From: Nathan TeBlunthuis Date: Wed, 6 Apr 2022 18:14:13 +0000 (-0700) Subject: Merge remote-tracking branch 'refs/remotes/origin/excise_reindex' into excise_reindex X-Git-Url: https://code.communitydata.science/cdsc_reddit.git/commitdiff_plain/55b75ea6fcf421e95f4fe6b180dcec6e64676619?hp=197518a222a321a8027c3dc5a4121350c47d0779 Merge remote-tracking branch 'refs/remotes/origin/excise_reindex' into excise_reindex --- diff --git a/density/overlap_density.py b/density/overlap_density.py index 2036824..ef0eb26 100644 --- a/density/overlap_density.py +++ b/density/overlap_density.py @@ -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 diff --git a/similarities/lsi_similarities.py b/similarities/lsi_similarities.py index 493755f..57a2d0d 100644 --- a/similarities/lsi_similarities.py +++ b/similarities/lsi_similarities.py @@ -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 diff --git a/similarities/tfidf.py b/similarities/tfidf.py index bbae528..c44fd0d 100644 --- a/similarities/tfidf.py +++ b/similarities/tfidf.py @@ -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', [], diff --git a/similarities/weekly_cosine_similarities.py b/similarities/weekly_cosine_similarities.py index 6ce30b8..45327c7 100755 --- a/similarities/weekly_cosine_similarities.py +++ b/similarities/weekly_cosine_similarities.py @@ -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 - -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): +import pickle + +# 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; + +# 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" - - # simfunc = partial(lsi_column_similarities,n_components=n_components,n_iter=n_iter,random_state=random_state,algorithm='randomized',lsi_model_load=lsi_model) + # 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=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()) - - 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? @@ -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 }) + diff --git a/timeseries/cluster_timeseries.py b/timeseries/cluster_timeseries.py index 91fa705..2286ab0 100644 --- a/timeseries/cluster_timeseries.py +++ b/timeseries/cluster_timeseries.py @@ -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__":