Getting Started

Estimators in sortedl1 are compatible with the scikit-learn interface. Here is a simple example of fitting a model to some random data.

We start by generating the data.

import numpy as np
from numpy.random import default_rng

from sortedl1 import Slope

# Generate some random data
n = 100
p = 3

seed = 31
rng = default_rng(seed)

x = rng.standard_normal((n, p))
beta = rng.standard_normal(p)
y = x @ beta + rng.standard_normal(n)

Next, we create the estimator by calling Slope() with all the desired parameters.

model = Slope(alpha=0.1)

Finally, we fit the model to the data and inspect the coefficients.

model.fit(x, y)
print(model.coef_)

[[-1.12654445]
 [ 0.9238887 ]
 [-1.7007892 ]]