This is the official implementation of RecForest for anomaly detection, proposed in the paper "Reconstruction-based Anomaly Detection with Completely Random Forest," SIAM International Conference on Data Mining (SDM), 2021. It is highly optimized and provides Scikit-Learn like APIs.
RecForest is available at PyPI:
$ pip install recforestTo use the latest version of RecForest, you will need to install the package from source:
$ git clone https://github.com/xuyxu/RecForest.git
$ cd RecForest
$ python setup.py installNotice that a C compiler is required to compile the pyx files (e.g., GCC on Linux, and MSVC on Windows). Please refer to Cython Installation for details.
The code snippet below presents the 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)
anomaly_score = model.predict(X_test)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 processors for joblib parallelization.-1means 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.
- numpy >= 1.13.3
- scipy >= 0.19.1
- joblib >= 0.12
- cython >= 0.28.5
- scikit-learn >= 0.22
A Python environment installed from conda is highly recommended. In this case, there is no need to install any package listed above.