make pass keyword arg to dataframe.drop

Also lowercases included subreddits.

__init__.py

@@ -0,0 +1,2 @@
+from .timeseries import load_clusters, load_densities, build_cluster_timeseries
+

bots/good_bad_bot.py

@@ -1,74 +0,0 @@
-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

@@ -1,76 +1,36 @@
-#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=srun -p compute-bigmem -A comdata --time=48:00:00 --mem=362G -c 40 /bin/bash -c 
-srun_singularity=source /gscratch/comdata/users/nathante/cdsc_reddit/bin/activate && srun_singularity.sh
+similarity_data=../../data/reddit_similarity
-similarity_data=/gscratch/comdata/output/reddit_similarity
+clustering_data=../../data/reddit_clustering
-clustering_data=/gscratch/comdata/output/reddit_clustering
+kmeans_selection_grid=--max_iters=[3000] --n_inits=[10] --n_clusters=[100,500,1000,1250,1500,1750,2000]
-kmeans_selection_grid="--max_iter=3000 --n_init=[10] --n_clusters=[100,500,1000,1500,2000,2500,3000,2350,3500,3570,4000]"
+hdbscan_selection_grid=--min_cluster_sizes=[2,3,4,5] --min_samples=[2,3,4,5] --cluster_selection_epsilons=[0,0.01,0.05,0.1,0.15,0.2] --cluster_selection_methods=[eom,leaf]
-#selection_grid="--max_iter=3000 --convergence_iter=[15] --preference_quantile=[0.5] --damping=[0.99]"
+affinity_selection_grid=--dampings=[0.5,0.6,0.7,0.8,0.95,0.97,0.99] --preference_quantiles=[0.1,0.3,0.5,0.7,0.9] --convergence_iters=[15]
-all:$(clustering_data)/subreddit_comment_authors_10k/kmeans/selection_data.csv $(clustering_data)/subreddit_comment_authors-tf_10k/kmeans/selection_data.csv $(clustering_data)/subreddit_comment_terms_10k/kmeans/selection_data.csv $(clustering_data)/subreddit_comment_terms_10k/affinity/selection_data.csv $(clustering_data)/subreddit_comment_authors_10k/affinity/selection_data.csv $(clustering_data)/subreddit_comment_authors-tf_10k/affinity/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/kmeans/selection_data.csv:selection.py $(similarity_data)/subreddit_comment_authors_10k.feather clustering.py
+authors_tf_10k_input_lsi=$(similarity_data)/subreddit_comment_authors-tf_10k_LSI
-	$(srun_singularity) python3 selection.py kmeans $(similarity_data)/subreddit_comment_authors_10k.feather $(clustering_data)/subreddit_comment_authors_10k/kmeans $(clustering_data)/subreddit_comment_authors_10k/kmeans/selection_data.csv $(kmeans_selection_grid)
+authors_tf_10k_output_lsi=$(clustering_data)/subreddit_comment_authors-tf_10k_LSI
 
-$(clustering_data)/subreddit_comment_terms_10k/kmeans/selection_data.csv:selection.py $(similarity_data)/subreddit_comment_terms_10k.feather clustering.py
+all:authors_tf_10k_lsi
-	$(srun_singularity) python3 selection.py kmeans $(similarity_data)/subreddit_comment_terms_10k.feather $(clustering_data)/subreddit_comment_terms_10k/kmeans  $(clustering_data)/subreddit_comment_terms_10k/kmeans/selection_data.csv $(kmeans_selection_grid)
 
-$(clustering_data)/subreddit_comment_authors-tf_10k/kmeans/selection_data.csv:clustering.py $(similarity_data)/subreddit_comment_authors-tf_10k.feather
+authors_tf_10k_lsi:${authors_tf_10k_output_lsi}/kmeans/selection_data.csv ${authors_tf_10k_output_lsi}/hdbscan/selection_data.csv ${authors_tf_10k_output_lsi}/affinity/selection_data.csv
-	$(srun_singularity) python3 selection.py kmeans $(similarity_data)/subreddit_comment_authors-tf_10k.feather $(clustering_data)/subreddit_comment_authors-tf_10k/kmeans  $(clustering_data)/subreddit_comment_authors-tf_10k/kmeans/selection_data.csv $(kmeans_selection_grid)
 
+## LSI Models
+${authors_tf_10k_output_lsi}/kmeans/selection_data.csv:clustering.py ${authors_tf_10k_input_lsi} clustering_base.py kmeans_clustering.py
+	$(srun_singularity) -c "source ~/.bashrc; python3 kmeans_clustering_lsi.py --inpath=${authors_tf_10k_input_lsi} --outpath=${authors_tf_10k_output_lsi}/kmeans --savefile=${authors_tf_10k_output_lsi}/kmeans/selection_data.csv $(kmeans_selection_grid)"
 
-affinity_selection_grid="--max_iter=3000 --convergence_iter=[15] --preference_quantile=[0.5] --damping=[0.99]"
+${authors_tf_10k_output_lsi}/affinity/selection_data.csv:clustering.py ${authors_tf_10k_input_lsi} clustering_base.py affinity_clustering.py
-$(clustering_data)/subreddit_comment_authors_10k/affinity/selection_data.csv:selection.py $(similarity_data)/subreddit_comment_authors_10k.feather clustering.py
+	$(srun_singularity) -c "source ~/.bashrc; python3 affinity_clustering_lsi.py --inpath=${authors_tf_10k_input_lsi} --outpath=${authors_tf_10k_output_lsi}/affinity --savefile=${authors_tf_10k_output_lsi}/affinity/selection_data.csv $(affinity_selection_grid)"
-	$(srun_singularity) python3 selection.py affinity $(similarity_data)/subreddit_comment_authors_10k.feather $(clustering_data)/subreddit_comment_authors_10k/affinity $(clustering_data)/subreddit_comment_authors_10k/affinity/selection_data.csv $(affinity_selection_grid) -J 20
 
-$(clustering_data)/subreddit_comment_terms_10k/affinity/selection_data.csv:selection.py $(similarity_data)/subreddit_comment_terms_10k.feather clustering.py
+${authors_tf_10k_output_lsi}/hdbscan/selection_data.csv:clustering.py ${authors_tf_10k_input_lsi} clustering_base.py hdbscan_clustering.py
-	$(srun_singularity) python3 selection.py affinity $(similarity_data)/subreddit_comment_terms_10k.feather $(clustering_data)/subreddit_comment_terms_10k/affinity  $(clustering_data)/subreddit_comment_terms_10k/affinity/selection_data.csv $(affinity_selection_grid) -J 20 
+	$(srun_singularity) -c "source ~/.bashrc; python3 hdbscan_clustering_lsi.py --inpath=${authors_tf_10k_input_lsi} --outpath=${authors_tf_10k_output_lsi}/hdbscan --savefile=${authors_tf_10k_output_lsi}/hdbscan/selection_data.csv $(hdbscan_selection_grid)"
 
-$(clustering_data)/subreddit_comment_authors-tf_10k/affinity/selection_data.csv:clustering.py $(similarity_data)/subreddit_comment_authors-tf_10k.feather
+${authors_tf_10k_output_lsi}/best_hdbscan.feather:${authors_tf_10k_output_lsi}/hdbscan/selection_data.csv pick_best_clustering.py
-	$(srun_singularity) python3 selection.py affinity $(similarity_data)/subreddit_comment_authors-tf_10k.feather $(clustering_data)/subreddit_comment_authors-tf_10k/affinity  $(clustering_data)/subreddit_comment_authors-tf_10k/affinity/selection_data.csv $(affinity_selection_grid) -J 20
+	$(srun_singularity) -c "source ~/.bashrc; python3 pick_best_clustering.py $< $@ --min_clusters=50 --max_isolates=5000 --min_cluster_size=2"
+
+${authors_tf_10k_input_lsi}:
+	$(MAKE) -C ../similarities
 
 clean:
-	rm -f $(clustering_data)/subreddit_comment_authors-tf_10k/affinity/selection_data.csv
+	rm -f ${authors_tf_10k_output_lsi}/affinity/selection_data.csv
-	rm -f $(clustering_data)/subreddit_comment_authors_10k/affinity/selection_data.csv
+	rm -f ${authors_tf_10k_output_lsi}/kmeans/selection_data.csv
-	rm -f $(clustering_data)/subreddit_comment_terms_10k/affinity/selection_data.csv
+	rm -f ${authors_tf_10k_output_lsi}/hdbscan/selection_data.csv
-	rm -f $(clustering_data)/subreddit_comment_authors-tf_10k/kmeans/selection_data.csv
-	rm -f $(clustering_data)/subreddit_comment_authors_10k/kmeans/selection_data.csv
-	rm -f $(clustering_data)/subreddit_comment_terms_10k/kmeans/selection_data.csv
 
 PHONY: clean 
-
-# $(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/affinity_clustering.py

@@ -0,0 +1,129 @@
+from sklearn.cluster import AffinityPropagation
+from dataclasses import dataclass
+from clustering_base import clustering_result, clustering_job
+from grid_sweep import grid_sweep
+from pathlib import Path
+from itertools import product, starmap
+import fire
+import sys
+import numpy as np
+
+# 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 affinity_clustering_result(clustering_result):
+    damping:float
+    convergence_iter:int
+    preference_quantile:float
+    preference:float
+    max_iter:int
+
+class affinity_job(clustering_job):
+    def __init__(self, infile, outpath, name, damping=0.9, max_iter=100000, convergence_iter=30, preference_quantile=0.5, random_state=1968, verbose=True):
+        super().__init__(infile,
+                         outpath,
+                         name,
+                         call=self._affinity_clustering,
+                         preference_quantile=preference_quantile,
+                         damping=damping,
+                         max_iter=max_iter,
+                         convergence_iter=convergence_iter,
+                         random_state=1968,
+                         verbose=verbose)
+        self.damping=damping
+        self.max_iter=max_iter
+        self.convergence_iter=convergence_iter
+        self.preference_quantile=preference_quantile
+
+    def _affinity_clustering(self, mat, preference_quantile, *args, **kwargs):
+        mat = 1-mat
+        preference = np.quantile(mat, preference_quantile)
+        self.preference = preference
+        print(f"preference is {preference}")
+        print("data loaded")
+        sys.stdout.flush()
+        clustering = AffinityPropagation(*args,
+                                         preference=preference,
+                                         affinity='precomputed',
+                                         copy=False,
+                                         **kwargs).fit(mat)
+        return clustering
+
+    def get_info(self):
+        result = super().get_info()
+        self.result=affinity_clustering_result(**result.__dict__,
+                                               damping=self.damping,
+                                               max_iter=self.max_iter,
+                                               convergence_iter=self.convergence_iter,
+                                               preference_quantile=self.preference_quantile,
+                                               preference=self.preference)
+
+        return self.result
+
+class affinity_grid_sweep(grid_sweep):
+    def __init__(self,
+                 inpath,
+                 outpath,
+                 *args,
+                 **kwargs):
+
+        super().__init__(affinity_job,
+                         _afffinity_grid_sweep,
+                         inpath,
+                         outpath,
+                         self.namer,
+                         *args,
+                         **kwargs)
+    def namer(self,
+              damping,
+              max_iter,
+              convergence_iter,
+              preference_quantile):
+
+        return f"damp-{damping}_maxit-{max_iter}_convit-{convergence_iter}_prefq-{preference_quantile}"
+
+def run_affinity_grid_sweep(savefile, inpath, outpath, dampings=[0.8], max_iters=[3000], convergence_iters=[30], preference_quantiles=[0.5],n_cores=10):
+    """Run affinity clustering once or more with different parameters.
+    
+    Usage:
+    affinity_clustering.py --savefile=SAVEFILE --inpath=INPATH --outpath=OUTPATH --max_iters=<csv> --dampings=<csv> --preference_quantiles=<csv>
+
+    Keword arguments:
+    savefile: path to save the metadata and diagnostics 
+    inpath: path to feather data containing a labeled matrix of subreddit similarities.
+    outpath: path to output fit kmeans clusterings.
+    dampings:one or more numbers in [0.5, 1). damping parameter in affinity propagatin clustering. 
+    preference_quantiles:one or more numbers in (0,1) for selecting the 'preference' parameter.
+    convergence_iters:one or more integers of number of iterations without improvement before stopping.
+    max_iters: one or more numbers of different maximum interations.
+    """
+    obj = affinity_grid_sweep(inpath,
+                         outpath,
+                         map(float,dampings),
+                         map(int,max_iters),
+                         map(int,convergence_iters),
+                         map(float,preference_quantiles))
+    obj.run(n_cores)
+    obj.save(savefile)
+    
+def test_select_affinity_clustering():
+    # select_hdbscan_clustering("/gscratch/comdata/output/reddit_similarity/subreddit_comment_authors-tf_30k_LSI",
+    #                           "test_hdbscan_author30k",
+    #                           min_cluster_sizes=[2],
+    #                           min_samples=[1,2],
+    #                           cluster_selection_epsilons=[0,0.05,0.1,0.15],
+    #                           cluster_selection_methods=['eom','leaf'],
+    #                           lsi_dimensions='all')
+    inpath = "/gscratch/comdata/output/reddit_similarity/subreddit_comment_authors-tf_10k_LSI/"
+    outpath = "test_affinity";
+    dampings=[0.8,0.9]
+    max_iters=[100000]
+    convergence_iters=[15]
+    preference_quantiles=[0.5,0.7]
+    
+    gs = affinity_lsi_grid_sweep(inpath, 'all', outpath, dampings, max_iters, convergence_iters, preference_quantiles)
+    gs.run(20)
+    gs.save("test_affinity/lsi_sweep.csv")
+
+
+if __name__ == "__main__":
+    fire.Fire(run_affinity_grid_sweep)

clustering/affinity_clustering_lsi.py

@@ -0,0 +1,99 @@
+import fire
+from affinity_clustering import affinity_clustering_result, affinity_job, affinity_grid_sweep
+from grid_sweep import grid_sweep
+from lsi_base import lsi_result_mixin, lsi_grid_sweep, lsi_mixin
+from dataclasses import dataclass
+
+@dataclass
+class affinity_clustering_result_lsi(affinity_clustering_result, lsi_result_mixin):
+    pass
+
+
+class affinity_lsi_job(affinity_job, lsi_mixin):
+    def __init__(self, infile, outpath, name, lsi_dims, *args, **kwargs):
+        super().__init__(infile,
+                         outpath,
+                         name,
+                         *args,
+                         **kwargs)
+        super().set_lsi_dims(lsi_dims)
+
+    def get_info(self):
+        result = super().get_info()
+        self.result = affinity_clustering_result_lsi(**result.__dict__,
+                                                     lsi_dimensions=self.lsi_dims)
+        return self.result
+    
+class affinity_lsi_grid_sweep(lsi_grid_sweep):
+    def __init__(self,
+                 inpath,
+                 lsi_dims,
+                 outpath,
+                 dampings=[0.9],
+                 max_iters=[10000],
+                 convergence_iters=[30],
+                 preference_quantiles=[0.5]):
+
+        super().__init__(affinity_lsi_job,
+                         _affinity_lsi_grid_sweep,
+                         inpath,
+                         lsi_dims,
+                         outpath,
+                         dampings,
+                         max_iters,
+                         convergence_iters,
+                         preference_quantiles)
+    
+
+class _affinity_lsi_grid_sweep(grid_sweep):
+    def __init__(self,
+                 inpath,
+                 outpath,
+                 lsi_dim,
+                 *args,
+                 **kwargs):
+        self.lsi_dim = lsi_dim
+        self.jobtype = affinity_lsi_job
+        super().__init__(self.jobtype,
+                         inpath,
+                         outpath,
+                         self.namer,
+                         [self.lsi_dim],
+                         *args,
+                         **kwargs)
+
+    def namer(self, *args, **kwargs):
+        s = affinity_grid_sweep.namer(self, *args[1:], **kwargs)
+        s += f"_lsi-{self.lsi_dim}"
+        return s
+                         
+def run_affinity_lsi_grid_sweep(savefile, inpath, outpath, dampings=[0.8], max_iters=[3000], convergence_iters=[30], preference_quantiles=[0.5], lsi_dimensions='all',n_cores=30):
+    """Run affinity clustering once or more with different parameters.
+    
+    Usage:
+    affinity_clustering.py --savefile=SAVEFILE --inpath=INPATH --outpath=OUTPATH --max_iters=<csv> --dampings=<csv> --preference_quantiles=<csv> --lsi_dimensions: either "all" or one or more available lsi similarity dimensions at INPATH.
+
+    Keword arguments:
+    savefile: path to save the metadata and diagnostics 
+    inpath: path to folder containing feather files with LSI similarity labeled matrices of subreddit similarities.
+    outpath: path to output fit kmeans clusterings.
+    dampings:one or more numbers in [0.5, 1). damping parameter in affinity propagatin clustering. 
+    preference_quantiles:one or more numbers in (0,1) for selecting the 'preference' parameter.
+    convergence_iters:one or more integers of number of iterations without improvement before stopping.
+    max_iters: one or more numbers of different maximum interations.
+    lsi_dimensions: either "all" or one or more available lsi similarity dimensions at INPATH.
+    """
+    
+    obj = affinity_lsi_grid_sweep(inpath,
+                            lsi_dimensions,
+                            outpath,
+                            map(float,dampings),
+                            map(int,max_iters),
+                            map(int,convergence_iters),
+                            map(float,preference_quantiles))
+
+    obj.run(n_cores)
+    obj.save(savefile)
+
+if __name__ == "__main__":
+    fire.Fire(run_affinity_lsi_grid_sweep)

clustering/clustering.py

@@ -3,7 +3,7 @@
 import sys
 import pandas as pd
 import numpy as np
-from sklearn.cluster import AffinityPropagation, KMeans
+from sklearn.cluster import AffinityPropagation
 import fire
 from pathlib import Path
 from multiprocessing import cpu_count
@@ -46,24 +46,6 @@ def _affinity_clustering(mat, subreddits, output, damping=0.9, max_iter=100000,
     print(f"saved {output}")
     return clustering
 
-def kmeans_clustering(similarities, *args, **kwargs):
-    subreddits, mat = read_similarity_mat(similarities)
-    mat = sim_to_dist(mat)
-    clustering = _kmeans_clustering(mat, *args, **kwargs)
-    cluster_data = process_clustering_result(clustering, subreddits)
-    return(cluster_data)
-
-def _kmeans_clustering(mat, output, n_clusters, n_init=10, max_iter=100000, random_state=1968, verbose=True):
-
-    clustering = KMeans(n_clusters=n_clusters,
-                        n_init=n_init,
-                        max_iter=max_iter,
-                        random_state=random_state,
-                        verbose=verbose
-                        ).fit(mat)
-
-    return clustering
-
 
 
 if __name__ == "__main__":

clustering/clustering_base.py

@@ -1,49 +1,151 @@
+import pickle
 from pathlib import Path
 import numpy as np
 import pandas as pd
 from dataclasses import dataclass
+from sklearn.metrics import silhouette_score, silhouette_samples
+from collections import Counter
 
-def sim_to_dist(mat):
+# this is meant to be an interface, not created directly
-    dist = 1-mat
+class clustering_job:
-    dist[dist < 0] = 0
+    def __init__(self, infile, outpath, name, call, *args, **kwargs):
-    np.fill_diagonal(dist,0)
+        self.outpath = Path(outpath)
-    return dist
+        self.call = call
+        self.args = args
+        self.kwargs = kwargs
+        self.infile = Path(infile)
+        self.name = name
+        self.hasrun = False
 
-def process_clustering_result(clustering, subreddits):
+    def run(self):
+        self.subreddits, self.mat = self.read_distance_mat(self.infile)
+        self.clustering = self.call(self.mat, *self.args, **self.kwargs)
+        self.cluster_data = self.process_clustering(self.clustering, self.subreddits)
+        self.outpath.mkdir(parents=True, exist_ok=True)
+        self.cluster_data.to_feather(self.outpath/(self.name + ".feather"))
+
+        self.hasrun = True
+        self.cleanup()
+
+    def cleanup(self):
+        self.cluster_data = None
+        self.mat = None
+        self.clustering=None
+        self.subreddits=None
+        
+    def get_info(self):
+        if not self.hasrun:
+            self.run()
+
+        self.result = clustering_result(outpath=str(self.outpath.resolve()),
+                                        silhouette_score=self.score,
+                                        name=self.name,
+                                        n_clusters=self.n_clusters,
+                                        n_isolates=self.n_isolates,
+                                        silhouette_samples = self.silsampout
+                                        )
+        return self.result
+
+    def silhouette(self):
+        counts = Counter(self.clustering.labels_)
+        singletons = [key for key, value in counts.items() if value == 1]
+        isolates = (self.clustering.labels_ == -1) | (np.isin(self.clustering.labels_,np.array(singletons)))
+        scoremat = self.mat[~isolates][:,~isolates]
+        if self.n_clusters > 1:
+            score = silhouette_score(scoremat, self.clustering.labels_[~isolates], metric='precomputed')
+            silhouette_samp = silhouette_samples(self.mat, self.clustering.labels_, metric='precomputed')
+            silhouette_samp = pd.DataFrame({'subreddit':self.subreddits,'score':silhouette_samp})
+            self.outpath.mkdir(parents=True, exist_ok=True)
+            silsampout = self.outpath / ("silhouette_samples-" + self.name +  ".feather")
+            self.silsampout = silsampout.resolve()
+            silhouette_samp.to_feather(self.silsampout)
+        else:
+            score = None
+            self.silsampout = None
+
+        return score
+
+    def read_distance_mat(self, similarities, use_threads=True):
+        print(similarities)
+        df = pd.read_feather(similarities, use_threads=use_threads)
+        mat = np.array(df.drop('_subreddit',axis=1))
+        n = mat.shape[0]
+        mat[range(n),range(n)] = 1
+        return (df._subreddit,1-mat)
+
+    def process_clustering(self, clustering, subreddits):
 
         if hasattr(clustering,'n_iter_'):
             print(f"clustering took {clustering.n_iter_} iterations")
 
         clusters = clustering.labels_
+        self.n_clusters = len(set(clusters))
 
-    print(f"found {len(set(clusters))} clusters")
+        print(f"found {self.n_clusters} clusters")
-
         cluster_data = pd.DataFrame({'subreddit': subreddits,'cluster':clustering.labels_})
 
+
+        self.score = self.silhouette()
+        print(f"silhouette_score:{self.score}")
+
+
         cluster_sizes = cluster_data.groupby("cluster").count().reset_index()
         print(f"the largest cluster has {cluster_sizes.loc[cluster_sizes.cluster!=-1].subreddit.max()} members")
 
         print(f"the median cluster has {cluster_sizes.subreddit.median()} members")
+        n_isolates1 = (cluster_sizes.subreddit==1).sum()
 
-    print(f"{(cluster_sizes.subreddit==1).sum()} clusters have 1 member")
+        print(f"{n_isolates1} clusters have 1 member")
 
-    print(f"{(cluster_sizes.loc[cluster_sizes.cluster==-1,['subreddit']])} subreddits are in cluster -1",flush=True)
+        n_isolates2 = cluster_sizes.loc[cluster_sizes.cluster==-1,:]['subreddit'].to_list()
+        if len(n_isolates2) > 0:
+            n_isloates2 = n_isolates2[0]
+        print(f"{n_isolates2} subreddits are in cluster -1",flush=True)
+
+        if n_isolates1 == 0:
+            self.n_isolates = n_isolates2
+        else:
+            self.n_isolates = n_isolates1
 
         return cluster_data
 
+class twoway_clustering_job(clustering_job):
+    def __init__(self, infile, outpath, name, call1, call2, args1, args2):
+        self.outpath = Path(outpath)
+        self.call1 = call1
+        self.args1 = args1
+        self.call2 = call2
+        self.args2 = args2
+        self.infile = Path(infile)
+        self.name = name
+        self.hasrun = False
+        self.args = args1|args2
+
+    def run(self):
+        self.subreddits, self.mat = self.read_distance_mat(self.infile)
+        self.step1 = self.call1(self.mat, **self.args1)
+        self.clustering = self.call2(self.mat, self.step1, **self.args2)
+        self.cluster_data = self.process_clustering(self.clustering, self.subreddits)
+        self.hasrun = True
+        self.after_run()
+        self.cleanup()
+
+    def after_run(self):
+        self.score = self.silhouette()
+        self.outpath.mkdir(parents=True, exist_ok=True)
+        print(self.outpath/(self.name+".feather"))
+        self.cluster_data.to_feather(self.outpath/(self.name + ".feather"))
+
+
+    def cleanup(self):
+        super().cleanup()
+        self.step1 = None
 
 @dataclass
 class clustering_result:
     outpath:Path
-    max_iter:int
     silhouette_score:float
-    alt_silhouette_score:float
     name:str
     n_clusters:int
-
+    n_isolates:int
-def read_similarity_mat(similarities, use_threads=True):
+    silhouette_samples:str
-    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)

clustering/fit_tsne.py

@@ -1,34 +0,0 @@
-import fire
-import pyarrow
-import pandas as pd
-from numpy import random
-import numpy as np
-from sklearn.manifold import TSNE
-
-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):
-    '''
-    similarities: feather file with a dataframe of similarity scores
-    learning_rate: parameter controlling how fast the model converges. Too low and you get outliers. Too high and you get a ball.
-    perplexity: number of neighbors to use. the default of 50 is often good.
-
-    '''
-    df = pd.read_feather(similarities)
-
-    n = df.shape[0]
-    mat = np.array(df.drop('subreddit',1),dtype=np.float64)
-    mat[range(n),range(n)] = 1
-    mat[mat > 1] = 1
-    dist = 2*np.arccos(mat)/np.pi
-    tsne_model = TSNE(2,learning_rate=750,perplexity=50,n_iter=10000,metric='precomputed',early_exaggeration=20,n_jobs=-1)
-    tsne_fit_model = tsne_model.fit(dist)
-
-    tsne_fit_whole = tsne_fit_model.fit_transform(dist)
-
-    plot_data = pd.DataFrame({'x':tsne_fit_whole[:,0],'y':tsne_fit_whole[:,1], 'subreddit':df.subreddit})
-
-    plot_data.to_feather(output)
-
-if __name__ == "__main__":
-    fire.Fire(fit_tsne)

clustering/grid_sweep.py

@@ -0,0 +1,49 @@
+from pathlib import Path
+from multiprocessing import Pool, cpu_count
+from itertools import product, chain
+import pandas as pd
+
+class grid_sweep:
+    def __init__(self, jobtype, inpath, outpath, namer, *args):
+        self.jobtype = jobtype
+        self.namer = namer
+        print(*args)
+        grid = list(product(*args))
+        inpath = Path(inpath)
+        outpath = Path(outpath)
+        self.hasrun = False
+        self.grid = [(inpath,outpath,namer(*g)) + g for g in grid]
+        self.jobs = [jobtype(*g) for g in self.grid]
+
+    def run(self, cores=20):
+        if cores is not None and cores > 1:
+            with Pool(cores) as pool:
+                infos = pool.map(self.jobtype.get_info, self.jobs)
+        else:
+            infos = map(self.jobtype.get_info, self.jobs)
+
+        self.infos = pd.DataFrame(infos)
+        self.hasrun = True
+
+    def save(self, outcsv):
+        if not self.hasrun:
+            self.run()
+        outcsv = Path(outcsv)
+        outcsv.parent.mkdir(parents=True, exist_ok=True)
+        self.infos.to_csv(outcsv)
+
+
+class twoway_grid_sweep(grid_sweep):
+    def __init__(self, jobtype, inpath, outpath, namer, args1, args2, *args, **kwargs):
+        self.jobtype = jobtype
+        self.namer = namer
+        prod1 = product(* args1.values())
+        prod2 = product(* args2.values())
+        grid1 = [dict(zip(args1.keys(), pargs)) for pargs in prod1]
+        grid2 = [dict(zip(args2.keys(), pargs)) for pargs in prod2]
+        grid = product(grid1, grid2)
+        inpath = Path(inpath)
+        outpath = Path(outpath)
+        self.hasrun = False
+        self.grid = [(inpath,outpath,namer(**(g[0] | g[1])), g[0], g[1], *args) for g in grid]
+        self.jobs = [jobtype(*g) for g in self.grid]

clustering/hdbscan_clustering.py

@@ -1,32 +1,57 @@
-from clustering_base import sim_to_dist, process_clustering_result, clustering_result, read_similarity_mat
+from clustering_base import clustering_result, clustering_job
+from grid_sweep import grid_sweep
 from dataclasses import dataclass
 import hdbscan
 from sklearn.neighbors import NearestNeighbors
 import plotnine as pn
 import numpy as np
-from itertools import product, starmap
+from itertools import product, starmap, chain
 import pandas as pd
-from sklearn.metrics import silhouette_score, silhouette_samples
+from multiprocessing import cpu_count
-from pathlib import Path
-from multiprocessing import Pool, cpu_count
 import fire
-from pyarrow.feather import write_feather
 
 def test_select_hdbscan_clustering():
-    select_hdbscan_clustering("/gscratch/comdata/output/reddit_similarity/subreddit_comment_authors-tf_30k_LSI",
+    # select_hdbscan_clustering("/gscratch/comdata/output/reddit_similarity/subreddit_comment_authors-tf_30k_LSI",
-                              "test_hdbscan_author30k",
+    #                           "test_hdbscan_author30k",
-                              min_cluster_sizes=[2],
+    #                           min_cluster_sizes=[2],
-                              min_samples=[1,2],
+    #                           min_samples=[1,2],
-                              cluster_selection_epsilons=[0,0.05,0.1,0.15],
+    #                           cluster_selection_epsilons=[0,0.05,0.1,0.15],
-                              cluster_selection_methods=['eom','leaf'],
+    #                           cluster_selection_methods=['eom','leaf'],
-                              lsi_dimensions='all')
+    #                           lsi_dimensions='all')
-    inpath = "/gscratch/comdata/output/reddit_similarity/subreddit_comment_authors-tf_30k_LSI"
+    inpath = "/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/similarity/comment_authors_compex_LSI"
     outpath = "test_hdbscan";
     min_cluster_sizes=[2,3,4];
     min_samples=[1,2,3];
     cluster_selection_epsilons=[0,0.1,0.3,0.5];
-    cluster_selection_methods=['eom'];
+    cluster_selection_methods=[1];
     lsi_dimensions='all'
+    gs = hdbscan_lsi_grid_sweep(inpath, "all", outpath, min_cluster_sizes, min_samples, cluster_selection_epsilons, cluster_selection_methods)
+    gs.run(20)
+    gs.save("test_hdbscan/lsi_sweep.csv")
+    # job1 = hdbscan_lsi_job(infile=inpath, outpath=outpath, name="test", lsi_dims=500, min_cluster_size=2, min_samples=1,cluster_selection_epsilon=0,cluster_selection_method='eom')
+    # job1.run()
+    # print(job1.get_info())
+
+    # df = pd.read_csv("test_hdbscan/selection_data.csv")
+    # test_select_hdbscan_clustering()
+    # check_clusters = pd.read_feather("test_hdbscan/500_2_2_0.1_eom.feather")
+    # silscores = pd.read_feather("test_hdbscan/silhouette_samples500_2_2_0.1_eom.feather")
+    # c = check_clusters.merge(silscores,on='subreddit')#    fire.Fire(select_hdbscan_clustering)
+class hdbscan_grid_sweep(grid_sweep):
+    def __init__(self,
+                 inpath,
+                 outpath,
+                 *args,
+                 **kwargs):
+
+        super().__init__(hdbscan_job, inpath, outpath, self.namer, *args, **kwargs)
+
+    def namer(self,
+              min_cluster_size,
+              min_samples,
+              cluster_selection_epsilon,
+              cluster_selection_method):
+        return f"mcs-{min_cluster_size}_ms-{min_samples}_cse-{cluster_selection_epsilon}_csm-{cluster_selection_method}"
 
 @dataclass
 class hdbscan_clustering_result(clustering_result):
@@ -34,101 +59,31 @@ class hdbscan_clustering_result(clustering_result):
     min_samples:int
     cluster_selection_epsilon:float
     cluster_selection_method:str
-    lsi_dimensions:int
-    n_isolates:int
-    silhouette_samples:str
 
-def select_hdbscan_clustering(inpath,
+class hdbscan_job(clustering_job):
+    def __init__(self, infile, outpath, name, min_cluster_size=2, min_samples=1, cluster_selection_epsilon=0, cluster_selection_method='eom'):
+        super().__init__(infile,
                          outpath,
-                              outfile=None,
+                         name,
-                              min_cluster_sizes=[2],
+                         call=hdbscan_job._hdbscan_clustering,
-                              min_samples=[1],
-                              cluster_selection_epsilons=[0],
-                              cluster_selection_methods=['eom'],
-                              lsi_dimensions='all'
-                              ):
-
-    inpath = Path(inpath)
-    outpath = Path(outpath)
-    outpath.mkdir(exist_ok=True, parents=True)
-    
-    if lsi_dimensions == 'all':
-        lsi_paths = list(inpath.glob("*"))
-
-    else:
-        lsi_paths = [inpath / (dim + '.feather') for dim in lsi_dimensions]
-
-    lsi_nums = [p.stem for p in lsi_paths]
-    grid = list(product(lsi_nums,
-                        min_cluster_sizes,
-                        min_samples,
-                        cluster_selection_epsilons,
-                        cluster_selection_methods))
-
-    # fix the output file names
-    names = list(map(lambda t:'_'.join(map(str,t)),grid))
-
-    grid = [(inpath/(str(t[0])+'.feather'),outpath/(name + '.feather'), t[0], name) + t[1:] for t, name in zip(grid, names)]
-        
-    with Pool(int(cpu_count()/4)) as pool:
-        mods = starmap(hdbscan_clustering, grid)
-
-    res = pd.DataFrame(mods)
-    if outfile is None:
-        outfile = outpath / "selection_data.csv"
-
-    res.to_csv(outfile)
-
-def hdbscan_clustering(similarities, output, lsi_dim, name, min_cluster_size=2, min_samples=1, cluster_selection_epsilon=0, cluster_selection_method='eom'):
-    subreddits, mat = read_similarity_mat(similarities)
-    mat = sim_to_dist(mat)
-    clustering = _hdbscan_clustering(mat,
                          min_cluster_size=min_cluster_size,
                          min_samples=min_samples,
                          cluster_selection_epsilon=cluster_selection_epsilon,
-                                     cluster_selection_method=cluster_selection_method,
+                         cluster_selection_method=cluster_selection_method
-                                     metric='precomputed',
-                                     core_dist_n_jobs=cpu_count()
                          )
 
-    cluster_data = process_clustering_result(clustering, subreddits)
+        self.min_cluster_size = min_cluster_size
-    isolates = clustering.labels_ == -1
+        self.min_samples = min_samples
-    scoremat = mat[~isolates][:,~isolates]
+        self.cluster_selection_epsilon = cluster_selection_epsilon
-    score = silhouette_score(scoremat, clustering.labels_[~isolates], metric='precomputed')
+        self.cluster_selection_method = cluster_selection_method
-    cluster_data.to_feather(output)
+#        self.mat = 1 - self.mat
 
-    silhouette_samp = silhouette_samples(mat, clustering.labels_, metric='precomputed')
-    silhouette_samp = pd.DataFrame({'subreddit':subreddits,'score':silhouette_samp})
-    silsampout = output.parent / ("silhouette_samples" + output.name)
-    silhouette_samp.to_feather(silsampout)
-
-    result = hdbscan_clustering_result(outpath=output,
-                                       max_iter=None,
-                                       silhouette_samples=silsampout,
-                                       silhouette_score=score,
-                                       alt_silhouette_score=score,
-                                       name=name,
-                                       min_cluster_size=min_cluster_size,
-                                       min_samples=min_samples,
-                                       cluster_selection_epsilon=cluster_selection_epsilon,
-                                       cluster_selection_method=cluster_selection_method,
-                                       lsi_dimensions=lsi_dim,
-                                       n_isolates=isolates.sum(),
-                                       n_clusters=len(set(clustering.labels_))
-                                   )
-
-
-                                       
-    return(result)
-
-# for all runs we should try cluster_selection_epsilon = None
-# for terms we should try cluster_selection_epsilon around 0.56-0.66
-# for authors we should try cluster_selection_epsilon around 0.98-0.99
     def _hdbscan_clustering(mat, *args, **kwargs):
         print(f"running hdbscan clustering. args:{args}. kwargs:{kwargs}")
-
         print(mat)
-    clusterer = hdbscan.HDBSCAN(*args,
+        clusterer = hdbscan.HDBSCAN(metric='precomputed',
+                                    core_dist_n_jobs=cpu_count(),
+                                    *args,
                                     **kwargs,
                                     )
     
@@ -136,6 +91,39 @@ def _hdbscan_clustering(mat, *args, **kwargs):
     
         return(clustering)
 
+    def get_info(self):
+        result = super().get_info()
+        self.result = hdbscan_clustering_result(**result.__dict__,
+                                                min_cluster_size=self.min_cluster_size,
+                                                min_samples=self.min_samples,
+                                                cluster_selection_epsilon=self.cluster_selection_epsilon,
+                                                cluster_selection_method=self.cluster_selection_method)
+        return self.result
+
+def run_hdbscan_grid_sweep(savefile, inpath, outpath,  min_cluster_sizes=[2], min_samples=[1], cluster_selection_epsilons=[0], cluster_selection_methods=['eom']):
+    """Run hdbscan clustering once or more with different parameters.
+    
+    Usage:
+    hdbscan_clustering.py --savefile=SAVEFILE --inpath=INPATH --outpath=OUTPATH --min_cluster_sizes=<csv> --min_samples=<csv> --cluster_selection_epsilons=<csv> --cluster_selection_methods=<csv "eom"|"leaf">
+
+    Keword arguments:
+    savefile: path to save the metadata and diagnostics 
+    inpath: path to feather data containing a labeled matrix of subreddit similarities.
+    outpath: path to output fit kmeans clusterings.
+    min_cluster_sizes: one or more integers indicating the minumum cluster size
+    min_samples: one ore more integers indicating the minimum number of samples used in the algorithm
+    cluster_selection_epsilon: one or more similarity thresholds for transition from dbscan to hdbscan
+    cluster_selection_method: "eom" or "leaf" eom gives larger clusters. 
+    """    
+    obj = hdbscan_grid_sweep(inpath,
+                             outpath,
+                             map(int,min_cluster_sizes),
+                             map(int,min_samples),
+                             map(float,cluster_selection_epsilons),
+                             cluster_selection_methods)
+    obj.run()
+    obj.save(savefile)
+
 def KNN_distances_plot(mat,outname,k=2):
     nbrs = NearestNeighbors(n_neighbors=k,algorithm='auto',metric='precomputed').fit(mat)
     distances, indices = nbrs.kneighbors(mat)
@@ -165,8 +153,7 @@ def make_KNN_plots():
     KNN_distances_plot(mat,k=2,outname='authors-tf_knn_dist2.png')
 
 if __name__ == "__main__":
-    df = pd.read_csv("test_hdbscan/selection_data.csv")
+    fire.Fire(run_hdbscan_grid_sweep)
-    test_select_hdbscan_clustering()
+    
-    check_clusters = pd.read_feather("test_hdbscan/500_2_2_0.1_eom.feather")
+#    test_select_hdbscan_clustering()
-    silscores = pd.read_feather("test_hdbscan/silhouette_samples500_2_2_0.1_eom.feather")
+    #fire.Fire(select_hdbscan_clustering)  
-    c = check_clusters.merge(silscores,on='subreddit')#    fire.Fire(select_hdbscan_clustering)

clustering/hdbscan_clustering_lsi.py

@@ -0,0 +1,101 @@
+from hdbscan_clustering import hdbscan_job, hdbscan_grid_sweep, hdbscan_clustering_result
+from lsi_base import lsi_grid_sweep, lsi_mixin, lsi_result_mixin
+from grid_sweep import grid_sweep
+import fire
+from dataclasses import dataclass
+
+@dataclass
+class hdbscan_clustering_result_lsi(hdbscan_clustering_result, lsi_result_mixin):
+    pass 
+
+class hdbscan_lsi_job(hdbscan_job, lsi_mixin):
+    def __init__(self, infile, outpath, name, lsi_dims, *args, **kwargs):
+        super().__init__(
+                         infile,
+                         outpath,
+                         name,
+                         *args,
+                         **kwargs)
+        super().set_lsi_dims(lsi_dims)
+
+    def get_info(self):
+        partial_result = super().get_info()
+        self.result = hdbscan_clustering_result_lsi(**partial_result.__dict__,
+                                                    lsi_dimensions=self.lsi_dims)
+        return self.result
+
+class hdbscan_lsi_grid_sweep(lsi_grid_sweep):
+    def __init__(self,
+                 inpath,
+                 lsi_dims,
+                 outpath,
+                 min_cluster_sizes,
+                 min_samples,
+                 cluster_selection_epsilons,
+                 cluster_selection_methods
+                 ):
+
+        super().__init__(hdbscan_lsi_job,
+                         _hdbscan_lsi_grid_sweep,
+                         inpath,
+                         lsi_dims,
+                         outpath,
+                         min_cluster_sizes,
+                         min_samples,
+                         cluster_selection_epsilons,
+                         cluster_selection_methods)
+        
+
+
+class _hdbscan_lsi_grid_sweep(grid_sweep):
+    def __init__(self,
+                 inpath,
+                 outpath,
+                 lsi_dim,
+                 *args,
+                 **kwargs):
+        print(args)
+        print(kwargs)
+
+        self.lsi_dim = lsi_dim
+        self.jobtype = hdbscan_lsi_job
+        super().__init__(self.jobtype, inpath, outpath, self.namer, [self.lsi_dim], *args, **kwargs)
+
+
+    def namer(self, *args, **kwargs):
+        s = hdbscan_grid_sweep.namer(self, *args[1:], **kwargs)
+        s += f"_lsi-{self.lsi_dim}"
+        return s
+
+def run_hdbscan_lsi_grid_sweep(savefile, inpath, outpath,  min_cluster_sizes=[2], min_samples=[1], cluster_selection_epsilons=[0], cluster_selection_methods=[1],lsi_dimensions='all'):
+    """Run hdbscan clustering once or more with different parameters.
+    
+    Usage:
+    hdbscan_clustering_lsi --savefile=SAVEFILE --inpath=INPATH --outpath=OUTPATH --min_cluster_sizes=<csv> --min_samples=<csv> --cluster_selection_epsilons=<csv> --cluster_selection_methods=[eom]> --lsi_dimensions: either "all" or one or more available lsi similarity dimensions at INPATH.
+
+    Keword arguments:
+    savefile: path to save the metadata and diagnostics 
+    inpath: path to folder containing feather files with LSI similarity labeled matrices of subreddit similarities.
+    outpath: path to output fit clusterings.
+    min_cluster_sizes: one or more integers indicating the minumum cluster size
+    min_samples: one ore more integers indicating the minimum number of samples used in the algorithm
+    cluster_selection_epsilons: one or more similarity thresholds for transition from dbscan to hdbscan
+    cluster_selection_methods: one or more of "eom" or "leaf" eom gives larger clusters. 
+    lsi_dimensions: either "all" or one or more available lsi similarity dimensions at INPATH.
+    """    
+
+    obj = hdbscan_lsi_grid_sweep(inpath,
+                                 lsi_dimensions,
+                                 outpath,
+                                 list(map(int,min_cluster_sizes)),
+                                 list(map(int,min_samples)),
+                                 list(map(float,cluster_selection_epsilons)),
+                                 cluster_selection_methods)
+                                 
+
+    obj.run(10)
+    obj.save(savefile)
+
+
+if __name__ == "__main__":
+    fire.Fire(run_hdbscan_lsi_grid_sweep)

clustering/kmeans_clustering.py

@@ -0,0 +1,105 @@
+from sklearn.cluster import KMeans
+import fire
+from pathlib import Path
+from dataclasses import dataclass
+from clustering_base import clustering_result, clustering_job
+from grid_sweep import grid_sweep
+
+@dataclass
+class kmeans_clustering_result(clustering_result):
+    n_clusters:int
+    n_init:int
+    max_iter:int
+
+class kmeans_job(clustering_job):
+    def __init__(self, infile, outpath, name, n_clusters, n_init=10, max_iter=100000, random_state=1968, verbose=True):
+        super().__init__(infile,
+                         outpath,
+                         name,
+                         call=kmeans_job._kmeans_clustering,
+                         n_clusters=n_clusters,
+                         n_init=n_init,
+                         max_iter=max_iter,
+                         random_state=random_state,
+                         verbose=verbose)
+
+        self.n_clusters=n_clusters
+        self.n_init=n_init
+        self.max_iter=max_iter
+
+    def _kmeans_clustering(mat, *args, **kwargs):
+
+        clustering = KMeans(*args,
+                            **kwargs,
+                            ).fit(mat)
+
+        return clustering
+
+
+    def get_info(self):
+        result = super().get_info()
+        self.result = kmeans_clustering_result(**result.__dict__,
+                                               n_init=self.n_init,
+                                               max_iter=self.max_iter)
+        return self.result
+
+
+class kmeans_grid_sweep(grid_sweep):
+       
+    def __init__(self,
+                 inpath,
+                 outpath,
+                 *args,
+                 **kwargs):
+        super().__init__(kmeans_job, inpath, outpath, self.namer, *args, **kwargs)
+
+    def namer(self,
+             n_clusters,
+             n_init,
+             max_iter):
+        return f"nclusters-{n_clusters}_nit-{n_init}_maxit-{max_iter}"
+
+def test_select_kmeans_clustering():
+    inpath = "/gscratch/comdata/output/reddit_similarity/subreddit_comment_authors-tf_10k_LSI/"
+    outpath = "test_kmeans";
+    n_clusters=[200,300,400];
+    n_init=[1,2,3];
+    max_iter=[100000]
+
+    gs = kmeans_lsi_grid_sweep(inpath, 'all', outpath, n_clusters, n_init, max_iter)
+    gs.run(1)
+
+    cluster_selection_epsilons=[0,0.1,0.3,0.5];
+    cluster_selection_methods=['eom'];
+    lsi_dimensions='all'
+    gs = hdbscan_lsi_grid_sweep(inpath, "all", outpath, min_cluster_sizes, min_samples, cluster_selection_epsilons, cluster_selection_methods)
+    gs.run(20)
+    gs.save("test_hdbscan/lsi_sweep.csv")
+
+def run_kmeans_grid_sweep(savefile, inpath, outpath,  n_clusters=[500], n_inits=[1], max_iters=[3000]):
+    """Run kmeans clustering once or more with different parameters.
+    
+    Usage:
+    kmeans_clustering.py --savefile=SAVEFILE --inpath=INPATH --outpath=OUTPATH --n_clusters=<csv number of clusters> --n_inits=<csv> --max_iters=<csv>
+
+    Keword arguments:
+    savefile: path to save the metadata and diagnostics 
+    inpath: path to feather data containing a labeled matrix of subreddit similarities.
+    outpath: path to output fit kmeans clusterings.
+    n_clusters: one or more numbers of kmeans clusters to select.
+    n_inits: one or more numbers of different initializations to use for each clustering.
+    max_iters: one or more numbers of different maximum interations. 
+    """    
+
+    obj = kmeans_grid_sweep(inpath,
+                            outpath,
+                            map(int,n_clusters),
+                            map(int,n_inits),
+                            map(int,max_iters))
+
+
+    obj.run(1)
+    obj.save(savefile)
+
+if __name__ == "__main__":
+    fire.Fire(run_kmeans_grid_sweep)

clustering/kmeans_clustering_lsi.py

@@ -0,0 +1,93 @@
+import fire
+from dataclasses import dataclass
+from kmeans_clustering import kmeans_job, kmeans_clustering_result, kmeans_grid_sweep
+from lsi_base import lsi_mixin, lsi_result_mixin, lsi_grid_sweep
+from grid_sweep import grid_sweep
+
+@dataclass
+class kmeans_clustering_result_lsi(kmeans_clustering_result, lsi_result_mixin):
+    pass
+
+class kmeans_lsi_job(kmeans_job, lsi_mixin):
+    def __init__(self, infile, outpath, name, lsi_dims, *args, **kwargs):
+        super().__init__(infile,
+                         outpath,
+                         name,
+                         *args,
+                         **kwargs)
+        super().set_lsi_dims(lsi_dims)
+
+    def get_info(self):
+        result = super().get_info()
+        self.result = kmeans_clustering_result_lsi(**result.__dict__,
+                                                   lsi_dimensions=self.lsi_dims)
+        return self.result
+
+class _kmeans_lsi_grid_sweep(grid_sweep):
+    def __init__(self,
+                 inpath,
+                 outpath,
+                 lsi_dim,
+                 *args,
+                 **kwargs):
+        print(args)
+        print(kwargs)
+        self.lsi_dim = lsi_dim
+        self.jobtype = kmeans_lsi_job
+        super().__init__(self.jobtype, inpath, outpath, self.namer, [self.lsi_dim], *args, **kwargs)
+
+    def namer(self, *args, **kwargs):
+        s = kmeans_grid_sweep.namer(self, *args[1:], **kwargs)
+        s += f"_lsi-{self.lsi_dim}"
+        return s
+
+class kmeans_lsi_grid_sweep(lsi_grid_sweep):
+
+    def __init__(self,
+                 inpath,
+                 lsi_dims,
+                 outpath,
+                 n_clusters,
+                 n_inits,
+                 max_iters
+                 ):
+
+        super().__init__(kmeans_lsi_job,
+                         _kmeans_lsi_grid_sweep,
+                         inpath,
+                         lsi_dims,
+                         outpath,
+                         n_clusters,
+                         n_inits,
+                         max_iters)
+
+def run_kmeans_lsi_grid_sweep(savefile, inpath, outpath,  n_clusters=[500], n_inits=[1], max_iters=[3000], lsi_dimensions="all"):
+    """Run kmeans clustering once or more with different parameters.
+    
+    Usage:
+    kmeans_clustering_lsi.py --savefile=SAVEFILE --inpath=INPATH --outpath=OUTPATH d--lsi_dimensions=<"all"|csv number of LSI dimensions to use> --n_clusters=<csv number of clusters> --n_inits=<csv> --max_iters=<csv>
+
+    Keword arguments:
+    savefile: path to save the metadata and diagnostics 
+    inpath: path to folder containing feather files with LSI similarity labeled matrices of subreddit similarities.
+    outpath: path to output fit kmeans clusterings.
+    lsi_dimensions: either "all" or one or more available lsi similarity dimensions at INPATH.
+    n_clusters: one or more numbers of kmeans clusters to select.
+    n_inits: one or more numbers of different initializations to use for each clustering.
+    max_iters: one or more numbers of different maximum interations. 
+    """    
+
+    obj = kmeans_lsi_grid_sweep(inpath,
+                                lsi_dimensions,
+                                outpath,
+                                list(map(int,n_clusters)),
+                                list(map(int,n_inits)),
+                                list(map(int,max_iters))
+                                )
+
+    obj.run(1)
+    obj.save(savefile)
+
+
+if __name__ == "__main__":
+    fire.Fire(run_kmeans_lsi_grid_sweep)

clustering/lsi_base.py

@@ -0,0 +1,44 @@
+from clustering_base import clustering_job, clustering_result
+from grid_sweep import grid_sweep, twoway_grid_sweep
+from dataclasses import dataclass
+from itertools import chain
+from pathlib import Path
+
+class lsi_mixin():
+    def set_lsi_dims(self, lsi_dims):
+        self.lsi_dims = lsi_dims
+
+@dataclass
+class lsi_result_mixin:
+    lsi_dimensions:int
+
+class lsi_grid_sweep(grid_sweep):
+    def __init__(self, jobtype, subsweep, inpath, lsi_dimensions, outpath, *args, **kwargs):
+        self.jobtype = jobtype
+        self.subsweep = subsweep
+        inpath = Path(inpath)
+        if lsi_dimensions == 'all':
+            lsi_paths = list(inpath.glob("*.feather"))
+        else:
+            lsi_paths = [inpath / (str(dim) + '.feather') for dim in lsi_dimensions]
+
+        print(lsi_paths)
+        lsi_nums = [int(p.stem) for p in lsi_paths]
+        self.hasrun = False
+        self.subgrids = [self.subsweep(lsi_path, outpath,  lsi_dim, *args, **kwargs) for lsi_dim, lsi_path in zip(lsi_nums, lsi_paths)]
+        self.jobs = list(chain(*map(lambda gs: gs.jobs, self.subgrids)))
+
+class twoway_lsi_grid_sweep(twoway_grid_sweep):
+    def __init__(self, jobtype, subsweep, inpath, lsi_dimensions, outpath, args1, args2):
+        self.jobtype = jobtype
+        self.subsweep = subsweep
+        inpath = Path(inpath)
+        if lsi_dimensions == 'all':
+            lsi_paths = list(inpath.glob("*.feather"))
+        else:
+            lsi_paths = [inpath / (str(dim) + '.feather') for dim in lsi_dimensions]
+
+        lsi_nums = [int(p.stem) for p in lsi_paths]
+        self.hasrun = False
+        self.subgrids = [self.subsweep(lsi_path, outpath, lsi_dim, args1, args2) for lsi_dim, lsi_path in zip(lsi_nums, lsi_paths)]
+        self.jobs = list(chain(*map(lambda gs: gs.jobs, self.subgrids)))

clustering/pick_best_clustering.py

@@ -0,0 +1,33 @@
+#!/usr/bin/env python3
+import fire
+import pandas as pd
+from pathlib import Path
+import shutil
+selection_data="/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/clustering/comment_authors_compex_LSI/selection_data.csv"
+
+outpath = 'test_best.feather'
+min_clusters=50; max_isolates=7500; min_cluster_size=2
+
+# pick the best clustering according to silhouette score subject to contraints
+def pick_best_clustering(selection_data, output, min_clusters, max_isolates, min_cluster_size):
+    df = pd.read_csv(selection_data,index_col=0)
+    df = df.sort_values("silhouette_score",ascending=False)
+
+    # not sure I fixed the bug underlying this fully or not.
+    df['n_isolates_str'] = df.n_isolates.str.strip("[]")
+    df['n_isolates_0'] = df['n_isolates_str'].apply(lambda l: len(l) == 0)
+    df.loc[df.n_isolates_0,'n_isolates'] = 0
+    df.loc[~df.n_isolates_0,'n_isolates'] = df.loc[~df.n_isolates_0].n_isolates_str.apply(lambda l: int(l))
+    
+    best_cluster = df[(df.n_isolates <= max_isolates)&(df.n_clusters >= min_clusters)&(df.min_cluster_size==min_cluster_size)]
+
+    best_cluster = best_cluster.iloc[0]
+
+    best_lsi_dimensions = best_cluster.lsi_dimensions
+    print(best_cluster.to_dict())
+    best_path = Path(best_cluster.outpath) / (str(best_cluster['name']) + ".feather")
+    shutil.copy(best_path,output)
+    print(f"lsi dimensions:{best_lsi_dimensions}")
+    
+if __name__ == "__main__":
+    fire.Fire(pick_best_clustering)

clustering/select_affinity.py

@@ -1,132 +0,0 @@
-from sklearn.metrics import silhouette_score
-from sklearn.cluster import AffinityPropagation
-from functools import partial
-from dataclasses import dataclass
-from clustering import _affinity_clustering, read_similarity_mat, sim_to_dist, process_clustering_result, clustering_result
-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 affinity_clustering_result(clustering_result):
-    damping:float
-    convergence_iter:int
-    preference_quantile:float
-
-def do_affinity_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")
-    outpath.parent.mkdir(parents=True,exist_ok=True)
-    print(outpath)
-    clustering = _affinity_clustering(mat, outpath, damping, max_iter, convergence_iter, preference_quantile, random_state, verbose)
-    cluster_data = process_clustering_result(clustering, subreddits)
-    mat = sim_to_dist(clustering.affinity_matrix_)
-
-    try: 
-        score = silhouette_score(mat, clustering.labels_, metric='precomputed')
-    except ValueError:
-        score = None
-
-    if alt_mat is not None:
-        alt_distances = sim_to_dist(alt_mat)
-        try:
-            alt_score = silhouette_score(alt_mat, clustering.labels_, metric='precomputed')
-        except ValueError:
-            alt_score = None
-    
-    res = affinity_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
-
-def do_affinity_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")
-    outpath.parent.mkdir(parents=True,exist_ok=True)
-    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_)
-
-    try: 
-        score = silhouette_score(mat, clustering.labels_, metric='precomputed')
-    except ValueError:
-        score = None
-
-    if alt_mat is not None:
-        alt_distances = sim_to_dist(alt_mat)
-        try:
-            alt_score = silhouette_score(alt_mat, clustering.labels_, metric='precomputed')
-        except ValueError:
-            alt_score = None
-    
-    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_affinity_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)

clustering/select_kmeans.py

@@ -1,92 +0,0 @@
-from sklearn.metrics import silhouette_score
-from sklearn.cluster import AffinityPropagation
-from functools import partial
-from clustering import _kmeans_clustering, read_similarity_mat, sim_to_dist, process_clustering_result, clustering_result
-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
-
-@dataclass
-class kmeans_clustering_result(clustering_result):
-    n_clusters:int
-    n_init:int
-
-
-# silhouette is the only one that doesn't need the feature matrix. So it's probably the only one that's worth trying. 
-
-def do_clustering(n_clusters, n_init, 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)
-    mat = sim_to_dist(mat)
-    clustering = _kmeans_clustering(mat, outpath, n_clusters, n_init, max_iter, random_state, verbose)
-
-    outpath.parent.mkdir(parents=True,exist_ok=True)
-    cluster_data.to_feather(outpath)
-    cluster_data = process_clustering_result(clustering, subreddits)
-
-    try: 
-        score = silhouette_score(mat, clustering.labels_, metric='precomputed')
-    except ValueError:
-        score = None
-
-    if alt_mat is not None:
-        alt_distances = sim_to_dist(alt_mat)
-        try:
-            alt_score = silhouette_score(alt_mat, clustering.labels_, metric='precomputed')
-        except ValueError:
-            alt_score = None
-    
-    res = kmeans_clustering_result(outpath=outpath,
-                                   max_iter=max_iter,
-                                   n_clusters=n_clusters,
-                                   n_init = n_init,
-                                   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_kmeans_clustering(similarities, outdir, outinfo, n_clusters=[1000], max_iter=100000, n_init=10, random_state=1968, verbose=True, alt_similarities=None):
-
-    n_clusters = list(map(int,n_clusters))
-    n_init  = list(map(int,n_init))
-
-    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
-
-    # get list of tuples: the combinations of hyperparameters
-    hyper_grid = product(n_clusters, n_init)
-    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)
-
-    # call starmap
-    print("running clustering selection")
-    clustering_data = 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_kmeans_clustering)

clustering/selection.py

@@ -1,7 +1,38 @@
-import fire
+import pandas as pd
-from select_affinity import select_affinity_clustering
+import plotnine as pn
-from select_kmeans import select_kmeans_clustering
+from pathlib import Path
+from clustering.fit_tsne import fit_tsne
+from visualization.tsne_vis import build_visualization
+
+df = pd.read_csv("/gscratch/comdata/output/reddit_clustering/subreddit_comment_authors-tf_10k_LSI/hdbscan/selection_data.csv",index_col=0)
+
+# plot silhouette_score as a function of isolates
+df = df.sort_values("silhouette_score")
+
+df['n_isolates'] = df.n_isolates.str.split("\n0").apply(lambda rg: int(rg[1]))
+p = pn.ggplot(df,pn.aes(x='n_isolates',y='silhouette_score')) + pn.geom_point()
+p.save("isolates_x_score.png")
+
+p = pn.ggplot(df,pn.aes(y='n_clusters',x='n_isolates',color='silhouette_score')) + pn.geom_point()
+p.save("clusters_x_isolates.png")
+
+# the best result for hdbscan seems like this one: it has a decent number of 
+# i think I prefer the 'eom' clustering style because larger clusters are less likely to suffer from ommitted variables
+best_eom = df[(df.n_isolates <5000)&(df.silhouette_score>0.4)&(df.cluster_selection_method=='eom')&(df.min_cluster_size==2)].iloc[df.shape[1]]
+
+best_lsi = df[(df.n_isolates <5000)&(df.silhouette_score>0.4)&(df.cluster_selection_method=='leaf')&(df.min_cluster_size==2)].iloc[df.shape[1]]
+
+tsne_data = Path("./clustering/authors-tf_lsi850_tsne.feather")
+
+if not tnse_data.exists():
+    fit_tsne("/gscratch/comdata/output/reddit_similarity/subreddit_comment_authors-tf_10k_LSI/850.feather",
+             tnse_data)
+
+build_visualization("./clustering/authors-tf_lsi850_tsne.feather",
+                    Path(best_eom.outpath)/(best_eom['name']+'.feather'),
+                    "./authors-tf_lsi850_best_eom.html")
+
+build_visualization("./clustering/authors-tf_lsi850_tsne.feather",
+                    Path(best_leaf.outpath)/(best_leaf['name']+'.feather'),
+                    "./authors-tf_lsi850_best_leaf.html")
 
-if __name__ == "__main__":
-    fire.Fire({"kmeans":select_kmeans_clustering,
-               "affinity":select_affinity_clustering})

clustering/validation.py

@@ -0,0 +1,4 @@
+from sklearn import metrics
+from sklearn.cluster import AffinityPropagation
+from functools import partial
+# sillouette is the only one that doesn't need the feature matrix. So it's probably the only one that's worth trying. 

datasets/Makefile

@@ -0,0 +1,28 @@
+all: ../../data/reddit_comments_by_subreddit.parquet ../../data/reddit_submissions_by_subreddit.parquet
+
+../../data/reddit_comments_by_subreddit.parquet:../../data/temp/reddit_comments.parquet
+	../start_spark_and_run.sh 4 comments_2_parquet_part2.py
+
+../../data/temp/reddit_comments.parquet: comments_task_list.sh run_comments_jobs.sbatch
+	mkdir -p comments_jobs
+	mkdir -p ../../data/temp/
+	sbatch --wait --array=1-$(shell cat comments_task_list.sh | wc -l) run_comments_jobs.sbatch 0
+
+temp_reddit_comments.parquet: ../../data/temp/reddit_comments.parquet
+
+comments_task_list.sh: comments_2_parquet_part1.py
+	srun -p compute-bigmem -A comdata --nodes=1 --mem-per-cpu=9g -c 40 --time=120:00:00 bash -c "source ~/.bashrc && python3 comments_2_parquet_part1.py gen_task_list --overwrite=False"
+
+submissions_task_list.sh: submissions_2_parquet_part1.py
+	srun -p compute-bigmem -A comdata --nodes=1 --mem-per-cpu=9g -c 40 --time=120:00:00 python3 submissions_2_parquet_part1.py gen_task_list
+
+../../data/reddit_submissions_by_subreddit.parquet:../../data/temp/reddit_submissions.parquet
+	../start_spark_and_run.sh 4 submissions_2_parquet_part2.py
+
+../../data/temp/reddit_submissions.parquet: submissions_task_list.sh run_submissions_jobs.sbatch
+	mkdir -p submissions_jobs
+	rm -rf ../../data/temp/reddit_submissions.parquet
+	mkdir -p ../../data/temp/
+	sbatch --wait --array=1-$(shell cat submissions_task_list.sh | wc -l) run_submissions_jobs.sbatch 0
+
+temp_reddit_submissions.parquet: ../../data/temp/reddit_submissions.parquet

datasets/checkpoint_parallelsql.sbatch

@@ -1,26 +0,0 @@
-#!/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

datasets/comments_2_parquet.sh

@@ -1,10 +1,10 @@
+#!/usr/bin/env bash
 ## needs to be run by hand since i don't have a nice way of waiting on a parallel-sql job to complete 
 
-#!/usr/bin/env bash
 echo "#!/usr/bin/bash" > job_script.sh
 #echo "source $(pwd)/../bin/activate" >> job_script.sh
 echo "python3 $(pwd)/comments_2_parquet_part1.py" >> job_script.sh
 
-srun -p comdata -A comdata --nodes=1 --mem=120G --time=48:00:00 --pty job_script.sh
+srun -p compute-bigmem -A comdata --nodes=1 --mem-per-cpu=9g -c 40 --time=120:00:00 --pty job_script.sh
 
 start_spark_and_run.sh 1 $(pwd)/comments_2_parquet_part2.py

datasets/comments_2_parquet_part1.py

@@ -1,12 +1,15 @@
 #!/usr/bin/env python3
+import os
 import json
 from datetime import datetime
 from multiprocessing import Pool
 from itertools import islice
-from helper import find_dumps, open_fileset
+from helper import open_input_file, find_dumps
 import pandas as pd
 import pyarrow as pa
 import pyarrow.parquet as pq
+from pathlib import Path
+import fire
 
 def parse_comment(comment, names= None):
     if names is None:
@@ -44,19 +47,14 @@ def parse_comment(comment, names= None):
     return tuple(row)
 
 
-#    conf = sc._conf.setAll([('spark.executor.memory', '20g'), ('spark.app.name', 'extract_reddit_timeline'), ('spark.executor.cores', '26'), ('spark.cores.max', '26'), ('spark.driver.memory','84g'),('spark.driver.maxResultSize','0'),('spark.local.dir','/gscratch/comdata/spark_tmp')])
+#    conf = sc._conf.setAll([('spark.executor.memory', '20g'), ('spark.app.name', 'extract_reddit_timeline'), ('spark.executor.cores', '26'), ('spark.cores.max', '26'), ('spark.driver.memory','84g'),('spark.driver.maxResultSize','0'),('spark.local.dir','../../data/spark_tmp')])
 
-dumpdir = "/gscratch/comdata/raw_data/reddit_dumps/comments/"
+def parse_dump(partition):
 
-files = list(find_dumps(dumpdir, base_pattern="RC_20*"))
+    dumpdir = f"../../data/reddit_dumps/comments/{partition}"
 
-pool = Pool(28)
+    stream = open_input_file(dumpdir)
-
+    rows = map(parse_comment, stream)
-stream = open_fileset(files)
-
-N = int(1e4)
-
-rows = pool.imap_unordered(parse_comment, stream, chunksize=int(N/28))
 
     schema = pa.schema([
         pa.field('id', pa.string(), nullable=True),
@@ -78,33 +76,16 @@ schema = pa.schema([
         pa.field('error', pa.string(), nullable=True),
     ])
 
-from pathlib import Path
+    p = Path("../../data/temp/reddit_comments.parquet")
-p = Path("/gscratch/comdata/output/reddit_comments.parquet_temp2")
+    p.mkdir(exist_ok=True,parents=True)
 
-if not p.is_dir():
+    N=10000
-    if p.exists():
+    with pq.ParquetWriter(f"../../data/temp/reddit_comments.parquet/{partition}.parquet",
-        p.unlink()
+                          schema=schema,
-    p.mkdir()
+                          compression='snappy',
-
+                          flavor='spark') as writer:
-else:
-    list(map(Path.unlink,p.glob('*')))
-
-part_size = int(1e7)
-part = 1
-n_output = 0
-writer = pq.ParquetWriter(f"/gscratch/comdata/output/reddit_comments.parquet_temp2/part_{part}.parquet",schema=schema,compression='snappy',flavor='spark')
 
         while True:
-    if n_output > part_size:
-        if part > 1:
-            writer.close()
-
-        part = part + 1
-        n_output = 0
-    
-        writer = pq.ParquetWriter(f"/gscratch/comdata/output/reddit_comments.parquet_temp2/part_{part}.parquet",schema=schema,compression='snappy',flavor='spark')
-
-    n_output += N
             chunk = islice(rows,N)
             pddf = pd.DataFrame(chunk, columns=schema.names)
             table = pa.Table.from_pandas(pddf,schema=schema)
@@ -112,4 +93,19 @@ while True:
                 break
             writer.write_table(table)
 
+        writer.close()
+
+
+def gen_task_list(dumpdir="../../data/raw_data/reddit_dumps/comments", overwrite=True):
+    files = list(find_dumps(dumpdir,base_pattern="RC_20*.*"))
+    with open("comments_task_list.sh",'w') as of:
+        for fpath in files:
+            partition = os.path.split(fpath)[1]
+            if (not Path(f"../../data/temp/reddit_comments.parquet/{partition}.parquet").exists()) or (overwrite is True):
+                of.write(f'python3 comments_2_parquet_part1.py parse_dump {partition}\n')
+
+
+if __name__ == '__main__':
+    fire.Fire({'parse_dump':parse_dump,
+              'gen_task_list':gen_task_list})
 

datasets/comments_2_parquet_part2.py

@@ -2,12 +2,19 @@
 
 # spark script to make sorted, and partitioned parquet files 
 
+import pyspark
 from pyspark.sql import functions as f
 from pyspark.sql import SparkSession
 
 spark = SparkSession.builder.getOrCreate()
 
-df = spark.read.parquet("/gscratch/comdata/output/reddit_comments.parquet_temp2",compression='snappy')
+conf = pyspark.SparkConf().setAppName("Reddit submissions to parquet")
+conf = conf.set("spark.sql.shuffle.partitions",2400)
+conf = conf.set('spark.sql.crossJoin.enabled',"true")
+conf = conf.set('spark.debug.maxToStringFields',200)
+sc = spark.sparkContext
+
+df = spark.read.parquet("/gscratch/comdata/output/temp/reddit_comments.parquet",compression='snappy')
 
 df = df.withColumn("subreddit_2", f.lower(f.col('subreddit')))
 df = df.drop('subreddit')
@@ -18,12 +25,13 @@ df = df.withColumn("Month",f.month(f.col("CreatedAt")))
 df = df.withColumn("Year",f.year(f.col("CreatedAt")))
 df = df.withColumn("Day",f.dayofmonth(f.col("CreatedAt")))
 
-df = df.repartition('subreddit')
+# df = df.repartition(1200,'subreddit')
-df2 = df.sort(["subreddit","CreatedAt","link_id","parent_id","Year","Month","Day"],ascending=True)
+# df2 = df.sort(["subreddit","CreatedAt","link_id","parent_id","Year","Month","Day"],ascending=True)
-df2 = df2.sortWithinPartitions(["subreddit","CreatedAt","link_id","parent_id","Year","Month","Day"],ascending=True)
+# df2 = df2.sortWithinPartitions(["subreddit","CreatedAt","link_id","parent_id","Year","Month","Day"],ascending=True)
-df2.write.parquet("/gscratch/comdata/users/nathante/reddit_comments_by_subreddit.parquet_new", mode='overwrite', compression='snappy')
+# df2.write.parquet("/gscratch/scrubbed/comdata/reddit_comments_by_subreddit.parquet", mode='overwrite', compression='snappy')
 
-df = df.repartition('author')
+#df = spark.read.parquet("/gscratch/scrubbed/comdata/reddit_comments_by_subreddit.parquet")
-df3 = df.sort(["author","CreatedAt","subreddit","link_id","parent_id","Year","Month","Day"],ascending=True)
+df = df.repartition(2400,'author','subreddit',"Year","Month","Day")
-df3 = df3.sortWithinPartitions(["author","CreatedAt","subreddit","link_id","parent_id","Year","Month","Day"],ascending=True)
+df3 = df.sort(["author","subreddit","Year","Month","Day","CreatedAt","link_id","parent_id"],ascending=True)
-df3.write.parquet("/gscratch/comdata/users/nathante/reddit_comments_by_author.parquet_new", mode='overwrite',compression='snappy')
+df3 = df3.sortWithinPartitions(["author","subreddit","Year","Month","Day","CreatedAt","link_id","parent_id"],ascending=True)
+df3.write.parquet("/gscratch/scrubbed/comdata/reddit_comments_by_author.parquet", mode='overwrite',compression='snappy')

datasets/helper.py

@@ -24,8 +24,7 @@ def open_fileset(files):
     for fh in files:
         print(fh)
         lines = open_input_file(fh)
-        for line in lines:
+        yield from lines
-            yield line
 
 def open_input_file(input_filename):
     if re.match(r'.*\.7z$', input_filename):
@@ -39,7 +38,7 @@ def open_input_file(input_filename):
     elif re.match(r'.*\.xz', input_filename):
         cmd = ["xzcat",'-dk', '-T 20',input_filename]
     elif re.match(r'.*\.zst',input_filename):
-        cmd = ['zstd','-dck', input_filename]
+        cmd = ['/kloneusr/bin/zstd','-dck', input_filename,  '--memory=2048MB --stdout']
     elif re.match(r'.*\.gz',input_filename):
         cmd = ['gzip','-dc', input_filename]
     try:

datasets/job_script.sh

@@ -1,4 +0,0 @@
-#!/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

datasets/run_comments_jobs.sbatch

@@ -0,0 +1,24 @@
+#!/bin/bash
+## tf reddit comments
+#SBATCH --job-name="cdsc_reddit; parse comment dumps"
+## Allocation Definition
+#SBATCH --account=comdata
+#SBATCH --partition=compute-bigmem
+## Resources
+## Nodes. This should always be 1 for parallel-sql.
+#SBATCH --nodes=1    
+## Walltime (12 hours)
+#SBATCH --time=24:00:00
+## Memory per node
+#SBATCH --mem=8G
+#SBATCH --cpus-per-task=1
+#SBATCH --ntasks=1
+#SBATCH 
+#SBATCH --chdir /gscratch/comdata/users/nathante/partitioning_reddit/dataverse/cdsc_reddit/datasets
+#SBATCH --output=comments_jobs/%A_%a.out
+#SBATCH --error=comments_jobs/%A_%a.out
+. /opt/ohpc/admin/lmod/lmod/init/profile
+source ~/.bashrc
+TASK_NUM=$(( SLURM_ARRAY_TASK_ID + $1))
+TASK_CALL=$(sed -n ${TASK_NUM}p ./comments_task_list.sh)
+${TASK_CALL}

datasets/run_submissions_jobs.sbatch

@@ -0,0 +1,23 @@
+#!/bin/bash
+## tf reddit comments
+#SBATCH --job-name="cdsc_reddit; parse submission dumps"
+## 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=24:00:00
+## Memory per node
+#SBATCH --mem=8G
+#SBATCH --cpus-per-task=1
+#SBATCH --ntasks=1
+#SBATCH 
+#SBATCH --chdir /gscratch/comdata/users/nathante/cdsc_reddit/datasets
+#SBATCH --output=submissions_jobs/%A_%a.out
+#SBATCH --error=submissions_jobs/%A_%a.out
+
+TASK_NUM=$(( SLURM_ARRAY_TASK_ID + $1))
+TASK_CALL=$(sed -n ${TASK_NUM}p ./submissions_task_list.sh)
+${TASK_CALL}

datasets/submissions_2_parquet.sh

@@ -1,8 +1,8 @@
+#!/usr/bin/env bash
 ## this should be run manually since we don't have a nice way to wait on parallel_sql jobs
 
-#!/usr/bin/env bash
 
-./parse_submissions.sh
+srun -p compute-bigmem -A comdata --nodes=1 --mem-per-cpu=9g -c 40 --time=120:00:00 python3 $(pwd)/submissions_2_parquet_part1.py gen_task_list
 
 start_spark_and_run.sh 1 $(pwd)/submissions_2_parquet_part2.py
 

datasets/submissions_2_parquet_part1.py

@@ -3,26 +3,23 @@
 # two stages:
 # 1. from gz to arrow parquet (this script) 
 # 2. from arrow parquet to spark parquet (submissions_2_parquet_part2.py)
-
 from datetime import datetime
-from multiprocessing import Pool
+from pathlib import Path
 from itertools import islice
 from helper import find_dumps, open_fileset
 import pandas as pd
 import pyarrow as pa
 import pyarrow.parquet as pq
-import simdjson
 import fire
 import os
-
+import json
-parser = simdjson.Parser()
 
 def parse_submission(post, names = None):
     if names is None:
         names = ['id','author','subreddit','title','created_utc','permalink','url','domain','score','ups','downs','over_18','has_media','selftext','retrieved_on','num_comments','gilded','edited','time_edited','subreddit_type','subreddit_id','subreddit_subscribers','name','is_self','stickied','quarantine','error']
 
     try:
-        post = parser.parse(post)
+        post = json.loads(post)
     except (ValueError) as e:
         #        print(e)
         #        print(post)
@@ -61,7 +58,7 @@ def parse_submission(post, names = None):
 def parse_dump(partition):
 
     N=10000
-    stream = open_fileset([f"/gscratch/comdata/raw_data/reddit_dumps/submissions/{partition}"])
+    stream = open_fileset([f"/gscratch/comdata/raw_data/submissions/{partition}"])
     rows = map(parse_submission,stream)
     schema = pa.schema([
         pa.field('id', pa.string(),nullable=True),
@@ -92,8 +89,7 @@ def parse_dump(partition):
         pa.field('quarantine',pa.bool_(),nullable=True),
         pa.field('error',pa.string(),nullable=True)])
 
-    if not os.path.exists("/gscratch/comdata/output/temp/reddit_submissions.parquet/"):
+    Path("/gscratch/comdata/output/temp/reddit_submissions.parquet/").mkdir(exist_ok=True,parents=True)
-        os.mkdir("/gscratch/comdata/output/temp/reddit_submissions.parquet/")
 
     with pq.ParquetWriter(f"/gscratch/comdata/output/temp/reddit_submissions.parquet/{partition}",schema=schema,compression='snappy',flavor='spark') as writer:
         while True:
@@ -106,9 +102,9 @@ def parse_dump(partition):
 
         writer.close()
 
-def gen_task_list(dumpdir="/gscratch/comdata/raw_data/reddit_dumps/submissions"):
+def gen_task_list(dumpdir="/gscratch/comdata/raw_data/submissions"):
     files = list(find_dumps(dumpdir,base_pattern="RS_20*.*"))
-    with open("parse_submissions_task_list",'w') as of:
+    with open("submissions_task_list.sh",'w') as of:
         for fpath in files:
             partition = os.path.split(fpath)[1]
             of.write(f'python3 submissions_2_parquet_part1.py parse_dump {partition}\n')

datasets/submissions_2_parquet_part2.py

@@ -29,14 +29,14 @@ df = df.withColumn("Day",f.dayofmonth(f.col("CreatedAt")))
 df = df.withColumn("subreddit_hash",f.sha2(f.col("subreddit"), 256)[0:3])
 
 # next we gotta resort it all.
-df = df.repartition("subreddit")
+df = df.repartition(800,"subreddit","Year","Month")
-df2 = df.sort(["subreddit","CreatedAt","id"],ascending=True)
+df2 = df.sort(["subreddit","Year","Month","CreatedAt","id"],ascending=True)
 df2 = df.sortWithinPartitions(["subreddit","CreatedAt","id"],ascending=True)
 df2.write.parquet("/gscratch/comdata/output/temp/reddit_submissions_by_subreddit.parquet2", mode='overwrite',compression='snappy')
 
 
 # # we also want to have parquet files sorted by author then reddit. 
-df = df.repartition("author")
+df = df.repartition(800,"author","subreddit","Year","Month")
-df3 = df.sort(["author","CreatedAt","id"],ascending=True)
+df3 = df.sort(["author","Year","Month","CreatedAt","id"],ascending=True)
 df3 = df.sortWithinPartitions(["author","CreatedAt","id"],ascending=True)
 df3.write.parquet("/gscratch/comdata/output/temp/reddit_submissions_by_author.parquet2", mode='overwrite',compression='snappy')

density/Makefile

@@ -1,10 +1,7 @@
-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
+all: ../../data/reddit_density/subreddit_author_tf_similarities_10K_LSI/600.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
+../../data/reddit_density/subreddit_author_tf_similarities_10K_LSI/600.feather: overlap_density.py ../../data/reddit_similarity/subreddit_comment_authors-tf_10k_LSI/600.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
+	../start_spark_and_run.sh 1 overlap_density.py authors --inpath="../../data/reddit_similarity/subreddit_comment_authors-tf_10k_LSI/600.feather" --outpath="../../data/reddit_density/subreddit_author_tf_similarities_10K_LSI/600.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
+../../data/reddit_similarity/subreddit_comment_authors-tf_10k_LSI/600.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
+	$(MAKE) -C ../similarities
-
-/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

@@ -1,4 +1,6 @@
 #!/usr/bin/bash
+source ~/.bashrc
+echo $(hostname)
 start_spark_cluster.sh
-spark-submit --master spark://$(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
+spark-submit --verbose --master spark://$(hostname):43015 overlap_density.py authors --inpath=../../data/reddit_similarity/subreddit_comment_authors-tf_10k_LSI/600.feather --outpath=../../data/reddit_density/subreddit_author_tf_similarities_10K_LSI/600.feather --agg=pd.DataFrame.sum
 stop-all.sh

density/overlap_density.py

@@ -1,11 +1,12 @@
 import pandas as pd
 from pandas.core.groupby import DataFrameGroupBy as GroupBy
+from pathlib import Path
 import fire
 import numpy as np
 import sys
-sys.path.append("..")
+# sys.path.append("..")
-sys.path.append("../similarities")
+# sys.path.append("../similarities")
-from similarities.similarities_helper import reindex_tfidf, reindex_tfidf_time_interval
+# from similarities.similarities_helper import pull_tfidf
 
 # this is the mean of the ratio of the overlap to the focal size.
 # mean shared membership per focal community member
@@ -13,10 +14,12 @@ from similarities.similarities_helper import reindex_tfidf, reindex_tfidf_time_i
 
 def overlap_density(inpath, outpath, agg = pd.DataFrame.sum):
     df = pd.read_feather(inpath)
-    df = df.drop('subreddit',1)
+    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')
+    outpath = Path(outpath)
+    outpath.parent.mkdir(parents=True, exist_ok = True)
     df.to_feather(outpath)
     return df
 
@@ -25,6 +28,8 @@ def overlap_density_weekly(inpath, outpath, agg = GroupBy.sum):
     # exclude the diagonal
     df = df.loc[df.subreddit != df.variable]
     res = agg(df.groupby(['subreddit','week'])).reset_index()
+    outpath = Path(outpath)
+    outpath.parent.mkdir(parents=True, exist_ok = True)
     res.to_feather(outpath)
     return res
 

dumps/check_comments_shas.py

@@ -6,9 +6,9 @@ from os import path
 import hashlib
 
 shasums1 = requests.get("https://files.pushshift.io/reddit/comments/sha256sum.txt").text
-shasums2 = requests.get("https://files.pushshift.io/reddit/comments/daily/sha256sum.txt").text
+#shasums2 = requests.get("https://files.pushshift.io/reddit/comments/daily/sha256sum.txt").text
 
-shasums = shasums1 + shasums2
+shasums = shasums1 
 dumpdir = "/gscratch/comdata/raw_data/reddit_dumps/comments"
 
 for l in shasums.strip().split('\n'):

dumps/pull_pushshift_comments.sh

@@ -1,14 +1,12 @@
 #!/bin/bash
 
-user_agent='nathante teblunthuis <nathante@uw.edu>'
+user_agent='"nathante teblunthuis <nathante@uw.edu>"'
 output_dir='/gscratch/comdata/raw_data/reddit_dumps/comments'
 base_url='https://files.pushshift.io/reddit/comments/'
 
-wget -r --no-parent -A 'RC_201*.bz2' -U $user_agent -P $output_dir -nd -nc $base_url
+wget -r --no-parent -A 'RC_20*.bz2' -U $user_agent -P $output_dir -nd -nc $base_url
-wget -r --no-parent -A 'RC_201*.xz' -U $user_agent -P $output_dir -nd -nc $base_url
+wget -r --no-parent -A 'RC_20*.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
+wget -r --no-parent -A 'RC_20*.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

dumps/pull_pushshift_submissions.sh

@@ -1,14 +1,14 @@
 #!/bin/bash
 
-user_agent='nathante teblunthuis <nathante@uw.edu>'
+user_agent='"nathante teblunthuis <nathante@uw.edu>"'
 output_dir='/gscratch/comdata/raw_data/reddit_dumps/submissions'
 base_url='https://files.pushshift.io/reddit/submissions/'
 
-wget -r --no-parent -A 'RS_20*.bz2' -U $user_agent -P $output_dir -nd -nc $base_url
+wget -r --no-parent -A 'RS_20*.bz2' --user-agent=$user_agent -P $output_dir -nd -nc $base_url
-wget -r --no-parent -A 'RS_20*.xz' -U $user_agent -P $output_dir -nd -nc $base_url
+wget -r --no-parent -A 'RS_20*.xz' --user-agent=$user_agent -P $output_dir -nd -nc $base_url
-wget -r --no-parent -A 'RS_20*.zst' -U $user_agent -P $output_dir -nd -nc $base_url
+wget -r --no-parent -A 'RS_20*.zst' --user-agent=$user_agent -P $output_dir -nd -nc $base_url
-wget -r --no-parent -A 'RS_20*.bz2' -U $user_agent -P $output_dir -nd -nc $base_url/old_v1_data/
+wget -r --no-parent -A 'RS_20*.bz2' --user-agent=$user_agent -P $output_dir -nd -nc $base_url/old_v1_data/
-wget -r --no-parent -A 'RS_20*.xz' -U $user_agent -P $output_dir -nd -nc $base_url/old_v1_data/
+wget -r --no-parent -A 'RS_20*.xz' --user-agent=$user_agent -P $output_dir -nd -nc $base_url/old_v1_data/
-wget -r --no-parent -A 'RS_20*.zst' -U $user_agent -P $output_dir -nd -nc $base_url/old_v1_data/
+wget -r --no-parent -A 'RS_20*.zst' --user-agent=$user_agent -P $output_dir -nd -nc $base_url/old_v1_data/
 
 ./check_submission_shas.py

dumps/remove_duplicate_comments.py

@@ -0,0 +1,34 @@
+from pathlib import Path
+from itertools import chain, groupby
+
+dumpdir = Path("/gscratch/comdata/raw_data/reddit_dumps/comments")
+
+zst_files = dumpdir.glob("*.zst")
+bz2_files = dumpdir.glob("*.bz2")
+xz_files = dumpdir.glob("*.xz")
+all_files = sorted(list(chain(zst_files, bz2_files, xz_files)))
+groups = groupby(all_files, key = lambda p: p.stem)
+
+kept_paths = []
+removed_paths = []
+
+priority = ['.zst','.xz','.bz2']
+
+for stem, files in groups:
+    keep_file = None
+    remove_files = []
+    for f in files:
+        if keep_file is None:
+            keep_file = f
+        elif priority.index(keep_file.suffix) > priority.index(f.suffix):
+            remove_files.append(keep_file)
+            keep_file = f
+        else:
+            remove_files.append(f)
+    kept_paths.append(keep_file)
+    removed_paths.extend(remove_files)
+
+(dumpdir / "to_remove").mkdir()
+
+for f in removed_paths:
+    f.rename(f.parent / "to_remove" / f.name)

dumps/remove_duplicate_submissions.py

@@ -0,0 +1,34 @@
+from pathlib import Path
+from itertools import chain, groupby
+
+dumpdir = Path("/gscratch/comdata/raw_data/reddit_dumps/submissions")
+
+zst_files = dumpdir.glob("*.zst")
+bz2_files = dumpdir.glob("*.bz2")
+xz_files = dumpdir.glob("*.xz")
+all_files = sorted(list(chain(zst_files, bz2_files, xz_files)))
+groups = groupby(all_files, key = lambda p: p.stem)
+
+kept_paths = []
+removed_paths = []
+
+priority = ['.zst','.xz','.bz2']
+
+for stem, files in groups:
+    keep_file = None
+    remove_files = []
+    for f in files:
+        if keep_file is None:
+            keep_file = f
+        elif priority.index(keep_file.suffix) > priority.index(f.suffix):
+            remove_files.append(keep_file)
+            keep_file = f
+        else:
+            remove_files.append(f)
+    kept_paths.append(keep_file)
+    removed_paths.extend(remove_files)
+
+(dumpdir / "to_remove").mkdir()
+
+for f in removed_paths:
+    f.rename(f.parent / "to_remove" / f.name)

examples/pyarrow_reading.py

@@ -1,17 +0,0 @@
-import pyarrow.dataset as ds
-
-# A pyarrow dataset abstracts reading, writing, or filtering a parquet file. It does not read dataa into memory. 
-#dataset = ds.dataset(pathlib.Path('/gscratch/comdata/output/reddit_submissions_by_subreddit.parquet/'), format='parquet', partitioning='hive')
-dataset = ds.dataset('/gscratch/comdata/output/reddit_comments_by_subreddit.parquet/', format='parquet')
-
-# let's get all the comments to two subreddits:
-subreddits_to_pull = ['seattle','seattlewa']
-
-# a table is a low-level structured data format.  This line pulls data into memory. Setting metadata_n_threads > 1 gives a little speed boost.
-table = dataset.to_table(filter = ds.field('subreddit').isin(subreddits_to_pull), columns=['id','subreddit','CreatedAt','author','ups','downs','score','subreddit_id','stickied','title','url','is_self','selftext'])
-
-# Since data from just these 2 subreddits fits in memory we can just turn our table into a pandas dataframe.
-df = table.to_pandas()
-
-# We should save this smaller dataset so we don't have to wait 15 min to pull from parquet next time.
-df.to_csv("mydataset.csv")

examples/pyarrow_streaming.py

@@ -1,38 +0,0 @@
-import pyarrow.dataset as ds
-from itertools import groupby
-
-# A pyarrow dataset abstracts reading, writing, or filtering a parquet file. It does not read dataa into memory. 
-
-dataset = ds.dataset('/gscratch/comdata/output/reddit_submissions_by_author.parquet', format='parquet')
-
-# let's get all the comments to two subreddits:
-subreddits_to_pull = ['seattlewa','seattle']
-
-# instead of loading the data into a pandas dataframe all at once we can stream it.
-scan_tasks = dataset.scan(filter = ds.field('subreddit').isin(subreddits_to_pull), columns=['id','subreddit','CreatedAt','author','ups','downs','score','subreddit_id','stickied','title','url','is_self','selftext'])
-
-# simple function to execute scantasks and generate rows
-def iterate_rows(scan_tasks):
-    for st in scan_tasks:
-        for rb in st.execute():
-            df = rb.to_pandas()
-            for t in df.itertuples():
-                yield t
-
-row_iter = iterate_rows(scan_tasks)
-
-# now we can use python's groupby function to read one author at a time
-# note that the same author can appear more than once since the record batches may not be in the correct order.
-author_submissions = groupby(row_iter, lambda row: row.author)
-
-count_dict = {}
-
-for auth, posts in author_submissions:
-    if auth in count_dict:
-        count_dict[auth] = count_dict[auth] + 1
-    else:
-        count_dict[auth] = 1
-
-# since it's partitioned and sorted by author, we get one group for each author 
-any([ v != 1 for k,v in count_dict.items()])
-

ngrams/Makefile

@@ -0,0 +1,25 @@
+outputdir=../../data/reddit_ngrams/
+inputdir=../../data/reddit_comments_by_subreddit.parquet
+authors_tfdir=${outputdir}/comment_authors.parquet
+srun=sbatch --wait --verbose run_job.sbatch
+
+all: ${outputdir}/comment_authors_sorted.parquet/_SUCCESS 
+
+tf_task_list_1: tf_comments.py
+	${srun} bash -c "python3 tf_comments.py gen_task_list --mwe_pass='first' --outputdir=${outputdir} --tf_task_list=$@ --inputdir=${inputdir}"
+
+${outputdir}/comment_terms.parquet:tf_task_list_1
+	mkdir -p sbatch_log
+	sbatch --wait --verbose --array=1-$(shell cat $< | wc -l) run_array.sbatch 0 $<
+
+${outputdir}/comment_authors.parquet:${outputdir}/comment_terms.parquet
+	-
+
+${outputdir}/comment_authors_sorted.parquet:${outputdir}/comment_authors.parquet sort_tf_comments.py
+	../start_spark_and_run.sh 3 sort_tf_comments.py --inparquet=$< --outparquet=$@ --colname=author
+
+${outputdir}/comment_authors_sorted.parquet/_SUCCESS:${outputdir}/comment_authors_sorted.parquet
+
+
+${inputdir}:
+	$(MAKE) -C ../datasets

ngrams/run_array.sbatch

@@ -0,0 +1,19 @@
+#!/bin/bash
+#SBATCH --job-name=reddit_comment_term_frequencies
+#SBATCH --account=comdata
+#SBATCH --partition=compute-bigmem
+#SBATCH --nodes=1
+#SBATCH --ntasks-per-node=1
+#SBATCH --cpus-per-task=1
+#SBATCH --mem-per-cpu=9g
+#SBATCH --ntasks=1
+#SBATCH --export=ALL
+#SBATCH --time=48:00:00
+#SBATCH --chdir=/gscratch/comdata/users/nathante/partitioning_reddit/dataverse/cdsc_reddit/ngrams
+#SBATCH --error="sbatch_log/%A_%a.out"
+#SBATCH --output="sbatch_log/%A_%a.out"
+
+TASK_NUM=$(($SLURM_ARRAY_TASK_ID + $1))
+TASK_CALL=$(sed -n ${TASK_NUM}p $2)
+${TASK_CALL}
+

ngrams/run_job.sbatch

@@ -0,0 +1,18 @@
+#!/bin/bash
+#SBATCH --job-name="simulate measurement error models"
+## Allocation Definition
+#SBATCH --account=comdata
+#SBATCH --partition=compute-bigmem
+## Resources
+#SBATCH --nodes=1    
+## Walltime (4 hours)
+#SBATCH --time=4:00:00
+## Memory per node
+#SBATCH --mem=4G
+#SBATCH --cpus-per-task=1
+#SBATCH --ntasks-per-node=1
+#SBATCH --chdir /gscratch/comdata/users/nathante/partitioning_reddit/dataverse/cdsc_reddit/ngrams/
+#SBATCH --output=sbatch_log/%A_%a.out
+#SBATCH --error=sbatch_log/%A_%a.err
+echo "$@"
+"$@"

ngrams/run_tf_jobs.sh

@@ -1,8 +1,6 @@
 #!/usr/bin/env bash
-module load parallel_sql
+
 source ./bin/activate
 python3 tf_comments.py gen_task_list
-psu --del --Y
-cat tf_task_list | psu --load
 
 for job in $(seq 1 50); do sbatch checkpoint_parallelsql.sbatch; done;

ngrams/sort_tf_comments.py

@@ -2,12 +2,17 @@
 
 from pyspark.sql import functions as f
 from pyspark.sql import SparkSession
+import fire
 
+def main(inparquet, outparquet, colname):
     spark = SparkSession.builder.getOrCreate()
-df = spark.read.parquet("/gscratch/comdata/users/nathante/reddit_tfidf_test.parquet_temp/")
+    df = spark.read.parquet(inparquet)
 
-df = df.repartition(2000,'term')
+    df = df.repartition(2000,colname)
-df = df.sort(['term','week','subreddit'])
+    df = df.sort([colname,'week','subreddit'])
-df = df.sortWithinPartitions(['term','week','subreddit'])
+    df = df.sortWithinPartitions([colname,'week','subreddit'])
 
-df.write.parquet("/gscratch/comdata/users/nathante/reddit_tfidf_test_sorted_tf.parquet_temp",mode='overwrite',compression='snappy')
+    df.write.parquet(outparquet,mode='overwrite',compression='snappy')
+
+if __name__ == '__main__':
+    fire.Fire(main)

ngrams/tf_comments.py

@@ -3,6 +3,7 @@ import pandas as pd
 import pyarrow as pa
 import pyarrow.dataset as ds
 import pyarrow.parquet as pq
+import pyarrow.compute as pc
 from itertools import groupby, islice, chain
 import fire
 from collections import Counter
@@ -13,26 +14,33 @@ from nltk.corpus import stopwords
 from nltk.util import ngrams
 import string
 from random import random
-
+from redditcleaner import clean
-# remove urls
+from pathlib import Path
-# taken from https://stackoverflow.com/questions/3809401/what-is-a-good-regular-expression-to-match-a-url
+from datetime import datetime
-urlregex = re.compile(r"[-a-zA-Z0-9@:%._\+~#=]{1,256}\.[a-zA-Z0-9()]{1,6}\b([-a-zA-Z0-9()@:%_\+.~#?&//=]*)")
 
 # compute term frequencies for comments in each subreddit by week
-def weekly_tf(partition, mwe_pass = 'first'):
+def weekly_tf(partition, outputdir = '/gscratch/comdata/output/reddit_ngrams/', inputdir="/gscratch/comdata/output/reddit_comments_by_subreddit.parquet/", mwe_pass = 'first', excluded_users=None):
-    dataset = ds.dataset(f'/gscratch/comdata/output/reddit_comments_by_subreddit.parquet/{partition}', format='parquet')
-    if not os.path.exists("/gscratch/comdata/users/nathante/reddit_comment_ngrams_10p_sample/"):
-        os.mkdir("/gscratch/comdata/users/nathante/reddit_comment_ngrams_10p_sample/")
 
-    if not os.path.exists("/gscratch/comdata/users/nathante/reddit_tfidf_test_authors.parquet_temp/"):
+    dataset = ds.dataset(Path(inputdir)/partition, format='parquet')
-        os.mkdir("/gscratch/comdata/users/nathante/reddit_tfidf_test_authors.parquet_temp/")
+    outputdir = Path(outputdir)
+    samppath = outputdir / "reddit_comment_ngrams_10p_sample"
+
+    if not samppath.exists():
+        samppath.mkdir(parents=True, exist_ok=True)
 
     ngram_output = partition.replace("parquet","txt")
 
-    if mwe_pass == 'first':
+    if excluded_users is not None:
-        if os.path.exists(f"/gscratch/comdata/output/reddit_ngrams/comment_ngrams_10p_sample/{ngram_output}"):
+        excluded_users = set(map(str.strip,open(excluded_users)))
-            os.remove(f"/gscratch/comdata/output/reddit_ngrams/comment_ngrams_10p_sample/{ngram_output}")
+        df = df.filter(~ (f.col("author").isin(excluded_users)))
 
+
+    ngram_path = samppath / ngram_output
+    if mwe_pass == 'first':
+        if ngram_path.exists():
+            ngram_path.unlink()
+
+    dataset = dataset.filter(pc.field("CreatedAt") <= pa.scalar(datetime(2020,4,13)))
     batches = dataset.to_batches(columns=['CreatedAt','subreddit','body','author'])
 
 
@@ -65,8 +73,10 @@ def weekly_tf(partition, mwe_pass = 'first'):
 
     subreddit_weeks = groupby(rows, lambda r: (r.subreddit, r.week))
 
+    mwe_path = outputdir / "multiword_expressions.feather"
+
     if mwe_pass != 'first':
-        mwe_dataset = pd.read_feather(f'/gscratch/comdata/output/reddit_ngrams/multiword_expressions.feather')
+        mwe_dataset = pd.read_feather(mwe_path)
         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]
@@ -95,8 +105,8 @@ def weekly_tf(partition, mwe_pass = 'first'):
         # lowercase        
         text = text.lower()
 
-        # remove urls
+        # redditcleaner removes reddit markdown(newlines, quotes, bullet points, links, strikethrough, spoiler, code, superscript, table, headings)
-        text = urlregex.sub("", text)
+        text = clean(text)
 
         # sentence tokenize
         sentences = sent_tokenize(text)
@@ -107,19 +117,18 @@ def weekly_tf(partition, mwe_pass = 'first'):
         # remove punctuation
                         
         sentences = map(remove_punct, sentences)
-
-        # remove sentences with less than 2 words
-        sentences = filter(lambda sentence: len(sentence) > 2, sentences)
-
         # datta et al. select relatively common phrases from the reddit corpus, but they don't really explain how. We'll try that in a second phase.
         # they say that the extract 1-4 grams from 10% of the sentences and then find phrases that appear often relative to the original terms
         # here we take a 10 percent sample of sentences 
         if mwe_pass == 'first':
+
+            # remove sentences with less than 2 words
+            sentences = filter(lambda sentence: len(sentence) > 2, sentences)
             sentences = list(sentences)
             for sentence in sentences:
                 if random() <= 0.1:
                     grams = list(chain(*map(lambda i : ngrams(sentence,i),range(4))))
-                    with open(f'/gscratch/comdata/output/reddit_ngrams/comment_ngrams_10p_sample/{ngram_output}','a') as gram_file:
+                    with open(ngram_path,'a') as gram_file:
                         for ng in grams:
                             gram_file.write(' '.join(ng) + '\n')
                 for token in sentence:
@@ -154,7 +163,14 @@ def weekly_tf(partition, mwe_pass = 'first'):
 
     outchunksize = 10000
     
-    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:
+    termtf_outputdir = (outputdir / "comment_terms.parquet")
+    termtf_outputdir.mkdir(parents=True, exist_ok=True)
+    authortf_outputdir = (outputdir / "comment_authors.parquet")
+    authortf_outputdir.mkdir(parents=True, exist_ok=True)    
+    termtf_path = termtf_outputdir / partition
+    authortf_path = authortf_outputdir / partition
+    with pq.ParquetWriter(termtf_path, schema=schema, compression='snappy', flavor='spark') as writer, \
+         pq.ParquetWriter(authortf_path, schema=author_schema, compression='snappy', flavor='spark') as author_writer:
     
         while True:
 
@@ -183,12 +199,12 @@ def weekly_tf(partition, mwe_pass = 'first'):
         author_writer.close()
 
 
-def gen_task_list(mwe_pass='first'):
+def gen_task_list(mwe_pass='first', inputdir="/gscratch/comdata/output/reddit_comments_by_subreddit.parquet/", outputdir='/gscratch/comdata/output/reddit_ngrams/', tf_task_list='tf_task_list', excluded_users_file=None):
-    files = os.listdir("/gscratch/comdata/output/reddit_comments_by_subreddit.parquet/")
+    files = os.listdir(inputdir)
-    with open("tf_task_list",'w') as outfile:
+    with open(tf_task_list,'w') as outfile:
         for f in files:
             if f.endswith(".parquet"):
-                outfile.write(f"./tf_comments.py weekly_tf --mwe-pass {mwe_pass} {f}\n")
+                outfile.write(f"./tf_comments.py weekly_tf --mwe-pass {mwe_pass} --inputdir {inputdir} --outputdir {outputdir} --excluded_users {excluded_users_file} {f}\n")
 
 if __name__ == "__main__":
     fire.Fire({"gen_task_list":gen_task_list,

ngrams/top_comment_phrases.py

@@ -1,58 +0,0 @@
-from pyspark.sql import functions as f
-from pyspark.sql import Window
-from pyspark.sql import SparkSession
-import numpy as np
-
-spark = SparkSession.builder.getOrCreate()
-df = spark.read.text("/gscratch/comdata/users/nathante/reddit_comment_ngrams_10p_sample/")
-
-df = df.withColumnRenamed("value","phrase")
-
-# count phrase occurrances
-phrases = df.groupby('phrase').count()
-phrases = phrases.withColumnRenamed('count','phraseCount')
-phrases = phrases.filter(phrases.phraseCount > 10)
-
-
-# count overall
-N = phrases.select(f.sum(phrases.phraseCount).alias("phraseCount")).collect()[0].phraseCount
-
-print(f'analyzing PMI on a sample of {N} phrases') 
-logN = np.log(N)
-phrases = phrases.withColumn("phraseLogProb", f.log(f.col("phraseCount")) - logN)
-
-# count term occurrances
-phrases = phrases.withColumn('terms',f.split(f.col('phrase'),' '))
-terms = phrases.select(['phrase','phraseCount','phraseLogProb',f.explode(phrases.terms).alias('term')])
-
-win = Window.partitionBy('term')
-terms = terms.withColumn('termCount',f.sum('phraseCount').over(win))
-terms = terms.withColumnRenamed('count','termCount')
-terms = terms.withColumn('termLogProb',f.log(f.col('termCount')) - logN)
-
-terms = terms.groupBy(terms.phrase, terms.phraseLogProb, terms.phraseCount).sum('termLogProb')
-terms = terms.withColumnRenamed('sum(termLogProb)','termsLogProb')
-terms = terms.withColumn("phrasePWMI", f.col('phraseLogProb') - f.col('termsLogProb'))
-
-# join phrases to term counts
-
-
-df = terms.select(['phrase','phraseCount','phraseLogProb','phrasePWMI'])
-
-df = df.sort(['phrasePWMI'],descending=True)
-df = df.sortWithinPartitions(['phrasePWMI'],descending=True)
-df.write.parquet("/gscratch/comdata/users/nathante/reddit_comment_ngrams_pwmi.parquet/",mode='overwrite',compression='snappy')
-
-df = spark.read.parquet("/gscratch/comdata/users/nathante/reddit_comment_ngrams_pwmi.parquet/")
-
-df.write.csv("/gscratch/comdata/users/nathante/reddit_comment_ngrams_pwmi.csv/",mode='overwrite',compression='none')
-
-df = spark.read.parquet("/gscratch/comdata/users/nathante/reddit_comment_ngrams_pwmi.parquet")
-df = df.select('phrase','phraseCount','phraseLogProb','phrasePWMI')
-
-# choosing phrases occurring at least 3500 times in the 10% sample (35000 times) and then with a PWMI of at least 3 yeids about 65000 expressions.
-#
-df = df.filter(f.col('phraseCount') > 3500).filter(f.col("phrasePWMI")>3)
-df = df.toPandas()
-df.to_feather("/gscratch/comdata/users/nathante/reddit_multiword_expressions.feather")
-df.to_csv("/gscratch/comdata/users/nathante/reddit_multiword_expressions.csv")

run_array.sbatch

@@ -0,0 +1,22 @@
+#!/bin/bash
+## tf reddit comments
+#SBATCH --job-name="wikia ecology; fit var models"
+## 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=24:00:00
+## Memory per node
+#SBATCH --mem=8G
+#SBATCH --cpus-per-task=1
+#SBATCH --ntasks=1
+#SBATCH 
+#SBATCH --chdir /gscratch/comdata/users/nathante/wikia_ecology
+#SBATCH --output=var_jobs/%A_%a.out
+#SBATCH --error=var_jobs/%A_%a.out
+TASK_NUM=$(( SLURM_ARRAY_TASK_ID + $1))
+TASK_CALL=$(sed -n ${TASK_NUM}p ./var_jobs.sh)
+${TASK_CALL}

similarities/Makefile

@@ -1,130 +1,28 @@
-#all: /gscratch/comdata/output/reddit_similarity/tfidf/comment_terms_130k.parquet /gscratch/comdata/output/reddit_similarity/tfidf/comment_authors_130k.parquet /gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_terms_130k.parquet /gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_authors_130k.parquet
+srun=srun -p compute-bigmem -A comdata --mem-per-cpu=9g --time=200:00:00 -c 40
-srun_singularity=source /gscratch/comdata/users/nathante/cdsc_reddit/bin/activate && srun_singularity.sh
+srun_huge=srun -p compute-hugemem -A comdata --mem=724g --time=200:00:00 -c 40
-srun_singularity_huge=source /gscratch/comdata/users/nathante/cdsc_reddit/bin/activate && srun_singularity_huge.sh
+
-base_data=/gscratch/comdata/output/
+similarity_data=../../data/reddit_similarity
-similarity_data=${base_data}/reddit_similarity
 tfidf_data=${similarity_data}/tfidf
-tfidf_weekly_data=${similarity_data}/tfidf_weekly
+lsi_components=[10,50,100,200,300,400,500,600,700,850]
-similarity_weekly_data=${similarity_data}/weekly
-lsi_components=[10,50,100,200,300,400,500,600,700,850,1000,1500]
 
-lsi_similarities: ${similarity_data}/subreddit_comment_terms_10k_LSI ${similarity_data}/subreddit_comment_authors-tf_10k_LSI ${similarity_data}/subreddit_comment_authors_10k_LSI ${similarity_data}/subreddit_comment_terms_30k_LSI ${similarity_data}/subreddit_comment_authors-tf_30k_LSI ${similarity_data}/subreddit_comment_authors_30k_LSI
+lsi_similarities: ${similarity_data}/subreddit_comment_authors-tf_10k_LSI
 
-all: ${tfidf_data}/comment_terms_100k.parquet ${tfidf_data}/comment_terms_30k.parquet ${tfidf_data}/comment_terms_10k.parquet ${tfidf_data}/comment_authors_100k.parquet ${tfidf_data}/comment_authors_30k.parquet ${tfidf_data}/comment_authors_10k.parquet ${similarity_data}/subreddit_comment_authors_30k.feather ${similarity_data}/subreddit_comment_authors_10k.feather  ${similarity_data}/subreddit_comment_terms_10k.feather ${similarity_data}/subreddit_comment_terms_30k.feather ${similarity_data}/subreddit_comment_authors-tf_30k.feather ${similarity_data}/subreddit_comment_authors-tf_10k.feather ${similarity_data}/subreddit_comment_terms_100k.feather ${similarity_data}/subreddit_comment_authors_100k.feather ${similarity_data}/subreddit_comment_authors-tf_100k.feather ${similarity_weekly_data}/comment_terms.parquet
+all: ${similarity_data}/subreddit_comment_authors-tf_10k.feather
 
-#${tfidf_weekly_data}/comment_terms_100k.parquet ${tfidf_weekly_data}/comment_authors_100k.parquet ${tfidf_weekly_data}/comment_terms_30k.parquet ${tfidf_weekly_data}/comment_authors_30k.parquet ${similarity_weekly_data}/comment_terms_100k.parquet ${similarity_weekly_data}/comment_authors_100k.parquet  ${similarity_weekly_data}/comment_terms_30k.parquet ${similarity_weekly_data}/comment_authors_30k.parquet
+${similarity_data}/subreddit_comment_authors-tf_10k_LSI: ${tfidf_data}/comment_authors_100k.parquet similarities_helper.py ${similarity_data}/subreddits_by_num_comments_nonsfw.csv
+	 ${srun_huge} /bin/bash -c "source ~/.bashrc; python3 lsi_similarities.py author-tf --outfile=${similarity_data}/subreddit_comment_authors-tf_10k_LSI --topN=10000 --n_components=${lsi_components} --min_df=10 --inpath=$<"
 
-# /gscratch/comdata/output/reddit_similarity/subreddit_comment_authors_130k.parquet /gscratch/comdata/output/reddit_similarity/subreddit_comment_authors_130k.parquet /gscratch/comdata/output/reddit_similarity/subreddit_author_tf_similarities_130k.parquet /gscratch/comdata/output/reddit_similarity/subreddit_comment_terms_130k.parquet /gscratch/comdata/output/reddit_similarity/comment_terms_weekly_130k.parquet
+${similarity_data}/subreddits_by_num_comments_nonsfw.csv: ../../data/reddit_submissions_by_subreddit.parquet ../../data/reddit_comments_by_subreddit.parquet
+	../start_spark_and_run.sh 3 top_subreddits_by_comments.py
 
-# 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
+${tfidf_data}/comment_authors_100k.parquet: ../../data/reddit_ngrams/comment_authors_sorted.parquet ${similarity_data}/subreddits_by_num_comments_nonsfw.csv
+	../start_spark_and_run.sh 3 tfidf.py authors --topN=100000 --inpath=$< --outpath=${tfidf_data}/comment_authors_100k.parquet
 
-${similarity_weekly_data}/comment_terms.parquet: weekly_cosine_similarities.py similarities_helper.py /gscratch/comdata/output/reddit_ngrams/comment_terms.parquet ${similarity_data}/subreddits_by_num_comments.csv ${tfidf_weekly_data}/comment_terms.parquet
+../../data/reddit_ngrams/comment_authors_sorted.parquet:
-	${srun_singularity} python3 weekly_cosine_similarities.py terms --topN=10000 --outfile=${similarity_weekly_data}/comment_terms.parquet
+	$(MAKE) -C ../ngrams
 
-${similarity_data}/subreddit_comment_terms_10k.feather: ${tfidf_data}/comment_terms_100k.parquet similarities_helper.py
+../../data/reddit_submissions_by_subreddit.parquet:
-	${srun_singularity} python3 cosine_similarities.py term --outfile=${similarity_data}/subreddit_comment_terms_10k.feather --topN=10000
+	$(MAKE) -C ../datasets
 
-${similarity_data}/subreddit_comment_terms_10k_LSI: ${tfidf_data}/comment_terms_100k.parquet similarities_helper.py
+../../data/reddit_comments_by_subreddit.parquet:
-	${srun_singularity} python3 lsi_similarities.py term --outfile=${similarity_data}/subreddit_comment_terms_10k_LSI --topN=10000 --n_components=${lsi_components} --min_df=200
+	$(MAKE) -C ../datasets
-
-${similarity_data}/subreddit_comment_terms_30k_LSI: ${tfidf_data}/comment_terms_100k.parquet similarities_helper.py
-	${srun_singularity} python3 lsi_similarities.py term --outfile=${similarity_data}/subreddit_comment_terms_30k_LSI --topN=30000 --n_components=${lsi_components} --min_df=200
-
-${similarity_data}/subreddit_comment_terms_30k.feather: ${tfidf_data}/comment_terms_30k.parquet similarities_helper.py
-	${srun_singularity} python3 cosine_similarities.py term --outfile=${similarity_data}/subreddit_comment_terms_30k.feather --topN=30000
-
-${similarity_data}/subreddit_comment_authors_30k.feather: ${tfidf_data}/comment_authors_30k.parquet similarities_helper.py
-	${srun_singularity} python3 cosine_similarities.py author --outfile=${similarity_data}/subreddit_comment_authors_30k.feather --topN=30000
-
-${similarity_data}/subreddit_comment_authors_10k.feather: ${tfidf_data}/comment_authors_10k.parquet similarities_helper.py
-	${srun_singularity} python3 cosine_similarities.py author --outfile=${similarity_data}/subreddit_comment_authors_10k.feather --topN=10000
-
-${similarity_data}/subreddit_comment_authors_10k_LSI: ${tfidf_data}/comment_authors_100k.parquet similarities_helper.py
-	${srun_singularity} python3 lsi_similarities.py author --outfile=${similarity_data}/subreddit_comment_authors_10k_LSI --topN=10000 --n_components=${lsi_components} --min_df=2
-
-${similarity_data}/subreddit_comment_authors_30k_LSI: ${tfidf_data}/comment_authors_100k.parquet similarities_helper.py
-	${srun_singularity} python3 lsi_similarities.py author --outfile=${similarity_data}/subreddit_comment_authors_30k_LSI --topN=30000 --n_components=${lsi_components} --min_df=2
-
-${similarity_data}/subreddit_comment_authors-tf_30k.feather: ${tfidf_data}/comment_authors_30k.parquet similarities_helper.py
-	${srun_singularity} python3 cosine_similarities.py author-tf --outfile=${similarity_data}/subreddit_comment_authors-tf_30k.feather --topN=30000
-
-${similarity_data}/subreddit_comment_authors-tf_10k.feather: ${tfidf_data}/comment_authors_10k.parquet similarities_helper.py
-	${srun_singularity} python3 cosine_similarities.py author-tf --outfile=${similarity_data}/subreddit_comment_authors-tf_10k.feather --topN=10000
-
-${similarity_data}/subreddit_comment_authors-tf_10k_LSI: ${tfidf_data}/comment_authors_100k.parquet similarities_helper.py
-	${srun_singularity} python3 lsi_similarities.py author-tf --outfile=${similarity_data}/subreddit_comment_authors-tf_10k_LSI --topN=10000 --n_components=${lsi_components} --min_df=2
-
-${similarity_data}/subreddit_comment_authors-tf_30k_LSI: ${tfidf_data}/comment_authors_100k.parquet similarities_helper.py
-	${srun_singularity} python3 lsi_similarities.py author-tf --outfile=${similarity_data}/subreddit_comment_authors-tf_30k_LSI --topN=30000 --n_components=${lsi_components} --min_df=2
-
-${similarity_data}/subreddit_comment_terms_100k.feather: ${tfidf_data}/comment_terms_100k.parquet similarities_helper.py
-	${srun_singularity} python3 cosine_similarities.py term --outfile=${similarity_data}/subreddit_comment_terms_100k.feather --topN=100000
-
-${similarity_data}/subreddit_comment_authors_100k.feather: ${tfidf_data}/comment_authors_100k.parquet similarities_helper.py
-	${srun_singularity} python3 cosine_similarities.py author --outfile=${similarity_data}/subreddit_comment_authors_100k.feather --topN=100000
-
-${similarity_data}/subreddit_comment_authors-tf_100k.feather: ${tfidf_data}/comment_authors_100k.parquet similarities_helper.py
-	${srun_singularity} python3 cosine_similarities.py author-tf --outfile=${similarity_data}/subreddit_comment_authors-tf_100k.feather --topN=100000
-
-${tfidf_data}/comment_terms_100k.feather/: /gscratch/comdata/output/reddit_ngrams/comment_terms.parquet ${similarity_data}/subreddits_by_num_comments.csv
-	mkdir -p ${tfidf_data}/
-	start_spark_and_run.sh 4 tfidf.py terms --topN=100000 --outpath=${tfidf_data}/comment_terms_100k.feather 
-
-${tfidf_data}/comment_terms_30k.feather: /gscratch/comdata/output/reddit_ngrams/comment_terms.parquet ${similarity_data}/subreddits_by_num_comments.csv
-	mkdir -p ${tfidf_data}/
-	start_spark_and_run.sh 4 tfidf.py terms --topN=30000 --outpath=${tfidf_data}/comment_terms_30k.feather
-
-${tfidf_data}/comment_terms_10k.feather: /gscratch/comdata/output/reddit_ngrams/comment_terms.parquet ${similarity_data}/subreddits_by_num_comments.csv
-	mkdir -p ${tfidf_data}/
-	start_spark_and_run.sh 4 tfidf.py terms --topN=10000 --outpath=${tfidf_data}/comment_terms_10k.feather
-
-${tfidf_data}/comment_authors_100k.feather: /gscratch/comdata/output/reddit_ngrams/comment_authors.parquet ${similarity_data}/subreddits_by_num_comments.csv
-	mkdir -p ${tfidf_data}/
-	start_spark_and_run.sh 4 tfidf.py authors --topN=100000 --outpath=${tfidf_data}/comment_authors_100k.feather
-
-${tfidf_data}/comment_authors_10k.parquet: /gscratch/comdata/output/reddit_ngrams/comment_authors.parquet ${similarity_data}/subreddits_by_num_comments.csv
-	mkdir -p ${tfidf_data}/
-	start_spark_and_run.sh 4 tfidf.py authors --topN=10000 --outpath=${tfidf_data}/comment_authors_10k.parquet
-
-${tfidf_data}/comment_authors_30k.parquet: /gscratch/comdata/output/reddit_ngrams/comment_authors.parquet ${similarity_data}/subreddits_by_num_comments.csv
-	mkdir -p ${tfidf_data}/
-	start_spark_and_run.sh 4 tfidf.py authors --topN=30000 --outpath=${tfidf_data}/comment_authors_30k.parquet
-
-${tfidf_data}/tfidf_weekly/comment_terms_100k.parquet: /gscratch/comdata/output/reddit_ngrams/comment_terms.parquet ${similarity_data}/subreddits_by_num_comments.csv
-	start_spark_and_run.sh 4 tfidf.py terms_weekly --topN=100000 --outpath=${similarity_data}/tfidf_weekly/comment_authors_100k.parquet
-
-${tfidf_data}/tfidf_weekly/comment_authors_100k.parquet: /gscratch/comdata/output/reddit_ngrams/comment_terms.parquet ${similarity_data}/subreddits_by_ppnum_comments.csv
-	start_spark_and_run.sh 4 tfidf.py authors_weekly --topN=100000 --outpath=${tfidf_weekly_data}/comment_authors_100k.parquet
-
-${tfidf_weekly_data}/comment_terms_30k.parquet:  /gscratch/comdata/output/reddit_ngrams/comment_terms.parquet ${similarity_data}/subreddits_by_num_comments.csv
-	start_spark_and_run.sh 4 tfidf.py terms_weekly --topN=30000 --outpath=${tfidf_weekly_data}/comment_authors_30k.parquet
-
-${tfidf_weekly_data}/comment_authors_30k.parquet: /gscratch/comdata/output/reddit_ngrams/comment_terms.parquet ${similarity_data}/subreddits_by_num_comments.csv
-	start_spark_and_run.sh 4 tfidf.py authors_weekly --topN=30000 --outpath=${tfidf_weekly_data}/comment_authors_30k.parquet
-
-${similarity_weekly_data}/comment_terms_100k.parquet: weekly_cosine_similarities.py similarities_helper.py ${tfidf_weekly_data}/comment_terms_100k.parquet
-	${srun_singularity} python3 weekly_cosine_similarities.py terms --topN=100000 --outfile=${similarity_weekly_data}/comment_authors_100k.parquet
-
-${similarity_weekly_data}/comment_authors_100k.parquet: weekly_cosine_similarities.py similarities_helper.py /gscratch/comdata/output/reddit_ngrams/comment_terms.parquet ${similarity_data}/subreddits_by_num_comments.csv ${tfidf_weekly_data}/comment_authors_100k.parquet
-	${srun_singularity} python3 weekly_cosine_similarities.py authors --topN=100000 --outfile=${similarity_weekly_data}/comment_authors_100k.parquet
-
-${similarity_weekly_data}/comment_terms_30k.parquet: weekly_cosine_similarities.py similarities_helper.py /gscratch/comdata/output/reddit_ngrams/comment_terms.parquet ${similarity_data}/subreddits_by_num_comments.csv ${tfidf_weekly_data}/comment_terms_30k.parquet
-	${srun_singularity} python3 weekly_cosine_similarities.py terms --topN=30000 --outfile=${similarity_weekly_data}/comment_authors_30k.parquet
-
-${similarity_weekly_data}/comment_authors_30k.parquet: weekly_cosine_similarities.py similarities_helper.py /gscratch/comdata/output/reddit_ngrams/comment_terms.parquet ${similarity_data}/subreddits_by_num_comments.csv ${tfidf_weekly_data}/comment_authors_30k.parquet
-	${srun_singularity} python3 weekly_cosine_similarities.py authors --topN=30000 --outfile=${similarity_weekly_data}/comment_authors_30k.parquet
-
-# ${tfidf_weekly_data}/comment_authors_130k.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_weekly --topN=130000
-
-# /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 ${tfidf_weekly_data}/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/cosine_similarities.py

@@ -11,7 +11,9 @@ def cosine_similarities(infile, term_colname, outfile, min_df=None, max_df=None,
 # change so that these take in an input as an optional argument (for speed, but also for idf).
 def term_cosine_similarities(outfile, min_df=None, max_df=None, included_subreddits=None, topN=500, exclude_phrases=False, from_date=None, to_date=None):
 
-    return cosine_similarities('/gscratch/comdata/output/reddit_similarity/tfidf/comment_terms_100k.parquet',
+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,
@@ -23,8 +25,8 @@ def term_cosine_similarities(outfile, min_df=None, max_df=None, included_subredd
                                to_date
                                )
 
-def author_cosine_similarities(outfile, min_df=2, max_df=None, included_subreddits=None, topN=10000, from_date=None, to_date=None):
+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('/gscratch/comdata/output/reddit_similarity/tfidf/comment_authors_100k.parquet',
+    return cosine_similarities(infile,
                                'author',
                                outfile,
                                min_df,
@@ -36,8 +38,8 @@ def author_cosine_similarities(outfile, min_df=2, max_df=None, included_subreddi
                                to_date=to_date
                                )
 
-def author_tf_similarities(outfile, min_df=2, max_df=None, included_subreddits=None, topN=10000, from_date=None, to_date=None):
+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('/gscratch/comdata/output/reddit_similarity/tfidf/comment_authors_100k.parquet',
+    return cosine_similarities(infile,
                                'author',
                                outfile,
                                min_df,

similarities/job_script.sh

@@ -1,4 +1,6 @@
 #!/usr/bin/bash
+source ~/.bashrc
+echo $(hostname)
 start_spark_cluster.sh
-singularity exec  /gscratch/comdata/users/nathante/cdsc_base.sif spark-submit --master spark://$(hostname).hyak.local:7077 lsi_similarities.py author --outfile=/gscratch/comdata/output//reddit_similarity/subreddit_comment_authors_10k_LSI.feather --topN=10000
+spark-submit --verbose --master spark://$(hostname):43015 tfidf.py authors --topN=100000 --inpath=../../data/reddit_ngrams/comment_authors_sorted.parquet --outpath=../../data/reddit_similarity/tfidf/comment_authors_100k.parquet
-singularity exec /gscratch/comdata/users/nathante/cdsc_base.sif stop-all.sh
+stop-all.sh

similarities/lsi_similarities.py

@@ -1,20 +1,42 @@
 import pandas as pd
 import fire
 from pathlib import Path
-from similarities_helper import similarities, lsi_column_similarities
+from similarities_helper import *
+#from similarities_helper import similarities, lsi_column_similarities
 from functools import partial
 
-def lsi_similarities(infile, term_colname, outfile, min_df=None, max_df=None, included_subreddits=None, topN=500, from_date=None, to_date=None, tfidf_colname='tf_idf',n_components=100,n_iter=5,random_state=1968,algorithm='arpack'):
+# inpath = "/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/tfidf/comment_authors_compex.parquet"
+# term_colname='authors'
+# outfile='/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/similarity/comment_test_compex_LSI'
+# n_components=[10,50,100]
+# included_subreddits="/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/included_subreddits.txt"
+# n_iter=5
+# random_state=1968
+# algorithm='randomized'
+# topN = None
+# from_date=None
+# to_date=None
+# min_df=None
+# max_df=None
+
+def lsi_similarities(inpath, term_colname, outfile, min_df=None, max_df=None, included_subreddits=None, topN=None, from_date=None, to_date=None, tfidf_colname='tf_idf',n_components=100,n_iter=5,random_state=1968,algorithm='arpack',lsi_model=None):
     print(n_components,flush=True)
 
-    simfunc = partial(lsi_column_similarities,n_components=n_components,n_iter=n_iter,random_state=random_state,algorithm=algorithm)
         
-    return similarities(infile=infile, simfunc=simfunc, term_colname=term_colname, outfile=outfile, min_df=min_df, max_df=max_df, included_subreddits=included_subreddits, topN=topN, from_date=from_date, to_date=to_date, tfidf_colname=tfidf_colname)
+    if lsi_model is None:
+        if type(n_components) == list:
+            lsi_model = Path(outfile) / f'{max(n_components)}_{term_colname}_LSIMOD.pkl'
+        else:
+            lsi_model = Path(outfile) / f'{n_components}_{term_colname}_LSIMOD.pkl'
+
+    simfunc = partial(lsi_column_similarities,n_components=n_components,n_iter=n_iter,random_state=random_state,algorithm=algorithm,lsi_model_save=lsi_model)
+
+    return similarities(inpath=inpath, simfunc=simfunc, term_colname=term_colname, outfile=outfile, min_df=min_df, max_df=max_df, included_subreddits=included_subreddits, topN=topN, from_date=from_date, to_date=to_date, tfidf_colname=tfidf_colname)
 
 # change so that these take in an input as an optional argument (for speed, but also for idf).
-def term_lsi_similarities(outfile, min_df=None, max_df=None, included_subreddits=None, topN=500, from_date=None, to_date=None, n_components=300,n_iter=5,random_state=1968,algorithm='arpack'):
+def term_lsi_similarities(inpath='/gscratch/comdata/output/reddit_similarity/tfidf/comment_terms_100k.parquet',outfile=None, min_df=None, max_df=None, included_subreddits=None, topN=None, from_date=None, to_date=None, algorithm='arpack', n_components=300,n_iter=5,random_state=1968):
 
-    return lsi_similarities('/gscratch/comdata/output/reddit_similarity/tfidf/comment_terms_100k.parquet',
+    res =  lsi_similarities(inpath,
                             'term',
                             outfile,
                             min_df,
@@ -23,11 +45,13 @@ def term_lsi_similarities(outfile, min_df=None, max_df=None, included_subreddits
                             topN,
                             from_date,
                             to_date,
-                            n_components=n_components
+                            n_components=n_components,
+                            algorithm = algorithm
                             )
+    return res
 
-def author_lsi_similarities(outfile, min_df=2, max_df=None, included_subreddits=None, topN=10000, from_date=None, to_date=None,n_components=300,n_iter=5,random_state=1968,algorithm='arpack'):
+def author_lsi_similarities(inpath='/gscratch/comdata/output/reddit_similarity/tfidf/comment_authors_100k.parquet',outfile=None, min_df=2, max_df=None, included_subreddits=None, topN=None, from_date=None, to_date=None,algorithm='arpack',n_components=300,n_iter=5,random_state=1968):
-    return lsi_similarities('/gscratch/comdata/output/reddit_similarity/tfidf/comment_authors_100k.parquet',
+    return lsi_similarities(inpath,
                             'author',
                             outfile,
                             min_df,
@@ -39,8 +63,8 @@ def author_lsi_similarities(outfile, min_df=2, max_df=None, included_subreddits=
                             n_components=n_components
                                )
 
-def author_tf_similarities(outfile, min_df=2, max_df=None, included_subreddits=None, topN=10000, from_date=None, to_date=None,n_components=300,n_iter=5,random_state=1968,algorithm='arpack'):
+def author_tf_similarities(inpath='/gscratch/comdata/output/reddit_similarity/tfidf/comment_authors_100k.parquet',outfile=None, min_df=2, max_df=None, included_subreddits=None, topN=None, from_date=None, to_date=None,algorithm='arpack',n_components=300,n_iter=5,random_state=1968):
-    return lsi_similarities('/gscratch/comdata/output/reddit_similarity/tfidf/comment_authors_100k.parquet',
+    return lsi_similarities(inpath,
                             'author',
                             outfile,
                             min_df,
@@ -50,7 +74,8 @@ def author_tf_similarities(outfile, min_df=2, max_df=None, included_subreddits=N
                             from_date=from_date,
                             to_date=to_date,
                             tfidf_colname='relative_tf',
-                            n_components=n_components
+                            n_components=n_components,
+                            algorithm=algorithm
                             )
 
 

similarities/similarities_helper.py

@@ -15,27 +15,54 @@ import numpy as np
 import pathlib
 from datetime import datetime
 from pathlib import Path
+import pickle
 
 class tf_weight(Enum):
     MaxTF = 1
     Norm05 = 2
 
-infile = "/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_terms.parquet"
+# infile = "/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_terms.parquet"
-cache_file = "/gscratch/comdata/users/nathante/cdsc_reddit/similarities/term_tfidf_entries_bak.parquet"
+# cache_file = "/gscratch/comdata/users/nathante/cdsc_reddit/similarities/term_tfidf_entries_bak.parquet"
-
-def termauthor_tfidf(term_tfidf_callable, author_tfidf_callable):
-    
 
 # subreddits missing after this step don't have any terms that have a high enough idf
 # try rewriting without merges
-def reindex_tfidf(infile, term_colname, min_df=None, max_df=None, included_subreddits=None, topN=500, week=None, from_date=None, to_date=None, rescale_idf=True, tf_family=tf_weight.MaxTF):
+
-    print("loading tfidf", flush=True)
+# does reindex_tfidf, but without reindexing.
+def reindex_tfidf(*args, **kwargs):
+    df, tfidf_ds, ds_filter = _pull_or_reindex_tfidf(*args, **kwargs, reindex=True)
+
+    print("assigning names")
+    subreddit_names = tfidf_ds.to_table(filter=ds_filter,columns=['subreddit','subreddit_id'])
+    batches = subreddit_names.to_batches()
+    
+    with Pool(cpu_count()) as pool:
+        chunks = pool.imap_unordered(pull_names,batches) 
+        subreddit_names = pd.concat(chunks,copy=False).drop_duplicates()
+        subreddit_names = subreddit_names.set_index("subreddit_id")
+
+    new_ids = df.loc[:,['subreddit_id','subreddit_id_new']].drop_duplicates()
+    new_ids = new_ids.set_index('subreddit_id')
+    subreddit_names = subreddit_names.join(new_ids,on='subreddit_id').reset_index()
+    subreddit_names = subreddit_names.drop("subreddit_id",axis=1)
+    subreddit_names = subreddit_names.sort_values("subreddit_id_new")
+    return(df, subreddit_names)
+
+def pull_tfidf(*args, **kwargs):
+    df, _, _ =  _pull_or_reindex_tfidf(*args, **kwargs, reindex=False)
+    return df
+
+def _pull_or_reindex_tfidf(infile, term_colname, min_df=None, max_df=None, included_subreddits=None, topN=None, week=None, from_date=None, to_date=None, rescale_idf=True, tf_family=tf_weight.MaxTF, reindex=True):
+    print(f"loading tfidf {infile}, week {week}, min_df {min_df}, max_df {max_df}", flush=True)
+
+    if week is not None:
+        tfidf_ds = ds.dataset(infile, partitioning='hive')
+    else: 
         tfidf_ds = ds.dataset(infile)
 
     if included_subreddits is None:
         included_subreddits = select_topN_subreddits(topN)
     else:
-        included_subreddits = set(open(included_subreddits))
+        included_subreddits = set(map(str.strip,open(included_subreddits)))
 
     ds_filter = ds.field("subreddit").isin(included_subreddits)
 
@@ -71,15 +98,23 @@ def reindex_tfidf(infile, term_colname, min_df=None, max_df=None, included_subre
             'relative_tf':ds.field('relative_tf').cast('float32'),
             'tf_idf':ds.field('tf_idf').cast('float32')}
 
-    tfidf_ds = ds.dataset(infile)
+    print(projection, flush=True)
-
+    print(ds_filter, flush=True)
     df = tfidf_ds.to_table(filter=ds_filter,columns=projection)
 
     df = df.to_pandas(split_blocks=True,self_destruct=True)
     print("assigning indexes",flush=True)
-    df['subreddit_id_new'] = df.groupby("subreddit_id").ngroup()
+    if reindex:
+        print("assigning indexes",flush=True)
+        df['subreddit_id_new'] = df.groupby("subreddit_id").ngroup() + 1
+    else:
+        df['subreddit_id_new'] = df['subreddit_id']
+
+    if reindex:
         grouped = df.groupby(term_id)
-    df[term_id_new] = grouped.ngroup()
+        df[term_id_new] = grouped.ngroup() + 1 
+    else:
+        df[term_id_new] = df[term_id]
 
     if rescale_idf:
         print("computing idf", flush=True)
@@ -91,26 +126,13 @@ def reindex_tfidf(infile, term_colname, min_df=None, max_df=None, included_subre
         else: # tf_fam = tf_weight.Norm05
             df["tf_idf"] = (0.5 + 0.5 * df.relative_tf) * df.idf
 
-    print("assigning names")
+    return (df, tfidf_ds, ds_filter)
-    subreddit_names = tfidf_ds.to_table(filter=ds_filter,columns=['subreddit','subreddit_id'])
-    batches = subreddit_names.to_batches()
 
-    with Pool(cpu_count()) as pool:
-        chunks = pool.imap_unordered(pull_names,batches) 
-        subreddit_names = pd.concat(chunks,copy=False).drop_duplicates()
-
-    subreddit_names = subreddit_names.set_index("subreddit_id")
-    new_ids = df.loc[:,['subreddit_id','subreddit_id_new']].drop_duplicates()
-    new_ids = new_ids.set_index('subreddit_id')
-    subreddit_names = subreddit_names.join(new_ids,on='subreddit_id').reset_index()
-    subreddit_names = subreddit_names.drop("subreddit_id",1)
-    subreddit_names = subreddit_names.sort_values("subreddit_id_new")
-    return(df, subreddit_names)
 
 def pull_names(batch):
     return(batch.to_pandas().drop_duplicates())
 
-def similarities(infile, simfunc, term_colname, outfile, min_df=None, max_df=None, included_subreddits=None, topN=500, from_date=None, to_date=None, tfidf_colname='tf_idf'):
+def similarities(inpath, simfunc, term_colname, outfile, min_df=None, max_df=None, included_subreddits=None, topN=500, 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.
     '''
@@ -130,7 +152,7 @@ def similarities(infile, simfunc, term_colname, outfile, min_df=None, max_df=Non
         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"))
-        outfile.parent.mkdir(exist_ok=True, parents=True)
+        p.parent.mkdir(exist_ok=True, parents=True)
 
         sims.to_feather(outfile)
 
@@ -138,8 +160,8 @@ def similarities(infile, simfunc, term_colname, outfile, min_df=None, max_df=Non
     term_id = term + '_id'
     term_id_new = term + '_id_new'
 
-    entries, subreddit_names = reindex_tfidf(infile, term_colname=term_colname, min_df=min_df, max_df=max_df, included_subreddits=included_subreddits, topN=topN,from_date=from_date,to_date=to_date)
+    entries, subreddit_names = reindex_tfidf(inpath, term_colname=term_colname, min_df=min_df, max_df=max_df, included_subreddits=included_subreddits, topN=topN,from_date=from_date,to_date=to_date)
-    mat = csr_matrix((entries[tfidf_colname],(entries[term_id_new], entries.subreddit_id_new)))
+    mat = csr_matrix((entries[tfidf_colname],(entries[term_id_new]-1, entries.subreddit_id_new-1)))
 
     print("loading matrix")        
 
@@ -154,7 +176,7 @@ def similarities(infile, simfunc, term_colname, outfile, min_df=None, max_df=Non
         for simmat, name in sims:
             proc_sims(simmat, Path(outfile)/(str(name) + ".feather"))
     else:
-        proc_sims(simmat, outfile)
+        proc_sims(sims, outfile)
 
 def write_weekly_similarities(path, sims, week, names):
     sims['week'] = week
@@ -207,10 +229,9 @@ def test_lsi_sims():
 # if n_components is a list we'll return a list of similarities with different latent dimensionalities
 # if algorithm is 'randomized' instead of 'arpack' then n_iter gives the number of iterations.
 # this function takes the svd and then the column similarities of it
-def lsi_column_similarities(tfidfmat,n_components=300,n_iter=10,random_state=1968,algorithm='randomized'):
+def lsi_column_similarities(tfidfmat,n_components=300,n_iter=10,random_state=1968,algorithm='randomized',lsi_model_save=None,lsi_model_load=None):
     # first compute the lsi of the matrix
     # then take the column similarities
-    print("running LSI",flush=True)
 
     if type(n_components) is int:
         n_components = [n_components]
@@ -218,15 +239,28 @@ def lsi_column_similarities(tfidfmat,n_components=300,n_iter=10,random_state=196
     n_components = sorted(n_components,reverse=True)
     
     svd_components = n_components[0]
+    
+    if lsi_model_load is not None and Path(lsi_model_load).exists():
+        print("loading LSI")
+        mod = pickle.load(open(lsi_model_load ,'rb'))
+        lsi_model_save = lsi_model_load
+
+    else:
+        print("running LSI",flush=True)
         svd = TruncatedSVD(n_components=svd_components,random_state=random_state,algorithm=algorithm,n_iter=n_iter)
         mod = svd.fit(tfidfmat.T)
+
+    if lsi_model_save is not None:
+        Path(lsi_model_save).parent.mkdir(exist_ok=True, parents=True)
+        pickle.dump(mod, open(lsi_model_save,'wb'))
+
+    print(n_components, flush=True)
     lsimat = mod.transform(tfidfmat.T)
     for n_dims in n_components:
+        print("computing similarities", flush=True)
         sims = column_similarities(lsimat[:,np.arange(n_dims)])
-        if len(n_components) > 1:
         yield (sims, n_dims)
-        else:
+
-            return sims
     
 
 def column_similarities(mat):
@@ -257,20 +291,20 @@ def build_weekly_tfidf_dataset(df, include_subs, term_colname, tf_family=tf_weig
     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 = idf.select([term]).distinct() # terms are distinct
 
-    terms = terms.withColumn(term_id,f.row_number().over(Window.partitionBy('week').orderBy(term))) # term ids 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','week']).distinct()
+    subreddits = df.select(['subreddit']).distinct()
-    subreddits = subreddits.withColumn('subreddit_id',f.row_number().over(Window.partitionBy("week").orderBy("subreddit")))
+    subreddits = subreddits.withColumn('subreddit_id',f.row_number().over(Window.orderBy("subreddit")))
 
-    df = df.join(subreddits,on=['subreddit','week'])
+    df = df.join(subreddits,on=['subreddit'])
 
     # map terms to indexes in the tfs and the idfs
-    df = df.join(terms,on=[term,'week']) # subreddit-term-id is unique
+    df = df.join(terms,on=[term]) # subreddit-term-id is unique
 
-    idf = idf.join(terms,on=[term,'week'])
+    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,'week'])
@@ -282,11 +316,11 @@ def build_weekly_tfidf_dataset(df, include_subs, term_colname, tf_family=tf_weig
     else: # tf_fam = tf_weight.Norm05
         df = df.withColumn("tf_idf",  (0.5 + 0.5 * df.relative_tf) * df.idf)
 
-    df = df.repartition(400,'subreddit','week')
+    df = df.repartition('week')
-    dfwriter = df.write.partitionBy("week").sortBy("subreddit")
+    dfwriter = df.write.partitionBy("week")
     return dfwriter
 
-def _calc_tfidf(df, term_colname, tf_family):
+def _calc_tfidf(df, term_colname, tf_family, min_df=None, max_df=None):
     term = term_colname
     term_id = term + '_id'
 
@@ -304,7 +338,13 @@ def _calc_tfidf(df, term_colname, tf_family):
     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 = idf
+    if min_df is not None:
+        terms = terms.filter(f.col('count')>=min_df)
+    if max_df is not None:
+        terms = terms.filter(f.col('count')<=max_df)
+    
+    terms = terms.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
@@ -314,12 +354,12 @@ def _calc_tfidf(df, term_colname, tf_family):
     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
+    df = df.join(terms,on=term,how='inner') # subreddit-term-id is unique
 
-    idf = idf.join(terms,on=term)
+    idf = idf.join(terms,on=term,how='inner')
 
     # join on subreddit/term to create tf/dfs indexed by term
-    df = df.join(idf, on=[term_id, term])
+    df = df.join(idf, on=[term_id, term],how='inner')
 
     # agg terms by subreddit to make sparse tf/df vectors
     if tf_family == tf_weight.MaxTF:
@@ -330,19 +370,19 @@ def _calc_tfidf(df, term_colname, tf_family):
     return df
     
 
-def build_tfidf_dataset(df, include_subs, term_colname, tf_family=tf_weight.Norm05):
+def tfidf_dataset(df, include_subs, term_colname, tf_family=tf_weight.Norm05, min_df=None, max_df=None):
     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)
+    df = _calc_tfidf(df, term_colname, tf_family, min_df, max_df)
     df = df.repartition('subreddit')
-    dfwriter = df.write.sortBy("subreddit","tf")
+    dfwriter = df.write
     return dfwriter
 
-def select_topN_subreddits(topN, path="/gscratch/comdata/output/reddit_similarity/subreddits_by_num_comments_nonsfw.csv"):
+def select_topN_subreddits(topN, path="../../data/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

@@ -1,9 +1,12 @@
 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
+from similarities_helper import tfidf_dataset, build_weekly_tfidf_dataset, select_topN_subreddits
+from functools import partial
 
-def _tfidf_wrapper(func, inpath, outpath, topN, term_colname, exclude, included_subreddits):
+inpath = '/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/tfidf/comment_authors_compex.parquet'
+# include_terms is a path to a parquet file that contains a column of term_colname + '_id' to include.
+def _tfidf_wrapper(func, inpath, outpath, topN, term_colname, exclude, included_subreddits, included_terms=None, min_df=None, max_df=None):
     spark = SparkSession.builder.getOrCreate()
 
     df = spark.read.parquet(inpath)
@@ -11,64 +14,95 @@ def _tfidf_wrapper(func, inpath, outpath, topN, term_colname, exclude, included_
     df = df.filter(~ f.col(term_colname).isin(exclude))
 
     if included_subreddits is not None:
-        include_subs = list(open(included_subreddits))
+        include_subs = set(map(str.strip,open(included_subreddits)))
     else:
         include_subs = select_topN_subreddits(topN)
 
-    dfwriter = func(df, include_subs, term_colname)
+    include_subs = spark.sparkContext.broadcast(include_subs)
+
+    #    term_id = term_colname + "_id"
+
+    if included_terms is not None:
+        terms_df = spark.read.parquet(included_terms)
+        terms_df = terms_df.select(term_colname).distinct()
+        df = df.join(terms_df, on=term_colname, how='left_semi')
+
+    dfwriter = func(df, include_subs.value, term_colname)
 
     dfwriter.parquet(outpath,mode='overwrite',compression='snappy')
     spark.stop()
 
-def tfidf(inpath, outpath, topN, term_colname, exclude, included_subreddits):
+def tfidf(inpath, outpath, topN, term_colname, exclude, included_subreddits, min_df, max_df):
-    return _tfidf_wrapper(build_tfidf_dataset, inpath, outpath, topN, term_colname, exclude, included_subreddits)
+    tfidf_func = partial(tfidf_dataset, max_df=max_df, min_df=min_df)
+    return _tfidf_wrapper(tfidf_func, inpath, outpath, topN, term_colname, exclude, included_subreddits)
 
-def tfidf_weekly(inpath, outpath, topN, term_colname, exclude, included_subreddits):
+def tfidf_weekly(inpath, outpath, static_tfidf_path, topN, term_colname, exclude, included_subreddits):
-    return _tfidf_wrapper(build_weekly_tfidf_dataset, inpath, outpath, topN, term_colname, exclude, included_subreddits)
+    return _tfidf_wrapper(build_weekly_tfidf_dataset, inpath, outpath, topN, term_colname, exclude, included_subreddits, included_terms=static_tfidf_path)
 
-def tfidf_authors(outpath='/gscratch/comdata/output/reddit_similarity/tfidf/comment_authors.parquet',
-                  topN=25000):
 
-    return tfidf("/gscratch/comdata/output/reddit_ngrams/comment_authors.parquet",
+def tfidf_authors(inpath="/gscratch/comdata/output/reddit_ngrams/comment_authors.parquet",
+                  outpath='/gscratch/comdata/output/reddit_similarity/tfidf/comment_authors.parquet',
+                  topN=None,
+                  included_subreddits=None,
+                  min_df=None,
+                  max_df=None):
+
+    return tfidf(inpath,
                  outpath,
                  topN,
                  'author',
                  ['[deleted]','AutoModerator'],
-                 included_subreddits=None
+                 included_subreddits=included_subreddits,
+                 min_df=min_df,
+                 max_df=max_df
                  )
 
-def tfidf_terms(outpath='/gscratch/comdata/output/reddit_similarity/tfidf/comment_terms.parquet',
+def tfidf_terms(inpath="/gscratch/comdata/output/reddit_ngrams/comment_terms.parquet",
-                topN=25000):
+                outpath='/gscratch/comdata/output/reddit_similarity/tfidf/comment_terms.parquet',
+                topN=None,
+                included_subreddits=None,
+                min_df=None,
+                max_df=None):
 
-    return tfidf("/gscratch/comdata/output/reddit_ngrams/comment_terms.parquet",
+    return tfidf(inpath,
                  outpath,
                  topN,
                  'term',
                  [],
-                 included_subreddits=None
+                 included_subreddits=included_subreddits,
+                 min_df=min_df,
+                 max_df=max_df
                  )
 
-def tfidf_authors_weekly(outpath='/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_authors.parquet',
+def tfidf_authors_weekly(inpath="/gscratch/comdata/output/reddit_ngrams/comment_authors.parquet",
-                         topN=25000):
+                         static_tfidf_path="/gscratch/comdata/output/reddit_similarity/tfidf/comment_authors.parquet",
+                         outpath='/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_authors.parquet',
+                         topN=None,
+                         included_subreddits=None):
 
-    return tfidf_weekly("/gscratch/comdata/output/reddit_ngrams/comment_authors.parquet",
+    return tfidf_weekly(inpath,
                         outpath,
+                        static_tfidf_path,
                         topN,
                         'author',
                         ['[deleted]','AutoModerator'],
-                        included_subreddits=None
+                        included_subreddits=included_subreddits
                         )
 
-def tfidf_terms_weekly(outpath='/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_terms.parquet',
+def tfidf_terms_weekly(inpath="/gscratch/comdata/output/reddit_ngrams/comment_terms.parquet",
-                       topN=25000):
+                       static_tfidf_path="/gscratch/comdata/output/reddit_similarity/tfidf/comment_terms.parquet",
+                       outpath='/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_terms.parquet',
+                       topN=None,
+                       included_subreddits=None):
 
 
-    return tfidf_weekly("/gscratch/comdata/output/reddit_ngrams/comment_terms.parquet",
+    return tfidf_weekly(inpath,
                         outpath,
+                        static_tfidf_path,
                         topN,
                         'term',
                         [],
-                        included_subreddits=None
+                        included_subreddits=included_subreddits
                         )
 
 

similarities/top_subreddits_by_comments.py

@@ -1,16 +1,20 @@
 from pyspark.sql import functions as f
 from pyspark.sql import SparkSession
 from pyspark.sql import Window
+from datetime import datetime
+from pathlib import Path
 
 spark = SparkSession.builder.getOrCreate()
 conf = spark.sparkContext.getConf()
 
-submissions = spark.read.parquet("/gscratch/comdata/output/reddit_submissions_by_subreddit.parquet")
+submissions = spark.read.parquet("../../data/reddit_submissions_by_subreddit.parquet")
+
+submissions = submissions.filter(f.col("CreatedAt") <= datetime(2020,4,13))
 
 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")
+df = spark.read.parquet("../../data/reddit_comments_by_subreddit.parquet")
-
+df = df.filter(f.col("CreatedAt") <= datetime(2020,4,13))
 # remove /u/ pages
 df = df.filter(~df.subreddit.like("u_%"))
 
@@ -26,4 +30,6 @@ df = df.toPandas()
 
 df = df.sort_values("n_comments")
 
-df.to_csv('/gscratch/comdata/output/reddit_similarity/subreddits_by_num_comments.csv', index=False)
+outpath = Path("../../data/reddit_similarity/subreddits_by_num_comments_nonsfw.csv")
+outpath.parent.mkdir(exist_ok=True, parents=True)
+df.to_csv(str(outpath), index=False)

similarities/wang_similarity.py

@@ -1,18 +0,0 @@
-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

@@ -1,81 +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 pyarrow.dataset as ds
-import pandas as pd
-import fire
-from itertools import islice, chain
-from pathlib import Path
-from similarities_helper import *
-from multiprocessing import Pool, cpu_count
-from functools import partial
-
-
-def _week_similarities(week, simfunc, tfidf_path, term_colname, min_df, max_df, included_subreddits, topN, outdir:Path):
-    term = term_colname
-    term_id = term + '_id'
-    term_id_new = term + '_id_new'
-    print(f"loading matrix: {week}")
-    entries, subreddit_names = reindex_tfidf(infile = tfidf_path,
-                                             term_colname=term_colname,
-                                             min_df=min_df,
-                                             max_df=max_df,
-                                             included_subreddits=included_subreddits,
-                                             topN=topN,
-                                             week=week)
-    mat = csr_matrix((entries[tfidf_colname],(entries[term_id_new], entries.subreddit_id_new)))
-    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'] = names.subreddit.values
-    outfile = str(Path(outdir) / str(week))
-    write_weekly_similarities(outfile, sims, week, names)
-
-def pull_weeks(batch):
-    return set(batch.to_pandas()['week'])
-
-#tfidf = spark.read.parquet('/gscratch/comdata/users/nathante/subreddit_tfidf_weekly.parquet')
-def cosine_similarities_weekly(tfidf_path, outfile, term_colname, min_df = None, max_df=None, included_subreddits = None, topN = 500):
-    print(outfile)
-    tfidf_ds = ds.dataset(tfidf_path)
-    tfidf_ds = tfidf_ds.to_table(columns=["week"])
-    batches = tfidf_ds.to_batches()
-
-    with Pool(cpu_count()) as pool:
-        weeks = set(chain( * pool.imap_unordered(pull_weeks,batches)))
-
-    weeks = sorted(weeks)
-    # do this step in parallel if we have the memory for it.
-    # should be doable with pool.map
-
-    print(f"computing weekly similarities")
-    week_similarities_helper = partial(_week_similarities,simfunc=column_similarities, tfidf_path=tfidf_path, term_colname=term_colname, outdir=outfile, min_df=min_df,max_df=max_df,included_subreddits=included_subreddits,topN=topN)
-
-    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, max_df=None, included_subreddits=None, topN=500):
-    return cosine_similarities_weekly('/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_authors.parquet',
-                                      outfile,
-                                      'author',
-                                      min_df,
-                                      max_df,
-                                      included_subreddits,
-                                      topN)
-
-def term_cosine_similarities_weekly(outfile, min_df=None, max_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,
-                                          max_df,
-                                          included_subreddits,
-                                          topN)
-
-if __name__ == "__main__":
-    fire.Fire({'authors':author_cosine_similarities_weekly,
-               'terms':term_cosine_similarities_weekly})

start_spark_and_run.sh

@@ -0,0 +1,21 @@
+
+#!/usr/bin/env bash
+
+# Script to start a spark cluster and run a script on klone
+source $SPARK_CONF_DIR/spark-env.sh
+echo "#!/usr/bin/bash" > job_script.sh
+echo "source ~/.bashrc" >> job_script.sh
+echo "export PYSPARK_PYTHON=python3" >> job.script.sh
+echo "export JAVA_HOME=/gscratch/comdata/local/open-jdk" >> job.script.sh
+echo "export SPARK_CONF_DIR=/gscratch/comdata/local/spark_config" >> job.script.sh
+echo "echo \$(hostname)" >> job_script.sh
+echo "source $SPARK_CONF_DIR/spark-env.sh" >> job.script.sh
+echo "start_spark_cluster.sh" >> job_script.sh
+echo "spark-submit --verbose --master spark://\$(hostname):$SPARK_MASTER_PORT $2 ${@:3}" >> job_script.sh
+echo "stop-all.sh" >> job_script.sh
+#echo "singularity instance stop --all" >> job_script.sh
+chmod +x job_script.sh
+
+let "cpus = $1 * 40" 
+salloc -p compute-bigmem -A comdata --nodes=$1 --time=48:00:00 -c 40 --mem=362G --exclusive srun -n1 job_script.sh
+

start_spark_cluster.sh

@@ -0,0 +1,26 @@
+#!/usr/bin/env bash
+nodes="$(scontrol show hostnames)"
+
+export SPARK_MASTER_HOST=$(hostname)
+echo $SPARK_MASTER_HOST
+# singularity instance stop spark-boss
+# rm -r $HOME/.singularity/instances/sing/$(hostname)/nathante/spark-boss
+ 
+# for node in $nodes
+# dol
+#     echo $node
+#     ssh $node "singularity instance stop --all -F"
+# done
+
+# singularity instance start /gscratch/comdata/users/nathante/cdsc_base.sif spark-boss
+#apptainer exec /gscratch/comdata/users/nathante/containers/nathante.sif
+start-master.sh 
+for node in $nodes
+do
+    # if [ "$node" != "$SPARK_BOSS" ]
+    # then
+    echo $node
+    ssh -t $node start_spark_worker.sh $SPARK_MASTER_HOST
+   # fi				
+done
+

start_spark_worker.sh

@@ -0,0 +1,18 @@
+#!/usr/bin/env bash
+# runs on worker node
+# instance_name=spark-worker-$(hostname)
+# echo $hostname
+# instance_url="instance://$instance_name"
+# singularity instance list
+# singularity instance stop -F "$instance_name"
+# singularity instance list
+# sleep 5
+# ls $HOME/.singularity/instances/sing/$(hostname)/nathante/$instance_name
+# rm -r $HOME/.singularity/instances/sing/$(hostname)/nathante/$instance_name
+# singularity instance start /gscratch/comdata/users/nathante/cdsc_base.sif $instance_name
+source /gscratch/comdata/env/cdsc_klone_bashrc
+source $SPARK_CONF_DIR/spark-env.sh
+echo $(which python3)
+echo $PYSPARK_PYTHON
+echo "start-worker.sh spark://$1:$SPARK_MASTER_PORT"
+start-worker.sh spark://$1:$SPARK_MASTER_PORT

timeseries/choose_clusters.py

@@ -1,96 +0,0 @@
-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

@@ -1,37 +0,0 @@
-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)

tsne_subreddit_fit.feather

@@ -1 +0,0 @@
-/annex/objects/SHA256E-s60874--d536adb0ec637fca262c4e1ec908dd8b4a5d1464047b583cd1a99cc6dba87191

visualization/Makefile

@@ -1,11 +0,0 @@
-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/data/term_affinityprop_10000.feather

@@ -1 +0,0 @@
-../../.git/annex/objects/Qk/wG/SHA256E-s145210--14a2ad6660d1e4015437eff556ec349dd10a115a4f96594152a29e83d00aa784/SHA256E-s145210--14a2ad6660d1e4015437eff556ec349dd10a115a4f96594152a29e83d00aa784

visualization/data/term_affinityprop_3000.feather

@@ -1 +0,0 @@
-../../.git/annex/objects/w7/2f/SHA256E-s44458--f1c5247775ecf06514a0ff9e523e944bc8fcd9d0fdb6f214cc1329b759d4354e/SHA256E-s44458--f1c5247775ecf06514a0ff9e523e944bc8fcd9d0fdb6f214cc1329b759d4354e

visualization/data/term_tsne_10000.feather

@@ -1 +0,0 @@
-../../.git/annex/objects/WX/v3/SHA256E-s190874--c2aea719f989dde297ca5f13371e156693c574e44acd9a0e313e5e3a3ad4b543/SHA256E-s190874--c2aea719f989dde297ca5f13371e156693c574e44acd9a0e313e5e3a3ad4b543

visualization/data/term_tsne_3000.feather

@@ -1 +0,0 @@
-../../.git/annex/objects/mq/2z/SHA256E-s58834--2e7b3ee11f47011fd9b34bddf8f1e788d35ab9c9e0bb6a1301b0b916135400cf/SHA256E-s58834--2e7b3ee11f47011fd9b34bddf8f1e788d35ab9c9e0bb6a1301b0b916135400cf

visualization/tsne_vis.py

@@ -1,175 +0,0 @@
-import pyarrow
-import altair as alt
-alt.data_transformers.disable_max_rows()
-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.interactive()
-    chart = chart.properties(width=1275,height=800)
-
-    return chart
-
-def viewport_plot(plot_data):
-    selector1 = alt.selection_interval(encodings=['x','y'],init={'x':(-65,65),'y':(-65,65)})
-    selectorx2 = alt.selection_interval(encodings=['x'],init={'x':(30,40)})
-    selectory2 = alt.selection_interval(encodings=['y'],init={'y':(-20,0)})
-
-    base = base_plot(plot_data)
-
-    viewport = base.mark_point(fillOpacity=0.2,opacity=0.2).encode(
-        alt.X('x',axis=alt.Axis(grid=False)),
-        alt.Y('y',axis=alt.Axis(grid=False)),
-    )
-   
-    viewport = viewport.properties(width=600,height=400)
-
-    viewport1 = viewport.add_selection(selector1)
-
-    viewport2 = viewport.encode(
-        alt.X('x',axis=alt.Axis(grid=False),scale=alt.Scale(domain=selector1)),
-        alt.Y('y',axis=alt.Axis(grid=False),scale=alt.Scale(domain=selector1))
-    )
-
-    viewport2 = viewport2.add_selection(selectorx2)
-    viewport2 = viewport2.add_selection(selectory2)
-
-    sr = base.encode(alt.X('x',axis=alt.Axis(grid=False),scale=alt.Scale(domain=selectorx2)),
-                     alt.Y('y',axis=alt.Axis(grid=False),scale=alt.Scale(domain=selectory2))
-    )
-
-
-    sr = sr.properties(width=1275,height=600)
-
-
-    chart = (viewport1 | viewport2) & sr
-
-
-    return chart
-
-def assign_cluster_colors(tsne_data, clusters, n_colors, n_neighbors = 4):
-    tsne_data = tsne_data.merge(clusters,on='subreddit')
-    
-    centroids = tsne_data.groupby('cluster').agg({'x':np.mean,'y':np.mean})
-
-    color_ids = np.arange(n_colors)
-
-    distances = np.empty(shape=(centroids.shape[0],centroids.shape[0]))
-
-    groups = tsne_data.groupby('cluster')
-    
-    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()
-
-    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))
-
-    for i in range(len(centroids)):
-        knn = indices[i]
-        knn_colors = color_assignments[knn]
-        available_colors = color_ids[list(set(color_ids) - set(knn_colors))]
-
-        if(len(available_colors) > 0):
-            color_assignments[i] = available_colors[0]
-        else:
-            raise Exception("Can't color this many neighbors with this many colors")
-
-
-    centroids = centroids.reset_index()
-    colors = centroids.loc[:,['cluster']]
-    colors['color'] = color_assignments
-
-    tsne_data = tsne_data.merge(colors,on='cluster')
-    return(tsne_data)
-
-def build_visualization(tsne_data, clusters, output):
-
-    # 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(output)
-
-    term_viewport_plot = viewport_plot(tsne_data)
-
-    term_viewport_plot.save(output.replace(".html","_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")