--- /dev/null
+from clustering_base import sim_to_dist, process_clustering_result, clustering_result, read_similarity_mat
+from dataclasses import dataclass
+import hdbscan
+from sklearn.neighbors import NearestNeighbors
+import plotnine as pn
+import numpy as np
+from itertools import product, starmap
+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_30k_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'
+
+@dataclass
+class hdbscan_clustering_result(clustering_result):
+ min_cluster_size:int
+ min_samples:int
+ cluster_selection_epsilon:float
+ cluster_selection_method:str
+ lsi_dimensions:int
+ n_isolates:int
+ silhouette_samples:str
+
+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 == 'all':
+ lsi_paths = list(inpath.glob("*"))
+
+ else:
+ lsi_paths = [inpath / (dim + '.feather') for dim in lsi_dimensions]
+
+ lsi_nums = [p.stem for p in lsi_paths]
+ 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,
+ max_iter=None,
+ silhouette_samples=silsampout,
+ silhouette_score=score,
+ alt_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__":
+ 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)
projects / cdsc_reddit.git / blobdiff