ValueError异常：类的数量必须大于一个（蟒蛇）更大

Question

当传递fitx,y，我收到以下错误：

回溯（最近通话最后一个）：

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.

以下是我的代码：

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) 

Answer 1

您可能在训练集目前只有一个唯一的类别标签。正如错误消息所述，您需要在数据集中至少有两个独特的类。例如，您可以运行np.unique(y)以查看数据集中唯一的类标签。