Sklearn GridSearchCV vs. CrossValidation

cvdiff.py

from sklearn.linear_model import SGDClassifier
from sklearn import cross_validation
from sklearn import metrics
from sklearn.grid_search import GridSearchCV

from sklearn.datasets import load_iris

data = load_iris()
sample_vector = data.data
targets = data.target

cv = cross_validation.StratifiedKFold(targets, 10)
score_func = metrics.f1_score

parameters = {
    'seed': [0],
    'loss': ('log', 'hinge'),
    'penalty': ['l1', 'l2', 'elasticnet'],
    'alpha': [0.001, 0.0001, 0.00001, 0.000001]
}

print
print "GRID SEARCH:"
grid_search = GridSearchCV(SGDClassifier(), parameters,
     score_func=score_func, cv=cv)
grid_search.fit(sample_vector, targets)
print "Best %s: %0.3f" % (score_func.__name__, grid_search.best_score_)
print "Best parameters set:"
best_parameters = grid_search.best_estimator_.get_params()
for param_name in sorted(parameters.keys()):
    print "\t%s: %r" % (param_name, best_parameters[param_name])

print
print "CROSS VALIDATION:"
clf = SGDClassifier(**best_parameters)
scores = cross_validation.cross_val_score(clf, sample_vector, targets,
    cv=cv, score_func=score_func)
print 'Best %s: %0.2f (+/- %0.2f)' % \
    (score_func.__name__, scores.mean(), scores.std() / 2)