My colleagues and I will be giving a talk on BOSS at Yahoo!’s Hack Day in NYC on October 9. To show developers the versatility of an open search API, I developed a simple toy example (see my past ones: TweetNews, Q&A) on the flight over that uses BOSS to generate data for training a machine learned text classifier. The resulting application basically takes two tags, some text, and tells you which tag best classifies that text. For example, you can ask the system if some piece of text is more liberal or conservative.
How does it work? BOSS offers delicious metadata for many search results that have been saved in delicious. This includes top tags, their frequencies, and the number of user saves. Additionally, BOSS makes available an option to retrieve extended search result abstracts. So, to generate a training set, I first build up a query list (100 delicious popular tags), search each query through BOSS (asking for 500 results per), and filter the results to just those that have delicious tags.
Basically, the collection logically looks like this:
[(result_1, delicious_tags), (result_2, delicious_tags) …]
Then, I invert the collection on the tags while retaining each result’s extended abstract and title fields (concatenated together)
This logically looks like this now:
[(tag_1, result_1.abstract + result_1.title), (tag_2, result_1.abstract + result_1.title), …, (tag_1, result_2.abstract + result_2.title), (tag_2, result_2.abstract + result_2.title) …]
To build a model comparing 2 tags, the system selects pairs from the above collection that have matching tags, converts the abstract + title text into features, and then passes the resulting pairs over to LibSVM to train a binary classification model.
Here’s how it works:
tagger viksi$ python gen_training_test_set.py liberal conservative
tagger viksi$ python autosvm.py training_data.txt test_data.txt
__Searching / Training Best Model
____Trained A Better Model: 60.5263
____Trained A Better Model: 68.4211
__Predicting Test Data
get_training_test_set finds the pairs with matching tags and split those results into a training (80% of the pairs) and test set (20%), saving the data as training_data.txt and test_data.txt respectively. autosvm learns the best model (brute forcing the parameters for you – could be handy by itself as a general learning tool) and then applies it to the test set, reporting how well it did. In the above case, the system achieved 80% accuracy over 20 test instances.
Here’s another way to use it:
tagger viksi$ python classify.py apple microsoft bill gates steve ballmer windows vista xp
tagger viksi$ python classify.py apple microsoft steve jobs ipod iphone macbook
classify combines the above steps into an application that, given two tags and some text, will return which tag more likely describes the text. Or, in command line form, ‘python classify.py [tag1] [tag2] [some free text]’ => ‘tag1’ or ‘tag2’
My main goal here is not to build a perfect experiment or classifier (see caveats below), but to show a proof of concept of how BOSS or open search can be leveraged to build intelligent applications. BOSS isn’t just a search API, but really a general data API for powering any application that needs to party on a lot of the world’s knowledge.
I’ve open sourced the code here:
Although the total lines of code is ~200 lines, the system is fairly state-of-the-art as it employs LibSVM for its learning model. However, this classifier setup has several caveats due to my time constraints and goals, as my main intention for this example was to show the awesomeness of the BOSS data. For example, training and testing on abstracts and titles means the top features will probably be inclusive of the query, so the test set may be fairly easy to score well on as well as not be representative of real input data. I did later add code to remove query related features from the test set and the accuracy seemed to dip just slightly. For classify.py, the ‘some free text’ input needs to be fairly large (about an extended abstract’s size) to be more accurate. Another caveat is what happens when both tags have been used to label a particular search result. The current system may only choose one tag, which may incur an error depending on what’s selected in the test set. Furthermore, the features I’m using are super simple and can be greatly improved with TFIDF scaling, normalization, feature selection (mutual information gain), etc. Also, more training / test instances (and check the distribution of the labels), baselines and evaluation measures should be tested.
I could have made this code a lot cleaner and shorter if I just used LibSVM’s python interface, but I for some reason forgot about that and wrote up scripts that parsed the stdout messages of the binaries to get something working fast (but dirty).