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A machine learning toolkit for classification and assisted experimentation.

Project description

A machine learning toolkit for classification and assisted experimentation.

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Assisted experimentation

We provide an experimental foundation for assisted experimentation by allowing the user to modularize the tasks that are common, but unique in implementation, to every experiment, including knowing how to:

  • Perform any required initial setup to get the model ready.

  • Parse the training dataset.

  • Parse the testing dataset.

  • Train the model given a training instance.

  • Make a prediction for a test instance using the current model.

Once the task specific implementations are known, we then coordinate the use of those functions to perform the experiment in an automated fashion by automatically performing tasks that can be common to any experiment:

  • Run an experiment given an implementation of all the modular tasks.

  • Incrementally train the model and get the current model’s performance.

  • Get the test instances that were incorrectly labeled using the current model.

Feature extraction

A feature extractor takes a document and extracts features to be used with a machine learning classifier (during training and prediction).

We wanted to provide a way to easily perform the common task of extracting textual features from documents, while at the same time making it easy for the researcher to experiment with the kinds of features that are extracted. The researcher can currently specify:

  • The function used to tokenize the raw document string.

  • The range of n-grams to extract from the text


Performance metrics

We provide a means of measuring the performance of your classifier by providing standard performance metrics, expanded to allow for multinomial classifiers, including:

  • Confusion matrix

  • Accuracy

  • Recall (average, weighted average, and per class)

  • Precision (average, weighted average, and per class)

  • F-measure with a selectable beta parameter (average, weighted average, and per class)

(Multinomial) Naive Bayes

Some of the existing implementations of Naive Bayes that are available in various libraries we have found to be very memory inefficient. Because of this, we decided to write our own implementation that can hopefully be better optimized.

In addition, there are lots of ways you can experiment with using and optimizing the performance of Naive Bayes that we wanted to be able to easily experiment with.

Feature selection

Feature selection is another tool that the researcher should be able to experiment with.

Dictionary trimming

Currently, we provide a form of feature selection that is similar to dictionary trimming, by having the classifier ignore all but the top-k most frequent features. This often gives us 90% of the benefit of feature selection without the work of computing more complex metrics.

Dictionary trimming normally involves making a pass over your dataset in order to extract the (feature) vocabulary. However, this is infeasible when you are attempting to learn in an online (streaming) setting, such as when your documents are continuously coming in, like tweets off of a stream from Twitter. To handle this case, we created a data structure that efficiently keeps track of the top-k most common terms that is updated while training. This allows O(1) checking if a feature is in the top-k features without having to make a pass over the vocabulary to make the check.

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