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 ba6d2c9..0000000
--- a/term_cosine_similarity.py
+++ /dev/null
@@ -1,105 +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
-
-min_df = 1000
-
-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 spark_similarities(outfile, min_df = None, included_subreddits=None, similarity_threshold=0):
-    '''
-    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
-'''
-
-    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"),500))
-        included_subreddits = [s.strip('\n') for s in included_subreddits]
-
-    else:
-        included_subreddits = set(open(included_subreddits))
-
-    if min_df is None:
-        min_df = 0.1 * len(included_subreddits)
-
-    tfidf = tfidf.filter(f.col("subreddit").isin(included_subreddits))
-
-    # reset the subreddit ids
-    sub_ids = tfidf.select('subreddit_id').distinct()
-    sub_ids = sub_ids.withColumn("subreddit_id_new",f.row_number().over(Window.orderBy("subreddit_id")))
-    tfidf = tfidf.join(sub_ids,'subreddit_id')
-
-    # only use terms in at least min_df included subreddits
-    new_count = tfidf.groupBy('term_id').agg(f.count('term_id').alias('new_count'))
-    term_ids = term_ids.join(new_count,'term_id')
-    term_ids = term_ids.filter(new_count >= min_df)
-
-    # reset the term ids
-    term_ids = tfidf.select('term_id').distinct()
-    term_ids = term_ids.withColumn("term_id_new",f.row_number().over(Window.orderBy("term_id")))
-    tfidf = tfidf.join(term_ids,'term_id')
-
-    # step 1 make an rdd of entires
-    # sorted by (dense) spark subreddit id
-    entries = tfidf.select(f.col("term_id_new")-1,f.col("subreddit_id_new")-1,"tf_idf").rdd
-
-    # step 2 make it into a distributed.RowMatrix
-    coordMat = CoordinateMatrix(entries)
-
-    # this needs to be an IndexedRowMatrix()
-    mat = coordMat.toRowMatrix()
-
-    #goal: build a matrix of subreddit columns and tf-idfs rows
-    sim_dist = mat.columnSimilarities(threshold=similarity_threshold)
-
-    print(sim_dist.numRows(), sim_dist.numCols())
-
-    #instead of toLocalMatrix() why not read as entries and put strait into numpy
-    sim_entries = sim_dist.entries.collect()
-
-    sim_entries = pd.DataFrame([{'i':me.i,'j':me.j,'value':me.value} for me in sim_entries])
-
-    df = tfidf.select('subreddit','subreddit_id_new').distinct().toPandas()
-
-    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 = sim_entries.rename(columns={'subreddit':"subreddit_i"})
-    similarities = sim_entries.join(df, on='j')
-    similarities = sim_entries.rename(columns={'subreddit':"subreddit_j"})
-
-    p = Path(outfile)
-    output_feather =  Path(str(p).replace("".join(p.suffixes), ".feather"))
-    output_csv =  Path(str(p).replace("".join(p.suffixes), ".csv"))
-
-    pyarrow.write_feather(similarities,output_feather)
-    pyarrow.write_csv(similarities,output_csv)
-    return similarities
-    
-if __name__ == '__main__':
-    fire.Fire(spark_similarities)