下記のような２値分類のSVMを組んだのですが，グリッドサーチの結果を格納してあるclf.grid_score_でエラーが出ます。

エラーメッセージ

AttributeError: 'GridSearchCV' object has no attribute 'grid_scores_'

プログラム

# -*- coding: utf-8 -*-
import numpy as np
import numpy.random as nr
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
from sklearn.model_selection import GridSearchCV
from sklearn.metrics import classification_report, confusion_matrix
from sklearn.svm import SVC

## データの読み込み
Injury = np.loadtxt("./Injury_All.csv",delimiter=',',skiprows=1)
nr.shuffle(Injury)
#怪我の有無
labels = Injury[:,0:1]
#標準化
sd = StandardScaler()
feature = sd.fit_transform(Injury[:,1:])
#2割をテストデータへ
X_train, X_test = train_test_split(feature,test_size=0.2, random_state=None)
y_train, y_test = train_test_split(labels,test_size=0.2, random_state=None)

## チューニングパラメータ
tuned_parameters = [{'kernel': ['rbf'], 'gamma': [1e-1, 1e-2, 1e-3, 1e-4],
                     'C': [1, 10, 100, 1000]},
                    ]

scores = ['accuracy', 'precision', 'recall']

 for score in scores:
    print ('\n' + '='*50)
    print (score)
    print ('='*50)

    clf = GridSearchCV(SVC(C=1), tuned_parameters, cv=5, scoring=score, n_jobs=-1)
    clf.fit(X_train, y_train)

    print ("\n+ ベストパラメータ:\n")
    print (clf.best_estimator_)

    print("\n+ トレーニングデータでCVした時の平均スコア:\n")
    for params, mean_score, all_scores in clf.grid_scores_:
        print ("{:.3f} (+/- {:.3f}) for {}".format(mean_score, all_scores.std() / 2, params))

    print ("\n+ テストデータでの識別結果:\n")
    y_true, y_pred = y_test, clf.predict(X_test)
    print (classification_report(y_true, y_pred))