102 lines
4.0 KiB
Python
102 lines
4.0 KiB
Python
from hdbscan_clustering import hdbscan_job, hdbscan_grid_sweep, hdbscan_clustering_result
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from lsi_base import lsi_grid_sweep, lsi_mixin, lsi_result_mixin
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from grid_sweep import grid_sweep
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import fire
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from dataclasses import dataclass
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@dataclass
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class hdbscan_clustering_result_lsi(hdbscan_clustering_result, lsi_result_mixin):
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pass
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class hdbscan_lsi_job(hdbscan_job, lsi_mixin):
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def __init__(self, infile, outpath, name, lsi_dims, *args, **kwargs):
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super().__init__(
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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 get_info(self):
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partial_result = super().get_info()
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self.result = hdbscan_clustering_result_lsi(**partial_result.__dict__,
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lsi_dimensions=self.lsi_dims)
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return self.result
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class hdbscan_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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min_cluster_sizes,
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min_samples,
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cluster_selection_epsilons,
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cluster_selection_methods
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):
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super().__init__(hdbscan_lsi_job,
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_hdbscan_lsi_grid_sweep,
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inpath,
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lsi_dims,
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outpath,
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min_cluster_sizes,
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min_samples,
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cluster_selection_epsilons,
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cluster_selection_methods)
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class _hdbscan_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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print(args)
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print(kwargs)
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self.lsi_dim = lsi_dim
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self.jobtype = hdbscan_lsi_job
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super().__init__(self.jobtype, inpath, outpath, self.namer, [self.lsi_dim], *args, **kwargs)
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def namer(self, *args, **kwargs):
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s = hdbscan_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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def run_hdbscan_lsi_grid_sweep(savefile, inpath, outpath, min_cluster_sizes=[2], min_samples=[1], cluster_selection_epsilons=[0], cluster_selection_methods=[1],lsi_dimensions='all'):
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"""Run hdbscan clustering once or more with different parameters.
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Usage:
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hdbscan_clustering_lsi --savefile=SAVEFILE --inpath=INPATH --outpath=OUTPATH --min_cluster_sizes=<csv> --min_samples=<csv> --cluster_selection_epsilons=<csv> --cluster_selection_methods=[eom]> --lsi_dimensions: either "all" or one or more available lsi similarity dimensions at INPATH.
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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 folder containing feather files with LSI similarity labeled matrices of subreddit similarities.
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outpath: path to output fit clusterings.
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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_epsilons: one or more similarity thresholds for transition from dbscan to hdbscan
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cluster_selection_methods: one or more of "eom" or "leaf" eom gives larger clusters.
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lsi_dimensions: either "all" or one or more available lsi similarity dimensions at INPATH.
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"""
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obj = hdbscan_lsi_grid_sweep(inpath,
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lsi_dimensions,
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outpath,
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list(map(int,min_cluster_sizes)),
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list(map(int,min_samples)),
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list(map(float,cluster_selection_epsilons)),
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cluster_selection_methods)
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obj.run(10)
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obj.save(savefile)
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if __name__ == "__main__":
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fire.Fire(run_hdbscan_lsi_grid_sweep)
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