X-Git-Url: https://code.communitydata.science/cdsc_reddit.git/blobdiff_plain/ce549c6c97058325ac6f1b9dab20406af1dbb2af..55b75ea6fcf421e95f4fe6b180dcec6e64676619:/similarities/weekly_cosine_similarities.py?ds=sidebyside diff --git a/similarities/weekly_cosine_similarities.py b/similarities/weekly_cosine_similarities.py old mode 100644 new mode 100755 index 7cafcb9..45327c7 --- a/similarities/weekly_cosine_similarities.py +++ b/similarities/weekly_cosine_similarities.py @@ -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 @@ -8,21 +9,27 @@ 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 +import pickle -# infile = "/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_authors_test.parquet" -# tfidf_path = infile -# min_df=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/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' @@ -30,8 +37,6 @@ 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.isoformat(), @@ -40,10 +45,11 @@ def _week_similarities(week, simfunc, tfidf_path, term_colname, min_df, max_df, 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.T) + 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 @@ -53,14 +59,30 @@ 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(*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_authors_compex_LSI/1000_author_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) + + 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, 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 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 @@ -68,26 +90,29 @@ 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=None, subreddit_names=subreddit_names,nterms=nterms) - pool = Pool(cpu_count()) - - list(pool.imap(week_similarities_helper,weeks)) - pool.close() + for week in weeks: + week_similarities_helper(week) + # 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, 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, max_df, included_subreddits, - topN) + topN, + min_df=2 +) -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 +120,30 @@ 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', included_subreddits=None, n_components=100,lsi_model=None): + return cosine_similarities_weekly_lsi(infile, + outfile, + 'author', + included_subreddits=included_subreddits, + 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', included_subreddits=None, n_components=100,lsi_model=None): + return cosine_similarities_weekly_lsi(infile, + outfile, + 'term', + included_subreddits=included_subreddits, + 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_lsi + }) +