from sklearn.manifold import SpectralEmbedding
model = SpectralEmbedding(n_components = 2, n_neighbors = 50)
se = model.fit_transform(np.log(X_train + 1))
plt.figure(figsize=(20,15))
plt.scatter(se[:, 0], se[:, 1], c = y_train.astype(int), cmap = 'tab10', s = 50)
plt.title('Laplacian Eigenmap', fontsize = 20)
plt.xlabel("LAP1", fontsize = 20)
plt.ylabel("LAP2", fontsize = 20)
plt.show()