n",
"Auto-assigning NUTS sampler...\n",
"Initializing NUTS using jitter+adapt_diag...\n",
"Multiprocess sampling (2 chains in 2 jobs)\n",
"NUTS: [slope, Intercept, sigma]\n"
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"/home/utkarsh/.local/lib/python3.8/site-packages/pymc3/math.py:246: RuntimeWarning: divide by zero encountered in log1p\n",
" return np.where(x < 0.6931471805599453, np.log(-np.expm1(-x)), np.log1p(-np.exp(-x)))\n",
"/home/utkarsh/.local/lib/python3.8/site-packages/pymc3/math.py:246: RuntimeWarning: divide by zero encountered in log1p\n",
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"Sampling 2 chains for 1_000 tune and 1_000 draw iterations (2_000 + 2_000 draws total) took 10 seconds.\n",
"/home/utkarsh/.local/lib/python3.8/site-packages/pymc3/sampling.py:1687: UserWarning: samples parameter is smaller than nchains times ndraws, some draws and/or chains may not be represented in the returned posterior predictive sample\n",
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"with pm.Model() as model:\n",
" mom_iq = pm.Data(\"mom_iq\", data[\"mom_iq\"])\n",
"\n",
" sigma = pm.HalfNormal('sigma', sd=10)\n",
" intercept = pm.Normal('Intercept', 0, sd=10)\n",
" x_coeff = pm.Normal('slope', 0, sd=10)\n",
" \n",
" mean = intercept + x_coeff * mom_iq\n",
" likelihood = pm.Normal('kid_score', mu=mean, \n",
" sd=sigma, observed=data[\"kid_score\"])\n",
" \n",
" trace = pm.sample(1000)\n",
" prior = pm.sample_prior_predictive()\n",
" posterior_predictive = pm.sample_posterior_predictive(trace, samples=1000)"
]
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"cell_type": "code",
"execution_count": 4,
"id": "difficult-criterion",
"metadata": {
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"outputs": [
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"name": "stderr",
"output_type": "stream",
"text": [
"posterior predictive variable kid_score's shape not compatible with number of chains and draws. This can mean that some draws or even whole chains are not represented.\n"
]
}
],
"source": [
"idata = az.from_pymc3(trace = trace, model=model,posterior_predictive=posterior_predictive, prior=prior)"
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"cell_type": "code",
"execution_count": 5,
"id": "presidential-egypt",
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"</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
"Dimensions: (chain: 2, draw: 1000)\n",
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"Data variables:\n",
" Intercept (chain, draw) float64 11.27 14.19 22.88 ... 17.57 13.84 12.32\n",
" slope (chain, draw) float64 0.7523 0.7261 0.6379 ... 0.7163 0.7321\n",
" sigma (chain, draw) float64 17.52 19.43 17.26 ... 18.33 18.33 18.4\n",
"Attributes:\n",
" created_at: 2021-06-15T15:15:09.217079\n",
" arviz_version: 0.11.0\n",
" inference_library: pymc3\n",
" inference_library_version: 3.11.0\n",
" sampling_time: 10.380492687225342\n",
" tuning_steps: 1000</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-f741ad38-d9d2-491b-80c5-1e1fe0ea5a4f' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-f741ad38-d9d2-491b-80c5-1e1fe0ea5a4f' class='xr-section-summary' title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 2</li><li><span class='xr-has-index'>draw</span>: 1000</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-e3ced9f5-6a00-459d-b900-7f015355a7ee' class='xr-section-summary-in' type='checkbox' checked><label for='section-e3ced9f5-6a00-459d-b900-7f015355a7ee' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1</div><input id='attrs-ac4757d6-d7f1-439b-8b55-f3e4ebb080a3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ac4757d6-d7f1-439b-8b55-f3e4ebb080a3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-01b56860-c9df-4d48-85de-d4230cd80324' class='xr-var-data-in' type='checkbox'><label for='data-01b56860-c9df-4d48-85de-d4230cd80324' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 995 996 997 998 999</div><input id='attrs-4a94bad6-fe63-4c52-bfe4-068afc0da0d2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4a94bad6-fe63-4c52-bfe4-068afc0da0d2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-828be16d-f2e2-42f2-98ba-12dc0db503ed' class='xr-var-data-in' type='checkbox'><label for='data-828be16d-f2e2-42f2-98ba-12dc0db503ed' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 0, 1, 2, ..., 997, 998, 999])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f6e2f530-8a68-48ce-93bc-a76524f246dd' class='xr-section-summary-in' type='checkbox' checked><label for='section-f6e2f530-8a68-48ce-93bc-a76524f246dd' class='xr-section-summary' >Data variables: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>Intercept</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>11.27 14.19 22.88 ... 13.84 12.32</div><input id='attrs-fa75781c-fa10-433e-a817-09b3a012bf1a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fa75781c-fa10-433e-a817-09b3a012bf1a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-93500a59-7dc4-46f5-8b3b-ea9251157d44' class='xr-var-data-in' type='checkbox'><label for='data-93500a59-7dc4-46f5-8b3b-ea9251157d44' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[11.26928977, 14.19306244, 22.88274013, ..., 16.32811615,\n",
" 19.26306346, 16.9252134 ],\n",
" [15.14566935, 14.39494712, 22.68566092, ..., 17.57217911,\n",
" 13.84332848, 12.32040358]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>slope</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.7523 0.7261 ... 0.7163 0.7321</div><input id='attrs-a24109d5-5e8f-499c-b729-52ad7a993a2a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a24109d5-5e8f-499c-b729-52ad7a993a2a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a6682af4-d737-4a9f-b7f3-ea349277049d' class='xr-var-data-in' type='checkbox'><label for='data-a6682af4-d737-4a9f-b7f3-ea349277049d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.75230345, 0.72607822, 0.63792803, ..., 0.70138169, 0.67171236,\n",
" 0.69341899],\n",
" [0.72586782, 0.72148957, 0.63236106, ..., 0.6737719 , 0.71634507,\n",
" 0.73208427]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sigma</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>17.52 19.43 17.26 ... 18.33 18.4</div><input id='attrs-561744bd-1e33-4a5e-ab46-6e1a4a400dd4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-561744bd-1e33-4a5e-ab46-6e1a4a400dd4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-96cf4eca-4c93-404c-ac24-2bdd4030b08f' class='xr-var-data-in' type='checkbox'><label for='data-96cf4eca-4c93-404c-ac24-2bdd4030b08f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[17.51960991, 19.42832645, 17.25911837, ..., 17.89855899,\n",
" 18.44774444, 18.31957596],\n",
" [18.46386703, 18.44884244, 18.00080427, ..., 18.3257646 ,\n",
" 18.32623576, 18.39816879]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b78fe7be-5f3a-472a-a99a-7077c1008afb' class='xr-section-summary-in' type='checkbox' checked><label for='section-b78fe7be-5f3a-472a-a99a-7077c1008afb' class='xr-section-summary' >Attributes: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2021-06-15T15:15:09.217079</dd><dt><span>arviz_version :</span></dt><dd>0.11.0</dd><dt><span>inference_library :</span></dt><dd>pymc3</dd><dt><span>inference_library_version :</span></dt><dd>3.11.0</dd><dt><span>sampling_time :</span></dt><dd>10.380492687225342</dd><dt><span>tuning_steps :</span></dt><dd>1000</dd></dl></div></li></ul></div></div><br></div>\n",
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" <div style=\"padding-left:2rem;\"><div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
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" fill: currentColor;\n",
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"</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
"Dimensions: (chain: 1, draw: 1000, kid_score_dim_0: 434)\n",
"Coordinates:\n",
" * chain (chain) int64 0\n",
" * draw (draw) int64 0 1 2 3 4 5 6 ... 993 994 995 996 997 998 999\n",
" * kid_score_dim_0 (kid_score_dim_0) int64 0 1 2 3 4 5 ... 429 430 431 432 433\n",
"Data variables:\n",
" kid_score (chain, draw, kid_score_dim_0) float64 113.2 76.8 ... 92.6\n",
"Attributes:\n",
" created_at: 2021-06-15T15:15:09.515243\n",
" arviz_version: 0.11.0\n",
" inference_library: pymc3\n",
" inference_library_version: 3.11.0</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-c433b68c-dd58-4b37-ac95-47070fe868a2' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-c433b68c-dd58-4b37-ac95-47070fe868a2' class='xr-section-summary' title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 1</li><li><span class='xr-has-index'>draw</span>: 1000</li><li><span class='xr-has-index'>kid_score_dim_0</span>: 434</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-396667ac-8809-4239-8436-705b3759cfab' class='xr-section-summary-in' type='checkbox' checked><label for='section-396667ac-8809-4239-8436-705b3759cfab' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-ee5451c3-e60b-4446-9aa8-8080fc80f0c1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ee5451c3-e60b-4446-9aa8-8080fc80f0c1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-15ee25ed-4d45-4437-84a9-03e76f088a47' class='xr-var-data-in' type='checkbox'><label for='data-15ee25ed-4d45-4437-84a9-03e76f088a47' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 995 996 997 998 999</div><input id='attrs-991b746a-a38a-47ec-a05e-3f03818a646d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-991b746a-a38a-47ec-a05e-3f03818a646d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-28e12bfc-2949-4721-9bcc-0191b5ad2e6f' class='xr-var-data-in' type='checkbox'><label for='data-28e12bfc-2949-4721-9bcc-0191b5ad2e6f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 0, 1, 2, ..., 997, 998, 999])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>kid_score_dim_0</span></div><div class='xr-var-dims'>(kid_score_dim_0)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 429 430 431 432 433</div><input id='attrs-383e4140-204b-4982-aafd-5a7179d74935' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-383e4140-204b-4982-aafd-5a7179d74935' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9b1fdaad-da0f-4b8c-b8b5-95cfd2c4db97' class='xr-var-data-in' type='checkbox'><label for='data-9b1fdaad-da0f-4b8c-b8b5-95cfd2c4db97' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 0, 1, 2, ..., 431, 432, 433])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f0a7752d-4fdc-4ab3-a613-2b04eb2e0ea8' class='xr-section-summary-in' type='checkbox' checked><label for='section-f0a7752d-4fdc-4ab3-a613-2b04eb2e0ea8' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>kid_score</span></div><div class='xr-var-dims'>(chain, draw, kid_score_dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>113.2 76.8 109.5 ... 113.9 92.6</div><input id='attrs-f0237702-c9df-441f-b8a9-ed1937376c1f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f0237702-c9df-441f-b8a9-ed1937376c1f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e1fe9063-7c54-4ba6-8877-34f41d96673e' class='xr-var-data-in' type='checkbox'><label for='data-e1fe9063-7c54-4ba6-8877-34f41d96673e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[113.24663126, 76.80414696, 109.51962934, ..., 87.62575334,\n",
" 96.59764657, 92.768621 ],\n",
" [125.21659402, 94.99521861, 93.81372475, ..., 53.84769432,\n",
" 89.0298554 , 53.4444988 ],\n",
" [ 96.04332501, 58.05957765, 115.63182686, ..., 83.32805698,\n",
" 73.9404732 , 66.70155317],\n",
" ...,\n",
" [131.58835327, 97.7050399 , 104.56734648, ..., 122.77587061,\n",
" 107.44591701, 104.9870184 ],\n",
" [ 93.87212015, 80.29365064, 74.39006769, ..., 96.76617403,\n",
" 66.78065316, 80.68424414],\n",
" [104.50004484, 95.18145509, 85.79731002, ..., 94.22881714,\n",
" 113.93686372, 92.59526035]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-829be09f-c51b-47c2-a7f3-d3bc8b68cb6e' class='xr-section-summary-in' type='checkbox' checked><label for='section-829be09f-c51b-47c2-a7f3-d3bc8b68cb6e' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2021-06-15T15:15:09.515243</dd><dt><span>arviz_version :</span></dt><dd>0.11.0</dd><dt><span>inference_library :</span></dt><dd>pymc3</dd><dt><span>inference_library_version :</span></dt><dd>3.11.0</dd></dl></div></li></ul></div></div><br></div>\n",
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"</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
"Dimensions: (chain: 2, draw: 1000, kid_score_dim_0: 434)\n",
"Coordinates:\n",
" * chain (chain) int64 0 1\n",
" * draw (draw) int64 0 1 2 3 4 5 6 ... 993 994 995 996 997 998 999\n",
" * kid_score_dim_0 (kid_score_dim_0) int64 0 1 2 3 4 5 ... 429 430 431 432 433\n",
"Data variables:\n",
" kid_score (chain, draw, kid_score_dim_0) float64 -6.059 ... -3.954\n",
"Attributes:\n",
" created_at: 2021-06-15T15:15:09.512123\n",
" arviz_version: 0.11.0\n",
" inference_library: pymc3\n",
" inference_library_version: 3.11.0</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-d46db0a3-55c4-4497-bf4c-ae4b2cc44bf5' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d46db0a3-55c4-4497-bf4c-ae4b2cc44bf5' class='xr-section-summary' title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 2</li><li><span class='xr-has-index'>draw</span>: 1000</li><li><span class='xr-has-index'>kid_score_dim_0</span>: 434</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-c2224acc-b056-4104-b308-ed72be7f6007' class='xr-section-summary-in' type='checkbox' checked><label for='section-c2224acc-b056-4104-b308-ed72be7f6007' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1</div><input id='attrs-fbc69045-63d2-47c6-bb0e-0a56980c51e9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fbc69045-63d2-47c6-bb0e-0a56980c51e9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0e04698a-f028-4f76-a626-eb8dae5764e8' class='xr-var-data-in' type='checkbox'><label for='data-0e04698a-f028-4f76-a626-eb8dae5764e8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 995 996 997 998 999</div><input id='attrs-5ef1b7b7-6f97-456f-95d1-f0775c5b9f2c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5ef1b7b7-6f97-456f-95d1-f0775c5b9f2c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f82f2fd2-434d-44cf-ace2-9ad3333805aa' class='xr-var-data-in' type='checkbox'><label for='data-f82f2fd2-434d-44cf-ace2-9ad3333805aa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 0, 1, 2, ..., 997, 998, 999])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>kid_score_dim_0</span></div><div class='xr-var-dims'>(kid_score_dim_0)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 429 430 431 432 433</div><input id='attrs-8d3fd6da-119d-4d30-b49e-62cd24bd564c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8d3fd6da-119d-4d30-b49e-62cd24bd564c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4f683452-4f8e-4acf-92c3-862e12d926ab' class='xr-var-data-in' type='checkbox'><label for='data-4f683452-4f8e-4acf-92c3-862e12d926ab' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 0, 1, 2, ..., 431, 432, 433])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6c80c2f8-3fda-41b2-b2cc-91029025456e' class='xr-section-summary-in' type='checkbox' checked><label for='section-6c80c2f8-3fda-41b2-b2cc-91029025456e' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>kid_score</span></div><div class='xr-var-dims'>(chain, draw, kid_score_dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-6.059 -4.402 ... -3.865 -3.954</div><input id='attrs-243fa06e-c631-4f2e-bd4a-98c9c6cce2f0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-243fa06e-c631-4f2e-bd4a-98c9c6cce2f0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a4293f46-6e94-4184-b2e8-9f46f526440c' class='xr-var-data-in' type='checkbox'><label for='data-a4293f46-6e94-4184-b2e8-9f46f526440c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[-6.05918586, -4.40190639, -4.06256295, ..., -5.51695725,\n",
" -3.80659546, -3.94254691],\n",
" [-5.71225391, -4.36001023, -4.11001211, ..., -5.33421452,\n",
" -3.90172602, -4.03032851],\n",
" [-5.84080504, -4.31784412, -3.99031681, ..., -5.63939305,\n",
" -3.78588905, -3.97393613],\n",
" ...,\n",
" [-5.85772359, -4.36679937, -4.0397019 , ..., -5.48902717,\n",
" -3.82547148, -3.97025437],\n",
" [-5.69790631, -4.34827788, -4.03871908, ..., -5.43205558,\n",
" -3.85374639, -3.99768813],\n",
" [-5.74814088, -4.37096096, -4.04057735, ..., -5.42024471,\n",
" -3.84971543, -3.98197262]],\n",
"\n",
" [[-5.95939128, -4.30938818, -4.11984559, ..., -5.53032214,\n",
" -3.84428527, -4.02480891],\n",
" [-5.82120043, -4.37162117, -4.07035799, ..., -5.41778295,\n",
" -3.85431308, -3.98777732],\n",
" [-5.6221892 , -4.35504963, -3.98560622, ..., -5.456446 ,\n",
" -3.83486535, -3.97595584],\n",
" ...,\n",
" [-5.56638093, -4.43585065, -3.98687388, ..., -5.30322613,\n",
" -3.86701903, -3.94935038],\n",
" [-5.71462073, -4.43130436, -4.02554837, ..., -5.33234625,\n",
" -3.86120123, -3.95361509],\n",
" [-5.7443515 , -4.43745682, -4.03807169, ..., -5.32170956,\n",
" -3.86483245, -3.95419886]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d42f2f94-8b5c-4964-9eaf-11cbabaf09f6' class='xr-section-summary-in' type='checkbox' checked><label for='section-d42f2f94-8b5c-4964-9eaf-11cbabaf09f6' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2021-06-15T15:15:09.512123</dd><dt><span>arviz_version :</span></dt><dd>0.11.0</dd><dt><span>inference_library :</span></dt><dd>pymc3</dd><dt><span>inference_library_version :</span></dt><dd>3.11.0</dd></dl></div></li></ul></div></div><br></div>\n",
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"</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
"Dimensions: (chain: 2, draw: 1000)\n",
"Coordinates:\n",
" * chain (chain) int64 0 1\n",
" * draw (draw) int64 0 1 2 3 4 5 6 ... 994 995 996 997 998 999\n",
"Data variables:\n",
" depth (chain, draw) int64 1 5 4 3 3 5 3 4 ... 4 4 4 4 4 4 5 3\n",
" step_size_bar (chain, draw) float64 0.1839 0.1839 ... 0.194 0.194\n",
" step_size (chain, draw) float64 0.1491 0.1491 ... 0.1585 0.1585\n",
" tree_size (chain, draw) float64 1.0 31.0 15.0 ... 15.0 23.0 7.0\n",
" mean_tree_accept (chain, draw) float64 1.0 0.9779 ... 0.9607 0.8147\n",
" process_time_diff (chain, draw) float64 0.001811 0.006793 ... 0.003794\n",
" diverging (chain, draw) bool False False False ... False False\n",
" energy (chain, draw) float64 1.889e+03 1.889e+03 ... 1.889e+03\n",
" max_energy_error (chain, draw) float64 -0.6959 0.06348 ... -1.336 2.41\n",
" energy_error (chain, draw) float64 -0.6959 0.01824 ... -0.03808\n",
" perf_counter_start (chain, draw) float64 1.343e+03 1.343e+03 ... 1.347e+03\n",
" lp (chain, draw) float64 -1.888e+03 ... -1.887e+03\n",
" perf_counter_diff (chain, draw) float64 0.001895 0.006843 ... 0.003793\n",
"Attributes:\n",
" created_at: 2021-06-15T15:15:09.225808\n",
" arviz_version: 0.11.0\n",
" inference_library: pymc3\n",
" inference_library_version: 3.11.0\n",
" sampling_time: 10.380492687225342\n",
" tuning_steps: 1000</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-627fcf1a-22a7-4d22-8473-9c416b2bc0a0' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-627fcf1a-22a7-4d22-8473-9c416b2bc0a0' class='xr-section-summary' title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 2</li><li><span class='xr-has-index'>draw</span>: 1000</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-94969e99-e7b7-452a-bb0e-c7b0e0af804c' class='xr-section-summary-in' type='checkbox' checked><label for='section-94969e99-e7b7-452a-bb0e-c7b0e0af804c' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1</div><input id='attrs-0462bf15-a411-479a-9ea1-68e316dd73a1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0462bf15-a411-479a-9ea1-68e316dd73a1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dd9ad094-efee-46e5-8692-6f9c10054981' class='xr-var-data-in' type='checkbox'><label for='data-dd9ad094-efee-46e5-8692-6f9c10054981' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 995 996 997 998 999</div><input id='attrs-4cb8a190-483f-47d0-9e5b-d07c70d2ede1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4cb8a190-483f-47d0-9e5b-d07c70d2ede1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-76737c59-fd1c-4497-a21b-910cc6fb0f8e' class='xr-var-data-in' type='checkbox'><label for='data-76737c59-fd1c-4497-a21b-910cc6fb0f8e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 0, 1, 2, ..., 997, 998, 999])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-added979-a1ad-4d3a-bbb1-6ff3dcbd25a5' class='xr-section-summary-in' type='checkbox' checked><label for='section-added979-a1ad-4d3a-bbb1-6ff3dcbd25a5' class='xr-section-summary' >Data variables: <span>(13)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>depth</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 5 4 3 3 5 3 4 ... 4 4 4 4 4 4 5 3</div><input id='attrs-2feb0687-db40-4c9b-a995-363c5fc9fbed' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2feb0687-db40-4c9b-a995-363c5fc9fbed' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-61126597-541a-4173-9c20-24c749d9ab54' class='xr-var-data-in' type='checkbox'><label for='data-61126597-541a-4173-9c20-24c749d9ab54' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[1, 5, 4, ..., 4, 4, 5],\n",
" [5, 1, 5, ..., 4, 5, 3]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>step_size_bar</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.1839 0.1839 ... 0.194 0.194</div><input id='attrs-8897df20-f9fb-4a0f-be90-aa2242b2beb1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8897df20-f9fb-4a0f-be90-aa2242b2beb1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fb234acb-0efb-42d2-865b-b43a5dfd8dbe' class='xr-var-data-in' type='checkbox'><label for='data-fb234acb-0efb-42d2-865b-b43a5dfd8dbe' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.18388609, 0.18388609, 0.18388609, ..., 0.18388609, 0.18388609,\n",
" 0.18388609],\n",
" [0.19401131, 0.19401131, 0.19401131, ..., 0.19401131, 0.19401131,\n",
" 0.19401131]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>step_size</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.1491 0.1491 ... 0.1585 0.1585</div><input id='attrs-ac7ead3c-2ed8-4d78-912d-3247787a39d7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ac7ead3c-2ed8-4d78-912d-3247787a39d7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f0832b4e-c48f-4166-a4e6-4347a6f178e2' class='xr-var-data-in' type='checkbox'><label for='data-f0832b4e-c48f-4166-a4e6-4347a6f178e2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.14910355, 0.14910355, 0.14910355, ..., 0.14910355, 0.14910355,\n",
" 0.14910355],\n",
" [0.15851516, 0.15851516, 0.15851516, ..., 0.15851516, 0.15851516,\n",
" 0.15851516]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tree_size</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.0 31.0 15.0 7.0 ... 15.0 23.0 7.0</div><input id='attrs-656fc58f-e0bb-4487-93ad-f6558197869e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-656fc58f-e0bb-4487-93ad-f6558197869e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fed67a62-c36e-452c-adcb-c6c8c3559bfb' class='xr-var-data-in' type='checkbox'><label for='data-fed67a62-c36e-452c-adcb-c6c8c3559bfb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 1., 31., 15., ..., 15., 15., 31.],\n",
" [31., 1., 19., ..., 15., 23., 7.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mean_tree_accept</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.0 0.9779 0.9956 ... 0.9607 0.8147</div><input id='attrs-3f4169c4-3a13-4e03-8266-e812c2614b70' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3f4169c4-3a13-4e03-8266-e812c2614b70' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9ca4a112-0991-44ba-ad8f-20526b079ac3' class='xr-var-data-in' type='checkbox'><label for='data-9ca4a112-0991-44ba-ad8f-20526b079ac3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[1. , 0.97793956, 0.99557532, ..., 0.99022166, 0.98615302,\n",
" 0.97244754],\n",
" [0.81317969, 1. , 0.7557581 , ..., 0.70060683, 0.96073667,\n",
" 0.8146903 ]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>process_time_diff</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.001811 0.006793 ... 0.003794</div><input id='attrs-ed63868c-a268-4854-965e-708d54daf232' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ed63868c-a268-4854-965e-708d54daf232' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b7e303a5-910d-4d2c-9db3-3c78e8a27cb1' class='xr-var-data-in' type='checkbox'><label for='data-b7e303a5-910d-4d2c-9db3-3c78e8a27cb1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.00181122, 0.00679293, 0.00432861, ..., 0.00326219, 0.00316276,\n",
" 0.00620602],\n",
" [0.00646669, 0.00053283, 0.00385936, ..., 0.00443767, 0.00812759,\n",
" 0.00379443]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diverging</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>bool</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-57d91d55-5ba4-4a8a-b149-32ffa7366ae1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-57d91d55-5ba4-4a8a-b149-32ffa7366ae1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-256ce7bc-c7cf-48f9-a768-0b833bf8efb5' class='xr-var-data-in' type='checkbox'><label for='data-256ce7bc-c7cf-48f9-a768-0b833bf8efb5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[False, False, False, ..., False, False, False],\n",
" [False, False, False, ..., False, False, False]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>energy</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.889e+03 1.889e+03 ... 1.889e+03</div><input id='attrs-7d4d9c84-5746-4ed2-bed5-63fd543ddf46' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7d4d9c84-5746-4ed2-bed5-63fd543ddf46' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f4f48cfb-bb04-4349-bb12-5f046b6d3baf' class='xr-var-data-in' type='checkbox'><label for='data-f4f48cfb-bb04-4349-bb12-5f046b6d3baf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[1888.51838298, 1889.18607264, 1888.21032796, ..., 1889.57686152,\n",
" 1886.36648754, 1886.13745641],\n",
" [1888.7428628 , 1886.57987942, 1887.68488319, ..., 1888.12341863,\n",
" 1887.75777627, 1888.7385242 ]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>max_energy_error</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.6959 0.06348 ... -1.336 2.41</div><input id='attrs-7a44ad19-b555-468b-a353-de853b63547f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7a44ad19-b555-468b-a353-de853b63547f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4fd6e1e2-da30-4b37-ac2e-015364bef1ff' class='xr-var-data-in' type='checkbox'><label for='data-4fd6e1e2-da30-4b37-ac2e-015364bef1ff' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[-0.69589796, 0.06347923, -0.0316909 , ..., -0.08527398,\n",
" 0.0390462 , 0.06977709],\n",
" [ 0.81942504, -0.5657915 , 0.8498141 , ..., 1.62186679,\n",
" -1.33596151, 2.41030501]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>energy_error</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.6959 0.01824 ... -0.03808</div><input id='attrs-5a27eec2-b20d-43e3-8355-575b36d10fc9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5a27eec2-b20d-43e3-8355-575b36d10fc9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-80964050-ca0a-47a3-a3af-d777e54cc702' class='xr-var-data-in' type='checkbox'><label for='data-80964050-ca0a-47a3-a3af-d777e54cc702' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[-0.69589796, 0.0182368 , -0.02563344, ..., -0.07559656,\n",
" 0.00704768, 0.03341379],\n",
" [ 0.46697027, -0.5657915 , 0.24470348, ..., 1.34059344,\n",
" -0.74014053, -0.03808431]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>perf_counter_start</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.343e+03 1.343e+03 ... 1.347e+03</div><input id='attrs-e259b6c3-fbe7-4652-871e-80f1b7f3b7f9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e259b6c3-fbe7-4652-871e-80f1b7f3b7f9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-89e40a7f-1df8-4e4d-8894-3a5ae6fc3749' class='xr-var-data-in' type='checkbox'><label for='data-89e40a7f-1df8-4e4d-8894-3a5ae6fc3749' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[1342.56629137, 1342.56849609, 1342.57561549, ..., 1347.02053015,\n",
" 1347.02407092, 1347.02749258],\n",
" [1342.37388479, 1342.3806811 , 1342.38146836, ..., 1346.61243997,\n",
" 1346.61721937, 1346.62580751]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lp</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-1.888e+03 ... -1.887e+03</div><input id='attrs-dea60db1-6ed3-4a7d-bd23-c905f65c913c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-dea60db1-6ed3-4a7d-bd23-c905f65c913c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3d69f3f7-9487-455d-a62f-bf72a6a20ee6' class='xr-var-data-in' type='checkbox'><label for='data-3d69f3f7-9487-455d-a62f-bf72a6a20ee6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[-1887.8585014 , -1887.95324136, -1887.31121475, ...,\n",
" -1886.11916557, -1885.91724122, -1885.98831302],\n",
" [-1887.06713029, -1886.29023829, -1886.53931317, ...,\n",
" -1887.70649998, -1887.09041571, -1887.3374102 ]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>perf_counter_diff</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.001895 0.006843 ... 0.003793</div><input id='attrs-06773593-b754-41f5-847e-7eb8a23003f1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-06773593-b754-41f5-847e-7eb8a23003f1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9b9cbda3-b3d1-4e66-b5a1-3f5e69c4863f' class='xr-var-data-in' type='checkbox'><label for='data-9b9cbda3-b3d1-4e66-b5a1-3f5e69c4863f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.0018953 , 0.00684347, 0.0044074 , ..., 0.00326927, 0.00316255,\n",
" 0.00621832],\n",
" [0.0064814 , 0.00053245, 0.00385762, ..., 0.00443657, 0.0081372 ,\n",
" 0.00379315]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e340ba73-e000-4935-bfa1-4a19bdb1c7bf' class='xr-section-summary-in' type='checkbox' checked><label for='section-e340ba73-e000-4935-bfa1-4a19bdb1c7bf' class='xr-section-summary' >Attributes: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2021-06-15T15:15:09.225808</dd><dt><span>arviz_version :</span></dt><dd>0.11.0</dd><dt><span>inference_library :</span></dt><dd>pymc3</dd><dt><span>inference_library_version :</span></dt><dd>3.11.0</dd><dt><span>sampling_time :</span></dt><dd>10.380492687225342</dd><dt><span>tuning_steps :</span></dt><dd>1000</dd></dl></div></li></ul></div></div><br></div>\n",
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"\n",
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"\n",
".xr-var-data > table {\n",
" float: right;\n",
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"\n",
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"\n",
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"\n",
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"\n",
".xr-attrs dt,\n",
".xr-attrs dd {\n",
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"\n",
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"\n",
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"\n",
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"\n",
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"}\n",
"</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
"Dimensions: (chain: 1, draw: 500)\n",
"Coordinates:\n",
" * chain (chain) int64 0\n",
" * draw (draw) int64 0 1 2 3 4 5 6 7 ... 493 494 495 496 497 498 499\n",
"Data variables:\n",
" slope (chain, draw) float64 10.98 9.91 5.374 ... -1.552 10.79 -2.3\n",
" sigma (chain, draw) float64 6.629 2.118 3.224 ... 4.108 14.31 21.71\n",
" Intercept (chain, draw) float64 -6.822 -2.422 -6.68 ... -0.02565 7.577\n",
" sigma_log__ (chain, draw) float64 1.892 0.7504 1.171 ... 1.413 2.661 3.078\n",
"Attributes:\n",
" created_at: 2021-06-15T15:15:09.517550\n",
" arviz_version: 0.11.0\n",
" inference_library: pymc3\n",
" inference_library_version: 3.11.0</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-83300a1b-573d-46c7-a969-2e546e1d4464' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-83300a1b-573d-46c7-a969-2e546e1d4464' class='xr-section-summary' title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 1</li><li><span class='xr-has-index'>draw</span>: 500</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-6b404d53-9d6e-4e50-ad36-09867bd9a512' class='xr-section-summary-in' type='checkbox' checked><label for='section-6b404d53-9d6e-4e50-ad36-09867bd9a512' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-4f2455bd-b433-4c22-96b9-3efb62b3cc7c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4f2455bd-b433-4c22-96b9-3efb62b3cc7c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a040f412-b2cf-46bb-add0-49422d70ed04' class='xr-var-data-in' type='checkbox'><label for='data-a040f412-b2cf-46bb-add0-49422d70ed04' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 495 496 497 498 499</div><input id='attrs-88f7d94b-595b-4fed-b889-8c55a2cf5c09' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-88f7d94b-595b-4fed-b889-8c55a2cf5c09' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-11ed73c7-ecca-4ac5-a34b-f2aa73828d58' class='xr-var-data-in' type='checkbox'><label for='data-11ed73c7-ecca-4ac5-a34b-f2aa73828d58' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 0, 1, 2, ..., 497, 498, 499])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6d0a32eb-d098-42fb-a096-2f7decf26b9c' class='xr-section-summary-in' type='checkbox' checked><label for='section-6d0a32eb-d098-42fb-a096-2f7decf26b9c' class='xr-section-summary' >Data variables: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>slope</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>10.98 9.91 5.374 ... 10.79 -2.3</div><input id='attrs-ce3b5942-6e7b-499b-9a47-dbb749fae046' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ce3b5942-6e7b-499b-9a47-dbb749fae046' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f2722f91-4698-49ec-a8cc-57f6d81df4a3' class='xr-var-data-in' type='checkbox'><label for='data-f2722f91-4698-49ec-a8cc-57f6d81df4a3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 1.09773393e+01, 9.91045435e+00, 5.37377174e+00,\n",
" -1.10296254e+01, 2.08926922e+01, 5.13523349e-02,\n",
" -1.05544535e+01, 4.50223171e+00, 5.28135088e+00,\n",
" -6.38454522e+00, 2.75129640e+00, 1.26986761e+01,\n",
" 4.14260454e+00, 7.01900467e+00, 5.81436487e+00,\n",
" -6.87840829e-01, -1.61193600e+01, -6.82969438e-01,\n",
" -7.50287041e+00, 1.19475156e+01, -1.51545995e+01,\n",
" 5.68252453e+00, -1.93299088e+01, 3.65297576e+00,\n",
" -1.98011480e+00, 7.27173158e+00, -1.06996381e+01,\n",
" -4.16266665e+00, 1.18641170e+01, 8.77433219e+00,\n",
" 1.12776686e+01, 6.31260600e+00, 6.77592284e+00,\n",
" 6.87188281e+00, -1.62864279e+01, 9.26539277e+00,\n",
" -8.89349424e+00, -3.20736641e+00, 8.28211461e+00,\n",
" 9.34319711e+00, -1.25237622e+01, -5.51065550e+00,\n",
" -4.36695253e+00, -1.88986526e+01, -1.86650776e-01,\n",
" 1.92391020e+00, 6.46501584e+00, 1.34299114e+01,\n",
" -1.05397361e+01, -2.69032282e+00, -7.53568363e+00,\n",
" 9.60367386e-01, -3.90073991e+00, -2.73544654e+00,\n",
" 5.12052950e+00, 1.69961594e+01, -1.04782679e+01,\n",
" 7.07231625e-01, 3.00219040e+00, -7.35911028e+00,\n",
"...\n",
" -6.41970282e+00, 5.53631306e+00, -3.36888134e+00,\n",
" -7.26028792e+00, 6.92380665e+00, -3.01373084e+00,\n",
" -1.46112914e+01, 3.88406123e+00, 4.70656107e+00,\n",
" 1.25494464e+01, 4.59053783e-01, -6.00996920e+00,\n",
" -2.71805579e+00, -7.35945451e+00, -1.71445480e+01,\n",
" -8.06266150e+00, -6.16409747e+00, 5.93900976e+00,\n",
" 1.74124158e+00, 7.65685835e+00, 6.61019155e+00,\n",
" 6.49161111e+00, -6.95272891e-01, 1.99461963e+01,\n",
" 2.24782369e+01, 1.83651580e+01, 5.70882471e+00,\n",
" -1.47894626e+01, 1.23068410e+01, 1.35635408e+01,\n",
" 3.49930475e+00, -1.44207623e+01, -2.27709270e+01,\n",
" 2.38181822e+01, -1.35515553e+00, 9.37746695e+00,\n",
" 1.27730806e+01, -5.14147001e+00, 4.19563365e+00,\n",
" -7.67923366e+00, 1.04396288e+01, 9.51242459e+00,\n",
" -5.10296693e+00, 1.52188987e-01, -9.58438024e+00,\n",
" -3.64338428e+00, -1.08275196e+01, -1.35713727e+00,\n",
" -3.22233590e+00, 2.86236109e+00, 5.68932826e+00,\n",
" 1.89241126e+01, -1.17733137e+01, 3.22105018e+00,\n",
" -6.15455179e+00, 2.78223372e-01, -1.55156645e+00,\n",
" 1.07861248e+01, -2.29982884e+00]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sigma</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>6.629 2.118 3.224 ... 14.31 21.71</div><input id='attrs-bd90ccf0-6b67-4024-a07a-2f3e08d34291' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bd90ccf0-6b67-4024-a07a-2f3e08d34291' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-24b155ca-2362-4527-9cc8-e865d12d35cb' class='xr-var-data-in' type='checkbox'><label for='data-24b155ca-2362-4527-9cc8-e865d12d35cb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[6.62932439e+00, 2.11792057e+00, 3.22376389e+00, 1.13842585e+01,\n",
" 1.51477652e+01, 1.86669231e+00, 4.92087090e+00, 6.07055540e+00,\n",
" 6.96940989e+00, 1.62731836e+01, 1.71864179e+01, 3.19464286e+00,\n",
" 4.35896254e+00, 9.47481352e+00, 1.60237975e+00, 3.69904765e+00,\n",
" 1.91301784e+00, 3.53321477e+00, 1.03389377e+01, 3.69732259e-03,\n",
" 1.72201003e+01, 7.11807240e+00, 5.99614479e-01, 9.82695256e+00,\n",
" 2.97461644e+00, 1.00897225e+01, 1.07252201e+01, 1.22709495e+01,\n",
" 1.57869199e+01, 7.39320451e+00, 3.39455486e+00, 1.50309783e+01,\n",
" 4.39489447e-01, 6.18958192e+00, 8.62699617e+00, 1.31659423e+00,\n",
" 9.51653502e+00, 1.41864316e+01, 1.67909305e+01, 8.44903988e-01,\n",
" 1.04291671e+01, 6.90875186e+00, 5.78216213e+00, 5.58840233e+00,\n",
" 1.34154907e+01, 3.72557638e+00, 1.13744301e+01, 1.52745846e+00,\n",
" 2.28422651e+01, 9.62090736e+00, 3.71794304e-01, 1.01113128e+01,\n",
" 1.13697738e+01, 1.08028603e+01, 1.22189327e+01, 6.22119218e+00,\n",
" 1.72479542e+01, 1.08305927e+01, 9.61909709e+00, 1.45956488e+01,\n",
" 8.44847326e+00, 2.44263094e+00, 6.51280030e-01, 3.94995788e+00,\n",
" 3.42543833e+00, 7.16991176e+00, 1.60628315e+01, 1.34074579e-01,\n",
" 1.79041186e+01, 1.33102429e+01, 7.58820206e+00, 5.00438298e-01,\n",
" 1.79363776e+00, 1.95383478e+00, 1.22432260e+01, 5.19374596e+00,\n",
" 1.04683961e+01, 1.42243836e+00, 1.27349976e+01, 1.10743915e+01,\n",
"...\n",
" 5.44814662e+00, 1.76039574e+01, 7.34530312e+00, 4.67364411e+00,\n",
" 1.24600837e+01, 1.33853865e+01, 9.56084960e+00, 1.57370474e+01,\n",
" 1.32421271e-01, 1.64014044e-01, 8.79781177e+00, 4.47260267e+00,\n",
" 1.14058110e+01, 1.73827962e+01, 5.12827936e+00, 1.41445834e+01,\n",
" 2.19974049e+00, 6.66799090e+00, 3.69877926e-01, 3.12457690e+00,\n",
" 1.25665046e+01, 1.46240178e+01, 1.94261230e+01, 6.42617639e+00,\n",
" 1.57990376e+01, 5.63551751e+00, 2.46396564e+00, 1.56850179e+01,\n",
" 1.01691982e+01, 1.07986042e+01, 2.99061615e+00, 6.18926357e+00,\n",
" 5.76849487e+00, 1.43072909e+01, 5.20950771e+00, 3.51852891e+00,\n",
" 2.89994097e+00, 1.68556633e+01, 8.73256197e+00, 2.18017551e+00,\n",
" 4.33853079e+00, 2.99736573e+00, 1.64959691e+01, 4.59340594e+00,\n",
" 1.53903184e+01, 1.43214111e+01, 7.50564059e+00, 8.79316113e+00,\n",
" 1.45692138e+01, 5.14035808e-01, 1.35296163e+01, 6.01628426e+00,\n",
" 1.91312706e+01, 1.66732704e+00, 7.71581737e+00, 8.05640827e+00,\n",
" 7.47740473e+00, 6.56432066e+00, 4.91415997e+00, 1.24426226e+00,\n",
" 1.39686546e+00, 1.03157342e+01, 8.61947830e+00, 8.72611563e+00,\n",
" 9.51778703e+00, 3.08647506e+00, 7.18850539e-01, 1.64081336e+01,\n",
" 1.20946560e+00, 1.70349578e+01, 2.87003160e+00, 1.69757702e+01,\n",
" 3.52207438e+00, 5.60792309e+00, 4.30038636e+00, 1.57063379e+01,\n",
" 1.55654537e+01, 4.10811093e+00, 1.43116651e+01, 2.17100563e+01]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Intercept</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-6.822 -2.422 ... -0.02565 7.577</div><input id='attrs-372fc3fb-9842-47de-886c-2ea709cb3e30' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-372fc3fb-9842-47de-886c-2ea709cb3e30' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-72301db3-826e-48a5-9aaf-491ef4eb58da' class='xr-var-data-in' type='checkbox'><label for='data-72301db3-826e-48a5-9aaf-491ef4eb58da' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[-6.82214233e+00, -2.42246080e+00, -6.68031075e+00,\n",
" -2.28549207e+01, 2.70775740e+00, 9.87238393e+00,\n",
" -8.20977727e+00, -8.00636096e-01, -4.19416784e+00,\n",
" -1.40493564e+01, 1.60076524e+01, 5.44631252e+00,\n",
" -1.69110911e+01, 4.00071264e+00, -6.93326874e+00,\n",
" -2.15622829e+01, -2.00026898e+00, -1.28962251e+01,\n",
" -1.24929051e+01, -8.29435358e+00, -9.69018590e+00,\n",
" -2.80843645e+00, 7.58494033e-01, -8.77049930e+00,\n",
" -4.36177503e+00, -7.12063098e+00, -4.69774949e-01,\n",
" -2.61494770e+01, 5.98497502e-01, -3.25171859e+00,\n",
" -6.28542282e+00, -1.15873064e+00, 2.07927296e+01,\n",
" 6.88736114e+00, -2.50707275e+00, -2.57064463e+00,\n",
" -5.69584947e+00, 4.19879926e+00, 5.95250923e+00,\n",
" 1.01077600e+01, -1.11018706e+00, 1.98185940e+01,\n",
" 1.48503490e+00, -1.01340697e+00, -6.53682724e+00,\n",
" 1.37511034e+01, -6.28017332e+00, -1.29494679e+01,\n",
" 8.73480559e+00, -1.36666050e+01, 1.32398186e+01,\n",
" 1.33300211e+00, 5.69709669e+00, 1.48108535e+01,\n",
" 2.31542541e+00, 3.04421193e+00, -5.02970526e-01,\n",
" 5.99647693e+00, 3.06937232e-01, 1.05504769e+01,\n",
"...\n",
" -1.63509731e+01, 6.04143885e-01, 1.01089485e+01,\n",
" -5.17124678e+00, 4.52026052e-02, 1.00905694e+01,\n",
" -1.33707767e+01, 2.24382044e+00, 3.90271822e-01,\n",
" -7.51538023e+00, 5.80165739e+00, 2.00436507e+00,\n",
" -7.66935316e+00, 9.06492864e+00, -3.82893426e+00,\n",
" 4.72048737e+00, -1.26064102e+01, 7.81159659e+00,\n",
" 1.59947303e+01, -4.21208601e-01, 1.10626795e-01,\n",
" 7.40740245e+00, -9.97548140e+00, -6.18659121e+00,\n",
" -5.17572078e+00, 2.31430919e+00, 8.85829478e+00,\n",
" 9.36014721e+00, -2.04696493e+00, -5.94167925e+00,\n",
" 8.96116567e+00, 1.16037433e+01, -2.04540683e+00,\n",
" -9.89025601e-03, -1.01772828e+01, -9.69102181e+00,\n",
" 5.93564307e+00, 1.85233231e+01, -1.00673443e+01,\n",
" 1.02665379e+01, -2.29041587e+00, 8.69286716e+00,\n",
" 5.27642570e+00, -4.28046233e+00, 5.60465850e+00,\n",
" -2.39734775e+00, 1.28496132e+01, -9.64756099e+00,\n",
" -1.54450293e+01, -3.94892259e+00, -6.58674904e+00,\n",
" -3.39458415e+00, -9.15379330e+00, -7.14643469e+00,\n",
" -2.35416633e+00, -5.98263488e-01, -4.17910045e+00,\n",
" -2.56528265e-02, 7.57717981e+00]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sigma_log__</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.892 0.7504 1.171 ... 2.661 3.078</div><input id='attrs-42473094-7988-464c-9816-ccc651a2f965' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-42473094-7988-464c-9816-ccc651a2f965' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d924ccf9-d844-4f4d-8b4f-3f9eb9c82908' class='xr-var-data-in' type='checkbox'><label for='data-d924ccf9-d844-4f4d-8b4f-3f9eb9c82908' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 1.89150290e+00, 7.50434743e-01, 1.17054959e+00,\n",
" 2.43223157e+00, 2.71785301e+00, 6.24168046e-01,\n",
" 1.59348553e+00, 1.80345010e+00, 1.94153056e+00,\n",
" 2.78951857e+00, 2.84411942e+00, 1.16147530e+00,\n",
" 1.47223408e+00, 2.24863707e+00, 4.71489865e-01,\n",
" 1.30807539e+00, 6.48682017e-01, 1.26220816e+00,\n",
" 2.33591712e+00, -5.60014635e+00, 2.84607732e+00,\n",
" 1.96263696e+00, -5.11468365e-01, 2.28512887e+00,\n",
" 1.09011510e+00, 2.31151733e+00, 2.37259799e+00,\n",
" 2.50723464e+00, 2.75918174e+00, 2.00056127e+00,\n",
" 1.22217264e+00, 2.71011329e+00, -8.22141573e-01,\n",
" 1.82286754e+00, 2.15489638e+00, 2.75048274e-01,\n",
" 2.25303081e+00, 2.65228598e+00, 2.82083889e+00,\n",
" -1.68532282e-01, 2.34460641e+00, 1.93278899e+00,\n",
" 1.75477768e+00, 1.72069344e+00, 2.59641006e+00,\n",
" 1.31522157e+00, 2.43136786e+00, 4.23605219e-01,\n",
" 3.12861255e+00, 2.26393858e+00, -9.89414524e-01,\n",
" 2.31365487e+00, 2.43095841e+00, 2.37981094e+00,\n",
" 2.50298661e+00, 1.82796156e+00, 2.84769354e+00,\n",
" 2.38237479e+00, 2.26375040e+00, 2.68072345e+00,\n",
"...\n",
" 2.68266523e+00, 2.96661871e+00, 1.86037971e+00,\n",
" 2.75994903e+00, 1.72908898e+00, 9.01772099e-01,\n",
" 2.75270598e+00, 2.31936337e+00, 2.37941689e+00,\n",
" 1.09547944e+00, 1.82281611e+00, 1.75241119e+00,\n",
" 2.66076926e+00, 1.65048536e+00, 1.25804298e+00,\n",
" 1.06469038e+00, 2.82468670e+00, 2.16705879e+00,\n",
" 7.79405382e-01, 1.46753576e+00, 1.09773381e+00,\n",
" 2.80311606e+00, 1.52462178e+00, 2.73373864e+00,\n",
" 2.66175570e+00, 2.01565482e+00, 2.17397428e+00,\n",
" 2.67891066e+00, -6.65462351e-01, 2.60488108e+00,\n",
" 1.79446984e+00, 2.95132420e+00, 5.11221770e-01,\n",
" 2.04327243e+00, 2.08646783e+00, 2.01188577e+00,\n",
" 1.88164902e+00, 1.59212083e+00, 2.18542790e-01,\n",
" 3.34230771e-01, 2.33367032e+00, 2.15402456e+00,\n",
" 2.16632033e+00, 2.25316237e+00, 1.12702968e+00,\n",
" -3.30101816e-01, 2.79777716e+00, 1.90178611e-01,\n",
" 2.83526758e+00, 1.05432304e+00, 2.83178704e+00,\n",
" 1.25905013e+00, 1.72418044e+00, 1.45870487e+00,\n",
" 2.75406432e+00, 2.74505395e+00, 1.41296329e+00,\n",
" 2.66107494e+00, 3.07777558e+00]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-21b27ca2-58fe-4906-b913-9b909391b4a3' class='xr-section-summary-in' type='checkbox' checked><label for='section-21b27ca2-58fe-4906-b913-9b909391b4a3' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2021-06-15T15:15:09.517550</dd><dt><span>arviz_version :</span></dt><dd>0.11.0</dd><dt><span>inference_library :</span></dt><dd>pymc3</dd><dt><span>inference_library_version :</span></dt><dd>3.11.0</dd></dl></div></li></ul></div></div><br></div>\n",
" </ul>\n",
" </div>\n",
" </li>\n",
" \n",
" <li class = \"xr-section-item\">\n",
" <input id=\"idata_prior_predictive9a5e3441-e1f4-4030-a46e-4076b59d8319\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
" <label for=\"idata_prior_predictive9a5e3441-e1f4-4030-a46e-4076b59d8319\" class = \"xr-section-summary\">prior_predictive</label>\n",
" <div class=\"xr-section-inline-details\"></div>\n",
" <div class=\"xr-section-details\">\n",
" <ul id=\"xr-dataset-coord-list\" class=\"xr-var-list\">\n",
" <div style=\"padding-left:2rem;\"><div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
"<defs>\n",
"<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
"<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
"<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
"<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
"</symbol>\n",
"<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
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"</defs>\n",
"</svg>\n",
"<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
" *\n",
" */\n",
"\n",
":root {\n",
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" --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
" --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
" --xr-background-color: var(--jp-layout-color0, white);\n",
" --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
" --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
"}\n",
"\n",
"html[theme=dark],\n",
"body.vscode-dark {\n",
" --xr-font-color0: rgba(255, 255, 255, 1);\n",
" --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
" --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
" --xr-border-color: #1F1F1F;\n",
" --xr-disabled-color: #515151;\n",
" --xr-background-color: #111111;\n",
" --xr-background-color-row-even: #111111;\n",
" --xr-background-color-row-odd: #313131;\n",
"}\n",
"\n",
".xr-wrap {\n",
" display: block;\n",
" min-width: 300px;\n",
" max-width: 700px;\n",
"}\n",
"\n",
".xr-text-repr-fallback {\n",
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"</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
"Dimensions: (chain: 1, draw: 500, kid_score_dim_0: 434)\n",
"Coordinates:\n",
" * chain (chain) int64 0\n",
" * draw (draw) int64 0 1 2 3 4 5 6 ... 493 494 495 496 497 498 499\n",
" * kid_score_dim_0 (kid_score_dim_0) int64 0 1 2 3 4 5 ... 429 430 431 432 433\n",
"Data variables:\n",
" kid_score (chain, draw, kid_score_dim_0) float64 1.305e+03 ... -170.1\n",
"Attributes:\n",
" created_at: 2021-06-15T15:15:09.519889\n",
" arviz_version: 0.11.0\n",
" inference_library: pymc3\n",
" inference_library_version: 3.11.0</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-ab20d86e-1984-4dba-a3e6-882ff9987173' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-ab20d86e-1984-4dba-a3e6-882ff9987173' class='xr-section-summary' title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 1</li><li><span class='xr-has-index'>draw</span>: 500</li><li><span class='xr-has-index'>kid_score_dim_0</span>: 434</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-20059354-1766-4c3f-ae78-7b1fdd4ee41d' class='xr-section-summary-in' type='checkbox' checked><label for='section-20059354-1766-4c3f-ae78-7b1fdd4ee41d' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-4965382e-c831-4365-9634-87b4f228be64' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4965382e-c831-4365-9634-87b4f228be64' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-52e71ab6-c706-423d-9dc4-b8d49c27301a' class='xr-var-data-in' type='checkbox'><label for='data-52e71ab6-c706-423d-9dc4-b8d49c27301a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 495 496 497 498 499</div><input id='attrs-c1296053-f355-4b40-8111-9f9e6f16c7bc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c1296053-f355-4b40-8111-9f9e6f16c7bc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fe06039b-25cc-4db3-9163-ef1f8aa8ea6c' class='xr-var-data-in' type='checkbox'><label for='data-fe06039b-25cc-4db3-9163-ef1f8aa8ea6c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 0, 1, 2, ..., 497, 498, 499])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>kid_score_dim_0</span></div><div class='xr-var-dims'>(kid_score_dim_0)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 429 430 431 432 433</div><input id='attrs-9487d521-7c9f-4fc2-97db-8054f7c74b94' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9487d521-7c9f-4fc2-97db-8054f7c74b94' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7f14a8d2-8b5c-48ce-ac35-2888a8331f68' class='xr-var-data-in' type='checkbox'><label for='data-7f14a8d2-8b5c-48ce-ac35-2888a8331f68' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 0, 1, 2, ..., 431, 432, 433])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ef20fe33-3a33-434f-b0c9-2a45ff0a162e' class='xr-section-summary-in' type='checkbox' checked><label for='section-ef20fe33-3a33-434f-b0c9-2a45ff0a162e' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>kid_score</span></div><div class='xr-var-dims'>(chain, draw, kid_score_dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.305e+03 976.4 ... -225.3 -170.1</div><input id='attrs-24c7ecad-e149-46b2-8249-784105f0eac4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-24c7ecad-e149-46b2-8249-784105f0eac4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9060b0d5-5cf7-4d95-96c4-9b29d25928b8' class='xr-var-data-in' type='checkbox'><label for='data-9060b0d5-5cf7-4d95-96c4-9b29d25928b8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[1305.45757255, 976.40117289, 1267.69233667, ...,\n",
" 1029.06138989, 1070.37121751, 1000.75736148],\n",
" [1195.16752516, 882.97071244, 1143.39272075, ...,\n",
" 934.62591093, 959.92617197, 899.80344855],\n",
" [ 642.22148154, 471.46438801, 612.47839159, ...,\n",
" 501.62705412, 512.06787779, 481.061784 ],\n",
" ...,\n",
" [-199.16875097, -143.90487054, -176.70068551, ...,\n",
" -150.40504191, -154.88688848, -136.94591513],\n",
" [1310.96525462, 951.42776269, 1244.11639689, ...,\n",
" 1010.82799804, 1064.18733064, 988.50534798],\n",
" [-291.14684147, -220.36108043, -273.70830641, ...,\n",
" -220.57057449, -225.28714676, -170.1180978 ]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c039bafd-15a6-4202-b432-886d2d1b1af5' class='xr-section-summary-in' type='checkbox' checked><label for='section-c039bafd-15a6-4202-b432-886d2d1b1af5' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2021-06-15T15:15:09.519889</dd><dt><span>arviz_version :</span></dt><dd>0.11.0</dd><dt><span>inference_library :</span></dt><dd>pymc3</dd><dt><span>inference_library_version :</span></dt><dd>3.11.0</dd></dl></div></li></ul></div></div><br></div>\n",
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"</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
"Dimensions: (kid_score_dim_0: 434)\n",
"Coordinates:\n",
" * kid_score_dim_0 (kid_score_dim_0) int64 0 1 2 3 4 5 ... 429 430 431 432 433\n",
"Data variables:\n",
" kid_score (kid_score_dim_0) int32 65 98 85 83 115 ... 94 76 50 88 70\n",
"Attributes:\n",
" created_at: 2021-06-15T15:15:09.521337\n",
" arviz_version: 0.11.0\n",
" inference_library: pymc3\n",
" inference_library_version: 3.11.0</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-3649deb6-b55d-49ca-970e-9358220d0ef8' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-3649deb6-b55d-49ca-970e-9358220d0ef8' class='xr-section-summary' title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>kid_score_dim_0</span>: 434</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-fad4b312-34a4-4d2e-99bb-6749632861bd' class='xr-section-summary-in' type='checkbox' checked><label for='section-fad4b312-34a4-4d2e-99bb-6749632861bd' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>kid_score_dim_0</span></div><div class='xr-var-dims'>(kid_score_dim_0)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 429 430 431 432 433</div><input id='attrs-3b418b86-0088-408f-9759-9540bdb99529' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3b418b86-0088-408f-9759-9540bdb99529' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bc6670db-0c54-4c91-8681-5c50cfffe5b2' class='xr-var-data-in' type='checkbox'><label for='data-bc6670db-0c54-4c91-8681-5c50cfffe5b2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 0, 1, 2, ..., 431, 432, 433])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-fb51a358-3f2d-4e4c-b000-f039d1341355' class='xr-section-summary-in' type='checkbox' checked><label for='section-fb51a358-3f2d-4e4c-b000-f039d1341355' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>kid_score</span></div><div class='xr-var-dims'>(kid_score_dim_0)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>65 98 85 83 115 ... 94 76 50 88 70</div><input id='attrs-24f6fdb6-f434-468f-8da7-2c719808161e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-24f6fdb6-f434-468f-8da7-2c719808161e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-357d5148-ce5b-4cf8-88b9-e758351352af' class='xr-var-data-in' type='checkbox'><label for='data-357d5148-ce5b-4cf8-88b9-e758351352af' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 65, 98, 85, 83, 115, 98, 69, 106, 102, 95, 91, 58, 84,\n",
" 78, 102, 110, 102, 99, 105, 101, 102, 115, 100, 87, 99, 96,\n",
" 72, 78, 77, 98, 69, 130, 109, 106, 92, 100, 107, 86, 90,\n",
" 110, 107, 113, 65, 102, 103, 111, 42, 100, 67, 92, 100, 110,\n",
" 56, 107, 97, 56, 95, 78, 76, 86, 79, 81, 79, 79, 56,\n",
" 52, 63, 80, 87, 88, 92, 100, 94, 117, 102, 107, 99, 73,\n",
" 56, 78, 94, 110, 109, 86, 92, 91, 123, 102, 105, 114, 96,\n",
" 66, 104, 108, 84, 83, 83, 92, 109, 95, 93, 114, 106, 87,\n",
" 65, 95, 61, 73, 112, 113, 49, 105, 122, 96, 97, 94, 117,\n",
" 136, 85, 116, 106, 99, 94, 89, 119, 112, 104, 92, 86, 69,\n",
" 45, 57, 94, 104, 89, 144, 52, 102, 106, 98, 97, 94, 111,\n",
" 100, 105, 90, 98, 121, 106, 121, 102, 64, 99, 81, 69, 84,\n",
" 104, 104, 107, 88, 67, 103, 94, 109, 94, 98, 102, 104, 114,\n",
" 87, 102, 77, 109, 94, 93, 86, 97, 97, 88, 103, 87, 87,\n",
" 90, 65, 111, 109, 87, 58, 87, 113, 64, 78, 97, 95, 75,\n",
" 91, 99, 108, 95, 100, 85, 97, 108, 90, 100, 82, 94, 95,\n",
" 119, 98, 100, 112, 136, 122, 126, 116, 98, 94, 93, 90, 70,\n",
" 110, 104, 83, 99, 81, 104, 109, 113, 95, 74, 81, 89, 93,\n",
" 102, 95, 85, 97, 92, 78, 104, 120, 83, 105, 68, 104, 80,\n",
" 120, 94, 81, 101, 61, 68, 110, 89, 98, 113, 50, 57, 86,\n",
" 83, 106, 106, 104, 78, 99, 91, 40, 42, 69, 84, 58, 42,\n",
" 72, 80, 58, 52, 101, 63, 73, 68, 60, 69, 73, 75, 20,\n",
" 56, 49, 71, 46, 54, 54, 44, 74, 58, 46, 76, 43, 60,\n",
" 58, 89, 43, 94, 88, 79, 87, 46, 95, 92, 42, 62, 52,\n",
" 101, 97, 85, 98, 94, 90, 72, 92, 75, 83, 64, 101, 82,\n",
" 77, 101, 50, 90, 103, 96, 50, 47, 73, 62, 77, 64, 52,\n",
" 61, 86, 41, 83, 64, 83, 116, 100, 42, 74, 76, 92, 98,\n",
" 96, 67, 84, 111, 41, 68, 107, 82, 89, 83, 73, 74, 94,\n",
" 58, 76, 61, 38, 100, 84, 99, 86, 94, 90, 50, 112, 58,\n",
" 87, 76, 68, 110, 88, 87, 54, 49, 56, 79, 82, 80, 60,\n",
" 102, 87, 42, 119, 84, 86, 113, 72, 104, 94, 78, 80, 67,\n",
" 104, 96, 65, 64, 95, 56, 75, 91, 106, 76, 90, 108, 86,\n",
" 85, 104, 87, 41, 106, 76, 100, 89, 42, 102, 104, 59, 93,\n",
" 94, 76, 50, 88, 70], dtype=int32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-bc8cf9e7-7c49-4b34-a20a-f2f304e0ad0b' class='xr-section-summary-in' type='checkbox' checked><label for='section-bc8cf9e7-7c49-4b34-a20a-f2f304e0ad0b' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2021-06-15T15:15:09.521337</dd><dt><span>arviz_version :</span></dt><dd>0.11.0</dd><dt><span>inference_library :</span></dt><dd>pymc3</dd><dt><span>inference_library_version :</span></dt><dd>3.11.0</dd></dl></div></li></ul></div></div><br></div>\n",
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"</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
"Dimensions: (mom_iq_dim_0: 434)\n",
"Coordinates:\n",
" * mom_iq_dim_0 (mom_iq_dim_0) int64 0 1 2 3 4 5 6 ... 428 429 430 431 432 433\n",
"Data variables:\n",
" mom_iq (mom_iq_dim_0) float64 121.1 89.36 115.4 ... 94.86 96.86 91.25\n",
"Attributes:\n",
" created_at: 2021-06-15T15:15:09.522399\n",
" arviz_version: 0.11.0\n",
" inference_library: pymc3\n",
" inference_library_version: 3.11.0</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-68aaf1ce-d88c-4406-9d6a-0907ab5cdbcc' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-68aaf1ce-d88c-4406-9d6a-0907ab5cdbcc' class='xr-section-summary' title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>mom_iq_dim_0</span>: 434</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-6df0ce27-bbc1-4218-b307-9b14f6bf2c60' class='xr-section-summary-in' type='checkbox' checked><label for='section-6df0ce27-bbc1-4218-b307-9b14f6bf2c60' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>mom_iq_dim_0</span></div><div class='xr-var-dims'>(mom_iq_dim_0)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 429 430 431 432 433</div><input id='attrs-72178972-b8a5-4d03-a1a0-4030c5f74a77' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-72178972-b8a5-4d03-a1a0-4030c5f74a77' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ad0b8198-db01-444f-92a8-c4dba05db56c' class='xr-var-data-in' type='checkbox'><label for='data-ad0b8198-db01-444f-92a8-c4dba05db56c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 0, 1, 2, ..., 431, 432, 433])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d1416505-c72b-4eb1-912c-00d4c59d734b' class='xr-section-summary-in' type='checkbox' checked><label for='section-d1416505-c72b-4eb1-912c-00d4c59d734b' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>mom_iq</span></div><div class='xr-var-dims'>(mom_iq_dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>121.1 89.36 115.4 ... 96.86 91.25</div><input id='attrs-df4b83d6-8c4c-47ce-a63c-b3817a7c3137' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-df4b83d6-8c4c-47ce-a63c-b3817a7c3137' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bf5800fc-b6c3-47ad-a8f8-8559dca731b3' class='xr-var-data-in' type='checkbox'><label for='data-bf5800fc-b6c3-47ad-a8f8-8559dca731b3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([121.1175286 , 89.36188171, 115.44316488, 99.44963944,\n",
" 92.74571 , 107.90183776, 138.89310607, 125.14511948,\n",
" 81.61952618, 95.07306862, 88.57699772, 94.85970819,\n",
" 88.96280085, 114.11429701, 100.53407192, 120.41914559,\n",
" 114.42687689, 111.59235758, 133.84922721, 97.26480106,\n",
" 110.09680614, 126.72399417, 97.91159031, 99.92572516,\n",
" 97.59500805, 121.74801346, 98.7480787 , 97.91525433,\n",
" 80.35855646, 114.30786063, 109.1383163 , 101.81717973,\n",
" 117.96510431, 108.63349691, 96.52861915, 92.87137547,\n",
" 95.89813429, 107.01545773, 87.19701175, 89.36188171,\n",
" 102.53098785, 130.16686024, 83.4141026 , 125.75346623,\n",
" 85.81037656, 126.52007263, 79.2030481 , 113.16590718,\n",
" 110.33138787, 99.40922374, 102.42552649, 124.90043775,\n",
" 94.85970819, 92.36856455, 101.81717973, 79.83353296,\n",
" 96.22613927, 82.35547239, 76.57564732, 110.01348289,\n",
" 111.59235758, 113.65740737, 101.16455677, 101.34109401,\n",
" 101.69816348, 98.81915458, 93.4981963 , 87.2938899 ,\n",
" 87.316028 , 86.20945742, 89.83796743, 121.87001491,\n",
" 127.54437863, 109.99134478, 90.44631419, 88.8949027 ,\n",
" 113.04689092, 78.01315427, 95.38965088, 83.61644211,\n",
"...\n",
" 108.00301206, 80.38935225, 99.80304488, 90.02918976,\n",
" 93.93878156, 103.70863431, 77.10925402, 117.55716308,\n",
" 79.83353296, 83.61644211, 88.73139685, 82.78361774,\n",
" 86.87676547, 88.73139685, 92.24089061, 90.55177555,\n",
" 84.14146561, 93.07371498, 85.83486773, 98.85597777,\n",
" 95.51233116, 101.06767862, 85.93237801, 82.78361774,\n",
" 98.66475544, 82.14946886, 85.61579575, 79.43762983,\n",
" 75.3368157 , 80.89216317, 115.77116986, 97.159104 ,\n",
" 80.89216317, 79.50552798, 98.01213248, 108.86649661,\n",
" 80.69859954, 100.74743234, 91.09893715, 82.98595725,\n",
" 89.71528716, 131.01070552, 124.51463462, 91.61040575,\n",
" 92.99039172, 91.88382114, 88.69098116, 126.09350931,\n",
" 84.98531089, 82.98595725, 101.79504163, 100.24363014,\n",
" 110.01348289, 88.57699772, 106.74204234, 95.2921406 ,\n",
" 108.31325064, 110.33138787, 116.82980007, 86.68554314,\n",
" 96.85662413, 90.24826174, 89.29080583, 74.23024512,\n",
" 91.88382114, 96.46072099, 97.40378573, 131.53329131,\n",
" 78.24455827, 127.67571659, 124.51463462, 80.46401782,\n",
" 74.86072998, 84.87741183, 92.99039172, 94.85970819,\n",
" 96.85662413, 91.25333628])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c1daf3e7-d1d0-4b82-bd9f-8efe62d5618b' class='xr-section-summary-in' type='checkbox' checked><label for='section-c1daf3e7-d1d0-4b82-bd9f-8efe62d5618b' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2021-06-15T15:15:09.522399</dd><dt><span>arviz_version :</span></dt><dd>0.11.0</dd><dt><span>inference_library :</span></dt><dd>pymc3</dd><dt><span>inference_library_version :</span></dt><dd>3.11.0</dd></dl></div></li></ul></div></div><br></div>\n",
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"\n",
".xr-var-name {\n",
" grid-column: 1;\n",
"}\n",
"\n",
".xr-var-dims {\n",
" grid-column: 2;\n",
"}\n",
"\n",
".xr-var-dtype {\n",
" grid-column: 3;\n",
" text-align: right;\n",
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
".xr-var-preview {\n",
" grid-column: 4;\n",
"}\n",
"\n",
".xr-var-name,\n",
".xr-var-dims,\n",
".xr-var-dtype,\n",
".xr-preview,\n",
".xr-attrs dt {\n",
" white-space: nowrap;\n",
" overflow: hidden;\n",
" text-overflow: ellipsis;\n",
" padding-right: 10px;\n",
"}\n",
"\n",
".xr-var-name:hover,\n",
".xr-var-dims:hover,\n",
".xr-var-dtype:hover,\n",
".xr-attrs dt:hover {\n",
" overflow: visible;\n",
" width: auto;\n",
" z-index: 1;\n",
"}\n",
"\n",
".xr-var-attrs,\n",
".xr-var-data {\n",
" display: none;\n",
" background-color: var(--xr-background-color) !important;\n",
" padding-bottom: 5px !important;\n",
"}\n",
"\n",
".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
".xr-var-data-in:checked ~ .xr-var-data {\n",
" display: block;\n",
"}\n",
"\n",
".xr-var-data > table {\n",
" float: right;\n",
"}\n",
"\n",
".xr-var-name span,\n",
".xr-var-data,\n",
".xr-attrs {\n",
" padding-left: 25px !important;\n",
"}\n",
"\n",
".xr-attrs,\n",
".xr-var-attrs,\n",
".xr-var-data {\n",
" grid-column: 1 / -1;\n",
"}\n",
"\n",
"dl.xr-attrs {\n",
" padding: 0;\n",
" margin: 0;\n",
" display: grid;\n",
" grid-template-columns: 125px auto;\n",
"}\n",
"\n",
".xr-attrs dt, dd {\n",
" padding: 0;\n",
" margin: 0;\n",
" float: left;\n",
" padding-right: 10px;\n",
" width: auto;\n",
"}\n",
"\n",
".xr-attrs dt {\n",
" font-weight: normal;\n",
" grid-column: 1;\n",
"}\n",
"\n",
".xr-attrs dt:hover span {\n",
" display: inline-block;\n",
" background: var(--xr-background-color);\n",
" padding-right: 10px;\n",
"}\n",
"\n",
".xr-attrs dd {\n",
" grid-column: 2;\n",
" white-space: pre-wrap;\n",
" word-break: break-all;\n",
"}\n",
"\n",
".xr-icon-database,\n",
".xr-icon-file-text2 {\n",
" display: inline-block;\n",
" vertical-align: middle;\n",
" width: 1em;\n",
" height: 1.5em !important;\n",
" stroke-width: 0;\n",
" stroke: currentColor;\n",
" fill: currentColor;\n",
"}\n",
".xr-wrap{width:700px!important;} </style>"
],
"text/plain": [
"Inference data with groups:\n",
"\t> posterior\n",
"\t> posterior_predictive\n",
"\t> log_likelihood\n",
"\t> sample_stats\n",
"\t> prior\n",
"\t> prior_predictive\n",
"\t> observed_data\n",
"\t> constant_data"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"idata"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "thousand-metro",
"metadata": {},
"outputs": [],
"source": [
"def plot_lm(\n",
" y_model_values,\n",
" x_values,\n",
" y_data_values,\n",
" y_ppc_values,\n",
" num_ppc_samples,\n",
"):\n",
"\n",
" _, ax = plt.subplots(1,1, figsize=(6,4), dpi=100)\n",
"\n",
" # plot data\n",
" ax.plot(x_values, y_data_values, marker='.', color='C3', lw=0, zorder=10)\n",
"\n",
" # plot uncertainty in y\n",
" az.plot_hdi(x_values, y_ppc_values, color='C1') \n",
"\n",
" # plot uncertainty in mean line\n",
" az.plot_hdi(x_values, y_model_values, color='k', fill_kwargs={\"alpha\":0.5, \"lw\":0.5, \"zorder\":9})\n",
"\n",
" #plot mean line\n",
" slicer = np.random.choice(list(range(1000)), size=num_ppc_samples, replace=False)\n",
" y_model_values_ = y_model_values.stack(sample=(\"chain\", \"draw\"))[... ,slicer]\n",
" ax.plot(x_values, y_model_values_.mean(\"sample\"), color='y', lw=0.8, zorder=11)\n",
"\n",
" for spine in ax.spines.values():\n",
" spine.set_visible(False)\n",
" ax.grid(True)\n",
" return ax\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "purple-mineral",
"metadata": {},
"outputs": [],
"source": [
"def plot_lm_userapi(\n",
" y_model,\n",
" x,\n",
" y_data=None,\n",
" y_ppc=None,\n",
" data=None,\n",
" x_group=None,\n",
" y_ppc_group=None,\n",
" y_data_group=None,\n",
" num_ppc_samples=100,\n",
" backend=None,\n",
"):\n",
" \n",
" groups = data._groups\n",
" if hasattr(data, \"observed_data\"):\n",
" groups = [\"observed_data\"] + [\n",
" group for group in groups if group != \"observed_data\"\n",
" ]\n",
"\n",
" y_model_values = y_model\n",
" if isinstance(y_model, str):\n",
" if data is None:\n",
" raise ValueError(\"data must be present\")\n",
"\n",
" lhs, rhs = y_model.split('~')\n",
" y_funcs = []\n",
"\n",
" y_var_name = lhs.strip()\n",
" while '(' in y_var_name:\n",
" idx = lhs.find('(')\n",
" y_funcs = y_funcs + [lhs[:idx]]\n",
" y_var_name = y_var_name[idx+1:-1]\n",
"\n",
" if y_data is None:\n",
" y_data = y_var_name\n",
"\n",
" if y_ppc is None:\n",
" y_ppc = y_var_name\n",
"\n",
" y_model_values=0\n",
" for rhs_terms in rhs.split('+'):\n",
" temp = 1\n",
" for var in rhs_terms.split('*'):\n",
" var_name = var.strip()\n",
" funcs = []\n",
" while '(' in var_name:\n",
" idx = var_name.find('(')\n",
" funcs += [var_name[:idx]]\n",
" var_name = var_name[idx+1:-1]\n",
"\n",
" for group in groups:\n",
" item = getattr(data, group)\n",
" if var_name in item:\n",
" var_group = group\n",
" break\n",
"\n",
" print(\"Using {} from group {}\".format(var_name, var_group))\n",
" var_values = getattr(data, var_group)[var_name]\n",
" while funcs:\n",
" var_values = getattr(np, funcs.pop())(var_values)\n",
" temp = temp*var_values\n",
" y_model_values = y_model_values + temp\n",
"\n",
" if y_data is None:\n",
" raise ValueError(\"y_data must be present\")\n",
"\n",
" x_values = x\n",
" if isinstance(x, str):\n",
" if data is None:\n",
" raise ValueError(\"data must be present\")\n",
"\n",
" if x_group is None:\n",
" for group in groups:\n",
" item = getattr(data, group)\n",
" if x in item and x_group is None:\n",
" x_group = group\n",
" elif x in item:\n",
" print(\n",
" \"Warning, duplicate variable names for x,\"\n",
" \"using variable from group {}\"\n",
" .format(x_group)\n",
" )\n",
" x_values = getattr(data, x_group)[x]\n",
"\n",
" y_data_values = y_data\n",
" if isinstance(y_data, str):\n",
" if data is None:\n",
" raise ValueError(\"data must be present\")\n",
"\n",
" if y_data_group is None:\n",
" for group in groups:\n",
" item = getattr(data, group)\n",
" if y_data in item and y_data_group is None:\n",
" y_data_group = group\n",
" elif y_data in item:\n",
" print(\"Warning, duplicate variable names for y_data, \"\n",
" \"using variable from group {}\".format(y_data_group))\n",
" y_data_values = getattr(data, y_data_group)[y_data]\n",
"\n",
" y_ppc_values = y_ppc\n",
" if isinstance(y_ppc, str):\n",
" if data is None:\n",
" raise ValueError(\"data must be present\")\n",
"\n",
" if y_ppc_group is None:\n",
" groups = [\"posterior_predictive\", \"prior_predictive\"]\n",
" for group in groups:\n",
" item = getattr(data, group)\n",
" if y_ppc in item and y_ppc_group is None:\n",
" y_ppc_group = group\n",
" elif y_ppc in item:\n",
" print(\"Warning, duplicate variable names for y_ppc, \"\n",
" \"using variable from group {}\".format(y_ppc_group))\n",
" y_ppc_values = getattr(data, y_ppc_group)[y_ppc]\n",
"\n",
"\n",
" lmplot_kwargs = dict(\n",
" y_model_values = y_model_values,\n",
" x_values=x_values,\n",
" y_data_values=y_data_values,\n",
" y_ppc_values=y_ppc_values,\n",
" num_ppc_samples=num_ppc_samples\n",
" )\n",
"\n",
"# if backend is None:\n",
"# backend = rcParams[\"plot.backend\"]\n",
"# backend = backend.lower()\n",
" \n",
"# plot = get_plotting_function(\"plot_lm\", \"lmplot\", backend)\n",
" ax = plot_lm(**lmplot_kwargs)\n",
" return ax"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "affiliated-cement",
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Using Intercept from group posterior\n",
"Using slope from group posterior\n",
"Using mom_iq from group constant_data\n",
"Warning, duplicate variable names for y_data, using variable from group observed_data\n",
"Warning, duplicate variable names for y_data, using variable from group observed_data\n",
"Warning, duplicate variable names for y_data, using variable from group observed_data\n",
"Warning, duplicate variable names for y_ppc, using variable from group posterior_predictive\n"
]
},
{
"data": {
"text/plain": [
"<AxesSubplot:>"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 600x400 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plot_lm_userapi(y_model = \"(kid_score) ~ Intercept + slope * mom_iq\", data = idata, x = \"mom_iq\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "balanced-hotel",
"metadata": {},
"outputs": [],
"source": [
"with model:\n",
" posterior_predictive = pm.sample_posterior_predictive(idata, samples=1000)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "homeless-pantyhose",
"metadata": {},
"outputs": [],
"source": [
"p_p = idata.prior['slope'] * idata.constant_data['mom_iq'] + idata.prior['Intercept']"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "civic-digest",
"metadata": {},
"outputs": [],
"source": [
"p_p.shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "acquired-senate",
"metadata": {},
"outputs": [],
"source": [
"x_values = getattr(idata, 'constant_data')['mom_iq']\n",
"y_data_values = getattr(idata, 'observed_data')['kid_score']\n",
"\n",
"fig, ax = plt.subplots(1,1, figsize=(6,4), dpi=100)\n",
"\n",
"ax.plot(x_values, y_data_values, marker='.', color='C3', lw=0, zorder=10)\n",
"az.plot_hdi(x_values, p_p, color='C1') \n",
"# for spine in ax.spines.values():\n",
"# spine.set_visible(False)\n",
"ax.grid(True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "scientific-malpractice",
"metadata": {},
"outputs": [],
"source": [
"var_ = \"sum(avg(x))\"\n",
"func =[]\n",
"while '(' in var_:\n",
" idx = var_.find('(')\n",
" func += [(var_[:idx])]\n",
" var_ = var_[idx+1:-1]\n",
"func\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "orange-white",
"metadata": {},
"outputs": [],
"source": [
"var_[0:3]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "falling-sailing",
"metadata": {},
"outputs": [],
"source": [
"f = []\n",
"f = f + [var_[:3]]\n",
"f"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "specified-duration",
"metadata": {},
"outputs": [],
"source": [
"func.pop()\n",
"func"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "challenging-worse",
"metadata": {},
"outputs": [],
"source": [
"func"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "dominican-gospel",
"metadata": {},
"outputs": [],
"source": [
"getattr(xr, func.pop())([1,2,3])"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "universal-sydney",
"metadata": {},
"outputs": [],
"source": [
"f = []\n",
"while f:\n",
" print(\"yes\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "complimentary-housing",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.5"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
@utkarsh-maheshwari
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If posterior_predivtive group is present in the input inferencedata, then I guess we don't need y_model from the user.? y_ppc name will be assumed to be as name of y unless stated otherwise. We just need x(str or sequence), y(str only), idata in this case.

If no posterior_predivtive group is there, then user must provide y_model (str or sequence).

  • If y_model : str ---> we need idata only. x,y can be extracted.

  • If y_model : seq ---> x, y are needed can be str or seq.

      If x : str or y : str ---> idata must be there.
  else ---> no idata is required

@utkarsh-maheshwari
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@ahartikainen @canyon289
I think ppc can be calculated by 2 ways:

  1. posterior_predictive = pm.sample_posterior_predictive(trace, samples=1000, model) : (User will probably calculate like this)
  2. y_ppc_values = data.posterior[intercept] + data.posterior[slope] * data.constant_data[x] :

How will the both ppc values differ? I believed both are same. But

y_ppc= idata.posterior["Intercept"] + idata.posterior["slope"] * idata.constant_data["mom_iq"]
plt.scatter(idata.constant_data["mom_iq"], idata.observed_data["kid_score"])
az.plot_hdi(x=idata.constant_data["mom_iq"], y=idata.posterior_predictive["kid_score"], color='c')
az.plot_hdi(idata.constant_data["mom_iq"], y_ppc, color='r')

produces:

image

How can this be explained?

@utkarsh-maheshwari
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  1. posterior_predictive = pm.sample_posterior_predictive(trace, samples=1000, model) : (User will probably calculate like this)

Should I not compute ppc like this?

@utkarsh-maheshwari
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Maybe, posterior_predictive is more suitable to plot uncertainty in points and not uncertainty lines 🤔

@canyon289
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canyon289 commented Jun 9, 2021 •
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Both are suitable and preferable in different circumstances. One is a plot of the uncertainty of the mean in red, the other is of the data in blue. Havent had a look through the notebook yet but its on my TODO list. Thank you for putting this together!

@utkarsh-maheshwari
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Got it, Thanks!

@utkarsh-maheshwari
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@canyon289
I tried to implement what you suggested by extending @ahartikainen's function. I can see some bugs though. But it might be a fair start.

@utkarsh-maheshwari
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Approach to parse the y_model

rhs, lhs = y_model.split()
terms = rhs.split('+') : gives terms of the expression. 
var_name = terms.split('*') : gives variable names to be searched in data

Iterate over terms
calculate value of each term
add it to the y_model_values

@canyon289
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Seems like the right approach! Any guidance you need from us?

@utkarsh-maheshwari
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Seems like the right approach! Any guidance you need from us?

A brief review would be great.

  • Now, that it supports plotting simple linear regression models, what should be the immediate next goal of the function?
  • Any bugs you see in the function?
  • Other things need to be done for simple regression?
  • Should I start adding the function to the library?

@canyon289
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If you feel like youre done prototyping the next thing you should do is move this into a PR in ArviZ. notably adding many tests. This answers the first question and the last question.

For finding bugs writing tests with hell both you and us find bugs if any exist.

At a glance it looks good but I have some questions about the api. This however will be easier to assess with a full test suite and when part of PR.

None of this is to say this isnt great work! IMO youre right on track and really appreciate the way youre doing this.

@utkarsh-maheshwari
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Thank you! Reviews are very helpful and encouraging for me be they positive or negative.
Not sure if prototyping is enough but since functions output something, I am gonna open a PR.
I'll get more reviews there 😋

@mattiasthalen
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Not sure if this has been abandoned. But I tried it in colab with just the prior predictive.

It failed due to this:

groups = ["posterior_predictive", "prior_predictive"]

I removed that and modified the loop to check for groups with _predictive in its name:

for group in groups:
    if '_predictive' in group:
        item = getattr(data, group)
        if y_ppc in item and y_ppc_group is None:
            y_ppc_group = group
        elif y_ppc in item:
            print("Warning, duplicate variable names for y_ppc, "
            "using variable from group {}".format(y_ppc_group))

Unfortunately it crashed.

@utkarsh-maheshwari
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Hi @mattiasthalen! Did you try plot_lm function? This gist is just a prototype.

@mattiasthalen
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@utkarsh-maheshwari no, I stumbled on this when googling. But I did now and it seems to work good :)

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