Use Latent semantic indexing and hdbscan
This commit is contained in:
Nate E TeBlunthuis
@@ -1,25 +1,130 @@
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all: /gscratch/comdata/output/reddit_similarity/subreddit_comment_authors_10000.parquet /gscratch/comdata/output/reddit_similarity/subreddit_comment_authors_10000.parquet /gscratch/comdata/output/reddit_similarity/subreddit_author_tf_similarities_10000.parquet /gscratch/comdata/output/reddit_similarity/subreddit_comment_authors_10000.parquet /gscratch/comdata/output/reddit_similarity/comment_terms.parquet
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#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
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srun_singularity=source /gscratch/comdata/users/nathante/cdsc_reddit/bin/activate && srun_singularity.sh
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srun_singularity_huge=source /gscratch/comdata/users/nathante/cdsc_reddit/bin/activate && srun_singularity_huge.sh
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base_data=/gscratch/comdata/output/
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similarity_data=${base_data}/reddit_similarity
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tfidf_data=${similarity_data}/tfidf
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tfidf_weekly_data=${similarity_data}/tfidf_weekly
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similarity_weekly_data=${similarity_data}/weekly
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lsi_components=[10,50,100,200,300,400,500,600,700,850,1000,1500]
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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
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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 ${similarity_weekly_data}/comment_terms.parquet
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#${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 ${similarity_weekly_data}/comment_authors_30k.parquet
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# /gscratch/comdata/output/reddit_similarity/subreddit_comment_authors_130k.parquet /gscratch/comdata/output/reddit_similarity/subreddit_comment_authors_130k.parquet /gscratch/comdata/output/reddit_similarity/subreddit_author_tf_similarities_130k.parquet /gscratch/comdata/output/reddit_similarity/subreddit_comment_terms_130k.parquet /gscratch/comdata/output/reddit_similarity/comment_terms_weekly_130k.parquet
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# all: /gscratch/comdata/output/reddit_similarity/subreddit_comment_terms_25000.parquet /gscratch/comdata/output/reddit_similarity/subreddit_comment_authors_25000.parquet /gscratch/comdata/output/reddit_similarity/subreddit_comment_authors_10000.parquet /gscratch/comdata/output/reddit_similarity/comment_terms_10000_weekly.parquet
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${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.csv ${tfidf_weekly_data}/comment_terms.parquet
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${srun_singularity} python3 weekly_cosine_similarities.py terms --topN=10000 --outfile=${similarity_weekly_data}/comment_terms.parquet
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# /gscratch/comdata/output/reddit_similarity/subreddit_comment_authors_25000.parquet: cosine_similarities.py /gscratch/comdata/output/reddit_similarity/tfidf/comment_authors.parquet
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# start_spark_and_run.sh 1 cosine_similarities.py author --outfile=/gscratch/comdata/output/reddit_similarity/subreddit_comment_authors_25000.feather
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${similarity_data}/subreddit_comment_terms_10k.feather: ${tfidf_data}/comment_terms_100k.parquet similarities_helper.py
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${srun_singularity} python3 cosine_similarities.py term --outfile=${similarity_data}/subreddit_comment_terms_10k.feather --topN=10000
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/gscratch/comdata/output/reddit_similarity/tfidf/comment_terms.parquet: tfidf.py similarities_helper.py /gscratch/comdata/output/reddit_ngrams/comment_terms.parquet /gscratch/comdata/output/reddit_similarity/subreddits_by_num_comments.csv
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start_spark_and_run.sh 1 tfidf.py terms --topN=10000
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${similarity_data}/subreddit_comment_terms_10k_LSI: ${tfidf_data}/comment_terms_100k.parquet similarities_helper.py
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${srun_singularity} python3 lsi_similarities.py term --outfile=${similarity_data}/subreddit_comment_terms_10k_LSI --topN=10000 --n_components=${lsi_components} --min_df=200
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/gscratch/comdata/output/reddit_similarity/tfidf/comment_authors.parquet: tfidf.py similarities_helper.py /gscratch/comdata/output/reddit_ngrams/comment_authors.parquet /gscratch/comdata/output/reddit_similarity/subreddits_by_num_comments.csv
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start_spark_and_run.sh 1 tfidf.py authors --topN=10000
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${similarity_data}/subreddit_comment_terms_30k_LSI: ${tfidf_data}/comment_terms_100k.parquet similarities_helper.py
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${srun_singularity} python3 lsi_similarities.py term --outfile=${similarity_data}/subreddit_comment_terms_30k_LSI --topN=30000 --n_components=${lsi_components} --min_df=200
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/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
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start_spark_and_run.sh 1 cosine_similarities.py author --outfile=/gscratch/comdata/output/reddit_similarity/comment_authors_10000.feather
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${similarity_data}/subreddit_comment_terms_30k.feather: ${tfidf_data}/comment_terms_30k.parquet similarities_helper.py
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${srun_singularity} python3 cosine_similarities.py term --outfile=${similarity_data}/subreddit_comment_terms_30k.feather --topN=30000
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/gscratch/comdata/output/reddit_similarity/comment_terms.parquet: cosine_similarities.py similarities_helper.py /gscratch/comdata/output/reddit_similarity/tfidf/comment_terms.parquet
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start_spark_and_run.sh 1 cosine_similarities.py term --outfile=/gscratch/comdata/output/reddit_similarity/comment_terms_10000.feather
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${similarity_data}/subreddit_comment_authors_30k.feather: ${tfidf_data}/comment_authors_30k.parquet similarities_helper.py
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${srun_singularity} python3 cosine_similarities.py author --outfile=${similarity_data}/subreddit_comment_authors_30k.feather --topN=30000
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# /gscratch/comdata/output/reddit_similarity/comment_terms_10000_weekly.parquet: cosine_similarities.py /gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_authors.parquet
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${similarity_data}/subreddit_comment_authors_10k.feather: ${tfidf_data}/comment_authors_10k.parquet similarities_helper.py
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${srun_singularity} python3 cosine_similarities.py author --outfile=${similarity_data}/subreddit_comment_authors_10k.feather --topN=10000
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${similarity_data}/subreddit_comment_authors_10k_LSI: ${tfidf_data}/comment_authors_100k.parquet similarities_helper.py
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${srun_singularity} python3 lsi_similarities.py author --outfile=${similarity_data}/subreddit_comment_authors_10k_LSI --topN=10000 --n_components=${lsi_components} --min_df=2
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${similarity_data}/subreddit_comment_authors_30k_LSI: ${tfidf_data}/comment_authors_100k.parquet similarities_helper.py
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${srun_singularity} python3 lsi_similarities.py author --outfile=${similarity_data}/subreddit_comment_authors_30k_LSI --topN=30000 --n_components=${lsi_components} --min_df=2
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${similarity_data}/subreddit_comment_authors-tf_30k.feather: ${tfidf_data}/comment_authors_30k.parquet similarities_helper.py
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${srun_singularity} python3 cosine_similarities.py author-tf --outfile=${similarity_data}/subreddit_comment_authors-tf_30k.feather --topN=30000
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${similarity_data}/subreddit_comment_authors-tf_10k.feather: ${tfidf_data}/comment_authors_10k.parquet similarities_helper.py
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${srun_singularity} python3 cosine_similarities.py author-tf --outfile=${similarity_data}/subreddit_comment_authors-tf_10k.feather --topN=10000
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${similarity_data}/subreddit_comment_authors-tf_10k_LSI: ${tfidf_data}/comment_authors_100k.parquet similarities_helper.py
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${srun_singularity} python3 lsi_similarities.py author-tf --outfile=${similarity_data}/subreddit_comment_authors-tf_10k_LSI --topN=10000 --n_components=${lsi_components} --min_df=2
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${similarity_data}/subreddit_comment_authors-tf_30k_LSI: ${tfidf_data}/comment_authors_100k.parquet similarities_helper.py
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${srun_singularity} python3 lsi_similarities.py author-tf --outfile=${similarity_data}/subreddit_comment_authors-tf_30k_LSI --topN=30000 --n_components=${lsi_components} --min_df=2
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${similarity_data}/subreddit_comment_terms_100k.feather: ${tfidf_data}/comment_terms_100k.parquet similarities_helper.py
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${srun_singularity} python3 cosine_similarities.py term --outfile=${similarity_data}/subreddit_comment_terms_100k.feather --topN=100000
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${similarity_data}/subreddit_comment_authors_100k.feather: ${tfidf_data}/comment_authors_100k.parquet similarities_helper.py
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${srun_singularity} python3 cosine_similarities.py author --outfile=${similarity_data}/subreddit_comment_authors_100k.feather --topN=100000
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${similarity_data}/subreddit_comment_authors-tf_100k.feather: ${tfidf_data}/comment_authors_100k.parquet similarities_helper.py
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${srun_singularity} python3 cosine_similarities.py author-tf --outfile=${similarity_data}/subreddit_comment_authors-tf_100k.feather --topN=100000
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${tfidf_data}/comment_terms_100k.feather/: /gscratch/comdata/output/reddit_ngrams/comment_terms.parquet ${similarity_data}/subreddits_by_num_comments.csv
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mkdir -p ${tfidf_data}/
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start_spark_and_run.sh 4 tfidf.py terms --topN=100000 --outpath=${tfidf_data}/comment_terms_100k.feather
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${tfidf_data}/comment_terms_30k.feather: /gscratch/comdata/output/reddit_ngrams/comment_terms.parquet ${similarity_data}/subreddits_by_num_comments.csv
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mkdir -p ${tfidf_data}/
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start_spark_and_run.sh 4 tfidf.py terms --topN=30000 --outpath=${tfidf_data}/comment_terms_30k.feather
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${tfidf_data}/comment_terms_10k.feather: /gscratch/comdata/output/reddit_ngrams/comment_terms.parquet ${similarity_data}/subreddits_by_num_comments.csv
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mkdir -p ${tfidf_data}/
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start_spark_and_run.sh 4 tfidf.py terms --topN=10000 --outpath=${tfidf_data}/comment_terms_10k.feather
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${tfidf_data}/comment_authors_100k.feather: /gscratch/comdata/output/reddit_ngrams/comment_authors.parquet ${similarity_data}/subreddits_by_num_comments.csv
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mkdir -p ${tfidf_data}/
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start_spark_and_run.sh 4 tfidf.py authors --topN=100000 --outpath=${tfidf_data}/comment_authors_100k.feather
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${tfidf_data}/comment_authors_10k.parquet: /gscratch/comdata/output/reddit_ngrams/comment_authors.parquet ${similarity_data}/subreddits_by_num_comments.csv
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mkdir -p ${tfidf_data}/
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start_spark_and_run.sh 4 tfidf.py authors --topN=10000 --outpath=${tfidf_data}/comment_authors_10k.parquet
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${tfidf_data}/comment_authors_30k.parquet: /gscratch/comdata/output/reddit_ngrams/comment_authors.parquet ${similarity_data}/subreddits_by_num_comments.csv
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mkdir -p ${tfidf_data}/
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start_spark_and_run.sh 4 tfidf.py authors --topN=30000 --outpath=${tfidf_data}/comment_authors_30k.parquet
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${tfidf_data}/tfidf_weekly/comment_terms_100k.parquet: /gscratch/comdata/output/reddit_ngrams/comment_terms.parquet ${similarity_data}/subreddits_by_num_comments.csv
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start_spark_and_run.sh 4 tfidf.py terms_weekly --topN=100000 --outpath=${similarity_data}/tfidf_weekly/comment_authors_100k.parquet
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${tfidf_data}/tfidf_weekly/comment_authors_100k.parquet: /gscratch/comdata/output/reddit_ngrams/comment_terms.parquet ${similarity_data}/subreddits_by_ppnum_comments.csv
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start_spark_and_run.sh 4 tfidf.py authors_weekly --topN=100000 --outpath=${tfidf_weekly_data}/comment_authors_100k.parquet
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${tfidf_weekly_data}/comment_terms_30k.parquet: /gscratch/comdata/output/reddit_ngrams/comment_terms.parquet ${similarity_data}/subreddits_by_num_comments.csv
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start_spark_and_run.sh 4 tfidf.py terms_weekly --topN=30000 --outpath=${tfidf_weekly_data}/comment_authors_30k.parquet
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${tfidf_weekly_data}/comment_authors_30k.parquet: /gscratch/comdata/output/reddit_ngrams/comment_terms.parquet ${similarity_data}/subreddits_by_num_comments.csv
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start_spark_and_run.sh 4 tfidf.py authors_weekly --topN=30000 --outpath=${tfidf_weekly_data}/comment_authors_30k.parquet
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${similarity_weekly_data}/comment_terms_100k.parquet: weekly_cosine_similarities.py similarities_helper.py ${tfidf_weekly_data}/comment_terms_100k.parquet
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${srun_singularity} python3 weekly_cosine_similarities.py terms --topN=100000 --outfile=${similarity_weekly_data}/comment_authors_100k.parquet
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${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.csv ${tfidf_weekly_data}/comment_authors_100k.parquet
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${srun_singularity} python3 weekly_cosine_similarities.py authors --topN=100000 --outfile=${similarity_weekly_data}/comment_authors_100k.parquet
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${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.csv ${tfidf_weekly_data}/comment_terms_30k.parquet
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${srun_singularity} python3 weekly_cosine_similarities.py terms --topN=30000 --outfile=${similarity_weekly_data}/comment_authors_30k.parquet
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${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.csv ${tfidf_weekly_data}/comment_authors_30k.parquet
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${srun_singularity} python3 weekly_cosine_similarities.py authors --topN=30000 --outfile=${similarity_weekly_data}/comment_authors_30k.parquet
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# ${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.csv
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# start_spark_and_run.sh 1 tfidf.py authors_weekly --topN=130000
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# /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
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# start_spark_and_run.sh 1 cosine_similarities.py author --outfile=/gscratch/comdata/output/reddit_similarity/comment_authors_10000.feather
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# /gscratch/comdata/output/reddit_similarity/comment_terms.parquet: cosine_similarities.py similarities_helper.py /gscratch/comdata/output/reddit_similarity/tfidf/comment_terms.parquet
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# start_spark_and_run.sh 1 cosine_similarities.py term --outfile=/gscratch/comdata/output/reddit_similarity/comment_terms_10000.feather
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# /gscratch/comdata/output/reddit_similarity/comment_terms_10000_weekly.parquet: cosine_similarities.py ${tfidf_weekly_data}/comment_authors.parquet
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# start_spark_and_run.sh 1 weekly_cosine_similarities.py term --outfile=/gscratch/comdata/output/reddit_similarity/subreddit_comment_terms_10000_weely.parquet
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/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
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start_spark_and_run.sh 1 cosine_similarities.py author-tf --outfile=/gscratch/comdata/output/reddit_similarity/subreddit_author_tf_similarities_10000.parquet
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# /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
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# start_spark_and_run.sh 1 cosine_similarities.py author-tf --outfile=/gscratch/comdata/output/reddit_similarity/subreddit_author_tf_similarities_10000.parquet
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@@ -2,12 +2,13 @@ import pandas as pd
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import fire
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from pathlib import Path
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from similarities_helper import similarities, column_similarities
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from functools import partial
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def cosine_similarities(infile, term_colname, outfile, min_df=None, max_df=None, included_subreddits=None, topN=500, exclude_phrases=False, from_date=None, to_date=None, tfidf_colname='tf_idf'):
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return similarities(infile=infile, simfunc=column_similarities, term_colname=term_colname, outfile=outfile, min_df=min_df, max_df=max_df, included_subreddits=included_subreddits, topN=topN, exclude_phrases=exclude_phrases,from_date=from_date, to_date=to_date, tfidf_colname=tfidf_colname)
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# change so that these take in an input as an optional argument (for speed, but also for idf).
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def term_cosine_similarities(outfile, min_df=None, max_df=None, included_subreddits=None, topN=500, exclude_phrases=False, from_date=None, to_date=None):
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return cosine_similarities('/gscratch/comdata/output/reddit_similarity/tfidf/comment_terms_100k.parquet',
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@@ -1,4 +1,4 @@
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#!/usr/bin/bash
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start_spark_cluster.sh
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spark-submit --master spark://$(hostname):18899 cosine_similarities.py term --outfile=/gscratch/comdata/output/reddit_similarity/comment_terms_10000.feather
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stop-all.sh
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singularity exec /gscratch/comdata/users/nathante/cdsc_base.sif spark-submit --master spark://$(hostname).hyak.local:7077 lsi_similarities.py author --outfile=/gscratch/comdata/output//reddit_similarity/subreddit_comment_authors_10k_LSI.feather --topN=10000
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singularity exec /gscratch/comdata/users/nathante/cdsc_base.sif stop-all.sh
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61
similarities/lsi_similarities.py
Normal file
61
similarities/lsi_similarities.py
Normal file
@@ -0,0 +1,61 @@
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import pandas as pd
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import fire
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from pathlib import Path
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from similarities_helper import similarities, lsi_column_similarities
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from functools import partial
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def lsi_similarities(infile, term_colname, outfile, min_df=None, max_df=None, included_subreddits=None, topN=500, from_date=None, to_date=None, tfidf_colname='tf_idf',n_components=100,n_iter=5,random_state=1968,algorithm='arpack'):
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print(n_components,flush=True)
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simfunc = partial(lsi_column_similarities,n_components=n_components,n_iter=n_iter,random_state=random_state,algorithm=algorithm)
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|
||||
return similarities(infile=infile, simfunc=simfunc, term_colname=term_colname, outfile=outfile, min_df=min_df, max_df=max_df, included_subreddits=included_subreddits, topN=topN, from_date=from_date, to_date=to_date, tfidf_colname=tfidf_colname)
|
||||
|
||||
# change so that these take in an input as an optional argument (for speed, but also for idf).
|
||||
def term_lsi_similarities(outfile, min_df=None, max_df=None, included_subreddits=None, topN=500, from_date=None, to_date=None, n_components=300,n_iter=5,random_state=1968,algorithm='arpack'):
|
||||
|
||||
return lsi_similarities('/gscratch/comdata/output/reddit_similarity/tfidf/comment_terms_100k.parquet',
|
||||
'term',
|
||||
outfile,
|
||||
min_df,
|
||||
max_df,
|
||||
included_subreddits,
|
||||
topN,
|
||||
from_date,
|
||||
to_date,
|
||||
n_components=n_components
|
||||
)
|
||||
|
||||
def author_lsi_similarities(outfile, min_df=2, max_df=None, included_subreddits=None, topN=10000, from_date=None, to_date=None,n_components=300,n_iter=5,random_state=1968,algorithm='arpack'):
|
||||
return lsi_similarities('/gscratch/comdata/output/reddit_similarity/tfidf/comment_authors_100k.parquet',
|
||||
'author',
|
||||
outfile,
|
||||
min_df,
|
||||
max_df,
|
||||
included_subreddits,
|
||||
topN,
|
||||
from_date=from_date,
|
||||
to_date=to_date,
|
||||
n_components=n_components
|
||||
)
|
||||
|
||||
def author_tf_similarities(outfile, min_df=2, max_df=None, included_subreddits=None, topN=10000, from_date=None, to_date=None,n_components=300,n_iter=5,random_state=1968,algorithm='arpack'):
|
||||
return lsi_similarities('/gscratch/comdata/output/reddit_similarity/tfidf/comment_authors_100k.parquet',
|
||||
'author',
|
||||
outfile,
|
||||
min_df,
|
||||
max_df,
|
||||
included_subreddits,
|
||||
topN,
|
||||
from_date=from_date,
|
||||
to_date=to_date,
|
||||
tfidf_colname='relative_tf',
|
||||
n_components=n_components
|
||||
)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
fire.Fire({'term':term_lsi_similarities,
|
||||
'author':author_lsi_similarities,
|
||||
'author-tf':author_tf_similarities})
|
||||
|
||||
@@ -2,6 +2,7 @@ from pyspark.sql import SparkSession
|
||||
from pyspark.sql import Window
|
||||
from pyspark.sql import functions as f
|
||||
from enum import Enum
|
||||
from multiprocessing import cpu_count, Pool
|
||||
from pyspark.mllib.linalg.distributed import CoordinateMatrix
|
||||
from tempfile import TemporaryDirectory
|
||||
import pyarrow
|
||||
@@ -19,46 +20,16 @@ class tf_weight(Enum):
|
||||
MaxTF = 1
|
||||
Norm05 = 2
|
||||
|
||||
infile = "/gscratch/comdata/output/reddit_similarity/tfidf/comment_authors_100k.parquet"
|
||||
infile = "/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_terms.parquet"
|
||||
cache_file = "/gscratch/comdata/users/nathante/cdsc_reddit/similarities/term_tfidf_entries_bak.parquet"
|
||||
|
||||
def reindex_tfidf_time_interval(infile, term_colname, min_df=None, max_df=None, included_subreddits=None, topN=500, exclude_phrases=False, from_date=None, to_date=None):
|
||||
term = term_colname
|
||||
term_id = term + '_id'
|
||||
term_id_new = term + '_id_new'
|
||||
|
||||
spark = SparkSession.builder.getOrCreate()
|
||||
conf = spark.sparkContext.getConf()
|
||||
print(exclude_phrases)
|
||||
tfidf_weekly = spark.read.parquet(infile)
|
||||
|
||||
# create the time interval
|
||||
if from_date is not None:
|
||||
if type(from_date) is str:
|
||||
from_date = datetime.fromisoformat(from_date)
|
||||
|
||||
tfidf_weekly = tfidf_weekly.filter(tfidf_weekly.week >= from_date)
|
||||
|
||||
if to_date is not None:
|
||||
if type(to_date) is str:
|
||||
to_date = datetime.fromisoformat(to_date)
|
||||
tfidf_weekly = tfidf_weekly.filter(tfidf_weekly.week < to_date)
|
||||
|
||||
tfidf = tfidf_weekly.groupBy(["subreddit","week", term_id, term]).agg(f.sum("tf").alias("tf"))
|
||||
tfidf = _calc_tfidf(tfidf, term_colname, tf_weight.Norm05)
|
||||
tempdir = prep_tfidf_entries(tfidf, term_colname, min_df, max_df, included_subreddits)
|
||||
tfidf = spark.read_parquet(tempdir.name)
|
||||
subreddit_names = tfidf.select(['subreddit','subreddit_id_new']).distinct().toPandas()
|
||||
subreddit_names = subreddit_names.sort_values("subreddit_id_new")
|
||||
subreddit_names['subreddit_id_new'] = subreddit_names['subreddit_id_new'] - 1
|
||||
return(tempdir, subreddit_names)
|
||||
def termauthor_tfidf(term_tfidf_callable, author_tfidf_callable):
|
||||
|
||||
|
||||
# subreddits missing after this step don't have any terms that have a high enough idf
|
||||
def reindex_tfidf(infile, term_colname, min_df=None, max_df=None, included_subreddits=None, topN=500, tf_family=tf_weight.MaxTF):
|
||||
spark = SparkSession.builder.getOrCreate()
|
||||
conf = spark.sparkContext.getConf()
|
||||
print(exclude_phrases)
|
||||
|
||||
# try rewriting without merges
|
||||
def 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):
|
||||
print("loading tfidf", flush=True)
|
||||
tfidf_ds = ds.dataset(infile)
|
||||
|
||||
if included_subreddits is None:
|
||||
@@ -74,94 +45,116 @@ def reindex_tfidf(infile, term_colname, min_df=None, max_df=None, included_subre
|
||||
if max_df is not None:
|
||||
ds_filter &= ds.field("count") <= max_df
|
||||
|
||||
if week is not None:
|
||||
ds_filter &= ds.field("week") == week
|
||||
|
||||
if from_date is not None:
|
||||
ds_filter &= ds.field("week") >= from_date
|
||||
|
||||
if to_date is not None:
|
||||
ds_filter &= ds.field("week") <= to_date
|
||||
|
||||
term = term_colname
|
||||
term_id = term + '_id'
|
||||
term_id_new = term + '_id_new'
|
||||
|
||||
projection = {
|
||||
'subreddit_id':ds.field('subreddit_id'),
|
||||
term_id:ds.field(term_id),
|
||||
'relative_tf':ds.field("relative_tf").cast('float32')
|
||||
}
|
||||
|
||||
if not rescale_idf:
|
||||
projection = {
|
||||
'subreddit_id':ds.field('subreddit_id'),
|
||||
term_id:ds.field(term_id),
|
||||
'relative_tf':ds.field('relative_tf').cast('float32'),
|
||||
'tf_idf':ds.field('tf_idf').cast('float32')}
|
||||
|
||||
tfidf_ds = ds.dataset(infile)
|
||||
|
||||
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)
|
||||
df['subreddit_id_new'] = df.groupby("subreddit_id").ngroup()
|
||||
grouped = df.groupby(term_id)
|
||||
df[term_id_new] = grouped.ngroup()
|
||||
|
||||
if rescale_idf:
|
||||
print("computing idf", flush=True)
|
||||
df['new_count'] = grouped[term_id].transform('count')
|
||||
N_docs = df.subreddit_id_new.max() + 1
|
||||
df['idf'] = np.log(N_docs/(1+df.new_count),dtype='float32') + 1
|
||||
if tf_family == tf_weight.MaxTF:
|
||||
df["tf_idf"] = df.relative_tf * df.idf
|
||||
else: # tf_fam = tf_weight.Norm05
|
||||
df["tf_idf"] = (0.5 + 0.5 * df.relative_tf) * df.idf
|
||||
|
||||
print("assigning names")
|
||||
subreddit_names = tfidf_ds.to_table(filter=ds_filter,columns=['subreddit','subreddit_id'])
|
||||
batches = subreddit_names.to_batches()
|
||||
|
||||
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())
|
||||
|
||||
def similarities(infile, simfunc, term_colname, outfile, min_df=None, max_df=None, included_subreddits=None, topN=500, from_date=None, to_date=None, tfidf_colname='tf_idf'):
|
||||
'''
|
||||
tfidf_colname: set to 'relative_tf' to use normalized term frequency instead of tf-idf, which can be useful for author-based similarities.
|
||||
'''
|
||||
|
||||
def proc_sims(sims, outfile):
|
||||
if issparse(sims):
|
||||
sims = sims.todense()
|
||||
|
||||
print(f"shape of sims:{sims.shape}")
|
||||
print(f"len(subreddit_names.subreddit.values):{len(subreddit_names.subreddit.values)}",flush=True)
|
||||
sims = pd.DataFrame(sims)
|
||||
sims = sims.rename({i:sr for i, sr in enumerate(subreddit_names.subreddit.values)}, axis=1)
|
||||
sims['_subreddit'] = subreddit_names.subreddit.values
|
||||
|
||||
p = Path(outfile)
|
||||
|
||||
output_feather = Path(str(p).replace("".join(p.suffixes), ".feather"))
|
||||
output_csv = Path(str(p).replace("".join(p.suffixes), ".csv"))
|
||||
output_parquet = Path(str(p).replace("".join(p.suffixes), ".parquet"))
|
||||
outfile.parent.mkdir(exist_ok=True, parents=True)
|
||||
|
||||
sims.to_feather(outfile)
|
||||
|
||||
term = term_colname
|
||||
term_id = term + '_id'
|
||||
term_id_new = term + '_id_new'
|
||||
|
||||
df = tfidf_ds.to_table(filter=ds_filter,columns=['subreddit','subreddit_id',term_id,'relative_tf']).to_pandas()
|
||||
|
||||
sub_ids = df.subreddit_id.drop_duplicates()
|
||||
new_sub_ids = pd.DataFrame({'subreddit_id':old,'subreddit_id_new':new} for new, old in enumerate(sorted(sub_ids)))
|
||||
df = df.merge(new_sub_ids,on='subreddit_id',how='inner',validate='many_to_one')
|
||||
|
||||
new_count = df.groupby(term_id)[term_id].aggregate(new_count='count').reset_index()
|
||||
df = df.merge(new_count,on=term_id,how='inner',validate='many_to_one')
|
||||
|
||||
term_ids = df[term_id].drop_duplicates()
|
||||
new_term_ids = pd.DataFrame({term_id:old,term_id_new:new} for new, old in enumerate(sorted(term_ids)))
|
||||
|
||||
df = df.merge(new_term_ids, on=term_id, validate='many_to_one')
|
||||
N_docs = sub_ids.shape[0]
|
||||
|
||||
df['idf'] = np.log(N_docs/(1+df.new_count)) + 1
|
||||
|
||||
# agg terms by subreddit to make sparse tf/df vectors
|
||||
if tf_family == tf_weight.MaxTF:
|
||||
df["tf_idf"] = df.relative_tf * df.idf
|
||||
else: # tf_fam = tf_weight.Norm05
|
||||
df["tf_idf"] = (0.5 + 0.5 * df.relative_tf) * df.idf
|
||||
|
||||
subreddit_names = df.loc[:,['subreddit','subreddit_id_new']].drop_duplicates()
|
||||
subreddit_names = subreddit_names.sort_values("subreddit_id_new")
|
||||
return(df, subreddit_names)
|
||||
|
||||
|
||||
def similarities(infile, simfunc, term_colname, outfile, min_df=None, max_df=None, included_subreddits=None, topN=500, exclude_phrases=False, from_date=None, to_date=None, tfidf_colname='tf_idf'):
|
||||
'''
|
||||
tfidf_colname: set to 'relative_tf' to use normalized term frequency instead of tf-idf, which can be useful for author-based similarities.
|
||||
'''
|
||||
if from_date is not None or to_date is not None:
|
||||
tempdir, subreddit_names = reindex_tfidf_time_interval(infile, term_colname=term_colname, min_df=min_df, max_df=max_df, included_subreddits=included_subreddits, topN=topN, exclude_phrases=False, from_date=from_date, to_date=to_date)
|
||||
mat = read_tfidf_matrix(tempdir.name, term_colname, tfidf_colname)
|
||||
else:
|
||||
entries, subreddit_names = reindex_tfidf(infile, term_colname=term_colname, min_df=min_df, max_df=max_df, included_subreddits=included_subreddits, topN=topN, exclude_phrases=False)
|
||||
mat = csr_matrix((entries[tfidf_colname],(entries[term_id_new]-1, entries.subreddit_id_new-1)))
|
||||
entries, subreddit_names = reindex_tfidf(infile, 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)))
|
||||
|
||||
print("loading matrix")
|
||||
|
||||
# mat = read_tfidf_matrix("term_tfidf_entries7ejhvnvl.parquet", term_colname)
|
||||
|
||||
print(f'computing similarities on mat. mat.shape:{mat.shape}')
|
||||
print(f"size of mat is:{mat.data.nbytes}")
|
||||
print(f"size of mat is:{mat.data.nbytes}",flush=True)
|
||||
sims = simfunc(mat)
|
||||
del mat
|
||||
|
||||
if issparse(sims):
|
||||
sims = sims.todense()
|
||||
|
||||
print(f"shape of sims:{sims.shape}")
|
||||
print(f"len(subreddit_names.subreddit.values):{len(subreddit_names.subreddit.values)}")
|
||||
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
|
||||
|
||||
p = Path(outfile)
|
||||
|
||||
output_feather = Path(str(p).replace("".join(p.suffixes), ".feather"))
|
||||
output_csv = Path(str(p).replace("".join(p.suffixes), ".csv"))
|
||||
output_parquet = Path(str(p).replace("".join(p.suffixes), ".parquet"))
|
||||
|
||||
sims.to_feather(outfile)
|
||||
# tempdir.cleanup()
|
||||
|
||||
def read_tfidf_matrix_weekly(path, term_colname, week, tfidf_colname='tf_idf'):
|
||||
term = term_colname
|
||||
term_id = term + '_id'
|
||||
term_id_new = term + '_id_new'
|
||||
|
||||
dataset = ds.dataset(path,format='parquet')
|
||||
entries = dataset.to_table(columns=[tfidf_colname,'subreddit_id_new', term_id_new],filter=ds.field('week')==week).to_pandas()
|
||||
return(csr_matrix((entries[tfidf_colname], (entries[term_id_new]-1, entries.subreddit_id_new-1))))
|
||||
|
||||
def read_tfidf_matrix(path, term_colname, tfidf_colname='tf_idf'):
|
||||
term = term_colname
|
||||
term_id = term + '_id'
|
||||
term_id_new = term + '_id_new'
|
||||
dataset = ds.dataset(path,format='parquet')
|
||||
print(f"tfidf_colname:{tfidf_colname}")
|
||||
entries = dataset.to_table(columns=[tfidf_colname, 'subreddit_id_new',term_id_new]).to_pandas()
|
||||
return(csr_matrix((entries[tfidf_colname],(entries[term_id_new]-1, entries.subreddit_id_new-1))))
|
||||
|
||||
if hasattr(sims,'__next__'):
|
||||
for simmat, name in sims:
|
||||
proc_sims(simmat, Path(outfile)/(str(name) + ".feather"))
|
||||
else:
|
||||
proc_sims(simmat, outfile)
|
||||
|
||||
def write_weekly_similarities(path, sims, week, names):
|
||||
sims['week'] = week
|
||||
@@ -182,155 +175,62 @@ def column_overlaps(mat):
|
||||
|
||||
return intersection / den
|
||||
|
||||
def test_lsi_sims():
|
||||
term = "term"
|
||||
term_id = term + '_id'
|
||||
term_id_new = term + '_id_new'
|
||||
|
||||
t1 = time.perf_counter()
|
||||
entries, subreddit_names = reindex_tfidf("/gscratch/comdata/output/reddit_similarity/tfidf/comment_terms_100k_repartitioned.parquet",
|
||||
term_colname='term',
|
||||
min_df=2000,
|
||||
topN=10000
|
||||
)
|
||||
t2 = time.perf_counter()
|
||||
print(f"first load took:{t2 - t1}s")
|
||||
|
||||
entries, subreddit_names = reindex_tfidf("/gscratch/comdata/output/reddit_similarity/tfidf/comment_terms_100k.parquet",
|
||||
term_colname='term',
|
||||
min_df=2000,
|
||||
topN=10000
|
||||
)
|
||||
t3=time.perf_counter()
|
||||
|
||||
print(f"second load took:{t3 - t2}s")
|
||||
|
||||
mat = csr_matrix((entries['tf_idf'],(entries[term_id_new], entries.subreddit_id_new)))
|
||||
sims = list(lsi_column_similarities(mat, [10,50]))
|
||||
sims_og = sims
|
||||
sims_test = list(lsi_column_similarities(mat,[10,50],algorithm='randomized',n_iter=10))
|
||||
|
||||
# n_components is the latent dimensionality. sklearn recommends 100. More might be better
|
||||
# if algorithm is 'random' instead of 'arpack' then n_iter gives the number of iterations.
|
||||
# if n_components is a list we'll return a list of similarities with different latent dimensionalities
|
||||
# if algorithm is 'randomized' instead of 'arpack' then n_iter gives the number of iterations.
|
||||
# this function takes the svd and then the column similarities of it
|
||||
def lsi_column_similarities(tfidfmat,n_components=300,n_iter=5,random_state=1968,algorithm='arpack'):
|
||||
def lsi_column_similarities(tfidfmat,n_components=300,n_iter=10,random_state=1968,algorithm='randomized'):
|
||||
# first compute the lsi of the matrix
|
||||
# then take the column similarities
|
||||
svd = TruncatedSVD(n_components=n_components,random_state=random_state,algorithm='arpack')
|
||||
print("running LSI",flush=True)
|
||||
|
||||
if type(n_components) is int:
|
||||
n_components = [n_components]
|
||||
|
||||
n_components = sorted(n_components,reverse=True)
|
||||
|
||||
svd_components = n_components[0]
|
||||
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)
|
||||
sims = column_similarities(lsimat)
|
||||
return sims
|
||||
for n_dims in n_components:
|
||||
sims = column_similarities(lsimat[:,np.arange(n_dims)])
|
||||
if len(n_components) > 1:
|
||||
yield (sims, n_dims)
|
||||
else:
|
||||
return sims
|
||||
|
||||
|
||||
def column_similarities(mat):
|
||||
return 1 - pairwise_distances(mat,metric='cosine')
|
||||
# if issparse(mat):
|
||||
# norm = np.matrix(np.power(mat.power(2).sum(axis=0),0.5,dtype=np.float32))
|
||||
# mat = mat.multiply(1/norm)
|
||||
# else:
|
||||
# norm = np.matrix(np.power(np.power(mat,2).sum(axis=0),0.5,dtype=np.float32))
|
||||
# mat = np.multiply(mat,1/norm)
|
||||
# sims = mat.T @ mat
|
||||
# return(sims)
|
||||
|
||||
|
||||
def prep_tfidf_entries_weekly(tfidf, term_colname, min_df, max_df, included_subreddits):
|
||||
term = term_colname
|
||||
term_id = term + '_id'
|
||||
term_id_new = term + '_id_new'
|
||||
|
||||
if min_df is None:
|
||||
min_df = 0.1 * len(included_subreddits)
|
||||
tfidf = tfidf.filter(f.col('count') >= min_df)
|
||||
if max_df is not None:
|
||||
tfidf = tfidf.filter(f.col('count') <= max_df)
|
||||
|
||||
tfidf = tfidf.filter(f.col("subreddit").isin(included_subreddits))
|
||||
|
||||
# we might not have the same terms or subreddits each week, so we need to make unique ids for each week.
|
||||
sub_ids = tfidf.select(['subreddit_id','week']).distinct()
|
||||
sub_ids = sub_ids.withColumn("subreddit_id_new",f.row_number().over(Window.partitionBy('week').orderBy("subreddit_id")))
|
||||
tfidf = tfidf.join(sub_ids,['subreddit_id','week'])
|
||||
|
||||
# only use terms in at least min_df included subreddits in a given week
|
||||
new_count = tfidf.groupBy([term_id,'week']).agg(f.count(term_id).alias('new_count'))
|
||||
tfidf = tfidf.join(new_count,[term_id,'week'],how='inner')
|
||||
|
||||
# reset the term ids
|
||||
term_ids = tfidf.select([term_id,'week']).distinct()
|
||||
term_ids = term_ids.withColumn(term_id_new,f.row_number().over(Window.partitionBy('week').orderBy(term_id)))
|
||||
tfidf = tfidf.join(term_ids,[term_id,'week'])
|
||||
|
||||
tfidf = tfidf.withColumnRenamed("tf_idf","tf_idf_old")
|
||||
tfidf = tfidf.withColumn("tf_idf", (tfidf.relative_tf * tfidf.idf).cast('float'))
|
||||
|
||||
tempdir =TemporaryDirectory(suffix='.parquet',prefix='term_tfidf_entries',dir='.')
|
||||
|
||||
tfidf = tfidf.repartition('week')
|
||||
|
||||
tfidf.write.parquet(tempdir.name,mode='overwrite',compression='snappy')
|
||||
return(tempdir)
|
||||
|
||||
|
||||
def prep_tfidf_entries(tfidf, term_colname, min_df, max_df, included_subreddits):
|
||||
term = term_colname
|
||||
term_id = term + '_id'
|
||||
term_id_new = term + '_id_new'
|
||||
|
||||
if min_df is None:
|
||||
min_df = 0.1 * len(included_subreddits)
|
||||
|
||||
tfidf = tfidf.filter(f.col('count') >= min_df)
|
||||
if max_df is not None:
|
||||
tfidf = tfidf.filter(f.col('count') <= max_df)
|
||||
|
||||
tfidf = tfidf.filter(f.col("subreddit").isin(included_subreddits))
|
||||
|
||||
# reset the subreddit ids
|
||||
sub_ids = tfidf.select('subreddit_id').distinct()
|
||||
sub_ids = sub_ids.withColumn("subreddit_id_new", f.row_number().over(Window.orderBy("subreddit_id")))
|
||||
tfidf = tfidf.join(sub_ids,'subreddit_id')
|
||||
|
||||
# only use terms in at least min_df included subreddits
|
||||
new_count = tfidf.groupBy(term_id).agg(f.count(term_id).alias('new_count'))
|
||||
tfidf = tfidf.join(new_count,term_id,how='inner')
|
||||
|
||||
# reset the term ids
|
||||
term_ids = tfidf.select([term_id]).distinct()
|
||||
term_ids = term_ids.withColumn(term_id_new,f.row_number().over(Window.orderBy(term_id)))
|
||||
tfidf = tfidf.join(term_ids,term_id)
|
||||
|
||||
tfidf = tfidf.withColumnRenamed("tf_idf","tf_idf_old")
|
||||
tfidf = tfidf.withColumn("tf_idf", (tfidf.relative_tf * tfidf.idf).cast('float'))
|
||||
|
||||
tempdir =TemporaryDirectory(suffix='.parquet',prefix='term_tfidf_entries',dir='.')
|
||||
|
||||
tfidf.write.parquet(tempdir.name,mode='overwrite',compression='snappy')
|
||||
return tempdir
|
||||
|
||||
|
||||
# try computing cosine similarities using spark
|
||||
def spark_cosine_similarities(tfidf, term_colname, min_df, included_subreddits, similarity_threshold):
|
||||
term = term_colname
|
||||
term_id = term + '_id'
|
||||
term_id_new = term + '_id_new'
|
||||
|
||||
if min_df is None:
|
||||
min_df = 0.1 * len(included_subreddits)
|
||||
|
||||
tfidf = tfidf.filter(f.col("subreddit").isin(included_subreddits))
|
||||
tfidf = tfidf.cache()
|
||||
|
||||
# reset the subreddit ids
|
||||
sub_ids = tfidf.select('subreddit_id').distinct()
|
||||
sub_ids = sub_ids.withColumn("subreddit_id_new",f.row_number().over(Window.orderBy("subreddit_id")))
|
||||
tfidf = tfidf.join(sub_ids,'subreddit_id')
|
||||
|
||||
# only use terms in at least min_df included subreddits
|
||||
new_count = tfidf.groupBy(term_id).agg(f.count(term_id).alias('new_count'))
|
||||
tfidf = tfidf.join(new_count,term_id,how='inner')
|
||||
|
||||
# reset the term ids
|
||||
term_ids = tfidf.select([term_id]).distinct()
|
||||
term_ids = term_ids.withColumn(term_id_new,f.row_number().over(Window.orderBy(term_id)))
|
||||
tfidf = tfidf.join(term_ids,term_id)
|
||||
|
||||
tfidf = tfidf.withColumnRenamed("tf_idf","tf_idf_old")
|
||||
tfidf = tfidf.withColumn("tf_idf", tfidf.relative_tf * tfidf.idf)
|
||||
|
||||
# step 1 make an rdd of entires
|
||||
# sorted by (dense) spark subreddit id
|
||||
n_partitions = int(len(included_subreddits)*2 / 5)
|
||||
|
||||
entries = tfidf.select(f.col(term_id_new)-1,f.col("subreddit_id_new")-1,"tf_idf").rdd.repartition(n_partitions)
|
||||
|
||||
# put like 10 subredis in each partition
|
||||
|
||||
# step 2 make it into a distributed.RowMatrix
|
||||
coordMat = CoordinateMatrix(entries)
|
||||
|
||||
coordMat = CoordinateMatrix(coordMat.entries.repartition(n_partitions))
|
||||
|
||||
# this needs to be an IndexedRowMatrix()
|
||||
mat = coordMat.toRowMatrix()
|
||||
|
||||
#goal: build a matrix of subreddit columns and tf-idfs rows
|
||||
sim_dist = mat.columnSimilarities(threshold=similarity_threshold)
|
||||
|
||||
return (sim_dist, tfidf)
|
||||
|
||||
|
||||
def build_weekly_tfidf_dataset(df, include_subs, term_colname, tf_family=tf_weight.Norm05):
|
||||
@@ -382,7 +282,9 @@ def build_weekly_tfidf_dataset(df, include_subs, term_colname, tf_family=tf_weig
|
||||
else: # tf_fam = tf_weight.Norm05
|
||||
df = df.withColumn("tf_idf", (0.5 + 0.5 * df.relative_tf) * df.idf)
|
||||
|
||||
return df
|
||||
df = df.repartition(400,'subreddit','week')
|
||||
dfwriter = df.write.partitionBy("week").sortBy("subreddit")
|
||||
return dfwriter
|
||||
|
||||
def _calc_tfidf(df, term_colname, tf_family):
|
||||
term = term_colname
|
||||
@@ -393,7 +295,7 @@ def _calc_tfidf(df, term_colname, tf_family):
|
||||
|
||||
df = df.join(max_subreddit_terms, on='subreddit')
|
||||
|
||||
df = df.withColumn("relative_tf", df.tf / df.sr_max_tf)
|
||||
df = df.withColumn("relative_tf", (df.tf / df.sr_max_tf))
|
||||
|
||||
# group by term. term is unique
|
||||
idf = df.groupby([term]).count()
|
||||
@@ -436,8 +338,9 @@ def build_tfidf_dataset(df, include_subs, term_colname, tf_family=tf_weight.Norm
|
||||
df = df.groupBy(['subreddit',term]).agg(f.sum('tf').alias('tf'))
|
||||
|
||||
df = _calc_tfidf(df, term_colname, tf_family)
|
||||
|
||||
return df
|
||||
df = df.repartition('subreddit')
|
||||
dfwriter = df.write.sortBy("subreddit","tf")
|
||||
return dfwriter
|
||||
|
||||
def select_topN_subreddits(topN, path="/gscratch/comdata/output/reddit_similarity/subreddits_by_num_comments_nonsfw.csv"):
|
||||
rankdf = pd.read_csv(path)
|
||||
@@ -445,3 +348,18 @@ def select_topN_subreddits(topN, path="/gscratch/comdata/output/reddit_similarit
|
||||
return included_subreddits
|
||||
|
||||
|
||||
def repartition_tfidf(inpath="/gscratch/comdata/output/reddit_similarity/tfidf/comment_terms_100k.parquet",
|
||||
outpath="/gscratch/comdata/output/reddit_similarity/tfidf/comment_terms_100k_repartitioned.parquet"):
|
||||
spark = SparkSession.builder.getOrCreate()
|
||||
df = spark.read.parquet(inpath)
|
||||
df = df.repartition(400,'subreddit')
|
||||
df.write.parquet(outpath,mode='overwrite')
|
||||
|
||||
|
||||
def repartition_tfidf_weekly(inpath="/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_terms.parquet",
|
||||
outpath="/gscratch/comdata/output/reddit_similarity/tfidf/comment_terms_repartitioned.parquet"):
|
||||
spark = SparkSession.builder.getOrCreate()
|
||||
df = spark.read.parquet(inpath)
|
||||
df = df.repartition(400,'subreddit','week')
|
||||
dfwriter = df.write.partitionBy("week")
|
||||
dfwriter.parquet(outpath,mode='overwrite')
|
||||
|
||||
@@ -15,10 +15,9 @@ def _tfidf_wrapper(func, inpath, outpath, topN, term_colname, exclude, included_
|
||||
else:
|
||||
include_subs = select_topN_subreddits(topN)
|
||||
|
||||
df = func(df, include_subs, term_colname)
|
||||
|
||||
df.write.parquet(outpath,mode='overwrite',compression='snappy')
|
||||
dfwriter = func(df, include_subs, term_colname)
|
||||
|
||||
dfwriter.parquet(outpath,mode='overwrite',compression='snappy')
|
||||
spark.stop()
|
||||
|
||||
def tfidf(inpath, outpath, topN, term_colname, exclude, included_subreddits):
|
||||
|
||||
@@ -3,78 +3,78 @@ 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
|
||||
from itertools import islice, chain
|
||||
from pathlib import Path
|
||||
from similarities_helper import *
|
||||
from multiprocessing import Pool, cpu_count
|
||||
from functools import partial
|
||||
|
||||
def _week_similarities(tempdir, term_colname, week):
|
||||
print(f"loading matrix: {week}")
|
||||
mat = read_tfidf_matrix_weekly(tempdir.name, term_colname, week)
|
||||
print('computing similarities')
|
||||
sims = column_similarities(mat)
|
||||
del mat
|
||||
|
||||
names = subreddit_names.loc[subreddit_names.week == week]
|
||||
sims = pd.DataFrame(sims.todense())
|
||||
def _week_similarities(week, simfunc, tfidf_path, term_colname, min_df, max_df, included_subreddits, topN, outdir:Path):
|
||||
term = term_colname
|
||||
term_id = term + '_id'
|
||||
term_id_new = term + '_id_new'
|
||||
print(f"loading matrix: {week}")
|
||||
entries, subreddit_names = reindex_tfidf(infile = tfidf_path,
|
||||
term_colname=term_colname,
|
||||
min_df=min_df,
|
||||
max_df=max_df,
|
||||
included_subreddits=included_subreddits,
|
||||
topN=topN,
|
||||
week=week)
|
||||
mat = csr_matrix((entries[tfidf_colname],(entries[term_id_new], entries.subreddit_id_new)))
|
||||
print('computing similarities')
|
||||
sims = column_similarities(mat)
|
||||
del mat
|
||||
sims = pd.DataFrame(sims.todense())
|
||||
sims = sims.rename({i: sr for i, sr in enumerate(subreddit_names.subreddit.values)}, axis=1)
|
||||
sims['_subreddit'] = names.subreddit.values
|
||||
outfile = str(Path(outdir) / str(week))
|
||||
write_weekly_similarities(outfile, sims, week, names)
|
||||
|
||||
sims = sims.rename({i: sr for i, sr in enumerate(names.subreddit.values)}, axis=1)
|
||||
sims['_subreddit'] = names.subreddit.values
|
||||
|
||||
write_weekly_similarities(outfile, sims, week, names)
|
||||
def pull_weeks(batch):
|
||||
return set(batch.to_pandas()['week'])
|
||||
|
||||
#tfidf = spark.read.parquet('/gscratch/comdata/users/nathante/subreddit_tfidf_weekly.parquet')
|
||||
def cosine_similarities_weekly(tfidf_path, outfile, term_colname, min_df = None, included_subreddits = None, topN = 500):
|
||||
spark = SparkSession.builder.getOrCreate()
|
||||
conf = spark.sparkContext.getConf()
|
||||
def cosine_similarities_weekly(tfidf_path, outfile, term_colname, min_df = None, max_df=None, included_subreddits = None, topN = 500):
|
||||
print(outfile)
|
||||
tfidf = spark.read.parquet(tfidf_path)
|
||||
|
||||
if included_subreddits is None:
|
||||
included_subreddits = select_topN_subreddits(topN)
|
||||
else:
|
||||
included_subreddits = set(open(included_subreddits))
|
||||
tfidf_ds = ds.dataset(tfidf_path)
|
||||
tfidf_ds = tfidf_ds.to_table(columns=["week"])
|
||||
batches = tfidf_ds.to_batches()
|
||||
|
||||
print(f"computing weekly similarities for {len(included_subreddits)} subreddits")
|
||||
with Pool(cpu_count()) as pool:
|
||||
weeks = set(chain( * pool.imap_unordered(pull_weeks,batches)))
|
||||
|
||||
print("creating temporary parquet with matrix indicies")
|
||||
tempdir = prep_tfidf_entries_weekly(tfidf, term_colname, min_df, max_df=None, included_subreddits=included_subreddits)
|
||||
|
||||
tfidf = spark.read.parquet(tempdir.name)
|
||||
|
||||
# the ids can change each week.
|
||||
subreddit_names = tfidf.select(['subreddit','subreddit_id_new','week']).distinct().toPandas()
|
||||
subreddit_names = subreddit_names.sort_values("subreddit_id_new")
|
||||
subreddit_names['subreddit_id_new'] = subreddit_names['subreddit_id_new'] - 1
|
||||
spark.stop()
|
||||
|
||||
weeks = sorted(list(subreddit_names.week.drop_duplicates()))
|
||||
weeks = sorted(weeks)
|
||||
# do this step in parallel if we have the memory for it.
|
||||
# should be doable with pool.map
|
||||
|
||||
def week_similarities_helper(week):
|
||||
_week_similarities(tempdir, term_colname, week)
|
||||
print(f"computing weekly similarities")
|
||||
week_similarities_helper = partial(_week_similarities,simfunc=column_similarities, tfidf_path=tfidf_path, term_colname=term_colname, outdir=outfile, min_df=min_df,max_df=max_df,included_subreddits=included_subreddits,topN=topN)
|
||||
|
||||
with Pool(cpu_count()) as pool: # maybe it can be done with 40 cores on the huge machine?
|
||||
list(pool.map(week_similarities_helper,weeks))
|
||||
|
||||
def author_cosine_similarities_weekly(outfile, min_df=2 , included_subreddits=None, topN=500):
|
||||
def author_cosine_similarities_weekly(outfile, min_df=2, max_df=None, included_subreddits=None, topN=500):
|
||||
return cosine_similarities_weekly('/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_authors.parquet',
|
||||
outfile,
|
||||
'author',
|
||||
min_df,
|
||||
max_df,
|
||||
included_subreddits,
|
||||
topN)
|
||||
|
||||
def term_cosine_similarities_weekly(outfile, min_df=None, included_subreddits=None, topN=500):
|
||||
return cosine_similarities_weekly('/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_terms.parquet',
|
||||
outfile,
|
||||
'term',
|
||||
min_df,
|
||||
included_subreddits,
|
||||
topN)
|
||||
def term_cosine_similarities_weekly(outfile, min_df=None, max_df=None, included_subreddits=None, topN=500):
|
||||
return cosine_similarities_weekly('/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_terms.parquet',
|
||||
outfile,
|
||||
'term',
|
||||
min_df,
|
||||
max_df,
|
||||
included_subreddits,
|
||||
topN)
|
||||
|
||||
if __name__ == "__main__":
|
||||
fire.Fire({'authors':author_cosine_similarities_weekly,
|
||||
|
||||
Reference in New Issue
Block a user