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lsi support for weekly similarities

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This commit is contained in:
Nathan TeBlunthuis
2021-08-11 22:48:33 -07:00
parent b7c39a3494
commit 541e125b28
7 changed files with 95 additions and 38 deletions
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8
similarities/similarities_helper.py
View File

@@ -97,6 +97,7 @@ def _pull_or_reindex_tfidf(infile, term_colname, min_df=None, max_df=None, inclu
'relative_tf':ds.field('relative_tf').cast('float32'),
'tf_idf':ds.field('tf_idf').cast('float32')}
print(projection)
df = tfidf_ds.to_table(filter=ds_filter,columns=projection)
@@ -240,7 +241,6 @@ def test_lsi_sims():
def lsi_column_similarities(tfidfmat,n_components=300,n_iter=10,random_state=1968,algorithm='randomized',lsi_model_save=None,lsi_model_load=None):
# first compute the lsi of the matrix
# then take the column similarities
print("running LSI",flush=True)
if type(n_components) is int:
n_components = [n_components]
@@ -249,10 +249,14 @@ def lsi_column_similarities(tfidfmat,n_components=300,n_iter=10,random_state=196
svd_components = n_components[0]
if lsi_model_load is not None:
if lsi_model_load is not None and Path(lsi_model_load).exists():
print("loading LSI")
mod = pickle.load(open(lsi_model_load ,'rb'))
lsi_model_save = lsi_model_load
else:
print("running LSI",flush=True)
svd = TruncatedSVD(n_components=svd_components,random_state=random_state,algorithm=algorithm,n_iter=n_iter)
mod = svd.fit(tfidfmat.T)

22
similarities/tfidf.py
View File

@@ -4,7 +4,7 @@ from pyspark.sql import functions as f
from similarities_helper import tfidf_dataset, build_weekly_tfidf_dataset, select_topN_subreddits
def _tfidf_wrapper(func, inpath, outpath, topN, term_colname, exclude, included_subreddits):
spark = SparkSession.builder.getOrCreate()
spark = SparkSession.builder.getOrCreate()y
df = spark.read.parquet(inpath)
@@ -26,11 +26,12 @@ def tfidf(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(outpath='/gscratch/comdata/output/reddit_similarity/tfidf/comment_authors.parquet',
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):
return tfidf("/gscratch/comdata/output/reddit_ngrams/comment_authors.parquet",
return tfidf(inpath,
outpath,
topN,
'author',
@@ -38,11 +39,12 @@ def tfidf_authors(outpath='/gscratch/comdata/output/reddit_similarity/tfidf/comm
included_subreddits=included_subreddits
)
def tfidf_terms(outpath='/gscratch/comdata/output/reddit_similarity/tfidf/comment_terms.parquet',
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):
return tfidf("/gscratch/comdata/output/reddit_ngrams/comment_terms.parquet",
return tfidf(inpath,
outpath,
topN,
'term',
@@ -50,11 +52,12 @@ def tfidf_terms(outpath='/gscratch/comdata/output/reddit_similarity/tfidf/commen
included_subreddits=included_subreddits
)
def tfidf_authors_weekly(outpath='/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_authors.parquet',
def tfidf_authors_weekly(inpath="/gscratch/comdata/output/reddit_ngrams/comment_authors.parquet",
outpath='/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_authors.parquet',
topN=None,
included_subreddits=None):
return tfidf_weekly("/gscratch/comdata/output/reddit_ngrams/comment_authors.parquet",
return tfidf_weekly(inpath,
outpath,
topN,
'author',
@@ -62,12 +65,13 @@ def tfidf_authors_weekly(outpath='/gscratch/comdata/output/reddit_similarity/tfi
included_subreddits=included_subreddits
)
def tfidf_terms_weekly(outpath='/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_terms.parquet',
def tfidf_terms_weekly(inpath="/gscratch/comdata/output/reddit_ngrams/comment_terms.parquet",
outpath='/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_terms.parquet',
topN=None,
included_subreddits=None):
return tfidf_weekly("/gscratch/comdata/output/reddit_ngrams/comment_terms.parquet",
return tfidf_weekly(inpath,
outpath,
topN,
'term',

75
similarities/weekly_cosine_similarities.py Normal file → Executable file
View File

@@ -1,3 +1,4 @@
#!/usr/bin/env python3
from pyspark.sql import functions as f
from pyspark.sql import SparkSession
from pyspark.sql import Window
@@ -8,17 +9,18 @@ import pandas as pd
import fire
from itertools import islice, chain
from pathlib import Path
from similarities_helper import pull_tfidf, column_similarities, write_weekly_similarities
from similarities_helper import pull_tfidf, column_similarities, write_weekly_similarities, lsi_column_similarities
from scipy.sparse import csr_matrix
from multiprocessing import Pool, cpu_count
from functools import partial
# infile = "/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_authors_test.parquet"
# tfidf_path = infile
# min_df=None
# max_df = None
# topN=100
# term_colname='author'
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
@@ -34,7 +36,7 @@ def _week_similarities(week, simfunc, tfidf_path, term_colname, min_df, max_df,
max_df=max_df,
included_subreddits=included_subreddits,
topN=topN,
week=week.isoformat(),
week=week,
rescale_idf=False)
tfidf_colname='tf_idf'
@@ -42,7 +44,7 @@ def _week_similarities(week, simfunc, tfidf_path, term_colname, min_df, max_df,
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')
sims = simfunc(mat.T)
sims = simfunc(mat)
del mat
sims = pd.DataFrame(sims)
sims = sims.rename({i: sr for i, sr in enumerate(subreddit_names.subreddit.values)}, axis=1)
@@ -53,14 +55,28 @@ def _week_similarities(week, simfunc, tfidf_path, term_colname, min_df, max_df,
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):
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"
# 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, min_df = None, max_df=None, included_subreddits = None, topN = 500):
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
spark = SparkSession.builder.getOrCreate()
df = spark.read.parquet(tfidf_path)
# load subreddits + topN
subreddit_names = df.select(['subreddit','subreddit_id']).distinct().toPandas()
subreddit_names = subreddit_names.sort_values("subreddit_id")
nterms = df.select(f.max(f.col(term_colname + "_id")).alias('max')).collect()[0].max
@@ -68,7 +84,7 @@ 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=column_similarities, tfidf_path=tfidf_path, term_colname=term_colname, outdir=outfile, min_df=min_df,max_df=max_df,included_subreddits=included_subreddits,topN=topN, 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)
pool = Pool(cpu_count())
@@ -77,8 +93,8 @@ def cosine_similarities_weekly(tfidf_path, outfile, term_colname, min_df = None,
# with Pool(cpu_count()) as pool: # maybe it can be done with 40 cores on the huge machine?
def author_cosine_similarities_weekly(outfile, min_df=2, max_df=None, included_subreddits=None, topN=500):
return cosine_similarities_weekly('/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_authors_test.parquet',
def author_cosine_similarities_weekly(outfile, infile='/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_authors_test.parquet', min_df=2, max_df=None, included_subreddits=None, topN=500):
return cosine_similarities_weekly(infile,
outfile,
'author',
min_df,
@@ -86,8 +102,8 @@ def author_cosine_similarities_weekly(outfile, min_df=2, max_df=None, included_s
included_subreddits,
topN)
def term_cosine_similarities_weekly(outfile, min_df=None, max_df=None, included_subreddits=None, topN=500):
return cosine_similarities_weekly('/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_terms.parquet',
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,
outfile,
'term',
min_df,
@@ -95,6 +111,33 @@ def term_cosine_similarities_weekly(outfile, min_df=None, max_df=None, included_
included_subreddits,
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):
return cosine_similarities_weekly_lsi(infile,
outfile,
'author',
min_df,
max_df,
included_subreddits,
topN,
n_components=n_components,
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):
return cosine_similarities_weekly_lsi(infile,
outfile,
'term',
min_df,
max_df,
included_subreddits,
topN,
n_components=n_components,
lsi_model=lsi_model)
if __name__ == "__main__":
fire.Fire({'authors':author_cosine_similarities_weekly,
'terms':term_cosine_similarities_weekly})
'terms':term_cosine_similarities_weekly,
'authors-lsi':author_cosine_similarities_weekly_lsi,
'terms-lsi':term_cosine_similarities_weekly
})