clustering/clustering_base.py

   1 from pathlib import Path
   2 import numpy as np
   3 import pandas as pd
   4 from dataclasses import dataclass
   5 from sklearn.metrics import silhouette_score, silhouette_samples
   6 from collections import Counter
   7
   8 # this is meant to be an interface, not created directly
   9 class clustering_job:
  10     def __init__(self, infile, outpath, name, call, *args, **kwargs):
  11         self.outpath = Path(outpath)
  12         self.call = call
  13         self.args = args
  14         self.kwargs = kwargs
  15         self.infile = Path(infile)
  16         self.name = name
  17         self.hasrun = False
  18
  19     def run(self):
  20         self.subreddits, self.mat = self.read_distance_mat(self.infile)
  21         self.clustering = self.call(self.mat, *self.args, **self.kwargs)
  22         self.cluster_data = self.process_clustering(self.clustering, self.subreddits)
  23         self.score = self.silhouette()
  24         self.outpath.mkdir(parents=True, exist_ok=True)
  25         self.cluster_data.to_feather(self.outpath/(self.name + ".feather"))
  26         self.hasrun = True
  27
  28     def get_info(self):
  29         if not self.hasrun:
  30             self.run()
  31
  32         self.result = clustering_result(outpath=str(self.outpath.resolve()),
  33                                         silhouette_score=self.score,
  34                                         name=self.name,
  35                                         n_clusters=self.n_clusters,
  36                                         n_isolates=self.n_isolates,
  37                                         silhouette_samples = self.silsampout
  38                                         )
  39         return self.result
  40
  41     def silhouette(self):
  42         counts = Counter(self.clustering.labels_)
  43         singletons = [key for key, value in counts.items() if value == 1]
  44         isolates = (self.clustering.labels_ == -1) | (np.isin(self.clustering.labels_,np.array(singletons)))
  45         scoremat = self.mat[~isolates][:,~isolates]
  46         if self.n_clusters > 1:
  47             score = silhouette_score(scoremat, self.clustering.labels_[~isolates], metric='precomputed')
  48             silhouette_samp = silhouette_samples(self.mat, self.clustering.labels_, metric='precomputed')
  49             silhouette_samp = pd.DataFrame({'subreddit':self.subreddits,'score':silhouette_samp})
  50             self.outpath.mkdir(parents=True, exist_ok=True)
  51             silsampout = self.outpath / ("silhouette_samples-" + self.name +  ".feather")
  52             self.silsampout = silsampout.resolve()
  53             silhouette_samp.to_feather(self.silsampout)
  54         else:
  55             score = None
  56             self.silsampout = None
  57         return score
  58
  59     def read_distance_mat(self, similarities, use_threads=True):
  60         df = pd.read_feather(similarities, use_threads=use_threads)
  61         mat = np.array(df.drop('_subreddit',1))
  62         n = mat.shape[0]
  63         mat[range(n),range(n)] = 1
  64         return (df._subreddit,1-mat)
  65
  66     def process_clustering(self, clustering, subreddits):
  67
  68         if hasattr(clustering,'n_iter_'):
  69             print(f"clustering took {clustering.n_iter_} iterations")
  70
  71         clusters = clustering.labels_
  72         self.n_clusters = len(set(clusters))
  73
  74         print(f"found {self.n_clusters} clusters")
  75
  76         cluster_data = pd.DataFrame({'subreddit': subreddits,'cluster':clustering.labels_})
  77
  78         cluster_sizes = cluster_data.groupby("cluster").count().reset_index()
  79         print(f"the largest cluster has {cluster_sizes.loc[cluster_sizes.cluster!=-1].subreddit.max()} members")
  80
  81         print(f"the median cluster has {cluster_sizes.subreddit.median()} members")
  82         n_isolates1 = (cluster_sizes.subreddit==1).sum()
  83
  84         print(f"{n_isolates1} clusters have 1 member")
  85
  86         n_isolates2 = cluster_sizes.loc[cluster_sizes.cluster==-1,:]['subreddit'].to_list()
  87         if len(n_isolates2) > 0:
  88             n_isloates2 = n_isolates2[0]
  89         print(f"{n_isolates2} subreddits are in cluster -1",flush=True)
  90
  91         if n_isolates1 == 0:
  92             self.n_isolates = n_isolates2
  93         else:
  94             self.n_isolates = n_isolates1
  95
  96         return cluster_data
  97
  98 @dataclass
  99 class clustering_result:
 100     outpath:Path
 101     silhouette_score:float
 102     name:str
 103     n_clusters:int
 104     n_isolates:int
 105     silhouette_samples:str