X-Git-Url: https://code.communitydata.science/cdsc_reddit.git/blobdiff_plain/811a0d87c4d394c2c7849a613f6aec2d81e49138..07b0dff9bc0dae2ab6f7fb7334007a5269a512ad:/ngrams/top_comment_phrases.py

diff --git a/ngrams/top_comment_phrases.py b/ngrams/top_comment_phrases.py
deleted file mode 100755
index ff1c4f0..0000000
--- a/ngrams/top_comment_phrases.py
+++ /dev/null
@@ -1,69 +0,0 @@
-#!/usr/bin/env python3
-from pyspark.sql import functions as f
-from pyspark.sql import Window
-from pyspark.sql import SparkSession
-import numpy as np
-import fire
-from pathlib import Path
-
-
-def main(ngram_dir="/gscratch/comdata/output/reddit_ngrams"):
-    spark = SparkSession.builder.getOrCreate()
-    ngram_dir = Path(ngram_dir)
-    ngram_sample = ngram_dir / "reddit_comment_ngrams_10p_sample"
-    df = spark.read.text(str(ngram_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)
-
-    pwmi_dir = ngram_dir / "reddit_comment_ngrams_pwmi.parquet/"
-    df.write.parquet(str(pwmi_dir), mode='overwrite', compression='snappy')
-
-    df = spark.read.parquet(str(pwmi_dir))
-
-    df.write.csv(str(ngram_dir / "reddit_comment_ngrams_pwmi.csv/"),mode='overwrite',compression='none')
-
-    df = spark.read.parquet(str(pwmi_dir))
-    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(ngram_dir / "multiword_expressions.feather")
-    df.to_csv(ngram_dir / "multiword_expressions.csv")
-
-if __name__ == '__main__':
-    fire.Fire(main)