Refactor and reorganze.

[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 dd92b2c..0000000
--- a/term_cosine_similarity.py
+++ /dev/null
@@ -1,127 +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, prep_tfidf_entries, read_tfidf_matrix, column_similarities
-import scipy
-# outfile='test_similarities_500.feather';
-# min_df = None;
-# included_subreddits=None; topN=100; exclude_phrases=True;
-
-def term_cosine_similarities(outfile, min_df = None, included_subreddits=None, topN=500, exclude_phrases=False):
-    spark = SparkSession.builder.getOrCreate()
-    conf = spark.sparkContext.getConf()
-    print(outfile)
-    print(exclude_phrases)
-
-    tfidf = spark.read.parquet('/gscratch/comdata/users/nathante/subreddit_tfidf.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))
-
-    if exclude_phrases == True:
-        tfidf = tfidf.filter(~f.col(term).contains("_"))
-
-    print("creating temporary parquet with matrix indicies")
-    tempdir = prep_tfidf_entries(tfidf, 'term', 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,'term')
-    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()
-    path = "term_tfidf_entriesaukjy5gv.parquet"
-    
-
-# 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)