Скачать или смотреть Understanding FuzzyWuzzy Partial Ratio String Matching in Python

Discover why you might be getting unexpected scores with FuzzyWuzzy's partial ratio and learn how to achieve accurate string matching in Python!
---
This video is based on the question https://stackoverflow.com/q/63286322/ asked by the user 'Mohamed Niyaz' ( https://stackoverflow.com/u/10738536/ ) and on the answer https://stackoverflow.com/a/63450825/ provided by the user 'Mohamed Niyaz' ( https://stackoverflow.com/u/10738536/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

Visit these links for original content and any more details, such as alternate solutions, latest updates/developments on topic, comments, revision history etc. For example, the original title of the Question was: FuzzyWuzzy partial_ratio string match

Also, Content (except music) licensed under CC BY-SA https://meta.stackexchange.com/help/l...
The original Question post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license, and the original Answer post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license.

If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com.
---
Introduction

String matching can often be a tricky business, especially when you're trying to find a sub-string within a main string. If you've ever used the FuzzyWuzzy library in Python, you might have encountered situations where the score returned by the partial_ratio method isn’t what you expected. For example, you might have a sub-string like "Aspire 1 14" and want to match it against a detailed product description in the main string. However, instead of the anticipated score of 100, you receive only a score of 55. What could be going wrong? Let's unravel this mystery together.

Understanding the FuzzyWuzzy Partial Ratio

FuzzyWuzzy uses the concept of 'string similarity' to generate scores based on how closely two strings match. The partial_ratio function is particularly useful when you want to check if a short sub-string exists within a longer main string. Generally, you would expect that if the sub-string perfectly matches part of the main string, the score should be 100. But this isn’t always the case, and several factors can affect the accuracy of your results.

The Role of String Length

One of the main reasons why the partial_ratio function might give you an unexpected score is related to the length of the strings involved:

Threshold Value: FuzzyWuzzy sets a threshold limit for the length of the strings. If either string exceeds this threshold, the function utilizes the Sequence Matcher to assess the similarity, which can lead to lower scores even if there is a significant match.

Context Consideration: The existence of excessive characters can confuse the algorithm, leading to a degradation in match quality if the context in which the words are found is less than optimal.

Possible Solutions to Improve Matching Accuracy

If you find that your partial ratio scores are not aligning with expectations, consider the following strategies to improve string matching accuracy:

1. Limit String Lengths

Ensure the main string and sub-string lengths do not exceed recommended lengths or consider truncating the main string to match the sub-string length before applying the partial_ratio.

2. Normalize Strings

Standardize your strings by:

Converting them to lower case to avoid case sensitivity issues.

Removing any special characters or extra spaces that might interfere with a clean comparison.

3. Refine the Matching Algorithm

If the problem persists despite the above steps, consider using alternative string matching algorithms provided within FuzzyWuzzy or other libraries like difflib or RapidFuzz, which may offer different approaches that suit your specific needs better.

Conclusion

When using FuzzyWuzzy’s partial_ratio, it's essential to be mindful of the surrounding factors that can influence your results, particularly string length and normalization. By implementing smart strategies to tailor your string matching efforts, you’ll be well on your way to ensuring that the scores align closer to your expectations. Whether you’re working with product descriptions, user queries, or any other type of textual data, understanding these nuances will enable you to utilize FuzzyWuzzy effectively for your string comparison needs.

Feel free to share your experiences or ask further questions about string matching in the comments below!