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Introduction
RWebPPL is an R package providing an interface to WebPPL, a probabilistic programming language. The purpose of this introduction is to provide an overview of basic functionality. See rwebppl README.md for installation instructions.
Section 1 focuses on the primary interface to the Webppl programming language through rwebppl::webppl()
.
Section 2 provides examples of passing data from R
to your WebPPL program.
Section 3 lists additional functionality included in the rwebppl package.
The rwebppl::webppl()
function is the primary interface with the WebPPl programming language. This first section provides an overview of this function.
- A model written in the probabilistic programming language webppl instantiated as a string or in an external file with
.wppl
extension. - [Optional] A data object, typically a data.frame in
R
. - [Optional] Inference parameters. (See the
Full webppl function argument list
section for more detail.)
Example calls:
webppl(model) # Pass model object only (see `Running a simple model`).
webppl(model, data_var=my_data, data=df) # Pass model object and data (see `Passing data from R to WebPPL`).
Example: we instantiate a webppl model in the my_model
variable. This is passed to
webppl()
.
my_model <- "
var model = function () {
var a = flip(0.3)
var b = flip(0.6)
return a + b
}
model()
"
webppl(program_code=my_model) # call with string
Alternatively, if my_model
is saved in an external file (e.g. path/to/model/model.wppl
) we could call:
webppl(program_file="path/to/model/model.wppl") # call with file
WebPPL packages can be used in more complex models:
webppl(program_file="path/to/model/model.wppl", packages=c("linked-list", "timeit"))
-
program_code
: A string of a webppl program -
program_file
: A file containing a webppl program -
data
: A data frame (or other serializable object) to be passed from R to the webppl program -
data_var
: A name by which the data can be referenced in the webppl program -
packages
: A character vector of names of external webppl package to use -
model_var
: When using inference opts, the name by which the model be referenced in the program. -
inference_opts
: A list with options for inference of a particular model in the program. (see http://webppl.readthedocs.io/en/master/inference.html) [N.B.: requires usingmodel_var
] -
random_seed
: Seed for random number generator. -
sort_by
: Sort probability table by probability or support. -
chains
: Number of times to run program (defaults to 1). -
cores
: Number of cores to use when running multiple chains (defaults to 1).
Data can be passed directly from R to WebPPL as in:
my_model <- "
var model = function () {
var a = flip(0.3)
var b = flip(0.6)
var scores = map( function(d) {
return a + b - d
}, myDF)
return scores
}
model()
"
webppl(my_model,
data = df,
data_var = "myDF")
In this example, myDF
is not defined inside the WebPPL program, but is passed into it from R, using data = df
. The argument data_var
tells WebPPL what the data should be called.
If myDF
looks like this in R:
Participant | Condition | Response |
---|---|---|
1 | A | 0.4 |
1 | B | 0.8 |
2 | A | 0.2 |
It will exist in WebPPL as a list of js objects e.g.
[
{
participant: 0,
condition: "A",
response: 0.4
},
{
participant: 0,
condition: "B",
response: 0.8
},
{
participant: 1,
condition: "A",
response: 0.2
},
...
]
-
install_webppl
: install webppl on local machine. Supports both official npm versions (e.g. '0.9.7') and also commit hashes from github repository for custom configurations.install_webppl('0.9.7') # install version 0.9.7 install_webppl('4bd2452333d24c122aee98c3206584bc39c6096a') # install from github commit hash
-
get_webppl_version
: Return current webppl version.get_webppl_version()
-
install_webppl_package
: Install an npm package to webppl's global installation.install_webppl_package("PapaParse")
-
uninstall_webppl_package
: Uninstall an npm package from webppl's global installation.uninstall_webppl_package("PapaParse")
-
get_samples
: Convert webppl "histogram" into samples.num_samples <- 10 df <- data.frame(prob = c(0.1, 0.3, 0.5, 0.1), support = c("a","b","c","d")) get_samples(df, num_samples)
-
kill_webppl
: Kill rwebppl process.kill_webppl() kill_webppl(6939) # passing optional pid(s)
Also make sure to look at this list of useful packages.