clustering/hdbscan_clustering.py

   1 from clustering_base import sim_to_dist, process_clustering_result, clustering_result, read_similarity_mat
   2 from dataclasses import dataclass
   3 import hdbscan
   4 from sklearn.neighbors import NearestNeighbors
   5 import plotnine as pn
   6 import numpy as np
   7 from itertools import product, starmap
   8 import pandas as pd
   9 from sklearn.metrics import silhouette_score, silhouette_samples
  10 from pathlib import Path
  11 from multiprocessing import Pool, cpu_count
  12 import fire
  13 from pyarrow.feather import write_feather
  14
  15 def test_select_hdbscan_clustering():
  16     select_hdbscan_clustering("/gscratch/comdata/output/reddit_similarity/subreddit_comment_authors-tf_30k_LSI",
  17                               "test_hdbscan_author30k",
  18                               min_cluster_sizes=[2],
  19                               min_samples=[1,2],
  20                               cluster_selection_epsilons=[0,0.05,0.1,0.15],
  21                               cluster_selection_methods=['eom','leaf'],
  22                               lsi_dimensions='all')
  23     inpath = "/gscratch/comdata/output/reddit_similarity/subreddit_comment_authors-tf_30k_LSI"
  24     outpath = "test_hdbscan";
  25     min_cluster_sizes=[2,3,4];
  26     min_samples=[1,2,3];
  27     cluster_selection_epsilons=[0,0.1,0.3,0.5];
  28     cluster_selection_methods=['eom'];
  29     lsi_dimensions='all'
  30
  31 @dataclass
  32 class hdbscan_clustering_result(clustering_result):
  33     min_cluster_size:int
  34     min_samples:int
  35     cluster_selection_epsilon:float
  36     cluster_selection_method:str
  37     lsi_dimensions:int
  38     n_isolates:int
  39     silhouette_samples:str
  40
  41 def select_hdbscan_clustering(inpath,
  42                               outpath,
  43                               outfile=None,
  44                               min_cluster_sizes=[2],
  45                               min_samples=[1],
  46                               cluster_selection_epsilons=[0],
  47                               cluster_selection_methods=['eom'],
  48                               lsi_dimensions='all'
  49                               ):
  50
  51     inpath = Path(inpath)
  52     outpath = Path(outpath)
  53     outpath.mkdir(exist_ok=True, parents=True)
  54
  55     if lsi_dimensions == 'all':
  56         lsi_paths = list(inpath.glob("*"))
  57
  58     else:
  59         lsi_paths = [inpath / (dim + '.feather') for dim in lsi_dimensions]
  60
  61     lsi_nums = [p.stem for p in lsi_paths]
  62     grid = list(product(lsi_nums,
  63                         min_cluster_sizes,
  64                         min_samples,
  65                         cluster_selection_epsilons,
  66                         cluster_selection_methods))
  67
  68     # fix the output file names
  69     names = list(map(lambda t:'_'.join(map(str,t)),grid))
  70
  71     grid = [(inpath/(str(t[0])+'.feather'),outpath/(name + '.feather'), t[0], name) + t[1:] for t, name in zip(grid, names)]
  72
  73     with Pool(int(cpu_count()/4)) as pool:
  74         mods = starmap(hdbscan_clustering, grid)
  75
  76     res = pd.DataFrame(mods)
  77     if outfile is None:
  78         outfile = outpath / "selection_data.csv"
  79
  80     res.to_csv(outfile)
  81
  82 def hdbscan_clustering(similarities, output, lsi_dim, name, min_cluster_size=2, min_samples=1, cluster_selection_epsilon=0, cluster_selection_method='eom'):
  83     subreddits, mat = read_similarity_mat(similarities)
  84     mat = sim_to_dist(mat)
  85     clustering = _hdbscan_clustering(mat,
  86                                      min_cluster_size=min_cluster_size,
  87                                      min_samples=min_samples,
  88                                      cluster_selection_epsilon=cluster_selection_epsilon,
  89                                      cluster_selection_method=cluster_selection_method,
  90                                      metric='precomputed',
  91                                      core_dist_n_jobs=cpu_count()
  92                                      )
  93
  94     cluster_data = process_clustering_result(clustering, subreddits)
  95     isolates = clustering.labels_ == -1
  96     scoremat = mat[~isolates][:,~isolates]
  97     score = silhouette_score(scoremat, clustering.labels_[~isolates], metric='precomputed')
  98     cluster_data.to_feather(output)
  99
 100     silhouette_samp = silhouette_samples(mat, clustering.labels_, metric='precomputed')
 101     silhouette_samp = pd.DataFrame({'subreddit':subreddits,'score':silhouette_samp})
 102     silsampout = output.parent / ("silhouette_samples" + output.name)
 103     silhouette_samp.to_feather(silsampout)
 104
 105     result = hdbscan_clustering_result(outpath=output,
 106                                        max_iter=None,
 107                                        silhouette_samples=silsampout,
 108                                        silhouette_score=score,
 109                                        alt_silhouette_score=score,
 110                                        name=name,
 111                                        min_cluster_size=min_cluster_size,
 112                                        min_samples=min_samples,
 113                                        cluster_selection_epsilon=cluster_selection_epsilon,
 114                                        cluster_selection_method=cluster_selection_method,
 115                                        lsi_dimensions=lsi_dim,
 116                                        n_isolates=isolates.sum(),
 117                                        n_clusters=len(set(clustering.labels_))
 118                                    )
 119
 120
 121
 122     return(result)
 123
 124 # for all runs we should try cluster_selection_epsilon = None
 125 # for terms we should try cluster_selection_epsilon around 0.56-0.66
 126 # for authors we should try cluster_selection_epsilon around 0.98-0.99
 127 def _hdbscan_clustering(mat, *args, **kwargs):
 128     print(f"running hdbscan clustering. args:{args}. kwargs:{kwargs}")
 129
 130     print(mat)
 131     clusterer = hdbscan.HDBSCAN(*args,
 132                                 **kwargs,
 133                                 )
 134
 135     clustering = clusterer.fit(mat.astype('double'))
 136
 137     return(clustering)
 138
 139 def KNN_distances_plot(mat,outname,k=2):
 140     nbrs = NearestNeighbors(n_neighbors=k,algorithm='auto',metric='precomputed').fit(mat)
 141     distances, indices = nbrs.kneighbors(mat)
 142     d2 = distances[:,-1]
 143     df = pd.DataFrame({'dist':d2})
 144     df = df.sort_values("dist",ascending=False)
 145     df['idx'] = np.arange(0,d2.shape[0]) + 1
 146     p = pn.qplot(x='idx',y='dist',data=df,geom='line') + pn.scales.scale_y_continuous(minor_breaks = np.arange(0,50)/50,
 147                                                                                       breaks = np.arange(0,10)/10)
 148     p.save(outname,width=16,height=10)
 149
 150 def make_KNN_plots():
 151     similarities = "/gscratch/comdata/output/reddit_similarity/subreddit_comment_terms_10k.feather"
 152     subreddits, mat = read_similarity_mat(similarities)
 153     mat = sim_to_dist(mat)
 154
 155     KNN_distances_plot(mat,k=2,outname='terms_knn_dist2.png')
 156
 157     similarities = "/gscratch/comdata/output/reddit_similarity/subreddit_comment_authors_10k.feather"
 158     subreddits, mat = read_similarity_mat(similarities)
 159     mat = sim_to_dist(mat)
 160     KNN_distances_plot(mat,k=2,outname='authors_knn_dist2.png')
 161
 162     similarities = "/gscratch/comdata/output/reddit_similarity/subreddit_comment_authors-tf_10k.feather"
 163     subreddits, mat = read_similarity_mat(similarities)
 164     mat = sim_to_dist(mat)
 165     KNN_distances_plot(mat,k=2,outname='authors-tf_knn_dist2.png')
 166
 167 if __name__ == "__main__":
 168     df = pd.read_csv("test_hdbscan/selection_data.csv")
 169     test_select_hdbscan_clustering()
 170     check_clusters = pd.read_feather("test_hdbscan/500_2_2_0.1_eom.feather")
 171     silscores = pd.read_feather("test_hdbscan/silhouette_samples500_2_2_0.1_eom.feather")
 172     c = check_clusters.merge(silscores,on='subreddit')#    fire.Fire(select_hdbscan_clustering)