A surface language for programming Stan models using python syntax

## Project description

# YAPS

Yaps is a new surface language for Stan. It lets
users write Stan programs using Python syntax. For example, consider the
following Stan program, which models tosses `x`

of a coin with bias `theta`

:

data { int<lower=0,upper=1> x[10]; } parameters { real<lower=0,upper=1> theta; } model { theta ~ uniform(0,1); for (i in 1:10) x[i] ~ bernoulli(theta); }

It can be rewritten in Python has follows:

import yaps from yaps.lib import int, real, uniform, bernoulli @yaps.model def coin(x: int(lower=0, upper=1)[10]): theta: real(lower=0, upper=1) <~ uniform(0, 1) for i in range(1,11): x[i] <~ bernoulli(theta)

The `@yaps.model`

decorator indicates that the function following it
is a Stan program. While being syntactically Python, it is
semantically reinterpreted as Stan.

The argument of the function corresponds to the `data`

block. The
type of the data must be declared. Here, you can see that `x`

is an
array of 10 integers between `0`

and `1`

(`int(lower=0, upper=1)[10]`

).

Parameters are declared as variables with their type in the body of
the function. Their prior can be defined using the sampling operator
`<~`

(or `is`

).

The body of the function corresponds to the Stan model. Python syntax
is used for the imperative constructs of the model, like the `for`

loop in the example. The operator `<~`

is used to represent sampling
and `x.T[a,b]`

for truncated distribution.

Note that Stan array are 1-based. The range of the loop is thus `range(1, 11)`

,
that is 1,2, ... 10.

Other Stan blocks can be introduced using the `with`

syntax of Python.
For example, the previous program could also be written as follows:

@yaps.model def coin(x: int(lower=0, upper=1)[10]): with parameters: theta: real(lower=0, upper=1) with model: theta <~ uniform(0, 1) for i in range(1,11): x[i] <~ bernoulli(theta)

The corresponding Stan program can be displayed using the `print`

function:

print(coin)

Finally, it is possible to launch Bayesian inference on the defined model applied to some data. The communication with the Stan inference engine is based on on PyCmdStan.

flips = np.array([0, 1, 0, 0, 0, 0, 0, 0, 0, 1]) constrained_coin = coin(x=flips) constrained_coin.sample(data=constrained_coin.data)

Note that arrays must be cast into numpy arrays (see pycmdstan documentation).

After the inference the attribute `posterior`

of the constrained model is an object with fields for the latent model parameters:

theta_mean = constrained_coin.posterior.theta.mean() print("mean of theta: {:.3f}".format(theta_mean))

Yaps provides a lighter syntax to Stan programs. Since Yaps uses Python syntax, users can take advantage of Python tooling for syntax highlighting, indentation, error reporting, ...

## Install

Yaps depends on the following python packages:

- astor
- graphviz
- antlr4-python3-runtime
- pycmdstan

To install Yaps and all its dependencies run:

```
pip install yaps
```

To install from source, first clone the repo, then:

```
pip install .
```

By default, communication with the Stan inference engine is based on PyCmdStan. To run inference, you first need to install CmdStan and set the CMDSTAN environment variable to point to your CmdStan directory.

```
export CMDSTAN=/path/to/cmdstan
```

## Tools

We provide a tool to compile Stan files to Yaps syntax.
For instance, if `path/to/coin.stan`

contain the Stan model presented at the beginning, then:

```
stan2yaps path/to/coin.stan
```

outputs:

```
# -------------
# tests/stan/coin.stan
# -------------
@yaps.model
def stan_model(x: int(lower=0, upper=1)[10]):
theta: real
theta is uniform(0.0, 1.0)
for i in range(1, 10 + 1):
x[(i),] is bernoulli(theta)
print(x)
```

Compilers from Yaps to Stan and from Stan to Yaps can also be invoked programmatically using the following functions:

yaps.from_stan(code_string=None, code_file=None) # Compile a Stan model to Yaps yaps.to_stan(code_string=None, code_file=None) # Compile a Yaps model to Stan

## Documentation

The full documentation is available at https://yaps.readthedocs.io. You can find more details in the following article:

```
@article{2018-yaps-stan,
author = {Baudart, Guillaume and Hirzel, Martin and Kate, Kiran and Mandel, Louis and Shinnar, Avraham},
title = "{Yaps: Python Frontend to Stan}",
journal = {arXiv e-prints},
year = 2018,
month = Dec,
url = {https://arxiv.org/abs/1812.04125},
}
```

## License

Yaps is distributed under the terms of the Apache 2.0 License, see LICENSE.txt

## Contributions

Yaps is still at an early phase of development and we welcome contributions. Contributors are expected to submit a 'Developer's Certificate of Origin', which can be found in DCO1.1.txt.

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