70 lines
2.7 KiB
Python
Executable File
70 lines
2.7 KiB
Python
Executable File
#!/usr/bin/env python3
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from pyspark.sql import functions as f
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from pyspark.sql import Window
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from pyspark.sql import SparkSession
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import numpy as np
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import fire
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from pathlib import Path
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def main(ngram_dir="/gscratch/comdata/output/reddit_ngrams"):
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spark = SparkSession.builder.getOrCreate()
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ngram_dir = Path(ngram_dir)
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ngram_sample = ngram_dir / "reddit_comment_ngrams_10p_sample"
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df = spark.read.text(str(ngram_sample))
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df = df.withColumnRenamed("value","phrase")
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# count phrase occurrances
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phrases = df.groupby('phrase').count()
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phrases = phrases.withColumnRenamed('count','phraseCount')
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phrases = phrases.filter(phrases.phraseCount > 10)
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# count overall
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N = phrases.select(f.sum(phrases.phraseCount).alias("phraseCount")).collect()[0].phraseCount
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print(f'analyzing PMI on a sample of {N} phrases')
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logN = np.log(N)
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phrases = phrases.withColumn("phraseLogProb", f.log(f.col("phraseCount")) - logN)
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# count term occurrances
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phrases = phrases.withColumn('terms',f.split(f.col('phrase'),' '))
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terms = phrases.select(['phrase','phraseCount','phraseLogProb',f.explode(phrases.terms).alias('term')])
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win = Window.partitionBy('term')
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terms = terms.withColumn('termCount',f.sum('phraseCount').over(win))
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terms = terms.withColumnRenamed('count','termCount')
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terms = terms.withColumn('termLogProb',f.log(f.col('termCount')) - logN)
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terms = terms.groupBy(terms.phrase, terms.phraseLogProb, terms.phraseCount).sum('termLogProb')
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terms = terms.withColumnRenamed('sum(termLogProb)','termsLogProb')
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terms = terms.withColumn("phrasePWMI", f.col('phraseLogProb') - f.col('termsLogProb'))
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# join phrases to term counts
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df = terms.select(['phrase','phraseCount','phraseLogProb','phrasePWMI'])
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df = df.sort(['phrasePWMI'],descending=True)
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df = df.sortWithinPartitions(['phrasePWMI'],descending=True)
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pwmi_dir = ngram_dir / "reddit_comment_ngrams_pwmi.parquet/"
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df.write.parquet(str(pwmi_dir), mode='overwrite', compression='snappy')
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df = spark.read.parquet(str(pwmi_dir))
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df.write.csv(str(ngram_dir / "reddit_comment_ngrams_pwmi.csv/"),mode='overwrite',compression='none')
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df = spark.read.parquet(str(pwmi_dir))
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df = df.select('phrase','phraseCount','phraseLogProb','phrasePWMI')
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# 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.
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#
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df = df.filter(f.col('phraseCount') > 3500).filter(f.col("phrasePWMI")>3)
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df = df.toPandas()
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df.to_feather(ngram_dir / "multiword_expressions.feather")
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df.to_csv(ngram_dir / "multiword_expressions.csv")
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if __name__ == '__main__':
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fire.Fire(main)
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