clustering/umap_hdbscan_clustering.py

   1 from clustering_base import clustering_result, clustering_job, twoway_clustering_job
   2 from hdbscan_clustering import hdbscan_clustering_result
   3 import umap
   4 from grid_sweep import twoway_grid_sweep
   5 from dataclasses import dataclass
   6 import hdbscan
   7 from sklearn.neighbors import NearestNeighbors
   8 import plotnine as pn
   9 import numpy as np
  10 from itertools import product, starmap, chain
  11 import pandas as pd
  12 from multiprocessing import cpu_count
  13 import fire
  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_10k_LSI"
  24     outpath = "test_umap_hdbscan_lsi"
  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=[1]
  29     lsi_dimensions='all'
  30     n_neighbors = [5,10,15,25,35,70,100]
  31     learning_rate = [0.1,0.5,1,2]
  32     min_dist = [0.5,1,1.5,2]
  33     local_connectivity = [1,2,3,4,5]
  34
  35     hdbscan_params = {"min_cluster_sizes":min_cluster_sizes, "min_samples":min_samples, "cluster_selection_epsilons":cluster_selection_epsilons, "cluster_selection_methods":cluster_selection_methods}
  36     umap_params = {"n_neighbors":n_neighbors, "learning_rate":learning_rate, "min_dist":min_dist, "local_connectivity":local_connectivity}
  37     gs = umap_hdbscan_grid_sweep(inpath, "all", outpath, hdbscan_params,umap_params)
  38
  39     # gs.run(20)
  40     # gs.save("test_hdbscan/lsi_sweep.csv")
  41
  42
  43     # job1 = hdbscan_lsi_job(infile=inpath, outpath=outpath, name="test", lsi_dims=500, min_cluster_size=2, min_samples=1,cluster_selection_epsilon=0,cluster_selection_method='eom')
  44     # job1.run()
  45     # print(job1.get_info())
  46
  47     # df = pd.read_csv("test_hdbscan/selection_data.csv")
  48     # test_select_hdbscan_clustering()
  49     # check_clusters = pd.read_feather("test_hdbscan/500_2_2_0.1_eom.feather")
  50     # silscores = pd.read_feather("test_hdbscan/silhouette_samples500_2_2_0.1_eom.feather")
  51     # c = check_clusters.merge(silscores,on='subreddit')#    fire.Fire(select_hdbscan_clustering)
  52 class umap_hdbscan_grid_sweep(twoway_grid_sweep):
  53     def __init__(self,
  54                  inpath,
  55                  outpath,
  56                  umap_params,
  57                  hdbscan_params):
  58
  59         super().__init__(umap_hdbscan_job, inpath, outpath, self.namer, umap_params, hdbscan_params)
  60
  61     def namer(self,
  62               min_cluster_size,
  63               min_samples,
  64               cluster_selection_epsilon,
  65               cluster_selection_method,
  66               n_neighbors,
  67               learning_rate,
  68               min_dist,
  69               local_connectivity
  70               ):
  71         return f"mcs-{min_cluster_size}_ms-{min_samples}_cse-{cluster_selection_epsilon}_csm-{cluster_selection_method}_nn-{n_neighbors}_lr-{learning_rate}_md-{min_dist}_lc-{local_connectivity}"
  72
  73 @dataclass
  74 class umap_hdbscan_clustering_result(hdbscan_clustering_result):
  75     n_neighbors:int
  76     learning_rate:float
  77     min_dist:float
  78     local_connectivity:int
  79
  80 class umap_hdbscan_job(twoway_clustering_job):
  81     def __init__(self, infile, outpath, name,
  82                  umap_args = {"n_neighbors":15, "learning_rate":1, "min_dist":1, "local_connectivity":1},
  83                  hdbscan_args = {"min_cluster_size":2, "min_samples":1, "cluster_selection_epsilon":0, "cluster_selection_method":'eom'},
  84                  save_step1 = False,
  85                  *args,
  86                  **kwargs):
  87         super().__init__(infile,
  88                          outpath,
  89                          name,
  90                          call1=umap_hdbscan_job._umap_embedding,
  91                          call2=umap_hdbscan_job._hdbscan_clustering,
  92                          args1=umap_args,
  93                          args2=hdbscan_args,
  94                          save_step1=save_step1,
  95                          *args,
  96                          **kwargs
  97                          )
  98
  99         self.n_neighbors = umap_args['n_neighbors']
 100         self.learning_rate = umap_args['learning_rate']
 101         self.min_dist = umap_args['min_dist']
 102         self.local_connectivity = umap_args['local_connectivity']
 103         self.min_cluster_size = hdbscan_args['min_cluster_size']
 104         self.min_samples = hdbscan_args['min_samples']
 105         self.cluster_selection_epsilon = hdbscan_args['cluster_selection_epsilon']
 106         self.cluster_selection_method = hdbscan_args['cluster_selection_method']
 107
 108     def after_run(self):
 109         coords = self.step1.emedding_
 110         self.cluster_data['x'] = coords[:,0]
 111         self.cluster_data['y'] = coords[:,1]
 112         super().after_run()
 113
 114
 115     def _umap_embedding(mat, **umap_args):
 116         print(f"running umap embedding. umap_args:{umap_args}")
 117         umapmodel = umap.UMAP(metric='precomputed', **umap_args)
 118         umapmodel = umapmodel.fit(mat)
 119         return umapmodel
 120
 121     def _hdbscan_clustering(mat, umapmodel, **hdbscan_args):
 122         print(f"running hdbascan clustering. hdbscan_args:{hdbscan_args}")
 123
 124         umap_coords = umapmodel.transform(mat)
 125
 126         clusterer = hdbscan.HDBSCAN(metric='euclidean',
 127                                     core_dist_n_jobs=cpu_count(),
 128                                     **hdbscan_args
 129                                     )
 130
 131         clustering = clusterer.fit(umap_coords)
 132
 133         return(clustering)
 134
 135     def get_info(self):
 136         result = super().get_info()
 137         self.result = umap_hdbscan_clustering_result(**result.__dict__,
 138                                                      min_cluster_size=self.min_cluster_size,
 139                                                      min_samples=self.min_samples,
 140                                                      cluster_selection_epsilon=self.cluster_selection_epsilon,
 141                                                      cluster_selection_method=self.cluster_selection_method,
 142                                                      n_neighbors = self.n_neighbors,
 143                                                      learning_rate = self.learning_rate,
 144                                                      min_dist = self.min_dist,
 145                                                      local_connectivity=self.local_connectivity
 146                                                      )
 147         return self.result
 148
 149 def run_umap_hdbscan_grid_sweep(savefile, inpath, outpath, n_neighbors = [15], learning_rate=[1], min_dist=[1], local_connectivity=[1],
 150                                 min_cluster_sizes=[2], min_samples=[1], cluster_selection_epsilons=[0], cluster_selection_methods=['eom']):
 151     """Run umap + hdbscan clustering once or more with different parameters.
 152
 153     Usage:
 154     umap_hdbscan_clustering.py --savefile=SAVEFILE --inpath=INPATH --outpath=OUTPATH --n_neighbors=<csv> --learning_rate=<csv> --min_dist=<csv> --local_connectivity=<csv> --min_cluster_sizes=<csv> --min_samples=<csv> --cluster_selection_epsilons=<csv> --cluster_selection_methods=<csv "eom"|"leaf">
 155
 156     Keword arguments:
 157     savefile: path to save the metadata and diagnostics
 158     inpath: path to feather data containing a labeled matrix of subreddit similarities.
 159     outpath: path to output fit kmeans clusterings.
 160     n_neighbors: umap parameter takes integers greater than 1
 161     learning_rate: umap parameter takes positive real values
 162     min_dist: umap parameter takes positive real values
 163     local_connectivity: umap parameter takes positive integers
 164     min_cluster_sizes: one or more integers indicating the minumum cluster size
 165     min_samples: one ore more integers indicating the minimum number of samples used in the algorithm
 166     cluster_selection_epsilon: one or more similarity thresholds for transition from dbscan to hdbscan
 167     cluster_selection_method: "eom" or "leaf" eom gives larger clusters.
 168     """
 169
 170     umap_args = {'n_neighbors':list(map(int, n_neighbors)),
 171                  'learning_rate':list(map(float,learning_rate)),
 172                  'min_dist':list(map(float,min_dist)),
 173                  'local_connectivity':list(map(int,local_connectivity)),
 174                  }
 175
 176     hdbscan_args = {'min_cluster_size':list(map(int,min_cluster_sizes)),
 177                     'min_samples':list(map(int,min_samples)),
 178                     'cluster_selection_epsilon':list(map(float,cluster_selection_epsilons)),
 179                     'cluster_selection_method':cluster_selection_methods}
 180
 181     obj = umap_hdbscan_grid_sweep(inpath,
 182                                   outpath,
 183                                   umap_args,
 184                                   hdbscan_args)
 185     obj.run(cores=10)
 186     obj.save(savefile)
 187
 188
 189 def KNN_distances_plot(mat,outname,k=2):
 190     nbrs = NearestNeighbors(n_neighbors=k,algorithm='auto',metric='precomputed').fit(mat)
 191     distances, indices = nbrs.kneighbors(mat)
 192     d2 = distances[:,-1]
 193     df = pd.DataFrame({'dist':d2})
 194     df = df.sort_values("dist",ascending=False)
 195     df['idx'] = np.arange(0,d2.shape[0]) + 1
 196     p = pn.qplot(x='idx',y='dist',data=df,geom='line') + pn.scales.scale_y_continuous(minor_breaks = np.arange(0,50)/50,
 197                                                                                       breaks = np.arange(0,10)/10)
 198     p.save(outname,width=16,height=10)
 199
 200 def make_KNN_plots():
 201     similarities = "/gscratch/comdata/output/reddit_similarity/subreddit_comment_terms_10k.feather"
 202     subreddits, mat = read_similarity_mat(similarities)
 203     mat = sim_to_dist(mat)
 204
 205     KNN_distances_plot(mat,k=2,outname='terms_knn_dist2.png')
 206
 207     similarities = "/gscratch/comdata/output/reddit_similarity/subreddit_comment_authors_10k.feather"
 208     subreddits, mat = read_similarity_mat(similarities)
 209     mat = sim_to_dist(mat)
 210     KNN_distances_plot(mat,k=2,outname='authors_knn_dist2.png')
 211
 212     similarities = "/gscratch/comdata/output/reddit_similarity/subreddit_comment_authors-tf_10k.feather"
 213     subreddits, mat = read_similarity_mat(similarities)
 214     mat = sim_to_dist(mat)
 215     KNN_distances_plot(mat,k=2,outname='authors-tf_knn_dist2.png')
 216
 217 if __name__ == "__main__":
 218     fire.Fire(run_umap_hdbscan_grid_sweep)
 219
 220 #    test_select_hdbscan_clustering()
 221     #fire.Fire(select_hdbscan_clustering)