add note to try other tf normalization strategies.

Also lowercases included subreddits.

author_cosine_similarity.py

@@ -1,79 +0,0 @@
-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):
-    '''
-    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)
-
-    tfidf = spark.read.parquet('/gscratch/comdata/users/nathante/subreddit_tfidf_authors.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')
-    
-
-
-    #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(author_cosine_similarities)

bots/good_bad_bot.py

@@ -0,0 +1,74 @@
+from pyspark.sql import functions as f
+from pyspark.sql import SparkSession
+from pyspark.sql import Window
+from pyspark.sql.types import FloatType
+import zlib
+
+def zlib_entropy_rate(s):
+    sb = s.encode()
+    if len(sb) == 0:
+        return None
+    else:
+        return len(zlib.compress(s.encode(),level=6))/len(s.encode())
+    
+zlib_entropy_rate_udf = f.udf(zlib_entropy_rate,FloatType())
+
+spark = SparkSession.builder.getOrCreate()
+
+df = spark.read.parquet("/gscratch/comdata/output/reddit_comments_by_author.parquet",compression='snappy')
+
+df = df.withColumn("saidbot",f.lower(f.col("body")).like("%bot%"))
+
+# df = df.filter(df.subreddit=='seattle')
+# df = df.cache()
+botreplies = df.filter(f.lower(df.body).rlike(".*[good|bad] bot.*"))
+botreplies = botreplies.select([f.col("parent_id").substr(4,100).alias("bot_comment_id"),f.lower(f.col("body")).alias("good_bad_bot"),f.col("link_id").alias("gbbb_link_id")])
+botreplies = botreplies.groupby(['bot_comment_id']).agg(f.count('good_bad_bot').alias("N_goodbad_votes"),
+                                                        f.sum((f.lower(f.col('good_bad_bot')).like('%good bot%').astype("double"))).alias("n_good_votes"),
+                                                        f.sum((f.lower(f.col('good_bad_bot')).like('%bad bot%').astype("double"))).alias("n_bad_votes"))
+
+comments_by_author = df.select(['author','id','saidbot']).groupBy('author').agg(f.count('id').alias("N_comments"),
+                                                                                f.mean(f.col('saidbot').astype("double")).alias("prop_saidbot"),
+                                                                                f.sum(f.col('saidbot').astype("double")).alias("n_saidbot"))
+
+# pd_comments_by_author = comments_by_author.toPandas()
+# pd_comments_by_author['frac'] = 500 / pd_comments_by_author['N_comments']
+# pd_comments_by_author.loc[pd_comments_by_author.frac > 1, 'frac'] = 1
+# fractions = pd_comments_by_author.loc[:,['author','frac']]
+# fractions = fractions.set_index('author').to_dict()['frac']
+
+# sampled_author_comments = df.sampleBy("author",fractions).groupBy('author').agg(f.concat_ws(" ", f.collect_list('body')).alias('comments'))
+df = df.withColumn("randn",f.randn(seed=1968))
+
+win = Window.partitionBy("author").orderBy("randn")
+
+df = df.withColumn("randRank",f.rank().over(win))
+sampled_author_comments = df.filter(f.col("randRank") <= 1000)
+sampled_author_comments = sampled_author_comments.groupBy('author').agg(f.concat_ws(" ", f.collect_list('body')).alias('comments'))
+
+author_entropy_rates = sampled_author_comments.select(['author',zlib_entropy_rate_udf(f.col('comments')).alias("entropy_rate")])
+
+parents = df.join(botreplies, on=df.id==botreplies.bot_comment_id,how='right_outer')
+
+win1 = Window.partitionBy("author")
+parents = parents.withColumn("first_bot_reply",f.min(f.col("CreatedAt")).over(win1))
+
+first_bot_reply = parents.filter(f.col("first_bot_reply")==f.col("CreatedAt"))
+first_bot_reply = first_bot_reply.withColumnRenamed("CreatedAt","FB_CreatedAt")
+first_bot_reply = first_bot_reply.withColumnRenamed("id","FB_id")
+
+comments_since_first_bot_reply = df.join(first_bot_reply,on = 'author',how='right_outer').filter(f.col("CreatedAt")>=f.col("first_bot_reply"))
+comments_since_first_bot_reply = comments_since_first_bot_reply.groupBy("author").agg(f.count("id").alias("N_comments_since_firstbot"))
+
+bots = parents.groupby(['author']).agg(f.sum('N_goodbad_votes').alias("N_goodbad_votes"),
+                                          f.sum(f.col('n_good_votes')).alias("n_good_votes"),
+                                          f.sum(f.col('n_bad_votes')).alias("n_bad_votes"),
+                                          f.count(f.col('author')).alias("N_bot_posts"))
+
+bots = bots.join(comments_by_author,on="author",how='left_outer')
+bots = bots.join(comments_since_first_bot_reply,on="author",how='left_outer')
+bots = bots.join(author_entropy_rates,on='author',how='left_outer')
+
+bots = bots.orderBy("N_goodbad_votes",ascending=False)
+bots = bots.repartition(1)
+bots.write.parquet("/gscratch/comdata/output/reddit_good_bad_bot.parquet",mode='overwrite')

clustering/Makefile

@@ -0,0 +1,55 @@
+#srun_cdsc='srun -p comdata-int -A comdata --time=300:00:00 --time-min=00:15:00 --mem=100G --ntasks=1 --cpus-per-task=28'
+srun_singularity=source /gscratch/comdata/users/nathante/cdsc_reddit/bin/activate && srun_singularity.sh
+similarity_data=/gscratch/comdata/output/reddit_similarity
+clustering_data=/gscratch/comdata/output/reddit_clustering
+selection_grid="--max_iter=3000 --convergence_iter=15,30,100 --damping=0.5,0.6,0.7,0.8,0.85,0.9,0.95,0.97,0.99, --preference_quantile=0.1,0.3,0.5,0.7,0.9"
+#selection_grid="--max_iter=3000 --convergence_iter=[15] --preference_quantile=[0.5] --damping=[0.99]"
+all:$(clustering_data)/subreddit_comment_authors_10k/selection_data.csv $(clustering_data)/subreddit_comment_authors-tf_10k/selection_data.csv $(clustering_data)/subreddit_comment_terms_10k/selection_data.csv
+# $(clustering_data)/subreddit_comment_authors_30k.feather/SUCCESS $(clustering_data)/subreddit_authors-tf_similarities_30k.feather/SUCCESS
+# $(clustering_data)/subreddit_comment_terms_30k.feather/SUCCESS
+
+$(clustering_data)/subreddit_comment_authors_10k/selection_data.csv:selection.py $(similarity_data)/subreddit_comment_authors_10k.feather clustering.py
+	$(srun_singularity) python3 selection.py $(similarity_data)/subreddit_comment_authors_10k.feather $(clustering_data)/subreddit_comment_authors_10k $(clustering_data)/subreddit_comment_authors_10k/selection_data.csv $(selection_grid) -J 20
+
+$(clustering_data)/subreddit_comment_terms_10k/selection_data.csv:selection.py $(similarity_data)/subreddit_comment_terms_10k.feather clustering.py
+	$(srun_singularity) python3 selection.py $(similarity_data)/subreddit_comment_terms_10k.feather $(clustering_data)/subreddit_comment_terms_10k  $(clustering_data)/subreddit_comment_terms_10k/selection_data.csv $(selection_grid) -J 20 
+
+$(clustering_data)/subreddit_comment_authors-tf_10k/selection_data.csv:clustering.py $(similarity_data)/subreddit_comment_authors-tf_10k.feather
+	$(srun_singularity) python3 selection.py $(similarity_data)/subreddit_comment_authors-tf_10k.feather $(clustering_data)/subreddit_comment_authors-tf_10k  $(clustering_data)/subreddit_comment_authors-tf_10k/selection_data.csv $(selection_grid) -J 20
+
+# $(clustering_data)/subreddit_comment_authors_30k.feather/SUCCESS:selection.py $(similarity_data)/subreddit_comment_authors_30k.feather clustering.py
+# 	$(srun_singularity) python3 selection.py $(similarity_data)/subreddit_comment_authors_30k.feather $(clustering_data)/subreddit_comment_authors_30k $(selection_grid) -J 10 && touch $(clustering_data)/subreddit_comment_authors_30k.feather/SUCCESS
+
+# $(clustering_data)/subreddit_comment_terms_30k.feather/SUCCESS:selection.py $(similarity_data)/subreddit_comment_terms_30k.feather clustering.py
+# 	$(srun_singularity) python3 selection.py $(similarity_data)/subreddit_comment_terms_30k.feather $(clustering_data)/subreddit_comment_terms_30k $(selection_grid) -J 10 && touch $(clustering_data)/subreddit_comment_terms_30k.feather/SUCCESS
+
+# $(clustering_data)/subreddit_authors-tf_similarities_30k.feather/SUCCESS:clustering.py $(similarity_data)/subreddit_comment_authors-tf_30k.feather
+# 	$(srun_singularity) python3 selection.py $(similarity_data)/subreddit_comment_authors-tf_30k.feather $(clustering_data)/subreddit_comment_authors-tf_30k $(selection_grid) -J 8 && touch $(clustering_data)/subreddit_authors-tf_similarities_30k.feather/SUCCESS
+
+
+# $(clustering_data)/subreddit_comment_authors_100k.feather:clustering.py $(similarity_data)/subreddit_comment_authors_100k.feather
+# 	 $(srun_singularity) python3 clustering.py $(similarity_data)/subreddit_comment_authors_100k.feather $(clustering_data)/subreddit_comment_authors_100k.feather ---max_iter=400 --convergence_iter=15 --preference_quantile=0.85 --damping=0.85
+
+# $(clustering_data)/comment_terms_100k.feather:clustering.py $(similarity_data)/subreddit_comment_terms_100k.feather
+# 	$(srun_singularity) python3 clustering.py $(similarity_data)/comment_terms_10000.feather $(clustering_data)/comment_terms_10000.feather ---max_iter=1000 --convergence_iter=15 --preference_quantile=0.9 --damping=0.5
+
+# $(clustering_data)/subreddit_comment_author-tf_100k.feather:clustering.py $(similarity_data)/subreddit_comment_author-tf_100k.feather
+# 	$(srun_singularity) python3 clustering.py $(similarity_data)/subreddit_comment_author-tf_100k.parquet $(clustering_data)/subreddit_comment_author-tf_100k.feather ---max_iter=400 --convergence_iter=15 --preference_quantile=0.5 --damping=0.85
+
+
+# it's pretty difficult to get a result that isn't one huge megacluster. A sign that it's bullcrap
+# /gscratch/comdata/output/reddit_clustering/wang_similarity_10000.feather:clustering.py /gscratch/comdata/output/reddit_similarity/wang_similarity_10000.feather
+# 	./clustering.py /gscratch/comdata/output/reddit_similarity/wang_similarity_10000.feather /gscratch/comdata/output/reddit_clustering/wang_similarity_10000.feather ---max_iter=400 --convergence_iter=15 --preference_quantile=0.9 --damping=0.85
+
+# /gscratch/comdata/output/reddit_tsne/subreddit_author_tf_similarities_10000.feather:fit_tsne.py /gscratch/comdata/output/reddit_similarity/subreddit_author_tf_similarities_10000.parquet
+
+# 	start_spark_and_run.sh 1 fit_tsne.py --similarities=/gscratch/comdata/output/reddit_similarity/subreddit_author_tf_similarities_10000.parquet --output=/gscratch/comdata/output/reddit_tsne/subreddit_author_tf_similarities_10000.feather
+
+
+# /gscratch/comdata/output/reddit_tsne/wang_similarity_10000.feather:fit_tsne.py /gscratch/comdata/output/reddit_similarity/wang_similarity_10000.feather
+
+# 	python3 fit_tsne.py --similarities=/gscratch/comdata/output/reddit_similarity/wang_similarity_10000.feather --output=/gscratch/comdata/output/reddit_tsne/wang_similarity_10000.feather
+
+# /gscratch/comdata/output/reddit_tsne/comment_authors_10000.feather:clustering.py /gscratch/comdata/output/reddit_similarity/comment_authors_10000.feather
+# #	$srun_cdsc python3
+# 	start_spark_and_run.sh 1 fit_tsne.py --similarities=/gscratch/comdata/output/reddit_similarity/comment_authors_10000.feather --output=/gscratch/comdata/output/reddit_tsne/comment_authors_10000.feather

clustering/clustering.py

@@ -1,27 +1,43 @@
+#!/usr/bin/env python3
+# TODO: replace prints with logging.
+import sys
 import pandas as pd
 import numpy as np
 from sklearn.cluster import AffinityPropagation
 import fire
+from pathlib import Path
 
-def affinity_clustering(similarities, output, damping=0.5, max_iter=100000, convergence_iter=30, preference_quantile=0.5, random_state=1968):
+def read_similarity_mat(similarities, use_threads=True):
+    df = pd.read_feather(similarities, use_threads=use_threads)
+    mat = np.array(df.drop('_subreddit',1))
+    n = mat.shape[0]
+    mat[range(n),range(n)] = 1
+    return (df._subreddit,mat)
+
+def affinity_clustering(similarities, *args, **kwargs):
+    subreddits, mat = read_similarity_mat(similarities)
+    return _affinity_clustering(mat, subreddits, *args, **kwargs)
+
+def _affinity_clustering(mat, subreddits, output, damping=0.9, max_iter=100000, convergence_iter=30, preference_quantile=0.5, random_state=1968, verbose=True):
     '''
     similarities: feather file with a dataframe of similarity scores
     preference_quantile: parameter controlling how many clusters to make. higher values = more clusters. 0.85 is a good value with 3000 subreddits.
+    damping: parameter controlling how iterations are merged. Higher values make convergence faster and more dependable. 0.85 is a good value for the 10000 subreddits by author. 
     '''
-
-    df = pd.read_feather(similarities)
-    n = df.shape[0]
-    mat = np.array(df.drop('subreddit',1))
-    mat[range(n),range(n)] = 1
+    print(f"damping:{damping}; convergenceIter:{convergence_iter}; preferenceQuantile:{preference_quantile}")
 
     preference = np.quantile(mat,preference_quantile)
 
+    print(f"preference is {preference}")
+    print("data loaded")
+    sys.stdout.flush()
     clustering = AffinityPropagation(damping=damping,
                                      max_iter=max_iter,
                                      convergence_iter=convergence_iter,
                                      copy=False,
                                      preference=preference,
                                      affinity='precomputed',
+                                     verbose=verbose,
                                      random_state=random_state).fit(mat)
 
 
@@ -30,7 +46,7 @@ def affinity_clustering(similarities, output, damping=0.5, max_iter=100000, conv
 
     print(f"found {len(set(clusters))} clusters")
 
-    cluster_data = pd.DataFrame({'subreddit': df.subreddit,'cluster':clustering.labels_})
+    cluster_data = pd.DataFrame({'subreddit': subreddits,'cluster':clustering.labels_})
 
     cluster_sizes = cluster_data.groupby("cluster").count()
     print(f"the largest cluster has {cluster_sizes.subreddit.max()} members")
@@ -39,7 +55,10 @@ def affinity_clustering(similarities, output, damping=0.5, max_iter=100000, conv
 
     print(f"{(cluster_sizes.subreddit==1).sum()} clusters have 1 member")
 
+    sys.stdout.flush()
     cluster_data.to_feather(output)
+    print(f"saved {output}")
+    return clustering
 
 if __name__ == "__main__":
     fire.Fire(affinity_clustering)

clustering/fit_tsne.py

@@ -5,7 +5,7 @@ from numpy import random
 import numpy as np
 from sklearn.manifold import TSNE
 
-similarities = "term_similarities_10000.feather"
+similarities = "/gscratch/comdata/output/reddit_similarity/subreddit_author_tf_similarities_10000.parquet"
 
 def fit_tsne(similarities, output, learning_rate=750, perplexity=50, n_iter=10000, early_exaggeration=20):
     '''

clustering/selection.py

@@ -0,0 +1,101 @@
+from sklearn.metrics import silhouette_score
+from sklearn.cluster import AffinityPropagation
+from functools import partial
+from clustering import _affinity_clustering, read_similarity_mat
+from dataclasses import dataclass
+from multiprocessing  import Pool, cpu_count, Array, Process
+from pathlib import Path
+from itertools import product, starmap
+import numpy as np
+import pandas as pd
+import fire
+import sys
+
+# silhouette is the only one that doesn't need the feature matrix. So it's probably the only one that's worth trying. 
+
+@dataclass
+class clustering_result:
+    outpath:Path
+    damping:float
+    max_iter:int
+    convergence_iter:int
+    preference_quantile:float
+    silhouette_score:float
+    alt_silhouette_score:float
+    name:str
+
+
+def sim_to_dist(mat):
+    dist = 1-mat
+    dist[dist < 0] = 0
+    np.fill_diagonal(dist,0)
+    return dist
+
+def do_clustering(damping, convergence_iter, preference_quantile, name, mat, subreddits,  max_iter,  outdir:Path, random_state, verbose, alt_mat, overwrite=False):
+    if name is None:
+        name = f"damping-{damping}_convergenceIter-{convergence_iter}_preferenceQuantile-{preference_quantile}"
+    print(name)
+    sys.stdout.flush()
+    outpath = outdir / (str(name) + ".feather")
+    print(outpath)
+    clustering = _affinity_clustering(mat, subreddits, outpath, damping, max_iter, convergence_iter, preference_quantile, random_state, verbose)
+    mat = sim_to_dist(clustering.affinity_matrix_)
+
+    score = silhouette_score(mat, clustering.labels_, metric='precomputed')
+
+    if alt_mat is not None:
+        alt_distances = sim_to_dist(alt_mat)
+        alt_score = silhouette_score(alt_mat, clustering.labels_, metric='precomputed')
+    
+    res = clustering_result(outpath=outpath,
+                            damping=damping,
+                            max_iter=max_iter,
+                            convergence_iter=convergence_iter,
+                            preference_quantile=preference_quantile,
+                            silhouette_score=score,
+                            alt_silhouette_score=score,
+                            name=str(name))
+
+    return res
+
+# alt similiarities is for checking the silhouette coefficient of an alternative measure of similarity (e.g., topic similarities for user clustering).
+
+def select_affinity_clustering(similarities, outdir, outinfo, damping=[0.9], max_iter=100000, convergence_iter=[30], preference_quantile=[0.5], random_state=1968, verbose=True, alt_similarities=None, J=None):
+
+    damping = list(map(float,damping))
+    convergence_iter = convergence_iter = list(map(int,convergence_iter))
+    preference_quantile = list(map(float,preference_quantile))
+
+    if type(outdir) is str:
+        outdir = Path(outdir)
+
+    outdir.mkdir(parents=True,exist_ok=True)
+
+    subreddits, mat = read_similarity_mat(similarities,use_threads=True)
+
+    if alt_similarities is not None:
+        alt_mat = read_similarity_mat(alt_similarities,use_threads=True)
+    else:
+        alt_mat = None
+
+    if J is None:
+        J = cpu_count()
+    pool = Pool(J)
+
+    # get list of tuples: the combinations of hyperparameters
+    hyper_grid = product(damping, convergence_iter, preference_quantile)
+    hyper_grid = (t + (str(i),) for i, t in enumerate(hyper_grid))
+
+    _do_clustering = partial(do_clustering,  mat=mat, subreddits=subreddits, outdir=outdir, max_iter=max_iter, random_state=random_state, verbose=verbose, alt_mat=alt_mat)
+
+    #    similarities = Array('d', mat)
+    # call pool.starmap
+    print("running clustering selection")
+    clustering_data = pool.starmap(_do_clustering, hyper_grid)
+    clustering_data = pd.DataFrame(list(clustering_data))
+    clustering_data.to_csv(outinfo)
+    
+    return clustering_data
+
+if __name__ == "__main__":
+    x = fire.Fire(select_affinity_clustering)

datasets/job_script.sh

@@ -0,0 +1,4 @@
+#!/usr/bin/bash
+start_spark_cluster.sh
+spark-submit --master spark://$(hostname):18899 weekly_cosine_similarities.py term --outfile=/gscratch/comdata/users/nathante/subreddit_term_similarity_weekly_5000.parquet --topN=5000
+stop-all.sh

density/Makefile

@@ -0,0 +1,10 @@
+all: /gscratch/comdata/output/reddit_density/comment_terms_10000.feather /gscratch/comdata/output/reddit_density/comment_authors_10000.feather /gscratch/comdata/output/reddit_density/subreddit_author_tf_similarities_10000.feather
+
+/gscratch/comdata/output/reddit_density/comment_terms_10000.feather:overlap_density.py /gscratch/comdata/output/reddit_similarity/comment_terms_10000.feather /gscratch/comdata/output/reddit_similarity/comment_terms_10000.feather
+	start_spark_and_run.sh 1 overlap_density.py terms --inpath="/gscratch/comdata/output/reddit_similarity/comment_terms_10000.feather" --outpath="/gscratch/comdata/output/reddit_density/comment_terms_10000.feather" --agg=pd.DataFrame.sum
+
+/gscratch/comdata/output/reddit_density/comment_authors_10000.feather:overlap_density.py /gscratch/comdata/output/reddit_similarity/comment_authors_10000.feather
+	start_spark_and_run.sh 1 overlap_density.py authors --inpath="/gscratch/comdata/output/reddit_similarity/comment_authors_10000.feather" --outpath="/gscratch/comdata/output/reddit_density/comment_authors_10000.feather" --agg=pd.DataFrame.sum
+
+/gscratch/comdata/output/reddit_density/subreddit_author_tf_similarities_10000.feather: overlap_density.py /gscratch/comdata/output/reddit_similarity/subreddit_author_tf_similarities_10000.parquet
+	start_spark_and_run.sh 1 overlap_density.py authors --inpath="/gscratch/comdata/output/reddit_similarity/subreddit_author_tf_similarities_10000.parquet" --outpath="/gscratch/comdata/output/reddit_density/subreddit_author_tf_similarities_10000.feather" --agg=pd.DataFrame.sum

density/job_script.sh

@@ -0,0 +1,4 @@
+#!/usr/bin/bash
+start_spark_cluster.sh
+spark-submit --master spark://$(hostname):18899 overlap_density.py authors --inpath=/gscratch/comdata/output/reddit_similarity/comment_authors_10000.feather --outpath=/gscratch/comdata/output/reddit_density/comment_authors_10000.feather --agg=pd.DataFrame.sum
+stop-all.sh

density/overlap_density.py

@@ -0,0 +1,76 @@
+import pandas as pd
+from pandas.core.groupby import DataFrameGroupBy as GroupBy
+import fire
+import numpy as np
+import sys
+sys.path.append("..")
+sys.path.append("../similarities")
+from similarities.similarities_helper import reindex_tfidf, reindex_tfidf_time_interval
+
+# this is the mean of the ratio of the overlap to the focal size.
+# mean shared membership per focal community member
+# the input is the author tf-idf matrix
+
+def overlap_density(inpath, outpath, agg = pd.DataFrame.sum):
+    df = pd.read_feather(inpath)
+    df = df.drop('subreddit',1)
+    np.fill_diagonal(df.values,0)
+    df = agg(df, 0).reset_index()
+    df = df.rename({0:'overlap_density'},axis='columns')
+    df.to_feather(outpath)
+    return df
+
+def overlap_density_weekly(inpath, outpath, agg = GroupBy.sum):
+    df = pd.read_parquet(inpath)
+    # exclude the diagonal
+    df = df.loc[df.subreddit != df.variable]
+    res = agg(df.groupby(['subreddit','week'])).reset_index()
+    res.to_feather(outpath)
+    return res
+
+
+# inpath="/gscratch/comdata/output/reddit_similarity/tfidf/comment_authors.parquet";
+# min_df=1;
+# included_subreddits=None;
+# topN=10000;
+# outpath="/gscratch/comdata/output/reddit_density/wang_overlaps_10000.feather"
+
+# to_date=2019-10-28
+
+
+def author_overlap_density(inpath="/gscratch/comdata/output/reddit_similarity/comment_authors_10000.feather",
+                           outpath="/gscratch/comdata/output/reddit_density/comment_authors_10000.feather", agg=pd.DataFrame.sum):
+    if type(agg) == str:
+        agg = eval(agg)
+
+    overlap_density(inpath, outpath, agg)
+
+def term_overlap_density(inpath="/gscratch/comdata/output/reddit_similarity/comment_terms_10000.feather",
+                         outpath="/gscratch/comdata/output/reddit_density/comment_term_similarity_10000.feather", agg=pd.DataFrame.sum):
+
+    if type(agg) == str:
+        agg = eval(agg)
+
+    overlap_density(inpath, outpath, agg)
+
+def author_overlap_density_weekly(inpath="/gscratch/comdata/output/reddit_similarity/subreddit_authors_10000_weekly.parquet",
+                                  outpath="/gscratch/comdata/output/reddit_density/comment_authors_10000_weekly.feather", agg=GroupBy.sum):
+    if type(agg) == str:
+        agg = eval(agg)
+
+    overlap_density_weekly(inpath, outpath, agg)
+
+def term_overlap_density_weekly(inpath="/gscratch/comdata/output/reddit_similarity/comment_terms_10000_weekly.parquet",
+                                outpath="/gscratch/comdata/output/reddit_density/comment_terms_10000_weekly.parquet", agg=GroupBy.sum):
+    if type(agg) == str:
+        agg = eval(agg)
+
+    overlap_density_weekly(inpath, outpath, agg)
+
+
+if __name__ == "__main__":
+    fire.Fire({'authors':author_overlap_density,
+               'terms':term_overlap_density,
+               'author_weekly':author_overlap_density_weekly,
+               'term_weekly':term_overlap_density_weekly})
+

dumps/pull_pushshift_comments.sh

@@ -8,7 +8,5 @@ wget -r --no-parent -A 'RC_201*.bz2' -U $user_agent -P $output_dir -nd -nc $base
 wget -r --no-parent -A 'RC_201*.xz' -U $user_agent -P $output_dir -nd -nc $base_url
 wget -r --no-parent -A 'RC_201*.zst' -U $user_agent -P $output_dir -nd -nc $base_url
 
-# starting in 2020 we use daily dumps not monthly dumps
-wget -r --no-parent -A 'RC_202*.gz' -U $user_agent -P $output_dir -nd -nc $base_url/daily/
 
 ./check_comments_shas.py

ngrams/checkpoint_parallelsql.sbatch

@@ -0,0 +1,26 @@
+#!/bin/bash
+## parallel_sql_job.sh
+#SBATCH --job-name=tf_subreddit_comments
+## Allocation Definition
+#SBATCH --account=comdata-ckpt
+#SBATCH --partition=ckpt
+## Resources
+## Nodes. This should always be 1 for parallel-sql.
+#SBATCH --nodes=1    
+## Walltime (12 hours)
+#SBATCH --time=12:00:00
+## Memory per node
+#SBATCH --mem=32G
+#SBATCH --cpus-per-task=4
+#SBATCH --ntasks=1
+#SBATCH -D /gscratch/comdata/users/nathante/cdsc-reddit
+source ./bin/activate
+module load parallel_sql
+echo $(which perl)
+conda list pyarrow
+which python3
+#Put here commands to load other modules (e.g. matlab etc.)
+#Below command means that parallel_sql will get tasks from the database
+#and run them on the node (in parallel). So a 16 core node will have
+#16 tasks running at one time.
+parallel-sql --sql -a parallel --exit-on-term --jobs 4

ngrams/tf_comments.py

@@ -7,7 +7,6 @@ from itertools import groupby, islice, chain
 import fire
 from collections import Counter
 import os
-import datetime
 import re
 from nltk import wordpunct_tokenize, MWETokenizer, sent_tokenize
 from nltk.corpus import stopwords
@@ -31,8 +30,8 @@ def weekly_tf(partition, mwe_pass = 'first'):
     ngram_output = partition.replace("parquet","txt")
 
     if mwe_pass == 'first':
-        if os.path.exists(f"/gscratch/comdata/users/nathante/reddit_comment_ngrams_10p_sample/{ngram_output}"):
-            os.remove(f"/gscratch/comdata/users/nathante/reddit_comment_ngrams_10p_sample/{ngram_output}")
+        if os.path.exists(f"/gscratch/comdata/output/reddit_ngrams/comment_ngrams_10p_sample/{ngram_output}"):
+            os.remove(f"/gscratch/comdata/output/reddit_ngrams/comment_ngrams_10p_sample/{ngram_output}")
     
     batches = dataset.to_batches(columns=['CreatedAt','subreddit','body','author'])
 
@@ -67,7 +66,7 @@ def weekly_tf(partition, mwe_pass = 'first'):
     subreddit_weeks = groupby(rows, lambda r: (r.subreddit, r.week))
 
     if mwe_pass != 'first':
-        mwe_dataset = pd.read_feather(f'/gscratch/comdata/users/nathante/reddit_multiword_expressions.feather')
+        mwe_dataset = pd.read_feather(f'/gscratch/comdata/output/reddit_ngrams/multiword_expressions.feather')
         mwe_dataset = mwe_dataset.sort_values(['phrasePWMI'],ascending=False)
         mwe_phrases = list(mwe_dataset.phrase)
         mwe_phrases = [tuple(s.split(' ')) for s in mwe_phrases]
@@ -88,7 +87,6 @@ def weekly_tf(partition, mwe_pass = 'first'):
                 new_sentence.append(new_token)
         return new_sentence
 
-
     stopWords = set(stopwords.words('english'))
 
     # we follow the approach described in datta, phelan, adar 2017
@@ -121,7 +119,7 @@ def weekly_tf(partition, mwe_pass = 'first'):
             for sentence in sentences:
                 if random() <= 0.1:
                     grams = list(chain(*map(lambda i : ngrams(sentence,i),range(4))))
-                    with open(f'/gscratch/comdata/users/nathante/reddit_comment_ngrams_10p_sample/{ngram_output}','a') as gram_file:
+                    with open(f'/gscratch/comdata/output/reddit_ngrams/comment_ngrams_10p_sample/{ngram_output}','a') as gram_file:
                         for ng in grams:
                             gram_file.write(' '.join(ng) + '\n')
                 for token in sentence:
@@ -156,7 +154,7 @@ def weekly_tf(partition, mwe_pass = 'first'):
 
     outchunksize = 10000
 
-    with pq.ParquetWriter(f"/gscratch/comdata/users/nathante/reddit_tfidf_test.parquet_temp/{partition}",schema=schema,compression='snappy',flavor='spark') as writer, pq.ParquetWriter(f"/gscratch/comdata/users/nathante/reddit_tfidf_test_authors.parquet_temp/{partition}",schema=author_schema,compression='snappy',flavor='spark') as author_writer:
+    with pq.ParquetWriter(f"/gscratch/comdata/output/reddit_ngrams/comment_terms.parquet/{partition}",schema=schema,compression='snappy',flavor='spark') as writer, pq.ParquetWriter(f"/gscratch/comdata/output/reddit_ngrams/comment_authors.parquet/{partition}",schema=author_schema,compression='snappy',flavor='spark') as author_writer:
     
         while True:
 

old/#tfidf_authors.py#

@@ -0,0 +1,21 @@
+from pyspark.sql import SparkSession
+from similarities_helper import build_tfidf_dataset
+import pandas as pd
+
+spark = SparkSession.builder.getOrCreate()
+
+df = spark.read.parquet("/gscratch/comdata/output/reddit_ngrams/comment_authors.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_tfidf_dataset(df, include_subs, 'author')
+
+df.write.parquet('/gscratch/comdata/output/reddit_similarity/tfidf/subreddit_comment_authors.parquet',mode='overwrite',compression='snappy')
+
+spark.stop()

old/#tfidf_comments_weekly.py#

@@ -0,0 +1,27 @@
+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
+
+
+## TODO:need to exclude automoderator / bot posts.
+## TODO:need to exclude better handle hyperlinks. 
+
+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()
+

old/author_cosine_similarity.py

@@ -0,0 +1,106 @@
+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/output/reddit_similarity/tfidf_weekly/subreddit_terms.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("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("loading matrix")
+        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 = 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)
+
+
+
+def cosine_similarities(outfile, min_df = None, included_subreddits=None, topN=500):
+    '''
+    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
+
+    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
+'''
+
+    spark = SparkSession.builder.getOrCreate()
+    conf = spark.sparkContext.getConf()
+    print(outfile)
+
+    tfidf = spark.read.parquet('/gscratch/comdata/output/reddit_similarity/tfidf/subreddit_comment_authors.parquet')
+
+    if included_subreddits is None:
+        included_subreddits = select_topN_subreddits(topN)
+
+    else:
+        included_subreddits = set(open(included_subreddits))
+
+    print("creating temporary parquet with matrix indicies")
+    tempdir = prep_tfidf_entries(tfidf, 'author', 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,'author')
+    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()
+    
+if __name__ == '__main__':
+    fire.Fire(author_cosine_similarities)

old/term_cosine_similarity.py

@@ -0,0 +1,61 @@
+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 prep_tfidf_entries, read_tfidf_matrix, column_similarities, select_topN
+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/output/reddit_similarity/tfidf/subreddit_terms.parquet')
+
+    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).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()
+    
+if __name__ == '__main__':
+    fire.Fire(term_cosine_similarities)

old/tfidf_authors.py

@@ -0,0 +1,21 @@
+from pyspark.sql import SparkSession
+from similarities_helper import build_tfidf_dataset
+import pandas as pd
+
+spark = SparkSession.builder.getOrCreate()
+
+df = spark.read.parquet("/gscratch/comdata/output/reddit_ngrams/comment_authors.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_tfidf_dataset(df, include_subs, 'author')
+
+df.write.parquet('/gscratch/comdata/output/reddit_similarity/tfidf/subreddit_comment_authors.parquet',mode='overwrite',compression='snappy')
+
+spark.stop()

old/tfidf_authors_weekly.py

@@ -0,0 +1,21 @@
+from pyspark.sql import SparkSession
+from similarities_helper import build_weekly_tfidf_dataset
+import pandas as pd
+
+spark = SparkSession.builder.getOrCreate()
+
+df = spark.read.parquet("/gscratch/comdata/output/reddit_ngrams/comment_authors.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, 'author')
+
+df.write.parquet('/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_authors.parquet', mode='overwrite', compression='snappy')
+
+spark.stop()

old/tfidf_comments.py

@@ -0,0 +1,18 @@
+from pyspark.sql import functions as f
+from pyspark.sql import SparkSession
+from pyspark.sql import Window
+from similarities_helper import build_tfidf_dataset
+
+## TODO:need to exclude automoderator / bot posts.
+## TODO:need to exclude better handle hyperlinks. 
+
+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'])
+
+df = build_tfidf_dataset(df, include_subs, 'term')
+
+df.write.parquet('/gscratch/comdata/output/reddit_similarity/reddit_similarity/subreddit_terms.parquet',mode='overwrite',compression='snappy')
+spark.stop()

old/tfidf_comments_weekly.py

@@ -0,0 +1,27 @@
+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
+
+
+## TODO:need to exclude automoderator / bot posts.
+## TODO:need to exclude better handle hyperlinks. 
+
+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/#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/Makefile

@@ -0,0 +1,25 @@
+all: /gscratch/comdata/output/reddit_similarity/subreddit_comment_authors_10000.parquet /gscratch/comdata/output/reddit_similarity/subreddit_comment_authors_10000.parquet /gscratch/comdata/output/reddit_similarity/subreddit_author_tf_similarities_10000.parquet /gscratch/comdata/output/reddit_similarity/subreddit_comment_authors_10000.parquet /gscratch/comdata/output/reddit_similarity/comment_terms.parquet
+
+# all: /gscratch/comdata/output/reddit_similarity/subreddit_comment_terms_25000.parquet /gscratch/comdata/output/reddit_similarity/subreddit_comment_authors_25000.parquet /gscratch/comdata/output/reddit_similarity/subreddit_comment_authors_10000.parquet /gscratch/comdata/output/reddit_similarity/comment_terms_10000_weekly.parquet
+
+
+# /gscratch/comdata/output/reddit_similarity/subreddit_comment_authors_25000.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_25000.feather
+
+/gscratch/comdata/output/reddit_similarity/tfidf/comment_terms.parquet: tfidf.py similarities_helper.py /gscratch/comdata/output/reddit_ngrams/comment_terms.parquet /gscratch/comdata/output/reddit_similarity/subreddits_by_num_comments.csv
+	start_spark_and_run.sh 1 tfidf.py terms --topN=10000
+
+/gscratch/comdata/output/reddit_similarity/tfidf/comment_authors.parquet: tfidf.py similarities_helper.py /gscratch/comdata/output/reddit_ngrams/comment_authors.parquet /gscratch/comdata/output/reddit_similarity/subreddits_by_num_comments.csv
+	start_spark_and_run.sh 1 tfidf.py authors --topN=10000
+
+/gscratch/comdata/output/reddit_similarity/comment_authors_10000.parquet: cosine_similarities.py similarities_helper.py /gscratch/comdata/output/reddit_similarity/tfidf/comment_authors.parquet /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/comment_authors_10000.feather
+
+/gscratch/comdata/output/reddit_similarity/comment_terms.parquet: cosine_similarities.py similarities_helper.py /gscratch/comdata/output/reddit_similarity/tfidf/comment_terms.parquet
+	start_spark_and_run.sh 1 cosine_similarities.py term --outfile=/gscratch/comdata/output/reddit_similarity/comment_terms_10000.feather
+
+# /gscratch/comdata/output/reddit_similarity/comment_terms_10000_weekly.parquet: cosine_similarities.py /gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_authors.parquet
+# 	start_spark_and_run.sh 1 weekly_cosine_similarities.py term --outfile=/gscratch/comdata/output/reddit_similarity/subreddit_comment_terms_10000_weely.parquet
+
+/gscratch/comdata/output/reddit_similarity/subreddit_author_tf_similarities_10000.parquet: cosine_similarities.py similarities_helper.py /gscratch/comdata/output/reddit_similarity/tfidf/comment_authors.parquet /gscratch/comdata/output/reddit_similarity/tfidf/comment_authors.parquet
+	start_spark_and_run.sh 1 cosine_similarities.py author-tf --outfile=/gscratch/comdata/output/reddit_similarity/subreddit_author_tf_similarities_10000.parquet

similarities/TODO

@@ -0,0 +1 @@
+Try normalizing tf by the mean or std instead of the max to avoid penalizing subreddits with very active users.

similarities/cosine_similarities.py

@@ -0,0 +1,57 @@
+import pandas as pd
+import fire
+from pathlib import Path
+from similarities_helper import similarities, column_similarities
+
+def cosine_similarities(infile, term_colname, outfile, min_df=None, max_df=None, included_subreddits=None, topN=500, exclude_phrases=False, from_date=None, to_date=None, tfidf_colname='tf_idf'):
+
+    return similarities(infile=infile, simfunc=column_similarities, term_colname=term_colname, outfile=outfile, min_df=min_df, max_df=max_df, included_subreddits=included_subreddits, topN=topN, exclude_phrases=exclude_phrases,from_date=from_date, to_date=to_date, tfidf_colname=tfidf_colname)
+
+
+def term_cosine_similarities(outfile, infile='/gscratch/comdata/output/reddit_similarity/tfidf/comment_terms_100k.parquet', min_df=None, max_df=None, included_subreddits=None, topN=500, exclude_phrases=False, from_date=None, to_date=None):
+
+    return cosine_similarities(infile,
+                               'term',
+                               outfile,
+                               min_df,
+                               max_df,
+                               included_subreddits,
+                               topN,
+                               exclude_phrases,
+                               from_date,
+                               to_date
+                               )
+
+def author_cosine_similarities(outfile, infile='/gscratch/comdata/output/reddit_similarity/tfidf/comment_authors_100k.parquet', min_df=2, max_df=None, included_subreddits=None, topN=10000, from_date=None, to_date=None):
+    return cosine_similarities(infile,
+                               'author',
+                               outfile,
+                               min_df,
+                               max_df,
+                               included_subreddits,
+                               topN,
+                               exclude_phrases=False,
+                               from_date=from_date,
+                               to_date=to_date
+                               )
+
+def author_tf_similarities(outfile, infile='/gscratch/comdata/output/reddit_similarity/tfidf/comment_authors_100k.parquet', min_df=2, max_df=None, included_subreddits=None, topN=10000, from_date=None, to_date=None):
+    return cosine_similarities(infile,
+                               'author',
+                               outfile,
+                               min_df,
+                               max_df,
+                               included_subreddits,
+                               topN,
+                               exclude_phrases=False,
+                               from_date=from_date,
+                               to_date=to_date,
+                               tfidf_colname='relative_tf'
+                               )
+
+
+if __name__ == "__main__":
+    fire.Fire({'term':term_cosine_similarities,
+               'author':author_cosine_similarities,
+               'author-tf':author_tf_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 term --outfile=/gscratch/comdata/output/reddit_similarity/comment_terms_10000.feather
+stop-all.sh

similarities/similarities_helper.py

@@ -0,0 +1,394 @@
+from pyspark.sql import SparkSession
+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, issparse
+import pandas as pd
+import numpy as np
+import pathlib
+from datetime import datetime
+from pathlib import Path
+
+class tf_weight(Enum):
+    MaxTF = 1
+    Norm05 = 2
+
+infile = "/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_authors.parquet"
+
+def reindex_tfidf_time_interval(infile, term_colname, min_df=None, max_df=None, included_subreddits=None, topN=500, exclude_phrases=False, from_date=None, to_date=None):
+    term = term_colname
+    term_id = term + '_id'
+    term_id_new = term + '_id_new'
+
+    spark = SparkSession.builder.getOrCreate()
+    conf = spark.sparkContext.getConf()
+    print(exclude_phrases)
+    tfidf_weekly = spark.read.parquet(infile)
+
+    # create the time interval
+    if from_date is not None:
+        if type(from_date) is str:
+            from_date = datetime.fromisoformat(from_date)
+
+        tfidf_weekly = tfidf_weekly.filter(tfidf_weekly.week >= from_date)
+        
+    if to_date is not None:
+        if type(to_date) is str:
+            to_date = datetime.fromisoformat(to_date)
+        tfidf_weekly = tfidf_weekly.filter(tfidf_weekly.week < to_date)
+
+    tfidf = tfidf_weekly.groupBy(["subreddit","week", term_id, term]).agg(f.sum("tf").alias("tf"))
+    tfidf = _calc_tfidf(tfidf, term_colname, tf_weight.Norm05)
+    tempdir = prep_tfidf_entries(tfidf, term_colname, min_df, max_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
+    return(tempdir, subreddit_names)
+
+def reindex_tfidf(infile, term_colname, min_df=None, max_df=None, included_subreddits=None, topN=500, exclude_phrases=False):
+    spark = SparkSession.builder.getOrCreate()
+    conf = spark.sparkContext.getConf()
+    print(exclude_phrases)
+
+    tfidf = spark.read.parquet(infile)
+
+    if included_subreddits is None:
+        included_subreddits = select_topN_subreddits(topN)
+    else:
+        included_subreddits = set(map(str.strip,map(str.lower,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, max_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()
+    return (tempdir, subreddit_names)
+
+
+def similarities(infile, simfunc, term_colname, outfile, min_df=None, max_df=None, included_subreddits=None, topN=500, exclude_phrases=False, from_date=None, to_date=None, tfidf_colname='tf_idf'):
+    '''
+    tfidf_colname: set to 'relative_tf' to use normalized term frequency instead of tf-idf, which can be useful for author-based similarities.
+    '''
+    if from_date is not None or to_date is not None:
+        tempdir, subreddit_names = reindex_tfidf_time_interval(infile, term_colname=term_colname, min_df=min_df, max_df=max_df, included_subreddits=included_subreddits, topN=topN, exclude_phrases=False, from_date=from_date, to_date=to_date)
+        
+    else:
+        tempdir, subreddit_names = reindex_tfidf(infile, term_colname=term_colname, min_df=min_df, max_df=max_df, included_subreddits=included_subreddits, topN=topN, exclude_phrases=False)
+
+    print("loading matrix")
+    #    mat = read_tfidf_matrix("term_tfidf_entries7ejhvnvl.parquet", term_colname)
+    mat = read_tfidf_matrix(tempdir.name, term_colname, tfidf_colname)
+    print(f'computing similarities on mat. mat.shape:{mat.shape}')
+    print(f"size of mat is:{mat.data.nbytes}")
+    sims = simfunc(mat)
+    del mat
+
+    if issparse(sims):
+        sims = sims.todense()
+
+    print(f"shape of sims:{sims.shape}")
+    print(f"len(subreddit_names.subreddit.values):{len(subreddit_names.subreddit.values)}")
+    sims = pd.DataFrame(sims)
+    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 read_tfidf_matrix_weekly(path, term_colname, week, tfidf_colname='tf_idf'):
+    term = term_colname
+    term_id = term + '_id'
+    term_id_new = term + '_id_new'
+
+    dataset = ds.dataset(path,format='parquet')
+    entries = dataset.to_table(columns=[tfidf_colname,'subreddit_id_new', term_id_new],filter=ds.field('week')==week).to_pandas()
+    return(csr_matrix((entries[tfidf_colname], (entries[term_id_new]-1, entries.subreddit_id_new-1))))
+
+def read_tfidf_matrix(path, term_colname, tfidf_colname='tf_idf'):
+    term = term_colname
+    term_id = term + '_id'
+    term_id_new = term + '_id_new'
+    dataset = ds.dataset(path,format='parquet')
+    print(f"tfidf_colname:{tfidf_colname}")
+    entries = dataset.to_table(columns=[tfidf_colname, 'subreddit_id_new',term_id_new]).to_pandas()
+    return(csr_matrix((entries[tfidf_colname],(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 column_overlaps(mat):
+    non_zeros = (mat != 0).astype('double')
+    
+    intersection = non_zeros.T @ non_zeros
+    card1 = non_zeros.sum(axis=0)
+    den = np.add.outer(card1,card1) - intersection
+
+    return intersection / den
+    
+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, max_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('count') >= min_df)
+    if max_df is not None:
+        tfidf = tfidf.filter(f.col('count') <= max_df)
+
+    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, max_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('count') >= min_df)
+    if max_df is not None:
+        tfidf = tfidf.filter(f.col('count') <= max_df)
+
+    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 _calc_tfidf(df, term_colname, tf_family):
+    term = term_colname
+    term_id = term + '_id'
+
+    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 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'))
+
+    df = _calc_tfidf(df, term_colname, tf_family)
+
+    return df
+
+def select_topN_subreddits(topN, path="/gscratch/comdata/output/reddit_similarity/subreddits_by_num_comments_nonsfw.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 +1,84 @@
+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, included_subreddits):
+    spark = SparkSession.builder.getOrCreate()
+
+    df = spark.read.parquet(inpath)
+
+    df = df.filter(~ f.col(term_colname).isin(exclude))
+
+    if included_subreddits is not None:
+        include_subs = set(map(str.strip,map(str.lower, open(included_subreddits))))
+    else:
+        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, included_subreddits):
+    return _tfidf_wrapper(build_tfidf_dataset, inpath, outpath, topN, term_colname, exclude, included_subreddits)
+
+def tfidf_weekly(inpath, outpath, topN, term_colname, exclude, included_subreddits):
+    return _tfidf_wrapper(build_weekly_tfidf_dataset, inpath, outpath, topN, term_colname, exclude, included_subreddits)
+
+def tfidf_authors(outpath='/gscratch/comdata/output/reddit_similarity/tfidf/comment_authors.parquet',
+                  topN=25000,
+                  included_subreddits=None):
+
+    return tfidf("/gscratch/comdata/output/reddit_ngrams/comment_authors.parquet",
+                 outpath,
+                 topN,
+                 'author',
+                 ['[deleted]','AutoModerator'],
+                 included_subreddits=included_subreddits
+                 )
+
+def tfidf_terms(outpath='/gscratch/comdata/output/reddit_similarity/tfidf/comment_terms.parquet',
+                topN=25000,
+                included_subreddits=None):
+
+    return tfidf("/gscratch/comdata/output/reddit_ngrams/comment_terms.parquet",
+                 outpath,
+                 topN,
+                 'term',
+                 [],
+                 included_subreddits=included_subreddits
+                 )
+
+def tfidf_authors_weekly(outpath='/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_authors.parquet',
+                         topN=25000,
+                         included_subreddits=None):
+
+    return tfidf_weekly("/gscratch/comdata/output/reddit_ngrams/comment_authors.parquet",
+                        outpath,
+                        topN,
+                        'author',
+                        ['[deleted]','AutoModerator'],
+                        included_subreddits=included_subreddits
+                        )
+
+def tfidf_terms_weekly(outpath='/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_terms.parquet',
+                       topN=25000,
+                       included_subreddits=None):
+
+
+    return tfidf_weekly("/gscratch/comdata/output/reddit_ngrams/comment_terms.parquet",
+                        outpath,
+                        topN,
+                        'term',
+                        [],
+                        included_subreddits=included_subreddits
+                        )
+
+
+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

@@ -1,18 +1,14 @@
 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
 
 spark = SparkSession.builder.getOrCreate()
 conf = spark.sparkContext.getConf()
 
+submissions = spark.read.parquet("/gscratch/comdata/output/reddit_submissions_by_subreddit.parquet")
+
+prop_nsfw = submissions.select(['subreddit','over_18']).groupby('subreddit').agg(f.mean(f.col('over_18').astype('double')).alias('prop_nsfw'))
+
 df = spark.read.parquet("/gscratch/comdata/output/reddit_comments_by_subreddit.parquet")
 
 # remove /u/ pages
@@ -20,6 +16,9 @@ df = df.filter(~df.subreddit.like("u_%"))
 
 df = df.groupBy('subreddit').agg(f.count('id').alias("n_comments"))
 
+df = df.join(prop_nsfw,on='subreddit')
+df = df.filter(df.prop_nsfw < 0.5)
+
 win = Window.orderBy(f.col('n_comments').desc())
 df = df.withColumn('comments_rank', f.rank().over(win))
 
@@ -27,4 +26,4 @@ df = df.toPandas()
 
 df = df.sort_values("n_comments")
 
-df.to_csv('/gscratch/comdata/users/nathante/cdsc-reddit/subreddits_by_num_comments.csv',index=False)
+df.to_csv('/gscratch/comdata/output/reddit_similarity/subreddits_by_num_comments.csv', index=False)

similarities/wang_similarity.py

@@ -0,0 +1,18 @@
+from similarities_helper import similarities
+import numpy as np
+import fire 
+
+def wang_similarity(mat):
+    non_zeros = (mat != 0).astype(np.float32)
+    intersection = non_zeros.T @ non_zeros
+    return intersection
+
+
+infile="/gscratch/comdata/output/reddit_similarity/tfidf/comment_authors.parquet"; outfile="/gscratch/comdata/output/reddit_similarity/wang_similarity_10000.feather"; min_df=1; included_subreddits=None; topN=10000; exclude_phrases=False; from_date=None; to_date=None
+    
+def wang_overlaps(infile, outfile="/gscratch/comdata/output/reddit_similarity/wang_similarity_10000.feather", min_df=1, max_df=None, included_subreddits=None, topN=10000, exclude_phrases=False, from_date=None, to_date=None):
+
+    return similarities(infile=infile, simfunc=wang_similarity, term_colname='author', outfile=outfile, min_df=min_df, max_df=max_df, included_subreddits=included_subreddits, topN=topN, exclude_phrases=exclude_phrases, from_date=from_date, to_date=to_date)
+
+if __name__ == "__main__":
+    fire.Fire(wang_overlaps)

similarities/weekly_cosine_similarities.py

@@ -0,0 +1,81 @@
+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 *
+from multiprocessing import Pool, cpu_count
+
+def _week_similarities(tempdir, term_colname, week):
+        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)
+
+#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, max_df=None, included_subreddits=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 = sorted(list(subreddit_names.week.drop_duplicates()))
+    # do this step in parallel if we have the memory for it.
+    # should be doable with pool.map
+
+    def week_similarities_helper(week):
+        _week_similarities(tempdir, term_colname, week)
+
+    with Pool(cpu_count()) as pool: # maybe it can be done with 40 cores on the huge machine?
+        list(pool.map(week_similarities_helper,weeks))
+
+def author_cosine_similarities_weekly(outfile, min_df=2 , 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({'authors':author_cosine_similarities_weekly,
+               'terms':term_cosine_similarities_weekly})

similarities_helper.py

@@ -1,172 +0,0 @@
-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
-
-class tf_weight(Enum):
-    MaxTF = 1
-    Norm05 = 2
-
-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(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'))
-#    new_count = new_count.filter(f.col('new_count') >= min_df)
-    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.withColumnRenamed("idf","idf_old")
-    # tfidf = tfidf.withColumn("idf",f.log(25000/f.col("count")))
-    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
-
-def 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'))
-#    new_count = new_count.filter(f.col('new_count') >= min_df)
-    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.withColumnRenamed("idf","idf_old")
-    # tfidf = tfidf.withColumn("idf",f.log(25000/f.col("count")))
-    tfidf = tfidf.withColumn("tf_idf", tfidf.relative_tf * tfidf.idf)
-
-    # step 1 make an rdd of entires
-    # sorted by (dense) spark subreddit id
-    #    entries = tfidf.filter((f.col('subreddit') == 'asoiaf') | (f.col('subreddit') == 'gameofthrones') | (f.col('subreddit') == 'christianity')).select(f.col("term_id_new")-1,f.col("subreddit_id_new")-1,"tf_idf").rdd
- 
-    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_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
-
-

term_cosine_similarity.py

@@ -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)

tfidf_authors.py

@@ -1,19 +0,0 @@
-from pyspark.sql import SparkSession
-from similarities_helper import build_tfidf_dataset
-
-spark = SparkSession.builder.getOrCreate()
-
-df = spark.read.parquet("/gscratch/comdata/users/nathante/reddit_tfidf_test_authors.parquet_temp")
-
-include_subs = set(open("/gscratch/comdata/users/nathante/cdsc-reddit/top_25000_subs_by_comments.txt"))
-include_subs = {s.strip('\n') for s in include_subs}
-
-# remove [deleted] and AutoModerator (TODO remove other bots)
-df = df.filter(df.author != '[deleted]')
-df = df.filter(df.author != 'AutoModerator')
-
-df = build_tfidf_dataset(df, include_subs, 'author')
-
-df.write.parquet('/gscratch/comdata/users/nathante/subreddit_tfidf_authors.parquet',mode='overwrite',compression='snappy')
-
-spark.stop()

tfidf_comments.py

@@ -1,18 +0,0 @@
-from pyspark.sql import functions as f
-from pyspark.sql import SparkSession
-from pyspark.sql import Window
-from similarities_helper import build_tfidf_dataset
-
-## TODO:need to exclude automoderator / bot posts.
-## TODO:need to exclude better handle hyperlinks. 
-
-spark = SparkSession.builder.getOrCreate()
-df = spark.read.parquet("/gscratch/comdata/users/nathante/reddit_tfidf_test.parquet_temp")
-
-include_subs = set(open("/gscratch/comdata/users/nathante/cdsc-reddit/top_25000_subs_by_comments.txt"))
-include_subs = {s.strip('\n') for s in include_subs}
-
-df = build_tfidf_dataset(df, include_subs, 'term')
-
-df.write.parquet('/gscratch/comdata/users/nathante/subreddit_tfidf.parquet',mode='overwrite',compression='snappy')
-spark.stop()

timeseries/choose_clusters.py

@@ -0,0 +1,96 @@
+from pyarrow import dataset as ds
+import numpy as np
+import pandas as pd
+import plotnine as pn
+random = np.random.RandomState(1968)
+
+def load_densities(term_density_file="/gscratch/comdata/output/reddit_density/comment_terms_10000.feather",
+                   author_density_file="/gscratch/comdata/output/reddit_density/comment_authors_10000.feather"):
+
+    term_density = pd.read_feather(term_density_file)
+    author_density = pd.read_feather(author_density_file)
+
+    term_density.rename({'overlap_density':'term_density','index':'subreddit'},axis='columns',inplace=True)
+    author_density.rename({'overlap_density':'author_density','index':'subreddit'},axis='columns',inplace=True)
+
+    density = term_density.merge(author_density,on='subreddit',how='inner')
+
+    return density
+
+def load_clusters(term_clusters_file="/gscratch/comdata/output/reddit_clustering/comment_terms_10000.feather",
+                  author_clusters_file="/gscratch/comdata/output/reddit_clustering/comment_authors_10000.feather"):
+    term_clusters = pd.read_feather(term_clusters_file)
+    author_clusters = pd.read_feather(author_clusters_file)
+
+    # rename, join and return
+    term_clusters.rename({'cluster':'term_cluster'},axis='columns',inplace=True)
+    author_clusters.rename({'cluster':'author_cluster'},axis='columns',inplace=True)
+
+    clusters = term_clusters.merge(author_clusters,on='subreddit',how='inner')
+
+    return clusters
+
+if __name__ == '__main__':
+
+    df = load_densities()
+    cl = load_clusters()
+
+    df['td_rank'] = df.term_density.rank()
+    df['ad_rank'] = df.author_density.rank()
+
+    df['td_percentile'] = df.td_rank / df.shape[0]
+    df['ad_percentile'] = df.ad_rank / df.shape[0]
+
+    df = df.merge(cl, on='subreddit',how='inner')
+
+    term_cluster_density = df.groupby('term_cluster').agg({'td_rank':['mean','min','max'],
+                                                         'ad_rank':['mean','min','max'],
+                                                         'td_percentile':['mean','min','max'],
+                                                           'ad_percentile':['mean','min','max'],
+                                                           'subreddit':['count']})
+                                                         
+
+    author_cluster_density = df.groupby('author_cluster').agg({'td_rank':['mean','min','max'],
+                                                         'ad_rank':['mean','min','max'],
+                                                         'td_percentile':['mean','min','max'],
+                                                           'ad_percentile':['mean','min','max'],
+                                                           'subreddit':['count']})
+                                                         
+    # which clusters have the most term_density?
+    term_cluster_density.iloc[term_cluster_density.td_rank['mean'].sort_values().index]
+
+    # which clusters have the most author_density?
+    term_cluster_density.iloc[term_cluster_density.ad_rank['mean'].sort_values(ascending=False).index].loc[term_cluster_density.subreddit['count'] >= 5][0:20]
+
+    high_density_term_clusters = term_cluster_density.loc[(term_cluster_density.td_percentile['mean'] > 0.75) & (term_cluster_density.subreddit['count'] > 5)]
+
+    # let's just use term density instead of author density for now. We can do a second batch with author density next.
+    chosen_clusters = high_density_term_clusters.sample(3,random_state=random)
+
+    cluster_info = df.loc[df.term_cluster.isin(chosen_clusters.index.values)]
+
+    chosen_subreddits = cluster_info.subreddit.values
+
+    dataset = ds.dataset("/gscratch/comdata/output/reddit_comments_by_subreddit.parquet",format='parquet')
+    comments = dataset.to_table(filter=ds.field("subreddit").isin(chosen_subreddits),columns=['id','subreddit','author','CreatedAt'])
+
+    comments = comments.to_pandas()
+
+    comments['week'] = comments.CreatedAt.dt.date - pd.to_timedelta(comments['CreatedAt'].dt.dayofweek, unit='d')
+
+    author_timeseries = comments.loc[:,['subreddit','author','week']].drop_duplicates().groupby(['subreddit','week']).count().reset_index()
+
+    for clid in chosen_clusters.index.values:
+
+        ts = pd.read_feather(f"data/ts_term_cluster_{clid}.feather")
+
+        pn.options.figure_size = (11.7,8.27)
+        p = pn.ggplot(ts)
+        p = p + pn.geom_line(pn.aes('week','value',group='subreddit'))
+        p = p + pn.facet_wrap('~ subreddit')
+        p.save(f"plots/ts_term_cluster_{clid}.png")
+        
+
+        fig, ax = pyplot.subplots(figsize=(11.7,8.27))
+        g = sns.FacetGrid(ts,row='subreddit')
+        g.map_dataframe(sns.scatterplot,'week','value',data=ts,ax=ax)

timeseries/cluster_timeseries.py

@@ -0,0 +1,37 @@
+import pandas as pd
+import numpy as np
+from pyspark.sql import functions as f
+from pyspark.sql import SparkSession
+from choose_clusters import load_clusters, load_densities
+import fire
+from pathlib import Path
+
+def main(term_clusters_path="/gscratch/comdata/output/reddit_clustering/comment_terms_10000.feather",
+         author_clusters_path="/gscratch/comdata/output/reddit_clustering/comment_authors_10000.feather",
+         term_densities_path="/gscratch/comdata/output/reddit_density/comment_terms_10000.feather",
+         author_densities_path="/gscratch/comdata/output/reddit_density/comment_authors_10000.feather",
+         output="data/subreddit_timeseries.parquet"):
+
+
+    clusters = load_clusters(term_clusters_path, author_clusters_path)
+    densities = load_densities(term_densities_path, author_densities_path)
+    
+    spark = SparkSession.builder.getOrCreate()
+    
+    df = spark.read.parquet("/gscratch/comdata/output/reddit_comments_by_subreddit.parquet")
+    
+    df = df.withColumn('week', f.date_trunc('week', f.col("CreatedAt")))
+    
+    # time of unique authors by series by week
+    ts = df.select(['subreddit','week','author']).distinct().groupby(['subreddit','week']).count()
+    
+    ts = ts.repartition('subreddit')
+    spk_clusters = spark.createDataFrame(clusters)
+    
+    ts = ts.join(spk_clusters, on='subreddit', how='inner')
+    spk_densities = spark.createDataFrame(densities)
+    ts = ts.join(spk_densities, on='subreddit', how='inner')
+    ts.write.parquet(output, mode='overwrite')
+
+if __name__ == "__main__":
+    fire.Fire(main)

visualization/Makefile

@@ -0,0 +1,11 @@
+all: subreddit_author_tf_similarities_10000.html #comment_authors_10000.html
+
+# wang_tsne_10000.html
+# wang_tsne_10000.html:/gscratch/comdata/output/reddit_tsne/wang_similarity_10000.feather /gscratch/comdata/output/reddit_clustering/wang_similarity_10000.feather tsne_vis.py
+# 	python3 tsne_vis.py --tsne_data=/gscratch/comdata/output/reddit_tsne/wang_similarity_10000.feather --clusters=/gscratch/comdata/output/reddit_clustering/wang_similarity_10000.feather --output=wang_tsne_10000.html
+
+# comment_authors_10000.html:/gscratch/comdata/output/reddit_tsne/comment_authors_10000.feather /gscratch/comdata/output/reddit_clustering/comment_authors_10000.feather tsne_vis.py
+# 	python3 tsne_vis.py --tsne_data=/gscratch/comdata/output/reddit_similarity/comment_authors_10000.feather --clusters=/gscratch/comdata/output/reddit_clustering/comment_authors_10000.feather --output=comment_authors_10000.html
+
+subreddit_author_tf_similarities_10000.html:/gscratch/comdata/output/reddit_tsne/subreddit_author_tf_similarities_10000.feather /gscratch/comdata/output/reddit_clustering/subreddit_author_tf_similarities_10000.feather tsne_vis.py
+	start_spark_and_run.sh 1 tsne_vis.py --tsne_data=/gscratch/comdata/output/reddit_tsne/subreddit_author_tf_similarities_10000.feather --clusters=/gscratch/comdata/output/reddit_clustering/subreddit_author_tf_similarities_10000.feather --output=subreddit_author_tf_similarities_10000.html

visualization/tsne_vis.py

@@ -5,21 +5,44 @@ alt.data_transformers.enable('default')
 from sklearn.neighbors import NearestNeighbors
 import pandas as pd
 from numpy import random
+import fire
 import numpy as np
 
 def base_plot(plot_data):
+
+#    base = base.encode(alt.Color(field='color',type='nominal',scale=alt.Scale(scheme='category10')))
+
+    cluster_dropdown = alt.binding_select(options=[str(c) for c in sorted(set(plot_data.cluster))])
+
+    #    subreddit_dropdown = alt.binding_select(options=sorted(plot_data.subreddit))
+
+    cluster_click_select = alt.selection_single(on='click',fields=['cluster'], bind=cluster_dropdown, name=' ')
+    # cluster_select = alt.selection_single(fields=['cluster'], bind=cluster_dropdown, name='cluster')
+    # cluster_select_and = cluster_click_select & cluster_select
+    #
+    #    subreddit_select = alt.selection_single(on='click',fields=['subreddit'],bind=subreddit_dropdown,name='subreddit_click')
+    
+    color = alt.condition(cluster_click_select ,
+                          alt.Color(field='color',type='nominal',scale=alt.Scale(scheme='category10')),
+                          alt.value("lightgray"))
+  
+    
     base = alt.Chart(plot_data).mark_text().encode(
         alt.X('x',axis=alt.Axis(grid=False),scale=alt.Scale(domain=(-65,65))),
         alt.Y('y',axis=alt.Axis(grid=False),scale=alt.Scale(domain=(-65,65))),
+        color=color,
         text='subreddit')
 
+    base = base.add_selection(cluster_click_select)
+ 
+
     return base
 
 def zoom_plot(plot_data):
     chart = base_plot(plot_data)
-    chart = chart.encode(alt.Color(field='color',type='nominal',scale=alt.Scale(scheme='category10')))
+
     chart = chart.interactive()
-    chart = chart.properties(width=1275,height=1000)
+    chart = chart.properties(width=1275,height=800)
 
     return chart
 
@@ -51,7 +74,7 @@ def viewport_plot(plot_data):
                      alt.Y('y',axis=alt.Axis(grid=False),scale=alt.Scale(domain=selectory2))
     )
 
-    sr = sr.encode(alt.Color(field='color',type='nominal',scale=alt.Scale(scheme='category10')))
+
     sr = sr.properties(width=1275,height=600)
 
 
@@ -70,15 +93,29 @@ def assign_cluster_colors(tsne_data, clusters, n_colors, n_neighbors = 4):
     distances = np.empty(shape=(centroids.shape[0],centroids.shape[0]))
 
     groups = tsne_data.groupby('cluster')
-    for centroid in centroids.itertuples():
-        c_dists = groups.apply(lambda r: min(np.sqrt(np.square(centroid.x - r.x) + np.square(centroid.y-r.y))))
-        distances[:,centroid.Index] = c_dists
+    
+    points = np.array(tsne_data.loc[:,['x','y']])
+    centers = np.array(centroids.loc[:,['x','y']])
+
+    # point x centroid
+    point_center_distances = np.linalg.norm((points[:,None,:] - centers[None,:,:]),axis=-1)
+    
+    # distances is cluster x point
+    for gid, group in groups:
+        c_dists = point_center_distances[group.index.values,:].min(axis=0)
+        distances[group.cluster.values[0],] = c_dists        
 
     # nbrs = NearestNeighbors(n_neighbors=n_neighbors).fit(centroids) 
     # distances, indices = nbrs.kneighbors()
 
-    nbrs = NearestNeighbors(n_neighbors=n_neighbors,metric='precomputed').fit(distances) 
-    distances, indices = nbrs.kneighbors()
+    nearest = distances.argpartition(n_neighbors,0)
+    indices = nearest[:n_neighbors,:].T
+    # neighbor_distances = np.copy(distances)
+    # neighbor_distances.sort(0)
+    # neighbor_distances = neighbor_distances[0:n_neighbors,:]
+    
+    # nbrs = NearestNeighbors(n_neighbors=n_neighbors,metric='precomputed').fit(distances) 
+    # distances, indices = nbrs.kneighbors()
 
     color_assignments = np.repeat(-1,len(centroids))
 
@@ -100,26 +137,39 @@ def assign_cluster_colors(tsne_data, clusters, n_colors, n_neighbors = 4):
     tsne_data = tsne_data.merge(colors,on='cluster')
     return(tsne_data)
 
-term_data = pd.read_feather("tsne_subreddit_fit.feather")
-clusters = pd.read_feather("term_3000_clusters.feather")
+def build_visualization(tsne_data, clusters, output):
 
-tsne_data = assign_cluster_colors(term_data,clusters,10,8)
+    # tsne_data = "/gscratch/comdata/output/reddit_tsne/subreddit_author_tf_similarities_10000.feather"
+    # clusters = "/gscratch/comdata/output/reddit_clustering/subreddit_author_tf_similarities_10000.feather"
+
+    tsne_data = pd.read_feather(tsne_data)
+    clusters = pd.read_feather(clusters)
+
+    tsne_data = assign_cluster_colors(tsne_data,clusters,10,8)
+
+    # sr_per_cluster = tsne_data.groupby('cluster').subreddit.count().reset_index()
+    # sr_per_cluster = sr_per_cluster.rename(columns={'subreddit':'cluster_size'})
+
+    tsne_data = tsne_data.merge(sr_per_cluster,on='cluster')
 
     term_zoom_plot = zoom_plot(tsne_data)
 
-term_zoom_plot.save("subreddit_terms_tsne_3000.html")
+    term_zoom_plot.save(output)
 
     term_viewport_plot = viewport_plot(tsne_data)
 
-term_viewport_plot.save("subreddit_terms_tsne_3000_viewport.html")
+    term_viewport_plot.save(output.replace(".html","_viewport.html"))
 
-commenter_data = pd.read_feather("tsne_author_fit.feather")
-clusters = pd.read_feather('author_3000_clusters.feather')
-commenter_data = assign_cluster_colors(commenter_data,clusters,10,8)
-commenter_zoom_plot = zoom_plot(commenter_data)
-commenter_viewport_plot = viewport_plot(commenter_data)
-commenter_zoom_plot.save("subreddit_commenters_tsne_3000.html")
-commenter_viewport_plot.save("subreddit_commenters_tsne_3000_viewport.html")
+if __name__ == "__main__":
+    fire.Fire(build_visualization)
+
+# commenter_data = pd.read_feather("tsne_author_fit.feather")
+# clusters = pd.read_feather('author_3000_clusters.feather')
+# commenter_data = assign_cluster_colors(commenter_data,clusters,10,8)
+# commenter_zoom_plot = zoom_plot(commenter_data)
+# commenter_viewport_plot = viewport_plot(commenter_data)
+# commenter_zoom_plot.save("subreddit_commenters_tsne_3000.html")
+# commenter_viewport_plot.save("subreddit_commenters_tsne_3000_viewport.html")
 
 # chart = chart.properties(width=10000,height=10000)
 # chart.save("test_tsne_whole.svg")