Merge pull request '9126-impact-indicators-wf-optimisation' (#471) from 9126-impact-indicators-wf-optimisation into beta
Reviewed-on: #471
This commit is contained in:
commit
4a9aeb6238
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@ -1,63 +0,0 @@
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#/usr/bin/bash
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# Read log files from ranking scripts and create a two-line file
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# with score limits for the various measures. To be used by Kleanthis
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attrank_file=$(ls *attrank*.log);
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pr_file=$(ls *pagerank*.log)
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ram_file=$(ls *ram*.log);
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cc_file=$(ls *cc*.log);
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impulse_file=$(ls *impulse*.log);
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echo
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echo "-----------------------------"
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echo "Attrank file:${attrank_file}";
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echo "PageRank file:${pr_file}";
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echo "RAM file:${ram_file}";
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echo "CC file:${cc_file}";
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echo "Impulse file:${impulse_file}";
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echo "-----------------------------"
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echo
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echo
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# output file will be called score_limits.csv
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echo -e "influence_top001\tinfluence_top01\tinfluence_top1\tinfluence_top10\tpopularity_top001\tpopularity_top01\tpopularity_top1\tpopularity_top10\timpulse_top001\timpulse_top01\timpulse_top1\timpulse_top10\tcc_top001\tcc_top01\tcc_top1\tcc_top10" > score_limits.csv
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# ---------------------------------------------------- #
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# Get respective score limits (we don't need RAM)
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inf_001=$(grep "^0.01%" ${pr_file} | cut -f 2);
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inf_01=$(grep "^0.1%" ${pr_file} | cut -f 2);
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inf_1=$(grep "^1%" ${pr_file} | cut -f 2);
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inf_10=$(grep "^10%" ${pr_file} | cut -f 2);
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echo "Influnence limits:"
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echo -e "${inf_001}\t${inf_01}\t${inf_1}\t${inf_10}";
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# ---------------------------------------------------- #
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pop_001=$(grep "^0.01%" ${attrank_file} | cut -f 2);
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pop_01=$(grep "^0.1%" ${attrank_file} | cut -f 2);
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pop_1=$(grep "^1%" ${attrank_file} | cut -f 2);
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pop_10=$(grep "^10%" ${attrank_file} | cut -f 2);
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echo "Popularity limits:";
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echo -e "${pop_001}\t${pop_01}\t${pop_1}\t${pop_10}";
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# ---------------------------------------------------- #
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imp_001=$(grep "^0.01%" ${impulse_file} | cut -f 2);
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imp_01=$(grep "^0.1%" ${impulse_file} | cut -f 2);
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imp_1=$(grep "^1%" ${impulse_file} | cut -f 2);
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imp_10=$(grep "^10%" ${impulse_file} | cut -f 2);
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echo "Popularity limits:";
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echo -e "${imp_001}\t${imp_01}\t${imp_1}\t${imp_10}";
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# ---------------------------------------------------- #
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cc_001=$(grep "^0.01%" ${cc_file} | cut -f 2);
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cc_01=$(grep "^0.1%" ${cc_file} | cut -f 2);
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cc_1=$(grep "^1%" ${cc_file} | cut -f 2);
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cc_10=$(grep "^10%" ${cc_file} | cut -f 2);
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echo "Popularity limits:";
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echo -e "${cc_001}\t${cc_01}\t${cc_1}\t${cc_10}";
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# ---------------------------------------------------- #
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echo -e "${inf_001}\t${inf_01}\t${inf_1}\t${inf_10}\t${pop_001}\t${pop_01}\t${pop_1}\t${pop_10}\t${imp_001}\t${imp_01}\t${imp_1}\t${imp_10}\t${cc_001}\t${cc_01}\t${cc_1}\t${cc_10}" >> score_limits.csv
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echo
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echo "score_limits.csv contents:"
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cat score_limits.csv
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echo;
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echo;
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@ -1,60 +0,0 @@
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import json
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import sys
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from pyspark.sql import SparkSession
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from pyspark import SparkConf, SparkContext
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if len(sys.argv) != 3:
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print("Usage: map_openaire_ids_to_dois.py <hdfs_src_dir> <hdfs_output_dir>")
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sys.exit(-1)
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conf = SparkConf().setAppName('BIP!: Map OpenAIRE IDs to DOIs')
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sc = SparkContext(conf = conf)
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spark = SparkSession.builder.appName('BIP!: Map OpenAIRE IDs to DOIs').getOrCreate()
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sc.setLogLevel('OFF')
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src_dir = sys.argv[1]
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output = sys.argv[2]
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# src_dir = "/tmp/beta_provision/graph/21_graph_cleaned/"
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# output = '/tmp/openaireid_to_dois/'
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def transform(doc):
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# get publication year from 'doc.dateofacceptance.value'
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dateofacceptance = doc.get('dateofacceptance', {}).get('value')
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year = 0
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if (dateofacceptance is not None):
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year = dateofacceptance.split('-')[0]
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# for each pid get 'pid.value' if 'pid.qualifier.classid' equals to 'doi'
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dois = [ pid['value'] for pid in doc.get('pid', []) if (pid.get('qualifier', {}).get('classid') == 'doi' and pid['value'] is not None)]
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num_dois = len(dois)
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# exlcude openaire ids that do not correspond to DOIs
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if (num_dois == 0):
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return None
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fields = [ doc['id'], str(num_dois), chr(0x02).join(dois), str(year) ]
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return '\t'.join([ v.encode('utf-8') for v in fields ])
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docs = None
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for result_type in ["publication", "dataset", "software", "otherresearchproduct"]:
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tmp = sc.textFile(src_dir + result_type).map(json.loads)
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if (docs is None):
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docs = tmp
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else:
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# append all result types in one RDD
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docs = docs.union(tmp)
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docs = docs.filter(lambda d: d.get('dataInfo', {}).get('deletedbyinference') == False and d.get('dataInfo', {}).get('invisible') == False)
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docs = docs.map(transform).filter(lambda d: d is not None)
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docs.saveAsTextFile(output)
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@ -1,168 +0,0 @@
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#!/usr/bin/python
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# This program reads the openaire to doi mapping from the ${synonymFolder} of the workflow
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# and uses this mapping to create doi-based score files in the format required by BiP! DB.
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# This is done by reading each openaire-id based ranking file and joining the openaire based
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# score and classes to all the corresponding dois.
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#################################################################################################
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# Imports
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import sys
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# Sparksession lib to communicate with cluster via session object
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from pyspark.sql import SparkSession
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# Import sql types to define schemas
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from pyspark.sql.types import *
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# Import sql functions with shorthand alias
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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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#################################################################################################
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#################################################################################################
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# Clean up directory name - no longer needed in final workflow version
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'''
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def clean_directory_name(dir_name):
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# We have a name with the form *_bip_universe<digits>_* or *_graph_universe<digits>_*
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# and we need to keep the parts in *
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dir_name_parts = dir_name.split('_')
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dir_name_parts = [part for part in dir_name_parts if ('bip' not in part and 'graph' not in part and 'universe' not in part and 'from' not in part)]
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dir_name = dir_name.replace("openaire_id_graph", "openaire_ids")
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clean_name = dir_name + ".txt.gz"
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# clean_name = '_'.join(dir_name_parts)
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# if '_ids' not in clean_name:
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# clean_name = clean_name.replace('id_', 'ids_')
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# clean_name = clean_name.replace('.txt', '')
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# clean_name = clean_name.replace('.gz', '')
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# if 'openaire_ids_' in clean_name:
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# clean_name = clean_name.replace('openaire_ids_', '')
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# clean_name = clean_name + '.txt.gz'
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# else:
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# clean_name = clean_name + '.txt.gz'
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return clean_name
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'''
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#################################################################################################
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if len(sys.argv) < 3:
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print ("Usage: ./map_scores_to_dois.py <synonym_folder> <num_partitions> <score_file_1> <score_file_2> <...etc...>")
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sys.exit(-1)
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# Read arguments
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synonyms_folder = sys.argv[1]
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num_partitions = int(sys.argv[2])
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input_file_list = [argument.replace("_openaire_id_graph", "").replace("_openaire_id_graph_", "") + "_openaire_ids.txt.gz" for argument in sys.argv[3:]]
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# input_file_list = [clean_directory_name(item) for item in input_file_list]
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# Prepare output specific variables
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output_file_list = [item.replace("_openaire_ids", "") for item in input_file_list]
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output_file_list = [item + ".txt.gz" if not item.endswith(".txt.gz") else item for item in output_file_list]
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# --- INFO MESSAGES --- #
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print ("\n\n----------------------------")
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print ("Mpping openaire ids to DOIs")
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print ("Reading input from: " + synonyms_folder)
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print ("Num partitions: " + str(num_partitions))
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print ("Input files:" + " -- ".join(input_file_list))
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print ("Output files: " + " -- ".join(output_file_list))
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print ("----------------------------\n\n")
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#######################################################################################
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# We weill define the following schemas:
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# --> the schema of the openaire - doi mapping file [string - int - doi_list] (the separator of the doi-list is a non printable character)
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# --> a schema for floating point ranking scores [string - float - string] (the latter string is the class)
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# --> a schema for integer ranking scores [string - int - string] (the latter string is the class)
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float_schema = StructType([
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StructField('id', StringType(), False),
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StructField('score', FloatType(), False),
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StructField('class', StringType(), False)
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])
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int_schema = StructType([
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StructField('id', StringType(), False),
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StructField('score', IntegerType(), False),
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StructField('class', StringType(), False)
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])
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# This schema concerns the output of the file
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# containing the number of references of each doi
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synonyms_schema = StructType([
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StructField('id', StringType(), False),
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StructField('num_synonyms', IntegerType(), False),
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StructField('doi_list', StringType(), False),
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])
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#######################################################################################
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# Start spark session
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spark = SparkSession.builder.appName('Map openaire scores to DOIs').getOrCreate()
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# Set Log Level for spark session
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spark.sparkContext.setLogLevel('WARN')
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#######################################################################################
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# MAIN Program
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# Read and repartition the synonym folder - also cache it since we will need to perform multiple joins
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synonym_df = spark.read.schema(synonyms_schema).option('delimiter', '\t').csv(synonyms_folder)
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synonym_df = synonym_df.select('id', F.split(F.col('doi_list'), chr(0x02)).alias('doi_list'))
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synonym_df = synonym_df.select('id', F.explode('doi_list').alias('doi')).repartition(num_partitions, 'id').cache()
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# TESTING
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# print ("Synonyms: " + str(synonym_df.count()))
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# print ("DF looks like this:" )
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# synonym_df.show(1000, False)
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print ("\n\n-----------------------------")
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# Now we need to join the score files on the openaire-id with the synonyms and then keep
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# only doi - score - class and write this to the output
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for offset, input_file in enumerate(input_file_list):
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print ("Mapping scores from " + input_file)
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# Select correct schema
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schema = int_schema
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if "attrank" in input_file.lower() or "pr" in input_file.lower() or "ram" in input_file.lower():
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schema = float_schema
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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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# 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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# print ("Loaded df sample:")
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# ranking_df.show(1000, False)
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# Join scores to synonyms and keep required fields
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doi_score_df = synonym_df.join(ranking_df, ['id']).select('doi', 'score', 'class').repartition(num_partitions, 'doi').cache()
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# Write output
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output_file = output_file_list[offset]
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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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# 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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ranking_df.unpersist(True)
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print ("-----------------------------")
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print ("\n\nFinished!\n\n")
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@ -17,10 +17,6 @@
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<name>openaireGraphInputPath</name>
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<value>${nameNode}/${workingDir}/openaire_id_graph</value>
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</property>
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<property>
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<name>synonymFolder</name>
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<value>${nameNode}/${workingDir}/openaireid_to_dois/</value>
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</property>
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<property>
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<name>checkpointDir</name>
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<value>${nameNode}/${workingDir}/check/</value>
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@ -32,29 +28,34 @@
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</configuration>
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</global>
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<!-- start using a decision node, so as to determine from which point onwards a job will continue -->
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<!-- Start using a decision node, to determine from which point onwards a job will continue -->
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<start to="entry-point-decision" />
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<decision name="entry-point-decision">
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<switch>
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<!-- The default will be set as the normal start, a.k.a. get-doi-synonyms -->
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<!-- If any different condition is set, go to the corresponding start -->
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<!-- Start from creating the citation network (i.e., normal execution should start from here) -->
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<case to="create-openaire-ranking-graph">${wf:conf('resume') eq "start"}</case>
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<!-- Different citation-based impact indicators are computed -->
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<case to="spark-cc">${wf:conf('resume') eq "cc"}</case>
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<case to="spark-ram">${wf:conf('resume') eq "ram"}</case>
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<case to="spark-impulse">${wf:conf('resume') eq "impulse"}</case>
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<case to="spark-pagerank">${wf:conf('resume') eq "pagerank"}</case>
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<case to="spark-attrank">${wf:conf('resume') eq "attrank"}</case>
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<!-- <case to="iterative-rankings">${wf:conf('resume') eq "rankings-iterative"}</case> -->
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<case to="get-file-names">${wf:conf('resume') eq "format-results"}</case>
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<case to="map-openaire-to-doi">${wf:conf('resume') eq "map-ids"}</case>
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<case to="map-scores-to-dois">${wf:conf('resume') eq "map-scores"}</case>
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<case to="create-openaire-ranking-graph">${wf:conf('resume') eq "start"}</case>
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<!-- Aggregation of impact scores on the project level -->
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<!-- Format the results appropriately before transforming them to action sets -->
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<case to="get-file-names">${wf:conf('resume') eq "format-results"}</case>
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<!-- Aggregation of impact scores on the project level -->
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<case to="project-impact-indicators">${wf:conf('resume') eq "projects-impact"}</case>
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<!-- Create action sets -->
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<case to="create-actionset">${wf:conf('resume') eq "create-actionset"}</case>
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||||
<!-- The default will be set as the normal start, a.k.a. create-openaire-ranking-graph -->
|
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<default to="create-openaire-ranking-graph" />
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</switch>
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</decision>
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@ -295,18 +296,11 @@
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<capture-output/>
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</shell>
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||||
<ok to="format-result-files" />
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<ok to="format-json-files" />
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||||
<error to="filename-getting-error" />
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||||
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||||
</action>
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||||
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||||
<!-- Now we will run in parallel the formatting of ranking files for BiP! DB and openaire (json files) -->
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||||
<fork name="format-result-files">
|
||||
<path start="format-bip-files"/>
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||||
<path start="format-json-files"/>
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||||
</fork>
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||||
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||||
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||||
<!-- Format json files -->
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||||
<!-- Two parts: a) format files b) make the file endings .json.gz -->
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||||
<action name="format-json-files">
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||||
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@ -345,139 +339,8 @@
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<file>${wfAppPath}/format_ranking_results.py#format_ranking_results.py</file>
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</spark>
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||||
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||||
<ok to="join-file-formatting" />
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<error to="json-formatting-fail" />
|
||||
</action>
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||||
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||||
<!-- This is the second line of parallel workflow execution where we create the BiP! DB files -->
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||||
<action name="format-bip-files">
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||||
<!-- This is required as a tag for spark jobs, regardless of programming language -->
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||||
<spark xmlns="uri:oozie:spark-action:0.2">
|
||||
|
||||
<!-- using configs from an example on openaire -->
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||||
<master>yarn-cluster</master>
|
||||
<mode>cluster</mode>
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||||
|
||||
<!-- This is the name of our job -->
|
||||
<name>Format Ranking Results BiP! DB</name>
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||||
<!-- Script name goes here -->
|
||||
<jar>format_ranking_results.py</jar>
|
||||
<!-- spark configuration options: I've taken most of them from an example from dhp workflows / Master value stolen from sandro -->
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||||
|
||||
<spark-opts>
|
||||
--executor-memory=${sparkNormalExecutorMemory}
|
||||
--executor-cores=${sparkExecutorCores}
|
||||
--driver-memory=${sparkNormalDriverMemory}
|
||||
--conf spark.executor.memoryOverhead=${sparkNormalExecutorMemory}
|
||||
--conf spark.sql.shuffle.partitions=${sparkShufflePartitions}
|
||||
--conf spark.extraListeners=${spark2ExtraListeners}
|
||||
--conf spark.sql.queryExecutionListeners=${spark2SqlQueryExecutionListeners}
|
||||
--conf spark.yarn.historyServer.address=${spark2YarnHistoryServerAddress}
|
||||
--conf spark.eventLog.dir=${nameNode}${spark2EventLogDir}
|
||||
</spark-opts>
|
||||
|
||||
<!-- Script arguments here -->
|
||||
<arg>zenodo</arg>
|
||||
<!-- Input files must be identified dynamically -->
|
||||
<arg>${nameNode}/${workingDir}/${wf:actionData('get-file-names')['pr_file']}</arg>
|
||||
<arg>${nameNode}/${workingDir}/${wf:actionData('get-file-names')['attrank_file']}</arg>
|
||||
<arg>${nameNode}/${workingDir}/${wf:actionData('get-file-names')['cc_file']}</arg>
|
||||
<arg>${nameNode}/${workingDir}/${wf:actionData('get-file-names')['impulse_file']}</arg>
|
||||
<arg>${nameNode}/${workingDir}/${wf:actionData('get-file-names')['ram_file']}</arg>
|
||||
<!-- Num partitions -->
|
||||
<arg>${sparkShufflePartitions}</arg>
|
||||
<!-- Type of data to be produced [bip (dois) / openaire (openaire-ids) ] -->
|
||||
<arg>openaire</arg>
|
||||
<!-- This needs to point to the file on the hdfs i think -->
|
||||
<file>${wfAppPath}/format_ranking_results.py#format_ranking_results.py</file>
|
||||
</spark>
|
||||
|
||||
<ok to="join-file-formatting" />
|
||||
<error to="bip-formatting-fail" />
|
||||
</action>
|
||||
|
||||
<!-- Finish formatting jobs -->
|
||||
<join name="join-file-formatting" to="map-openaire-to-doi"/>
|
||||
|
||||
<!-- maps openaire ids to DOIs -->
|
||||
<action name="map-openaire-to-doi">
|
||||
<spark xmlns="uri:oozie:spark-action:0.2">
|
||||
|
||||
<!-- Delete previously created doi synonym folder -->
|
||||
<prepare>
|
||||
<delete path="${synonymFolder}"/>
|
||||
</prepare>
|
||||
|
||||
<master>yarn-cluster</master>
|
||||
<mode>cluster</mode>
|
||||
<name>Openaire-DOI synonym collection</name>
|
||||
<jar>map_openaire_ids_to_dois.py</jar>
|
||||
|
||||
<spark-opts>
|
||||
--executor-memory=${sparkHighExecutorMemory}
|
||||
--executor-cores=${sparkExecutorCores}
|
||||
--driver-memory=${sparkHighDriverMemory}
|
||||
--conf spark.executor.memoryOverhead=${sparkHighExecutorMemory}
|
||||
--conf spark.sql.shuffle.partitions=${sparkShufflePartitions}
|
||||
--conf spark.extraListeners=${spark2ExtraListeners}
|
||||
--conf spark.sql.queryExecutionListeners=${spark2SqlQueryExecutionListeners}
|
||||
--conf spark.yarn.historyServer.address=${spark2YarnHistoryServerAddress}
|
||||
--conf spark.eventLog.dir=${nameNode}${spark2EventLogDir}
|
||||
</spark-opts>
|
||||
|
||||
<!-- Script arguments here -->
|
||||
<arg>${openaireDataInput}/</arg>
|
||||
<!-- number of partitions to be used on joins -->
|
||||
<arg>${synonymFolder}</arg>
|
||||
|
||||
<file>${wfAppPath}/map_openaire_ids_to_dois.py#map_openaire_ids_to_dois.py</file>
|
||||
</spark>
|
||||
|
||||
<ok to="map-scores-to-dois" />
|
||||
<error to="synonym-collection-fail" />
|
||||
|
||||
</action>
|
||||
|
||||
<!-- mapping openaire scores to DOIs -->
|
||||
<action name="map-scores-to-dois">
|
||||
<!-- This is required as a tag for spark jobs, regardless of programming language -->
|
||||
<spark xmlns="uri:oozie:spark-action:0.2">
|
||||
|
||||
<!-- using configs from an example on openaire -->
|
||||
<master>yarn-cluster</master>
|
||||
<mode>cluster</mode>
|
||||
<name>Mapping Openaire Scores to DOIs</name>
|
||||
<jar>map_scores_to_dois.py</jar>
|
||||
|
||||
<spark-opts>
|
||||
--executor-memory=${sparkHighExecutorMemory}
|
||||
--executor-cores=${sparkExecutorCores}
|
||||
--driver-memory=${sparkHighDriverMemory}
|
||||
--conf spark.executor.memoryOverhead=${sparkHighExecutorMemory}
|
||||
--conf spark.sql.shuffle.partitions=${sparkShufflePartitions}
|
||||
--conf spark.extraListeners=${spark2ExtraListeners}
|
||||
--conf spark.sql.queryExecutionListeners=${spark2SqlQueryExecutionListeners}
|
||||
--conf spark.yarn.historyServer.address=${spark2YarnHistoryServerAddress}
|
||||
--conf spark.eventLog.dir=${nameNode}${spark2EventLogDir}
|
||||
</spark-opts>
|
||||
|
||||
<!-- Script arguments here -->
|
||||
<arg>${synonymFolder}</arg>
|
||||
<!-- Number of partitions -->
|
||||
<arg>${sparkShufflePartitions}</arg>
|
||||
<!-- The remaining input are the ranking files fproduced for bip db-->
|
||||
<arg>${nameNode}/${workingDir}/${wf:actionData('get-file-names')['pr_file']}</arg>
|
||||
<arg>${nameNode}/${workingDir}/${wf:actionData('get-file-names')['attrank_file']}</arg>
|
||||
<arg>${nameNode}/${workingDir}/${wf:actionData('get-file-names')['cc_file']}</arg>
|
||||
<arg>${nameNode}/${workingDir}/${wf:actionData('get-file-names')['impulse_file']}</arg>
|
||||
<arg>${nameNode}/${workingDir}/${wf:actionData('get-file-names')['ram_file']}</arg>
|
||||
|
||||
<file>${wfAppPath}/map_scores_to_dois.py#map_scores_to_dois.py</file>
|
||||
</spark>
|
||||
|
||||
<ok to="project-impact-indicators" />
|
||||
<error to="map-scores-fail" />
|
||||
|
||||
<error to="json-formatting-fail" />
|
||||
</action>
|
||||
|
||||
<action name="project-impact-indicators">
|
||||
|
|
@ -594,18 +457,6 @@
|
|||
<message>Error formatting json files, error message[${wf:errorMessage(wf:lastErrorNode())}]</message>
|
||||
</kill>
|
||||
|
||||
<kill name="bip-formatting-fail">
|
||||
<message>Error formatting BIP files, error message[${wf:errorMessage(wf:lastErrorNode())}]</message>
|
||||
</kill>
|
||||
|
||||
<kill name="synonym-collection-fail">
|
||||
<message>Synonym collection failed, error message[${wf:errorMessage(wf:lastErrorNode())}]</message>
|
||||
</kill>
|
||||
|
||||
<kill name="map-scores-fail">
|
||||
<message>Mapping scores to DOIs failed, error message[${wf:errorMessage(wf:lastErrorNode())}]</message>
|
||||
</kill>
|
||||
|
||||
<kill name="actionset-delete-fail">
|
||||
<message>Deleting output path for actionsets failed, error message[${wf:errorMessage(wf:lastErrorNode())}]</message>
|
||||
</kill>
|
||||
|
|
|
|||
Loading…
Reference in New Issue