79 lines
3.2 KiB
Python
79 lines
3.2 KiB
Python
|
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)
|