"""
===========================
Randomized LASSO example
===========================
An example plot of the stability scores for each variable after fitting :class:`stability_selection.StabilitySelection`
with :class:`stability_selection.RandomizedLasso`
"""
import numpy as np
from sklearn.utils import check_random_state
from stability_selection import StabilitySelection, RandomizedLasso, plot_stability_path
def generate_experiment_data(n=200, p=200, rho=0.6, random_state=3245):
rng = check_random_state(random_state)
sigma = np.eye(p)
sigma[0, 2] = rho
sigma[2, 0] = rho
sigma[1, 2] = rho
sigma[2, 1] = rho
X = rng.multivariate_normal(mean=np.zeros(p), cov=sigma, size=(n,))
beta = np.zeros(p)
beta[:2] = 1.0
epsilon = rng.normal(0.0, 0.25, size=(n,))
y = np.matmul(X, beta) + epsilon
return X, y
if __name__ == '__main__':
n, p = 200, 200
rho = 0.6
X, y = generate_experiment_data()
lambda_grid = np.linspace(0.001, 0.5, num=100)
for weakness in [0.2, 0.5, 1.0]:
estimator = RandomizedLasso(weakness=weakness)
selector = StabilitySelection(base_estimator=estimator, lambda_name='alpha',
lambda_grid=lambda_grid, threshold=0.9, verbose=1)
selector.fit(X, y)
fig, ax = plot_stability_path(selector)
fig.show()
