Merge branch 'excise_reindex' of code:cdsc_reddit into excise_reindex

[cdsc_reddit.git] / clustering / clustering.py

diff --git a/clustering/clustering.py b/clustering/clustering.py
index 4cde71787eb5f208a0e51afb68ef57f1f99c1106..6ee78420824c0af5cdf59410eff7bda5226e39c1 100755 (executable)
--- a/clustering/clustering.py
+++ b/clustering/clustering.py
@@ -1,29 +1,35 @@
 #!/usr/bin/env python3
 #!/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
 import pandas as pd
 import numpy as np
 from sklearn.cluster import AffinityPropagation
 import fire

-
-def affinity_clustering(similarities, output, damping=0.9, max_iter=100000, convergence_iter=30, preference_quantile=0.5, random_state=1968, verbose=True):
+from pathlib import Path
+from multiprocessing import cpu_count
+from dataclasses import dataclass
+from clustering_base import sim_to_dist, process_clustering_result, clustering_result, read_similarity_mat
+
+def affinity_clustering(similarities, output, *args, **kwargs):
+    subreddits, mat = read_similarity_mat(similarities)
+    clustering = _affinity_clustering(mat, *args, **kwargs)
+    cluster_data = process_clustering_result(clustering, subreddits)
+    cluster_data['algorithm'] = 'affinity'
+    return(cluster_data)
+
+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
+    similarities: matrix 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. 
     '''
     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}")
 
     preference = np.quantile(mat,preference_quantile)
 
     print(f"preference is {preference}")

-

     print("data loaded")
     print("data loaded")

-
+    sys.stdout.flush()

     clustering = AffinityPropagation(damping=damping,
                                      max_iter=max_iter,
                                      convergence_iter=convergence_iter,
     clustering = AffinityPropagation(damping=damping,
                                      max_iter=max_iter,
                                      convergence_iter=convergence_iter,


@@ -33,22 +39,14 @@ def affinity_clustering(similarities, output, damping=0.9, max_iter=100000, conv
                                      verbose=verbose,
                                      random_state=random_state).fit(mat)
 
                                      verbose=verbose,
                                      random_state=random_state).fit(mat)
 

+    cluster_data = process_clustering_result(clustering, subreddits)
+    output = Path(output)
+    output.parent.mkdir(parents=True,exist_ok=True)
+    cluster_data.to_feather(output)
+    print(f"saved {output}")
+    return clustering

 
 

-    print(f"clustering took {clustering.n_iter_} iterations")
-    clusters = clustering.labels_
-
-    print(f"found {len(set(clusters))} clusters")
-
-    cluster_data = pd.DataFrame({'subreddit': df.subreddit,'cluster':clustering.labels_})
-
-    cluster_sizes = cluster_data.groupby("cluster").count()
-    print(f"the largest cluster has {cluster_sizes.subreddit.max()} members")
-
-    print(f"the median cluster has {cluster_sizes.subreddit.median()} members")
-
-    print(f"{(cluster_sizes.subreddit==1).sum()} clusters have 1 member")

 
 

-    cluster_data.to_feather(output)

 
 if __name__ == "__main__":
     fire.Fire(affinity_clustering)
 
 if __name__ == "__main__":
     fire.Fire(affinity_clustering)