MCTSExtent

This repo holds the code related to "Anytime Mining of Sequential Discriminative Patternsin Labeled Sequences", Romain Mathonat, Diana Nurbakova, Jean-Francois Boulicaut and Mehdi Kaytoue, Knowledge and Information Systems

MCTSExtent finds subgroups for sequential datasets. More precisely, you give a labeled dataset of sequences of itemset, a target class, and the algorithm finds the top-k best pattern discriminative of this class.

This is interesting for two reasons:

1. You can use found patterns to better predict classes of sequences
2. You can use patterns to better understand a phenomena (= what are the sequence of events, i.e the patterns, that appears for this target class)

To use this module, you need to have data in a kosarak-like format. For exemple, the sequence "{1 5},{5 8 9}, {2}", with a class of "+" is encoded this way:

+ 1 5 -1 5 8 9 -1 2 -1 -2

Each line then corresponds to a new sequence.

You also need to specify a target class. In this case, you could launch the algorithm this way:

from mctsextent.mctsextent.main import get_patterns

get_patterns(path='my_path', target_class='+')

Which would give you, by default, the top-5 non redundant patterns for target class '+'

In the following code we specify the number of patterns we want to get, and the value of theta for non-redundancy.

from mctsextent.mctsextent.main import get_patterns
from mctsextent.utils import print_results

results = get_patterns(path='../data/figures_rc.dat', target_class='3', time=10, top_k=10, theta=0.5)

Code organization:

• Main code is present in mctsextent
• Tests contains unit test.
• Competitors holds the code of beam_search, misere, and an exhaustive one that we created to access the ground truth (see experiments of paper)

Experiments

xp folder hold the code for experiments. For reproducibility, you can relaunch experiments very easily by doing a:

pip install -r requirements.txt
cd xp
python3 xp_main.py