+#!/usr/bin/env python3
from pyspark.sql import functions as f
from pyspark.sql import SparkSession
from pyspark.sql import Window
import numpy as np
import pyarrow
+import pyarrow.dataset as ds
import pandas as pd
import fire
-from itertools import islice
+from itertools import islice, chain
from pathlib import Path
-from similarities_helper import *
-from multiprocessing import pool
+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
-def _week_similarities(tempdir, term_colname, week):
- print(f"loading matrix: {week}")
- mat = read_tfidf_matrix_weekly(tempdir.name, term_colname, week)
- print('computing similarities')
- sims = column_similarities(mat)
- del mat
+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
- names = subreddit_names.loc[subreddit_names.week == week]
- sims = pd.DataFrame(sims.todense())
+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'
+ print(f"loading matrix: {week}")
- sims = sims.rename({i: sr for i, sr in enumerate(names.subreddit.values)}, axis=1)
- sims['_subreddit'] = names.subreddit.values
+ 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,
+ 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]))
- write_weekly_similarities(outfile, sims, week, names)
+ print('computing similarities')
+ 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)
+ sims['_subreddit'] = subreddit_names.subreddit.values
+ outfile = str(Path(outdir) / str(week))
+ write_weekly_similarities(outfile, sims, week, subreddit_names)
-#tfidf = spark.read.parquet('/gscratch/comdata/users/nathante/subreddit_tfidf_weekly.parquet')
-def cosine_similarities_weekly(tfidf_path, outfile, term_colname, min_df = None, included_subreddits = None, topN = 500):
- spark = SparkSession.builder.getOrCreate()
- conf = spark.sparkContext.getConf()
- print(outfile)
- tfidf = spark.read.parquet(tfidf_path)
-
- if included_subreddits is None:
- included_subreddits = select_topN_subreddits(topN)
- else:
- included_subreddits = set(open(included_subreddits))
+def pull_weeks(batch):
+ return set(batch.to_pandas()['week'])
- print(f"computing weekly similarities for {len(included_subreddits)} subreddits")
+# 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"
- print("creating temporary parquet with matrix indicies")
- tempdir = prep_tfidf_entries_weekly(tfidf, term_colname, min_df, included_subreddits)
+ # simfunc = partial(lsi_column_similarities,n_components=n_components,n_iter=n_iter,random_state=random_state,algorithm='randomized',lsi_model_load=lsi_model)
- tfidf = spark.read.parquet(tempdir.name)
+ 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)
- # the ids can change each week.
- subreddit_names = tfidf.select(['subreddit','subreddit_id_new','week']).distinct().toPandas()
- subreddit_names = subreddit_names.sort_values("subreddit_id_new")
- subreddit_names['subreddit_id_new'] = subreddit_names['subreddit_id_new'] - 1
- spark.stop()
+ return cosine_similarities_weekly(*args, simfunc=simfunc, **kwargs)
- weeks = sorted(list(subreddit_names.week.drop_duplicates()))
+#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):
+ print(outfile)
# do this step in parallel if we have the memory for it.
# should be doable with pool.map
- def week_similarities_helper(week):
- _week_similarities(tempdir, term_colname, week)
+ 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
+ weeks = df.select(f.col("week")).distinct().toPandas().week.values
+ 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)
+
+ 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?
- with Pool(40) as pool: # maybe it can be done with 40 cores on the huge machine?
- list(pool.map(weeks,week_similarities_helper))
-def author_cosine_similarities_weekly(outfile, min_df=2 , included_subreddits=None, topN=500):
- return cosine_similarities_weekly('/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_authors_100k.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,
+ max_df,
included_subreddits,
topN)
-def term_cosine_similarities_weekly(outfile, min_df=None, included_subreddits=None, topN=500):
- return cosine_similarities_weekly('/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_terms_100k.parquet',
- outfile,
- 'term',
- min_df,
- included_subreddits,
- 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,
+ outfile,
+ 'term',
+ min_df,
+ max_df,
+ 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
+ })
projects / cdsc_reddit.git / blobdiff