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Add code for running tf-idf at the weekly level.

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Nate E TeBlunthuis 2020-12-01 22:54:48 -08:00
parent db5879d6c9
commit a60747292e
3 changed files with 118 additions and 23 deletions
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82
author_cosine_similarity.py
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@ -7,7 +7,7 @@ import pandas as pd
import fire
from itertools import islice
from pathlib import Path
from similarities_helper import cosine_similarities
from similarities_helper import cosine_similarities, prep_tfidf_entries, read_tfidf_matrix, column_similarities
spark = SparkSession.builder.getOrCreate()
conf = spark.sparkContext.getConf()
@ -31,49 +31,89 @@ https://stanford.edu/~rezab/papers/dimsum.pdf. If similarity_threshold=0 we get
where to output csv and feather outputs
'''
spark = SparkSession.builder.getOrCreate()
conf = spark.sparkContext.getConf()
print(outfile)
tfidf = spark.read.parquet('/gscratch/comdata/users/nathante/subreddit_tfidf_authors.parquet')
if included_subreddits is None:
included_subreddits = list(islice(open("/gscratch/comdata/users/nathante/cdsc-reddit/top_25000_subs_by_comments.txt"),topN))
included_subreddits = {s.strip('\n') for s in included_subreddits}
rankdf = pd.read_csv("/gscratch/comdata/users/nathante/cdsc-reddit/subreddits_by_num_comments.csv")
included_subreddits = set(rankdf.loc[rankdf.comments_rank <= topN,'subreddit'].values)
else:
included_subreddits = set(open(included_subreddits))
sim_dist, tfidf = cosine_similarities(tfidf, 'author', min_df, included_subreddits, similarity_threshold)
print("creating temporary parquet with matrix indicies")
tempdir = prep_tfidf_entries(tfidf, 'author', min_df, included_subreddits)
tfidf = spark.read.parquet(tempdir.name)
subreddit_names = tfidf.select(['subreddit','subreddit_id_new']).distinct().toPandas()
subreddit_names = subreddit_names.sort_values("subreddit_id_new")
subreddit_names['subreddit_id_new'] = subreddit_names['subreddit_id_new'] - 1
spark.stop()
print("loading matrix")
mat = read_tfidf_matrix(tempdir.name,'author')
print('computing similarities')
sims = column_similarities(mat)
del mat
sims = pd.DataFrame(sims.todense())
sims = sims.rename({i:sr for i, sr in enumerate(subreddit_names.subreddit.values)},axis=1)
sims['subreddit'] = subreddit_names.subreddit.values
p = Path(outfile)
output_feather = Path(str(p).replace("".join(p.suffixes), ".feather"))
output_csv = Path(str(p).replace("".join(p.suffixes), ".csv"))
output_parquet = Path(str(p).replace("".join(p.suffixes), ".parquet"))
sim_dist = sim_dist.entries.toDF()
sim_dist = sim_dist.repartition(1)
sim_dist.write.parquet(str(output_parquet),mode='overwrite',compression='snappy')
sims.to_feather(outfile)
tempdir.cleanup()
# print(outfile)
# tfidf = spark.read.parquet('/gscratch/comdata/users/nathante/subreddit_tfidf_authors.parquet')
# if included_subreddits is None:
# included_subreddits = list(islice(open("/gscratch/comdata/users/nathante/cdsc-reddit/top_25000_subs_by_comments.txt"),topN))
# included_subreddits = {s.strip('\n') for s in included_subreddits}
# else:
# included_subreddits = set(open(included_subreddits))
# sim_dist, tfidf = cosine_similarities(tfidf, 'author', min_df, included_subreddits, similarity_threshold)
# p = Path(outfile)
# output_feather = Path(str(p).replace("".join(p.suffixes), ".feather"))
# output_csv = Path(str(p).replace("".join(p.suffixes), ".csv"))
# output_parquet = Path(str(p).replace("".join(p.suffixes), ".parquet"))
# sim_dist = sim_dist.entries.toDF()
# sim_dist = sim_dist.repartition(1)
# sim_dist.write.parquet(str(output_parquet),mode='overwrite',compression='snappy')
#instead of toLocalMatrix() why not read as entries and put strait into numpy
sim_entries = pd.read_parquet(output_parquet)
# #instead of toLocalMatrix() why not read as entries and put strait into numpy
# sim_entries = pd.read_parquet(output_parquet)
df = tfidf.select('subreddit','subreddit_id_new').distinct().toPandas()
# df = tfidf.select('subreddit','subreddit_id_new').distinct().toPandas()
spark.stop()
df['subreddit_id_new'] = df['subreddit_id_new'] - 1
df = df.sort_values('subreddit_id_new').reset_index(drop=True)
df = df.set_index('subreddit_id_new')
# spark.stop()
# df['subreddit_id_new'] = df['subreddit_id_new'] - 1
# df = df.sort_values('subreddit_id_new').reset_index(drop=True)
# df = df.set_index('subreddit_id_new')
similarities = sim_entries.join(df, on='i')
similarities = similarities.rename(columns={'subreddit':"subreddit_i"})
similarities = similarities.join(df, on='j')
similarities = similarities.rename(columns={'subreddit':"subreddit_j"})
# similarities = sim_entries.join(df, on='i')
# similarities = similarities.rename(columns={'subreddit':"subreddit_i"})
# similarities = similarities.join(df, on='j')
# similarities = similarities.rename(columns={'subreddit':"subreddit_j"})
similarities.to_feather(output_feather)
similarities.to_csv(output_csv)
return similarities
# similarities.to_feather(output_feather)
# similarities.to_csv(output_csv)
# return similarities
if __name__ == '__main__':
fire.Fire(author_cosine_similarities)

53
similarities_helper.py
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@ -119,6 +119,59 @@ def cosine_similarities(tfidf, term_colname, min_df, included_subreddits, simila
return (sim_dist, tfidf)
def build_weekly_tfidf_dataset(df, include_subs, term_colname, tf_family=tf_weight.Norm05):
term = term_colname
term_id = term + '_id'
# aggregate counts by week. now subreddit-term is distinct
df = df.filter(df.subreddit.isin(include_subs))
df = df.groupBy(['subreddit',term,'week']).agg(f.sum('tf').alias('tf'))
max_subreddit_terms = df.groupby(['subreddit','week']).max('tf') # subreddits are unique
max_subreddit_terms = max_subreddit_terms.withColumnRenamed('max(tf)','sr_max_tf')
df = df.join(max_subreddit_terms, on=['subreddit','week'])
df = df.withColumn("relative_tf", df.tf / df.sr_max_tf)
# group by term. term is unique
idf = df.groupby([term,'week']).count()
N_docs = df.select(['subreddit','week']).distinct().groupby(['week']).agg(f.count("subreddit").alias("subreddits_in_week"))
idf = idf.join(N_docs, on=['week'])
# add a little smoothing to the idf
idf = idf.withColumn('idf',f.log(idf.subreddits_in_week) / (1+f.col('count'))+1)
# collect the dictionary to make a pydict of terms to indexes
terms = idf.select([term,'week']).distinct() # terms are distinct
terms = terms.withColumn(term_id,f.row_number().over(Window.partitionBy('week').orderBy(term))) # term ids are distinct
# make subreddit ids
subreddits = df.select(['subreddit','week']).distinct()
subreddits = subreddits.withColumn('subreddit_id',f.row_number().over(Window.partitionBy("week").orderBy("subreddit")))
df = df.join(subreddits,on=['subreddit','week'])
# map terms to indexes in the tfs and the idfs
df = df.join(terms,on=[term,'week']) # subreddit-term-id is unique
idf = idf.join(terms,on=[term,'week'])
# join on subreddit/term to create tf/dfs indexed by term
df = df.join(idf, on=[term_id, term,'week'])
# agg terms by subreddit to make sparse tf/df vectors
if tf_family == tf_weight.MaxTF:
df = df.withColumn("tf_idf", df.relative_tf * df.idf)
else: # tf_fam = tf_weight.Norm05
df = df.withColumn("tf_idf", (0.5 + 0.5 * df.relative_tf) * df.idf)
return df
def build_tfidf_dataset(df, include_subs, term_colname, tf_family=tf_weight.Norm05):
term = term_colname

6
tfidf_authors.py
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@ -1,12 +1,14 @@
from pyspark.sql import SparkSession
from similarities_helper import build_tfidf_dataset
import pandas as pd
spark = SparkSession.builder.getOrCreate()
df = spark.read.parquet("/gscratch/comdata/users/nathante/reddit_tfidf_test_authors.parquet_temp")
include_subs = set(open("/gscratch/comdata/users/nathante/cdsc-reddit/top_25000_subs_by_comments.txt"))
include_subs = {s.strip('\n') for s in include_subs}
include_subs = pd.read_csv("/gscratch/comdata/users/nathante/cdsc-reddit/subreddits_by_num_comments.csv")
#include_subs = set(include_subs.loc[include_subs.comments_rank < 300]['subreddit'])
# remove [deleted] and AutoModerator (TODO remove other bots)
df = df.filter(df.author != '[deleted]')