from sklearn.cross_validation import train_test_split
from sklearn.cross_validation import cross_val_score
from sklearn.cross_validation import KFold
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import SGDClassifier
from sklearn.datasets import load_iris
from sklearn.metrics import classification_report
import numpy as np

dataset = load_iris()
X = dataset.data
y = dataset.target

clf =  Pipeline([('scaler', StandardScaler()),('linear_model',
    SGDClassifier())])
cv = KFold(X.shape[0],5,shuffle=True, random_state=42)
score = cross_val_score(clf, X, y, cv=cv)

print "Mean = {}".format(np.mean(score))