106 lines
4.1 KiB
Python
106 lines
4.1 KiB
Python
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from pyspark.sql import functions as f
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from pyspark.sql import SparkSession
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from pyspark.sql import Window
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from pyspark.mllib.linalg.distributed import RowMatrix, CoordinateMatrix
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import numpy as np
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import pyarrow
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import pandas as pd
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import fire
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from itertools import islice
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from pathlib import Path
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min_df = 1000
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spark = SparkSession.builder.getOrCreate()
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conf = spark.sparkContext.getConf()
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# outfile = '/gscratch/comdata/users/nathante/test_similarities_500.feather'; min_df = None; included_subreddits=None; similarity_threshold=0;
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def spark_similarities(outfile, min_df = None, included_subreddits=None, similarity_threshold=0):
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'''
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Compute similarities between subreddits based on tfi-idf vectors of comment texts
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included_subreddits : string
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Text file containing a list of subreddits to include (one per line) if included_subreddits is None then do the top 500 subreddits
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similarity_threshold : double (default = 0)
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set > 0 for large numbers of subreddits to get an approximate solution using the DIMSUM algorithm
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https://stanford.edu/~rezab/papers/dimsum.pdf. If similarity_threshold=0 we get an exact solution using an O(N^2) algorithm.
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min_df : int (default = 0.1 * (number of included_subreddits)
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exclude terms that appear in fewer than this number of documents.
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outfile: string
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where to output csv and feather outputs
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'''
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tfidf = spark.read.parquet('/gscratch/comdata/users/nathante/subreddit_tfidf.parquet')
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if included_subreddits is None:
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included_subreddits = list(islice(open("/gscratch/comdata/users/nathante/cdsc-reddit/top_25000_subs_by_comments.txt"),500))
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included_subreddits = [s.strip('\n') for s in included_subreddits]
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else:
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included_subreddits = set(open(included_subreddits))
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if min_df is None:
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min_df = 0.1 * len(included_subreddits)
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tfidf = tfidf.filter(f.col("subreddit").isin(included_subreddits))
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# reset the subreddit ids
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sub_ids = tfidf.select('subreddit_id').distinct()
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sub_ids = sub_ids.withColumn("subreddit_id_new",f.row_number().over(Window.orderBy("subreddit_id")))
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tfidf = tfidf.join(sub_ids,'subreddit_id')
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# only use terms in at least min_df included subreddits
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new_count = tfidf.groupBy('term_id').agg(f.count('term_id').alias('new_count'))
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term_ids = term_ids.join(new_count,'term_id')
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term_ids = term_ids.filter(new_count >= min_df)
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# reset the term ids
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term_ids = tfidf.select('term_id').distinct()
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term_ids = term_ids.withColumn("term_id_new",f.row_number().over(Window.orderBy("term_id")))
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tfidf = tfidf.join(term_ids,'term_id')
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# step 1 make an rdd of entires
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# sorted by (dense) spark subreddit id
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entries = tfidf.select(f.col("term_id_new")-1,f.col("subreddit_id_new")-1,"tf_idf").rdd
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# step 2 make it into a distributed.RowMatrix
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coordMat = CoordinateMatrix(entries)
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# this needs to be an IndexedRowMatrix()
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mat = coordMat.toRowMatrix()
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#goal: build a matrix of subreddit columns and tf-idfs rows
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sim_dist = mat.columnSimilarities(threshold=similarity_threshold)
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print(sim_dist.numRows(), sim_dist.numCols())
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#instead of toLocalMatrix() why not read as entries and put strait into numpy
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sim_entries = sim_dist.entries.collect()
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sim_entries = pd.DataFrame([{'i':me.i,'j':me.j,'value':me.value} for me in sim_entries])
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df = tfidf.select('subreddit','subreddit_id_new').distinct().toPandas()
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df = df.sort_values('subreddit_id_new').reset_index(drop=True)
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df = df.set_index('subreddit_id_new')
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similarities = sim_entries.join(df, on='i')
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similarities = sim_entries.rename(columns={'subreddit':"subreddit_i"})
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similarities = sim_entries.join(df, on='j')
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similarities = sim_entries.rename(columns={'subreddit':"subreddit_j"})
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p = Path(outfile)
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output_feather = Path(str(p).replace("".join(p.suffixes), ".feather"))
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output_csv = Path(str(p).replace("".join(p.suffixes), ".csv"))
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pyarrow.write_feather(similarities,output_feather)
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pyarrow.write_csv(similarities,output_csv)
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return similarities
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if __name__ == '__main__':
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fire.Fire(spark_similarities)
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