+from clustering_base import clustering_result, clustering_job, twoway_clustering_job
+from hdbscan_clustering import hdbscan_clustering_result
+import umap
+from grid_sweep import twoway_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 multiprocessing import cpu_count
+import fire
+
+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_umap_hdbscan_lsi"
+ min_cluster_sizes=[2,3,4]
+ min_samples=[1,2,3]
+ cluster_selection_epsilons=[0,0.1,0.3,0.5]
+ cluster_selection_methods=[1]
+ lsi_dimensions='all'
+ n_neighbors = [5,10,15,25,35,70,100]
+ learning_rate = [0.1,0.5,1,2]
+ min_dist = [0.5,1,1.5,2]
+ local_connectivity = [1,2,3,4,5]
+
+ hdbscan_params = {"min_cluster_sizes":min_cluster_sizes, "min_samples":min_samples, "cluster_selection_epsilons":cluster_selection_epsilons, "cluster_selection_methods":cluster_selection_methods}
+ umap_params = {"n_neighbors":n_neighbors, "learning_rate":learning_rate, "min_dist":min_dist, "local_connectivity":local_connectivity}
+ gs = umap_hdbscan_grid_sweep(inpath, "all", outpath, hdbscan_params,umap_params)
+
+ # 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 umap_hdbscan_grid_sweep(twoway_grid_sweep):
+ def __init__(self,
+ inpath,
+ outpath,
+ umap_params,
+ hdbscan_params):
+
+ super().__init__(umap_hdbscan_job, inpath, outpath, self.namer, umap_params, hdbscan_params)
+
+ def namer(self,
+ min_cluster_size,
+ min_samples,
+ cluster_selection_epsilon,
+ cluster_selection_method,
+ n_neighbors,
+ learning_rate,
+ min_dist,
+ local_connectivity
+ ):
+ 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}"
+
+@dataclass
+class umap_hdbscan_clustering_result(hdbscan_clustering_result):
+ n_neighbors:int
+ learning_rate:float
+ min_dist:float
+ local_connectivity:int
+
+class umap_hdbscan_job(twoway_clustering_job):
+ def __init__(self, infile, outpath, name,
+ umap_args = {"n_neighbors":15, "learning_rate":1, "min_dist":1, "local_connectivity":1},
+ hdbscan_args = {"min_cluster_size":2, "min_samples":1, "cluster_selection_epsilon":0, "cluster_selection_method":'eom'},
+ save_step1 = False,
+ *args,
+ **kwargs):
+ super().__init__(infile,
+ outpath,
+ name,
+ call1=umap_hdbscan_job._umap_embedding,
+ call2=umap_hdbscan_job._hdbscan_clustering,
+ args1=umap_args,
+ args2=hdbscan_args,
+ save_step1=save_step1,
+ *args,
+ **kwargs
+ )
+
+ self.n_neighbors = umap_args['n_neighbors']
+ self.learning_rate = umap_args['learning_rate']
+ self.min_dist = umap_args['min_dist']
+ self.local_connectivity = umap_args['local_connectivity']
+ self.min_cluster_size = hdbscan_args['min_cluster_size']
+ self.min_samples = hdbscan_args['min_samples']
+ self.cluster_selection_epsilon = hdbscan_args['cluster_selection_epsilon']
+ self.cluster_selection_method = hdbscan_args['cluster_selection_method']
+
+ def after_run(self):
+ coords = self.step1.emedding_
+ self.cluster_data['x'] = coords[:,0]
+ self.cluster_data['y'] = coords[:,1]
+ super().after_run()
+
+
+ def _umap_embedding(mat, **umap_args):
+ print(f"running umap embedding. umap_args:{umap_args}")
+ umapmodel = umap.UMAP(metric='precomputed', **umap_args)
+ umapmodel = umapmodel.fit(mat)
+ return umapmodel
+
+ def _hdbscan_clustering(mat, umapmodel, **hdbscan_args):
+ print(f"running hdbascan clustering. hdbscan_args:{hdbscan_args}")
+
+ umap_coords = umapmodel.transform(mat)
+
+ clusterer = hdbscan.HDBSCAN(metric='euclidean',
+ core_dist_n_jobs=cpu_count(),
+ **hdbscan_args
+ )
+
+ clustering = clusterer.fit(umap_coords)
+
+ return(clustering)
+
+ def get_info(self):
+ result = super().get_info()
+ self.result = umap_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,
+ n_neighbors = self.n_neighbors,
+ learning_rate = self.learning_rate,
+ min_dist = self.min_dist,
+ local_connectivity=self.local_connectivity
+ )
+ return self.result
+
+def run_umap_hdbscan_grid_sweep(savefile, inpath, outpath, n_neighbors = [15], learning_rate=[1], min_dist=[1], local_connectivity=[1],
+ min_cluster_sizes=[2], min_samples=[1], cluster_selection_epsilons=[0], cluster_selection_methods=['eom']):
+ """Run umap + hdbscan clustering once or more with different parameters.
+
+ Usage:
+ 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">
+
+ Keword arguments:
+ savefile: path to save the metadata and diagnostics
+ inpath: path to feather data containing a labeled matrix of subreddit similarities.
+ outpath: path to output fit kmeans clusterings.
+ n_neighbors: umap parameter takes integers greater than 1
+ learning_rate: umap parameter takes positive real values
+ min_dist: umap parameter takes positive real values
+ local_connectivity: umap parameter takes positive integers
+ min_cluster_sizes: one or more integers indicating the minumum cluster size
+ min_samples: one ore more integers indicating the minimum number of samples used in the algorithm
+ cluster_selection_epsilon: one or more similarity thresholds for transition from dbscan to hdbscan
+ cluster_selection_method: "eom" or "leaf" eom gives larger clusters.
+ """
+
+ umap_args = {'n_neighbors':list(map(int, n_neighbors)),
+ 'learning_rate':list(map(float,learning_rate)),
+ 'min_dist':list(map(float,min_dist)),
+ 'local_connectivity':list(map(int,local_connectivity)),
+ }
+
+ hdbscan_args = {'min_cluster_size':list(map(int,min_cluster_sizes)),
+ 'min_samples':list(map(int,min_samples)),
+ 'cluster_selection_epsilon':list(map(float,cluster_selection_epsilons)),
+ 'cluster_selection_method':cluster_selection_methods}
+
+ obj = umap_hdbscan_grid_sweep(inpath,
+ outpath,
+ umap_args,
+ hdbscan_args)
+ obj.run(cores=10)
+ obj.save(savefile)
+
+
+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(run_umap_hdbscan_grid_sweep)
+
+# test_select_hdbscan_clustering()
+ #fire.Fire(select_hdbscan_clustering)
projects / cdsc_reddit.git / blobdiff