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MRE_missing_values
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# MRE_missing_values"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Issue: `sample_prior_predictive` fails for a model with missing values. Is there any way to correct it?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Setup"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Imports"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Author: jon@sedar.co\n",
"\n",
"Last updated: 2021-04-01 02:03:52\n",
"\n",
"Python implementation: CPython\n",
"Python version : 3.8.5\n",
"IPython version : 7.20.0\n",
"\n",
"Compiler : Clang 10.0.0 \n",
"OS : Darwin\n",
"Release : 19.6.0\n",
"Machine : x86_64\n",
"Processor : i386\n",
"CPU cores : 8\n",
"Architecture: 64bit\n",
"\n",
"matplotlib: 3.3.2\n",
"theano : 1.0.5\n",
"arviz : 0.11.0\n",
"seaborn : 0.11.1\n",
"numpy : 1.19.2\n",
"pymc3 : 3.9.3\n",
"\n"
]
}
],
"source": [
"import arviz as az\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import pymc3 as pm\n",
"import seaborn as sns\n",
"import theano.tensor as tt\n",
"import warnings\n",
"\n",
"%load_ext watermark\n",
"%watermark -a \"jon@sedar.co\" -udtmv -iv"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Notebook config"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"%config InlineBackend.figure_format = 'retina'\n",
"sns.set(style='darkgrid', palette='muted', context='notebook')\n",
"plt.rcParams['figure.figsize'] = 16, 4\n",
"RANDOM_SEED = 42\n",
"RNG = np.random.default_rng(seed=RANDOM_SEED)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n",
"\n",
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 1. Simple linreg, no missing values, everything works as expected"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create dataset"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[ 8.01092742]\n",
" [11.5490581 ]\n",
" [ 7.20564887]\n",
" [ 9.03575594]\n",
" [ 7.91899853]]\n",
"[[-0.69528292 1.96001589]\n",
" [-0.2495488 3.94056472]\n",
" [-2.95103519 1.69782049]\n",
" [-0.8721596 2.68375741]\n",
" [-1.01680116 2.14695607]]\n"
]
}
],
"source": [
"n = 20\n",
"X_MU = np.array([-1., 3.])\n",
"INTERCEPT = 2.\n",
"BETA = np.array([-0.5, 2.])\n",
"S = 1.\n",
"\n",
"x0 = RNG.normal(loc=0, scale=1, size=(n, 2)) + X_MU\n",
"s = RNG.normal(loc=0, scale=1, size=n) + S\n",
"\n",
"y = INTERCEPT + np.dot(x0, BETA.T) + s\n",
"\n",
"print(y.reshape(-1, 1)[:5])\n",
"print(x0[:5])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Define model"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
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"source": [
"coords={'names': ['a', 'b'],\n",
" 'obs': np.arange(y.shape[0])}\n",
"\n",
"# don't standardize: want to demonstrate param recovery\n",
"# x0s = (x0 - np.nanmean(x0, axis=0)) / (2 * np.nanstd(x0, axis=0))\n",
"\n",
"with pm.Model(coords=coords) as mdl0:\n",
" x_obs = pm.Data('x_obs', x0, dims=['obs', 'names'])\n",
" intercept = pm.Normal('intercept', 0, 2)\n",
" beta = pm.Normal('beta', 0, 2, dims='names')\n",
" sigma = pm.InverseGamma('sigma', alpha=6, beta=5)\n",
" like = pm.Normal('like', mu=intercept + tt.dot(beta, x_obs.T),\n",
" sigma=sigma, observed=y, dims='obs')\n",
"\n",
"rvs = ['intercept', 'beta', 'sigma']\n",
"display(pm.model_graph.model_to_graphviz(mdl0))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### `sample_prior_predictive` works as normal"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"with mdl0:\n",
" trc0_prior = pm.sample_prior_predictive(random_seed=RANDOM_SEED)\n",
"azid0 = az.from_pymc3(model=mdl0, prior=trc0_prior)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 864x316.8 with 4 Axes>"
]
},
"metadata": {
"image/png": {
"height": 306,
"width": 852
}
},
"output_type": "display_data"
}
],
"source": [
"extra = 1\n",
"m, n = 2, ((len(rvs)+extra) // 2) + ((len(rvs)+extra) % 2)\n",
"f, axs = plt.subplots(n, m, figsize=(m*6, 2.2*n))\n",
"_ = az.plot_posterior(azid0, group='prior', var_names=rvs, ax=axs)\n",
"f.tight_layout()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Just for good measure, show that `sample` works fine"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Auto-assigning NUTS sampler...\n",
"Initializing NUTS using jitter+adapt_diag...\n",
"Multiprocess sampling (4 chains in 4 jobs)\n",
"NUTS: [sigma, beta, intercept]\n"
]
},
{
"data": {
"text/html": [
"\n",
" <div>\n",
" <style>\n",
" /* Turns off some styling */\n",
" progress {\n",
" /* gets rid of default border in Firefox and Opera. */\n",
" border: none;\n",
" /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
" background-size: auto;\n",
" }\n",
" .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
" background: #F44336;\n",
" }\n",
" </style>\n",
" <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
" 100.00% [8000/8000 00:12<00:00 Sampling 4 chains, 0 divergences]\n",
" </div>\n",
" "
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 20 seconds.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"0, dim: obs, 20 =? 20\n"
]
}
],
"source": [
"with mdl0:\n",
" trc0 = pm.sample(target_accept=0.85, random_seed=RANDOM_SEED)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0, dim: obs, 20 =? 20\n"
]
}
],
"source": [
"azid0.extend(az.from_pymc3(model=mdl0, trace=trc0))"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 864x432 with 6 Axes>"
]
},
"metadata": {
"image/png": {
"height": 440,
"width": 872
}
},
"output_type": "display_data"
}
],
"source": [
"_ = az.plot_trace(azid0, compact=True, combined=False, kind=\"rank_bars\")"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>mean</th>\n",
" <th>sd</th>\n",
" <th>hdi_3%</th>\n",
" <th>hdi_97%</th>\n",
" <th>mcse_mean</th>\n",
" <th>mcse_sd</th>\n",
" <th>ess_mean</th>\n",
" <th>ess_sd</th>\n",
" <th>ess_bulk</th>\n",
" <th>ess_tail</th>\n",
" <th>r_hat</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>intercept</th>\n",
" <td>1.906</td>\n",
" <td>0.706</td>\n",
" <td>0.597</td>\n",
" <td>3.305</td>\n",
" <td>0.020</td>\n",
" <td>0.014</td>\n",
" <td>1284.0</td>\n",
" <td>1284.0</td>\n",
" <td>1294.0</td>\n",
" <td>1725.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>beta[0]</th>\n",
" <td>-0.480</td>\n",
" <td>0.209</td>\n",
" <td>-0.865</td>\n",
" <td>-0.075</td>\n",
" <td>0.005</td>\n",
" <td>0.003</td>\n",
" <td>2140.0</td>\n",
" <td>2028.0</td>\n",
" <td>2145.0</td>\n",
" <td>1938.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>beta[1]</th>\n",
" <td>2.429</td>\n",
" <td>0.219</td>\n",
" <td>2.015</td>\n",
" <td>2.829</td>\n",
" <td>0.006</td>\n",
" <td>0.004</td>\n",
" <td>1325.0</td>\n",
" <td>1325.0</td>\n",
" <td>1331.0</td>\n",
" <td>1995.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>sigma</th>\n",
" <td>0.731</td>\n",
" <td>0.124</td>\n",
" <td>0.503</td>\n",
" <td>0.944</td>\n",
" <td>0.003</td>\n",
" <td>0.002</td>\n",
" <td>2038.0</td>\n",
" <td>2022.0</td>\n",
" <td>1950.0</td>\n",
" <td>1231.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" mean sd hdi_3% hdi_97% mcse_mean mcse_sd ess_mean \\\n",
"intercept 1.906 0.706 0.597 3.305 0.020 0.014 1284.0 \n",
"beta[0] -0.480 0.209 -0.865 -0.075 0.005 0.003 2140.0 \n",
"beta[1] 2.429 0.219 2.015 2.829 0.006 0.004 1325.0 \n",
"sigma 0.731 0.124 0.503 0.944 0.003 0.002 2038.0 \n",
"\n",
" ess_sd ess_bulk ess_tail r_hat \n",
"intercept 1284.0 1294.0 1725.0 1.0 \n",
"beta[0] 2028.0 2145.0 1938.0 1.0 \n",
"beta[1] 1325.0 1331.0 1995.0 1.0 \n",
"sigma 2022.0 1950.0 1231.0 1.0 "
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pm.summary(azid0)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x316.8 with 4 Axes>"
]
},
"metadata": {
"image/png": {
"height": 306,
"width": 852
}
},
"output_type": "display_data"
}
],
"source": [
"extra = 1\n",
"m, n = 2, ((len(rvs)+extra) // 2) + ((len(rvs)+extra) % 2)\n",
"f, axs = plt.subplots(n, m, figsize=(m*6, 2.2*n))\n",
"_ = az.plot_posterior(azid0, group='posterior', var_names=rvs, ax=axs, ref_val=[INTERCEPT, BETA[0], BETA[1], S])\n",
"f.tight_layout()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Observe:**\n",
"\n",
"+ Seems to have converged, traces look good, no divergences, rhat and ESS look good\n",
"+ Posteriors (mostly) contain the original parameter values"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n",
"\n",
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 2. Same model but dataset has missing values, `sample_prior_predictive` fails"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Lossy copy dataset with missing vals, and create masked array"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"masked_array(\n",
" data=[[-0.6952829202455686, --],\n",
" [-0.24954880419354275, 3.940564716391214],\n",
" [-2.9510351886538366, 1.697820493137682],\n",
" [-0.8721595968327146, --],\n",
" [-1.0168011575042888, 2.14695607242642],\n",
" [-0.12060202513717144, 3.777791935428948],\n",
" [-0.933969302438784, --],\n",
" [-0.5324906577479545, 2.1407075371167616],\n",
" [-0.6312492159175012, 2.041117399171001],\n",
" [-0.12154969869272747, 2.950074089013747],\n",
" [-1.1848623635452606, 2.3190704555960586],\n",
" [0.22254133867403025, 2.8454705179311977],\n",
" [-1.4283278221631073, 2.6478664495117705],\n",
" [-0.4676908144466513, 3.365444064364078],\n",
" [-0.5872673884040116, --],\n",
" [1.1416476008704612, 2.5935849836153846],\n",
" [-1.5122427290715375, 2.1862272717521223],\n",
" [--, 4.128972292720892],\n",
" [-1.1139474576548751, 2.159843523037472],\n",
" [-1.8244812156912396, 3.650592787824701]],\n",
" mask=[[False, True],\n",
" [False, False],\n",
" [False, False],\n",
" [False, True],\n",
" [False, False],\n",
" [False, False],\n",
" [False, True],\n",
" [False, False],\n",
" [False, False],\n",
" [False, False],\n",
" [False, False],\n",
" [False, False],\n",
" [False, False],\n",
" [False, False],\n",
" [False, True],\n",
" [False, False],\n",
" [False, False],\n",
" [ True, False],\n",
" [False, False],\n",
" [False, False]],\n",
" fill_value=1e+20)"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x1 = np.where(RNG.binomial(n=1, p=0.2, size=x0.shape), np.nan, x0)\n",
"x1ma = np.ma.masked_array(x1, mask = np.isnan(x1))\n",
"x1ma"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Define model"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/jon/opt/anaconda3/envs/freberg_trk/lib/python3.8/site-packages/pymc3/model.py:1668: ImputationWarning: Data in x_pred contains missing values and will be automatically imputed from the sampling distribution.\n",
" warnings.warn(impute_message, ImputationWarning)\n"
]
},
{
"data": {
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"</g>\n",
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"</g>\n",
"<!-- beta&#45;&gt;like -->\n",
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],
"text/plain": [
"<graphviz.dot.Digraph at 0x7f8b9c8da820>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"coords={'names': ['a', 'b'],\n",
" 'obs': np.arange(y.shape[0])}\n",
"\n",
"with pm.Model(coords=coords) as mdl1:\n",
" \n",
" x_mu = pm.Normal('x_mu', 0, 2, dims='names')\n",
" x_pred = pm.Normal('x_pred', mu=x_mu, sigma=1., observed=x1ma, dims=['obs', 'names'])\n",
" \n",
" intercept = pm.Normal('intercept', 0, 2)\n",
" beta = pm.Normal('beta', 0, 2, dims='names')\n",
" sigma = pm.InverseGamma('sigma', alpha=11, beta=10)\n",
" like = pm.Normal('like', mu=intercept + tt.dot(beta, x_pred.T),\n",
" sigma=sigma, observed=y, dims='obs')\n",
"\n",
"rvs = ['x_mu', 'intercept', 'beta', 'sigma']\n",
"display(pm.model_graph.model_to_graphviz(mdl1))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### `sample_prior_predictive` fails"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"ename": "ValueError",
"evalue": "shape mismatch: value array of shape (2,) could not be broadcast to indexing result of shape (5,)\nApply node that caused the error: AdvancedIncSubtensor{inplace=False, set_instead_of_inc=True}(TensorConstant{[[-6.95282..9279e+00]]}, x_pred_missing, TensorConstant{[ 0 3 6 14 17]}, TensorConstant{[1 1 1 1 0]})\nToposort index: 0\nInputs types: [TensorType(float64, matrix), TensorType(float64, vector), TensorType(int64, vector), TensorType(int64, vector)]\nInputs shapes: [(20, 2), (2,), (5,), (5,)]\nInputs strides: [(16, 8), (8,), (16,), (16,)]\nInputs values: ['not shown', array([-1.26155332, -1.39000285]), array([ 0, 3, 6, 14, 17]), array([1, 1, 1, 1, 0])]\nOutputs clients: [[CGemv{inplace}(AllocEmpty{dtype='float64'}.0, TensorConstant{1.0}, x_pred, beta, TensorConstant{0.0})]]\n\nBacktrace when the node is created(use Theano flag traceback.limit=N to make it longer):\n File \"/Users/jon/opt/anaconda3/envs/freberg_trk/lib/python3.8/site-packages/IPython/core/interactiveshell.py\", line 3155, in run_cell_async\n has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n File \"/Users/jon/opt/anaconda3/envs/freberg_trk/lib/python3.8/site-packages/IPython/core/interactiveshell.py\", line 3347, in run_ast_nodes\n if (await self.run_code(code, result, async_=asy)):\n File \"/Users/jon/opt/anaconda3/envs/freberg_trk/lib/python3.8/site-packages/IPython/core/interactiveshell.py\", line 3427, in run_code\n exec(code_obj, self.user_global_ns, self.user_ns)\n File \"<ipython-input-24-5ab5bac64eed>\", line 7, in <module>\n x_pred = pm.Normal('x_pred', mu=x_mu, sigma=1., observed=x1ma, dims=['obs', 'names'])\n File \"/Users/jon/opt/anaconda3/envs/freberg_trk/lib/python3.8/site-packages/pymc3/distributions/distribution.py\", line 83, in __new__\n return model.Var(name, dist, data, total_size, dims=dims)\n File \"/Users/jon/opt/anaconda3/envs/freberg_trk/lib/python3.8/site-packages/pymc3/model.py\", line 1112, in Var\n var = ObservedRV(\n File \"/Users/jon/opt/anaconda3/envs/freberg_trk/lib/python3.8/site-packages/pymc3/model.py\", line 1737, in __init__\n data = as_tensor(data, name, model, distribution)\n File \"/Users/jon/opt/anaconda3/envs/freberg_trk/lib/python3.8/site-packages/pymc3/model.py\", line 1683, in as_tensor\n dataTensor = tt.set_subtensor(constant[data.mask.nonzero()], missing_values)\n\nHINT: Use the Theano flag 'exception_verbosity=high' for a debugprint and storage map footprint of this apply node.",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m~/opt/anaconda3/envs/freberg_trk/lib/python3.8/site-packages/pymc3/distributions/distribution.py\u001b[0m in \u001b[0;36m_draw_value\u001b[0;34m(param, point, givens, size)\u001b[0m\n\u001b[1;32m 834\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 835\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mdist_tmp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpoint\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpoint\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 836\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mValueError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/opt/anaconda3/envs/freberg_trk/lib/python3.8/site-packages/pymc3/distributions/continuous.py\u001b[0m in \u001b[0;36mrandom\u001b[0;34m(self, point, size)\u001b[0m\n\u001b[1;32m 512\u001b[0m \"\"\"\n\u001b[0;32m--> 513\u001b[0;31m mu, tau, _ = draw_values([self.mu, self.tau, self.sigma],\n\u001b[0m\u001b[1;32m 514\u001b[0m point=point, size=size)\n",
"\u001b[0;32m~/opt/anaconda3/envs/freberg_trk/lib/python3.8/site-packages/pymc3/distributions/distribution.py\u001b[0m in \u001b[0;36mdraw_values\u001b[0;34m(params, point, size)\u001b[0m\n\u001b[1;32m 694\u001b[0m )\n\u001b[0;32m--> 695\u001b[0;31m value = _draw_value(param,\n\u001b[0m\u001b[1;32m 696\u001b[0m \u001b[0mpoint\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpoint\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/opt/anaconda3/envs/freberg_trk/lib/python3.8/site-packages/pymc3/distributions/distribution.py\u001b[0m in \u001b[0;36m_draw_value\u001b[0;34m(param, point, givens, size)\u001b[0m\n\u001b[1;32m 875\u001b[0m \u001b[0mfunc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_compile_theano_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparam\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput_vars\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 876\u001b[0;31m \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput_vals\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 877\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0moutput\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/opt/anaconda3/envs/freberg_trk/lib/python3.8/site-packages/numpy/lib/function_base.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 2107\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2108\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_vectorize_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2109\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/opt/anaconda3/envs/freberg_trk/lib/python3.8/site-packages/numpy/lib/function_base.py\u001b[0m in \u001b[0;36m_vectorize_call\u001b[0;34m(self, func, args)\u001b[0m\n\u001b[1;32m 2181\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msignature\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2182\u001b[0;31m \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_vectorize_call_with_signature\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2183\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/opt/anaconda3/envs/freberg_trk/lib/python3.8/site-packages/numpy/lib/function_base.py\u001b[0m in \u001b[0;36m_vectorize_call_with_signature\u001b[0;34m(self, func, args)\u001b[0m\n\u001b[1;32m 2210\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2211\u001b[0;31m broadcast_shape, dim_sizes = _parse_input_dimensions(\n\u001b[0m\u001b[1;32m 2212\u001b[0m args, input_core_dims)\n",
"\u001b[0;32m~/opt/anaconda3/envs/freberg_trk/lib/python3.8/site-packages/numpy/lib/function_base.py\u001b[0m in \u001b[0;36m_parse_input_dimensions\u001b[0;34m(args, input_core_dims)\u001b[0m\n\u001b[1;32m 1876\u001b[0m \u001b[0mbroadcast_args\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdummy_array\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1877\u001b[0;31m \u001b[0mbroadcast_shape\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstride_tricks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_broadcast_shape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mbroadcast_args\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1878\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mbroadcast_shape\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdim_sizes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/opt/anaconda3/envs/freberg_trk/lib/python3.8/site-packages/numpy/lib/stride_tricks.py\u001b[0m in \u001b[0;36m_broadcast_shape\u001b[0;34m(*args)\u001b[0m\n\u001b[1;32m 188\u001b[0m \u001b[0;31m# consistently\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 189\u001b[0;31m \u001b[0mb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbroadcast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m32\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 190\u001b[0m \u001b[0;31m# unfortunately, it cannot handle 32 or more arguments directly\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mValueError\u001b[0m: shape mismatch: objects cannot be broadcast to a single shape",
"\nDuring handling of the above exception, another exception occurred:\n",
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m~/opt/anaconda3/envs/freberg_trk/lib/python3.8/site-packages/theano/compile/function_module.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 902\u001b[0m \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 903\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0moutput_subset\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 904\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput_subset\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moutput_subset\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/opt/anaconda3/envs/freberg_trk/lib/python3.8/site-packages/theano/gof/op.py\u001b[0m in \u001b[0;36mrval\u001b[0;34m(p, i, o, n)\u001b[0m\n\u001b[1;32m 891\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mrval\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnode_input_storage\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mo\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnode_output_storage\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnode\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 892\u001b[0;31m \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mp\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 893\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mo\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mnode\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/opt/anaconda3/envs/freberg_trk/lib/python3.8/site-packages/theano/tensor/subtensor.py\u001b[0m in \u001b[0;36mperform\u001b[0;34m(self, node, inputs, out_)\u001b[0m\n\u001b[1;32m 2338\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_instead_of_inc\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2339\u001b[0;31m \u001b[0mout\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2340\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mValueError\u001b[0m: shape mismatch: value array of shape (2,) could not be broadcast to indexing result of shape (5,)",
"\nDuring handling of the above exception, another exception occurred:\n",
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-25-344174df0c1a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mmdl1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mtrc1_prior\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msample_prior_predictive\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrandom_seed\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mRANDOM_SEED\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mazid1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maz\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_pymc3\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmdl1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprior\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtrc1_prior\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/opt/anaconda3/envs/freberg_trk/lib/python3.8/site-packages/pymc3/sampling.py\u001b[0m in \u001b[0;36msample_prior_predictive\u001b[0;34m(samples, model, vars, var_names, random_seed)\u001b[0m\n\u001b[1;32m 1955\u001b[0m \u001b[0mnames\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_default_varnames\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvars_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minclude_transformed\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1956\u001b[0m \u001b[0;31m# draw_values fails with auto-transformed variables. transform them later!\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1957\u001b[0;31m \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdraw_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mnames\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msamples\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1958\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1959\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnames\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/opt/anaconda3/envs/freberg_trk/lib/python3.8/site-packages/pymc3/distributions/distribution.py\u001b[0m in \u001b[0;36mdraw_values\u001b[0;34m(params, point, size)\u001b[0m\n\u001b[1;32m 649\u001b[0m \u001b[0;31m# This may fail for autotransformed RVs, which don't\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 650\u001b[0m \u001b[0;31m# have the random method\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 651\u001b[0;31m value = _draw_value(next_,\n\u001b[0m\u001b[1;32m 652\u001b[0m \u001b[0mpoint\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpoint\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 653\u001b[0m \u001b[0mgivens\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtemp_givens\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/opt/anaconda3/envs/freberg_trk/lib/python3.8/site-packages/pymc3/distributions/distribution.py\u001b[0m in \u001b[0;36m_draw_value\u001b[0;34m(param, point, givens, size)\u001b[0m\n\u001b[1;32m 841\u001b[0m \u001b[0;31m# we don't want to store these drawn values to the context\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 842\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0m_DrawValuesContextBlocker\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 843\u001b[0;31m val = np.atleast_1d(dist_tmp.random(point=point,\n\u001b[0m\u001b[1;32m 844\u001b[0m size=None))\n\u001b[1;32m 845\u001b[0m \u001b[0;31m# Sometimes point may change the size of val but not the\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/opt/anaconda3/envs/freberg_trk/lib/python3.8/site-packages/pymc3/distributions/continuous.py\u001b[0m in \u001b[0;36mrandom\u001b[0;34m(self, point, size)\u001b[0m\n\u001b[1;32m 511\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 512\u001b[0m \"\"\"\n\u001b[0;32m--> 513\u001b[0;31m mu, tau, _ = draw_values([self.mu, self.tau, self.sigma],\n\u001b[0m\u001b[1;32m 514\u001b[0m point=point, size=size)\n\u001b[1;32m 515\u001b[0m return generate_samples(stats.norm.rvs, loc=mu, scale=tau**-0.5,\n",
"\u001b[0;32m~/opt/anaconda3/envs/freberg_trk/lib/python3.8/site-packages/pymc3/distributions/distribution.py\u001b[0m in \u001b[0;36mdraw_values\u001b[0;34m(params, point, size)\u001b[0m\n\u001b[1;32m 693\u001b[0m \u001b[0mdrawn\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msize\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 694\u001b[0m )\n\u001b[0;32m--> 695\u001b[0;31m value = _draw_value(param,\n\u001b[0m\u001b[1;32m 696\u001b[0m \u001b[0mpoint\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpoint\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 697\u001b[0m \u001b[0mgivens\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mgivens\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/opt/anaconda3/envs/freberg_trk/lib/python3.8/site-packages/pymc3/distributions/distribution.py\u001b[0m in \u001b[0;36m_draw_value\u001b[0;34m(param, point, givens, size)\u001b[0m\n\u001b[1;32m 874\u001b[0m \u001b[0minput_vals\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 875\u001b[0m \u001b[0mfunc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_compile_theano_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparam\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput_vars\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 876\u001b[0;31m \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput_vals\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 877\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0moutput\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 878\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Unexpected type in draw_value: %s'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparam\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/opt/anaconda3/envs/freberg_trk/lib/python3.8/site-packages/numpy/lib/function_base.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 2106\u001b[0m \u001b[0mvargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0m_n\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0m_n\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mnames\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2107\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2108\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_vectorize_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2109\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2110\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_get_ufunc_and_otypes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/opt/anaconda3/envs/freberg_trk/lib/python3.8/site-packages/numpy/lib/function_base.py\u001b[0m in \u001b[0;36m_vectorize_call\u001b[0;34m(self, func, args)\u001b[0m\n\u001b[1;32m 2180\u001b[0m \u001b[0;34m\"\"\"Vectorized call to `func` over positional `args`.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2181\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msignature\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2182\u001b[0;31m \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_vectorize_call_with_signature\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2183\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2184\u001b[0m \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/opt/anaconda3/envs/freberg_trk/lib/python3.8/site-packages/numpy/lib/function_base.py\u001b[0m in \u001b[0;36m_vectorize_call_with_signature\u001b[0;34m(self, func, args)\u001b[0m\n\u001b[1;32m 2221\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2222\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mindex\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mbroadcast_shape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2223\u001b[0;31m \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0marg\u001b[0m \u001b[0;32min\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2224\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2225\u001b[0m \u001b[0mn_results\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresults\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresults\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/opt/anaconda3/envs/freberg_trk/lib/python3.8/site-packages/theano/compile/function_module.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 912\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'thunks'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 913\u001b[0m \u001b[0mthunk\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mthunks\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mposition_of_error\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 914\u001b[0;31m gof.link.raise_with_op(\n\u001b[0m\u001b[1;32m 915\u001b[0m \u001b[0mnode\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnodes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mposition_of_error\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 916\u001b[0m \u001b[0mthunk\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mthunk\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/opt/anaconda3/envs/freberg_trk/lib/python3.8/site-packages/theano/gof/link.py\u001b[0m in \u001b[0;36mraise_with_op\u001b[0;34m(node, thunk, exc_info, storage_map)\u001b[0m\n\u001b[1;32m 323\u001b[0m \u001b[0;31m# extra long error message in that case.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 324\u001b[0m \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 325\u001b[0;31m \u001b[0mreraise\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexc_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexc_value\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexc_trace\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 326\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 327\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/opt/anaconda3/envs/freberg_trk/lib/python3.8/site-packages/six.py\u001b[0m in \u001b[0;36mreraise\u001b[0;34m(tp, value, tb)\u001b[0m\n\u001b[1;32m 700\u001b[0m \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtp\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 701\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mtb\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 702\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 703\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 704\u001b[0m \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/opt/anaconda3/envs/freberg_trk/lib/python3.8/site-packages/theano/compile/function_module.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 901\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 902\u001b[0m \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 903\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0moutput_subset\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 904\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput_subset\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moutput_subset\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 905\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/opt/anaconda3/envs/freberg_trk/lib/python3.8/site-packages/theano/gof/op.py\u001b[0m in \u001b[0;36mrval\u001b[0;34m(p, i, o, n)\u001b[0m\n\u001b[1;32m 890\u001b[0m \u001b[0;31m# default arguments are stored in the closure of `rval`\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 891\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mrval\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnode_input_storage\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mo\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnode_output_storage\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnode\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 892\u001b[0;31m \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mp\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 893\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mo\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mnode\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 894\u001b[0m \u001b[0mcompute_map\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mo\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/opt/anaconda3/envs/freberg_trk/lib/python3.8/site-packages/theano/tensor/subtensor.py\u001b[0m in \u001b[0;36mperform\u001b[0;34m(self, node, inputs, out_)\u001b[0m\n\u001b[1;32m 2337\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2338\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_instead_of_inc\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2339\u001b[0;31m \u001b[0mout\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2340\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2341\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mValueError\u001b[0m: shape mismatch: value array of shape (2,) could not be broadcast to indexing result of shape (5,)\nApply node that caused the error: AdvancedIncSubtensor{inplace=False, set_instead_of_inc=True}(TensorConstant{[[-6.95282..9279e+00]]}, x_pred_missing, TensorConstant{[ 0 3 6 14 17]}, TensorConstant{[1 1 1 1 0]})\nToposort index: 0\nInputs types: [TensorType(float64, matrix), TensorType(float64, vector), TensorType(int64, vector), TensorType(int64, vector)]\nInputs shapes: [(20, 2), (2,), (5,), (5,)]\nInputs strides: [(16, 8), (8,), (16,), (16,)]\nInputs values: ['not shown', array([-1.26155332, -1.39000285]), array([ 0, 3, 6, 14, 17]), array([1, 1, 1, 1, 0])]\nOutputs clients: [[CGemv{inplace}(AllocEmpty{dtype='float64'}.0, TensorConstant{1.0}, x_pred, beta, TensorConstant{0.0})]]\n\nBacktrace when the node is created(use Theano flag traceback.limit=N to make it longer):\n File \"/Users/jon/opt/anaconda3/envs/freberg_trk/lib/python3.8/site-packages/IPython/core/interactiveshell.py\", line 3155, in run_cell_async\n has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n File \"/Users/jon/opt/anaconda3/envs/freberg_trk/lib/python3.8/site-packages/IPython/core/interactiveshell.py\", line 3347, in run_ast_nodes\n if (await self.run_code(code, result, async_=asy)):\n File \"/Users/jon/opt/anaconda3/envs/freberg_trk/lib/python3.8/site-packages/IPython/core/interactiveshell.py\", line 3427, in run_code\n exec(code_obj, self.user_global_ns, self.user_ns)\n File \"<ipython-input-24-5ab5bac64eed>\", line 7, in <module>\n x_pred = pm.Normal('x_pred', mu=x_mu, sigma=1., observed=x1ma, dims=['obs', 'names'])\n File \"/Users/jon/opt/anaconda3/envs/freberg_trk/lib/python3.8/site-packages/pymc3/distributions/distribution.py\", line 83, in __new__\n return model.Var(name, dist, data, total_size, dims=dims)\n File \"/Users/jon/opt/anaconda3/envs/freberg_trk/lib/python3.8/site-packages/pymc3/model.py\", line 1112, in Var\n var = ObservedRV(\n File \"/Users/jon/opt/anaconda3/envs/freberg_trk/lib/python3.8/site-packages/pymc3/model.py\", line 1737, in __init__\n data = as_tensor(data, name, model, distribution)\n File \"/Users/jon/opt/anaconda3/envs/freberg_trk/lib/python3.8/site-packages/pymc3/model.py\", line 1683, in as_tensor\n dataTensor = tt.set_subtensor(constant[data.mask.nonzero()], missing_values)\n\nHINT: Use the Theano flag 'exception_verbosity=high' for a debugprint and storage map footprint of this apply node."
]
}
],
"source": [
"with mdl1:\n",
" trc1_prior = pm.sample_prior_predictive(random_seed=RANDOM_SEED)\n",
"azid1 = az.from_pymc3(model=mdl1, prior=trc1_prior)"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [],
"source": [
"# extra = 3\n",
"# m, n = 2, ((len(rvs)+extra) // 2) + ((len(rvs)+extra) % 2)\n",
"# f, axs = plt.subplots(n, m, figsize=(m*6, 2.2*n))\n",
"# _ = az.plot_posterior(azid1, group='prior', var_names=rvs, ax=axs)\n",
"# f.tight_layout()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Just for good measure, show that `sample` works fine"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Auto-assigning NUTS sampler...\n",
"Initializing NUTS using jitter+adapt_diag...\n",
"Multiprocess sampling (4 chains in 4 jobs)\n",
"NUTS: [sigma, beta, intercept, x_pred_missing, x_mu]\n"
]
},
{
"data": {
"text/html": [
"\n",
" <div>\n",
" <style>\n",
" /* Turns off some styling */\n",
" progress {\n",
" /* gets rid of default border in Firefox and Opera. */\n",
" border: none;\n",
" /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
" background-size: auto;\n",
" }\n",
" .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
" background: #F44336;\n",
" }\n",
" </style>\n",
" <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
" 100.00% [8000/8000 00:13<00:00 Sampling 4 chains, 1 divergences]\n",
" </div>\n",
" "
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 23 seconds.\n",
"There was 1 divergence after tuning. Increase `target_accept` or reparameterize.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"0, dim: obs, 20 =? 20\n",
"1, dim: names, 2 =? 2\n",
"0, dim: obs, 20 =? 20\n"
]
}
],
"source": [
"with mdl1:\n",
" trc1 = pm.sample(target_accept=0.85, random_seed=RANDOM_SEED)"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0, dim: obs, 20 =? 20\n",
"1, dim: names, 2 =? 2\n",
"0, dim: obs, 20 =? 20\n"
]
}
],
"source": [
"azid1 = az.from_pymc3(model=mdl1, trace=trc1)"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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