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 .
-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
-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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