From: Nate E TeBlunthuis Date: Mon, 2 Nov 2020 18:40:12 +0000 (-0800) Subject: Merge branch 'master' of code:cdsc_reddit X-Git-Url: https://code.communitydata.science/cdsc_reddit.git/commitdiff_plain/8b8c45ee2da18a0c7139e7ff88991aa2a1fff12f?hp=0882878166e2d3608411185bbe19e448b5983b86 Merge branch 'master' of code:cdsc_reddit --- diff --git a/term_cosine_similarity.py b/term_cosine_similarity.py new file mode 100644 index 0000000..ba6d2c9 --- /dev/null +++ b/term_cosine_similarity.py @@ -0,0 +1,105 @@ +from pyspark.sql import functions as f +from pyspark.sql import SparkSession +from pyspark.sql import Window +from pyspark.mllib.linalg.distributed import RowMatrix, CoordinateMatrix +import numpy as np +import pyarrow +import pandas as pd +import fire +from itertools import islice +from pathlib import Path + +min_df = 1000 + +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): + ''' + Compute similarities between subreddits based on tfi-idf vectors of comment texts + + 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 +''' + + 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] + + 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) + + # 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') + + # 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() + + #goal: build a matrix of subreddit columns and tf-idfs rows + sim_dist = mat.columnSimilarities(threshold=similarity_threshold) + + print(sim_dist.numRows(), sim_dist.numCols()) + + #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]) + + df = tfidf.select('subreddit','subreddit_id_new').distinct().toPandas() + + 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")) + + pyarrow.write_feather(similarities,output_feather) + pyarrow.write_csv(similarities,output_csv) + return similarities + +if __name__ == '__main__': + fire.Fire(spark_similarities)