Refactor and reorganze.

similarities/#cosine_similarities.py#

@@ -0,0 +1,73 @@
+from pyspark.sql import functions as f
+from pyspark.sql import SparkSession
+import pandas as pd
+import fire
+from pathlib import Path
+from similarities_helper import prep_tfidf_entries, read_tfidf_matrix, select_topN_subreddits
+
+
+def cosine_similarities(infile, term_colname, 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(infile)
+
+    if included_subreddits is None:
+        included_subreddits = select_topN_subreddits(topN)
+    else:
+        included_subreddits = set(open(included_subreddits))
+
+    if exclude_phrases == True:
+        tfidf = tfidf.filter(~f.col(term_colname).contains("_"))
+
+    print("creating temporary parquet with matrix indicies")
+    tempdir = prep_tfidf_entries(tfidf, term_colname, 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_colname)
+    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()
+
+def term_cosine_similarities(outfile, min_df=None, included_subreddits=None, topN=500, exclude_phrases=False):
+    return cosine_similarities('/gscratch/comdata/output/reddit_similarity/tfidf/comment_terms.parquet',
+                               'term',
+                               outfile,
+                               min_df,
+                               included_subreddits,
+                               topN,
+                               exclude_phrases)
+
+def author_cosine_similarities(outfile, min_df=2, included_subreddits=None, topN=10000):
+    return cosine_similarities('/gscratch/comdata/output/reddit_similarity/tfidf/comment_authors.parquet',
+                               'author',
+                               outfile,
+                               min_df,
+                               included_subreddits,
+                               topN,
+                               exclude_phrases=False)
+
+if __name__ == "__main__":
+    fire.Fire({'term':term_cosine_similarities,
+               'author':author_cosine_similarities})
+

similarities/#tfidf_weekly.py#

@@ -0,0 +1,24 @@
+from pyspark.sql import functions as f
+from pyspark.sql import SparkSession
+from pyspark.sql import Window
+from similarities_helper import build_weekly_tfidf_dataset
+import pandas as pd
+
+def tfidf_weekly(inpath, outpath, topN, term_colname, exclude):
+    
+spark = SparkSession.builder.getOrCreate()
+df = spark.read.parquet("/gscratch/comdata/output/reddit_ngrams/comment_terms.parquet")
+
+include_subs = pd.read_csv("/gscratch/comdata/output/reddit_similarity/subreddits_by_num_comments.csv")
+
+include_subs = set(include_subs.loc[include_subs.comments_rank <= 25000]['subreddit'])
+
+# remove [deleted] and AutoModerator (TODO remove other bots)
+# df = df.filter(df.author != '[deleted]')
+# df = df.filter(df.author != 'AutoModerator')
+
+df = build_weekly_tfidf_dataset(df, include_subs, 'term')
+
+
+df.write.parquet('/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_terms.parquet', mode='overwrite', compression='snappy')
+spark.stop()

similarities/.#cosine_similarities.py

@@ -0,0 +1 @@
+nathante@n2347.hyak.local.31061:1602221800

similarities/.#tfidf_weekly.py

@@ -0,0 +1 @@
+nathante@n2347.hyak.local.31061:1602221800

similarities/Makefile

@@ -0,0 +1,2 @@
+/gscratch/comdata/output/reddit_similarity/subreddit_comment_authors_10000.parquet: cosine_similarities.py /gscratch/comdata/output/reddit_similarity/tfidf/comment_authors.parquet
+	start_spark_and_run.sh 1 cosine_similarities.py author --outfile=/gscratch/comdata/output/reddit_similarity/subreddit_comment_authors_10000.parquet

similarities/cosine_similarities.py

@@ -0,0 +1,73 @@
+from pyspark.sql import functions as f
+from pyspark.sql import SparkSession
+import pandas as pd
+import fire
+from pathlib import Path
+from similarities_helper import prep_tfidf_entries, read_tfidf_matrix, select_topN_subreddits
+
+
+def cosine_similarities(infile, term_colname, 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(infile)
+
+    if included_subreddits is None:
+        included_subreddits = select_topN_subreddits(topN)
+    else:
+        included_subreddits = set(open(included_subreddits))
+
+    if exclude_phrases == True:
+        tfidf = tfidf.filter(~f.col(term_colname).contains("_"))
+
+    print("creating temporary parquet with matrix indicies")
+    tempdir = prep_tfidf_entries(tfidf, term_colname, 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_colname)
+    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()
+
+def term_cosine_similarities(outfile, min_df=None, included_subreddits=None, topN=500, exclude_phrases=False):
+    return cosine_similarities('/gscratch/comdata/output/reddit_similarity/tfidf/comment_terms.parquet',
+                               'term',
+                               outfile,
+                               min_df,
+                               included_subreddits,
+                               topN,
+                               exclude_phrases)
+
+def author_cosine_similarities(outfile, min_df=2, included_subreddits=None, topN=10000):
+    return cosine_similarities('/gscratch/comdata/output/reddit_similarity/tfidf/comment_authors.parquet',
+                               'author',
+                               outfile,
+                               min_df,
+                               included_subreddits,
+                               topN,
+                               exclude_phrases=False)
+
+if __name__ == "__main__":
+    fire.Fire({'term':term_cosine_similarities,
+               'author':author_cosine_similarities})
+

similarities/job_script.sh

@@ -0,0 +1,4 @@
+#!/usr/bin/bash
+start_spark_cluster.sh
+spark-submit --master spark://$(hostname):18899 cosine_similarities.py author --outfile=/gscratch/comdata/output/reddit_similarity/subreddit_comment_authors_10000.parquet
+stop-all.sh

similarities/similarities_helper.py

@@ -0,0 +1,278 @@
+from pyspark.sql import Window
+from pyspark.sql import functions as f
+from enum import Enum
+from pyspark.mllib.linalg.distributed import CoordinateMatrix
+from tempfile import TemporaryDirectory
+import pyarrow
+import pyarrow.dataset as ds
+from scipy.sparse import csr_matrix
+import pandas as pd
+import numpy as np
+import pathlib
+
+class tf_weight(Enum):
+    MaxTF = 1
+    Norm05 = 2
+
+def read_tfidf_matrix_weekly(path, term_colname, week):
+    term = term_colname
+    term_id = term + '_id'
+    term_id_new = term + '_id_new'
+
+    dataset = ds.dataset(path,format='parquet')
+    entries = dataset.to_table(columns=['tf_idf','subreddit_id_new',term_id_new],filter=ds.field('week')==week).to_pandas()
+    return(csr_matrix((entries.tf_idf,(entries[term_id_new]-1, entries.subreddit_id_new-1))))
+
+def write_weekly_similarities(path, sims, week, names):
+    sims['week'] = week
+    p = pathlib.Path(path)
+    if not p.is_dir():
+        p.mkdir()
+        
+    # reformat as a pairwise list
+    sims = sims.melt(id_vars=['subreddit','week'],value_vars=names.subreddit.values)
+    sims.to_parquet(p / week.isoformat())
+
+
+
+def read_tfidf_matrix(path,term_colname):
+    term = term_colname
+    term_id = term + '_id'
+    term_id_new = term + '_id_new'
+
+    dataset = ds.dataset(path,format='parquet')
+    entries = dataset.to_table(columns=['tf_idf','subreddit_id_new',term_id_new]).to_pandas()
+    return(csr_matrix((entries.tf_idf,(entries[term_id_new]-1, entries.subreddit_id_new-1))))
+    
+def column_similarities(mat):
+    norm = np.matrix(np.power(mat.power(2).sum(axis=0),0.5,dtype=np.float32))
+    mat = mat.multiply(1/norm)
+    sims = mat.T @ mat
+    return(sims)
+
+
+def prep_tfidf_entries_weekly(tfidf, term_colname, min_df, included_subreddits):
+    term = term_colname
+    term_id = term + '_id'
+    term_id_new = term + '_id_new'
+
+    if min_df is None:
+        min_df = 0.1 * len(included_subreddits)
+
+    tfidf = tfidf.filter(f.col("subreddit").isin(included_subreddits))
+
+    # we might not have the same terms or subreddits each week, so we need to make unique ids for each week.
+    sub_ids = tfidf.select(['subreddit_id','week']).distinct()
+    sub_ids = sub_ids.withColumn("subreddit_id_new",f.row_number().over(Window.partitionBy('week').orderBy("subreddit_id")))
+    tfidf = tfidf.join(sub_ids,['subreddit_id','week'])
+
+    # only use terms in at least min_df included subreddits in a given week
+    new_count = tfidf.groupBy([term_id,'week']).agg(f.count(term_id).alias('new_count'))
+    tfidf = tfidf.join(new_count,[term_id,'week'],how='inner')
+
+    # reset the term ids
+    term_ids = tfidf.select([term_id,'week']).distinct()
+    term_ids = term_ids.withColumn(term_id_new,f.row_number().over(Window.partitionBy('week').orderBy(term_id)))
+    tfidf = tfidf.join(term_ids,[term_id,'week'])
+
+    tfidf = tfidf.withColumnRenamed("tf_idf","tf_idf_old")
+    tfidf = tfidf.withColumn("tf_idf", (tfidf.relative_tf * tfidf.idf).cast('float'))
+
+    tempdir =TemporaryDirectory(suffix='.parquet',prefix='term_tfidf_entries',dir='.')
+
+    tfidf = tfidf.repartition('week')
+
+    tfidf.write.parquet(tempdir.name,mode='overwrite',compression='snappy')
+    return(tempdir)
+    
+
+def prep_tfidf_entries(tfidf, term_colname, min_df, included_subreddits):
+    term = term_colname
+    term_id = term + '_id'
+    term_id_new = term + '_id_new'
+
+    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'))
+    tfidf = tfidf.join(new_count,term_id,how='inner')
+    
+    # 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)
+
+    tfidf = tfidf.withColumnRenamed("tf_idf","tf_idf_old")
+    tfidf = tfidf.withColumn("tf_idf", (tfidf.relative_tf * tfidf.idf).cast('float'))
+    
+    tempdir =TemporaryDirectory(suffix='.parquet',prefix='term_tfidf_entries',dir='.')
+    
+    tfidf.write.parquet(tempdir.name,mode='overwrite',compression='snappy')
+    return tempdir
+
+
+# try computing cosine similarities using spark
+def spark_cosine_similarities(tfidf, term_colname, min_df, included_subreddits, similarity_threshold):
+    term = term_colname
+    term_id = term + '_id'
+    term_id_new = term + '_id_new'
+
+    if min_df is None:
+        min_df = 0.1 * len(included_subreddits)
+
+    tfidf = tfidf.filter(f.col("subreddit").isin(included_subreddits))
+    tfidf = tfidf.cache()
+
+    # 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'))
+    tfidf = tfidf.join(new_count,term_id,how='inner')
+    
+    # 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)
+
+    tfidf = tfidf.withColumnRenamed("tf_idf","tf_idf_old")
+    tfidf = tfidf.withColumn("tf_idf", tfidf.relative_tf * tfidf.idf)
+
+    # step 1 make an rdd of entires
+    # sorted by (dense) spark subreddit id
+    n_partitions = int(len(included_subreddits)*2 / 5)
+
+    entries = tfidf.select(f.col(term_id_new)-1,f.col("subreddit_id_new")-1,"tf_idf").rdd.repartition(n_partitions)
+
+    # put like 10 subredis in each partition
+
+    # step 2 make it into a distributed.RowMatrix
+    coordMat = CoordinateMatrix(entries)
+
+    coordMat = CoordinateMatrix(coordMat.entries.repartition(n_partitions))
+
+    # 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)
+
+    return (sim_dist, tfidf)
+
+
+def build_weekly_tfidf_dataset(df, include_subs, term_colname, tf_family=tf_weight.Norm05):
+    term = term_colname
+    term_id = term + '_id'
+
+    # aggregate counts by week. now subreddit-term is distinct
+    df = df.filter(df.subreddit.isin(include_subs))
+    df = df.groupBy(['subreddit',term,'week']).agg(f.sum('tf').alias('tf'))
+
+    max_subreddit_terms = df.groupby(['subreddit','week']).max('tf') # subreddits are unique
+    max_subreddit_terms = max_subreddit_terms.withColumnRenamed('max(tf)','sr_max_tf')
+    df = df.join(max_subreddit_terms, on=['subreddit','week'])
+    df = df.withColumn("relative_tf", df.tf / df.sr_max_tf)
+
+    # group by term. term is unique
+    idf = df.groupby([term,'week']).count()
+
+    N_docs = df.select(['subreddit','week']).distinct().groupby(['week']).agg(f.count("subreddit").alias("subreddits_in_week"))
+
+    idf = idf.join(N_docs, on=['week'])
+
+    # add a little smoothing to the idf
+    idf = idf.withColumn('idf',f.log(idf.subreddits_in_week) / (1+f.col('count'))+1)
+
+    # collect the dictionary to make a pydict of terms to indexes
+    terms = idf.select([term,'week']).distinct() # terms are distinct
+
+    terms = terms.withColumn(term_id,f.row_number().over(Window.partitionBy('week').orderBy(term))) # term ids are distinct
+
+    # make subreddit ids
+    subreddits = df.select(['subreddit','week']).distinct()
+    subreddits = subreddits.withColumn('subreddit_id',f.row_number().over(Window.partitionBy("week").orderBy("subreddit")))
+
+    df = df.join(subreddits,on=['subreddit','week'])
+
+    # map terms to indexes in the tfs and the idfs
+    df = df.join(terms,on=[term,'week']) # subreddit-term-id is unique
+
+    idf = idf.join(terms,on=[term,'week'])
+
+    # join on subreddit/term to create tf/dfs indexed by term
+    df = df.join(idf, on=[term_id, term,'week'])
+
+    # agg terms by subreddit to make sparse tf/df vectors
+    
+    if tf_family == tf_weight.MaxTF:
+        df = df.withColumn("tf_idf",  df.relative_tf * df.idf)
+    else: # tf_fam = tf_weight.Norm05
+        df = df.withColumn("tf_idf",  (0.5 + 0.5 * df.relative_tf) * df.idf)
+
+    return df
+
+
+
+def build_tfidf_dataset(df, include_subs, term_colname, tf_family=tf_weight.Norm05):
+
+    term = term_colname
+    term_id = term + '_id'
+    # aggregate counts by week. now subreddit-term is distinct
+    df = df.filter(df.subreddit.isin(include_subs))
+    df = df.groupBy(['subreddit',term]).agg(f.sum('tf').alias('tf'))
+
+    max_subreddit_terms = df.groupby(['subreddit']).max('tf') # subreddits are unique
+    max_subreddit_terms = max_subreddit_terms.withColumnRenamed('max(tf)','sr_max_tf')
+
+    df = df.join(max_subreddit_terms, on='subreddit')
+
+    df = df.withColumn("relative_tf", df.tf / df.sr_max_tf)
+
+    # group by term. term is unique
+    idf = df.groupby([term]).count()
+
+    N_docs = df.select('subreddit').distinct().count()
+
+    # add a little smoothing to the idf
+    idf = idf.withColumn('idf',f.log(N_docs/(1+f.col('count')))+1)
+
+    # collect the dictionary to make a pydict of terms to indexes
+    terms = idf.select(term).distinct() # terms are distinct
+    terms = terms.withColumn(term_id,f.row_number().over(Window.orderBy(term))) # term ids are distinct
+
+    # make subreddit ids
+    subreddits = df.select(['subreddit']).distinct()
+    subreddits = subreddits.withColumn('subreddit_id',f.row_number().over(Window.orderBy("subreddit")))
+
+    df = df.join(subreddits,on='subreddit')
+
+    # map terms to indexes in the tfs and the idfs
+    df = df.join(terms,on=term) # subreddit-term-id is unique
+
+    idf = idf.join(terms,on=term)
+
+    # join on subreddit/term to create tf/dfs indexed by term
+    df = df.join(idf, on=[term_id, term])
+
+    # agg terms by subreddit to make sparse tf/df vectors
+    if tf_family == tf_weight.MaxTF:
+        df = df.withColumn("tf_idf",  df.relative_tf * df.idf)
+    else: # tf_fam = tf_weight.Norm05
+        df = df.withColumn("tf_idf",  (0.5 + 0.5 * df.relative_tf) * df.idf)
+
+    return df
+
+def select_topN_subreddits(topN, path="/gscratch/comdata/output/reddit_similarity/subreddits_by_num_comments.csv"):
+    rankdf = pd.read_csv(path)
+    included_subreddits = set(rankdf.loc[rankdf.comments_rank <= topN,'subreddit'].values)
+    return included_subreddits

similarities/tfidf.py

@@ -0,0 +1,73 @@
+import fire
+from pyspark.sql import SparkSession
+from pyspark.sql import functions as f
+from similarities_helper import build_tfidf_dataset, build_weekly_tfidf_dataset, select_topN_subreddits
+
+
+def _tfidf_wrapper(func, inpath, outpath, topN, term_colname, exclude):
+    spark = SparkSession.builder.getOrCreate()
+
+    df = spark.read.parquet(inpath)
+
+    df = df.filter(~ f.col(term_colname).isin(exclude))
+
+    include_subs = select_topN_subreddits(topN)
+
+    df = func(df, include_subs, term_colname)
+
+    df.write.parquet(outpath,mode='overwrite',compression='snappy')
+
+    spark.stop()
+
+def tfidf(inpath, outpath, topN, term_colname, exclude):
+    return _tfidf_wrapper(build_tfidf_dataset, inpath, outpath, topN, term_colname, exclude)
+
+def tfidf_weekly(inpath, outpath, topN, term_colname, exclude):
+    return _tfidf_wrapper(build_weekly_tfidf_dataset, inpath, outpath, topN, term_colname, exclude)
+
+def tfidf_authors(outpath='/gscratch/comdata/output/reddit_similarity/tfidf/comment_authors.parquet',
+                  topN=25000):
+
+    return tfidf("/gscratch/comdata/output/reddit_ngrams/comment_authors.parquet",
+                 outpath,
+                 topN,
+                 'author',
+                 ['[deleted]','AutoModerator']
+                 )
+
+def tfidf_terms(outpath='/gscratch/comdata/output/reddit_similarity/tfidf/comment_terms.parquet',
+                topN=25000):
+
+    return tfidf("/gscratch/comdata/output/reddit_ngrams/comment_terms.parquet",
+                 outpath,
+                 topN,
+                 'term',
+                 []
+                 )
+
+def tfidf_authors_weekly(outpath='/gscratch/comdata/output/reddit_similarity/tfidf/comment_authors.parquet',
+                  topN=25000):
+
+    return tfidf_weekly("/gscratch/comdata/output/reddit_ngrams/comment_authors.parquet",
+                 outpath,
+                 topN,
+                 'author',
+                 ['[deleted]','AutoModerator']
+                 )
+
+def tfidf_terms_weekly(outpath='/gscratch/comdata/output/reddit_similarity/tfidf/comment_terms.parquet',
+                topN=25000):
+
+    return tfidf_weekly("/gscratch/comdata/output/reddit_ngrams/comment_terms.parquet",
+                 outpath,
+                 topN,
+                 'term',
+                 []
+                 )
+
+
+if __name__ == "__main__":
+    fire.Fire({'authors':tfidf_authors,
+               'terms':tfidf_terms,
+               'authors_weekly':tfidf_authors_weekly,
+               'terms_weekly':tfidf_terms_weekly})

similarities/top_subreddits_by_comments.py

@@ -0,0 +1,22 @@
+from pyspark.sql import functions as f
+from pyspark.sql import SparkSession
+from pyspark.sql import Window
+
+spark = SparkSession.builder.getOrCreate()
+conf = spark.sparkContext.getConf()
+
+df = spark.read.parquet("/gscratch/comdata/output/reddit_comments_by_subreddit.parquet")
+
+# remove /u/ pages
+df = df.filter(~df.subreddit.like("u_%"))
+
+df = df.groupBy('subreddit').agg(f.count('id').alias("n_comments"))
+
+win = Window.orderBy(f.col('n_comments').desc())
+df = df.withColumn('comments_rank', f.rank().over(win))
+
+df = df.toPandas()
+
+df = df.sort_values("n_comments")
+
+df.to_csv('/gscratch/comdata/output/reddit_similarity/subreddits_by_num_comments.csv', index=False)

similarities/weekly_cosine_similarities.py

@@ -0,0 +1,73 @@
+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 *
+
+
+#tfidf = spark.read.parquet('/gscratch/comdata/users/nathante/subreddit_tfidf_weekly.parquet')
+def cosine_similarities_weekly(tfidf_path, outfile, term_colname, min_df = None, included_subreddits = None, topN = 500):
+    spark = SparkSession.builder.getOrCreate()
+    conf = spark.sparkContext.getConf()
+    print(outfile)
+    tfidf = spark.read.parquet(tfidf_path)
+    
+    if included_subreddits is None:
+        included_subreddits = select_topN_subreddits(topN)
+    else:
+        included_subreddits = set(open(included_subreddits))
+
+    print(f"computing weekly similarities for {len(included_subreddits)} subreddits")
+
+    print("creating temporary parquet with matrix indicies")
+    tempdir = prep_tfidf_entries_weekly(tfidf, term_colname, min_df, included_subreddits)
+
+    tfidf = spark.read.parquet(tempdir.name)
+
+    # the ids can change each week.
+    subreddit_names = tfidf.select(['subreddit','subreddit_id_new','week']).distinct().toPandas()
+    subreddit_names = subreddit_names.sort_values("subreddit_id_new")
+    subreddit_names['subreddit_id_new'] = subreddit_names['subreddit_id_new'] - 1
+    spark.stop()
+
+    weeks = list(subreddit_names.week.drop_duplicates())
+    for week in weeks:
+        print(f"loading matrix: {week}")
+        mat = read_tfidf_matrix_weekly(tempdir.name, term_colname, week)
+        print('computing similarities')
+        sims = column_similarities(mat)
+        del mat
+
+        names = subreddit_names.loc[subreddit_names.week == week]
+        sims = pd.DataFrame(sims.todense())
+
+        sims = sims.rename({i: sr for i, sr in enumerate(names.subreddit.values)}, axis=1)
+        sims['subreddit'] = names.subreddit.values
+
+        write_weekly_similarities(outfile, sims, week, names)
+
+
+def author_cosine_similarities_weekly(outfile, min_df=None , included_subreddits=None, topN=500):
+    return cosine_similarities_weekly('/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_authors.parquet',
+                                      outfile,
+                                      'author',
+                                      min_df,
+                                      included_subreddits,
+                                      topN)
+
+def term_cosine_similarities_weekly(outfile, min_df=None, included_subreddits=None, topN=500):
+    return cosine_similarities_weekly('/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_terms.parquet',
+                                      outfile,
+                                      'term',
+                                      min_df,
+                                      included_subreddits,
+                                      topN)
+
+if __name__ == "__main__":
+    fire.Fire({'author':author_cosine_similarities_weekly,
+               'term':term_cosine_similarities_weekly})