From: Nathan TeBlunthuis Date: Thu, 12 Aug 2021 05:48:33 +0000 (-0700) Subject: lsi support for weekly similarities X-Git-Url: https://code.communitydata.science/cdsc_reddit.git/commitdiff_plain/541e125b28dbca5c06d2160a5cd59ce112657b2a?ds=sidebyside;hp=b7c39a3494ce214f315fd7e3bb0bf99bc58070d1 lsi support for weekly similarities --- diff --git a/clustering/hdbscan_clustering.py b/clustering/hdbscan_clustering.py index e533808..32cdf95 100644 --- a/clustering/hdbscan_clustering.py +++ b/clustering/hdbscan_clustering.py @@ -18,12 +18,12 @@ def test_select_hdbscan_clustering(): # cluster_selection_epsilons=[0,0.05,0.1,0.15], # cluster_selection_methods=['eom','leaf'], # lsi_dimensions='all') - inpath = "/gscratch/comdata/output/reddit_similarity/subreddit_comment_authors-tf_10k_LSI/" + inpath = "/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/similarity/comment_authors_compex_LSI" outpath = "test_hdbscan"; min_cluster_sizes=[2,3,4]; min_samples=[1,2,3]; cluster_selection_epsilons=[0,0.1,0.3,0.5]; - cluster_selection_methods=['eom']; + cluster_selection_methods=[1]; lsi_dimensions='all' gs = hdbscan_lsi_grid_sweep(inpath, "all", outpath, min_cluster_sizes, min_samples, cluster_selection_epsilons, cluster_selection_methods) gs.run(20) @@ -120,7 +120,7 @@ def run_hdbscan_grid_sweep(savefile, inpath, outpath, min_cluster_sizes=[2], mi map(int,min_cluster_sizes), map(int,min_samples), map(float,cluster_selection_epsilons), - map(float,cluster_selection_methods)) + cluster_selection_methods) obj.run() obj.save(savefile) diff --git a/clustering/hdbscan_clustering_lsi.py b/clustering/hdbscan_clustering_lsi.py index cbd44bd..a4c1efd 100644 --- a/clustering/hdbscan_clustering_lsi.py +++ b/clustering/hdbscan_clustering_lsi.py @@ -67,7 +67,7 @@ class _hdbscan_lsi_grid_sweep(grid_sweep): s += f"_lsi-{self.lsi_dim}" return s -def run_hdbscan_lsi_grid_sweep(savefile, inpath, outpath, min_cluster_sizes=[2], min_samples=[1], cluster_selection_epsilons=[0], cluster_selection_methods=['eom'],lsi_dimensions='all'): +def run_hdbscan_lsi_grid_sweep(savefile, inpath, outpath, min_cluster_sizes=[2], min_samples=[1], cluster_selection_epsilons=[0], cluster_selection_methods=[1],lsi_dimensions='all'): """Run hdbscan clustering once or more with different parameters. Usage: @@ -90,8 +90,8 @@ def run_hdbscan_lsi_grid_sweep(savefile, inpath, outpath, min_cluster_sizes=[2] list(map(int,min_cluster_sizes)), list(map(int,min_samples)), list(map(float,cluster_selection_epsilons)), - cluster_selection_methods - ) + cluster_selection_methods) + obj.run(10) obj.save(savefile) diff --git a/clustering/lsi_base.py b/clustering/lsi_base.py index f07bca6..80b7101 100644 --- a/clustering/lsi_base.py +++ b/clustering/lsi_base.py @@ -18,10 +18,11 @@ class lsi_grid_sweep(grid_sweep): self.subsweep = subsweep inpath = Path(inpath) if lsi_dimensions == 'all': - lsi_paths = list(inpath.glob("*")) + lsi_paths = list(inpath.glob("*.feather")) else: lsi_paths = [inpath / (str(dim) + '.feather') for dim in lsi_dimensions] + print(lsi_paths) lsi_nums = [int(p.stem) for p in lsi_paths] self.hasrun = False self.subgrids = [self.subsweep(lsi_path, outpath, lsi_dim, *args, **kwargs) for lsi_dim, lsi_path in zip(lsi_nums, lsi_paths)] diff --git a/clustering/pick_best_clustering.py b/clustering/pick_best_clustering.py old mode 100644 new mode 100755 index c541d23..e05e3ac --- a/clustering/pick_best_clustering.py +++ b/clustering/pick_best_clustering.py @@ -1,11 +1,12 @@ +#!/usr/bin/env python3 import fire import pandas as pd from pathlib import Path import shutil -selection_data="/gscratch/comdata/output/reddit_clustering/subreddit_comment_authors-tf_10k_LSI/hdbscan/selection_data.csv" +selection_data="/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/clustering/comment_authors_compex_LSI/selection_data.csv" outpath = 'test_best.feather' -min_clusters=50; max_isolates=5000; min_cluster_size=2 +min_clusters=50; max_isolates=7500; min_cluster_size=2 # pick the best clustering according to silhouette score subject to contraints def pick_best_clustering(selection_data, output, min_clusters, max_isolates, min_cluster_size): @@ -18,11 +19,15 @@ def pick_best_clustering(selection_data, output, min_clusters, max_isolates, min df.loc[df.n_isolates_0,'n_isolates'] = 0 df.loc[~df.n_isolates_0,'n_isolates'] = df.loc[~df.n_isolates_0].n_isolates_str.apply(lambda l: int(l)) - best_cluster = df[(df.n_isolates <= max_isolates)&(df.n_clusters >= min_clusters)&(df.min_cluster_size==min_cluster_size)].iloc[df.shape[1]] + best_cluster = df[(df.n_isolates <= max_isolates)&(df.n_clusters >= min_clusters)&(df.min_cluster_size==min_cluster_size)] + best_cluster = best_cluster.iloc[0] + + best_lsi_dimensions = best_cluster.lsi_dimensions print(best_cluster.to_dict()) best_path = Path(best_cluster.outpath) / (str(best_cluster['name']) + ".feather") shutil.copy(best_path,output) - + print(f"lsi dimensions:{best_lsi_dimensions}") + if __name__ == "__main__": fire.Fire(pick_best_clustering) diff --git a/similarities/similarities_helper.py b/similarities/similarities_helper.py index 13845d1..d97e519 100644 --- a/similarities/similarities_helper.py +++ b/similarities/similarities_helper.py @@ -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) diff --git a/similarities/tfidf.py b/similarities/tfidf.py index 19d3013..01b0b20 100644 --- a/similarities/tfidf.py +++ b/similarities/tfidf.py @@ -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', diff --git a/similarities/weekly_cosine_similarities.py b/similarities/weekly_cosine_similarities.py old mode 100644 new mode 100755 index 7cafcb9..6ce30b8 --- 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 @@ -8,17 +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 + })