clustering/selection.py

   1 from sklearn.metrics import silhouette_score
   2 from sklearn.cluster import AffinityPropagation
   3 from functools import partial
   4 from clustering import _affinity_clustering, read_similarity_mat
   5 from dataclasses import dataclass
   6 from multiprocessing  import Pool, cpu_count, Array, Process
   7 from pathlib import Path
   8 from itertools import product, starmap
   9 import pandas as pd
  10 import fire
  11 import sys
  12
  13 # silhouette is the only one that doesn't need the feature matrix. So it's probably the only one that's worth trying.
  14
  15 @dataclass
  16 class clustering_result:
  17     outpath:Path
  18     damping:float
  19     max_iter:int
  20     convergence_iter:int
  21     preference_quantile:float
  22     silhouette_score:float
  23     alt_silhouette_score:float
  24     name:str
  25
  26 def do_clustering(damping, convergence_iter, preference_quantile, name, mat, subreddits,  max_iter,  outdir:Path, random_state, verbose, alt_mat):
  27     if name is None:
  28         name = f"damping-{damping}_convergenceIter-{convergence_iter}_preferenceQuantile-{convergence_iter}"
  29     print(name)
  30     sys.stdout.flush()
  31     outpath = outdir / (str(name) + ".feather")
  32     print(outpath)
  33     clustering = _affinity_clustering(mat, subreddits, outpath, damping, max_iter, convergence_iter, preference_quantile, random_state, verbose)
  34     score = silhouette_score(clustering.affinity_matrix_, clustering.labels_, metric='precomputed')
  35     alt_score = silhouette_score(alt_mat, clustering.labels_, metric='precomputed')
  36
  37     res = clustering_result(outpath=outpath,
  38                             damping=damping,
  39                             max_iter=max_iter,
  40                             convergence_iter=convergence_iter,
  41                             preference_quantile=preference_quantile,
  42                             silhouette_score=score,
  43                             alt_silhouette_score=score,
  44                             name=str(name))
  45
  46     return res
  47
  48 # alt similiarities is for checking the silhouette coefficient of an alternative measure of similarity (e.g., topic similarities for user clustering).
  49
  50 def select_affinity_clustering(similarities, outdir, damping=[0.9], max_iter=100000, convergence_iter=[30], preference_quantile=[0.5], random_state=1968, verbose=True, alt_similarities=None, J=None):
  51
  52     damping = list(map(float,damping))
  53     convergence_iter = convergence_iter = list(map(int,convergence_iter))
  54     preference_quantile = list(map(float,preference_quantile))
  55
  56     if type(outdir) is str:
  57         outdir = Path(outdir)
  58
  59     outdir.mkdir(parents=True,exist_ok=True)
  60
  61     subreddits, mat = read_similarity_mat(similarities,use_threads=True)
  62
  63     if alt_similarities is not None:
  64         alt_mat = read_similarity_mat(alt_similarities,use_threads=True)
  65     else:
  66         alt_mat = None
  67
  68     if J is None:
  69         J = cpu_count()
  70     pool = Pool(J)
  71
  72     # get list of tuples: the combinations of hyperparameters
  73     hyper_grid = product(damping, convergence_iter, preference_quantile)
  74     hyper_grid = (t + (str(i),) for i, t in enumerate(hyper_grid))
  75
  76     _do_clustering = partial(do_clustering,  mat=mat, subreddits=subreddits, outdir=outdir, max_iter=max_iter, random_state=random_state, verbose=verbose, alt_mat=alt_mat)
  77
  78     #    similarities = Array('d', mat)
  79     # call pool.starmap
  80     print("running clustering selection")
  81     clustering_data = pool.starmap(_do_clustering, hyper_grid)
  82     clustering_data = pd.DataFrame(list(clustering_data))
  83     return clustering_data
  84
  85
  86 if __name__ == "__main__":
  87     fire.Fire(select_affinity_clustering)