imgurPCA v2

Machine Learning on imgur. Under development. Basic interface is complete.

imgurpca is a set of tools that help parse, analyze, and act on textual data on imgur.com. It provides a modular interface that lets you build trivial to complex workflows.

While initially written for imgur, the project has been structured so that it can be easily ported to another API. Additionally, many machine learning functions in imgurpca can be universally used with any data set out of the box.

imgurpca provides an option to use Principal Component Analysis (PCA) as a way to reduce the dimensionality of data points. For a given set of data, it finds vectors that best describe the set's distribution. Those vectors can be used as axes for later computations. For example, posts on imgur.com may have thousands of unique words which would mean thousands of dimensions in the data. But with PCA, a few vectors can be used to distinguish posts without significant loss in accuracy.

With fewer vectors needed to describe posts and users (by their comments), other computations become less costly. For example, you can try to predict:

• What score a comment would get based on existing gallery comments (linear regression),
• If a user is likely to favourite a post based on their history (logistic regression),
• Posts that are similar in 'tone' (k-means clustering),
• Tags a post would get based on comments/title/description (decision tree).

All of the machine learning methods above in parentheses have been implemented in the Learner class, with more to come.

imgurpca also provides the Bot class. It can post comments, upload pictures, send messages etc. interactively or in the backround on whatever schedule you choose.

See docs folder for more details on usage.

Built With

• numpy
• python