__init__()得到了一个意想不到的关键字“n_”错误。

I was going to try the code in this link:

我想试试这个链接的代码:

I am getting error from the line which refers to StratifiedKFold(n_splits=60). Can anybody tell me how i can solve this error?

我从这条线中得到了一个错误，它指的是层状褶皱(n_= 60)。有人能告诉我怎么解决这个错误吗?

Here is the Code:

这是代码:

import numpy as np
from scipy import interp
import matplotlib.pyplot as plt
from itertools import cycle

from sklearn import svm, datasets
from sklearn.metrics import roc_curve, auc
from sklearn.cross_validation import StratifiedKFold

iris = datasets.load_iris()
X = iris.data
y = iris.target
X, y = X[y != 2], y
X, y

cv = StratifiedKFold(n_splits=6)
classifier = svm.SVC(kernel='linear', probability=True,
                     random_state=random_state)

mean_tpr = 0.0
mean_fpr = np.linspace(0, 1, 100)

Here is the error:

这是错误:

TypeError                                 Traceback (most recent call last)
<ipython-input-227-2af2773f4987> in <module>()
----> 1 sklearn.cross_validation.StratifiedKFold(n_splits=6)
      2 #cv = StratifiedKFold(n_splits=6,  shuffle=True, random_state=1)
      3 classifier = svm.SVC(kernel='linear', probability=True,
      4                      random_state=random_state)
      5 

TypeError: __init__() got an unexpected keyword argument 'n_splits'

1 个解决方案

#1

You are not getting any warnings while importing the sklearn.cross-validation module. This means that your installed version is less than 0.18.

在导入sklearn时，您没有得到任何警告。交叉验证模块。这意味着您安装的版本小于0.18。

If your scikit-learn version is < 0.18, then change the below lines: (Taken from StratifiedKFold documentation for version 0.17)

如果您的scikitt -学习版本是< 0.18，那么更改以下代码:(从0.17版本的分层文档中提取)

#Notice the extra parameter y and change of name for n_splits to n_folds
cv = StratifiedKFold(y, n_folds=6)

#Also note that the cv is called directly in for loop
for train_index, test_index in cv:
   print("TRAIN:", train_index, "TEST:", test_index)
   X_train, X_test = X[train_index], X[test_index]
   y_train, y_test = y[train_index], y[test_index]

If your scikit-learn version is >=0.18, then only you can use the n_splits parameter for the cv: (Taken from StratifiedKFold current documentation, which is what I think you are referring to)

如果您的scikitt -学习版本是>=0.18，那么只有您可以为cv使用n_参数(从分层的当前文档中提取，这是我认为您所指的)

#Notice the extra parameter y is removed here
cv = StratifiedKFold(n_splits=6)

#Also note that the cv.split() is called here (opposed to cv in ver 0.17 above)
for train_index, test_index in cv.split(X, y):
   print("TRAIN:", train_index, "TEST:", test_index)
   X_train, X_test = X[train_index], X[test_index]
   y_train, y_test = y[train_index], y[test_index]

Recommendation:

#1