Metadata-Version: 2.1
Name: hi_lasso
Version: 0.1.1
Summary: High-Demensional LASSO
Home-page: https://github.com/datax-lab/Hi-LASSO
Author: Jongkwon Jo
Author-email: jongkwon.jo@gmail.com
License: UNKNOWN
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](https://hi_lasso.readthedocs.io/en/latest/)
        
        ## Quick Start
        ```python
        #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_
        ```
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
