Coding Style Guidelines

Main Goals

  • Code should be compatible with Python 2.4 and above (using 2to3 for conversion to Python 3.x). This may not be possible in the short term for Theano-dependent code.
  • Code should be easy to read, understand and update by developers and users.
  • Code should be well-documented and well-tested.

Python Coding Guidelines

Official Guidelines

Source Material

The four main documents describing our Python coding guidelines are:

However, there are a few points mentioned in those documents that we decided to do differently:

  • Use only one space (not two) after a sentence-ending period in comments.

    # Good.
    # This is the first sentence. It is followed by a single blank space.
    # Bad.
    # This is the first sentence.  It is followed by two blank spaces.
  • You do not need to add an extra blank line before the closing quotes of a multi-line docstring. Also, we ask that the first line of a multi-line docstring should contain only the opening quotes.

    # Good.
    This is a multi-line docstring.
    Which means it has more than one line.
    # Bad.
    """This is a multi-line docstring.
    Which means it has more than one line.
  • Standard library imports can (and should) be on the same line, to avoid wasting space on straighforward imports:

    # Good.
    import os, sys, time
    # Good when it does not fit on a single line.
    import std_lib_module_1, std_lib_module_2, std_lib_module_3
    import std_lib_module_4, std_lib_module_5, std_lib_module_6
    # Bad.
    import os
    import sys
    import time
  • Importing class / functions from a module is allowed when these are used multiple times, and no ambiguity is possible.

    # Good when Bar and Blah are used many times.
    from foo import Bar, Blah
    do_something_with(Bar(), Blah(), Bar(), Blah(), Bar(), Blah())
    # Good in most situations.
    import foo
    do_something_with(foo.Bar(), foo.Blah())
    # Bad.
    from foo import *
    from numpy import any   # Potential ambiguity with __builtin__.any


We emphasize here a few important topics that are found in the official guidelines:

  • Only use ASCII characters in code files.

  • Code indent must be done with four blank characters (no tabs).

  • Limit lines to 79 characters.

  • No trailing spaces.

  • Naming conventions: ClassName, TOP_LEVEL_CONSTANT, everything_else.

  • Comments should start with a capital letter (unless the first word is a code identifier) and end with a period (short inline comments may skip the period at the end).

  • Imports should be listed in alphabetical order. It makes it easier to verify that something is imported, and avoids duplicated imports.

  • Use absolute imports only. This is compatible across a wider range of Python versions, and avoids confusion about what is being imported.

  • Avoid renaming imported modules. This makes code more difficult to re-use, and is not grep-friendly.

    # Good.
    from theano import tensor
    # Bad.
    from theano import tensor as T
  • Avoid using lists if all you care about is iterating on something. Using lists:

    • uses more memory (and possibly more CPU if the code may break out of the iteration),
    • can lead to ugly code when converted to Python 3 with 2to3,
    • can have a different behavior if evaluating elements in the list has side effects (if you want these side effects, make it explicit by assigning the list to some variable before iterating on it).
    Iterative version List version

    Code example with map:

    # Good.
    for f_x in imap(f, x):
    all_f_x = map(f, x)
    map(f, x)   # f has some side effect.
    # Bad.
    for element in map(f, x):
    imap(f, x)
  • Generally prefer list comprehensions to map / filter, as the former are easier to read.

    # Good.
    non_comments = [line.strip() for line in my_file.readlines()
                                 if not line.startswith('#')]
    # Bad.
    non_comments = map(str.strip,
                       ifilter(lambda line: not line.startswith('#'),
  • Use in on container objects instead of using class-specific methods: it is easier to read and may allow you to re-use your code with different container types.

    # Good.
    has_key = key in my_dict
    has_substring = substring in my_string
    # Bad.
    has_key = my_dict.has_key(key)
    has_substring = my_string.find(substring) >= 0
  • Do not use mutable arguments as default values. Instead, use a helper function (conditional expressions are forbidden at this point, see below).

    # Good.
    def f(array=None):
        array = pylearn.if_none(array, [])
    # Bad.
    def f(array=[]): # Dangerous if `array` is modified down the road.
  • All top-level classes should inherit from object. It makes some ‘under-the-hood’ differences that can be very useful for Python black magic adepts.

    # Good.
    class MyClass(object):
    # Bad.
    class MyClass:
  • Always raise an exception with raise MyException(args) where MyException inherits from Exception. This is required for compatibility across all versions of Python.

    # Good.
    raise NotImplementedError('The Pylearn team is too lazy.')
    # Bad.
    raise NotImplementedError, 'The Pylearn team is too lazy.'
    raise 'The Pylearn team is too lazy to implement this.'
  • Use a leading underscore ‘_’ in names of internal attributes / methods, but avoid the double underscore ‘__’ unless you know what you are doing.

Additional Recommendations

Things you should do even if they are not listed in official guidelines:

  • All Python code files should start like this:

    """Module docstring as the first line, as usual."""
    __authors__   = "Olivier Delalleau, Frederic Bastien, David Warde-Farley"
    __copyright__ = "(c) 2010, Universite de Montreal"
    __license__   = "3-clause BSD License"
    __contact__   = "Name Of Current Guardian of this file <email@address>"
  • Use // for integer division and / float(...) if you want the floating point operation (for readability and compatibility across all versions of Python).

    # Good.
    n_samples_per_split = n_samples // n_splits
    mean_x = sum(x) / float(len(x))
    # Bad.
    n_samples_per_split = n_samples / n_splits
    mean_x = sum(x) / len(x)
  • If you really have to catch all exceptions, in general you should use except Exception: rather than except:, as the latter also catches interrupts like when hitting Ctrl-C.

    # Good (assuming you *must* be catching all exceptions).
    except Exception:
    # Bad.
  • Use either try ... except or try ... finally, but do not mix except with finally (which is not supported in Python 2.4). You can however embed one into the other to mimic the try ... except ... finally behavior.

    # Good.
        except SomeError:
    # Bad.
    except SomeError:
  • No conditional expression (not supported in Python 2.4). These are expressions of the form x = y if condition else z.

  • Do not use the all and any builtin functions (they are not supported in Python 2.4). Instead, import them from theano.gof.python25 (or use numpy.all / numpy.any for array data).

  • Do not use the hashlib module (not supported in Python 2.4). We will probably provide a wrapper around it to be compatible with all Python versions.

  • Use numpy.inf and numpy.nan rather than float('inf') / float('nan') (should be slightly more efficient even if efficiency is typically not an issue here, the main goal being code consistency). Also, always use numpy.isinf / numpy.isnan to test infinite / NaN values. This is important because numpy.nan != float('nan').

  • Whenever possible, mimic the numpy / scipy interfaces when writing code similar to what can be found in these packages.

  • Avoid backslashes whenever possible. They make it more difficult to edit code, and they are ugly (as well as potentially dangerous if there are trailing white spaces).

    # Good.
    if (cond_1 and
        cond_2 and
        # Note that we added a blank line above to avoid confusion between
        # conditions and the rest of the code (this would not have been
        # needed if they were at significantly different indentation levels).
    # Bad.
    if cond_1 and \
       cond_2 and \
  • When indenting multi-line statements like lists or function arguments, keep elements of the same level aligned with each other. The position of the first element (on the same line or a new line) should be chosen depending on what is easiest to read (sometimes both can be ok). Other formattings may be ok depending on the specific situation, use common sense and pick whichever looks best.

    # Good.
    for my_very_long_variable_name in [my_foo, my_bar, my_love,
    for my_very_long_variable_name in [
            my_foo, my_bar, my_love, my_everything]:
    # Good iff the list needs to be frequently updated or is easier to
    # understand when each element is on its own line.
    for my_very_long_variable_name in [
    # Good as long as it does not require more than two lines.
    for my_very_long_variable_name in [my_foo,
    # Bad.
    for my_very_long_variable_name in [my_foo, my_bar, my_love,
    for my_very_long_variable_name in [my_foo,
  • Use the key argument instead of cmp when sorting (for Python 3 compatibility).

    # Good.
    # Bad.
    my_list.sort(cmp=lambda x, y: cmp(abs(x), abs(y)))
  • Whenever you read / write binary files, specify it in the mode (‘rb’ for reading, ‘wb’ for writing). This is important for cross-platform and Python 3 compatibility (e.g. when pickling / unpickling objects).

    # Good.
    cPickle.dump(obj, open('my_obj.pkl', 'wb', protocol=-1))
    # Bad.
    cPickle.dump(obj, open('my_obj.pkl', 'w', protocol=-1))
  • Avoid tuple parameter unpacking as it can lead to very ugly code when converting to Python 3.

    # Good.
    def f(x, y_z):
        y, z = y_z
    # Bad.
    def f(x, (y, z)):
  • Only use cPickle, not pickle (except for debugging purpose since error messages from pickle are sometimes easier to understand).

  • A script’s only top-level code should be something like:

    if __name__ == '__main__':
  • Avoid isinstance(x, str) as this don’t work with unicode string, use:

    isinstance(x, basestring)

The logging Module vs. the warning Module

The logging Module

A central logging facility for Python capable of logging messages of various categories/urgency and choosing with some granularity which messages are displayed/suppressed, as well as where they are displayed or written. This includes an INFO level for innocuous status information, a WARNING level for unexpected state that is still recoverable, DEBUG for detailed information which is only really of interest when things are going wrong, etc.

In addition to the library documentation, see this helpful tutorial, Python Logging 101.

The warning Module

The warning module in the standard library and its main interface, the warn() function, allows the programmer to issue warnings in situations where they wish to alert the user to some condition, but the situation is not urgent enough to throw an exception. By default, a warning issued at a given line of the code will only be displayed the first time that line is executed. By default, warnings are written to sys.stderr but the warning module contains flexible facilities for altering the defaults, redirecting, etc.

Which? When?

It is our feeling that the logging module’s WARNING level be used to log warnings more meant for internal, developer consumption, to log situations where something unexpected happened that may be indicative of a problem but is several layers of abstraction below what a user of the library would care about.

By contrast, the warning module should be used for warnings intended for user consumption, e.g. alerting them that their version of Pylearn is older than this plugin requires, so things may not work as expected, or that a given function/class/method is slated for deprecation in a coming release (early in the library’s lifetime, DeprecationWarning will likely be the most common case). The warning message issued through this facility should avoid referring to Pylearn internals.

Code Sample

The following code sample illustrates some of the coding guidelines one should follow in Pylearn. This is still a work-in-progress. Feel free to improve it and add more!

#! /usr/env/bin python

"""Sample code. Edit it as you like!"""

__authors__   = "Olivier Delalleau"
__copyright__ = "(c) 2010, Universite de Montreal"
__license__   = "3-clause BSD License"
__contact__   = "Olivier Delalleau <delallea@iro>"

# Standard library imports are on a single line.
import os, sys, time

# Third-party imports come after standard library imports, and there is
# only one import per line. Imports are sorted lexicographically.
import numpy
import scipy
import theano
# Individual 'from' imports come after packages.
from numpy import argmax
from theano import tensor

# Application-specific imports come last.
# The absolute path should always be used.
from pylearn import datasets, learner
from pylearn.formulas import noise

# All exceptions inherit from Exception.
class PylearnError(Exception):
    # TODO Write doc.

# All top-level classes inherit from object.
class StorageExample(object):
    # TODO Write doc.

# Two blank lines between definitions of top-level classes and functions.
class AwesomeLearner(learner.Learner):
    # TODO Write doc.

    def __init__(self, print_fields=None):
        # TODO Write doc.
        # print_fields is a list of strings whose counts found in the
        # training set should be printed at the end of training. If None,
        # then nothing is printed.
        # Do not forget to call the parent class constructor.
        super(AwesomeLearner, self).__init__()
        # Use None instead of an empty list as default argument to
        # print_fields to avoid issues with mutable default arguments.
        self.print_fields = if_none(print_fields, [])

    # One blank line between method definitions.
    def add_field(self, field):
        # TODO Write doc.
        # Test if something belongs to a container with `in`, not
        # container-specific methods like `index`.
        if field in self.print_fields:
            # TODO Print a warning and do nothing.
            # This is why using [] as default to print_fields in the
            # constructor would have been a bad idea.

    def train(self, dataset):
        # TODO Write doc (store the mean of each field in the training
        # set).
        self.mean_fields = {}
        count = {}
        for sample_dict in dataset:
            # Whenever it is enough for what you need, use iterative
            # instead of list versions of dictionary methods.
            for field, value in sample_dict.iteritems():
                # Keep line length to max 80 characters, using parentheses
                # instead of \ to continue long lines.
                self.mean_fields[field] = (self.mean_fields.get(field, 0) +
                count[field] = count.get(field, 0) + 1
        for field in self.mean_fields:
            self.mean_fields[field] /= float(count[field])
        for field in self.print_fields:
            # Test is done with `in`, not `has_key`.
            if field in self.sum_fields:
                # TODO Use log module instead.
                print '%s: %s' % (field, self.sum_fields[field])
                # TODO Print warning.

    def test_error(self, dataset):
        # TODO Write doc.
        if not hasattr(self, 'sum_fields'):
            # Exceptions should be raised as follows (in particular, no
            # string exceptions!).
            raise PylearnError('Cannot test a learner that was not '
        error = 0
        count = 0
        for sample_dict in dataset:
            for field, value in sample_dict.iteritems():
                    # Minimize code into a try statement.
                    mean = self.mean_fields[field]
                # Always specicy which kind of exception you are
                # intercepting with except.
                except KeyError:
                    raise PylearnError(
                        "Found in a test sample a field ('%s') that had "
                        "never been seen in the training set." % field)
                error += (value - self.mean_fields[field])**2
                count += 1
        # Remember to divide by a floating point number unless you
        # explicitly want an integer division (in which case you should
        # use //).
        mse = error / float(count)
        # TODO Use log module instead.
        print 'MSE: %s' % mse
        return mse

def if_none(val_if_not_none, val_if_none):
    # TODO Write doc.
    if val_if_not_none is not None:
        return val_if_not_none
        return val_if_none

def print_subdirs_in(directory):
    # TODO Write doc.
    # Using list comprehension rather than filter.
    sub_dirs = sorted([d for d in os.listdir(directory)
                         if os.path.isdir(os.path.join(directory, d))])
    print '%s: %s' % (directory, ' '.join(sub_dirs))
    # A `for` loop is often easier to read than a call to `map`.
    for d in sub_dirs:
        print_subdirs_in(os.path.join(directory, d))

def main():
    if len(sys.argv) != 2:
        # Note: conventions on how to display script documentation and
        # parse arguments are still to-be-determined. This is just one
        # way to do it.
Usage: %s <directory>
For the given directory and all sub-directories found inside it, print
the list of the directories they contain."""
              % os.path.basename(sys.argv[0]))
        return 1
    return 0

# Top-level executable code should be minimal.
if __name__ == '__main__':

Automatic Code Verification

Tools will be available to make it easier to automatically ensure that code committed to Pylearn complies to above specifications. This work is not finalized yet, but David started a Wiki page with helpful configuration tips for Vim.

Commit message

  • A one line summary. Try to keep it short, and provide the information that seems most useful to other developers: in particular the goal of a change is more useful than its description (which is always available through the changeset patch log). E.g. say “Improved stability of cost computation” rather than “Replaced log(exp(a) + exp(b)) by a * log(1 + exp(b -a)) in cost computation”.

  • If needed a blank line followed by a more detailed summary

  • Make a commit for each logical modification
    • This makes reviews easier to do
    • This makes debugging easier as we can more easily pinpoint errors in commits with hg bisect
  • NEVER commit reformatting with functionality changes

  • Review your change before commiting
    • “hg diff <files>...” to see the diff you have done

    • “hg record” allows you to select which changes to a file should be committed. To enable it, put into the file ~/.hgrc:

    • hg record / diff force you to review your code, never commit without running one of these two commands first

  • Write detailed commit messages in the past tense, not present tense.
    • Good: “Fixed Unicode bug in RSS API.”
    • Bad: “Fixes Unicode bug in RSS API.”
    • Bad: “Fixing Unicode bug in RSS API.”
  • Separate bug fixes from feature changes.

  • When fixing a ticket, start the message with “Fixed #abc”
    • Can make a system to change the ticket?
  • When referencing a ticket, start the message with “Refs #abc”
    • Can make a system to put a comment to the ticket?


Theano used many standard for its docstring. I think we should consentrate on the more frequent:


To make a file UTF-8 compatible, just add this line at the begining of the file:

# -*- coding: utf-8 -*-


Things still missing from this document, being discussed in coding_style.txt:
  • Proper style for C code
  • Enforcing 100% test coverage of the code base
  • Providing ways to add type checking for function arguments
  • Conventions for script usage documentation and argument parsing
  • Conventions for class / method / function documentation
  • Guidelines for serialization-friendly code (hint: nested and lambda functions, as well as instance methods, cannot be serialized, and apparently there are some issues with decorators – to be investigated).