支持向量类型错误。 none 类型对象不可迭代

Question

我在 python 中有这段代码，它使用不同的内核创建一个 SVM：

from sklearn.svm import SVC
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, roc_auc_score

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3)

# The gamma parameter is the kernel coefficient for kernels rbf/poly/sigmoid
svm = SVC(gamma='auto', probability=True)

svm.fit(X_train,y_train.values.ravel())
prediction = svm.predict(X_test)
prediction_prob = svm.predict_proba(X_test)
print('Accuracy:', accuracy_score(y_test,prediction))
print('AUC:',roc_auc_score(y_test,prediction_prob[:,1]))

print(X_train)
print(y_train)

现在我想用不同的内核 rbf 构建它并将值存储到数组中。我需要将折叠的结果存储到列表中，如下所示：所以像这样

def svm_grid_search(parameters, cv):

# Store the outcome of the folds in these lists
means = []
stds = []
params = []

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3)

for parameter in parameters: 
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3)
# The gamma parameter is the kernel coefficient for kernels rbf/poly/sigmoid
svm = SVC(gamma=1,kernel ='rbf',probability=True)
svm.fit(X_train,y_train.values.ravel())
prediction = svm.predict(X_test)
prediction_prob = svm.predict_proba(X_test)
return means, stddevs, params

答案应该如下所示，使用不同的参数和内核函数：

from sklearn.model_selection import GridSearchCV

parameters = {'kernel':['linear','poly','rbf'],'C':[0.2,0.5,1.0]}

means, stddevs, params = svm_grid_search(parameters, 10)

print('Mean AUC (+/- standard deviation), for parameters')
for mean, std, params in zip(means, stddevs, params):
print("%0.3f (+/- %0.03f) for %r"
% (mean, std, params))

但是我不断收到此错误，这表明我没有正确通过列表的折叠？

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-7-3f5f99897a92> in <module>()
3 parameters = {'kernel':['linear','poly','rbf'],'C':[0.2,0.5,1.0]}
4 
----> 5 means, stddevs, params = svm_grid_search(parameters, 10)
6 
7 print('Mean AUC (+/- standard deviation), for parameters')

TypeError: 'NoneType' object is not iterable

如果有人能指出正确的方向，不胜感激

Answer 1

看起来您实际上并没有在函数中创建的列表中存储任何内容。此外，您正在创建一个名为 stds 但返回 stddevs 的空列表（您从未定义过）。

编辑：我认为您正在尝试使用 sklearns GridSerachCV，但您从未调用过任何一个。以下是一些帮助您入门的代码：

from sklearn.model_selection import GridSearchCV
from sklearn.svm import SVC
from sklearn import datasets
import pandas as pd


iris = datasets.load_iris()
X = iris.data[:, :2]  # we only take the first two features.
y = iris.target

parameters = {'kernel':['linear','poly','rbf'],'C':[0.2,0.5,1.0]}

svc = SVC()
clf = GridSearchCV(svc, parameters)
clf.fit(X,y)

results = pd.DataFrame.from_dict(clf.cv_results_)

其中 results 就是您的网格搜索的结果。

   mean_fit_time  std_fit_time  mean_score_time  std_score_time param_C
0       0.000600      0.000490           0.0004        0.000490     0.2
1       0.000400      0.000490           0.0002        0.000400     0.2
2       0.000600      0.000490           0.0004        0.000490     0.2
3       0.001008      0.000013           0.0000        0.000000     0.5
4       0.000800      0.000400           0.0002        0.000399     0.5
5       0.000800      0.000400           0.0004        0.000490     0.5
6       0.000800      0.000400           0.0000        0.000000     1.0
7       0.000800      0.000400           0.0002        0.000400     1.0
8       0.000800      0.000400           0.0002        0.000400     1.0

如果你想将它包装在你自己的函数中，那么你需要阅读一些关于 python 函数以及 GridSearchCV 返回的内容。 希望对您有所帮助。