From: Nate E TeBlunthuis Date: Wed, 21 Apr 2021 23:56:25 +0000 (-0700) Subject: bugfixes in clustering selection. X-Git-Url: https://code.communitydata.science/cdsc_reddit.git/commitdiff_plain/37dd0ef55fbc9e73f97747aaa81089509a69aa6f?ds=inline bugfixes in clustering selection. --- diff --git a/clustering/Makefile b/clustering/Makefile index adaa8fe..338f0a6 100644 --- a/clustering/Makefile +++ b/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 diff --git a/clustering/clustering.py b/clustering/clustering.py index cac5730..153a5c9 100755 --- a/clustering/clustering.py +++ b/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) diff --git a/clustering/selection.py b/clustering/selection.py index bfa1c31..520857d 100644 --- a/clustering/selection.py +++ b/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 @@ -23,16 +24,28 @@ 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') - 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, @@ -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)