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Merge remote-tracking branch 'refs/remotes/origin/excise_reindex' into excise_reindex

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Nathan TeBlunthuis 2022-04-06 11:14:13 -07:00
commit 55b75ea6fc
5 changed files with 96 additions and 64 deletions
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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 reindex_tfidf
# sys.path.append("..")
# sys.path.append("../similarities")
# from similarities.similarities_helper import pull_tfidf
# this is the mean of the ratio of the overlap to the focal size.
# mean shared membership per focal community member

8
similarities/lsi_similarities.py
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@ -5,14 +5,14 @@ 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'
# 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='arpack'
# algorithm='randomized'
# topN = None
# from_date=None
# to_date=None

46
similarities/tfidf.py
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@ -2,8 +2,11 @@ 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
def _tfidf_wrapper(func, inpath, outpath, topN, term_colname, exclude, included_subreddits):
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):
spark = SparkSession.builder.getOrCreate()
df = spark.read.parquet(inpath)
@ -15,50 +18,71 @@ def _tfidf_wrapper(func, inpath, outpath, topN, term_colname, exclude, included_
else:
include_subs = select_topN_subreddits(topN)
dfwriter = func(df, include_subs, term_colname)
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.parquet(outpath,mode='overwrite',compression='snappy')
spark.stop()
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(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_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):
included_subreddits=None,
min_df=None,
max_df=None):
return tfidf(inpath,
outpath,
topN,
'author',
['[deleted]','AutoModerator'],
included_subreddits=included_subreddits
included_subreddits=included_subreddits,
min_df=min_df,
max_df=max_df
)
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):
included_subreddits=None,
min_df=None,
max_df=None):
return tfidf(inpath,
outpath,
topN,
'term',
[],
included_subreddits=included_subreddits
included_subreddits=included_subreddits,
min_df=min_df,
max_df=max_df
)
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'],
@ -66,6 +90,7 @@ 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):
@ -73,6 +98,7 @@ def tfidf_terms_weekly(inpath="/gscratch/comdata/output/reddit_ngrams/comment_te
return tfidf_weekly(inpath,
outpath,
static_tfidf_path,
topN,
'term',
[],

84
similarities/weekly_cosine_similarities.py
View File

@ -13,18 +13,23 @@ 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
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
# 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;
def _week_similarities(week, simfunc, tfidf_path, term_colname, min_df, max_df, included_subreddits, topN, outdir:Path, subreddit_names, nterms):
# static_tfidf = "/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/tfidf/comment_authors_compex.parquet"
# dftest = spark.read.parquet(static_tfidf)
def _week_similarities(week, simfunc, tfidf_path, term_colname, included_subreddits, outdir:Path, subreddit_names, nterms, topN=None, min_df=None, max_df=None):
term = term_colname
term_id = term + '_id'
term_id_new = term + '_id_new'
@ -32,20 +37,19 @@ def _week_similarities(week, simfunc, tfidf_path, term_colname, min_df, max_df,
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,
week=week.isoformat(),
rescale_idf=False)
tfidf_colname='tf_idf'
# if the max subreddit id we found is less than the number of subreddit names then we have to fill in 0s
mat = csr_matrix((entries[tfidf_colname],(entries[term_id_new]-1, entries.subreddit_id_new-1)),shape=(nterms,subreddit_names.shape[0]))
print('computing similarities')
print(simfunc)
sims = simfunc(mat)
del mat
sims = next(sims)[0]
sims = pd.DataFrame(sims)
sims = sims.rename({i: sr for i, sr in enumerate(subreddit_names.subreddit.values)}, axis=1)
sims['_subreddit'] = subreddit_names.subreddit.values
@ -56,18 +60,20 @@ 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(n_components=100, lsi_model=None, *args, **kwargs):
def cosine_similarities_weekly_lsi(*args, n_components=100, lsi_model=None, **kwargs):
print(args)
print(kwargs)
term_colname= kwargs.get('term_colname')
#lsi_model = "/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/similarity/comment_terms_compex_LSI/1000_term_LSIMOD.pkl"
# lsi_model = "/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/similarity/comment_authors_compex_LSI/1000_author_LSIMOD.pkl"
# 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)
lsi_model = pickle.load(open(lsi_model,'rb'))
#simfunc = partial(lsi_column_similarities,n_components=n_components,random_state=random_state,algorithm='randomized',lsi_model=lsi_model)
simfunc = partial(lsi_column_similarities,n_components=n_components,random_state=kwargs.get('random_state'),lsi_model=lsi_model)
return cosine_similarities_weekly(*args, simfunc=simfunc, **kwargs)
#tfidf = spark.read.parquet('/gscratch/comdata/users/nathante/subreddit_tfidf_weekly.parquet')
def cosine_similarities_weekly(tfidf_path, outfile, term_colname, min_df = None, max_df=None, included_subreddits = None, topN = 500, simfunc=column_similarities):
def cosine_similarities_weekly(tfidf_path, outfile, term_colname, included_subreddits = None, topN = None, simfunc=column_similarities, min_df=None,max_df=None):
print(outfile)
# do this step in parallel if we have the memory for it.
# should be doable with pool.map
@ -84,12 +90,14 @@ def cosine_similarities_weekly(tfidf_path, outfile, term_colname, min_df = None,
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=topN, 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=None, subreddit_names=subreddit_names,nterms=nterms)
pool = Pool(cpu_count())
for week in weeks:
week_similarities_helper(week)
# pool = Pool(cpu_count())
list(pool.imap(week_similarities_helper,weeks))
pool.close()
# 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?
@ -97,10 +105,11 @@ def author_cosine_similarities_weekly(outfile, infile='/gscratch/comdata/output/
return cosine_similarities_weekly(infile,
outfile,
'author',
min_df,
max_df,
included_subreddits,
topN)
topN,
min_df=2
)
def term_cosine_similarities_weekly(outfile, infile='/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_terms.parquet', min_df=None, max_df=None, included_subreddits=None, topN=None):
return cosine_similarities_weekly(infile,
@ -112,32 +121,29 @@ 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', min_df=2, max_df=None, included_subreddits=None, topN=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', included_subreddits=None, n_components=100,lsi_model=None):
return cosine_similarities_weekly_lsi(infile,
outfile,
'author',
min_df,
max_df,
included_subreddits,
topN,
included_subreddits=included_subreddits,
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', min_df=None, max_df=None, included_subreddits=None, topN=500,n_components=100,lsi_model=None):
def term_cosine_similarities_weekly_lsi(outfile, infile = '/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_terms.parquet', included_subreddits=None, n_components=100,lsi_model=None):
return cosine_similarities_weekly_lsi(infile,
outfile,
'term',
min_df,
max_df,
included_subreddits,
topN,
included_subreddits=included_subreddits,
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
'terms-lsi':term_cosine_similarities_weekly_lsi
})

14
timeseries/cluster_timeseries.py
View File

@ -12,10 +12,6 @@ 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")
@ -26,11 +22,15 @@ 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')
spk_clusters = spark.createDataFrame(clusters)
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__":