clustering.py

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