Question

我的数据集高度不平衡，我想执行SMOTE来平衡数据集，并执行交叉验证以测量准确性。但是，大多数现有教程仅使用单个public class CarPolicyIdAttributeConverter implements GenericConverter { @Override public Set<ConvertiblePair> getConvertibleTypes() { final ConvertiblePair[] pairs = new ConvertiblePair[] { new ConvertiblePair(String.class, String.class) }; return ImmutableSet.copyOf(pairs); } @Override public Object convert(final Object source, final TypeDescriptor sourceType, final TypeDescriptor targetType) { if (targetType.getAnnotation(Uppercase.class) != null) { return ((String)source).toUppercase(); } return source; } }和training迭代来执行SMOTE。

因此，我想知道使用交叉验证执行SMOTE的正确过程。

我当前的代码如下。但是，如上所述，它仅使用一次迭代。

testing

很高兴在需要时提供更多详细信息。

Answer 1

您需要每次折叠执行次。因此，您需要避免使用train_test_split来支持KFold：

from sklearn.model_selection import KFold
from imblearn.over_sampling import SMOTE
from sklearn.metrics import f1_score

kf = KFold(n_splits=5)

for fold, (train_index, test_index) in enumerate(kf.split(X), 1):
    X_train = X[train_index]
    y_train = y[train_index]  # Based on your code, you might need a ravel call here, but I would look into how you're generating your y
    X_test = X[test_index]
    y_test = y[test_index]  # See comment on ravel and  y_train
    sm = SMOTE()
    X_train_oversampled, y_train_oversampled = sm.fit_sample(X_train, y_train)
    model = ...  # Choose a model here
    model.fit(X_train, y_train)  
    y_pred = model.predict(X_test)
    print(f'For fold {fold}:')
    print(f'Accuracy: {model.score(X_test, y_test)}')
    print(f'f-score: {f1_score(y_test, y_pred)}')

例如，您还可以将分数附加到外部定义的list上。

Answer 2

from sklearn.model_selection import StratifiedKFold
from imblearn.over_sampling import SMOTE

cv = StratifiedKFold(n_splits=5)
for train_idx, test_idx, in cv.split(X, y):
    X_train, y_train = X[train_idx], y[train_idx]
    X_test, y_test = X[test_idx], y[test_idx]
    X_train, y_train = SMOTE().fit_sample(X_train, y_train)
    ....

如何在sklearn中使用交叉验证执行SMOTE

2 个答案: