warned-dataclasses 0.4.0

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warned-dataclasses

This package adds functionality to Python's dataclasses feature to emit a warning or raise an exception if an explicit value for an attribute was used to initialize a dataclass but some user-specified condition that that attribute logically relies on was not met.

Motivation

The primary use case for this package is for tools (such as 🤗 Transformers) that use dataclasses for command-line parsing, where different command-line parameters make sense in different scenarios.

In the simple case, a programmer can just emit a warning or raise an exception if an explicit value was passed to one of these parameters in a context where it is not appropriate. However, the programmer may also want to set sensible defaults for such parameters when they are appropriate.

One approach to this problem is to compare the runtime value against the default value, and assume an explicit value was passed if they do not match. However, this approach presents two concerns:

1. complex and difficult-to-introspect default_factory objects may be used in the dataclass's fields
2. the programmer may want to warn the user even if they explicitly pass the default value

This package presents a solution to both of these problems.

Installation

This package is on PyPI for Python >=3.8:

pip install warned-dataclasses

Usage

There are two simple usage paradigms for this package. The following (contrived) examples should illustrate them.

1. warn_for_condition

With this approach, warnings are emitted as unmet conditions are discovered. This could be useful for emitting relevant warnings amidst other logging statements describing program flow, or (when using error=True) if an unmet condition in a critical point in the program should terminate the program immediately.

import json

from dataclasses import dataclass, field

from warned_dataclasses import Warned, warned, warn_for_condition


@warned
@dataclass
class User:
    id: int
    admin_level: Warned[int, 'admin_only'] = field(default=1)


def check_admin(user: User):
    with open('admins.json', 'r') as admins_fd:
        admins = json.load(admins_fd)

    if user.id not in admins:
        # uh-oh, user is not an admin
        warn_for_condition(user, 'admin_only')


if __name__ == '__main__':
    user = User(123, admin_level=2)
    check_admin(user)

2. satisfy and warn_all

With this approach, warnings are suppressed as conditions are judged to be met and marked as satisfied, and all warnings for a dataclass are emitted at once when warn_all is called.

import json

from dataclasses import dataclass, field

from warned_dataclasses import Warned, warned, warn_all, satisfy


@warned
@dataclass
class User:
    id: int
    admin_level: Warned[int, 'admin_only'] = field(default=1)
    fileshare_home_dir: Warned[str, 'db_access'] = field(default='~')


def check_admin(user: User):
    with open('admins.json', 'r') as admins_fd:
        admins = json.load(admins_fd)

    if user.id in admins:
        # user is an admin; no warning
        satisfy(user, 'admin_only')


def check_db_access(user: User):
    with open('db_users.json', 'r') as db_users_fd:
        db_users = json.load(db_users_fd)

    if user.id in db_users:
        # user is a db user; no warning
        satisfy(user, 'db_access')


if __name__ == '__main__':
    user = User(123, admin_level=2, fileshare_home_dir='/')
    check_admin(user)
    check_db_access(user)

    # now emit all unsatisfied errors
    warn_all(user)

Advanced Usage

1. Decorator options

• By default, a warned dataclass will emit a warning to the current logging logger. To raise an exception instead, use @warned(error=True).

• By default, a warned dataclass will only emit a warning once for each condition; future calls to warn_for_condition or warn_all will treat that condition as satisfied. To disable this behavior and emit a warning every time it is invoked, use @warned(satisfy_on_warn=False).

• By default, a warned dataclass will emit a warning if a value equal to the default value for a field is passed explicitly. Sometimes this is undesirable, and explicitly-passed default values should be ignored. To disable warnings for explicit default values, use @warned(warn_on_default=False).

• A plain @warned can be used with or without parentheses.

2. Multiple warned dataclasses and ConditionSet

If your code uses multiple warned dataclasses that share some conditions, the methods illustrated above become clunky, as you end up having to call warn_for_condition(obj, condition) for every warned dataclass object that uses condition. Instead, you can collect conditions immediately after instantiation using ConditionSet, then call any of the usual functions, either with the ConditionSet object as the first parameter or as a method on ConditionSet:

from dataclasses import dataclass, field

from warned_dataclasses import warned, Warned, ConditionSet, warn_for_condition, warn_all, satisfy


@warned
@dataclass
class One:
    ...
    some_attr: Warned[int, 'abc'] = field(default=32)
    other_attr: Warned[str, 'shared_condition'] = field(default='')
    ...


@warned
@dataclass
class Two:
    ...
    some_attr: Warned[int, 'shared_condition'] = field(default=5)
    other_attr: Warned[float, '123'] = field(default=0.0)
    ...


def main():
    ...
    one = One(...)
    two = Two(...)
    
    conditions = ConditionSet(one, two)
    
    ...
    
    conditions.warn_for_condition("shared_condition")
    conditions.satisfy("123")
    conditions.warn_all()
    
    # or:
    
    warn_for_condition(conditions, "shared_condition")
    satisfy(conditions, "123")
    warn_all(conditions)