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add details about zuko density estimators #1387

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Feb 13, 2025
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add details about zuko density estimators #1387

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32 changes: 21 additions & 11 deletions tutorials/03_density_estimators.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -11,21 +11,31 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"`sbi` allows to specify a custom density estimator for each of the implemented methods. For all options, check the API reference [here](https://sbi-dev.github.io/sbi/reference/models/).\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Changing the type of density estimator\n"
"`sbi` allows to specify a specific density estimator for each of the implemented methods.\n",
"We support a variety of density estimators, e.g., mixtures of Gaussians, normalizing\n",
"flows, and diffusion models. Some of the density estimators are implemented as part of\n",
"`sbi`, for others we rely on other packages like\n",
"[`nflows`](https://github.com/bayesiains/nflows/) (via `pyknos`) or [`zuko`](https://github.com/probabilists/zuko). \n",
"\n",
"For all options, check the API reference\n",
"[here](https://sbi-dev.github.io/sbi/reference/models/).\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"One option is using one of the preconfigured density estimators by passing a string in the `density_estimator` keyword argument to the inference object (`NPE` or `NLE`), e.g., \"maf\" for a Masked Autoregressive Flow, of \"nsf\" for a Neural Spline Flow with default hyperparameters.\n"
"## Changing the type of density estimator\n",
"\n",
"One option is using one of the preconfigured density estimators by passing a string in\n",
"the `density_estimator` keyword argument to the inference object (`NPE` or `NLE`), e.g.,\n",
"\"maf\" for a Masked Autoregressive Flow, of \"nsf\" for a Neural Spline Flow with default\n",
"hyperparameters.\n",
"\n",
"**New with sbi 0.23:** Note that `\"maf\"` or `\"nsf\"` correspond to `nflows` density\n",
"estimators. Those have proven to work well, but the `nflows` package is not maintained\n",
"anymore. To use more recent and actively maintained density estimators, we tentatively\n",
"recommend using `zuko`, e.g., by passing `zuko_maf` or `zuko_nsf`. \n"
]
},
{
Expand All @@ -47,7 +57,7 @@
"outputs": [],
"source": [
"prior = BoxUniform(torch.zeros(2), torch.ones(2))\n",
"inference = NPE(prior=prior, density_estimator=\"maf\")"
"inference = NPE(prior=prior, density_estimator=\"zuko_maf\")"
]
},
{
Expand Down Expand Up @@ -92,7 +102,7 @@
"from sbi.neural_nets import posterior_nn\n",
"\n",
"density_estimator_build_fun = posterior_nn(\n",
" model=\"nsf\", hidden_features=60, num_transforms=3\n",
" model=\"zuko_nsf\", hidden_features=60, num_transforms=3\n",
")\n",
"inference = NPE(prior=prior, density_estimator=density_estimator_build_fun)"
]
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