Merge remote-tracking branch 'origin/icwsm_dataverse'
This commit is contained in:
Nathan TeBlunthuis
commit
3d192ab82f
2
__init__.py
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2
__init__.py
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from .timeseries import load_clusters, load_densities, build_cluster_timeseries
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@ -1,74 +0,0 @@
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from pyspark.sql import functions as f
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from pyspark.sql import SparkSession
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from pyspark.sql import Window
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from pyspark.sql.types import FloatType
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import zlib
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def zlib_entropy_rate(s):
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sb = s.encode()
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if len(sb) == 0:
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return None
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else:
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return len(zlib.compress(s.encode(),level=6))/len(s.encode())
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zlib_entropy_rate_udf = f.udf(zlib_entropy_rate,FloatType())
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spark = SparkSession.builder.getOrCreate()
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df = spark.read.parquet("/gscratch/comdata/output/reddit_comments_by_author.parquet",compression='snappy')
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df = df.withColumn("saidbot",f.lower(f.col("body")).like("%bot%"))
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# df = df.filter(df.subreddit=='seattle')
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# df = df.cache()
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botreplies = df.filter(f.lower(df.body).rlike(".*[good|bad] bot.*"))
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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")])
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botreplies = botreplies.groupby(['bot_comment_id']).agg(f.count('good_bad_bot').alias("N_goodbad_votes"),
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f.sum((f.lower(f.col('good_bad_bot')).like('%good bot%').astype("double"))).alias("n_good_votes"),
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f.sum((f.lower(f.col('good_bad_bot')).like('%bad bot%').astype("double"))).alias("n_bad_votes"))
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comments_by_author = df.select(['author','id','saidbot']).groupBy('author').agg(f.count('id').alias("N_comments"),
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f.mean(f.col('saidbot').astype("double")).alias("prop_saidbot"),
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f.sum(f.col('saidbot').astype("double")).alias("n_saidbot"))
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# pd_comments_by_author = comments_by_author.toPandas()
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# pd_comments_by_author['frac'] = 500 / pd_comments_by_author['N_comments']
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# pd_comments_by_author.loc[pd_comments_by_author.frac > 1, 'frac'] = 1
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# fractions = pd_comments_by_author.loc[:,['author','frac']]
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# fractions = fractions.set_index('author').to_dict()['frac']
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# sampled_author_comments = df.sampleBy("author",fractions).groupBy('author').agg(f.concat_ws(" ", f.collect_list('body')).alias('comments'))
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df = df.withColumn("randn",f.randn(seed=1968))
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win = Window.partitionBy("author").orderBy("randn")
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df = df.withColumn("randRank",f.rank().over(win))
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sampled_author_comments = df.filter(f.col("randRank") <= 1000)
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sampled_author_comments = sampled_author_comments.groupBy('author').agg(f.concat_ws(" ", f.collect_list('body')).alias('comments'))
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author_entropy_rates = sampled_author_comments.select(['author',zlib_entropy_rate_udf(f.col('comments')).alias("entropy_rate")])
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parents = df.join(botreplies, on=df.id==botreplies.bot_comment_id,how='right_outer')
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win1 = Window.partitionBy("author")
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parents = parents.withColumn("first_bot_reply",f.min(f.col("CreatedAt")).over(win1))
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first_bot_reply = parents.filter(f.col("first_bot_reply")==f.col("CreatedAt"))
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first_bot_reply = first_bot_reply.withColumnRenamed("CreatedAt","FB_CreatedAt")
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first_bot_reply = first_bot_reply.withColumnRenamed("id","FB_id")
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comments_since_first_bot_reply = df.join(first_bot_reply,on = 'author',how='right_outer').filter(f.col("CreatedAt")>=f.col("first_bot_reply"))
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comments_since_first_bot_reply = comments_since_first_bot_reply.groupBy("author").agg(f.count("id").alias("N_comments_since_firstbot"))
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bots = parents.groupby(['author']).agg(f.sum('N_goodbad_votes').alias("N_goodbad_votes"),
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f.sum(f.col('n_good_votes')).alias("n_good_votes"),
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f.sum(f.col('n_bad_votes')).alias("n_bad_votes"),
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f.count(f.col('author')).alias("N_bot_posts"))
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bots = bots.join(comments_by_author,on="author",how='left_outer')
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bots = bots.join(comments_since_first_bot_reply,on="author",how='left_outer')
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bots = bots.join(author_entropy_rates,on='author',how='left_outer')
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bots = bots.orderBy("N_goodbad_votes",ascending=False)
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bots = bots.repartition(1)
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bots.write.parquet("/gscratch/comdata/output/reddit_good_bad_bot.parquet",mode='overwrite')
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@ -1,55 +1,36 @@
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#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'
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srun_singularity=source /gscratch/comdata/users/nathante/cdsc_reddit/bin/activate && srun_singularity.sh
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similarity_data=/gscratch/comdata/output/reddit_similarity
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clustering_data=/gscratch/comdata/output/reddit_clustering
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selection_grid="--max_iter=3000 --convergence_iter=15,30,100 --damping=0.5,0.6,0.7,0.8,0.85,0.9,0.95,0.97,0.99, --preference_quantile=0.1,0.3,0.5,0.7,0.9"
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#selection_grid="--max_iter=3000 --convergence_iter=[15] --preference_quantile=[0.5] --damping=[0.99]"
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all:$(clustering_data)/subreddit_comment_authors_10k/selection_data.csv $(clustering_data)/subreddit_comment_authors-tf_10k/selection_data.csv $(clustering_data)/subreddit_comment_terms_10k/selection_data.csv
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# $(clustering_data)/subreddit_comment_authors_30k.feather/SUCCESS $(clustering_data)/subreddit_authors-tf_similarities_30k.feather/SUCCESS
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# $(clustering_data)/subreddit_comment_terms_30k.feather/SUCCESS
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srun_singularity=srun -p compute-bigmem -A comdata --time=48:00:00 --mem=362G -c 40 /bin/bash -c
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similarity_data=../../data/reddit_similarity
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clustering_data=../../data/reddit_clustering
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kmeans_selection_grid=--max_iters=[3000] --n_inits=[10] --n_clusters=[100,500,1000,1250,1500,1750,2000]
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hdbscan_selection_grid=--min_cluster_sizes=[2,3,4,5] --min_samples=[2,3,4,5] --cluster_selection_epsilons=[0,0.01,0.05,0.1,0.15,0.2] --cluster_selection_methods=[eom,leaf]
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affinity_selection_grid=--dampings=[0.5,0.6,0.7,0.8,0.95,0.97,0.99] --preference_quantiles=[0.1,0.3,0.5,0.7,0.9] --convergence_iters=[15]
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$(clustering_data)/subreddit_comment_authors_10k/selection_data.csv:selection.py $(similarity_data)/subreddit_comment_authors_10k.feather clustering.py
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$(srun_singularity) python3 selection.py $(similarity_data)/subreddit_comment_authors_10k.feather $(clustering_data)/subreddit_comment_authors_10k $(clustering_data)/subreddit_comment_authors_10k/selection_data.csv $(selection_grid) -J 20
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authors_tf_10k_input_lsi=$(similarity_data)/subreddit_comment_authors-tf_10k_LSI
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authors_tf_10k_output_lsi=$(clustering_data)/subreddit_comment_authors-tf_10k_LSI
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$(clustering_data)/subreddit_comment_terms_10k/selection_data.csv:selection.py $(similarity_data)/subreddit_comment_terms_10k.feather clustering.py
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$(srun_singularity) python3 selection.py $(similarity_data)/subreddit_comment_terms_10k.feather $(clustering_data)/subreddit_comment_terms_10k $(clustering_data)/subreddit_comment_terms_10k/selection_data.csv $(selection_grid) -J 20
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all:authors_tf_10k_lsi
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$(clustering_data)/subreddit_comment_authors-tf_10k/selection_data.csv:clustering.py $(similarity_data)/subreddit_comment_authors-tf_10k.feather
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$(srun_singularity) python3 selection.py $(similarity_data)/subreddit_comment_authors-tf_10k.feather $(clustering_data)/subreddit_comment_authors-tf_10k $(clustering_data)/subreddit_comment_authors-tf_10k/selection_data.csv $(selection_grid) -J 20
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authors_tf_10k_lsi:${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
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# $(clustering_data)/subreddit_comment_authors_30k.feather/SUCCESS:selection.py $(similarity_data)/subreddit_comment_authors_30k.feather clustering.py
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# $(srun_singularity) python3 selection.py $(similarity_data)/subreddit_comment_authors_30k.feather $(clustering_data)/subreddit_comment_authors_30k $(selection_grid) -J 10 && touch $(clustering_data)/subreddit_comment_authors_30k.feather/SUCCESS
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## LSI Models
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${authors_tf_10k_output_lsi}/kmeans/selection_data.csv:clustering.py ${authors_tf_10k_input_lsi} clustering_base.py kmeans_clustering.py
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$(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)"
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# $(clustering_data)/subreddit_comment_terms_30k.feather/SUCCESS:selection.py $(similarity_data)/subreddit_comment_terms_30k.feather clustering.py
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# $(srun_singularity) python3 selection.py $(similarity_data)/subreddit_comment_terms_30k.feather $(clustering_data)/subreddit_comment_terms_30k $(selection_grid) -J 10 && touch $(clustering_data)/subreddit_comment_terms_30k.feather/SUCCESS
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${authors_tf_10k_output_lsi}/affinity/selection_data.csv:clustering.py ${authors_tf_10k_input_lsi} clustering_base.py affinity_clustering.py
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$(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)"
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# $(clustering_data)/subreddit_authors-tf_similarities_30k.feather/SUCCESS:clustering.py $(similarity_data)/subreddit_comment_authors-tf_30k.feather
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# $(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 $(clustering_data)/subreddit_authors-tf_similarities_30k.feather/SUCCESS
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${authors_tf_10k_output_lsi}/hdbscan/selection_data.csv:clustering.py ${authors_tf_10k_input_lsi} clustering_base.py hdbscan_clustering.py
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$(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 $(hdbscan_selection_grid)"
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${authors_tf_10k_output_lsi}/best_hdbscan.feather:${authors_tf_10k_output_lsi}/hdbscan/selection_data.csv pick_best_clustering.py
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$(srun_singularity) -c "source ~/.bashrc; python3 pick_best_clustering.py $< $@ --min_clusters=50 --max_isolates=5000 --min_cluster_size=2"
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# $(clustering_data)/subreddit_comment_authors_100k.feather:clustering.py $(similarity_data)/subreddit_comment_authors_100k.feather
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# $(srun_singularity) python3 clustering.py $(similarity_data)/subreddit_comment_authors_100k.feather $(clustering_data)/subreddit_comment_authors_100k.feather ---max_iter=400 --convergence_iter=15 --preference_quantile=0.85 --damping=0.85
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${authors_tf_10k_input_lsi}:
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$(MAKE) -C ../similarities
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# $(clustering_data)/comment_terms_100k.feather:clustering.py $(similarity_data)/subreddit_comment_terms_100k.feather
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# $(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
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clean:
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rm -f ${authors_tf_10k_output_lsi}/affinity/selection_data.csv
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rm -f ${authors_tf_10k_output_lsi}/kmeans/selection_data.csv
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rm -f ${authors_tf_10k_output_lsi}/hdbscan/selection_data.csv
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# $(clustering_data)/subreddit_comment_author-tf_100k.feather:clustering.py $(similarity_data)/subreddit_comment_author-tf_100k.feather
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# $(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
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# it's pretty difficult to get a result that isn't one huge megacluster. A sign that it's bullcrap
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# /gscratch/comdata/output/reddit_clustering/wang_similarity_10000.feather:clustering.py /gscratch/comdata/output/reddit_similarity/wang_similarity_10000.feather
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# ./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
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# /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
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# 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
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# /gscratch/comdata/output/reddit_tsne/wang_similarity_10000.feather:fit_tsne.py /gscratch/comdata/output/reddit_similarity/wang_similarity_10000.feather
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# python3 fit_tsne.py --similarities=/gscratch/comdata/output/reddit_similarity/wang_similarity_10000.feather --output=/gscratch/comdata/output/reddit_tsne/wang_similarity_10000.feather
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# /gscratch/comdata/output/reddit_tsne/comment_authors_10000.feather:clustering.py /gscratch/comdata/output/reddit_similarity/comment_authors_10000.feather
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# # $srun_cdsc python3
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# 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
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PHONY: clean
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Binary file not shown.
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clustering/affinity_clustering.py
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clustering/affinity_clustering.py
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from sklearn.cluster import AffinityPropagation
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from dataclasses import dataclass
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from clustering_base import clustering_result, clustering_job
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from grid_sweep import grid_sweep
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from pathlib import Path
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from itertools import product, starmap
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import fire
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import sys
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import numpy as np
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# silhouette is the only one that doesn't need the feature matrix. So it's probably the only one that's worth trying.
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@dataclass
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class affinity_clustering_result(clustering_result):
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damping:float
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convergence_iter:int
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preference_quantile:float
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preference:float
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max_iter:int
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class affinity_job(clustering_job):
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def __init__(self, infile, outpath, name, damping=0.9, max_iter=100000, convergence_iter=30, preference_quantile=0.5, random_state=1968, verbose=True):
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super().__init__(infile,
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outpath,
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name,
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call=self._affinity_clustering,
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preference_quantile=preference_quantile,
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damping=damping,
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max_iter=max_iter,
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convergence_iter=convergence_iter,
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random_state=1968,
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verbose=verbose)
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self.damping=damping
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self.max_iter=max_iter
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self.convergence_iter=convergence_iter
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self.preference_quantile=preference_quantile
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def _affinity_clustering(self, mat, preference_quantile, *args, **kwargs):
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mat = 1-mat
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preference = np.quantile(mat, preference_quantile)
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self.preference = preference
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print(f"preference is {preference}")
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print("data loaded")
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sys.stdout.flush()
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clustering = AffinityPropagation(*args,
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preference=preference,
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affinity='precomputed',
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copy=False,
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**kwargs).fit(mat)
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return clustering
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def get_info(self):
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result = super().get_info()
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self.result=affinity_clustering_result(**result.__dict__,
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damping=self.damping,
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max_iter=self.max_iter,
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convergence_iter=self.convergence_iter,
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preference_quantile=self.preference_quantile,
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preference=self.preference)
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return self.result
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class affinity_grid_sweep(grid_sweep):
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def __init__(self,
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inpath,
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outpath,
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*args,
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**kwargs):
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super().__init__(affinity_job,
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_afffinity_grid_sweep,
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inpath,
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outpath,
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self.namer,
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*args,
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**kwargs)
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def namer(self,
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damping,
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max_iter,
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convergence_iter,
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preference_quantile):
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return f"damp-{damping}_maxit-{max_iter}_convit-{convergence_iter}_prefq-{preference_quantile}"
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def run_affinity_grid_sweep(savefile, inpath, outpath, dampings=[0.8], max_iters=[3000], convergence_iters=[30], preference_quantiles=[0.5],n_cores=10):
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"""Run affinity clustering once or more with different parameters.
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Usage:
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affinity_clustering.py --savefile=SAVEFILE --inpath=INPATH --outpath=OUTPATH --max_iters=<csv> --dampings=<csv> --preference_quantiles=<csv>
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Keword arguments:
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savefile: path to save the metadata and diagnostics
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inpath: path to feather data containing a labeled matrix of subreddit similarities.
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outpath: path to output fit kmeans clusterings.
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dampings:one or more numbers in [0.5, 1). damping parameter in affinity propagatin clustering.
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preference_quantiles:one or more numbers in (0,1) for selecting the 'preference' parameter.
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convergence_iters:one or more integers of number of iterations without improvement before stopping.
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max_iters: one or more numbers of different maximum interations.
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"""
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obj = affinity_grid_sweep(inpath,
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outpath,
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map(float,dampings),
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map(int,max_iters),
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map(int,convergence_iters),
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||||
map(float,preference_quantiles))
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obj.run(n_cores)
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obj.save(savefile)
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def test_select_affinity_clustering():
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# select_hdbscan_clustering("/gscratch/comdata/output/reddit_similarity/subreddit_comment_authors-tf_30k_LSI",
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# "test_hdbscan_author30k",
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||||
# min_cluster_sizes=[2],
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||||
# min_samples=[1,2],
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||||
# cluster_selection_epsilons=[0,0.05,0.1,0.15],
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||||
# cluster_selection_methods=['eom','leaf'],
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||||
# lsi_dimensions='all')
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inpath = "/gscratch/comdata/output/reddit_similarity/subreddit_comment_authors-tf_10k_LSI/"
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outpath = "test_affinity";
|
||||
dampings=[0.8,0.9]
|
||||
max_iters=[100000]
|
||||
convergence_iters=[15]
|
||||
preference_quantiles=[0.5,0.7]
|
||||
|
||||
gs = affinity_lsi_grid_sweep(inpath, 'all', outpath, dampings, max_iters, convergence_iters, preference_quantiles)
|
||||
gs.run(20)
|
||||
gs.save("test_affinity/lsi_sweep.csv")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
fire.Fire(run_affinity_grid_sweep)
|
||||
99
clustering/affinity_clustering_lsi.py
Normal file
99
clustering/affinity_clustering_lsi.py
Normal file
@ -0,0 +1,99 @@
|
||||
import fire
|
||||
from affinity_clustering import affinity_clustering_result, affinity_job, affinity_grid_sweep
|
||||
from grid_sweep import grid_sweep
|
||||
from lsi_base import lsi_result_mixin, lsi_grid_sweep, lsi_mixin
|
||||
from dataclasses import dataclass
|
||||
|
||||
@dataclass
|
||||
class affinity_clustering_result_lsi(affinity_clustering_result, lsi_result_mixin):
|
||||
pass
|
||||
|
||||
|
||||
class affinity_lsi_job(affinity_job, lsi_mixin):
|
||||
def __init__(self, infile, outpath, name, lsi_dims, *args, **kwargs):
|
||||
super().__init__(infile,
|
||||
outpath,
|
||||
name,
|
||||
*args,
|
||||
**kwargs)
|
||||
super().set_lsi_dims(lsi_dims)
|
||||
|
||||
def get_info(self):
|
||||
result = super().get_info()
|
||||
self.result = affinity_clustering_result_lsi(**result.__dict__,
|
||||
lsi_dimensions=self.lsi_dims)
|
||||
return self.result
|
||||
|
||||
class affinity_lsi_grid_sweep(lsi_grid_sweep):
|
||||
def __init__(self,
|
||||
inpath,
|
||||
lsi_dims,
|
||||
outpath,
|
||||
dampings=[0.9],
|
||||
max_iters=[10000],
|
||||
convergence_iters=[30],
|
||||
preference_quantiles=[0.5]):
|
||||
|
||||
super().__init__(affinity_lsi_job,
|
||||
_affinity_lsi_grid_sweep,
|
||||
inpath,
|
||||
lsi_dims,
|
||||
outpath,
|
||||
dampings,
|
||||
max_iters,
|
||||
convergence_iters,
|
||||
preference_quantiles)
|
||||
|
||||
|
||||
class _affinity_lsi_grid_sweep(grid_sweep):
|
||||
def __init__(self,
|
||||
inpath,
|
||||
outpath,
|
||||
lsi_dim,
|
||||
*args,
|
||||
**kwargs):
|
||||
self.lsi_dim = lsi_dim
|
||||
self.jobtype = affinity_lsi_job
|
||||
super().__init__(self.jobtype,
|
||||
inpath,
|
||||
outpath,
|
||||
self.namer,
|
||||
[self.lsi_dim],
|
||||
*args,
|
||||
**kwargs)
|
||||
|
||||
def namer(self, *args, **kwargs):
|
||||
s = affinity_grid_sweep.namer(self, *args[1:], **kwargs)
|
||||
s += f"_lsi-{self.lsi_dim}"
|
||||
return s
|
||||
|
||||
def run_affinity_lsi_grid_sweep(savefile, inpath, outpath, dampings=[0.8], max_iters=[3000], convergence_iters=[30], preference_quantiles=[0.5], lsi_dimensions='all',n_cores=30):
|
||||
"""Run affinity clustering once or more with different parameters.
|
||||
|
||||
Usage:
|
||||
affinity_clustering.py --savefile=SAVEFILE --inpath=INPATH --outpath=OUTPATH --max_iters=<csv> --dampings=<csv> --preference_quantiles=<csv> --lsi_dimensions: either "all" or one or more available lsi similarity dimensions at INPATH.
|
||||
|
||||
Keword arguments:
|
||||
savefile: path to save the metadata and diagnostics
|
||||
inpath: path to folder containing feather files with LSI similarity labeled matrices of subreddit similarities.
|
||||
outpath: path to output fit kmeans clusterings.
|
||||
dampings:one or more numbers in [0.5, 1). damping parameter in affinity propagatin clustering.
|
||||
preference_quantiles:one or more numbers in (0,1) for selecting the 'preference' parameter.
|
||||
convergence_iters:one or more integers of number of iterations without improvement before stopping.
|
||||
max_iters: one or more numbers of different maximum interations.
|
||||
lsi_dimensions: either "all" or one or more available lsi similarity dimensions at INPATH.
|
||||
"""
|
||||
|
||||
obj = affinity_lsi_grid_sweep(inpath,
|
||||
lsi_dimensions,
|
||||
outpath,
|
||||
map(float,dampings),
|
||||
map(int,max_iters),
|
||||
map(int,convergence_iters),
|
||||
map(float,preference_quantiles))
|
||||
|
||||
obj.run(n_cores)
|
||||
obj.save(savefile)
|
||||
|
||||
if __name__ == "__main__":
|
||||
fire.Fire(run_affinity_lsi_grid_sweep)
|
||||
@ -6,21 +6,20 @@ import numpy as np
|
||||
from sklearn.cluster import AffinityPropagation
|
||||
import fire
|
||||
from pathlib import Path
|
||||
from multiprocessing import cpu_count
|
||||
from dataclasses import dataclass
|
||||
from clustering_base import sim_to_dist, process_clustering_result, clustering_result, read_similarity_mat
|
||||
|
||||
def read_similarity_mat(similarities, use_threads=True):
|
||||
df = pd.read_feather(similarities, use_threads=use_threads)
|
||||
mat = np.array(df.drop('_subreddit',1))
|
||||
n = mat.shape[0]
|
||||
mat[range(n),range(n)] = 1
|
||||
return (df._subreddit,mat)
|
||||
|
||||
def affinity_clustering(similarities, *args, **kwargs):
|
||||
def affinity_clustering(similarities, output, *args, **kwargs):
|
||||
subreddits, mat = read_similarity_mat(similarities)
|
||||
return _affinity_clustering(mat, subreddits, *args, **kwargs)
|
||||
clustering = _affinity_clustering(mat, *args, **kwargs)
|
||||
cluster_data = process_clustering_result(clustering, subreddits)
|
||||
cluster_data['algorithm'] = 'affinity'
|
||||
return(cluster_data)
|
||||
|
||||
def _affinity_clustering(mat, subreddits, output, damping=0.9, max_iter=100000, convergence_iter=30, preference_quantile=0.5, random_state=1968, verbose=True):
|
||||
'''
|
||||
similarities: feather file with a dataframe of similarity scores
|
||||
similarities: matrix of similarity scores
|
||||
preference_quantile: parameter controlling how many clusters to make. higher values = more clusters. 0.85 is a good value with 3000 subreddits.
|
||||
damping: parameter controlling how iterations are merged. Higher values make convergence faster and more dependable. 0.85 is a good value for the 10000 subreddits by author.
|
||||
'''
|
||||
@ -40,25 +39,14 @@ def _affinity_clustering(mat, subreddits, output, damping=0.9, max_iter=100000,
|
||||
verbose=verbose,
|
||||
random_state=random_state).fit(mat)
|
||||
|
||||
|
||||
print(f"clustering took {clustering.n_iter_} iterations")
|
||||
clusters = clustering.labels_
|
||||
|
||||
print(f"found {len(set(clusters))} clusters")
|
||||
|
||||
cluster_data = pd.DataFrame({'subreddit': subreddits,'cluster':clustering.labels_})
|
||||
|
||||
cluster_sizes = cluster_data.groupby("cluster").count()
|
||||
print(f"the largest cluster has {cluster_sizes.subreddit.max()} members")
|
||||
|
||||
print(f"the median cluster has {cluster_sizes.subreddit.median()} members")
|
||||
|
||||
print(f"{(cluster_sizes.subreddit==1).sum()} clusters have 1 member")
|
||||
|
||||
sys.stdout.flush()
|
||||
cluster_data = process_clustering_result(clustering, subreddits)
|
||||
output = Path(output)
|
||||
output.parent.mkdir(parents=True,exist_ok=True)
|
||||
cluster_data.to_feather(output)
|
||||
print(f"saved {output}")
|
||||
return clustering
|
||||
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
fire.Fire(affinity_clustering)
|
||||
|
||||
151
clustering/clustering_base.py
Normal file
151
clustering/clustering_base.py
Normal file
@ -0,0 +1,151 @@
|
||||
import pickle
|
||||
from pathlib import Path
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
from dataclasses import dataclass
|
||||
from sklearn.metrics import silhouette_score, silhouette_samples
|
||||
from collections import Counter
|
||||
|
||||
# this is meant to be an interface, not created directly
|
||||
class clustering_job:
|
||||
def __init__(self, infile, outpath, name, call, *args, **kwargs):
|
||||
self.outpath = Path(outpath)
|
||||
self.call = call
|
||||
self.args = args
|
||||
self.kwargs = kwargs
|
||||
self.infile = Path(infile)
|
||||
self.name = name
|
||||
self.hasrun = False
|
||||
|
||||
def run(self):
|
||||
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.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:
|
||||
self.run()
|
||||
|
||||
self.result = clustering_result(outpath=str(self.outpath.resolve()),
|
||||
silhouette_score=self.score,
|
||||
name=self.name,
|
||||
n_clusters=self.n_clusters,
|
||||
n_isolates=self.n_isolates,
|
||||
silhouette_samples = self.silsampout
|
||||
)
|
||||
return self.result
|
||||
|
||||
def silhouette(self):
|
||||
counts = Counter(self.clustering.labels_)
|
||||
singletons = [key for key, value in counts.items() if value == 1]
|
||||
isolates = (self.clustering.labels_ == -1) | (np.isin(self.clustering.labels_,np.array(singletons)))
|
||||
scoremat = self.mat[~isolates][:,~isolates]
|
||||
if self.n_clusters > 1:
|
||||
score = silhouette_score(scoremat, self.clustering.labels_[~isolates], metric='precomputed')
|
||||
silhouette_samp = silhouette_samples(self.mat, self.clustering.labels_, metric='precomputed')
|
||||
silhouette_samp = pd.DataFrame({'subreddit':self.subreddits,'score':silhouette_samp})
|
||||
self.outpath.mkdir(parents=True, exist_ok=True)
|
||||
silsampout = self.outpath / ("silhouette_samples-" + self.name + ".feather")
|
||||
self.silsampout = silsampout.resolve()
|
||||
silhouette_samp.to_feather(self.silsampout)
|
||||
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',axis=1))
|
||||
n = mat.shape[0]
|
||||
mat[range(n),range(n)] = 1
|
||||
return (df._subreddit,1-mat)
|
||||
|
||||
def process_clustering(self, clustering, subreddits):
|
||||
|
||||
if hasattr(clustering,'n_iter_'):
|
||||
print(f"clustering took {clustering.n_iter_} iterations")
|
||||
|
||||
clusters = clustering.labels_
|
||||
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")
|
||||
|
||||
print(f"the median cluster has {cluster_sizes.subreddit.median()} members")
|
||||
n_isolates1 = (cluster_sizes.subreddit==1).sum()
|
||||
|
||||
print(f"{n_isolates1} clusters have 1 member")
|
||||
|
||||
n_isolates2 = cluster_sizes.loc[cluster_sizes.cluster==-1,:]['subreddit'].to_list()
|
||||
if len(n_isolates2) > 0:
|
||||
n_isloates2 = n_isolates2[0]
|
||||
print(f"{n_isolates2} subreddits are in cluster -1",flush=True)
|
||||
|
||||
if n_isolates1 == 0:
|
||||
self.n_isolates = n_isolates2
|
||||
else:
|
||||
self.n_isolates = n_isolates1
|
||||
|
||||
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
|
||||
silhouette_score:float
|
||||
name:str
|
||||
n_clusters:int
|
||||
n_isolates:int
|
||||
silhouette_samples:str
|
||||
@ -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)
|
||||
49
clustering/grid_sweep.py
Normal file
49
clustering/grid_sweep.py
Normal file
@ -0,0 +1,49 @@
|
||||
from pathlib import Path
|
||||
from multiprocessing import Pool, cpu_count
|
||||
from itertools import product, chain
|
||||
import pandas as pd
|
||||
|
||||
class grid_sweep:
|
||||
def __init__(self, jobtype, inpath, outpath, namer, *args):
|
||||
self.jobtype = jobtype
|
||||
self.namer = namer
|
||||
print(*args)
|
||||
grid = list(product(*args))
|
||||
inpath = Path(inpath)
|
||||
outpath = Path(outpath)
|
||||
self.hasrun = False
|
||||
self.grid = [(inpath,outpath,namer(*g)) + g for g in grid]
|
||||
self.jobs = [jobtype(*g) for g in self.grid]
|
||||
|
||||
def run(self, cores=20):
|
||||
if cores is not None and cores > 1:
|
||||
with Pool(cores) as pool:
|
||||
infos = pool.map(self.jobtype.get_info, self.jobs)
|
||||
else:
|
||||
infos = map(self.jobtype.get_info, self.jobs)
|
||||
|
||||
self.infos = pd.DataFrame(infos)
|
||||
self.hasrun = True
|
||||
|
||||
def save(self, outcsv):
|
||||
if not self.hasrun:
|
||||
self.run()
|
||||
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]
|
||||
159
clustering/hdbscan_clustering.py
Normal file
159
clustering/hdbscan_clustering.py
Normal file
@ -0,0 +1,159 @@
|
||||
from clustering_base import clustering_result, clustering_job
|
||||
from grid_sweep import grid_sweep
|
||||
from dataclasses import dataclass
|
||||
import hdbscan
|
||||
from sklearn.neighbors import NearestNeighbors
|
||||
import plotnine as pn
|
||||
import numpy as np
|
||||
from itertools import product, starmap, chain
|
||||
import pandas as pd
|
||||
from multiprocessing import cpu_count
|
||||
import fire
|
||||
|
||||
def test_select_hdbscan_clustering():
|
||||
# select_hdbscan_clustering("/gscratch/comdata/output/reddit_similarity/subreddit_comment_authors-tf_30k_LSI",
|
||||
# "test_hdbscan_author30k",
|
||||
# min_cluster_sizes=[2],
|
||||
# min_samples=[1,2],
|
||||
# cluster_selection_epsilons=[0,0.05,0.1,0.15],
|
||||
# cluster_selection_methods=['eom','leaf'],
|
||||
# lsi_dimensions='all')
|
||||
inpath = "/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/similarity/comment_authors_compex_LSI"
|
||||
outpath = "test_hdbscan";
|
||||
min_cluster_sizes=[2,3,4];
|
||||
min_samples=[1,2,3];
|
||||
cluster_selection_epsilons=[0,0.1,0.3,0.5];
|
||||
cluster_selection_methods=[1];
|
||||
lsi_dimensions='all'
|
||||
gs = hdbscan_lsi_grid_sweep(inpath, "all", outpath, min_cluster_sizes, min_samples, cluster_selection_epsilons, cluster_selection_methods)
|
||||
gs.run(20)
|
||||
gs.save("test_hdbscan/lsi_sweep.csv")
|
||||
# job1 = hdbscan_lsi_job(infile=inpath, outpath=outpath, name="test", lsi_dims=500, min_cluster_size=2, min_samples=1,cluster_selection_epsilon=0,cluster_selection_method='eom')
|
||||
# job1.run()
|
||||
# print(job1.get_info())
|
||||
|
||||
# df = pd.read_csv("test_hdbscan/selection_data.csv")
|
||||
# test_select_hdbscan_clustering()
|
||||
# check_clusters = pd.read_feather("test_hdbscan/500_2_2_0.1_eom.feather")
|
||||
# silscores = pd.read_feather("test_hdbscan/silhouette_samples500_2_2_0.1_eom.feather")
|
||||
# c = check_clusters.merge(silscores,on='subreddit')# fire.Fire(select_hdbscan_clustering)
|
||||
class hdbscan_grid_sweep(grid_sweep):
|
||||
def __init__(self,
|
||||
inpath,
|
||||
outpath,
|
||||
*args,
|
||||
**kwargs):
|
||||
|
||||
super().__init__(hdbscan_job, inpath, outpath, self.namer, *args, **kwargs)
|
||||
|
||||
def namer(self,
|
||||
min_cluster_size,
|
||||
min_samples,
|
||||
cluster_selection_epsilon,
|
||||
cluster_selection_method):
|
||||
return f"mcs-{min_cluster_size}_ms-{min_samples}_cse-{cluster_selection_epsilon}_csm-{cluster_selection_method}"
|
||||
|
||||
@dataclass
|
||||
class hdbscan_clustering_result(clustering_result):
|
||||
min_cluster_size:int
|
||||
min_samples:int
|
||||
cluster_selection_epsilon:float
|
||||
cluster_selection_method:str
|
||||
|
||||
class hdbscan_job(clustering_job):
|
||||
def __init__(self, infile, outpath, name, min_cluster_size=2, min_samples=1, cluster_selection_epsilon=0, cluster_selection_method='eom'):
|
||||
super().__init__(infile,
|
||||
outpath,
|
||||
name,
|
||||
call=hdbscan_job._hdbscan_clustering,
|
||||
min_cluster_size=min_cluster_size,
|
||||
min_samples=min_samples,
|
||||
cluster_selection_epsilon=cluster_selection_epsilon,
|
||||
cluster_selection_method=cluster_selection_method
|
||||
)
|
||||
|
||||
self.min_cluster_size = min_cluster_size
|
||||
self.min_samples = min_samples
|
||||
self.cluster_selection_epsilon = cluster_selection_epsilon
|
||||
self.cluster_selection_method = cluster_selection_method
|
||||
# self.mat = 1 - self.mat
|
||||
|
||||
def _hdbscan_clustering(mat, *args, **kwargs):
|
||||
print(f"running hdbscan clustering. args:{args}. kwargs:{kwargs}")
|
||||
print(mat)
|
||||
clusterer = hdbscan.HDBSCAN(metric='precomputed',
|
||||
core_dist_n_jobs=cpu_count(),
|
||||
*args,
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
clustering = clusterer.fit(mat.astype('double'))
|
||||
|
||||
return(clustering)
|
||||
|
||||
def get_info(self):
|
||||
result = super().get_info()
|
||||
self.result = hdbscan_clustering_result(**result.__dict__,
|
||||
min_cluster_size=self.min_cluster_size,
|
||||
min_samples=self.min_samples,
|
||||
cluster_selection_epsilon=self.cluster_selection_epsilon,
|
||||
cluster_selection_method=self.cluster_selection_method)
|
||||
return self.result
|
||||
|
||||
def run_hdbscan_grid_sweep(savefile, inpath, outpath, min_cluster_sizes=[2], min_samples=[1], cluster_selection_epsilons=[0], cluster_selection_methods=['eom']):
|
||||
"""Run hdbscan clustering once or more with different parameters.
|
||||
|
||||
Usage:
|
||||
hdbscan_clustering.py --savefile=SAVEFILE --inpath=INPATH --outpath=OUTPATH --min_cluster_sizes=<csv> --min_samples=<csv> --cluster_selection_epsilons=<csv> --cluster_selection_methods=<csv "eom"|"leaf">
|
||||
|
||||
Keword arguments:
|
||||
savefile: path to save the metadata and diagnostics
|
||||
inpath: path to feather data containing a labeled matrix of subreddit similarities.
|
||||
outpath: path to output fit kmeans clusterings.
|
||||
min_cluster_sizes: one or more integers indicating the minumum cluster size
|
||||
min_samples: one ore more integers indicating the minimum number of samples used in the algorithm
|
||||
cluster_selection_epsilon: one or more similarity thresholds for transition from dbscan to hdbscan
|
||||
cluster_selection_method: "eom" or "leaf" eom gives larger clusters.
|
||||
"""
|
||||
obj = hdbscan_grid_sweep(inpath,
|
||||
outpath,
|
||||
map(int,min_cluster_sizes),
|
||||
map(int,min_samples),
|
||||
map(float,cluster_selection_epsilons),
|
||||
cluster_selection_methods)
|
||||
obj.run()
|
||||
obj.save(savefile)
|
||||
|
||||
def KNN_distances_plot(mat,outname,k=2):
|
||||
nbrs = NearestNeighbors(n_neighbors=k,algorithm='auto',metric='precomputed').fit(mat)
|
||||
distances, indices = nbrs.kneighbors(mat)
|
||||
d2 = distances[:,-1]
|
||||
df = pd.DataFrame({'dist':d2})
|
||||
df = df.sort_values("dist",ascending=False)
|
||||
df['idx'] = np.arange(0,d2.shape[0]) + 1
|
||||
p = pn.qplot(x='idx',y='dist',data=df,geom='line') + pn.scales.scale_y_continuous(minor_breaks = np.arange(0,50)/50,
|
||||
breaks = np.arange(0,10)/10)
|
||||
p.save(outname,width=16,height=10)
|
||||
|
||||
def make_KNN_plots():
|
||||
similarities = "/gscratch/comdata/output/reddit_similarity/subreddit_comment_terms_10k.feather"
|
||||
subreddits, mat = read_similarity_mat(similarities)
|
||||
mat = sim_to_dist(mat)
|
||||
|
||||
KNN_distances_plot(mat,k=2,outname='terms_knn_dist2.png')
|
||||
|
||||
similarities = "/gscratch/comdata/output/reddit_similarity/subreddit_comment_authors_10k.feather"
|
||||
subreddits, mat = read_similarity_mat(similarities)
|
||||
mat = sim_to_dist(mat)
|
||||
KNN_distances_plot(mat,k=2,outname='authors_knn_dist2.png')
|
||||
|
||||
similarities = "/gscratch/comdata/output/reddit_similarity/subreddit_comment_authors-tf_10k.feather"
|
||||
subreddits, mat = read_similarity_mat(similarities)
|
||||
mat = sim_to_dist(mat)
|
||||
KNN_distances_plot(mat,k=2,outname='authors-tf_knn_dist2.png')
|
||||
|
||||
if __name__ == "__main__":
|
||||
fire.Fire(run_hdbscan_grid_sweep)
|
||||
|
||||
# test_select_hdbscan_clustering()
|
||||
#fire.Fire(select_hdbscan_clustering)
|
||||
101
clustering/hdbscan_clustering_lsi.py
Normal file
101
clustering/hdbscan_clustering_lsi.py
Normal file
@ -0,0 +1,101 @@
|
||||
from hdbscan_clustering import hdbscan_job, hdbscan_grid_sweep, hdbscan_clustering_result
|
||||
from lsi_base import lsi_grid_sweep, lsi_mixin, lsi_result_mixin
|
||||
from grid_sweep import grid_sweep
|
||||
import fire
|
||||
from dataclasses import dataclass
|
||||
|
||||
@dataclass
|
||||
class hdbscan_clustering_result_lsi(hdbscan_clustering_result, lsi_result_mixin):
|
||||
pass
|
||||
|
||||
class hdbscan_lsi_job(hdbscan_job, lsi_mixin):
|
||||
def __init__(self, infile, outpath, name, lsi_dims, *args, **kwargs):
|
||||
super().__init__(
|
||||
infile,
|
||||
outpath,
|
||||
name,
|
||||
*args,
|
||||
**kwargs)
|
||||
super().set_lsi_dims(lsi_dims)
|
||||
|
||||
def get_info(self):
|
||||
partial_result = super().get_info()
|
||||
self.result = hdbscan_clustering_result_lsi(**partial_result.__dict__,
|
||||
lsi_dimensions=self.lsi_dims)
|
||||
return self.result
|
||||
|
||||
class hdbscan_lsi_grid_sweep(lsi_grid_sweep):
|
||||
def __init__(self,
|
||||
inpath,
|
||||
lsi_dims,
|
||||
outpath,
|
||||
min_cluster_sizes,
|
||||
min_samples,
|
||||
cluster_selection_epsilons,
|
||||
cluster_selection_methods
|
||||
):
|
||||
|
||||
super().__init__(hdbscan_lsi_job,
|
||||
_hdbscan_lsi_grid_sweep,
|
||||
inpath,
|
||||
lsi_dims,
|
||||
outpath,
|
||||
min_cluster_sizes,
|
||||
min_samples,
|
||||
cluster_selection_epsilons,
|
||||
cluster_selection_methods)
|
||||
|
||||
|
||||
|
||||
class _hdbscan_lsi_grid_sweep(grid_sweep):
|
||||
def __init__(self,
|
||||
inpath,
|
||||
outpath,
|
||||
lsi_dim,
|
||||
*args,
|
||||
**kwargs):
|
||||
print(args)
|
||||
print(kwargs)
|
||||
|
||||
self.lsi_dim = lsi_dim
|
||||
self.jobtype = hdbscan_lsi_job
|
||||
super().__init__(self.jobtype, inpath, outpath, self.namer, [self.lsi_dim], *args, **kwargs)
|
||||
|
||||
|
||||
def namer(self, *args, **kwargs):
|
||||
s = hdbscan_grid_sweep.namer(self, *args[1:], **kwargs)
|
||||
s += f"_lsi-{self.lsi_dim}"
|
||||
return s
|
||||
|
||||
def run_hdbscan_lsi_grid_sweep(savefile, inpath, outpath, min_cluster_sizes=[2], min_samples=[1], cluster_selection_epsilons=[0], cluster_selection_methods=[1],lsi_dimensions='all'):
|
||||
"""Run hdbscan clustering once or more with different parameters.
|
||||
|
||||
Usage:
|
||||
hdbscan_clustering_lsi --savefile=SAVEFILE --inpath=INPATH --outpath=OUTPATH --min_cluster_sizes=<csv> --min_samples=<csv> --cluster_selection_epsilons=<csv> --cluster_selection_methods=[eom]> --lsi_dimensions: either "all" or one or more available lsi similarity dimensions at INPATH.
|
||||
|
||||
Keword arguments:
|
||||
savefile: path to save the metadata and diagnostics
|
||||
inpath: path to folder containing feather files with LSI similarity labeled matrices of subreddit similarities.
|
||||
outpath: path to output fit clusterings.
|
||||
min_cluster_sizes: one or more integers indicating the minumum cluster size
|
||||
min_samples: one ore more integers indicating the minimum number of samples used in the algorithm
|
||||
cluster_selection_epsilons: one or more similarity thresholds for transition from dbscan to hdbscan
|
||||
cluster_selection_methods: one or more of "eom" or "leaf" eom gives larger clusters.
|
||||
lsi_dimensions: either "all" or one or more available lsi similarity dimensions at INPATH.
|
||||
"""
|
||||
|
||||
obj = hdbscan_lsi_grid_sweep(inpath,
|
||||
lsi_dimensions,
|
||||
outpath,
|
||||
list(map(int,min_cluster_sizes)),
|
||||
list(map(int,min_samples)),
|
||||
list(map(float,cluster_selection_epsilons)),
|
||||
cluster_selection_methods)
|
||||
|
||||
|
||||
obj.run(10)
|
||||
obj.save(savefile)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
fire.Fire(run_hdbscan_lsi_grid_sweep)
|
||||
105
clustering/kmeans_clustering.py
Normal file
105
clustering/kmeans_clustering.py
Normal file
@ -0,0 +1,105 @@
|
||||
from sklearn.cluster import KMeans
|
||||
import fire
|
||||
from pathlib import Path
|
||||
from dataclasses import dataclass
|
||||
from clustering_base import clustering_result, clustering_job
|
||||
from grid_sweep import grid_sweep
|
||||
|
||||
@dataclass
|
||||
class kmeans_clustering_result(clustering_result):
|
||||
n_clusters:int
|
||||
n_init:int
|
||||
max_iter:int
|
||||
|
||||
class kmeans_job(clustering_job):
|
||||
def __init__(self, infile, outpath, name, n_clusters, n_init=10, max_iter=100000, random_state=1968, verbose=True):
|
||||
super().__init__(infile,
|
||||
outpath,
|
||||
name,
|
||||
call=kmeans_job._kmeans_clustering,
|
||||
n_clusters=n_clusters,
|
||||
n_init=n_init,
|
||||
max_iter=max_iter,
|
||||
random_state=random_state,
|
||||
verbose=verbose)
|
||||
|
||||
self.n_clusters=n_clusters
|
||||
self.n_init=n_init
|
||||
self.max_iter=max_iter
|
||||
|
||||
def _kmeans_clustering(mat, *args, **kwargs):
|
||||
|
||||
clustering = KMeans(*args,
|
||||
**kwargs,
|
||||
).fit(mat)
|
||||
|
||||
return clustering
|
||||
|
||||
|
||||
def get_info(self):
|
||||
result = super().get_info()
|
||||
self.result = kmeans_clustering_result(**result.__dict__,
|
||||
n_init=self.n_init,
|
||||
max_iter=self.max_iter)
|
||||
return self.result
|
||||
|
||||
|
||||
class kmeans_grid_sweep(grid_sweep):
|
||||
|
||||
def __init__(self,
|
||||
inpath,
|
||||
outpath,
|
||||
*args,
|
||||
**kwargs):
|
||||
super().__init__(kmeans_job, inpath, outpath, self.namer, *args, **kwargs)
|
||||
|
||||
def namer(self,
|
||||
n_clusters,
|
||||
n_init,
|
||||
max_iter):
|
||||
return f"nclusters-{n_clusters}_nit-{n_init}_maxit-{max_iter}"
|
||||
|
||||
def test_select_kmeans_clustering():
|
||||
inpath = "/gscratch/comdata/output/reddit_similarity/subreddit_comment_authors-tf_10k_LSI/"
|
||||
outpath = "test_kmeans";
|
||||
n_clusters=[200,300,400];
|
||||
n_init=[1,2,3];
|
||||
max_iter=[100000]
|
||||
|
||||
gs = kmeans_lsi_grid_sweep(inpath, 'all', outpath, n_clusters, n_init, max_iter)
|
||||
gs.run(1)
|
||||
|
||||
cluster_selection_epsilons=[0,0.1,0.3,0.5];
|
||||
cluster_selection_methods=['eom'];
|
||||
lsi_dimensions='all'
|
||||
gs = hdbscan_lsi_grid_sweep(inpath, "all", outpath, min_cluster_sizes, min_samples, cluster_selection_epsilons, cluster_selection_methods)
|
||||
gs.run(20)
|
||||
gs.save("test_hdbscan/lsi_sweep.csv")
|
||||
|
||||
def run_kmeans_grid_sweep(savefile, inpath, outpath, n_clusters=[500], n_inits=[1], max_iters=[3000]):
|
||||
"""Run kmeans clustering once or more with different parameters.
|
||||
|
||||
Usage:
|
||||
kmeans_clustering.py --savefile=SAVEFILE --inpath=INPATH --outpath=OUTPATH --n_clusters=<csv number of clusters> --n_inits=<csv> --max_iters=<csv>
|
||||
|
||||
Keword arguments:
|
||||
savefile: path to save the metadata and diagnostics
|
||||
inpath: path to feather data containing a labeled matrix of subreddit similarities.
|
||||
outpath: path to output fit kmeans clusterings.
|
||||
n_clusters: one or more numbers of kmeans clusters to select.
|
||||
n_inits: one or more numbers of different initializations to use for each clustering.
|
||||
max_iters: one or more numbers of different maximum interations.
|
||||
"""
|
||||
|
||||
obj = kmeans_grid_sweep(inpath,
|
||||
outpath,
|
||||
map(int,n_clusters),
|
||||
map(int,n_inits),
|
||||
map(int,max_iters))
|
||||
|
||||
|
||||
obj.run(1)
|
||||
obj.save(savefile)
|
||||
|
||||
if __name__ == "__main__":
|
||||
fire.Fire(run_kmeans_grid_sweep)
|
||||
93
clustering/kmeans_clustering_lsi.py
Normal file
93
clustering/kmeans_clustering_lsi.py
Normal file
@ -0,0 +1,93 @@
|
||||
import fire
|
||||
from dataclasses import dataclass
|
||||
from kmeans_clustering import kmeans_job, kmeans_clustering_result, kmeans_grid_sweep
|
||||
from lsi_base import lsi_mixin, lsi_result_mixin, lsi_grid_sweep
|
||||
from grid_sweep import grid_sweep
|
||||
|
||||
@dataclass
|
||||
class kmeans_clustering_result_lsi(kmeans_clustering_result, lsi_result_mixin):
|
||||
pass
|
||||
|
||||
class kmeans_lsi_job(kmeans_job, lsi_mixin):
|
||||
def __init__(self, infile, outpath, name, lsi_dims, *args, **kwargs):
|
||||
super().__init__(infile,
|
||||
outpath,
|
||||
name,
|
||||
*args,
|
||||
**kwargs)
|
||||
super().set_lsi_dims(lsi_dims)
|
||||
|
||||
def get_info(self):
|
||||
result = super().get_info()
|
||||
self.result = kmeans_clustering_result_lsi(**result.__dict__,
|
||||
lsi_dimensions=self.lsi_dims)
|
||||
return self.result
|
||||
|
||||
class _kmeans_lsi_grid_sweep(grid_sweep):
|
||||
def __init__(self,
|
||||
inpath,
|
||||
outpath,
|
||||
lsi_dim,
|
||||
*args,
|
||||
**kwargs):
|
||||
print(args)
|
||||
print(kwargs)
|
||||
self.lsi_dim = lsi_dim
|
||||
self.jobtype = kmeans_lsi_job
|
||||
super().__init__(self.jobtype, inpath, outpath, self.namer, [self.lsi_dim], *args, **kwargs)
|
||||
|
||||
def namer(self, *args, **kwargs):
|
||||
s = kmeans_grid_sweep.namer(self, *args[1:], **kwargs)
|
||||
s += f"_lsi-{self.lsi_dim}"
|
||||
return s
|
||||
|
||||
class kmeans_lsi_grid_sweep(lsi_grid_sweep):
|
||||
|
||||
def __init__(self,
|
||||
inpath,
|
||||
lsi_dims,
|
||||
outpath,
|
||||
n_clusters,
|
||||
n_inits,
|
||||
max_iters
|
||||
):
|
||||
|
||||
super().__init__(kmeans_lsi_job,
|
||||
_kmeans_lsi_grid_sweep,
|
||||
inpath,
|
||||
lsi_dims,
|
||||
outpath,
|
||||
n_clusters,
|
||||
n_inits,
|
||||
max_iters)
|
||||
|
||||
def run_kmeans_lsi_grid_sweep(savefile, inpath, outpath, n_clusters=[500], n_inits=[1], max_iters=[3000], lsi_dimensions="all"):
|
||||
"""Run kmeans clustering once or more with different parameters.
|
||||
|
||||
Usage:
|
||||
kmeans_clustering_lsi.py --savefile=SAVEFILE --inpath=INPATH --outpath=OUTPATH d--lsi_dimensions=<"all"|csv number of LSI dimensions to use> --n_clusters=<csv number of clusters> --n_inits=<csv> --max_iters=<csv>
|
||||
|
||||
Keword arguments:
|
||||
savefile: path to save the metadata and diagnostics
|
||||
inpath: path to folder containing feather files with LSI similarity labeled matrices of subreddit similarities.
|
||||
outpath: path to output fit kmeans clusterings.
|
||||
lsi_dimensions: either "all" or one or more available lsi similarity dimensions at INPATH.
|
||||
n_clusters: one or more numbers of kmeans clusters to select.
|
||||
n_inits: one or more numbers of different initializations to use for each clustering.
|
||||
max_iters: one or more numbers of different maximum interations.
|
||||
"""
|
||||
|
||||
obj = kmeans_lsi_grid_sweep(inpath,
|
||||
lsi_dimensions,
|
||||
outpath,
|
||||
list(map(int,n_clusters)),
|
||||
list(map(int,n_inits)),
|
||||
list(map(int,max_iters))
|
||||
)
|
||||
|
||||
obj.run(1)
|
||||
obj.save(savefile)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
fire.Fire(run_kmeans_lsi_grid_sweep)
|
||||
44
clustering/lsi_base.py
Normal file
44
clustering/lsi_base.py
Normal file
@ -0,0 +1,44 @@
|
||||
from clustering_base import clustering_job, clustering_result
|
||||
from grid_sweep import grid_sweep, twoway_grid_sweep
|
||||
from dataclasses import dataclass
|
||||
from itertools import chain
|
||||
from pathlib import Path
|
||||
|
||||
class lsi_mixin():
|
||||
def set_lsi_dims(self, lsi_dims):
|
||||
self.lsi_dims = lsi_dims
|
||||
|
||||
@dataclass
|
||||
class lsi_result_mixin:
|
||||
lsi_dimensions:int
|
||||
|
||||
class lsi_grid_sweep(grid_sweep):
|
||||
def __init__(self, jobtype, subsweep, inpath, lsi_dimensions, outpath, *args, **kwargs):
|
||||
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]
|
||||
|
||||
print(lsi_paths)
|
||||
lsi_nums = [int(p.stem) for p in lsi_paths]
|
||||
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)))
|
||||
33
clustering/pick_best_clustering.py
Executable file
33
clustering/pick_best_clustering.py
Executable file
@ -0,0 +1,33 @@
|
||||
#!/usr/bin/env python3
|
||||
import fire
|
||||
import pandas as pd
|
||||
from pathlib import Path
|
||||
import shutil
|
||||
selection_data="/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/clustering/comment_authors_compex_LSI/selection_data.csv"
|
||||
|
||||
outpath = 'test_best.feather'
|
||||
min_clusters=50; max_isolates=7500; min_cluster_size=2
|
||||
|
||||
# pick the best clustering according to silhouette score subject to contraints
|
||||
def pick_best_clustering(selection_data, output, min_clusters, max_isolates, min_cluster_size):
|
||||
df = pd.read_csv(selection_data,index_col=0)
|
||||
df = df.sort_values("silhouette_score",ascending=False)
|
||||
|
||||
# not sure I fixed the bug underlying this fully or not.
|
||||
df['n_isolates_str'] = df.n_isolates.str.strip("[]")
|
||||
df['n_isolates_0'] = df['n_isolates_str'].apply(lambda l: len(l) == 0)
|
||||
df.loc[df.n_isolates_0,'n_isolates'] = 0
|
||||
df.loc[~df.n_isolates_0,'n_isolates'] = df.loc[~df.n_isolates_0].n_isolates_str.apply(lambda l: int(l))
|
||||
|
||||
best_cluster = df[(df.n_isolates <= max_isolates)&(df.n_clusters >= min_clusters)&(df.min_cluster_size==min_cluster_size)]
|
||||
|
||||
best_cluster = best_cluster.iloc[0]
|
||||
|
||||
best_lsi_dimensions = best_cluster.lsi_dimensions
|
||||
print(best_cluster.to_dict())
|
||||
best_path = Path(best_cluster.outpath) / (str(best_cluster['name']) + ".feather")
|
||||
shutil.copy(best_path,output)
|
||||
print(f"lsi dimensions:{best_lsi_dimensions}")
|
||||
|
||||
if __name__ == "__main__":
|
||||
fire.Fire(pick_best_clustering)
|
||||
@ -1,101 +1,38 @@
|
||||
from sklearn.metrics import silhouette_score
|
||||
from sklearn.cluster import AffinityPropagation
|
||||
from functools import partial
|
||||
from clustering import _affinity_clustering, read_similarity_mat
|
||||
from dataclasses import dataclass
|
||||
from multiprocessing import Pool, cpu_count, Array, Process
|
||||
from pathlib import Path
|
||||
from itertools import product, starmap
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
import fire
|
||||
import sys
|
||||
import plotnine as pn
|
||||
from pathlib import Path
|
||||
from clustering.fit_tsne import fit_tsne
|
||||
from visualization.tsne_vis import build_visualization
|
||||
|
||||
# silhouette is the only one that doesn't need the feature matrix. So it's probably the only one that's worth trying.
|
||||
df = pd.read_csv("/gscratch/comdata/output/reddit_clustering/subreddit_comment_authors-tf_10k_LSI/hdbscan/selection_data.csv",index_col=0)
|
||||
|
||||
@dataclass
|
||||
class clustering_result:
|
||||
outpath:Path
|
||||
damping:float
|
||||
max_iter:int
|
||||
convergence_iter:int
|
||||
preference_quantile:float
|
||||
silhouette_score:float
|
||||
alt_silhouette_score:float
|
||||
name:str
|
||||
# plot silhouette_score as a function of isolates
|
||||
df = df.sort_values("silhouette_score")
|
||||
|
||||
df['n_isolates'] = df.n_isolates.str.split("\n0").apply(lambda rg: int(rg[1]))
|
||||
p = pn.ggplot(df,pn.aes(x='n_isolates',y='silhouette_score')) + pn.geom_point()
|
||||
p.save("isolates_x_score.png")
|
||||
|
||||
def sim_to_dist(mat):
|
||||
dist = 1-mat
|
||||
dist[dist < 0] = 0
|
||||
np.fill_diagonal(dist,0)
|
||||
return dist
|
||||
p = pn.ggplot(df,pn.aes(y='n_clusters',x='n_isolates',color='silhouette_score')) + pn.geom_point()
|
||||
p.save("clusters_x_isolates.png")
|
||||
|
||||
def do_clustering(damping, convergence_iter, preference_quantile, name, mat, subreddits, max_iter, outdir:Path, random_state, verbose, alt_mat, overwrite=False):
|
||||
if name is None:
|
||||
name = f"damping-{damping}_convergenceIter-{convergence_iter}_preferenceQuantile-{preference_quantile}"
|
||||
print(name)
|
||||
sys.stdout.flush()
|
||||
outpath = outdir / (str(name) + ".feather")
|
||||
print(outpath)
|
||||
clustering = _affinity_clustering(mat, subreddits, outpath, damping, max_iter, convergence_iter, preference_quantile, random_state, verbose)
|
||||
mat = sim_to_dist(clustering.affinity_matrix_)
|
||||
# the best result for hdbscan seems like this one: it has a decent number of
|
||||
# i think I prefer the 'eom' clustering style because larger clusters are less likely to suffer from ommitted variables
|
||||
best_eom = df[(df.n_isolates <5000)&(df.silhouette_score>0.4)&(df.cluster_selection_method=='eom')&(df.min_cluster_size==2)].iloc[df.shape[1]]
|
||||
|
||||
score = silhouette_score(mat, clustering.labels_, metric='precomputed')
|
||||
best_lsi = df[(df.n_isolates <5000)&(df.silhouette_score>0.4)&(df.cluster_selection_method=='leaf')&(df.min_cluster_size==2)].iloc[df.shape[1]]
|
||||
|
||||
if alt_mat is not None:
|
||||
alt_distances = sim_to_dist(alt_mat)
|
||||
alt_score = silhouette_score(alt_mat, clustering.labels_, metric='precomputed')
|
||||
tsne_data = Path("./clustering/authors-tf_lsi850_tsne.feather")
|
||||
|
||||
res = clustering_result(outpath=outpath,
|
||||
damping=damping,
|
||||
max_iter=max_iter,
|
||||
convergence_iter=convergence_iter,
|
||||
preference_quantile=preference_quantile,
|
||||
silhouette_score=score,
|
||||
alt_silhouette_score=score,
|
||||
name=str(name))
|
||||
if not tnse_data.exists():
|
||||
fit_tsne("/gscratch/comdata/output/reddit_similarity/subreddit_comment_authors-tf_10k_LSI/850.feather",
|
||||
tnse_data)
|
||||
|
||||
return res
|
||||
build_visualization("./clustering/authors-tf_lsi850_tsne.feather",
|
||||
Path(best_eom.outpath)/(best_eom['name']+'.feather'),
|
||||
"./authors-tf_lsi850_best_eom.html")
|
||||
|
||||
# alt similiarities is for checking the silhouette coefficient of an alternative measure of similarity (e.g., topic similarities for user clustering).
|
||||
build_visualization("./clustering/authors-tf_lsi850_tsne.feather",
|
||||
Path(best_leaf.outpath)/(best_leaf['name']+'.feather'),
|
||||
"./authors-tf_lsi850_best_leaf.html")
|
||||
|
||||
def select_affinity_clustering(similarities, outdir, outinfo, damping=[0.9], max_iter=100000, convergence_iter=[30], preference_quantile=[0.5], random_state=1968, verbose=True, alt_similarities=None, J=None):
|
||||
|
||||
damping = list(map(float,damping))
|
||||
convergence_iter = convergence_iter = list(map(int,convergence_iter))
|
||||
preference_quantile = list(map(float,preference_quantile))
|
||||
|
||||
if type(outdir) is str:
|
||||
outdir = Path(outdir)
|
||||
|
||||
outdir.mkdir(parents=True,exist_ok=True)
|
||||
|
||||
subreddits, mat = read_similarity_mat(similarities,use_threads=True)
|
||||
|
||||
if alt_similarities is not None:
|
||||
alt_mat = read_similarity_mat(alt_similarities,use_threads=True)
|
||||
else:
|
||||
alt_mat = None
|
||||
|
||||
if J is None:
|
||||
J = cpu_count()
|
||||
pool = Pool(J)
|
||||
|
||||
# get list of tuples: the combinations of hyperparameters
|
||||
hyper_grid = product(damping, convergence_iter, preference_quantile)
|
||||
hyper_grid = (t + (str(i),) for i, t in enumerate(hyper_grid))
|
||||
|
||||
_do_clustering = partial(do_clustering, mat=mat, subreddits=subreddits, outdir=outdir, max_iter=max_iter, random_state=random_state, verbose=verbose, alt_mat=alt_mat)
|
||||
|
||||
# similarities = Array('d', mat)
|
||||
# call pool.starmap
|
||||
print("running clustering selection")
|
||||
clustering_data = pool.starmap(_do_clustering, hyper_grid)
|
||||
clustering_data = pd.DataFrame(list(clustering_data))
|
||||
clustering_data.to_csv(outinfo)
|
||||
|
||||
return clustering_data
|
||||
|
||||
if __name__ == "__main__":
|
||||
x = fire.Fire(select_affinity_clustering)
|
||||
|
||||
4
clustering/validation.py
Normal file
4
clustering/validation.py
Normal file
@ -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.
|
||||
28
datasets/Makefile
Normal file
28
datasets/Makefile
Normal file
@ -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
|
||||
@ -1,26 +0,0 @@
|
||||
#!/bin/bash
|
||||
## parallel_sql_job.sh
|
||||
#SBATCH --job-name=tf_subreddit_comments
|
||||
## 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=12:00:00
|
||||
## Memory per node
|
||||
#SBATCH --mem=32G
|
||||
#SBATCH --cpus-per-task=4
|
||||
#SBATCH --ntasks=1
|
||||
#SBATCH -D /gscratch/comdata/users/nathante/cdsc-reddit
|
||||
source ./bin/activate
|
||||
module load parallel_sql
|
||||
echo $(which perl)
|
||||
conda list pyarrow
|
||||
which python3
|
||||
#Put here commands to load other modules (e.g. matlab etc.)
|
||||
#Below command means that parallel_sql will get tasks from the database
|
||||
#and run them on the node (in parallel). So a 16 core node will have
|
||||
#16 tasks running at one time.
|
||||
parallel-sql --sql -a parallel --exit-on-term --jobs 4
|
||||
@ -1,10 +1,10 @@
|
||||
#!/usr/bin/env bash
|
||||
## needs to be run by hand since i don't have a nice way of waiting on a parallel-sql job to complete
|
||||
|
||||
#!/usr/bin/env bash
|
||||
echo "#!/usr/bin/bash" > job_script.sh
|
||||
#echo "source $(pwd)/../bin/activate" >> job_script.sh
|
||||
echo "python3 $(pwd)/comments_2_parquet_part1.py" >> job_script.sh
|
||||
|
||||
srun -p comdata -A comdata --nodes=1 --mem=120G --time=48:00:00 --pty job_script.sh
|
||||
srun -p compute-bigmem -A comdata --nodes=1 --mem-per-cpu=9g -c 40 --time=120:00:00 --pty job_script.sh
|
||||
|
||||
start_spark_and_run.sh 1 $(pwd)/comments_2_parquet_part2.py
|
||||
|
||||
@ -1,12 +1,15 @@
|
||||
#!/usr/bin/env python3
|
||||
import os
|
||||
import json
|
||||
from datetime import datetime
|
||||
from multiprocessing import Pool
|
||||
from itertools import islice
|
||||
from helper import find_dumps, open_fileset
|
||||
from helper import open_input_file, find_dumps
|
||||
import pandas as pd
|
||||
import pyarrow as pa
|
||||
import pyarrow.parquet as pq
|
||||
from pathlib import Path
|
||||
import fire
|
||||
|
||||
def parse_comment(comment, names= None):
|
||||
if names is None:
|
||||
@ -44,19 +47,14 @@ 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')])
|
||||
|
||||
dumpdir = "/gscratch/comdata/raw_data/reddit_dumps/comments/"
|
||||
def parse_dump(partition):
|
||||
|
||||
files = list(find_dumps(dumpdir, base_pattern="RC_20*"))
|
||||
dumpdir = f"../../data/reddit_dumps/comments/{partition}"
|
||||
|
||||
pool = Pool(28)
|
||||
|
||||
stream = open_fileset(files)
|
||||
|
||||
N = int(1e4)
|
||||
|
||||
rows = pool.imap_unordered(parse_comment, stream, chunksize=int(N/28))
|
||||
stream = open_input_file(dumpdir)
|
||||
rows = map(parse_comment, stream)
|
||||
|
||||
schema = pa.schema([
|
||||
pa.field('id', pa.string(), nullable=True),
|
||||
@ -78,33 +76,16 @@ schema = pa.schema([
|
||||
pa.field('error', pa.string(), nullable=True),
|
||||
])
|
||||
|
||||
from pathlib import Path
|
||||
p = Path("/gscratch/comdata/output/reddit_comments.parquet_temp2")
|
||||
p = Path("../../data/temp/reddit_comments.parquet")
|
||||
p.mkdir(exist_ok=True,parents=True)
|
||||
|
||||
if not p.is_dir():
|
||||
if p.exists():
|
||||
p.unlink()
|
||||
p.mkdir()
|
||||
|
||||
else:
|
||||
list(map(Path.unlink,p.glob('*')))
|
||||
|
||||
part_size = int(1e7)
|
||||
part = 1
|
||||
n_output = 0
|
||||
writer = pq.ParquetWriter(f"/gscratch/comdata/output/reddit_comments.parquet_temp2/part_{part}.parquet",schema=schema,compression='snappy',flavor='spark')
|
||||
N=10000
|
||||
with pq.ParquetWriter(f"../../data/temp/reddit_comments.parquet/{partition}.parquet",
|
||||
schema=schema,
|
||||
compression='snappy',
|
||||
flavor='spark') as writer:
|
||||
|
||||
while True:
|
||||
if n_output > part_size:
|
||||
if part > 1:
|
||||
writer.close()
|
||||
|
||||
part = part + 1
|
||||
n_output = 0
|
||||
|
||||
writer = pq.ParquetWriter(f"/gscratch/comdata/output/reddit_comments.parquet_temp2/part_{part}.parquet",schema=schema,compression='snappy',flavor='spark')
|
||||
|
||||
n_output += N
|
||||
chunk = islice(rows,N)
|
||||
pddf = pd.DataFrame(chunk, columns=schema.names)
|
||||
table = pa.Table.from_pandas(pddf,schema=schema)
|
||||
@ -112,4 +93,19 @@ while True:
|
||||
break
|
||||
writer.write_table(table)
|
||||
|
||||
writer.close()
|
||||
|
||||
|
||||
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"../../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')
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
fire.Fire({'parse_dump':parse_dump,
|
||||
'gen_task_list':gen_task_list})
|
||||
|
||||
|
||||
@ -2,12 +2,19 @@
|
||||
|
||||
# spark script to make sorted, and partitioned parquet files
|
||||
|
||||
import pyspark
|
||||
from pyspark.sql import functions as f
|
||||
from pyspark.sql import SparkSession
|
||||
|
||||
spark = SparkSession.builder.getOrCreate()
|
||||
|
||||
df = spark.read.parquet("/gscratch/comdata/output/reddit_comments.parquet_temp2",compression='snappy')
|
||||
conf = pyspark.SparkConf().setAppName("Reddit submissions to parquet")
|
||||
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
|
||||
|
||||
df = spark.read.parquet("/gscratch/comdata/output/temp/reddit_comments.parquet",compression='snappy')
|
||||
|
||||
df = df.withColumn("subreddit_2", f.lower(f.col('subreddit')))
|
||||
df = df.drop('subreddit')
|
||||
@ -18,12 +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')
|
||||
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.write.parquet("/gscratch/comdata/users/nathante/reddit_comments_by_subreddit.parquet_new", mode='overwrite', compression='snappy')
|
||||
# df = df.repartition(1200,'subreddit')
|
||||
# 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.write.parquet("/gscratch/scrubbed/comdata/reddit_comments_by_subreddit.parquet", mode='overwrite', compression='snappy')
|
||||
|
||||
df = df.repartition('author')
|
||||
df3 = df.sort(["author","CreatedAt","subreddit","link_id","parent_id","Year","Month","Day"],ascending=True)
|
||||
df3 = df3.sortWithinPartitions(["author","CreatedAt","subreddit","link_id","parent_id","Year","Month","Day"],ascending=True)
|
||||
df3.write.parquet("/gscratch/comdata/users/nathante/reddit_comments_by_author.parquet_new", mode='overwrite',compression='snappy')
|
||||
#df = spark.read.parquet("/gscratch/scrubbed/comdata/reddit_comments_by_subreddit.parquet")
|
||||
df = df.repartition(2400,'author','subreddit',"Year","Month","Day")
|
||||
df3 = df.sort(["author","subreddit","Year","Month","Day","CreatedAt","link_id","parent_id"],ascending=True)
|
||||
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')
|
||||
|
||||
@ -24,8 +24,7 @@ def open_fileset(files):
|
||||
for fh in files:
|
||||
print(fh)
|
||||
lines = open_input_file(fh)
|
||||
for line in lines:
|
||||
yield line
|
||||
yield from lines
|
||||
|
||||
def open_input_file(input_filename):
|
||||
if re.match(r'.*\.7z$', input_filename):
|
||||
@ -39,7 +38,7 @@ def open_input_file(input_filename):
|
||||
elif re.match(r'.*\.xz', input_filename):
|
||||
cmd = ["xzcat",'-dk', '-T 20',input_filename]
|
||||
elif re.match(r'.*\.zst',input_filename):
|
||||
cmd = ['zstd','-dck', input_filename]
|
||||
cmd = ['/kloneusr/bin/zstd','-dck', input_filename, '--memory=2048MB --stdout']
|
||||
elif re.match(r'.*\.gz',input_filename):
|
||||
cmd = ['gzip','-dc', input_filename]
|
||||
try:
|
||||
|
||||
@ -1,4 +0,0 @@
|
||||
#!/usr/bin/bash
|
||||
start_spark_cluster.sh
|
||||
spark-submit --master spark://$(hostname):18899 weekly_cosine_similarities.py term --outfile=/gscratch/comdata/users/nathante/subreddit_term_similarity_weekly_5000.parquet --topN=5000
|
||||
stop-all.sh
|
||||
24
datasets/run_comments_jobs.sbatch
Normal file
24
datasets/run_comments_jobs.sbatch
Normal file
@ -0,0 +1,24 @@
|
||||
#!/bin/bash
|
||||
## tf reddit comments
|
||||
#SBATCH --job-name="cdsc_reddit; parse comment dumps"
|
||||
## Allocation Definition
|
||||
#SBATCH --account=comdata
|
||||
#SBATCH --partition=compute-bigmem
|
||||
## Resources
|
||||
## Nodes. This should always be 1 for parallel-sql.
|
||||
#SBATCH --nodes=1
|
||||
## Walltime (12 hours)
|
||||
#SBATCH --time=24:00:00
|
||||
## Memory per node
|
||||
#SBATCH --mem=8G
|
||||
#SBATCH --cpus-per-task=1
|
||||
#SBATCH --ntasks=1
|
||||
#SBATCH
|
||||
#SBATCH --chdir /gscratch/comdata/users/nathante/partitioning_reddit/dataverse/cdsc_reddit/datasets
|
||||
#SBATCH --output=comments_jobs/%A_%a.out
|
||||
#SBATCH --error=comments_jobs/%A_%a.out
|
||||
. /opt/ohpc/admin/lmod/lmod/init/profile
|
||||
source ~/.bashrc
|
||||
TASK_NUM=$(( SLURM_ARRAY_TASK_ID + $1))
|
||||
TASK_CALL=$(sed -n ${TASK_NUM}p ./comments_task_list.sh)
|
||||
${TASK_CALL}
|
||||
23
datasets/run_submissions_jobs.sbatch
Normal file
23
datasets/run_submissions_jobs.sbatch
Normal file
@ -0,0 +1,23 @@
|
||||
#!/bin/bash
|
||||
## tf reddit comments
|
||||
#SBATCH --job-name="cdsc_reddit; parse submission dumps"
|
||||
## Allocation Definition
|
||||
#SBATCH --account=comdata-ckpt
|
||||
#SBATCH --partition=ckpt
|
||||
## Resources
|
||||
## Nodes. This should always be 1 for parallel-sql.
|
||||
#SBATCH --nodes=1
|
||||
## Walltime (12 hours)
|
||||
#SBATCH --time=24:00:00
|
||||
## Memory per node
|
||||
#SBATCH --mem=8G
|
||||
#SBATCH --cpus-per-task=1
|
||||
#SBATCH --ntasks=1
|
||||
#SBATCH
|
||||
#SBATCH --chdir /gscratch/comdata/users/nathante/cdsc_reddit/datasets
|
||||
#SBATCH --output=submissions_jobs/%A_%a.out
|
||||
#SBATCH --error=submissions_jobs/%A_%a.out
|
||||
|
||||
TASK_NUM=$(( SLURM_ARRAY_TASK_ID + $1))
|
||||
TASK_CALL=$(sed -n ${TASK_NUM}p ./submissions_task_list.sh)
|
||||
${TASK_CALL}
|
||||
4
datasets/submissions_2_parquet.sh
Normal file → Executable file
4
datasets/submissions_2_parquet.sh
Normal file → Executable file
@ -1,8 +1,8 @@
|
||||
#!/usr/bin/env bash
|
||||
## this should be run manually since we don't have a nice way to wait on parallel_sql jobs
|
||||
|
||||
#!/usr/bin/env bash
|
||||
|
||||
./parse_submissions.sh
|
||||
srun -p compute-bigmem -A comdata --nodes=1 --mem-per-cpu=9g -c 40 --time=120:00:00 python3 $(pwd)/submissions_2_parquet_part1.py gen_task_list
|
||||
|
||||
start_spark_and_run.sh 1 $(pwd)/submissions_2_parquet_part2.py
|
||||
|
||||
|
||||
@ -3,26 +3,23 @@
|
||||
# two stages:
|
||||
# 1. from gz to arrow parquet (this script)
|
||||
# 2. from arrow parquet to spark parquet (submissions_2_parquet_part2.py)
|
||||
|
||||
from datetime import datetime
|
||||
from multiprocessing import Pool
|
||||
from pathlib import Path
|
||||
from itertools import islice
|
||||
from helper import find_dumps, open_fileset
|
||||
import pandas as pd
|
||||
import pyarrow as pa
|
||||
import pyarrow.parquet as pq
|
||||
import simdjson
|
||||
import fire
|
||||
import os
|
||||
|
||||
parser = simdjson.Parser()
|
||||
import json
|
||||
|
||||
def parse_submission(post, names = None):
|
||||
if names is None:
|
||||
names = ['id','author','subreddit','title','created_utc','permalink','url','domain','score','ups','downs','over_18','has_media','selftext','retrieved_on','num_comments','gilded','edited','time_edited','subreddit_type','subreddit_id','subreddit_subscribers','name','is_self','stickied','quarantine','error']
|
||||
|
||||
try:
|
||||
post = parser.parse(post)
|
||||
post = json.loads(post)
|
||||
except (ValueError) as e:
|
||||
# print(e)
|
||||
# print(post)
|
||||
@ -61,7 +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),
|
||||
@ -92,8 +89,7 @@ def parse_dump(partition):
|
||||
pa.field('quarantine',pa.bool_(),nullable=True),
|
||||
pa.field('error',pa.string(),nullable=True)])
|
||||
|
||||
if not os.path.exists("/gscratch/comdata/output/temp/reddit_submissions.parquet/"):
|
||||
os.mkdir("/gscratch/comdata/output/temp/reddit_submissions.parquet/")
|
||||
Path("/gscratch/comdata/output/temp/reddit_submissions.parquet/").mkdir(exist_ok=True,parents=True)
|
||||
|
||||
with pq.ParquetWriter(f"/gscratch/comdata/output/temp/reddit_submissions.parquet/{partition}",schema=schema,compression='snappy',flavor='spark') as writer:
|
||||
while True:
|
||||
@ -106,9 +102,9 @@ 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("parse_submissions_task_list",'w') as of:
|
||||
with open("submissions_task_list.sh",'w') as of:
|
||||
for fpath in files:
|
||||
partition = os.path.split(fpath)[1]
|
||||
of.write(f'python3 submissions_2_parquet_part1.py parse_dump {partition}\n')
|
||||
|
||||
@ -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")
|
||||
df2 = df.sort(["subreddit","CreatedAt","id"],ascending=True)
|
||||
df = df.repartition(800,"subreddit","Year","Month")
|
||||
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")
|
||||
df3 = df.sort(["author","CreatedAt","id"],ascending=True)
|
||||
df = df.repartition(800,"author","subreddit","Year","Month")
|
||||
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')
|
||||
|
||||
@ -1,10 +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
|
||||
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
|
||||
../../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 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
|
||||
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
|
||||
|
||||
/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
|
||||
../../data/reddit_similarity/subreddit_comment_authors-tf_10k_LSI/600.feather:
|
||||
$(MAKE) -C ../similarities
|
||||
|
||||
@ -1,4 +1,6 @@
|
||||
#!/usr/bin/bash
|
||||
source ~/.bashrc
|
||||
echo $(hostname)
|
||||
start_spark_cluster.sh
|
||||
spark-submit --master spark://$(hostname):18899 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
|
||||
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
|
||||
stop-all.sh
|
||||
|
||||
@ -1,11 +1,12 @@
|
||||
import pandas as pd
|
||||
from pandas.core.groupby import DataFrameGroupBy as GroupBy
|
||||
from pathlib import Path
|
||||
import fire
|
||||
import numpy as np
|
||||
import sys
|
||||
sys.path.append("..")
|
||||
sys.path.append("../similarities")
|
||||
from similarities.similarities_helper import reindex_tfidf, reindex_tfidf_time_interval
|
||||
# sys.path.append("..")
|
||||
# sys.path.append("../similarities")
|
||||
# from similarities.similarities_helper import pull_tfidf
|
||||
|
||||
# this is the mean of the ratio of the overlap to the focal size.
|
||||
# mean shared membership per focal community member
|
||||
@ -13,10 +14,12 @@ from similarities.similarities_helper import reindex_tfidf, reindex_tfidf_time_i
|
||||
|
||||
def overlap_density(inpath, outpath, agg = pd.DataFrame.sum):
|
||||
df = pd.read_feather(inpath)
|
||||
df = df.drop('subreddit',1)
|
||||
df = df.drop('_subreddit',1)
|
||||
np.fill_diagonal(df.values,0)
|
||||
df = agg(df, 0).reset_index()
|
||||
df = df.rename({0:'overlap_density'},axis='columns')
|
||||
outpath = Path(outpath)
|
||||
outpath.parent.mkdir(parents=True, exist_ok = True)
|
||||
df.to_feather(outpath)
|
||||
return df
|
||||
|
||||
@ -25,6 +28,8 @@ def overlap_density_weekly(inpath, outpath, agg = GroupBy.sum):
|
||||
# exclude the diagonal
|
||||
df = df.loc[df.subreddit != df.variable]
|
||||
res = agg(df.groupby(['subreddit','week'])).reset_index()
|
||||
outpath = Path(outpath)
|
||||
outpath.parent.mkdir(parents=True, exist_ok = True)
|
||||
res.to_feather(outpath)
|
||||
return res
|
||||
|
||||
|
||||
@ -6,9 +6,9 @@ from os import path
|
||||
import hashlib
|
||||
|
||||
shasums1 = requests.get("https://files.pushshift.io/reddit/comments/sha256sum.txt").text
|
||||
shasums2 = requests.get("https://files.pushshift.io/reddit/comments/daily/sha256sum.txt").text
|
||||
#shasums2 = requests.get("https://files.pushshift.io/reddit/comments/daily/sha256sum.txt").text
|
||||
|
||||
shasums = shasums1 + shasums2
|
||||
shasums = shasums1
|
||||
dumpdir = "/gscratch/comdata/raw_data/reddit_dumps/comments"
|
||||
|
||||
for l in shasums.strip().split('\n'):
|
||||
|
||||
@ -1,12 +1,12 @@
|
||||
#!/bin/bash
|
||||
|
||||
user_agent='nathante teblunthuis <nathante@uw.edu>'
|
||||
user_agent='"nathante teblunthuis <nathante@uw.edu>"'
|
||||
output_dir='/gscratch/comdata/raw_data/reddit_dumps/comments'
|
||||
base_url='https://files.pushshift.io/reddit/comments/'
|
||||
|
||||
wget -r --no-parent -A 'RC_201*.bz2' -U $user_agent -P $output_dir -nd -nc $base_url
|
||||
wget -r --no-parent -A 'RC_201*.xz' -U $user_agent -P $output_dir -nd -nc $base_url
|
||||
wget -r --no-parent -A 'RC_201*.zst' -U $user_agent -P $output_dir -nd -nc $base_url
|
||||
wget -r --no-parent -A 'RC_20*.bz2' -U $user_agent -P $output_dir -nd -nc $base_url
|
||||
wget -r --no-parent -A 'RC_20*.xz' -U $user_agent -P $output_dir -nd -nc $base_url
|
||||
wget -r --no-parent -A 'RC_20*.zst' -U $user_agent -P $output_dir -nd -nc $base_url
|
||||
|
||||
|
||||
./check_comments_shas.py
|
||||
|
||||
@ -1,14 +1,14 @@
|
||||
#!/bin/bash
|
||||
|
||||
user_agent='nathante teblunthuis <nathante@uw.edu>'
|
||||
user_agent='"nathante teblunthuis <nathante@uw.edu>"'
|
||||
output_dir='/gscratch/comdata/raw_data/reddit_dumps/submissions'
|
||||
base_url='https://files.pushshift.io/reddit/submissions/'
|
||||
|
||||
wget -r --no-parent -A 'RS_20*.bz2' -U $user_agent -P $output_dir -nd -nc $base_url
|
||||
wget -r --no-parent -A 'RS_20*.xz' -U $user_agent -P $output_dir -nd -nc $base_url
|
||||
wget -r --no-parent -A 'RS_20*.zst' -U $user_agent -P $output_dir -nd -nc $base_url
|
||||
wget -r --no-parent -A 'RS_20*.bz2' -U $user_agent -P $output_dir -nd -nc $base_url/old_v1_data/
|
||||
wget -r --no-parent -A 'RS_20*.xz' -U $user_agent -P $output_dir -nd -nc $base_url/old_v1_data/
|
||||
wget -r --no-parent -A 'RS_20*.zst' -U $user_agent -P $output_dir -nd -nc $base_url/old_v1_data/
|
||||
wget -r --no-parent -A 'RS_20*.bz2' --user-agent=$user_agent -P $output_dir -nd -nc $base_url
|
||||
wget -r --no-parent -A 'RS_20*.xz' --user-agent=$user_agent -P $output_dir -nd -nc $base_url
|
||||
wget -r --no-parent -A 'RS_20*.zst' --user-agent=$user_agent -P $output_dir -nd -nc $base_url
|
||||
wget -r --no-parent -A 'RS_20*.bz2' --user-agent=$user_agent -P $output_dir -nd -nc $base_url/old_v1_data/
|
||||
wget -r --no-parent -A 'RS_20*.xz' --user-agent=$user_agent -P $output_dir -nd -nc $base_url/old_v1_data/
|
||||
wget -r --no-parent -A 'RS_20*.zst' --user-agent=$user_agent -P $output_dir -nd -nc $base_url/old_v1_data/
|
||||
|
||||
./check_submission_shas.py
|
||||
|
||||
34
dumps/remove_duplicate_comments.py
Normal file
34
dumps/remove_duplicate_comments.py
Normal file
@ -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)
|
||||
34
dumps/remove_duplicate_submissions.py
Normal file
34
dumps/remove_duplicate_submissions.py
Normal file
@ -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)
|
||||
@ -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")
|
||||
@ -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()])
|
||||
|
||||
25
ngrams/Makefile
Normal file
25
ngrams/Makefile
Normal file
@ -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}:
|
||||
$(MAKE) -C ../datasets
|
||||
19
ngrams/run_array.sbatch
Executable file
19
ngrams/run_array.sbatch
Executable file
@ -0,0 +1,19 @@
|
||||
#!/bin/bash
|
||||
#SBATCH --job-name=reddit_comment_term_frequencies
|
||||
#SBATCH --account=comdata
|
||||
#SBATCH --partition=compute-bigmem
|
||||
#SBATCH --nodes=1
|
||||
#SBATCH --ntasks-per-node=1
|
||||
#SBATCH --cpus-per-task=1
|
||||
#SBATCH --mem-per-cpu=9g
|
||||
#SBATCH --ntasks=1
|
||||
#SBATCH --export=ALL
|
||||
#SBATCH --time=48:00:00
|
||||
#SBATCH --chdir=/gscratch/comdata/users/nathante/partitioning_reddit/dataverse/cdsc_reddit/ngrams
|
||||
#SBATCH --error="sbatch_log/%A_%a.out"
|
||||
#SBATCH --output="sbatch_log/%A_%a.out"
|
||||
|
||||
TASK_NUM=$(($SLURM_ARRAY_TASK_ID + $1))
|
||||
TASK_CALL=$(sed -n ${TASK_NUM}p $2)
|
||||
${TASK_CALL}
|
||||
|
||||
18
ngrams/run_job.sbatch
Normal file
18
ngrams/run_job.sbatch
Normal file
@ -0,0 +1,18 @@
|
||||
#!/bin/bash
|
||||
#SBATCH --job-name="simulate measurement error models"
|
||||
## Allocation Definition
|
||||
#SBATCH --account=comdata
|
||||
#SBATCH --partition=compute-bigmem
|
||||
## Resources
|
||||
#SBATCH --nodes=1
|
||||
## Walltime (4 hours)
|
||||
#SBATCH --time=4:00:00
|
||||
## Memory per node
|
||||
#SBATCH --mem=4G
|
||||
#SBATCH --cpus-per-task=1
|
||||
#SBATCH --ntasks-per-node=1
|
||||
#SBATCH --chdir /gscratch/comdata/users/nathante/partitioning_reddit/dataverse/cdsc_reddit/ngrams/
|
||||
#SBATCH --output=sbatch_log/%A_%a.out
|
||||
#SBATCH --error=sbatch_log/%A_%a.err
|
||||
echo "$@"
|
||||
"$@"
|
||||
@ -1,8 +1,6 @@
|
||||
#!/usr/bin/env bash
|
||||
module load parallel_sql
|
||||
|
||||
source ./bin/activate
|
||||
python3 tf_comments.py gen_task_list
|
||||
psu --del --Y
|
||||
cat tf_task_list | psu --load
|
||||
|
||||
for job in $(seq 1 50); do sbatch checkpoint_parallelsql.sbatch; done;
|
||||
|
||||
@ -2,12 +2,17 @@
|
||||
|
||||
from pyspark.sql import functions as f
|
||||
from pyspark.sql import SparkSession
|
||||
import fire
|
||||
|
||||
def main(inparquet, outparquet, colname):
|
||||
spark = SparkSession.builder.getOrCreate()
|
||||
df = spark.read.parquet("/gscratch/comdata/users/nathante/reddit_tfidf_test.parquet_temp/")
|
||||
df = spark.read.parquet(inparquet)
|
||||
|
||||
df = df.repartition(2000,'term')
|
||||
df = df.sort(['term','week','subreddit'])
|
||||
df = df.sortWithinPartitions(['term','week','subreddit'])
|
||||
df = df.repartition(2000,colname)
|
||||
df = df.sort([colname,'week','subreddit'])
|
||||
df = df.sortWithinPartitions([colname,'week','subreddit'])
|
||||
|
||||
df.write.parquet("/gscratch/comdata/users/nathante/reddit_tfidf_test_sorted_tf.parquet_temp",mode='overwrite',compression='snappy')
|
||||
df.write.parquet(outparquet,mode='overwrite',compression='snappy')
|
||||
|
||||
if __name__ == '__main__':
|
||||
fire.Fire(main)
|
||||
|
||||
211
ngrams/tf_comments.py
Executable file
211
ngrams/tf_comments.py
Executable file
@ -0,0 +1,211 @@
|
||||
#!/usr/bin/env python3
|
||||
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
|
||||
import os
|
||||
import re
|
||||
from nltk import wordpunct_tokenize, MWETokenizer, sent_tokenize
|
||||
from nltk.corpus import stopwords
|
||||
from nltk.util import ngrams
|
||||
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/', inputdir="/gscratch/comdata/output/reddit_comments_by_subreddit.parquet/", mwe_pass = 'first', excluded_users=None):
|
||||
|
||||
dataset = ds.dataset(Path(inputdir)/partition, format='parquet')
|
||||
outputdir = Path(outputdir)
|
||||
samppath = outputdir / "reddit_comment_ngrams_10p_sample"
|
||||
|
||||
if not samppath.exists():
|
||||
samppath.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
ngram_output = partition.replace("parquet","txt")
|
||||
|
||||
if excluded_users is not None:
|
||||
excluded_users = set(map(str.strip,open(excluded_users)))
|
||||
df = df.filter(~ (f.col("author").isin(excluded_users)))
|
||||
|
||||
|
||||
ngram_path = samppath / ngram_output
|
||||
if mwe_pass == 'first':
|
||||
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'])
|
||||
|
||||
|
||||
schema = pa.schema([pa.field('subreddit', pa.string(), nullable=False),
|
||||
pa.field('term', pa.string(), nullable=False),
|
||||
pa.field('week', pa.date32(), nullable=False),
|
||||
pa.field('tf', pa.int64(), nullable=False)]
|
||||
)
|
||||
|
||||
author_schema = pa.schema([pa.field('subreddit', pa.string(), nullable=False),
|
||||
pa.field('author', pa.string(), nullable=False),
|
||||
pa.field('week', pa.date32(), nullable=False),
|
||||
pa.field('tf', pa.int64(), nullable=False)]
|
||||
)
|
||||
|
||||
dfs = (b.to_pandas() for b in batches)
|
||||
|
||||
def add_week(df):
|
||||
df['week'] = (df.CreatedAt - pd.to_timedelta(df.CreatedAt.dt.dayofweek, unit='d')).dt.date
|
||||
return(df)
|
||||
|
||||
dfs = (add_week(df) for df in dfs)
|
||||
|
||||
def iterate_rows(dfs):
|
||||
for df in dfs:
|
||||
for row in df.itertuples():
|
||||
yield row
|
||||
|
||||
rows = iterate_rows(dfs)
|
||||
|
||||
subreddit_weeks = groupby(rows, lambda r: (r.subreddit, r.week))
|
||||
|
||||
mwe_path = outputdir / "multiword_expressions.feather"
|
||||
|
||||
if mwe_pass != 'first':
|
||||
mwe_dataset = pd.read_feather(mwe_path)
|
||||
mwe_dataset = mwe_dataset.sort_values(['phrasePWMI'],ascending=False)
|
||||
mwe_phrases = list(mwe_dataset.phrase)
|
||||
mwe_phrases = [tuple(s.split(' ')) for s in mwe_phrases]
|
||||
mwe_tokenizer = MWETokenizer(mwe_phrases)
|
||||
mwe_tokenize = mwe_tokenizer.tokenize
|
||||
|
||||
else:
|
||||
mwe_tokenize = MWETokenizer().tokenize
|
||||
|
||||
def remove_punct(sentence):
|
||||
new_sentence = []
|
||||
for token in sentence:
|
||||
new_token = ''
|
||||
for c in token:
|
||||
if c not in string.punctuation:
|
||||
new_token += c
|
||||
if len(new_token) > 0:
|
||||
new_sentence.append(new_token)
|
||||
return new_sentence
|
||||
|
||||
stopWords = set(stopwords.words('english'))
|
||||
|
||||
# we follow the approach described in datta, phelan, adar 2017
|
||||
def my_tokenizer(text):
|
||||
# remove stopwords, punctuation, urls, lower case
|
||||
# lowercase
|
||||
text = text.lower()
|
||||
|
||||
# redditcleaner removes reddit markdown(newlines, quotes, bullet points, links, strikethrough, spoiler, code, superscript, table, headings)
|
||||
text = clean(text)
|
||||
|
||||
# sentence tokenize
|
||||
sentences = sent_tokenize(text)
|
||||
|
||||
# wordpunct_tokenize
|
||||
sentences = map(wordpunct_tokenize, sentences)
|
||||
|
||||
# remove punctuation
|
||||
|
||||
sentences = map(remove_punct, sentences)
|
||||
# datta et al. select relatively common phrases from the reddit corpus, but they don't really explain how. We'll try that in a second phase.
|
||||
# they say that the extract 1-4 grams from 10% of the sentences and then find phrases that appear often relative to the original terms
|
||||
# here we take a 10 percent sample of sentences
|
||||
if mwe_pass == 'first':
|
||||
|
||||
# remove sentences with less than 2 words
|
||||
sentences = filter(lambda sentence: len(sentence) > 2, sentences)
|
||||
sentences = list(sentences)
|
||||
for sentence in sentences:
|
||||
if random() <= 0.1:
|
||||
grams = list(chain(*map(lambda i : ngrams(sentence,i),range(4))))
|
||||
with open(ngram_path,'a') as gram_file:
|
||||
for ng in grams:
|
||||
gram_file.write(' '.join(ng) + '\n')
|
||||
for token in sentence:
|
||||
if token not in stopWords:
|
||||
yield token
|
||||
|
||||
else:
|
||||
# remove stopWords
|
||||
sentences = map(mwe_tokenize, sentences)
|
||||
sentences = map(lambda s: filter(lambda token: token not in stopWords, s), sentences)
|
||||
for sentence in sentences:
|
||||
for token in sentence:
|
||||
yield token
|
||||
|
||||
def tf_comments(subreddit_weeks):
|
||||
for key, posts in subreddit_weeks:
|
||||
subreddit, week = key
|
||||
tfs = Counter([])
|
||||
authors = Counter([])
|
||||
for post in posts:
|
||||
tokens = my_tokenizer(post.body)
|
||||
tfs.update(tokens)
|
||||
authors.update([post.author])
|
||||
|
||||
for term, tf in tfs.items():
|
||||
yield [True, subreddit, term, week, tf]
|
||||
|
||||
for author, tf in authors.items():
|
||||
yield [False, subreddit, author, week, tf]
|
||||
|
||||
outrows = tf_comments(subreddit_weeks)
|
||||
|
||||
outchunksize = 10000
|
||||
|
||||
termtf_outputdir = (outputdir / "comment_terms.parquet")
|
||||
termtf_outputdir.mkdir(parents=True, exist_ok=True)
|
||||
authortf_outputdir = (outputdir / "comment_authors.parquet")
|
||||
authortf_outputdir.mkdir(parents=True, exist_ok=True)
|
||||
termtf_path = termtf_outputdir / partition
|
||||
authortf_path = authortf_outputdir / partition
|
||||
with pq.ParquetWriter(termtf_path, schema=schema, compression='snappy', flavor='spark') as writer, \
|
||||
pq.ParquetWriter(authortf_path, schema=author_schema, compression='snappy', flavor='spark') as author_writer:
|
||||
|
||||
while True:
|
||||
|
||||
chunk = islice(outrows,outchunksize)
|
||||
chunk = (c for c in chunk if c[1] is not None)
|
||||
pddf = pd.DataFrame(chunk, columns=["is_token"] + schema.names)
|
||||
author_pddf = pddf.loc[pddf.is_token == False, schema.names]
|
||||
pddf = pddf.loc[pddf.is_token == True, schema.names]
|
||||
author_pddf = author_pddf.rename({'term':'author'}, axis='columns')
|
||||
author_pddf = author_pddf.loc[:,author_schema.names]
|
||||
table = pa.Table.from_pandas(pddf,schema=schema)
|
||||
author_table = pa.Table.from_pandas(author_pddf,schema=author_schema)
|
||||
do_break = True
|
||||
|
||||
if table.shape[0] != 0:
|
||||
writer.write_table(table)
|
||||
do_break = False
|
||||
if author_table.shape[0] != 0:
|
||||
author_writer.write_table(author_table)
|
||||
do_break = False
|
||||
|
||||
if do_break:
|
||||
break
|
||||
|
||||
writer.close()
|
||||
author_writer.close()
|
||||
|
||||
|
||||
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(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} --inputdir {inputdir} --outputdir {outputdir} --excluded_users {excluded_users_file} {f}\n")
|
||||
|
||||
if __name__ == "__main__":
|
||||
fire.Fire({"gen_task_list":gen_task_list,
|
||||
"weekly_tf":weekly_tf})
|
||||
22
run_array.sbatch
Normal file
22
run_array.sbatch
Normal file
@ -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)
|
||||
${TASK_CALL}
|
||||
@ -1,25 +1,28 @@
|
||||
all: /gscratch/comdata/output/reddit_similarity/subreddit_comment_authors_10000.parquet /gscratch/comdata/output/reddit_similarity/subreddit_comment_authors_10000.parquet /gscratch/comdata/output/reddit_similarity/subreddit_author_tf_similarities_10000.parquet /gscratch/comdata/output/reddit_similarity/subreddit_comment_authors_10000.parquet /gscratch/comdata/output/reddit_similarity/comment_terms.parquet
|
||||
srun=srun -p compute-bigmem -A comdata --mem-per-cpu=9g --time=200:00:00 -c 40
|
||||
srun_huge=srun -p compute-hugemem -A comdata --mem=724g --time=200:00:00 -c 40
|
||||
|
||||
# all: /gscratch/comdata/output/reddit_similarity/subreddit_comment_terms_25000.parquet /gscratch/comdata/output/reddit_similarity/subreddit_comment_authors_25000.parquet /gscratch/comdata/output/reddit_similarity/subreddit_comment_authors_10000.parquet /gscratch/comdata/output/reddit_similarity/comment_terms_10000_weekly.parquet
|
||||
similarity_data=../../data/reddit_similarity
|
||||
tfidf_data=${similarity_data}/tfidf
|
||||
lsi_components=[10,50,100,200,300,400,500,600,700,850]
|
||||
|
||||
lsi_similarities: ${similarity_data}/subreddit_comment_authors-tf_10k_LSI
|
||||
|
||||
# /gscratch/comdata/output/reddit_similarity/subreddit_comment_authors_25000.parquet: cosine_similarities.py /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/subreddit_comment_authors_25000.feather
|
||||
all: ${similarity_data}/subreddit_comment_authors-tf_10k.feather
|
||||
|
||||
/gscratch/comdata/output/reddit_similarity/tfidf/comment_terms.parquet: tfidf.py similarities_helper.py /gscratch/comdata/output/reddit_ngrams/comment_terms.parquet /gscratch/comdata/output/reddit_similarity/subreddits_by_num_comments.csv
|
||||
start_spark_and_run.sh 1 tfidf.py terms --topN=10000
|
||||
${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=${lsi_components} --min_df=10 --inpath=$<"
|
||||
|
||||
/gscratch/comdata/output/reddit_similarity/tfidf/comment_authors.parquet: tfidf.py similarities_helper.py /gscratch/comdata/output/reddit_ngrams/comment_authors.parquet /gscratch/comdata/output/reddit_similarity/subreddits_by_num_comments.csv
|
||||
start_spark_and_run.sh 1 tfidf.py authors --topN=10000
|
||||
${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
|
||||
|
||||
/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
|
||||
${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/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
|
||||
../../data/reddit_ngrams/comment_authors_sorted.parquet:
|
||||
$(MAKE) -C ../ngrams
|
||||
|
||||
# /gscratch/comdata/output/reddit_similarity/comment_terms_10000_weekly.parquet: cosine_similarities.py /gscratch/comdata/output/reddit_similarity/tfidf_weekly/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
|
||||
../../data/reddit_submissions_by_subreddit.parquet:
|
||||
$(MAKE) -C ../datasets
|
||||
|
||||
/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
|
||||
../../data/reddit_comments_by_subreddit.parquet:
|
||||
$(MAKE) -C ../datasets
|
||||
|
||||
Binary file not shown.
@ -2,11 +2,14 @@ import pandas as pd
|
||||
import fire
|
||||
from pathlib import Path
|
||||
from similarities_helper import similarities, column_similarities
|
||||
from functools import partial
|
||||
|
||||
def cosine_similarities(infile, term_colname, outfile, min_df=None, max_df=None, included_subreddits=None, topN=500, exclude_phrases=False, from_date=None, to_date=None, tfidf_colname='tf_idf'):
|
||||
|
||||
return similarities(infile=infile, simfunc=column_similarities, term_colname=term_colname, 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, tfidf_colname=tfidf_colname)
|
||||
|
||||
# change so that these take in an input as an optional argument (for speed, but also for idf).
|
||||
def term_cosine_similarities(outfile, min_df=None, max_df=None, included_subreddits=None, topN=500, exclude_phrases=False, from_date=None, to_date=None):
|
||||
|
||||
def term_cosine_similarities(outfile, infile='/gscratch/comdata/output/reddit_similarity/tfidf/comment_terms_100k.parquet', min_df=None, max_df=None, included_subreddits=None, topN=500, exclude_phrases=False, from_date=None, to_date=None):
|
||||
|
||||
|
||||
@ -1,4 +1,6 @@
|
||||
#!/usr/bin/bash
|
||||
source ~/.bashrc
|
||||
echo $(hostname)
|
||||
start_spark_cluster.sh
|
||||
spark-submit --master spark://$(hostname):18899 cosine_similarities.py term --outfile=/gscratch/comdata/output/reddit_similarity/comment_terms_10000.feather
|
||||
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
|
||||
stop-all.sh
|
||||
|
||||
86
similarities/lsi_similarities.py
Normal file
86
similarities/lsi_similarities.py
Normal file
@ -0,0 +1,86 @@
|
||||
import pandas as pd
|
||||
import fire
|
||||
from pathlib import Path
|
||||
from similarities_helper import *
|
||||
#from similarities_helper import similarities, lsi_column_similarities
|
||||
from functools import partial
|
||||
|
||||
# inpath = "/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/tfidf/comment_authors_compex.parquet"
|
||||
# term_colname='authors'
|
||||
# outfile='/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/similarity/comment_test_compex_LSI'
|
||||
# n_components=[10,50,100]
|
||||
# included_subreddits="/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/included_subreddits.txt"
|
||||
# n_iter=5
|
||||
# random_state=1968
|
||||
# algorithm='randomized'
|
||||
# topN = None
|
||||
# from_date=None
|
||||
# to_date=None
|
||||
# min_df=None
|
||||
# max_df=None
|
||||
|
||||
def lsi_similarities(inpath, term_colname, outfile, min_df=None, max_df=None, included_subreddits=None, topN=None, from_date=None, to_date=None, tfidf_colname='tf_idf',n_components=100,n_iter=5,random_state=1968,algorithm='arpack',lsi_model=None):
|
||||
print(n_components,flush=True)
|
||||
|
||||
|
||||
if lsi_model is None:
|
||||
if type(n_components) == list:
|
||||
lsi_model = Path(outfile) / f'{max(n_components)}_{term_colname}_LSIMOD.pkl'
|
||||
else:
|
||||
lsi_model = Path(outfile) / f'{n_components}_{term_colname}_LSIMOD.pkl'
|
||||
|
||||
simfunc = partial(lsi_column_similarities,n_components=n_components,n_iter=n_iter,random_state=random_state,algorithm=algorithm,lsi_model_save=lsi_model)
|
||||
|
||||
return similarities(inpath=inpath, simfunc=simfunc, term_colname=term_colname, outfile=outfile, min_df=min_df, max_df=max_df, included_subreddits=included_subreddits, topN=topN, from_date=from_date, to_date=to_date, tfidf_colname=tfidf_colname)
|
||||
|
||||
# change so that these take in an input as an optional argument (for speed, but also for idf).
|
||||
def term_lsi_similarities(inpath='/gscratch/comdata/output/reddit_similarity/tfidf/comment_terms_100k.parquet',outfile=None, min_df=None, max_df=None, included_subreddits=None, topN=None, from_date=None, to_date=None, algorithm='arpack', n_components=300,n_iter=5,random_state=1968):
|
||||
|
||||
res = lsi_similarities(inpath,
|
||||
'term',
|
||||
outfile,
|
||||
min_df,
|
||||
max_df,
|
||||
included_subreddits,
|
||||
topN,
|
||||
from_date,
|
||||
to_date,
|
||||
n_components=n_components,
|
||||
algorithm = algorithm
|
||||
)
|
||||
return res
|
||||
|
||||
def author_lsi_similarities(inpath='/gscratch/comdata/output/reddit_similarity/tfidf/comment_authors_100k.parquet',outfile=None, min_df=2, max_df=None, included_subreddits=None, topN=None, from_date=None, to_date=None,algorithm='arpack',n_components=300,n_iter=5,random_state=1968):
|
||||
return lsi_similarities(inpath,
|
||||
'author',
|
||||
outfile,
|
||||
min_df,
|
||||
max_df,
|
||||
included_subreddits,
|
||||
topN,
|
||||
from_date=from_date,
|
||||
to_date=to_date,
|
||||
n_components=n_components
|
||||
)
|
||||
|
||||
def author_tf_similarities(inpath='/gscratch/comdata/output/reddit_similarity/tfidf/comment_authors_100k.parquet',outfile=None, min_df=2, max_df=None, included_subreddits=None, topN=None, from_date=None, to_date=None,algorithm='arpack',n_components=300,n_iter=5,random_state=1968):
|
||||
return lsi_similarities(inpath,
|
||||
'author',
|
||||
outfile,
|
||||
min_df,
|
||||
max_df,
|
||||
included_subreddits,
|
||||
topN,
|
||||
from_date=from_date,
|
||||
to_date=to_date,
|
||||
tfidf_colname='relative_tf',
|
||||
n_components=n_components,
|
||||
algorithm=algorithm
|
||||
)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
fire.Fire({'term':term_lsi_similarities,
|
||||
'author':author_lsi_similarities,
|
||||
'author-tf':author_tf_similarities})
|
||||
|
||||
@ -2,143 +2,190 @@ from pyspark.sql import SparkSession
|
||||
from pyspark.sql import Window
|
||||
from pyspark.sql import functions as f
|
||||
from enum import Enum
|
||||
from multiprocessing import cpu_count, Pool
|
||||
from pyspark.mllib.linalg.distributed import CoordinateMatrix
|
||||
from tempfile import TemporaryDirectory
|
||||
import pyarrow
|
||||
import pyarrow.dataset as ds
|
||||
from sklearn.metrics import pairwise_distances
|
||||
from scipy.sparse import csr_matrix, issparse
|
||||
from sklearn.decomposition import TruncatedSVD
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
import pathlib
|
||||
from datetime import datetime
|
||||
from pathlib import Path
|
||||
import pickle
|
||||
|
||||
class tf_weight(Enum):
|
||||
MaxTF = 1
|
||||
Norm05 = 2
|
||||
|
||||
infile = "/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_authors.parquet"
|
||||
# infile = "/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_terms.parquet"
|
||||
# cache_file = "/gscratch/comdata/users/nathante/cdsc_reddit/similarities/term_tfidf_entries_bak.parquet"
|
||||
|
||||
def reindex_tfidf_time_interval(infile, term_colname, min_df=None, max_df=None, included_subreddits=None, topN=500, exclude_phrases=False, from_date=None, to_date=None):
|
||||
term = term_colname
|
||||
term_id = term + '_id'
|
||||
term_id_new = term + '_id_new'
|
||||
# subreddits missing after this step don't have any terms that have a high enough idf
|
||||
# try rewriting without merges
|
||||
|
||||
spark = SparkSession.builder.getOrCreate()
|
||||
conf = spark.sparkContext.getConf()
|
||||
print(exclude_phrases)
|
||||
tfidf_weekly = spark.read.parquet(infile)
|
||||
# does reindex_tfidf, but without reindexing.
|
||||
def reindex_tfidf(*args, **kwargs):
|
||||
df, tfidf_ds, ds_filter = _pull_or_reindex_tfidf(*args, **kwargs, reindex=True)
|
||||
|
||||
# create the time interval
|
||||
if from_date is not None:
|
||||
if type(from_date) is str:
|
||||
from_date = datetime.fromisoformat(from_date)
|
||||
print("assigning names")
|
||||
subreddit_names = tfidf_ds.to_table(filter=ds_filter,columns=['subreddit','subreddit_id'])
|
||||
batches = subreddit_names.to_batches()
|
||||
|
||||
tfidf_weekly = tfidf_weekly.filter(tfidf_weekly.week >= from_date)
|
||||
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")
|
||||
|
||||
if to_date is not None:
|
||||
if type(to_date) is str:
|
||||
to_date = datetime.fromisoformat(to_date)
|
||||
tfidf_weekly = tfidf_weekly.filter(tfidf_weekly.week < to_date)
|
||||
|
||||
tfidf = tfidf_weekly.groupBy(["subreddit","week", term_id, term]).agg(f.sum("tf").alias("tf"))
|
||||
tfidf = _calc_tfidf(tfidf, term_colname, tf_weight.Norm05)
|
||||
tempdir = prep_tfidf_entries(tfidf, term_colname, min_df, max_df, included_subreddits)
|
||||
tfidf = spark.read_parquet(tempdir.name)
|
||||
subreddit_names = tfidf.select(['subreddit','subreddit_id_new']).distinct().toPandas()
|
||||
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",axis=1)
|
||||
subreddit_names = subreddit_names.sort_values("subreddit_id_new")
|
||||
subreddit_names['subreddit_id_new'] = subreddit_names['subreddit_id_new'] - 1
|
||||
return(tempdir, subreddit_names)
|
||||
return(df, subreddit_names)
|
||||
|
||||
def reindex_tfidf(infile, term_colname, min_df=None, max_df=None, included_subreddits=None, topN=500, exclude_phrases=False):
|
||||
spark = SparkSession.builder.getOrCreate()
|
||||
conf = spark.sparkContext.getConf()
|
||||
print(exclude_phrases)
|
||||
def pull_tfidf(*args, **kwargs):
|
||||
df, _, _ = _pull_or_reindex_tfidf(*args, **kwargs, reindex=False)
|
||||
return df
|
||||
|
||||
tfidf = spark.read.parquet(infile)
|
||||
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}, 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:
|
||||
tfidf_ds = ds.dataset(infile)
|
||||
|
||||
if included_subreddits is None:
|
||||
included_subreddits = select_topN_subreddits(topN)
|
||||
else:
|
||||
included_subreddits = set(map(str.strip,map(str.lower,open(included_subreddits))))
|
||||
included_subreddits = set(map(str.strip,open(included_subreddits)))
|
||||
|
||||
if exclude_phrases == True:
|
||||
tfidf = tfidf.filter(~f.col(term_colname).contains("_"))
|
||||
ds_filter = ds.field("subreddit").isin(included_subreddits)
|
||||
|
||||
print("creating temporary parquet with matrix indicies")
|
||||
tempdir = prep_tfidf_entries(tfidf, term_colname, min_df, max_df, included_subreddits)
|
||||
if min_df is not None:
|
||||
ds_filter &= ds.field("count") >= min_df
|
||||
|
||||
tfidf = spark.read.parquet(tempdir.name)
|
||||
subreddit_names = tfidf.select(['subreddit','subreddit_id_new']).distinct().toPandas()
|
||||
subreddit_names = subreddit_names.sort_values("subreddit_id_new")
|
||||
subreddit_names['subreddit_id_new'] = subreddit_names['subreddit_id_new'] - 1
|
||||
spark.stop()
|
||||
return (tempdir, subreddit_names)
|
||||
if max_df is not None:
|
||||
ds_filter &= ds.field("count") <= max_df
|
||||
|
||||
if week is not None:
|
||||
ds_filter &= ds.field("week") == week
|
||||
|
||||
if from_date is not None:
|
||||
ds_filter &= ds.field("week") >= from_date
|
||||
|
||||
if to_date is not None:
|
||||
ds_filter &= ds.field("week") <= to_date
|
||||
|
||||
term = term_colname
|
||||
term_id = term + '_id'
|
||||
term_id_new = term + '_id_new'
|
||||
|
||||
projection = {
|
||||
'subreddit_id':ds.field('subreddit_id'),
|
||||
term_id:ds.field(term_id),
|
||||
'relative_tf':ds.field("relative_tf").cast('float32')
|
||||
}
|
||||
|
||||
if not rescale_idf:
|
||||
projection = {
|
||||
'subreddit_id':ds.field('subreddit_id'),
|
||||
term_id:ds.field(term_id),
|
||||
'relative_tf':ds.field('relative_tf').cast('float32'),
|
||||
'tf_idf':ds.field('tf_idf').cast('float32')}
|
||||
|
||||
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:
|
||||
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() + 1
|
||||
else:
|
||||
df[term_id_new] = df[term_id]
|
||||
|
||||
if rescale_idf:
|
||||
print("computing idf", flush=True)
|
||||
df['new_count'] = grouped[term_id].transform('count')
|
||||
N_docs = df.subreddit_id_new.max() + 1
|
||||
df['idf'] = np.log(N_docs/(1+df.new_count),dtype='float32') + 1
|
||||
if tf_family == tf_weight.MaxTF:
|
||||
df["tf_idf"] = df.relative_tf * df.idf
|
||||
else: # tf_fam = tf_weight.Norm05
|
||||
df["tf_idf"] = (0.5 + 0.5 * df.relative_tf) * df.idf
|
||||
|
||||
return (df, tfidf_ds, ds_filter)
|
||||
|
||||
|
||||
def similarities(infile, simfunc, term_colname, outfile, min_df=None, max_df=None, included_subreddits=None, topN=500, exclude_phrases=False, from_date=None, to_date=None, tfidf_colname='tf_idf'):
|
||||
def pull_names(batch):
|
||||
return(batch.to_pandas().drop_duplicates())
|
||||
|
||||
def similarities(inpath, simfunc, term_colname, outfile, min_df=None, max_df=None, included_subreddits=None, topN=500, from_date=None, to_date=None, tfidf_colname='tf_idf'):
|
||||
'''
|
||||
tfidf_colname: set to 'relative_tf' to use normalized term frequency instead of tf-idf, which can be useful for author-based similarities.
|
||||
'''
|
||||
if from_date is not None or to_date is not None:
|
||||
tempdir, subreddit_names = reindex_tfidf_time_interval(infile, term_colname=term_colname, min_df=min_df, max_df=max_df, included_subreddits=included_subreddits, topN=topN, exclude_phrases=False, from_date=from_date, to_date=to_date)
|
||||
|
||||
else:
|
||||
tempdir, subreddit_names = reindex_tfidf(infile, term_colname=term_colname, min_df=min_df, max_df=max_df, included_subreddits=included_subreddits, topN=topN, exclude_phrases=False)
|
||||
|
||||
print("loading matrix")
|
||||
# mat = read_tfidf_matrix("term_tfidf_entries7ejhvnvl.parquet", term_colname)
|
||||
mat = read_tfidf_matrix(tempdir.name, term_colname, tfidf_colname)
|
||||
print(f'computing similarities on mat. mat.shape:{mat.shape}')
|
||||
print(f"size of mat is:{mat.data.nbytes}")
|
||||
sims = simfunc(mat)
|
||||
del mat
|
||||
|
||||
def proc_sims(sims, outfile):
|
||||
if issparse(sims):
|
||||
sims = sims.todense()
|
||||
|
||||
print(f"shape of sims:{sims.shape}")
|
||||
print(f"len(subreddit_names.subreddit.values):{len(subreddit_names.subreddit.values)}")
|
||||
print(f"len(subreddit_names.subreddit.values):{len(subreddit_names.subreddit.values)}",flush=True)
|
||||
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
|
||||
sims['_subreddit'] = subreddit_names.subreddit.values
|
||||
|
||||
p = Path(outfile)
|
||||
|
||||
output_feather = Path(str(p).replace("".join(p.suffixes), ".feather"))
|
||||
output_csv = Path(str(p).replace("".join(p.suffixes), ".csv"))
|
||||
output_parquet = Path(str(p).replace("".join(p.suffixes), ".parquet"))
|
||||
p.parent.mkdir(exist_ok=True, parents=True)
|
||||
|
||||
sims.to_feather(outfile)
|
||||
tempdir.cleanup()
|
||||
|
||||
def read_tfidf_matrix_weekly(path, term_colname, week, tfidf_colname='tf_idf'):
|
||||
term = term_colname
|
||||
term_id = term + '_id'
|
||||
term_id_new = term + '_id_new'
|
||||
|
||||
dataset = ds.dataset(path,format='parquet')
|
||||
entries = dataset.to_table(columns=[tfidf_colname,'subreddit_id_new', term_id_new],filter=ds.field('week')==week).to_pandas()
|
||||
return(csr_matrix((entries[tfidf_colname], (entries[term_id_new]-1, entries.subreddit_id_new-1))))
|
||||
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]-1, entries.subreddit_id_new-1)))
|
||||
|
||||
def read_tfidf_matrix(path, term_colname, tfidf_colname='tf_idf'):
|
||||
term = term_colname
|
||||
term_id = term + '_id'
|
||||
term_id_new = term + '_id_new'
|
||||
dataset = ds.dataset(path,format='parquet')
|
||||
print(f"tfidf_colname:{tfidf_colname}")
|
||||
entries = dataset.to_table(columns=[tfidf_colname, 'subreddit_id_new',term_id_new]).to_pandas()
|
||||
return(csr_matrix((entries[tfidf_colname],(entries[term_id_new]-1, entries.subreddit_id_new-1))))
|
||||
print("loading matrix")
|
||||
|
||||
# mat = read_tfidf_matrix("term_tfidf_entries7ejhvnvl.parquet", term_colname)
|
||||
|
||||
print(f'computing similarities on mat. mat.shape:{mat.shape}')
|
||||
print(f"size of mat is:{mat.data.nbytes}",flush=True)
|
||||
sims = simfunc(mat)
|
||||
del mat
|
||||
|
||||
if hasattr(sims,'__next__'):
|
||||
for simmat, name in sims:
|
||||
proc_sims(simmat, Path(outfile)/(str(name) + ".feather"))
|
||||
else:
|
||||
proc_sims(sims, outfile)
|
||||
|
||||
def write_weekly_similarities(path, sims, week, names):
|
||||
sims['week'] = week
|
||||
p = pathlib.Path(path)
|
||||
if not p.is_dir():
|
||||
p.mkdir()
|
||||
p.mkdir(exist_ok=True,parents=True)
|
||||
|
||||
# reformat as a pairwise list
|
||||
sims = sims.melt(id_vars=['subreddit','week'],value_vars=names.subreddit.values)
|
||||
sims = sims.melt(id_vars=['_subreddit','week'],value_vars=names.subreddit.values)
|
||||
sims.to_parquet(p / week.isoformat())
|
||||
|
||||
def column_overlaps(mat):
|
||||
@ -150,136 +197,74 @@ def column_overlaps(mat):
|
||||
|
||||
return intersection / den
|
||||
|
||||
def test_lsi_sims():
|
||||
term = "term"
|
||||
term_id = term + '_id'
|
||||
term_id_new = term + '_id_new'
|
||||
|
||||
t1 = time.perf_counter()
|
||||
entries, subreddit_names = reindex_tfidf("/gscratch/comdata/output/reddit_similarity/tfidf/comment_terms_100k_repartitioned.parquet",
|
||||
term_colname='term',
|
||||
min_df=2000,
|
||||
topN=10000
|
||||
)
|
||||
t2 = time.perf_counter()
|
||||
print(f"first load took:{t2 - t1}s")
|
||||
|
||||
entries, subreddit_names = reindex_tfidf("/gscratch/comdata/output/reddit_similarity/tfidf/comment_terms_100k.parquet",
|
||||
term_colname='term',
|
||||
min_df=2000,
|
||||
topN=10000
|
||||
)
|
||||
t3=time.perf_counter()
|
||||
|
||||
print(f"second load took:{t3 - t2}s")
|
||||
|
||||
mat = csr_matrix((entries['tf_idf'],(entries[term_id_new], entries.subreddit_id_new)))
|
||||
sims = list(lsi_column_similarities(mat, [10,50]))
|
||||
sims_og = sims
|
||||
sims_test = list(lsi_column_similarities(mat,[10,50],algorithm='randomized',n_iter=10))
|
||||
|
||||
# n_components is the latent dimensionality. sklearn recommends 100. More might be better
|
||||
# if n_components is a list we'll return a list of similarities with different latent dimensionalities
|
||||
# if algorithm is 'randomized' instead of 'arpack' then n_iter gives the number of iterations.
|
||||
# this function takes the svd and then the column similarities of it
|
||||
def lsi_column_similarities(tfidfmat,n_components=300,n_iter=10,random_state=1968,algorithm='randomized',lsi_model_save=None,lsi_model_load=None):
|
||||
# first compute the lsi of the matrix
|
||||
# then take the column similarities
|
||||
|
||||
if type(n_components) is int:
|
||||
n_components = [n_components]
|
||||
|
||||
n_components = sorted(n_components,reverse=True)
|
||||
|
||||
svd_components = n_components[0]
|
||||
|
||||
if lsi_model_load is not None and Path(lsi_model_load).exists():
|
||||
print("loading LSI")
|
||||
mod = pickle.load(open(lsi_model_load ,'rb'))
|
||||
lsi_model_save = lsi_model_load
|
||||
|
||||
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)
|
||||
|
||||
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'))
|
||||
|
||||
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)])
|
||||
yield (sims, n_dims)
|
||||
|
||||
|
||||
|
||||
def column_similarities(mat):
|
||||
norm = np.matrix(np.power(mat.power(2).sum(axis=0),0.5,dtype=np.float32))
|
||||
mat = mat.multiply(1/norm)
|
||||
sims = mat.T @ mat
|
||||
return(sims)
|
||||
|
||||
|
||||
def prep_tfidf_entries_weekly(tfidf, term_colname, min_df, max_df, included_subreddits):
|
||||
term = term_colname
|
||||
term_id = term + '_id'
|
||||
term_id_new = term + '_id_new'
|
||||
|
||||
if min_df is None:
|
||||
min_df = 0.1 * len(included_subreddits)
|
||||
tfidf = tfidf.filter(f.col('count') >= min_df)
|
||||
if max_df is not None:
|
||||
tfidf = tfidf.filter(f.col('count') <= max_df)
|
||||
|
||||
tfidf = tfidf.filter(f.col("subreddit").isin(included_subreddits))
|
||||
|
||||
# we might not have the same terms or subreddits each week, so we need to make unique ids for each week.
|
||||
sub_ids = tfidf.select(['subreddit_id','week']).distinct()
|
||||
sub_ids = sub_ids.withColumn("subreddit_id_new",f.row_number().over(Window.partitionBy('week').orderBy("subreddit_id")))
|
||||
tfidf = tfidf.join(sub_ids,['subreddit_id','week'])
|
||||
|
||||
# only use terms in at least min_df included subreddits in a given week
|
||||
new_count = tfidf.groupBy([term_id,'week']).agg(f.count(term_id).alias('new_count'))
|
||||
tfidf = tfidf.join(new_count,[term_id,'week'],how='inner')
|
||||
|
||||
# reset the term ids
|
||||
term_ids = tfidf.select([term_id,'week']).distinct()
|
||||
term_ids = term_ids.withColumn(term_id_new,f.row_number().over(Window.partitionBy('week').orderBy(term_id)))
|
||||
tfidf = tfidf.join(term_ids,[term_id,'week'])
|
||||
|
||||
tfidf = tfidf.withColumnRenamed("tf_idf","tf_idf_old")
|
||||
tfidf = tfidf.withColumn("tf_idf", (tfidf.relative_tf * tfidf.idf).cast('float'))
|
||||
|
||||
tempdir =TemporaryDirectory(suffix='.parquet',prefix='term_tfidf_entries',dir='.')
|
||||
|
||||
tfidf = tfidf.repartition('week')
|
||||
|
||||
tfidf.write.parquet(tempdir.name,mode='overwrite',compression='snappy')
|
||||
return(tempdir)
|
||||
|
||||
|
||||
def prep_tfidf_entries(tfidf, term_colname, min_df, max_df, included_subreddits):
|
||||
term = term_colname
|
||||
term_id = term + '_id'
|
||||
term_id_new = term + '_id_new'
|
||||
|
||||
if min_df is None:
|
||||
min_df = 0.1 * len(included_subreddits)
|
||||
tfidf = tfidf.filter(f.col('count') >= min_df)
|
||||
if max_df is not None:
|
||||
tfidf = tfidf.filter(f.col('count') <= max_df)
|
||||
|
||||
tfidf = tfidf.filter(f.col("subreddit").isin(included_subreddits))
|
||||
|
||||
# reset the subreddit ids
|
||||
sub_ids = tfidf.select('subreddit_id').distinct()
|
||||
sub_ids = sub_ids.withColumn("subreddit_id_new", f.row_number().over(Window.orderBy("subreddit_id")))
|
||||
tfidf = tfidf.join(sub_ids,'subreddit_id')
|
||||
|
||||
# only use terms in at least min_df included subreddits
|
||||
new_count = tfidf.groupBy(term_id).agg(f.count(term_id).alias('new_count'))
|
||||
tfidf = tfidf.join(new_count,term_id,how='inner')
|
||||
|
||||
# reset the term ids
|
||||
term_ids = tfidf.select([term_id]).distinct()
|
||||
term_ids = term_ids.withColumn(term_id_new,f.row_number().over(Window.orderBy(term_id)))
|
||||
tfidf = tfidf.join(term_ids,term_id)
|
||||
|
||||
tfidf = tfidf.withColumnRenamed("tf_idf","tf_idf_old")
|
||||
tfidf = tfidf.withColumn("tf_idf", (tfidf.relative_tf * tfidf.idf).cast('float'))
|
||||
|
||||
tempdir =TemporaryDirectory(suffix='.parquet',prefix='term_tfidf_entries',dir='.')
|
||||
|
||||
tfidf.write.parquet(tempdir.name,mode='overwrite',compression='snappy')
|
||||
return tempdir
|
||||
|
||||
|
||||
# try computing cosine similarities using spark
|
||||
def spark_cosine_similarities(tfidf, term_colname, min_df, included_subreddits, similarity_threshold):
|
||||
term = term_colname
|
||||
term_id = term + '_id'
|
||||
term_id_new = term + '_id_new'
|
||||
|
||||
if min_df is None:
|
||||
min_df = 0.1 * len(included_subreddits)
|
||||
|
||||
tfidf = tfidf.filter(f.col("subreddit").isin(included_subreddits))
|
||||
tfidf = tfidf.cache()
|
||||
|
||||
# reset the subreddit ids
|
||||
sub_ids = tfidf.select('subreddit_id').distinct()
|
||||
sub_ids = sub_ids.withColumn("subreddit_id_new",f.row_number().over(Window.orderBy("subreddit_id")))
|
||||
tfidf = tfidf.join(sub_ids,'subreddit_id')
|
||||
|
||||
# only use terms in at least min_df included subreddits
|
||||
new_count = tfidf.groupBy(term_id).agg(f.count(term_id).alias('new_count'))
|
||||
tfidf = tfidf.join(new_count,term_id,how='inner')
|
||||
|
||||
# reset the term ids
|
||||
term_ids = tfidf.select([term_id]).distinct()
|
||||
term_ids = term_ids.withColumn(term_id_new,f.row_number().over(Window.orderBy(term_id)))
|
||||
tfidf = tfidf.join(term_ids,term_id)
|
||||
|
||||
tfidf = tfidf.withColumnRenamed("tf_idf","tf_idf_old")
|
||||
tfidf = tfidf.withColumn("tf_idf", tfidf.relative_tf * tfidf.idf)
|
||||
|
||||
# step 1 make an rdd of entires
|
||||
# sorted by (dense) spark subreddit id
|
||||
n_partitions = int(len(included_subreddits)*2 / 5)
|
||||
|
||||
entries = tfidf.select(f.col(term_id_new)-1,f.col("subreddit_id_new")-1,"tf_idf").rdd.repartition(n_partitions)
|
||||
|
||||
# put like 10 subredis in each partition
|
||||
|
||||
# step 2 make it into a distributed.RowMatrix
|
||||
coordMat = CoordinateMatrix(entries)
|
||||
|
||||
coordMat = CoordinateMatrix(coordMat.entries.repartition(n_partitions))
|
||||
|
||||
# this needs to be an IndexedRowMatrix()
|
||||
mat = coordMat.toRowMatrix()
|
||||
|
||||
#goal: build a matrix of subreddit columns and tf-idfs rows
|
||||
sim_dist = mat.columnSimilarities(threshold=similarity_threshold)
|
||||
|
||||
return (sim_dist, tfidf)
|
||||
return 1 - pairwise_distances(mat,metric='cosine')
|
||||
|
||||
|
||||
def build_weekly_tfidf_dataset(df, include_subs, term_colname, tf_family=tf_weight.Norm05):
|
||||
@ -306,20 +291,20 @@ def build_weekly_tfidf_dataset(df, include_subs, term_colname, tf_family=tf_weig
|
||||
idf = idf.withColumn('idf',f.log(idf.subreddits_in_week) / (1+f.col('count'))+1)
|
||||
|
||||
# collect the dictionary to make a pydict of terms to indexes
|
||||
terms = idf.select([term,'week']).distinct() # terms are distinct
|
||||
terms = idf.select([term]).distinct() # terms are distinct
|
||||
|
||||
terms = terms.withColumn(term_id,f.row_number().over(Window.partitionBy('week').orderBy(term))) # term ids are distinct
|
||||
terms = terms.withColumn(term_id,f.row_number().over(Window.orderBy(term))) # term ids are distinct
|
||||
|
||||
# make subreddit ids
|
||||
subreddits = df.select(['subreddit','week']).distinct()
|
||||
subreddits = subreddits.withColumn('subreddit_id',f.row_number().over(Window.partitionBy("week").orderBy("subreddit")))
|
||||
subreddits = df.select(['subreddit']).distinct()
|
||||
subreddits = subreddits.withColumn('subreddit_id',f.row_number().over(Window.orderBy("subreddit")))
|
||||
|
||||
df = df.join(subreddits,on=['subreddit','week'])
|
||||
df = df.join(subreddits,on=['subreddit'])
|
||||
|
||||
# map terms to indexes in the tfs and the idfs
|
||||
df = df.join(terms,on=[term,'week']) # subreddit-term-id is unique
|
||||
df = df.join(terms,on=[term]) # subreddit-term-id is unique
|
||||
|
||||
idf = idf.join(terms,on=[term,'week'])
|
||||
idf = idf.join(terms,on=[term])
|
||||
|
||||
# join on subreddit/term to create tf/dfs indexed by term
|
||||
df = df.join(idf, on=[term_id, term,'week'])
|
||||
@ -331,9 +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)
|
||||
|
||||
return df
|
||||
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'
|
||||
|
||||
@ -342,7 +329,7 @@ def _calc_tfidf(df, term_colname, tf_family):
|
||||
|
||||
df = df.join(max_subreddit_terms, on='subreddit')
|
||||
|
||||
df = df.withColumn("relative_tf", df.tf / df.sr_max_tf)
|
||||
df = df.withColumn("relative_tf", (df.tf / df.sr_max_tf))
|
||||
|
||||
# group by term. term is unique
|
||||
idf = df.groupby([term]).count()
|
||||
@ -351,7 +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
|
||||
@ -361,12 +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:
|
||||
@ -377,18 +370,36 @@ def _calc_tfidf(df, term_colname, tf_family):
|
||||
return df
|
||||
|
||||
|
||||
def build_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
|
||||
|
||||
return df
|
||||
|
||||
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
|
||||
|
||||
|
||||
def repartition_tfidf(inpath="/gscratch/comdata/output/reddit_similarity/tfidf/comment_terms_100k.parquet",
|
||||
outpath="/gscratch/comdata/output/reddit_similarity/tfidf/comment_terms_100k_repartitioned.parquet"):
|
||||
spark = SparkSession.builder.getOrCreate()
|
||||
df = spark.read.parquet(inpath)
|
||||
df = df.repartition(400,'subreddit')
|
||||
df.write.parquet(outpath,mode='overwrite')
|
||||
|
||||
|
||||
def repartition_tfidf_weekly(inpath="/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_terms.parquet",
|
||||
outpath="/gscratch/comdata/output/reddit_similarity/tfidf/comment_terms_repartitioned.parquet"):
|
||||
spark = SparkSession.builder.getOrCreate()
|
||||
df = spark.read.parquet(inpath)
|
||||
df = df.repartition(400,'subreddit','week')
|
||||
dfwriter = df.write.partitionBy("week")
|
||||
dfwriter.parquet(outpath,mode='overwrite')
|
||||
|
||||
@ -2,35 +2,45 @@
|
||||
import fire
|
||||
from pyspark.sql import SparkSession
|
||||
from pyspark.sql import functions as f
|
||||
from similarities_helper import build_tfidf_dataset, build_weekly_tfidf_dataset, select_topN_subreddits
|
||||
from similarities_helper import tfidf_dataset, build_weekly_tfidf_dataset, select_topN_subreddits
|
||||
from functools import partial
|
||||
|
||||
def _tfidf_wrapper(func, inpath, outpath, topN, term_colname, exclude, included_subreddits):
|
||||
def _tfidf_wrapper(func, inpath, outpath, topN, term_colname, exclude, included_subreddits, included_terms=None, min_df=None, max_df=None):
|
||||
spark = SparkSession.builder.config(map={'spark.executor.memory':'900g','spark.executor.cores':128}).getOrCreate()
|
||||
df = spark.read.parquet(inpath)
|
||||
|
||||
df = df.filter(~ f.col(term_colname).isin(exclude))
|
||||
|
||||
if included_subreddits is not None:
|
||||
include_subs = set(map(str.strip,map(str.lower, open(included_subreddits))))
|
||||
include_subs = set(map(str.strip,open(included_subreddits)))
|
||||
else:
|
||||
include_subs = select_topN_subreddits(topN)
|
||||
|
||||
df = func(df, include_subs, term_colname)
|
||||
include_subs = spark.sparkContext.broadcast(include_subs)
|
||||
|
||||
df.write.parquet(outpath,mode='overwrite',compression='snappy')
|
||||
# term_id = term_colname + "_id"
|
||||
|
||||
if included_terms is not None:
|
||||
terms_df = spark.read.parquet(included_terms)
|
||||
terms_df = terms_df.select(term_colname).distinct()
|
||||
df = df.join(terms_df, on=term_colname, how='left_semi')
|
||||
|
||||
dfwriter = func(df, include_subs.value, term_colname)
|
||||
|
||||
dfwriter.parquet(outpath,mode='overwrite',compression='snappy')
|
||||
spark.stop()
|
||||
|
||||
def tfidf(inpath, outpath, topN, term_colname, exclude, included_subreddits):
|
||||
return _tfidf_wrapper(build_tfidf_dataset, inpath, outpath, topN, term_colname, exclude, included_subreddits)
|
||||
def tfidf(inpath, outpath, topN, term_colname, exclude, included_subreddits, min_df, max_df):
|
||||
tfidf_func = partial(tfidf_dataset, max_df=max_df, min_df=min_df)
|
||||
return _tfidf_wrapper(tfidf_func, inpath, outpath, topN, term_colname, exclude, included_subreddits)
|
||||
|
||||
def tfidf_weekly(inpath, outpath, static_tfidf_path, topN, term_colname, exclude, included_subreddits):
|
||||
return _tfidf_wrapper(build_weekly_tfidf_dataset, inpath, outpath, topN, term_colname, exclude, included_subreddits, included_terms=static_tfidf_path)
|
||||
|
||||
def tfidf_weekly(inpath, outpath, topN, term_colname, exclude, included_subreddits):
|
||||
return _tfidf_wrapper(build_weekly_tfidf_dataset, inpath, outpath, topN, term_colname, exclude, included_subreddits)
|
||||
|
||||
def tfidf_post_comment_authors(outpath='/gscratch/comdata/output/reddit_similarity/tfidf/post_authors.parquet',
|
||||
topN=25000,
|
||||
included_subreddits=None):
|
||||
|
||||
return tfidf("/gscratch/comdata/output/reddit_ngrams/post_comment_authors.parquet",
|
||||
outpath,
|
||||
topN,
|
||||
@ -41,49 +51,64 @@ def tfidf_post_comment_authors(outpath='/gscratch/comdata/output/reddit_similari
|
||||
|
||||
def tfidf_authors(inpath="/gscratch/comdata/output/reddit_ngrams/comment_authors.parquet",
|
||||
outpath='/gscratch/comdata/output/reddit_similarity/tfidf/comment_authors.parquet',
|
||||
topN=25000,
|
||||
included_subreddits=None):
|
||||
topN=None,
|
||||
included_subreddits=None,
|
||||
min_df=None,
|
||||
max_df=None):
|
||||
|
||||
return tfidf(inpath,
|
||||
outpath,
|
||||
topN,
|
||||
'author',
|
||||
['[deleted]','AutoModerator'],
|
||||
included_subreddits=included_subreddits
|
||||
included_subreddits=included_subreddits,
|
||||
min_df=min_df,
|
||||
max_df=max_df
|
||||
)
|
||||
|
||||
def tfidf_terms(outpath='/gscratch/comdata/output/reddit_similarity/tfidf/comment_terms.parquet',
|
||||
topN=25000,
|
||||
included_subreddits=None):
|
||||
def tfidf_terms(inpath="/gscratch/comdata/output/reddit_ngrams/comment_terms.parquet",
|
||||
outpath='/gscratch/comdata/output/reddit_similarity/tfidf/comment_terms.parquet',
|
||||
topN=None,
|
||||
included_subreddits=None,
|
||||
min_df=None,
|
||||
max_df=None):
|
||||
|
||||
return tfidf("/gscratch/comdata/output/reddit_ngrams/comment_terms.parquet",
|
||||
return tfidf(inpath,
|
||||
outpath,
|
||||
topN,
|
||||
'term',
|
||||
[],
|
||||
included_subreddits=included_subreddits
|
||||
included_subreddits=included_subreddits,
|
||||
min_df=min_df,
|
||||
max_df=max_df
|
||||
)
|
||||
|
||||
|
||||
def tfidf_authors_weekly(inpath="/gscratch/comdata/output/reddit_ngrams/comment_authors.parquet",
|
||||
static_tfidf_path="/gscratch/comdata/output/reddit_similarity/tfidf/comment_authors.parquet",
|
||||
outpath='/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_authors.parquet',
|
||||
topN=25000,
|
||||
topN=None,
|
||||
included_subreddits=None):
|
||||
|
||||
return tfidf_weekly(inpath,
|
||||
outpath,
|
||||
static_tfidf_path,
|
||||
topN,
|
||||
'author',
|
||||
['[deleted]','AutoModerator'],
|
||||
included_subreddits=included_subreddits
|
||||
)
|
||||
|
||||
def tfidf_terms_weekly(outpath='/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_terms.parquet',
|
||||
topN=25000,
|
||||
def tfidf_terms_weekly(inpath="/gscratch/comdata/output/reddit_ngrams/comment_terms.parquet",
|
||||
static_tfidf_path="/gscratch/comdata/output/reddit_similarity/tfidf/comment_terms.parquet",
|
||||
outpath='/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_terms.parquet',
|
||||
topN=None,
|
||||
included_subreddits=None):
|
||||
|
||||
|
||||
return tfidf_weekly("/gscratch/comdata/output/reddit_ngrams/comment_terms.parquet",
|
||||
return tfidf_weekly(inpath,
|
||||
outpath,
|
||||
static_tfidf_path,
|
||||
topN,
|
||||
'term',
|
||||
[],
|
||||
|
||||
@ -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/comdata/output/reddit_similarity/subreddits_by_num_comments.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)
|
||||
|
||||
@ -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)
|
||||
@ -1,81 +0,0 @@
|
||||
from pyspark.sql import functions as f
|
||||
from pyspark.sql import SparkSession
|
||||
from pyspark.sql import Window
|
||||
import numpy as np
|
||||
import pyarrow
|
||||
import pandas as pd
|
||||
import fire
|
||||
from itertools import islice
|
||||
from pathlib import Path
|
||||
from similarities_helper import *
|
||||
from multiprocessing import Pool, cpu_count
|
||||
|
||||
def _week_similarities(tempdir, term_colname, week):
|
||||
print(f"loading matrix: {week}")
|
||||
mat = read_tfidf_matrix_weekly(tempdir.name, term_colname, week)
|
||||
print('computing similarities')
|
||||
sims = column_similarities(mat)
|
||||
del mat
|
||||
|
||||
names = subreddit_names.loc[subreddit_names.week == week]
|
||||
sims = pd.DataFrame(sims.todense())
|
||||
|
||||
sims = sims.rename({i: sr for i, sr in enumerate(names.subreddit.values)}, axis=1)
|
||||
sims['_subreddit'] = names.subreddit.values
|
||||
|
||||
write_weekly_similarities(outfile, sims, week, names)
|
||||
|
||||
#tfidf = spark.read.parquet('/gscratch/comdata/users/nathante/subreddit_tfidf_weekly.parquet')
|
||||
def cosine_similarities_weekly(tfidf_path, outfile, term_colname, min_df = None, included_subreddits = None, topN = 500):
|
||||
spark = SparkSession.builder.getOrCreate()
|
||||
conf = spark.sparkContext.getConf()
|
||||
print(outfile)
|
||||
tfidf = spark.read.parquet(tfidf_path)
|
||||
|
||||
if included_subreddits is None:
|
||||
included_subreddits = select_topN_subreddits(topN)
|
||||
else:
|
||||
included_subreddits = set(open(included_subreddits))
|
||||
|
||||
print(f"computing weekly similarities for {len(included_subreddits)} subreddits")
|
||||
|
||||
print("creating temporary parquet with matrix indicies")
|
||||
tempdir = prep_tfidf_entries_weekly(tfidf, term_colname, min_df, max_df=None, included_subreddits=included_subreddits)
|
||||
|
||||
tfidf = spark.read.parquet(tempdir.name)
|
||||
|
||||
# the ids can change each week.
|
||||
subreddit_names = tfidf.select(['subreddit','subreddit_id_new','week']).distinct().toPandas()
|
||||
subreddit_names = subreddit_names.sort_values("subreddit_id_new")
|
||||
subreddit_names['subreddit_id_new'] = subreddit_names['subreddit_id_new'] - 1
|
||||
spark.stop()
|
||||
|
||||
weeks = sorted(list(subreddit_names.week.drop_duplicates()))
|
||||
# do this step in parallel if we have the memory for it.
|
||||
# should be doable with pool.map
|
||||
|
||||
def week_similarities_helper(week):
|
||||
_week_similarities(tempdir, term_colname, week)
|
||||
|
||||
with Pool(cpu_count()) as pool: # maybe it can be done with 40 cores on the huge machine?
|
||||
list(pool.map(week_similarities_helper,weeks))
|
||||
|
||||
def author_cosine_similarities_weekly(outfile, min_df=2 , included_subreddits=None, topN=500):
|
||||
return cosine_similarities_weekly('/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_authors.parquet',
|
||||
outfile,
|
||||
'author',
|
||||
min_df,
|
||||
included_subreddits,
|
||||
topN)
|
||||
|
||||
def term_cosine_similarities_weekly(outfile, min_df=None, included_subreddits=None, topN=500):
|
||||
return cosine_similarities_weekly('/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_terms.parquet',
|
||||
outfile,
|
||||
'term',
|
||||
min_df,
|
||||
included_subreddits,
|
||||
topN)
|
||||
|
||||
if __name__ == "__main__":
|
||||
fire.Fire({'authors':author_cosine_similarities_weekly,
|
||||
'terms':term_cosine_similarities_weekly})
|
||||
21
start_spark_and_run.sh
Executable file
21
start_spark_and_run.sh
Executable file
@ -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=$1 --time=48:00:00 -c 40 --mem=362G --exclusive srun -n1 job_script.sh
|
||||
|
||||
26
start_spark_cluster.sh
Executable file
26
start_spark_cluster.sh
Executable file
@ -0,0 +1,26 @@
|
||||
#!/usr/bin/env bash
|
||||
nodes="$(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 $SPARK_MASTER_HOST
|
||||
# fi
|
||||
done
|
||||
|
||||
18
start_spark_worker.sh
Executable file
18
start_spark_worker.sh
Executable file
@ -0,0 +1,18 @@
|
||||
#!/usr/bin/env bash
|
||||
# runs on worker node
|
||||
# instance_name=spark-worker-$(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
|
||||
@ -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)
|
||||
@ -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 main(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"):
|
||||
|
||||
|
||||
clusters = load_clusters(term_clusters_path, author_clusters_path)
|
||||
densities = load_densities(term_densities_path, author_densities_path)
|
||||
|
||||
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')
|
||||
spk_clusters = spark.createDataFrame(clusters)
|
||||
|
||||
ts = ts.join(spk_clusters, on='subreddit', how='inner')
|
||||
spk_densities = spark.createDataFrame(densities)
|
||||
ts = ts.join(spk_densities, on='subreddit', how='inner')
|
||||
ts.write.parquet(output, mode='overwrite')
|
||||
|
||||
if __name__ == "__main__":
|
||||
fire.Fire(main)
|
||||
@ -1 +0,0 @@
|
||||
/annex/objects/SHA256E-s60874--d536adb0ec637fca262c4e1ec908dd8b4a5d1464047b583cd1a99cc6dba87191
|
||||
@ -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
|
||||
@ -1 +0,0 @@
|
||||
../../.git/annex/objects/Qk/wG/SHA256E-s145210--14a2ad6660d1e4015437eff556ec349dd10a115a4f96594152a29e83d00aa784/SHA256E-s145210--14a2ad6660d1e4015437eff556ec349dd10a115a4f96594152a29e83d00aa784
|
||||
@ -1 +0,0 @@
|
||||
../../.git/annex/objects/w7/2f/SHA256E-s44458--f1c5247775ecf06514a0ff9e523e944bc8fcd9d0fdb6f214cc1329b759d4354e/SHA256E-s44458--f1c5247775ecf06514a0ff9e523e944bc8fcd9d0fdb6f214cc1329b759d4354e
|
||||
@ -1 +0,0 @@
|
||||
../../.git/annex/objects/WX/v3/SHA256E-s190874--c2aea719f989dde297ca5f13371e156693c574e44acd9a0e313e5e3a3ad4b543/SHA256E-s190874--c2aea719f989dde297ca5f13371e156693c574e44acd9a0e313e5e3a3ad4b543
|
||||
@ -1 +0,0 @@
|
||||
../../.git/annex/objects/mq/2z/SHA256E-s58834--2e7b3ee11f47011fd9b34bddf8f1e788d35ab9c9e0bb6a1301b0b916135400cf/SHA256E-s58834--2e7b3ee11f47011fd9b34bddf8f1e788d35ab9c9e0bb6a1301b0b916135400cf
|
||||
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
@ -1,175 +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')
|
||||
|
||||
color = alt.condition(cluster_click_select ,
|
||||
alt.Color(field='color',type='nominal',scale=alt.Scale(scheme='category10')),
|
||||
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):
|
||||
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)):
|
||||
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)
|
||||
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")
|
||||
Loading…
Reference in New Issue
Block a user