forked from D-Net/dnet-hadoop
Produce 5 classes of ranking scores
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@ -421,7 +421,7 @@ elif mode == 'json':
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# Score-specific dataframe - read inputs
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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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attrank_df = spark.read.schema(float_schema).option('delimiter', '\t').option('header',False).csv(attrank_dir).repartition(num_partitions, 'id')
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attrank_df = spark.read.schema(float_schema).option('delimiter', '\t').option('header',True).csv(attrank_dir).repartition(num_partitions, 'id')
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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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@ -601,7 +601,7 @@ elif mode == 'json-5-way':
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# Score-specific dataframe - read inputs
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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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attrank_df = spark.read.schema(float_schema).option('delimiter', '\t').option('header',False).csv(attrank_dir).repartition(num_partitions, 'id')
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attrank_df = spark.read.schema(float_schema).option('delimiter', '\t').option('header',True).csv(attrank_dir).repartition(num_partitions, 'id')
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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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@ -753,15 +753,36 @@ elif mode == 'json-5-way':
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# -------------------------------------------- #
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# Write json output
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# -------------------------------------------- #
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# Write json output - set the directory here
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output_dir = "/".join(pagerank_dir.split('/')[:-1])
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if graph_type == 'bip':
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output_dir = output_dir + '/bip_universe_doi_scores_5_classes/'
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output_dir = output_dir + '/bip_universe_doi_scores/'
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else:
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output_dir = output_dir + '/openaire_universe_scores_5_classes/'
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output_dir = output_dir + '/openaire_universe_scores/'
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# Write the dataframe
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print ("Writing output to: " + output_dir)
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results_df.write.mode('overwrite').option('header', False).text(output_dir, compression='gzip')
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# Rename the files to .json.gz now
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sc = spark.sparkContext
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URI = sc._gateway.jvm.java.net.URI
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Path = sc._gateway.jvm.org.apache.hadoop.fs.Path
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FileSystem = sc._gateway.jvm.org.apache.hadoop.fs.FileSystem
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# Get master prefix from input file path
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master_prefix = "/".join(pagerank_dir.split('/')[:5])
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fs = FileSystem.get(URI(master_prefix), sc._jsc.hadoopConfiguration())
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path = Path(output_dir)
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print ("Path is:" + path.toString())
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file_list = fs.listStatus(Path(output_dir))
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print ("Renaming files:")
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for f in file_list:
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initial_filename = f.getPath().toString()
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if "part" in initial_filename:
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print (initial_filename + " => " + initial_filename.replace(".txt.gz", ".json.gz"))
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fs.rename(Path(initial_filename), Path(initial_filename.replace(".txt.gz", ".json.gz")))
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# Close spark session
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spark.stop()
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@ -390,7 +390,7 @@
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--conf spark.yarn.historyServer.address=${spark2YarnHistoryServerAddress}
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--conf spark.eventLog.dir=${nameNode}${spark2EventLogDir}</spark-opts>
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<!-- Script arguments here -->
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<arg>json</arg>
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<arg>json-5-way</arg>
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<!-- Input files must be identified dynamically -->
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<arg>${nameNode}/${workflowDataDir}/${wf:actionData('get-file-names')['pr_file']}</arg>
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<arg>${nameNode}/${workflowDataDir}/${wf:actionData('get-file-names')['attrank_file']}</arg>
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