He has a bunch of strings (paragraphs) in a research paper. To make things more clear, when a professor wants to run a plagiarism check, he won’t be looking at just one string/sentence. Now, what happens when you are not looking at just two strings, but dealing with a list of strings. The above strings “Fat Cat” and “Cat Fat” give a 100% match with the Token Sort Ratio. However, the token sort ratio isn’t as flexible as token set ratio in terms of repetitions of words. An interesting feature about this ratio is that it doesn’t take into consideration the order of the tokens appearing in the strings. The tokens (words) are then compared using the simple ratio mechanism. Instead of directly comparing the strings, Token Sort Ratio also splits the strings into tokens. Hence, the Token sort ratio gave a 100% match. String1 = "Humpty Dumpty sat on a wall" String2 = "Dumpty Humpty wall on sat a" fuzz.token_sort_ratio("Humpty Dumpty sat on a wall","Dumpty Humpty wall on sat a") > 100Īs noticed from the above example, the strings only differed in the arrangement of words. If the order in which the words are placed in a particular sentence doesn’t matter then the best way to match two strings is by the use of Token Sort Ratio from the package. The final match is the result of the similarities existing between the transformed strings (in form of tokens). Furthermore, the token function removes all the punctuations by eliminating all non-alpha, non-numeric characters. The set of tokens are split up into two different sets: Intersection set and Remainder set. Then takes place the sorting of these tokens. The process of getting the similarity percentage, first involves splitting the strings into tokens (or words). The similarity between given strings is an integer (int) measure ranging from. For an instance, it recognizes missing punctuations, case-sensitive words, misspelled words etc. The unique feature of ‘fuzz.ratio’ lies in the fact that it takes into consideration minimal differences existing between both strings. This ratio uses a simple technique which involves calculating the edit distance (Levenshtein distance) between two strings. The difference lies in the missing exclamation mark ‘!’.
#Fuzzy logic python code
Here is the code to understand the match using the simple ratio: String1 = "Humpty Dumpty sat on a wall"String2 = "Humpty Dumpty Sat on a Wall! fuzz.ratio("Humpty Dumpty sat on a wall", "Humpty Dumpty Sat on a Wall!") > 91Īs seen in the above code, the first string matches to the second one with 91%. When you have a very simple set of strings which look almost similar with their words, you can use the simple ratio from the FuzzyWuzzy package. Gaining a deeper understanding about the method of calculating similarity percentages, let’s look at the different types of Fuzz ratios that run the whole process.
#Fuzzy logic python install
The library can be installed by using pip: pip install fuzzywuzzy pip-install python-Levenshtein The core method being used here is the calculation of Levenshtein Distance between two strings. It is a Python library which has been originally developed by SeatGeek. Now let’s have a look at the most used library for string matching - FuzzyWuzzy package. Substituting a letter or removing it also gives +1. For an instance, adding an alphabet results in +1. The operations involved in the above process include:Įvery operation done to change a given string into the other one adds 1 to the Levenshtein distance counter. Let’s enhance our knowledge about Levenshtein distance! Levenshtein DistanceĪlso known as the edit distance, Levenshtein Distance (LD) is a parameter used in String matching, that basically measures the minimum number of operations/edits required to change a particular string into some other string. The core of the string matching process is the calculation of edit distance (levenshtein distance). The technique involved behind checking the similarity percentage between certain sentences, paragraphs or words isn’t rocket science! Understanding the mechanism behind how a few lines of code can help you find the similarity percentage between given strings, can help you overcome the above mentioned issues. How amazing is it to just input an address and get a list of best matched address suggestions! Or detecting the misspelled words! Being a professor, have you ever worried about examining a research paper and getting the similarity percentage to check how much the student has copied from the internet? The logic and the mechanism behind the concepts of Fuzzy Address Matching, Spelling Checkers, and Plagiarism is as interesting as the concepts itself.