version of weekly_cosine_similarities.py from klone

[cdsc_reddit.git] / clustering / clustering.py

diff --git a/clustering/clustering.py b/clustering/clustering.py
index 4cde71787eb5f208a0e51afb68ef57f1f99c1106..153a5c940d520d60fc27acfff06a6bd70107e909 100755 (executable)
--- a/clustering/clustering.py
+++ b/clustering/clustering.py
@@ -1,29 +1,36 @@
 #!/usr/bin/env python3
-
+# TODO: replace prints with logging.
+import sys
 import pandas as pd
 import numpy as np
 from sklearn.cluster import AffinityPropagation
 import fire
+from pathlib import Path
+
+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)
+
+def affinity_clustering(similarities, *args, **kwargs):
+    subreddits, mat = read_similarity_mat(similarities)
+    return _affinity_clustering(mat, subreddits, *args, **kwargs)
 
-def affinity_clustering(similarities, output, damping=0.9, max_iter=100000, convergence_iter=30, preference_quantile=0.5, random_state=1968, verbose=True):
+def _affinity_clustering(mat, subreddits, 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.
     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)
-    n = df.shape[0]
-    mat = np.array(df.drop('_subreddit',1))
-    mat[range(n),range(n)] = 1
-    assert(all(np.diag(mat)==1))
+    print(f"damping:{damping}; convergenceIter:{convergence_iter}; preferenceQuantile:{preference_quantile}")
 
     preference = np.quantile(mat,preference_quantile)
 
     print(f"preference is {preference}")
-
     print("data loaded")
-
+    sys.stdout.flush()
     clustering = AffinityPropagation(damping=damping,
                                      max_iter=max_iter,
                                      convergence_iter=convergence_iter,
@@ -39,7 +46,7 @@ def affinity_clustering(similarities, output, damping=0.9, max_iter=100000, conv
 
     print(f"found {len(set(clusters))} clusters")
 
-    cluster_data = pd.DataFrame({'subreddit': df.subreddit,'cluster':clustering.labels_})
+    cluster_data = pd.DataFrame({'subreddit': subreddits,'cluster':clustering.labels_})
 
     cluster_sizes = cluster_data.groupby("cluster").count()
     print(f"the largest cluster has {cluster_sizes.subreddit.max()} members")
@@ -48,7 +55,10 @@ def affinity_clustering(similarities, output, damping=0.9, max_iter=100000, conv
 
     print(f"{(cluster_sizes.subreddit==1).sum()} clusters have 1 member")
 
+    sys.stdout.flush()
     cluster_data.to_feather(output)
+    print(f"saved {output}")
+    return clustering
 
 if __name__ == "__main__":
     fire.Fire(affinity_clustering)