+from clustering_base import sim_to_dist, process_clustering_result, clustering_result, read_similarity_mat
+from clustering_base import lsi_result_mixin, lsi_mixin, clustering_job, grid_sweep, lsi_grid_sweep
+from dataclasses import dataclass
+import hdbscan
+from sklearn.neighbors import NearestNeighbors
+import plotnine as pn
+import numpy as np
+from itertools import product, starmap, chain
+import pandas as pd
+from sklearn.metrics import silhouette_score, silhouette_samples
+from pathlib import Path
+from multiprocessing import Pool, cpu_count
+import fire
+from pyarrow.feather import write_feather
+
+def test_select_hdbscan_clustering():
+ # select_hdbscan_clustering("/gscratch/comdata/output/reddit_similarity/subreddit_comment_authors-tf_30k_LSI",
+ # "test_hdbscan_author30k",
+ # min_cluster_sizes=[2],
+ # min_samples=[1,2],
+ # cluster_selection_epsilons=[0,0.05,0.1,0.15],
+ # cluster_selection_methods=['eom','leaf'],
+ # lsi_dimensions='all')
+ inpath = "/gscratch/comdata/output/reddit_similarity/subreddit_comment_authors-tf_10k_LSI/"
+ outpath = "test_hdbscan";
+ min_cluster_sizes=[2,3,4];
+ min_samples=[1,2,3];
+ cluster_selection_epsilons=[0,0.1,0.3,0.5];
+ cluster_selection_methods=['eom'];
+ lsi_dimensions='all'
+ gs = hdbscan_lsi_grid_sweep(inpath, "all", outpath, min_cluster_sizes, min_samples, cluster_selection_epsilons, cluster_selection_methods)
+ gs.run(20)
+ gs.save("test_hdbscan/lsi_sweep.csv")
+ # 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')
+ # job1.run()
+ # print(job1.get_info())
+
+ # df = pd.read_csv("test_hdbscan/selection_data.csv")
+ # test_select_hdbscan_clustering()
+ # check_clusters = pd.read_feather("test_hdbscan/500_2_2_0.1_eom.feather")
+ # silscores = pd.read_feather("test_hdbscan/silhouette_samples500_2_2_0.1_eom.feather")
+ # c = check_clusters.merge(silscores,on='subreddit')# fire.Fire(select_hdbscan_clustering)
+
+class hdbscan_lsi_grid_sweep(lsi_grid_sweep):
+ def __init__(self,
+ inpath,
+ lsi_dims,
+ outpath,
+ min_cluster_sizes,
+ min_samples,
+ cluster_selection_epsilons,
+ cluster_selection_methods
+ ):
+
+ super().__init__(hdbscan_lsi_job,
+ _hdbscan_lsi_grid_sweep,
+ inpath,
+ lsi_dims,
+ outpath,
+ min_cluster_sizes,
+ min_samples,
+ cluster_selection_epsilons,
+ cluster_selection_methods)
+
+class hdbscan_grid_sweep(grid_sweep):
+ def __init__(self,
+ inpath,
+ outpath,
+ *args,
+ **kwargs):
+
+ super().__init__(hdbscan_job, inpath, outpath, self.namer, *args, **kwargs)
+
+ def namer(self,
+ min_cluster_size,
+ min_samples,
+ cluster_selection_epsilon,
+ cluster_selection_method):
+ return f"mcs-{min_cluster_size}_ms-{min_samples}_cse-{cluster_selection_epsilon}_csm-{cluster_selection_method}"
+
+
+class _hdbscan_lsi_grid_sweep(grid_sweep):
+ def __init__(self,
+ inpath,
+ outpath,
+ lsi_dim,
+ *args,
+ **kwargs):
+
+ self.lsi_dim = lsi_dim
+ self.jobtype = hdbscan_lsi_job
+ super().__init__(self.jobtype, inpath, outpath, self.namer, self.lsi_dim, *args, **kwargs)
+
+
+ def namer(self, *args, **kwargs):
+ s = hdbscan_grid_sweep.namer(self, *args[1:], **kwargs)
+ s += f"_lsi-{self.lsi_dim}"
+ return s
+
+@dataclass
+class hdbscan_clustering_result(clustering_result):
+ min_cluster_size:int
+ min_samples:int
+ cluster_selection_epsilon:float
+ cluster_selection_method:str
+
+@dataclass
+class hdbscan_clustering_result_lsi(hdbscan_clustering_result, lsi_result_mixin):
+ pass
+
+class hdbscan_job(clustering_job):
+ def __init__(self, infile, outpath, name, min_cluster_size=2, min_samples=1, cluster_selection_epsilon=0, cluster_selection_method='eom'):
+ super().__init__(infile,
+ outpath,
+ name,
+ call=hdbscan_job._hdbscan_clustering,
+ min_cluster_size=min_cluster_size,
+ min_samples=min_samples,
+ cluster_selection_epsilon=cluster_selection_epsilon,
+ cluster_selection_method=cluster_selection_method
+ )
+
+ self.min_cluster_size = min_cluster_size
+ self.min_samples = min_samples
+ self.cluster_selection_epsilon = cluster_selection_epsilon
+ self.cluster_selection_method = cluster_selection_method
+# self.mat = 1 - self.mat
+
+ def _hdbscan_clustering(mat, *args, **kwargs):
+ print(f"running hdbscan clustering. args:{args}. kwargs:{kwargs}")
+ print(mat)
+ clusterer = hdbscan.HDBSCAN(metric='precomputed',
+ core_dist_n_jobs=cpu_count(),
+ *args,
+ **kwargs,
+ )
+
+ clustering = clusterer.fit(mat.astype('double'))
+
+ return(clustering)
+
+ def get_info(self):
+ result = super().get_info()
+ self.result = hdbscan_clustering_result(**result.__dict__,
+ min_cluster_size=self.min_cluster_size,
+ min_samples=self.min_samples,
+ cluster_selection_epsilon=self.cluster_selection_epsilon,
+ cluster_selection_method=self.cluster_selection_method)
+ return self.result
+
+class hdbscan_lsi_job(hdbscan_job, lsi_mixin):
+ def __init__(self, infile, outpath, name, lsi_dims, *args, **kwargs):
+ super().__init__(
+ infile,
+ outpath,
+ name,
+ *args,
+ **kwargs)
+ super().set_lsi_dims(lsi_dims)
+
+ def get_info(self):
+ partial_result = super().get_info()
+ self.result = hdbscan_clustering_result_lsi(**partial_result.__dict__,
+ lsi_dimensions=self.lsi_dims)
+ return self.result
+
+# def select_hdbscan_clustering(inpath,
+# outpath,
+# outfile=None,
+# min_cluster_sizes=[2],
+# min_samples=[1],
+# cluster_selection_epsilons=[0],
+# cluster_selection_methods=['eom'],
+# lsi_dimensions='all'
+# ):
+
+# inpath = Path(inpath)
+# outpath = Path(outpath)
+# outpath.mkdir(exist_ok=True, parents=True)
+
+# if lsi_dimensions is None:
+# lsi_paths = [inpath]
+# elif lsi_dimensions == 'all':
+# lsi_paths = list(inpath.glob("*"))
+
+# else:
+# lsi_paths = [inpath / (dim + '.feather') for dim in lsi_dimensions]
+
+# if lsi_dimensions is not None:
+# lsi_nums = [p.stem for p in lsi_paths]
+# else:
+# lsi_nums = [None]
+# grid = list(product(lsi_nums,
+# min_cluster_sizes,
+# min_samples,
+# cluster_selection_epsilons,
+# cluster_selection_methods))
+
+# # fix the output file names
+# names = list(map(lambda t:'_'.join(map(str,t)),grid))
+
+# grid = [(inpath/(str(t[0])+'.feather'),outpath/(name + '.feather'), t[0], name) + t[1:] for t, name in zip(grid, names)]
+
+# with Pool(int(cpu_count()/4)) as pool:
+# mods = starmap(hdbscan_clustering, grid)
+
+# res = pd.DataFrame(mods)
+# if outfile is None:
+# outfile = outpath / "selection_data.csv"
+
+# res.to_csv(outfile)
+
+# def hdbscan_clustering(similarities, output, lsi_dim, name, min_cluster_size=2, min_samples=1, cluster_selection_epsilon=0, cluster_selection_method='eom'):
+# subreddits, mat = read_similarity_mat(similarities)
+# mat = sim_to_dist(mat)
+# clustering = _hdbscan_clustering(mat,
+# min_cluster_size=min_cluster_size,
+# min_samples=min_samples,
+# cluster_selection_epsilon=cluster_selection_epsilon,
+# cluster_selection_method=cluster_selection_method,
+# metric='precomputed',
+# core_dist_n_jobs=cpu_count()
+# )
+
+# cluster_data = process_clustering_result(clustering, subreddits)
+# isolates = clustering.labels_ == -1
+# scoremat = mat[~isolates][:,~isolates]
+# score = silhouette_score(scoremat, clustering.labels_[~isolates], metric='precomputed')
+# cluster_data.to_feather(output)
+# silhouette_samp = silhouette_samples(mat, clustering.labels_, metric='precomputed')
+# silhouette_samp = pd.DataFrame({'subreddit':subreddits,'score':silhouette_samp})
+# silsampout = output.parent / ("silhouette_samples" + output.name)
+# silhouette_samp.to_feather(silsampout)
+
+# result = hdbscan_clustering_result(outpath=output,
+# silhouette_samples=silsampout,
+# silhouette_score=score,
+# name=name,
+# min_cluster_size=min_cluster_size,
+# min_samples=min_samples,
+# cluster_selection_epsilon=cluster_selection_epsilon,
+# cluster_selection_method=cluster_selection_method,
+# lsi_dimensions=lsi_dim,
+# n_isolates=isolates.sum(),
+# n_clusters=len(set(clustering.labels_))
+# )
+
+
+
+# return(result)
+
+# # for all runs we should try cluster_selection_epsilon = None
+# # for terms we should try cluster_selection_epsilon around 0.56-0.66
+# # for authors we should try cluster_selection_epsilon around 0.98-0.99
+# def _hdbscan_clustering(mat, *args, **kwargs):
+# print(f"running hdbscan clustering. args:{args}. kwargs:{kwargs}")
+
+# print(mat)
+# clusterer = hdbscan.HDBSCAN(*args,
+# **kwargs,
+# )
+
+# clustering = clusterer.fit(mat.astype('double'))
+
+# return(clustering)
+
+def KNN_distances_plot(mat,outname,k=2):
+ nbrs = NearestNeighbors(n_neighbors=k,algorithm='auto',metric='precomputed').fit(mat)
+ distances, indices = nbrs.kneighbors(mat)
+ d2 = distances[:,-1]
+ df = pd.DataFrame({'dist':d2})
+ df = df.sort_values("dist",ascending=False)
+ df['idx'] = np.arange(0,d2.shape[0]) + 1
+ p = pn.qplot(x='idx',y='dist',data=df,geom='line') + pn.scales.scale_y_continuous(minor_breaks = np.arange(0,50)/50,
+ breaks = np.arange(0,10)/10)
+ p.save(outname,width=16,height=10)
+
+def make_KNN_plots():
+ similarities = "/gscratch/comdata/output/reddit_similarity/subreddit_comment_terms_10k.feather"
+ subreddits, mat = read_similarity_mat(similarities)
+ mat = sim_to_dist(mat)
+
+ KNN_distances_plot(mat,k=2,outname='terms_knn_dist2.png')
+
+ similarities = "/gscratch/comdata/output/reddit_similarity/subreddit_comment_authors_10k.feather"
+ subreddits, mat = read_similarity_mat(similarities)
+ mat = sim_to_dist(mat)
+ KNN_distances_plot(mat,k=2,outname='authors_knn_dist2.png')
+
+ similarities = "/gscratch/comdata/output/reddit_similarity/subreddit_comment_authors-tf_10k.feather"
+ subreddits, mat = read_similarity_mat(similarities)
+ mat = sim_to_dist(mat)
+ KNN_distances_plot(mat,k=2,outname='authors-tf_knn_dist2.png')
+
+if __name__ == "__main__":
+ fire.Fire{'grid_sweep':hdbscan_grid_sweep,
+ 'grid_sweep_lsi':hdbscan_lsi_grid_sweep
+ 'cluster':hdbscan_job,
+ 'cluster_lsi':hdbscan_lsi_job}
+
+# test_select_hdbscan_clustering()
+ #fire.Fire(select_hdbscan_clustering)
projects / cdsc_reddit.git / blobdiff