From 39c581bee915c97acb67e0de9e0c75e234f55050 Mon Sep 17 00:00:00 2001 From: Nate E TeBlunthuis Date: Tue, 10 Nov 2020 13:18:57 -0800 Subject: [PATCH] Reuse code for term and author cosine similarity. --- author_cosine_similarity.py | 78 +++++++++++++++++++++++++++++++++++++ term_cosine_similarity.py | 75 ++++++++++++----------------------- 2 files changed, 103 insertions(+), 50 deletions(-) create mode 100644 author_cosine_similarity.py diff --git a/author_cosine_similarity.py b/author_cosine_similarity.py new file mode 100644 index 0000000..7b2a766 --- /dev/null +++ b/author_cosine_similarity.py @@ -0,0 +1,78 @@ +from pyspark.sql import functions as f +from pyspark.sql import SparkSession +from pyspark.sql import Window +import numpy as np +import pyarrow +import pandas as pd +import fire +from itertools import islice +from pathlib import Path +from similarities_helper import cosine_similarities + +spark = SparkSession.builder.getOrCreate() +conf = spark.sparkContext.getConf() + +# outfile = '/gscratch/comdata/users/nathante/test_similarities_500.feather'; min_df = None; included_subreddits=None; similarity_threshold=0; +def author_cosine_similarities(outfile, min_df = None, included_subreddits=None, similarity_threshold=0, topN=500, exclude_phrases=True): + ''' + Compute similarities between subreddits based on tfi-idf vectors of author comments + + included_subreddits : string + Text file containing a list of subreddits to include (one per line) if included_subreddits is None then do the top 500 subreddits + + similarity_threshold : double (default = 0) + set > 0 for large numbers of subreddits to get an approximate solution using the DIMSUM algorithm +https://stanford.edu/~rezab/papers/dimsum.pdf. If similarity_threshold=0 we get an exact solution using an O(N^2) algorithm. + + min_df : int (default = 0.1 * (number of included_subreddits) + exclude terms that appear in fewer than this number of documents. + + outfile: string + where to output csv and feather outputs +''' + + print(outfile) + print(exclude_phrases) + + tfidf = spark.read.parquet('/gscratch/comdata/users/nathante/subreddit_tfidf_authors.parquet_test1/part-00000-107cee94-92d8-4265-b804-40f1e7f1aaf2-c000.snappy.parquet') + + if included_subreddits is None: + included_subreddits = list(islice(open("/gscratch/comdata/users/nathante/cdsc-reddit/top_25000_subs_by_comments.txt"),topN)) + included_subreddits = {s.strip('\n') for s in included_subreddits} + + else: + included_subreddits = set(open(included_subreddits)) + + sim_dist, tfidf = cosine_similarities(tfidf, 'author', min_df, included_subreddits, similarity_threshold) + + p = Path(outfile) + + output_feather = Path(str(p).replace("".join(p.suffixes), ".feather")) + output_csv = Path(str(p).replace("".join(p.suffixes), ".csv")) + output_parquet = Path(str(p).replace("".join(p.suffixes), ".parquet")) + sim_dist = sim_dist.entries.toDF() + + sim_dist = sim_dist.repartition(1) + sim_dist.write.parquet(str(output_parquet),mode='overwrite',compression='snappy') + + spark.stop() + + #instead of toLocalMatrix() why not read as entries and put strait into numpy + sim_entries = pd.read_parquet(output_parquet) + + df = tfidf.select('subreddit','subreddit_id_new').distinct().toPandas() + df['subreddit_id_new'] = df['subreddit_id_new'] - 1 + df = df.sort_values('subreddit_id_new').reset_index(drop=True) + df = df.set_index('subreddit_id_new') + + similarities = sim_entries.join(df, on='i') + similarities = similarities.rename(columns={'subreddit':"subreddit_i"}) + similarities = similarities.join(df, on='j') + similarities = similarities.rename(columns={'subreddit':"subreddit_j"}) + + similarities.write_feather(output_feather) + similarities.write_csv(output_csv) + return similarities + +if __name__ == '__main__': + fire.Fire(term_cosine_similarities) diff --git a/term_cosine_similarity.py b/term_cosine_similarity.py index ba6d2c9..c487c5b 100644 --- a/term_cosine_similarity.py +++ b/term_cosine_similarity.py @@ -8,14 +8,13 @@ import pandas as pd import fire from itertools import islice from pathlib import Path - -min_df = 1000 +from similarities_helper import build_cosine_similarities spark = SparkSession.builder.getOrCreate() conf = spark.sparkContext.getConf() # outfile = '/gscratch/comdata/users/nathante/test_similarities_500.feather'; min_df = None; included_subreddits=None; similarity_threshold=0; -def spark_similarities(outfile, min_df = None, included_subreddits=None, similarity_threshold=0): +def term_cosine_similarities(outfile, min_df = None, included_subreddits=None, similarity_threshold=0, topN=500, exclude_phrases=True): ''' Compute similarities between subreddits based on tfi-idf vectors of comment texts @@ -33,73 +32,49 @@ https://stanford.edu/~rezab/papers/dimsum.pdf. If similarity_threshold=0 we get where to output csv and feather outputs ''' + print(outfile) + print(exclude_phrases) + tfidf = spark.read.parquet('/gscratch/comdata/users/nathante/subreddit_tfidf.parquet') if included_subreddits is None: - included_subreddits = list(islice(open("/gscratch/comdata/users/nathante/cdsc-reddit/top_25000_subs_by_comments.txt"),500)) - included_subreddits = [s.strip('\n') for s in included_subreddits] + included_subreddits = list(islice(open("/gscratch/comdata/users/nathante/cdsc-reddit/top_25000_subs_by_comments.txt"),topN)) + included_subreddits = {s.strip('\n') for s in included_subreddits} else: included_subreddits = set(open(included_subreddits)) - if min_df is None: - min_df = 0.1 * len(included_subreddits) - - tfidf = tfidf.filter(f.col("subreddit").isin(included_subreddits)) - - # reset the subreddit ids - sub_ids = tfidf.select('subreddit_id').distinct() - sub_ids = sub_ids.withColumn("subreddit_id_new",f.row_number().over(Window.orderBy("subreddit_id"))) - tfidf = tfidf.join(sub_ids,'subreddit_id') - - # only use terms in at least min_df included subreddits - new_count = tfidf.groupBy('term_id').agg(f.count('term_id').alias('new_count')) - term_ids = term_ids.join(new_count,'term_id') - term_ids = term_ids.filter(new_count >= min_df) + if exclude_phrases == True: + tfidf = tfidf.filter(~f.col(term).contains("_")) - # reset the term ids - term_ids = tfidf.select('term_id').distinct() - term_ids = term_ids.withColumn("term_id_new",f.row_number().over(Window.orderBy("term_id"))) - tfidf = tfidf.join(term_ids,'term_id') + sim_dist, tfidf = cosine_similarities(tfidf, 'term', min_df, include_subreddits, similarity_threshold) - # step 1 make an rdd of entires - # sorted by (dense) spark subreddit id - entries = tfidf.select(f.col("term_id_new")-1,f.col("subreddit_id_new")-1,"tf_idf").rdd - - # step 2 make it into a distributed.RowMatrix - coordMat = CoordinateMatrix(entries) - - # this needs to be an IndexedRowMatrix() - mat = coordMat.toRowMatrix() + p = Path(outfile) - #goal: build a matrix of subreddit columns and tf-idfs rows - sim_dist = mat.columnSimilarities(threshold=similarity_threshold) + output_feather = Path(str(p).replace("".join(p.suffixes), ".feather")) + output_csv = Path(str(p).replace("".join(p.suffixes), ".csv")) + output_parquet = Path(str(p).replace("".join(p.suffixes), ".parquet")) - print(sim_dist.numRows(), sim_dist.numCols()) + sim_dist.entries.toDF().write.parquet(str(output_parquet),mode='overwrite',compression='snappy') + + spark.stop() #instead of toLocalMatrix() why not read as entries and put strait into numpy - sim_entries = sim_dist.entries.collect() - - sim_entries = pd.DataFrame([{'i':me.i,'j':me.j,'value':me.value} for me in sim_entries]) + sim_entries = pd.read_parquet(output_parquet) df = tfidf.select('subreddit','subreddit_id_new').distinct().toPandas() - + df['subreddit_id_new'] = df['subreddit_id_new'] - 1 df = df.sort_values('subreddit_id_new').reset_index(drop=True) - df = df.set_index('subreddit_id_new') similarities = sim_entries.join(df, on='i') - similarities = sim_entries.rename(columns={'subreddit':"subreddit_i"}) - similarities = sim_entries.join(df, on='j') - similarities = sim_entries.rename(columns={'subreddit':"subreddit_j"}) - - p = Path(outfile) - output_feather = Path(str(p).replace("".join(p.suffixes), ".feather")) - output_csv = Path(str(p).replace("".join(p.suffixes), ".csv")) + similarities = similarities.rename(columns={'subreddit':"subreddit_i"}) + similarities = similarities.join(df, on='j') + similarities = similarities.rename(columns={'subreddit':"subreddit_j"}) - pyarrow.write_feather(similarities,output_feather) - pyarrow.write_csv(similarities,output_csv) + similarities.write_feather(output_feather) + similarities.write_csv(output_csv) return similarities if __name__ == '__main__': - fire.Fire(spark_similarities) + fire.Fire(term_cosine_similarities) -- 2.39.5