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Refactor tfidf code to for code resuse.

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This commit is contained in:
Nate E TeBlunthuis 2020-11-10 13:18:19 -08:00
parent 772f3a8fbd
commit 5632a971c6
3 changed files with 129 additions and 73 deletions
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116
similarities_helper.py Normal file
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from pyspark.sql import Window
from pyspark.sql import functions as f
from enum import Enum
from pyspark.mllib.linalg.distributed import CoordinateMatrix
class tf_weight(Enum):
MaxTF = 1
Norm05 = 2
def cosine_similarities(tfidf, term_colname, min_df, included_subreddits, similarity_threshold):
term = term_colname
term_id = term + '_id'
term_id_new = term + '_id_new'
if min_df is None:
min_df = 0.1 * len(included_subreddits)
tfidf = tfidf.filter(f.col("subreddit").isin(included_subreddits))
tfidf = tfidf.cache()
# reset the subreddit ids
sub_ids = tfidf.select('subreddit_id').distinct()
sub_ids = sub_ids.withColumn("subreddit_id_new",f.row_number().over(Window.orderBy("subreddit_id")))
tfidf = tfidf.join(sub_ids,'subreddit_id')
# only use terms in at least min_df included subreddits
new_count = tfidf.groupBy(term_id).agg(f.count(term_id).alias('new_count'))
# new_count = new_count.filter(f.col('new_count') >= min_df)
tfidf = tfidf.join(new_count,term_id,how='inner')
# reset the term ids
term_ids = tfidf.select([term_id]).distinct()
term_ids = term_ids.withColumn(term_id_new,f.row_number().over(Window.orderBy(term_id)))
tfidf = tfidf.join(term_ids,term_id)
tfidf = tfidf.withColumnRenamed("tf_idf","tf_idf_old")
# tfidf = tfidf.withColumnRenamed("idf","idf_old")
# tfidf = tfidf.withColumn("idf",f.log(25000/f.col("count")))
tfidf = tfidf.withColumn("tf_idf", tfidf.relative_tf * tfidf.idf)
# step 1 make an rdd of entires
# sorted by (dense) spark subreddit id
# entries = tfidf.filter((f.col('subreddit') == 'asoiaf') | (f.col('subreddit') == 'gameofthrones') | (f.col('subreddit') == 'christianity')).select(f.col("term_id_new")-1,f.col("subreddit_id_new")-1,"tf_idf").rdd
n_partitions = int(len(included_subreddits)*2 / 5)
entries = tfidf.select(f.col(term_id_new)-1,f.col("subreddit_id_new")-1,"tf_idf").rdd.repartition(n_partitions)
# put like 10 subredis in each partition
# step 2 make it into a distributed.RowMatrix
coordMat = CoordinateMatrix(entries)
coordMat = CoordinateMatrix(coordMat.entries.repartition(n_partitions))
# this needs to be an IndexedRowMatrix()
mat = coordMat.toRowMatrix()
#goal: build a matrix of subreddit columns and tf-idfs rows
sim_dist = mat.columnSimilarities(threshold=similarity_threshold)
return (sim_dist, tfidf)
def build_tfidf_dataset(df, include_subs, term_colname, tf_family=tf_weight.Norm05):
term = term_colname
term_id = term + '_id'
# aggregate counts by week. now subreddit-term is distinct
df = df.filter(df.subreddit.isin(include_subs))
df = df.groupBy(['subreddit',term]).agg(f.sum('tf').alias('tf'))
max_subreddit_terms = df.groupby(['subreddit']).max('tf') # subreddits are unique
max_subreddit_terms = max_subreddit_terms.withColumnRenamed('max(tf)','sr_max_tf')
df = df.join(max_subreddit_terms, on='subreddit')
df = df.withColumn("relative_tf", df.tf / df.sr_max_tf)
# group by term. term is unique
idf = df.groupby([term]).count()
N_docs = df.select('subreddit').distinct().count()
# add a little smoothing to the idf
idf = idf.withColumn('idf',f.log(N_docs/(1+f.col('count')))+1)
# collect the dictionary to make a pydict of terms to indexes
terms = idf.select(term).distinct() # terms are distinct
terms = terms.withColumn(term_id,f.row_number().over(Window.orderBy(term))) # term ids are distinct
# make subreddit ids
subreddits = df.select(['subreddit']).distinct()
subreddits = subreddits.withColumn('subreddit_id',f.row_number().over(Window.orderBy("subreddit")))
df = df.join(subreddits,on='subreddit')
# map terms to indexes in the tfs and the idfs
df = df.join(terms,on=term) # subreddit-term-id is unique
idf = idf.join(terms,on=term)
# join on subreddit/term to create tf/dfs indexed by term
df = df.join(idf, on=[term_id, term])
# agg terms by subreddit to make sparse tf/df vectors
if tf_family == tf_weight.MaxTF:
df = df.withColumn("tf_idf", df.relative_tf * df.idf)
else: # tf_fam = tf_weight.Norm05
df = df.withColumn("tf_idf", (0.5 + 0.5 * df.relative_tf) * df.idf)
return df

46
tfidf_authors.py
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from pyspark.sql import functions as f
from pyspark.sql import SparkSession
from similarities_helper import build_tfidf_dataset
## TODO:need to exclude automoderator / bot posts.
## TODO:need to exclude better handle hyperlinks.
spark = SparkSession.builder.getOrCreate()
df = spark.read.parquet("/gscratch/comdata/users/nathante/reddit_tfidf_test_authors.parquet_temp/")
max_subreddit_week_authors = df.groupby(['subreddit','week']).max('tf')
max_subreddit_week_authors = max_subreddit_week_authors.withColumnRenamed('max(tf)','sr_week_max_tf')
df = spark.read.parquet("/gscratch/comdata/users/nathante/reddit_tfidf_test_authors.parquet_temp/part-00000-d61007de-9cbe-4970-857f-b9fd4b35b741-c000.snappy.parquet")
df = df.join(max_subreddit_week_authors, ['subreddit','week'])
include_subs = set(open("/gscratch/comdata/users/nathante/cdsc-reddit/top_25000_subs_by_comments.txt"))
include_subs = {s.strip('\n') for s in include_subs}
df = df.filter(df.author != '[deleted]')
df = df.filter(df.author != 'AutoModerator')
df = df.withColumn("relative_tf", df.tf / df.sr_week_max_tf)
df = build_tfidf_dataset(df, include_subs, 'author')
# group by term / week
idf = df.groupby(['author','week']).count()
df.cache()
idf = idf.withColumnRenamed('count','idf')
# output: term | week | df
#idf.write.parquet("/gscratch/comdata/users/nathante/reddit_tfidf_test_sorted_tf.parquet_temp",mode='overwrite',compression='snappy')
# collect the dictionary to make a pydict of terms to indexes
authors = idf.select('author').distinct()
authors = authors.withColumn('author_id',f.monotonically_increasing_id())
# map terms to indexes in the tfs and the idfs
df = df.join(authors,on='author')
idf = idf.join(authors,on='author')
# join on subreddit/term/week to create tf/dfs indexed by term
df = df.join(idf, on=['author_id','week','author'])
# agg terms by subreddit to make sparse tf/df vectors
df = df.withColumn("tf_idf",df.relative_tf / df.sr_week_max_tf)
df = df.groupby(['subreddit','week']).agg(f.collect_list(f.struct('author_id','tf_idf')).alias('tfidf_maps'))
df = df.withColumn('tfidf_vec', f.map_from_entries('tfidf_maps'))
# output: subreddit | week | tf/df
df.write.json('/gscratch/comdata/users/nathante/test_tfidf_authors.parquet',mode='overwrite',compression='snappy')
df.write.parquet('/gscratch/comdata/users/nathante/subreddit_tfidf_authors.parquet',mode='overwrite',compression='snappy')

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tfidf_comments.py
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from pyspark.sql import functions as f
from pyspark.sql import SparkSession
from pyspark.sql import Window
from similarities_helper import build_tfidf_dataset
## TODO:need to exclude automoderator / bot posts.
## TODO:need to exclude better handle hyperlinks.
@ -11,43 +12,6 @@ df = spark.read.parquet("/gscratch/comdata/users/nathante/reddit_tfidf_test.parq
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}
# aggregate counts by week. now subreddit-term is distinct
df = df.filter(df.subreddit.isin(include_subs))
df = df.groupBy(['subreddit','term']).agg(f.sum('tf').alias('tf'))
max_subreddit_terms = df.groupby(['subreddit']).max('tf') # subreddits are unique
max_subreddit_terms = max_subreddit_terms.withColumnRenamed('max(tf)','sr_max_tf')
df = df.join(max_subreddit_terms, on='subreddit')
df = df.withColumn("relative_tf", df.tf / df.sr_max_tf)
# group by term. term is unique
idf = df.groupby(['term']).count()
N_docs = df.select('subreddit').distinct().count()
idf = idf.withColumn('idf',f.log(N_docs/f.col('count')))
# collect the dictionary to make a pydict of terms to indexes
terms = idf.select('term').distinct() # terms are distinct
terms = terms.withColumn('term_id',f.row_number().over(Window.orderBy("term"))) # term ids are distinct
# make subreddit ids
subreddits = df.select(['subreddit']).distinct()
subreddits = subreddits.withColumn('subreddit_id',f.row_number().over(Window.orderBy("subreddit")))
df = df.join(subreddits,on='subreddit')
# map terms to indexes in the tfs and the idfs
df = df.join(terms,on='term') # subreddit-term-id is unique
idf = idf.join(terms,on='term')
# join on subreddit/term to create tf/dfs indexed by term
df = df.join(idf, on=['term_id','term'])
# agg terms by subreddit to make sparse tf/df vectors
df = df.withColumn("tf_idf", (0.5 + (0.5 * df.relative_tf) * df.idf))
df = build_tfidf_dataset(df, include_subs, 'term')
df.write.parquet('/gscratch/comdata/users/nathante/subreddit_tfidf.parquet',mode='overwrite',compression='snappy')