]> 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
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
20     preference = np.quantile(mat,preference_quantile)
21
22     print(f"preference is {preference}")
23
24     print("data loaded")
25
26     clustering = AffinityPropagation(damping=damping,
27                                      max_iter=max_iter,
28                                      convergence_iter=convergence_iter,
29                                      copy=False,
30                                      preference=preference,
31                                      affinity='precomputed',
32                                      verbose=verbose,
33                                      random_state=random_state).fit(mat)
34
35
36     print(f"clustering took {clustering.n_iter_} iterations")
37     clusters = clustering.labels_
38
39     print(f"found {len(set(clusters))} clusters")
40
41     cluster_data = pd.DataFrame({'subreddit': df.subreddit,'cluster':clustering.labels_})
42
43     cluster_sizes = cluster_data.groupby("cluster").count()
44     print(f"the largest cluster has {cluster_sizes.subreddit.max()} members")
45
46     print(f"the median cluster has {cluster_sizes.subreddit.median()} members")
47
48     print(f"{(cluster_sizes.subreddit==1).sum()} clusters have 1 member")
49
50     cluster_data.to_feather(output)
51
52 if __name__ == "__main__":
53     fire.Fire(affinity_clustering)

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