cross_val_predict
(参见文档,v0.18)在下面的代码中使用k-fold方法时,是否计算每个折叠的准确性并最终取平均值?
cv = KFold(len(labels), n_folds=20)
clf = SVC()
ypred = cross_val_predict(clf, td, labels, cv=cv)
accuracy = accuracy_score(labels, ypred)
print accuracy
cross_val_predict
(参见文档,v0.18)在下面的代码中使用k-fold方法时,是否计算每个折叠的准确性并最终取平均值?
cv = KFold(len(labels), n_folds=20)
clf = SVC()
ypred = cross_val_predict(clf, td, labels, cv=cv)
accuracy = accuracy_score(labels, ypred)
print accuracy
cross_val_predict
仅基于某种策略返回标签,而不返回任何分数,该策略在此处描述:accuracy_score(labels, ypred)
,您仅计算了由上述特定策略预测的标签的准确性得分,与真实标签进行比较。同一文档页面中也指明了这一点。
如果您需要不同折叠的准确性评分,则可以尝试以下操作:These prediction can then be used to evaluate the classifier:
predicted = cross_val_predict(clf, iris.data, iris.target, cv=10) metrics.accuracy_score(iris.target, predicted)
Note that the result of this computation may be slightly different from those obtained using cross_val_score as the elements are grouped in different ways.
>>> scores = cross_val_score(clf, X, y, cv=cv)
>>> scores
array([ 0.96..., 1. ..., 0.96..., 0.96..., 1. ])
然后对于所有折叠的平均准确率使用 scores.mean()
:
>>> print("Accuracy: %0.2f (+/- %0.2f)" % (scores.mean(), scores.std() * 2))
Accuracy: 0.98 (+/- 0.03)
为了计算Cohen kappa系数和混淆矩阵,我假设您指的是真实标签和每个折叠预测标签之间的kappa系数和混淆矩阵:
from sklearn.model_selection import KFold
from sklearn.svm.classes import SVC
from sklearn.metrics.classification import cohen_kappa_score
from sklearn.metrics import confusion_matrix
cv = KFold(len(labels), n_folds=20)
clf = SVC()
for train_index, test_index in cv.split(X):
clf.fit(X[train_index], labels[train_index])
ypred = clf.predict(X[test_index])
kappa_score = cohen_kappa_score(labels[test_index], ypred)
confusion_matrix = confusion_matrix(labels[test_index], ypred)
cross_val_predict
返回什么?它使用KFold将数据分为k
份,然后进行i=1..k
次迭代:
i'th
部分作为测试数据,将所有其他部分作为训练数据i'th
)来训练模型i'th
部分(测试数据)的标签在每次迭代中,预测i'th
部分的标签。最终,cross_val_predict
将所有部分预测的标签合并,并将其作为最终结果返回。
以下代码逐步显示了此过程:
X = np.array([[0], [1], [2], [3], [4], [5]])
labels = np.array(['a', 'a', 'a', 'b', 'b', 'b'])
cv = KFold(len(labels), n_folds=3)
clf = SVC()
ypred_all = np.chararray((labels.shape))
i = 1
for train_index, test_index in cv.split(X):
print("iteration", i, ":")
print("train indices:", train_index)
print("train data:", X[train_index])
print("test indices:", test_index)
print("test data:", X[test_index])
clf.fit(X[train_index], labels[train_index])
ypred = clf.predict(X[test_index])
print("predicted labels for data of indices", test_index, "are:", ypred)
ypred_all[test_index] = ypred
print("merged predicted labels:", ypred_all)
i = i+1
print("=====================================")
y_cross_val_predict = cross_val_predict(clf, X, labels, cv=cv)
print("predicted labels by cross_val_predict:", y_cross_val_predict)
iteration 1 :
train indices: [2 3 4 5]
train data: [[2] [3] [4] [5]]
test indices: [0 1]
test data: [[0] [1]]
predicted labels for data of indices [0 1] are: ['b' 'b']
merged predicted labels: ['b' 'b' '' '' '' '']
=====================================
iteration 2 :
train indices: [0 1 4 5]
train data: [[0] [1] [4] [5]]
test indices: [2 3]
test data: [[2] [3]]
predicted labels for data of indices [2 3] are: ['a' 'b']
merged predicted labels: ['b' 'b' 'a' 'b' '' '']
=====================================
iteration 3 :
train indices: [0 1 2 3]
train data: [[0] [1] [2] [3]]
test indices: [4 5]
test data: [[4] [5]]
predicted labels for data of indices [4 5] are: ['a' 'a']
merged predicted labels: ['b' 'b' 'a' 'b' 'a' 'a']
=====================================
predicted labels by cross_val_predict: ['b' 'b' 'a' 'b' 'a' 'a']
从github的cross_val_predict
代码中可以看出,该函数为每个折叠计算预测值并将它们连接起来。这些预测是基于从其他折叠中学习到的模型进行的。
下面是您的代码和示例的组合:
from sklearn import datasets, linear_model
from sklearn.model_selection import cross_val_predict, KFold
from sklearn.metrics import accuracy_score
diabetes = datasets.load_diabetes()
X = diabetes.data[:400]
y = diabetes.target[:400]
cv = KFold(n_splits=20)
lasso = linear_model.Lasso()
y_pred = cross_val_predict(lasso, X, y, cv=cv)
accuracy = accuracy_score(y_pred.astype(int), y.astype(int))
print(accuracy)
# >>> 0.0075
from sklearn.model_selection import cross_val_score, cross_val_predict
from sklearn.metrics import precision_recall_fscore_support as score
y_pred = cross_val_predict(lm,df,y,cv=5)
precision, recall, fscore, support = score(y, y_pred, average='micro')
print(fscore)
这在数学上是可行的,因为微平均值给出了混淆矩阵的加权平均值。
祝你好运。
cross_val_predict
将所有部分预测的标签合并,并将它们作为整体返回。 - Omid