<｜begin▁of▁sentence｜>### Instruction:
As an agronomist focusing on optimizing agricultural practices for sustainable crop production, imagine you're tasked with analyzing the interactions between different crop species in a diverse farming system. You have a network representation of these interactions stored in a GML file named "crop_interactions.gml". Using the greedy_modularity function from the CDlib library, your objective is to identify distinct communities within the network, each representing a group of crops that tend to interact more frequently with each other. Furthermore, you need to assess the purity of these communities by randomly assigning each crop a label indicating its type (e.g., grains, legumes, fruits) and then evaluating how well the detected communities align with these random labels.
### Response:
from cdlib import algorithms, evaluation
import networkx as nx
import random

# Read the polbooks graph
G = nx.read_gml('polbooks.gml')

# Apply a community detection algorithm (e.g., greedy_modularity)
communities = algorithms.greedy_modularity(G)

# Suppose we have predefined labels for the books (dummy example)
labels = {node: {'genre': random.choice(['Politics', 'History', 'Economics'])} for node in G.nodes()}

# Evaluate the purity of the detected communities
pur = evaluation.purity(communities, labels)

print(pur.score)
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