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@lukasheinrich
Created December 1, 2022 20:56
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{
"cells": [
{
"cell_type": "code",
"execution_count": 252,
"id": "4a7383bf",
"metadata": {},
"outputs": [],
"source": [
"from aesara import tensor as at\n",
"from aesara.graph.basic import Apply\n",
"\n",
"import pymc as pm\n",
"import arviz as az\n",
"import pyhf\n",
"import jax\n",
"import jax.numpy as jnp\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"def make_op(func,name):\n",
" class Op(at.Op):\n",
" def make_node(self, *inputs):\n",
" inputs = [at.as_tensor_variable(inp) for inp in inputs]\n",
" outputs = [at.dvector(name = name)]\n",
" return Apply(self, inputs, outputs)\n",
"\n",
" def perform(self, node, inputs, outputs):\n",
" result = func(*inputs)\n",
" outputs[0][0] = np.asarray(result, dtype=node.outputs[0].dtype)\n",
" return Op()\n",
"\n",
"def _real_hifa(model):\n",
" return model.expected_actualdata,model.expected_auxdata, jnp.array(model.config.auxdata)\n",
"\n",
"\n",
"def prepare_ops(model):\n",
" _hifa_main, _hifa_aux, _aux_data = _real_hifa(model)\n",
" hifa_main = make_op(_hifa_main,'hifa_main')\n",
" hifa_aux = make_op(_hifa_aux,'hifa_aux')\n",
" aux_data = np.asarray([int(x) for x in _aux_data])\n",
" return hifa_main,hifa_aux,aux_data"
]
},
{
"cell_type": "code",
"execution_count": 276,
"id": "44e2a05b",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Sampling: [aux, gamma0, gamma1, gamma10, gamma11, gamma12, gamma13, gamma14, gamma15, gamma16, gamma17, gamma18, gamma19, gamma2, gamma20, gamma21, gamma22, gamma23, gamma24, gamma25, gamma26, gamma27, gamma28, gamma29, gamma3, gamma30, gamma31, gamma32, gamma33, gamma34, gamma35, gamma36, gamma37, gamma38, gamma39, gamma4, gamma40, gamma41, gamma42, gamma43, gamma44, gamma45, gamma46, gamma47, gamma48, gamma49, gamma5, gamma6, gamma7, gamma8, gamma9, main, mu]\n",
"INFO:pymc:Sampling: [aux, gamma0, gamma1, gamma10, gamma11, gamma12, gamma13, gamma14, gamma15, gamma16, gamma17, gamma18, gamma19, gamma2, gamma20, gamma21, gamma22, gamma23, gamma24, gamma25, gamma26, gamma27, gamma28, gamma29, gamma3, gamma30, gamma31, gamma32, gamma33, gamma34, gamma35, gamma36, gamma37, gamma38, gamma39, gamma4, gamma40, gamma41, gamma42, gamma43, gamma44, gamma45, gamma46, gamma47, gamma48, gamma49, gamma5, gamma6, gamma7, gamma8, gamma9, main, mu]\n",
"Multiprocess sampling (4 chains in 4 jobs)\n",
"INFO:pymc:Multiprocess sampling (4 chains in 4 jobs)\n",
"CompoundStep\n",
"INFO:pymc:CompoundStep\n",
">Slice: [mu]\n",
"INFO:pymc:>Slice: [mu]\n",
">Slice: [gamma0]\n",
"INFO:pymc:>Slice: [gamma0]\n",
">Slice: [gamma1]\n",
"INFO:pymc:>Slice: [gamma1]\n",
">Slice: [gamma2]\n",
"INFO:pymc:>Slice: [gamma2]\n",
">Slice: [gamma3]\n",
"INFO:pymc:>Slice: [gamma3]\n",
">Slice: [gamma4]\n",
"INFO:pymc:>Slice: [gamma4]\n",
">Slice: [gamma5]\n",
"INFO:pymc:>Slice: [gamma5]\n",
">Slice: [gamma6]\n",
"INFO:pymc:>Slice: [gamma6]\n",
">Slice: [gamma7]\n",
"INFO:pymc:>Slice: [gamma7]\n",
">Slice: [gamma8]\n",
"INFO:pymc:>Slice: [gamma8]\n",
">Slice: [gamma9]\n",
"INFO:pymc:>Slice: [gamma9]\n",
">Slice: [gamma10]\n",
"INFO:pymc:>Slice: [gamma10]\n",
">Slice: [gamma11]\n",
"INFO:pymc:>Slice: [gamma11]\n",
">Slice: [gamma12]\n",
"INFO:pymc:>Slice: [gamma12]\n",
">Slice: [gamma13]\n",
"INFO:pymc:>Slice: [gamma13]\n",
">Slice: [gamma14]\n",
"INFO:pymc:>Slice: [gamma14]\n",
">Slice: [gamma15]\n",
"INFO:pymc:>Slice: [gamma15]\n",
">Slice: [gamma16]\n",
"INFO:pymc:>Slice: [gamma16]\n",
">Slice: [gamma17]\n",
"INFO:pymc:>Slice: [gamma17]\n",
">Slice: [gamma18]\n",
"INFO:pymc:>Slice: [gamma18]\n",
">Slice: [gamma19]\n",
"INFO:pymc:>Slice: [gamma19]\n",
">Slice: [gamma20]\n",
"INFO:pymc:>Slice: [gamma20]\n",
">Slice: [gamma21]\n",
"INFO:pymc:>Slice: [gamma21]\n",
">Slice: [gamma22]\n",
"INFO:pymc:>Slice: [gamma22]\n",
">Slice: [gamma23]\n",
"INFO:pymc:>Slice: [gamma23]\n",
">Slice: [gamma24]\n",
"INFO:pymc:>Slice: [gamma24]\n",
">Slice: [gamma25]\n",
"INFO:pymc:>Slice: [gamma25]\n",
">Slice: [gamma26]\n",
"INFO:pymc:>Slice: [gamma26]\n",
">Slice: [gamma27]\n",
"INFO:pymc:>Slice: [gamma27]\n",
">Slice: [gamma28]\n",
"INFO:pymc:>Slice: [gamma28]\n",
">Slice: [gamma29]\n",
"INFO:pymc:>Slice: [gamma29]\n",
">Slice: [gamma30]\n",
"INFO:pymc:>Slice: [gamma30]\n",
">Slice: [gamma31]\n",
"INFO:pymc:>Slice: [gamma31]\n",
">Slice: [gamma32]\n",
"INFO:pymc:>Slice: [gamma32]\n",
">Slice: [gamma33]\n",
"INFO:pymc:>Slice: [gamma33]\n",
">Slice: [gamma34]\n",
"INFO:pymc:>Slice: [gamma34]\n",
">Slice: [gamma35]\n",
"INFO:pymc:>Slice: [gamma35]\n",
">Slice: [gamma36]\n",
"INFO:pymc:>Slice: [gamma36]\n",
">Slice: [gamma37]\n",
"INFO:pymc:>Slice: [gamma37]\n",
">Slice: [gamma38]\n",
"INFO:pymc:>Slice: [gamma38]\n",
">Slice: [gamma39]\n",
"INFO:pymc:>Slice: [gamma39]\n",
">Slice: [gamma40]\n",
"INFO:pymc:>Slice: [gamma40]\n",
">Slice: [gamma41]\n",
"INFO:pymc:>Slice: [gamma41]\n",
">Slice: [gamma42]\n",
"INFO:pymc:>Slice: [gamma42]\n",
">Slice: [gamma43]\n",
"INFO:pymc:>Slice: [gamma43]\n",
">Slice: [gamma44]\n",
"INFO:pymc:>Slice: [gamma44]\n",
">Slice: [gamma45]\n",
"INFO:pymc:>Slice: [gamma45]\n",
">Slice: [gamma46]\n",
"INFO:pymc:>Slice: [gamma46]\n",
">Slice: [gamma47]\n",
"INFO:pymc:>Slice: [gamma47]\n",
">Slice: [gamma48]\n",
"INFO:pymc:>Slice: [gamma48]\n",
">Slice: [gamma49]\n",
"INFO:pymc:>Slice: [gamma49]\n"
]
},
{
"data": {
"text/html": [
"\n",
"<style>\n",
" /* Turns off some styling */\n",
" progress {\n",
" /* gets rid of default border in Firefox and Opera. */\n",
" border: none;\n",
" /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
" background-size: auto;\n",
" }\n",
" progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
" background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
" }\n",
" .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
" background: #F44336;\n",
" }\n",
"</style>\n"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"\n",
" <div>\n",
" <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
" 100.00% [8000/8000 02:28&lt;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 150 seconds.\n",
"INFO:pymc:Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 150 seconds.\n",
"Sampling: [aux, main]\n",
"INFO:pymc:Sampling: [aux, main]\n"
]
},
{
"data": {
"text/html": [
"\n",
"<style>\n",
" /* Turns off some styling */\n",
" progress {\n",
" /* gets rid of default border in Firefox and Opera. */\n",
" border: none;\n",
" /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
" background-size: auto;\n",
" }\n",
" progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
" background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
" }\n",
" .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
" background: #F44336;\n",
" }\n",
"</style>\n"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"\n",
" <div>\n",
" <progress value='4000' class='' max='4000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
" 100.00% [4000/4000 00:00&lt;00:00]\n",
" </div>\n",
" "
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"Nbins = 50\n",
"sig = (3*np.exp(-(np.arange(Nbins)-Nbins/2)**2/20))\n",
"bkg = (100*np.exp(-np.arange(Nbins)/20))\n",
"db = (bkg*0.1).tolist()\n",
"bkg = bkg.tolist()\n",
"\n",
"model = pyhf.simplemodels.uncorrelated_background(sig,bkg,db)\n",
"hifa_main,hifa_aux,aux_data = prepare_ops(model)\n",
"obs_pars = model.config.suggested_init()\n",
"obs_pars[0] = 0.0\n",
"obs_data = model.expected_actualdata(obs_pars)\n",
"print\n",
"basic_model = pm.Model()\n",
"with basic_model:\n",
" pars = []\n",
" pars.append(pm.Uniform(\"mu\", lower = 0, upper = 10))\n",
" for i in range(Nbins):\n",
" pars.append(pm.Gamma(f\"gamma{i}\", alpha = 20, beta = 20))\n",
" pars = at.stack(pars)\n",
" raparam_main = pm.Deterministic(\"raparam_main\",hifa_main(pars))\n",
" raparam_aux = pm.Deterministic(\"raparam_aux\",hifa_aux(pars))\n",
" pm.Poisson(\"main\", mu = raparam_main, observed = obs_data)\n",
" pm.Poisson(\"aux\", mu = raparam_aux, observed = aux_data)\n",
" \n",
"N = 1000\n",
"with basic_model:\n",
" prior_idata = pm.sample_prior_predictive(samples=N)\n",
" post_idata = pm.sample(draws=N)\n",
" post_pred = pm.sample_posterior_predictive(post_idata)"
]
},
{
"cell_type": "code",
"execution_count": 277,
"id": "9a05a6fc",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.collections.PathCollection at 0x2dbd49d00>"
]
},
"execution_count": 277,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.step(np.linspace(0,Nbins-1,Nbins),prior_idata.prior_predictive.main[0].T, alpha = 0.01, c = 'steelblue', where = 'mid');\n",
"plt.step(np.linspace(0,Nbins-1,Nbins),post_pred.posterior_predictive.main[0].T, alpha = 0.01, c = 'orange', where = 'mid');\n",
"plt.vlines(np.arange(Nbins),*np.quantile(prior_idata.prior_predictive.main[0],[.15,.85],axis=0), colors = 'steelblue')\n",
"plt.vlines(np.arange(Nbins),*np.quantile(post_pred.posterior_predictive.main[0],[.15,.85],axis=0), colors = 'k')\n",
"plt.scatter(np.arange(Nbins),obs_data, c = 'k', zorder = 999)"
]
},
{
"cell_type": "code",
"execution_count": 279,
"id": "28660f4b",
"metadata": {},
"outputs": [],
"source": [
"!open ."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a4bb0948",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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": "3.9.11"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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