sklearn loocv.split 返回比预期更小的测试和训练数组

苦参

因为我有一个小数据集，所以我LOOCV在sklearn.

当我运行分类器时，收到以下错误：

"Number of labels=41 does not match number of samples=42".

我使用以下代码生成了测试集和训练集：

otu_trans = test_train.transpose()
# transpose otu table 
merged = pd.concat([otu_trans, metadata[status]], axis=1, join='inner')
# merge phenotype column from metadata file with transposed otu table

X = merged.drop([status],axis=1)

# drop status from X 
y = merged[status]


encoder = LabelEncoder()
y = pd.Series(encoder.fit_transform(y),
index=y.index, name=y.name)
# convert T and TF lables to 0 and 1 respectively

loocv = LeaveOneOut()
loocv.get_n_splits(X)

for train_index, test_index in loocv.split(X):
    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]
    print(X_train, X_test, y_train, y_test)

输入数据

当我检查它的形状时X_train，X_test它42,41不是41,257我认为应该的样子，因此似乎数据沿错误的轴进行了分区。

谁能向我解释为什么会发生这种情况？

谢谢

塞拉鲁克

首先，初始矩阵X根本不会受到影响。它仅用于生成索引和拆分数据。

首字母的形状X将始终相同。

现在，这是一个使用拆分的简单示例LOOCV：

import numpy as np
from sklearn.model_selection import LeaveOneOut

# I produce fake data with same dimensions as yours.
#fake data
X = np.random.rand(41,257)
#fake labels
y = np.random.rand(41)

#Now check that the shapes are correct:
X.shape
y.shape

这会给你：

(41, 257)
(41,)

现在拆分：

loocv = LeaveOneOut()
loocv.get_n_splits(X)

for train_index, test_index in loocv.split(X):
    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]

    #classifier.fit(X_train, y_train)
    #classifier.predict(X_test)


X_train.shape
X_test.shape

这打印：