如何在python sklearn中为NMF选择最佳的组件数量？

from sklearn.decomposition import NMF
from sklearn.metrics import mean_squared_error
from sklearn.datasets import make_moons
from sklearn.model_selection import train_test_split
import numpy as np
import matplotlib.pyplot as plt

def compute_nmf_rmse(n_components):
    model = NMF(n_components=n_components)
    W = model.fit_transform(X_train)
    H = model.components_
    X_train_approx = np.dot(W, H)
    rmse = np.sqrt(mean_squared_error(X_train, X_train_approx))
    return rmse

X, _ = make_moons(n_samples=200, noise=0.05, random_state=42)
X_train, X_test = train_test_split(X, test_size=0.2, random_state=42)

n_components_range = range(1, 10)
errors = []
for n_components in n_components_range:
    rmse = compute_nmf_rmse(n_components)
    errors.append(rmse)

plt.plot(n_components_range, errors)
plt.xlabel('Number of Components')
plt.ylabel('RMSE')
plt.title('NMF Component Selection')
plt.show()