X-Git-Url: https://code.communitydata.science/cdsc_reddit.git/blobdiff_plain/a60747292e91a47d122158659182f82bfd2e922a..e6294b5b90135a5163441c8dc62252dd6a188412:/author_cosine_similarity.py

diff --git a/author_cosine_similarity.py b/author_cosine_similarity.py
deleted file mode 100644
index 7ae708b..0000000
--- a/author_cosine_similarity.py
+++ /dev/null
@@ -1,119 +0,0 @@
-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, prep_tfidf_entries, read_tfidf_matrix, column_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):
-    '''
-    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
-'''
-
-    spark = SparkSession.builder.getOrCreate()
-    conf = spark.sparkContext.getConf()
-    print(outfile)
-
-    tfidf = spark.read.parquet('/gscratch/comdata/users/nathante/subreddit_tfidf_authors.parquet')
-
-    if included_subreddits is None:
-        rankdf = pd.read_csv("/gscratch/comdata/users/nathante/cdsc-reddit/subreddits_by_num_comments.csv")
-        included_subreddits = set(rankdf.loc[rankdf.comments_rank <= topN,'subreddit'].values)
-
-    else:
-        included_subreddits = set(open(included_subreddits))
-
-    print("creating temporary parquet with matrix indicies")
-    tempdir = prep_tfidf_entries(tfidf, 'author', min_df, included_subreddits)
-    tfidf = spark.read.parquet(tempdir.name)
-    subreddit_names = tfidf.select(['subreddit','subreddit_id_new']).distinct().toPandas()
-    subreddit_names = subreddit_names.sort_values("subreddit_id_new")
-    subreddit_names['subreddit_id_new'] = subreddit_names['subreddit_id_new'] - 1
-    spark.stop()
-
-    print("loading matrix")
-    mat = read_tfidf_matrix(tempdir.name,'author')
-    print('computing similarities')
-    sims = column_similarities(mat)
-    del mat
-    
-    sims = pd.DataFrame(sims.todense())
-    sims = sims.rename({i:sr for i, sr in enumerate(subreddit_names.subreddit.values)},axis=1)
-    sims['subreddit'] = subreddit_names.subreddit.values
-
-    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"))
-
-    sims.to_feather(outfile)
-    tempdir.cleanup()
-
-    # print(outfile)
-
-    # tfidf = spark.read.parquet('/gscratch/comdata/users/nathante/subreddit_tfidf_authors.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')
-    
-
-
-    # #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(author_cosine_similarities)