Non-negative least squares¶
In this example, we fit a linear model with positive constraints on the regression coefficients and compare the estimated coefficients to a classic linear regression.
print(__doc__) import numpy as np import matplotlib.pyplot as plt from sklearn.metrics import r2_score
Generate some random data
np.random.seed(42) n_samples, n_features = 200, 50 X = np.random.randn(n_samples, n_features) true_coef = 3 * np.random.randn(n_features) # Threshold coefficients to render them non-negative true_coef[true_coef < 0] = 0 y = np.dot(X, true_coef) # Add some noise y += 5 * np.random.normal(size=(n_samples, ))
Split the data in train set and test set
Fit the Non-Negative least squares.
NNLS R2 score 0.8225220806196525
Fit an OLS.
OLS R2 score 0.7436926291700345
Comparing the regression coefficients between OLS and NNLS, we can observe they are highly correlated (the dashed line is the identity relation), but the non-negative constraint shrinks some to 0. The Non-Negative Least squares inherently yield sparse results.
fig, ax = plt.subplots() ax.plot(reg_ols.coef_, reg_nnls.coef_, linewidth=0, marker=".") low_x, high_x = ax.get_xlim() low_y, high_y = ax.get_ylim() low = max(low_x, low_y) high = min(high_x, high_y) ax.plot([low, high], [low, high], ls="--", c=".3", alpha=.5) ax.set_xlabel("OLS regression coefficients", fontweight="bold") ax.set_ylabel("NNLS regression coefficients", fontweight="bold")
Text(55.847222222222214, 0.5, 'NNLS regression coefficients')
Total running time of the script: ( 0 minutes 0.083 seconds)