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jessegrabowski/Shapes and Dims.ipynb

Last active January 4, 2023 21:53
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Shapes, Dims, and Coords in PyMC
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Shapes and Dims.ipynb
{
"cells": [
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "import pymc as pm\nimport numpy as np",
"execution_count": 1,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "k = 6\np = 10\nn = 100\n\ncoords = {\"player\" : np.arange(p),\n \"obs_id\" : np.arange(n),\n \"predictors\" : np.arange(k)}",
"execution_count": null,
"outputs": []
},
{
"metadata": {},
"cell_type": "markdown",
"source": "# Nd draws from a single normal "
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "with pm.Model(coords=coords) as shape_demo:\n beta_mu = pm.Normal('mu')\n beta_sigma = pm.HalfNormal('sigma')\n \n beta = pm.Normal('beta', mu=beta_mu, sigma=beta_sigma, dims=['player', 'predictors'])",
"execution_count": 2,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "beta_draws = pm.draw(beta, 100_000)\nwith np.printoptions(precision=3, suppress=True, linewidth=10_000):\n print(beta_draws.mean(axis=0))\n print(beta_draws.std(axis=0))",
"execution_count": 3,
"outputs": [
{
"output_type": "stream",
"text": "[[ 0.004 0.002 0.003 0.002 0.001 0.007]\n [ 0.004 -0. 0.005 0.008 0.002 0.006]\n [ 0.004 0.002 0.007 0.002 0.001 0.003]\n [ 0.008 0.008 0. 0.006 -0. 0.002]\n [-0. 0. 0.012 0.006 0.008 0.005]\n [ 0.005 0.001 0. 0.005 0.002 0.004]\n [ 0.009 0.002 0.004 0.003 -0.001 0.001]\n [ 0.002 0.007 0.005 0.006 0.001 0.006]\n [-0.001 0.006 0.004 -0.001 0.004 0.001]\n [ 0.003 0.001 0.007 -0. 0.008 0.003]]\n[[1.408 1.408 1.403 1.403 1.406 1.412]\n [1.403 1.409 1.411 1.41 1.411 1.414]\n [1.415 1.416 1.413 1.413 1.408 1.406]\n [1.405 1.406 1.411 1.415 1.412 1.4 ]\n [1.406 1.409 1.408 1.412 1.41 1.415]\n [1.411 1.417 1.412 1.407 1.409 1.409]\n [1.406 1.411 1.407 1.414 1.409 1.405]\n [1.409 1.408 1.404 1.412 1.414 1.406]\n [1.413 1.412 1.411 1.403 1.411 1.408]\n [1.412 1.414 1.41 1.408 1.409 1.41 ]]\n",
"name": "stdout"
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "## Show order doesn't matter when mu, sigma are scalar"
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "with pm.Model(coords=coords) as shape_demo:\n beta_mu = pm.Normal('mu')\n beta_sigma = pm.HalfNormal('sigma')\n \n beta = pm.Normal('beta', mu=beta_mu, sigma=beta_sigma, dims=['predictors', 'player'])",
"execution_count": 18,
"outputs": []
},
{
"metadata": {
"trusted": true,
"scrolled": true
},
"cell_type": "code",
"source": "beta_draws = pm.draw(beta, 100_000)\nwith np.printoptions(precision=3, suppress=True, linewidth=10_000):\n print(beta_draws.mean(axis=0))\n print(beta_draws.std(axis=0))",
"execution_count": 19,
"outputs": [
{
"output_type": "stream",
"text": "[[-0.004 -0.004 -0.005 -0.001 -0.008 -0.006 -0.005 -0.005 -0.005 -0.001]\n [-0.003 -0.008 -0.001 -0.002 -0.006 -0.002 -0.006 -0.008 -0.008 -0.004]\n [ 0.002 -0.005 -0.012 -0.009 -0.005 -0.004 0.005 -0.006 0.003 -0.004]\n [-0.002 -0.004 -0.001 -0.008 -0.004 0.001 -0.005 0. -0.002 -0.004]\n [-0.008 -0.008 -0.005 -0.001 -0.007 -0.005 -0.002 -0.001 -0.006 -0.002]\n [-0.01 0.001 -0.004 -0.004 0.002 0.002 -0.008 -0.003 -0.004 -0.001]]\n[[1.412 1.411 1.41 1.407 1.407 1.41 1.414 1.415 1.406 1.409]\n [1.413 1.41 1.41 1.406 1.412 1.412 1.417 1.416 1.415 1.409]\n [1.411 1.412 1.407 1.411 1.409 1.406 1.416 1.409 1.408 1.41 ]\n [1.408 1.411 1.408 1.412 1.411 1.415 1.409 1.405 1.408 1.413]\n [1.411 1.407 1.419 1.406 1.404 1.412 1.41 1.41 1.41 1.411]\n [1.412 1.405 1.409 1.406 1.412 1.408 1.407 1.41 1.41 1.406]]\n",
"name": "stdout"
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "# Vector-valued mu, sigma for matrix of k independent normals"
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "with pm.Model(coords=coords) as shape_demo:\n beta_mu = pm.Normal('mu', mu=np.arange(k), dims=['predictors'])\n beta_sigma = pm.HalfNormal('sigma', sigma=np.arange(k) + 1, dims=['predictors'])\n \n # player is the \"batch dimension\", and MUST come first!\n beta = pm.Normal('beta', mu=beta_mu, sigma=beta_sigma, dims=['player', 'predictors'])",
"execution_count": 12,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "beta_draws = pm.draw(beta, 100_000)\nwith np.printoptions(precision=3, suppress=True, linewidth=10_000):\n print(beta_draws.mean(axis=0))\n print(beta_draws.std(axis=0))",
"execution_count": 13,
"outputs": [
{
"output_type": "stream",
"text": "[[0.002 1.002 1.978 2.983 3.964 4.993]\n [0.009 0.998 2.016 2.994 3.984 4.977]\n [0.01 1.008 2.02 2.992 4.005 4.968]\n [0.001 1.006 2.003 3.004 3.991 5.003]\n [0.003 1.006 2.009 3.008 4.018 4.974]\n [0.007 1.006 2.013 3.004 4.006 5.002]\n [0.006 1.01 1.999 3. 3.991 5.013]\n [0.005 0.996 1.991 2.979 4.005 4.999]\n [0.002 1.005 1.993 3.002 4.02 4.984]\n [0.001 1.004 1.998 2.993 3.992 5.012]]\n[[1.415 2.235 3.148 4.144 5.071 6.062]\n [1.411 2.236 3.155 4.128 5.108 6.101]\n [1.414 2.235 3.139 4.136 5.109 6.116]\n [1.42 2.231 3.166 4.133 5.099 6.102]\n [1.42 2.237 3.177 4.13 5.129 6.12 ]\n [1.417 2.226 3.154 4.135 5.1 6.088]\n [1.41 2.237 3.141 4.104 5.112 6.11 ]\n [1.413 2.229 3.152 4.115 5.091 6.117]\n [1.415 2.218 3.154 4.132 5.079 6.131]\n [1.408 2.227 3.164 4.119 5.118 6.139]]\n",
"name": "stdout"
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "## \"batch\" dims must come first"
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "with pm.Model(coords=coords) as shape_demo:\n beta_mu = pm.Normal('mu', mu=np.arange(k), dims=['predictors'])\n beta_sigma = pm.HalfNormal('sigma', sigma=np.arange(k) + 1, dims=['predictors'])\n \n # we want \"player\" draws from each of \"predictor\" distributions, but if the batch dim isn't first PyMC doesn't get it\n beta = pm.Normal('beta', mu=beta_mu, sigma=beta_sigma, dims=['predictors', 'player'])",
"execution_count": 14,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "beta_draws = pm.draw(beta, 100_000)\nwith np.printoptions(precision=3, suppress=True, linewidth=10_000):\n print(beta_draws.mean(axis=0))\n print(beta_draws.std(axis=0))",
"execution_count": 15,
"outputs": [
{
"output_type": "error",
"ename": "ValueError",
"evalue": "shape mismatch: objects cannot be broadcast to a single shape. Mismatch is between arg 0 with shape (6, 10) and arg 1 with shape (6,).\nApply node that caused the error: normal_rv{0, (0, 0), floatX, True}(RandomGeneratorSharedVariable(<Generator(PCG64) at 0x12D533580>), TensorConstant{[ 6 10]}, TensorConstant{11}, mu, sigma)\nToposort index: 2\nInputs types: [RandomGeneratorType, TensorType(int64, (2,)), TensorType(int64, ()), TensorType(float64, (6,)), TensorType(float64, (6,))]\nInputs shapes: ['No shapes', (2,), (), (6,), (6,)]\nInputs strides: ['No strides', (8,), (), (8,), (8,)]\nInputs values: [Generator(PCG64) at 0x12D533580, array([ 6, 10]), array(11), 'not shown', 'not shown']\nOutputs clients: [['output'], ['output']]\n\nHINT: Re-running with most PyTensor optimizations disabled could provide a back-trace showing when this node was created. This can be done by setting the PyTensor flag 'optimizer=fast_compile'. If that does not work, PyTensor optimizations can be disabled with 'optimizer=None'.\nHINT: Use the PyTensor flag `exception_verbosity=high` for a debug print-out and storage map footprint of this Apply node.",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
"File \u001b[0;32m~/mambaforge/envs/econ/lib/python3.9/site-packages/pytensor/compile/function/types.py:972\u001b[0m, in \u001b[0;36mFunction.__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 970\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 971\u001b[0m outputs \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m--> 972\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvm\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 973\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m output_subset \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 974\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvm(output_subset\u001b[38;5;241m=\u001b[39moutput_subset)\n\u001b[1;32m 975\u001b[0m )\n\u001b[1;32m 976\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m:\n",
"File \u001b[0;32m~/mambaforge/envs/econ/lib/python3.9/site-packages/pytensor/graph/op.py:542\u001b[0m, in \u001b[0;36mOp.make_py_thunk.<locals>.rval\u001b[0;34m(p, i, o, n, params)\u001b[0m\n\u001b[1;32m 538\u001b[0m \u001b[38;5;129m@is_thunk_type\u001b[39m\n\u001b[1;32m 539\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mrval\u001b[39m(\n\u001b[1;32m 540\u001b[0m p\u001b[38;5;241m=\u001b[39mp, i\u001b[38;5;241m=\u001b[39mnode_input_storage, o\u001b[38;5;241m=\u001b[39mnode_output_storage, n\u001b[38;5;241m=\u001b[39mnode, params\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 541\u001b[0m ):\n\u001b[0;32m--> 542\u001b[0m r \u001b[38;5;241m=\u001b[39m \u001b[43mp\u001b[49m\u001b[43m(\u001b[49m\u001b[43mn\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[43mx\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mi\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mo\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 543\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m o \u001b[38;5;129;01min\u001b[39;00m node\u001b[38;5;241m.\u001b[39moutputs:\n",
"File \u001b[0;32m~/mambaforge/envs/econ/lib/python3.9/site-packages/pytensor/tensor/random/op.py:379\u001b[0m, in \u001b[0;36mRandomVariable.perform\u001b[0;34m(self, node, inputs, outputs)\u001b[0m\n\u001b[1;32m 377\u001b[0m rng_var_out[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m=\u001b[39m rng\n\u001b[0;32m--> 379\u001b[0m smpl_val \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrng_fn\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrng\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[43msize\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 381\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[1;32m 382\u001b[0m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(smpl_val, np\u001b[38;5;241m.\u001b[39mndarray)\n\u001b[1;32m 383\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mstr\u001b[39m(smpl_val\u001b[38;5;241m.\u001b[39mdtype) \u001b[38;5;241m!=\u001b[39m out_var\u001b[38;5;241m.\u001b[39mtype\u001b[38;5;241m.\u001b[39mdtype\n\u001b[1;32m 384\u001b[0m ):\n",
"File \u001b[0;32m~/mambaforge/envs/econ/lib/python3.9/site-packages/pytensor/tensor/random/op.py:164\u001b[0m, in \u001b[0;36mRandomVariable.rng_fn\u001b[0;34m(self, rng, *args, **kwargs)\u001b[0m\n\u001b[1;32m 163\u001b[0m \u001b[38;5;124;03m\"\"\"Sample a numeric random variate.\"\"\"\u001b[39;00m\n\u001b[0;32m--> 164\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mgetattr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mrng\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m_generator.pyx:1136\u001b[0m, in \u001b[0;36mnumpy.random._generator.Generator.normal\u001b[0;34m()\u001b[0m\n",
"File \u001b[0;32m_common.pyx:600\u001b[0m, in \u001b[0;36mnumpy.random._common.cont\u001b[0;34m()\u001b[0m\n",
"File \u001b[0;32m_common.pyx:517\u001b[0m, in \u001b[0;36mnumpy.random._common.cont_broadcast_2\u001b[0;34m()\u001b[0m\n",
"File \u001b[0;32m__init__.pxd:741\u001b[0m, in \u001b[0;36mnumpy.PyArray_MultiIterNew3\u001b[0;34m()\u001b[0m\n",
"\u001b[0;31mValueError\u001b[0m: shape mismatch: objects cannot be broadcast to a single shape. Mismatch is between arg 0 with shape (6, 10) and arg 1 with shape (6,).",
"\nDuring handling of the above exception, another exception occurred:\n",
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[15], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m beta_draws \u001b[38;5;241m=\u001b[39m \u001b[43mpm\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbeta\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m100_000\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m np\u001b[38;5;241m.\u001b[39mprintoptions(precision\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m3\u001b[39m, suppress\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, linewidth\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m10_000\u001b[39m):\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28mprint\u001b[39m(beta_draws\u001b[38;5;241m.\u001b[39mmean(axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m))\n",
"File \u001b[0;32m~/mambaforge/envs/econ/lib/python3.9/site-packages/pymc/sampling/forward.py:326\u001b[0m, in \u001b[0;36mdraw\u001b[0;34m(vars, draws, random_seed, **kwargs)\u001b[0m\n\u001b[1;32m 324\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\u001b[38;5;28mvars\u001b[39m, (\u001b[38;5;28mlist\u001b[39m, \u001b[38;5;28mtuple\u001b[39m)):\n\u001b[1;32m 325\u001b[0m cast(Callable[[], np\u001b[38;5;241m.\u001b[39mndarray], draw_fn)\n\u001b[0;32m--> 326\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m np\u001b[38;5;241m.\u001b[39mstack([draw_fn() \u001b[38;5;28;01mfor\u001b[39;00m _ \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(draws)])\n\u001b[1;32m 328\u001b[0m \u001b[38;5;66;03m# Multiple variable output\u001b[39;00m\n\u001b[1;32m 329\u001b[0m cast(Callable[[], List[np\u001b[38;5;241m.\u001b[39mndarray]], draw_fn)\n",
"File \u001b[0;32m~/mambaforge/envs/econ/lib/python3.9/site-packages/pymc/sampling/forward.py:326\u001b[0m, in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 324\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\u001b[38;5;28mvars\u001b[39m, (\u001b[38;5;28mlist\u001b[39m, \u001b[38;5;28mtuple\u001b[39m)):\n\u001b[1;32m 325\u001b[0m cast(Callable[[], np\u001b[38;5;241m.\u001b[39mndarray], draw_fn)\n\u001b[0;32m--> 326\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m np\u001b[38;5;241m.\u001b[39mstack([\u001b[43mdraw_fn\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mfor\u001b[39;00m _ \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(draws)])\n\u001b[1;32m 328\u001b[0m \u001b[38;5;66;03m# Multiple variable output\u001b[39;00m\n\u001b[1;32m 329\u001b[0m cast(Callable[[], List[np\u001b[38;5;241m.\u001b[39mndarray]], draw_fn)\n",
"File \u001b[0;32m~/mambaforge/envs/econ/lib/python3.9/site-packages/pytensor/compile/function/types.py:985\u001b[0m, in \u001b[0;36mFunction.__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 983\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvm, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mthunks\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[1;32m 984\u001b[0m thunk \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvm\u001b[38;5;241m.\u001b[39mthunks[\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvm\u001b[38;5;241m.\u001b[39mposition_of_error]\n\u001b[0;32m--> 985\u001b[0m \u001b[43mraise_with_op\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 986\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmaker\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfgraph\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 987\u001b[0m \u001b[43m \u001b[49m\u001b[43mnode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvm\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnodes\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvm\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mposition_of_error\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 988\u001b[0m \u001b[43m \u001b[49m\u001b[43mthunk\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mthunk\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 989\u001b[0m \u001b[43m \u001b[49m\u001b[43mstorage_map\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mgetattr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvm\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstorage_map\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 990\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 991\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 992\u001b[0m \u001b[38;5;66;03m# old-style linkers raise their own exceptions\u001b[39;00m\n\u001b[1;32m 993\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m\n",
"File \u001b[0;32m~/mambaforge/envs/econ/lib/python3.9/site-packages/pytensor/link/utils.py:536\u001b[0m, in \u001b[0;36mraise_with_op\u001b[0;34m(fgraph, node, thunk, exc_info, storage_map)\u001b[0m\n\u001b[1;32m 531\u001b[0m warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[1;32m 532\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mexc_type\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m error does not allow us to add an extra error message\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 533\u001b[0m )\n\u001b[1;32m 534\u001b[0m \u001b[38;5;66;03m# Some exception need extra parameter in inputs. So forget the\u001b[39;00m\n\u001b[1;32m 535\u001b[0m \u001b[38;5;66;03m# extra long error message in that case.\u001b[39;00m\n\u001b[0;32m--> 536\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m exc_value\u001b[38;5;241m.\u001b[39mwith_traceback(exc_trace)\n",
"File \u001b[0;32m~/mambaforge/envs/econ/lib/python3.9/site-packages/pytensor/compile/function/types.py:972\u001b[0m, in \u001b[0;36mFunction.__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 969\u001b[0m t0_fn \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mperf_counter()\n\u001b[1;32m 970\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 971\u001b[0m outputs \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m--> 972\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvm\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 973\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m output_subset \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 974\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvm(output_subset\u001b[38;5;241m=\u001b[39moutput_subset)\n\u001b[1;32m 975\u001b[0m )\n\u001b[1;32m 976\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m:\n\u001b[1;32m 977\u001b[0m restore_defaults()\n",
"File \u001b[0;32m~/mambaforge/envs/econ/lib/python3.9/site-packages/pytensor/graph/op.py:542\u001b[0m, in \u001b[0;36mOp.make_py_thunk.<locals>.rval\u001b[0;34m(p, i, o, n, params)\u001b[0m\n\u001b[1;32m 538\u001b[0m \u001b[38;5;129m@is_thunk_type\u001b[39m\n\u001b[1;32m 539\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mrval\u001b[39m(\n\u001b[1;32m 540\u001b[0m p\u001b[38;5;241m=\u001b[39mp, i\u001b[38;5;241m=\u001b[39mnode_input_storage, o\u001b[38;5;241m=\u001b[39mnode_output_storage, n\u001b[38;5;241m=\u001b[39mnode, params\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 541\u001b[0m ):\n\u001b[0;32m--> 542\u001b[0m r \u001b[38;5;241m=\u001b[39m \u001b[43mp\u001b[49m\u001b[43m(\u001b[49m\u001b[43mn\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[43mx\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mi\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mo\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 543\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m o \u001b[38;5;129;01min\u001b[39;00m node\u001b[38;5;241m.\u001b[39moutputs:\n\u001b[1;32m 544\u001b[0m compute_map[o][\u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n",
"File \u001b[0;32m~/mambaforge/envs/econ/lib/python3.9/site-packages/pytensor/tensor/random/op.py:379\u001b[0m, in \u001b[0;36mRandomVariable.perform\u001b[0;34m(self, node, inputs, outputs)\u001b[0m\n\u001b[1;32m 375\u001b[0m rng \u001b[38;5;241m=\u001b[39m copy(rng)\n\u001b[1;32m 377\u001b[0m rng_var_out[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m=\u001b[39m rng\n\u001b[0;32m--> 379\u001b[0m smpl_val \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrng_fn\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrng\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[43msize\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 381\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[1;32m 382\u001b[0m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(smpl_val, np\u001b[38;5;241m.\u001b[39mndarray)\n\u001b[1;32m 383\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mstr\u001b[39m(smpl_val\u001b[38;5;241m.\u001b[39mdtype) \u001b[38;5;241m!=\u001b[39m out_var\u001b[38;5;241m.\u001b[39mtype\u001b[38;5;241m.\u001b[39mdtype\n\u001b[1;32m 384\u001b[0m ):\n\u001b[1;32m 385\u001b[0m smpl_val \u001b[38;5;241m=\u001b[39m _asarray(smpl_val, dtype\u001b[38;5;241m=\u001b[39mout_var\u001b[38;5;241m.\u001b[39mtype\u001b[38;5;241m.\u001b[39mdtype)\n",
"File \u001b[0;32m~/mambaforge/envs/econ/lib/python3.9/site-packages/pytensor/tensor/random/op.py:164\u001b[0m, in \u001b[0;36mRandomVariable.rng_fn\u001b[0;34m(self, rng, *args, **kwargs)\u001b[0m\n\u001b[1;32m 162\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mrng_fn\u001b[39m(\u001b[38;5;28mself\u001b[39m, rng, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Union[\u001b[38;5;28mint\u001b[39m, \u001b[38;5;28mfloat\u001b[39m, np\u001b[38;5;241m.\u001b[39mndarray]:\n\u001b[1;32m 163\u001b[0m \u001b[38;5;124;03m\"\"\"Sample a numeric random variate.\"\"\"\u001b[39;00m\n\u001b[0;32m--> 164\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mgetattr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mrng\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m_generator.pyx:1136\u001b[0m, in \u001b[0;36mnumpy.random._generator.Generator.normal\u001b[0;34m()\u001b[0m\n",
"File \u001b[0;32m_common.pyx:600\u001b[0m, in \u001b[0;36mnumpy.random._common.cont\u001b[0;34m()\u001b[0m\n",
"File \u001b[0;32m_common.pyx:517\u001b[0m, in \u001b[0;36mnumpy.random._common.cont_broadcast_2\u001b[0;34m()\u001b[0m\n",
"File \u001b[0;32m__init__.pxd:741\u001b[0m, in \u001b[0;36mnumpy.PyArray_MultiIterNew3\u001b[0;34m()\u001b[0m\n",
"\u001b[0;31mValueError\u001b[0m: shape mismatch: objects cannot be broadcast to a single shape. Mismatch is between arg 0 with shape (6, 10) and arg 1 with shape (6,).\nApply node that caused the error: normal_rv{0, (0, 0), floatX, True}(RandomGeneratorSharedVariable(<Generator(PCG64) at 0x12D533580>), TensorConstant{[ 6 10]}, TensorConstant{11}, mu, sigma)\nToposort index: 2\nInputs types: [RandomGeneratorType, TensorType(int64, (2,)), TensorType(int64, ()), TensorType(float64, (6,)), TensorType(float64, (6,))]\nInputs shapes: ['No shapes', (2,), (), (6,), (6,)]\nInputs strides: ['No strides', (8,), (), (8,), (8,)]\nInputs values: [Generator(PCG64) at 0x12D533580, array([ 6, 10]), array(11), 'not shown', 'not shown']\nOutputs clients: [['output'], ['output']]\n\nHINT: Re-running with most PyTensor optimizations disabled could provide a back-trace showing when this node was created. This can be done by setting the PyTensor flag 'optimizer=fast_compile'. If that does not work, PyTensor optimizations can be disabled with 'optimizer=None'.\nHINT: Use the PyTensor flag `exception_verbosity=high` for a debug print-out and storage map footprint of this Apply node."
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