refactor clustring in object oriented style
This commit is contained in:
Nate E TeBlunthuis
parent
8d1df5b26e
commit
f05cb962e0
@ -2,7 +2,8 @@ from sklearn.metrics import silhouette_score
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from sklearn.cluster import AffinityPropagation
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from functools import partial
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from dataclasses import dataclass
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from clustering import _affinity_clustering, read_similarity_mat, sim_to_dist, process_clustering_result, clustering_result
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from clustering_base import sim_to_dist, process_clustering_result, clustering_result, read_similarity_mat
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from clustering_base import lsi_result_mixin, lsi_mixin, clustering_job, grid_sweep, lsi_grid_sweep
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from multiprocessing import Pool, cpu_count, Array, Process
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from pathlib import Path
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from itertools import product, starmap
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@ -17,116 +18,158 @@ class affinity_clustering_result(clustering_result):
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damping:float
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convergence_iter:int
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preference_quantile:float
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preference:float
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max_iter:int
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def affinity_clustering(similarities, output, *args, **kwargs):
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subreddits, mat = read_similarity_mat(similarities)
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clustering = _affinity_clustering(mat, *args, **kwargs)
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cluster_data = process_clustering_result(clustering, subreddits)
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cluster_data['algorithm'] = 'affinity'
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return(cluster_data)
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@dataclass
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class affinity_clustering_result_lsi(affinity_clustering_result, lsi_result_mixin):
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pass
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def _affinity_clustering(mat, subreddits, output, damping=0.9, max_iter=100000, convergence_iter=30, preference_quantile=0.5, random_state=1968, verbose=True):
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'''
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similarities: matrix of similarity scores
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preference_quantile: parameter controlling how many clusters to make. higher values = more clusters. 0.85 is a good value with 3000 subreddits.
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damping: parameter controlling how iterations are merged. Higher values make convergence faster and more dependable. 0.85 is a good value for the 10000 subreddits by author.
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'''
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print(f"damping:{damping}; convergenceIter:{convergence_iter}; preferenceQuantile:{preference_quantile}")
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class affinity_job(clustering_job):
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def __init__(self, infile, outpath, name, damping=0.9, max_iter=100000, convergence_iter=30, preference_quantile=0.5, random_state=1968, verbose=True):
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super().__init__(infile,
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outpath,
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name,
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call=self._affinity_clustering,
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preference_quantile=preference_quantile,
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damping=damping,
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max_iter=max_iter,
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convergence_iter=convergence_iter,
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random_state=1968,
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verbose=verbose)
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self.damping=damping
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self.max_iter=max_iter
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self.convergence_iter=convergence_iter
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self.preference_quantile=preference_quantile
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preference = np.quantile(mat,preference_quantile)
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def _affinity_clustering(self, mat, preference_quantile, *args, **kwargs):
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mat = 1-mat
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preference = np.quantile(mat, preference_quantile)
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self.preference = preference
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print(f"preference is {preference}")
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print("data loaded")
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sys.stdout.flush()
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clustering = AffinityPropagation(*args,
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preference=preference,
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affinity='precomputed',
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copy=False,
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**kwargs).fit(mat)
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return clustering
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print(f"preference is {preference}")
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print("data loaded")
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sys.stdout.flush()
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clustering = AffinityPropagation(damping=damping,
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max_iter=max_iter,
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convergence_iter=convergence_iter,
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copy=False,
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preference=preference,
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affinity='precomputed',
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verbose=verbose,
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random_state=random_state).fit(mat)
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def get_info(self):
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result = super().get_info()
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self.result=affinity_clustering_result(**result.__dict__,
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damping=self.damping,
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max_iter=self.max_iter,
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convergence_iter=self.convergence_iter,
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preference_quantile=self.preference_quantile,
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preference=self.preference)
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cluster_data = process_clustering_result(clustering, subreddits)
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output = Path(output)
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output.parent.mkdir(parents=True,exist_ok=True)
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cluster_data.to_feather(output)
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print(f"saved {output}")
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return clustering
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return self.result
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class affinity_lsi_job(affinity_job, lsi_mixin):
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def __init__(self, infile, outpath, name, lsi_dims, *args, **kwargs):
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super().__init__(infile,
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outpath,
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name,
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*args,
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**kwargs)
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super().set_lsi_dims(lsi_dims)
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def do_clustering(damping, convergence_iter, preference_quantile, name, mat, subreddits, max_iter, outdir:Path, random_state, verbose, alt_mat, overwrite=False):
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if name is None:
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name = f"damping-{damping}_convergenceIter-{convergence_iter}_preferenceQuantile-{preference_quantile}"
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print(name)
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sys.stdout.flush()
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outpath = outdir / (str(name) + ".feather")
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outpath.parent.mkdir(parents=True,exist_ok=True)
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print(outpath)
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clustering = _affinity_clustering(mat, outpath, damping, max_iter, convergence_iter, preference_quantile, random_state, verbose)
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cluster_data = process_clustering_result(clustering, subreddits)
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mat = sim_to_dist(clustering.affinity_matrix_)
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def get_info(self):
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result = super().get_info()
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self.result = affinity_clustering_result_lsi(**result.__dict__,
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lsi_dimensions=self.lsi_dims)
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return self.result
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try:
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score = silhouette_score(mat, clustering.labels_, metric='precomputed')
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except ValueError:
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score = None
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class affinity_grid_sweep(grid_sweep):
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def __init__(self,
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inpath,
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outpath,
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*args,
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**kwargs):
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if alt_mat is not None:
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alt_distances = sim_to_dist(alt_mat)
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try:
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alt_score = silhouette_score(alt_mat, clustering.labels_, metric='precomputed')
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except ValueError:
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alt_score = None
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super().__init__(affinity_job,
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_afffinity_grid_sweep,
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inpath,
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outpath,
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self.namer,
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*args,
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**kwargs)
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def namer(self,
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damping,
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max_iter,
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convergence_iter,
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preference_quantile):
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return f"damp-{damping}_maxit-{max_iter}_convit-{convergence_iter}_prefq-{preference_quantile}"
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class _affinity_lsi_grid_sweep(grid_sweep):
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def __init__(self,
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inpath,
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outpath,
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lsi_dim,
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*args,
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**kwargs):
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self.lsi_dim = lsi_dim
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self.jobtype = affinity_lsi_job
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super().__init__(self.jobtype,
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inpath,
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outpath,
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self.namer,
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self.lsi_dim,
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*args,
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**kwargs)
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def namer(self, *args, **kwargs):
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s = affinity_grid_sweep.namer(self, *args[1:], **kwargs)
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s += f"_lsi-{self.lsi_dim}"
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return s
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class affinity_lsi_grid_sweep(lsi_grid_sweep):
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def __init__(self,
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inpath,
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lsi_dims,
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outpath,
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dampings=[0.9],
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max_iters=[10000],
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convergence_iters=[30],
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preference_quantiles=[0.5]):
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super().__init__(affinity_lsi_job,
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_affinity_lsi_grid_sweep,
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inpath,
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lsi_dims,
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outpath,
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dampings,
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max_iters,
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convergence_iters,
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preference_quantiles)
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res = affinity_clustering_result(outpath=outpath,
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damping=damping,
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max_iter=max_iter,
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convergence_iter=convergence_iter,
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preference_quantile=preference_quantile,
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silhouette_score=score,
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alt_silhouette_score=score,
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name=str(name))
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def test_select_affinity_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_affinity";
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dampings=[0.8,0.9]
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max_iters=[100000]
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convergence_iters=[15]
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preference_quantiles=[0.5,0.7]
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gs = affinity_lsi_grid_sweep(inpath, 'all', outpath, dampings, max_iters, convergence_iters, preference_quantiles)
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gs.run(20)
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gs.save("test_affinity/lsi_sweep.csv")
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return res
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# alt similiarities is for checking the silhouette coefficient of an alternative measure of similarity (e.g., topic similarities for user clustering).
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def select_affinity_clustering(similarities, outdir, outinfo, damping=[0.9], max_iter=100000, convergence_iter=[30], preference_quantile=[0.5], random_state=1968, verbose=True, alt_similarities=None, J=None):
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damping = list(map(float,damping))
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convergence_iter = convergence_iter = list(map(int,convergence_iter))
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preference_quantile = list(map(float,preference_quantile))
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if type(outdir) is str:
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outdir = Path(outdir)
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outdir.mkdir(parents=True,exist_ok=True)
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subreddits, mat = read_similarity_mat(similarities,use_threads=True)
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if alt_similarities is not None:
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alt_mat = read_similarity_mat(alt_similarities,use_threads=True)
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else:
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alt_mat = None
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if J is None:
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J = cpu_count()
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pool = Pool(J)
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# get list of tuples: the combinations of hyperparameters
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hyper_grid = product(damping, convergence_iter, preference_quantile)
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hyper_grid = (t + (str(i),) for i, t in enumerate(hyper_grid))
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_do_clustering = partial(do_clustering, mat=mat, subreddits=subreddits, outdir=outdir, max_iter=max_iter, random_state=random_state, verbose=verbose, alt_mat=alt_mat)
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# similarities = Array('d', mat)
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# call pool.starmap
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print("running clustering selection")
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clustering_data = pool.starmap(_do_clustering, hyper_grid)
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clustering_data = pd.DataFrame(list(clustering_data))
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clustering_data.to_csv(outinfo)
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return clustering_data
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if __name__ == "__main__":
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x = fire.Fire(select_affinity_clustering)
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fire.Fire{'grid_sweep':affinity_grid_sweep,
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'grid_sweep_lsi':affinity_lsi_grid_sweep
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'cluster':affinity_job,
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'cluster_lsi':affinity_lsi_job}
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@ -2,6 +2,9 @@ from pathlib import Path
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import numpy as np
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import pandas as pd
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from dataclasses import dataclass
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from sklearn.metrics import silhouette_score, silhouette_samples
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from itertools import product, chain
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from multiprocessing import Pool, cpu_count
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def sim_to_dist(mat):
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dist = 1-mat
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@ -9,41 +12,147 @@ def sim_to_dist(mat):
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np.fill_diagonal(dist,0)
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return dist
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def process_clustering_result(clustering, subreddits):
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class grid_sweep:
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def __init__(self, jobtype, inpath, outpath, namer, *args):
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self.jobtype = jobtype
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self.namer = namer
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grid = list(product(*args))
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inpath = Path(inpath)
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outpath = Path(outpath)
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self.hasrun = False
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self.grid = [(inpath,outpath,namer(*g)) + g for g in grid]
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self.jobs = [jobtype(*g) for g in self.grid]
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if hasattr(clustering,'n_iter_'):
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print(f"clustering took {clustering.n_iter_} iterations")
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def run(self, cores=20):
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if cores is not None and cores > 1:
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with Pool(cores) as pool:
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infos = pool.map(self.jobtype.get_info, self.jobs)
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else:
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infos = map(self.jobtype.get_info, self.jobs)
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clusters = clustering.labels_
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self.infos = pd.DataFrame(infos)
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self.hasrun = True
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print(f"found {len(set(clusters))} clusters")
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def save(self, outcsv):
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if not self.hasrun:
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self.run()
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outcsv = Path(outcsv)
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outcsv.parent.mkdir(parents=True, exist_ok=True)
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self.infos.to_csv(outcsv)
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cluster_data = pd.DataFrame({'subreddit': subreddits,'cluster':clustering.labels_})
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cluster_sizes = cluster_data.groupby("cluster").count().reset_index()
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print(f"the largest cluster has {cluster_sizes.loc[cluster_sizes.cluster!=-1].subreddit.max()} members")
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class lsi_grid_sweep(grid_sweep):
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def __init__(self, jobtype, subsweep, inpath, lsi_dimensions, outpath, *args, **kwargs):
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self.jobtype = jobtype
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self.subsweep = subsweep
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inpath = Path(inpath)
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if lsi_dimensions == 'all':
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lsi_paths = list(inpath.glob("*"))
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else:
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lsi_paths = [inpath / (dim + '.feather') for dim in lsi_dimensions]
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print(f"the median cluster has {cluster_sizes.subreddit.median()} members")
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lsi_nums = [p.stem for p in lsi_paths]
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self.hasrun = False
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self.subgrids = [self.subsweep(lsi_path, outpath, lsi_dim, *args, **kwargs) for lsi_dim, lsi_path in zip(lsi_nums, lsi_paths)]
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self.jobs = list(chain(*map(lambda gs: gs.jobs, self.subgrids)))
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print(f"{(cluster_sizes.subreddit==1).sum()} clusters have 1 member")
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print(f"{(cluster_sizes.loc[cluster_sizes.cluster==-1,['subreddit']])} subreddits are in cluster -1",flush=True)
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# this is meant to be an interface, not created directly
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class clustering_job:
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def __init__(self, infile, outpath, name, call, *args, **kwargs):
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self.outpath = Path(outpath)
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self.call = call
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self.args = args
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self.kwargs = kwargs
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self.infile = Path(infile)
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self.name = name
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self.hasrun = False
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return cluster_data
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def run(self):
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self.subreddits, self.mat = self.read_distance_mat(self.infile)
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self.clustering = self.call(self.mat, *self.args, **self.kwargs)
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self.cluster_data = self.process_clustering(self.clustering, self.subreddits)
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self.score = self.silhouette()
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self.outpath.mkdir(parents=True, exist_ok=True)
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self.cluster_data.to_feather(self.outpath/(self.name + ".feather"))
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self.hasrun = True
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def get_info(self):
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if not self.hasrun:
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self.run()
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self.result = clustering_result(outpath=str(self.outpath.resolve()),
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silhouette_score=self.score,
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name=self.name,
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n_clusters=self.n_clusters,
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n_isolates=self.n_isolates,
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silhouette_samples = str(self.silsampout.resolve())
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)
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return self.result
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def silhouette(self):
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isolates = self.clustering.labels_ == -1
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scoremat = self.mat[~isolates][:,~isolates]
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score = silhouette_score(scoremat, self.clustering.labels_[~isolates], metric='precomputed')
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silhouette_samp = silhouette_samples(self.mat, self.clustering.labels_, metric='precomputed')
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silhouette_samp = pd.DataFrame({'subreddit':self.subreddits,'score':silhouette_samp})
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self.outpath.mkdir(parents=True, exist_ok=True)
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self.silsampout = self.outpath / ("silhouette_samples-" + self.name + ".feather")
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silhouette_samp.to_feather(self.silsampout)
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return score
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def read_distance_mat(self, similarities, use_threads=True):
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df = pd.read_feather(similarities, use_threads=use_threads)
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mat = np.array(df.drop('_subreddit',1))
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n = mat.shape[0]
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mat[range(n),range(n)] = 1
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return (df._subreddit,1-mat)
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def process_clustering(self, clustering, subreddits):
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if hasattr(clustering,'n_iter_'):
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print(f"clustering took {clustering.n_iter_} iterations")
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clusters = clustering.labels_
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self.n_clusters = len(set(clusters))
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print(f"found {self.n_clusters} clusters")
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cluster_data = pd.DataFrame({'subreddit': subreddits,'cluster':clustering.labels_})
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cluster_sizes = cluster_data.groupby("cluster").count().reset_index()
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print(f"the largest cluster has {cluster_sizes.loc[cluster_sizes.cluster!=-1].subreddit.max()} members")
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print(f"the median cluster has {cluster_sizes.subreddit.median()} members")
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n_isolates1 = (cluster_sizes.subreddit==1).sum()
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print(f"{n_isolates1} clusters have 1 member")
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n_isolates2 = (cluster_sizes.loc[cluster_sizes.cluster==-1,['subreddit']])
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print(f"{n_isolates2} subreddits are in cluster -1",flush=True)
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if n_isolates1 == 0:
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self.n_isolates = n_isolates2
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else:
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self.n_isolates = n_isolates1
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return cluster_data
|
||||
|
||||
|
||||
class lsi_mixin():
|
||||
def set_lsi_dims(self, lsi_dims):
|
||||
self.lsi_dims = lsi_dims
|
||||
|
||||
@dataclass
|
||||
class clustering_result:
|
||||
outpath:Path
|
||||
max_iter:int
|
||||
silhouette_score:float
|
||||
alt_silhouette_score:float
|
||||
name:str
|
||||
n_clusters:int
|
||||
n_isolates:int
|
||||
silhouette_samples:str
|
||||
|
||||
def read_similarity_mat(similarities, use_threads=True):
|
||||
df = pd.read_feather(similarities, use_threads=use_threads)
|
||||
mat = np.array(df.drop('_subreddit',1))
|
||||
n = mat.shape[0]
|
||||
mat[range(n),range(n)] = 1
|
||||
return (df._subreddit,mat)
|
||||
@dataclass
|
||||
class lsi_result_mixin:
|
||||
lsi_dimensions:int
|
||||
|
||||
@ -1,10 +1,11 @@
|
||||
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
|
||||
from itertools import product, starmap, chain
|
||||
import pandas as pd
|
||||
from sklearn.metrics import silhouette_score, silhouette_samples
|
||||
from pathlib import Path
|
||||
@ -13,27 +14,88 @@ 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"
|
||||
# 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)
|
||||
# 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):
|
||||
@ -41,107 +103,166 @@ class hdbscan_clustering_result(clustering_result):
|
||||
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'
|
||||
):
|
||||
@dataclass
|
||||
class hdbscan_clustering_result_lsi(hdbscan_clustering_result, lsi_result_mixin):
|
||||
pass
|
||||
|
||||
inpath = Path(inpath)
|
||||
outpath = Path(outpath)
|
||||
outpath.mkdir(exist_ok=True, parents=True)
|
||||
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,
|
||||
)
|
||||
|
||||
if lsi_dimensions == 'all':
|
||||
lsi_paths = list(inpath.glob("*"))
|
||||
clustering = clusterer.fit(mat.astype('double'))
|
||||
|
||||
return(clustering)
|
||||
|
||||
else:
|
||||
lsi_paths = [inpath / (dim + '.feather') for dim in lsi_dimensions]
|
||||
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
|
||||
|
||||
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))
|
||||
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)
|
||||
|
||||
# fix the output file names
|
||||
names = list(map(lambda t:'_'.join(map(str,t)),grid))
|
||||
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
|
||||
|
||||
grid = [(inpath/(str(t[0])+'.feather'),outpath/(name + '.feather'), t[0], name) + t[1:] for t, name in zip(grid, names)]
|
||||
# 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)
|
||||
# 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 = pd.DataFrame(mods)
|
||||
# if outfile is None:
|
||||
# outfile = outpath / "selection_data.csv"
|
||||
|
||||
res.to_csv(outfile)
|
||||
# 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()
|
||||
)
|
||||
# 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)
|
||||
# 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)
|
||||
|
||||
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_))
|
||||
)
|
||||
# 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)
|
||||
# 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}")
|
||||
# # 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,
|
||||
)
|
||||
# print(mat)
|
||||
# clusterer = hdbscan.HDBSCAN(*args,
|
||||
# **kwargs,
|
||||
# )
|
||||
|
||||
clustering = clusterer.fit(mat.astype('double'))
|
||||
# clustering = clusterer.fit(mat.astype('double'))
|
||||
|
||||
return(clustering)
|
||||
# return(clustering)
|
||||
|
||||
def KNN_distances_plot(mat,outname,k=2):
|
||||
nbrs = NearestNeighbors(n_neighbors=k,algorithm='auto',metric='precomputed').fit(mat)
|
||||
@ -172,4 +293,10 @@ def make_KNN_plots():
|
||||
KNN_distances_plot(mat,k=2,outname='authors-tf_knn_dist2.png')
|
||||
|
||||
if __name__ == "__main__":
|
||||
fire.Fire(select_hdbscan_clustering)
|
||||
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)
|
||||
|
||||
@ -4,98 +4,145 @@ from pathlib import Path
|
||||
from multiprocessing import cpu_count
|
||||
from dataclasses import dataclass
|
||||
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
|
||||
|
||||
|
||||
@dataclass
|
||||
class kmeans_clustering_result(clustering_result):
|
||||
n_clusters:int
|
||||
n_init:int
|
||||
max_iter:int
|
||||
|
||||
def kmeans_clustering(similarities, *args, **kwargs):
|
||||
subreddits, mat = read_similarity_mat(similarities)
|
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mat = sim_to_dist(mat)
|
||||
clustering = _kmeans_clustering(mat, *args, **kwargs)
|
||||
cluster_data = process_clustering_result(clustering, subreddits)
|
||||
return(cluster_data)
|
||||
@dataclass
|
||||
class kmeans_clustering_result_lsi(kmeans_clustering_result, lsi_result_mixin):
|
||||
pass
|
||||
|
||||
def _kmeans_clustering(mat, output, n_clusters, n_init=10, max_iter=100000, random_state=1968, verbose=True):
|
||||
class kmeans_job(clustering_job):
|
||||
def __init__(self, infile, outpath, name, n_clusters, n_init=10, max_iter=100000, random_state=1968, verbose=True):
|
||||
super().__init__(infile,
|
||||
outpath,
|
||||
name,
|
||||
call=kmeans_job._kmeans_clustering,
|
||||
n_clusters=n_clusters,
|
||||
n_init=n_init,
|
||||
max_iter=max_iter,
|
||||
random_state=random_state,
|
||||
verbose=verbose)
|
||||
|
||||
clustering = KMeans(n_clusters=n_clusters,
|
||||
n_init=n_init,
|
||||
max_iter=max_iter,
|
||||
random_state=random_state,
|
||||
verbose=verbose
|
||||
).fit(mat)
|
||||
self.n_clusters=n_clusters
|
||||
self.n_init=n_init
|
||||
self.max_iter=max_iter
|
||||
|
||||
return clustering
|
||||
def _kmeans_clustering(mat, *args, **kwargs):
|
||||
|
||||
def do_clustering(n_clusters, n_init, name, mat, subreddits, max_iter, outdir:Path, random_state, verbose, alt_mat, overwrite=False):
|
||||
if name is None:
|
||||
name = f"damping-{damping}_convergenceIter-{convergence_iter}_preferenceQuantile-{preference_quantile}"
|
||||
print(name)
|
||||
sys.stdout.flush()
|
||||
outpath = outdir / (str(name) + ".feather")
|
||||
print(outpath)
|
||||
mat = sim_to_dist(mat)
|
||||
clustering = _kmeans_clustering(mat, outpath, n_clusters, n_init, max_iter, random_state, verbose)
|
||||
clustering = KMeans(*args,
|
||||
**kwargs,
|
||||
).fit(mat)
|
||||
|
||||
outpath.parent.mkdir(parents=True,exist_ok=True)
|
||||
cluster_data.to_feather(outpath)
|
||||
cluster_data = process_clustering_result(clustering, subreddits)
|
||||
return clustering
|
||||
|
||||
try:
|
||||
score = silhouette_score(mat, clustering.labels_, metric='precomputed')
|
||||
except ValueError:
|
||||
score = None
|
||||
|
||||
if alt_mat is not None:
|
||||
alt_distances = sim_to_dist(alt_mat)
|
||||
try:
|
||||
alt_score = silhouette_score(alt_mat, clustering.labels_, metric='precomputed')
|
||||
except ValueError:
|
||||
alt_score = None
|
||||
def get_info(self):
|
||||
result = super().get_info()
|
||||
self.result = kmeans_clustering_result(**result.__dict__,
|
||||
n_init=n_init,
|
||||
max_iter=max_iter)
|
||||
return self.result
|
||||
|
||||
|
||||
class kmeans_lsi_job(kmeans_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):
|
||||
result = super().get_info()
|
||||
self.result = kmeans_clustering_result_lsi(**result.__dict__,
|
||||
lsi_dimensions=self.lsi_dims)
|
||||
return self.result
|
||||
|
||||
res = kmeans_clustering_result(outpath=outpath,
|
||||
max_iter=max_iter,
|
||||
n_clusters=n_clusters,
|
||||
n_init = n_init,
|
||||
silhouette_score=score,
|
||||
alt_silhouette_score=score,
|
||||
name=str(name))
|
||||
|
||||
return res
|
||||
class kmeans_grid_sweep(grid_sweep):
|
||||
def __init__(self,
|
||||
inpath,
|
||||
outpath,
|
||||
*args,
|
||||
**kwargs):
|
||||
super().__init__(kmeans_job, inpath, outpath, self.namer, *args, **kwargs)
|
||||
|
||||
def namer(self,
|
||||
n_clusters,
|
||||
n_init,
|
||||
max_iter):
|
||||
return f"nclusters-{n_clusters}_nit-{n_init}_maxit-{max_iter}"
|
||||
|
||||
# alt similiarities is for checking the silhouette coefficient of an alternative measure of similarity (e.g., topic similarities for user clustering).
|
||||
def select_kmeans_clustering(similarities, outdir, outinfo, n_clusters=[1000], max_iter=100000, n_init=10, random_state=1968, verbose=True, alt_similarities=None):
|
||||
class _kmeans_lsi_grid_sweep(grid_sweep):
|
||||
def __init__(self,
|
||||
inpath,
|
||||
outpath,
|
||||
lsi_dim,
|
||||
*args,
|
||||
**kwargs):
|
||||
self.lsi_dim = lsi_dim
|
||||
self.jobtype = kmeans_lsi_job
|
||||
super().__init__(self.jobtype, inpath, outpath, self.namer, self.lsi_dim, *args, **kwargs)
|
||||
|
||||
n_clusters = list(map(int,n_clusters))
|
||||
n_init = list(map(int,n_init))
|
||||
def namer(self, *args, **kwargs):
|
||||
s = kmeans_grid_sweep.namer(self, *args[1:], **kwargs)
|
||||
s += f"_lsi-{self.lsi_dim}"
|
||||
return s
|
||||
|
||||
if type(outdir) is str:
|
||||
outdir = Path(outdir)
|
||||
class kmeans_lsi_grid_sweep(lsi_grid_sweep):
|
||||
def __init__(self,
|
||||
inpath,
|
||||
lsi_dims,
|
||||
outpath,
|
||||
n_clusters,
|
||||
n_inits,
|
||||
max_iters
|
||||
):
|
||||
|
||||
outdir.mkdir(parents=True,exist_ok=True)
|
||||
super().__init__(kmeans_lsi_job,
|
||||
_kmeans_lsi_grid_sweep,
|
||||
inpath,
|
||||
lsi_dims,
|
||||
outpath,
|
||||
n_clusters,
|
||||
n_inits,
|
||||
max_iters)
|
||||
|
||||
subreddits, mat = read_similarity_mat(similarities,use_threads=True)
|
||||
def test_select_kmeans_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_kmeans";
|
||||
n_clusters=[200,300,400];
|
||||
n_init=[1,2,3];
|
||||
max_iter=[100000]
|
||||
|
||||
if alt_similarities is not None:
|
||||
alt_mat = read_similarity_mat(alt_similarities,use_threads=True)
|
||||
else:
|
||||
alt_mat = None
|
||||
gs = kmeans_lsi_grid_sweep(inpath, 'all', outpath, n_clusters, n_init, max_iter)
|
||||
gs.run(1)
|
||||
|
||||
# get list of tuples: the combinations of hyperparameters
|
||||
hyper_grid = product(n_clusters, n_init)
|
||||
hyper_grid = (t + (str(i),) for i, t in enumerate(hyper_grid))
|
||||
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")
|
||||
|
||||
_do_clustering = partial(do_clustering, mat=mat, subreddits=subreddits, outdir=outdir, max_iter=max_iter, random_state=random_state, verbose=verbose, alt_mat=alt_mat)
|
||||
|
||||
# call starmap
|
||||
print("running clustering selection")
|
||||
clustering_data = starmap(_do_clustering, hyper_grid)
|
||||
clustering_data = pd.DataFrame(list(clustering_data))
|
||||
clustering_data.to_csv(outinfo)
|
||||
|
||||
return clustering_data
|
||||
|
||||
if __name__ == "__main__":
|
||||
x = fire.Fire(select_kmeans_clustering)
|
||||
|
||||
fire.Fire{'grid_sweep':kmeans_grid_sweep,
|
||||
'grid_sweep_lsi':kmeans_lsi_grid_sweep
|
||||
'cluster':kmeans_job,
|
||||
'cluster_lsi':kmeans_lsi_job}
|
||||
|
||||
Loading…
Reference in New Issue
Block a user