MNIST classification using multinomial logistic + L1¶
Here we fit a multinomial logistic regression with L1 penalty on a subset of the MNIST digits classification task. We use the SAGA algorithm for this purpose: this a solver that is fast when the number of samples is significantly larger than the number of features and is able to finely optimize non-smooth objective functions which is the case with the l1-penalty. Test accuracy reaches > 0.8, while weight vectors remains sparse and therefore more easily interpretable.
Note that this accuracy of this l1-penalized linear model is significantly below what can be reached by an l2-penalized linear model or a non-linear multi-layer perceptron model on this dataset.
Sparsity with L1 penalty: 74.57% Test score with L1 penalty: 0.8253 Example run in 20.613 s
# Author: Arthur Mensch <email@example.com> # License: BSD 3 clause import time import matplotlib.pyplot as plt import numpy as np from sklearn.datasets import fetch_openml from sklearn.linear_model import LogisticRegression from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler from sklearn.utils import check_random_state # Turn down for faster convergence t0 = time.time() train_samples = 5000 # Load data from https://www.openml.org/d/554 X, y = fetch_openml("mnist_784", version=1, return_X_y=True, as_frame=False) random_state = check_random_state(0) permutation = random_state.permutation(X.shape) X = X[permutation] y = y[permutation] X = X.reshape((X.shape, -1)) X_train, X_test, y_train, y_test = train_test_split( X, y, train_size=train_samples, test_size=10000 ) scaler = StandardScaler() X_train = scaler.fit_transform(X_train) X_test = scaler.transform(X_test) # Turn up tolerance for faster convergence clf = LogisticRegression(C=50.0 / train_samples, penalty="l1", solver="saga", tol=0.1) clf.fit(X_train, y_train) sparsity = np.mean(clf.coef_ == 0) * 100 score = clf.score(X_test, y_test) # print('Best C % .4f' % clf.C_) print("Sparsity with L1 penalty: %.2f%%" % sparsity) print("Test score with L1 penalty: %.4f" % score) coef = clf.coef_.copy() plt.figure(figsize=(10, 5)) scale = np.abs(coef).max() for i in range(10): l1_plot = plt.subplot(2, 5, i + 1) l1_plot.imshow( coef[i].reshape(28, 28), interpolation="nearest", cmap=plt.cm.RdBu, vmin=-scale, vmax=scale, ) l1_plot.set_xticks(()) l1_plot.set_yticks(()) l1_plot.set_xlabel("Class %i" % i) plt.suptitle("Classification vector for...") run_time = time.time() - t0 print("Example run in %.3f s" % run_time) plt.show()
Total running time of the script: ( 0 minutes 20.679 seconds)