clustering/clustering.py

   1 #!/usr/bin/env python3
   2
   3 import pandas as pd
   4 import numpy as np
   5 from sklearn.cluster import AffinityPropagation
   6 import fire
   7
   8 def affinity_clustering(similarities, output, damping=0.9, max_iter=100000, convergence_iter=30, preference_quantile=0.5, random_state=1968, verbose=True):
   9     '''
  10     similarities: feather file with a dataframe of similarity scores
  11     preference_quantile: parameter controlling how many clusters to make. higher values = more clusters. 0.85 is a good value with 3000 subreddits.
  12     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.
  13     '''
  14
  15     df = pd.read_feather(similarities)
  16     n = df.shape[0]
  17     mat = np.array(df.drop('_subreddit',1))
  18     mat[range(n),range(n)] = 1
  19     assert(all(np.diag(mat)==1))
  20
  21     preference = np.quantile(mat,preference_quantile)
  22
  23     print(f"preference is {preference}")
  24
  25     print("data loaded")
  26
  27     clustering = AffinityPropagation(damping=damping,
  28                                      max_iter=max_iter,
  29                                      convergence_iter=convergence_iter,
  30                                      copy=False,
  31                                      preference=preference,
  32                                      affinity='precomputed',
  33                                      verbose=verbose,
  34                                      random_state=random_state).fit(mat)
  35
  36
  37     print(f"clustering took {clustering.n_iter_} iterations")
  38     clusters = clustering.labels_
  39
  40     print(f"found {len(set(clusters))} clusters")
  41
  42     cluster_data = pd.DataFrame({'subreddit': df.subreddit,'cluster':clustering.labels_})
  43
  44     cluster_sizes = cluster_data.groupby("cluster").count()
  45     print(f"the largest cluster has {cluster_sizes.subreddit.max()} members")
  46
  47     print(f"the median cluster has {cluster_sizes.subreddit.median()} members")
  48
  49     print(f"{(cluster_sizes.subreddit==1).sum()} clusters have 1 member")
  50
  51     cluster_data.to_feather(output)
  52
  53 if __name__ == "__main__":
  54     fire.Fire(affinity_clustering)