Revision as of 16:50, 9 March 2017

Prerequisites

IDE: Eclipse Java EE IDE for Web Developers. Version: 3.7+

We advice you to also follow this video:

http://i-marine.eu/Content/eTraining.aspx?id=e1777006-a08c-49ad-b2e6-c13e094f27d4

Hello World Algorithm (Maven Tutorial^[1]):

Step by Step

Let's start by creating a project using the eclipse IDE that is mavenized according to our indications. After having mavenized the project in eclipse you have to put dependencies.

Maven coordinates

The maven artifact coordinates are:

<dependency>
	<groupId>org.gcube.dataanalysis</groupId>
	<artifactId>ecological-engine</artifactId>
	<version>(1.6.1-SNAPSHOT,2.0.0-SNAPSHOT)</version>
</dependency>

Let's start creating a new call which implements a basic algorithm; it will be executed by the DataMiner. The next step is to extend a basic interface StandardLocalExternalAlgorithm. The following snippet shows unimplemented interface methods that we are going to fulfill.

public class SimpleAlgorithm extends StandardLocalExternalAlgorithm{
 
	@Override
	public void init() throws Exception {
		// TODO Auto-generated method stub		
	}
	@Override
	public String getDescription() {
		// TODO Auto-generated method stub
		return null;
	}
	@Override
	protected void process() throws Exception {
		// TODO Auto-generated method stub
 
	}
	@Override
	protected void setInputParameters() {
		// TODO Auto-generated method stub
 
	}
	@Override
	public void shutdown() {
		// TODO Auto-generated method stub		
	}
	@Override
	public StatisticalType getOutput() {
		return null;
	}
}

The init() is the initialization method. In this simple example we need to initialize the loging facility and we use the logger from the ecological engine library. In case the algorithm uses a database, we have to open its connection in this method. The shutdown() closes database connection. In the getDescription() method we add a simple description for the algorithm.

Customize input visualization

String input parameters

The user's input is obtained by calling from setInputParameters() the method addStringInput with following parameters:

name of the variable ;
description for the variable;
default value;

User input is retrieved using getInputParameter() passing name used as parameter into setInputParameters().

protected void setInputParameters() {
		addStringInput(NameOfVariable, "Description", "DefaultInput");
 
}

The input parameter will be automatically passed by DataMiner to the procedure. In particular, to process the method we can retrieve such parameter by name that we set in the addStringInput method.

@Override
protected void process() throws Exception {
....
String userInputValue = getInputParameter(NameOfVariable);
}

Combo box input parameter

In order to obtain a combo box we have to define a enumerator that contains the possible choices that could be selected in the combo box and you have to pass it to the method addEnumerateInput as follows:

public enum Enum {
FIRST_ENUM,
SECOND_ENUM
}
 
protected void setInputParameters() {
addEnumerateInput(Enum.values(), variableName, "Description",
					Enum.FIRST_ENUM.name());
}

addEnumerateInput parameters are respectively:

values of declared enumerator;
name of variable used to extract value insert by user;
description of value;
default value visualized in comboBox

Import input from the DataMiner database

User can be upload his data in the DataMiner "Access to the Data Space" Section. After the uploading of a file (for example csv file), it's possible to use uploaded data as input for an algorithm. In order to select the columns values of a table that is extrapolated from csv, an algorithm developer fulfills the methods in the following way:

@Override
protected void setInputParameters() {
List<TableTemplates> templates = new ArrayList<TableTemplates>();
templates.add(TableTemplates.GENERIC);
InputTable tinput = new InputTable(templates, "Table","Table Description");
ColumnTypesList columns = new ColumnTypesList("Table","Columns", "Selceted Columns Description", false);
inputs.add(tinput);
inputs.add(columns);
DatabaseType.addDefaultDBPars(inputs);
 
}
 
@Override
protected void process() throws Exception {
{
config.setParam("DatabaseDriver", "org.postgresql.Driver");
SessionFactory dbconnection = DatabaseUtils.initDBSession(config);
String[] columnlist = columnnames.split(AlgorithmConfiguration.getListSeparator());
List<Object> speciesList = DatabaseFactory.executeSQLQuery("select " + columnlist[0]+ " from " + tablename, dbconnection);
}

Advanced Input

It is possible to indicate spatial inputs or time/date inputs. The details for the definition of these dare are reported in the Advanced Input page .

Case of algorithms using databases

In order to use a database it is required to call, into setInputParameters(), the method addRemoteDatabaseInput(). An important step is to pass as first parameter the name of the Runtime Resource addressing the database. The DataMiner automatically retrieves thew following parameters from the runtime resource: url ,user and password. Into the process method, before database connection, url,user and password will be retrieve using getInputParameter. Each of them is retrieved using the name and passing it into addRemoteDatabaseInput as parameters.

@Override
protected void setInputParameters() {	
...	
addRemoteDatabaseInput("Obis2Repository", urlParameterName,userParameterName, passwordParameterName, "driver", "dialect");
 
@Override
protected void process() throws Exception {
...
 
String databaseJdbc = getInputParameter(urlParameterName);
String databaseUser = getInputParameter(userParameterName);
String databasePwd = getInputParameter(passwordParameterName);
 
connection = DriverManager.getConnection(databaseJdbc, databaseUser,databasePwd);
...
 
}

Customize output

The last step is to set and to specify output of procedure. For this purpose we override the method getOutput() which return StatisticalType. First output parameter we instantiate is a PrimitiveType object that wraps a string; so, we set type as string. We associate name and description to the output value. We can istantiate a second output as an another PrimitiveType We set them as a map which will keep the order of the parameter used to store both output. We add both the output object into the map.

getOutput() procedure which will invoke DataMiner to understand type of the output object and at this point in the ecological engine library the algorithm will be indexed with the name set in the file of property.

String Output

In ordert to have a string as output you have to create a PrimitiveType as follows:

@Override
public StatisticalType getOutput() {
….
PrimitiveType val = new PrimitiveType(String.class.getName(), myString , PrimitiveTypes.STRING, stringName, defaultValue);
return val;
 
}

Bar Chart Output

In order to create an Histogram Chart you have to fulfill a DafaultCategoryDataser object and use it to create chart

DefaultCategoryDataset dataset;
…
dataset.addValue(...);	
….
 
 
@Override
public StatisticalType getOutput() {
….
HashMap<String, Image> producedImages = new HashMap<String, Image>();
JFreeChart chart = HistogramGraph.createStaticChart(dataset);
Image image = ImageTools.toImage(chart.createBufferedImage(680, 420));
producedImages.put("Species Observations", image);
…
}

Timeseries Chart Output

In order to create a TimeSeries Chart you have to fulfill a DafaultCategoryDataser object and use it to create the chart. The second parameter of createStatiChart method is the format of time.

DefaultCategoryDataset dataset;
…
dataset.addValue(...);	
….
@Override
public StatisticalType getOutput() {
...
HashMap<String, Image> producedImages = new HashMap<String, Image>();
JFreeChart chart = TimeSeriesGraph.createStaticChart(dataset, "yyyy");
Image image = ImageTools.toImage(chart.createBufferedImage(680, 420));
producedImages.put("TimeSeries chart", image);
... 
}

File Output

In order to create a results file that user can download, algorithm developers have to add following code:

protected String fileName;
protected BufferedWriter out;
 
@Override
protected void process() throws Exception {
fileName = super.config.getPersistencePath() + "results.csv";
out = new BufferedWriter(new FileWriter(fileName));
out.write(results);
out.newLine();
}
 
@Override
public StatisticalType getOutput() {
...
PrimitiveType file = new PrimitiveType(File.class.getName(), new File(fileName), PrimitiveTypes.FILE, "Description ", "Default value");
map.put("Output",file);
...
}

Test the algorithm

This is a template example to test an algorithm from Eclipse. The same Factory exist for Clusterers, Evaluators, Modellers and Generators. Download the following folder https://goo.gl/r16rfF and put it locally to the code. For new algorithms just edit one among the Transducers, Clusterers, Evaluators, Modellers or Generators files, adding your class. Just edit only the file which is suited to the category of your agorithm.

package org.gcube.dataanalysis.ecoengine.test.regression;
 
import java.util.List;
 
import org.gcube.dataanalysis.ecoengine.configuration.AlgorithmConfiguration;
import org.gcube.dataanalysis.ecoengine.evaluation.bioclimate.InterpolateTables.INTERPOLATIONFUNCTIONS;
import org.gcube.dataanalysis.ecoengine.interfaces.ComputationalAgent;
import org.gcube.dataanalysis.ecoengine.interfaces.Transducerer;
import org.gcube.dataanalysis.ecoengine.processing.factories.TransducerersFactory;
 
public class TestTransducers {
 
public static void main(String[] args) throws Exception {
System.out.println("TEST 1");
List<ComputationalAgent> trans = null;
trans = TransducerersFactory.getTransducerers(testConfigLocal());
trans.get(0).init();
Regressor.process(trans.get(0));
trans = null;
}
 
private static AlgorithmConfiguration testConfigLocal() {
 
 AlgorithmConfiguration config = Regressor.getConfig();
 config.setAgent("OCCURRENCES_DUPLICATES_DELETER");
 
 config.setParam("longitudeColumn", "decimallongitude");
 config.setParam("latitudeColumn", "decimallatitude");
 config.setParam("recordedByColumn", "recordedby");
 config.setParam("scientificNameColumn", "scientificname");
 config.setParam("eventDateColumn", "eventdate");
 config.setParam("lastModificationColumn", "modified");
 config.setParam("OccurrencePointsTableName", "whitesharkoccurrences2");
 config.setParam("finalTableName", "whitesharkoccurrencesnoduplicates");
 config.setParam("spatialTolerance", "0.5");
 config.setParam("confidence", "80");
 
return config;
}
 
}

Properties File and Deploy

In order to deploy an algorithm we must create:

the jar corresponding to the eclipse Java project containing the algorithm;
a file of property containing the name you want the algorithm to be displayed on the GUI and the classpath to algorithm class. E.g. MY_ALGORITHM=org.gcube.cnr.Myalgorithm

You must provide these two files to the i-Marine team. They will move the algorithm onto a DataMiner instance and the interface will be automatically generated.

In the following example, inside the src/main/java folder, the package org.gcube.dataanalysis.myAlgorithms exists that contains the class SimpleAlgorithm implementing an algorithm. SIMPLE_ALGORITHM=org.gcube.dataanalysis.myrAlgorithms.SimpleAlgorithm

Complete Example with multiple outputs

public class AbsoluteSpeciesBarChartsAlgorithm  extends
StandardLocalExternalAlgorithm  {
	LinkedHashMap<String, StatisticalType> map = new LinkedHashMap<String, StatisticalType>();
	static String databaseName = "DatabaseName";
	static String userParameterName = "DatabaseUserName";
	static String passwordParameterName = "DatabasePassword";
	static String urlParameterName = "DatabaseURL";
	private String firstSpeciesNumber="Species";
	private String yearStart="Starting_year";
	private String yearEnd="Ending_year";
	private int speciesNumber;
	private DefaultCategoryDataset defaultcategorydataset;
	@Override
	public void init() throws Exception {
		AnalysisLogger.getLogger().debug("Initialization");		
	}
 
	@Override
	public String getDescription() {
		return "Algorithm returning bar chart of most observed species in a specific years range (with respect to the OBIS database)";
	}
 
	@Override
	protected void process() throws Exception {
		defaultcategorydataset = new DefaultCategoryDataset();
		String driverName = "org.postgresql.Driver";
		String tmp=getInputParameter(firstSpeciesNumber);
 
		speciesNumber = Integer.parseInt(tmp);
		Class driverClass = Class.forName(driverName);
		Driver driver = (Driver) driverClass.newInstance();
		String databaseJdbc = getInputParameter(urlParameterName);
		String year_start = getInputParameter(yearStart);
		String year_end = getInputParameter(yearEnd);
 
		String databaseUser = getInputParameter(userParameterName);
		String databasePwd = getInputParameter(passwordParameterName);
		Connection connection = null;
		connection = DriverManager.getConnection(databaseJdbc, databaseUser,
				databasePwd);
		Statement stmt = connection.createStatement();
		String query = "SELECT  tname, sum(count)AS count FROM public.count_species_per_year WHERE year::integer >="
				+ year_start
				+ "AND year::integer <="
				+ year_end
				+ "GROUP BY tname ORDER BY count desc;";
		ResultSet rs = stmt.executeQuery(query);
		int i =0;
		String s = "Species";
			while (rs.next()&& i<speciesNumber) {
 
				String tname = rs.getString("tname");
				String count = rs.getString("count");
				int countOcc=Integer.parseInt(count);
 
                                // First output (list of string)
				PrimitiveType val = new PrimitiveType(String.class.getName(), count, PrimitiveTypes.STRING, tname, tname);
				map.put(tname, val);	
				if(i<16)
				defaultcategorydataset.addValue(countOcc,s,tname);	
				else
					break;
				i++;
 
		}
		connection.close();
 
 
 
	}
 
	@Override
	protected void setInputParameters() {
		addStringInput(firstSpeciesNumber,
				"Number of shown species", "10");
		addStringInput(yearStart, "Starting year of observations",
				"1800");
		addStringInput(yearEnd, "Ending year of observations", "2020");
		addRemoteDatabaseInput("Obis2Repository", urlParameterName,
				userParameterName, passwordParameterName, "driver", "dialect");
 
 
	}
 
	@Override
	public void shutdown() {
		AnalysisLogger.getLogger().debug("Shutdown");		
	}
 
 
	@Override
	public StatisticalType getOutput() {
		PrimitiveType p = new PrimitiveType(Map.class.getName(), PrimitiveType.stringMap2StatisticalMap(outputParameters), PrimitiveTypes.MAP, "Discrepancy Analysis","");
		AnalysisLogger.getLogger().debug("MapsComparator: Producing Gaussian Distribution for the errors");	
		//build image:
		HashMap<String, Image> producedImages = new HashMap<String, Image>();
 
		JFreeChart chart = HistogramGraph.createStaticChart(defaultcategorydataset);
	     Image image = ImageTools.toImage(chart.createBufferedImage(680, 420));
	     producedImages.put("Species Observations", image);
 
		PrimitiveType images = new PrimitiveType(HashMap.class.getName(), producedImages, PrimitiveTypes.IMAGES, "ErrorRepresentation", "Graphical representation of the error spread");
 
		//end build image
		AnalysisLogger.getLogger().debug("Bar Charts Species Occurrences Produced");
		//collect all the outputs
 
		map.put("Result", p);
		map.put("Images", images);
 
		//generate a primitive type for the collection
		PrimitiveType output = new PrimitiveType(HashMap.class.getName(), map, PrimitiveTypes.MAP, "ResultsMap", "Results Map");
 
 
		return output;
	}
 
}

Integrating R Scripts

DataMiner (DM) supports R scripts integration. This section explains how to integrate R scripts that will be executed by one single powerful machine in sequential mode. The calculation will be distributed on one of the machines that make up the DataMiner system, and the DM will automatically account for multi-users requests management. This section does not deal with parallel processing enabled for the script, which will be discussed later.

In the Eclipse project, download the following configuration folder: http://goo.gl/bNKrZK Then add the following maven dependency:

<dependency>
   <groupId>org.gcube.dataanalysis</groupId>
   <artifactId>ecological-engine-smart-executor</artifactId>
   <version>[1.0.0-SNAPSHOT,2.0.0)</version>
</dependency>

Then copy an R script inside the cfg folder. The DM framework assumes that the R file (i) accepts an input file whose name is hard-coded in the script, (ii) produces an output file whose name is hard-coded in the script, (iii) requires an R context made up of user's variables, (iv) possibly requires custom adjustment to the code.

The DM framework facilitates the call to the script by adding context variables "on the fly" and managing multi-user synchronous calls. This mechanism is performed by generating new on-the-fly temporary R scripts for each user. The DM will be also responsible for distributing the script on one powerful machine. Required packages are assumed to be preinstalled on the backend system.

One example of an algorithm calling a complex interpolation model is the following:

package org.gcube.dataanalysis.executor.rscripts;
 
import java.io.File;
import java.util.HashMap;
import java.util.LinkedHashMap;
 
import org.gcube.contentmanagement.lexicalmatcher.utils.AnalysisLogger;
import org.gcube.dataanalysis.ecoengine.datatypes.PrimitiveType;
import org.gcube.dataanalysis.ecoengine.datatypes.StatisticalType;
import org.gcube.dataanalysis.ecoengine.datatypes.enumtypes.PrimitiveTypes;
import org.gcube.dataanalysis.ecoengine.interfaces.StandardLocalExternalAlgorithm;
import org.gcube.dataanalysis.executor.util.RScriptsManager;
 
public class SGVMS_Interpolation extends StandardLocalExternalAlgorithm {
 
	private static int maxPoints = 10000;
	public enum methodEnum { cHs, SL};
	RScriptsManager scriptmanager;
	String outputFile;
 
	@Override
	public void init() throws Exception {
		AnalysisLogger.getLogger().debug("Initializing SGVMS_Interpolation");
	}
 
	@Override
	public String getDescription() {
		return "An interpolation method relying on the implementation by the Study Group on VMS (SGVMS). The method uses two interpolation approached to simulate vessels points at a certain temporal resolution. The input is a file in TACSAT format uploaded on the DataMiner. The output is another TACSAT file containing interpolated points." +
				"The underlying R code has been extracted from the SGVM VMSTools framework. This algorithm comes after a feasibility study (http://goo.gl/risQre) which clarifies the features an e-Infrastructure adds to the original scripts. Limitation: the input will be processed up to "+maxPoints+" vessels trajectory points.";
	}
 
	@Override
	protected void process() throws Exception {
 
		status = 0;
		//instantiate the R Script executor
		scriptmanager = new RScriptsManager();
		//this is the script name
		String scriptName = "interpolateTacsat.r";
		//absolute path to the input, provided by the DM  
		String inputFile = config.getParam("InputFile");
 
		AnalysisLogger.getLogger().debug("Starting SGVM Interpolation-> Config path "+config.getConfigPath()+" Persistence path: "+config.getPersistencePath());
		//default input and outputs		
		String defaultInputFileInTheScript = "tacsat.csv";
		String defaultOutputFileInTheScript = "tacsat_interpolated.csv";
		//input parameters: represent the context of the script. Values will be assigned in the R environment.
		LinkedHashMap<String,String> inputParameters = new LinkedHashMap<String, String>();
		inputParameters.put("npoints",config.getParam("npoints"));
		inputParameters.put("interval",config.getParam("interval"));
		inputParameters.put("margin",config.getParam("margin"));
		inputParameters.put("res",config.getParam("res"));
		inputParameters.put("fm",config.getParam("fm"));
		inputParameters.put("distscale",config.getParam("distscale"));
		inputParameters.put("sigline",config.getParam("sigline"));
		inputParameters.put("minspeedThr",config.getParam("minspeedThr"));
		inputParameters.put("maxspeedThr",config.getParam("maxspeedThr"));
		inputParameters.put("headingAdjustment",config.getParam("headingAdjustment"));
		inputParameters.put("equalDist",config.getParam("equalDist").toUpperCase());
		//add static context variables
		inputParameters.put("st", "c(minspeedThr,maxspeedThr)");
		inputParameters.put("fast", "TRUE");
		inputParameters.put("method", "\""+config.getParam("method")+"\"");
 
		AnalysisLogger.getLogger().debug("Starting SGVM Interpolation-> Input Parameters: "+inputParameters);
		//if other code injection is required, put the strings to substitute as keys and the substituting ones as values
		HashMap<String,String> codeInjection = null;
		//force the script to produce an output file, otherwise generate an exception 
		boolean scriptMustReturnAFile = true;
		boolean uploadScriptOnTheInfrastructureWorkspace = false; //the DataMiner service will manage the upload
		AnalysisLogger.getLogger().debug("SGVM Interpolation-> Executing the script ");
		status = 10;
		//execute the script in multi-user mode
                scriptmanager.executeRScript(config, scriptName, inputFile, inputParameters, defaultInputFileInTheScript, defaultOutputFileInTheScript, codeInjection, scriptMustReturnAFile,uploadScriptOnTheInfrastructureWorkspace, config.getConfigPath());
		//assign the file path to an output variable for the DM
		outputFile = scriptmanager.currentOutputFileName;
		AnalysisLogger.getLogger().debug("SGVM Interpolation-> Output File is "+outputFile);
		status = 100;
	}
 
	@Override
	protected void setInputParameters() {
		//declare the input parameters the user will set: they will basically correspond to the R context
		inputs.add(new PrimitiveType(File.class.getName(), null, PrimitiveTypes.FILE, "InputFile", "Input file in TACSAT format. E.g. http://goo.gl/i16kPw"));
		addIntegerInput("npoints", "The number of pings or positions required between each real or actual vessel position or ping", "10");
		addIntegerInput("interval", "Average time in minutes between two adjacent datapoints", "120");
		addIntegerInput("margin", "Maximum deviation from specified interval to find adjacent datapoints (tolerance)", "10");
		addIntegerInput("res", "Number of points to use to create interpolation (including start and end point)", "100");
		addEnumerateInput(methodEnum.values(), "method","Set to cHs for cubic Hermite spline or SL for Straight Line interpolation", "cHs");
		addDoubleInput("fm", "The FM parameter in cubic interpolation", "0.5");
		addIntegerInput("distscale", "The DistScale parameter for cubic interpolation", "20");
		addDoubleInput("sigline", "The Sigline parameter in cubic interpolation", "0.2");
		addDoubleInput("minspeedThr", "A filter on the minimum speed to take into account for interpolation", "2");
		addDoubleInput("maxspeedThr", "A filter on the maximum speed to take into account for interpolation", "6");
		addIntegerInput("headingAdjustment", "Parameter to adjust the choice of heading depending on its own or previous point (0 or 1). Set 1 in case the heading at the endpoint does not represent the heading of the arriving vessel to that point but the departing vessel.", "0");
		inputs.add(new PrimitiveType(Boolean.class.getName(), null, PrimitiveTypes.BOOLEAN, "equalDist", "Whether the number of positions returned should be equally spaced or not", "true"));
	}
 
	@Override
	public StatisticalType getOutput() {
		//return the output file by the procedure to the DM
		PrimitiveType o = new PrimitiveType(File.class.getName(), new File(outputFile), PrimitiveTypes.FILE, "OutputFile", "Output file in TACSAT format.");
		return o;
	}
 
	@Override
	public void shutdown() {
		//in the case of forced shutdown, stop the R process
		if (scriptmanager!=null)
			scriptmanager.stop();
		System.gc();
	}
 
}

In order to test the above algorithm, just modify the "transducerers.properties" file inside the cfg folder by adding the following string:

SGVM_INTERPOLATION=org.gcube.dataanalysis.executor.rscripts.SGVMS_Interpolation

which will assign a name to the algorithm. Then a test class for this algorithm will be the following:

package org.gcube.dataanalysis.executor.tests;
 
import java.util.List;
 
import org.gcube.dataanalysis.ecoengine.configuration.AlgorithmConfiguration;
import org.gcube.dataanalysis.ecoengine.datatypes.PrimitiveType;
import org.gcube.dataanalysis.ecoengine.datatypes.StatisticalType;
import org.gcube.dataanalysis.ecoengine.interfaces.ComputationalAgent;
import org.gcube.dataanalysis.ecoengine.processing.factories.TransducerersFactory;
import org.gcube.dataanalysis.ecoengine.test.regression.Regressor;
 
public class TestSGVMInterpolation {
 
	public static void main(String[] args) throws Exception {
		// setup the configuration
		AlgorithmConfiguration config = new AlgorithmConfiguration();
		// set the path to the cfg folder and to the PARALLEL_PROCESSING folder
		config.setConfigPath("./cfg/");
		config.setPersistencePath("./PARALLEL_PROCESSING");
		//set the user's inputs. They will passed by the DM to the script in the following way:
		config.setParam("InputFile", "<absolute path to the file>/tacsatmini.csv"); //put the absolute path to the input file
		config.setParam("npoints", "10");
		config.setParam("interval", "120");
		config.setParam("margin", "10");
		config.setParam("res", "100");
		config.setParam("method", "SL");
		config.setParam("fm", "0.5");
		config.setParam("distscale", "20");
		config.setParam("sigline", "0.2");
		config.setParam("minspeedThr", "2");
		config.setParam("maxspeedThr", "6");
		config.setParam("headingAdjustment", "0");
		config.setParam("equalDist", "true");
 
		//set the scope and the user (optional for this test)
		config.setGcubeScope( "/gcube/devsec/devVRE");
		config.setParam("ServiceUserName", "test.user");
 
		//set the name of the algorithm to call, as is is in the transducerer.properties file
		config.setAgent("SGVM_INTERPOLATION");
 
		//recall the transducerer with the above name 
		List<ComputationalAgent> transducers = TransducerersFactory.getTransducerers(config);
		ComputationalAgent transducer =transducers.get(0);
		//init the transducerer
		transducer.init();
		//start the process
		Regressor.process(transducer);
		//retrieve the output
		StatisticalType st = transducer.getOutput();
		System.out.println("st:"+((PrimitiveType)st).getContent());
	}
 
}

Enabling Cloud Computing for R Scripts

In the case of a process running in the Infrastructure and using Cloud computing, you have to extend the ActorNode class, define how to setup the process, chunkize the input space, run the script and perform the Reduce phase. These steps are performed using the following methods respectively:

setup(AlgorithmConfiguration config)
getNumberOfRightElements()
getNumberOfLeftElements()
postProcess(boolean manageDuplicates, boolean manageFault)

package org.gcube.dataanalysis.executor.nodes.algorithms;
 
public class LWR extends ActorNode {
 
	public String destinationTable;
	public String destinationTableLabel;
	public String originTable;
	public String familyColumn;
	public int count;
 
	public float status = 0;
 
	//specify the kind of parallel process: the following performs a matrix-to-matrix comparison
	@Override
	public ALG_PROPS[] getProperties() {
		ALG_PROPS[] p = { ALG_PROPS.PHENOMENON_VS_PARALLEL_PHENOMENON };
		return p;
	}
 
	@Override
	public String getName() {
		return "LWR";
	}
 
	@Override
	public String getDescription() {
		return "An algorithm to estimate Length-Weight relationship parameters for marine species, using Bayesian methods. Runs an R procedure. Based on the Cube-law theory.";
	}
 
	@Override
	public List<StatisticalType> getInputParameters() {
		List<TableTemplates> templateLWRInput = new ArrayList<TableTemplates>();
		templateLWRInput.add(TableTemplates.GENERIC);
		InputTable p1 = new InputTable(templateLWRInput, "LWR_Input", "Input table containing taxa and species information", "lwr");
		ColumnType p3 = new ColumnType("LWR_Input", "FamilyColumn", "The column containing Family information", "Family", false);
		ServiceType p4 = new ServiceType(ServiceParameters.RANDOMSTRING, "RealOutputTable", "name of the resulting table", "lwr_");
		PrimitiveType p2 = new PrimitiveType(String.class.getName(), null, PrimitiveTypes.STRING, "TableLabel", "Name of the table which will contain the model output", "lwrout");
 
		List<StatisticalType> parameters = new ArrayList<StatisticalType>();
		parameters.add(p1);
		parameters.add(p3);
		parameters.add(p2);
		parameters.add(p4);
 
		DatabaseType.addDefaultDBPars(parameters);
 
		return parameters;
	}
 
	@Override
	public StatisticalType getOutput() {
		List<TableTemplates> template = new ArrayList<TableTemplates>();
		template.add(TableTemplates.GENERIC);
		OutputTable p = new OutputTable(template, destinationTableLabel, destinationTable, "Output lwr table");
		return p;
	}
 
	@Override
	public void initSingleNode(AlgorithmConfiguration config) {
 
	}
 
	@Override
	public float getInternalStatus() {
		return status;
	}
 
	private static String scriptName = "UpdateLWR_4.R";
 
	//the inputs delivered by the DM are: the index and number of elements to take from the left and right tables, the indication on if the same requeste was yet asked to another worker node (in the case of errors), the sandobox folder in which the script will be executed, the configuration of the algorithm
	@Override
	public int executeNode(int leftStartIndex, int numberOfLeftElementsToProcess, int rightStartIndex, int numberOfRightElementsToProcess,  boolean duplicate, String sandboxFolder, String nodeConfigurationFileObject, String logfileNameToProduce) {
		String insertQuery = null;
		try {
			status = 0;
			//reconstruct the configuration
			AlgorithmConfiguration config = Transformations.restoreConfig(nodeConfigurationFileObject);
			config.setConfigPath(sandboxFolder);
			System.out.println("Initializing DB");
			//take the parameters and possibly initialize connection to the DB
			dbconnection = DatabaseUtils.initDBSession(config);
			destinationTableLabel = config.getParam("TableLabel");
			destinationTable = config.getParam("RealOutputTable");
			System.out.println("Destination Table: "+destinationTable);
			System.out.println("Destination Table Label: "+destinationTableLabel);
			originTable = config.getParam("LWR_Input");
			familyColumn = config.getParam("FamilyColumn");
			System.out.println("Origin Table: "+originTable);
 
			// take the families to process
			List<Object> families = DatabaseFactory.executeSQLQuery(DatabaseUtils.getDinstictElements(originTable, familyColumn, ""), dbconnection);
 
			// transform the families into a string
			StringBuffer familiesFilter = new StringBuffer();
			familiesFilter.append("Families <- Fam.All[");
 
			int end = rightStartIndex + numberOfRightElementsToProcess;
			//build the substitution string
			for (int i = rightStartIndex; i < end; i++) {
				familiesFilter.append("Fam.All == \"" + families.get(i) + "\"");
				if (i < end - 1)
					familiesFilter.append(" | ");
			}
			familiesFilter.append("]");
 
			//substitution to perform in the script
			String substitutioncommand = "sed -i 's/Families <- Fam.All[Fam.All== \"Acanthuridae\" | Fam.All == \"Achiridae\"]/" + familiesFilter + "/g' " + "UpdateLWR_Test2.R";
			System.out.println("Preparing for processing the families names: "+familiesFilter.toString());
 
			substituteInScript(sandboxFolder+scriptName,sandboxFolder+"UpdateLWR_Tester.R","Families <- Fam.All[Fam.All== \"Acanthuridae\" | Fam.All == \"Achiridae\"]",familiesFilter.toString());
			//for test only
 
			System.out.println("Creating local file from remote table");
			// download the table in csv format to feed the procedure
			DatabaseUtils.createLocalFileFromRemoteTable(sandboxFolder+"RF_LWR.csv", originTable, ",", config.getDatabaseUserName(),config.getDatabasePassword(),config.getDatabaseURL());
 
			String headers = "Subfamily,Family,Genus,Species,FBname,SpecCode,AutoCtr,Type,a,b,CoeffDetermination,Number,LengthMin,Score,BodyShapeI";
			System.out.println("Adding headers to the file");
 
			String headerscommand = "sed -i '1s/^/"+headers+"\\n/g' "+"RF_LWR2.csv";
			// substitute the string in the RCode
			addheader(sandboxFolder+"RF_LWR.csv",sandboxFolder+"RF_LWR2.csv",headers);
			System.out.println("Headers added");
			System.out.println("Executing R script " + "R --no-save < UpdateLWR_Tester.R");
			// run the R code: it can be alternatively made with the methods of the previous example
			Process process = Runtime.getRuntime().exec("R --no-save");
			BufferedWriter bw = new BufferedWriter(new OutputStreamWriter(process.getOutputStream()));
			bw.write("source('UpdateLWR_Tester.R')\n");
			bw.write("q()\n");
			bw.close();
			BufferedReader br = new BufferedReader(new InputStreamReader(process.getInputStream()));
			String line = br.readLine();
			System.out.println(line);
			while (line!=null){
				line = br.readLine();
				System.out.println(line);
			}
			process.destroy();
			System.out.println("Appending csv to table");
			// transform the output into table
			StringBuffer lines = readFromCSV("LWR_Test1.csv");
			insertQuery = DatabaseUtils.insertFromBuffer(destinationTable, columnNames, lines);
			DatabaseFactory.executeSQLUpdate(insertQuery, dbconnection);
			System.out.println("The procedure was successful");
			status = 1f;
		} catch (Exception e) {
			e.printStackTrace();
			System.out.println("warning: error in node execution " + e.getLocalizedMessage());
			System.out.println("Insertion Query: "+insertQuery);
			System.err.println("Error in node execution " + e.getLocalizedMessage());
			return -1;
		} finally {
			if (dbconnection != null)
				try {
					dbconnection.close();
				} catch (Exception e) {
				}
		}
		return 0;
	}
 
	//setup phase of the algorithm
	@Override
	public void setup(AlgorithmConfiguration config) throws Exception {
 
		destinationTableLabel = config.getParam("TableLabel");
		AnalysisLogger.getLogger().info("Table Label: "+destinationTableLabel);
		destinationTable = config.getParam("RealOutputTable");
		AnalysisLogger.getLogger().info("Uderlying Table Name: "+destinationTable);
		originTable = config.getParam("LWR_Input");
		AnalysisLogger.getLogger().info("Original Table: "+originTable);
		familyColumn = config.getParam("FamilyColumn");
		AnalysisLogger.getLogger().info("Family Column: "+familyColumn);
		haspostprocessed = false;
 
		AnalysisLogger.getLogger().info("Initializing DB Connection");
		dbconnection = DatabaseUtils.initDBSession(config);
		List<Object> families = DatabaseFactory.executeSQLQuery(DatabaseUtils.getDinstictElements(originTable, familyColumn, ""), dbconnection);
		count = families.size();
 
		//create the table were the script will write the output
		DatabaseFactory.executeSQLUpdate(String.format(createOutputTable, destinationTable), dbconnection);
		AnalysisLogger.getLogger().info("Destination Table Created! Addressing " + count + " species");
	}	
 
	@Override
	public int getNumberOfRightElements() {
		return count; //each Worker node has to get all the elements in the right table
	}
 
	@Override
	public int getNumberOfLeftElements() {
		return 1; //each Worker node has to get only one element in the left table
	}
 
	@Override
	public void stop() {
 
		//if has not postprocessed, then abort the computations by removing the database table
		if (!haspostprocessed){
			try{
				AnalysisLogger.getLogger().info("The procedure did NOT correctly postprocessed ....Removing Table "+destinationTable+" because of computation stop!");
				DatabaseFactory.executeSQLUpdate(DatabaseUtils.dropTableStatement(destinationTable), dbconnection);
			}catch (Exception e) {
				AnalysisLogger.getLogger().info("Table "+destinationTable+" did not exist");
			}
		}
		else
			AnalysisLogger.getLogger().info("The procedure has correctly postprocessed: shutting down the connection!");
		if (dbconnection != null)
			try {
				dbconnection.close();
			} catch (Exception e) {
			}
	}
 
	boolean haspostprocessed = false;
	@Override
	public void postProcess(boolean manageDuplicates, boolean manageFault) {
		haspostprocessed=true;
	}
 
}

Video

We advice you to also follow this video which practically show how to build an algorithm:

http://i-marine.eu/Content/eTraining.aspx?id=e1777006-a08c-49ad-b2e6-c13e094f27d4

@@ Line 9: / Line 9: @@
 http://i-marine.eu/Content/eTraining.aspx?id=e1777006-a08c-49ad-b2e6-c13e094f27d4
-Hello World Algorithm (Maven Tutorial<ref>[https://maven.apache.org/guides/getting-started/maven-in-five-minutes.html Maven Tutorial]</ref>):
+Hello World Algorithm (Maven Tutorial<ref>[https://maven.apache.org/guides/getting-started/index.html Maven Tutorial]</ref>):
 [[File:hello-world-algorithm.zip|hello-world-algorithm.zip]]

Difference between revisions of "How-to Implement Algorithms for DataMiner"

Revision as of 16:50, 9 March 2017

Contents

Prerequisites

Step by Step

Maven coordinates

Customize input visualization

String input parameters

Combo box input parameter

Import input from the DataMiner database

Advanced Input

Case of algorithms using databases

Customize output

String Output

Bar Chart Output

Timeseries Chart Output

File Output

Test the algorithm

Properties File and Deploy

Complete Example with multiple outputs

Integrating R Scripts

Enabling Cloud Computing for R Scripts

Video

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References

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