Скачать или смотреть Obtain the Relative Frequency Matrix from CountVectorizer in Python

Learn how to convert an absolute frequency matrix into a relative frequency matrix using TfidfVectorizer in Python with Scikit-learn.
---
This video is based on the question https://stackoverflow.com/q/68051225/ asked by the user 'LJG' ( https://stackoverflow.com/u/13983136/ ) and on the answer https://stackoverflow.com/a/68059200/ provided by the user 'MaximeKan' ( https://stackoverflow.com/u/10956606/ ) 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: Term relative frequency matrix from CountVectorizer

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.
---
How to Obtain a Relative Frequency Matrix from CountVectorizer in Python

When working with natural language processing and text analysis in Python, one common task is to convert a set of documents into a numerical format that can be processed by machine learning algorithms. The CountVectorizer class from Scikit-learn does exactly that by creating a bag-of-words model. However, there may come a time when you need to convert the absolute frequency counts into relative frequencies. In this guide, we will discuss how to achieve just that.

Understanding the Problem

You may already be familiar with the CountVectorizer and how it generates a sparse matrix representing the absolute frequency of terms in your documents. For instance, given a list of sentences, CountVectorizer will produce a matrix where each row corresponds to a document and each column corresponds to a term from the vocabulary, with entries indicating the count of occurrences.

However, the goal here is to obtain a relative frequency matrix—a matrix where each term's frequency in a document is expressed as a fraction of the total term count in that document. This normalization can aid in various downstream tasks, such as clustering or classification.

The Solution

You can easily obtain a relative frequency matrix by using the TfidfVectorizer class instead of CountVectorizer. Although TfidfVectorizer computes Term Frequency-Inverse Document Frequency (TF-IDF), it can be adjusted to return the normalized frequency that you desire.

Steps to Convert to Relative Frequency

Import Necessary Libraries:
Load the TfidfVectorizer from Scikit-learn, just like you would with CountVectorizer.

Set Up Your Data:
Use the same document list that you used with CountVectorizer.

Configure the TfidfVectorizer:
Adjust parameters to use only term frequency without IDF and set the norm to L1 (which normalizes by the sum of counts).

Example Code

Here’s how you can implement these steps in code:

[[See Video to Reveal this Text or Code Snippet]]

Expected Output

The output of running the above code will produce a sparse matrix similar to this:

[[See Video to Reveal this Text or Code Snippet]]

You will also retrieve the feature names from the vectorizer:

[[See Video to Reveal this Text or Code Snippet]]

Conclusion

By making use of the TfidfVectorizer instead of CountVectorizer, you can effectively transform your absolute frequency matrix into a relative frequency matrix. Adjusting the parameters accordingly ensures that your text data is ready for further analysis or modeling.

Incorporating relative frequencies can provide more insight and improve your model's performance in various tasks. Now, you're equipped with the knowledge to make this conversion smoothly. Happy coding!