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"(parameters_recovery)=\n",
"# Recovering computational parameters from observed behaviours"
]
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"[](https://colab.research.google.com/github/ComputationalPsychiatry/pyhgf/blob/master/docs/source/notebooks/4-Parameter_recovery.ipynb)"
]
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"source": [
"import sys\n",
"from IPython.utils import io\n",
"if 'google.colab' in sys.modules:\n",
"\n",
" with io.capture_output() as captured:\n",
" ! pip install pyhgf watermark"
]
},
{
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"id": "4bc4fb2a-0224-4374-b046-76ab5904e757",
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"name": "stderr",
"output_type": "stream",
"text": [
"An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n"
]
}
],
"source": [
"import arviz as az\n",
"import jax.numpy as jnp\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import pymc as pm\n",
"import seaborn as sns\n",
"from pyhgf import load_data\n",
"from pyhgf.distribution import HGFDistribution\n",
"from pyhgf.math import sigmoid_inverse_temperature\n",
"from pyhgf.model import HGF\n",
"from pyhgf.response import binary_softmax_inverse_temperature"
]
},
{
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"execution_count": 3,
"id": "20aba0ed-e8c6-4276-8778-496528d41232",
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"source": [
"np.random.seed(123)"
]
},
{
"cell_type": "markdown",
"id": "dbef25ef-44e0-4d35-bfa4-5be3bb3a393e",
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"source": [
"An important application of Hierarchical Gaussian Filters consists in the inference of computational parameters from observed behaviours, as well as the inference of data-generating models (e.g. are the participants answering randomly or are they learning environmental volatilities that are better approached with a Rescorla-Wagner or a Hierarchical Gaussian Filter?). **Parameter recovery** refers to the ability to recover true data-generating parameters; **model recovery** refers to the ability to correctly identify the true data-generating model using model comparison techniques. It is often a good idea to test parameter/model recovery of a computational model using simulated data before applying this model to experimental data {cite:p}`RobertCollins2019`. In this tutorial, we demonstrate how to recover some parameters of the generative model of the Hierarchical Gaussian Filter."
]
},
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"source": [
"## Simulate behaviours from a one-armed bandit task\n",
"Using a given task structure, we simulate behaviours from a group of participants assuming that they are updating beliefs of environmental volatility using a two-level Hierarchical Gaussian Filter, using a simple sigmoid as a response function parametrized by an inverse temperature parameter. For each participant, the inverse temperature and the tonic volatility at the second level are free parameters that will be estimated during the inference step."
]
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"id": "70400fe9-3277-4674-97de-d17543d42054",
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"u, _ = load_data(\"binary\") # the vector encoding the presence/absence of association\n",
"\n",
"N = 20 # the number of agents to simulate\n",
"\n",
"# sample one value for the inverse temperature\n",
"temperatures = np.linspace(0.5, 6.0, num=N)\n",
"\n",
"# sample one new value of the tonic volatility at the second level\n",
"volatilities = np.linspace(-6.0, -1.0, num=N)"
]
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"id": "7d8a095c-2360-4a12-9539-7a6c94b99c48",
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"source": [
"# create just one default network - we will simply change the values of interest before fitting to save time\n",
"agent = HGF(\n",
" n_levels=2,\n",
" model_type=\"binary\",\n",
" initial_mean={\"1\": 0.5, \"2\": 0.0},\n",
")"
]
},
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"source": [
"# observations (always the same), simulated decisions, sample values for temperature and volatility\n",
"responses = []\n",
"for i in range(N):\n",
" # set the tonic volatility for this agent and run the perceptual model forward\n",
" agent.attributes[1][\"tonic_volatility\"] = volatilities[i]\n",
" agent.input_data(input_data=u)\n",
"\n",
" # get decision probabilities using the belief trajectories\n",
" # and the sigmoid decision function with inverse temperature\n",
" p = sigmoid_inverse_temperature(\n",
" x=agent.node_trajectories[0][\"expected_mean\"], temperature=temperatures[i]\n",
" )\n",
"\n",
" # save the observations and decisions separately\n",
" responses.append(np.random.binomial(p=p, n=1))"
]
},
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"id": "b2f1c173-ebf9-4f65-9003-a07b547bc670",
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"source": [
"## Inference from the simulated behaviours"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "92a9d137-7c0c-4dd7-bdd0-403dde0a6f90",
"metadata": {
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"source": [
"hgf_logp_op = HGFDistribution(\n",
" n_levels=2,\n",
" model_type=\"binary\",\n",
" input_data=jnp.array(\n",
" [u] * N\n",
" ), # the inputs are the same for all agents - just duplicate the array\n",
" response_function=binary_softmax_inverse_temperature,\n",
" response_function_inputs=jnp.array(responses),\n",
")"
]
},
{
"cell_type": "markdown",
"id": "fd8b6cc5-2eca-4d5f-89e4-eb450e0832e8",
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"source": [
"Here, we are not assuming hyperpriors to ensure that individual estimates are independent and avoid hierarchical partial pooling."
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "2e4177b3-2e49-43f8-9f3b-64226420ef05",
"metadata": {
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"source": [
"with pm.Model() as two_levels_binary_hgf:\n",
"\n",
" # tonic volatility\n",
" volatility = pm.Normal.dist(-3.0, 5, shape=N)\n",
" censored_volatility = pm.Censored(\n",
" \"censored_volatility\", volatility, lower=-8, upper=2\n",
" )\n",
"\n",
" # inverse temperature\n",
" inverse_temperature = pm.Uniform(\n",
" \"inverse_temperature\", 0.2, 20, shape=N, initval=np.ones(N)\n",
" )\n",
"\n",
" # The multi-HGF distribution\n",
" # --------------------------\n",
" pm.Potential(\n",
" \"hgf_loglike\",\n",
" hgf_logp_op(\n",
" tonic_volatility_2=censored_volatility,\n",
" response_function_parameters=inverse_temperature,\n",
" ),\n",
" )"
]
},
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"execution_count": 9,
"id": "5154c464-7218-4ebf-a974-0fbaf82685d5",
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"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Initializing NUTS using jitter+adapt_diag...\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Sequential sampling (2 chains in 1 job)\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"NUTS: [censored_volatility, inverse_temperature]\n"
]
},
{
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"application/vnd.jupyter.widget-view+json": {
"model_id": "9e4d5ab127874749a255603e295f687a",
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"text/plain": [
"Output()"
]
},
"metadata": {},
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},
{
"data": {
"text/html": [
"\n"
],
"text/plain": []
},
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"Output()"
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"text/html": [
"\n"
],
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},
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},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Sampling 2 chains for 1_000 tune and 1_000 draw iterations (2_000 + 2_000 draws total) took 53 seconds.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"There were 1998 divergences after tuning. Increase `target_accept` or reparameterize.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"We recommend running at least 4 chains for robust computation of convergence diagnostics\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"The rhat statistic is larger than 1.01 for some parameters. This indicates problems during sampling. See https://arxiv.org/abs/1903.08008 for details\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"The effective sample size per chain is smaller than 100 for some parameters. A higher number is needed for reliable rhat and ess computation. See https://arxiv.org/abs/1903.08008 for details\n"
]
}
],
"source": [
"with two_levels_binary_hgf:\n",
" two_level_hgf_idata = pm.sample(chains=2, cores=1)"
]
},
{
"cell_type": "markdown",
"id": "51c38758-8394-40ca-ae32-676d44f59dd4",
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"source": [
"## Visualizing parameters recovery"
]
},
{
"cell_type": "markdown",
"id": "5ba30cbb-bf8c-4e00-9198-0bbe411daeae",
"metadata": {
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"source": [
"A successful parameter recovery is usually inferred from the scatterplot of simulated values and inferred values of the parameters. Here, we can see that the model can recover fairly accurate values close to the underlying parameters. Additionally, we can report the coefficient of correlation between the two variables, as a more objective measure of correspondence."
]
},
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"scrolled": true,
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{
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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, axs = plt.subplots(figsize=(12, 5), ncols=2)\n",
"\n",
"axs[0].plot([-6.0, 0.0], [-6.0, 0.0], color=\"grey\", linestyle=\"--\", zorder=-1)\n",
"axs[1].plot([0.0, 7.0], [0.0, 7.0], color=\"grey\", linestyle=\"--\", zorder=-1)\n",
"\n",
"for var_name, refs, idx in zip(\n",
" [\"censored_volatility\", \"inverse_temperature\"],\n",
" [volatilities, temperatures],\n",
" [0, 1],\n",
"):\n",
" inferred_parameters = az.summary(two_level_hgf_idata, var_names=var_name)[\n",
" \"mean\"\n",
" ].tolist()\n",
"\n",
" sns.kdeplot(\n",
" x=refs,\n",
" y=inferred_parameters,\n",
" ax=axs[idx],\n",
" fill=True,\n",
" cmap=\"Reds\" if var_name == \"censored_volatility\" else \"Greens\",\n",
" alpha=0.4,\n",
" )\n",
"\n",
" axs[idx].scatter(\n",
" refs,\n",
" az.summary(two_level_hgf_idata, var_names=var_name)[\"mean\"].tolist(),\n",
" s=60,\n",
" alpha=0.8,\n",
" edgecolors=\"k\",\n",
" color=\"#c44e52\" if var_name == \"censored_volatility\" else \"#55a868\",\n",
" )\n",
"\n",
" axs[idx].grid(True, linestyle=\"--\")\n",
" axs[idx].set_xlabel(\"Simulated parameter\")\n",
" axs[idx].set_ylabel(\"Infered parameter\")\n",
"\n",
"\n",
"axs[0].set_title(\"Second level tonic volatility\")\n",
"axs[1].set_title(\"Inverse temperature\")\n",
"sns.despine()"
]
},
{
"cell_type": "markdown",
"id": "e4cf5f0c-6263-45a1-860a-6694b9a02279",
"metadata": {
"editable": true,
"slideshow": {
"slide_type": ""
},
"tags": []
},
"source": [
"# System configuration"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "f26d0d76-8705-4f03-86fc-e8d1b996b99c",
"metadata": {
"editable": true,
"execution": {
"iopub.execute_input": "2025-01-10T13:57:49.339359Z",
"iopub.status.busy": "2025-01-10T13:57:49.339155Z",
"iopub.status.idle": "2025-01-10T13:57:49.353520Z",
"shell.execute_reply": "2025-01-10T13:57:49.352737Z"
},
"slideshow": {
"slide_type": ""
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Last updated: Fri Jan 10 2025\n",
"\n",
"Python implementation: CPython\n",
"Python version : 3.12.3\n",
"IPython version : 8.31.0\n",
"\n",
"pyhgf : 0.2.1.post4.dev0+d49aafe9\n",
"jax : 0.4.31\n",
"jaxlib: 0.4.31\n",
"\n",
"pyhgf : 0.2.1.post4.dev0+d49aafe9\n",
"jax : 0.4.31\n",
"arviz : 0.20.0\n",
"pymc : 5.20.0\n",
"numpy : 1.26.0\n",
"sys : 3.12.3 | packaged by conda-forge | (main, Apr 15 2024, 18:38:13) [GCC 12.3.0]\n",
"seaborn : 0.13.2\n",
"matplotlib: 3.10.0\n",
"IPython : 8.31.0\n",
"\n",
"Watermark: 2.5.0\n",
"\n"
]
}
],
"source": [
"%load_ext watermark\n",
"%watermark -n -u -v -iv -w -p pyhgf,jax,jaxlib"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1467d1a4-9bc8-4cf9-9b71-85c797d7973e",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"interpreter": {
"hash": "40d3a090f54c6569ab1632332b64b2c03c39dcf918b08424e98f38b5ae0af88f"
},
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"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",
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"_view_name": "OutputView",
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"msg_id": "",
"outputs": [
{
"data": {
"text/html": "Sampling chain 1, 997 divergences ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:00 / 0:00:23\n
\n",
"text/plain": "Sampling chain 1, 997 divergences \u001b[32m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100%\u001b[0m \u001b[36m0:00:00\u001b[0m / \u001b[33m0:00:23\u001b[0m\n"
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{
"data": {
"text/html": "Sampling chain 0, 999 divergences ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:00 / 0:00:30\n
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