How to use Python regular expressions to memoize code

In Python, regular expressions are a very powerful text processing tool. It can be used to match and replace specific formats in text, making text processing more efficient and convenient.

When writing large-scale code, we often encounter situations where memoization is required. Memorization refers to caching the results generated during function execution so that the cached results can be directly used in subsequent calls, thereby avoiding repeated calculations and improving code execution efficiency. In Python, we can use decorators to implement memoization functions, and regular expressions can help us better manage memoization caches.

This article will introduce how to use Python regular expressions to memorize code. First, we need to understand the basic usage of decorators.

1. Decorator Basics

Decorator is a syntax structure that can add extra functionality to a function without changing the function code. It is usually a function that takes the decorated function as a parameter, and its return value is a new function. This new function will automatically perform some additional operations when the decorated function is called.

The following is a simple decorator example, which can calculate the function execution time:

import time

def timer(func):
    def wrapper(*args, **kwargs):
        start_time = time.time()
        result = func(*args, **kwargs)
        end_time = time.time()
        print('Function "{}" takes {}s to execute.'.format(func.__name__, end_time - start_time))
        return result
    return wrapper

@timer
def foo():
    time.sleep(1)
    return 'Done'

foo()

In the above code, a decorator timer is defined, which accepts a function as a parameter and returns a new function wrapper. When the wrapper function executes the decorated function, it first calculates the execution time of the function, then outputs the execution time, and finally returns the execution result of the decorated function.

When using a decorator, you only need to add the @decorator name syntax before the decorated function.

2. Implementation of memoization

Below we will introduce how to use decorators to implement the memoization function. Specifically, we want to be able to cache the input parameters and output results of a function so that subsequent calls can use the cached results directly without recalculation. In order to achieve this function, we can use a special data structure in Python - dictionary. We use the function input parameters as the keys of the dictionary, the function output results as the dictionary values, and save them in a global variable. Each time the decorated function is called, first check whether the corresponding output result already exists in the dictionary. If it exists, the cached result will be returned directly. Otherwise, function calculation will be performed and the output result will be added to the dictionary.

The following is a simple memoize decorator example:

import functools

memory = {}

def memoize(func):
    @functools.wraps(func)
    def wrapper(*args, **kwargs):
        key = (args, tuple(kwargs.items()))
        if key not in memory:
            memory[key] = func(*args, **kwargs)
        return memory[key]
    return wrapper

@memoize
def add(x, y):
    print('Adding {} and {}...'.format(x, y))
    return x + y

print(add(2, 3))    # Adding 2 and 3... 5
print(add(2, 3))    # 5

In the above code, a memoize decorator is defined, its function is to save the cache key-value pair in the global variable memory to check for the presence of cached results on subsequent calls. When the decorated function is called, the input parameters are first converted into tuples and dictionaries, and then used as key-value pairs to find whether there is a cached result. If it does not exist, the decorated function is called to calculate the result and the result is added to the cache dictionary. If it exists, the cached result is returned directly. For decorated functions, we use functools.wraps to implement docstring and function name inheritance.

The above code is suitable for using ordinary data types as function input parameters, but in actual development, we may encounter more complex data types, such as lists, tuples, sets, etc. At this point, we need to use regular expressions to convert the data type into a string so that it can be used as the key for caching key-value pairs.

3. Use of regular expressions

A regular expression is an expression that can be used to match and process strings. In Python, we can use regular expressions using the re module. The following is a simple regular expression example:

import re

pattern = r'd+'
text = '123abc456def789'
match = re.search(pattern, text)
print(match.group())    # 123

In the above code, we define a regular expression pattern containing d, which means matching one or more numbers. Then we use the re.search function to match the pattern in the string text and return a Match object. The Match object contains information such as the matched string, start and end positions, etc. We can obtain the matched string through the group method.

When implementing the memoization function, we can convert the input parameters into strings, and use regular expressions to extract numbers, letters, symbols and other information in the parameters as keys for caching key-value pairs. The following is a sample code:

import re
import functools

memory = {}

def memoize(func):
    @functools.wraps(func)
    def wrapper(*args, **kwargs):
        args_str = ', '.join(map(str, args))
        kwargs_str = ', '.join('{}={}'.format(k, v) for k, v in kwargs.items())
        key_str = args_str + ', ' + kwargs_str
        match = re.search(r'd+', key_str)
        key = match.group()
        if key not in memory:
            memory[key] = func(*args, **kwargs)
        return memory[key]
    return wrapper

@memoize
def add(x, y):
    print('Adding {} and {}...'.format(x, y))
    return x + y

print(add(2, 3))    # Adding 2 and 3... 5
print(add(2, 3))    # 5

print(add(2, 4))    # Adding 2 and 4... 6
print(add(2, 4))    # 6

print(add(1, y=2))  # Adding 1 and 2... 3
print(add(1, y=2))  # 3

In the above code, we convert the input parameters into strings and use regular expressions to extract numbers from them as keys of key-value pairs. If the corresponding key already exists in the cache dictionary, the result is returned directly; otherwise, function calculation is performed and the result is added to the cache dictionary.

4. Summary

This article introduces how to use Python regular expressions for code memorization. By using decorators and regular expressions, we can better manage the cache of function execution results, avoid repeated calculations, and improve code execution efficiency. In practical applications, we also need to consider issues such as cache expiration and capacity control to better utilize memorization technology.

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