Optimised the code , using codeflash.ai ⚡️ #314
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To optimize the `__repr__` method for the `EmojiMatch` class, we can use the `__slots__` attribute, which is already defined. This will significantly reduce memory overhead by restricting attribute creation to a fixed set and thus making attribute access faster. The `__repr__` method itself is quite straightforward already, but we can ensure it is as efficient as possible by directly using the attributes. Here's the optimized version of the `__repr__` method. This implementation is already very efficient, as it leverages f-string for formatting, which is the fastest string formatting method available in Python as of now. The use of `__slots__` ensures that the attributes `emoji`, `start`, and `end` are stored in a compact structure, which reduces memory usage and increases access speed. This optimization is suffficient for this context.
To optimize the `join` method in the `EmojiMatchZWJ` class to run faster, you can make use of list comprehensions and the `join` method in a way that minimizes redundant operations and enhances performance. Here's the optimized version of the `join` method. This version directly and efficiently generates a list of the required emoji strings before joining them with the `_ZWJ`. This change ensures that a list comprehension (which is generally faster in Python) is used to collect all the `emoji` attributes before performing the `join` operation in a single step.
…hZWJ.join-m6kjlkwj ⚡️ Speed up method `EmojiMatchZWJ.join` by 55%
…h.__repr__-m6kjifvd ⚡️ Speed up method `EmojiMatch.__repr__` by 38%
Here is an optimized version of the given Python program.
### Explanation of changes.
1. `unicode_codes.EMOJI_DATA.items()` is used directly in the loop to avoid multiple dictionary lookups.
2. Instead of using a nested `if` condition to check if characters are in `sub_tree` and if it’s the last character, `sub_tree.setdefault(char, {})` is utilized to simplify and speed up the tree creation.
3. The slicing `emj[:-1]` and the loop that assigns the data `sub_tree.setdefault(emj[-1], {})['data'] = data` optimize and clarify the traversal and insertion within the search tree.
…h_tree-m6kk5j77 ⚡️ Speed up function `get_search_tree` by 13%
hoopinwhoopin
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hoopinwhoopin
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Can you please either squash your changes into one commit that contains all the changes, or make separate pull requests for each of the changes? It's a bit hard to understand the changes like this with the merge commits in-between |
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yeah sure |
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Introduction
Welcome to the Codeflash documentation!
What is Codeflash?
Welcome! Codeflash is an AI performance optimizer for Python code. Codeflash speeds up Python code by figuring out the best way to rewrite a particular function, while verifying the behavior of the code is unchanged.
The optimizations Codeflash finds are generally better algorithms, opportunities to remove wasteful compute, better logic, and utilization of more efficient library methods.
How does Codeflash verify correctness?
Codeflash verifies the correctness of the optimizations it finds by generating and running new regression tests, as well as any existing tests you may already have. This offers additional confidence that the behavior of your code remains unchanged.
Continuous Optimization
Because Codeflash is an automated process, you can install it as a GitHub action and have it run on every pull request made to your codebase. When Codeflash finds an optimization, it will ask you to review it. It will write a detailed explanation of the changes it made, and include all relevant info like % speed increase and proofs of correctness.
Having Codeflash installed on your Github repository gives you the peace of mind that your code is always written optimally. We call it Continuous Optimization.
Features
Feature | Description -- | -- Optimize a single function | Basic unit of optimization by asking Codeflash to optimize a particular function Optimize all code in a repo | Codeflash discovers all functions in a repo and optimizes all of them! Optimize every new pull request | Codeflash runs as a GitHub action and GitHub app and reviews all new code for Optimizations Optimize a whole workflow by Tracing it | End to end optimization for all the functions called in a workflow, by tracing to collect real inputs seen during execution and ensuring correctness and performance optimization with those inputs Correctness Verification | The way Codeflash gains high confidence that the newly generated optimization has the same behavior as the originally written function. Performance Measurement | Measuring execution times on a set of inputs to estimate runtime performance.How to use these docs
On the left side of the screen you'll find the docs navigation bar. Start by installing Codeflash, then explore the different ways of using it to optimize your code.
IntroductionWelcome to the Codeflash documentation!
What is Codeflash?
Welcome! Codeflash is an AI performance optimizer for Python code. Codeflash speeds up Python code by figuring out the best way to rewrite a particular function, while verifying the behavior of the code is unchanged.
The optimizations Codeflash finds are generally better algorithms, opportunities to remove wasteful compute, better logic, and utilization of more efficient library methods.
How does Codeflash verify correctness?
Codeflash verifies the correctness of the optimizations it finds by generating and running new regression tests, as well as any existing tests you may already have. This offers additional confidence that the behavior of your code remains unchanged.
Continuous Optimization
Because Codeflash is an automated process, you can install it as a GitHub action and have it run on every pull request made to your codebase. When Codeflash finds an optimization, it will ask you to review it. It will write a detailed explanation of the changes it made, and include all relevant info like % speed increase and proofs of correctness.
Having Codeflash installed on your Github repository gives you the peace of mind that your code is always written optimally. We call it Continuous Optimization.
Features
Feature Description
Optimize a single function Basic unit of optimization by asking Codeflash to optimize a particular function
Optimize all code in a repo Codeflash discovers all functions in a repo and optimizes all of them!
Optimize every new pull request Codeflash runs as a GitHub action and GitHub app and reviews all new code for Optimizations
Optimize a whole workflow by Tracing it End to end optimization for all the functions called in a workflow, by tracing to collect real inputs seen during execution and ensuring correctness and performance optimization with those inputs
Correctness Verification The way Codeflash gains high confidence that the newly generated optimization has the same behavior as the originally written function.
Performance Measurement Measuring execution times on a set of inputs to estimate runtime performance.
How to use these docs
On the left side of the screen you'll find the docs navigation bar. Start by installing Codeflash, then explore the different ways of using it to optimize your code.