The speed up vs lru_cache provided by functools in 3.3 or 3.4 is 10x-30x depending on the function signature and whether one is comparing with 3.3 or 3.4. Teams. Download the file for your platform. Download the file for your platform. type 'numpy.int64' unhashable. This workaround allows caching functions that take an arbitrary numpy.array as first parameter, other parameters are passed as is. If you're not sure which to choose, learn more about installing packages. Please try enabling it if you encounter problems. lru_cache does not support unhashable types, which means function arguments cannot contain dict or list. Q&A for Work. We use optional third-party analytics cookies to understand how you use GitHub.com so we can build better products. Learn more. You can always update your selection by clicking Cookie Preferences at the bottom of the page. You need to create a decorator that attaches the cache to a function created just onceper decorated target. We use optional third-party analytics cookies to understand how you use GitHub.com so we can build better products. If you're not sure which to choose, learn more about installing packages. Name Description; python报错提示unhashable type: 'list' 主要代码如下: 大家有谁遇到过类似问题吗,这个应该改哪里呢 unhashable type : 'list' cocosion的博客 pip install lru_cache. The following are 30 code examples for showing how to use functools.lru_cache().These examples are extracted from open source projects. Donate today! Do check it out. Simply using functools.lru_cache won't work because numpy.array is mutable and not hashable. That's exactly what I was hoping to find! If maxsize is set to None, the LRU feature is disabled and the cache can grow without bound.. PropTypes.objectOf can be used to ensure that the prop is an object in which all property values match the specified type. Sometimes processing numpy arrays can be slow, even more if we are doing image analysis. Since you’re sorting the list anyway, just place the duplicate removal after the list is already sorted. Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. @lru_cache - The One-Liner To Memoise In Python. User must be very careful when mutable objects (list, dict, numpy.array...) are returned. Decorator accepts lru_cache standard parameters (maxsize=128, typed=False). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. Download the file for your platform. lru, Keep that in mind when using it. Decorators for function scope control, overloading, type safety, thread safety, cache control, tracing and even self awareness! An empty cache will always have to re-calculate the value. However, it complains with TypeError: unhashable type: 'DatetimeIndex'.. Since DatetimeIndex objects are immutable, there should be a good way to use them as a key for memoization, right?. We can safely replace all expressions last(l) with the output value, without changing the program’s behavior. We can't apply lru_cache to minHeightShelves directly because books is a list, which is an unhashable type. Status: The wrapped function is instrumented with a cache_parameters() function that returns a new dict showing the values for … def f():pass print type(f) print f.__hash__() print hash(f) Output 1265925978 1265925978. We use essential cookies to perform essential website functions, e.g. Thanks, I see what you're trying to do now: 1) Given a slow function 2) that takes a complex argument 2a) that includes a hashable unique identifier 2b) and some unhashable data 3) Cache the function result using only the unique identifier The lru_cache() currently can't be used directly because all the function arguments must be hashable. Here is a fine article by Caktus Group in which they caught a bug in Django which occurred due to lru_cache. Any mutable object in Python cannot be hashed simply due to the definition of hashing. $ python3 functools_lru_cache_arguments.py (1, 2) called expensive(1, 2) ([1], 2) ERROR: unhashable type: 'list' (1, {'2': 'two'}) ERROR: unhashable type: 'dict' Reducing a Data Set ¶ The reduce() function takes a callable and a sequence of data as input and produces a single value as output based on invoking the callable with the values from the sequence and accumulating the resulting output. they're used to gather information about the pages you visit and how many clicks you need to accomplish a task. TypeError: unhashable type: 'numpy.ndarray' [closed] Ask Question Asked 2 years, 1 month ago. If result were a nested list, deepcopy must be used. If maxsize is set to None, the LRU feature is disabled and the cache can grow without bound.. I'm trying to memoize a method foo(dti: DatetimeIndex) using the @functools.lru_cache() annotation. from functools import lru_cache class Solution: def minHeightShelves (self, books: List[List[int]], shelf_width: int)-> int: @lru_cache def shelf_height (index:int= 0, remaining_shelf_width:int=shelf_width, last_height:int= 0): return min(shelf_height(index + 1, remaining_shelf_width - books[index][0], … lru_cache is vulnerable to hash collision attack and can be hacked or compromised. Download files. Contribute to acmerfight/lru_cache development by creating an account on GitHub. Active 2 years, 1 month ago. It is not currently accepting answers. We can't apply lru_cache to minHeightShelves directly because books is a list, which is an unhashable type. lru_cache, The author of this package has not provided a project description. hashable, This shows that any function is hashable as it has a … Do check it out. The docs say that objectOf is for "An object with property values of a certain type" like PropTypes.number.