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157 lines
4.4 KiB
Python
157 lines
4.4 KiB
Python
"""Utilities for memoization
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Note: a memoized function should always return an _immutable_
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result to avoid later modifications polluting cached results.
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"""
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from collections import OrderedDict
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from functools import wraps
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class DoNotCache:
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"""Wrapper to return a result without caching it.
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In a function decorated with `@lru_cache_key`:
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return DoNotCache(result)
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is equivalent to:
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return result # but don't cache it!
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"""
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def __init__(self, result):
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self.result = result
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class LRUCache:
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"""A simple Least-Recently-Used (LRU) cache with a max size"""
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def __init__(self, maxsize=1024):
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self._cache = OrderedDict()
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self.maxsize = maxsize
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def __contains__(self, key):
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return key in self._cache
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def get(self, key, default=None):
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"""Get an item from the cache"""
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if key in self._cache:
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# cache hit, bump to front of the queue for LRU
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result = self._cache[key]
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self._cache.move_to_end(key)
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return result
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return default
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def set(self, key, value):
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"""Store an entry in the cache
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Purges oldest entry if cache is full
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"""
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self._cache[key] = value
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# cache is full, purge oldest entry
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if len(self._cache) > self.maxsize:
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self._cache.popitem(last=False)
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__getitem__ = get
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__setitem__ = set
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def lru_cache_key(key_func, maxsize=1024):
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"""Like functools.lru_cache, but takes a custom key function,
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as seen in sorted(key=func).
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Useful for non-hashable arguments which have a known hashable equivalent (e.g. sets, lists),
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or mutable objects where only immutable fields might be used
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(e.g. User, where only username affects output).
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For safety: Cached results should always be immutable,
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such as using `frozenset` instead of mutable `set`.
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Example:
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@lru_cache_key(lambda user: user.name)
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def func_user(user):
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# output only varies by name
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Args:
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key (callable):
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Should have the same signature as the decorated function.
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Returns a hashable key to use in the cache
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maxsize (int):
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The maximum size of the cache.
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"""
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def cache_func(func):
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cache = LRUCache(maxsize=maxsize)
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# the actual decorated function:
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@wraps(func)
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def cached(*args, **kwargs):
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cache_key = key_func(*args, **kwargs)
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if cache_key in cache:
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# cache hit
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return cache[cache_key]
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else:
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# cache miss, call function and cache result
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result = func(*args, **kwargs)
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if isinstance(result, DoNotCache):
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# DoNotCache prevents caching
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result = result.result
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else:
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cache[cache_key] = result
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return result
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return cached
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return cache_func
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class FrozenDict(dict):
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"""A frozen dictionary subclass
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Immutable and hashable, so it can be used as a cache key
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Values will be frozen with `.freeze(value)`
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and must be hashable after freezing.
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Not rigorous, but enough for our purposes.
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"""
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_hash = None
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def __init__(self, d):
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dict_set = dict.__setitem__
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for key, value in d.items():
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dict.__setitem__(self, key, self._freeze(value))
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def _freeze(self, item):
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"""Make values of a dict hashable
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- list, set -> frozenset
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- dict -> recursive _FrozenDict
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- anything else: assumed hashable
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"""
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if isinstance(item, FrozenDict):
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return item
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elif isinstance(item, list):
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return tuple(self._freeze(e) for e in item)
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elif isinstance(item, set):
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return frozenset(item)
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elif isinstance(item, dict):
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return FrozenDict(item)
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else:
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# any other type is assumed hashable
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return item
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def __setitem__(self, key):
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raise RuntimeError("Cannot modify frozen {type(self).__name__}")
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def update(self, other):
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raise RuntimeError("Cannot modify frozen {type(self).__name__}")
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def __hash__(self):
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"""Cache hash because we are immutable"""
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if self._hash is None:
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self._hash = hash(tuple((key, value) for key, value in self.items()))
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return self._hash
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