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How to Contribute to aima-python

Thanks for considering contributing to aima-python! Whether you are an aspiring Google Summer of Code student, or an independent contributor, here is a guide to how you can help:

Read the Code and Start on an Issue

  • First, read and understand the code to get a feel for the extent and the style.
  • Look at the issues and pick one to work on.
  • One of the issues is that some algorithms are missing from the list of algorithms.

Port to Python 3; Pythonic Idioms; py.test

  • Check for common problems in porting to Python 3, such as: print is now a function; range and map and other functions no longer produce lists; objects of different types can no longer be compared with <; strings are now Unicode; it would be nice to move % string formating to .format; there is a new next function for generators; integer division now returns a float; we can now use set literals.
  • Replace old Lisp-based idioms with proper Python idioms. For example, we have many functions that were taken directly from Common Lisp, such as the every function: every(callable, items) returns true if every element of items is callable. This is good Lisp style, but good Python style would be to use all and a generator expression: all(callable(f) for f in items). Eventually, fix all calls to these legacy Lisp functions and then remove the functions.
  • Add more tests in _test.py files. Strive for terseness; it is ok to group multiple asserts into one def test_something(): function. Move most tests to _test.py, but it is fine to have a single doctest example in the docstring of a function in the .py file, if the purpose of the doctest is to explain how to use the function, rather than test the implementation.

New and Improved Algorithms

  • Implement functions that were in the third edition of the book but were not yet implemented in the code. Check the list of pseudocode algorithms (pdf) to see what's missing.
  • As we finish chapters for the new fourth edition, we will share the new pseudocode in the aima-pseudocode repository, and describe what changes are necessary. We hope to have a algorithm-name.md file for each algorithm, eventually; it would be great if contributors could add some for the existing algorithms.
  • Give examples of how to use the code in the .ipynb file.

We still support a legacy branch, aima3python2 (for the third edition of the textbook and for Python 2 code).

Style Guide

There are a few style rules that are unique to this project:

  • The first rule is that the code should correspond directly to the pseudocode in the book. When possible this will be almost one-to-one, just allowing for the syntactic differences between Python and pseudocode, and for different library functions.
  • Don't make a function more complicated than the pseudocode in the book, even if the complication would add a nice feature, or give an efficiency gain. Instead, remain faithful to the pseudocode, and if you must, add a new function (not in the book) with the added feature.
  • I use functional programming (functions with no side effects) in many cases, but not exclusively (sometimes classes and/or functions with side effects are used). Let the book's pseudocode be the guide.

Beyond the above rules, we use Pep 8, with a few minor exceptions:

  • I have set --max-line-length 100, not 79.
  • You don't need two spaces after a sentence-ending period.
  • Strunk and White is not a good guide for English.
  • I prefer more concise docstrings; I don't follow Pep 257. In most cases, a one-line docstring suffices. It is rarely necessary to list what each argument does; the name of the argument usually is enough.
  • Not all constants have to be UPPERCASE.
  • At some point I may add Pep 484 type annotations, but I think I'll hold off for now; I want to get more experience with them, and some people may still be in Python 3.4.

Contributing a Patch

  1. Submit an issue describing your proposed change to the repo in question (or work on an existing issue).
  2. The repo owner will respond to your issue promptly.
  3. Fork the desired repo, develop and test your code changes.
  4. Submit a pull request.

Reporting Issues

  • Under which versions of Python does this happen?

  • Is anybody working on this?

Patch Rules

  • Ensure that the patch is python 3.4 compliant.

  • Include tests if your patch is supposed to solve a bug, and explain clearly under which circumstances the bug happens. Make sure the test fails without your patch.

  • Follow the style guidelines described above.

Running the Test-Suite

The minimal requirement for running the testsuite is py.test. You can install it with::

pip install pytest

Clone this repository::

git clone https://github.com/aimacode/aima-python.git

Fetch the aima-data submodule::

cd aima-python
git submodule init
git submodule update

Then you can run the testsuite with::

py.test

Choice of Programming Languages

Are we right to concentrate on Java and Python versions of the code? I think so; both languages are popular; Java is fast enough for our purposes, and has reasonable type declarations (but can be verbose); Python is popular and has a very direct mapping to the pseudocode in the book (but lacks type declarations and can be slow). The TIOBE Index says the top seven most popular languages, in order, are:

    Java, C, C++, C#, Python, PHP, Javascript

So it might be reasonable to also support C++/C# at some point in the future. It might also be reasonable to support a language that combines the terse readability of Python with the type safety and speed of Java; perhaps Go or Julia. I see no reason to support PHP. Javascript is the language of the browser; it would be nice to have code that runs in the browser without need for any downloads; this would be in Javascript or a variant such as Typescript.

There is also a aima-lisp project; in 1995 when we wrote the first edition of the book, Lisp was the right choice, but today it is less popular (currently #31 on the TIOBE index).

What languages are instructors recommending for their AI class? To get an approximate idea, I gave the query [norvig russell "Modern Approach"] along with the names of various languages and looked at the estimated counts of results on various dates. However, I don't have much confidence in these figures...

Language 2004 2005 2007 2010 2016
none 8,080 20,100 75,200 150,000 132,000
java 1,990 4,930 44,200 37,000 50,000
c++ 875 1,820 35,300 105,000 35,000
lisp 844 974 30,100 19,000 14,000
prolog 789 2,010 23,200 17,000 16,000
python 785 1,240 18,400 11,000 12,000