pandas

• Information Retrieving with ….. a lot of libraries

  Implementing a Information Retrieval system is a fun thing to do. However, doing it efficiently is not (at least to me). So my first few attempts didn’t really end well (mostly uses just Go/golang with some bash tricks here and there, with or without a database). Then I jumped back to Python, which I am more familiar with and was very surprised with all the options available. So I started with Pandas and Scikit-learn combo.

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• Random notes on Pandas and Scikit-learn

  So I first heard about Panda probably a year ago when I was in my previous job. It looked nice, but I didn’t really get the chance to use it. So practically it is a library that makes data looks like a mix of relational database table and excel sheet. It is easy to do query with it, and provides a way to process it fast if you know how to do it properly (no, I don’t, so I cheated).

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Random Posts

• Regression with Annoy (cont’d)

  After a year and half, a lot of things changed, and annoy also changed the splitting strategy too. However, I always wanted to do a proper follow up to the original post, where I compared boosting to Annoy. I still remember the reason I started that (flawed) experiment was because I found boosting easy.

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• Hitting a nail with the wrong hammer (my writing tools)

  I just failed a programming assessment test miserably yesterday and thought I should at least document it down. However, the problem with this is that the questions are copyrighted, so I guess I would write it from another point of view. So the main reason I failed was because I chose the wrong strategy to the problem, thinking it should be solution but as I put in time to that I ended up creating more problems.

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• Linting a libsvm-formatted data

  While working on a text classification task, I spent quite some time preparing the training set for a given document collection. The project is supposed to be a pure golang implementation, so after some quick searching I found some libraries that are either a wrapper to libsvm, or a re-implementation. So I happily started to prepare my training set in the libsvm format.

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• Revisiting semantic web (plus wheel building with Poetry)

  Back then, when I was still working on my postgraduate degree research, I used RDF, which was the preferred format in the world of Semantic Web to represent data. I eventually dropped the degree, and stopped following the development of the related technology and standards. Until I volunteered to update the import script for popit when I was looking for the next job/project.

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• Collaborative Tagging & Folksonomy

  Folksonomy is a neologism of two words, ’folk’ and ’taxonomy’ which describes conceptual structures created by users ^{[4, 5]}. A folksonomy is a set of unstructured collaborative usage of tags for content classification and knowledge representation that is popularized by Web 2.0 and social applications ^{[1, 5]}. Unlike taxonomy that is commonly used to organize resources to form a category hierarchy, folksonomy is non-hierarchical and non-exclusive ^[3]. Both content hierarchy and folksonomy can be used together to better content classification.

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