X-Git-Url: https://code.communitydata.science/cdsc_reddit.git/blobdiff_plain/b4dd9acbd8e499d87714413b8260240341ebf7d7..e1c9d9af6fccf3f2de24d192f9678318ad04a4ea:/clustering/Makefile

diff --git a/clustering/Makefile b/clustering/Makefile
index 115b218..d09cfd9 100644
--- a/clustering/Makefile
+++ b/clustering/Makefile
@@ -1,10 +1,76 @@
 #srun_cdsc='srun -p comdata-int -A comdata --time=300:00:00 --time-min=00:15:00 --mem=100G --ntasks=1 --cpus-per-task=28'
-all:/gscratch/comdata/output/reddit_clustering/comment_authors_10000.feather /gscratch/comdata/output/reddit_clustering/comment_terms_10000.feather
+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
+kmeans_selection_grid="--max_iter=3000 --n_init=[10] --n_clusters=[100,500,1000,1500,2000,2500,3000,2350,3500,3570,4000]"
+#selection_grid="--max_iter=3000 --convergence_iter=[15] --preference_quantile=[0.5] --damping=[0.99]"
+all:$(clustering_data)/subreddit_comment_authors_10k/kmeans/selection_data.csv $(clustering_data)/subreddit_comment_authors-tf_10k/kmeans/selection_data.csv $(clustering_data)/subreddit_comment_terms_10k/kmeans/selection_data.csv $(clustering_data)/subreddit_comment_terms_10k/affinity/selection_data.csv $(clustering_data)/subreddit_comment_authors_10k/affinity/selection_data.csv $(clustering_data)/subreddit_comment_authors-tf_10k/affinity/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
 
-/gscratch/comdata/output/reddit_clustering/comment_authors_10000.feather:clustering.py /gscratch/comdata/output/reddit_similarity/comment_authors_10000.feather
-#	$srun_cdsc python3
-	./clustering.py /gscratch/comdata/output/reddit_similarity/comment_authors_10000.feather /gscratch/comdata/output/reddit_clustering/comment_authors_10000.feather ---max_iter=400 --convergence_iter=15 --preference_quantile=0.85 --damping=0.85
+$(clustering_data)/subreddit_comment_authors_10k/kmeans/selection_data.csv:selection.py $(similarity_data)/subreddit_comment_authors_10k.feather clustering.py
+	$(srun_singularity) python3 selection.py kmeans $(similarity_data)/subreddit_comment_authors_10k.feather $(clustering_data)/subreddit_comment_authors_10k/kmeans $(clustering_data)/subreddit_comment_authors_10k/kmeans/selection_data.csv $(kmeans_selection_grid)
 
-/gscratch/comdata/output/reddit_clustering/comment_terms_10000.feather:clustering.py /gscratch/comdata/output/reddit_similarity/comment_terms_10000.feather
-#	$srun_cdsc python3
-	./clustering.py /gscratch/comdata/output/reddit_similarity/comment_terms_10000.feather /gscratch/comdata/output/reddit_clustering/comment_terms_10000.feather ---max_iter=1000 --convergence_iter=15 --preference_quantile=0.9 --damping=0.5
+$(clustering_data)/subreddit_comment_terms_10k/kmeans/selection_data.csv:selection.py $(similarity_data)/subreddit_comment_terms_10k.feather clustering.py
+	$(srun_singularity) python3 selection.py kmeans $(similarity_data)/subreddit_comment_terms_10k.feather $(clustering_data)/subreddit_comment_terms_10k/kmeans  $(clustering_data)/subreddit_comment_terms_10k/kmeans/selection_data.csv $(kmeans_selection_grid)
+
+$(clustering_data)/subreddit_comment_authors-tf_10k/kmeans/selection_data.csv:clustering.py $(similarity_data)/subreddit_comment_authors-tf_10k.feather
+	$(srun_singularity) python3 selection.py kmeans $(similarity_data)/subreddit_comment_authors-tf_10k.feather $(clustering_data)/subreddit_comment_authors-tf_10k/kmeans  $(clustering_data)/subreddit_comment_authors-tf_10k/kmeans/selection_data.csv $(kmeans_selection_grid)
+
+
+affinity_selection_grid="--max_iter=3000 --convergence_iter=[15] --preference_quantile=[0.5] --damping=[0.99]"
+$(clustering_data)/subreddit_comment_authors_10k/affinity/selection_data.csv:selection.py $(similarity_data)/subreddit_comment_authors_10k.feather clustering.py
+	$(srun_singularity) python3 selection.py affinity $(similarity_data)/subreddit_comment_authors_10k.feather $(clustering_data)/subreddit_comment_authors_10k/affinity $(clustering_data)/subreddit_comment_authors_10k/affinity/selection_data.csv $(affinity_selection_grid) -J 20
+
+$(clustering_data)/subreddit_comment_terms_10k/affinity/selection_data.csv:selection.py $(similarity_data)/subreddit_comment_terms_10k.feather clustering.py
+	$(srun_singularity) python3 selection.py affinity $(similarity_data)/subreddit_comment_terms_10k.feather $(clustering_data)/subreddit_comment_terms_10k/affinity  $(clustering_data)/subreddit_comment_terms_10k/affinity/selection_data.csv $(affinity_selection_grid) -J 20 
+
+$(clustering_data)/subreddit_comment_authors-tf_10k/affinity/selection_data.csv:clustering.py $(similarity_data)/subreddit_comment_authors-tf_10k.feather
+	$(srun_singularity) python3 selection.py affinity $(similarity_data)/subreddit_comment_authors-tf_10k.feather $(clustering_data)/subreddit_comment_authors-tf_10k/affinity  $(clustering_data)/subreddit_comment_authors-tf_10k/affinity/selection_data.csv $(affinity_selection_grid) -J 20
+
+clean:
+	rm -f $(clustering_data)/subreddit_comment_authors-tf_10k/affinity/selection_data.csv
+	rm -f $(clustering_data)/subreddit_comment_authors_10k/affinity/selection_data.csv
+	rm -f $(clustering_data)/subreddit_comment_terms_10k/affinity/selection_data.csv
+	rm -f $(clustering_data)/subreddit_comment_authors-tf_10k/kmeans/selection_data.csv
+	rm -f $(clustering_data)/subreddit_comment_authors_10k/kmeans/selection_data.csv
+	rm -f $(clustering_data)/subreddit_comment_terms_10k/kmeans/selection_data.csv
+
+PHONY: clean
+
+# $(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/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/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
+# 	 $(srun_singularity) python3 clustering.py $(similarity_data)/subreddit_comment_authors_100k.feather $(clustering_data)/subreddit_comment_authors_100k.feather ---max_iter=400 --convergence_iter=15 --preference_quantile=0.85 --damping=0.85
+
+# $(clustering_data)/comment_terms_100k.feather:clustering.py $(similarity_data)/subreddit_comment_terms_100k.feather
+# 	$(srun_singularity) python3 clustering.py $(similarity_data)/comment_terms_10000.feather $(clustering_data)/comment_terms_10000.feather ---max_iter=1000 --convergence_iter=15 --preference_quantile=0.9 --damping=0.5
+
+# $(clustering_data)/subreddit_comment_author-tf_100k.feather:clustering.py $(similarity_data)/subreddit_comment_author-tf_100k.feather
+# 	$(srun_singularity) python3 clustering.py $(similarity_data)/subreddit_comment_author-tf_100k.parquet $(clustering_data)/subreddit_comment_author-tf_100k.feather ---max_iter=400 --convergence_iter=15 --preference_quantile=0.5 --damping=0.85
+
+
+# it's pretty difficult to get a result that isn't one huge megacluster. A sign that it's bullcrap
+# /gscratch/comdata/output/reddit_clustering/wang_similarity_10000.feather:clustering.py /gscratch/comdata/output/reddit_similarity/wang_similarity_10000.feather
+# 	./clustering.py /gscratch/comdata/output/reddit_similarity/wang_similarity_10000.feather /gscratch/comdata/output/reddit_clustering/wang_similarity_10000.feather ---max_iter=400 --convergence_iter=15 --preference_quantile=0.9 --damping=0.85
+
+# /gscratch/comdata/output/reddit_tsne/subreddit_author_tf_similarities_10000.feather:fit_tsne.py /gscratch/comdata/output/reddit_similarity/subreddit_author_tf_similarities_10000.parquet
+
+# 	start_spark_and_run.sh 1 fit_tsne.py --similarities=/gscratch/comdata/output/reddit_similarity/subreddit_author_tf_similarities_10000.parquet --output=/gscratch/comdata/output/reddit_tsne/subreddit_author_tf_similarities_10000.feather
+
+
+# /gscratch/comdata/output/reddit_tsne/wang_similarity_10000.feather:fit_tsne.py /gscratch/comdata/output/reddit_similarity/wang_similarity_10000.feather
+
+# 	python3 fit_tsne.py --similarities=/gscratch/comdata/output/reddit_similarity/wang_similarity_10000.feather --output=/gscratch/comdata/output/reddit_tsne/wang_similarity_10000.feather
+
+# /gscratch/comdata/output/reddit_tsne/comment_authors_10000.feather:clustering.py /gscratch/comdata/output/reddit_similarity/comment_authors_10000.feather
+# #	$srun_cdsc python3
+# 	start_spark_and_run.sh 1 fit_tsne.py --similarities=/gscratch/comdata/output/reddit_similarity/comment_authors_10000.feather --output=/gscratch/comdata/output/reddit_tsne/comment_authors_10000.feather