Python 相当于 Scala 的惰性 val

Question

I'm currently trying to port some Scala code to a Python project and I came across the following bit of Scala code:

  lazy val numNonZero = weights.filter { case (k,w) => w > 0 }.keys

weights is a really long list of tuples of items and their associated probability weighting. Elements are frequently added and removed from this list but checking how many elements have a non-zero probability is relatively rare. There are a few other rare-but-expensive operations like this in the code I'm porting that seem to benefit greatly from usage of lazy val . What is the most idiomatic Python way to do something similar to Scala's lazy val ?

Answer 1

In Scala, lazy val is a final variable that is evaluated once at the time it is first accessed, rather than at the time it is declared. It is essentially a memoized function with no arguments. Here's one way you can implement a memoization decorator in Python:

from functools import wraps

def memoize(f):
    @wraps(f)
    def memoized(*args, **kwargs):
        key = (args, tuple(sorted(kwargs.items()))) # make args hashable
        result = memoized._cache.get(key, None)
        if result is None:
            result = f(*args, **kwargs)
            memoized._cache[key] = result
        return result
    memoized._cache = {}
    return memoized

---

Here's how it can be used. With property you can even drop the empty parentheses, just like Scala:

>>> class Foo:
...     @property
...     @memoize
...     def my_lazy_val(self):
...         print "calculating"
...         return "some expensive value"

>>> a = Foo()
>>> a.my_lazy_val
calculating
'some expensive value'

>>> a.my_lazy_val
'some expensive value'

Answer 2

Essentially, you want to change how attribute access works for numNonZero . Python does that with a descriptor . In particular, take a look at their application to Properties .

With that, you can defer calculation until the attribute is accessed, caching it for later use.

Answer 3

Generator expression

>>> weights = [(1,2), (2,0), (3, 1)]
>>> numNonZero = (k for k, w in weights if w > 0)
>>> next(numNonZero)
1
>>> next(numNonZero)
3
>>> next(numNonZero)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
StopIteration
>>> next(numNonZero, -1)
-1

---

>>> numNonZero = (k for k, w in weights if w > 0)
>>> for k in numNonZero:
...     print(k)
... 
1
3

Python tutorial: Generator expressions

Answer 4

You can use @functools.lru_cache(maxsize=None) on a nullary function to emulate a lazy val .

Python 3.6.5 (default, Mar 30 2018, 06:41:53) 
[GCC 4.2.1 Compatible Apple LLVM 9.0.0 (clang-900.0.39.2)] on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> import functools
>>> import random
>>> def foo():
...     @functools.lru_cache(maxsize=None)
...     def bar():
...         return random.random()
...     return bar
... 
>>> f1 = foo()
>>> f2 = foo()
>>> f1()
0.11043217592970156
>>> f1()
0.11043217592970156
>>> f2()
0.3545457696543922
>>> f2()
0.3545457696543922

Answer 5

A simpler variant of @sam-thomson's approach (inspired by his approach):

In [1]:     class Foo:
   ...:         def __init__(self):
   ...:             self.cached_lazy_val=None
   ...:         @property
   ...:         def my_lazy_val(self):
   ...:             if not self.cached_lazy_val:
   ...:                 print("calculating")
   ...:                 self.cached_lazy_val='some expensive value'
   ...:             return self.cached_lazy_val
   ...:

In [2]: f=Foo()

In [3]: f.my_lazy_val
calculating
Out[3]: 'some expensive value'

In [4]: f.my_lazy_val
Out[4]: 'some expensive value'