make pass keyword arg to dataframe.drop

bots/good_bad_bot.py

@@ -1,74 +0,0 @@
-from pyspark.sql import functions as f
-from pyspark.sql import SparkSession
-from pyspark.sql import Window
-from pyspark.sql.types import FloatType
-import zlib
-
-def zlib_entropy_rate(s):
-    sb = s.encode()
-    if len(sb) == 0:
-        return None
-    else:
-        return len(zlib.compress(s.encode(),level=6))/len(s.encode())
-    
-zlib_entropy_rate_udf = f.udf(zlib_entropy_rate,FloatType())
-
-spark = SparkSession.builder.getOrCreate()
-
-df = spark.read.parquet("/gscratch/comdata/output/reddit_comments_by_author.parquet",compression='snappy')
-
-df = df.withColumn("saidbot",f.lower(f.col("body")).like("%bot%"))
-
-# df = df.filter(df.subreddit=='seattle')
-# df = df.cache()
-botreplies = df.filter(f.lower(df.body).rlike(".*[good|bad] bot.*"))
-botreplies = botreplies.select([f.col("parent_id").substr(4,100).alias("bot_comment_id"),f.lower(f.col("body")).alias("good_bad_bot"),f.col("link_id").alias("gbbb_link_id")])
-botreplies = botreplies.groupby(['bot_comment_id']).agg(f.count('good_bad_bot').alias("N_goodbad_votes"),
-                                                        f.sum((f.lower(f.col('good_bad_bot')).like('%good bot%').astype("double"))).alias("n_good_votes"),
-                                                        f.sum((f.lower(f.col('good_bad_bot')).like('%bad bot%').astype("double"))).alias("n_bad_votes"))
-
-comments_by_author = df.select(['author','id','saidbot']).groupBy('author').agg(f.count('id').alias("N_comments"),
-                                                                                f.mean(f.col('saidbot').astype("double")).alias("prop_saidbot"),
-                                                                                f.sum(f.col('saidbot').astype("double")).alias("n_saidbot"))
-
-# pd_comments_by_author = comments_by_author.toPandas()
-# pd_comments_by_author['frac'] = 500 / pd_comments_by_author['N_comments']
-# pd_comments_by_author.loc[pd_comments_by_author.frac > 1, 'frac'] = 1
-# fractions = pd_comments_by_author.loc[:,['author','frac']]
-# fractions = fractions.set_index('author').to_dict()['frac']
-
-# sampled_author_comments = df.sampleBy("author",fractions).groupBy('author').agg(f.concat_ws(" ", f.collect_list('body')).alias('comments'))
-df = df.withColumn("randn",f.randn(seed=1968))
-
-win = Window.partitionBy("author").orderBy("randn")
-
-df = df.withColumn("randRank",f.rank().over(win))
-sampled_author_comments = df.filter(f.col("randRank") <= 1000)
-sampled_author_comments = sampled_author_comments.groupBy('author').agg(f.concat_ws(" ", f.collect_list('body')).alias('comments'))
-
-author_entropy_rates = sampled_author_comments.select(['author',zlib_entropy_rate_udf(f.col('comments')).alias("entropy_rate")])
-
-parents = df.join(botreplies, on=df.id==botreplies.bot_comment_id,how='right_outer')
-
-win1 = Window.partitionBy("author")
-parents = parents.withColumn("first_bot_reply",f.min(f.col("CreatedAt")).over(win1))
-
-first_bot_reply = parents.filter(f.col("first_bot_reply")==f.col("CreatedAt"))
-first_bot_reply = first_bot_reply.withColumnRenamed("CreatedAt","FB_CreatedAt")
-first_bot_reply = first_bot_reply.withColumnRenamed("id","FB_id")
-
-comments_since_first_bot_reply = df.join(first_bot_reply,on = 'author',how='right_outer').filter(f.col("CreatedAt")>=f.col("first_bot_reply"))
-comments_since_first_bot_reply = comments_since_first_bot_reply.groupBy("author").agg(f.count("id").alias("N_comments_since_firstbot"))
-
-bots = parents.groupby(['author']).agg(f.sum('N_goodbad_votes').alias("N_goodbad_votes"),
-                                          f.sum(f.col('n_good_votes')).alias("n_good_votes"),
-                                          f.sum(f.col('n_bad_votes')).alias("n_bad_votes"),
-                                          f.count(f.col('author')).alias("N_bot_posts"))
-
-bots = bots.join(comments_by_author,on="author",how='left_outer')
-bots = bots.join(comments_since_first_bot_reply,on="author",how='left_outer')
-bots = bots.join(author_entropy_rates,on='author',how='left_outer')
-
-bots = bots.orderBy("N_goodbad_votes",ascending=False)
-bots = bots.repartition(1)
-bots.write.parquet("/gscratch/comdata/output/reddit_good_bad_bot.parquet",mode='overwrite')

clustering/Makefile

@@ -1,199 +1,36 @@
-#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'
+srun_singularity=srun -p compute-bigmem -A comdata --time=48:00:00 --mem=362G -c 40 /bin/bash -c 
-srun_singularity=source /gscratch/comdata/users/nathante/cdsc_reddit/bin/activate && srun_singularity.sh
+similarity_data=../../data/reddit_similarity
-similarity_data=/gscratch/comdata/output/reddit_similarity
+clustering_data=../../data/reddit_clustering
-clustering_data=/gscratch/comdata/output/reddit_clustering
 kmeans_selection_grid=--max_iters=[3000] --n_inits=[10] --n_clusters=[100,500,1000,1250,1500,1750,2000]
 hdbscan_selection_grid=--min_cluster_sizes=[2,3,4,5] --min_samples=[2,3,4,5] --cluster_selection_epsilons=[0,0.01,0.05,0.1,0.15,0.2] --cluster_selection_methods=[eom,leaf]
 affinity_selection_grid=--dampings=[0.5,0.6,0.7,0.8,0.95,0.97,0.99] --preference_quantiles=[0.1,0.3,0.5,0.7,0.9] --convergence_iters=[15]
 
-authors_10k_input=$(similarity_data)/subreddit_comment_authors_10k.feather
-authors_10k_input_lsi=$(similarity_data)/subreddit_comment_authors_10k_LSI
-authors_10k_output=$(clustering_data)/subreddit_comment_authors_10k
-authors_10k_output_lsi=$(clustering_data)/subreddit_comment_authors_10k_LSI
-
-authors_tf_10k_input=$(similarity_data)/subreddit_comment_authors-tf_10k.feather
 authors_tf_10k_input_lsi=$(similarity_data)/subreddit_comment_authors-tf_10k_LSI
-authors_tf_10k_output=$(clustering_data)/subreddit_comment_authors-tf_10k
 authors_tf_10k_output_lsi=$(clustering_data)/subreddit_comment_authors-tf_10k_LSI
 
-terms_10k_input=$(similarity_data)/subreddit_comment_terms_10k.feather
+all:authors_tf_10k_lsi
-terms_10k_input_lsi=$(similarity_data)/subreddit_comment_terms_10k_LSI
-terms_10k_output=$(clustering_data)/subreddit_comment_terms_10k
-terms_10k_output_lsi=$(clustering_data)/subreddit_comment_terms_10k_LSI
-
-all:terms_10k authors_10k authors_tf_10k terms_10k_lsi authors_10k_lsi authors_tf_10k_lsi
-
-terms_10k:${terms_10k_output}/kmeans/selection_data.csv ${terms_10k_output}/affinity/selection_data.csv ${terms_10k_output}/hdbscan/selection_data.csv
-
-authors_10k:${authors_10k_output}/kmeans/selection_data.csv ${authors_10k_output}/hdbscan/selection_data.csv ${authors_10k_output}/affinity/selection_data.csv
-
-authors_tf_10k:${authors_tf_10k_output}/kmeans/selection_data.csv ${authors_tf_10k_output}/hdbscan/selection_data.csv ${authors_tf_10k_output}/affinity/selection_data.csv
-
-terms_10k_lsi:${terms_10k_output_lsi}/kmeans/selection_data.csv ${terms_10k_output_lsi}/affinity/selection_data.csv ${terms_10k_output_lsi}/hdbscan/selection_data.csv
-
-authors_10k_lsi:${authors_10k_output_lsi}/kmeans/selection_data.csv ${authors_10k_output_lsi}/hdbscan/selection_data.csv ${authors_10k_output_lsi}/affinity/selection_data.csv
 
 authors_tf_10k_lsi:${authors_tf_10k_output_lsi}/kmeans/selection_data.csv ${authors_tf_10k_output_lsi}/hdbscan/selection_data.csv ${authors_tf_10k_output_lsi}/affinity/selection_data.csv
 
-${authors_10k_output}/kmeans/selection_data.csv:selection.py ${authors_10k_input} clustering_base.py kmeans_clustering.py
-	$(srun_singularity) python3 kmeans_clustering.py --inpath=${authors_10k_input} --outpath=${authors_10k_output}/kmeans --savefile=${authors_10k_output}/kmeans/selection_data.csv $(kmeans_selection_grid) 
-
-${terms_10k_output}/kmeans/selection_data.csv:selection.py ${terms_10k_input} clustering_base.py kmeans_clustering.py
-	$(srun_singularity) python3 kmeans_clustering.py --inpath=${terms_10k_input} --outpath=${terms_10k_output}/kmeans  --savefile=${terms_10k_output}/kmeans/selection_data.csv $(kmeans_selection_grid) 
-
-${authors_tf_10k_output}/kmeans/selection_data.csv:clustering.py ${authors_tf_10k_input} clustering_base.py kmeans_clustering.py
-	$(srun_singularity) python3 kmeans_clustering.py --inpath=${authors_tf_10k_input} --outpath=${authors_tf_10k_output}/kmeans --savefile=${authors_tf_10k_output}/kmeans/selection_data.csv $(kmeans_selection_grid) 
-
-${authors_10k_output}/affinity/selection_data.csv:selection.py ${authors_10k_input} clustering_base.py affinity_clustering.py
-	$(srun_singularity) python3 affinity_clustering.py --inpath=${authors_10k_input} --outpath=${authors_10k_output}/affinity --savefile=${authors_10k_output}/affinity/selection_data.csv $(affinity_selection_grid) 
-
-${terms_10k_output}/affinity/selection_data.csv:selection.py ${terms_10k_input} clustering_base.py affinity_clustering.py
-	$(srun_singularity) python3 affinity_clustering.py --inpath=${terms_10k_input} --outpath=${terms_10k_output}/affinity  --savefile=${terms_10k_output}/affinity/selection_data.csv $(affinity_selection_grid) 
-
-${authors_tf_10k_output}/affinity/selection_data.csv:clustering.py ${authors_tf_10k_input} clustering_base.py affinity_clustering.py
-	$(srun_singularity) python3 affinity_clustering.py --inpath=${authors_tf_10k_input} --outpath=${authors_tf_10k_output}/affinity --savefile=${authors_tf_10k_output}/affinity/selection_data.csv $(affinity_selection_grid) 
-
-${authors_10k_output}/hdbscan/selection_data.csv:selection.py ${authors_10k_input} clustering_base.py hdbscan_clustering.py
-	$(srun_singularity) python3 hdbscan_clustering.py --inpath=${authors_10k_input} --outpath=${authors_10k_output}/hdbscan --savefile=${authors_10k_output}/hdbscan/selection_data.csv $(hdbscan_selection_grid) 
-
-${terms_10k_output}/hdbscan/selection_data.csv:selection.py ${terms_10k_input} clustering_base.py hdbscan_clustering.py
-	$(srun_singularity) python3 hdbscan_clustering.py --inpath=${terms_10k_input} --outpath=${terms_10k_output}/hdbscan  --savefile=${terms_10k_output}/hdbscan/selection_data.csv $(hdbscan_selection_grid) 
-
-${authors_tf_10k_output}/hdbscan/selection_data.csv:clustering.py ${authors_tf_10k_input} clustering_base.py hdbscan_clustering.py
-	$(srun_singularity) python3 hdbscan_clustering.py --inpath=${authors_tf_10k_input} --outpath=${authors_tf_10k_output}/hdbscan --savefile=${authors_tf_10k_output}/hdbscan/selection_data.csv $(hdbscan_selection_grid) 
-
-
 ## LSI Models
-${authors_10k_output_lsi}/kmeans/selection_data.csv:selection.py ${authors_10k_input_lsi} clustering_base.py kmeans_clustering.py
-	$(srun_singularity) python3 kmeans_clustering_lsi.py --inpath=${authors_10k_input_lsi} --outpath=${authors_10k_output_lsi}/kmeans --savefile=${authors_10k_output_lsi}/kmeans/selection_data.csv $(kmeans_selection_grid)
-
-${terms_10k_output_lsi}/kmeans/selection_data.csv:selection.py ${terms_10k_input_lsi} clustering_base.py kmeans_clustering.py
-	$(srun_singularity) python3 kmeans_clustering_lsi.py --inpath=${terms_10k_input_lsi} --outpath=${terms_10k_output_lsi}/kmeans  --savefile=${terms_10k_output_lsi}/kmeans/selection_data.csv $(kmeans_selection_grid)
-
 ${authors_tf_10k_output_lsi}/kmeans/selection_data.csv:clustering.py ${authors_tf_10k_input_lsi} clustering_base.py kmeans_clustering.py
-	$(srun_singularity) python3 kmeans_clustering_lsi.py --inpath=${authors_tf_10k_input_lsi} --outpath=${authors_tf_10k_output_lsi}/kmeans --savefile=${authors_tf_10k_output_lsi}/kmeans/selection_data.csv $(kmeans_selection_grid)
+	$(srun_singularity) -c "source ~/.bashrc; python3 kmeans_clustering_lsi.py --inpath=${authors_tf_10k_input_lsi} --outpath=${authors_tf_10k_output_lsi}/kmeans --savefile=${authors_tf_10k_output_lsi}/kmeans/selection_data.csv $(kmeans_selection_grid)"
-
-${authors_10k_output_lsi}/affinity/selection_data.csv:selection.py ${authors_10k_input_lsi} clustering_base.py affinity_clustering.py
-	$(srun_singularity) python3 affinity_clustering_lsi.py --inpath=${authors_10k_input_lsi} --outpath=${authors_10k_output_lsi}/affinity --savefile=${authors_10k_output_lsi}/affinity/selection_data.csv $(affinity_selection_grid)
-
-${terms_10k_output_lsi}/affinity/selection_data.csv:selection.py ${terms_10k_input_lsi} clustering_base.py affinity_clustering.py
-	$(srun_singularity) python3 affinity_clustering_lsi.py --inpath=${terms_10k_input_lsi} --outpath=${terms_10k_output_lsi}/affinity  --savefile=${terms_10k_output_lsi}/affinity/selection_data.csv $(affinity_selection_grid)
 
 ${authors_tf_10k_output_lsi}/affinity/selection_data.csv:clustering.py ${authors_tf_10k_input_lsi} clustering_base.py affinity_clustering.py
-	$(srun_singularity) python3 affinity_clustering_lsi.py --inpath=${authors_tf_10k_input_lsi} --outpath=${authors_tf_10k_output_lsi}/affinity --savefile=${authors_tf_10k_output_lsi}/affinity/selection_data.csv $(affinity_selection_grid)
+	$(srun_singularity) -c "source ~/.bashrc; python3 affinity_clustering_lsi.py --inpath=${authors_tf_10k_input_lsi} --outpath=${authors_tf_10k_output_lsi}/affinity --savefile=${authors_tf_10k_output_lsi}/affinity/selection_data.csv $(affinity_selection_grid)"
-
-${authors_10k_output_lsi}/hdbscan/selection_data.csv:selection.py ${authors_10k_input_lsi} clustering_base.py hdbscan_clustering.py
-	$(srun_singularity) python3 hdbscan_clustering_lsi.py --inpath=${authors_10k_input_lsi} --outpath=${authors_10k_output_lsi}/hdbscan --savefile=${authors_10k_output_lsi}/hdbscan/selection_data.csv $(hdbscan_selection_grid)
-
-${terms_10k_output_lsi}/hdbscan/selection_data.csv:selection.py ${terms_10k_input_lsi} clustering_base.py hdbscan_clustering.py
-	$(srun_singularity) python3 hdbscan_clustering_lsi.py --inpath=${terms_10k_input_lsi} --outpath=${terms_10k_output_lsi}/hdbscan  --savefile=${terms_10k_output_lsi}/hdbscan/selection_data.csv $(hdbscan_selection_grid)
 
 ${authors_tf_10k_output_lsi}/hdbscan/selection_data.csv:clustering.py ${authors_tf_10k_input_lsi} clustering_base.py hdbscan_clustering.py
-	$(srun_singularity) python3 hdbscan_clustering_lsi.py --inpath=${authors_tf_10k_input_lsi} --outpath=${authors_tf_10k_output_lsi}/hdbscan --savefile=${authors_tf_10k_output_lsi}/hdbscan/selection_data.csv $(hdbscan_selection_grid)
+	$(srun_singularity) -c "source ~/.bashrc; python3 hdbscan_clustering_lsi.py --inpath=${authors_tf_10k_input_lsi} --outpath=${authors_tf_10k_output_lsi}/hdbscan --savefile=${authors_tf_10k_output_lsi}/hdbscan/selection_data.csv $(hdbscan_selection_grid)"
-
-${terms_10k_output_lsi}/best_hdbscan.feather:${terms_10k_output_lsi}/hdbscan/selection_data.csv pick_best_clustering.py
-	$(srun_singularity) python3 pick_best_clustering.py $< $@ --min_clusters=50 --max_isolates=5000 --min_cluster_size=2
 
 ${authors_tf_10k_output_lsi}/best_hdbscan.feather:${authors_tf_10k_output_lsi}/hdbscan/selection_data.csv pick_best_clustering.py
-	$(srun_singularity) python3 pick_best_clustering.py $< $@ --min_clusters=50 --max_isolates=5000 --min_cluster_size=2
+	$(srun_singularity) -c "source ~/.bashrc; python3 pick_best_clustering.py $< $@ --min_clusters=50 --max_isolates=5000 --min_cluster_size=2"
 
-clean_affinity:
+${authors_tf_10k_input_lsi}:
-	rm -f ${authors_10k_output}/affinity/selection_data.csv
+	$(MAKE) -C ../similarities
-	rm -f ${authors_tf_10k_output}/affinity/selection_data.csv
-	rm -f ${terms_10k_output}/affinity/selection_data.csv
 
-clean_kmeans:
+clean:
-	rm -f ${authors_10k_output}/kmeans/selection_data.csv
-	rm -f ${authors_tf_10k_output}/kmeans/selection_data.csv
-	rm -f ${terms_10k_output}/kmeans/selection_data.csv
-
-clean_hdbscan:
-	rm -f ${authors_10k_output}/hdbscan/selection_data.csv
-	rm -f ${authors_tf_10k_output}/hdbscan/selection_data.csv
-	rm -f ${terms_10k_output}/hdbscan/selection_data.csv
-
-clean_authors:
-	rm -f ${authors_10k_output}/affinity/selection_data.csv
-	rm -f ${authors_10k_output}/kmeans/selection_data.csv
-	rm -f ${authors_10k_output}/hdbscan/selection_data.csv
-
-clean_authors_tf:
-	rm -f ${authors_tf_10k_output}/affinity/selection_data.csv
-	rm -f ${authors_tf_10k_output}/kmeans/selection_data.csv
-	rm -f ${authors_tf_10k_output}/hdbscan/selection_data.csv
-
-clean_terms:
-	rm -f ${terms_10k_output}/affinity/selection_data.csv
-	rm -f ${terms_10k_output}/kmeans/selection_data.csv
-	rm -f ${terms_10k_output}/hdbscan/selection_data.csv
-
-clean_lsi_affinity:
-	rm -f ${authors_10k_output_lsi}/affinity/selection_data.csv
-	rm -f ${authors_tf_10k_output_lsi}/affinity/selection_data.csv
-	rm -f ${terms_10k_output_lsi}/affinity/selection_data.csv
-
-clean_lsi_kmeans:
-	rm -f ${authors_10k_output_lsi}/kmeans/selection_data.csv
-	rm -f ${authors_tf_10k_output_lsi}/kmeans/selection_data.csv
-	rm -f ${terms_10k_output_lsi}/kmeans/selection_data.csv
-
-clean_lsi_hdbscan:
-	rm -f ${authors_10k_output_lsi}/hdbscan/selection_data.csv
-	rm -f ${authors_tf_10k_output_lsi}/hdbscan/selection_data.csv
-	rm -f ${terms_10k_output_lsi}/hdbscan/selection_data.csv
-
-clean_lsi_authors:
-	rm -f ${authors_10k_output_lsi}/affinity/selection_data.csv
-	rm -f ${authors_10k_output_lsi}/kmeans/selection_data.csv
-	rm -f ${authors_10k_output_lsi}/hdbscan/selection_data.csv
-
-clean_lsi_authors_tf:
 	rm -f ${authors_tf_10k_output_lsi}/affinity/selection_data.csv
 	rm -f ${authors_tf_10k_output_lsi}/kmeans/selection_data.csv
 	rm -f ${authors_tf_10k_output_lsi}/hdbscan/selection_data.csv
 
-clean_lsi_terms:
+PHONY: clean 
-	rm -f ${terms_10k_output_lsi}/affinity/selection_data.csv
-	rm -f ${terms_10k_output_lsi}/kmeans/selection_data.csv
-	rm -f ${terms_10k_output_lsi}/hdbscan/selection_data.csv
-
-clean: clean_affinity clean_kmeans clean_hdbscan
-
-PHONY: clean clean_affinity clean_kmeans clean_hdbscan clean_authors clean_authors_tf clean_terms terms_10k authors_10k authors_tf_10k
-
-# $(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

clustering/clustering_base.py

@@ -1,3 +1,4 @@
+import pickle
 from pathlib import Path
 import numpy as np
 import pandas as pd
@@ -20,10 +21,17 @@ class clustering_job:
         self.subreddits, self.mat = self.read_distance_mat(self.infile)
         self.clustering = self.call(self.mat, *self.args, **self.kwargs)
         self.cluster_data = self.process_clustering(self.clustering, self.subreddits)
-        self.score = self.silhouette()
         self.outpath.mkdir(parents=True, exist_ok=True)
         self.cluster_data.to_feather(self.outpath/(self.name + ".feather"))
+
         self.hasrun = True
+        self.cleanup()
+
+    def cleanup(self):
+        self.cluster_data = None
+        self.mat = None
+        self.clustering=None
+        self.subreddits=None
         
     def get_info(self):
         if not self.hasrun:
@@ -54,11 +62,13 @@ class clustering_job:
         else:
             score = None
             self.silsampout = None
+
         return score
 
     def read_distance_mat(self, similarities, use_threads=True):
+        print(similarities)
         df = pd.read_feather(similarities, use_threads=use_threads)
-        mat = np.array(df.drop('_subreddit',1))
+        mat = np.array(df.drop('_subreddit',axis=1))
         n = mat.shape[0]
         mat[range(n),range(n)] = 1
         return (df._subreddit,1-mat)
@@ -72,9 +82,13 @@ class clustering_job:
         self.n_clusters = len(set(clusters))
 
         print(f"found {self.n_clusters} clusters")
-
         cluster_data = pd.DataFrame({'subreddit': subreddits,'cluster':clustering.labels_})
 
+
+        self.score = self.silhouette()
+        print(f"silhouette_score:{self.score}")
+
+
         cluster_sizes = cluster_data.groupby("cluster").count().reset_index()
         print(f"the largest cluster has {cluster_sizes.loc[cluster_sizes.cluster!=-1].subreddit.max()} members")
 
@@ -95,6 +109,38 @@ class clustering_job:
 
         return cluster_data
 
+class twoway_clustering_job(clustering_job):
+    def __init__(self, infile, outpath, name, call1, call2, args1, args2):
+        self.outpath = Path(outpath)
+        self.call1 = call1
+        self.args1 = args1
+        self.call2 = call2
+        self.args2 = args2
+        self.infile = Path(infile)
+        self.name = name
+        self.hasrun = False
+        self.args = args1|args2
+
+    def run(self):
+        self.subreddits, self.mat = self.read_distance_mat(self.infile)
+        self.step1 = self.call1(self.mat, **self.args1)
+        self.clustering = self.call2(self.mat, self.step1, **self.args2)
+        self.cluster_data = self.process_clustering(self.clustering, self.subreddits)
+        self.hasrun = True
+        self.after_run()
+        self.cleanup()
+
+    def after_run(self):
+        self.score = self.silhouette()
+        self.outpath.mkdir(parents=True, exist_ok=True)
+        print(self.outpath/(self.name+".feather"))
+        self.cluster_data.to_feather(self.outpath/(self.name + ".feather"))
+
+
+    def cleanup(self):
+        super().cleanup()
+        self.step1 = None
+
 @dataclass
 class clustering_result:
     outpath:Path

clustering/fit_tsne.py

@@ -1,34 +0,0 @@
-import fire
-import pyarrow
-import pandas as pd
-from numpy import random
-import numpy as np
-from sklearn.manifold import TSNE
-
-similarities = "/gscratch/comdata/output/reddit_similarity/subreddit_author_tf_similarities_10000.parquet"
-
-def fit_tsne(similarities, output, learning_rate=750, perplexity=50, n_iter=10000, early_exaggeration=20):
-    '''
-    similarities: feather file with a dataframe of similarity scores
-    learning_rate: parameter controlling how fast the model converges. Too low and you get outliers. Too high and you get a ball.
-    perplexity: number of neighbors to use. the default of 50 is often good.
-
-    '''
-    df = pd.read_feather(similarities)
-
-    n = df.shape[0]
-    mat = np.array(df.drop('_subreddit',1),dtype=np.float64)
-    mat[range(n),range(n)] = 1
-    mat[mat > 1] = 1
-    dist = 2*np.arccos(mat)/np.pi
-    tsne_model = TSNE(2,learning_rate=750,perplexity=50,n_iter=10000,metric='precomputed',early_exaggeration=20,n_jobs=-1)
-    tsne_fit_model = tsne_model.fit(dist)
-
-    tsne_fit_whole = tsne_fit_model.fit_transform(dist)
-
-    plot_data = pd.DataFrame({'x':tsne_fit_whole[:,0],'y':tsne_fit_whole[:,1], '_subreddit':df['_subreddit']})
-
-    plot_data.to_feather(output)
-
-if __name__ == "__main__":
-    fire.Fire(fit_tsne)

clustering/grid_sweep.py

@@ -31,3 +31,19 @@ class grid_sweep:
         outcsv = Path(outcsv)
         outcsv.parent.mkdir(parents=True, exist_ok=True)
         self.infos.to_csv(outcsv)
+
+
+class twoway_grid_sweep(grid_sweep):
+    def __init__(self, jobtype, inpath, outpath, namer, args1, args2, *args, **kwargs):
+        self.jobtype = jobtype
+        self.namer = namer
+        prod1 = product(* args1.values())
+        prod2 = product(* args2.values())
+        grid1 = [dict(zip(args1.keys(), pargs)) for pargs in prod1]
+        grid2 = [dict(zip(args2.keys(), pargs)) for pargs in prod2]
+        grid = product(grid1, grid2)
+        inpath = Path(inpath)
+        outpath = Path(outpath)
+        self.hasrun = False
+        self.grid = [(inpath,outpath,namer(**(g[0] | g[1])), g[0], g[1], *args) for g in grid]
+        self.jobs = [jobtype(*g) for g in self.grid]

clustering/lsi_base.py

@@ -1,5 +1,5 @@
 from clustering_base import clustering_job, clustering_result
-from grid_sweep import grid_sweep
+from grid_sweep import grid_sweep, twoway_grid_sweep
 from dataclasses import dataclass
 from itertools import chain
 from pathlib import Path
@@ -27,3 +27,18 @@ class lsi_grid_sweep(grid_sweep):
         self.hasrun = False
         self.subgrids = [self.subsweep(lsi_path, outpath,  lsi_dim, *args, **kwargs) for lsi_dim, lsi_path in zip(lsi_nums, lsi_paths)]
         self.jobs = list(chain(*map(lambda gs: gs.jobs, self.subgrids)))
+
+class twoway_lsi_grid_sweep(twoway_grid_sweep):
+    def __init__(self, jobtype, subsweep, inpath, lsi_dimensions, outpath, args1, args2):
+        self.jobtype = jobtype
+        self.subsweep = subsweep
+        inpath = Path(inpath)
+        if lsi_dimensions == 'all':
+            lsi_paths = list(inpath.glob("*.feather"))
+        else:
+            lsi_paths = [inpath / (str(dim) + '.feather') for dim in lsi_dimensions]
+
+        lsi_nums = [int(p.stem) for p in lsi_paths]
+        self.hasrun = False
+        self.subgrids = [self.subsweep(lsi_path, outpath, lsi_dim, args1, args2) for lsi_dim, lsi_path in zip(lsi_nums, lsi_paths)]
+        self.jobs = list(chain(*map(lambda gs: gs.jobs, self.subgrids)))

clustering/validation.py

@@ -0,0 +1,4 @@
+from sklearn import metrics
+from sklearn.cluster import AffinityPropagation
+from functools import partial
+# sillouette is the only one that doesn't need the feature matrix. So it's probably the only one that's worth trying. 

datasets/Makefile

@@ -0,0 +1,28 @@
+all: ../../data/reddit_comments_by_subreddit.parquet ../../data/reddit_submissions_by_subreddit.parquet
+
+../../data/reddit_comments_by_subreddit.parquet:../../data/temp/reddit_comments.parquet
+	../start_spark_and_run.sh 4 comments_2_parquet_part2.py
+
+../../data/temp/reddit_comments.parquet: comments_task_list.sh run_comments_jobs.sbatch
+	mkdir -p comments_jobs
+	mkdir -p ../../data/temp/
+	sbatch --wait --array=1-$(shell cat comments_task_list.sh | wc -l) run_comments_jobs.sbatch 0
+
+temp_reddit_comments.parquet: ../../data/temp/reddit_comments.parquet
+
+comments_task_list.sh: comments_2_parquet_part1.py
+	srun -p compute-bigmem -A comdata --nodes=1 --mem-per-cpu=9g -c 40 --time=120:00:00 bash -c "source ~/.bashrc && python3 comments_2_parquet_part1.py gen_task_list --overwrite=False"
+
+submissions_task_list.sh: submissions_2_parquet_part1.py
+	srun -p compute-bigmem -A comdata --nodes=1 --mem-per-cpu=9g -c 40 --time=120:00:00 python3 submissions_2_parquet_part1.py gen_task_list
+
+../../data/reddit_submissions_by_subreddit.parquet:../../data/temp/reddit_submissions.parquet
+	../start_spark_and_run.sh 4 submissions_2_parquet_part2.py
+
+../../data/temp/reddit_submissions.parquet: submissions_task_list.sh run_submissions_jobs.sbatch
+	mkdir -p submissions_jobs
+	rm -rf ../../data/temp/reddit_submissions.parquet
+	mkdir -p ../../data/temp/
+	sbatch --wait --array=1-$(shell cat submissions_task_list.sh | wc -l) run_submissions_jobs.sbatch 0
+
+temp_reddit_submissions.parquet: ../../data/temp/reddit_submissions.parquet

datasets/comments_2_parquet_part1.py

@@ -47,11 +47,11 @@ def parse_comment(comment, names= None):
     return tuple(row)
 
 
-#    conf = sc._conf.setAll([('spark.executor.memory', '20g'), ('spark.app.name', 'extract_reddit_timeline'), ('spark.executor.cores', '26'), ('spark.cores.max', '26'), ('spark.driver.memory','84g'),('spark.driver.maxResultSize','0'),('spark.local.dir','/gscratch/comdata/spark_tmp')])
+#    conf = sc._conf.setAll([('spark.executor.memory', '20g'), ('spark.app.name', 'extract_reddit_timeline'), ('spark.executor.cores', '26'), ('spark.cores.max', '26'), ('spark.driver.memory','84g'),('spark.driver.maxResultSize','0'),('spark.local.dir','../../data/spark_tmp')])
 
 def parse_dump(partition):
 
-    dumpdir = f"/gscratch/comdata/raw_data/reddit_dumps/comments/{partition}"
+    dumpdir = f"../../data/reddit_dumps/comments/{partition}"
 
     stream = open_input_file(dumpdir)
     rows = map(parse_comment, stream)
@@ -76,11 +76,11 @@ def parse_dump(partition):
         pa.field('error', pa.string(), nullable=True),
     ])
 
-    p = Path("/gscratch/comdata/output/temp/reddit_comments.parquet")
+    p = Path("../../data/temp/reddit_comments.parquet")
     p.mkdir(exist_ok=True,parents=True)
 
     N=10000
-    with pq.ParquetWriter(f"/gscratch/comdata/output/temp/reddit_comments.parquet/{partition}.parquet",
+    with pq.ParquetWriter(f"../../data/temp/reddit_comments.parquet/{partition}.parquet",
                           schema=schema,
                           compression='snappy',
                           flavor='spark') as writer:
@@ -96,12 +96,12 @@ def parse_dump(partition):
         writer.close()
 
 
-def gen_task_list(dumpdir="/gscratch/comdata/raw_data/reddit_dumps/comments", overwrite=True):
+def gen_task_list(dumpdir="../../data/raw_data/reddit_dumps/comments", overwrite=True):
     files = list(find_dumps(dumpdir,base_pattern="RC_20*.*"))
     with open("comments_task_list.sh",'w') as of:
         for fpath in files:
             partition = os.path.split(fpath)[1]
-            if (not Path(f"/gscratch/comdata/output/temp/reddit_comments.parquet/{partition}.parquet").exists()) or (overwrite is True):
+            if (not Path(f"../../data/temp/reddit_comments.parquet/{partition}.parquet").exists()) or (overwrite is True):
                 of.write(f'python3 comments_2_parquet_part1.py parse_dump {partition}\n')
 
 

datasets/comments_2_parquet_part2.py

@@ -9,7 +9,7 @@ from pyspark.sql import SparkSession
 spark = SparkSession.builder.getOrCreate()
 
 conf = pyspark.SparkConf().setAppName("Reddit submissions to parquet")
-conf = conf.set("spark.sql.shuffle.partitions",2000)
+conf = conf.set("spark.sql.shuffle.partitions",2400)
 conf = conf.set('spark.sql.crossJoin.enabled',"true")
 conf = conf.set('spark.debug.maxToStringFields',200)
 sc = spark.sparkContext
@@ -25,12 +25,13 @@ df = df.withColumn("Month",f.month(f.col("CreatedAt")))
 df = df.withColumn("Year",f.year(f.col("CreatedAt")))
 df = df.withColumn("Day",f.dayofmonth(f.col("CreatedAt")))
 
-df = df.repartition('subreddit')
+# df = df.repartition(1200,'subreddit')
-df2 = df.sort(["subreddit","CreatedAt","link_id","parent_id","Year","Month","Day"],ascending=True)
+# df2 = df.sort(["subreddit","CreatedAt","link_id","parent_id","Year","Month","Day"],ascending=True)
-df2 = df2.sortWithinPartitions(["subreddit","CreatedAt","link_id","parent_id","Year","Month","Day"],ascending=True)
+# df2 = df2.sortWithinPartitions(["subreddit","CreatedAt","link_id","parent_id","Year","Month","Day"],ascending=True)
-df2.write.parquet("/gscratch/scrubbed/comdata/output/reddit_comments_by_subreddit.parquet", mode='overwrite', compression='snappy')
+# df2.write.parquet("/gscratch/scrubbed/comdata/reddit_comments_by_subreddit.parquet", mode='overwrite', compression='snappy')
 
-df = df.repartition('author')
+#df = spark.read.parquet("/gscratch/scrubbed/comdata/reddit_comments_by_subreddit.parquet")
-df3 = df.sort(["author","CreatedAt","subreddit","link_id","parent_id","Year","Month","Day"],ascending=True)
+df = df.repartition(2400,'author','subreddit',"Year","Month","Day")
-df3 = df3.sortWithinPartitions(["author","CreatedAt","subreddit","link_id","parent_id","Year","Month","Day"],ascending=True)
+df3 = df.sort(["author","subreddit","Year","Month","Day","CreatedAt","link_id","parent_id"],ascending=True)
-df3.write.parquet("/gscratch/scrubbed/comdata/output/reddit_comments_by_author.parquet", mode='overwrite',compression='snappy')
+df3 = df3.sortWithinPartitions(["author","subreddit","Year","Month","Day","CreatedAt","link_id","parent_id"],ascending=True)
+df3.write.parquet("/gscratch/scrubbed/comdata/reddit_comments_by_author.parquet", mode='overwrite',compression='snappy')

datasets/job_script.sh

@@ -1,4 +0,0 @@
-#!/usr/bin/bash
-start_spark_cluster.sh
-singularity exec  /gscratch/comdata/users/nathante/containers/nathante.sif spark-submit --master spark://$(hostname):7077 comments_2_parquet_part2.py 
-singularity exec /gscratch/comdata/users/nathante/containers/nathante.sif stop-all.sh

datasets/run_comments_jobs.sbatch

@@ -0,0 +1,24 @@
+#!/bin/bash
+## tf reddit comments
+#SBATCH --job-name="cdsc_reddit; parse comment dumps"
+## Allocation Definition
+#SBATCH --account=comdata
+#SBATCH --partition=compute-bigmem
+## Resources
+## Nodes. This should always be 1 for parallel-sql.
+#SBATCH --nodes=1    
+## Walltime (12 hours)
+#SBATCH --time=24:00:00
+## Memory per node
+#SBATCH --mem=8G
+#SBATCH --cpus-per-task=1
+#SBATCH --ntasks=1
+#SBATCH 
+#SBATCH --chdir /gscratch/comdata/users/nathante/partitioning_reddit/dataverse/cdsc_reddit/datasets
+#SBATCH --output=comments_jobs/%A_%a.out
+#SBATCH --error=comments_jobs/%A_%a.out
+. /opt/ohpc/admin/lmod/lmod/init/profile
+source ~/.bashrc
+TASK_NUM=$(( SLURM_ARRAY_TASK_ID + $1))
+TASK_CALL=$(sed -n ${TASK_NUM}p ./comments_task_list.sh)
+${TASK_CALL}

datasets/run_submissions_jobs.sbatch

@@ -0,0 +1,23 @@
+#!/bin/bash
+## tf reddit comments
+#SBATCH --job-name="cdsc_reddit; parse submission dumps"
+## Allocation Definition
+#SBATCH --account=comdata-ckpt
+#SBATCH --partition=ckpt
+## Resources
+## Nodes. This should always be 1 for parallel-sql.
+#SBATCH --nodes=1    
+## Walltime (12 hours)
+#SBATCH --time=24:00:00
+## Memory per node
+#SBATCH --mem=8G
+#SBATCH --cpus-per-task=1
+#SBATCH --ntasks=1
+#SBATCH 
+#SBATCH --chdir /gscratch/comdata/users/nathante/cdsc_reddit/datasets
+#SBATCH --output=submissions_jobs/%A_%a.out
+#SBATCH --error=submissions_jobs/%A_%a.out
+
+TASK_NUM=$(( SLURM_ARRAY_TASK_ID + $1))
+TASK_CALL=$(sed -n ${TASK_NUM}p ./submissions_task_list.sh)
+${TASK_CALL}

datasets/submissions_2_parquet_part1.py

@@ -58,7 +58,7 @@ def parse_submission(post, names = None):
 def parse_dump(partition):
 
     N=10000
-    stream = open_fileset([f"/gscratch/comdata/raw_data/reddit_dumps/submissions/{partition}"])
+    stream = open_fileset([f"/gscratch/comdata/raw_data/submissions/{partition}"])
     rows = map(parse_submission,stream)
     schema = pa.schema([
         pa.field('id', pa.string(),nullable=True),
@@ -102,7 +102,7 @@ def parse_dump(partition):
 
         writer.close()
 
-def gen_task_list(dumpdir="/gscratch/comdata/raw_data/reddit_dumps/submissions"):
+def gen_task_list(dumpdir="/gscratch/comdata/raw_data/submissions"):
     files = list(find_dumps(dumpdir,base_pattern="RS_20*.*"))
     with open("submissions_task_list.sh",'w') as of:
         for fpath in files:

datasets/submissions_2_parquet_part2.py

@@ -29,14 +29,14 @@ df = df.withColumn("Day",f.dayofmonth(f.col("CreatedAt")))
 df = df.withColumn("subreddit_hash",f.sha2(f.col("subreddit"), 256)[0:3])
 
 # next we gotta resort it all.
-df = df.repartition("subreddit")
+df = df.repartition(800,"subreddit","Year","Month")
-df2 = df.sort(["subreddit","CreatedAt","id"],ascending=True)
+df2 = df.sort(["subreddit","Year","Month","CreatedAt","id"],ascending=True)
 df2 = df.sortWithinPartitions(["subreddit","CreatedAt","id"],ascending=True)
 df2.write.parquet("/gscratch/comdata/output/temp/reddit_submissions_by_subreddit.parquet2", mode='overwrite',compression='snappy')
 
 
 # # we also want to have parquet files sorted by author then reddit. 
-df = df.repartition("author")
+df = df.repartition(800,"author","subreddit","Year","Month")
-df3 = df.sort(["author","CreatedAt","id"],ascending=True)
+df3 = df.sort(["author","Year","Month","CreatedAt","id"],ascending=True)
 df3 = df.sortWithinPartitions(["author","CreatedAt","id"],ascending=True)
 df3.write.parquet("/gscratch/comdata/output/temp/reddit_submissions_by_author.parquet2", mode='overwrite',compression='snappy')

density/Makefile

@@ -1,16 +1,7 @@
-all: /gscratch/comdata/output/reddit_density/comment_terms_10000.feather /gscratch/comdata/output/reddit_density/comment_authors_10000.feather /gscratch/comdata/output/reddit_density/subreddit_author_tf_similarities_10000.feather
+all: ../../data/reddit_density/subreddit_author_tf_similarities_10K_LSI/600.feather
 
-/gscratch/comdata/output/reddit_density/comment_terms_10000.feather:overlap_density.py /gscratch/comdata/output/reddit_similarity/comment_terms_10000.feather /gscratch/comdata/output/reddit_similarity/comment_terms_10000.feather
+../../data/reddit_density/subreddit_author_tf_similarities_10K_LSI/600.feather: overlap_density.py ../../data/reddit_similarity/subreddit_comment_authors-tf_10k_LSI/600.feather
-	start_spark_and_run.sh 1 overlap_density.py terms --inpath="/gscratch/comdata/output/reddit_similarity/comment_terms_10000.feather" --outpath="/gscratch/comdata/output/reddit_density/comment_terms_10000.feather" --agg=pd.DataFrame.sum
+	../start_spark_and_run.sh 1 overlap_density.py authors --inpath="../../data/reddit_similarity/subreddit_comment_authors-tf_10k_LSI/600.feather" --outpath="../../data/reddit_density/subreddit_author_tf_similarities_10K_LSI/600.feather" --agg=pd.DataFrame.sum
 
-/gscratch/comdata/output/reddit_density/comment_authors_10000.feather:overlap_density.py /gscratch/comdata/output/reddit_similarity/comment_authors_10000.feather
+../../data/reddit_similarity/subreddit_comment_authors-tf_10k_LSI/600.feather:
-	start_spark_and_run.sh 1 overlap_density.py authors --inpath="/gscratch/comdata/output/reddit_similarity/comment_authors_10000.feather" --outpath="/gscratch/comdata/output/reddit_density/comment_authors_10000.feather" --agg=pd.DataFrame.sum
+	$(MAKE) -C ../similarities
-
-/gscratch/comdata/output/reddit_density/subreddit_author_tf_similarities_10000.feather: overlap_density.py /gscratch/comdata/output/reddit_similarity/subreddit_author_tf_similarities_10000.parquet
-	start_spark_and_run.sh 1 overlap_density.py authors --inpath="/gscratch/comdata/output/reddit_similarity/subreddit_author_tf_similarities_10000.parquet" --outpath="/gscratch/comdata/output/reddit_density/subreddit_author_tf_similarities_10000.feather" --agg=pd.DataFrame.sum
-
-/gscratch/comdata/output/reddit_density/subreddit_author_tf_similarities_10K_LSI/850.feather: overlap_density.py /gscratch/comdata/output/reddit_similarity/subreddit_comment_authors-tf_10k_LSI/850.feather
-	start_spark_and_run.sh 1 overlap_density.py authors --inpath="/gscratch/comdata/output/reddit_similarity/subreddit_comment_authors-tf_10k_LSI/850.feather" --outpath="/gscratch/comdata/output/reddit_density/subreddit_author_tf_similarities_10K_LSI/850.feather" --agg=pd.DataFrame.sum
-
-/gscratch/comdata/output/reddit_density/subreddit_author_tf_similarities_10K_LSI/600.feather: overlap_density.py /gscratch/comdata/output/reddit_similarity/subreddit_comment_authors-tf_10k_LSI/600.feather
-	start_spark_and_run.sh 1 overlap_density.py authors --inpath="/gscratch/comdata/output/reddit_similarity/subreddit_comment_authors-tf_10k_LSI/600.feather" --outpath="/gscratch/comdata/output/reddit_density/subreddit_author_tf_similarities_10K_LSI/600.feather" --agg=pd.DataFrame.sum

density/job_script.sh

@@ -1,4 +1,6 @@
 #!/usr/bin/bash
+source ~/.bashrc
+echo $(hostname)
 start_spark_cluster.sh
-singularity exec  /gscratch/comdata/users/nathante/cdsc_base.sif spark-submit --master spark://$(hostname).hyak.local:7077 overlap_density.py authors --inpath=/gscratch/comdata/output/reddit_similarity/subreddit_comment_authors-tf_10k_LSI/600.feather --outpath=/gscratch/comdata/output/reddit_density/subreddit_author_tf_similarities_10K_LSI/600.feather --agg=pd.DataFrame.sum
+spark-submit --verbose --master spark://$(hostname):43015 overlap_density.py authors --inpath=../../data/reddit_similarity/subreddit_comment_authors-tf_10k_LSI/600.feather --outpath=../../data/reddit_density/subreddit_author_tf_similarities_10K_LSI/600.feather --agg=pd.DataFrame.sum
-singularity exec /gscratch/comdata/users/nathante/cdsc_base.sif stop-all.sh
+stop-all.sh

dumps/remove_duplicate_comments.py

@@ -0,0 +1,34 @@
+from pathlib import Path
+from itertools import chain, groupby
+
+dumpdir = Path("/gscratch/comdata/raw_data/reddit_dumps/comments")
+
+zst_files = dumpdir.glob("*.zst")
+bz2_files = dumpdir.glob("*.bz2")
+xz_files = dumpdir.glob("*.xz")
+all_files = sorted(list(chain(zst_files, bz2_files, xz_files)))
+groups = groupby(all_files, key = lambda p: p.stem)
+
+kept_paths = []
+removed_paths = []
+
+priority = ['.zst','.xz','.bz2']
+
+for stem, files in groups:
+    keep_file = None
+    remove_files = []
+    for f in files:
+        if keep_file is None:
+            keep_file = f
+        elif priority.index(keep_file.suffix) > priority.index(f.suffix):
+            remove_files.append(keep_file)
+            keep_file = f
+        else:
+            remove_files.append(f)
+    kept_paths.append(keep_file)
+    removed_paths.extend(remove_files)
+
+(dumpdir / "to_remove").mkdir()
+
+for f in removed_paths:
+    f.rename(f.parent / "to_remove" / f.name)

dumps/remove_duplicate_submissions.py

@@ -0,0 +1,34 @@
+from pathlib import Path
+from itertools import chain, groupby
+
+dumpdir = Path("/gscratch/comdata/raw_data/reddit_dumps/submissions")
+
+zst_files = dumpdir.glob("*.zst")
+bz2_files = dumpdir.glob("*.bz2")
+xz_files = dumpdir.glob("*.xz")
+all_files = sorted(list(chain(zst_files, bz2_files, xz_files)))
+groups = groupby(all_files, key = lambda p: p.stem)
+
+kept_paths = []
+removed_paths = []
+
+priority = ['.zst','.xz','.bz2']
+
+for stem, files in groups:
+    keep_file = None
+    remove_files = []
+    for f in files:
+        if keep_file is None:
+            keep_file = f
+        elif priority.index(keep_file.suffix) > priority.index(f.suffix):
+            remove_files.append(keep_file)
+            keep_file = f
+        else:
+            remove_files.append(f)
+    kept_paths.append(keep_file)
+    removed_paths.extend(remove_files)
+
+(dumpdir / "to_remove").mkdir()
+
+for f in removed_paths:
+    f.rename(f.parent / "to_remove" / f.name)

examples/pyarrow_reading.py

@@ -1,17 +0,0 @@
-import pyarrow.dataset as ds
-
-# A pyarrow dataset abstracts reading, writing, or filtering a parquet file. It does not read dataa into memory. 
-#dataset = ds.dataset(pathlib.Path('/gscratch/comdata/output/reddit_submissions_by_subreddit.parquet/'), format='parquet', partitioning='hive')
-dataset = ds.dataset('/gscratch/comdata/output/reddit_comments_by_subreddit.parquet/', format='parquet')
-
-# let's get all the comments to two subreddits:
-subreddits_to_pull = ['seattle','seattlewa']
-
-# a table is a low-level structured data format.  This line pulls data into memory. Setting metadata_n_threads > 1 gives a little speed boost.
-table = dataset.to_table(filter = ds.field('subreddit').isin(subreddits_to_pull), columns=['id','subreddit','CreatedAt','author','ups','downs','score','subreddit_id','stickied','title','url','is_self','selftext'])
-
-# Since data from just these 2 subreddits fits in memory we can just turn our table into a pandas dataframe.
-df = table.to_pandas()
-
-# We should save this smaller dataset so we don't have to wait 15 min to pull from parquet next time.
-df.to_csv("mydataset.csv")

examples/pyarrow_streaming.py

@@ -1,38 +0,0 @@
-import pyarrow.dataset as ds
-from itertools import groupby
-
-# A pyarrow dataset abstracts reading, writing, or filtering a parquet file. It does not read dataa into memory. 
-
-dataset = ds.dataset('/gscratch/comdata/output/reddit_submissions_by_author.parquet', format='parquet')
-
-# let's get all the comments to two subreddits:
-subreddits_to_pull = ['seattlewa','seattle']
-
-# instead of loading the data into a pandas dataframe all at once we can stream it.
-scan_tasks = dataset.scan(filter = ds.field('subreddit').isin(subreddits_to_pull), columns=['id','subreddit','CreatedAt','author','ups','downs','score','subreddit_id','stickied','title','url','is_self','selftext'])
-
-# simple function to execute scantasks and generate rows
-def iterate_rows(scan_tasks):
-    for st in scan_tasks:
-        for rb in st.execute():
-            df = rb.to_pandas()
-            for t in df.itertuples():
-                yield t
-
-row_iter = iterate_rows(scan_tasks)
-
-# now we can use python's groupby function to read one author at a time
-# note that the same author can appear more than once since the record batches may not be in the correct order.
-author_submissions = groupby(row_iter, lambda row: row.author)
-
-count_dict = {}
-
-for auth, posts in author_submissions:
-    if auth in count_dict:
-        count_dict[auth] = count_dict[auth] + 1
-    else:
-        count_dict[auth] = 1
-
-# since it's partitioned and sorted by author, we get one group for each author 
-any([ v != 1 for k,v in count_dict.items()])
-

ngrams/Makefile

@@ -0,0 +1,25 @@
+outputdir=../../data/reddit_ngrams/
+inputdir=../../data/reddit_comments_by_subreddit.parquet
+authors_tfdir=${outputdir}/comment_authors.parquet
+srun=sbatch --wait --verbose run_job.sbatch
+
+all: ${outputdir}/comment_authors_sorted.parquet/_SUCCESS 
+
+tf_task_list_1: tf_comments.py
+	${srun} bash -c "python3 tf_comments.py gen_task_list --mwe_pass='first' --outputdir=${outputdir} --tf_task_list=$@ --inputdir=${inputdir}"
+
+${outputdir}/comment_terms.parquet:tf_task_list_1
+	mkdir -p sbatch_log
+	sbatch --wait --verbose --array=1-$(shell cat $< | wc -l) run_array.sbatch 0 $<
+
+${outputdir}/comment_authors.parquet:${outputdir}/comment_terms.parquet
+	-
+
+${outputdir}/comment_authors_sorted.parquet:${outputdir}/comment_authors.parquet sort_tf_comments.py
+	../start_spark_and_run.sh 3 sort_tf_comments.py --inparquet=$< --outparquet=$@ --colname=author
+
+${outputdir}/comment_authors_sorted.parquet/_SUCCESS:${outputdir}/comment_authors_sorted.parquet
+
+
+${inputdir}:
+	$(MAKE) -C ../datasets

ngrams/run_array.sbatch

@@ -0,0 +1,19 @@
+#!/bin/bash
+#SBATCH --job-name=reddit_comment_term_frequencies
+#SBATCH --account=comdata
+#SBATCH --partition=compute-bigmem
+#SBATCH --nodes=1
+#SBATCH --ntasks-per-node=1
+#SBATCH --cpus-per-task=1
+#SBATCH --mem-per-cpu=9g
+#SBATCH --ntasks=1
+#SBATCH --export=ALL
+#SBATCH --time=48:00:00
+#SBATCH --chdir=/gscratch/comdata/users/nathante/partitioning_reddit/dataverse/cdsc_reddit/ngrams
+#SBATCH --error="sbatch_log/%A_%a.out"
+#SBATCH --output="sbatch_log/%A_%a.out"
+
+TASK_NUM=$(($SLURM_ARRAY_TASK_ID + $1))
+TASK_CALL=$(sed -n ${TASK_NUM}p $2)
+${TASK_CALL}
+

ngrams/run_job.sbatch

@@ -0,0 +1,18 @@
+#!/bin/bash
+#SBATCH --job-name="simulate measurement error models"
+## Allocation Definition
+#SBATCH --account=comdata
+#SBATCH --partition=compute-bigmem
+## Resources
+#SBATCH --nodes=1    
+## Walltime (4 hours)
+#SBATCH --time=4:00:00
+## Memory per node
+#SBATCH --mem=4G
+#SBATCH --cpus-per-task=1
+#SBATCH --ntasks-per-node=1
+#SBATCH --chdir /gscratch/comdata/users/nathante/partitioning_reddit/dataverse/cdsc_reddit/ngrams/
+#SBATCH --output=sbatch_log/%A_%a.out
+#SBATCH --error=sbatch_log/%A_%a.err
+echo "$@"
+"$@"

ngrams/tf_comments.py

@@ -3,6 +3,7 @@ import pandas as pd
 import pyarrow as pa
 import pyarrow.dataset as ds
 import pyarrow.parquet as pq
+import pyarrow.compute as pc
 from itertools import groupby, islice, chain
 import fire
 from collections import Counter
@@ -15,11 +16,12 @@ import string
 from random import random
 from redditcleaner import clean
 from pathlib import Path
+from datetime import datetime
 
 # compute term frequencies for comments in each subreddit by week
-def weekly_tf(partition, outputdir = '/gscratch/comdata/output/reddit_ngrams/', input_dir="/gscratch/comdata/output/reddit_comments_by_subreddit.parquet/", mwe_pass = 'first', excluded_users=None):
+def weekly_tf(partition, outputdir = '/gscratch/comdata/output/reddit_ngrams/', inputdir="/gscratch/comdata/output/reddit_comments_by_subreddit.parquet/", mwe_pass = 'first', excluded_users=None):
 
-    dataset = ds.dataset(Path(input_dir)/partition, format='parquet')
+    dataset = ds.dataset(Path(inputdir)/partition, format='parquet')
     outputdir = Path(outputdir)
     samppath = outputdir / "reddit_comment_ngrams_10p_sample"
 
@@ -38,6 +40,7 @@ def weekly_tf(partition, outputdir = '/gscratch/comdata/output/reddit_ngrams/',
         if ngram_path.exists():
             ngram_path.unlink()
 
+    dataset = dataset.filter(pc.field("CreatedAt") <= pa.scalar(datetime(2020,4,13)))
     batches = dataset.to_batches(columns=['CreatedAt','subreddit','body','author'])
 
 
@@ -160,9 +163,9 @@ def weekly_tf(partition, outputdir = '/gscratch/comdata/output/reddit_ngrams/',
 
     outchunksize = 10000
     
-    termtf_outputdir = (outputdir / "comment_terms")
+    termtf_outputdir = (outputdir / "comment_terms.parquet")
     termtf_outputdir.mkdir(parents=True, exist_ok=True)
-    authortf_outputdir = (outputdir / "comment_authors")
+    authortf_outputdir = (outputdir / "comment_authors.parquet")
     authortf_outputdir.mkdir(parents=True, exist_ok=True)    
     termtf_path = termtf_outputdir / partition
     authortf_path = authortf_outputdir / partition
@@ -196,12 +199,12 @@ def weekly_tf(partition, outputdir = '/gscratch/comdata/output/reddit_ngrams/',
         author_writer.close()
 
 
-def gen_task_list(mwe_pass='first', outputdir='/gscratch/comdata/output/reddit_ngrams/', tf_task_list='tf_task_list', excluded_users_file=None):
+def gen_task_list(mwe_pass='first', inputdir="/gscratch/comdata/output/reddit_comments_by_subreddit.parquet/", outputdir='/gscratch/comdata/output/reddit_ngrams/', tf_task_list='tf_task_list', excluded_users_file=None):
-    files = os.listdir("/gscratch/comdata/output/reddit_comments_by_subreddit.parquet/")
+    files = os.listdir(inputdir)
     with open(tf_task_list,'w') as outfile:
         for f in files:
             if f.endswith(".parquet"):
-                outfile.write(f"./tf_comments.py weekly_tf --mwe-pass {mwe_pass} --outputdir {outputdir} --excluded_users {excluded_users_file} {f}\n")
+                outfile.write(f"./tf_comments.py weekly_tf --mwe-pass {mwe_pass} --inputdir {inputdir} --outputdir {outputdir} --excluded_users {excluded_users_file} {f}\n")
 
 if __name__ == "__main__":
     fire.Fire({"gen_task_list":gen_task_list,

ngrams/top_comment_phrases.py

@@ -1,69 +0,0 @@
-#!/usr/bin/env python3
-from pyspark.sql import functions as f
-from pyspark.sql import Window
-from pyspark.sql import SparkSession
-import numpy as np
-import fire
-from pathlib import Path
-
-
-def main(ngram_dir="/gscratch/comdata/output/reddit_ngrams"):
-    spark = SparkSession.builder.getOrCreate()
-    ngram_dir = Path(ngram_dir)
-    ngram_sample = ngram_dir / "reddit_comment_ngrams_10p_sample"
-    df = spark.read.text(str(ngram_sample))
-
-    df = df.withColumnRenamed("value","phrase")
-
-    # count phrase occurrances
-    phrases = df.groupby('phrase').count()
-    phrases = phrases.withColumnRenamed('count','phraseCount')
-    phrases = phrases.filter(phrases.phraseCount > 10)
-
-    # count overall
-    N = phrases.select(f.sum(phrases.phraseCount).alias("phraseCount")).collect()[0].phraseCount
-
-    print(f'analyzing PMI on a sample of {N} phrases') 
-    logN = np.log(N)
-    phrases = phrases.withColumn("phraseLogProb", f.log(f.col("phraseCount")) - logN)
-
-    # count term occurrances
-    phrases = phrases.withColumn('terms',f.split(f.col('phrase'),' '))
-    terms = phrases.select(['phrase','phraseCount','phraseLogProb',f.explode(phrases.terms).alias('term')])
-
-    win = Window.partitionBy('term')
-    terms = terms.withColumn('termCount',f.sum('phraseCount').over(win))
-    terms = terms.withColumnRenamed('count','termCount')
-    terms = terms.withColumn('termLogProb',f.log(f.col('termCount')) - logN)
-
-    terms = terms.groupBy(terms.phrase, terms.phraseLogProb, terms.phraseCount).sum('termLogProb')
-    terms = terms.withColumnRenamed('sum(termLogProb)','termsLogProb')
-    terms = terms.withColumn("phrasePWMI", f.col('phraseLogProb') - f.col('termsLogProb'))
-
-    # join phrases to term counts
-
-
-    df = terms.select(['phrase','phraseCount','phraseLogProb','phrasePWMI'])
-
-    df = df.sort(['phrasePWMI'],descending=True)
-    df = df.sortWithinPartitions(['phrasePWMI'],descending=True)
-
-    pwmi_dir = ngram_dir / "reddit_comment_ngrams_pwmi.parquet/"
-    df.write.parquet(str(pwmi_dir), mode='overwrite', compression='snappy')
-
-    df = spark.read.parquet(str(pwmi_dir))
-
-    df.write.csv(str(ngram_dir / "reddit_comment_ngrams_pwmi.csv/"),mode='overwrite',compression='none')
-
-    df = spark.read.parquet(str(pwmi_dir))
-    df = df.select('phrase','phraseCount','phraseLogProb','phrasePWMI')
-
-    # choosing phrases occurring at least 3500 times in the 10% sample (35000 times) and then with a PWMI of at least 3 yeids about 65000 expressions.
-    #
-    df = df.filter(f.col('phraseCount') > 3500).filter(f.col("phrasePWMI")>3)
-    df = df.toPandas()
-    df.to_feather(ngram_dir / "multiword_expressions.feather")
-    df.to_csv(ngram_dir / "multiword_expressions.csv")
-
-if __name__ == '__main__':
-    fire.Fire(main)

run_array.sbatch

@@ -0,0 +1,22 @@
+#!/bin/bash
+## tf reddit comments
+#SBATCH --job-name="wikia ecology; fit var models"
+## Allocation Definition
+#SBATCH --account=comdata-ckpt
+#SBATCH --partition=ckpt
+## Resources
+## Nodes. This should always be 1 for parallel-sql.
+#SBATCH --nodes=1    
+## Walltime (12 hours)
+#SBATCH --time=24:00:00
+## Memory per node
+#SBATCH --mem=8G
+#SBATCH --cpus-per-task=1
+#SBATCH --ntasks=1
+#SBATCH 
+#SBATCH --chdir /gscratch/comdata/users/nathante/wikia_ecology
+#SBATCH --output=var_jobs/%A_%a.out
+#SBATCH --error=var_jobs/%A_%a.out
+TASK_NUM=$(( SLURM_ARRAY_TASK_ID + $1))
+TASK_CALL=$(sed -n ${TASK_NUM}p ./var_jobs.sh)
+${TASK_CALL}

similarities/Makefile

@@ -1,138 +1,28 @@
-
-#all: /gscratch/comdata/output/reddit_similarity/tfidf/comment_terms_130k.parquet /gscratch/comdata/output/reddit_similarity/tfidf/comment_authors_130k.parquet /gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_terms_130k.parquet /gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_authors_130k.parquet
-# srun_singularity=source /gscratch/comdata/users/nathante/cdsc_reddit/bin/activate && srun_singularity.sh
-# srun_singularity_huge=source /gscratch/comdata/users/nathante/cdsc_reddit/bin/activate && srun_singularity_huge.sh
 srun=srun -p compute-bigmem -A comdata --mem-per-cpu=9g --time=200:00:00 -c 40
-srun_huge=srun -p compute-hugemem -A comdata --mem-per-cpu=9g --time=200:00:00 -c 40 
+srun_huge=srun -p compute-hugemem -A comdata --mem=724g --time=200:00:00 -c 40
-similarity_data=/gscratch/scrubbed/comdata/reddit_similarity
+
+similarity_data=../../data/reddit_similarity
 tfidf_data=${similarity_data}/tfidf
-tfidf_weekly_data=${similarity_data}/tfidf_weekly
+lsi_components=[10,50,100,200,300,400,500,600,700,850]
-similarity_weekly_data=${similarity_data}/weekly
-lsi_components=[10,50,100,200,300,400,500,600,700,850,1000,1500]
 
-lsi_similarities: ${similarity_data}/subreddit_comment_terms_10k_LSI ${similarity_data}/subreddit_comment_authors-tf_10k_LSI ${similarity_data}/subreddit_comment_authors_10k_LSI ${similarity_data}/subreddit_comment_terms_30k_LSI ${similarity_data}/subreddit_comment_authors-tf_30k_LSI ${similarity_data}/subreddit_comment_authors_30k_LSI
+lsi_similarities: ${similarity_data}/subreddit_comment_authors-tf_10k_LSI
 
+all: ${similarity_data}/subreddit_comment_authors-tf_10k.feather
 
-all: ${tfidf_data}/comment_terms_30k.parquet ${tfidf_data}/comment_terms_10k.parquet ${tfidf_data}/comment_authors_30k.parquet ${tfidf_data}/comment_authors_10k.parquet ${similarity_data}/subreddit_comment_authors_30k.feather ${similarity_data}/subreddit_comment_authors_10k.feather  ${similarity_data}/subreddit_comment_terms_10k.feather ${similarity_data}/subreddit_comment_terms_30k.feather ${similarity_data}/subreddit_comment_authors-tf_30k.feather ${similarity_data}/subreddit_comment_authors-tf_10k.feather
+${similarity_data}/subreddit_comment_authors-tf_10k_LSI: ${tfidf_data}/comment_authors_100k.parquet similarities_helper.py ${similarity_data}/subreddits_by_num_comments_nonsfw.csv
+	 ${srun_huge} /bin/bash -c "source ~/.bashrc; python3 lsi_similarities.py author-tf --outfile=${similarity_data}/subreddit_comment_authors-tf_10k_LSI --topN=10000 --n_components=${lsi_components} --min_df=10 --inpath=$<"
 
-#all: ${tfidf_data}/comment_terms_100k.parquet ${tfidf_data}/comment_terms_30k.parquet ${tfidf_data}/comment_terms_10k.parquet ${tfidf_data}/comment_authors_100k.parquet ${tfidf_data}/comment_authors_30k.parquet ${tfidf_data}/comment_authors_10k.parquet ${similarity_data}/subreddit_comment_authors_30k.feather ${similarity_data}/subreddit_comment_authors_10k.feather  ${similarity_data}/subreddit_comment_terms_10k.feather ${similarity_data}/subreddit_comment_terms_30k.feather ${similarity_data}/subreddit_comment_authors-tf_30k.feather ${similarity_data}/subreddit_comment_authors-tf_10k.feather ${similarity_data}/subreddit_comment_terms_100k.feather ${similarity_data}/subreddit_comment_authors_100k.feather ${similarity_data}/subreddit_comment_authors-tf_100k.feather ${similarity_weekly_data}/comment_terms.parquet
+${similarity_data}/subreddits_by_num_comments_nonsfw.csv: ../../data/reddit_submissions_by_subreddit.parquet ../../data/reddit_comments_by_subreddit.parquet
+	../start_spark_and_run.sh 3 top_subreddits_by_comments.py
 
-#${tfidf_weekly_data}/comment_terms_100k.parquet ${tfidf_weekly_data}/comment_authors_100k.parquet ${tfidf_weekly_data}/comment_terms_30k.parquet ${tfidf_weekly_data}/comment_authors_30k.parquet ${similarity_weekly_data}/comment_terms_100k.parquet ${similarity_weekly_data}/comment_authors_100k.parquet  ${similarity_weekly_data}/comment_terms_30k.parquet ${similarity_weekly_data}/comment_authors_30k.parquet
+${tfidf_data}/comment_authors_100k.parquet: ../../data/reddit_ngrams/comment_authors_sorted.parquet ${similarity_data}/subreddits_by_num_comments_nonsfw.csv
+	../start_spark_and_run.sh 3 tfidf.py authors --topN=100000 --inpath=$< --outpath=${tfidf_data}/comment_authors_100k.parquet
 
-# /gscratch/comdata/output/reddit_similarity/subreddit_comment_authors_130k.parquet /gscratch/comdata/output/reddit_similarity/subreddit_comment_authors_130k.parquet /gscratch/comdata/output/reddit_similarity/subreddit_author_tf_similarities_130k.parquet /gscratch/comdata/output/reddit_similarity/subreddit_comment_terms_130k.parquet /gscratch/comdata/output/reddit_similarity/comment_terms_weekly_130k.parquet
+../../data/reddit_ngrams/comment_authors_sorted.parquet:
+	$(MAKE) -C ../ngrams
 
-# all: /gscratch/comdata/output/reddit_similarity/subreddit_comment_terms_25000.parquet /gscratch/comdata/output/reddit_similarity/subreddit_comment_authors_25000.parquet /gscratch/comdata/output/reddit_similarity/subreddit_comment_authors_10000.parquet /gscratch/comdata/output/reddit_similarity/comment_terms_10000_weekly.parquet
+../../data/reddit_submissions_by_subreddit.parquet:
+	$(MAKE) -C ../datasets
 
-${similarity_weekly_data}/comment_terms.parquet: weekly_cosine_similarities.py similarities_helper.py /gscratch/comdata/output/reddit_ngrams/comment_terms.parquet ${similarity_data}/subreddits_by_num_comments_nonsfw.csv ${tfidf_weekly_data}/comment_terms.parquet
+../../data/reddit_comments_by_subreddit.parquet:
-	 ${srun} python3 weekly_cosine_similarities.py terms --topN=10000 --outfile=${similarity_weekly_data}/comment_terms.parquet
+	$(MAKE) -C ../datasets
-
-${similarity_data}/subreddit_comment_terms_10k.feather: ${tfidf_data}/comment_terms_100k.parquet similarities_helper.py
-	 ${srun} python3 cosine_similarities.py term --outfile=${similarity_data}/subreddit_comment_terms_10k.feather --topN=10000
-
-${similarity_data}/subreddit_comment_terms_10k_LSI: ${tfidf_data}/comment_terms_100k.parquet similarities_helper.py
-	 ${srun_huge} python3 lsi_similarities.py term --outfile=${similarity_data}/subreddit_comment_terms_10k_LSI --topN=10000 --n_components=${lsi_components} --min_df=200
-
-${similarity_data}/subreddit_comment_terms_30k_LSI: ${tfidf_data}/comment_terms_100k.parquet similarities_helper.py
-	 ${srun_huge} python3 lsi_similarities.py term --outfile=${similarity_data}/subreddit_comment_terms_30k_LSI --topN=30000 --n_components=${lsi_components} --min_df=200 --inpath=$<
-
-${similarity_data}/subreddit_comment_terms_30k.feather: ${tfidf_data}/comment_terms_30k.parquet similarities_helper.py
-	 ${srun_huge} python3 cosine_similarities.py term --outfile=${similarity_data}/subreddit_comment_terms_30k.feather --topN=30000 --inpath=$<
-
-${similarity_data}/subreddit_comment_authors_30k.feather: ${tfidf_data}/comment_authors_30k.parquet similarities_helper.py
-	 ${srun_huge} python3 cosine_similarities.py author --outfile=${similarity_data}/subreddit_comment_authors_30k.feather --topN=30000 --inpath=$<
-
-${similarity_data}/subreddit_comment_authors_10k.feather: ${tfidf_data}/comment_authors_10k.parquet similarities_helper.py
-	 ${srun_huge} python3 cosine_similarities.py author --outfile=${similarity_data}/subreddit_comment_authors_10k.feather --topN=10000 --inpath=$<
-
-${similarity_data}/subreddit_comment_authors_10k_LSI: ${tfidf_data}/comment_authors_100k.parquet similarities_helper.py
-	 ${srun_huge} python3 lsi_similarities.py author --outfile=${similarity_data}/subreddit_comment_authors_10k_LSI --topN=10000 --n_components=${lsi_components} --min_df=10 --inpath=$<
-
-${similarity_data}/subreddit_comment_authors_30k_LSI: ${tfidf_data}/comment_authors_100k.parquet similarities_helper.py
-	 ${srun_huge} python3 lsi_similarities.py author --outfile=${similarity_data}/subreddit_comment_authors_30k_LSI --topN=30000 --n_components=${lsi_components} --min_df=10 --inpath=$<
-
-${similarity_data}/subreddit_comment_authors-tf_30k.feather: ${tfidf_data}/comment_authors_100k.parquet similarities_helper.py
-	 ${srun} python3 cosine_similarities.py author-tf --outfile=${similarity_data}/subreddit_comment_authors-tf_30k.feather --topN=30000 --inpath=$<
-
-${similarity_data}/subreddit_comment_authors-tf_10k.feather: ${tfidf_data}/comment_authors_100k.parquet similarities_helper.py
-	 ${srun} python3 cosine_similarities.py author-tf --outfile=${similarity_data}/subreddit_comment_authors-tf_10k.feather --topN=10000
-
-${similarity_data}/subreddit_comment_authors-tf_10k_LSI: ${tfidf_data}/comment_authors_100k.parquet similarities_helper.py
-	 ${srun_huge} python3 lsi_similarities.py author-tf --outfile=${similarity_data}/subreddit_comment_authors-tf_10k_LSI --topN=10000 --n_components=${lsi_components} --min_df=10 --inpath=$<
-
-${similarity_data}/subreddit_comment_authors-tf_30k_LSI: ${tfidf_data}/comment_authors_100k.parquet similarities_helper.py
-	 ${srun_huge} python3 lsi_similarities.py author-tf --outfile=${similarity_data}/subreddit_comment_authors-tf_30k_LSI --topN=30000 --n_components=${lsi_components} --min_df=10 --inpath=$<
-
-${similarity_data}/subreddit_comment_terms_100k.feather: ${tfidf_data}/comment_terms_100k.parquet similarities_helper.py
-	 ${srun} python3 cosine_similarities.py term --outfile=${similarity_data}/subreddit_comment_terms_100k.feather --topN=100000
-
-${similarity_data}/subreddit_comment_authors_100k.feather: ${tfidf_data}/comment_authors_100k.parquet similarities_helper.py
-	 ${srun} python3 cosine_similarities.py author --outfile=${similarity_data}/subreddit_comment_authors_100k.feather --topN=100000
-
-${similarity_data}/subreddit_comment_authors-tf_100k.feather: ${tfidf_data}/comment_authors_100k.parquet similarities_helper.py
-	 ${srun} python3 cosine_similarities.py author-tf --outfile=${similarity_data}/subreddit_comment_authors-tf_100k.feather --topN=100000
-
-${similarity_data}/subreddits_by_num_comments_nonsfw.csv:
-	start_spark_and_run.sh 3 top_subreddits_by_comments.py
-
-${tfidf_data}/comment_terms_100k.parquet: /gscratch/comdata/output/reddit_ngrams/comment_terms.parquet ${similarity_data}/subreddits_by_num_comments_nonsfw.csv
-#	mkdir -p ${tfidf_data}/
-	start_spark_and_run.sh 3 tfidf.py terms --topN=100000 --inpath=$< --outpath=${tfidf_data}/comment_terms_100k.parquet
-
-${tfidf_data}/comment_terms_30k.feather: /gscratch/comdata/output/reddit_ngrams/comment_terms.parquet ${similarity_data}/subreddits_by_num_comments_nonsfw.csv
-#	mkdir -p ${tfidf_data}/
-	start_spark_and_run.sh 3 tfidf.py terms --topN=30000 --inpath=$< --outpath=${tfidf_data}/comment_terms_30k.feather
-
-${tfidf_data}/comment_terms_10k.feather: /gscratch/comdata/output/reddit_ngrams/comment_terms.parquet ${similarity_data}/subreddits_by_num_comments_nonsfw.csv
-#	mkdir -p ${tfidf_data}/
-	start_spark_and_run.sh 3 tfidf.py terms --topN=10000 --inpath=$< --outpath=${tfidf_data}/comment_terms_10k.feather
-
-${tfidf_data}/comment_authors_100k.parquet: /gscratch/comdata/output/reddit_ngrams/comment_authors.parquet ${similarity_data}/subreddits_by_num_comments_nonsfw.csv
-#	mkdir -p ${tfidf_data}/
-	start_spark_and_run.sh 3 tfidf.py authors --topN=100000 --inpath=$< --outpath=${tfidf_data}/comment_authors_100k.parquet
-
-${tfidf_data}/comment_authors_10k.parquet: /gscratch/comdata/output/reddit_ngrams/comment_authors.parquet ${similarity_data}/subreddits_by_num_comments_nonsfw.csv
-#	mkdir -p ${tfidf_data}/
-	start_spark_and_run.sh 3 tfidf.py authors --topN=10000 --inpath=$< --outpath=${tfidf_data}/comment_authors_10k.parquet
-
-${tfidf_data}/comment_authors_30k.parquet: /gscratch/comdata/output/reddit_ngrams/comment_authors.parquet ${similarity_data}/subreddits_by_num_comments_nonsfw.csv
-#	mkdir -p ${tfidf_data}/
-	start_spark_and_run.sh 3 tfidf.py authors --topN=30000 --inpath=$< --outpath=${tfidf_data}/comment_authors_30k.parquet
-
-${tfidf_data}/tfidf_weekly/comment_terms_100k.parquet: /gscratch/comdata/output/reddit_ngrams/comment_terms.parquet ${similarity_data}/subreddits_by_num_comments_nonsfw.csv
-	start_spark_and_run.sh 3 tfidf.py terms_weekly --topN=100000 --outpath=${similarity_data}/tfidf_weekly/comment_authors_100k.parquet
-
-${tfidf_data}/tfidf_weekly/comment_authors_100k.parquet: /gscratch/comdata/output/reddit_ngrams/comment_terms.parquet ${similarity_data}/subreddits_by_ppnum_comments.csv
-	start_spark_and_run.sh 3 tfidf.py authors_weekly --topN=100000 --inpath=$< --outpath=${tfidf_weekly_data}/comment_authors_100k.parquet
-
-${tfidf_weekly_data}/comment_terms_30k.parquet:  /gscratch/comdata/output/reddit_ngrams/comment_terms.parquet ${similarity_data}/subreddits_by_num_comments_nonsfw.csv
-	start_spark_and_run.sh 2 tfidf.py terms_weekly --topN=30000 --inpath=$< --outpath=${tfidf_weekly_data}/comment_authors_30k.parquet
-
-${tfidf_weekly_data}/comment_authors_30k.parquet: /gscratch/comdata/output/reddit_ngrams/comment_terms.parquet ${similarity_data}/subreddits_by_num_comments_nonsfw.csv
-	start_spark_and_run.sh 3 tfidf.py authors_weekly --topN=30000 --inpath=$< --outpath=${tfidf_weekly_data}/comment_authors_30k.parquet
-
-${similarity_weekly_data}/comment_terms_100k.parquet: weekly_cosine_similarities.py similarities_helper.py ${tfidf_weekly_data}/comment_terms_100k.parquet
-	 ${srun} python3 weekly_cosine_similarities.py terms --topN=100000 --outfile=${similarity_weekly_data}/comment_terms_100k.parquet
-
-${similarity_weekly_data}/comment_authors_100k.parquet: weekly_cosine_similarities.py similarities_helper.py /gscratch/comdata/output/reddit_ngrams/comment_terms.parquet ${similarity_data}/subreddits_by_num_comments_nonsfw.csv ${tfidf_weekly_data}/comment_authors_100k.parquet
-	 ${srun} python3 weekly_cosine_similarities.py authors --topN=100000 --outfile=${similarity_weekly_data}/comment_authors_100k.parquet
-
-${similarity_weekly_data}/comment_terms_30k.parquet: weekly_cosine_similarities.py similarities_helper.py /gscratch/comdata/output/reddit_ngrams/comment_terms.parquet ${similarity_data}/subreddits_by_num_comments_nonsfw.csv ${tfidf_weekly_data}/comment_terms_30k.parquet
-	 ${srun} python3 weekly_cosine_similarities.py terms --topN=30000 --outfile=${similarity_weekly_data}/comment_authors_30k.parquet
-
-,${similarity_weekly_data}/comment_authors_30k.parquet: weekly_cosine_similarities.py similarities_helper.py /gscratch/comdata/output/reddit_ngrams/comment_terms.parquet ${similarity_data}/subreddits_by_num_comments_nonsfw.csv ${tfidf_weekly_data}/comment_authors_30k.parquet
-	 ${srun} python3 weekly_cosine_similarities.py authors --topN=30000 --outfile=${similarity_weekly_data}/comment_authors_30k.parquet
-
-# ${tfidf_weekly_data}/comment_authors_130k.parquet: tfidf.py similarities_helper.py /gscratch/comdata/output/reddit_ngrams/comment_authors.parquet /gscratch/comdata/output/reddit_similarity/subreddits_by_num_comments_nonsfw.csv
-# 	start_spark_and_run.sh 1 tfidf.py authors_weekly --topN=130000
-
-# /gscratch/comdata/output/reddit_similarity/comment_authors_10000.parquet: cosine_similarities.py similarities_helper.py /gscratch/comdata/output/reddit_similarity/tfidf/comment_authors.parquet /gscratch/comdata/output/reddit_similarity/tfidf/comment_authors.parquet 
-# 	start_spark_and_run.sh 1 cosine_similarities.py author --outfile=/gscratch/comdata/output/reddit_similarity/comment_authors_10000.feather
-
-# /gscratch/comdata/output/reddit_similarity/comment_terms.parquet: cosine_similarities.py similarities_helper.py /gscratch/comdata/output/reddit_similarity/tfidf/comment_terms.parquet
-# 	start_spark_and_run.sh 1 cosine_similarities.py term --outfile=/gscratch/comdata/output/reddit_similarity/comment_terms_10000.feather
-
-# /gscratch/comdata/output/reddit_similarity/comment_terms_10000_weekly.parquet: cosine_similarities.py ${tfidf_weekly_data}/comment_authors.parquet
-# 	start_spark_and_run.sh 1 weekly_cosine_similarities.py term --outfile=/gscratch/comdata/output/reddit_similarity/subreddit_comment_terms_10000_weely.parquet
-
-# /gscratch/comdata/output/reddit_similarity/subreddit_author_tf_similarities_10000.parquet: cosine_similarities.py similarities_helper.py /gscratch/comdata/output/reddit_similarity/tfidf/comment_authors.parquet /gscratch/comdata/output/reddit_similarity/tfidf/comment_authors.parquet
-# 	start_spark_and_run.sh 1 cosine_similarities.py author-tf --outfile=/gscratch/comdata/output/reddit_similarity/subreddit_author_tf_similarities_10000.parquet

similarities/job_script.sh

@@ -1,4 +1,6 @@
 #!/usr/bin/bash
+source ~/.bashrc
+echo $(hostname)
 start_spark_cluster.sh
-singularity exec  /gscratch/comdata/users/nathante/containers/nathante.sif spark-submit --master spark://$(hostname):7077 tfidf.py authors --topN=100000 --inpath=/gscratch/comdata/output/reddit_ngrams/comment_authors.parquet --outpath=/gscratch/scrubbed/comdata/reddit_similarity/tfidf/comment_authors_100k.parquet
+spark-submit --verbose --master spark://$(hostname):43015 tfidf.py authors --topN=100000 --inpath=../../data/reddit_ngrams/comment_authors_sorted.parquet --outpath=../../data/reddit_similarity/tfidf/comment_authors_100k.parquet
-singularity exec /gscratch/comdata/users/nathante/containers/nathante.sif stop-all.sh
+stop-all.sh

similarities/similarities_helper.py

@@ -43,7 +43,7 @@ def reindex_tfidf(*args, **kwargs):
     new_ids = df.loc[:,['subreddit_id','subreddit_id_new']].drop_duplicates()
     new_ids = new_ids.set_index('subreddit_id')
     subreddit_names = subreddit_names.join(new_ids,on='subreddit_id').reset_index()
-    subreddit_names = subreddit_names.drop("subreddit_id",1)
+    subreddit_names = subreddit_names.drop("subreddit_id",axis=1)
     subreddit_names = subreddit_names.sort_values("subreddit_id_new")
     return(df, subreddit_names)
 
@@ -51,8 +51,9 @@ def pull_tfidf(*args, **kwargs):
     df, _, _ =  _pull_or_reindex_tfidf(*args, **kwargs, reindex=False)
     return df
 
-def _pull_or_reindex_tfidf(infile, term_colname, min_df=None, max_df=None, included_subreddits=None, topN=500, week=None, from_date=None, to_date=None, rescale_idf=True, tf_family=tf_weight.MaxTF, reindex=True):
+def _pull_or_reindex_tfidf(infile, term_colname, min_df=None, max_df=None, included_subreddits=None, topN=None, week=None, from_date=None, to_date=None, rescale_idf=True, tf_family=tf_weight.MaxTF, reindex=True):
-    print(f"loading tfidf {infile}", flush=True)
+    print(f"loading tfidf {infile}, week {week}, min_df {min_df}, max_df {max_df}", flush=True)
+
     if week is not None:
         tfidf_ds = ds.dataset(infile, partitioning='hive')
     else: 
@@ -97,20 +98,21 @@ def _pull_or_reindex_tfidf(infile, term_colname, min_df=None, max_df=None, inclu
             'relative_tf':ds.field('relative_tf').cast('float32'),
             'tf_idf':ds.field('tf_idf').cast('float32')}
 
-        print(projection)
+    print(projection, flush=True)
-
+    print(ds_filter, flush=True)
     df = tfidf_ds.to_table(filter=ds_filter,columns=projection)
 
     df = df.to_pandas(split_blocks=True,self_destruct=True)
     print("assigning indexes",flush=True)
     if reindex:
-        df['subreddit_id_new'] = df.groupby("subreddit_id").ngroup()
+        print("assigning indexes",flush=True)
+        df['subreddit_id_new'] = df.groupby("subreddit_id").ngroup() + 1
     else:
         df['subreddit_id_new'] = df['subreddit_id']
 
     if reindex:
         grouped = df.groupby(term_id)
-        df[term_id_new] = grouped.ngroup()
+        df[term_id_new] = grouped.ngroup() + 1 
     else:
         df[term_id_new] = df[term_id]
 
@@ -126,17 +128,6 @@ def _pull_or_reindex_tfidf(infile, term_colname, min_df=None, max_df=None, inclu
 
     return (df, tfidf_ds, ds_filter)
 
-    with Pool(cpu_count()) as pool:
-        chunks = pool.imap_unordered(pull_names,batches) 
-        subreddit_names = pd.concat(chunks,copy=False).drop_duplicates()
-
-    subreddit_names = subreddit_names.set_index("subreddit_id")
-    new_ids = df.loc[:,['subreddit_id','subreddit_id_new']].drop_duplicates()
-    new_ids = new_ids.set_index('subreddit_id')
-    subreddit_names = subreddit_names.join(new_ids,on='subreddit_id').reset_index()
-    subreddit_names = subreddit_names.drop("subreddit_id",1)
-    subreddit_names = subreddit_names.sort_values("subreddit_id_new")
-    return(df, subreddit_names)
 
 def pull_names(batch):
     return(batch.to_pandas().drop_duplicates())
@@ -170,7 +161,7 @@ def similarities(inpath, simfunc, term_colname, outfile, min_df=None, max_df=Non
     term_id_new = term + '_id_new'
 
     entries, subreddit_names = reindex_tfidf(inpath, term_colname=term_colname, min_df=min_df, max_df=max_df, included_subreddits=included_subreddits, topN=topN,from_date=from_date,to_date=to_date)
-    mat = csr_matrix((entries[tfidf_colname],(entries[term_id_new], entries.subreddit_id_new)))
+    mat = csr_matrix((entries[tfidf_colname],(entries[term_id_new]-1, entries.subreddit_id_new-1)))
 
     print("loading matrix")        
 
@@ -256,22 +247,20 @@ def lsi_column_similarities(tfidfmat,n_components=300,n_iter=10,random_state=196
 
     else:
         print("running LSI",flush=True)
-
         svd = TruncatedSVD(n_components=svd_components,random_state=random_state,algorithm=algorithm,n_iter=n_iter)
         mod = svd.fit(tfidfmat.T)
 
-    lsimat = mod.transform(tfidfmat.T)
     if lsi_model_save is not None:
         Path(lsi_model_save).parent.mkdir(exist_ok=True, parents=True)
         pickle.dump(mod, open(lsi_model_save,'wb'))
 
-    sims_list = []
+    print(n_components, flush=True)
+    lsimat = mod.transform(tfidfmat.T)
     for n_dims in n_components:
+        print("computing similarities", flush=True)
         sims = column_similarities(lsimat[:,np.arange(n_dims)])
-        if len(n_components) > 1:
         yield (sims, n_dims)
-        else:
+
-            return sims
     
 
 def column_similarities(mat):
@@ -327,11 +316,11 @@ def build_weekly_tfidf_dataset(df, include_subs, term_colname, tf_family=tf_weig
     else: # tf_fam = tf_weight.Norm05
         df = df.withColumn("tf_idf",  (0.5 + 0.5 * df.relative_tf) * df.idf)
 
-    df = df.repartition(400,'subreddit','week')
+    df = df.repartition('week')
     dfwriter = df.write.partitionBy("week")
     return dfwriter
 
-def _calc_tfidf(df, term_colname, tf_family):
+def _calc_tfidf(df, term_colname, tf_family, min_df=None, max_df=None):
     term = term_colname
     term_id = term + '_id'
 
@@ -349,7 +338,13 @@ def _calc_tfidf(df, term_colname, tf_family):
     idf = idf.withColumn('idf',f.log(N_docs/(1+f.col('count')))+1)
 
     # collect the dictionary to make a pydict of terms to indexes
-    terms = idf.select(term).distinct() # terms are distinct
+    terms = idf
+    if min_df is not None:
+        terms = terms.filter(f.col('count')>=min_df)
+    if max_df is not None:
+        terms = terms.filter(f.col('count')<=max_df)
+    
+    terms = terms.select(term).distinct() # terms are distinct
     terms = terms.withColumn(term_id,f.row_number().over(Window.orderBy(term))) # term ids are distinct
 
     # make subreddit ids
@@ -359,12 +354,12 @@ def _calc_tfidf(df, term_colname, tf_family):
     df = df.join(subreddits,on='subreddit')
 
     # map terms to indexes in the tfs and the idfs
-    df = df.join(terms,on=term) # subreddit-term-id is unique
+    df = df.join(terms,on=term,how='inner') # subreddit-term-id is unique
 
-    idf = idf.join(terms,on=term)
+    idf = idf.join(terms,on=term,how='inner')
 
     # join on subreddit/term to create tf/dfs indexed by term
-    df = df.join(idf, on=[term_id, term])
+    df = df.join(idf, on=[term_id, term],how='inner')
 
     # agg terms by subreddit to make sparse tf/df vectors
     if tf_family == tf_weight.MaxTF:
@@ -375,19 +370,19 @@ def _calc_tfidf(df, term_colname, tf_family):
     return df
     
 
-def tfidf_dataset(df, include_subs, term_colname, tf_family=tf_weight.Norm05):
+def tfidf_dataset(df, include_subs, term_colname, tf_family=tf_weight.Norm05, min_df=None, max_df=None):
     term = term_colname
     term_id = term + '_id'
     # aggregate counts by week. now subreddit-term is distinct
     df = df.filter(df.subreddit.isin(include_subs))
     df = df.groupBy(['subreddit',term]).agg(f.sum('tf').alias('tf'))
 
-    df = _calc_tfidf(df, term_colname, tf_family)
+    df = _calc_tfidf(df, term_colname, tf_family, min_df, max_df)
     df = df.repartition('subreddit')
     dfwriter = df.write
     return dfwriter
 
-def select_topN_subreddits(topN, path="/gscratch/comdata/output/reddit_similarity/subreddits_by_num_comments_nonsfw.csv"):
+def select_topN_subreddits(topN, path="../../data/reddit_similarity/subreddits_by_num_comments_nonsfw.csv"):
     rankdf = pd.read_csv(path)
     included_subreddits = set(rankdf.loc[rankdf.comments_rank <= topN,'subreddit'].values)
     return included_subreddits

similarities/top_subreddits_by_comments.py

@@ -1,16 +1,20 @@
 from pyspark.sql import functions as f
 from pyspark.sql import SparkSession
 from pyspark.sql import Window
+from datetime import datetime
+from pathlib import Path
 
 spark = SparkSession.builder.getOrCreate()
 conf = spark.sparkContext.getConf()
 
-submissions = spark.read.parquet("/gscratch/comdata/output/reddit_submissions_by_subreddit.parquet")
+submissions = spark.read.parquet("../../data/reddit_submissions_by_subreddit.parquet")
+
+submissions = submissions.filter(f.col("CreatedAt") <= datetime(2020,4,13))
 
 prop_nsfw = submissions.select(['subreddit','over_18']).groupby('subreddit').agg(f.mean(f.col('over_18').astype('double')).alias('prop_nsfw'))
 
-df = spark.read.parquet("/gscratch/comdata/output/reddit_comments_by_subreddit.parquet")
+df = spark.read.parquet("../../data/reddit_comments_by_subreddit.parquet")
-
+df = df.filter(f.col("CreatedAt") <= datetime(2020,4,13))
 # remove /u/ pages
 df = df.filter(~df.subreddit.like("u_%"))
 
@@ -26,4 +30,6 @@ df = df.toPandas()
 
 df = df.sort_values("n_comments")
 
-df.to_csv('/gscratch/scrubbed/comdata/reddit_similarity/subreddits_by_num_comments_nonsfw.csv', index=False)
+outpath = Path("../../data/reddit_similarity/subreddits_by_num_comments_nonsfw.csv")
+outpath.parent.mkdir(exist_ok=True, parents=True)
+df.to_csv(str(outpath), index=False)

similarities/wang_similarity.py

@@ -1,18 +0,0 @@
-from similarities_helper import similarities
-import numpy as np
-import fire 
-
-def wang_similarity(mat):
-    non_zeros = (mat != 0).astype(np.float32)
-    intersection = non_zeros.T @ non_zeros
-    return intersection
-
-
-infile="/gscratch/comdata/output/reddit_similarity/tfidf/comment_authors.parquet"; outfile="/gscratch/comdata/output/reddit_similarity/wang_similarity_10000.feather"; min_df=1; included_subreddits=None; topN=10000; exclude_phrases=False; from_date=None; to_date=None
-    
-def wang_overlaps(infile, outfile="/gscratch/comdata/output/reddit_similarity/wang_similarity_10000.feather", min_df=1, max_df=None, included_subreddits=None, topN=10000, exclude_phrases=False, from_date=None, to_date=None):
-
-    return similarities(infile=infile, simfunc=wang_similarity, term_colname='author', outfile=outfile, min_df=min_df, max_df=max_df, included_subreddits=included_subreddits, topN=topN, exclude_phrases=exclude_phrases, from_date=from_date, to_date=to_date)
-
-if __name__ == "__main__":
-    fire.Fire(wang_overlaps)

similarities/weekly_cosine_similarities.py

@@ -1,149 +0,0 @@
-#!/usr/bin/env python3
-from pyspark.sql import functions as f
-from pyspark.sql import SparkSession
-from pyspark.sql import Window
-import numpy as np
-import pyarrow
-import pyarrow.dataset as ds
-import pandas as pd
-import fire
-from itertools import islice, chain
-from pathlib import Path
-from similarities_helper import pull_tfidf, column_similarities, write_weekly_similarities, lsi_column_similarities
-from scipy.sparse import csr_matrix
-from multiprocessing import Pool, cpu_count
-from functools import partial
-import pickle
-
-# tfidf_path = "/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/similarity_weekly/comment_authors_tfidf.parquet"
-# #tfidf_path = "/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data//comment_authors_compex.parquet"
-# min_df=2
-# included_subreddits="/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/included_subreddits.txt"
-# max_df = None
-# topN=100
-# term_colname='author'
-# # outfile = '/gscratch/comdata/output/reddit_similarity/weekly/comment_authors_test.parquet'
-# # included_subreddits=None
-outfile="/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/similarity_weekly/comment_authors.parquet"; infile="/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/tfidf_weekly/comment_authors_tfidf.parquet"; included_subreddits="/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/included_subreddits.txt"; lsi_model="/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/similarity/comment_authors_compex_LSI/2000_authors_LSIMOD.pkl"; n_components=1500; algorithm="randomized"; term_colname='author'; tfidf_path=infile; random_state=1968;
-
-# static_tfidf = "/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/tfidf/comment_authors_compex.parquet"
-# dftest = spark.read.parquet(static_tfidf)
-
-def _week_similarities(week, simfunc, tfidf_path, term_colname, included_subreddits, outdir:Path, subreddit_names, nterms, topN=None, min_df=None, max_df=None):
-    term = term_colname
-    term_id = term + '_id'
-    term_id_new = term + '_id_new'
-    print(f"loading matrix: {week}")
-
-    entries = pull_tfidf(infile = tfidf_path,
-                         term_colname=term_colname,
-                         included_subreddits=included_subreddits,
-                         topN=topN,
-                         week=week.isoformat(),
-                         rescale_idf=False)
-    
-    tfidf_colname='tf_idf'
-    # if the max subreddit id we found is less than the number of subreddit names then we have to fill in 0s
-    mat = csr_matrix((entries[tfidf_colname],(entries[term_id_new]-1, entries.subreddit_id_new-1)),shape=(nterms,subreddit_names.shape[0]))
-    print('computing similarities')
-    print(simfunc)
-    sims = simfunc(mat)
-    del mat
-    sims = next(sims)[0]
-    sims = pd.DataFrame(sims)
-    sims = sims.rename({i: sr for i, sr in enumerate(subreddit_names.subreddit.values)}, axis=1)
-    sims['_subreddit'] = subreddit_names.subreddit.values
-    outfile = str(Path(outdir) / str(week))
-    write_weekly_similarities(outfile, sims, week, subreddit_names)
-
-def pull_weeks(batch):
-    return set(batch.to_pandas()['week'])
-
-# This requires a prefit LSI model, since we shouldn't fit different LSI models for every week. 
-def cosine_similarities_weekly_lsi(*args, n_components=100, lsi_model=None, **kwargs):
-    print(args)
-    print(kwargs)
-    term_colname= kwargs.get('term_colname')
-    # lsi_model = "/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/similarity/comment_authors_compex_LSI/1000_author_LSIMOD.pkl"
-
-    lsi_model = pickle.load(open(lsi_model,'rb'))
-    #simfunc = partial(lsi_column_similarities,n_components=n_components,random_state=random_state,algorithm='randomized',lsi_model=lsi_model)
-    simfunc = partial(lsi_column_similarities,n_components=n_components,random_state=kwargs.get('random_state'),lsi_model=lsi_model)
-
-    return cosine_similarities_weekly(*args, simfunc=simfunc, **kwargs)
-
-#tfidf = spark.read.parquet('/gscratch/comdata/users/nathante/subreddit_tfidf_weekly.parquet')
-def cosine_similarities_weekly(tfidf_path, outfile, term_colname, included_subreddits = None, topN = None, simfunc=column_similarities, min_df=None,max_df=None):
-    print(outfile)
-    # do this step in parallel if we have the memory for it.
-    # should be doable with pool.map
-
-    spark = SparkSession.builder.getOrCreate()
-    df = spark.read.parquet(tfidf_path)
-
-    # load subreddits + topN
-        
-    subreddit_names = df.select(['subreddit','subreddit_id']).distinct().toPandas()
-    subreddit_names = subreddit_names.sort_values("subreddit_id")
-    nterms = df.select(f.max(f.col(term_colname + "_id")).alias('max')).collect()[0].max
-    weeks = df.select(f.col("week")).distinct().toPandas().week.values
-    spark.stop()
-
-    print(f"computing weekly similarities")
-    week_similarities_helper = partial(_week_similarities,simfunc=simfunc, tfidf_path=tfidf_path, term_colname=term_colname, outdir=outfile, min_df=min_df, max_df=max_df, included_subreddits=included_subreddits, topN=None, subreddit_names=subreddit_names,nterms=nterms)
-
-    for week in weeks:
-        week_similarities_helper(week)
-    # pool = Pool(cpu_count())
-        
-    # list(pool.imap(week_similarities_helper, weeks))
-    # pool.close()
-    #    with Pool(cpu_count()) as pool: # maybe it can be done with 40 cores on the huge machine?
-
-
-def author_cosine_similarities_weekly(outfile, infile='/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_authors_test.parquet', min_df=2, max_df=None, included_subreddits=None, topN=500):
-    return cosine_similarities_weekly(infile,
-                                      outfile,
-                                      'author',
-                                      max_df,
-                                      included_subreddits,
-                                      topN,
-                                      min_df=2
-)
-
-def term_cosine_similarities_weekly(outfile, infile='/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_terms.parquet', min_df=None, max_df=None, included_subreddits=None, topN=None):
-        return cosine_similarities_weekly(infile,
-                                          outfile,
-                                          'term',
-                                          min_df,
-                                          max_df,
-                                          included_subreddits,
-                                          topN)
-
-
-def author_cosine_similarities_weekly_lsi(outfile, infile = '/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_authors_test.parquet', included_subreddits=None, n_components=100,lsi_model=None):
-    return cosine_similarities_weekly_lsi(infile,
-                                          outfile,
-                                          'author',
-                                          included_subreddits=included_subreddits,
-                                          n_components=n_components,
-                                          lsi_model=lsi_model
-                                          )
-
-
-def term_cosine_similarities_weekly_lsi(outfile, infile = '/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_terms.parquet', included_subreddits=None, n_components=100,lsi_model=None):
-        return cosine_similarities_weekly_lsi(infile,
-                                              outfile,
-                                              'term',
-                                              included_subreddits=included_subreddits,
-                                              n_components=n_components,
-                                              lsi_model=lsi_model,
-                                              )
-
-if __name__ == "__main__":
-    fire.Fire({'authors':author_cosine_similarities_weekly,
-               'terms':term_cosine_similarities_weekly,
-               'authors-lsi':author_cosine_similarities_weekly_lsi,
-               'terms-lsi':term_cosine_similarities_weekly_lsi
-               })
-

start_spark_and_run.sh

@@ -0,0 +1,21 @@
+
+#!/usr/bin/env bash
+
+# Script to start a spark cluster and run a script on klone
+source $SPARK_CONF_DIR/spark-env.sh
+echo "#!/usr/bin/bash" > job_script.sh
+echo "source ~/.bashrc" >> job_script.sh
+echo "export PYSPARK_PYTHON=python3" >> job.script.sh
+echo "export JAVA_HOME=/gscratch/comdata/local/open-jdk" >> job.script.sh
+echo "export SPARK_CONF_DIR=/gscratch/comdata/local/spark_config" >> job.script.sh
+echo "echo \$(hostname)" >> job_script.sh
+echo "source $SPARK_CONF_DIR/spark-env.sh" >> job.script.sh
+echo "start_spark_cluster.sh" >> job_script.sh
+echo "spark-submit --verbose --master spark://\$(hostname):$SPARK_MASTER_PORT $2 ${@:3}" >> job_script.sh
+echo "stop-all.sh" >> job_script.sh
+#echo "singularity instance stop --all" >> job_script.sh
+chmod +x job_script.sh
+
+let "cpus = $1 * 40" 
+salloc -p compute-bigmem -A comdata --nodes=$1 --time=48:00:00 -c 40 --mem=362G --exclusive srun -n1 job_script.sh
+

start_spark_cluster.sh

@@ -0,0 +1,26 @@
+#!/usr/bin/env bash
+nodes="$(scontrol show hostnames)"
+
+export SPARK_MASTER_HOST=$(hostname)
+echo $SPARK_MASTER_HOST
+# singularity instance stop spark-boss
+# rm -r $HOME/.singularity/instances/sing/$(hostname)/nathante/spark-boss
+ 
+# for node in $nodes
+# dol
+#     echo $node
+#     ssh $node "singularity instance stop --all -F"
+# done
+
+# singularity instance start /gscratch/comdata/users/nathante/cdsc_base.sif spark-boss
+#apptainer exec /gscratch/comdata/users/nathante/containers/nathante.sif
+start-master.sh 
+for node in $nodes
+do
+    # if [ "$node" != "$SPARK_BOSS" ]
+    # then
+    echo $node
+    ssh -t $node start_spark_worker.sh $SPARK_MASTER_HOST
+   # fi				
+done
+

start_spark_worker.sh

@@ -0,0 +1,18 @@
+#!/usr/bin/env bash
+# runs on worker node
+# instance_name=spark-worker-$(hostname)
+# echo $hostname
+# instance_url="instance://$instance_name"
+# singularity instance list
+# singularity instance stop -F "$instance_name"
+# singularity instance list
+# sleep 5
+# ls $HOME/.singularity/instances/sing/$(hostname)/nathante/$instance_name
+# rm -r $HOME/.singularity/instances/sing/$(hostname)/nathante/$instance_name
+# singularity instance start /gscratch/comdata/users/nathante/cdsc_base.sif $instance_name
+source /gscratch/comdata/env/cdsc_klone_bashrc
+source $SPARK_CONF_DIR/spark-env.sh
+echo $(which python3)
+echo $PYSPARK_PYTHON
+echo "start-worker.sh spark://$1:$SPARK_MASTER_PORT"
+start-worker.sh spark://$1:$SPARK_MASTER_PORT

timeseries/__init__.py

@@ -1,2 +0,0 @@
-from .choose_clusters import load_clusters, load_densities
-from .cluster_timeseries import build_cluster_timeseries

timeseries/choose_clusters.py

@@ -1,96 +0,0 @@
-from pyarrow import dataset as ds
-import numpy as np
-import pandas as pd
-import plotnine as pn
-random = np.random.RandomState(1968)
-
-def load_densities(term_density_file="/gscratch/comdata/output/reddit_density/comment_terms_10000.feather",
-                   author_density_file="/gscratch/comdata/output/reddit_density/comment_authors_10000.feather"):
-
-    term_density = pd.read_feather(term_density_file)
-    author_density = pd.read_feather(author_density_file)
-
-    term_density.rename({'overlap_density':'term_density','index':'subreddit'},axis='columns',inplace=True)
-    author_density.rename({'overlap_density':'author_density','index':'subreddit'},axis='columns',inplace=True)
-
-    density = term_density.merge(author_density,on='subreddit',how='inner')
-
-    return density
-
-def load_clusters(term_clusters_file="/gscratch/comdata/output/reddit_clustering/comment_terms_10000.feather",
-                  author_clusters_file="/gscratch/comdata/output/reddit_clustering/comment_authors_10000.feather"):
-    term_clusters = pd.read_feather(term_clusters_file)
-    author_clusters = pd.read_feather(author_clusters_file)
-
-    # rename, join and return
-    term_clusters.rename({'cluster':'term_cluster'},axis='columns',inplace=True)
-    author_clusters.rename({'cluster':'author_cluster'},axis='columns',inplace=True)
-
-    clusters = term_clusters.merge(author_clusters,on='subreddit',how='inner')
-
-    return clusters
-
-if __name__ == '__main__':
-
-    df = load_densities()
-    cl = load_clusters()
-
-    df['td_rank'] = df.term_density.rank()
-    df['ad_rank'] = df.author_density.rank()
-
-    df['td_percentile'] = df.td_rank / df.shape[0]
-    df['ad_percentile'] = df.ad_rank / df.shape[0]
-
-    df = df.merge(cl, on='subreddit',how='inner')
-
-    term_cluster_density = df.groupby('term_cluster').agg({'td_rank':['mean','min','max'],
-                                                         'ad_rank':['mean','min','max'],
-                                                         'td_percentile':['mean','min','max'],
-                                                           'ad_percentile':['mean','min','max'],
-                                                           'subreddit':['count']})
-                                                         
-
-    author_cluster_density = df.groupby('author_cluster').agg({'td_rank':['mean','min','max'],
-                                                         'ad_rank':['mean','min','max'],
-                                                         'td_percentile':['mean','min','max'],
-                                                           'ad_percentile':['mean','min','max'],
-                                                           'subreddit':['count']})
-                                                         
-    # which clusters have the most term_density?
-    term_cluster_density.iloc[term_cluster_density.td_rank['mean'].sort_values().index]
-
-    # which clusters have the most author_density?
-    term_cluster_density.iloc[term_cluster_density.ad_rank['mean'].sort_values(ascending=False).index].loc[term_cluster_density.subreddit['count'] >= 5][0:20]
-
-    high_density_term_clusters = term_cluster_density.loc[(term_cluster_density.td_percentile['mean'] > 0.75) & (term_cluster_density.subreddit['count'] > 5)]
-
-    # let's just use term density instead of author density for now. We can do a second batch with author density next.
-    chosen_clusters = high_density_term_clusters.sample(3,random_state=random)
-
-    cluster_info = df.loc[df.term_cluster.isin(chosen_clusters.index.values)]
-
-    chosen_subreddits = cluster_info.subreddit.values
-
-    dataset = ds.dataset("/gscratch/comdata/output/reddit_comments_by_subreddit.parquet",format='parquet')
-    comments = dataset.to_table(filter=ds.field("subreddit").isin(chosen_subreddits),columns=['id','subreddit','author','CreatedAt'])
-
-    comments = comments.to_pandas()
-
-    comments['week'] = comments.CreatedAt.dt.date - pd.to_timedelta(comments['CreatedAt'].dt.dayofweek, unit='d')
-
-    author_timeseries = comments.loc[:,['subreddit','author','week']].drop_duplicates().groupby(['subreddit','week']).count().reset_index()
-
-    for clid in chosen_clusters.index.values:
-
-        ts = pd.read_feather(f"data/ts_term_cluster_{clid}.feather")
-
-        pn.options.figure_size = (11.7,8.27)
-        p = pn.ggplot(ts)
-        p = p + pn.geom_line(pn.aes('week','value',group='subreddit'))
-        p = p + pn.facet_wrap('~ subreddit')
-        p.save(f"plots/ts_term_cluster_{clid}.png")
-        
-
-        fig, ax = pyplot.subplots(figsize=(11.7,8.27))
-        g = sns.FacetGrid(ts,row='subreddit')
-        g.map_dataframe(sns.scatterplot,'week','value',data=ts,ax=ax)

timeseries/cluster_timeseries.py

@@ -1,37 +0,0 @@
-import pandas as pd
-import numpy as np
-from pyspark.sql import functions as f
-from pyspark.sql import SparkSession
-from .choose_clusters import load_clusters, load_densities
-import fire
-from pathlib import Path
-
-def build_cluster_timeseries(term_clusters_path="/gscratch/comdata/output/reddit_clustering/comment_terms_10000.feather",
-         author_clusters_path="/gscratch/comdata/output/reddit_clustering/comment_authors_10000.feather",
-         term_densities_path="/gscratch/comdata/output/reddit_density/comment_terms_10000.feather",
-         author_densities_path="/gscratch/comdata/output/reddit_density/comment_authors_10000.feather",
-         output="data/subreddit_timeseries.parquet"):
-
-    spark = SparkSession.builder.getOrCreate()
-    
-    df = spark.read.parquet("/gscratch/comdata/output/reddit_comments_by_subreddit.parquet")
-    
-    df = df.withColumn('week', f.date_trunc('week', f.col("CreatedAt")))
-    
-    # time of unique authors by series by week
-    ts = df.select(['subreddit','week','author']).distinct().groupby(['subreddit','week']).count()
-    
-    ts = ts.repartition('subreddit')
-
-    if term_densities_path is not None and author_densities_path is not None:
-        densities = load_densities(term_densities_path, author_densities_path)
-        spk_densities = spark.createDataFrame(densities)
-        ts = ts.join(spk_densities, on='subreddit', how='inner')
-    
-    clusters = load_clusters(term_clusters_path, author_clusters_path)
-    spk_clusters = spark.createDataFrame(clusters)
-    ts = ts.join(spk_clusters, on='subreddit', how='inner')
-    ts.write.parquet(output, mode='overwrite')
-
-if __name__ == "__main__":
-    fire.Fire(build_cluster_timeseries)

tsne_subreddit_fit.feather

@@ -1 +0,0 @@
-/annex/objects/SHA256E-s60874--d536adb0ec637fca262c4e1ec908dd8b4a5d1464047b583cd1a99cc6dba87191

visualization/Makefile

@@ -1,11 +0,0 @@
-all: subreddit_author_tf_similarities_10000.html #comment_authors_10000.html
-
-# wang_tsne_10000.html
-# wang_tsne_10000.html:/gscratch/comdata/output/reddit_tsne/wang_similarity_10000.feather /gscratch/comdata/output/reddit_clustering/wang_similarity_10000.feather tsne_vis.py
-# 	python3 tsne_vis.py --tsne_data=/gscratch/comdata/output/reddit_tsne/wang_similarity_10000.feather --clusters=/gscratch/comdata/output/reddit_clustering/wang_similarity_10000.feather --output=wang_tsne_10000.html
-
-# comment_authors_10000.html:/gscratch/comdata/output/reddit_tsne/comment_authors_10000.feather /gscratch/comdata/output/reddit_clustering/comment_authors_10000.feather tsne_vis.py
-# 	python3 tsne_vis.py --tsne_data=/gscratch/comdata/output/reddit_similarity/comment_authors_10000.feather --clusters=/gscratch/comdata/output/reddit_clustering/comment_authors_10000.feather --output=comment_authors_10000.html
-
-subreddit_author_tf_similarities_10000.html:/gscratch/comdata/output/reddit_tsne/subreddit_author_tf_similarities_10000.feather /gscratch/comdata/output/reddit_clustering/subreddit_author_tf_similarities_10000.feather tsne_vis.py
-	start_spark_and_run.sh 1 tsne_vis.py --tsne_data=/gscratch/comdata/output/reddit_tsne/subreddit_author_tf_similarities_10000.feather --clusters=/gscratch/comdata/output/reddit_clustering/subreddit_author_tf_similarities_10000.feather --output=subreddit_author_tf_similarities_10000.html

visualization/data/term_affinityprop_10000.feather

@@ -1 +0,0 @@
-../../.git/annex/objects/Qk/wG/SHA256E-s145210--14a2ad6660d1e4015437eff556ec349dd10a115a4f96594152a29e83d00aa784/SHA256E-s145210--14a2ad6660d1e4015437eff556ec349dd10a115a4f96594152a29e83d00aa784

visualization/data/term_affinityprop_3000.feather

@@ -1 +0,0 @@
-../../.git/annex/objects/w7/2f/SHA256E-s44458--f1c5247775ecf06514a0ff9e523e944bc8fcd9d0fdb6f214cc1329b759d4354e/SHA256E-s44458--f1c5247775ecf06514a0ff9e523e944bc8fcd9d0fdb6f214cc1329b759d4354e

visualization/data/term_tsne_10000.feather

@@ -1 +0,0 @@
-../../.git/annex/objects/WX/v3/SHA256E-s190874--c2aea719f989dde297ca5f13371e156693c574e44acd9a0e313e5e3a3ad4b543/SHA256E-s190874--c2aea719f989dde297ca5f13371e156693c574e44acd9a0e313e5e3a3ad4b543

visualization/data/term_tsne_3000.feather

@@ -1 +0,0 @@
-../../.git/annex/objects/mq/2z/SHA256E-s58834--2e7b3ee11f47011fd9b34bddf8f1e788d35ab9c9e0bb6a1301b0b916135400cf/SHA256E-s58834--2e7b3ee11f47011fd9b34bddf8f1e788d35ab9c9e0bb6a1301b0b916135400cf

visualization/tsne_vis.py

@@ -1,187 +0,0 @@
-import pyarrow
-import altair as alt
-alt.data_transformers.disable_max_rows()
-alt.data_transformers.enable('default')
-from sklearn.neighbors import NearestNeighbors
-import pandas as pd
-from numpy import random
-import fire
-import numpy as np
-
-def base_plot(plot_data):
-
-#    base = base.encode(alt.Color(field='color',type='nominal',scale=alt.Scale(scheme='category10')))
-
-    cluster_dropdown = alt.binding_select(options=[str(c) for c in sorted(set(plot_data.cluster))])
-
-    #    subreddit_dropdown = alt.binding_select(options=sorted(plot_data.subreddit))
-
-    cluster_click_select = alt.selection_single(on='click',fields=['cluster'], bind=cluster_dropdown, name=' ')
-    # cluster_select = alt.selection_single(fields=['cluster'], bind=cluster_dropdown, name='cluster')
-    # cluster_select_and = cluster_click_select & cluster_select
-    #
-    #    subreddit_select = alt.selection_single(on='click',fields=['subreddit'],bind=subreddit_dropdown,name='subreddit_click')
-    
-    base_scale = alt.Scale(scheme={"name":'category10',
-                                   "extent":[0,100],
-                                   "count":10})
-
-    color = alt.condition(cluster_click_select ,
-                          alt.Color(field='color',type='nominal',scale=base_scale),
-                          alt.value("lightgray"))
-  
-    
-    base = alt.Chart(plot_data).mark_text().encode(
-        alt.X('x',axis=alt.Axis(grid=False),scale=alt.Scale(domain=(-65,65))),
-        alt.Y('y',axis=alt.Axis(grid=False),scale=alt.Scale(domain=(-65,65))),
-        color=color,
-        text='subreddit')
-
-    base = base.add_selection(cluster_click_select)
- 
-
-    return base
-
-def zoom_plot(plot_data):
-    chart = base_plot(plot_data)
-
-    chart = chart.interactive()
-    chart = chart.properties(width=1275,height=800)
-
-    return chart
-
-def viewport_plot(plot_data):
-    selector1 = alt.selection_interval(encodings=['x','y'],init={'x':(-65,65),'y':(-65,65)})
-    selectorx2 = alt.selection_interval(encodings=['x'],init={'x':(30,40)})
-    selectory2 = alt.selection_interval(encodings=['y'],init={'y':(-20,0)})
-
-    base = base_plot(plot_data)
-
-    viewport = base.mark_point(fillOpacity=0.2,opacity=0.2).encode(
-        alt.X('x',axis=alt.Axis(grid=False)),
-        alt.Y('y',axis=alt.Axis(grid=False)),
-    )
-   
-    viewport = viewport.properties(width=600,height=400)
-
-    viewport1 = viewport.add_selection(selector1)
-
-    viewport2 = viewport.encode(
-        alt.X('x',axis=alt.Axis(grid=False),scale=alt.Scale(domain=selector1)),
-        alt.Y('y',axis=alt.Axis(grid=False),scale=alt.Scale(domain=selector1))
-    )
-
-    viewport2 = viewport2.add_selection(selectorx2)
-    viewport2 = viewport2.add_selection(selectory2)
-
-    sr = base.encode(alt.X('x',axis=alt.Axis(grid=False),scale=alt.Scale(domain=selectorx2)),
-                     alt.Y('y',axis=alt.Axis(grid=False),scale=alt.Scale(domain=selectory2))
-    )
-
-
-    sr = sr.properties(width=1275,height=600)
-
-
-    chart = (viewport1 | viewport2) & sr
-
-
-    return chart
-
-def assign_cluster_colors(tsne_data, clusters, n_colors, n_neighbors = 4):
-    isolate_color = 101
-
-    cluster_sizes = clusters.groupby('cluster').count()
-    singletons = set(cluster_sizes.loc[cluster_sizes.subreddit == 1].reset_index().cluster)
-
-    tsne_data = tsne_data.merge(clusters,on='subreddit')
-    
-    centroids = tsne_data.groupby('cluster').agg({'x':np.mean,'y':np.mean})
-
-    color_ids = np.arange(n_colors)
-
-    distances = np.empty(shape=(centroids.shape[0],centroids.shape[0]))
-
-    groups = tsne_data.groupby('cluster')
-    
-    points = np.array(tsne_data.loc[:,['x','y']])
-    centers = np.array(centroids.loc[:,['x','y']])
-
-    # point x centroid
-    point_center_distances = np.linalg.norm((points[:,None,:] - centers[None,:,:]),axis=-1)
-    
-    # distances is cluster x point
-    for gid, group in groups:
-        c_dists = point_center_distances[group.index.values,:].min(axis=0)
-        distances[group.cluster.values[0],] = c_dists        
-
-    # nbrs = NearestNeighbors(n_neighbors=n_neighbors).fit(centroids) 
-    # distances, indices = nbrs.kneighbors()
-
-    nearest = distances.argpartition(n_neighbors,0)
-    indices = nearest[:n_neighbors,:].T
-    # neighbor_distances = np.copy(distances)
-    # neighbor_distances.sort(0)
-    # neighbor_distances = neighbor_distances[0:n_neighbors,:]
-    
-    # nbrs = NearestNeighbors(n_neighbors=n_neighbors,metric='precomputed').fit(distances) 
-    # distances, indices = nbrs.kneighbors()
-
-    color_assignments = np.repeat(-1,len(centroids))
-
-    for i in range(len(centroids)):
-        if (centroids.iloc[i].name == -1) or (i in singletons):
-            color_assignments[i] = isolate_color
-        else:
-            knn = indices[i]
-            knn_colors = color_assignments[knn]
-            available_colors = color_ids[list(set(color_ids) - set(knn_colors))]
-
-            if(len(available_colors) > 0):
-                color_assignments[i] = available_colors[0]
-            else:
-                raise Exception("Can't color this many neighbors with this many colors")
-
-    centroids = centroids.reset_index()
-    colors = centroids.loc[:,['cluster']]
-    colors['color'] = color_assignments
-
-    tsne_data = tsne_data.merge(colors,on='cluster')
-    return(tsne_data)
-
-def build_visualization(tsne_data, clusters, output):
-
-    # tsne_data = "/gscratch/comdata/output/reddit_tsne/subreddit_author_tf_similarities_10000.feather"
-    # clusters = "/gscratch/comdata/output/reddit_clustering/subreddit_author_tf_similarities_10000.feather"
-
-    tsne_data = pd.read_feather(tsne_data)
-    tsne_data = tsne_data.rename(columns={'_subreddit':'subreddit'})
-    clusters = pd.read_feather(clusters)
-
-    tsne_data = assign_cluster_colors(tsne_data,clusters,10,8)
-
-    sr_per_cluster = tsne_data.groupby('cluster').subreddit.count().reset_index()
-    sr_per_cluster = sr_per_cluster.rename(columns={'subreddit':'cluster_size'})
-
-    tsne_data = tsne_data.merge(sr_per_cluster,on='cluster')
-
-    term_zoom_plot = zoom_plot(tsne_data)
-
-    term_zoom_plot.save(output)
-
-    term_viewport_plot = viewport_plot(tsne_data)
-
-    term_viewport_plot.save(output.replace(".html","_viewport.html"))
-
-if __name__ == "__main__":
-    fire.Fire(build_visualization)
-
-# commenter_data = pd.read_feather("tsne_author_fit.feather")
-# clusters = pd.read_feather('author_3000_clusters.feather')
-# commenter_data = assign_cluster_colors(commenter_data,clusters,10,8)
-# commenter_zoom_plot = zoom_plot(commenter_data)
-# commenter_viewport_plot = viewport_plot(commenter_data)
-# commenter_zoom_plot.save("subreddit_commenters_tsne_3000.html")
-# commenter_viewport_plot.save("subreddit_commenters_tsne_3000_viewport.html")
-
-# chart = chart.properties(width=10000,height=10000)
-# chart.save("test_tsne_whole.svg")