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
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)
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
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')
- alt_score = silhouette_score(alt_mat, 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,
damping=damping,
# 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))
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)