Notes on codes, projects and everything
haproxy
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Deploying docker containers with HAProxy on a VPS
With most of my stuff more or less set, I guess it is time to start documenting the steps before I forget. So I heard a lot of good things about docker for quite some time, but haven’t really have the time to do it due to laziness (plus my relatively n00b-ness in the field of dev-ops). Just a few months ago, I decided to finally migrate away from webfaction (thanks for all the superb support) to a VPS so I can run more things on it.
Random Posts
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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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Re-implementing Approximate Nearest Neighbour Search
Recently I switched my search code to Annoy because the input dataset is huge (7.5mil records with 20k dictionary count). It wasn’t without issues though, however I would probably talk about it next time. In order to figure out what each parameters meant, I spent some time watching through the talk given by the author @fulhack.
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Evil evil update
This update took me quite a bit more time than I initially expected. Anyway, I have done some refactoring work to the original code, and thought it would be nice to document the changes. Overall, most of the changes involved the refactoring of function names. I am not sure if this would stick, but I am quite satisfied for now.
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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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Processing JSON with dask.bag
Often times, I am dealing with JSONL files, though panda’s DataFrame is great (and blaze to certain extend), however it is offering too much for the job. Most of the received data is in the form of structured text and I do all sorts of work with them. For example checking for consistency, doing replace based on values of other columns, stripping whitespace etc.
