clustering/clustering.py

   1 #!/usr/bin/env python3
   2 # TODO: replace prints with logging.
   3 import sys
   4 import pandas as pd
   5 import numpy as np
   6 from sklearn.cluster import AffinityPropagation
   7 import fire
   8 from pathlib import Path
   9 from multiprocessing import cpu_count
  10 from dataclasses import dataclass
  11 from clustering_base import sim_to_dist, process_clustering_result, clustering_result, read_similarity_mat
  12
  13 def affinity_clustering(similarities, output, *args, **kwargs):
  14     subreddits, mat = read_similarity_mat(similarities)
  15     clustering = _affinity_clustering(mat, *args, **kwargs)
  16     cluster_data = process_clustering_result(clustering, subreddits)
  17     cluster_data['algorithm'] = 'affinity'
  18     return(cluster_data)
  19
  20 def _affinity_clustering(mat, subreddits, output, damping=0.9, max_iter=100000, convergence_iter=30, preference_quantile=0.5, random_state=1968, verbose=True):
  21     '''
  22     similarities: matrix of similarity scores
  23     preference_quantile: parameter controlling how many clusters to make. higher values = more clusters. 0.85 is a good value with 3000 subreddits.
  24     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.
  25     '''
  26     print(f"damping:{damping}; convergenceIter:{convergence_iter}; preferenceQuantile:{preference_quantile}")
  27
  28     preference = np.quantile(mat,preference_quantile)
  29
  30     print(f"preference is {preference}")
  31     print("data loaded")
  32     sys.stdout.flush()
  33     clustering = AffinityPropagation(damping=damping,
  34                                      max_iter=max_iter,
  35                                      convergence_iter=convergence_iter,
  36                                      copy=False,
  37                                      preference=preference,
  38                                      affinity='precomputed',
  39                                      verbose=verbose,
  40                                      random_state=random_state).fit(mat)
  41
  42     cluster_data = process_clustering_result(clustering, subreddits)
  43     output = Path(output)
  44     output.parent.mkdir(parents=True,exist_ok=True)
  45     cluster_data.to_feather(output)
  46     print(f"saved {output}")
  47     return clustering
  48
  49
  50
  51 if __name__ == "__main__":
  52     fire.Fire(affinity_clustering)