changes for archiving.
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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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@ -1,218 +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=srun -p compute-bigmem -A comdata --time=48:00:00 --mem=362G -c 40
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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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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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umap_hdbscan_selection_grid=--min_cluster_sizes=[2] --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] --n_neighbors=[5,15,25,50,75,100] --learning_rate=[1] --min_dist=[0,0.1,0.25,0.5,0.75,0.9,0.99] --local_connectivity=[1] --densmap=[True,False] --n_components=[2,5,10,15,25]
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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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authors_10k_input=$(similarity_data)/subreddit_comment_authors_10k.feather
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authors_10k_input_lsi=$(similarity_data)/subreddit_comment_authors_10k_LSI
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authors_10k_output=$(clustering_data)/subreddit_comment_authors_10k
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authors_10k_output_lsi=$(clustering_data)/subreddit_comment_authors_10k_LSI
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authors_tf_10k_input=$(similarity_data)/subreddit_comment_authors-tf_10k.feather
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authors_tf_10k_input_lsi=$(similarity_data)/subreddit_comment_authors-tf_10k_LSI
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authors_tf_10k_output=$(clustering_data)/subreddit_comment_authors-tf_10k
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authors_tf_10k_output_lsi=$(clustering_data)/subreddit_comment_authors-tf_10k_LSI
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terms_10k_input=$(similarity_data)/subreddit_comment_terms_10k.feather
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terms_10k_input_lsi=$(similarity_data)/subreddit_comment_terms_10k_LSI
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terms_10k_output=$(clustering_data)/subreddit_comment_terms_10k
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terms_10k_output_lsi=$(clustering_data)/subreddit_comment_terms_10k_LSI
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all:terms_10k authors_10k authors_tf_10k terms_10k_lsi authors_10k_lsi authors_tf_10k_lsi
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terms_10k:${terms_10k_output}/kmeans/selection_data.csv ${terms_10k_output}/affinity/selection_data.csv ${terms_10k_output}/hdbscan/selection_data.csv
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authors_10k:${authors_10k_output}/kmeans/selection_data.csv ${authors_10k_output}/hdbscan/selection_data.csv ${authors_10k_output}/affinity/selection_data.csv
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authors_tf_10k:${authors_tf_10k_output}/kmeans/selection_data.csv ${authors_tf_10k_output}/hdbscan/selection_data.csv ${authors_tf_10k_output}/affinity/selection_data.csv
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terms_10k_lsi:${terms_10k_output_lsi}/kmeans/selection_data.csv ${terms_10k_output_lsi}/affinity/selection_data.csv ${terms_10k_output_lsi}/hdbscan/selection_data.csv
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authors_10k_lsi:${authors_10k_output_lsi}/kmeans/selection_data.csv ${authors_10k_output_lsi}/hdbscan/selection_data.csv ${authors_10k_output_lsi}/affinity/selection_data.csv
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all:authors_tf_10k_lsi
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authors_tf_10k_lsi:${authors_tf_10k_output_lsi}/kmeans/selection_data.csv ${authors_tf_10k_output_lsi}/hdbscan/selection_data.csv ${authors_tf_10k_output_lsi}/affinity/selection_data.csv
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${authors_10k_output}/kmeans/selection_data.csv:selection.py ${authors_10k_input} clustering_base.py kmeans_clustering.py
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$(srun_singularity) python3 kmeans_clustering.py --inpath=${authors_10k_input} --outpath=${authors_10k_output}/kmeans --savefile=${authors_10k_output}/kmeans/selection_data.csv $(kmeans_selection_grid)
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${terms_10k_output}/kmeans/selection_data.csv:selection.py ${terms_10k_input} clustering_base.py kmeans_clustering.py
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$(srun_singularity) python3 kmeans_clustering.py --inpath=${terms_10k_input} --outpath=${terms_10k_output}/kmeans --savefile=${terms_10k_output}/kmeans/selection_data.csv $(kmeans_selection_grid)
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${authors_tf_10k_output}/kmeans/selection_data.csv:clustering.py ${authors_tf_10k_input} clustering_base.py kmeans_clustering.py
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$(srun_singularity) python3 kmeans_clustering.py --inpath=${authors_tf_10k_input} --outpath=${authors_tf_10k_output}/kmeans --savefile=${authors_tf_10k_output}/kmeans/selection_data.csv $(kmeans_selection_grid)
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${authors_10k_output}/affinity/selection_data.csv:selection.py ${authors_10k_input} clustering_base.py affinity_clustering.py
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$(srun_singularity) python3 affinity_clustering.py --inpath=${authors_10k_input} --outpath=${authors_10k_output}/affinity --savefile=${authors_10k_output}/affinity/selection_data.csv $(affinity_selection_grid)
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${terms_10k_output}/affinity/selection_data.csv:selection.py ${terms_10k_input} clustering_base.py affinity_clustering.py
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$(srun_singularity) python3 affinity_clustering.py --inpath=${terms_10k_input} --outpath=${terms_10k_output}/affinity --savefile=${terms_10k_output}/affinity/selection_data.csv $(affinity_selection_grid)
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${authors_tf_10k_output}/affinity/selection_data.csv:clustering.py ${authors_tf_10k_input} clustering_base.py affinity_clustering.py
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$(srun_singularity) python3 affinity_clustering.py --inpath=${authors_tf_10k_input} --outpath=${authors_tf_10k_output}/affinity --savefile=${authors_tf_10k_output}/affinity/selection_data.csv $(affinity_selection_grid)
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${authors_10k_output}/hdbscan/selection_data.csv:selection.py ${authors_10k_input} clustering_base.py hdbscan_clustering.py
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$(srun_singularity) python3 hdbscan_clustering.py --inpath=${authors_10k_input} --outpath=${authors_10k_output}/hdbscan --savefile=${authors_10k_output}/hdbscan/selection_data.csv $(hdbscan_selection_grid)
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${terms_10k_output}/hdbscan/selection_data.csv:selection.py ${terms_10k_input} clustering_base.py hdbscan_clustering.py
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$(srun_singularity) python3 hdbscan_clustering.py --inpath=${terms_10k_input} --outpath=${terms_10k_output}/hdbscan --savefile=${terms_10k_output}/hdbscan/selection_data.csv $(hdbscan_selection_grid)
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${authors_tf_10k_output}/hdbscan/selection_data.csv:clustering.py ${authors_tf_10k_input} clustering_base.py hdbscan_clustering.py
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$(srun_singularity) python3 hdbscan_clustering.py --inpath=${authors_tf_10k_input} --outpath=${authors_tf_10k_output}/hdbscan --savefile=${authors_tf_10k_output}/hdbscan/selection_data.csv $(hdbscan_selection_grid)
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## LSI Models
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${authors_10k_output_lsi}/kmeans/selection_data.csv:selection.py ${authors_10k_input_lsi} clustering_base.py kmeans_clustering.py
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$(srun_singularity) python3 kmeans_clustering_lsi.py --inpath=${authors_10k_input_lsi} --outpath=${authors_10k_output_lsi}/kmeans --savefile=${authors_10k_output_lsi}/kmeans/selection_data.csv $(kmeans_selection_grid)
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${terms_10k_output_lsi}/kmeans/selection_data.csv:selection.py ${terms_10k_input_lsi} clustering_base.py kmeans_clustering.py
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$(srun_singularity) python3 kmeans_clustering_lsi.py --inpath=${terms_10k_input_lsi} --outpath=${terms_10k_output_lsi}/kmeans --savefile=${terms_10k_output_lsi}/kmeans/selection_data.csv $(kmeans_selection_grid)
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${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) 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 $(kmeans_selection_grid)
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${authors_10k_output_lsi}/affinity/selection_data.csv:selection.py ${authors_10k_input_lsi} clustering_base.py affinity_clustering.py
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$(srun_singularity) python3 affinity_clustering_lsi.py --inpath=${authors_10k_input_lsi} --outpath=${authors_10k_output_lsi}/affinity --savefile=${authors_10k_output_lsi}/affinity/selection_data.csv $(affinity_selection_grid)
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${terms_10k_output_lsi}/affinity/selection_data.csv:selection.py ${terms_10k_input_lsi} clustering_base.py affinity_clustering.py
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$(srun_singularity) python3 affinity_clustering_lsi.py --inpath=${terms_10k_input_lsi} --outpath=${terms_10k_output_lsi}/affinity --savefile=${terms_10k_output_lsi}/affinity/selection_data.csv $(affinity_selection_grid)
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$(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 $(kmeans_selection_grid)"
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${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) 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 $(affinity_selection_grid)
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${authors_10k_output_lsi}/hdbscan/selection_data.csv:selection.py ${authors_10k_input_lsi} clustering_base.py hdbscan_clustering.py
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$(srun_singularity) python3 hdbscan_clustering_lsi.py --inpath=${authors_10k_input_lsi} --outpath=${authors_10k_output_lsi}/hdbscan --savefile=${authors_10k_output_lsi}/hdbscan/selection_data.csv $(hdbscan_selection_grid)
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${terms_10k_output_lsi}/hdbscan/selection_data.csv:selection.py ${terms_10k_input_lsi} clustering_base.py hdbscan_clustering.py
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$(srun_singularity) python3 hdbscan_clustering_lsi.py --inpath=${terms_10k_input_lsi} --outpath=${terms_10k_output_lsi}/hdbscan --savefile=${terms_10k_output_lsi}/hdbscan/selection_data.csv $(hdbscan_selection_grid)
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$(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 $(affinity_selection_grid)"
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${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) 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}/umap_hdbscan/selection_data.csv:umap_hdbscan_clustering_lsi.py
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$(srun_singularity) python3 umap_hdbscan_clustering_lsi.py --inpath=${authors_tf_10k_input_lsi} --outpath=${authors_tf_10k_output_lsi}/umap_hdbscan --savefile=${authors_tf_10k_output_lsi}/umap_hdbscan/selection_data.csv $(umap_hdbscan_selection_grid)
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${terms_10k_output_lsi}/best_hdbscan.feather:${terms_10k_output_lsi}/hdbscan/selection_data.csv pick_best_clustering.py
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$(srun_singularity) python3 pick_best_clustering.py $< $@ --min_clusters=50 --max_isolates=5000 --min_cluster_size=2
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$(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) python3 pick_best_clustering.py $< $@ --min_clusters=50 --max_isolates=5000 --min_cluster_size=2
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$(srun_singularity) -c "source ~/.bashrc; python3 pick_best_clustering.py $< $@ --min_clusters=50 --max_isolates=5000 --min_cluster_size=2"
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${authors_tf_10k_output_lsi}/best_umap_hdbscan_2.feather:${authors_tf_10k_output_lsi}/umap_hdbscan/selection_data.csv pick_best_clustering.py
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$(srun_singularity) python3 pick_best_clustering.py $< $@ --min_clusters=50 --max_isolates=5000 --min_cluster_size=2
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${authors_tf_10k_input_lsi}:
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$(MAKE) -C ../similarities
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best_umap_hdbscan.feather:${authors_tf_10k_output_lsi}/best_umap_hdbscan_2.feather
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# {'lsi_dimensions': 700, 'outpath': '/gscratch/comdata/output/reddit_clustering/subreddit_comment_authors-tf_10k_LSI/umap_hdbscan', 'silhouette_score': 0.27616957, 'name': 'mcs-2_ms-5_cse-0.05_csm-leaf_nn-15_lr-1.0_md-0.1_lc-1_lsi-700', 'n_clusters': 547, 'n_isolates': 2093, 'silhouette_samples': '/gscratch/comdata/output/reddit_clustering/subreddit_comment_authors-tf_10k_LSI/umap_hdbscan/silhouette_samples-mcs-2_ms-5_cse-0.05_csm-leaf_nn-15_lr-1.0_md-0.1_lc-1_lsi-700.feather', 'min_cluster_size': 2, 'min_samples': 5, 'cluster_selection_epsilon': 0.05, 'cluster_selection_method': 'leaf', 'n_neighbors': 15, 'learning_rate': 1.0, 'min_dist': 0.1, 'local_connectivity': 1, 'n_isolates_str': '2093', 'n_isolates_0': False}
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best_umap_grid=--min_cluster_sizes=[2] --min_samples=[5] --cluster_selection_epsilons=[0.05] --cluster_selection_methods=[leaf] --n_neighbors=[15] --learning_rate=[1] --min_dist=[0.1] --local_connectivity=[1] --save_step1=True
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umap_hdbscan_coords:
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python3 umap_hdbscan_clustering_lsi.py --inpath=${authors_tf_10k_input_lsi} --outpath=${authors_tf_10k_output_lsi}/umap_hdbscan --savefile=/dev/null ${best_umap_grid}
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clean_affinity:
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rm -f ${authors_10k_output}/affinity/selection_data.csv
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rm -f ${authors_tf_10k_output}/affinity/selection_data.csv
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rm -f ${terms_10k_output}/affinity/selection_data.csv
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clean_kmeans:
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rm -f ${authors_10k_output}/kmeans/selection_data.csv
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rm -f ${authors_tf_10k_output}/kmeans/selection_data.csv
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rm -f ${terms_10k_output}/kmeans/selection_data.csv
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clean_hdbscan:
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rm -f ${authors_10k_output}/hdbscan/selection_data.csv
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rm -f ${authors_tf_10k_output}/hdbscan/selection_data.csv
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rm -f ${terms_10k_output}/hdbscan/selection_data.csv
|
||||
|
||||
clean_authors:
|
||||
rm -f ${authors_10k_output}/affinity/selection_data.csv
|
||||
rm -f ${authors_10k_output}/kmeans/selection_data.csv
|
||||
rm -f ${authors_10k_output}/hdbscan/selection_data.csv
|
||||
|
||||
clean_authors_tf:
|
||||
rm -f ${authors_tf_10k_output}/affinity/selection_data.csv
|
||||
rm -f ${authors_tf_10k_output}/kmeans/selection_data.csv
|
||||
rm -f ${authors_tf_10k_output}/hdbscan/selection_data.csv
|
||||
|
||||
clean_terms:
|
||||
rm -f ${terms_10k_output}/affinity/selection_data.csv
|
||||
rm -f ${terms_10k_output}/kmeans/selection_data.csv
|
||||
rm -f ${terms_10k_output}/hdbscan/selection_data.csv
|
||||
|
||||
clean_lsi_affinity:
|
||||
rm -f ${authors_10k_output_lsi}/affinity/selection_data.csv
|
||||
rm -f ${authors_tf_10k_output_lsi}/affinity/selection_data.csv
|
||||
rm -f ${terms_10k_output_lsi}/affinity/selection_data.csv
|
||||
|
||||
clean_lsi_kmeans:
|
||||
rm -f ${authors_10k_output_lsi}/kmeans/selection_data.csv
|
||||
rm -f ${authors_tf_10k_output_lsi}/kmeans/selection_data.csv
|
||||
rm -f ${terms_10k_output_lsi}/kmeans/selection_data.csv
|
||||
|
||||
clean_lsi_hdbscan:
|
||||
rm -f ${authors_10k_output_lsi}/hdbscan/selection_data.csv
|
||||
rm -f ${authors_tf_10k_output_lsi}/hdbscan/selection_data.csv
|
||||
rm -f ${terms_10k_output_lsi}/hdbscan/selection_data.csv
|
||||
|
||||
clean_lsi_authors:
|
||||
rm -f ${authors_10k_output_lsi}/affinity/selection_data.csv
|
||||
rm -f ${authors_10k_output_lsi}/kmeans/selection_data.csv
|
||||
rm -f ${authors_10k_output_lsi}/hdbscan/selection_data.csv
|
||||
|
||||
clean_lsi_authors_tf:
|
||||
clean:
|
||||
rm -f ${authors_tf_10k_output_lsi}/affinity/selection_data.csv
|
||||
rm -f ${authors_tf_10k_output_lsi}/kmeans/selection_data.csv
|
||||
rm -f ${authors_tf_10k_output_lsi}/hdbscan/selection_data.csv
|
||||
|
||||
clean_lsi_terms:
|
||||
rm -f ${terms_10k_output_lsi}/affinity/selection_data.csv
|
||||
rm -f ${terms_10k_output_lsi}/kmeans/selection_data.csv
|
||||
rm -f ${terms_10k_output_lsi}/hdbscan/selection_data.csv
|
||||
|
||||
clean: clean_affinity clean_kmeans clean_hdbscan
|
||||
|
||||
PHONY: clean clean_affinity clean_kmeans clean_hdbscan clean_authors clean_authors_tf clean_terms terms_10k authors_10k authors_tf_10k best_umap_hdbscan.feather umap_hdbscan_coords
|
||||
|
||||
# $(clustering_data)/subreddit_comment_authors_30k.feather/SUCCESS:selection.py $(similarity_data)/subreddit_comment_authors_30k.feather clustering.py
|
||||
# $(srun_singularity) python3 selection.py $(similarity_data)/subreddit_comment_authors_30k.feather $(clustering_data)/subreddit_comment_authors_30k $(selection_grid) -J 10 && touch $(clustering_data)/subreddit_comment_authors_30k.feather/SUCCESS
|
||||
|
||||
# $(clustering_data)/subreddit_comment_terms_30k.feather/SUCCESS:selection.py $(similarity_data)/subreddit_comment_terms_30k.feather clustering.py
|
||||
# $(srun_singularity) python3 selection.py $(similarity_data)/subreddit_comment_terms_30k.feather $(clustering_data)/subreddit_comment_terms_30k $(selection_grid) -J 10 && touch $(clustering_data)/subreddit_comment_terms_30k.feather/SUCCESS
|
||||
|
||||
# $(clustering_data)/subreddit_authors-tf_similarities_30k.feather/SUCCESS:clustering.py $(similarity_data)/subreddit_comment_authors-tf_30k.feather
|
||||
# $(srun_singularity) python3 selection.py $(similarity_data)/subreddit_comment_authors-tf_30k.feather $(clustering_data)/subreddit_comment_authors-tf_30k $(selection_grid) -J 8 && touch $(clustering_data)/subreddit_authors-tf_similarities_30k.feather/SUCCESS
|
||||
|
||||
|
||||
# $(clustering_data)/subreddit_comment_authors_100k.feather:clustering.py $(similarity_data)/subreddit_comment_authors_100k.feather
|
||||
# $(srun_singularity) python3 clustering.py $(similarity_data)/subreddit_comment_authors_100k.feather $(clustering_data)/subreddit_comment_authors_100k.feather ---max_iter=400 --convergence_iter=15 --preference_quantile=0.85 --damping=0.85
|
||||
|
||||
# $(clustering_data)/comment_terms_100k.feather:clustering.py $(similarity_data)/subreddit_comment_terms_100k.feather
|
||||
# $(srun_singularity) python3 clustering.py $(similarity_data)/comment_terms_10000.feather $(clustering_data)/comment_terms_10000.feather ---max_iter=1000 --convergence_iter=15 --preference_quantile=0.9 --damping=0.5
|
||||
|
||||
# $(clustering_data)/subreddit_comment_author-tf_100k.feather:clustering.py $(similarity_data)/subreddit_comment_author-tf_100k.feather
|
||||
# $(srun_singularity) python3 clustering.py $(similarity_data)/subreddit_comment_author-tf_100k.parquet $(clustering_data)/subreddit_comment_author-tf_100k.feather ---max_iter=400 --convergence_iter=15 --preference_quantile=0.5 --damping=0.85
|
||||
|
||||
|
||||
# it's pretty difficult to get a result that isn't one huge megacluster. A sign that it's bullcrap
|
||||
# /gscratch/comdata/output/reddit_clustering/wang_similarity_10000.feather:clustering.py /gscratch/comdata/output/reddit_similarity/wang_similarity_10000.feather
|
||||
# ./clustering.py /gscratch/comdata/output/reddit_similarity/wang_similarity_10000.feather /gscratch/comdata/output/reddit_clustering/wang_similarity_10000.feather ---max_iter=400 --convergence_iter=15 --preference_quantile=0.9 --damping=0.85
|
||||
|
||||
# /gscratch/comdata/output/reddit_tsne/subreddit_author_tf_similarities_10000.feather:fit_tsne.py /gscratch/comdata/output/reddit_similarity/subreddit_author_tf_similarities_10000.parquet
|
||||
|
||||
# start_spark_and_run.sh 1 fit_tsne.py --similarities=/gscratch/comdata/output/reddit_similarity/subreddit_author_tf_similarities_10000.parquet --output=/gscratch/comdata/output/reddit_tsne/subreddit_author_tf_similarities_10000.feather
|
||||
|
||||
|
||||
# /gscratch/comdata/output/reddit_tsne/wang_similarity_10000.feather:fit_tsne.py /gscratch/comdata/output/reddit_similarity/wang_similarity_10000.feather
|
||||
|
||||
# python3 fit_tsne.py --similarities=/gscratch/comdata/output/reddit_similarity/wang_similarity_10000.feather --output=/gscratch/comdata/output/reddit_tsne/wang_similarity_10000.feather
|
||||
|
||||
# /gscratch/comdata/output/reddit_tsne/comment_authors_10000.feather:clustering.py /gscratch/comdata/output/reddit_similarity/comment_authors_10000.feather
|
||||
# # $srun_cdsc python3
|
||||
# start_spark_and_run.sh 1 fit_tsne.py --similarities=/gscratch/comdata/output/reddit_similarity/comment_authors_10000.feather --output=/gscratch/comdata/output/reddit_tsne/comment_authors_10000.feather
|
||||
PHONY: clean
|
||||
|
||||
Binary file not shown.
@ -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)
|
||||
@ -1,230 +0,0 @@
|
||||
from clustering_base import clustering_result, clustering_job, twoway_clustering_job
|
||||
from hdbscan_clustering import hdbscan_clustering_result
|
||||
import umap
|
||||
from grid_sweep import twoway_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/output/reddit_similarity/subreddit_comment_authors-tf_10k_LSI"
|
||||
outpath = "test_umap_hdbscan_lsi"
|
||||
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'
|
||||
n_neighbors = [5,10,15,25,35,70,100]
|
||||
learning_rate = [0.1,0.5,1,2]
|
||||
min_dist = [0.5,1,1.5,2]
|
||||
local_connectivity = [1,2,3,4,5]
|
||||
|
||||
hdbscan_params = {"min_cluster_sizes":min_cluster_sizes, "min_samples":min_samples, "cluster_selection_epsilons":cluster_selection_epsilons, "cluster_selection_methods":cluster_selection_methods}
|
||||
umap_params = {"n_neighbors":n_neighbors, "learning_rate":learning_rate, "min_dist":min_dist, "local_connectivity":local_connectivity}
|
||||
gs = umap_hdbscan_grid_sweep(inpath, "all", outpath, hdbscan_params,umap_params)
|
||||
|
||||
# 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 umap_hdbscan_grid_sweep(twoway_grid_sweep):
|
||||
def __init__(self,
|
||||
inpath,
|
||||
outpath,
|
||||
umap_params,
|
||||
hdbscan_params):
|
||||
|
||||
super().__init__(umap_hdbscan_job, inpath, outpath, self.namer, umap_params, hdbscan_params)
|
||||
|
||||
def namer(self,
|
||||
min_cluster_size,
|
||||
min_samples,
|
||||
cluster_selection_epsilon,
|
||||
cluster_selection_method,
|
||||
n_components,
|
||||
n_neighbors,
|
||||
learning_rate,
|
||||
min_dist,
|
||||
local_connectivity,
|
||||
densmap
|
||||
):
|
||||
return f"mcs-{min_cluster_size}_ms-{min_samples}_cse-{cluster_selection_epsilon}_csm-{cluster_selection_method}_nc-{n_components}_nn-{n_neighbors}_lr-{learning_rate}_md-{min_dist}_lc-{local_connectivity}_dm-{densmap}"
|
||||
|
||||
@dataclass
|
||||
class umap_hdbscan_clustering_result(hdbscan_clustering_result):
|
||||
n_components:int
|
||||
n_neighbors:int
|
||||
learning_rate:float
|
||||
min_dist:float
|
||||
local_connectivity:int
|
||||
densmap:bool
|
||||
|
||||
class umap_hdbscan_job(twoway_clustering_job):
|
||||
def __init__(self, infile, outpath, name,
|
||||
umap_args = {"n_components":2,"n_neighbors":15, "learning_rate":1, "min_dist":1, "local_connectivity":1,'densmap':False},
|
||||
hdbscan_args = {"min_cluster_size":2, "min_samples":1, "cluster_selection_epsilon":0, "cluster_selection_method":'eom'},
|
||||
*args,
|
||||
**kwargs):
|
||||
super().__init__(infile,
|
||||
outpath,
|
||||
name,
|
||||
call1=umap_hdbscan_job._umap_embedding,
|
||||
call2=umap_hdbscan_job._hdbscan_clustering,
|
||||
args1=umap_args,
|
||||
args2=hdbscan_args,
|
||||
*args,
|
||||
**kwargs
|
||||
)
|
||||
|
||||
self.n_components = umap_args['n_components']
|
||||
self.n_neighbors = umap_args['n_neighbors']
|
||||
self.learning_rate = umap_args['learning_rate']
|
||||
self.min_dist = umap_args['min_dist']
|
||||
self.local_connectivity = umap_args['local_connectivity']
|
||||
self.densmap = umap_args['densmap']
|
||||
self.min_cluster_size = hdbscan_args['min_cluster_size']
|
||||
self.min_samples = hdbscan_args['min_samples']
|
||||
self.cluster_selection_epsilon = hdbscan_args['cluster_selection_epsilon']
|
||||
self.cluster_selection_method = hdbscan_args['cluster_selection_method']
|
||||
|
||||
def after_run(self):
|
||||
coords = self.step1.embedding_
|
||||
self.cluster_data['x'] = coords[:,0]
|
||||
self.cluster_data['y'] = coords[:,1]
|
||||
super().after_run()
|
||||
|
||||
|
||||
def _umap_embedding(mat, **umap_args):
|
||||
print(f"running umap embedding. umap_args:{umap_args}")
|
||||
umapmodel = umap.UMAP(metric='precomputed', **umap_args)
|
||||
umapmodel = umapmodel.fit(mat)
|
||||
return umapmodel
|
||||
|
||||
def _hdbscan_clustering(mat, umapmodel, **hdbscan_args):
|
||||
print(f"running hdbascan clustering. hdbscan_args:{hdbscan_args}")
|
||||
|
||||
umap_coords = umapmodel.transform(mat)
|
||||
|
||||
clusterer = hdbscan.HDBSCAN(metric='euclidean',
|
||||
core_dist_n_jobs=cpu_count(),
|
||||
**hdbscan_args
|
||||
)
|
||||
|
||||
clustering = clusterer.fit(umap_coords)
|
||||
|
||||
return(clustering)
|
||||
|
||||
def get_info(self):
|
||||
result = super().get_info()
|
||||
self.result = umap_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,
|
||||
n_components = self.n_components,
|
||||
n_neighbors = self.n_neighbors,
|
||||
learning_rate = self.learning_rate,
|
||||
min_dist = self.min_dist,
|
||||
local_connectivity=self.local_connectivity,
|
||||
densmap=self.densmap
|
||||
)
|
||||
return self.result
|
||||
|
||||
def run_umap_hdbscan_grid_sweep(savefile, inpath, outpath, n_neighbors = [15], n_components=[2], learning_rate=[1], min_dist=[1], local_connectivity=[1],
|
||||
densmap=[False],
|
||||
min_cluster_sizes=[2], min_samples=[1], cluster_selection_epsilons=[0], cluster_selection_methods=['eom']):
|
||||
"""Run umap + hdbscan clustering once or more with different parameters.
|
||||
|
||||
Usage:
|
||||
umap_hdbscan_clustering.py --savefile=SAVEFILE --inpath=INPATH --outpath=OUTPATH --n_neighbors=<csv> --learning_rate=<csv> --min_dist=<csv> --local_connectivity=<csv> --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.
|
||||
n_neighbors: umap parameter takes integers greater than 1
|
||||
learning_rate: umap parameter takes positive real values
|
||||
min_dist: umap parameter takes positive real values
|
||||
local_connectivity: umap parameter takes positive integers
|
||||
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.
|
||||
"""
|
||||
|
||||
umap_args = {'n_neighbors':list(map(int, n_neighbors)),
|
||||
'learning_rate':list(map(float,learning_rate)),
|
||||
'min_dist':list(map(float,min_dist)),
|
||||
'local_connectivity':list(map(int,local_connectivity)),
|
||||
'n_components':list(map(int, n_components)),
|
||||
'densmap':list(map(bool,densmap))
|
||||
}
|
||||
|
||||
hdbscan_args = {'min_cluster_size':list(map(int,min_cluster_sizes)),
|
||||
'min_samples':list(map(int,min_samples)),
|
||||
'cluster_selection_epsilon':list(map(float,cluster_selection_epsilons)),
|
||||
'cluster_selection_method':cluster_selection_methods}
|
||||
|
||||
obj = umap_hdbscan_grid_sweep(inpath,
|
||||
outpath,
|
||||
umap_args,
|
||||
hdbscan_args)
|
||||
obj.run(cores=10)
|
||||
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_umap_hdbscan_grid_sweep)
|
||||
|
||||
# test_select_hdbscan_clustering()
|
||||
#fire.Fire(select_hdbscan_clustering)
|
||||
@ -1,113 +0,0 @@
|
||||
from umap_hdbscan_clustering import umap_hdbscan_job, umap_hdbscan_grid_sweep, umap_hdbscan_clustering_result
|
||||
from lsi_base import twoway_lsi_grid_sweep, lsi_mixin, lsi_result_mixin
|
||||
from grid_sweep import twoway_grid_sweep
|
||||
import fire
|
||||
from dataclasses import dataclass
|
||||
|
||||
@dataclass
|
||||
class umap_hdbscan_clustering_result_lsi(umap_hdbscan_clustering_result, lsi_result_mixin):
|
||||
pass
|
||||
|
||||
class umap_hdbscan_lsi_job(umap_hdbscan_job, lsi_mixin):
|
||||
def __init__(self, infile, outpath, name, umap_args, hdbscan_args, lsi_dims):
|
||||
super().__init__(
|
||||
infile,
|
||||
outpath,
|
||||
name,
|
||||
umap_args,
|
||||
hdbscan_args
|
||||
)
|
||||
super().set_lsi_dims(lsi_dims)
|
||||
|
||||
def get_info(self):
|
||||
partial_result = super().get_info()
|
||||
self.result = umap_hdbscan_clustering_result_lsi(**partial_result.__dict__,
|
||||
lsi_dimensions=self.lsi_dims)
|
||||
return self.result
|
||||
|
||||
class umap_hdbscan_lsi_grid_sweep(twoway_lsi_grid_sweep):
|
||||
def __init__(self,
|
||||
inpath,
|
||||
lsi_dims,
|
||||
outpath,
|
||||
umap_args,
|
||||
hdbscan_args
|
||||
):
|
||||
|
||||
super().__init__(umap_hdbscan_lsi_job,
|
||||
_umap_hdbscan_lsi_grid_sweep,
|
||||
inpath,
|
||||
lsi_dims,
|
||||
outpath,
|
||||
umap_args,
|
||||
hdbscan_args
|
||||
)
|
||||
|
||||
|
||||
|
||||
class _umap_hdbscan_lsi_grid_sweep(twoway_grid_sweep):
|
||||
def __init__(self,
|
||||
inpath,
|
||||
outpath,
|
||||
lsi_dim,
|
||||
umap_args,
|
||||
hdbscan_args,
|
||||
):
|
||||
|
||||
self.lsi_dim = lsi_dim
|
||||
self.jobtype = umap_hdbscan_lsi_job
|
||||
super().__init__(self.jobtype, inpath, outpath, self.namer, umap_args, hdbscan_args, lsi_dim)
|
||||
|
||||
|
||||
def namer(self, *args, **kwargs):
|
||||
s = umap_hdbscan_grid_sweep.namer(self, *args, **kwargs)
|
||||
s += f"_lsi-{self.lsi_dim}"
|
||||
return s
|
||||
|
||||
def run_umap_hdbscan_lsi_grid_sweep(savefile, inpath, outpath, n_neighbors = [15], n_components=[2], learning_rate=[1], min_dist=[1], local_connectivity=[1],
|
||||
densmap=[False],
|
||||
min_cluster_sizes=[2], min_samples=[1], cluster_selection_epsilons=[0], cluster_selection_methods=['eom'], 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.
|
||||
"""
|
||||
|
||||
|
||||
umap_args = {'n_neighbors':list(map(int, n_neighbors)),
|
||||
'learning_rate':list(map(float,learning_rate)),
|
||||
'min_dist':list(map(float,min_dist)),
|
||||
'local_connectivity':list(map(int,local_connectivity)),
|
||||
'n_components':list(map(int, n_components)),
|
||||
'densmap':list(map(bool,densmap))
|
||||
}
|
||||
|
||||
hdbscan_args = {'min_cluster_size':list(map(int,min_cluster_sizes)),
|
||||
'min_samples':list(map(int,min_samples)),
|
||||
'cluster_selection_epsilon':list(map(float,cluster_selection_epsilons)),
|
||||
'cluster_selection_method':cluster_selection_methods}
|
||||
|
||||
obj = umap_hdbscan_lsi_grid_sweep(inpath,
|
||||
lsi_dimensions,
|
||||
outpath,
|
||||
umap_args,
|
||||
hdbscan_args
|
||||
)
|
||||
|
||||
|
||||
obj.run(10)
|
||||
obj.save(savefile)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
fire.Fire(run_umap_hdbscan_lsi_grid_sweep)
|
||||
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
|
||||
@ -47,11 +47,11 @@ def parse_comment(comment, names= None):
|
||||
return tuple(row)
|
||||
|
||||
|
||||
# conf = sc._conf.setAll([('spark.executor.memory', '20g'), ('spark.app.name', 'extract_reddit_timeline'), ('spark.executor.cores', '26'), ('spark.cores.max', '26'), ('spark.driver.memory','84g'),('spark.driver.maxResultSize','0'),('spark.local.dir','/gscratch/comdata/spark_tmp')])
|
||||
# conf = sc._conf.setAll([('spark.executor.memory', '20g'), ('spark.app.name', 'extract_reddit_timeline'), ('spark.executor.cores', '26'), ('spark.cores.max', '26'), ('spark.driver.memory','84g'),('spark.driver.maxResultSize','0'),('spark.local.dir','../../data/spark_tmp')])
|
||||
|
||||
def parse_dump(partition):
|
||||
|
||||
dumpdir = f"/gscratch/comdata/raw_data/reddit_dumps/comments/{partition}"
|
||||
dumpdir = f"../../data/reddit_dumps/comments/{partition}"
|
||||
|
||||
stream = open_input_file(dumpdir)
|
||||
rows = map(parse_comment, stream)
|
||||
@ -76,11 +76,11 @@ def parse_dump(partition):
|
||||
pa.field('error', pa.string(), nullable=True),
|
||||
])
|
||||
|
||||
p = Path("/gscratch/comdata/output/temp/reddit_comments.parquet")
|
||||
p = Path("../../data/temp/reddit_comments.parquet")
|
||||
p.mkdir(exist_ok=True,parents=True)
|
||||
|
||||
N=10000
|
||||
with pq.ParquetWriter(f"/gscratch/comdata/output/temp/reddit_comments.parquet/{partition}.parquet",
|
||||
with pq.ParquetWriter(f"../../data/temp/reddit_comments.parquet/{partition}.parquet",
|
||||
schema=schema,
|
||||
compression='snappy',
|
||||
flavor='spark') as writer:
|
||||
@ -96,12 +96,12 @@ def parse_dump(partition):
|
||||
writer.close()
|
||||
|
||||
|
||||
def gen_task_list(dumpdir="/gscratch/comdata/raw_data/reddit_dumps/comments", overwrite=True):
|
||||
def gen_task_list(dumpdir="../../data/raw_data/reddit_dumps/comments", overwrite=True):
|
||||
files = list(find_dumps(dumpdir,base_pattern="RC_20*.*"))
|
||||
with open("comments_task_list.sh",'w') as of:
|
||||
for fpath in files:
|
||||
partition = os.path.split(fpath)[1]
|
||||
if (not Path(f"/gscratch/comdata/output/temp/reddit_comments.parquet/{partition}.parquet").exists()) or (overwrite is True):
|
||||
if (not Path(f"../../data/temp/reddit_comments.parquet/{partition}.parquet").exists()) or (overwrite is True):
|
||||
of.write(f'python3 comments_2_parquet_part1.py parse_dump {partition}\n')
|
||||
|
||||
|
||||
|
||||
@ -1,6 +0,0 @@
|
||||
#!/usr/bin/bash
|
||||
source ~/.bashrc
|
||||
echo $(hostname)
|
||||
start_spark_cluster.sh
|
||||
spark-submit --verbose --master spark://$(hostname):43015 submissions_2_parquet_part2.py
|
||||
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}
|
||||
@ -1,16 +1,7 @@
|
||||
all: /gscratch/comdata/output/reddit_density/comment_terms_10000.feather /gscratch/comdata/output/reddit_density/comment_authors_10000.feather /gscratch/comdata/output/reddit_density/subreddit_author_tf_similarities_10000.feather
|
||||
all: ../../data/reddit_density/subreddit_author_tf_similarities_10K_LSI/600.feather
|
||||
|
||||
/gscratch/comdata/output/reddit_density/comment_terms_10000.feather:overlap_density.py /gscratch/comdata/output/reddit_similarity/comment_terms_10000.feather /gscratch/comdata/output/reddit_similarity/comment_terms_10000.feather
|
||||
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
|
||||
|
||||
/gscratch/comdata/output/reddit_density/subreddit_author_tf_similarities_10K_LSI/850.feather: overlap_density.py /gscratch/comdata/output/reddit_similarity/subreddit_comment_authors-tf_10k_LSI/850.feather
|
||||
start_spark_and_run.sh 1 overlap_density.py authors --inpath="/gscratch/comdata/output/reddit_similarity/subreddit_comment_authors-tf_10k_LSI/850.feather" --outpath="/gscratch/comdata/output/reddit_density/subreddit_author_tf_similarities_10K_LSI/850.feather" --agg=pd.DataFrame.sum
|
||||
|
||||
/gscratch/comdata/output/reddit_density/subreddit_author_tf_similarities_10K_LSI/600.feather: overlap_density.py /gscratch/comdata/output/reddit_similarity/subreddit_comment_authors-tf_10k_LSI/600.feather
|
||||
start_spark_and_run.sh 1 overlap_density.py authors --inpath="/gscratch/comdata/output/reddit_similarity/subreddit_comment_authors-tf_10k_LSI/600.feather" --outpath="/gscratch/comdata/output/reddit_density/subreddit_author_tf_similarities_10K_LSI/600.feather" --agg=pd.DataFrame.sum
|
||||
../../data/reddit_similarity/subreddit_comment_authors-tf_10k_LSI/600.feather:
|
||||
$(MAKE) -C ../similarities
|
||||
|
||||
@ -1,4 +1,6 @@
|
||||
#!/usr/bin/bash
|
||||
source ~/.bashrc
|
||||
echo $(hostname)
|
||||
start_spark_cluster.sh
|
||||
singularity exec /gscratch/comdata/users/nathante/cdsc_base.sif spark-submit --master spark://$(hostname).hyak.local:7077 overlap_density.py authors --inpath=/gscratch/comdata/output/reddit_similarity/subreddit_comment_authors-tf_10k_LSI/600.feather --outpath=/gscratch/comdata/output/reddit_density/subreddit_author_tf_similarities_10K_LSI/600.feather --agg=pd.DataFrame.sum
|
||||
singularity exec /gscratch/comdata/users/nathante/cdsc_base.sif stop-all.sh
|
||||
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
|
||||
|
||||
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)
|
||||
${TASK_CALL}
|
||||
|
||||
18
ngrams/run_job.sbatch
Normal file
18
ngrams/run_job.sbatch
Normal file
@ -0,0 +1,18 @@
|
||||
#!/bin/bash
|
||||
#SBATCH --job-name="simulate measurement error models"
|
||||
## Allocation Definition
|
||||
#SBATCH --account=comdata
|
||||
#SBATCH --partition=compute-bigmem
|
||||
## Resources
|
||||
#SBATCH --nodes=1
|
||||
## Walltime (4 hours)
|
||||
#SBATCH --time=4:00:00
|
||||
## Memory per node
|
||||
#SBATCH --mem=4G
|
||||
#SBATCH --cpus-per-task=1
|
||||
#SBATCH --ntasks-per-node=1
|
||||
#SBATCH --chdir /gscratch/comdata/users/nathante/partitioning_reddit/dataverse/cdsc_reddit/ngrams/
|
||||
#SBATCH --output=sbatch_log/%A_%a.out
|
||||
#SBATCH --error=sbatch_log/%A_%a.err
|
||||
echo "$@"
|
||||
"$@"
|
||||
@ -3,6 +3,7 @@ import pandas as pd
|
||||
import pyarrow as pa
|
||||
import pyarrow.dataset as ds
|
||||
import pyarrow.parquet as pq
|
||||
import pyarrow.compute as pc
|
||||
from itertools import groupby, islice, chain
|
||||
import fire
|
||||
from collections import Counter
|
||||
@ -15,11 +16,12 @@ import string
|
||||
from random import random
|
||||
from redditcleaner import clean
|
||||
from pathlib import Path
|
||||
from datetime import datetime
|
||||
|
||||
# compute term frequencies for comments in each subreddit by week
|
||||
def weekly_tf(partition, outputdir = '/gscratch/comdata/output/reddit_ngrams/', input_dir="/gscratch/comdata/output/reddit_comments_by_subreddit.parquet/", mwe_pass = 'first', excluded_users=None):
|
||||
def weekly_tf(partition, outputdir = '/gscratch/comdata/output/reddit_ngrams/', inputdir="/gscratch/comdata/output/reddit_comments_by_subreddit.parquet/", mwe_pass = 'first', excluded_users=None):
|
||||
|
||||
dataset = ds.dataset(Path(input_dir)/partition, format='parquet')
|
||||
dataset = ds.dataset(Path(inputdir)/partition, format='parquet')
|
||||
outputdir = Path(outputdir)
|
||||
samppath = outputdir / "reddit_comment_ngrams_10p_sample"
|
||||
|
||||
@ -38,6 +40,7 @@ def weekly_tf(partition, outputdir = '/gscratch/comdata/output/reddit_ngrams/',
|
||||
if ngram_path.exists():
|
||||
ngram_path.unlink()
|
||||
|
||||
dataset = dataset.filter(pc.field("CreatedAt") <= pa.scalar(datetime(2020,4,13)))
|
||||
batches = dataset.to_batches(columns=['CreatedAt','subreddit','body','author'])
|
||||
|
||||
|
||||
@ -160,9 +163,9 @@ def weekly_tf(partition, outputdir = '/gscratch/comdata/output/reddit_ngrams/',
|
||||
|
||||
outchunksize = 10000
|
||||
|
||||
termtf_outputdir = (outputdir / "comment_terms")
|
||||
termtf_outputdir = (outputdir / "comment_terms.parquet")
|
||||
termtf_outputdir.mkdir(parents=True, exist_ok=True)
|
||||
authortf_outputdir = (outputdir / "comment_authors")
|
||||
authortf_outputdir = (outputdir / "comment_authors.parquet")
|
||||
authortf_outputdir.mkdir(parents=True, exist_ok=True)
|
||||
termtf_path = termtf_outputdir / partition
|
||||
authortf_path = authortf_outputdir / partition
|
||||
@ -196,12 +199,12 @@ def weekly_tf(partition, outputdir = '/gscratch/comdata/output/reddit_ngrams/',
|
||||
author_writer.close()
|
||||
|
||||
|
||||
def gen_task_list(mwe_pass='first', outputdir='/gscratch/comdata/output/reddit_ngrams/', tf_task_list='tf_task_list', excluded_users_file=None):
|
||||
files = os.listdir("/gscratch/comdata/output/reddit_comments_by_subreddit.parquet/")
|
||||
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} --outputdir {outputdir} --excluded_users {excluded_users_file} {f}\n")
|
||||
outfile.write(f"./tf_comments.py weekly_tf --mwe-pass {mwe_pass} --inputdir {inputdir} --outputdir {outputdir} --excluded_users {excluded_users_file} {f}\n")
|
||||
|
||||
if __name__ == "__main__":
|
||||
fire.Fire({"gen_task_list":gen_task_list,
|
||||
|
||||
@ -1,69 +0,0 @@
|
||||
#!/usr/bin/env python3
|
||||
from pyspark.sql import functions as f
|
||||
from pyspark.sql import Window
|
||||
from pyspark.sql import SparkSession
|
||||
import numpy as np
|
||||
import fire
|
||||
from pathlib import Path
|
||||
|
||||
|
||||
def main(ngram_dir="/gscratch/comdata/output/reddit_ngrams"):
|
||||
spark = SparkSession.builder.getOrCreate()
|
||||
ngram_dir = Path(ngram_dir)
|
||||
ngram_sample = ngram_dir / "reddit_comment_ngrams_10p_sample"
|
||||
df = spark.read.text(str(ngram_sample))
|
||||
|
||||
df = df.withColumnRenamed("value","phrase")
|
||||
|
||||
# count phrase occurrances
|
||||
phrases = df.groupby('phrase').count()
|
||||
phrases = phrases.withColumnRenamed('count','phraseCount')
|
||||
phrases = phrases.filter(phrases.phraseCount > 10)
|
||||
|
||||
# count overall
|
||||
N = phrases.select(f.sum(phrases.phraseCount).alias("phraseCount")).collect()[0].phraseCount
|
||||
|
||||
print(f'analyzing PMI on a sample of {N} phrases')
|
||||
logN = np.log(N)
|
||||
phrases = phrases.withColumn("phraseLogProb", f.log(f.col("phraseCount")) - logN)
|
||||
|
||||
# count term occurrances
|
||||
phrases = phrases.withColumn('terms',f.split(f.col('phrase'),' '))
|
||||
terms = phrases.select(['phrase','phraseCount','phraseLogProb',f.explode(phrases.terms).alias('term')])
|
||||
|
||||
win = Window.partitionBy('term')
|
||||
terms = terms.withColumn('termCount',f.sum('phraseCount').over(win))
|
||||
terms = terms.withColumnRenamed('count','termCount')
|
||||
terms = terms.withColumn('termLogProb',f.log(f.col('termCount')) - logN)
|
||||
|
||||
terms = terms.groupBy(terms.phrase, terms.phraseLogProb, terms.phraseCount).sum('termLogProb')
|
||||
terms = terms.withColumnRenamed('sum(termLogProb)','termsLogProb')
|
||||
terms = terms.withColumn("phrasePWMI", f.col('phraseLogProb') - f.col('termsLogProb'))
|
||||
|
||||
# join phrases to term counts
|
||||
|
||||
|
||||
df = terms.select(['phrase','phraseCount','phraseLogProb','phrasePWMI'])
|
||||
|
||||
df = df.sort(['phrasePWMI'],descending=True)
|
||||
df = df.sortWithinPartitions(['phrasePWMI'],descending=True)
|
||||
|
||||
pwmi_dir = ngram_dir / "reddit_comment_ngrams_pwmi.parquet/"
|
||||
df.write.parquet(str(pwmi_dir), mode='overwrite', compression='snappy')
|
||||
|
||||
df = spark.read.parquet(str(pwmi_dir))
|
||||
|
||||
df.write.csv(str(ngram_dir / "reddit_comment_ngrams_pwmi.csv/"),mode='overwrite',compression='none')
|
||||
|
||||
df = spark.read.parquet(str(pwmi_dir))
|
||||
df = df.select('phrase','phraseCount','phraseLogProb','phrasePWMI')
|
||||
|
||||
# choosing phrases occurring at least 3500 times in the 10% sample (35000 times) and then with a PWMI of at least 3 yeids about 65000 expressions.
|
||||
#
|
||||
df = df.filter(f.col('phraseCount') > 3500).filter(f.col("phrasePWMI")>3)
|
||||
df = df.toPandas()
|
||||
df.to_feather(ngram_dir / "multiword_expressions.feather")
|
||||
df.to_csv(ngram_dir / "multiword_expressions.csv")
|
||||
|
||||
if __name__ == '__main__':
|
||||
fire.Fire(main)
|
||||
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)
|
||||
${TASK_CALL}
|
||||
@ -1,138 +1,28 @@
|
||||
|
||||
#all: /gscratch/comdata/output/reddit_similarity/tfidf/comment_terms_130k.parquet /gscratch/comdata/output/reddit_similarity/tfidf/comment_authors_130k.parquet /gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_terms_130k.parquet /gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_authors_130k.parquet
|
||||
# srun_singularity=source /gscratch/comdata/users/nathante/cdsc_reddit/bin/activate && srun_singularity.sh
|
||||
# srun_singularity_huge=source /gscratch/comdata/users/nathante/cdsc_reddit/bin/activate && srun_singularity_huge.sh
|
||||
srun=srun -p compute-bigmem -A comdata --mem-per-cpu=9g --time=200:00:00 -c 40
|
||||
srun_huge=srun -p compute-hugemem -A comdata --mem-per-cpu=9g --time=200:00:00 -c 40
|
||||
similarity_data=/gscratch/scrubbed/comdata/reddit_similarity
|
||||
srun_huge=srun -p compute-hugemem -A comdata --mem=724g --time=200:00:00 -c 40
|
||||
|
||||
similarity_data=../../data/reddit_similarity
|
||||
tfidf_data=${similarity_data}/tfidf
|
||||
tfidf_weekly_data=${similarity_data}/tfidf_weekly
|
||||
similarity_weekly_data=${similarity_data}/weekly
|
||||
lsi_components=[10,50,100,200,300,400,500,600,700,850,1000,1500]
|
||||
lsi_components=[10,50,100,200,300,400,500,600,700,850]
|
||||
|
||||
lsi_similarities: ${similarity_data}/subreddit_comment_terms_10k_LSI ${similarity_data}/subreddit_comment_authors-tf_10k_LSI ${similarity_data}/subreddit_comment_authors_10k_LSI ${similarity_data}/subreddit_comment_terms_30k_LSI ${similarity_data}/subreddit_comment_authors-tf_30k_LSI ${similarity_data}/subreddit_comment_authors_30k_LSI
|
||||
lsi_similarities: ${similarity_data}/subreddit_comment_authors-tf_10k_LSI
|
||||
|
||||
all: ${similarity_data}/subreddit_comment_authors-tf_10k.feather
|
||||
|
||||
all: ${tfidf_data}/comment_terms_30k.parquet ${tfidf_data}/comment_terms_10k.parquet ${tfidf_data}/comment_authors_30k.parquet ${tfidf_data}/comment_authors_10k.parquet ${similarity_data}/subreddit_comment_authors_30k.feather ${similarity_data}/subreddit_comment_authors_10k.feather ${similarity_data}/subreddit_comment_terms_10k.feather ${similarity_data}/subreddit_comment_terms_30k.feather ${similarity_data}/subreddit_comment_authors-tf_30k.feather ${similarity_data}/subreddit_comment_authors-tf_10k.feather
|
||||
${similarity_data}/subreddit_comment_authors-tf_10k_LSI: ${tfidf_data}/comment_authors_100k.parquet similarities_helper.py ${similarity_data}/subreddits_by_num_comments_nonsfw.csv
|
||||
${srun_huge} /bin/bash -c "source ~/.bashrc; python3 lsi_similarities.py author-tf --outfile=${similarity_data}/subreddit_comment_authors-tf_10k_LSI --topN=10000 --n_components=${lsi_components} --min_df=10 --inpath=$<"
|
||||
|
||||
#all: ${tfidf_data}/comment_terms_100k.parquet ${tfidf_data}/comment_terms_30k.parquet ${tfidf_data}/comment_terms_10k.parquet ${tfidf_data}/comment_authors_100k.parquet ${tfidf_data}/comment_authors_30k.parquet ${tfidf_data}/comment_authors_10k.parquet ${similarity_data}/subreddit_comment_authors_30k.feather ${similarity_data}/subreddit_comment_authors_10k.feather ${similarity_data}/subreddit_comment_terms_10k.feather ${similarity_data}/subreddit_comment_terms_30k.feather ${similarity_data}/subreddit_comment_authors-tf_30k.feather ${similarity_data}/subreddit_comment_authors-tf_10k.feather ${similarity_data}/subreddit_comment_terms_100k.feather ${similarity_data}/subreddit_comment_authors_100k.feather ${similarity_data}/subreddit_comment_authors-tf_100k.feather ${similarity_weekly_data}/comment_terms.parquet
|
||||
${similarity_data}/subreddits_by_num_comments_nonsfw.csv: ../../data/reddit_submissions_by_subreddit.parquet ../../data/reddit_comments_by_subreddit.parquet
|
||||
../start_spark_and_run.sh 3 top_subreddits_by_comments.py
|
||||
|
||||
#${tfidf_weekly_data}/comment_terms_100k.parquet ${tfidf_weekly_data}/comment_authors_100k.parquet ${tfidf_weekly_data}/comment_terms_30k.parquet ${tfidf_weekly_data}/comment_authors_30k.parquet ${similarity_weekly_data}/comment_terms_100k.parquet ${similarity_weekly_data}/comment_authors_100k.parquet ${similarity_weekly_data}/comment_terms_30k.parquet ${similarity_weekly_data}/comment_authors_30k.parquet
|
||||
${tfidf_data}/comment_authors_100k.parquet: ../../data/reddit_ngrams/comment_authors_sorted.parquet ${similarity_data}/subreddits_by_num_comments_nonsfw.csv
|
||||
../start_spark_and_run.sh 3 tfidf.py authors --topN=100000 --inpath=$< --outpath=${tfidf_data}/comment_authors_100k.parquet
|
||||
|
||||
# /gscratch/comdata/output/reddit_similarity/subreddit_comment_authors_130k.parquet /gscratch/comdata/output/reddit_similarity/subreddit_comment_authors_130k.parquet /gscratch/comdata/output/reddit_similarity/subreddit_author_tf_similarities_130k.parquet /gscratch/comdata/output/reddit_similarity/subreddit_comment_terms_130k.parquet /gscratch/comdata/output/reddit_similarity/comment_terms_weekly_130k.parquet
|
||||
../../data/reddit_ngrams/comment_authors_sorted.parquet:
|
||||
$(MAKE) -C ../ngrams
|
||||
|
||||
# all: /gscratch/comdata/output/reddit_similarity/subreddit_comment_terms_25000.parquet /gscratch/comdata/output/reddit_similarity/subreddit_comment_authors_25000.parquet /gscratch/comdata/output/reddit_similarity/subreddit_comment_authors_10000.parquet /gscratch/comdata/output/reddit_similarity/comment_terms_10000_weekly.parquet
|
||||
../../data/reddit_submissions_by_subreddit.parquet:
|
||||
$(MAKE) -C ../datasets
|
||||
|
||||
${similarity_weekly_data}/comment_terms.parquet: weekly_cosine_similarities.py similarities_helper.py /gscratch/comdata/output/reddit_ngrams/comment_terms.parquet ${similarity_data}/subreddits_by_num_comments_nonsfw.csv ${tfidf_weekly_data}/comment_terms.parquet
|
||||
${srun} python3 weekly_cosine_similarities.py terms --topN=10000 --outfile=${similarity_weekly_data}/comment_terms.parquet
|
||||
|
||||
${similarity_data}/subreddit_comment_terms_10k.feather: ${tfidf_data}/comment_terms_100k.parquet similarities_helper.py
|
||||
${srun} python3 cosine_similarities.py term --outfile=${similarity_data}/subreddit_comment_terms_10k.feather --topN=10000
|
||||
|
||||
${similarity_data}/subreddit_comment_terms_10k_LSI: ${tfidf_data}/comment_terms_100k.parquet similarities_helper.py
|
||||
${srun_huge} python3 lsi_similarities.py term --outfile=${similarity_data}/subreddit_comment_terms_10k_LSI --topN=10000 --n_components=${lsi_components} --min_df=200
|
||||
|
||||
${similarity_data}/subreddit_comment_terms_30k_LSI: ${tfidf_data}/comment_terms_100k.parquet similarities_helper.py
|
||||
${srun_huge} python3 lsi_similarities.py term --outfile=${similarity_data}/subreddit_comment_terms_30k_LSI --topN=30000 --n_components=${lsi_components} --min_df=200 --inpath=$<
|
||||
|
||||
${similarity_data}/subreddit_comment_terms_30k.feather: ${tfidf_data}/comment_terms_30k.parquet similarities_helper.py
|
||||
${srun_huge} python3 cosine_similarities.py term --outfile=${similarity_data}/subreddit_comment_terms_30k.feather --topN=30000 --inpath=$<
|
||||
|
||||
${similarity_data}/subreddit_comment_authors_30k.feather: ${tfidf_data}/comment_authors_30k.parquet similarities_helper.py
|
||||
${srun_huge} python3 cosine_similarities.py author --outfile=${similarity_data}/subreddit_comment_authors_30k.feather --topN=30000 --inpath=$<
|
||||
|
||||
${similarity_data}/subreddit_comment_authors_10k.feather: ${tfidf_data}/comment_authors_10k.parquet similarities_helper.py
|
||||
${srun_huge} python3 cosine_similarities.py author --outfile=${similarity_data}/subreddit_comment_authors_10k.feather --topN=10000 --inpath=$<
|
||||
|
||||
${similarity_data}/subreddit_comment_authors_10k_LSI: ${tfidf_data}/comment_authors_100k.parquet similarities_helper.py
|
||||
${srun_huge} python3 lsi_similarities.py author --outfile=${similarity_data}/subreddit_comment_authors_10k_LSI --topN=10000 --n_components=${lsi_components} --min_df=10 --inpath=$<
|
||||
|
||||
${similarity_data}/subreddit_comment_authors_30k_LSI: ${tfidf_data}/comment_authors_100k.parquet similarities_helper.py
|
||||
${srun_huge} python3 lsi_similarities.py author --outfile=${similarity_data}/subreddit_comment_authors_30k_LSI --topN=30000 --n_components=${lsi_components} --min_df=10 --inpath=$<
|
||||
|
||||
${similarity_data}/subreddit_comment_authors-tf_30k.feather: ${tfidf_data}/comment_authors_100k.parquet similarities_helper.py
|
||||
${srun} python3 cosine_similarities.py author-tf --outfile=${similarity_data}/subreddit_comment_authors-tf_30k.feather --topN=30000 --inpath=$<
|
||||
|
||||
${similarity_data}/subreddit_comment_authors-tf_10k.feather: ${tfidf_data}/comment_authors_100k.parquet similarities_helper.py
|
||||
${srun} python3 cosine_similarities.py author-tf --outfile=${similarity_data}/subreddit_comment_authors-tf_10k.feather --topN=10000
|
||||
|
||||
${similarity_data}/subreddit_comment_authors-tf_10k_LSI: ${tfidf_data}/comment_authors_100k.parquet similarities_helper.py
|
||||
${srun_huge} python3 lsi_similarities.py author-tf --outfile=${similarity_data}/subreddit_comment_authors-tf_10k_LSI --topN=10000 --n_components=${lsi_components} --min_df=10 --inpath=$<
|
||||
|
||||
${similarity_data}/subreddit_comment_authors-tf_30k_LSI: ${tfidf_data}/comment_authors_100k.parquet similarities_helper.py
|
||||
${srun_huge} python3 lsi_similarities.py author-tf --outfile=${similarity_data}/subreddit_comment_authors-tf_30k_LSI --topN=30000 --n_components=${lsi_components} --min_df=10 --inpath=$<
|
||||
|
||||
${similarity_data}/subreddit_comment_terms_100k.feather: ${tfidf_data}/comment_terms_100k.parquet similarities_helper.py
|
||||
${srun} python3 cosine_similarities.py term --outfile=${similarity_data}/subreddit_comment_terms_100k.feather --topN=100000
|
||||
|
||||
${similarity_data}/subreddit_comment_authors_100k.feather: ${tfidf_data}/comment_authors_100k.parquet similarities_helper.py
|
||||
${srun} python3 cosine_similarities.py author --outfile=${similarity_data}/subreddit_comment_authors_100k.feather --topN=100000
|
||||
|
||||
${similarity_data}/subreddit_comment_authors-tf_100k.feather: ${tfidf_data}/comment_authors_100k.parquet similarities_helper.py
|
||||
${srun} python3 cosine_similarities.py author-tf --outfile=${similarity_data}/subreddit_comment_authors-tf_100k.feather --topN=100000
|
||||
|
||||
${similarity_data}/subreddits_by_num_comments_nonsfw.csv:
|
||||
start_spark_and_run.sh 3 top_subreddits_by_comments.py
|
||||
|
||||
${tfidf_data}/comment_terms_100k.parquet: /gscratch/comdata/output/reddit_ngrams/comment_terms.parquet ${similarity_data}/subreddits_by_num_comments_nonsfw.csv
|
||||
# mkdir -p ${tfidf_data}/
|
||||
start_spark_and_run.sh 3 tfidf.py terms --topN=100000 --inpath=$< --outpath=${tfidf_data}/comment_terms_100k.parquet
|
||||
|
||||
${tfidf_data}/comment_terms_30k.feather: /gscratch/comdata/output/reddit_ngrams/comment_terms.parquet ${similarity_data}/subreddits_by_num_comments_nonsfw.csv
|
||||
# mkdir -p ${tfidf_data}/
|
||||
start_spark_and_run.sh 3 tfidf.py terms --topN=30000 --inpath=$< --outpath=${tfidf_data}/comment_terms_30k.feather
|
||||
|
||||
${tfidf_data}/comment_terms_10k.feather: /gscratch/comdata/output/reddit_ngrams/comment_terms.parquet ${similarity_data}/subreddits_by_num_comments_nonsfw.csv
|
||||
# mkdir -p ${tfidf_data}/
|
||||
start_spark_and_run.sh 3 tfidf.py terms --topN=10000 --inpath=$< --outpath=${tfidf_data}/comment_terms_10k.feather
|
||||
|
||||
${tfidf_data}/comment_authors_100k.parquet: /gscratch/comdata/output/reddit_ngrams/comment_authors.parquet ${similarity_data}/subreddits_by_num_comments_nonsfw.csv
|
||||
# mkdir -p ${tfidf_data}/
|
||||
start_spark_and_run.sh 3 tfidf.py authors --topN=100000 --inpath=$< --outpath=${tfidf_data}/comment_authors_100k.parquet
|
||||
|
||||
${tfidf_data}/comment_authors_10k.parquet: /gscratch/comdata/output/reddit_ngrams/comment_authors.parquet ${similarity_data}/subreddits_by_num_comments_nonsfw.csv
|
||||
# mkdir -p ${tfidf_data}/
|
||||
start_spark_and_run.sh 3 tfidf.py authors --topN=10000 --inpath=$< --outpath=${tfidf_data}/comment_authors_10k.parquet
|
||||
|
||||
${tfidf_data}/comment_authors_30k.parquet: /gscratch/comdata/output/reddit_ngrams/comment_authors.parquet ${similarity_data}/subreddits_by_num_comments_nonsfw.csv
|
||||
# mkdir -p ${tfidf_data}/
|
||||
start_spark_and_run.sh 3 tfidf.py authors --topN=30000 --inpath=$< --outpath=${tfidf_data}/comment_authors_30k.parquet
|
||||
|
||||
${tfidf_data}/tfidf_weekly/comment_terms_100k.parquet: /gscratch/comdata/output/reddit_ngrams/comment_terms.parquet ${similarity_data}/subreddits_by_num_comments_nonsfw.csv
|
||||
start_spark_and_run.sh 3 tfidf.py terms_weekly --topN=100000 --outpath=${similarity_data}/tfidf_weekly/comment_authors_100k.parquet
|
||||
|
||||
${tfidf_data}/tfidf_weekly/comment_authors_100k.parquet: /gscratch/comdata/output/reddit_ngrams/comment_terms.parquet ${similarity_data}/subreddits_by_ppnum_comments.csv
|
||||
start_spark_and_run.sh 3 tfidf.py authors_weekly --topN=100000 --inpath=$< --outpath=${tfidf_weekly_data}/comment_authors_100k.parquet
|
||||
|
||||
${tfidf_weekly_data}/comment_terms_30k.parquet: /gscratch/comdata/output/reddit_ngrams/comment_terms.parquet ${similarity_data}/subreddits_by_num_comments_nonsfw.csv
|
||||
start_spark_and_run.sh 2 tfidf.py terms_weekly --topN=30000 --inpath=$< --outpath=${tfidf_weekly_data}/comment_authors_30k.parquet
|
||||
|
||||
${tfidf_weekly_data}/comment_authors_30k.parquet: /gscratch/comdata/output/reddit_ngrams/comment_terms.parquet ${similarity_data}/subreddits_by_num_comments_nonsfw.csv
|
||||
start_spark_and_run.sh 3 tfidf.py authors_weekly --topN=30000 --inpath=$< --outpath=${tfidf_weekly_data}/comment_authors_30k.parquet
|
||||
|
||||
${similarity_weekly_data}/comment_terms_100k.parquet: weekly_cosine_similarities.py similarities_helper.py ${tfidf_weekly_data}/comment_terms_100k.parquet
|
||||
${srun} python3 weekly_cosine_similarities.py terms --topN=100000 --outfile=${similarity_weekly_data}/comment_terms_100k.parquet
|
||||
|
||||
${similarity_weekly_data}/comment_authors_100k.parquet: weekly_cosine_similarities.py similarities_helper.py /gscratch/comdata/output/reddit_ngrams/comment_terms.parquet ${similarity_data}/subreddits_by_num_comments_nonsfw.csv ${tfidf_weekly_data}/comment_authors_100k.parquet
|
||||
${srun} python3 weekly_cosine_similarities.py authors --topN=100000 --outfile=${similarity_weekly_data}/comment_authors_100k.parquet
|
||||
|
||||
${similarity_weekly_data}/comment_terms_30k.parquet: weekly_cosine_similarities.py similarities_helper.py /gscratch/comdata/output/reddit_ngrams/comment_terms.parquet ${similarity_data}/subreddits_by_num_comments_nonsfw.csv ${tfidf_weekly_data}/comment_terms_30k.parquet
|
||||
${srun} python3 weekly_cosine_similarities.py terms --topN=30000 --outfile=${similarity_weekly_data}/comment_authors_30k.parquet
|
||||
|
||||
,${similarity_weekly_data}/comment_authors_30k.parquet: weekly_cosine_similarities.py similarities_helper.py /gscratch/comdata/output/reddit_ngrams/comment_terms.parquet ${similarity_data}/subreddits_by_num_comments_nonsfw.csv ${tfidf_weekly_data}/comment_authors_30k.parquet
|
||||
${srun} python3 weekly_cosine_similarities.py authors --topN=30000 --outfile=${similarity_weekly_data}/comment_authors_30k.parquet
|
||||
|
||||
# ${tfidf_weekly_data}/comment_authors_130k.parquet: tfidf.py similarities_helper.py /gscratch/comdata/output/reddit_ngrams/comment_authors.parquet /gscratch/comdata/output/reddit_similarity/subreddits_by_num_comments_nonsfw.csv
|
||||
# start_spark_and_run.sh 1 tfidf.py authors_weekly --topN=130000
|
||||
|
||||
# /gscratch/comdata/output/reddit_similarity/comment_authors_10000.parquet: cosine_similarities.py similarities_helper.py /gscratch/comdata/output/reddit_similarity/tfidf/comment_authors.parquet /gscratch/comdata/output/reddit_similarity/tfidf/comment_authors.parquet
|
||||
# start_spark_and_run.sh 1 cosine_similarities.py author --outfile=/gscratch/comdata/output/reddit_similarity/comment_authors_10000.feather
|
||||
|
||||
# /gscratch/comdata/output/reddit_similarity/comment_terms.parquet: cosine_similarities.py similarities_helper.py /gscratch/comdata/output/reddit_similarity/tfidf/comment_terms.parquet
|
||||
# start_spark_and_run.sh 1 cosine_similarities.py term --outfile=/gscratch/comdata/output/reddit_similarity/comment_terms_10000.feather
|
||||
|
||||
# /gscratch/comdata/output/reddit_similarity/comment_terms_10000_weekly.parquet: cosine_similarities.py ${tfidf_weekly_data}/comment_authors.parquet
|
||||
# start_spark_and_run.sh 1 weekly_cosine_similarities.py term --outfile=/gscratch/comdata/output/reddit_similarity/subreddit_comment_terms_10000_weely.parquet
|
||||
|
||||
# /gscratch/comdata/output/reddit_similarity/subreddit_author_tf_similarities_10000.parquet: cosine_similarities.py similarities_helper.py /gscratch/comdata/output/reddit_similarity/tfidf/comment_authors.parquet /gscratch/comdata/output/reddit_similarity/tfidf/comment_authors.parquet
|
||||
# start_spark_and_run.sh 1 cosine_similarities.py author-tf --outfile=/gscratch/comdata/output/reddit_similarity/subreddit_author_tf_similarities_10000.parquet
|
||||
../../data/reddit_comments_by_subreddit.parquet:
|
||||
$(MAKE) -C ../datasets
|
||||
|
||||
Binary file not shown.
@ -1,4 +1,6 @@
|
||||
#!/usr/bin/bash
|
||||
source ~/.bashrc
|
||||
echo $(hostname)
|
||||
start_spark_cluster.sh
|
||||
singularity exec /gscratch/comdata/users/nathante/containers/nathante.sif spark-submit --master spark://$(hostname):7077 tfidf.py authors --topN=100000 --inpath=/gscratch/comdata/output/reddit_ngrams/comment_authors.parquet --outpath=/gscratch/scrubbed/comdata/reddit_similarity/tfidf/comment_authors_100k.parquet
|
||||
singularity exec /gscratch/comdata/users/nathante/containers/nathante.sif stop-all.sh
|
||||
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
|
||||
|
||||
@ -43,7 +43,7 @@ def reindex_tfidf(*args, **kwargs):
|
||||
new_ids = df.loc[:,['subreddit_id','subreddit_id_new']].drop_duplicates()
|
||||
new_ids = new_ids.set_index('subreddit_id')
|
||||
subreddit_names = subreddit_names.join(new_ids,on='subreddit_id').reset_index()
|
||||
subreddit_names = subreddit_names.drop("subreddit_id",1)
|
||||
subreddit_names = subreddit_names.drop("subreddit_id",axis=1)
|
||||
subreddit_names = subreddit_names.sort_values("subreddit_id_new")
|
||||
return(df, subreddit_names)
|
||||
|
||||
@ -51,8 +51,9 @@ def pull_tfidf(*args, **kwargs):
|
||||
df, _, _ = _pull_or_reindex_tfidf(*args, **kwargs, reindex=False)
|
||||
return df
|
||||
|
||||
def _pull_or_reindex_tfidf(infile, term_colname, min_df=None, max_df=None, included_subreddits=None, topN=500, week=None, from_date=None, to_date=None, rescale_idf=True, tf_family=tf_weight.MaxTF, reindex=True):
|
||||
print(f"loading tfidf {infile}", flush=True)
|
||||
def _pull_or_reindex_tfidf(infile, term_colname, min_df=None, max_df=None, included_subreddits=None, topN=None, week=None, from_date=None, to_date=None, rescale_idf=True, tf_family=tf_weight.MaxTF, reindex=True):
|
||||
print(f"loading tfidf {infile}, week {week}, min_df {min_df}, max_df {max_df}", flush=True)
|
||||
|
||||
if week is not None:
|
||||
tfidf_ds = ds.dataset(infile, partitioning='hive')
|
||||
else:
|
||||
@ -97,20 +98,21 @@ def _pull_or_reindex_tfidf(infile, term_colname, min_df=None, max_df=None, inclu
|
||||
'relative_tf':ds.field('relative_tf').cast('float32'),
|
||||
'tf_idf':ds.field('tf_idf').cast('float32')}
|
||||
|
||||
print(projection)
|
||||
|
||||
print(projection, flush=True)
|
||||
print(ds_filter, flush=True)
|
||||
df = tfidf_ds.to_table(filter=ds_filter,columns=projection)
|
||||
|
||||
df = df.to_pandas(split_blocks=True,self_destruct=True)
|
||||
print("assigning indexes",flush=True)
|
||||
if reindex:
|
||||
df['subreddit_id_new'] = df.groupby("subreddit_id").ngroup()
|
||||
print("assigning indexes",flush=True)
|
||||
df['subreddit_id_new'] = df.groupby("subreddit_id").ngroup() + 1
|
||||
else:
|
||||
df['subreddit_id_new'] = df['subreddit_id']
|
||||
|
||||
if reindex:
|
||||
grouped = df.groupby(term_id)
|
||||
df[term_id_new] = grouped.ngroup()
|
||||
df[term_id_new] = grouped.ngroup() + 1
|
||||
else:
|
||||
df[term_id_new] = df[term_id]
|
||||
|
||||
@ -126,17 +128,6 @@ def _pull_or_reindex_tfidf(infile, term_colname, min_df=None, max_df=None, inclu
|
||||
|
||||
return (df, tfidf_ds, ds_filter)
|
||||
|
||||
with Pool(cpu_count()) as pool:
|
||||
chunks = pool.imap_unordered(pull_names,batches)
|
||||
subreddit_names = pd.concat(chunks,copy=False).drop_duplicates()
|
||||
|
||||
subreddit_names = subreddit_names.set_index("subreddit_id")
|
||||
new_ids = df.loc[:,['subreddit_id','subreddit_id_new']].drop_duplicates()
|
||||
new_ids = new_ids.set_index('subreddit_id')
|
||||
subreddit_names = subreddit_names.join(new_ids,on='subreddit_id').reset_index()
|
||||
subreddit_names = subreddit_names.drop("subreddit_id",1)
|
||||
subreddit_names = subreddit_names.sort_values("subreddit_id_new")
|
||||
return(df, subreddit_names)
|
||||
|
||||
def pull_names(batch):
|
||||
return(batch.to_pandas().drop_duplicates())
|
||||
@ -170,7 +161,7 @@ def similarities(inpath, simfunc, term_colname, outfile, min_df=None, max_df=Non
|
||||
term_id_new = term + '_id_new'
|
||||
|
||||
entries, subreddit_names = reindex_tfidf(inpath, term_colname=term_colname, min_df=min_df, max_df=max_df, included_subreddits=included_subreddits, topN=topN,from_date=from_date,to_date=to_date)
|
||||
mat = csr_matrix((entries[tfidf_colname],(entries[term_id_new], entries.subreddit_id_new)))
|
||||
mat = csr_matrix((entries[tfidf_colname],(entries[term_id_new]-1, entries.subreddit_id_new-1)))
|
||||
|
||||
print("loading matrix")
|
||||
|
||||
@ -256,22 +247,20 @@ def lsi_column_similarities(tfidfmat,n_components=300,n_iter=10,random_state=196
|
||||
|
||||
else:
|
||||
print("running LSI",flush=True)
|
||||
|
||||
svd = TruncatedSVD(n_components=svd_components,random_state=random_state,algorithm=algorithm,n_iter=n_iter)
|
||||
mod = svd.fit(tfidfmat.T)
|
||||
|
||||
lsimat = mod.transform(tfidfmat.T)
|
||||
if lsi_model_save is not None:
|
||||
Path(lsi_model_save).parent.mkdir(exist_ok=True, parents=True)
|
||||
pickle.dump(mod, open(lsi_model_save,'wb'))
|
||||
|
||||
sims_list = []
|
||||
print(n_components, flush=True)
|
||||
lsimat = mod.transform(tfidfmat.T)
|
||||
for n_dims in n_components:
|
||||
print("computing similarities", flush=True)
|
||||
sims = column_similarities(lsimat[:,np.arange(n_dims)])
|
||||
if len(n_components) > 1:
|
||||
yield (sims, n_dims)
|
||||
else:
|
||||
return sims
|
||||
|
||||
|
||||
|
||||
def column_similarities(mat):
|
||||
@ -327,11 +316,11 @@ def build_weekly_tfidf_dataset(df, include_subs, term_colname, tf_family=tf_weig
|
||||
else: # tf_fam = tf_weight.Norm05
|
||||
df = df.withColumn("tf_idf", (0.5 + 0.5 * df.relative_tf) * df.idf)
|
||||
|
||||
df = df.repartition(400,'subreddit','week')
|
||||
df = df.repartition('week')
|
||||
dfwriter = df.write.partitionBy("week")
|
||||
return dfwriter
|
||||
|
||||
def _calc_tfidf(df, term_colname, tf_family):
|
||||
def _calc_tfidf(df, term_colname, tf_family, min_df=None, max_df=None):
|
||||
term = term_colname
|
||||
term_id = term + '_id'
|
||||
|
||||
@ -349,7 +338,13 @@ def _calc_tfidf(df, term_colname, tf_family):
|
||||
idf = idf.withColumn('idf',f.log(N_docs/(1+f.col('count')))+1)
|
||||
|
||||
# collect the dictionary to make a pydict of terms to indexes
|
||||
terms = idf.select(term).distinct() # terms are distinct
|
||||
terms = idf
|
||||
if min_df is not None:
|
||||
terms = terms.filter(f.col('count')>=min_df)
|
||||
if max_df is not None:
|
||||
terms = terms.filter(f.col('count')<=max_df)
|
||||
|
||||
terms = terms.select(term).distinct() # terms are distinct
|
||||
terms = terms.withColumn(term_id,f.row_number().over(Window.orderBy(term))) # term ids are distinct
|
||||
|
||||
# make subreddit ids
|
||||
@ -359,12 +354,12 @@ def _calc_tfidf(df, term_colname, tf_family):
|
||||
df = df.join(subreddits,on='subreddit')
|
||||
|
||||
# map terms to indexes in the tfs and the idfs
|
||||
df = df.join(terms,on=term) # subreddit-term-id is unique
|
||||
df = df.join(terms,on=term,how='inner') # subreddit-term-id is unique
|
||||
|
||||
idf = idf.join(terms,on=term)
|
||||
idf = idf.join(terms,on=term,how='inner')
|
||||
|
||||
# join on subreddit/term to create tf/dfs indexed by term
|
||||
df = df.join(idf, on=[term_id, term])
|
||||
df = df.join(idf, on=[term_id, term],how='inner')
|
||||
|
||||
# agg terms by subreddit to make sparse tf/df vectors
|
||||
if tf_family == tf_weight.MaxTF:
|
||||
@ -375,19 +370,19 @@ def _calc_tfidf(df, term_colname, tf_family):
|
||||
return df
|
||||
|
||||
|
||||
def tfidf_dataset(df, include_subs, term_colname, tf_family=tf_weight.Norm05):
|
||||
def tfidf_dataset(df, include_subs, term_colname, tf_family=tf_weight.Norm05, min_df=None, max_df=None):
|
||||
term = term_colname
|
||||
term_id = term + '_id'
|
||||
# aggregate counts by week. now subreddit-term is distinct
|
||||
df = df.filter(df.subreddit.isin(include_subs))
|
||||
df = df.groupBy(['subreddit',term]).agg(f.sum('tf').alias('tf'))
|
||||
|
||||
df = _calc_tfidf(df, term_colname, tf_family)
|
||||
df = _calc_tfidf(df, term_colname, tf_family, min_df, max_df)
|
||||
df = df.repartition('subreddit')
|
||||
dfwriter = df.write
|
||||
return dfwriter
|
||||
|
||||
def select_topN_subreddits(topN, path="/gscratch/comdata/output/reddit_similarity/subreddits_by_num_comments_nonsfw.csv"):
|
||||
def select_topN_subreddits(topN, path="../../data/reddit_similarity/subreddits_by_num_comments_nonsfw.csv"):
|
||||
rankdf = pd.read_csv(path)
|
||||
included_subreddits = set(rankdf.loc[rankdf.comments_rank <= topN,'subreddit'].values)
|
||||
return included_subreddits
|
||||
|
||||
@ -1,16 +1,20 @@
|
||||
from pyspark.sql import functions as f
|
||||
from pyspark.sql import SparkSession
|
||||
from pyspark.sql import Window
|
||||
from datetime import datetime
|
||||
from pathlib import Path
|
||||
|
||||
spark = SparkSession.builder.getOrCreate()
|
||||
conf = spark.sparkContext.getConf()
|
||||
|
||||
submissions = spark.read.parquet("/gscratch/comdata/output/reddit_submissions_by_subreddit.parquet")
|
||||
submissions = spark.read.parquet("../../data/reddit_submissions_by_subreddit.parquet")
|
||||
|
||||
submissions = submissions.filter(f.col("CreatedAt") <= datetime(2020,4,13))
|
||||
|
||||
prop_nsfw = submissions.select(['subreddit','over_18']).groupby('subreddit').agg(f.mean(f.col('over_18').astype('double')).alias('prop_nsfw'))
|
||||
|
||||
df = spark.read.parquet("/gscratch/comdata/output/reddit_comments_by_subreddit.parquet")
|
||||
|
||||
df = spark.read.parquet("../../data/reddit_comments_by_subreddit.parquet")
|
||||
df = df.filter(f.col("CreatedAt") <= datetime(2020,4,13))
|
||||
# remove /u/ pages
|
||||
df = df.filter(~df.subreddit.like("u_%"))
|
||||
|
||||
@ -26,4 +30,6 @@ df = df.toPandas()
|
||||
|
||||
df = df.sort_values("n_comments")
|
||||
|
||||
df.to_csv('/gscratch/scrubbed/comdata/reddit_similarity/subreddits_by_num_comments_nonsfw.csv', index=False)
|
||||
outpath = Path("../../data/reddit_similarity/subreddits_by_num_comments_nonsfw.csv")
|
||||
outpath.parent.mkdir(exist_ok=True, parents=True)
|
||||
df.to_csv(str(outpath), index=False)
|
||||
|
||||
@ -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)
|
||||
@ -1,149 +0,0 @@
|
||||
#!/usr/bin/env python3
|
||||
from pyspark.sql import functions as f
|
||||
from pyspark.sql import SparkSession
|
||||
from pyspark.sql import Window
|
||||
import numpy as np
|
||||
import pyarrow
|
||||
import pyarrow.dataset as ds
|
||||
import pandas as pd
|
||||
import fire
|
||||
from itertools import islice, chain
|
||||
from pathlib import Path
|
||||
from similarities_helper import pull_tfidf, column_similarities, write_weekly_similarities, lsi_column_similarities
|
||||
from scipy.sparse import csr_matrix
|
||||
from multiprocessing import Pool, cpu_count
|
||||
from functools import partial
|
||||
import pickle
|
||||
|
||||
# tfidf_path = "/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/similarity_weekly/comment_authors_tfidf.parquet"
|
||||
# #tfidf_path = "/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data//comment_authors_compex.parquet"
|
||||
# min_df=2
|
||||
# included_subreddits="/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/included_subreddits.txt"
|
||||
# max_df = None
|
||||
# topN=100
|
||||
# term_colname='author'
|
||||
# # outfile = '/gscratch/comdata/output/reddit_similarity/weekly/comment_authors_test.parquet'
|
||||
# # included_subreddits=None
|
||||
outfile="/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/similarity_weekly/comment_authors.parquet"; infile="/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/tfidf_weekly/comment_authors_tfidf.parquet"; included_subreddits="/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/included_subreddits.txt"; lsi_model="/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/similarity/comment_authors_compex_LSI/2000_authors_LSIMOD.pkl"; n_components=1500; algorithm="randomized"; term_colname='author'; tfidf_path=infile; random_state=1968;
|
||||
|
||||
# static_tfidf = "/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/tfidf/comment_authors_compex.parquet"
|
||||
# dftest = spark.read.parquet(static_tfidf)
|
||||
|
||||
def _week_similarities(week, simfunc, tfidf_path, term_colname, included_subreddits, outdir:Path, subreddit_names, nterms, topN=None, min_df=None, max_df=None):
|
||||
term = term_colname
|
||||
term_id = term + '_id'
|
||||
term_id_new = term + '_id_new'
|
||||
print(f"loading matrix: {week}")
|
||||
|
||||
entries = pull_tfidf(infile = tfidf_path,
|
||||
term_colname=term_colname,
|
||||
included_subreddits=included_subreddits,
|
||||
topN=topN,
|
||||
week=week.isoformat(),
|
||||
rescale_idf=False)
|
||||
|
||||
tfidf_colname='tf_idf'
|
||||
# if the max subreddit id we found is less than the number of subreddit names then we have to fill in 0s
|
||||
mat = csr_matrix((entries[tfidf_colname],(entries[term_id_new]-1, entries.subreddit_id_new-1)),shape=(nterms,subreddit_names.shape[0]))
|
||||
print('computing similarities')
|
||||
print(simfunc)
|
||||
sims = simfunc(mat)
|
||||
del mat
|
||||
sims = next(sims)[0]
|
||||
sims = pd.DataFrame(sims)
|
||||
sims = sims.rename({i: sr for i, sr in enumerate(subreddit_names.subreddit.values)}, axis=1)
|
||||
sims['_subreddit'] = subreddit_names.subreddit.values
|
||||
outfile = str(Path(outdir) / str(week))
|
||||
write_weekly_similarities(outfile, sims, week, subreddit_names)
|
||||
|
||||
def pull_weeks(batch):
|
||||
return set(batch.to_pandas()['week'])
|
||||
|
||||
# This requires a prefit LSI model, since we shouldn't fit different LSI models for every week.
|
||||
def cosine_similarities_weekly_lsi(*args, n_components=100, lsi_model=None, **kwargs):
|
||||
print(args)
|
||||
print(kwargs)
|
||||
term_colname= kwargs.get('term_colname')
|
||||
# lsi_model = "/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/similarity/comment_authors_compex_LSI/1000_author_LSIMOD.pkl"
|
||||
|
||||
lsi_model = pickle.load(open(lsi_model,'rb'))
|
||||
#simfunc = partial(lsi_column_similarities,n_components=n_components,random_state=random_state,algorithm='randomized',lsi_model=lsi_model)
|
||||
simfunc = partial(lsi_column_similarities,n_components=n_components,random_state=kwargs.get('random_state'),lsi_model=lsi_model)
|
||||
|
||||
return cosine_similarities_weekly(*args, simfunc=simfunc, **kwargs)
|
||||
|
||||
#tfidf = spark.read.parquet('/gscratch/comdata/users/nathante/subreddit_tfidf_weekly.parquet')
|
||||
def cosine_similarities_weekly(tfidf_path, outfile, term_colname, included_subreddits = None, topN = None, simfunc=column_similarities, min_df=None,max_df=None):
|
||||
print(outfile)
|
||||
# do this step in parallel if we have the memory for it.
|
||||
# should be doable with pool.map
|
||||
|
||||
spark = SparkSession.builder.getOrCreate()
|
||||
df = spark.read.parquet(tfidf_path)
|
||||
|
||||
# load subreddits + topN
|
||||
|
||||
subreddit_names = df.select(['subreddit','subreddit_id']).distinct().toPandas()
|
||||
subreddit_names = subreddit_names.sort_values("subreddit_id")
|
||||
nterms = df.select(f.max(f.col(term_colname + "_id")).alias('max')).collect()[0].max
|
||||
weeks = df.select(f.col("week")).distinct().toPandas().week.values
|
||||
spark.stop()
|
||||
|
||||
print(f"computing weekly similarities")
|
||||
week_similarities_helper = partial(_week_similarities,simfunc=simfunc, tfidf_path=tfidf_path, term_colname=term_colname, outdir=outfile, min_df=min_df, max_df=max_df, included_subreddits=included_subreddits, topN=None, subreddit_names=subreddit_names,nterms=nterms)
|
||||
|
||||
for week in weeks:
|
||||
week_similarities_helper(week)
|
||||
# pool = Pool(cpu_count())
|
||||
|
||||
# list(pool.imap(week_similarities_helper, weeks))
|
||||
# pool.close()
|
||||
# with Pool(cpu_count()) as pool: # maybe it can be done with 40 cores on the huge machine?
|
||||
|
||||
|
||||
def author_cosine_similarities_weekly(outfile, infile='/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_authors_test.parquet', min_df=2, max_df=None, included_subreddits=None, topN=500):
|
||||
return cosine_similarities_weekly(infile,
|
||||
outfile,
|
||||
'author',
|
||||
max_df,
|
||||
included_subreddits,
|
||||
topN,
|
||||
min_df=2
|
||||
)
|
||||
|
||||
def term_cosine_similarities_weekly(outfile, infile='/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_terms.parquet', min_df=None, max_df=None, included_subreddits=None, topN=None):
|
||||
return cosine_similarities_weekly(infile,
|
||||
outfile,
|
||||
'term',
|
||||
min_df,
|
||||
max_df,
|
||||
included_subreddits,
|
||||
topN)
|
||||
|
||||
|
||||
def author_cosine_similarities_weekly_lsi(outfile, infile = '/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_authors_test.parquet', included_subreddits=None, n_components=100,lsi_model=None):
|
||||
return cosine_similarities_weekly_lsi(infile,
|
||||
outfile,
|
||||
'author',
|
||||
included_subreddits=included_subreddits,
|
||||
n_components=n_components,
|
||||
lsi_model=lsi_model
|
||||
)
|
||||
|
||||
|
||||
def term_cosine_similarities_weekly_lsi(outfile, infile = '/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_terms.parquet', included_subreddits=None, n_components=100,lsi_model=None):
|
||||
return cosine_similarities_weekly_lsi(infile,
|
||||
outfile,
|
||||
'term',
|
||||
included_subreddits=included_subreddits,
|
||||
n_components=n_components,
|
||||
lsi_model=lsi_model,
|
||||
)
|
||||
|
||||
if __name__ == "__main__":
|
||||
fire.Fire({'authors':author_cosine_similarities_weekly,
|
||||
'terms':term_cosine_similarities_weekly,
|
||||
'authors-lsi':author_cosine_similarities_weekly_lsi,
|
||||
'terms-lsi':term_cosine_similarities_weekly_lsi
|
||||
})
|
||||
|
||||
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
|
||||
@ -1,2 +0,0 @@
|
||||
from .choose_clusters import load_clusters, load_densities
|
||||
from .cluster_timeseries import build_cluster_timeseries
|
||||
@ -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 build_cluster_timeseries(term_clusters_path="/gscratch/comdata/output/reddit_clustering/comment_terms_10000.feather",
|
||||
author_clusters_path="/gscratch/comdata/output/reddit_clustering/comment_authors_10000.feather",
|
||||
term_densities_path="/gscratch/comdata/output/reddit_density/comment_terms_10000.feather",
|
||||
author_densities_path="/gscratch/comdata/output/reddit_density/comment_authors_10000.feather",
|
||||
output="data/subreddit_timeseries.parquet"):
|
||||
|
||||
spark = SparkSession.builder.getOrCreate()
|
||||
|
||||
df = spark.read.parquet("/gscratch/comdata/output/reddit_comments_by_subreddit.parquet")
|
||||
|
||||
df = df.withColumn('week', f.date_trunc('week', f.col("CreatedAt")))
|
||||
|
||||
# time of unique authors by series by week
|
||||
ts = df.select(['subreddit','week','author']).distinct().groupby(['subreddit','week']).count()
|
||||
|
||||
ts = ts.repartition('subreddit')
|
||||
|
||||
if term_densities_path is not None and author_densities_path is not None:
|
||||
densities = load_densities(term_densities_path, author_densities_path)
|
||||
spk_densities = spark.createDataFrame(densities)
|
||||
ts = ts.join(spk_densities, on='subreddit', how='inner')
|
||||
|
||||
clusters = load_clusters(term_clusters_path, author_clusters_path)
|
||||
spk_clusters = spark.createDataFrame(clusters)
|
||||
ts = ts.join(spk_clusters, on='subreddit', how='inner')
|
||||
ts.write.parquet(output, mode='overwrite')
|
||||
|
||||
if __name__ == "__main__":
|
||||
fire.Fire(build_cluster_timeseries)
|
||||
@ -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
@ -1,187 +0,0 @@
|
||||
import pyarrow
|
||||
import altair as alt
|
||||
alt.data_transformers.disable_max_rows()
|
||||
alt.data_transformers.enable('default')
|
||||
from sklearn.neighbors import NearestNeighbors
|
||||
import pandas as pd
|
||||
from numpy import random
|
||||
import fire
|
||||
import numpy as np
|
||||
|
||||
def base_plot(plot_data):
|
||||
|
||||
# base = base.encode(alt.Color(field='color',type='nominal',scale=alt.Scale(scheme='category10')))
|
||||
|
||||
cluster_dropdown = alt.binding_select(options=[str(c) for c in sorted(set(plot_data.cluster))])
|
||||
|
||||
# subreddit_dropdown = alt.binding_select(options=sorted(plot_data.subreddit))
|
||||
|
||||
cluster_click_select = alt.selection_single(on='click',fields=['cluster'], bind=cluster_dropdown, name=' ')
|
||||
# cluster_select = alt.selection_single(fields=['cluster'], bind=cluster_dropdown, name='cluster')
|
||||
# cluster_select_and = cluster_click_select & cluster_select
|
||||
#
|
||||
# subreddit_select = alt.selection_single(on='click',fields=['subreddit'],bind=subreddit_dropdown,name='subreddit_click')
|
||||
|
||||
base_scale = alt.Scale(scheme={"name":'category10',
|
||||
"extent":[0,100],
|
||||
"count":10})
|
||||
|
||||
color = alt.condition(cluster_click_select ,
|
||||
alt.Color(field='color',type='nominal',scale=base_scale),
|
||||
alt.value("lightgray"))
|
||||
|
||||
|
||||
base = alt.Chart(plot_data).mark_text().encode(
|
||||
alt.X('x',axis=alt.Axis(grid=False),scale=alt.Scale(domain=(-65,65))),
|
||||
alt.Y('y',axis=alt.Axis(grid=False),scale=alt.Scale(domain=(-65,65))),
|
||||
color=color,
|
||||
text='subreddit')
|
||||
|
||||
base = base.add_selection(cluster_click_select)
|
||||
|
||||
|
||||
return base
|
||||
|
||||
def zoom_plot(plot_data):
|
||||
chart = base_plot(plot_data)
|
||||
|
||||
chart = chart.interactive()
|
||||
chart = chart.properties(width=1275,height=800)
|
||||
|
||||
return chart
|
||||
|
||||
def viewport_plot(plot_data):
|
||||
selector1 = alt.selection_interval(encodings=['x','y'],init={'x':(-65,65),'y':(-65,65)})
|
||||
selectorx2 = alt.selection_interval(encodings=['x'],init={'x':(30,40)})
|
||||
selectory2 = alt.selection_interval(encodings=['y'],init={'y':(-20,0)})
|
||||
|
||||
base = base_plot(plot_data)
|
||||
|
||||
viewport = base.mark_point(fillOpacity=0.2,opacity=0.2).encode(
|
||||
alt.X('x',axis=alt.Axis(grid=False)),
|
||||
alt.Y('y',axis=alt.Axis(grid=False)),
|
||||
)
|
||||
|
||||
viewport = viewport.properties(width=600,height=400)
|
||||
|
||||
viewport1 = viewport.add_selection(selector1)
|
||||
|
||||
viewport2 = viewport.encode(
|
||||
alt.X('x',axis=alt.Axis(grid=False),scale=alt.Scale(domain=selector1)),
|
||||
alt.Y('y',axis=alt.Axis(grid=False),scale=alt.Scale(domain=selector1))
|
||||
)
|
||||
|
||||
viewport2 = viewport2.add_selection(selectorx2)
|
||||
viewport2 = viewport2.add_selection(selectory2)
|
||||
|
||||
sr = base.encode(alt.X('x',axis=alt.Axis(grid=False),scale=alt.Scale(domain=selectorx2)),
|
||||
alt.Y('y',axis=alt.Axis(grid=False),scale=alt.Scale(domain=selectory2))
|
||||
)
|
||||
|
||||
|
||||
sr = sr.properties(width=1275,height=600)
|
||||
|
||||
|
||||
chart = (viewport1 | viewport2) & sr
|
||||
|
||||
|
||||
return chart
|
||||
|
||||
def assign_cluster_colors(tsne_data, clusters, n_colors, n_neighbors = 4):
|
||||
isolate_color = 101
|
||||
|
||||
cluster_sizes = clusters.groupby('cluster').count()
|
||||
singletons = set(cluster_sizes.loc[cluster_sizes.subreddit == 1].reset_index().cluster)
|
||||
|
||||
tsne_data = tsne_data.merge(clusters,on='subreddit')
|
||||
|
||||
centroids = tsne_data.groupby('cluster').agg({'x':np.mean,'y':np.mean})
|
||||
|
||||
color_ids = np.arange(n_colors)
|
||||
|
||||
distances = np.empty(shape=(centroids.shape[0],centroids.shape[0]))
|
||||
|
||||
groups = tsne_data.groupby('cluster')
|
||||
|
||||
points = np.array(tsne_data.loc[:,['x','y']])
|
||||
centers = np.array(centroids.loc[:,['x','y']])
|
||||
|
||||
# point x centroid
|
||||
point_center_distances = np.linalg.norm((points[:,None,:] - centers[None,:,:]),axis=-1)
|
||||
|
||||
# distances is cluster x point
|
||||
for gid, group in groups:
|
||||
c_dists = point_center_distances[group.index.values,:].min(axis=0)
|
||||
distances[group.cluster.values[0],] = c_dists
|
||||
|
||||
# nbrs = NearestNeighbors(n_neighbors=n_neighbors).fit(centroids)
|
||||
# distances, indices = nbrs.kneighbors()
|
||||
|
||||
nearest = distances.argpartition(n_neighbors,0)
|
||||
indices = nearest[:n_neighbors,:].T
|
||||
# neighbor_distances = np.copy(distances)
|
||||
# neighbor_distances.sort(0)
|
||||
# neighbor_distances = neighbor_distances[0:n_neighbors,:]
|
||||
|
||||
# nbrs = NearestNeighbors(n_neighbors=n_neighbors,metric='precomputed').fit(distances)
|
||||
# distances, indices = nbrs.kneighbors()
|
||||
|
||||
color_assignments = np.repeat(-1,len(centroids))
|
||||
|
||||
for i in range(len(centroids)):
|
||||
if (centroids.iloc[i].name == -1) or (i in singletons):
|
||||
color_assignments[i] = isolate_color
|
||||
else:
|
||||
knn = indices[i]
|
||||
knn_colors = color_assignments[knn]
|
||||
available_colors = color_ids[list(set(color_ids) - set(knn_colors))]
|
||||
|
||||
if(len(available_colors) > 0):
|
||||
color_assignments[i] = available_colors[0]
|
||||
else:
|
||||
raise Exception("Can't color this many neighbors with this many colors")
|
||||
|
||||
centroids = centroids.reset_index()
|
||||
colors = centroids.loc[:,['cluster']]
|
||||
colors['color'] = color_assignments
|
||||
|
||||
tsne_data = tsne_data.merge(colors,on='cluster')
|
||||
return(tsne_data)
|
||||
|
||||
def build_visualization(tsne_data, clusters, output):
|
||||
|
||||
# tsne_data = "/gscratch/comdata/output/reddit_tsne/subreddit_author_tf_similarities_10000.feather"
|
||||
# clusters = "/gscratch/comdata/output/reddit_clustering/subreddit_author_tf_similarities_10000.feather"
|
||||
|
||||
tsne_data = pd.read_feather(tsne_data)
|
||||
tsne_data = tsne_data.rename(columns={'_subreddit':'subreddit'})
|
||||
clusters = pd.read_feather(clusters)
|
||||
|
||||
tsne_data = assign_cluster_colors(tsne_data,clusters,10,8)
|
||||
|
||||
sr_per_cluster = tsne_data.groupby('cluster').subreddit.count().reset_index()
|
||||
sr_per_cluster = sr_per_cluster.rename(columns={'subreddit':'cluster_size'})
|
||||
|
||||
tsne_data = tsne_data.merge(sr_per_cluster,on='cluster')
|
||||
|
||||
term_zoom_plot = zoom_plot(tsne_data)
|
||||
|
||||
term_zoom_plot.save(output)
|
||||
|
||||
term_viewport_plot = viewport_plot(tsne_data)
|
||||
|
||||
term_viewport_plot.save(output.replace(".html","_viewport.html"))
|
||||
|
||||
if __name__ == "__main__":
|
||||
fire.Fire(build_visualization)
|
||||
|
||||
# commenter_data = pd.read_feather("tsne_author_fit.feather")
|
||||
# clusters = pd.read_feather('author_3000_clusters.feather')
|
||||
# commenter_data = assign_cluster_colors(commenter_data,clusters,10,8)
|
||||
# commenter_zoom_plot = zoom_plot(commenter_data)
|
||||
# commenter_viewport_plot = viewport_plot(commenter_data)
|
||||
# commenter_zoom_plot.save("subreddit_commenters_tsne_3000.html")
|
||||
# commenter_viewport_plot.save("subreddit_commenters_tsne_3000_viewport.html")
|
||||
|
||||
# chart = chart.properties(width=10000,height=10000)
|
||||
# chart.save("test_tsne_whole.svg")
|
||||
Loading…
Reference in New Issue
Block a user