53 lines
2.2 KiB
Python
Executable File
53 lines
2.2 KiB
Python
Executable File
#!/usr/bin/env python3
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# TODO: replace prints with logging.
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import sys
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import pandas as pd
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import numpy as np
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from sklearn.cluster import AffinityPropagation
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import fire
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from pathlib import Path
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from multiprocessing import cpu_count
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from dataclasses import dataclass
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from clustering_base import sim_to_dist, process_clustering_result, clustering_result, read_similarity_mat
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def affinity_clustering(similarities, output, *args, **kwargs):
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subreddits, mat = read_similarity_mat(similarities)
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clustering = _affinity_clustering(mat, *args, **kwargs)
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cluster_data = process_clustering_result(clustering, subreddits)
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cluster_data['algorithm'] = 'affinity'
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return(cluster_data)
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def _affinity_clustering(mat, subreddits, output, damping=0.9, max_iter=100000, convergence_iter=30, preference_quantile=0.5, random_state=1968, verbose=True):
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'''
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similarities: matrix of similarity scores
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preference_quantile: parameter controlling how many clusters to make. higher values = more clusters. 0.85 is a good value with 3000 subreddits.
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damping: parameter controlling how iterations are merged. Higher values make convergence faster and more dependable. 0.85 is a good value for the 10000 subreddits by author.
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'''
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print(f"damping:{damping}; convergenceIter:{convergence_iter}; preferenceQuantile:{preference_quantile}")
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preference = np.quantile(mat,preference_quantile)
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print(f"preference is {preference}")
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print("data loaded")
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sys.stdout.flush()
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clustering = AffinityPropagation(damping=damping,
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max_iter=max_iter,
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convergence_iter=convergence_iter,
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copy=False,
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preference=preference,
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affinity='precomputed',
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verbose=verbose,
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random_state=random_state).fit(mat)
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cluster_data = process_clustering_result(clustering, subreddits)
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output = Path(output)
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output.parent.mkdir(parents=True,exist_ok=True)
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cluster_data.to_feather(output)
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print(f"saved {output}")
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return clustering
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if __name__ == "__main__":
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fire.Fire(affinity_clustering)
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