94 lines
3.4 KiB
Python
94 lines
3.4 KiB
Python
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import fire
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from dataclasses import dataclass
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from kmeans_clustering import kmeans_job, kmeans_clustering_result, kmeans_grid_sweep
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from lsi_base import lsi_mixin, lsi_result_mixin, lsi_grid_sweep
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from grid_sweep import grid_sweep
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@dataclass
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class kmeans_clustering_result_lsi(kmeans_clustering_result, lsi_result_mixin):
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pass
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class kmeans_lsi_job(kmeans_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 get_info(self):
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result = super().get_info()
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self.result = kmeans_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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class _kmeans_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 = kmeans_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 = kmeans_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 kmeans_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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n_clusters,
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n_inits,
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max_iters
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):
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super().__init__(kmeans_lsi_job,
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_kmeans_lsi_grid_sweep,
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inpath,
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lsi_dims,
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outpath,
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n_clusters,
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n_inits,
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max_iters)
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def run_kmeans_lsi_grid_sweep(savefile, inpath, outpath, n_clusters=[500], n_inits=[1], max_iters=[3000], lsi_dimensions="all"):
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"""Run kmeans clustering once or more with different parameters.
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Usage:
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kmeans_clustering_lsi.py --savefile=SAVEFILE --inpath=INPATH --outpath=OUTPATH d--lsi_dimensions=<"all"|csv number of LSI dimensions to use> --n_clusters=<csv number of clusters> --n_inits=<csv> --max_iters=<csv>
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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 kmeans clusterings.
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lsi_dimensions: either "all" or one or more available lsi similarity dimensions at INPATH.
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n_clusters: one or more numbers of kmeans clusters to select.
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n_inits: one or more numbers of different initializations to use for each clustering.
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max_iters: one or more numbers of different maximum interations.
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"""
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obj = kmeans_lsi_grid_sweep(inpath,
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lsi_dimensions,
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outpath,
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list(map(int,n_clusters)),
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list(map(int,n_inits)),
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list(map(int,max_iters))
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)
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obj.run(1)
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obj.save(savefile)
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if __name__ == "__main__":
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fire.Fire(run_kmeans_lsi_grid_sweep)
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