Mutable types, such as lists and dicts, are not hashable because a change of their contents would change the … Simply using functools.lru_cache won't work because numpy.array is mutable and not hashable. Teams. When we try to use them as a parameter in the hash function. However, in memoization, caching is always typed, which means f(3) and f(3.0) will be treated as different calls and cached separately. Posted on September 14, 2018 in Python. Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. lru_cache is vulnerable to hash collision attack and can be hacked or compromised. I end up using geopandas on a regular basis, and one of its minor irritants is getting the unique number of geometries in a GeoDataFrame. Using this technique, attackers can make your program unexpectedly slow by feeding the cached function with certain cleverly designed inputs. As you know that dict, list, byte array, set, user-defined classes, etc are unhashable objects in python. Download files. The reason you’re getting the unhashable type: 'list' exception is because k = list[0:j] sets k to be a “slice” of the list, which is another, usually shorter, list. Explanation. This workaround allows caching functions that take an arbitrary numpy.array as first parameter, other parameters are passed as is.Decorator accepts lru_cache standard parameters (maxsize=128, … Q&A for Work. Unhashable in Python - Getting the unique number of locations in a GeoDataFrame. If you're not sure which to choose, learn more about installing packages. What you need is to get just the first item in list, written like so k = list[0].The same for v = list[j + 1:] which should just be v = list[2] for the third element of the list returned from the call to readline.split(" "). Contact Information #3940 Sector 23, Gurgaon, Haryana (India) Pin :- 122015. -- New Sets require their items to be hashable.Out of types predefined by Python only the immutable ones, such as strings, numbers, and tuples, are hashable. Download the file for your platform. There are many ways to achieve fast and responsive applications. 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. When we try to use them as a parameter in the hash function. Sometimes processing numpy arrays can be slow, even more if we are doing image analysis. My point is that a pure Python version won’t 1 be faster than using a C-accelerated lru_cache, and if once can’t out-perform lru_cache there’s no point (beyond naming 2, which can be covered by once=lru_cache…) I totally agree that this discussion is all about a micro-optimisation that hasn’t yet been demonstrated to be worth the cost. Python’s functools module comes with the @lru_cache decorator, which gives you the ability to cache the result of your functions using the Least Recently Used (LRU) strategy. Caching is one approach that, when used correctly, makes things much faster while decreasing the load on computing resources. An extension of functools.lru_cache allowing unhashable objects to be.
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