如何正确地重塑sklearn分类器的predict_proba的多类输出？

from sklearn.datasets import make_classification
from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import LabelEncoder
import numpy as np

X, y = make_classification(n_samples=100, n_features=2, n_informative=2, n_redundant=0, n_classes=3, random_state=42)

le = LabelEncoder()
y_encoded = le.fit_transform(y)

clf = LogisticRegression()
clf.fit(X, y_encoded)

probs = clf.predict_proba(X)

reshaped_probs = np.zeros((probs.shape[0], 2))
for i, prob in enumerate(probs):
    reshaped_probs[i][0] = prob[1]  # 将第2个类别的概率作为第1个类别的概率
    reshaped_probs[i][1] = 1 - prob[1]  # 计算第1个类别的概率（1减去第2个类别的概率）

print(reshaped_probs)