Add script for aggregating impact indicators at the project level

dhp-workflows/dhp-impact-indicators/src/main/resources/eu/dnetlib/dhp/oa/graph/impact_indicators/oozie_app/job.properties

@@ -90,3 +90,6 @@ oozie.wf.application.path=${wfAppPath}
 # Path where the final output should be?
 actionSetOutputPath=${workflowDataDir}/bip_actionsets/
 
+# The directory to store project impact indicators
+projectImpactIndicatorsOutput=${workflowDataDir}/project_indicators
+

dhp-workflows/dhp-impact-indicators/src/main/resources/eu/dnetlib/dhp/oa/graph/impact_indicators/oozie_app/projects_impact.py

@@ -0,0 +1,109 @@
+import sys
+from pyspark.sql import SparkSession
+from pyspark import SparkConf, SparkContext
+import pyspark.sql.functions as F
+from pyspark.sql.types import StringType, IntegerType, StructType, StructField
+
+if len(sys.argv) < 8:
+    print("Usage: projects_impact.py <relations_folder> <influence_file> <popularity_file> <cc_file> <impulse_file> <num_partitions> <output_dir>")
+    sys.exit(-1)
+
+appName = 'Project Impact Indicators'
+conf = SparkConf().setAppName(appName)
+sc = SparkContext(conf = conf)
+spark = SparkSession.builder.appName(appName).getOrCreate()
+sc.setLogLevel('OFF')
+
+# input parameters
+relations_fd = sys.argv[1]
+influence_fd = sys.argv[2]
+popularity_fd = sys.argv[3]
+cc_fd = sys.argv[4]
+impulse_fd = sys.argv[5]
+num_partitions = int(sys.argv[6])
+output_dir = sys.argv[7]
+
+# schema for impact indicator files
+impact_files_schema = StructType([
+    StructField('resultId', StringType(), False),
+    StructField('score', IntegerType(), False),
+    StructField('class', StringType(), False),
+])
+
+# list of impact indicators
+impact_indicators = [
+    ('influence', influence_fd, 'class'),
+    ('popularity', popularity_fd, 'class'),
+    ('impulse', impulse_fd, 'score'),
+    ('citation_count', cc_fd, 'score')
+]
+
+'''
+    * Read impact indicator file and return a dataframe with the following schema:
+    *   resultId: String
+    *   indicator_name: Integer
+'''
+def read_df(fd, indicator_name, column_name):
+    return spark.read.schema(impact_files_schema)\
+        .option('delimiter', '\t')\
+        .option('header', False)\
+        .csv(fd)\
+        .select('resultId', F.col(column_name).alias(indicator_name))\
+        .repartition(num_partitions, 'resultId')
+
+# Print dataframe schema, first 5 rows, and count
+def print_df(df):
+    df.show(50)
+    df.printSchema()
+    print(df.count())
+
+# Sets a null value to the column if the value is equal to the given value
+def set_class_value_to_null(column, value):
+    return F.when(column != value, column).otherwise(F.lit(None))
+
+# load and filter Project-to-Result relations
+print("Reading relations")
+relations = spark.read.json(relations_fd)\
+			.select(F.col('source').alias('projectId'), F.col('target').alias('resultId'), 'relClass', 'dataInfo.deletedbyinference', 'dataInfo.invisible')\
+			.where( (F.col('relClass') == 'produces') \
+				& (F.col('deletedbyinference') == "false")\
+                & (F.col('invisible') == "false"))\
+			.drop('deletedbyinference')\
+			.drop('invisible')\
+            .drop('relClass')\
+			.repartition(num_partitions, 'resultId')
+
+for indicator_name, fd, column_name in impact_indicators:
+
+    print("Reading {} '{}' field from file".format(indicator_name, column_name))
+    df = read_df(fd, indicator_name, column_name)
+
+    # sets a zero value to the indicator column if the value is C5
+    if (column_name == 'class'):
+        df = df.withColumn(indicator_name, F.when(F.col(indicator_name).isin("C5"), 0).otherwise(1))
+
+    # print_df(df)
+
+    print("Joining {} to relations".format(indicator_name))
+
+    # NOTE: we use inner join because we want to keep only the results that have an impact score
+    # also note that all impact scores have the same set of results
+    relations = relations.join(df, 'resultId', 'inner')\
+        .repartition(num_partitions, 'resultId')
+
+# uncomment to print non-null values count for each indicator
+# for indicator_name, fd, column_name in impact_indicators:
+#     print("Counting non null values for {}".format(indicator_name))
+#     print(relations.filter(F.col(indicator_name).isNotNull()).count())
+
+sum the impact indicator values for each project
+relations.groupBy('projectId')\
+    .agg(\
+        F.sum('influence').alias('influence'),\
+        F.sum('popularity').alias('popularity'),\
+        F.sum('impulse').alias('impulse'),\
+        F.sum('citation_count').alias('citation_count')\
+    )\
+    .write.mode("overwrite")\
+    .option("delimiter", "\t")\
+    .csv(output_dir, compression="gzip")

dhp-workflows/dhp-impact-indicators/src/main/resources/eu/dnetlib/dhp/oa/graph/impact_indicators/oozie_app/workflow.xml

@@ -15,6 +15,8 @@
 			<case to="map-openaire-to-doi">${resume eq "map-ids"}</case> 
 			<case to="map-scores-to-dois">${resume eq "map-scores"}</case> 
 			<case to="create-openaire-ranking-graph">${resume eq "start"}</case>
+			<case to="project-impact-indicators">${resume eq "projects-impact"}</case>
+
 			<!-- TODO: add action set creation here -->
 			<default to="create-openaire-ranking-graph" />
 		</switch>
@@ -334,7 +336,7 @@
 			<!-- This should give the machine/root of the hdfs -->
 			<name-node>${nameNode}</name-node>
             		
-            		<!-- Exec is needed foor shell comands - points to type of shell command -->
+            		<!-- Exec is needed for shell commands - points to type of shell command -->
             		<exec>/usr/bin/bash</exec>
             		<!-- name of script to run -->
             		<argument>get_ranking_files.sh</argument>
@@ -592,11 +594,63 @@
             <arg>--inputPath</arg><arg>${bipScorePath}</arg>
             <arg>--outputPath</arg><arg>${actionSetOutputPath}</arg>
         </spark>
-        <ok to="end"/>
+        <ok to="project-impact-indicators"/>
         <error to="actionset-creation-fail"/>
     </action>
 
+	<!-- PAGERANK here -->
+	<action name="project-impact-indicators">
+		<!-- This is required as a tag for spark jobs, regardless of programming language -->
+		<spark xmlns="uri:oozie:spark-action:0.2">
+			<!-- Is this yarn? Probably the answers are at the link serafeim sent me -->
+			<job-tracker>${jobTracker}</job-tracker>
+			<!-- This should give the machine/root of the hdfs, serafeim has provided a link with the required job properties -->
+			<name-node>${nameNode}</name-node>
+			<!-- using configs from an example on openaire -->
+			<master>yarn-cluster</master>
+			<mode>cluster</mode>
 
+			<!-- This is the name of our job -->
+			<name>Spark Pagerank</name>
+			<!-- Script name goes here -->
+			<jar>PageRank.py</jar>
+			<!-- spark configuration options: I've taken most of them from an example from dhp workflows / Master value stolen from sandro -->
+			<spark-opts>--executor-memory 18G --executor-cores 4 --driver-memory 10G
+				--master yarn
+				--deploy-mode cluster
+				--conf spark.sql.shuffle.partitions=7680
+				--conf spark.extraListeners=${spark2ExtraListeners}
+				--conf spark.sql.queryExecutionListeners=${spark2SqlQueryExecutionListeners}
+				--conf spark.yarn.historyServer.address=${spark2YarnHistoryServerAddress}
+				--conf spark.eventLog.dir=${nameNode}${spark2EventLogDir}</spark-opts>
+
+			<!-- Script arguments here -->
+
+			<!-- graph data folder from which to read relations -->
+			<arg>${openaireDataInput}/relations</arg>
+
+			<!-- input files with impact indicators for results	-->
+			<arg>${nameNode}/${workflowDataDir}/${wf:actionData('get-file-names')['pr_file']}</arg>
+			<arg>${nameNode}/${workflowDataDir}/${wf:actionData('get-file-names')['attrank_file']}</arg>
+			<arg>${nameNode}/${workflowDataDir}/${wf:actionData('get-file-names')['cc_file']}</arg>
+			<arg>${nameNode}/${workflowDataDir}/${wf:actionData('get-file-names')['impulse_file']}</arg>
+
+			<!-- number of partitions to be used on joins -->
+			<arg>7680</arg>
+
+			<arg>${projectImpactIndicatorsOutput}</arg>
+
+			<!-- This needs to point to the file on the hdfs i think -->
+			<file>${wfAppPath}/projects_impact.py#projects_impact.py</file>
+		</spark>
+
+		<!-- Do this after finishing okay -->
+		<ok to="end" />
+
+		<!-- Go there if we have an error -->
+		<error to="project-impact-indicators-fail" />
+
+	</action>
 
 	<!-- TODO: end the workflow-->
 		
@@ -644,4 +698,8 @@
 		<message>ActionSet creation failed, error message[${wf:errorMessage(wf:lastErrorNode())}]</message>
 	</kill>
 
+	<kill name="project-impact-indicators-fail">
+		<message>Calculating project impact indicators failed, error message[${wf:errorMessage(wf:lastErrorNode())}]</message>
+	</kill>
+
 </workflow-app>