CONTRIBUTING.md

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 on how you can help.

The main ways you can contribute to the repository are the following:

1. Implement algorithms from the list of algorithms.
2. Add tests for algorithms that are missing them (you can also add more tests to algorithms that already have some).
3. Take care of issues.
4. Write on the notebooks (.ipynb files).
5. Add and edit documentation (the docstrings in .py files).

In more detail:

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 and that some don't have tests.

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 an 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 files.

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

Jupyter Notebooks

In this project we use Jupyter/IPython Notebooks to showcase the algorithms in the book. They serve as short tutorials on what the algorithms do, how they are implemented and how one can use them. To install Jupyter, you can follow the instructions here. These are some ways you can contribute to the notebooks:

• Proofread the notebooks for grammar mistakes, typos, or general errors.
• Move visualization and unrelated to the algorithm code from notebooks to notebook.py (a file used to store code for the notebooks, like visualization and other miscellaneous stuff). Make sure the notebooks still work and have their outputs showing!
• Replace the %psource magic notebook command with the function psource from notebook.py where needed. Examples where this is useful are a) when we want to show code for algorithm implementation and b) when we have consecutive cells with the magic keyword (in this case, if the code is large, it's best to leave the output hidden).
• Add the function pseudocode(algorithm_name) in algorithm sections. The function prints the pseudocode of the algorithm. You can see some example usage in knowledge.ipynb.
• Edit existing sections for algorithms to add more information and/or examples.
• Add visualizations for algorithms. The visualization code should go in notebook.py to keep things clean.
• Add new sections for algorithms not yet covered. The general format we use in the notebooks is the following: First start with an overview of the algorithm, printing the pseudocode and explaining how it works. Then, add some implementation details, including showing the code (using psource). Finally, add examples for the implementations, showing how the algorithms work. Don't fret with adding complex, real-world examples; the project is meant for educational purposes. You can of course choose another format if something better suits an algorithm.

Apart from the notebooks explaining how the algorithms work, we also have notebooks showcasing some indicative applications of the algorithms. These notebooks are in the *_apps.ipynb format. We aim to have an apps notebook for each module, so if you don't see one for the module you would like to contribute to, feel free to create it from scratch! In these notebooks we are looking for applications showing what the algorithms can do. The general format of these sections is this: Add a description of the problem you are trying to solve, then explain how you are going to solve it and finally provide your solution with examples. Note that any code you write should not require any external libraries apart from the ones already provided (like matplotlib).

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?

• Provide an example of the issue occuring.

• 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 from the aima-python or tests directory 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