Merge remote-tracking branch 'origin/excise_reindex' into temp

[cdsc_reddit.git] / clustering / kmeans_clustering.py

diff --git a/clustering/kmeans_clustering.py b/clustering/kmeans_clustering.py
new file mode 100644 (file)
index 0000000..e41b88b
--- /dev/null
+++ b/clustering/kmeans_clustering.py
@@ -0,0 +1,148 @@
+from sklearn.cluster import KMeans
+import fire
+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
+
+@dataclass
+class kmeans_clustering_result_lsi(kmeans_clustering_result, lsi_result_mixin):
+    pass
+
+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)
+
+        self.n_clusters=n_clusters
+        self.n_init=n_init
+        self.max_iter=max_iter
+
+    def _kmeans_clustering(mat, *args, **kwargs):
+
+        clustering = KMeans(*args,
+                            **kwargs,
+                            ).fit(mat)
+
+        return clustering
+
+
+    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
+    
+
+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}"
+
+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)
+
+    def namer(self, *args, **kwargs):
+        s = kmeans_grid_sweep.namer(self, *args[1:], **kwargs)
+        s += f"_lsi-{self.lsi_dim}"
+        return s
+
+class kmeans_lsi_grid_sweep(lsi_grid_sweep):
+    def __init__(self,
+                 inpath,
+                 lsi_dims,
+                 outpath,
+                 n_clusters,
+                 n_inits,
+                 max_iters
+                 ):
+
+        super().__init__(kmeans_lsi_job,
+                         _kmeans_lsi_grid_sweep,
+                         inpath,
+                         lsi_dims,
+                         outpath,
+                         n_clusters,
+                         n_inits,
+                         max_iters)
+
+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]
+
+    gs = kmeans_lsi_grid_sweep(inpath, 'all', outpath, n_clusters, n_init, max_iter)
+    gs.run(1)
+
+    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")
+
+
+if __name__ == "__main__":
+
+    fire.Fire{'grid_sweep':kmeans_grid_sweep,
+              'grid_sweep_lsi':kmeans_lsi_grid_sweep
+              'cluster':kmeans_job,
+              'cluster_lsi':kmeans_lsi_job}