关于scikit learn：ValueError：类的数量必须大于一（python）

ValueError: The number of classes has to be greater than one (python)

当传递x,y在fit，我得到以下错误：

追踪(最新的呼叫负载)：

File"C:/Classify/classifier.py", line 95, in

train_avg, test_avg, cms = train_model(X, y,"ceps", plot=True)
File"C:/Classify/classifier.py", line 47, in train_model

clf.fit(X_train, y_train) File"C:\Python27\lib\site-packages\sklearn\svm\base.py", line 676, in fit
raise ValueError("The number of classes has to be greater than" ValueError: The number of classes has to be greater than one.

下面是我的代码：

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def train_model(X, Y, name, plot=False):
"""
train_model(vector, vector, name[, plot=False])

Trains and saves model to disk.
"""
labels = np.unique(Y)

cv = ShuffleSplit(n=len(X), n_iter=1, test_size=0.3, indices=True, random_state=0)

train_errors = []
test_errors = []

scores = []
pr_scores = defaultdict(list)
precisions, recalls, thresholds = defaultdict(list), defaultdict(list), defaultdict(list)

roc_scores = defaultdict(list)
tprs = defaultdict(list)
fprs = defaultdict(list)

clfs = [] # for the median

cms = []

for train, test in cv:
X_train, y_train = X[train], Y[train]
X_test, y_test = X[test], Y[test]

clf = LogisticRegression()
clf.fit(X_train, y_train)
clfs.append(clf)