Logistic regression with \(\ell_1\) regularization

In this example, we use CVXPY to train a logistic regression classifier with \(\ell_1\) regularization. We are given data \((x_i,y_i)\), \(i=1,\ldots, m\). The \(x_i \in {\bf R}^n\) are feature vectors, while the \(y_i \in \{0, 1\}\) are associated boolean classes; we assume the first component of each \(x_i\) is \(1\).

Our goal is to construct a linear classifier \(\hat y = \mathbb{1}[\beta^T x > 0]\), which is \(1\) when \(\beta^T x\) is positive and \(0\) otherwise. We model the posterior probabilities of the classes given the data linearly, with

\[\log \frac{\mathrm{Pr} (Y=1 \mid X = x)}{\mathrm{Pr} (Y=0 \mid X = x)} = \beta^T x.\]

This implies that

\[\mathrm{Pr} (Y=1 \mid X = x) = \frac{\exp(\beta^T x)}{1 + \exp(\beta^T x)}, \quad \mathrm{Pr} (Y=0 \mid X = x) = \frac{1}{1 + \exp(\beta^T x)}.\]

We fit \(\beta\) by maximizing the log-likelihood of the data, plus a regularization term \(\lambda \|{\beta}\|_1\) with \(\lambda > 0\):

\[\ell(\beta) = \sum_{i=1}^{m} y_i \beta^T x_i - \log(1 + \exp (\beta^T x_i) - \lambda \|{\beta}\|_1.\]

Because \(\ell\) is a concave function of \(\beta\), this is a convex optimization problem.

from __future__ import division
import cvxpy as cp
import numpy as np
import matplotlib.pyplot as plt

In the following code we generate data with \(n=20\) features by randomly choosing \(x_i\) and a sparse \(\beta_{\mathrm{true}} \in {\bf R}^n\). We then set \(y_i = \mathbb{1}[\beta_{\mathrm{true}}^T x_i - z_i > 0]\), where the \(z_i\) are i.i.d. normal random variables. We divide the data into training and test sets with \(m=1000\) examples each.

n = 20
m = 1000
density = 0.2
beta_true = np.random.randn(n,1)
idxs = np.random.choice(range(n), int((1-density)*n), replace=False)
for idx in idxs:
    beta_true[idx] = 0

sigma = 45
X = np.random.normal(0, 5, size=(m,n))
X[:, 0] = 1.0
Y = X @ beta_true + np.random.normal(0, sigma, size=(m,1))
Y[Y > 0] = 1
Y[Y <= 0] = 0

X_test = np.random.normal(0, 5, size=(m, n))
X_test[:, 0] = 1.0
Y_test = X_test @ beta_true + np.random.normal(0, sigma, size=(m,1))
Y_test[Y_test > 0] = 1
Y_test[Y_test <= 0] = 0

We next formulate the optimization problem using CVXPY.

beta = cp.Variable((n,1))
lambd = cp.Parameter(nonneg=True)
log_likelihood = cp.sum(
    cp.reshape(cp.multiply(Y, X @ beta), (m,)) -
    cp.log_sum_exp(cp.hstack([np.zeros((m,1)), X @ beta]), axis=1) -
    lambd * cp.norm(beta, 1)
problem = cp.Problem(cp.Maximize(log_likelihood))

We solve the optimization problem for a range of \(\lambda\) to compute a trade-off curve. We then plot the train and test error over the trade-off curve. A reasonable choice of \(\lambda\) is the value that minimizes the test error.

def error(scores, labels):
  scores[scores > 0] = 1
  scores[scores <= 0] = 0
  return np.sum(np.abs(scores - labels)) / float(np.size(labels))
trials = 100
train_error = np.zeros(trials)
test_error = np.zeros(trials)
lambda_vals = np.logspace(-2, 0, trials)
beta_vals = []
for i in range(trials):
    lambd.value = lambda_vals[i]
    train_error[i] = error(X @ beta.value, Y)
    test_error[i] = error(X_test @ beta.value, Y_test)
%matplotlib inline
%config InlineBackend.figure_format = 'svg'

plt.plot(lambda_vals, train_error, label="Train error")
plt.plot(lambda_vals, test_error, label="Test error")
plt.legend(loc='upper left')
plt.xlabel(r"$\lambda$", fontsize=16)

We also plot the regularization path, or the \(\beta_i\) versus \(\lambda\). Notice that a few features remain non-zero longer for larger \(\lambda\) then the rest, which suggests that these features are the most important.

for i in range(n):
    plt.plot(lambda_vals, [wi[i,0] for wi in beta_vals])
plt.xlabel(r"$\lambda$", fontsize=16)