NEP 23 — Backwards compatibility and deprecation policy

Author

Ralf Gommers <ralf.gommers@gmail.com>

Status

Final

Type

Process

Created

2018-07-14

Resolution

https://mail.python.org/pipermail/numpy-discussion/2021-January/081423.html

Abstract

In this NEP we describe NumPy’s approach to backwards compatibility, its deprecation and removal policy, and the trade-offs and decision processes for individual cases where breaking backwards compatibility is considered.

Motivation and Scope

NumPy has a very large user base. Those users rely on NumPy being stable and the code they write that uses NumPy functionality to keep working. NumPy is also actively maintained and improved – and sometimes improvements require, or are made easier, by breaking backwards compatibility. Finally, there are trade-offs in stability for existing users vs. avoiding errors or having a better user experience for new users. These competing needs often give rise to long debates and delay accepting or rejecting contributions. This NEP tries to address that by providing a policy as well as examples and rationales for when it is or isn’t a good idea to break backwards compatibility.

In addition, this NEP can serve as documentation for users about how the NumPy project treats backwards compatibility, and the speed at which they can expect changes to be made.

In scope for this NEP are:

  • Principles of NumPy’s approach to backwards compatibility.

  • How to deprecate functionality, and when to remove already deprecated functionality.

  • Decision making process for deprecations and removals.

  • How to ensure that users are well informed about any change.

Out of scope are:

  • Making concrete decisions about deprecations of particular functionality.

  • NumPy’s versioning scheme.

General principles

When considering proposed changes that are backwards incompatible, the main principles the NumPy developers use when making a decision are:

  1. Changes need to benefit more than they harm users.

  2. NumPy is widely used, so breaking changes should be assumed by default to be harmful.

  3. Decisions should be based on how they affect users and downstream packages and should be based on usage data where possible. It does not matter whether this use contradicts the documentation or best practices.

  4. The possibility of an incorrect result is worse than an error or even crash.

When assessing the costs of proposed changes, keep in mind that most users do not read the mailing list, do not notice deprecation warnings, and sometimes wait more than one or two years before upgrading from their old version. And that NumPy has millions of users, so “no one will do or use this” is likely incorrect.

Benefits of proposed changes can include improved functionality, usability and performance, as well as lower maintenance cost and improved future extensibility.

Fixes for clear bugs are exempt from this backwards compatibility policy. However, in case of serious impact on users even bug fixes may have to be delayed for one or more releases. For example, if a downstream library would no longer build or would give incorrect results.

Implementing deprecations and removals

Deprecation warnings are necessary in all cases where functionality will eventually be removed. If there is no intent to remove functionality, then it should not be deprecated. A “please don’t use this for new code” in the documentation or other type of warning should be used instead, and the documentation can be organized such that the preferred alternative is more prominently shown.

Deprecations:

  • shall include the version number of the release in which the functionality was deprecated.

  • shall include information on alternatives to the deprecated functionality, or a reason for the deprecation if no clear alternative is available. Note that release notes can include longer messages if needed.

  • shall use DeprecationWarning by default, and VisibleDeprecation for changes that need attention again after already having been deprecated or needing extra attention for some reason.

  • shall be listed in the release notes of the release where the deprecation is first present.

  • shall not be introduced in micro (bug fix) releases.

  • shall set a stacklevel, so the warning appears to come from the correct place.

  • shall be mentioned in the documentation for the functionality. A .. deprecated:: directive can be used for this.

Examples of good deprecation warnings (also note standard form of the comments above the warning, helps when grepping):

# NumPy 1.15.0, 2018-09-02
warnings.warn('np.asscalar(a) is deprecated since NumPy 1.16.0, use '
              'a.item() instead', DeprecationWarning, stacklevel=3)

# NumPy 1.15.0, 2018-02-10
warnings.warn("Importing from numpy.testing.utils is deprecated "
              "since 1.15.0, import from numpy.testing instead.",
              DeprecationWarning, stacklevel=2)

# NumPy 1.14.0, 2017-07-14
warnings.warn(
    "Reading unicode strings without specifying the encoding "
    "argument is deprecated since NumPy 1.14.0. Set the encoding, "
    "use None for the system default.",
    np.VisibleDeprecationWarning, stacklevel=2)
/* DEPRECATED 2020-05-13, NumPy 1.20 */
if (PyErr_WarnFormat(PyExc_DeprecationWarning, 1,
        matrix_deprecation_msg, ufunc->name, "first") < 0) {
    return NULL;
}

Removal of deprecated functionality:

  • shall be done after at least 2 releases assuming the current 6-monthly release cycle; if that changes, there shall be at least 1 year between deprecation and removal.

  • shall be listed in the release notes of the release where the removal happened.

  • can be done in any minor, but not bugfix, release.

For backwards incompatible changes that aren’t “deprecate and remove” but for which code will start behaving differently, a FutureWarning should be used. Release notes, mentioning version number and using stacklevel should be done in the same way as for deprecation warnings. A .. versionchanged:: directive shall be used in the documentation after the behaviour change was made to indicate when the behavior changed:

def argsort(self, axis=np._NoValue, ...):
    """
    Parameters
    ----------
    axis : int, optional
        Axis along which to sort. If None, the default, the flattened array
        is used.

        ..  versionchanged:: 1.13.0
            Previously, the default was documented to be -1, but that was
            in error. At some future date, the default will change to -1, as
            originally intended.
            Until then, the axis should be given explicitly when
            ``arr.ndim > 1``, to avoid a FutureWarning.
    """
    ...
    warnings.warn(
        "In the future the default for argsort will be axis=-1, not the "
        "current None, to match its documentation and np.argsort. "
        "Explicitly pass -1 or None to silence this warning.",
        MaskedArrayFutureWarning, stacklevel=3)

Decision making

In concrete cases where this policy needs to be applied, decisions are made according to the NumPy governance model.

All deprecations must be proposed on the mailing list in order to give everyone with an interest in NumPy development a chance to comment. Removal of deprecated functionality does not need discussion on the mailing list.

Functionality with more strict deprecation policies

  • numpy.random has its own backwards compatibility policy with additional requirements on top of the ones in this NEP, see NEP 19.

  • The file format of .npy and .npz files is strictly versioned independent of the NumPy version; existing format versions must remain backwards compatible even if a newer format version is introduced.

Example cases

We now discuss a few concrete examples from NumPy’s history to illustrate typical issues and trade-offs.

Changing the behavior of a function

np.histogram is probably the most infamous example. First, a new keyword new=False was introduced, this was then switched over to None one release later, and finally it was removed again. Also, it has a normed keyword that had behavior that could be considered either suboptimal or broken (depending on ones opinion on the statistics). A new keyword density was introduced to replace it; normed started giving DeprecationWarning only in v.1.15.0. Evolution of histogram:

def histogram(a, bins=10, range=None, normed=False):  # v1.0.0

def histogram(a, bins=10, range=None, normed=False, weights=None, new=False):  #v1.1.0

def histogram(a, bins=10, range=None, normed=False, weights=None, new=None):  #v1.2.0

def histogram(a, bins=10, range=None, normed=False, weights=None):  #v1.5.0

def histogram(a, bins=10, range=None, normed=False, weights=None, density=None):  #v1.6.0

def histogram(a, bins=10, range=None, normed=None, weights=None, density=None):  #v1.15.0
    # v1.15.0 was the first release where `normed` started emitting
    # DeprecationWarnings

The new keyword was planned from the start to be temporary. Such a plan forces users to change their code more than once, which is almost never the right thing to do. Instead, a better approach here would have been to deprecate histogram and introduce a new function hist in its place.

Disallowing indexing with floats

Indexing an array with floats is asking for something ambiguous, and can be a sign of a bug in user code. After some discussion, it was deemed a good idea to deprecate indexing with floats. This was first tried for the v1.8.0 release, however in pre-release testing it became clear that this would break many libraries that depend on NumPy. Therefore it was reverted before release, to give those libraries time to fix their code first. It was finally introduced for v1.11.0 and turned into a hard error for v1.12.0.

This change was disruptive, however it did catch real bugs in, e.g., SciPy and scikit-learn. Overall the change was worth the cost, and introducing it in the main branch first to allow testing, then removing it again before a release, is a useful strategy.

Similar deprecations that also look like good examples of cleanups/improvements:

  • removing deprecated boolean indexing (in 2016, see gh-8312)

  • deprecating truth testing on empty arrays (in 2017, see gh-9718)

Removing the financial functions

The financial functions (e.g. np.pmt) had short non-descriptive names, were present in the main NumPy namespace, and didn’t really fit well within NumPy’s scope. They were added in 2008 after a discussion on the mailing list where opinion was divided (but a majority in favor). The financial functions didn’t cause a lot of overhead, however there were still multiple issues and PRs a year for them which cost maintainer time to deal with. And they cluttered up the numpy namespace. Discussion on removing them was discussed in 2013 (gh-2880, rejected) and in 2019 (NEP 32 — Remove the financial functions from NumPy, accepted without significant complaints).

Given that they were clearly outside of NumPy’s scope, moving them to a separate numpy-financial package and removing them from NumPy after a deprecation period made sense. That also gave users an easy way to update their code by doing pip install numpy-financial.

Alternatives

Being more aggressive with deprecations.

The goal of being more aggressive is to allow NumPy to move forward faster. This would avoid others inventing their own solutions (often in multiple places), as well as be a benefit to users without a legacy code base. We reject this alternative because of the place NumPy has in the scientific Python ecosystem - being fairly conservative is required in order to not increase the extra maintenance for downstream libraries and end users to an unacceptable level.