add term_cosine_similarity.py

term_cosine_similarity.py

@@ -0,0 +1,105 @@
+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)