Python package for implementing ensemble methods of rank-based trees
- The R version of this package is also available at R CRAN: https://CRAN.R-project.org/package=ranktreeEnsemble
Authors
Ruijie Yin (ruijieyin428@gmail.com), Chen Ye (cxy364@miami.edu) and Min Lu (m.lu6@umiami.edu)
Reference
Min Lu, Ruijie Yin and Steven X. Chen. Ensemble Methods of Rank-Based Trees for Single Sample Classification with Gene Expression Profiles. (Submitted)
Getting Started
Dependencies
For Python package:
- Prerequisites: pandas, sklearn, numpy, shap-hypetune, lightgbm
- Python (>= 3.8.0)
Installation:
In a Python console, type:
pip install ranktreeEnsemble
Examples:
- Build a Random Rank Forest with Variable Importance:
from ranktreeEnsemble.data.dataPrep import *
from ranktreeEnsemble.Method.ranktreeMethod import *
import pandas as pd
tnbc = pd.read_csv("data/tnbc.csv")
model = rforest(tnbc.drop(columns=['subtype']).head(100), tnbc["subtype"].head(100))
# get feature importance scores:
model.feature_importances_
# pair() to convert continuous variables to binary ranked pairs:
datp = pair(tnbc.iloc[100:111,:-1])
print(datp)
model.predict(datp)
- Build a Boosting with LogitBoost Cost model with Variable Importance:
model = rboost(tnbc.drop(columns=['subtype']).head(100), tnbc["subtype"].head(100))
# get feature importance scores:
model.feature_importances_
# Build a Boosting with LogitBoost Cost model with forward stepwise feature selection:
model_rfa = rboost_rfa(tnbc.drop(columns=['subtype']).head(100), tnbc["subtype"].head(100))
# Build a Boosting with LogitBoost Cost model with backward stepwise feature selection:
model_rfe = rboost_rfe(tnbc.drop(columns=['subtype']).head(100), tnbc["subtype"].head(100))
Version History
-
0.1.1
- Added functions for forward stepwise feature selection, backward stepwise feature selection.
-
0.1.0
- Initial Release
License
This project is licensed under the MIT License - see the LICENSE.md file for details
