Recursive-Rule eXtraction (Re-RX) algorithm

Environment

• python>=3.7.11
• If you want to use J48graft, you need to install Java.
  • See Custom Run -> BaseTree -> Setting for using J48graft

How to run

1. Install Re-RX

Run the following command in the directory where setup.py is located

pip install .

2. Run example

python example.py

Custom run

ReRX

You can use ReRX by from rerx import ReRX. The arguments of ReRX are as follows:

• base_model
  • A model that satisfies the requirements of BaseModel.
• tree
  • A model that satisfies the requirements of BaseTree.

Options

• output_dim [default 2].
  • Output dimension of class
• pruning_lamda [default 0.01].
  • Threshold of drop in classification performance to stop pruning
• pruning_step [default 0.01]
  • Step size for the pruning phase
• is_increasing_decision_score [default True]
  • High score is better score or not
• delta_1
  • Threshold of the support (coverage) rate
• delta_2
  • threshold for the error rate
• min_instance [default 10]
  • Minimum number of instances to fit the tree.
• is_pruning [default True]
  • Whether to prune or not in the fitting phase.
• is_eval [default False]
  • Whether to evaluate the model or not in the fitting phase.
• verbose [default 1]
  • 0: no log
  • 1: log
• log_func [default print]

Setting for using J48graft

J48graft operates based on weka and requires Java to be installed.

sudo apt update
sudo apt upgrade
sudo apt autoremove
sudo apt install net-tools
sudo apt install build-essential
sudo apt install make
sudo apt install cmake
sudo apt install screen
sudo apt install curl
sudo apt install vim-nox

Check the version of Java that can be installed.

sudo apt search openjdk-\(\.\)\+-jdk$

Install the installable version.

sudo apt install openjdk-{version}-jdk

Check installed version.

java -version

Examples

Use J48graft

from rerx import MLP, J48graft, ReRX

mlp = MLP()
j48graft = J48graft()
rerx = ReRX(base_model=mlp, tree=j48graft)
rerx.fit(X, y)
score = rerx.evaluate(X_test, y_test)

Use CART (DecisionTreeClassifier)

from rerx import MLP, ReRX
from sklearn.tree import DecisionTreeClassifier


mlp = MLP()
cart = DecisionTreeClassifier()
rerx = ReRX(base_model=mlp, tree=cart)
rerx.fit(X, y)
score = rerx.evaluate(X_test, y_test)