LANDMarkClassifier 2.0.2

LANDMark: An ensemble approach to the supervised selection of biomarkers in high-throughput sequencing data

LANDMark

Implementation of a decision tree ensemble which splits each node using learned linear and non-linear functions.

Install

From PyPI:

pip install LANDMarkClassifier

From source:

git clone https://github.com/jrudar/LANDMark.git
cd LANDMark
pip install .
# or create a virtual environment
python -m venv venv
source venv/bin/activate
pip install .

Interface

An overview of the API can be found here.

Usage and Examples

Comming Soon

Contributing

To contribute to the development of LANDMark please read our contributing guide

Basic Usage

from LANDMark import LANDMarkClassifier

from sklearn.datasets import load_wine
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split

# Create the dataset
X, y = load_wine(return_X_y = True)

# Split into train and test sets
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=0, stratify=y
)

# Standardize
X_trf = StandardScaler()
X_trf.fit(X_train)

X_train = X_trf.transform(X_train)
X_test = X_trf.transform(X_test)

# Setup a LANDMark model and fit
clf = LANDMarkClassifier()
clf.fit(X_train, y_train)

# Make a prediction
predictions = clf.predict(X_test)

Specal Notes

Starting with TensorFlow 2.11, GPU support on Windows 10 and higher requires Windows WSL2. See: https://www.tensorflow.org/install/pip

References

Rudar, J., Porter, T.M., Wright, M., Golding G.B., Hajibabaei, M. LANDMark: an ensemble 
approach to the supervised selection of biomarkers in high-throughput sequencing data. 
BMC Bioinformatics 23, 110 (2022). https://doi.org/10.1186/s12859-022-04631-z

Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, et al. Scikit-learn: 
Machine Learning in Python. Journal of Machine Learning Research. 2011;12:2825–30. 

Kuncheva LI, Rodriguez JJ. Classifier ensembles with a random linear oracle. 
IEEE Transactions on Knowledge and Data Engineering. 2007;19(4):500–8. 

Geurts P, Ernst D, Wehenkel L. Extremely Randomized Trees. Machine Learning. 2006;63(1):3–42.