forked from D-Net/dnet-hadoop
Merge branch '8172_impact_indicators_workflow' of https://code-repo.d4science.org/D-Net/dnet-hadoop into 8172_impact_indicators_workflow
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commit
c64e5e588f
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@ -213,7 +213,10 @@ if mode == 'bip':
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cc_dir = sys.argv[4]
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impulse_dir = sys.argv[5]
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ram_dir = sys.argv[6]
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refs_dir = sys.argv[7]
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# NOTE: This was used initial, but @Serafeim told me to remove it since we don't get doi-doi referencew anymore
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# In case of emergency, bring this back
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# refs_dir = sys.argv[7]
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# Score-specific dataframe
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pagerank_df = spark.read.schema(float_schema).option('delimiter', '\t').option('header',True).csv(pagerank_dir).repartition(num_partitions, 'id')
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@ -221,7 +224,7 @@ if mode == 'bip':
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cc_df = spark.read.schema(int_schema).option('delimiter', '\t').option('header',True).csv(cc_dir).repartition(num_partitions, 'id')
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impulse_df = spark.read.schema(int_schema).option('delimiter', '\t').option('header',True).csv(impulse_dir).repartition(num_partitions, 'id')
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ram_df = spark.read.schema(float_schema).option('delimiter', '\t').option('header', True).csv(ram_dir).repartition(num_partitions, 'id')
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refs_df = spark.read.schema(refs_schema).option('delimiter', '\t').option('header',True).csv(refs_dir).repartition(num_partitions, 'id')
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# refs_df = spark.read.schema(refs_schema).option('delimiter', '\t').option('header',True).csv(refs_dir).repartition(num_partitions, 'id')
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# ----------- TESTING CODE --------------- #
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# pagerank_entries = pagerank_df.count()
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@ -258,9 +261,10 @@ if mode == 'bip':
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.select(results_df.id, 'pagerank', 'pagerank_normalized', 'attrank', 'attrank_normalized', 'cc', 'cc_normalized',\
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'3-cc', '3-cc_normalized', F.col('score').alias('ram'))
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# Add references
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results_df = results_df.join(refs_df, ['id']).select(results_df.id, 'pagerank', 'pagerank_normalized', 'attrank', 'attrank_normalized', \
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'cc', 'cc_normalized', '3-cc', '3-cc_normalized', 'ram', 'num_refs')
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# Add references - THIS WAS REMOVED SINCE WE DON't GET DOI REFERENCES
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# In case of emergency bring back
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# results_df = results_df.join(refs_df, ['id']).select(results_df.id, 'pagerank', 'pagerank_normalized', 'attrank', 'attrank_normalized', \
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# 'cc', 'cc_normalized', '3-cc', '3-cc_normalized', 'ram', 'num_refs')
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# Write resulting dataframe to file
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output_dir = "/".join(pagerank_dir.split('/')[:-1])
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