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Nathan TeBlunthuis
2022-04-06 11:11:11 -07:00
parent 98c1317af5
commit 197518a222
19 changed files with 260 additions and 247 deletions
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similarities/Makefile
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@@ -1,8 +1,10 @@
#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
base_data=/gscratch/comdata/output
similarity_data=${base_data}/reddit_similarity
# srun_singularity=source /gscratch/comdata/users/nathante/cdsc_reddit/bin/activate && srun_singularity.sh
# srun_singularity_huge=source /gscratch/comdata/users/nathante/cdsc_reddit/bin/activate && srun_singularity_huge.sh
srun=srun -p compute-bigmem -A comdata --mem-per-cpu=9g --time=200:00:00 -c 40
srun_huge=srun -p compute-hugemem -A comdata --mem-per-cpu=9g --time=200:00:00 -c 40
similarity_data=/gscratch/scrubbed/comdata/reddit_similarity
tfidf_data=${similarity_data}/tfidf
tfidf_weekly_data=${similarity_data}/tfidf_weekly
similarity_weekly_data=${similarity_data}/weekly
@@ -10,7 +12,10 @@ lsi_components=[10,50,100,200,300,400,500,600,700,850,1000,1500]
lsi_similarities: ${similarity_data}/subreddit_comment_terms_10k_LSI ${similarity_data}/subreddit_comment_authors-tf_10k_LSI ${similarity_data}/subreddit_comment_authors_10k_LSI ${similarity_data}/subreddit_comment_terms_30k_LSI ${similarity_data}/subreddit_comment_authors-tf_30k_LSI ${similarity_data}/subreddit_comment_authors_30k_LSI
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
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
#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 ${similarity_weekly_data}/comment_terms.parquet
#${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
@@ -18,103 +23,106 @@ all: ${tfidf_data}/comment_terms_100k.parquet ${tfidf_data}/comment_terms_30k.pa
# all: /gscratch/comdata/output/reddit_similarity/subreddit_comment_terms_25000.parquet /gscratch/comdata/output/reddit_similarity/subreddit_comment_authors_25000.parquet /gscratch/comdata/output/reddit_similarity/subreddit_comment_authors_10000.parquet /gscratch/comdata/output/reddit_similarity/comment_terms_10000_weekly.parquet
${similarity_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
${srun_singularity} python3 weekly_cosine_similarities.py terms --topN=10000 --outfile=${similarity_weekly_data}/comment_terms.parquet
${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
${srun_singularity} python3 cosine_similarities.py term --outfile=${similarity_data}/subreddit_comment_terms_10k.feather --topN=10000
${srun} python3 cosine_similarities.py term --outfile=${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
${srun_singularity} python3 lsi_similarities.py term --outfile=${similarity_data}/subreddit_comment_terms_10k_LSI --topN=10000 --n_components=${lsi_components} --min_df=200
${srun_huge} python3 lsi_similarities.py term --outfile=${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
${srun_singularity} python3 lsi_similarities.py term --outfile=${similarity_data}/subreddit_comment_terms_30k_LSI --topN=30000 --n_components=${lsi_components} --min_df=200
${srun_huge} python3 lsi_similarities.py term --outfile=${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
${srun_singularity} python3 cosine_similarities.py term --outfile=${similarity_data}/subreddit_comment_terms_30k.feather --topN=30000
${srun_huge} python3 cosine_similarities.py term --outfile=${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
${srun_singularity} python3 cosine_similarities.py author --outfile=${similarity_data}/subreddit_comment_authors_30k.feather --topN=30000
${srun_huge} python3 cosine_similarities.py author --outfile=${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
${srun_singularity} python3 cosine_similarities.py author --outfile=${similarity_data}/subreddit_comment_authors_10k.feather --topN=10000
${srun_huge} python3 cosine_similarities.py author --outfile=${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
${srun_singularity} python3 lsi_similarities.py author --outfile=${similarity_data}/subreddit_comment_authors_10k_LSI --topN=10000 --n_components=${lsi_components} --min_df=2
${srun_huge} python3 lsi_similarities.py author --outfile=${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
${srun_singularity} python3 lsi_similarities.py author --outfile=${similarity_data}/subreddit_comment_authors_30k_LSI --topN=30000 --n_components=${lsi_components} --min_df=2
${srun_huge} python3 lsi_similarities.py author --outfile=${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_30k.parquet similarities_helper.py
${srun_singularity} python3 cosine_similarities.py author-tf --outfile=${similarity_data}/subreddit_comment_authors-tf_30k.feather --topN=30000
${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_10k.parquet similarities_helper.py
${srun_singularity} python3 cosine_similarities.py author-tf --outfile=${similarity_data}/subreddit_comment_authors-tf_10k.feather --topN=10000
${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
${srun_singularity} python3 lsi_similarities.py author-tf --outfile=${similarity_data}/subreddit_comment_authors-tf_10k_LSI --topN=10000 --n_components=${lsi_components} --min_df=2
${srun_huge} python3 lsi_similarities.py author-tf --outfile=${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
${srun_singularity} python3 lsi_similarities.py author-tf --outfile=${similarity_data}/subreddit_comment_authors-tf_30k_LSI --topN=30000 --n_components=${lsi_components} --min_df=2
${srun_huge} python3 lsi_similarities.py author-tf --outfile=${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
${srun_singularity} python3 cosine_similarities.py term --outfile=${similarity_data}/subreddit_comment_terms_100k.feather --topN=100000
${srun} python3 cosine_similarities.py term --outfile=${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
${srun_singularity} python3 cosine_similarities.py author --outfile=${similarity_data}/subreddit_comment_authors_100k.feather --topN=100000
${srun} python3 cosine_similarities.py author --outfile=${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
${srun_singularity} python3 cosine_similarities.py author-tf --outfile=${similarity_data}/subreddit_comment_authors-tf_100k.feather --topN=100000
${srun} python3 cosine_similarities.py author-tf --outfile=${similarity_data}/subreddit_comment_authors-tf_100k.feather --topN=100000
${tfidf_data}/comment_terms_100k.feather/: /gscratch/comdata/output/reddit_ngrams/comment_terms.parquet ${similarity_data}/subreddits_by_num_comments.csv
mkdir -p ${tfidf_data}/
start_spark_and_run.sh 4 tfidf.py terms --topN=100000 --outpath=${tfidf_data}/comment_terms_100k.feather
${similarity_data}/subreddits_by_num_comments_nonsfw.csv:
start_spark_and_run.sh 3 top_subreddits_by_comments.py
${tfidf_data}/comment_terms_30k.feather: /gscratch/comdata/output/reddit_ngrams/comment_terms.parquet ${similarity_data}/subreddits_by_num_comments.csv
mkdir -p ${tfidf_data}/
start_spark_and_run.sh 4 tfidf.py terms --topN=30000 --outpath=${tfidf_data}/comment_terms_30k.feather
${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_10k.feather: /gscratch/comdata/output/reddit_ngrams/comment_terms.parquet ${similarity_data}/subreddits_by_num_comments.csv
mkdir -p ${tfidf_data}/
start_spark_and_run.sh 4 tfidf.py terms --topN=10000 --outpath=${tfidf_data}/comment_terms_10k.feather
${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_authors_100k.feather: /gscratch/comdata/output/reddit_ngrams/comment_authors.parquet ${similarity_data}/subreddits_by_num_comments.csv
mkdir -p ${tfidf_data}/
start_spark_and_run.sh 4 tfidf.py authors --topN=100000 --outpath=${tfidf_data}/comment_authors_100k.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_10k.parquet: /gscratch/comdata/output/reddit_ngrams/comment_authors.parquet ${similarity_data}/subreddits_by_num_comments.csv
mkdir -p ${tfidf_data}/
start_spark_and_run.sh 4 tfidf.py authors --topN=10000 --outpath=${tfidf_data}/comment_authors_10k.parquet
${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_30k.parquet: /gscratch/comdata/output/reddit_ngrams/comment_authors.parquet ${similarity_data}/subreddits_by_num_comments.csv
mkdir -p ${tfidf_data}/
start_spark_and_run.sh 4 tfidf.py authors --topN=30000 --outpath=${tfidf_data}/comment_authors_30k.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}/tfidf_weekly/comment_terms_100k.parquet: /gscratch/comdata/output/reddit_ngrams/comment_terms.parquet ${similarity_data}/subreddits_by_num_comments.csv
start_spark_and_run.sh 4 tfidf.py terms_weekly --topN=100000 --outpath=${similarity_data}/tfidf_weekly/comment_authors_100k.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 ${similarity_data}/subreddits_by_ppnum_comments.csv
start_spark_and_run.sh 4 tfidf.py authors_weekly --topN=100000 --outpath=${tfidf_weekly_data}/comment_authors_100k.parquet
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 ${similarity_data}/subreddits_by_num_comments.csv
start_spark_and_run.sh 2 tfidf.py terms_weekly --topN=30000 --outpath=${tfidf_weekly_data}/comment_authors_30k.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 ${similarity_data}/subreddits_by_num_comments.csv
start_spark_and_run.sh 4 tfidf.py authors_weekly --topN=30000 --outpath=${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
${srun_singularity} python3 weekly_cosine_similarities.py terms --topN=100000 --outfile=${similarity_weekly_data}/comment_authors_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.csv ${tfidf_weekly_data}/comment_authors_100k.parquet
${srun_singularity} python3 weekly_cosine_similarities.py authors --topN=100000 --outfile=${similarity_weekly_data}/comment_authors_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.csv ${tfidf_weekly_data}/comment_terms_30k.parquet
${srun_singularity} python3 weekly_cosine_similarities.py terms --topN=30000 --outfile=${similarity_weekly_data}/comment_authors_30k.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.csv ${tfidf_weekly_data}/comment_authors_30k.parquet
${srun_singularity} python3 weekly_cosine_similarities.py authors --topN=30000 --outfile=${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.csv
# ${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

4
similarities/job_script.sh
View File

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

29
similarities/lsi_similarities.py
View File

@@ -5,19 +5,20 @@ from similarities_helper import *
#from similarities_helper import similarities, lsi_column_similarities
from functools import partial
inpath = "/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/tfidf/comment_terms_compex.parquet/"
term_colname='term'
outfile='/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/similarity/comment_terms_compex_LSI'
n_components=[10,50,100]
included_subreddits="/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/included_subreddits.txt"
n_iter=5
random_state=1968
algorithm='arpack'
topN = None
from_date=None
to_date=None
min_df=None
max_df=None
# inpath = "/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/tfidf/comment_terms_compex.parquet/"
# term_colname='term'
# outfile='/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/similarity/comment_terms_compex_LSI'
# n_components=[10,50,100]
# included_subreddits="/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/included_subreddits.txt"
# n_iter=5
# random_state=1968
# algorithm='arpack'
# topN = None
# from_date=None
# to_date=None
# min_df=None
# max_df=None
def lsi_similarities(inpath, term_colname, outfile, min_df=None, max_df=None, included_subreddits=None, topN=None, from_date=None, to_date=None, tfidf_colname='tf_idf',n_components=100,n_iter=5,random_state=1968,algorithm='arpack',lsi_model=None):
print(n_components,flush=True)
@@ -62,7 +63,7 @@ def author_lsi_similarities(inpath='/gscratch/comdata/output/reddit_similarity/t
n_components=n_components
)
def author_tf_similarities(inpath='/gscratch/comdata/output/reddit_similarity/tfidf/comment_authors_100k.parquet',outfile=None, min_df=2, max_df=None, included_subreddits=None, topN=None, from_date=None, to_date=None,n_components=300,n_iter=5,random_state=1968):
def author_tf_similarities(inpath='/gscratch/comdata/output/reddit_similarity/tfidf/comment_authors_100k.parquet',outfile=None, min_df=2, max_df=None, included_subreddits=None, topN=None, from_date=None, to_date=None,algorithm='arpack',n_components=300,n_iter=5,random_state=1968):
return lsi_similarities(inpath,
'author',
outfile,

1
similarities/similarities_helper.py
View File

@@ -262,6 +262,7 @@ def lsi_column_similarities(tfidfmat,n_components=300,n_iter=10,random_state=196
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 = []

2
similarities/tfidf.py
View File

@@ -4,7 +4,7 @@ from pyspark.sql import functions as f
from similarities_helper import tfidf_dataset, build_weekly_tfidf_dataset, select_topN_subreddits
def _tfidf_wrapper(func, inpath, outpath, topN, term_colname, exclude, included_subreddits):
spark = SparkSession.builder.getOrCreate()y
spark = SparkSession.builder.getOrCreate()
df = spark.read.parquet(inpath)

4
similarities/top_subreddits_by_comments.py
View File

@@ -17,7 +17,7 @@ df = df.filter(~df.subreddit.like("u_%"))
df = df.groupBy('subreddit').agg(f.count('id').alias("n_comments"))
df = df.join(prop_nsfw,on='subreddit')
#df = df.filter(df.prop_nsfw < 0.5)
df = df.filter(df.prop_nsfw < 0.5)
win = Window.orderBy(f.col('n_comments').desc())
df = df.withColumn('comments_rank', f.rank().over(win))
@@ -26,4 +26,4 @@ df = df.toPandas()
df = df.sort_values("n_comments")
df.to_csv('/gscratch/comdata/output/reddit_similarity/subreddits_by_num_comments_nsfw.csv', index=False)
df.to_csv('/gscratch/scrubbed/comdata/reddit_similarity/subreddits_by_num_comments_nonsfw.csv', index=False)