X-Git-Url: https://code.communitydata.science/cdsc_reddit.git/blobdiff_plain/4cb7eeec80c5a9c8f49339acd378c515e290ed81..87ffaa6858919bd830694d60dd4fc7b1857b462a:/clustering/clustering_base.py

diff --git a/clustering/clustering_base.py b/clustering/clustering_base.py
index 1d24533..3778fc3 100644
--- a/clustering/clustering_base.py
+++ b/clustering/clustering_base.py
@@ -3,6 +3,7 @@ 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:
@@ -38,9 +39,11 @@ class clustering_job:
         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]
-        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})
@@ -80,8 +83,9 @@ class clustering_job:
 
         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: