pyss3 0.3.3

Python package that implements the SS3 text classifier (with visualizations tools for XAI)

PySS3: A python package implementing a novel text classifier with visualization tools for Explainable AI

The SS3 text classifier is a novel supervised machine learning model for text classification. SS3 was originally introduced in Section 3 of the paper "A text classification framework for simple and effective early depression detection over social media streams" (preprint available here).

Some virtues of SS3:

• It has the ability to visually explain its rationale.
• Introduces a domain-independent classification model that does not require feature engineering.
• Naturally supports incremental (online) learning and incremental classification.
• Well suited to work over text streams.

What is PySS3?

PySS3 is a Python package that allows you to work with SS3 in a very straightforward, interactive and visual way. In addition to the implementations of the SS3 classifier, PySS3 comes with a set of tools to help you to develop your machine learning models in a clearer and faster way. These tools let you analyze, supervise and understand your models (what they have actually learned and why). To achieve this, PySS3 provides you 3 main components: the SS3 class, the Server class and the PySS3 Command Line tool, as pointed out below.

The SS3 class

which implements the classifier using a clear API (very similar to that of sklearn):

    from pyss3 import SS3
    clf = SS3()
    ...
    clf.fit(x_train, y_train)
    y_pred = clf.predict(x_test)

The Server class

which allows you to interactively test your model and visually see the reasons behind classification decisions, with just one line of code:

    import pyss3
    from pyss3 import SS3

    clf = SS3(name="my_model")
    ...
    clf.fit(x_train, y_train)
    pyss3.Server.serve(clf, x_test, y_test) # <- this one! cool uh? :)

As shown in the image below, this will open up, locally, an interactive tool in your browser which you can use to (live) test your models with the documents given in x_test (or typing in your own!). This will allow you to visualize and understand what your model is actually learning.

And last but not least, the PySS3 Command Line

This is probably the most useful component of PySS3. When you install the package (for instance by using pip install pyss3) a new command line is automatically added to your environment, called "pyss3". This command allows you to access to the PySS3 Command Line, an interactive command-line query tool. This tool will let you interact with your SS3 models through special commands while assisting you during the whole machine learning pipeline (model selection, training, testing, etc.). Probably one of the most important features is the ability to automatically (and permanently) record the history of every evaluation result of any type (tests, k-fold cross-validations, grid searches, etc.) that you've performed. This will allow you (with a single command) to interactively visualize and analyze your classifier performance in terms of its different hyper-parameters values (and select the best model according to your needs). For instance, let's perform a grid search with a 4-fold cross-validation on the three hyper-parameters, smoothness(s), significance(l), and sanction(p) as follows:

your@user:/your/project/path$ pyss3
(pyss3) >>> load my_model
(pyss3) >>> grid_search path/to/dataset 4-fold -s r(.2,.8,6) -l r(.1,2,6) -p r(.5,2,6)

In this illustrative example, s will take 6 different values between .2 and .8, l between .1 and 2, and p between .5 and 2. After the grid search finishes, we can use the following command to open up the interactive plot in the browser:

(pyss3) >>> plot evaluations

Each dot represents an experiment/evaluation performed using that particular combination of values (s, l, and p). Also, dots are painted proportional to how good the performance was using that configuration of the model. Researchers can interactively change the evaluation metrics to be used (accuracy, precision, recall, f1, etc.) and plots will update "on the fly". Additionally, when the cursor is moved over a data point, useful information is shown (including a "compact" representation of the confusion matrix obtained in that experiment). Finally, it is worth mentioning that, before showing the 3D plots, PySS3 creates a single and portable HTML file containing the plots and stores it locally. This allows researchers to store, send or upload the plots to another place using this single HTML file (their papers can now link to these types of plots to increase experimentation transparency!). For example, we have uploaded two of these files we've obtained for the "Tutorials" section: "Movie Review Classification" and "Topic Categorization" evaluation plots.

The PySS3 Workflow

The somewhat standard way

(TODO: tutorial WIP)

The "Command-Line" way

(TODO: tutorial WIP)

Installation

PyPi installation

Simply type:

$ pip install pyss3

Installation from source

To install latest version from github, clone the source from the project repository and install with setup.py:

$ git clone https://github.com/sergioburdisso/pyss3
$ cd pyss3
$ python setup.py install

API Documentation

Full API documentation can be found here