使用GroupKFold实现交叉验证时出现Key Error问题求助

我有一个包含label、embeddings（特征列）、chr三列的数据框，想要按染色体分组做10折交叉验证，确保同一染色体（比如chr1）的所有行要么全在训练集，要么全在测试集，不拆分到两个集合里。我觉得代码写得没问题，但一直触发Key Error。以下是我的代码：

import numpy as np
from sklearn.model_selection import GroupKFold

X = np.array([np.array(x) for x in mini_df['embeddings']])
y = mini_df['label']
groups = mini_df['chromosome']
group_kfold = GroupKFold(n_splits=10)

# Initialize figure for plotting
plt.figure(figsize=(10, 6))

# Perform cross-validation and plot ROC curves for each fold
for i, (train_idx, val_idx) in enumerate(group_kfold.split(X, y, groups)):
    X_train_fold, X_val_fold = X[train_idx], X[val_idx]
    y_train_fold, y_val_fold = y[train_idx], y[val_idx]
    
    # Initialize classifier
    rf_classifier = RandomForestClassifier(n_estimators=n_trees, random_state=42, max_depth=max_depth, n_jobs=-1)
    
    # Train the classifier on this fold
    rf_classifier.fit(X_train_fold, y_train_fold)
    
    # Make predictions on the validation set
    y_pred_proba = rf_classifier.predict_proba(X_val_fold)[:, 1]
    
    # Calculate ROC curve
    fpr, tpr, _ = roc_curve(y_val_fold, y_pred_proba)
    
    # Calculate AUC
    roc_auc = auc(fpr, tpr)
    
    # Plot ROC curve for this fold
    plt.plot(fpr, tpr, lw=1, alpha=0.7, label=f'ROC Fold {i+1} (AUC = {roc_auc:.2f})')

# Plot ROC for random classifier
plt.plot([0, 1], [0, 1], linestyle='--', lw=2, color='r', label='Random', alpha=0.8)

# Add labels and legend
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('ROC Curves for Random Forest Classifier')
plt.legend(loc='lower right')
plt.show()

触发Key Error的核心原因是列名不匹配：你数据框里的染色体列是chr，但代码里写的是mini_df['chromosome']，引用了不存在的列名，直接导致报错。

另外还要注意两个细节：

1. 代码里用到的n_trees和max_depth必须提前定义，否则会触发NameError
2. 缺少matplotlib.pyplot、RandomForestClassifier、roc_curve和auc的导入语句，运行时会报错

import numpy as np
import matplotlib.pyplot as plt
from sklearn.model_selection import GroupKFold
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import roc_curve, auc

# 提前定义模型参数，可根据需求调整
n_trees = 100
max_depth = 10

# 修正列名：使用数据框实际的'chr'列
X = np.array([np.array(x) for x in mini_df['embeddings']])
y = mini_df['label']
groups = mini_df['chr']  # 关键修正点
group_kfold = GroupKFold(n_splits=10)

plt.figure(figsize=(10, 6))

for i, (train_idx, val_idx) in enumerate(group_kfold.split(X, y, groups)):
    X_train_fold, X_val_fold = X[train_idx], X[val_idx]
    y_train_fold, y_val_fold = y[train_idx], y[val_idx]
    
    rf_classifier = RandomForestClassifier(n_estimators=n_trees, random_state=42, max_depth=max_depth, n_jobs=-1)
    rf_classifier.fit(X_train_fold, y_train_fold)
    
    y_pred_proba = rf_classifier.predict_proba(X_val_fold)[:, 1]
    fpr, tpr, _ = roc_curve(y_val_fold, y_pred_proba)
    roc_auc = auc(fpr, tpr)
    
    plt.plot(fpr, tpr, lw=1, alpha=0.7, label=f'ROC Fold {i+1} (AUC = {roc_auc:.2f})')

plt.plot([0, 1], [0, 1], linestyle='--', lw=2, color='r', label='Random', alpha=0.8)
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('ROC Curves for Random Forest Classifier')
plt.legend(loc='lower right')
plt.show()

• 运行print(mini_df.columns)确认数据框的列名，确保chr存在且拼写正确
• 如果embeddings列的元素本身就是numpy数组，可以用X = np.stack(mini_df['embeddings'].values)替代列表推导，更高效

内容的提问来源于stack exchange，提问作者youtube