231 lines
11 KiB
Python
231 lines
11 KiB
Python
from clustering_base import clustering_result, clustering_job, twoway_clustering_job
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from hdbscan_clustering import hdbscan_clustering_result
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import umap
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from grid_sweep import twoway_grid_sweep
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from dataclasses import dataclass
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import hdbscan
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from sklearn.neighbors import NearestNeighbors
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import plotnine as pn
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import numpy as np
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from itertools import product, starmap, chain
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import pandas as pd
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from multiprocessing import cpu_count
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import fire
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def test_select_hdbscan_clustering():
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# select_hdbscan_clustering("/gscratch/comdata/output/reddit_similarity/subreddit_comment_authors-tf_30k_LSI",
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# "test_hdbscan_author30k",
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# min_cluster_sizes=[2],
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# min_samples=[1,2],
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# cluster_selection_epsilons=[0,0.05,0.1,0.15],
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# cluster_selection_methods=['eom','leaf'],
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# lsi_dimensions='all')
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inpath = "/gscratch/comdata/output/reddit_similarity/subreddit_comment_authors-tf_10k_LSI"
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outpath = "test_umap_hdbscan_lsi"
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min_cluster_sizes=[2,3,4]
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min_samples=[1,2,3]
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cluster_selection_epsilons=[0,0.1,0.3,0.5]
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cluster_selection_methods=[1]
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lsi_dimensions='all'
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n_neighbors = [5,10,15,25,35,70,100]
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learning_rate = [0.1,0.5,1,2]
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min_dist = [0.5,1,1.5,2]
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local_connectivity = [1,2,3,4,5]
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hdbscan_params = {"min_cluster_sizes":min_cluster_sizes, "min_samples":min_samples, "cluster_selection_epsilons":cluster_selection_epsilons, "cluster_selection_methods":cluster_selection_methods}
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umap_params = {"n_neighbors":n_neighbors, "learning_rate":learning_rate, "min_dist":min_dist, "local_connectivity":local_connectivity}
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gs = umap_hdbscan_grid_sweep(inpath, "all", outpath, hdbscan_params,umap_params)
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# gs.run(20)
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# gs.save("test_hdbscan/lsi_sweep.csv")
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# 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')
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# job1.run()
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# print(job1.get_info())
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# df = pd.read_csv("test_hdbscan/selection_data.csv")
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# test_select_hdbscan_clustering()
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# check_clusters = pd.read_feather("test_hdbscan/500_2_2_0.1_eom.feather")
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# silscores = pd.read_feather("test_hdbscan/silhouette_samples500_2_2_0.1_eom.feather")
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# c = check_clusters.merge(silscores,on='subreddit')# fire.Fire(select_hdbscan_clustering)
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class umap_hdbscan_grid_sweep(twoway_grid_sweep):
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def __init__(self,
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inpath,
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outpath,
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umap_params,
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hdbscan_params):
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super().__init__(umap_hdbscan_job, inpath, outpath, self.namer, umap_params, hdbscan_params)
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def namer(self,
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min_cluster_size,
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min_samples,
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cluster_selection_epsilon,
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cluster_selection_method,
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n_components,
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n_neighbors,
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learning_rate,
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min_dist,
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local_connectivity,
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densmap
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):
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return f"mcs-{min_cluster_size}_ms-{min_samples}_cse-{cluster_selection_epsilon}_csm-{cluster_selection_method}_nc-{n_components}_nn-{n_neighbors}_lr-{learning_rate}_md-{min_dist}_lc-{local_connectivity}_dm-{densmap}"
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@dataclass
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class umap_hdbscan_clustering_result(hdbscan_clustering_result):
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n_components:int
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n_neighbors:int
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learning_rate:float
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min_dist:float
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local_connectivity:int
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densmap:bool
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class umap_hdbscan_job(twoway_clustering_job):
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def __init__(self, infile, outpath, name,
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umap_args = {"n_components":2,"n_neighbors":15, "learning_rate":1, "min_dist":1, "local_connectivity":1,'densmap':False},
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hdbscan_args = {"min_cluster_size":2, "min_samples":1, "cluster_selection_epsilon":0, "cluster_selection_method":'eom'},
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*args,
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**kwargs):
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super().__init__(infile,
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outpath,
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name,
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call1=umap_hdbscan_job._umap_embedding,
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call2=umap_hdbscan_job._hdbscan_clustering,
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args1=umap_args,
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args2=hdbscan_args,
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*args,
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**kwargs
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)
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self.n_components = umap_args['n_components']
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self.n_neighbors = umap_args['n_neighbors']
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self.learning_rate = umap_args['learning_rate']
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self.min_dist = umap_args['min_dist']
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self.local_connectivity = umap_args['local_connectivity']
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self.densmap = umap_args['densmap']
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self.min_cluster_size = hdbscan_args['min_cluster_size']
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self.min_samples = hdbscan_args['min_samples']
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self.cluster_selection_epsilon = hdbscan_args['cluster_selection_epsilon']
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self.cluster_selection_method = hdbscan_args['cluster_selection_method']
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def after_run(self):
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coords = self.step1.emedding_
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self.cluster_data['x'] = coords[:,0]
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self.cluster_data['y'] = coords[:,1]
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super().after_run()
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def _umap_embedding(mat, **umap_args):
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print(f"running umap embedding. umap_args:{umap_args}")
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umapmodel = umap.UMAP(metric='precomputed', **umap_args)
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umapmodel = umapmodel.fit(mat)
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return umapmodel
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def _hdbscan_clustering(mat, umapmodel, **hdbscan_args):
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print(f"running hdbascan clustering. hdbscan_args:{hdbscan_args}")
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umap_coords = umapmodel.transform(mat)
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clusterer = hdbscan.HDBSCAN(metric='euclidean',
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core_dist_n_jobs=cpu_count(),
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**hdbscan_args
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)
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clustering = clusterer.fit(umap_coords)
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return(clustering)
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def get_info(self):
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result = super().get_info()
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self.result = umap_hdbscan_clustering_result(**result.__dict__,
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min_cluster_size=self.min_cluster_size,
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min_samples=self.min_samples,
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cluster_selection_epsilon=self.cluster_selection_epsilon,
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cluster_selection_method=self.cluster_selection_method,
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n_components = self.n_components,
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n_neighbors = self.n_neighbors,
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learning_rate = self.learning_rate,
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min_dist = self.min_dist,
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local_connectivity=self.local_connectivity,
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densmap=self.densmap
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)
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return self.result
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def run_umap_hdbscan_grid_sweep(savefile, inpath, outpath, n_neighbors = [15], n_components=[2], learning_rate=[1], min_dist=[1], local_connectivity=[1],
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densmap=[False],
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min_cluster_sizes=[2], min_samples=[1], cluster_selection_epsilons=[0], cluster_selection_methods=['eom']):
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"""Run umap + hdbscan clustering once or more with different parameters.
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Usage:
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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">
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Keword arguments:
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savefile: path to save the metadata and diagnostics
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inpath: path to feather data containing a labeled matrix of subreddit similarities.
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outpath: path to output fit kmeans clusterings.
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n_neighbors: umap parameter takes integers greater than 1
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learning_rate: umap parameter takes positive real values
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min_dist: umap parameter takes positive real values
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local_connectivity: umap parameter takes positive integers
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min_cluster_sizes: one or more integers indicating the minumum cluster size
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min_samples: one ore more integers indicating the minimum number of samples used in the algorithm
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cluster_selection_epsilon: one or more similarity thresholds for transition from dbscan to hdbscan
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cluster_selection_method: "eom" or "leaf" eom gives larger clusters.
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"""
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umap_args = {'n_neighbors':list(map(int, n_neighbors)),
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'learning_rate':list(map(float,learning_rate)),
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'min_dist':list(map(float,min_dist)),
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'local_connectivity':list(map(int,local_connectivity)),
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'n_components':list(map(int, n_components)),
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'densmap':list(map(bool,densmap))
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}
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hdbscan_args = {'min_cluster_size':list(map(int,min_cluster_sizes)),
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'min_samples':list(map(int,min_samples)),
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'cluster_selection_epsilon':list(map(float,cluster_selection_epsilons)),
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'cluster_selection_method':cluster_selection_methods}
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obj = umap_hdbscan_grid_sweep(inpath,
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outpath,
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umap_args,
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hdbscan_args)
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obj.run(cores=10)
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obj.save(savefile)
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def KNN_distances_plot(mat,outname,k=2):
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nbrs = NearestNeighbors(n_neighbors=k,algorithm='auto',metric='precomputed').fit(mat)
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distances, indices = nbrs.kneighbors(mat)
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d2 = distances[:,-1]
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df = pd.DataFrame({'dist':d2})
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df = df.sort_values("dist",ascending=False)
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df['idx'] = np.arange(0,d2.shape[0]) + 1
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p = pn.qplot(x='idx',y='dist',data=df,geom='line') + pn.scales.scale_y_continuous(minor_breaks = np.arange(0,50)/50,
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breaks = np.arange(0,10)/10)
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p.save(outname,width=16,height=10)
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def make_KNN_plots():
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similarities = "/gscratch/comdata/output/reddit_similarity/subreddit_comment_terms_10k.feather"
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subreddits, mat = read_similarity_mat(similarities)
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mat = sim_to_dist(mat)
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KNN_distances_plot(mat,k=2,outname='terms_knn_dist2.png')
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similarities = "/gscratch/comdata/output/reddit_similarity/subreddit_comment_authors_10k.feather"
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subreddits, mat = read_similarity_mat(similarities)
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mat = sim_to_dist(mat)
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KNN_distances_plot(mat,k=2,outname='authors_knn_dist2.png')
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similarities = "/gscratch/comdata/output/reddit_similarity/subreddit_comment_authors-tf_10k.feather"
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subreddits, mat = read_similarity_mat(similarities)
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mat = sim_to_dist(mat)
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KNN_distances_plot(mat,k=2,outname='authors-tf_knn_dist2.png')
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if __name__ == "__main__":
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fire.Fire(run_umap_hdbscan_grid_sweep)
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# test_select_hdbscan_clustering()
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#fire.Fire(select_hdbscan_clustering)
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