]> code.communitydata.science - cdsc_reddit.git/blob - clustering/clustering.py
Merge branch 'master' of code:cdsc_reddit
[cdsc_reddit.git] / 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
10 def read_similarity_mat(similarities, use_threads=True):
11     df = pd.read_feather(similarities, use_threads=use_threads)
12     mat = np.array(df.drop('_subreddit',1))
13     n = mat.shape[0]
14     mat[range(n),range(n)] = 1
15     return (df._subreddit,mat)
16
17 def affinity_clustering(similarities, *args, **kwargs):
18     subreddits, mat = read_similarity_mat(similarities)
19     return _affinity_clustering(mat, subreddits, *args, **kwargs)
20
21 def _affinity_clustering(mat, subreddits, output, damping=0.9, max_iter=100000, convergence_iter=30, preference_quantile=0.5, random_state=1968, verbose=True):
22     '''
23     similarities: feather file with a dataframe of similarity scores
24     preference_quantile: parameter controlling how many clusters to make. higher values = more clusters. 0.85 is a good value with 3000 subreddits.
25     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. 
26     '''
27     print(f"damping:{damping}; convergenceIter:{convergence_iter}; preferenceQuantile:{preference_quantile}")
28
29     preference = np.quantile(mat,preference_quantile)
30
31     print(f"preference is {preference}")
32     print("data loaded")
33     sys.stdout.flush()
34     clustering = AffinityPropagation(damping=damping,
35                                      max_iter=max_iter,
36                                      convergence_iter=convergence_iter,
37                                      copy=False,
38                                      preference=preference,
39                                      affinity='precomputed',
40                                      verbose=verbose,
41                                      random_state=random_state).fit(mat)
42
43
44     print(f"clustering took {clustering.n_iter_} iterations")
45     clusters = clustering.labels_
46
47     print(f"found {len(set(clusters))} clusters")
48
49     cluster_data = pd.DataFrame({'subreddit': subreddits,'cluster':clustering.labels_})
50
51     cluster_sizes = cluster_data.groupby("cluster").count()
52     print(f"the largest cluster has {cluster_sizes.subreddit.max()} members")
53
54     print(f"the median cluster has {cluster_sizes.subreddit.median()} members")
55
56     print(f"{(cluster_sizes.subreddit==1).sum()} clusters have 1 member")
57
58     sys.stdout.flush()
59     cluster_data.to_feather(output)
60     print(f"saved {output}")
61     return clustering
62
63 if __name__ == "__main__":
64     fire.Fire(affinity_clustering)

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