add note to try other tf normalization strategies.

[cdsc_reddit.git] / term_cosine_similarity.py

diff --git a/term_cosine_similarity.py b/term_cosine_similarity.py
deleted file mode 100644 (file)
index 48132a8..0000000
--- a/term_cosine_similarity.py
+++ /dev/null
@@ -1,79 +0,0 @@
-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
-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 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 
-    
-    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.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))
-
-    if exclude_phrases == True:
-        tfidf = tfidf.filter(~f.col(term).contains("_"))
-
-    sim_dist, tfidf = cosine_similarities(tfidf, 'term', 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.entries.toDF().write.parquet(str(output_parquet),mode='overwrite',compression='snappy')
-    
-    #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()
-    spark.stop()
-    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.to_feather(output_feather)
-    similarities.to_csv(output_csv)
-    return similarities
-    
-if __name__ == '__main__':
-    fire.Fire(term_cosine_similarities)