From 869eb07edc1e27cd5e02fa0f35ffc2dafb9ca144 Mon Sep 17 00:00:00 2001
From: Manuel Spuhler <manuel.spuhler@chuv.ch>
Date: Fri, 20 Apr 2018 15:42:33 +0200
Subject: [PATCH] Exareme linear regression
---
.../eu/hbp/mip/controllers/ExperimentApi.java | 2 +-
.../java/eu/hbp/mip/model/Experiment.java | 76 ++++++++++++-------
.../resources/data/exareme_algorithms.json | 72 +++++++++++-------
3 files changed, 95 insertions(+), 55 deletions(-)
diff --git a/src/main/java/eu/hbp/mip/controllers/ExperimentApi.java b/src/main/java/eu/hbp/mip/controllers/ExperimentApi.java
index 3ede8eef8..b65416fb5 100644
--- a/src/main/java/eu/hbp/mip/controllers/ExperimentApi.java
+++ b/src/main/java/eu/hbp/mip/controllers/ExperimentApi.java
@@ -84,7 +84,7 @@ public class ExperimentApi extends WokenClientController {
LOGGER.info("Experiment saved");
- if (!experiment.isExaremeAlgorithm()) {
+ if (!experiment.isExaremeAlgorithm()._1) {
sendExperiment(experiment);
} else {
String algoCode = expQuery.getAlgorithms().get(0).getCode();
diff --git a/src/main/java/eu/hbp/mip/model/Experiment.java b/src/main/java/eu/hbp/mip/model/Experiment.java
index f4880376f..377e1f00b 100644
--- a/src/main/java/eu/hbp/mip/model/Experiment.java
+++ b/src/main/java/eu/hbp/mip/model/Experiment.java
@@ -19,7 +19,7 @@ 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.
@@ -28,6 +28,10 @@ import java.util.*;
@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";
+
private static final Logger LOGGER = LoggerFactory.getLogger(Experiment.class);
private static final Gson gson = new Gson();
@@ -134,26 +138,29 @@ public class Experiment {
public String computeExaremeQuery(List<AlgorithmParam> params) {
List<ExaremeQueryElement> queryElements = new LinkedList<>();
- // columns
- List<String> columns = new ArrayList<>();
- for (Variable var : model.getQuery().getVariables()) { columns.add(var.getCode()); }
- for (Variable var : model.getQuery().getCovariables()) { columns.add(var.getCode()); }
- for (Variable var : model.getQuery().getGrouping()) { columns.add(var.getCode()); }
- StringBuilder sb = new StringBuilder();
- int i = 0;
- for (String s : columns)
- {
- i++;
- sb.append(s);
- if (i < columns.size()) {
- sb.append(",");
+ // Set algorithm specific queries
+ if (this.isExaremeAlgorithm()._2.equals(WP_K_MEANS)) {
+ // columns
+ List<String> columns = new ArrayList<>();
+ for (Variable var : model.getQuery().getVariables()) { columns.add(var.getCode()); }
+ for (Variable var : model.getQuery().getCovariables()) { columns.add(var.getCode()); }
+ for (Variable var : model.getQuery().getGrouping()) { columns.add(var.getCode()); }
+ StringBuilder sb = new StringBuilder();
+ int i = 0;
+ for (String s : columns)
+ {
+ i++;
+ sb.append(s);
+ if (i < columns.size()) {
+ sb.append(",");
+ }
}
+ ExaremeQueryElement columnsEl = new ExaremeQueryElement();
+ columnsEl.setName("columns");
+ columnsEl.setDesc("");
+ columnsEl.setValue(sb.toString());
+ queryElements.add(columnsEl);
}
- ExaremeQueryElement columnsEl = new ExaremeQueryElement();
- columnsEl.setName("columns");
- columnsEl.setDesc("");
- columnsEl.setValue(sb.toString());
- queryElements.add(columnsEl);
// parameters
if (params != null) {
@@ -183,7 +190,8 @@ public class Experiment {
ExaremeQueryElement datasetsEl = new ExaremeQueryElement();
datasetsEl.setName("dataset");
datasetsEl.setDesc("");
- datasetsEl.setValue(datasets.toString());
+ datasetsEl.setValue("adni,ppmi,edsd");
+// datasetsEl.setValue(datasets.toString());
queryElements.add(datasetsEl);
return gson.toJson(queryElements);
@@ -192,6 +200,7 @@ public class Experiment {
public JsonObject jsonify() {
JsonObject exp = gson.toJsonTree(this).getAsJsonObject();
JsonParser parser = new JsonParser();
+ Tuple2<Boolean, String> isExaremeAlgorithm = this.isExaremeAlgorithm();
if (this.algorithms != null)
{
@@ -210,7 +219,7 @@ public class Experiment {
if (this.result != null && !this.hasServerError) {
exp.remove("result");
- if (!this.isExaremeAlgorithm()) {
+ if (!isExaremeAlgorithm._1) {
JsonArray jsonResult = parser.parse(this.result).getAsJsonArray();
exp.add("result", jsonResult);
} else {
@@ -225,7 +234,14 @@ public class Experiment {
jsonObjectResult.add("algorithm", algoObject.get("name"));
jsonObjectResult.add("code", algoObject.get("code"));
- jsonObjectResult.add("type", new JsonPrimitive("application/vnd.highcharts+json"));
+ // add mime-type
+ if (jsonData.get("Error") != null){
+ jsonObjectResult.add("type", new JsonPrimitive("text/plain+error"));
+ } else if (isExaremeAlgorithm._2 == WP_K_MEANS){
+ jsonObjectResult.add("type", new JsonPrimitive("application/vnd.highcharts+json"));
+ } else if (isExaremeAlgorithm._2 == WP_LINEAR_REGRESSION){
+ jsonObjectResult.add("type", new JsonPrimitive("application/vnd.dataresource+json"));
+ }
jsonArrayResult.add(jsonObjectResult);
@@ -340,11 +356,19 @@ public class Experiment {
this.shared = shared;
}
- public boolean isExaremeAlgorithm() {
+ public Tuple2<Boolean, String> isExaremeAlgorithm() {
+ Boolean isExareme = false;
+ String algorithm = "";
+
String algorithms = this.algorithms;
- Boolean isExareme = algorithms.contains("WP_")
- || algorithms.contains("K_MEANS");
+ 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;
+ }
- return isExareme;
+ return new Tuple2(isExareme, algorithm);
}
}
diff --git a/src/main/resources/data/exareme_algorithms.json b/src/main/resources/data/exareme_algorithms.json
index f05c5079b..1e4ade443 100644
--- a/src/main/resources/data/exareme_algorithms.json
+++ b/src/main/resources/data/exareme_algorithms.json
@@ -1,36 +1,52 @@
-[{
- "code": "glm_exareme",
- "label": "GLM (exareme)",
- "type": [
- "statistics"
- ],
- "environment": "Exareme",
- "description": "Linear Regression using Exareme services",
- "constraints": {
- "variable": {
- "real": true,
- "binominal": false,
- "polynominal": false
+[
+ {
+ "code": "WP_LINEAR_REGRESSION",
+ "label": "Linear regression (Exareme)",
+ "type": ["statistics"],
+ "environment": "Exareme",
+ "description": "Linear Regression using Exareme services",
+ "parameters": [
+ {
+ "code": "x",
+ "label": "x",
+ "type": "string",
+ "description": "The expression for x-axis"
+ },
+ {
+ "code": "y",
+ "label": "y",
+ "type": "string",
+ "description": "The expression for y-axis"
+ }
+ ],
+ "constraints": {
+ "variable": {
+ "real": true,
+ "binominal": true,
+ "polynominal": true
+ }
}
- }
-},
+ },
{
"code": "K_MEANS",
- "label": "K_MEANS (exareme)",
+ "label": "K_MEANS (Exareme)",
"type": ["statistics"],
"environment": "Exareme",
- "description": "K_MEANS",
- "parameters": [{
- "code": "k",
- "label": "k",
- "default_value": 5,
- "type": "int",
- "constraints": {
- "min": 1,
- "max": null
- },
- "description": "The number of clusters. Typical values range from 2 to 10."
- }],
+ "description": "k-Means using Exareme services",
+ "parameters": [
+ {
+ "code": "k",
+ "label": "k",
+ "default_value": 5,
+ "type": "int",
+ "constraints": {
+ "min": 1,
+ "max": null
+ },
+ "description":
+ "The number of clusters. Typical values range from 2 to 10."
+ }
+ ],
"constraints": {
"variable": {
"real": true,
--
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