Feature Ranking and Selection Using Gram Schmidt Orthogonalisation
fs-gram-schmidt is an open-source feature selection algorithm in Python. It is built upon scientific computing packages Numpy and Scipy. It is based on the paper Ranking a Random Feature for Variable and Feature Selection (Stoppiglia et al. 2003) published in Journal of Machine Learning Research
Python 2.7 or Python 3
Pandas
NumPy
SciPy
After you download/clone fs-gram-schmidt,
For Linux users, you can install the repository by the following command:
python setup.py install
For Windows users, you can also install the repository by the following command:
setup.py install
from fs-gram-schmidt.function import GSO
# feature_df : n*m pandas dataframe (preprocessed : should be scaled and works best when outliers are removed)
# target : n*1 pandas series of target values corresponding to feature_df
# risk : Predefined threshold of risk (0 < risk < 1)
gso_output = GSO.rank_features(train_X, target, risk=0.05)
# gso_output['ranked_features'] : List of ranked features ordered by their relevancy(descending) and selection risk (ascending)
# gso_output['feature_selection_risk'] : Mapping of nth feature in ranked_features with it's selection risk
Ameya Dahale
E-mail: [email protected]
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