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bench_rcv1_logreg_convergence.py
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bench_rcv1_logreg_convergence.py
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# Authors: Tom Dupre la Tour <[email protected]>
# Olivier Grisel <[email protected]>
#
# License: BSD 3 clause
import matplotlib.pyplot as plt
import numpy as np
import gc
import time
from sklearn.utils import Memory
from sklearn.linear_model import (LogisticRegression, SGDClassifier)
from sklearn.datasets import fetch_rcv1
from sklearn.linear_model.sag import get_auto_step_size
try:
import lightning.classification as lightning_clf
except ImportError:
lightning_clf = None
m = Memory(cachedir='.', verbose=0)
# compute logistic loss
def get_loss(w, intercept, myX, myy, C):
n_samples = myX.shape[0]
w = w.ravel()
p = np.mean(np.log(1. + np.exp(-myy * (myX.dot(w) + intercept))))
print("%f + %f" % (p, w.dot(w) / 2. / C / n_samples))
p += w.dot(w) / 2. / C / n_samples
return p
# We use joblib to cache individual fits. Note that we do not pass the dataset
# as argument as the hashing would be too slow, so we assume that the dataset
# never changes.
@m.cache()
def bench_one(name, clf_type, clf_params, n_iter):
clf = clf_type(**clf_params)
try:
clf.set_params(max_iter=n_iter, random_state=42)
except:
clf.set_params(n_iter=n_iter, random_state=42)
st = time.time()
clf.fit(X, y)
end = time.time()
try:
C = 1.0 / clf.alpha / n_samples
except:
C = clf.C
try:
intercept = clf.intercept_
except:
intercept = 0.
train_loss = get_loss(clf.coef_, intercept, X, y, C)
train_score = clf.score(X, y)
test_score = clf.score(X_test, y_test)
duration = end - st
return train_loss, train_score, test_score, duration
def bench(clfs):
for (name, clf, iter_range, train_losses, train_scores,
test_scores, durations) in clfs:
print("training %s" % name)
clf_type = type(clf)
clf_params = clf.get_params()
for n_iter in iter_range:
gc.collect()
train_loss, train_score, test_score, duration = bench_one(
name, clf_type, clf_params, n_iter)
train_losses.append(train_loss)
train_scores.append(train_score)
test_scores.append(test_score)
durations.append(duration)
print("classifier: %s" % name)
print("train_loss: %.8f" % train_loss)
print("train_score: %.8f" % train_score)
print("test_score: %.8f" % test_score)
print("time for fit: %.8f seconds" % duration)
print("")
print("")
return clfs
def plot_train_losses(clfs):
plt.figure()
for (name, _, _, train_losses, _, _, durations) in clfs:
plt.plot(durations, train_losses, '-o', label=name)
plt.legend(loc=0)
plt.xlabel("seconds")
plt.ylabel("train loss")
def plot_train_scores(clfs):
plt.figure()
for (name, _, _, _, train_scores, _, durations) in clfs:
plt.plot(durations, train_scores, '-o', label=name)
plt.legend(loc=0)
plt.xlabel("seconds")
plt.ylabel("train score")
plt.ylim((0.92, 0.96))
def plot_test_scores(clfs):
plt.figure()
for (name, _, _, _, _, test_scores, durations) in clfs:
plt.plot(durations, test_scores, '-o', label=name)
plt.legend(loc=0)
plt.xlabel("seconds")
plt.ylabel("test score")
plt.ylim((0.92, 0.96))
def plot_dloss(clfs):
plt.figure()
pobj_final = []
for (name, _, _, train_losses, _, _, durations) in clfs:
pobj_final.append(train_losses[-1])
indices = np.argsort(pobj_final)
pobj_best = pobj_final[indices[0]]
for (name, _, _, train_losses, _, _, durations) in clfs:
log_pobj = np.log(abs(np.array(train_losses) - pobj_best)) / np.log(10)
plt.plot(durations, log_pobj, '-o', label=name)
plt.legend(loc=0)
plt.xlabel("seconds")
plt.ylabel("log(best - train_loss)")
def get_max_squared_sum(X):
"""Get the maximum row-wise sum of squares"""
return np.sum(X ** 2, axis=1).max()
rcv1 = fetch_rcv1()
X = rcv1.data
n_samples, n_features = X.shape
# consider the binary classification problem 'CCAT' vs the rest
ccat_idx = rcv1.target_names.tolist().index('CCAT')
y = rcv1.target.tocsc()[:, ccat_idx].toarray().ravel().astype(np.float64)
y[y == 0] = -1
# parameters
C = 1.
fit_intercept = True
tol = 1.0e-14
# max_iter range
sgd_iter_range = list(range(1, 121, 10))
newton_iter_range = list(range(1, 25, 3))
lbfgs_iter_range = list(range(1, 242, 12))
liblinear_iter_range = list(range(1, 37, 3))
liblinear_dual_iter_range = list(range(1, 85, 6))
sag_iter_range = list(range(1, 37, 3))
clfs = [
("LR-liblinear",
LogisticRegression(C=C, tol=tol,
solver="liblinear", fit_intercept=fit_intercept,
intercept_scaling=1),
liblinear_iter_range, [], [], [], []),
("LR-liblinear-dual",
LogisticRegression(C=C, tol=tol, dual=True,
solver="liblinear", fit_intercept=fit_intercept,
intercept_scaling=1),
liblinear_dual_iter_range, [], [], [], []),
("LR-SAG",
LogisticRegression(C=C, tol=tol,
solver="sag", fit_intercept=fit_intercept),
sag_iter_range, [], [], [], []),
("LR-newton-cg",
LogisticRegression(C=C, tol=tol, solver="newton-cg",
fit_intercept=fit_intercept),
newton_iter_range, [], [], [], []),
("LR-lbfgs",
LogisticRegression(C=C, tol=tol,
solver="lbfgs", fit_intercept=fit_intercept),
lbfgs_iter_range, [], [], [], []),
("SGD",
SGDClassifier(alpha=1.0 / C / n_samples, penalty='l2', loss='log',
fit_intercept=fit_intercept, verbose=0),
sgd_iter_range, [], [], [], [])]
if lightning_clf is not None and not fit_intercept:
alpha = 1. / C / n_samples
# compute the same step_size than in LR-sag
max_squared_sum = get_max_squared_sum(X)
step_size = get_auto_step_size(max_squared_sum, alpha, "log",
fit_intercept)
clfs.append(
("Lightning-SVRG",
lightning_clf.SVRGClassifier(alpha=alpha, eta=step_size,
tol=tol, loss="log"),
sag_iter_range, [], [], [], []))
clfs.append(
("Lightning-SAG",
lightning_clf.SAGClassifier(alpha=alpha, eta=step_size,
tol=tol, loss="log"),
sag_iter_range, [], [], [], []))
# We keep only 200 features, to have a dense dataset,
# and compare to lightning SAG, which seems incorrect in the sparse case.
X_csc = X.tocsc()
nnz_in_each_features = X_csc.indptr[1:] - X_csc.indptr[:-1]
X = X_csc[:, np.argsort(nnz_in_each_features)[-200:]]
X = X.toarray()
print("dataset: %.3f MB" % (X.nbytes / 1e6))
# Split training and testing. Switch train and test subset compared to
# LYRL2004 split, to have a larger training dataset.
n = 23149
X_test = X[:n, :]
y_test = y[:n]
X = X[n:, :]
y = y[n:]
clfs = bench(clfs)
plot_train_scores(clfs)
plot_test_scores(clfs)
plot_train_losses(clfs)
plot_dloss(clfs)
plt.show()