program-induction

A library for program induction and learning representations.

Implements Bayesian program learning and genetic programming.

Installation

Install rust. In a new or existing project, add the following to your Cargo.toml:

[dependencies]
programinduction = "0.3"
# many examples also depend on polytype for the tp! and ptp! macros:
polytype = "4.1"

The documentation requires a custom HTML header to include KaTeX for math support. This isn't supported by cargo doc, so to build the documentation you may use:

cargo rustdoc -- --html-in-header rustdoc-include-katex-header.html

Usage

Specify a probabilistic context-free grammar (PCFG; see pcfg::Grammar) and induce a sentence that matches an example:

#[macro_use]
extern crate polytype;
extern crate programinduction;

use programinduction::{ECParams, EC};
use programinduction::pcfg::{task_by_evaluation, Grammar, Rule};

fn evaluate(name: &str, inps: &[i32]) -> i32 {
    match name {
        "0" => 0,
        "1" => 1,
        "plus" => inps[0] + inps[1],
        _ => unreachable!(),
    }
}

fn main() {
    let g = Grammar::new(
        tp!(EXPR),
        vec![
            Rule::new("0", tp!(EXPR), 1.0),
            Rule::new("1", tp!(EXPR), 1.0),
            Rule::new("plus", tp!(@arrow[tp!(EXPR), tp!(EXPR), tp!(EXPR)]), 1.0),
        ],
    );
    let ec_params = ECParams {
        frontier_limit: 1,
        search_limit_timeout: None,
        search_limit_description_length: Some(8.0),
    };
    // task: the number 4
    let task = task_by_evaluation(&evaluate, &4, tp!(EXPR));

    let frontiers = g.explore(&ec_params, &[task]);
    let sol = &frontiers[0].best_solution().unwrap().0;
    println!("{}", g.display(sol));
}

The Exploration-Compression (EC) algorithm iteratively learns a better representation by finding common structure in induced programs. We can run the EC algorithm with a polymorphically-typed lambda calculus representation lambda::Language in a Boolean circuit domain:

#[macro_use]
extern crate polytype;
extern crate programinduction;

use programinduction::{domains, lambda, ECParams, EC};

fn main() {
    // circuit DSL
    let dsl = lambda::Language::uniform(vec![
        // NAND takes two bools and returns a bool
        ("nand", ptp!(@arrow[tp!(bool), tp!(bool), tp!(bool)])),
    ]);
    // parameters
    let lambda_params = lambda::CompressionParams::default();
    let ec_params = ECParams {
        frontier_limit: 1,
        search_limit_timeout: Some(std::time::Duration::new(1, 0)),
        search_limit_description_length: None,
    };
    // randomly sample 250 circuit tasks
    let tasks = domains::circuits::make_tasks(250);

    // one iteration of EC:
    let (new_dsl, _solutions) = dsl.ec(&ec_params, &lambda_params, &tasks);
    // print the new concepts it invented, based on common structure:
    for &(ref expr, _, _) in &new_dsl.invented {
        println!("invented {}", new_dsl.display(expr))
        // one of the inventions was "(λ (nand $0 $0))",
        // which is the common and useful NOT operation!
    }
}

You may have noted the above use of domains::circuits. Some domains are already implemented for you. Currently, this only consists of circuits and strings.

TODO

(you could be the one who does one of these!)

• First-class function evaluation within Rust (and remove lisp interpreters).
• Fallible evaluation (e.g. see how domains::strings handles slice).
• Faster lambda calculus evaluation.
• PCFG compression is currently only estimating parameters, not actually learning pieces of programs. An adaptor grammar approach seems like a good direction to go, perhaps minus the Bayesian non-parametrics.
• impl GP for pcfg::Grammar is not yet complete.
• Add more representations
• Add more domains
• Add task generation function in domains::strings