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collective:master collective:icwsm_dataverse collective:excise_reindex collective:synced/excise_reindex collective:git-annex collective:synced/git-annex collective:factor_out_similarities collective:charliepatch collective:synced/master
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compare: collective:factor_out_similarities
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collective:master collective:icwsm_dataverse collective:excise_reindex collective:synced/excise_reindex collective:git-annex collective:synced/git-annex collective:factor_out_similarities collective:charliepatch collective:synced/master

1 Commits
synced/exc ... factor_out

Author SHA1 Message Date
Nathan TeBlunthuis
930ee47d2b refactor similarities to use submodule. 2022-01-19 15:05:49 -08:00
26 changed files with 319 additions and 359 deletions
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3
.gitmodules vendored Normal file
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@@ -0,0 +1,3 @@
[submodule "cdsc_ecology_utils"]
path = cdsc_ecology_utils
url = code:cdsc_ecology_utils

4
__init__.py
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@@ -1,2 +1,2 @@
from .timeseries import load_clusters, load_densities, build_cluster_timeseries
from timeseries import load_clusters, load_densities, build_cluster_timeseries
from cdsc_ecology_utils import similarity_functions

1
cdsc_ecology_utils Submodule

Submodule cdsc_ecology_utils added at 5b5fdbb3c0

26
datasets/checkpoint_parallelsql.sbatch Normal file
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@@ -0,0 +1,26 @@
#!/bin/bash
## parallel_sql_job.sh
#SBATCH --job-name=tf_subreddit_comments
## Allocation Definition
#SBATCH --account=comdata-ckpt
#SBATCH --partition=ckpt
## Resources
## Nodes. This should always be 1 for parallel-sql.
#SBATCH --nodes=1
## Walltime (12 hours)
#SBATCH --time=12:00:00
## Memory per node
#SBATCH --mem=32G
#SBATCH --cpus-per-task=4
#SBATCH --ntasks=1
#SBATCH -D /gscratch/comdata/users/nathante/cdsc-reddit
source ./bin/activate
module load parallel_sql
echo $(which perl)
conda list pyarrow
which python3
#Put here commands to load other modules (e.g. matlab etc.)
#Below command means that parallel_sql will get tasks from the database
#and run them on the node (in parallel). So a 16 core node will have
#16 tasks running at one time.
parallel-sql --sql -a parallel --exit-on-term --jobs 4

4
datasets/comments_2_parquet.sh
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@@ -1,10 +1,10 @@
#!/usr/bin/env bash
## needs to be run by hand since i don't have a nice way of waiting on a parallel-sql job to complete
#!/usr/bin/env bash
echo "#!/usr/bin/bash" > job_script.sh
#echo "source $(pwd)/../bin/activate" >> job_script.sh
echo "python3 $(pwd)/comments_2_parquet_part1.py" >> job_script.sh
srun -p compute-bigmem -A comdata --nodes=1 --mem-per-cpu=9g -c 40 --time=120:00:00 --pty job_script.sh
srun -p comdata -A comdata --nodes=1 --mem=120G --time=48:00:00 --pty job_script.sh
start_spark_and_run.sh 1 $(pwd)/comments_2_parquet_part2.py

114
datasets/comments_2_parquet_part1.py
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@@ -1,15 +1,12 @@
#!/usr/bin/env python3
import os
import json
from datetime import datetime
from multiprocessing import Pool
from itertools import islice
from helper import open_input_file, find_dumps
from helper import find_dumps, open_fileset
import pandas as pd
import pyarrow as pa
import pyarrow.parquet as pq
from pathlib import Path
import fire
def parse_comment(comment, names= None):
if names is None:
@@ -49,63 +46,70 @@ def parse_comment(comment, names= None):
# 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')])
def parse_dump(partition):
dumpdir = "/gscratch/comdata/raw_data/reddit_dumps/comments/"
dumpdir = f"/gscratch/comdata/raw_data/reddit_dumps/comments/{partition}"
files = list(find_dumps(dumpdir, base_pattern="RC_20*"))
stream = open_input_file(dumpdir)
rows = map(parse_comment, stream)
pool = Pool(28)
schema = pa.schema([
pa.field('id', pa.string(), nullable=True),
pa.field('subreddit', pa.string(), nullable=True),
pa.field('link_id', pa.string(), nullable=True),
pa.field('parent_id', pa.string(), nullable=True),
pa.field('created_utc', pa.timestamp('ms'), nullable=True),
pa.field('author', pa.string(), nullable=True),
pa.field('ups', pa.int64(), nullable=True),
pa.field('downs', pa.int64(), nullable=True),
pa.field('score', pa.int64(), nullable=True),
pa.field('edited', pa.bool_(), nullable=True),
pa.field('time_edited', pa.timestamp('ms'), nullable=True),
pa.field('subreddit_type', pa.string(), nullable=True),
pa.field('subreddit_id', pa.string(), nullable=True),
pa.field('stickied', pa.bool_(), nullable=True),
pa.field('is_submitter', pa.bool_(), nullable=True),
pa.field('body', pa.string(), nullable=True),
pa.field('error', pa.string(), nullable=True),
])
stream = open_fileset(files)
p = Path("/gscratch/comdata/output/temp/reddit_comments.parquet")
p.mkdir(exist_ok=True,parents=True)
N = int(1e4)
N=10000
with pq.ParquetWriter(f"/gscratch/comdata/output/temp/reddit_comments.parquet/{partition}.parquet",
schema=schema,
compression='snappy',
flavor='spark') as writer:
rows = pool.imap_unordered(parse_comment, stream, chunksize=int(N/28))
while True:
chunk = islice(rows,N)
pddf = pd.DataFrame(chunk, columns=schema.names)
table = pa.Table.from_pandas(pddf,schema=schema)
if table.shape[0] == 0:
break
writer.write_table(table)
schema = pa.schema([
pa.field('id', pa.string(), nullable=True),
pa.field('subreddit', pa.string(), nullable=True),
pa.field('link_id', pa.string(), nullable=True),
pa.field('parent_id', pa.string(), nullable=True),
pa.field('created_utc', pa.timestamp('ms'), nullable=True),
pa.field('author', pa.string(), nullable=True),
pa.field('ups', pa.int64(), nullable=True),
pa.field('downs', pa.int64(), nullable=True),
pa.field('score', pa.int64(), nullable=True),
pa.field('edited', pa.bool_(), nullable=True),
pa.field('time_edited', pa.timestamp('ms'), nullable=True),
pa.field('subreddit_type', pa.string(), nullable=True),
pa.field('subreddit_id', pa.string(), nullable=True),
pa.field('stickied', pa.bool_(), nullable=True),
pa.field('is_submitter', pa.bool_(), nullable=True),
pa.field('body', pa.string(), nullable=True),
pa.field('error', pa.string(), nullable=True),
])
writer.close()
from pathlib import Path
p = Path("/gscratch/comdata/output/reddit_comments.parquet_temp2")
if not p.is_dir():
if p.exists():
p.unlink()
p.mkdir()
else:
list(map(Path.unlink,p.glob('*')))
part_size = int(1e7)
part = 1
n_output = 0
writer = pq.ParquetWriter(f"/gscratch/comdata/output/reddit_comments.parquet_temp2/part_{part}.parquet",schema=schema,compression='snappy',flavor='spark')
while True:
if n_output > part_size:
if part > 1:
writer.close()
part = part + 1
n_output = 0
writer = pq.ParquetWriter(f"/gscratch/comdata/output/reddit_comments.parquet_temp2/part_{part}.parquet",schema=schema,compression='snappy',flavor='spark')
n_output += N
chunk = islice(rows,N)
pddf = pd.DataFrame(chunk, columns=schema.names)
table = pa.Table.from_pandas(pddf,schema=schema)
if table.shape[0] == 0:
break
writer.write_table(table)
def gen_task_list(dumpdir="/gscratch/comdata/raw_data/reddit_dumps/comments", overwrite=True):
files = list(find_dumps(dumpdir,base_pattern="RC_20*.*"))
with open("comments_task_list.sh",'w') as of:
for fpath in files:
partition = os.path.split(fpath)[1]
if (not Path(f"/gscratch/comdata/output/temp/reddit_comments.parquet/{partition}.parquet").exists()) or (overwrite is True):
of.write(f'python3 comments_2_parquet_part1.py parse_dump {partition}\n')
if __name__ == '__main__':
fire.Fire({'parse_dump':parse_dump,
'gen_task_list':gen_task_list})

13
datasets/comments_2_parquet_part2.py
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@@ -2,19 +2,12 @@
# spark script to make sorted, and partitioned parquet files
import pyspark
from pyspark.sql import functions as f
from pyspark.sql import SparkSession
spark = SparkSession.builder.getOrCreate()
conf = pyspark.SparkConf().setAppName("Reddit submissions to parquet")
conf = conf.set("spark.sql.shuffle.partitions",2000)
conf = conf.set('spark.sql.crossJoin.enabled',"true")
conf = conf.set('spark.debug.maxToStringFields',200)
sc = spark.sparkContext
df = spark.read.parquet("/gscratch/comdata/output/temp/reddit_comments.parquet",compression='snappy')
df = spark.read.parquet("/gscratch/comdata/output/reddit_comments.parquet_temp2",compression='snappy')
df = df.withColumn("subreddit_2", f.lower(f.col('subreddit')))
df = df.drop('subreddit')
@@ -28,9 +21,9 @@ df = df.withColumn("Day",f.dayofmonth(f.col("CreatedAt")))
df = df.repartition('subreddit')
df2 = df.sort(["subreddit","CreatedAt","link_id","parent_id","Year","Month","Day"],ascending=True)
df2 = df2.sortWithinPartitions(["subreddit","CreatedAt","link_id","parent_id","Year","Month","Day"],ascending=True)
df2.write.parquet("/gscratch/scrubbed/comdata/output/reddit_comments_by_subreddit.parquet", mode='overwrite', compression='snappy')
df2.write.parquet("/gscratch/comdata/users/nathante/reddit_comments_by_subreddit.parquet_new", mode='overwrite', compression='snappy')
df = df.repartition('author')
df3 = df.sort(["author","CreatedAt","subreddit","link_id","parent_id","Year","Month","Day"],ascending=True)
df3 = df3.sortWithinPartitions(["author","CreatedAt","subreddit","link_id","parent_id","Year","Month","Day"],ascending=True)
df3.write.parquet("/gscratch/scrubbed/comdata/output/reddit_comments_by_author.parquet", mode='overwrite',compression='snappy')
df3.write.parquet("/gscratch/comdata/users/nathante/reddit_comments_by_author.parquet_new", mode='overwrite',compression='snappy')

5
datasets/helper.py
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@@ -24,7 +24,8 @@ def open_fileset(files):
for fh in files:
print(fh)
lines = open_input_file(fh)
yield from lines
for line in lines:
yield line
def open_input_file(input_filename):
if re.match(r'.*\.7z$', input_filename):
@@ -38,7 +39,7 @@ def open_input_file(input_filename):
elif re.match(r'.*\.xz', input_filename):
cmd = ["xzcat",'-dk', '-T 20',input_filename]
elif re.match(r'.*\.zst',input_filename):
cmd = ['/kloneusr/bin/zstd','-dck', input_filename, '--memory=2048MB --stdout']
cmd = ['zstd','-dck', input_filename]
elif re.match(r'.*\.gz',input_filename):
cmd = ['gzip','-dc', input_filename]
try:

4
datasets/job_script.sh
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@@ -1,4 +1,4 @@
#!/usr/bin/bash
start_spark_cluster.sh
singularity exec /gscratch/comdata/users/nathante/containers/nathante.sif spark-submit --master spark://$(hostname):7077 comments_2_parquet_part2.py
singularity exec /gscratch/comdata/users/nathante/containers/nathante.sif stop-all.sh
spark-submit --master spark://$(hostname):18899 weekly_cosine_similarities.py term --outfile=/gscratch/comdata/users/nathante/subreddit_term_similarity_weekly_5000.parquet --topN=5000
stop-all.sh

4
datasets/submissions_2_parquet.sh Executable file → Normal file
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@@ -1,8 +1,8 @@
#!/usr/bin/env bash
## this should be run manually since we don't have a nice way to wait on parallel_sql jobs
#!/usr/bin/env bash
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
./parse_submissions.sh
start_spark_and_run.sh 1 $(pwd)/submissions_2_parquet_part2.py

14
datasets/submissions_2_parquet_part1.py
View File

@@ -3,23 +3,26 @@
# two stages:
# 1. from gz to arrow parquet (this script)
# 2. from arrow parquet to spark parquet (submissions_2_parquet_part2.py)
from datetime import datetime
from pathlib import Path
from multiprocessing import Pool
from itertools import islice
from helper import find_dumps, open_fileset
import pandas as pd
import pyarrow as pa
import pyarrow.parquet as pq
import simdjson
import fire
import os
import json
parser = simdjson.Parser()
def parse_submission(post, names = None):
if names is None:
names = ['id','author','subreddit','title','created_utc','permalink','url','domain','score','ups','downs','over_18','has_media','selftext','retrieved_on','num_comments','gilded','edited','time_edited','subreddit_type','subreddit_id','subreddit_subscribers','name','is_self','stickied','quarantine','error']
try:
post = json.loads(post)
post = parser.parse(post)
except (ValueError) as e:
# print(e)
# print(post)
@@ -89,7 +92,8 @@ def parse_dump(partition):
pa.field('quarantine',pa.bool_(),nullable=True),
pa.field('error',pa.string(),nullable=True)])
Path("/gscratch/comdata/output/temp/reddit_submissions.parquet/").mkdir(exist_ok=True,parents=True)
if not os.path.exists("/gscratch/comdata/output/temp/reddit_submissions.parquet/"):
os.mkdir("/gscratch/comdata/output/temp/reddit_submissions.parquet/")
with pq.ParquetWriter(f"/gscratch/comdata/output/temp/reddit_submissions.parquet/{partition}",schema=schema,compression='snappy',flavor='spark') as writer:
while True:
@@ -104,7 +108,7 @@ def parse_dump(partition):
def gen_task_list(dumpdir="/gscratch/comdata/raw_data/reddit_dumps/submissions"):
files = list(find_dumps(dumpdir,base_pattern="RS_20*.*"))
with open("submissions_task_list.sh",'w') as of:
with open("parse_submissions_task_list",'w') as of:
for fpath in files:
partition = os.path.split(fpath)[1]
of.write(f'python3 submissions_2_parquet_part1.py parse_dump {partition}\n')

6
density/overlap_density.py
View File

@@ -4,9 +4,9 @@ from pathlib import Path
import fire
import numpy as np
import sys
# sys.path.append("..")
# sys.path.append("../similarities")
# from similarities.similarities_helper import pull_tfidf
sys.path.append("..")
sys.path.append("../similarities")
from similarities.similarities_helper import reindex_tfidf
# this is the mean of the ratio of the overlap to the focal size.
# mean shared membership per focal community member

2
dumps/check_comments_shas.py
View File

@@ -8,7 +8,7 @@ import hashlib
shasums1 = requests.get("https://files.pushshift.io/reddit/comments/sha256sum.txt").text
#shasums2 = requests.get("https://files.pushshift.io/reddit/comments/daily/sha256sum.txt").text
shasums = shasums1
shasums = shasums1 + shasums2
dumpdir = "/gscratch/comdata/raw_data/reddit_dumps/comments"
for l in shasums.strip().split('\n'):

4
ngrams/run_tf_jobs.sh
View File

@@ -1,6 +1,8 @@
#!/usr/bin/env bash
module load parallel_sql
source ./bin/activate
python3 tf_comments.py gen_task_list
psu --del --Y
cat tf_task_list | psu --load
for job in $(seq 1 50); do sbatch checkpoint_parallelsql.sbatch; done;

17
ngrams/sort_tf_comments.py
View File

@@ -2,17 +2,12 @@
from pyspark.sql import functions as f
from pyspark.sql import SparkSession
import fire
def main(inparquet, outparquet, colname):
spark = SparkSession.builder.getOrCreate()
df = spark.read.parquet(inparquet)
spark = SparkSession.builder.getOrCreate()
df = spark.read.parquet("/gscratch/comdata/users/nathante/reddit_tfidf_test.parquet_temp/")
df = df.repartition(2000,colname)
df = df.sort([colname,'week','subreddit'])
df = df.sortWithinPartitions([colname,'week','subreddit'])
df = df.repartition(2000,'term')
df = df.sort(['term','week','subreddit'])
df = df.sortWithinPartitions(['term','week','subreddit'])
df.write.parquet(outparquet,mode='overwrite',compression='snappy')
if __name__ == '__main__':
fire.Fire(main)
df.write.parquet("/gscratch/comdata/users/nathante/reddit_tfidf_test_sorted_tf.parquet_temp",mode='overwrite',compression='snappy')

47
ngrams/tf_comments.py
View File

@@ -14,29 +14,21 @@ from nltk.util import ngrams
import string
from random import random
from redditcleaner import clean
from pathlib import Path
# compute term frequencies for comments in each subreddit by week
def weekly_tf(partition, outputdir = '/gscratch/comdata/output/reddit_ngrams/', input_dir="/gscratch/comdata/output/reddit_comments_by_subreddit.parquet/", mwe_pass = 'first', excluded_users=None):
def weekly_tf(partition, mwe_pass = 'first'):
dataset = ds.dataset(f'/gscratch/comdata/output/reddit_comments_by_subreddit.parquet/{partition}', format='parquet')
if not os.path.exists("/gscratch/comdata/users/nathante/reddit_comment_ngrams_10p_sample/"):
os.mkdir("/gscratch/comdata/users/nathante/reddit_comment_ngrams_10p_sample/")
dataset = ds.dataset(Path(input_dir)/partition, format='parquet')
outputdir = Path(outputdir)
samppath = outputdir / "reddit_comment_ngrams_10p_sample"
if not samppath.exists():
samppath.mkdir(parents=True, exist_ok=True)
if not os.path.exists("/gscratch/comdata/users/nathante/reddit_tfidf_test_authors.parquet_temp/"):
os.mkdir("/gscratch/comdata/users/nathante/reddit_tfidf_test_authors.parquet_temp/")
ngram_output = partition.replace("parquet","txt")
if excluded_users is not None:
excluded_users = set(map(str.strip,open(excluded_users)))
df = df.filter(~ (f.col("author").isin(excluded_users)))
ngram_path = samppath / ngram_output
if mwe_pass == 'first':
if ngram_path.exists():
ngram_path.unlink()
if os.path.exists(f"/gscratch/comdata/output/reddit_ngrams/comment_ngrams_10p_sample/{ngram_output}"):
os.remove(f"/gscratch/comdata/output/reddit_ngrams/comment_ngrams_10p_sample/{ngram_output}")
batches = dataset.to_batches(columns=['CreatedAt','subreddit','body','author'])
@@ -70,10 +62,8 @@ def weekly_tf(partition, outputdir = '/gscratch/comdata/output/reddit_ngrams/',
subreddit_weeks = groupby(rows, lambda r: (r.subreddit, r.week))
mwe_path = outputdir / "multiword_expressions.feather"
if mwe_pass != 'first':
mwe_dataset = pd.read_feather(mwe_path)
mwe_dataset = pd.read_feather(f'/gscratch/comdata/output/reddit_ngrams/multiword_expressions.feather')
mwe_dataset = mwe_dataset.sort_values(['phrasePWMI'],ascending=False)
mwe_phrases = list(mwe_dataset.phrase)
mwe_phrases = [tuple(s.split(' ')) for s in mwe_phrases]
@@ -125,7 +115,7 @@ def weekly_tf(partition, outputdir = '/gscratch/comdata/output/reddit_ngrams/',
for sentence in sentences:
if random() <= 0.1:
grams = list(chain(*map(lambda i : ngrams(sentence,i),range(4))))
with open(ngram_path,'a') as gram_file:
with open(f'/gscratch/comdata/output/reddit_ngrams/comment_ngrams_10p_sample/{ngram_output}','a') as gram_file:
for ng in grams:
gram_file.write(' '.join(ng) + '\n')
for token in sentence:
@@ -159,15 +149,8 @@ def weekly_tf(partition, outputdir = '/gscratch/comdata/output/reddit_ngrams/',
outrows = tf_comments(subreddit_weeks)
outchunksize = 10000
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:
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:
while True:
@@ -196,12 +179,12 @@ def weekly_tf(partition, outputdir = '/gscratch/comdata/output/reddit_ngrams/',
author_writer.close()
def gen_task_list(mwe_pass='first', outputdir='/gscratch/comdata/output/reddit_ngrams/', tf_task_list='tf_task_list', excluded_users_file=None):
def gen_task_list(mwe_pass='first'):
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} --outputdir {outputdir} --excluded_users {excluded_users_file} {f}\n")
outfile.write(f"./tf_comments.py weekly_tf --mwe-pass {mwe_pass} {f}\n")
if __name__ == "__main__":
fire.Fire({"gen_task_list":gen_task_list,

91
ngrams/top_comment_phrases.py Executable file → Normal file
View File

@@ -1,69 +1,58 @@
#!/usr/bin/env python3
from pyspark.sql import functions as f
from pyspark.sql import Window
from pyspark.sql import SparkSession
import numpy as np
import fire
from pathlib import Path
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)
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))
# count overall
N = phrases.select(f.sum(phrases.phraseCount).alias("phraseCount")).collect()[0].phraseCount
df = df.withColumnRenamed("value","phrase")
print(f'analyzing PMI on a sample of {N} phrases')
logN = np.log(N)
phrases = phrases.withColumn("phraseLogProb", f.log(f.col("phraseCount")) - logN)
# count phrase occurrances
phrases = df.groupby('phrase').count()
phrases = phrases.withColumnRenamed('count','phraseCount')
phrases = phrases.filter(phrases.phraseCount > 10)
# 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 overall
N = phrases.select(f.sum(phrases.phraseCount).alias("phraseCount")).collect()[0].phraseCount
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)
print(f'analyzing PMI on a sample of {N} phrases')
logN = np.log(N)
phrases = phrases.withColumn("phraseLogProb", f.log(f.col("phraseCount")) - 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'))
# 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
# 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 = 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')
pwmi_dir = ngram_dir / "reddit_comment_ngrams_pwmi.parquet/"
df.write.parquet(str(pwmi_dir), mode='overwrite', compression='snappy')
df = spark.read.parquet("/gscratch/comdata/users/nathante/reddit_comment_ngrams_pwmi.parquet/")
df = spark.read.parquet(str(pwmi_dir))
df.write.csv("/gscratch/comdata/users/nathante/reddit_comment_ngrams_pwmi.csv/",mode='overwrite',compression='none')
df.write.csv(str(ngram_dir / "reddit_comment_ngrams_pwmi.csv/"),mode='overwrite',compression='none')
df = spark.read.parquet("/gscratch/comdata/users/nathante/reddit_comment_ngrams_pwmi.parquet")
df = df.select('phrase','phraseCount','phraseLogProb','phrasePWMI')
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)
# 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")

122
similarities/Makefile
View File

@@ -1,10 +1,8 @@
#all: /gscratch/comdata/output/reddit_similarity/tfidf/comment_terms_130k.parquet /gscratch/comdata/output/reddit_similarity/tfidf/comment_authors_130k.parquet /gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_terms_130k.parquet /gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_authors_130k.parquet
# srun_singularity=source /gscratch/comdata/users/nathante/cdsc_reddit/bin/activate && srun_singularity.sh
# srun_singularity_huge=source /gscratch/comdata/users/nathante/cdsc_reddit/bin/activate && srun_singularity_huge.sh
srun=srun -p compute-bigmem -A comdata --mem-per-cpu=9g --time=200:00:00 -c 40
srun_huge=srun -p compute-hugemem -A comdata --mem-per-cpu=9g --time=200:00:00 -c 40
similarity_data=/gscratch/scrubbed/comdata/reddit_similarity
srun_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=${base_data}/reddit_similarity
tfidf_data=${similarity_data}/tfidf
tfidf_weekly_data=${similarity_data}/tfidf_weekly
similarity_weekly_data=${similarity_data}/weekly
@@ -12,10 +10,7 @@ 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_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
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
@@ -23,106 +18,103 @@ all: ${tfidf_data}/comment_terms_30k.parquet ${tfidf_data}/comment_terms_10k.par
# 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_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_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_data}/subreddit_comment_terms_10k.feather: ${tfidf_data}/comment_terms_100k.parquet similarities_helper.py
${srun} python3 cosine_similarities.py term --outfile=${similarity_data}/subreddit_comment_terms_10k.feather --topN=10000
${srun_singularity} python3 cosine_similarities.py term --outfile=${similarity_data}/subreddit_comment_terms_10k.feather --topN=10000
${similarity_data}/subreddit_comment_terms_10k_LSI: ${tfidf_data}/comment_terms_100k.parquet similarities_helper.py
${srun_huge} python3 lsi_similarities.py term --outfile=${similarity_data}/subreddit_comment_terms_10k_LSI --topN=10000 --n_components=${lsi_components} --min_df=200
${srun_singularity} python3 lsi_similarities.py term --outfile=${similarity_data}/subreddit_comment_terms_10k_LSI --topN=10000 --n_components=${lsi_components} --min_df=200
${similarity_data}/subreddit_comment_terms_30k_LSI: ${tfidf_data}/comment_terms_100k.parquet similarities_helper.py
${srun_huge} python3 lsi_similarities.py term --outfile=${similarity_data}/subreddit_comment_terms_30k_LSI --topN=30000 --n_components=${lsi_components} --min_df=200 --inpath=$<
${srun_singularity} python3 lsi_similarities.py term --outfile=${similarity_data}/subreddit_comment_terms_30k_LSI --topN=30000 --n_components=${lsi_components} --min_df=200
${similarity_data}/subreddit_comment_terms_30k.feather: ${tfidf_data}/comment_terms_30k.parquet similarities_helper.py
${srun_huge} python3 cosine_similarities.py term --outfile=${similarity_data}/subreddit_comment_terms_30k.feather --topN=30000 --inpath=$<
${srun_singularity} python3 cosine_similarities.py term --outfile=${similarity_data}/subreddit_comment_terms_30k.feather --topN=30000
${similarity_data}/subreddit_comment_authors_30k.feather: ${tfidf_data}/comment_authors_30k.parquet similarities_helper.py
${srun_huge} python3 cosine_similarities.py author --outfile=${similarity_data}/subreddit_comment_authors_30k.feather --topN=30000 --inpath=$<
${srun_singularity} python3 cosine_similarities.py author --outfile=${similarity_data}/subreddit_comment_authors_30k.feather --topN=30000
${similarity_data}/subreddit_comment_authors_10k.feather: ${tfidf_data}/comment_authors_10k.parquet similarities_helper.py
${srun_huge} python3 cosine_similarities.py author --outfile=${similarity_data}/subreddit_comment_authors_10k.feather --topN=10000 --inpath=$<
${srun_singularity} python3 cosine_similarities.py author --outfile=${similarity_data}/subreddit_comment_authors_10k.feather --topN=10000
${similarity_data}/subreddit_comment_authors_10k_LSI: ${tfidf_data}/comment_authors_100k.parquet similarities_helper.py
${srun_huge} python3 lsi_similarities.py author --outfile=${similarity_data}/subreddit_comment_authors_10k_LSI --topN=10000 --n_components=${lsi_components} --min_df=10 --inpath=$<
${srun_singularity} python3 lsi_similarities.py author --outfile=${similarity_data}/subreddit_comment_authors_10k_LSI --topN=10000 --n_components=${lsi_components} --min_df=2
${similarity_data}/subreddit_comment_authors_30k_LSI: ${tfidf_data}/comment_authors_100k.parquet similarities_helper.py
${srun_huge} python3 lsi_similarities.py author --outfile=${similarity_data}/subreddit_comment_authors_30k_LSI --topN=30000 --n_components=${lsi_components} --min_df=10 --inpath=$<
${srun_singularity} python3 lsi_similarities.py author --outfile=${similarity_data}/subreddit_comment_authors_30k_LSI --topN=30000 --n_components=${lsi_components} --min_df=2
${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_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_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.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_LSI: ${tfidf_data}/comment_authors_100k.parquet similarities_helper.py
${srun_huge} python3 lsi_similarities.py author-tf --outfile=${similarity_data}/subreddit_comment_authors-tf_10k_LSI --topN=10000 --n_components=${lsi_components} --min_df=10 --inpath=$<
${srun_singularity} python3 lsi_similarities.py author-tf --outfile=${similarity_data}/subreddit_comment_authors-tf_10k_LSI --topN=10000 --n_components=${lsi_components} --min_df=2
${similarity_data}/subreddit_comment_authors-tf_30k_LSI: ${tfidf_data}/comment_authors_100k.parquet similarities_helper.py
${srun_huge} python3 lsi_similarities.py author-tf --outfile=${similarity_data}/subreddit_comment_authors-tf_30k_LSI --topN=30000 --n_components=${lsi_components} --min_df=10 --inpath=$<
${srun_singularity} python3 lsi_similarities.py author-tf --outfile=${similarity_data}/subreddit_comment_authors-tf_30k_LSI --topN=30000 --n_components=${lsi_components} --min_df=2
${similarity_data}/subreddit_comment_terms_100k.feather: ${tfidf_data}/comment_terms_100k.parquet similarities_helper.py
${srun} python3 cosine_similarities.py term --outfile=${similarity_data}/subreddit_comment_terms_100k.feather --topN=100000
${srun_singularity} python3 cosine_similarities.py term --outfile=${similarity_data}/subreddit_comment_terms_100k.feather --topN=100000
${similarity_data}/subreddit_comment_authors_100k.feather: ${tfidf_data}/comment_authors_100k.parquet similarities_helper.py
${srun} python3 cosine_similarities.py author --outfile=${similarity_data}/subreddit_comment_authors_100k.feather --topN=100000
${srun_singularity} python3 cosine_similarities.py author --outfile=${similarity_data}/subreddit_comment_authors_100k.feather --topN=100000
${similarity_data}/subreddit_comment_authors-tf_100k.feather: ${tfidf_data}/comment_authors_100k.parquet similarities_helper.py
${srun} python3 cosine_similarities.py author-tf --outfile=${similarity_data}/subreddit_comment_authors-tf_100k.feather --topN=100000
${srun_singularity} python3 cosine_similarities.py author-tf --outfile=${similarity_data}/subreddit_comment_authors-tf_100k.feather --topN=100000
${similarity_data}/subreddits_by_num_comments_nonsfw.csv:
start_spark_and_run.sh 3 top_subreddits_by_comments.py
${tfidf_data}/comment_terms_100k.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
${tfidf_data}/comment_terms_100k.parquet: /gscratch/comdata/output/reddit_ngrams/comment_terms.parquet ${similarity_data}/subreddits_by_num_comments_nonsfw.csv
# mkdir -p ${tfidf_data}/
start_spark_and_run.sh 3 tfidf.py terms --topN=100000 --inpath=$< --outpath=${tfidf_data}/comment_terms_100k.parquet
${tfidf_data}/comment_terms_30k.feather: /gscratch/comdata/output/reddit_ngrams/comment_terms.parquet ${similarity_data}/subreddits_by_num_comments.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_30k.feather: /gscratch/comdata/output/reddit_ngrams/comment_terms.parquet ${similarity_data}/subreddits_by_num_comments_nonsfw.csv
# mkdir -p ${tfidf_data}/
start_spark_and_run.sh 3 tfidf.py terms --topN=30000 --inpath=$< --outpath=${tfidf_data}/comment_terms_30k.feather
${tfidf_data}/comment_terms_10k.feather: /gscratch/comdata/output/reddit_ngrams/comment_terms.parquet ${similarity_data}/subreddits_by_num_comments.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_10k.feather: /gscratch/comdata/output/reddit_ngrams/comment_terms.parquet ${similarity_data}/subreddits_by_num_comments_nonsfw.csv
# mkdir -p ${tfidf_data}/
start_spark_and_run.sh 3 tfidf.py terms --topN=10000 --inpath=$< --outpath=${tfidf_data}/comment_terms_10k.feather
${tfidf_data}/comment_authors_100k.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_authors_100k.parquet: /gscratch/comdata/output/reddit_ngrams/comment_authors.parquet ${similarity_data}/subreddits_by_num_comments_nonsfw.csv
# mkdir -p ${tfidf_data}/
start_spark_and_run.sh 3 tfidf.py authors --topN=100000 --inpath=$< --outpath=${tfidf_data}/comment_authors_100k.parquet
${tfidf_data}/comment_authors_10k.parquet: /gscratch/comdata/output/reddit_ngrams/comment_authors.parquet ${similarity_data}/subreddits_by_num_comments.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_10k.parquet: /gscratch/comdata/output/reddit_ngrams/comment_authors.parquet ${similarity_data}/subreddits_by_num_comments_nonsfw.csv
# mkdir -p ${tfidf_data}/
start_spark_and_run.sh 3 tfidf.py authors --topN=10000 --inpath=$< --outpath=${tfidf_data}/comment_authors_10k.parquet
${tfidf_data}/comment_authors_30k.parquet: /gscratch/comdata/output/reddit_ngrams/comment_authors.parquet ${similarity_data}/subreddits_by_num_comments.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_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_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}/tfidf_weekly/comment_authors_100k.parquet: /gscratch/comdata/output/reddit_ngrams/comment_terms.parquet ${similarity_data}/subreddits_by_ppnum_comments.csv
start_spark_and_run.sh 3 tfidf.py authors_weekly --topN=100000 --inpath=$< --outpath=${tfidf_weekly_data}/comment_authors_100k.parquet
start_spark_and_run.sh 4 tfidf.py authors_weekly --topN=100000 --outpath=${tfidf_weekly_data}/comment_authors_100k.parquet
${tfidf_weekly_data}/comment_terms_30k.parquet: /gscratch/comdata/output/reddit_ngrams/comment_terms.parquet ${similarity_data}/subreddits_by_num_comments_nonsfw.csv
start_spark_and_run.sh 2 tfidf.py terms_weekly --topN=30000 --inpath=$< --outpath=${tfidf_weekly_data}/comment_authors_30k.parquet
${tfidf_weekly_data}/comment_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_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
${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
${similarity_weekly_data}/comment_terms_100k.parquet: weekly_cosine_similarities.py similarities_helper.py ${tfidf_weekly_data}/comment_terms_100k.parquet
${srun} python3 weekly_cosine_similarities.py terms --topN=100000 --outfile=${similarity_weekly_data}/comment_terms_100k.parquet
${srun_singularity} python3 weekly_cosine_similarities.py terms --topN=100000 --outfile=${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_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_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_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_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
${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
# ${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
# ${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
# 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/cosine_similarities.py
View File

@@ -1,12 +1,12 @@
import pandas as pd
import fire
from pathlib import Path
from similarities_helper import similarities, column_similarities
from cdsc_ecology_utils.similarity import similarities, column_similarities
from functools import partial
def cosine_similarities(infile, term_colname, outfile, min_df=None, max_df=None, included_subreddits=None, topN=500, exclude_phrases=False, from_date=None, to_date=None, tfidf_colname='tf_idf'):
return similarities(infile=infile, simfunc=column_similarities, term_colname=term_colname, outfile=outfile, min_df=min_df, max_df=max_df, included_subreddits=included_subreddits, topN=topN, exclude_phrases=exclude_phrases,from_date=from_date, to_date=to_date, tfidf_colname=tfidf_colname)
return similarities(infile=infile, simfunc=column_similarities, term_colname=term_colname, outfile=outfile, min_df=min_df, max_df=max_df, included_communities=included_subreddits, topN=topN, exclude_phrases=exclude_phrases,from_date=from_date, to_date=to_date, tfidf_colname=tfidf_colname)
# change so that these take in an input as an optional argument (for speed, but also for idf).
def term_cosine_similarities(outfile, min_df=None, max_df=None, included_subreddits=None, topN=500, exclude_phrases=False, from_date=None, to_date=None):

4
similarities/job_script.sh
View File

@@ -1,4 +1,4 @@
#!/usr/bin/bash
start_spark_cluster.sh
singularity exec /gscratch/comdata/users/nathante/containers/nathante.sif spark-submit --master spark://$(hostname):7077 tfidf.py authors --topN=100000 --inpath=/gscratch/comdata/output/reddit_ngrams/comment_authors.parquet --outpath=/gscratch/scrubbed/comdata/reddit_similarity/tfidf/comment_authors_100k.parquet
singularity exec /gscratch/comdata/users/nathante/containers/nathante.sif stop-all.sh
singularity exec /gscratch/comdata/users/nathante/cdsc_base.sif spark-submit --master spark://$(hostname):7077 top_subreddits_by_comments.py
singularity exec /gscratch/comdata/users/nathante/cdsc_base.sif stop-all.sh

33
similarities/lsi_similarities.py
View File

@@ -1,24 +1,23 @@
import pandas as pd
import fire
from pathlib import Path
from similarities_helper import *
from cdsc_ecology_utils.similarity.similarity_functions import lsi_column_similarities, similarities,
#from similarities_helper import similarities, lsi_column_similarities
from functools import partial
# inpath = "/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/tfidf/comment_authors_compex.parquet"
# term_colname='authors'
# outfile='/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/similarity/comment_test_compex_LSI'
# n_components=[10,50,100]
# included_subreddits="/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/included_subreddits.txt"
# n_iter=5
# random_state=1968
# algorithm='randomized'
# topN = None
# from_date=None
# to_date=None
# min_df=None
# max_df=None
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)
@@ -31,7 +30,7 @@ def lsi_similarities(inpath, term_colname, outfile, min_df=None, max_df=None, in
simfunc = partial(lsi_column_similarities,n_components=n_components,n_iter=n_iter,random_state=random_state,algorithm=algorithm,lsi_model_save=lsi_model)
return similarities(inpath=inpath, simfunc=simfunc, term_colname=term_colname, outfile=outfile, min_df=min_df, max_df=max_df, included_subreddits=included_subreddits, topN=topN, from_date=from_date, to_date=to_date, tfidf_colname=tfidf_colname)
return similarities(inpath=inpath, simfunc=simfunc, term_colname=term_colname, outfile=outfile, min_df=min_df, max_df=max_df, included_communities=included_subreddits, topN=topN, from_date=from_date, to_date=to_date, tfidf_colname=tfidf_colname)
# change so that these take in an input as an optional argument (for speed, but also for idf).
def term_lsi_similarities(inpath='/gscratch/comdata/output/reddit_similarity/tfidf/comment_terms_100k.parquet',outfile=None, min_df=None, max_df=None, included_subreddits=None, topN=None, from_date=None, to_date=None, algorithm='arpack', n_components=300,n_iter=5,random_state=1968):
@@ -63,7 +62,7 @@ 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,algorithm='arpack',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,n_components=300,n_iter=5,random_state=1968):
return lsi_similarities(inpath,
'author',
outfile,

1
similarities/similarities_helper.py
View File

@@ -262,7 +262,6 @@ 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 = []

51
similarities/tfidf.py
View File

@@ -1,12 +1,10 @@
import fire
from pyspark.sql import SparkSession
from pyspark.sql import functions as f
from similarities_helper import tfidf_dataset, build_weekly_tfidf_dataset, select_topN_subreddits
from functools import partial
from cdsc_ecology_utils.similarity.similarity_functions import tfidf_dataset, \
build_weekly_tfidf_dataset, select_topN_communities
inpath = '/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/tfidf/comment_authors_compex.parquet'
# include_terms is a path to a parquet file that contains a column of term_colname + '_id' to include.
def _tfidf_wrapper(func, inpath, outpath, topN, term_colname, exclude, included_subreddits, included_terms=None, min_df=None, max_df=None):
def _tfidf_wrapper(func, inpath, outpath, topN, term_colname, exclude, included_subreddits):
spark = SparkSession.builder.getOrCreate()
df = spark.read.parquet(inpath)
@@ -16,73 +14,52 @@ def _tfidf_wrapper(func, inpath, outpath, topN, term_colname, exclude, included_
if included_subreddits is not None:
include_subs = set(map(str.strip,open(included_subreddits)))
else:
include_subs = select_topN_subreddits(topN)
include_subs = select_topN_communities(topN)
include_subs = spark.sparkContext.broadcast(include_subs)
# term_id = term_colname + "_id"
if included_terms is not None:
terms_df = spark.read.parquet(included_terms)
terms_df = terms_df.select(term_colname).distinct()
df = df.join(terms_df, on=term_colname, how='left_semi')
dfwriter = func(df, include_subs.value, term_colname)
dfwriter = 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, min_df, max_df):
tfidf_func = partial(tfidf_dataset, max_df=max_df, min_df=min_df)
return _tfidf_wrapper(tfidf_func, inpath, outpath, topN, term_colname, exclude, included_subreddits)
def tfidf_weekly(inpath, outpath, static_tfidf_path, topN, term_colname, exclude, included_subreddits):
return _tfidf_wrapper(build_weekly_tfidf_dataset, inpath, outpath, topN, term_colname, exclude, included_subreddits, included_terms=static_tfidf_path)
def tfidf(inpath, outpath, topN, term_colname, exclude, included_subreddits):
return _tfidf_wrapper(tfidf_dataset, inpath, outpath, topN, term_colname, exclude, included_subreddits)
def tfidf_weekly(inpath, outpath, topN, term_colname, exclude, included_subreddits):
return _tfidf_wrapper(build_weekly_tfidf_dataset, inpath, outpath, topN, term_colname, exclude, included_subreddits)
def tfidf_authors(inpath="/gscratch/comdata/output/reddit_ngrams/comment_authors.parquet",
outpath='/gscratch/comdata/output/reddit_similarity/tfidf/comment_authors.parquet',
topN=None,
included_subreddits=None,
min_df=None,
max_df=None):
included_subreddits=None):
return tfidf(inpath,
outpath,
topN,
'author',
['[deleted]','AutoModerator'],
included_subreddits=included_subreddits,
min_df=min_df,
max_df=max_df
included_subreddits=included_subreddits
)
def tfidf_terms(inpath="/gscratch/comdata/output/reddit_ngrams/comment_terms.parquet",
outpath='/gscratch/comdata/output/reddit_similarity/tfidf/comment_terms.parquet',
topN=None,
included_subreddits=None,
min_df=None,
max_df=None):
included_subreddits=None):
return tfidf(inpath,
outpath,
topN,
'term',
[],
included_subreddits=included_subreddits,
min_df=min_df,
max_df=max_df
included_subreddits=included_subreddits
)
def tfidf_authors_weekly(inpath="/gscratch/comdata/output/reddit_ngrams/comment_authors.parquet",
static_tfidf_path="/gscratch/comdata/output/reddit_similarity/tfidf/comment_authors.parquet",
outpath='/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_authors.parquet',
topN=None,
included_subreddits=None):
return tfidf_weekly(inpath,
outpath,
static_tfidf_path,
topN,
'author',
['[deleted]','AutoModerator'],
@@ -90,7 +67,6 @@ def tfidf_authors_weekly(inpath="/gscratch/comdata/output/reddit_ngrams/comment_
)
def tfidf_terms_weekly(inpath="/gscratch/comdata/output/reddit_ngrams/comment_terms.parquet",
static_tfidf_path="/gscratch/comdata/output/reddit_similarity/tfidf/comment_terms.parquet",
outpath='/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_terms.parquet',
topN=None,
included_subreddits=None):
@@ -98,7 +74,6 @@ def tfidf_terms_weekly(inpath="/gscratch/comdata/output/reddit_ngrams/comment_te
return tfidf_weekly(inpath,
outpath,
static_tfidf_path,
topN,
'term',
[],

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/scrubbed/comdata/reddit_similarity/subreddits_by_num_comments_nonsfw.csv', index=False)
df.to_csv('/gscratch/comdata/output/reddit_similarity/subreddits_by_num_comments_nsfw.csv', index=False)

86
similarities/weekly_cosine_similarities.py
View File

@@ -13,23 +13,18 @@ from similarities_helper import pull_tfidf, column_similarities, write_weekly_si
from scipy.sparse import csr_matrix
from multiprocessing import Pool, cpu_count
from functools import partial
import pickle
# tfidf_path = "/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/similarity_weekly/comment_authors_tfidf.parquet"
# #tfidf_path = "/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data//comment_authors_compex.parquet"
# min_df=2
# included_subreddits="/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/included_subreddits.txt"
# max_df = None
# topN=100
# term_colname='author'
# # outfile = '/gscratch/comdata/output/reddit_similarity/weekly/comment_authors_test.parquet'
# # included_subreddits=None
outfile="/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/similarity_weekly/comment_authors.parquet"; infile="/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/tfidf_weekly/comment_authors_tfidf.parquet"; included_subreddits="/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/included_subreddits.txt"; lsi_model="/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/similarity/comment_authors_compex_LSI/2000_authors_LSIMOD.pkl"; n_components=1500; algorithm="randomized"; term_colname='author'; tfidf_path=infile; random_state=1968;
infile = "/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_authors_10k.parquet"
tfidf_path = "/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/tfidf/comment_authors_compex.parquet"
min_df=None
included_subreddits="/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/included_subreddits.txt"
max_df = None
topN=100
term_colname='author'
# outfile = '/gscratch/comdata/output/reddit_similarity/weekly/comment_authors_test.parquet'
# included_subreddits=None
# static_tfidf = "/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/tfidf/comment_authors_compex.parquet"
# dftest = spark.read.parquet(static_tfidf)
def _week_similarities(week, simfunc, tfidf_path, term_colname, included_subreddits, outdir:Path, subreddit_names, nterms, topN=None, min_df=None, max_df=None):
def _week_similarities(week, simfunc, tfidf_path, term_colname, min_df, max_df, included_subreddits, topN, outdir:Path, subreddit_names, nterms):
term = term_colname
term_id = term + '_id'
term_id_new = term + '_id_new'
@@ -37,19 +32,20 @@ def _week_similarities(week, simfunc, tfidf_path, term_colname, included_subredd
entries = pull_tfidf(infile = tfidf_path,
term_colname=term_colname,
min_df=min_df,
max_df=max_df,
included_subreddits=included_subreddits,
topN=topN,
week=week.isoformat(),
week=week,
rescale_idf=False)
tfidf_colname='tf_idf'
# if the max subreddit id we found is less than the number of subreddit names then we have to fill in 0s
mat = csr_matrix((entries[tfidf_colname],(entries[term_id_new]-1, entries.subreddit_id_new-1)),shape=(nterms,subreddit_names.shape[0]))
print('computing similarities')
print(simfunc)
sims = simfunc(mat)
del mat
sims = next(sims)[0]
sims = pd.DataFrame(sims)
sims = sims.rename({i: sr for i, sr in enumerate(subreddit_names.subreddit.values)}, axis=1)
sims['_subreddit'] = subreddit_names.subreddit.values
@@ -60,20 +56,18 @@ def pull_weeks(batch):
return set(batch.to_pandas()['week'])
# This requires a prefit LSI model, since we shouldn't fit different LSI models for every week.
def cosine_similarities_weekly_lsi(*args, n_components=100, lsi_model=None, **kwargs):
print(args)
print(kwargs)
def cosine_similarities_weekly_lsi(n_components=100, lsi_model=None, *args, **kwargs):
term_colname= kwargs.get('term_colname')
# lsi_model = "/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/similarity/comment_authors_compex_LSI/1000_author_LSIMOD.pkl"
#lsi_model = "/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/similarity/comment_terms_compex_LSI/1000_term_LSIMOD.pkl"
lsi_model = pickle.load(open(lsi_model,'rb'))
#simfunc = partial(lsi_column_similarities,n_components=n_components,random_state=random_state,algorithm='randomized',lsi_model=lsi_model)
simfunc = partial(lsi_column_similarities,n_components=n_components,random_state=kwargs.get('random_state'),lsi_model=lsi_model)
# simfunc = partial(lsi_column_similarities,n_components=n_components,n_iter=n_iter,random_state=random_state,algorithm='randomized',lsi_model_load=lsi_model)
simfunc = partial(lsi_column_similarities,n_components=n_components,n_iter=kwargs.get('n_iter'),random_state=kwargs.get('random_state'),algorithm=kwargs.get('algorithm'),lsi_model_load=lsi_model)
return cosine_similarities_weekly(*args, simfunc=simfunc, **kwargs)
#tfidf = spark.read.parquet('/gscratch/comdata/users/nathante/subreddit_tfidf_weekly.parquet')
def cosine_similarities_weekly(tfidf_path, outfile, term_colname, included_subreddits = None, topN = None, simfunc=column_similarities, min_df=None,max_df=None):
def cosine_similarities_weekly(tfidf_path, outfile, term_colname, min_df = None, max_df=None, included_subreddits = None, topN = 500, simfunc=column_similarities):
print(outfile)
# do this step in parallel if we have the memory for it.
# should be doable with pool.map
@@ -90,14 +84,12 @@ def cosine_similarities_weekly(tfidf_path, outfile, term_colname, included_subre
spark.stop()
print(f"computing weekly similarities")
week_similarities_helper = partial(_week_similarities,simfunc=simfunc, tfidf_path=tfidf_path, term_colname=term_colname, outdir=outfile, min_df=min_df, max_df=max_df, included_subreddits=included_subreddits, topN=None, subreddit_names=subreddit_names,nterms=nterms)
week_similarities_helper = partial(_week_similarities,simfunc=simfunc, tfidf_path=tfidf_path, term_colname=term_colname, outdir=outfile, min_df=min_df,max_df=max_df,included_subreddits=included_subreddits,topN=topN, subreddit_names=subreddit_names,nterms=nterms)
for week in weeks:
week_similarities_helper(week)
# pool = Pool(cpu_count())
# list(pool.imap(week_similarities_helper, weeks))
# pool.close()
pool = Pool(cpu_count())
list(pool.imap(week_similarities_helper,weeks))
pool.close()
# with Pool(cpu_count()) as pool: # maybe it can be done with 40 cores on the huge machine?
@@ -105,11 +97,10 @@ def author_cosine_similarities_weekly(outfile, infile='/gscratch/comdata/output/
return cosine_similarities_weekly(infile,
outfile,
'author',
min_df,
max_df,
included_subreddits,
topN,
min_df=2
)
topN)
def term_cosine_similarities_weekly(outfile, infile='/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_terms.parquet', min_df=None, max_df=None, included_subreddits=None, topN=None):
return cosine_similarities_weekly(infile,
@@ -121,29 +112,32 @@ def term_cosine_similarities_weekly(outfile, infile='/gscratch/comdata/output/re
topN)
def author_cosine_similarities_weekly_lsi(outfile, infile = '/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_authors_test.parquet', included_subreddits=None, n_components=100,lsi_model=None):
def author_cosine_similarities_weekly_lsi(outfile, infile = '/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_authors_test.parquet', min_df=2, max_df=None, included_subreddits=None, topN=None,n_components=100,lsi_model=None):
return cosine_similarities_weekly_lsi(infile,
outfile,
'author',
included_subreddits=included_subreddits,
min_df,
max_df,
included_subreddits,
topN,
n_components=n_components,
lsi_model=lsi_model
)
lsi_model=lsi_model)
def term_cosine_similarities_weekly_lsi(outfile, infile = '/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_terms.parquet', included_subreddits=None, n_components=100,lsi_model=None):
def term_cosine_similarities_weekly_lsi(outfile, infile = '/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_terms.parquet', min_df=None, max_df=None, included_subreddits=None, topN=500,n_components=100,lsi_model=None):
return cosine_similarities_weekly_lsi(infile,
outfile,
'term',
included_subreddits=included_subreddits,
min_df,
max_df,
included_subreddits,
topN,
n_components=n_components,
lsi_model=lsi_model,
)
lsi_model=lsi_model)
if __name__ == "__main__":
fire.Fire({'authors':author_cosine_similarities_weekly,
'terms':term_cosine_similarities_weekly,
'authors-lsi':author_cosine_similarities_weekly_lsi,
'terms-lsi':term_cosine_similarities_weekly_lsi
'terms-lsi':term_cosine_similarities_weekly
})

14
timeseries/cluster_timeseries.py
View File

@@ -12,6 +12,10 @@ def build_cluster_timeseries(term_clusters_path="/gscratch/comdata/output/reddit
author_densities_path="/gscratch/comdata/output/reddit_density/comment_authors_10000.feather",
output="data/subreddit_timeseries.parquet"):
clusters = load_clusters(term_clusters_path, author_clusters_path)
densities = load_densities(term_densities_path, author_densities_path)
spark = SparkSession.builder.getOrCreate()
df = spark.read.parquet("/gscratch/comdata/output/reddit_comments_by_subreddit.parquet")
@@ -22,15 +26,11 @@ def build_cluster_timeseries(term_clusters_path="/gscratch/comdata/output/reddit
ts = df.select(['subreddit','week','author']).distinct().groupby(['subreddit','week']).count()
ts = ts.repartition('subreddit')
if term_densities_path is not None and author_densities_path is not None:
densities = load_densities(term_densities_path, author_densities_path)
spk_densities = spark.createDataFrame(densities)
ts = ts.join(spk_densities, on='subreddit', how='inner')
clusters = load_clusters(term_clusters_path, author_clusters_path)
spk_clusters = spark.createDataFrame(clusters)
ts = ts.join(spk_clusters, on='subreddit', how='inner')
spk_densities = spark.createDataFrame(densities)
ts = ts.join(spk_densities, on='subreddit', how='inner')
ts.write.parquet(output, mode='overwrite')
if __name__ == "__main__":