X-Git-Url: https://code.communitydata.science/cdsc_reddit.git/blobdiff_plain/4cb7eeec80c5a9c8f49339acd378c515e290ed81..refs/heads/icwsm_dataverse:/clustering/clustering_base.py

diff --git a/clustering/clustering_base.py b/clustering/clustering_base.py
index 1d24533..2f37b68 100644
--- 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 collections import Counter
 
 # this is meant to be an interface, not created directly
 class clustering_job:
@@ -19,10 +21,17 @@ class clustering_job:
         self.subreddits, self.mat = self.read_distance_mat(self.infile)
         self.clustering = self.call(self.mat, *self.args, **self.kwargs)
         self.cluster_data = self.process_clustering(self.clustering, self.subreddits)
-        self.score = self.silhouette()
         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:
@@ -38,9 +47,11 @@ 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})
@@ -51,11 +62,13 @@ class clustering_job:
         else:
             score = None
             self.silsampout = None
+
         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))
+        mat = np.array(df.drop('_subreddit',axis=1))
         n = mat.shape[0]
         mat[range(n),range(n)] = 1
         return (df._subreddit,1-mat)
@@ -69,9 +82,13 @@ class clustering_job:
         self.n_clusters = len(set(clusters))
 
         print(f"found {self.n_clusters} clusters")
-
         cluster_data = pd.DataFrame({'subreddit': subreddits,'cluster':clustering.labels_})
 
+
+        self.score = self.silhouette()
+        print(f"silhouette_score:{self.score}")
+
+
         cluster_sizes = cluster_data.groupby("cluster").count().reset_index()
         print(f"the largest cluster has {cluster_sizes.loc[cluster_sizes.cluster!=-1].subreddit.max()} members")
 
@@ -80,8 +97,9 @@ 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:
@@ -91,6 +109,38 @@ class clustering_job:
 
         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):
+        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