roc logreg

from sklearn.metrics import roc_curve, auc
C=np.linspace(1,100,10).tolist()
train_results = []
test_results = []
for i in C:
    clf = LogisticRegression(C=i)
    clf.fit(tfX_train, y_train)
    train_pred = clf.predict(tfX_train)
    false_positive_rate, true_positive_rate, thresholds = roc_curve(y_train, train_pred)
    roc_auc = auc(false_positive_rate, true_positive_rate)
    train_results.append(roc_auc)
    y_pred = clf.predict(tfX_test)
    false_positive_rate, true_positive_rate, thresholds = roc_curve(y_test, y_pred)
    roc_auc = auc(false_positive_rate, true_positive_rate)
    test_results.append(roc_auc)
from matplotlib.legend_handler import HandlerLine2D
line1, = plt.plot(C, train_results,"b", label="Train AUC")
line2, = plt.plot(C, test_results, "r", label="Test AUC")
plt.legend(handler_map={line1: HandlerLine2D(numpoints=2)})
plt.ylabel("AUC score")
plt.xlabel("C")
plt.grid(True)