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ahartikainen/posteriordb_reader.ipynb

Created August 15, 2020 08:09
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posteriordb to arviz.InferenceData
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posteriordb_reader.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import zipfile\n",
"from collections import defaultdict\n",
"from pathlib import Path\n",
"\n",
"import arviz as az\n",
"import orjson\n",
"import posteriordb"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"path = Path(posteriordb.__file__).parent.parent.parent.parent / \"posterior_database\"\n",
"pdb = posteriordb.PosteriorDatabase(path)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"# helper functions\n",
"import numpy as np\n",
"def transform_data(data, transpose=False):\n",
" \"\"\"Transform data to numpy array.\"\"\"\n",
" res = {}\n",
" for key, values in data.items():\n",
" if isinstance(values, (list, tuple)):\n",
" values = np.array(values)\n",
" if transpose:\n",
" values = values.T\n",
" res[key] = values\n",
" return res\n",
"\n",
"def get_stem(path):\n",
" return path.name[:-sum(map(len, path.suffixes))]\n",
"\n",
"def concat(data):\n",
" res = {}\n",
" for key in data[0]:\n",
" res[key] = np.column_stack([item[key] for item in data])\n",
" return res\n",
"\n",
"def read_posterior_data(path):\n",
" with zipfile.ZipFile(path, mode=\"r\") as f:\n",
" json_data = orjson.loads(f.read(f.namelist()[0]))\n",
" data = concat([transform_data(item) for item in json_data])\n",
" return data\n",
"\n",
"def transform_arrays(data):\n",
" main_keys = {}\n",
" \n",
" # solve dimensions\n",
" for key in data:\n",
" if \"[\" in key:\n",
" new_key = key.split(\"[\")[0]\n",
" dims = list(map(int, key.split(\"[\", 1)[-1].strip(\"]\").split(\",\")))\n",
" if new_key not in main_keys:\n",
" main_keys[new_key] = dims[:], dims[:], []\n",
" continue\n",
" main_keys[new_key][-1].append(data[key])\n",
" for i, d in enumerate(dims):\n",
" if main_keys[new_key][0][i] < d:\n",
" main_keys[new_key][0][i] = d\n",
" if main_keys[new_key][1][i] > d:\n",
" main_keys[new_key][1][i] = d\n",
" else:\n",
" main_keys[key] = None, None, data[key]\n",
" \n",
" # replace data\n",
" for key, (max_dims, min_dims, data) in main_keys.items():\n",
" if max_dims is None:\n",
" main_keys[key] = np.asarray(data).T\n",
" continue\n",
" data = np.asarray(data)\n",
" newshape = list(np.array(max_dims)-min_dims) + list(data.shape[1:])\n",
" data = np.reshape(data, newshape, order=\"F\")\n",
" main_keys[key] = data.T\n",
" return main_keys"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"# Read gold-standards\n",
"reference_posterior_draws_infos = {get_stem(item) : item for item in list(path.glob(\"reference_posteriors/draws/info/*\"))}\n",
"reference_posterior_draws_draws = {get_stem(item) : item for item in list(path.glob(\"reference_posteriors/draws/draws/*\"))}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Posterior_names"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"def get_posteriordb(name: str):\n",
" \"\"\"Get posterior, model, data.\"\"\"\n",
" \n",
" res_dict = {}\n",
" posterior_info = pdb.get_posterior_info(name)\n",
" posterior = pdb.posterior(name)\n",
" \n",
" res_dict.update({\n",
" \"posterior_name\" : name,\n",
" \"posterior_info\" : posterior_info,\n",
" })\n",
" \n",
" reference_posterior_name = posterior_info.get(\"reference_posterior_name\")\n",
" if reference_posterior_name in reference_posterior_draws_infos:\n",
" with reference_posterior_draws_infos[reference_posterior_name].open(\"rb\") as f:\n",
" draws_info = orjson.loads(f.read())\n",
" else:\n",
" draws_info = None\n",
" res_dict[\"draws_info\"] = draws_info\n",
" if reference_posterior_name in reference_posterior_draws_draws:\n",
" posterior_draws = transform_data(read_posterior_data(reference_posterior_draws_draws[reference_posterior_name]), transpose=True)\n",
" else:\n",
" posterior_draws = {}\n",
" \n",
" model_name = posterior_info.get(\"model_name\")\n",
" if model_name:\n",
" model_info = pdb.get_model_info(model_name)\n",
" model = pdb.model(model_name)\n",
" \n",
" res_dict.update({\n",
" \"model_name\" : model_name,\n",
" \"model_info\" : model_info,\n",
" \"model_stan\" : getattr(model, \"stan_code\", lambda: \"\")(),\n",
" \"model_pymc3\" : getattr(model, \"pymc3_code\", lambda: \"\")(),\n",
" })\n",
" \n",
" data_name = posterior_info.get(\"data_name\")\n",
" if data_name:\n",
" data_info = pdb.get_data_info(data_name)\n",
" data_obj = pdb.data(data_name)\n",
" data = transform_data(data_obj.values())\n",
" res_dict.update({\n",
" \"data_name\" : data_name,\n",
" \"data_info\" : data_info,\n",
" })\n",
" idata = az.from_dict(posterior=transform_arrays(posterior_draws), observed_data=data)\n",
" idata.posterior.attrs.update(res_dict)\n",
"\n",
" return idata"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"timings = {}\n",
"for name in pdb.posterior_names():\n",
" res = %timeit -n 1000 -r 100 -q -o None == None\n",
" timings[name] = res.timings"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'diamonds-diamonds'"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pdb.posterior_names()[5]"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Wall time: 174 ms\n"
]
}
],
"source": [
"%time posterior = get_posteriordb(\"diamonds-diamonds\")"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
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"</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
"Dimensions: (b_dim_0: 23, chain: 10, draw: 1000)\n",
"Coordinates:\n",
" * chain (chain) int32 0 1 2 3 4 5 6 7 8 9\n",
" * draw (draw) int32 0 1 2 3 4 5 6 7 ... 992 993 994 995 996 997 998 999\n",
" * b_dim_0 (b_dim_0) int32 0 1 2 3 4 5 6 7 8 ... 14 15 16 17 18 19 20 21 22\n",
"Data variables:\n",
" b (chain, draw, b_dim_0) float64 6.106 -4.492 ... 4.599 -1.474\n",
" Intercept (chain, draw) float64 7.793 7.788 7.789 ... 7.787 7.788 7.783\n",
" sigma (chain, draw) float64 0.1234 0.122 0.1231 ... 0.126 0.1227 0.123\n",
"Attributes:\n",
" created_at: 2020-08-15T08:08:36.715109\n",
" arviz_version: 0.9.0\n",
" posterior_name: diamonds-diamonds\n",
" posterior_info: {&#x27;name&#x27;: &#x27;diamonds-diamonds&#x27;, &#x27;keywords&#x27;: [&#x27;stan_benchma...\n",
" draws_info: {&#x27;name&#x27;: &#x27;diamonds-diamonds&#x27;, &#x27;inference&#x27;: {&#x27;method&#x27;: &#x27;s...\n",
" model_name: diamonds\n",
" model_info: {&#x27;name&#x27;: &#x27;diamonds&#x27;, &#x27;keywords&#x27;: [&#x27;stan_benchmark&#x27;, &#x27;lin...\n",
" model_stan: // generated with brms 2.10.0\\n\\nfunctions {\\n}\\ndata {\\...\n",
" model_pymc3: \n",
" data_name: diamonds\n",
" data_info: {&#x27;name&#x27;: &#x27;diamonds&#x27;, &#x27;keywords&#x27;: [&#x27;ggplot2&#x27;, &#x27;tidyverse&#x27;...</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-10e3ddb7-a827-498a-998f-4b377e56c746' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-10e3ddb7-a827-498a-998f-4b377e56c746' 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'>b_dim_0</span>: 23</li><li><span class='xr-has-index'>chain</span>: 10</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-1e6df167-2e3b-43ff-a768-e9b56aec3cd1' class='xr-section-summary-in' type='checkbox' checked><label for='section-1e6df167-2e3b-43ff-a768-e9b56aec3cd1' 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'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 7 8 9</div><input id='attrs-53b21fa3-3b9b-47bf-889b-493d9c127e19' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-53b21fa3-3b9b-47bf-889b-493d9c127e19' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-34cc7f46-cbd8-46e7-8bbb-f06843bf30f8' class='xr-var-data-in' type='checkbox'><label for='data-34cc7f46-cbd8-46e7-8bbb-f06843bf30f8' 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, 3, 4, 5, 6, 7, 8, 9])</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'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 995 996 997 998 999</div><input id='attrs-ff0f3a82-8326-4ea5-8d17-7f4ef93cc2aa' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ff0f3a82-8326-4ea5-8d17-7f4ef93cc2aa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a865b4d9-4b94-4d8e-9bcc-d994c2b5a378' class='xr-var-data-in' type='checkbox'><label for='data-a865b4d9-4b94-4d8e-9bcc-d994c2b5a378' 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'>b_dim_0</span></div><div class='xr-var-dims'>(b_dim_0)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 17 18 19 20 21 22</div><input id='attrs-a2dd2454-a86a-4dc3-8757-a9343e25b93f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a2dd2454-a86a-4dc3-8757-a9343e25b93f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3eb2b426-d2d1-4fbb-9460-838961740659' class='xr-var-data-in' type='checkbox'><label for='data-3eb2b426-d2d1-4fbb-9460-838961740659' 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, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
" 18, 19, 20, 21, 22])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9e840dc2-a120-4afa-b4e8-a12e7ac6079f' class='xr-section-summary-in' type='checkbox' checked><label for='section-9e840dc2-a120-4afa-b4e8-a12e7ac6079f' 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>b</span></div><div class='xr-var-dims'>(chain, draw, b_dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>6.106 -4.492 1.568 ... 4.599 -1.474</div><input id='attrs-27c75293-21aa-4d2a-aa97-d9767d1e938f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-27c75293-21aa-4d2a-aa97-d9767d1e938f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2f740b72-3c65-4f40-b466-37cf7f201fa8' class='xr-var-data-in' type='checkbox'><label for='data-2f740b72-3c65-4f40-b466-37cf7f201fa8' 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([[[ 6.10556805, -4.49200771, 1.56818739, ..., -5.75785956,\n",
" 4.38466344, -1.53241486],\n",
" [ 6.08897561, -4.30964506, 1.30259152, ..., -5.8221029 ,\n",
" 4.25030304, -1.42749267],\n",
" [ 6.01390816, -4.44977949, 1.57373858, ..., -5.86596508,\n",
" 4.39634851, -1.45133755],\n",
" ...,\n",
" [ 6.33635555, -4.54855589, 1.32796811, ..., -6.34520614,\n",
" 4.7363763 , -1.33004053],\n",
" [ 6.23175785, -4.59976728, 1.6041028 , ..., -6.08927227,\n",
" 4.69373975, -1.46813281],\n",
" [ 5.98161125, -4.33588597, 1.64109165, ..., -5.76691047,\n",
" 4.47143684, -1.42258016]],\n",
"\n",
" [[ 6.31664244, -4.80591574, 1.41609496, ..., -6.39349415,\n",
" 4.7543405 , -1.50376435],\n",
" [ 5.98520392, -4.25384241, 1.45851078, ..., -5.71762556,\n",
" 4.28919256, -1.40495076],\n",
" [ 6.35005863, -4.89527212, 1.66713015, ..., -6.27994807,\n",
" 4.90040511, -1.54751668],\n",
"...\n",
" [ 6.58448599, -4.8807725 , 1.48661078, ..., -6.10095395,\n",
" 4.72238685, -1.42392614],\n",
" [ 6.37575585, -4.50270027, 1.4224081 , ..., -6.02764533,\n",
" 4.58866097, -1.31253016],\n",
" [ 6.76487735, -4.93189642, 1.26456708, ..., -6.51145483,\n",
" 4.8532809 , -1.25961821]],\n",
"\n",
" [[ 5.97148297, -4.42974807, 1.55557561, ..., -5.89830577,\n",
" 4.54018957, -1.55829004],\n",
" [ 5.94148447, -4.1324976 , 1.33987992, ..., -5.70664303,\n",
" 4.15776838, -1.32567416],\n",
" [ 6.83055911, -5.18543199, 1.56173224, ..., -6.30524519,\n",
" 4.92947243, -1.45550993],\n",
" ...,\n",
" [ 6.24275438, -4.95220495, 1.53774509, ..., -6.19962752,\n",
" 4.75062656, -1.8183247 ],\n",
" [ 6.58526592, -4.72930691, 1.39898731, ..., -6.16019225,\n",
" 4.67492218, -1.27939865],\n",
" [ 6.41074979, -4.69569687, 1.3693479 , ..., -6.09175933,\n",
" 4.59948587, -1.4736066 ]]])</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'>7.793 7.788 7.789 ... 7.788 7.783</div><input id='attrs-1a62fa4d-d3f8-4602-a50a-204e43e4931a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1a62fa4d-d3f8-4602-a50a-204e43e4931a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b043839d-ddbf-44c6-9446-f3375317d93a' class='xr-var-data-in' type='checkbox'><label for='data-b043839d-ddbf-44c6-9446-f3375317d93a' 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([[7.79296696, 7.7880572 , 7.78923391, ..., 7.78817627, 7.78576273,\n",
" 7.78764544],\n",
" [7.79017363, 7.78692238, 7.78708052, ..., 7.78803925, 7.78769779,\n",
" 7.78723649],\n",
" [7.78662902, 7.78928928, 7.78447535, ..., 7.7889083 , 7.78891557,\n",
" 7.78989354],\n",
" ...,\n",
" [7.78880416, 7.78797326, 7.78980046, ..., 7.78709495, 7.78595024,\n",
" 7.79300133],\n",
" [7.79016417, 7.7893291 , 7.78712003, ..., 7.786297 , 7.78861734,\n",
" 7.78678212],\n",
" [7.78842425, 7.78721594, 7.78809777, ..., 7.78715768, 7.78834641,\n",
" 7.78347353]])</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'>0.1234 0.122 ... 0.1227 0.123</div><input id='attrs-978b99ee-0a22-437b-9de7-643438b5ef30' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-978b99ee-0a22-437b-9de7-643438b5ef30' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-67ac692b-07ba-4d74-8214-47c2e11c8abc' class='xr-var-data-in' type='checkbox'><label for='data-67ac692b-07ba-4d74-8214-47c2e11c8abc' 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([[0.12340134, 0.12200939, 0.12312824, ..., 0.12524257, 0.12258347,\n",
" 0.12468841],\n",
" [0.12136426, 0.12168967, 0.12359757, ..., 0.12140777, 0.12262495,\n",
" 0.12417345],\n",
" [0.12123008, 0.12399266, 0.12195029, ..., 0.1242312 , 0.12440633,\n",
" 0.12395845],\n",
" ...,\n",
" [0.12312386, 0.12246384, 0.12453582, ..., 0.12212189, 0.12107969,\n",
" 0.12372127],\n",
" [0.12221339, 0.12308872, 0.12317927, ..., 0.12193379, 0.12344136,\n",
" 0.12537551],\n",
" [0.12191504, 0.12287719, 0.12199612, ..., 0.12601803, 0.12274839,\n",
" 0.12300411]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-cb5c33e9-8df6-4e16-9a78-27a88cd13fa2' class='xr-section-summary-in' type='checkbox' ><label for='section-cb5c33e9-8df6-4e16-9a78-27a88cd13fa2' class='xr-section-summary' >Attributes: <span>(11)</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>2020-08-15T08:08:36.715109</dd><dt><span>arviz_version :</span></dt><dd>0.9.0</dd><dt><span>posterior_name :</span></dt><dd>diamonds-diamonds</dd><dt><span>posterior_info :</span></dt><dd>{&#x27;name&#x27;: &#x27;diamonds-diamonds&#x27;, &#x27;keywords&#x27;: [&#x27;stan_benchmark&#x27;, &#x27;linear regression&#x27;], &#x27;urls&#x27;: &#x27;https://github.com/bbbales2/cmdstan-warmup/blob/develop/examples/diamonds&#x27;, &#x27;model_name&#x27;: &#x27;diamonds&#x27;, &#x27;data_name&#x27;: &#x27;diamonds&#x27;, &#x27;reference_posterior_name&#x27;: &#x27;diamonds-diamonds&#x27;, &#x27;references&#x27;: [&#x27;bales2019selecting&#x27;, &#x27;wickham2016ggplot2&#x27;], &#x27;dimensions&#x27;: {&#x27;b&#x27;: 24, &#x27;Intercept&#x27;: 1, &#x27;sigma&#x27;: 1}, &#x27;added_date&#x27;: &#x27;2020-02-01&#x27;, &#x27;added_by&#x27;: &#x27;Oliver Järnefelt&#x27;}</dd><dt><span>draws_info :</span></dt><dd>{&#x27;name&#x27;: &#x27;diamonds-diamonds&#x27;, &#x27;inference&#x27;: {&#x27;method&#x27;: &#x27;stan_sampling&#x27;, &#x27;method_arguments&#x27;: {&#x27;chains&#x27;: 10, &#x27;iter&#x27;: 20000, &#x27;warmup&#x27;: 10000, &#x27;thin&#x27;: 10, &#x27;seed&#x27;: 4711, &#x27;control&#x27;: {&#x27;adapt_delta&#x27;: 0.99, &#x27;max_treedepth&#x27;: 20}}}, &#x27;diagnostics&#x27;: {&#x27;diagnostic_information&#x27;: {&#x27;names&#x27;: [&#x27;b[1]&#x27;, &#x27;b[2]&#x27;, &#x27;b[3]&#x27;, &#x27;b[4]&#x27;, &#x27;b[5]&#x27;, &#x27;b[6]&#x27;, &#x27;b[7]&#x27;, &#x27;b[8]&#x27;, &#x27;b[9]&#x27;, &#x27;b[10]&#x27;, &#x27;b[11]&#x27;, &#x27;b[12]&#x27;, &#x27;b[13]&#x27;, &#x27;b[14]&#x27;, &#x27;b[15]&#x27;, &#x27;b[16]&#x27;, &#x27;b[17]&#x27;, &#x27;b[18]&#x27;, &#x27;b[19]&#x27;, &#x27;b[20]&#x27;, &#x27;b[21]&#x27;, &#x27;b[22]&#x27;, &#x27;b[23]&#x27;, &#x27;b[24]&#x27;, &#x27;Intercept&#x27;, &#x27;sigma&#x27;]}, &#x27;ndraws&#x27;: 10000, &#x27;nchains&#x27;: 10, &#x27;effective_sample_size_bulk&#x27;: [9887.7459603498, 10310.438346761, 10137.7414052872, 10136.1230533179, 10057.2361933595, 9730.31848970419, 9574.49918784441, 9846.43494375318, 9861.5479075935, 10118.422678104, 9964.0802437337, 10171.9101417965, 9892.86685555493, 9628.9211135197, 9934.00450797643, 9527.67038371501, 10105.2621133498, 10106.4605745892, 9921.45279461561, 10165.0651379118, 10104.7170513001, 9975.19365420567, 9976.0976472878, 10049.5584138559, 9947.6813758736, 10051.4476397944], &#x27;effective_sample_size_tail&#x27;: [9999.00820858923, 9911.87449276965, 9614.38016382067, 9868.3004844549, 9508.70368262515, 9720.15412113097, 10035.4444163785, 9829.00981909016, 9914.97460942642, 9785.19452478898, 9798.10544540015, 9800.97918664471, 10008.7232729614, 10167.1469286514, 9617.40755010547, 9516.3010386735, 9850.06490228, 9891.55820340828, 9912.03389985128, 9787.56871633368, 9752.51243119974, 10076.2861558318, 9873.67187139235, 9857.11411442788, 9950.04654734129, 9921.99626148065], &#x27;r_hat&#x27;: [1.00020832161253, 1.00015457288796, 1.00027610561452, 1.0005942195255, 0.999583876746124, 0.999971342219562, 1.00003650122139, 0.999781450207121, 1.00041592530143, 1.00003166327389, 0.99978272825822, 1.00072474624433, 0.99982826481009, 1.00002864945976, 1.00053647443037, 1.00009244071397, 1.00015714091388, 1.0002390795141, 1.00023692338943, 0.999981941246108, 0.999916778582736, 1.00008702219742, 1.00063196297723, 0.999771167194997, 1.00017555748287, 1.00013374272954], &#x27;divergent_transitions&#x27;: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], &#x27;expected_fraction_of_missing_information&#x27;: [1.99715717129428, 2.05614891738547, 1.99318896349261, 1.88686691757097, 2.03102248822297, 1.96094555177724, 1.96747391143874, 1.96426717087559, 1.93037110414862, 2.03839876013752]}, &#x27;checks_made&#x27;: {&#x27;ndraws_is_10k&#x27;: True, &#x27;nchains_is_gte_4&#x27;: True, &#x27;ess_within_bounds&#x27;: True, &#x27;r_hat_below_1_01&#x27;: True, &#x27;efmi_above_0_2&#x27;: True}, &#x27;comments&#x27;: None, &#x27;added_by&#x27;: &#x27;Oliver Järnefelt&#x27;, &#x27;added_date&#x27;: &#x27;2020-05-27&#x27;, &#x27;versions&#x27;: {&#x27;rstan_version&#x27;: &#x27;rstan 2.19.3&#x27;, &#x27;r_Makevars&#x27;: &#x27;&#x27;, &#x27;r_version&#x27;: &#x27;R version 3.6.1 (2019-07-05)&#x27;, &#x27;r_session&#x27;: &#x27;R version 3.6.1 (2019-07-05)\\nPlatform: x86_64-pc-linux-gnu (64-bit)\\nRunning under: CentOS Linux 7 (Core)\\n\\nMatrix products: default\\nBLAS/LAPACK: /share/apps/spack/envs/fgci-centos7-generic/software/openblas/0.3.7/fqs4o7j/lib/libopenblasp-r0.3.7.so\\n\\nlocale:\\n [1] LC_CTYPE=en_GB.UTF-8 LC_NUMERIC=C \\n [3] LC_TIME=en_GB.UTF-8 LC_COLLATE=en_GB.UTF-8 \\n [5] LC_MONETARY=en_GB.UTF-8 LC_MESSAGES=en_GB.UTF-8 \\n [7] LC_PAPER=en_GB.UTF-8 LC_NAME=C \\n [9] LC_ADDRESS=C LC_TELEPHONE=C \\n[11] LC_MEASUREMENT=en_GB.UTF-8 LC_IDENTIFICATION=C \\n\\nattached base packages:\\n[1] stats graphics grDevices utils datasets methods base \\n\\nother attached packages:\\n[1] posterior_0.0.2 posteriordb_0.1 rstan_2.19.3 ggplot2_3.3.0 \\n[5] StanHeaders_2.19.2\\n\\nloaded via a namespace (and not attached):\\n [1] Rcpp_1.0.4.6 pillar_1.4.3 compiler_3.6.1 prettyunits_1.1.1 \\n [5] tools_3.6.1 pkgbuild_1.0.7 lifecycle_0.2.0 tibble_3.0.1 \\n [9] gtable_0.3.0 checkmate_2.0.0 pkgconfig_2.0.3 rlang_0.4.6 \\n[13] cli_2.0.2 parallel_3.6.1 loo_2.2.0 gridExtra_2.3 \\n[17] withr_2.2.0 dplyr_0.8.5 vctrs_0.2.4 stats4_3.6.1 \\n[21] grid_3.6.1 tidyselect_1.0.0 glue_1.4.0 inline_0.3.15 \\n[25] R6_2.4.1 processx_3.4.2 fansi_0.4.1 callr_3.4.3 \\n[29] purrr_0.3.4 magrittr_1.5 backports_1.1.6 scales_1.1.0 \\n[33] ps_1.3.2 ellipsis_0.3.0 matrixStats_0.56.0 abind_1.4-5 \\n[37] assertthat_0.2.1 colorspace_1.4-1 munsell_0.5.0 crayon_1.3.4 &#x27;}}</dd><dt><span>model_name :</span></dt><dd>diamonds</dd><dt><span>model_info :</span></dt><dd>{&#x27;name&#x27;: &#x27;diamonds&#x27;, &#x27;keywords&#x27;: [&#x27;stan_benchmark&#x27;, &#x27;linear regression&#x27;], &#x27;title&#x27;: &#x27;Multiple Highly Correlated Predictors Log-Log Model&#x27;, &#x27;prior&#x27;: {&#x27;keywords&#x27;: &#x27;stan_recommended_35dbfe6&#x27;}, &#x27;description&#x27;: &#x27;log(price) ~ carat * (log(x) + log(y) + log(z)) + cut + color + clarity&#x27;, &#x27;urls&#x27;: &#x27;https://github.com/bbbales2/cmdstan-warmup/blob/develop/examples/diamonds&#x27;, &#x27;model_implementations&#x27;: {&#x27;stan&#x27;: {&#x27;model_code&#x27;: &#x27;models/stan/diamonds.stan&#x27;}}, &#x27;references&#x27;: &#x27;bales2019selecting&#x27;, &#x27;added_date&#x27;: &#x27;2020-02-01&#x27;, &#x27;added_by&#x27;: &#x27;Oliver Järnefelt&#x27;}</dd><dt><span>model_stan :</span></dt><dd>// generated with brms 2.10.0\n",
"\n",
"functions {\n",
"}\n",
"data {\n",
" int&lt;lower=1&gt; N; // number of observations\n",
" vector[N] Y; // response variable\n",
" int&lt;lower=1&gt; K; // number of population-level effects\n",
" matrix[N, K] X; // population-level design matrix\n",
" int prior_only; // should the likelihood be ignored?\n",
"}\n",
"transformed data {\n",
" int Kc = K - 1;\n",
" matrix[N, Kc] Xc; // centered version of X without an intercept\n",
" vector[Kc] means_X; // column means of X before centering\n",
" for (i in 2:K) {\n",
" means_X[i - 1] = mean(X[, i]);\n",
" Xc[, i - 1] = X[, i] - means_X[i - 1];\n",
" }\n",
"}\n",
"parameters {\n",
" vector[Kc] b; // population-level effects\n",
" // temporary intercept for centered predictors\n",
" real Intercept;\n",
" real&lt;lower=0&gt; sigma; // residual SD\n",
"}\n",
"transformed parameters {\n",
"}\n",
"model {\n",
" // priors including all constants\n",
" target += normal_lpdf(b | 0, 1);\n",
" target += student_t_lpdf(Intercept | 3, 8, 10);\n",
" target += student_t_lpdf(sigma | 3, 0, 10)\n",
" - 1 * student_t_lccdf(0 | 3, 0, 10);\n",
" // likelihood including all constants\n",
" if (!prior_only) {\n",
" target += normal_id_glm_lpdf(Y | Xc, Intercept, b, sigma);\n",
" }\n",
"}\n",
"generated quantities {\n",
" // actual population-level intercept\n",
" real b_Intercept = Intercept - dot_product(means_X, b);\n",
"}\n",
"</dd><dt><span>model_pymc3 :</span></dt><dd></dd><dt><span>data_name :</span></dt><dd>diamonds</dd><dt><span>data_info :</span></dt><dd>{&#x27;name&#x27;: &#x27;diamonds&#x27;, &#x27;keywords&#x27;: [&#x27;ggplot2&#x27;, &#x27;tidyverse&#x27;], &#x27;title&#x27;: &#x27;Diamonds data frame for brms generated by brms::make_standata&#x27;, &#x27;description&#x27;: &#x27;ggplot2 Diamonds data set for brms&#x27;, &#x27;urls&#x27;: &#x27;https://github.com/bbbales2/cmdstan-warmup/blob/develop/examples/diamonds&#x27;, &#x27;data_file&#x27;: &#x27;data/data/diamonds.json&#x27;, &#x27;references&#x27;: &#x27;wickham2016ggplot2&#x27;, &#x27;added_date&#x27;: &#x27;2020-02-01&#x27;, &#x27;added_by&#x27;: &#x27;Oliver Järnefelt&#x27;}</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: (K_dim_0: 1, N_dim_0: 1, X_dim_0: 5000, X_dim_1: 25, Y_dim_0: 5000, prior_only_dim_0: 1)\n",
"Coordinates:\n",
" * N_dim_0 (N_dim_0) int32 0\n",
" * Y_dim_0 (Y_dim_0) int32 0 1 2 3 4 5 ... 4995 4996 4997 4998 4999\n",
" * K_dim_0 (K_dim_0) int32 0\n",
" * X_dim_0 (X_dim_0) int32 0 1 2 3 4 5 ... 4995 4996 4997 4998 4999\n",
" * X_dim_1 (X_dim_1) int32 0 1 2 3 4 5 6 7 ... 18 19 20 21 22 23 24\n",
" * prior_only_dim_0 (prior_only_dim_0) int32 0\n",
"Data variables:\n",
" N (N_dim_0) int32 5000\n",
" Y (Y_dim_0) float64 7.993 8.247 8.317 ... 8.186 7.945 8.946\n",
" K (K_dim_0) int32 25\n",
" X (X_dim_0, X_dim_1) float64 1.0 0.82 1.792 ... 2.58 1.925\n",
" prior_only (prior_only_dim_0) int32 0\n",
"Attributes:\n",
" created_at: 2020-08-15T08:08:36.718109\n",
" arviz_version: 0.9.0</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-83d2fe45-c72f-4a57-bff8-c032302bc65e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-83d2fe45-c72f-4a57-bff8-c032302bc65e' 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'>K_dim_0</span>: 1</li><li><span class='xr-has-index'>N_dim_0</span>: 1</li><li><span class='xr-has-index'>X_dim_0</span>: 5000</li><li><span class='xr-has-index'>X_dim_1</span>: 25</li><li><span class='xr-has-index'>Y_dim_0</span>: 5000</li><li><span class='xr-has-index'>prior_only_dim_0</span>: 1</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-f469ecda-86b6-43fa-8379-13f9910234f3' class='xr-section-summary-in' type='checkbox' checked><label for='section-f469ecda-86b6-43fa-8379-13f9910234f3' class='xr-section-summary' >Coordinates: <span>(6)</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'>N_dim_0</span></div><div class='xr-var-dims'>(N_dim_0)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-af92cf2c-1c93-48f8-b859-fef75c9b2ec9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-af92cf2c-1c93-48f8-b859-fef75c9b2ec9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f325ffcf-6acf-44b6-b9bd-3d7b3f285dc2' class='xr-var-data-in' type='checkbox'><label for='data-f325ffcf-6acf-44b6-b9bd-3d7b3f285dc2' 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'>Y_dim_0</span></div><div class='xr-var-dims'>(Y_dim_0)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 ... 4996 4997 4998 4999</div><input id='attrs-2792b2c5-33bf-45ff-9f6e-b9a4678ee50f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2792b2c5-33bf-45ff-9f6e-b9a4678ee50f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c299bee6-70a4-4c90-93f9-e461445713cd' class='xr-var-data-in' type='checkbox'><label for='data-c299bee6-70a4-4c90-93f9-e461445713cd' 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, ..., 4997, 4998, 4999])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>K_dim_0</span></div><div class='xr-var-dims'>(K_dim_0)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-679c0da7-51e9-4147-b277-1a2ee9f2baca' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-679c0da7-51e9-4147-b277-1a2ee9f2baca' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-18fdd559-88cb-4544-9b11-6fa3b944a2a7' class='xr-var-data-in' type='checkbox'><label for='data-18fdd559-88cb-4544-9b11-6fa3b944a2a7' 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'>X_dim_0</span></div><div class='xr-var-dims'>(X_dim_0)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 ... 4996 4997 4998 4999</div><input id='attrs-6d9cc7da-4b8c-44ce-8cfb-a8e0ec601423' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6d9cc7da-4b8c-44ce-8cfb-a8e0ec601423' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-35b204bc-904a-463a-8e86-59d4f126f069' class='xr-var-data-in' type='checkbox'><label for='data-35b204bc-904a-463a-8e86-59d4f126f069' 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, ..., 4997, 4998, 4999])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>X_dim_1</span></div><div class='xr-var-dims'>(X_dim_1)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 19 20 21 22 23 24</div><input id='attrs-e9181a94-148f-45ba-bbcf-bd4071ebf192' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e9181a94-148f-45ba-bbcf-bd4071ebf192' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-89973bf2-eadc-4fad-a9bb-fa3fad751d81' class='xr-var-data-in' type='checkbox'><label for='data-89973bf2-eadc-4fad-a9bb-fa3fad751d81' 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, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
" 18, 19, 20, 21, 22, 23, 24])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>prior_only_dim_0</span></div><div class='xr-var-dims'>(prior_only_dim_0)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-24e64b27-2aa0-4272-b976-bf0f1f3016f4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-24e64b27-2aa0-4272-b976-bf0f1f3016f4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-06b7792c-a281-4388-95a6-b821628050f9' class='xr-var-data-in' type='checkbox'><label for='data-06b7792c-a281-4388-95a6-b821628050f9' 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></ul></div></li><li class='xr-section-item'><input id='section-9b15941f-8532-4e0c-85f5-bf02ee0be63b' class='xr-section-summary-in' type='checkbox' checked><label for='section-9b15941f-8532-4e0c-85f5-bf02ee0be63b' class='xr-section-summary' >Data variables: <span>(5)</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>N</span></div><div class='xr-var-dims'>(N_dim_0)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>5000</div><input id='attrs-4b2a8575-5d63-4e47-8fe5-5419475b37ee' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4b2a8575-5d63-4e47-8fe5-5419475b37ee' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-22dfec44-5a78-4864-b861-7c9572f097b3' class='xr-var-data-in' type='checkbox'><label for='data-22dfec44-5a78-4864-b861-7c9572f097b3' 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([5000])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Y</span></div><div class='xr-var-dims'>(Y_dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>7.993 8.247 8.317 ... 7.945 8.946</div><input id='attrs-2a316f50-1e58-4f7a-91eb-4f1eb0a550c0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2a316f50-1e58-4f7a-91eb-4f1eb0a550c0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-552a52bc-2e67-4b28-9a0f-d15509d88834' class='xr-var-data-in' type='checkbox'><label for='data-552a52bc-2e67-4b28-9a0f-d15509d88834' 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([7.99260665, 8.247482 , 8.31678913, ..., 8.18646443, 7.94520113,\n",
" 8.94585386])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>K</span></div><div class='xr-var-dims'>(K_dim_0)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>25</div><input id='attrs-5325ccbf-cc17-4300-b1b4-458d3a977a65' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5325ccbf-cc17-4300-b1b4-458d3a977a65' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-241733ff-0691-4685-b1b4-d75b0159efb8' class='xr-var-data-in' type='checkbox'><label for='data-241733ff-0691-4685-b1b4-d75b0159efb8' 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([25])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>X</span></div><div class='xr-var-dims'>(X_dim_0, X_dim_1)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.0 0.82 1.792 ... 2.591 2.58 1.925</div><input id='attrs-a764e307-8f12-4496-90bc-bd99d4cfb91d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a764e307-8f12-4496-90bc-bd99d4cfb91d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cd3d70be-c4c8-42e9-8d0f-2d01a5dd1103' class='xr-var-data-in' type='checkbox'><label for='data-cd3d70be-c4c8-42e9-8d0f-2d01a5dd1103' 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. , 0.82 , 1.79175947, ..., 1.46924276, 1.47333255,\n",
" 1.07725341],\n",
" [1. , 0.9 , 1.8050047 , ..., 1.62450423, 1.62745989,\n",
" 1.21326583],\n",
" [1. , 0.9 , 1.83418019, ..., 1.65076217, 1.64354481,\n",
" 1.18476741],\n",
" ...,\n",
" [1. , 1.01 , 1.84687877, ..., 1.86534756, 1.87169978,\n",
" 1.38489253],\n",
" [1. , 0.7 , 1.74221902, ..., 1.21955332, 1.21339672,\n",
" 0.88883238],\n",
" [1. , 1.32 , 1.96290773, ..., 2.5910382 , 2.57986747,\n",
" 1.92537183]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>prior_only</span></div><div class='xr-var-dims'>(prior_only_dim_0)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-030f793a-fc78-40b6-99c9-a357e4039b86' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-030f793a-fc78-40b6-99c9-a357e4039b86' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a0c4d11c-0a3a-4efa-a46e-da8229c85bce' class='xr-var-data-in' type='checkbox'><label for='data-a0c4d11c-0a3a-4efa-a46e-da8229c85bce' 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></ul></div></li><li class='xr-section-item'><input id='section-8f99df63-837e-4ffd-87bc-390c217bd50a' class='xr-section-summary-in' type='checkbox' checked><label for='section-8f99df63-837e-4ffd-87bc-390c217bd50a' class='xr-section-summary' >Attributes: <span>(2)</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>2020-08-15T08:08:36.718109</dd><dt><span>arviz_version :</span></dt><dd>0.9.0</dd></dl></div></li></ul></div></div><br></div>\n",
" </ul>\n",
" </div>\n",
" </li>\n",
" \n",
" </ul>\n",
" </div>\n",
" <style> /* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
" *\n",
" */\n",
"\n",
":root {\n",
" --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
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"}\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",
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" --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",
" /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
" display: none;\n",
"}\n",
"\n",
".xr-header {\n",
" padding-top: 6px;\n",
" padding-bottom: 6px;\n",
" margin-bottom: 4px;\n",
" border-bottom: solid 1px var(--xr-border-color);\n",
"}\n",
"\n",
".xr-header > div,\n",
".xr-header > ul {\n",
" display: inline;\n",
" margin-top: 0;\n",
" margin-bottom: 0;\n",
"}\n",
"\n",
".xr-obj-type,\n",
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" margin-left: 2px;\n",
" margin-right: 10px;\n",
"}\n",
"\n",
".xr-obj-type {\n",
" color: var(--xr-font-color2);\n",
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"\n",
".xr-sections {\n",
" padding-left: 0 !important;\n",
" display: grid;\n",
" grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
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"\n",
".xr-section-item {\n",
" display: contents;\n",
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"\n",
".xr-section-item input {\n",
" display: none;\n",
"}\n",
"\n",
".xr-section-item input + label {\n",
" color: var(--xr-disabled-color);\n",
"}\n",
"\n",
".xr-section-item input:enabled + label {\n",
" cursor: pointer;\n",
" color: var(--xr-font-color2);\n",
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"\n",
".xr-section-item input:enabled + label:hover {\n",
" color: var(--xr-font-color0);\n",
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"\n",
".xr-section-summary {\n",
" grid-column: 1;\n",
" color: var(--xr-font-color2);\n",
" font-weight: 500;\n",
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"\n",
".xr-section-summary > span {\n",
" display: inline-block;\n",
" padding-left: 0.5em;\n",
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"\n",
".xr-section-summary-in:disabled + label {\n",
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
".xr-section-summary-in + label:before {\n",
" display: inline-block;\n",
" content: '►';\n",
" font-size: 11px;\n",
" width: 15px;\n",
" text-align: center;\n",
"}\n",
"\n",
".xr-section-summary-in:disabled + label:before {\n",
" color: var(--xr-disabled-color);\n",
"}\n",
"\n",
".xr-section-summary-in:checked + label:before {\n",
" content: '▼';\n",
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"\n",
".xr-section-summary-in:checked + label > span {\n",
" display: none;\n",
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"\n",
".xr-section-summary,\n",
".xr-section-inline-details {\n",
" padding-top: 4px;\n",
" padding-bottom: 4px;\n",
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"\n",
".xr-section-inline-details {\n",
" grid-column: 2 / -1;\n",
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"\n",
".xr-section-details {\n",
" display: none;\n",
" grid-column: 1 / -1;\n",
" margin-bottom: 5px;\n",
"}\n",
"\n",
".xr-section-summary-in:checked ~ .xr-section-details {\n",
" display: contents;\n",
"}\n",
"\n",
".xr-array-wrap {\n",
" grid-column: 1 / -1;\n",
" display: grid;\n",
" grid-template-columns: 20px auto;\n",
"}\n",
"\n",
".xr-array-wrap > label {\n",
" grid-column: 1;\n",
" vertical-align: top;\n",
"}\n",
"\n",
".xr-preview {\n",
" color: var(--xr-font-color3);\n",
"}\n",
"\n",
".xr-array-preview,\n",
".xr-array-data {\n",
" padding: 0 5px !important;\n",
" grid-column: 2;\n",
"}\n",
"\n",
".xr-array-data,\n",
".xr-array-in:checked ~ .xr-array-preview {\n",
" display: none;\n",
"}\n",
"\n",
".xr-array-in:checked ~ .xr-array-data,\n",
".xr-array-preview {\n",
" display: inline-block;\n",
"}\n",
"\n",
".xr-dim-list {\n",
" display: inline-block !important;\n",
" list-style: none;\n",
" padding: 0 !important;\n",
" margin: 0;\n",
"}\n",
"\n",
".xr-dim-list li {\n",
" display: inline-block;\n",
" padding: 0;\n",
" margin: 0;\n",
"}\n",
"\n",
".xr-dim-list:before {\n",
" content: '(';\n",
"}\n",
"\n",
".xr-dim-list:after {\n",
" content: ')';\n",
"}\n",
"\n",
".xr-dim-list li:not(:last-child):after {\n",
" content: ',';\n",
" padding-right: 5px;\n",
"}\n",
"\n",
".xr-has-index {\n",
" font-weight: bold;\n",
"}\n",
"\n",
".xr-var-list,\n",
".xr-var-item {\n",
" display: contents;\n",
"}\n",
"\n",
".xr-var-item > div,\n",
".xr-var-item label,\n",
".xr-var-item > .xr-var-name span {\n",
" background-color: var(--xr-background-color-row-even);\n",
" margin-bottom: 0;\n",
"}\n",
"\n",
".xr-var-item > .xr-var-name:hover span {\n",
" padding-right: 5px;\n",
"}\n",
"\n",
".xr-var-list > li:nth-child(odd) > div,\n",
".xr-var-list > li:nth-child(odd) > label,\n",
".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
" background-color: var(--xr-background-color-row-odd);\n",
"}\n",
"\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> observed_data"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"posterior"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"df = pd.DataFrame.from_dict(timings, orient=\"index\")"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.hist(df.values.ravel(), bins=30)\n",
"plt.title(\"Timings\")\n",
"[item.set_visible(False) for item in plt.gca().spines.values()]\n",
"plt.yscale(\"log\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"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": "3.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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