RecForest 0.1.0

Installation

RecForest is available at PyPI:

$ pip install recforest

$ git clone https://github.com/xuyxu/RecForest.git
$ cd RecForest
$ python setup.py install

Example

The code snippet below presents the minimal example on how to use RecForest for anomaly detection. Scripts on reproducing experiment results in the paper are available in the directory examples.

from recforest import RecForest
model = RecForest()
model.fit(X_train)
y_pred = model.predict(X_test)

Documentation

RecForest only has two hyper-parameters: n_estimators and max_depth. Docstrings on the input parameters are listed below.

• n_estimators: Specify the number of decision trees in Recforest;

• max_depth: Specify the maximum depth of decision trees in Recforest;

• n_jobs: Specify the number of workers for joblib parallelization. -1 means using all processors;

• random_state: Specify the random state for reproducibility.

RecForest has three public methods. Docstrings on these methods are listed below. Notice that for all methods, the accepted data format of input X is numpy array of the shape (n_samples, n_features).

• fit(X): Fit a RecForest using the input data X;

• apply(X): Return the leaf node ID of input data X in each decision tree;

• predict(X): Return the anomaly score on the input data X.