add note to try other tf normalization strategies.

clustering/Makefile

@@ -2,26 +2,29 @@
 srun_singularity=source /gscratch/comdata/users/nathante/cdsc_reddit/bin/activate && srun_singularity.sh
 similarity_data=/gscratch/comdata/output/reddit_similarity
 clustering_data=/gscratch/comdata/output/reddit_clustering
-selection_grid="--max_iter=10000 --convergence_iter=15,30,100 --preference_quantile=0.85 --damping=0.5,0.6,0.7,0.8,0.85,0.9,0.95,0.97,0.99, --preference_quantile=0.1,0.3,0.5,0.7,0.9"
-all:$(clustering_data)/subreddit_comment_authors_30k.feather $(clustering_data)/subreddit_authors-tf_similarities_30k.feather $(clustering_data)/subreddit_comment_authors_10k.feather $(clustering_data)/subreddit_authors-tf_similarities_10k.feather $(clustering_data)/subreddit_comment_terms_30k.feather $(clustering_data)/subreddit_comment_terms_10k.feather
+selection_grid="--max_iter=3000 --convergence_iter=15,30,100 --damping=0.5,0.6,0.7,0.8,0.85,0.9,0.95,0.97,0.99, --preference_quantile=0.1,0.3,0.5,0.7,0.9"
+#selection_grid="--max_iter=3000 --convergence_iter=[15] --preference_quantile=[0.5] --damping=[0.99]"
+all:$(clustering_data)/subreddit_comment_authors_10k/selection_data.csv $(clustering_data)/subreddit_comment_authors-tf_10k/selection_data.csv $(clustering_data)/subreddit_comment_terms_10k/selection_data.csv
+# $(clustering_data)/subreddit_comment_authors_30k.feather/SUCCESS $(clustering_data)/subreddit_authors-tf_similarities_30k.feather/SUCCESS
+# $(clustering_data)/subreddit_comment_terms_30k.feather/SUCCESS
 
-$(clustering_data)/subreddit_comment_authors_10k.feather:selection.py $(similarity_data)/subreddit_comment_authors_10k.feather clustering.py
-	$(srun_singularity) python3 selection.py $(similarity_data)/subreddit_comment_authors_10k.feather $(clustering_data)/subreddit_comment_authors_10k $(selection_grid) -J 20
+$(clustering_data)/subreddit_comment_authors_10k/selection_data.csv:selection.py $(similarity_data)/subreddit_comment_authors_10k.feather clustering.py
+	$(srun_singularity) python3 selection.py $(similarity_data)/subreddit_comment_authors_10k.feather $(clustering_data)/subreddit_comment_authors_10k $(clustering_data)/subreddit_comment_authors_10k/selection_data.csv $(selection_grid) -J 20
 
-$(clustering_data)/subreddit_comment_terms_10k.feather:selection.py $(similarity_data)/subreddit_comment_terms_10k.feather clustering.py
-	$(srun_singularity) python3 selection.py $(similarity_data)/subreddit_comment_terms_10k.feather $(clustering_data)/subreddit_comment_terms_10k $(selection_grid) -J 20
+$(clustering_data)/subreddit_comment_terms_10k/selection_data.csv:selection.py $(similarity_data)/subreddit_comment_terms_10k.feather clustering.py
+	$(srun_singularity) python3 selection.py $(similarity_data)/subreddit_comment_terms_10k.feather $(clustering_data)/subreddit_comment_terms_10k  $(clustering_data)/subreddit_comment_terms_10k/selection_data.csv $(selection_grid) -J 20 
 
-$(clustering_data)/subreddit_authors-tf_similarities_10k.feather:clustering.py $(similarity_data)/subreddit_comment_authors-tf_10k.feather
-	$(srun_singularity) python3 selection.py $(similarity_data)/subreddit_comment_authors-tf_10k.feather $(clustering_data)/subreddit_comment_authors-tf_10k $(selection_grid) -J 20
+$(clustering_data)/subreddit_comment_authors-tf_10k/selection_data.csv:clustering.py $(similarity_data)/subreddit_comment_authors-tf_10k.feather
+	$(srun_singularity) python3 selection.py $(similarity_data)/subreddit_comment_authors-tf_10k.feather $(clustering_data)/subreddit_comment_authors-tf_10k  $(clustering_data)/subreddit_comment_authors-tf_10k/selection_data.csv $(selection_grid) -J 20
 
-$(clustering_data)/subreddit_comment_authors_30k.feather:selection.py $(similarity_data)/subreddit_comment_authors_30k.feather clustering.py
-	$(srun_singularity) python3 selection.py $(similarity_data)/subreddit_comment_authors_30k.feather $(clustering_data)/subreddit_comment_authors_30k $(selection_grid) -J 10
+# $(clustering_data)/subreddit_comment_authors_30k.feather/SUCCESS:selection.py $(similarity_data)/subreddit_comment_authors_30k.feather clustering.py
+# 	$(srun_singularity) python3 selection.py $(similarity_data)/subreddit_comment_authors_30k.feather $(clustering_data)/subreddit_comment_authors_30k $(selection_grid) -J 10 && touch $(clustering_data)/subreddit_comment_authors_30k.feather/SUCCESS
 
-$(clustering_data)/subreddit_comment_terms_30k.feather:selection.py $(similarity_data)/subreddit_comment_terms_30k.feather clustering.py
-	$(srun_singularity) python3 selection.py $(similarity_data)/subreddit_comment_terms_30k.feather $(clustering_data)/subreddit_comment_terms_30k $(selection_grid) -J 10
+# $(clustering_data)/subreddit_comment_terms_30k.feather/SUCCESS:selection.py $(similarity_data)/subreddit_comment_terms_30k.feather clustering.py
+# 	$(srun_singularity) python3 selection.py $(similarity_data)/subreddit_comment_terms_30k.feather $(clustering_data)/subreddit_comment_terms_30k $(selection_grid) -J 10 && touch $(clustering_data)/subreddit_comment_terms_30k.feather/SUCCESS
 
-$(clustering_data)/subreddit_authors-tf_similarities_30k.feather:clustering.py $(similarity_data)/subreddit_comment_authors-tf_30k.feather
-	$(srun_singularity) python3 selection.py $(similarity_data)/subreddit_comment_authors-tf_30k.feather $(clustering_data)/subreddit_comment_authors-tf_30k $(selection_grid) -J 8
+# $(clustering_data)/subreddit_authors-tf_similarities_30k.feather/SUCCESS:clustering.py $(similarity_data)/subreddit_comment_authors-tf_30k.feather
+# 	$(srun_singularity) python3 selection.py $(similarity_data)/subreddit_comment_authors-tf_30k.feather $(clustering_data)/subreddit_comment_authors-tf_30k $(selection_grid) -J 8 && touch $(clustering_data)/subreddit_authors-tf_similarities_30k.feather/SUCCESS
 
 
 # $(clustering_data)/subreddit_comment_authors_100k.feather:clustering.py $(similarity_data)/subreddit_comment_authors_100k.feather

clustering/clustering.py

@@ -24,7 +24,7 @@ def _affinity_clustering(mat, subreddits, output, damping=0.9, max_iter=100000,
     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. 
     '''
-    print(f"damping:{damping}; convergenceIter:{convergence_iter}; preferenceQuantile:{preference_quantilne}")
+    print(f"damping:{damping}; convergenceIter:{convergence_iter}; preferenceQuantile:{preference_quantile}")
 
     preference = np.quantile(mat,preference_quantile)
 

clustering/selection.py

@@ -6,6 +6,7 @@ from dataclasses import dataclass
 from multiprocessing  import Pool, cpu_count, Array, Process
 from pathlib import Path
 from itertools import product, starmap
+import numpy as np
 import pandas as pd
 import fire
 import sys
@@ -23,15 +24,27 @@ class clustering_result:
     alt_silhouette_score:float
     name:str
 
-def do_clustering(damping, convergence_iter, preference_quantile, name, mat, subreddits,  max_iter,  outdir:Path, random_state, verbose, alt_mat):
+
+def sim_to_dist(mat):
+    dist = 1-mat
+    dist[dist < 0] = 0
+    np.fill_diagonal(dist,0)
+    return dist
+
+def do_clustering(damping, convergence_iter, preference_quantile, name, mat, subreddits,  max_iter,  outdir:Path, random_state, verbose, alt_mat, overwrite=False):
     if name is None:
-        name = f"damping-{damping}_convergenceIter-{convergence_iter}_preferenceQuantile-{convergence_iter}"
+        name = f"damping-{damping}_convergenceIter-{convergence_iter}_preferenceQuantile-{preference_quantile}"
     print(name)
     sys.stdout.flush()
     outpath = outdir / (str(name) + ".feather")
     print(outpath)
     clustering = _affinity_clustering(mat, subreddits, outpath, damping, max_iter, convergence_iter, preference_quantile, random_state, verbose)
-    score = silhouette_score(clustering.affinity_matrix_, clustering.labels_, metric='precomputed')
+    mat = sim_to_dist(clustering.affinity_matrix_)
+
+    score = silhouette_score(mat, clustering.labels_, metric='precomputed')
+
+    if alt_mat is not None:
+        alt_distances = sim_to_dist(alt_mat)
         alt_score = silhouette_score(alt_mat, clustering.labels_, metric='precomputed')
     
     res = clustering_result(outpath=outpath,
@@ -47,7 +60,7 @@ def do_clustering(damping, convergence_iter, preference_quantile, name, mat, sub
 
 # alt similiarities is for checking the silhouette coefficient of an alternative measure of similarity (e.g., topic similarities for user clustering).
 
-def select_affinity_clustering(similarities, outdir, damping=[0.9], max_iter=100000, convergence_iter=[30], preference_quantile=[0.5], random_state=1968, verbose=True, alt_similarities=None, J=None):
+def select_affinity_clustering(similarities, outdir, outinfo, damping=[0.9], max_iter=100000, convergence_iter=[30], preference_quantile=[0.5], random_state=1968, verbose=True, alt_similarities=None, J=None):
 
     damping = list(map(float,damping))
     convergence_iter = convergence_iter = list(map(int,convergence_iter))
@@ -80,8 +93,9 @@ def select_affinity_clustering(similarities, outdir, damping=[0.9], max_iter=100
     print("running clustering selection")
     clustering_data = pool.starmap(_do_clustering, hyper_grid)
     clustering_data = pd.DataFrame(list(clustering_data))
+    clustering_data.to_csv(outinfo)
+    
     return clustering_data
 
-
 if __name__ == "__main__":
-    fire.Fire(select_affinity_clustering)
+    x = fire.Fire(select_affinity_clustering)

dumps/pull_pushshift_comments.sh

@@ -8,7 +8,5 @@ wget -r --no-parent -A 'RC_201*.bz2' -U $user_agent -P $output_dir -nd -nc $base
 wget -r --no-parent -A 'RC_201*.xz' -U $user_agent -P $output_dir -nd -nc $base_url
 wget -r --no-parent -A 'RC_201*.zst' -U $user_agent -P $output_dir -nd -nc $base_url
 
-# starting in 2020 we use daily dumps not monthly dumps
-wget -r --no-parent -A 'RC_202*.gz' -U $user_agent -P $output_dir -nd -nc $base_url/daily/
 
 ./check_comments_shas.py

similarities/TODO

@@ -0,0 +1 @@
+Try normalizing tf by the mean or std instead of the max to avoid penalizing subreddits with very active users.