[Baypiggies] Type checking - argument for it being bad practice?
brent.tubbs at gmail.com
Fri Oct 8 21:07:31 CEST 2010
Here's a decorator recipe from ActiveState that does something similar:
It allows for overloading:
@require("x", int, float)
def foo(x, y):
print foo(1, 2.5) # Prints 3.5.
print foo(2.0, 2.5) # Prints 4.5.
print foo("asdf", 2.5) # Raises TypeError exception.
print foo(1, 2) # Raises TypeError exception.
On Thu, Oct 7, 2010 at 11:54 PM, Jason Lai <jason.lai at gmail.com> wrote:
> As an alternative to an explicit assert, you may consider creating a
> decorator, e.g.
> @enforce_type(inputNumber = Number)
> def myfunc(inputNumber):
> That way, it separates the type checking from the main body of code, and
> enforce_type could generate an appropriate detailed error message like
> "myfunc expected inputNumber to be of type 'Number' but was passed 'str'"
> which is nicer than just an assert. You could also easily demote it to a
> warning rather than an exception without changing all of your code (e.g.
> type enforcement only for debugging), although you might miss it if the
> method just stores the argument and the type error doesn't happen until much
> later, which is one argument for early typechecking.
> Python 3.0 supports function annotations, which allows you to annotate
> function parameters with arbitrary objects including type objects (although
> it doesn't assign any special meaning to them), which would allow you to do
> something like:
> def myfunc(inputNumber: Number):
> Note that numbers.Number (which is a superclass of the standard number types
> like int and float) is only available in Python 2.6+.
> For non-numeric/non-string types though, you have to be more careful -- for
> example it's not uncommon for people to pass around dictionary-like objects
> that implement the usual x["key"] protocol but aren't actually dictionaries.
> And file-like objects are enshrined in Python's API.
> Python 2.6+ has the concept of Abstract Base Classes to allow type-checking
> to ensure an object implements a particular interface. It seems a little
> inconsistent though -- if you implement __iter__ on a random class MyClass
> then isinstance(MyClass(), collections.Iterable) returns True. However, if
> you try to implement the collections.Mapping ABC, simply implementing the
> methods __getitem__, __iter__, and __len__ is insufficient;
> isinstance(MyClass(), collections.Mapping) will return False.
> In that case, you have to make MyClass subclass collections.Mapping, or use
> collections.Mapping.register(MyClass). This is OK if you control all the
> code, since you can just make sure all your dictionary-like objects inherit
> from the right ABC, but probably too strict for open-source library code
> where most people expect to pass an object that looks like a dictionary and
> have the duck typing just work.
> You could potentially work around this in the type-checking decorator by, if
> the initial type check fails, iterate over the __abstractmethods__ property
> of the the required class and make sure all the necessary methods exist.
> For your own user-defined classes which don't have a well-defined protocol
> like the collection ABCs though, it's probably more trouble than it's worth
> to define an explicit interface, in which case you might as well just stick
> to duck typing.
> - Jason
> On Thu, Oct 7, 2010 at 9:22 PM, Seth Friedman <sfseth at gmail.com> wrote:
>> Hi Mark,
>> Thanks for your response.
>> I can't quite wrap my head around your argument. I don't think one or two
>> lines per input is a lot of / too much time gatekeeping input variables.
>> Philisophically I think I see your point, I'm trying to impose some rigidity
>> in a domain that doesn't like it. But there are lots of other values that
>> python brings - it seems extreme to go to a different super-verbose language
>> just for type-checking. Not to mention that I don't trust those other
>> languages to actually do it for me as well as python's assert(isinstance...)
>> style - Java for instance strikes me as a prime candidate for false-negative
>> type checking, they've got a lot of subtle type variations.
>> Is something like
>> assert(isinstance(inputNumber,isActuallyANumberWeExpected)) really that
>> cumbersome, aside from my long variable names? The biggest examples I can
>> think of are the distinctions between number and string of number, and list
>> of numbers, which seem like totally legit "how far does this function need
>> to go" question, asserting what is expected seems to illuminate rather than
>> I mean, what's wrong with code asserting that what it got passed was what
>> was expected? If there's blind faith that stuff will just work beneath the
>> scenes, ok, but when i *know* it won't, why not fail there, and explicitly?
>> If I write a function that can be called from python code or command line,
>> I might get a string of a number or an int, and I will probably forget 6
>> months from now what I wrote the code to handle, without some kind of
>> prompt. The closer that prompt is to the disconnect, the better.
>> On Thu, Oct 7, 2010 at 8:20 PM, Mark Voorhies <mvoorhie at yahoo.com> wrote:
>>> On Thursday, October 07, 2010 07:32:18 pm Seth Friedman wrote:
>>> > Hi Baypiggies,
>>> > I've encountered, a couple of times now, a sort of categorical "dont do
>>> > typechecking" attitude on various blog posts and I haven't seen
>>> > rationale to
>>> > back it up. Enough that I'm now curious: what's the deal?
>>> > So I put the question to you all, typechecking: good or bad thing? Or
>>> > pointless philosophical debate?
>>> The lack of type signatures in function declarations are in line with
>>> as a rapid prototyping language" -- if you're spending a lot of lines on
>>> a function's arguments, you might be better off in a language that does
>>> work for you (e.g., Java, C++, ...). There is a similar argument for not
>>> a lot of work into enforcing private variables in Python.
>>> That said, I don't have a strong opinion on this, and I've heard the
>>> "traits" system
>>> in the Enthought Python Distribution recommended as a way to assert more
>>> control over the types that a function accepts.
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