<｜begin▁of▁sentence｜>### Instruction:
Given the US politics books Graph which you can read from a gml file named polbooks.gml, can you use both the Louvain and Leiden community detection algorithms to identify communities, and then compute the Normalized F1 score to compare the similarity between the community partitions generated by these two algorithms?

Notes: You need to print the Normalized F1 score that evaluates the similarity between the two partitions detected.
### Response:
import networkx as nx
from cdlib import algorithms, evaluation

# Read the graph of books about US politics
G = nx.read_gml('polbooks.gml')

# Detect communities using the Louvain and Leiden algorithms
louvain_communities = algorithms.louvain(G)
leiden_communities = algorithms.leiden(G)

# Compute the Normalized F1 score between the two partitions
nf1_score = evaluation.nf1(louvain_communities, leiden_communities)

# Print the Normalized F1 score
print(nf1_score.score)
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