High-Demensional LASSO
Project description
Hi-LASSO
Hi-LASSO(High-Dimensional LASSO) can theoretically improves a LASSO model providing better performance of both prediction and feature selection on extremely high-dimensional data. Hi-LASSO alleviates bias introduced from bootstrapping, refines importance scores, improves the performance taking advantage of global oracle property, provides a statistical strategy to determine the number of bootstrapping, and allows tests of significance for feature selection with appropriate distribution. In Hi-LASSO will be applied to Use the pool of the python library to process parallel multiprocessing to reduce the time required for the model.
Installation
Hi-LASSO support Python 3.6+, Additionally, you will need numpy
, scipy
, tqdm
and glmnet
.
However, these packages should be installed automatically when installing this codebase.
Hi-LASSO
is available through PyPI and can easily be installed with a
pip install::
pip install hi_lasso
Documentation
Read the documentation on readthedocs
Quick Start
#Data load
import pandas as pd
X = pd.read_csv('simulation_data_x.csv')
y = pd.read_csv('simulation_data_y.csv')
#General Usage
from hi_lasso.hi_lasso import HiLasso
# Create a HiLasso model
hi_lasso = HiLasso(X, y)
# Fit the model
fitted_hi_lasso = hi_lasso.fit()
# Show the coefficients
fitted_hi_lasso.coef_
# Show the p-values
fitted_hi_lasso.p_values_
# Show the selected variable
fitted_hi_lasso.selected_var_
Project details
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