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Commit f65d30a7 authored by Manuel Spuhler's avatar Manuel Spuhler
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Exareme setup WIP

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src/main/java/eu/hbp/mip/controllers/ExperimentApi.java
+ 3
− 2
View file @ f65d30a7
......@@ -76,11 +76,12 @@ public class ExperimentApi extends WokenClientController {
experiment.setName(expQuery.getName());
experiment.setCreatedBy(user);
experiment.setModel(modelRepository.findOne(expQuery.getModel()));
experiment.setSource(expQuery.getSource());
experimentRepository.save(experiment);
LOGGER.info("Experiment saved");
if (!experiment.isExaremeAlgorithm()._1) {
if (experiment.getSource() != "exareme") {
sendExperiment(experiment);
} else {
String algoCode = expQuery.getAlgorithms().get(0).getCode();
......@@ -286,7 +287,7 @@ public class ExperimentApi extends WokenClientController {
// >> Temporary: we should integrate exareme in a proper way in the future
// this runs in the background. For future optimization: use a thread pool
new Thread(() -> {
String query = experiment.computeExaremeQuery(params);
String query = ""; //experiment.computeExaremeQuery(params);
String url = miningExaremeQueryUrl + "/" + algoCode;
// Results are stored in the experiment object
......
src/main/java/eu/hbp/mip/controllers/MethodsApi.java
+ 1
− 1
View file @ f65d30a7
......@@ -26,7 +26,7 @@ public class MethodsApi extends WokenClientController {
private static final Logger LOGGER = LoggerFactory.getLogger(MethodsApi.class);
private static final Gson gson = new Gson();
d
@Value("#{'${services.exareme.algorithmsUrl:http://localhost:9090/mining/algorithms.json}'}")
private String exaremeAlgorithmsUrl;
......
src/main/java/eu/hbp/mip/model/Experiment.java
+ 11
− 193
View file @ f65d30a7
......@@ -19,7 +19,6 @@ import scala.collection.JavaConversions;
import javax.persistence.*;
import java.lang.reflect.Type;
import java.util.*;
import scala.Tuple2;
/**
* Created by habfast on 21/04/16.
......@@ -28,12 +27,6 @@ import scala.Tuple2;
@Table(name = "`experiment`")
public class Experiment {
public static final String WP_K_MEANS = "K_MEANS";
public static final String WP_LINEAR_REGRESSION = "WP_LINEAR_REGRESSION";
public static final String WP_VARIABLES_HISTOGRAM = "WP_VARIABLES_HISTOGRAM";
public static final String WP_REGRESSION_TREE = "PIPELINE_ISOUP_REGRESSION_TREE_SERIALIZER";
public static final String WP_MODEL_TREE = "PIPELINE_ISOUP_MODEL_TREE_SERIALIZER";
private static final Logger LOGGER = LoggerFactory.getLogger(Experiment.class);
private static final Gson gson = new Gson();
......@@ -89,6 +82,10 @@ public class Experiment {
@Expose
private boolean resultsViewed = false;
@Column(columnDefinition="TEXT")
@Expose
private String source;
public Experiment() {
/*
* Empty constructor is needed by Hibernate
......@@ -137,159 +134,10 @@ public class Experiment {
validationsSeq, Option.empty());
}
private String chainsParams(List<String> params, String operator) {
StringBuilder sb = new StringBuilder();
int i = 0;
for (String s : params)
{
i++;
sb.append(s);
if (i < params.size()) {
sb.append(operator);
}
}
return sb.toString();
}
public String computeExaremeQuery(List<AlgorithmParam> params) {
List<ExaremeQueryElement> queryElements = new LinkedList<>();
// parameters
String design = ""; // TODO: don't assign to a global
String nobuckets = "";
if (params != null) {
for (AlgorithmParam p : params)
{
ExaremeQueryElement paramEl = new ExaremeQueryElement();
paramEl.setName(p.getCode());
paramEl.setDesc("");
paramEl.setValue(p.getValue());
queryElements.add(paramEl);
if (p.getCode().equals("design")) {
design = p.getValue();
}
if (p.getCode().equals("nobuckets")) {
nobuckets = p.getValue();
}
}
}
List<String> variables = new ArrayList<>();
List<String> covariables = new ArrayList<>();
List<String> groupings = new ArrayList<>();
for (Variable var : model.getQuery().getVariables()) { variables.add(var.getCode()); }
for (Variable var : model.getQuery().getCovariables()) { covariables.add(var.getCode()); }
for (Variable var : model.getQuery().getGrouping()) { groupings.add(var.getCode()); }
// Set algorithm specific queries
String algoName = this.isExaremeAlgorithm()._2;
if (algoName.equals(WP_K_MEANS)) {
// columns
List<String> columns = variables;
columns.addAll(covariables);
columns.addAll(groupings);
ExaremeQueryElement columnsEl = new ExaremeQueryElement();
columnsEl.setName("columns");
columnsEl.setDesc("");
columnsEl.setValue(chainsParams(columns, ","));
queryElements.add(columnsEl);
} else if (algoName.equals(WP_LINEAR_REGRESSION)) {
List<String> nominals = covariables.isEmpty() ? groupings : covariables;
nominals.addAll(groupings);
String operator = design.equals("factorial") ? "*" : "+";
ExaremeQueryElement xEl = new ExaremeQueryElement();
xEl.setName("x");
xEl.setDesc("");
xEl.setValue(chainsParams(nominals, operator));
queryElements.add(xEl);
ExaremeQueryElement yEl = new ExaremeQueryElement();
yEl.setName("y");
yEl.setDesc("");
yEl.setValue(chainsParams(variables, ","));
queryElements.add(yEl);
} else if (algoName.equals(WP_VARIABLES_HISTOGRAM)) {
List<String> column1 = variables;
ExaremeQueryElement columnsEl = new ExaremeQueryElement();
columnsEl.setName("column1");
columnsEl.setDesc("");
columnsEl.setValue(chainsParams(column1, ","));
queryElements.add(columnsEl);
List<String> column2 = covariables.isEmpty() ? groupings : covariables;
ExaremeQueryElement columnsEl2 = new ExaremeQueryElement();
columnsEl2.setName("column2");
columnsEl2.setDesc("");
columnsEl2.setValue(chainsParams(column2, ","));
queryElements.add(columnsEl2);
if (!nobuckets.isEmpty()) {
ExaremeQueryElement columnsEl3 = new ExaremeQueryElement();
columnsEl3.setName("nobuckets");
columnsEl3.setDesc("");
columnsEl3.setValue(nobuckets);
queryElements.add(columnsEl3);
}
} else if (algoName.equals(WP_REGRESSION_TREE) || algoName.equals(WP_MODEL_TREE)) {
List<String> target = variables;
List<String> descriptive = covariables.isEmpty() ? groupings : covariables;
descriptive.addAll(groupings);
ExaremeQueryElement xEl = new ExaremeQueryElement();
xEl.setName("target_attributes");
xEl.setDesc("");
xEl.setValue(chainsParams(target, ","));
queryElements.add(xEl);
ExaremeQueryElement yEl = new ExaremeQueryElement();
yEl.setName("descriptive_attributes");
yEl.setDesc("");
yEl.setValue(chainsParams(descriptive, ","));
queryElements.add(yEl);
}
// datasets
StringBuilder datasets = new StringBuilder();
List<Variable> trainingDatasets = model.getQuery().getTrainingDatasets();
int j = 0;
for (Variable var : trainingDatasets)
{
j++;
datasets.append(var.getCode());
if (j < trainingDatasets.size() ) {
datasets.append(",");
}
}
ExaremeQueryElement datasetsEl = new ExaremeQueryElement();
datasetsEl.setName("dataset");
datasetsEl.setDesc("");
// datasetsEl.setValue("adni,ppmi,edsd,fbf,clm");
datasetsEl.setValue(datasets.toString());
queryElements.add(datasetsEl);
// filter
String filter = model.getQuery().getFilters();
ExaremeQueryElement filterEl = new ExaremeQueryElement();
filterEl.setName("filter");
filterEl.setDesc("");
filterEl.setValue(filter);
queryElements.add(filterEl);
return gson.toJson(queryElements);
}
public JsonObject jsonify() {
JsonObject exp = gson.toJsonTree(this).getAsJsonObject();
JsonParser parser = new JsonParser();
Tuple2<Boolean, String> isExaremeAlgorithm = this.isExaremeAlgorithm();
Boolean isExaremeAlgorithm = this.getSource() == "exareme";
if (this.algorithms != null)
{
......@@ -308,7 +156,7 @@ public class Experiment {
if (this.result != null && !this.hasServerError) {
exp.remove("result");
if (!isExaremeAlgorithm._1) {
if (!isExaremeAlgorithm) {
JsonElement jsonResult = parser.parse(this.result);
exp.add("result", jsonResult);
} else {
......@@ -331,18 +179,6 @@ public class Experiment {
JsonObject jsonData = tryJson;
jsonObjectResult.add("data", jsonData);
// add mime-type
String algo = isExaremeAlgorithm._2;
if (jsonData.get("Error") != null) {
jsonObjectResult.add("type", new JsonPrimitive("text/plain+error"));
} else if (algo.equals( WP_K_MEANS) || algo.equals( WP_VARIABLES_HISTOGRAM)) {
jsonObjectResult.add("type", new JsonPrimitive("application/vnd.highcharts+json"));
} else if (algo.equals(WP_LINEAR_REGRESSION)) {
jsonObjectResult.add("type", new JsonPrimitive("application/vnd.dataresource+json"));
} else if (algo.equals(WP_REGRESSION_TREE) || algo.equals(WP_MODEL_TREE)) {
jsonObjectResult.add("type", new JsonPrimitive("application/vnd.visjs+javascript"));
}
jsonArrayResult.add(jsonObjectResult);
exp.add("result", jsonArrayResult);
......@@ -456,29 +292,11 @@ public class Experiment {
this.shared = shared;
}
public Tuple2<Boolean, String> isExaremeAlgorithm() {
Boolean isExareme = false;
String algorithm = "";
String algorithms = this.algorithms;
if (algorithms.contains(WP_K_MEANS)) {
isExareme = true;
algorithm = WP_K_MEANS;
} else if (algorithms.contains(WP_LINEAR_REGRESSION)) {
isExareme = true;
algorithm = WP_LINEAR_REGRESSION;
} else if (algorithms.contains(WP_VARIABLES_HISTOGRAM)) {
isExareme = true;
algorithm = WP_VARIABLES_HISTOGRAM;
} else if (algorithms.contains(WP_REGRESSION_TREE)) {
isExareme = true;
algorithm = WP_REGRESSION_TREE;
} else if (algorithms.contains(WP_MODEL_TREE)) {
isExareme = true;
algorithm = WP_MODEL_TREE;
}
public String getSource() {
return this.source;
}
return new Tuple2<Boolean, String>(isExareme, algorithm);
public void setSource(String source) {
this.source = source;
}
}
src/main/java/eu/hbp/mip/model/ExperimentQuery.java
+ 9
− 0
View file @ f65d30a7
......@@ -9,6 +9,7 @@ public class ExperimentQuery {
private String name;
private String model;
private String source;
private List<ExperimentValidator> validations;
private List<Algorithm> algorithms;
......@@ -28,6 +29,14 @@ public class ExperimentQuery {
this.model = model;
}
public String getSource() {
return source;
}
public void setSource(String source) {
this.source = source;
}
public List<ExperimentValidator> getValidations() {
return validations;
}
......
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