ElasticNet Example

This page mirrors examples/elastic_net_example.py.

1 Fit A Regularized Linear Model

import numpy as np
from pprint import pprint

from crabbymetrics import ElasticNet

np.set_printoptions(precision=4, suppress=True)

rng = np.random.default_rng(1)
n = 600
k = 6
beta = np.array([2.0, -1.5, 0.0, 0.0, 0.8, -0.3])
intercept = -0.4

x = rng.normal(size=(n, k))
y = intercept + x @ beta + rng.normal(scale=0.7, size=n)

model = ElasticNet(penalty=0.1, l1_ratio=0.5)
model.fit(x, y)

print("true intercept:", intercept)
print("true coef:", beta)
pprint(model.summary())

true intercept: -0.4
true coef: [ 2.  -1.5  0.   0.   0.8 -0.3]
{'coef': array([ 1.849 , -1.3812,  0.    ,  0.    ,  0.6863, -0.2843]),
 'coef_se': array([0.0313, 0.0314, 0.0305, 0.0291, 0.0329, 0.03  ]),
 'intercept': -0.2741791532039864,
 'intercept_se': 0.031205312308178204}