Regularization path of L1- Logistic Regression — scikit-learn 0.20.4 documentation (original) (raw)
Note
Click here to download the full example code
Train l1-penalized logistic regression models on a binary classification problem derived from the Iris dataset.
The models are ordered from strongest regularized to least regularized. The 4 coefficients of the models are collected and plotted as a “regularization path”: on the left-hand side of the figure (strong regularizers), all the coefficients are exactly 0. When regularization gets progressively looser, coefficients can get non-zero values one after the other.
Here we choose the SAGA solver because it can efficiently optimize for the Logistic Regression loss with a non-smooth, sparsity inducing l1 penalty.
Also note that we set a low value for the tolerance to make sure that the model has converged before collecting the coefficients.
We also use warm_start=True which means that the coefficients of the models are reused to initialize the next model fit to speed-up the computation of the full-path.
Out:
Computing regularization path ... This took 3.768s
print(doc)
Author: Alexandre Gramfort alexandre.gramfort@inria.fr
License: BSD 3 clause
from time import time import numpy as np import matplotlib.pyplot as plt
from sklearn import linear_model from sklearn import datasets from sklearn.svm import l1_min_c
iris = datasets.load_iris() X = iris.data y = iris.target
X = X[y != 2] y = y[y != 2]
X /= X.max() # Normalize X to speed-up convergence
Demo path functions
cs = l1_min_c(X, y, loss='log') * np.logspace(0, 7, 16)
print("Computing regularization path ...") start = time() clf = linear_model.LogisticRegression(penalty='l1', solver='saga', tol=1e-6, max_iter=int(1e6), warm_start=True) coefs_ = [] for c in cs: clf.set_params(C=c) clf.fit(X, y) coefs_.append(clf.coef_.ravel().copy()) print("This took %0.3fs" % (time() - start))
coefs_ = np.array(coefs_) plt.plot(np.log10(cs), coefs_, marker='o') ymin, ymax = plt.ylim() plt.xlabel('log(C)') plt.ylabel('Coefficients') plt.title('Logistic Regression Path') plt.axis('tight') plt.show()
Total running time of the script: ( 0 minutes 3.914 seconds)