# nutpie: A fast sampler for Bayesian posteriors
## Installation
nutpie can be installed using Conda or Mamba from conda-forge with
```bash
mamba install -c conda-forge nutpie
```
Or using pip:
```bash
pip install nutpie
```
To install it from source, install a Rust compiler and maturin and then
```bash
maturin develop --release
```
If you want to use the nightly SIMD implementation for some of the math functions,
switch to Rust nightly and then install with the `simd_support` feature in then
nutpie directory:
```bash
rustup override set nightly
maturin develop --release --features=simd_support
```
## Usage with PyMC
First, PyMC and Numba need to be installed, for example using
```bash
mamba install -c conda-forge pymc numba
```
We need to create a model:
```python
import pymc as pm
import numpy as np
import nutpie
import pandas as pd
import seaborn as sns
# Load the radon dataset
data = pd.read_csv(pm.get_data("radon.csv"))
data["log_radon"] = data["log_radon"].astype(np.float64)
county_idx, counties = pd.factorize(data.county)
coords = {"county": counties, "obs_id": np.arange(len(county_idx))}
# Create a simple hierarchical model for the radon dataset
with pm.Model(coords=coords, check_bounds=False) as pymc_model:
intercept = pm.Normal("intercept", sigma=10)
# County effects
raw = pm.ZeroSumNormal("county_raw", dims="county")
sd = pm.HalfNormal("county_sd")
county_effect = pm.Deterministic("county_effect", raw * sd, dims="county")
# Global floor effect
floor_effect = pm.Normal("floor_effect", sigma=2)
# County:floor interaction
raw = pm.ZeroSumNormal("county_floor_raw", dims="county")
sd = pm.HalfNormal("county_floor_sd")
county_floor_effect = pm.Deterministic(
"county_floor_effect", raw * sd, dims="county"
)
mu = (
intercept
+ county_effect[county_idx]
+ floor_effect * data.floor.values
+ county_floor_effect[county_idx] * data.floor.values
)
sigma = pm.HalfNormal("sigma", sigma=1.5)
pm.Normal(
"log_radon", mu=mu, sigma=sigma, observed=data.log_radon.values, dims="obs_id"
)
```
We then compile this model and sample form the posterior:
```python
compiled_model = nutpie.compile_pymc_model(pymc_model)
trace_pymc = nutpie.sample(compiled_model)
```
`trace_pymc` now contains an ArviZ `InferenceData` object, including sampling
statistics and the posterior of the variables defined above.
We can also control the sampler in a non-blocking way:
```python
# The sampler will now run the the background
sampler = nutpie.sample(compiled_model, blocking=False)
# Pause and resume the sampling
sampler.pause()
sampler.resume()
# Wait for the sampler to finish (up to timeout seconds)
# sampler.wait(timeout=0.1)
# or we can also abort the sampler (and return the incomplete trace)
incomplete_trace = sampler.abort()
# or cancel and discard all progress:
sampler.cancel()
```
## Usage with Stan
In order to sample from Stan model, `bridgestan` needs to be installed.
A pip package is available, but right now this can not be installed using Conda.
```bash
pip install bridgestan
```
When we install nutpie with pip, we can also specify that we want optional
dependencies for Stan models using
```
pip install 'nutpie[stan]'
```
In addition, a C++ compiler needs to be available. For details see
[the Stan docs](https://mc-stan.org/docs/cmdstan-guide/cmdstan-installation.html#cpp-toolchain).
We can then compile a Stan model, and sample using nutpie:
```python
import nutpie
code = """
data {
real mu;
}
parameters {
real x;
}
model {
x ~ normal(mu, 1);
}
"""
compiled = nutpie.compile_stan_model(code=code)
# Provide data
compiled = compiled.with_data(mu=3.)
trace = nutpie.sample(compiled)
```
## Advantages
nutpie uses [`nuts-rs`](https://github.com/pymc-devs/nuts-rs), a library written in Rust, that implements NUTS as in
PyMC and Stan, but with a slightly different mass matrix tuning method as
those. It often produces a higher effective sample size per gradient
evaluation, and tends to converge faster and with fewer gradient evaluation.
Raw data
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"description": "# nutpie: A fast sampler for Bayesian posteriors\n\n## Installation\n\nnutpie can be installed using Conda or Mamba from conda-forge with\n\n```bash\nmamba install -c conda-forge nutpie\n```\n\nOr using pip:\n\n```bash\npip install nutpie\n```\n\nTo install it from source, install a Rust compiler and maturin and then\n\n```bash\nmaturin develop --release\n```\n\nIf you want to use the nightly SIMD implementation for some of the math functions,\nswitch to Rust nightly and then install with the `simd_support` feature in then\nnutpie directory:\n\n```bash\nrustup override set nightly\nmaturin develop --release --features=simd_support\n```\n\n## Usage with PyMC\n\nFirst, PyMC and Numba need to be installed, for example using\n\n```bash\nmamba install -c conda-forge pymc numba\n```\n\nWe need to create a model:\n\n```python\nimport pymc as pm\nimport numpy as np\nimport nutpie\nimport pandas as pd\nimport seaborn as sns\n\n# Load the radon dataset\ndata = pd.read_csv(pm.get_data(\"radon.csv\"))\ndata[\"log_radon\"] = data[\"log_radon\"].astype(np.float64)\ncounty_idx, counties = pd.factorize(data.county)\ncoords = {\"county\": counties, \"obs_id\": np.arange(len(county_idx))}\n\n# Create a simple hierarchical model for the radon dataset\nwith pm.Model(coords=coords, check_bounds=False) as pymc_model:\n intercept = pm.Normal(\"intercept\", sigma=10)\n\n # County effects\n raw = pm.ZeroSumNormal(\"county_raw\", dims=\"county\")\n sd = pm.HalfNormal(\"county_sd\")\n county_effect = pm.Deterministic(\"county_effect\", raw * sd, dims=\"county\")\n\n # Global floor effect\n floor_effect = pm.Normal(\"floor_effect\", sigma=2)\n\n # County:floor interaction\n raw = pm.ZeroSumNormal(\"county_floor_raw\", dims=\"county\")\n sd = pm.HalfNormal(\"county_floor_sd\")\n county_floor_effect = pm.Deterministic(\n \"county_floor_effect\", raw * sd, dims=\"county\"\n )\n\n mu = (\n intercept\n + county_effect[county_idx]\n + floor_effect * data.floor.values\n + county_floor_effect[county_idx] * data.floor.values\n )\n\n sigma = pm.HalfNormal(\"sigma\", sigma=1.5)\n pm.Normal(\n \"log_radon\", mu=mu, sigma=sigma, observed=data.log_radon.values, dims=\"obs_id\"\n )\n```\n\nWe then compile this model and sample form the posterior:\n\n```python\ncompiled_model = nutpie.compile_pymc_model(pymc_model)\ntrace_pymc = nutpie.sample(compiled_model)\n```\n\n`trace_pymc` now contains an ArviZ `InferenceData` object, including sampling\nstatistics and the posterior of the variables defined above.\n\nWe can also control the sampler in a non-blocking way:\n\n```python\n# The sampler will now run the the background\nsampler = nutpie.sample(compiled_model, blocking=False)\n\n# Pause and resume the sampling\nsampler.pause()\nsampler.resume()\n\n# Wait for the sampler to finish (up to timeout seconds)\n# sampler.wait(timeout=0.1)\n\n# or we can also abort the sampler (and return the incomplete trace)\nincomplete_trace = sampler.abort()\n\n# or cancel and discard all progress:\nsampler.cancel()\n```\n\n## Usage with Stan\n\nIn order to sample from Stan model, `bridgestan` needs to be installed.\nA pip package is available, but right now this can not be installed using Conda.\n\n```bash\npip install bridgestan\n```\n\nWhen we install nutpie with pip, we can also specify that we want optional\ndependencies for Stan models using\n\n```\npip install 'nutpie[stan]'\n```\n\nIn addition, a C++ compiler needs to be available. For details see\n[the Stan docs](https://mc-stan.org/docs/cmdstan-guide/cmdstan-installation.html#cpp-toolchain).\n\nWe can then compile a Stan model, and sample using nutpie:\n\n```python\nimport nutpie\n\ncode = \"\"\"\ndata {\n real mu;\n}\nparameters {\n real x;\n}\nmodel {\n x ~ normal(mu, 1);\n}\n\"\"\"\n\ncompiled = nutpie.compile_stan_model(code=code)\n# Provide data\ncompiled = compiled.with_data(mu=3.)\ntrace = nutpie.sample(compiled)\n```\n\n## Advantages\n\nnutpie uses [`nuts-rs`](https://github.com/pymc-devs/nuts-rs), a library written in Rust, that implements NUTS as in\nPyMC and Stan, but with a slightly different mass matrix tuning method as\nthose. It often produces a higher effective sample size per gradient\nevaluation, and tends to converge faster and with fewer gradient evaluation.\n\n",
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