Welcome to PySS3’s documentation!

PySS3 is a Python package that allows you to work with The SS3 Classification Model in a very straightforward, interactive and visual way. In addition to the implementation of the classifier, PySS3 comes with a set of tools to help you developing your machine learning models in a clearer and faster way. These tools let you analyze, monitor and understand your models by allowing you to see what they have actually learned and why. To achieve this, PySS3 provides you with 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:

from pyss3.server import Server
from pyss3 import SS3

clf = SS3(name="my_model")
clf.fit(x_train, y_train)
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.


For example, we have uploaded two of these live tests online for you to try out: “Movie Review (Sentiment Analysis)” and “Topic Categorization”, both were obtained following the Tutorials.

And last but not least, the PySS3 Command Line tool

This is probably the most useful component of PySS3. When you install the package (for instance by using pip install pyss3) a new command pyss3 is automatically added to your environment’s command line. 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 its 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 hyperparameters 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 hyperparameters, 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 0.2 and 0.8, l between 0.1 and 2, and p between 0.5 and 2. After the grid search finishes, we can use the following command to open up an interactive 3D plot in the browser:

(pyss3) >>> plot evaluations

Each point represents an experiment/evaluation performed using that particular combination of values (s, l, and p). Also, these points 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 in your project folder containing the interactive plots. This allows researchers to store, send or upload the plots to another place using this single HTML file (or even provide a link to this file in their own papers, which would be nicer for readers, plus it would increase experimentation transparency). For example, we have uploaded two of these files for you to see: “Movie Review (Sentiment Analysis)” and “Topic Categorization”, both evaluation plots were obtained following the Tutorials.

Want to give PySS3 a try?

Just go to the Getting Started page :D