python之sklearn 的 SVC 评分方法需要什么样的输入

所以我正在尝试构建一个分类器并对其性能进行评分。这是我的代码:

def svc(train_data, train_labels, test_data, test_labels): 
    from sklearn.svm import SVC 
    from sklearn.metrics import accuracy_score 
    svc = SVC(kernel='linear') 
    svc.fit(train_data, train_labels) 
    predicted = svc.predict(test_data) 
    actual = test_labels 
    score = svc.score(test_data, test_labels) 
    print ('svc score') 
    print (score) 
    print ('svc accuracy') 
    print (accuracy_score(predicted, actual)) 

现在，当我运行函数 svc(X, x, Y, y) 时:

X.shape = (1000, 150)     
x.shape = (1000, )    
Y.shape = (200, 150)    
y.shape = (200, ) 

我得到错误:

      6     predicted = svc.predict(test_classed_data) 
      7     actual = test_classed_labels 
----> 8     score = svc.score(test_classed_data, test_classed_labels) 
      9     print ('svc score') 
     10     print (score) 
 
local/lib/python3.4/site-packages/sklearn/base.py in score(self, X, y, sample_weight) 
    289         """ 
    290         from .metrics import accuracy_score 
--> 291         return accuracy_score(y, self.predict(X), sample_weight=sample_weight) 
    292  
    293  
 
    124     if (y_type not in ["binary", "multiclass", "multilabel-indicator", 
    125                        "multilabel-sequences"]): 
--> 126         raise ValueError("{0} is not supported".format(y_type)) 
    127  
    128     if y_type in ["binary", "multiclass"]: 
 
ValueError: continuous is not supported 

问题是我的 test_labels 或 y 的格式是:

[ 15.5  15.5  15.5  15.5  15.5  15.5  15.5  15.5  15.5  15.5  15.5  20.5 
  20.5  20.5  20.5  20.5  20.5  20.5  20.5  20.5  20.5  20.5  25.5  25.5 
  25.5  25.5  25.5  25.5  25.5  25.5  25.5  25.5  25.5  30.5  30.5  30.5 
  30.5  30.5  30.5  30.5  30.5  30.5  30.5  30.5  35.5  35.5  35.5  35.5 
  35.5  35.5  35.5  35.5  35.5  35.5  35.5... ] 

我真的很困惑，为什么 SVC 不能将这些识别为离散标签，而我看过的所有示例都与我的格式相似并且工作正常。请帮忙。

请您参考如下方法:

fit 和score 函数中的y 应该是整数或字符串，代表类标签。

例如如果您有两个类 "foo" 和 1，您可以像这样训练 SVM:

>>> from sklearn.svm import SVC 
>>> clf = SVC() 
>>> X = np.random.randn(10, 4) 
>>> y = ["foo"] * 5 + [1] * 5 
>>> clf.fit(X, y) 
SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.0, 
  kernel='rbf', max_iter=-1, probability=False, random_state=None, 
  shrinking=True, tol=0.001, verbose=False) 

然后用

>>> X_test = np.random.randn(6, 4) 
>>> y_test = ["foo", 1] * 3 
>>> clf.score(X_test, y_test) 
0.5 

浮点值显然仍被 fit 接受，但它们不应该被接受，因为类标签不应该是真实值。