Support vector machine classifier with \(\ell_1\)-regularization

In this example we use CVXPY to train a SVM 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 \(y_i \in \{\pm 1\}\) are associated boolean outcomes. Our goal is to construct a good linear classifier \(\hat y = {\rm sign}(\beta^T x - v)\). We find the parameters \(\beta,v\) by minimizing the (convex) function

\[f(\beta,v) = (1/m) \sum_i \left(1 - y_i ( \beta^T x_i-v) \right)_+ + \lambda \| \beta\|_1\]

The first term is the average hinge loss. The second term shrinks the coefficients in \(\beta\) and encourages sparsity. The scalar \(\lambda \geq 0\) is a (regularization) parameter. Minimizing \(f(\beta,v)\) simultaneously selects features and fits the classifier.

Example

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 \(y_i = {\rm sign}(\beta_{\mathrm{true}}^T x_i -v_{\mathrm{true}} - z_i)\), 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.

# Generate data for SVM classifier with L1 regularization.
from __future__ import division
import numpy as np
np.random.seed(1)
n = 20
m = 1000
TEST = m
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
offset = 0
sigma = 45
X = np.random.normal(0, 5, size=(m,n))
Y = np.sign(X.dot(beta_true) + offset + np.random.normal(0,sigma,size=(m,1)))
X_test = np.random.normal(0, 5, size=(TEST,n))
Y_test = np.sign(X_test.dot(beta_true) + offset + np.random.normal(0,sigma,size=(TEST,1)))

We next formulate the optimization problem using CVXPY.

# Form SVM with L1 regularization problem.
import cvxpy as cp
beta = cp.Variable((n,1))
v = cp.Variable()
loss = cp.sum(cp.pos(1 - cp.multiply(Y, X*beta - v)))
reg = cp.norm(beta, 1)
lambd = cp.Parameter(nonneg=True)
prob = cp.Problem(cp.Minimize(loss/m + lambd*reg))

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.

# Compute a trade-off curve and record train and test error.
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]
    prob.solve()
    train_error[i] = (np.sign(X.dot(beta_true) + offset) != np.sign(X.dot(beta.value) - v.value)).sum()/m
    test_error[i] = (np.sign(X_test.dot(beta_true) + offset) != np.sign(X_test.dot(beta.value) - v.value)).sum()/TEST
    beta_vals.append(beta.value)
# Plot the train and test error over the trade-off curve.
import matplotlib.pyplot as plt
%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.xscale('log')
plt.legend(loc='upper left')
plt.xlabel(r"$\lambda$", fontsize=16)
plt.show()
../../_images/svm_8_0.svg

We also plot the regularization path, or the \(\beta_i\) versus \(\lambda\). Notice that the \(\beta_i\) do not necessarily decrease monotonically as \(\lambda\) increases. 4 features remain non-zero longer for larger \(\lambda\) than the rest, which suggests that these features are the most important. In fact \(\beta_{\mathrm{true}}\) had 4 non-zero values.

# Plot the regularization path for beta.
for i in range(n):
    plt.plot(lambda_vals, [wi[i,0] for wi in beta_vals])
plt.xlabel(r"$\lambda$", fontsize=16)
plt.xscale("log")
../../_images/svm_10_0.svg