add support for umap->hdbscan clustering method

[cdsc_reddit.git] / clustering / clustering_base.py

diff --git a/clustering/clustering_base.py b/clustering/clustering_base.py
index 1d24533b520865d8e3f8bd53bad8a344178d8741..ced627d2863eb9448126c625020fb7aba530b21c 100644 (file)
--- a/clustering/clustering_base.py
+++ b/clustering/clustering_base.py
@@ -1,8 +1,10 @@
+import pickle

 from pathlib import Path
 import numpy as np
 import pandas as pd
 from dataclasses import dataclass
 from sklearn.metrics import silhouette_score, silhouette_samples
 from pathlib import Path
 import numpy as np
 import pandas as pd
 from dataclasses import dataclass
 from sklearn.metrics import silhouette_score, silhouette_samples

+from collections import Counter

 
 # this is meant to be an interface, not created directly
 class clustering_job:
 
 # this is meant to be an interface, not created directly
 class clustering_job:


@@ -23,6 +25,13 @@ class clustering_job:
         self.outpath.mkdir(parents=True, exist_ok=True)
         self.cluster_data.to_feather(self.outpath/(self.name + ".feather"))
         self.hasrun = True
         self.outpath.mkdir(parents=True, exist_ok=True)
         self.cluster_data.to_feather(self.outpath/(self.name + ".feather"))
         self.hasrun = True

+        self.cleanup()
+
+    def cleanup(self):
+        self.cluster_data = None
+        self.mat = None
+        self.clustering=None
+        self.subreddits=None

         
     def get_info(self):
         if not self.hasrun:
         
     def get_info(self):
         if not self.hasrun:


@@ -38,9 +47,11 @@ class clustering_job:
         return self.result
 
     def silhouette(self):
         return self.result
 
     def silhouette(self):

-        isolates = self.clustering.labels_ == -1
+        counts = Counter(self.clustering.labels_)
+        singletons = [key for key, value in counts.items() if value == 1]
+        isolates = (self.clustering.labels_ == -1) | (np.isin(self.clustering.labels_,np.array(singletons)))

         scoremat = self.mat[~isolates][:,~isolates]
         scoremat = self.mat[~isolates][:,~isolates]

-        if scoremat.shape[0] > 0:
+        if self.n_clusters > 1:

             score = silhouette_score(scoremat, self.clustering.labels_[~isolates], metric='precomputed')
             silhouette_samp = silhouette_samples(self.mat, self.clustering.labels_, metric='precomputed')
             silhouette_samp = pd.DataFrame({'subreddit':self.subreddits,'score':silhouette_samp})
             score = silhouette_score(scoremat, self.clustering.labels_[~isolates], metric='precomputed')
             silhouette_samp = silhouette_samples(self.mat, self.clustering.labels_, metric='precomputed')
             silhouette_samp = pd.DataFrame({'subreddit':self.subreddits,'score':silhouette_samp})


@@ -54,6 +65,7 @@ class clustering_job:
         return score
 
     def read_distance_mat(self, similarities, use_threads=True):
         return score
 
     def read_distance_mat(self, similarities, use_threads=True):

+        print(similarities)

         df = pd.read_feather(similarities, use_threads=use_threads)
         mat = np.array(df.drop('_subreddit',1))
         n = mat.shape[0]
         df = pd.read_feather(similarities, use_threads=use_threads)
         mat = np.array(df.drop('_subreddit',1))
         n = mat.shape[0]


@@ -80,8 +92,9 @@ class clustering_job:
 
         print(f"{n_isolates1} clusters have 1 member")
 
 
         print(f"{n_isolates1} clusters have 1 member")
 

-        n_isolates2 = (cluster_sizes.loc[cluster_sizes.cluster==-1,['subreddit']])
-
+        n_isolates2 = cluster_sizes.loc[cluster_sizes.cluster==-1,:]['subreddit'].to_list()
+        if len(n_isolates2) > 0:
+            n_isloates2 = n_isolates2[0]

         print(f"{n_isolates2} subreddits are in cluster -1",flush=True)
 
         if n_isolates1 == 0:
         print(f"{n_isolates2} subreddits are in cluster -1",flush=True)
 
         if n_isolates1 == 0:


@@ -91,6 +104,38 @@ class clustering_job:
 
         return cluster_data
 
 
         return cluster_data
 

+class twoway_clustering_job(clustering_job):
+    def __init__(self, infile, outpath, name, call1, call2, args1, args2):
+        self.outpath = Path(outpath)
+        self.call1 = call1
+        self.args1 = args1
+        self.call2 = call2
+        self.args2 = args2
+        self.infile = Path(infile)
+        self.name = name
+        self.hasrun = False
+        self.args = args1|args2
+
+    def run(self):
+        self.subreddits, self.mat = self.read_distance_mat(self.infile)
+        self.step1 = self.call1(self.mat, **self.args1)
+        self.clustering = self.call2(self.mat, self.step1, **self.args2)
+        self.cluster_data = self.process_clustering(self.clustering, self.subreddits)
+        self.hasrun = True
+        self.after_run()
+        self.cleanup()
+
+    def after_run():
+        self.score = self.silhouette()
+        self.outpath.mkdir(parents=True, exist_ok=True)
+        print(self.outpath/(self.name+".feather"))
+        self.cluster_data.to_feather(self.outpath/(self.name + ".feather"))
+
+
+    def cleanup(self):
+        super().cleanup()
+        self.step1 = None
+

 @dataclass
 class clustering_result:
     outpath:Path
 @dataclass
 class clustering_result:
     outpath:Path