forked from D-Net/dnet-hadoop
Produce additional bip update specific files
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@ -15,6 +15,8 @@ from pyspark.sql.types import *
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# Import sql functions with shorthand alias
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# Import sql functions with shorthand alias
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import pyspark.sql.functions as F
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import pyspark.sql.functions as F
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from pyspark.sql.functions import max
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# from pyspark.sql.functions import udf
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# from pyspark.sql.functions import udf
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#################################################################################################
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#################################################################################################
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#################################################################################################
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#################################################################################################
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@ -127,6 +129,10 @@ for offset, input_file in enumerate(input_file_list):
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# Load file to dataframe
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# Load file to dataframe
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ranking_df = spark.read.schema(schema).option('delimiter', '\t').csv(input_file).repartition(num_partitions, 'id')
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ranking_df = spark.read.schema(schema).option('delimiter', '\t').csv(input_file).repartition(num_partitions, 'id')
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# Get max score
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max_score = ranking_df.select(max('score').alias('max')).collect()[0]['max']
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print ("Max Score for " + str(input_file) + " is " + str(max_score))
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# TESTING
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# TESTING
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# print ("Loaded df sample:")
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# print ("Loaded df sample:")
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@ -138,6 +144,15 @@ for offset, input_file in enumerate(input_file_list):
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output_file = output_file_list[offset]
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output_file = output_file_list[offset]
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print ("Writing to: " + output_file)
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print ("Writing to: " + output_file)
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doi_score_df.write.mode('overwrite').option('delimiter','\t').option('header',False).csv(output_file, compression='gzip')
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doi_score_df.write.mode('overwrite').option('delimiter','\t').option('header',False).csv(output_file, compression='gzip')
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# Creata another file for the bip update process
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ranking_df = ranking_df.select('id', 'score', F.lit(F.col('score')/max_score).alias('normalized_score'), 'class', F.col('class').alias('class_dup'))
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doi_score_df = synonym_df.join(ranking_df, ['id']).select('doi', 'score', 'normalized_score', 'class', 'class_dup').repartition(num_partitions, 'doi').cache()
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output_file = output_file.replace(".txt.gz", "_for_bip_update.txt.gz")
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print ("Writing bip update to: " + output_file)
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doi_score_df.write.mode('overwrite').option('delimiter','\t').option('header',False).csv(output_file, compression='gzip')
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# Free memory?
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# Free memory?
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ranking_df.unpersist(True)
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ranking_df.unpersist(True)
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