59 lines
2.5 KiB
Python
59 lines
2.5 KiB
Python
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")
|