Refactor to make a decent api.

[cdsc_reddit.git] / clustering / hdbscan_clustering_lsi.py

diff --git a/clustering/hdbscan_clustering_lsi.py b/clustering/hdbscan_clustering_lsi.py
new file mode 100644 (file)
index 0000000..73b5276
--- /dev/null
+++ b/clustering/hdbscan_clustering_lsi.py
@@ -0,0 +1,101 @@
+from hdbscan_clustering import hdbscan_job, hdbscan_grid_sweep, hdbscan_clustering_result
+from lsi_base import lsi_grid_sweep, lsi_mixin, lsi_result_mixin
+from grid_sweep import grid_sweep
+import fire
+from dataclasses import dataclass
+
+@dataclass
+class hdbscan_clustering_result_lsi(hdbscan_clustering_result, lsi_result_mixin):
+    pass 
+
+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)
+
+    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
+
+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_lsi_grid_sweep(grid_sweep):
+    def __init__(self,
+                 inpath,
+                 outpath,
+                 lsi_dim,
+                 *args,
+                 **kwargs):
+        print(args)
+        print(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
+
+def run_hdbscan_lsi_grid_sweep(savefile, inpath, outpath,  min_cluster_sizes=[2], min_samples=[1], cluster_selection_epsilons=[0], cluster_selection_methods=['eom'],lsi_dimensions='all'):
+    """Run hdbscan clustering once or more with different parameters.
+    
+    Usage:
+    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.
+
+    Keword arguments:
+    savefile: path to save the metadata and diagnostics 
+    inpath: path to folder containing feather files with LSI similarity labeled matrices of subreddit similarities.
+    outpath: path to output fit clusterings.
+    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_epsilons: one or more similarity thresholds for transition from dbscan to hdbscan
+    cluster_selection_methods: one or more of "eom" or "leaf" eom gives larger clusters. 
+    lsi_dimensions: either "all" or one or more available lsi similarity dimensions at INPATH.
+    """    
+
+    obj = hdbscan_lsi_grid_sweep(inpath,
+                                 lsi_dimensions,
+                                 outpath,
+                                 map(int,min_cluster_sizes),
+                                 map(int,min_samples),
+                                 map(float,cluster_selection_epsilons),
+                                 cluster_selection_methods
+                                 )
+
+    obj.run(10)
+    obj.save(savefile)
+
+
+if __name__ == "__main__":
+    fire.Fire(run_hdbscan_lsi_grid_sweep)