script for picking the best clustering given constraints

[cdsc_reddit.git] / clustering / clustering_base.py

diff --git a/clustering/clustering_base.py b/clustering/clustering_base.py
index 1d864383cb791c30d803de69aa281be8a966cafe..3778fc3fa91259f49a1b1470e7d0901e1f3ee6ba 100644 (file)
--- a/clustering/clustering_base.py
+++ b/clustering/clustering_base.py
@@ -2,48 +2,104 @@ from pathlib import Path
 import numpy as np
 import pandas as pd
 from dataclasses import dataclass
 import numpy as np
 import pandas as pd
 from dataclasses import dataclass

+from sklearn.metrics import silhouette_score, silhouette_samples
+from collections import Counter

 
 

-def sim_to_dist(mat):
-    dist = 1-mat
-    dist[dist < 0] = 0
-    np.fill_diagonal(dist,0)
-    return dist
+# this is meant to be an interface, not created directly
+class clustering_job:
+    def __init__(self, infile, outpath, name, call, *args, **kwargs):
+        self.outpath = Path(outpath)
+        self.call = call
+        self.args = args
+        self.kwargs = kwargs
+        self.infile = Path(infile)
+        self.name = name
+        self.hasrun = False

 
 

-def process_clustering_result(clustering, subreddits):
+    def run(self):
+        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
+        
+    def get_info(self):
+        if not self.hasrun:
+            self.run()

 
 

-    if hasattr(clustering,'n_iter_'):
-        print(f"clustering took {clustering.n_iter_} iterations")
+        self.result = clustering_result(outpath=str(self.outpath.resolve()),
+                                        silhouette_score=self.score,
+                                        name=self.name,
+                                        n_clusters=self.n_clusters,
+                                        n_isolates=self.n_isolates,
+                                        silhouette_samples = self.silsampout
+                                        )
+        return self.result

 
 

-    clusters = clustering.labels_
+    def silhouette(self):
+        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 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})
+            self.outpath.mkdir(parents=True, exist_ok=True)
+            silsampout = self.outpath / ("silhouette_samples-" + self.name +  ".feather")
+            self.silsampout = silsampout.resolve()
+            silhouette_samp.to_feather(self.silsampout)
+        else:
+            score = None
+            self.silsampout = None
+        return score

 
 

-    print(f"found {len(set(clusters))} clusters")
+    def read_distance_mat(self, similarities, use_threads=True):
+        df = pd.read_feather(similarities, use_threads=use_threads)
+        mat = np.array(df.drop('_subreddit',1))
+        n = mat.shape[0]
+        mat[range(n),range(n)] = 1
+        return (df._subreddit,1-mat)

 
 

-    cluster_data = pd.DataFrame({'subreddit': subreddits,'cluster':clustering.labels_})
+    def process_clustering(self, clustering, subreddits):

 
 

-    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")
+        if hasattr(clustering,'n_iter_'):
+            print(f"clustering took {clustering.n_iter_} iterations")

 
 

-    print(f"the median cluster has {cluster_sizes.subreddit.median()} members")
+        clusters = clustering.labels_
+        self.n_clusters = len(set(clusters))

 
 

-    print(f"{(cluster_sizes.subreddit==1).sum()} clusters have 1 member")
+        print(f"found {self.n_clusters} clusters")

 
 

-    print(f"{(cluster_sizes.loc[cluster_sizes.cluster==-1,['subreddit']])} subreddits are in cluster -1",flush=True)
+        cluster_data = pd.DataFrame({'subreddit': subreddits,'cluster':clustering.labels_})

 
 

-    return cluster_data
+        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")

 
 

+        print(f"the median cluster has {cluster_sizes.subreddit.median()} members")
+        n_isolates1 = (cluster_sizes.subreddit==1).sum()
+
+        print(f"{n_isolates1} clusters have 1 member")
+
+        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:
+            self.n_isolates = n_isolates2
+        else:
+            self.n_isolates = n_isolates1
+
+        return cluster_data

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

-    max_iter:int

     silhouette_score:float
     silhouette_score:float

-    alt_silhouette_score:float

     name:str
     n_clusters:int
     name:str
     n_clusters:int

-
-def read_similarity_mat(similarities, use_threads=True):
-    df = pd.read_feather(similarities, use_threads=use_threads)
-    mat = np.array(df.drop('_subreddit',1))
-    n = mat.shape[0]
-    mat[range(n),range(n)] = 1
-    return (df._subreddit,mat)
+    n_isolates:int
+    silhouette_samples:str