+++ /dev/null
-#!/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, chain
-from pathlib import Path
-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_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
-
-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}")
-
- 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]))
-
- 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)
-
-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, 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
- 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?
-
-
-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, 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,
- 'authors-lsi':author_cosine_similarities_weekly_lsi,
- 'terms-lsi':term_cosine_similarities_weekly
- })
projects / cdsc_reddit.git / blobdiff