add visualization for 10000 subreddits based on author-tf similarities.

[cdsc_reddit.git] / clustering / clustering.py

diff --git a/clustering/clustering.py b/clustering/clustering.py
old mode 100644 (file)
new mode 100755 (executable)
index 38af31c..e652304
--- a/clustering/clustering.py
+++ b/clustering/clustering.py
@@ -1,12 +1,15 @@
+#!/usr/bin/env python3
+

 import pandas as pd
 import numpy as np
 from sklearn.cluster import AffinityPropagation
 import fire
 
 import pandas as pd
 import numpy as np
 from sklearn.cluster import AffinityPropagation
 import fire
 

-def affinity_clustering(similarities, output, damping=0.5, max_iter=100000, convergence_iter=30, preference_quantile=0.5, random_state=1968):
+def affinity_clustering(similarities, output, damping=0.9, max_iter=100000, convergence_iter=30, preference_quantile=0.5, random_state=1968, verbose=True):

     '''
     similarities: feather file with a dataframe of similarity scores
     preference_quantile: parameter controlling how many clusters to make. higher values = more clusters. 0.85 is a good value with 3000 subreddits.
     '''
     similarities: feather file with a dataframe of similarity scores
     preference_quantile: parameter controlling how many clusters to make. higher values = more clusters. 0.85 is a good value with 3000 subreddits.

+    damping: parameter controlling how iterations are merged. Higher values make convergence faster and more dependable. 0.85 is a good value for the 10000 subreddits by author. 

     '''
 
     df = pd.read_feather(similarities)
     '''
 
     df = pd.read_feather(similarities)


@@ -16,6 +19,8 @@ def affinity_clustering(similarities, output, damping=0.5, max_iter=100000, conv
 
     preference = np.quantile(mat,preference_quantile)
 
 
     preference = np.quantile(mat,preference_quantile)
 

+    print(f"preference is {preference}")
+

     print("data loaded")
 
     clustering = AffinityPropagation(damping=damping,
     print("data loaded")
 
     clustering = AffinityPropagation(damping=damping,


@@ -24,6 +29,7 @@ def affinity_clustering(similarities, output, damping=0.5, max_iter=100000, conv
                                      copy=False,
                                      preference=preference,
                                      affinity='precomputed',
                                      copy=False,
                                      preference=preference,
                                      affinity='precomputed',

+                                     verbose=verbose,

                                      random_state=random_state).fit(mat)
 
 
                                      random_state=random_state).fit(mat)