SLEP009: Keyword-only arguments

Author:Adrin Jalali
Status:Accepted
Type:Standards Track
Created:2019-07-13
Vote opened:2019-09-11

Abstract

This proposal discusses the path to gradually forcing users to pass arguments, or most of them, as keyword arguments only. It talks about the status-quo, and the motivation to introduce the change. It shall cover the pros and cons of the change. The original issue starting the discussion is located here.

Motivation

At the moment sklearn accepts all arguments both as positional and keyword arguments. For example, both of the following are valid:

# positional arguments
clf = svm.SVC(.1, 'rbf')
# keyword arguments
clf = svm.SVC(C=.1, kernel='rbf')

Using keyword arguments has a few benefits:

  • It is more readable.
  • For models which accept many parameters, especially numerical, it is less error-prone than positional arguments. Compare these examples:
cls = cluster.OPTICS(
    min_samples=5, max_eps=inf, metric=’minkowski’, p=2,
    metric_params=None, cluster_method=’xi’, eps=None, xi=0.05,
    predecessor_correction=True, min_cluster_size=None, algorithm=’auto’,
    leaf_size=30, n_jobs=None)

cls = cluster.OPTICS(5, inf, ’minkowski’, 2, None, ’xi’, None, 0.05,
                     True, None, ’auto’, 30, None)
  • It allows adding new parameters closer the other relevant parameters, instead of adding new ones at the end of the list without breaking backward compatibility. Right now all new parameters are added at the end of the signature. Once we move to a keyword only argument list, we can change their order and put related parameters together. Assuming at some point numpydoc would support sections for parameters, these groups of parameters would be in different sections for the documentation to be more readable. Also, note that we have previously assumed users would pass most parameters by name and have sometimes considered changes to be backwards compatible when they modified the order of parameters. For example, user code relying on positional arguments could break after a deprecated parameter was removed. Accepting this SLEP would make this requirement explicit.

Solution

The official supported way to have keyword only arguments is:

def func(arg1, arg2, *, arg3, arg4)

Which means the function can only be called with arg3 and arg4 specified as keyword arguments:

func(1, 2, arg3=3, arg4=4)

The feature was discussed and the related PEP PEP3102 was accepted and introduced in Python 3.0, in 2006.

For the change to happen in sklearn, we would need to add the * where we want all subsequent parameters to be passed as keyword only.

Considerations

We can identify the following main challenges: familiarity of the users with the syntax, and its support by different IDEs.

Syntax

Partly due to the fact that the Scipy/PyData has been supporting Python 2 until recently, the feature (among other Python 3 features) has seen limited adoption and the users may not be used to seeing the syntax. The similarity between the following two definitions may also be confusing to some users:

def f(arg1, *arg2, arg3): pass # variable length arguments at arg2

def f(arg1, *, arg3): pass # no arguments accepted at *

However, some other teams are already moving towards using the syntax, such as matplotlib which has introduced the syntax with a deprecation cycle using a decorator for this purpose in version 3.1. The related PRs can be found here and here. Soon users will be familiar with the syntax.

IDE Support

Many users rely on autocomplete and parameter hints of the IDE while coding. Here is how the hint looks like in two different IDEs. For instance, for the above function, defined in VSCode, the hint would be shown as:

           func(arg1, arg2, *, arg3, arg4)

           param arg3
func(1, 2, |)

The good news is that the IDE understands the syntax and tells the user it’s the arg3’s turn. But it doesn’t say it is a keyword only argument.

ipython, however, suggests all parameters be given with the keyword anyway:

In [1]: def func(arg1, arg2, *, arg3, arg4): pass

In [2]: func(
  abs()                          arg3=
  all()                          arg4=
  any()                          ArithmeticError                >
  arg1=                          ascii()
  arg2=                          AssertionError

Scope

An important open question is which functions/methods and/or parameters should follow this pattern, and which parameters should be keyword only. We can identify the following categories of functions/methods:

  • __init__
  • Main methods of the API, i.e. fit, transform, etc.
  • All other methods, e.g. SpectralBiclustering.get_submatrix
  • Functions

With regard to the common methods of the API, the decision for these methods should be the same throughout the library in order to keep a consistent interface to the user.

This proposal suggests making only most commonly used parameters positional. The most commonly used parameters are defined per method or function, to be defined as either of the following two ways:

  • The set defined and agreed upon by the core developers, which should cover the easy cases.
  • A set identified as being in the top 95% of the use cases, using some automated analysis such as this one or this one.

This way we would minimize the number of warnings the users would receive, which minimizes the friction cause by the change. This SLEP does not define these parameter sets, and the respective decisions shall be made in their corresponding pull requests.

Deprecation Path

For a smooth transition, we need an easy deprecation path. Similar to the decorators developed in matplotlib, a proposed solution is available at [#13311](https://github.com/scikit-learn/scikit-learn/pull/13311), which deprecates the usage of positional arguments on selected functions and methods. With the decorator, the user sees a warning if they pass the designated keyword-only arguments as positional, and removing the decorator would result in an error. Examples (borrowing from the PR):

@warn_args
def dbscan(X, eps=0.5, *, min_samples=4, metric='minkowski'):
    pass


class LogisticRegression:

    @warn_args
    def __init__(self, penalty='l2', *, dual=False):

        self.penalty = penalty
        self.dual = dual

Calling LogisticRegression('l2', True) will result with a DeprecationWarning:

Should use keyword args: dual=True

Once the deprecation period is over, we’d remove the decorator and calling the function/method with the positional arguments after * would fail.

The final decorator solution shall make sure it is well understood by most commonly used IDEs and editors such as IPython, Jupiter Lab, Emacs, vim, VSCode, and PyCharm.