#!/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)