git-annex in
This commit is contained in:
parent
98c1317af5
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197518a222
@ -1,26 +0,0 @@
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#!/bin/bash
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## parallel_sql_job.sh
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#SBATCH --job-name=tf_subreddit_comments
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## Allocation Definition
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#SBATCH --account=comdata-ckpt
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#SBATCH --partition=ckpt
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## Resources
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## Nodes. This should always be 1 for parallel-sql.
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#SBATCH --nodes=1
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## Walltime (12 hours)
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#SBATCH --time=12:00:00
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## Memory per node
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#SBATCH --mem=32G
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#SBATCH --cpus-per-task=4
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#SBATCH --ntasks=1
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#SBATCH -D /gscratch/comdata/users/nathante/cdsc-reddit
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source ./bin/activate
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module load parallel_sql
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echo $(which perl)
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conda list pyarrow
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which python3
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#Put here commands to load other modules (e.g. matlab etc.)
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#Below command means that parallel_sql will get tasks from the database
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#and run them on the node (in parallel). So a 16 core node will have
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#16 tasks running at one time.
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parallel-sql --sql -a parallel --exit-on-term --jobs 4
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@ -1,10 +1,10 @@
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#!/usr/bin/env bash
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## needs to be run by hand since i don't have a nice way of waiting on a parallel-sql job to complete
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#!/usr/bin/env bash
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echo "#!/usr/bin/bash" > job_script.sh
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#echo "source $(pwd)/../bin/activate" >> job_script.sh
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echo "python3 $(pwd)/comments_2_parquet_part1.py" >> job_script.sh
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srun -p comdata -A comdata --nodes=1 --mem=120G --time=48:00:00 --pty job_script.sh
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srun -p compute-bigmem -A comdata --nodes=1 --mem-per-cpu=9g -c 40 --time=120:00:00 --pty job_script.sh
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start_spark_and_run.sh 1 $(pwd)/comments_2_parquet_part2.py
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@ -1,12 +1,15 @@
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#!/usr/bin/env python3
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import os
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import json
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from datetime import datetime
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from multiprocessing import Pool
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from itertools import islice
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from helper import find_dumps, open_fileset
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from helper import open_input_file, find_dumps
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import pandas as pd
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import pyarrow as pa
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import pyarrow.parquet as pq
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from pathlib import Path
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import fire
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def parse_comment(comment, names= None):
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if names is None:
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@ -46,19 +49,14 @@ def parse_comment(comment, names= None):
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# 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')])
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dumpdir = "/gscratch/comdata/raw_data/reddit_dumps/comments/"
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def parse_dump(partition):
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files = list(find_dumps(dumpdir, base_pattern="RC_20*"))
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dumpdir = f"/gscratch/comdata/raw_data/reddit_dumps/comments/{partition}"
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pool = Pool(28)
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stream = open_input_file(dumpdir)
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rows = map(parse_comment, stream)
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stream = open_fileset(files)
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N = int(1e4)
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rows = pool.imap_unordered(parse_comment, stream, chunksize=int(N/28))
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schema = pa.schema([
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schema = pa.schema([
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pa.field('id', pa.string(), nullable=True),
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pa.field('subreddit', pa.string(), nullable=True),
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pa.field('link_id', pa.string(), nullable=True),
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@ -76,35 +74,18 @@ schema = pa.schema([
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pa.field('is_submitter', pa.bool_(), nullable=True),
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pa.field('body', pa.string(), nullable=True),
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pa.field('error', pa.string(), nullable=True),
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])
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])
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from pathlib import Path
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p = Path("/gscratch/comdata/output/reddit_comments.parquet_temp2")
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p = Path("/gscratch/comdata/output/temp/reddit_comments.parquet")
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p.mkdir(exist_ok=True,parents=True)
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if not p.is_dir():
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if p.exists():
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p.unlink()
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p.mkdir()
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N=10000
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with pq.ParquetWriter(f"/gscratch/comdata/output/temp/reddit_comments.parquet/{partition}.parquet",
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schema=schema,
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compression='snappy',
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flavor='spark') as writer:
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else:
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list(map(Path.unlink,p.glob('*')))
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part_size = int(1e7)
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part = 1
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n_output = 0
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writer = pq.ParquetWriter(f"/gscratch/comdata/output/reddit_comments.parquet_temp2/part_{part}.parquet",schema=schema,compression='snappy',flavor='spark')
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while True:
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if n_output > part_size:
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if part > 1:
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writer.close()
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part = part + 1
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n_output = 0
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writer = pq.ParquetWriter(f"/gscratch/comdata/output/reddit_comments.parquet_temp2/part_{part}.parquet",schema=schema,compression='snappy',flavor='spark')
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n_output += N
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while True:
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chunk = islice(rows,N)
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pddf = pd.DataFrame(chunk, columns=schema.names)
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table = pa.Table.from_pandas(pddf,schema=schema)
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@ -112,4 +93,19 @@ while True:
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break
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writer.write_table(table)
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writer.close()
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def gen_task_list(dumpdir="/gscratch/comdata/raw_data/reddit_dumps/comments", overwrite=True):
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files = list(find_dumps(dumpdir,base_pattern="RC_20*.*"))
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with open("comments_task_list.sh",'w') as of:
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for fpath in files:
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partition = os.path.split(fpath)[1]
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if (not Path(f"/gscratch/comdata/output/temp/reddit_comments.parquet/{partition}.parquet").exists()) or (overwrite is True):
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of.write(f'python3 comments_2_parquet_part1.py parse_dump {partition}\n')
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if __name__ == '__main__':
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fire.Fire({'parse_dump':parse_dump,
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'gen_task_list':gen_task_list})
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@ -2,12 +2,19 @@
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# spark script to make sorted, and partitioned parquet files
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import pyspark
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from pyspark.sql import functions as f
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from pyspark.sql import SparkSession
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spark = SparkSession.builder.getOrCreate()
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df = spark.read.parquet("/gscratch/comdata/output/reddit_comments.parquet_temp2",compression='snappy')
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conf = pyspark.SparkConf().setAppName("Reddit submissions to parquet")
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conf = conf.set("spark.sql.shuffle.partitions",2000)
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conf = conf.set('spark.sql.crossJoin.enabled',"true")
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conf = conf.set('spark.debug.maxToStringFields',200)
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sc = spark.sparkContext
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df = spark.read.parquet("/gscratch/comdata/output/temp/reddit_comments.parquet",compression='snappy')
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df = df.withColumn("subreddit_2", f.lower(f.col('subreddit')))
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df = df.drop('subreddit')
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@ -21,9 +28,9 @@ df = df.withColumn("Day",f.dayofmonth(f.col("CreatedAt")))
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df = df.repartition('subreddit')
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df2 = df.sort(["subreddit","CreatedAt","link_id","parent_id","Year","Month","Day"],ascending=True)
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df2 = df2.sortWithinPartitions(["subreddit","CreatedAt","link_id","parent_id","Year","Month","Day"],ascending=True)
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df2.write.parquet("/gscratch/comdata/users/nathante/reddit_comments_by_subreddit.parquet_new", mode='overwrite', compression='snappy')
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df2.write.parquet("/gscratch/scrubbed/comdata/output/reddit_comments_by_subreddit.parquet", mode='overwrite', compression='snappy')
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df = df.repartition('author')
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df3 = df.sort(["author","CreatedAt","subreddit","link_id","parent_id","Year","Month","Day"],ascending=True)
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df3 = df3.sortWithinPartitions(["author","CreatedAt","subreddit","link_id","parent_id","Year","Month","Day"],ascending=True)
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df3.write.parquet("/gscratch/comdata/users/nathante/reddit_comments_by_author.parquet_new", mode='overwrite',compression='snappy')
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df3.write.parquet("/gscratch/scrubbed/comdata/output/reddit_comments_by_author.parquet", mode='overwrite',compression='snappy')
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@ -24,8 +24,7 @@ def open_fileset(files):
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for fh in files:
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print(fh)
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lines = open_input_file(fh)
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for line in lines:
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yield line
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yield from lines
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def open_input_file(input_filename):
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if re.match(r'.*\.7z$', input_filename):
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@ -39,7 +38,7 @@ def open_input_file(input_filename):
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elif re.match(r'.*\.xz', input_filename):
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cmd = ["xzcat",'-dk', '-T 20',input_filename]
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elif re.match(r'.*\.zst',input_filename):
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cmd = ['zstd','-dck', input_filename]
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cmd = ['/kloneusr/bin/zstd','-dck', input_filename, '--memory=2048MB --stdout']
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elif re.match(r'.*\.gz',input_filename):
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cmd = ['gzip','-dc', input_filename]
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try:
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#!/usr/bin/bash
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start_spark_cluster.sh
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spark-submit --master spark://$(hostname):18899 weekly_cosine_similarities.py term --outfile=/gscratch/comdata/users/nathante/subreddit_term_similarity_weekly_5000.parquet --topN=5000
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stop-all.sh
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singularity exec /gscratch/comdata/users/nathante/containers/nathante.sif spark-submit --master spark://$(hostname):7077 comments_2_parquet_part2.py
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singularity exec /gscratch/comdata/users/nathante/containers/nathante.sif stop-all.sh
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4
datasets/submissions_2_parquet.sh
Normal file → Executable file
4
datasets/submissions_2_parquet.sh
Normal file → Executable file
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#!/usr/bin/env bash
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## this should be run manually since we don't have a nice way to wait on parallel_sql jobs
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#!/usr/bin/env bash
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./parse_submissions.sh
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srun -p compute-bigmem -A comdata --nodes=1 --mem-per-cpu=9g -c 40 --time=120:00:00 python3 $(pwd)/submissions_2_parquet_part1.py gen_task_list
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start_spark_and_run.sh 1 $(pwd)/submissions_2_parquet_part2.py
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@ -3,26 +3,23 @@
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# two stages:
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# 1. from gz to arrow parquet (this script)
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# 2. from arrow parquet to spark parquet (submissions_2_parquet_part2.py)
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from datetime import datetime
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from multiprocessing import Pool
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from pathlib import Path
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from itertools import islice
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from helper import find_dumps, open_fileset
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import pandas as pd
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import pyarrow as pa
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import pyarrow.parquet as pq
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import simdjson
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import fire
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import os
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parser = simdjson.Parser()
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import json
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def parse_submission(post, names = None):
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if names is None:
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names = ['id','author','subreddit','title','created_utc','permalink','url','domain','score','ups','downs','over_18','has_media','selftext','retrieved_on','num_comments','gilded','edited','time_edited','subreddit_type','subreddit_id','subreddit_subscribers','name','is_self','stickied','quarantine','error']
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try:
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post = parser.parse(post)
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post = json.loads(post)
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except (ValueError) as e:
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# print(e)
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# print(post)
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@ -92,8 +89,7 @@ def parse_dump(partition):
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pa.field('quarantine',pa.bool_(),nullable=True),
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pa.field('error',pa.string(),nullable=True)])
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if not os.path.exists("/gscratch/comdata/output/temp/reddit_submissions.parquet/"):
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os.mkdir("/gscratch/comdata/output/temp/reddit_submissions.parquet/")
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Path("/gscratch/comdata/output/temp/reddit_submissions.parquet/").mkdir(exist_ok=True,parents=True)
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with pq.ParquetWriter(f"/gscratch/comdata/output/temp/reddit_submissions.parquet/{partition}",schema=schema,compression='snappy',flavor='spark') as writer:
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while True:
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@ -108,7 +104,7 @@ def parse_dump(partition):
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def gen_task_list(dumpdir="/gscratch/comdata/raw_data/reddit_dumps/submissions"):
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files = list(find_dumps(dumpdir,base_pattern="RS_20*.*"))
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with open("parse_submissions_task_list",'w') as of:
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with open("submissions_task_list.sh",'w') as of:
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for fpath in files:
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partition = os.path.split(fpath)[1]
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of.write(f'python3 submissions_2_parquet_part1.py parse_dump {partition}\n')
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@ -8,7 +8,7 @@ import hashlib
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shasums1 = requests.get("https://files.pushshift.io/reddit/comments/sha256sum.txt").text
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#shasums2 = requests.get("https://files.pushshift.io/reddit/comments/daily/sha256sum.txt").text
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shasums = shasums1 + shasums2
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shasums = shasums1
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dumpdir = "/gscratch/comdata/raw_data/reddit_dumps/comments"
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for l in shasums.strip().split('\n'):
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@ -1,8 +1,6 @@
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#!/usr/bin/env bash
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module load parallel_sql
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source ./bin/activate
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python3 tf_comments.py gen_task_list
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psu --del --Y
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cat tf_task_list | psu --load
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for job in $(seq 1 50); do sbatch checkpoint_parallelsql.sbatch; done;
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@ -2,12 +2,17 @@
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from pyspark.sql import functions as f
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from pyspark.sql import SparkSession
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import fire
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spark = SparkSession.builder.getOrCreate()
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df = spark.read.parquet("/gscratch/comdata/users/nathante/reddit_tfidf_test.parquet_temp/")
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def main(inparquet, outparquet, colname):
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spark = SparkSession.builder.getOrCreate()
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df = spark.read.parquet(inparquet)
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df = df.repartition(2000,'term')
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df = df.sort(['term','week','subreddit'])
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df = df.sortWithinPartitions(['term','week','subreddit'])
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df = df.repartition(2000,colname)
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df = df.sort([colname,'week','subreddit'])
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df = df.sortWithinPartitions([colname,'week','subreddit'])
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df.write.parquet("/gscratch/comdata/users/nathante/reddit_tfidf_test_sorted_tf.parquet_temp",mode='overwrite',compression='snappy')
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df.write.parquet(outparquet,mode='overwrite',compression='snappy')
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if __name__ == '__main__':
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fire.Fire(main)
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@ -14,21 +14,29 @@ from nltk.util import ngrams
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import string
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from random import random
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from redditcleaner import clean
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from pathlib import Path
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# compute term frequencies for comments in each subreddit by week
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def weekly_tf(partition, mwe_pass = 'first'):
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dataset = ds.dataset(f'/gscratch/comdata/output/reddit_comments_by_subreddit.parquet/{partition}', format='parquet')
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if not os.path.exists("/gscratch/comdata/users/nathante/reddit_comment_ngrams_10p_sample/"):
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os.mkdir("/gscratch/comdata/users/nathante/reddit_comment_ngrams_10p_sample/")
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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):
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if not os.path.exists("/gscratch/comdata/users/nathante/reddit_tfidf_test_authors.parquet_temp/"):
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os.mkdir("/gscratch/comdata/users/nathante/reddit_tfidf_test_authors.parquet_temp/")
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dataset = ds.dataset(Path(input_dir)/partition, format='parquet')
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outputdir = Path(outputdir)
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samppath = outputdir / "reddit_comment_ngrams_10p_sample"
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if not samppath.exists():
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samppath.mkdir(parents=True, exist_ok=True)
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ngram_output = partition.replace("parquet","txt")
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if excluded_users is not None:
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excluded_users = set(map(str.strip,open(excluded_users)))
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df = df.filter(~ (f.col("author").isin(excluded_users)))
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ngram_path = samppath / ngram_output
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if mwe_pass == 'first':
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if os.path.exists(f"/gscratch/comdata/output/reddit_ngrams/comment_ngrams_10p_sample/{ngram_output}"):
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os.remove(f"/gscratch/comdata/output/reddit_ngrams/comment_ngrams_10p_sample/{ngram_output}")
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if ngram_path.exists():
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ngram_path.unlink()
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batches = dataset.to_batches(columns=['CreatedAt','subreddit','body','author'])
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@ -62,8 +70,10 @@ def weekly_tf(partition, mwe_pass = 'first'):
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subreddit_weeks = groupby(rows, lambda r: (r.subreddit, r.week))
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mwe_path = outputdir / "multiword_expressions.feather"
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if mwe_pass != 'first':
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mwe_dataset = pd.read_feather(f'/gscratch/comdata/output/reddit_ngrams/multiword_expressions.feather')
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mwe_dataset = pd.read_feather(mwe_path)
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mwe_dataset = mwe_dataset.sort_values(['phrasePWMI'],ascending=False)
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mwe_phrases = list(mwe_dataset.phrase)
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mwe_phrases = [tuple(s.split(' ')) for s in mwe_phrases]
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@ -115,7 +125,7 @@ def weekly_tf(partition, mwe_pass = 'first'):
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for sentence in sentences:
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if random() <= 0.1:
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grams = list(chain(*map(lambda i : ngrams(sentence,i),range(4))))
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with open(f'/gscratch/comdata/output/reddit_ngrams/comment_ngrams_10p_sample/{ngram_output}','a') as gram_file:
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with open(ngram_path,'a') as gram_file:
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for ng in grams:
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gram_file.write(' '.join(ng) + '\n')
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for token in sentence:
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@ -150,7 +160,14 @@ def weekly_tf(partition, mwe_pass = 'first'):
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outchunksize = 10000
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|
||||
with pq.ParquetWriter(f"/gscratch/comdata/output/reddit_ngrams/comment_terms.parquet/{partition}",schema=schema,compression='snappy',flavor='spark') as writer, pq.ParquetWriter(f"/gscratch/comdata/output/reddit_ngrams/comment_authors.parquet/{partition}",schema=author_schema,compression='snappy',flavor='spark') as author_writer:
|
||||
termtf_outputdir = (outputdir / "comment_terms")
|
||||
termtf_outputdir.mkdir(parents=True, exist_ok=True)
|
||||
authortf_outputdir = (outputdir / "comment_authors")
|
||||
authortf_outputdir.mkdir(parents=True, exist_ok=True)
|
||||
termtf_path = termtf_outputdir / partition
|
||||
authortf_path = authortf_outputdir / partition
|
||||
with pq.ParquetWriter(termtf_path, schema=schema, compression='snappy', flavor='spark') as writer, \
|
||||
pq.ParquetWriter(authortf_path, schema=author_schema, compression='snappy', flavor='spark') as author_writer:
|
||||
|
||||
while True:
|
||||
|
||||
@ -179,12 +196,12 @@ def weekly_tf(partition, mwe_pass = 'first'):
|
||||
author_writer.close()
|
||||
|
||||
|
||||
def gen_task_list(mwe_pass='first'):
|
||||
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/")
|
||||
with open("tf_task_list",'w') as outfile:
|
||||
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} {f}\n")
|
||||
outfile.write(f"./tf_comments.py weekly_tf --mwe-pass {mwe_pass} --outputdir {outputdir} --excluded_users {excluded_users_file} {f}\n")
|
||||
|
||||
if __name__ == "__main__":
|
||||
fire.Fire({"gen_task_list":gen_task_list,
|
||||
|
||||
91
ngrams/top_comment_phrases.py
Normal file → Executable file
91
ngrams/top_comment_phrases.py
Normal file → Executable file
@ -1,58 +1,69 @@
|
||||
#!/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
|
||||
|
||||
spark = SparkSession.builder.getOrCreate()
|
||||
df = spark.read.text("/gscratch/comdata/users/nathante/reddit_comment_ngrams_10p_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)
|
||||
import fire
|
||||
from pathlib import Path
|
||||
|
||||
|
||||
# count overall
|
||||
N = phrases.select(f.sum(phrases.phraseCount).alias("phraseCount")).collect()[0].phraseCount
|
||||
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))
|
||||
|
||||
print(f'analyzing PMI on a sample of {N} phrases')
|
||||
logN = np.log(N)
|
||||
phrases = phrases.withColumn("phraseLogProb", f.log(f.col("phraseCount")) - logN)
|
||||
df = df.withColumnRenamed("value","phrase")
|
||||
|
||||
# count term occurrances
|
||||
phrases = phrases.withColumn('terms',f.split(f.col('phrase'),' '))
|
||||
terms = phrases.select(['phrase','phraseCount','phraseLogProb',f.explode(phrases.terms).alias('term')])
|
||||
# count phrase occurrances
|
||||
phrases = df.groupby('phrase').count()
|
||||
phrases = phrases.withColumnRenamed('count','phraseCount')
|
||||
phrases = phrases.filter(phrases.phraseCount > 10)
|
||||
|
||||
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)
|
||||
# count overall
|
||||
N = phrases.select(f.sum(phrases.phraseCount).alias("phraseCount")).collect()[0].phraseCount
|
||||
|
||||
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'))
|
||||
print(f'analyzing PMI on a sample of {N} phrases')
|
||||
logN = np.log(N)
|
||||
phrases = phrases.withColumn("phraseLogProb", f.log(f.col("phraseCount")) - logN)
|
||||
|
||||
# join phrases to term counts
|
||||
# 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 = terms.select(['phrase','phraseCount','phraseLogProb','phrasePWMI'])
|
||||
|
||||
df = df.sort(['phrasePWMI'],descending=True)
|
||||
df = df.sortWithinPartitions(['phrasePWMI'],descending=True)
|
||||
df.write.parquet("/gscratch/comdata/users/nathante/reddit_comment_ngrams_pwmi.parquet/",mode='overwrite',compression='snappy')
|
||||
df = df.sort(['phrasePWMI'],descending=True)
|
||||
df = df.sortWithinPartitions(['phrasePWMI'],descending=True)
|
||||
|
||||
df = spark.read.parquet("/gscratch/comdata/users/nathante/reddit_comment_ngrams_pwmi.parquet/")
|
||||
pwmi_dir = ngram_dir / "reddit_comment_ngrams_pwmi.parquet/"
|
||||
df.write.parquet(str(pwmi_dir), mode='overwrite', compression='snappy')
|
||||
|
||||
df.write.csv("/gscratch/comdata/users/nathante/reddit_comment_ngrams_pwmi.csv/",mode='overwrite',compression='none')
|
||||
df = spark.read.parquet(str(pwmi_dir))
|
||||
|
||||
df = spark.read.parquet("/gscratch/comdata/users/nathante/reddit_comment_ngrams_pwmi.parquet")
|
||||
df = df.select('phrase','phraseCount','phraseLogProb','phrasePWMI')
|
||||
df.write.csv(str(ngram_dir / "reddit_comment_ngrams_pwmi.csv/"),mode='overwrite',compression='none')
|
||||
|
||||
# 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("/gscratch/comdata/users/nathante/reddit_multiword_expressions.feather")
|
||||
df.to_csv("/gscratch/comdata/users/nathante/reddit_multiword_expressions.csv")
|
||||
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)
|
||||
|
||||
@ -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
|
||||
# ${tfidf_weekly_data}/comment_authors_130k.parquet: tfidf.py similarities_helper.py /gscratch/comdata/output/reddit_ngrams/comment_authors.parquet /gscratch/comdata/output/reddit_similarity/subreddits_by_num_comments_nonsfw.csv
|
||||
# start_spark_and_run.sh 1 tfidf.py authors_weekly --topN=130000
|
||||
|
||||
# /gscratch/comdata/output/reddit_similarity/comment_authors_10000.parquet: cosine_similarities.py similarities_helper.py /gscratch/comdata/output/reddit_similarity/tfidf/comment_authors.parquet /gscratch/comdata/output/reddit_similarity/tfidf/comment_authors.parquet
|
||||
|
||||
@ -1,4 +1,4 @@
|
||||
#!/usr/bin/bash
|
||||
start_spark_cluster.sh
|
||||
singularity exec /gscratch/comdata/users/nathante/cdsc_base.sif spark-submit --master spark://$(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
|
||||
|
||||
@ -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,
|
||||
|
||||
@ -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 = []
|
||||
|
||||
@ -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)
|
||||
|
||||
|
||||
@ -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)
|
||||
|
||||
Loading…
Reference in New Issue
Block a user