Reuse code for term and author cosine similarity.

diff --git a/author_cosine_similarity.py b/author_cosine_similarity.py
new file mode 100644 (file)
index 0000000..7b2a766
--- /dev/null
+++ b/author_cosine_similarity.py
@@ -0,0 +1,78 @@
+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
+
+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, exclude_phrases=True):
+    '''
+    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
+'''
+
+    print(outfile)
+    print(exclude_phrases)
+
+    tfidf = spark.read.parquet('/gscratch/comdata/users/nathante/subreddit_tfidf_authors.parquet_test1/part-00000-107cee94-92d8-4265-b804-40f1e7f1aaf2-c000.snappy.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')
+    
+    spark.stop()
+
+    #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()
+    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.write_feather(output_feather)
+    similarities.write_csv(output_csv)
+    return similarities
+    
+if __name__ == '__main__':
+    fire.Fire(term_cosine_similarities)

diff --git a/term_cosine_similarity.py b/term_cosine_similarity.py
index ba6d2c93ae87aeeb53f3c6422f1082424cd80989..c487c5beb8a576644d7c9cf09e90f7ed66889bce 100644 (file)
--- a/term_cosine_similarity.py
+++ b/term_cosine_similarity.py
@@ -8,14 +8,13 @@ import pandas as pd
 import fire
 from itertools import islice
 from pathlib import Path
-
-min_df = 1000
+from similarities_helper import build_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 spark_similarities(outfile, 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 
     
@@ -33,73 +32,49 @@ https://stanford.edu/~rezab/papers/dimsum.pdf. If similarity_threshold=0 we get
          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"),500))
-        included_subreddits = [s.strip('\n') for s in included_subreddits]
+        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 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)
+    if exclude_phrases == True:
+        tfidf = tfidf.filter(~f.col(term).contains("_"))
 
-    # 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')
+    sim_dist, tfidf = cosine_similarities(tfidf, 'term', min_df, include_subreddits, similarity_threshold)
 
-    # 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()
+    p = Path(outfile)
 
-    #goal: build a matrix of subreddit columns and tf-idfs rows
-    sim_dist = mat.columnSimilarities(threshold=similarity_threshold)
+    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"))
 
-    print(sim_dist.numRows(), sim_dist.numCols())
+    sim_dist.entries.toDF().write.parquet(str(output_parquet),mode='overwrite',compression='snappy')
+    
+    spark.stop()
 
     #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])
+    sim_entries = pd.read_parquet(output_parquet)
 
     df = tfidf.select('subreddit','subreddit_id_new').distinct().toPandas()
-
+    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 = 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"))
+    similarities = similarities.rename(columns={'subreddit':"subreddit_i"})
+    similarities = similarities.join(df, on='j')
+    similarities = similarities.rename(columns={'subreddit':"subreddit_j"})
 
-    pyarrow.write_feather(similarities,output_feather)
-    pyarrow.write_csv(similarities,output_csv)
+    similarities.write_feather(output_feather)
+    similarities.write_csv(output_csv)
     return similarities
     
 if __name__ == '__main__':
-    fire.Fire(spark_similarities)
+    fire.Fire(term_cosine_similarities)