public Workflow doDelete(
Session mysession, Request myrequest, Response myresponse, Configuration myconfig, DB db) {
boolean accesspermission =
RequireUser.SuperAdministrator(
text,
mysession.get("username"),
myconfig.get(db, "superadmin"),
myrequest,
myresponse,
db.getDatabase(),
mysession.get("database"));
if (!accesspermission) return new Workflow(text);
Workflow workflow = new Workflow(text);
workflow.read(db, myrequest.getParameter("id"));
Cms.CMSAudit(
"action=delete workflow="
+ workflow.getTitle()
+ " - "
+ workflow.getAction()
+ " username="******"username")
+ " userid="
+ mysession.get("userid"));
workflow.delete(db);
return workflow;
}
@Override
protected void backPressed() {
// move workflow to previous state, start up a new thread to do it.
final Workflow pipe = getWizard().getWorkflow();
if (pipe.getCurrentState().getPreviousState() == null) {
// this means that this wizard is part of a larger wizard and we need to return control
// to it. See setAdapter for details
workflowWizardAdapter.backPressed();
}
IRunnableWithProgress runnable =
new IRunnableWithProgress() {
public void run(IProgressMonitor monitor)
throws InvocationTargetException, InterruptedException {
pipe.previous(monitor);
}
};
try {
run(false, true, runnable);
} catch (InvocationTargetException e) {
CatalogUIPlugin.log(e.getLocalizedMessage(), e);
} catch (InterruptedException e) {
CatalogUIPlugin.log(e.getLocalizedMessage(), e);
}
}
/**
* This method builds a decision tree model
*
* @param sparkContext JavaSparkContext initialized with the application
* @param modelID Model ID
* @param trainingData Training data as a JavaRDD of LabeledPoints
* @param testingData Testing data as a JavaRDD of LabeledPoints
* @param workflow Machine learning workflow
* @param mlModel Deployable machine learning model
* @throws MLModelBuilderException
*/
private ModelSummary buildDecisionTreeModel(
JavaSparkContext sparkContext,
long modelID,
JavaRDD<LabeledPoint> trainingData,
JavaRDD<LabeledPoint> testingData,
Workflow workflow,
MLModel mlModel,
SortedMap<Integer, String> includedFeatures,
Map<Integer, Integer> categoricalFeatureInfo)
throws MLModelBuilderException {
try {
Map<String, String> hyperParameters = workflow.getHyperParameters();
DecisionTree decisionTree = new DecisionTree();
DecisionTreeModel decisionTreeModel =
decisionTree.train(
trainingData,
getNoOfClasses(mlModel),
categoricalFeatureInfo,
hyperParameters.get(MLConstants.IMPURITY),
Integer.parseInt(hyperParameters.get(MLConstants.MAX_DEPTH)),
Integer.parseInt(hyperParameters.get(MLConstants.MAX_BINS)));
// remove from cache
trainingData.unpersist();
// add test data to cache
testingData.cache();
JavaPairRDD<Double, Double> predictionsAndLabels =
decisionTree.test(decisionTreeModel, testingData).cache();
ClassClassificationAndRegressionModelSummary classClassificationAndRegressionModelSummary =
SparkModelUtils.getClassClassificationModelSummary(
sparkContext, testingData, predictionsAndLabels);
// remove from cache
testingData.unpersist();
mlModel.setModel(new MLDecisionTreeModel(decisionTreeModel));
classClassificationAndRegressionModelSummary.setFeatures(
includedFeatures.values().toArray(new String[0]));
classClassificationAndRegressionModelSummary.setAlgorithm(
SUPERVISED_ALGORITHM.DECISION_TREE.toString());
MulticlassMetrics multiclassMetrics =
getMulticlassMetrics(sparkContext, predictionsAndLabels);
predictionsAndLabels.unpersist();
classClassificationAndRegressionModelSummary.setMulticlassConfusionMatrix(
getMulticlassConfusionMatrix(multiclassMetrics, mlModel));
Double modelAccuracy = getModelAccuracy(multiclassMetrics);
classClassificationAndRegressionModelSummary.setModelAccuracy(modelAccuracy);
classClassificationAndRegressionModelSummary.setDatasetVersion(workflow.getDatasetVersion());
return classClassificationAndRegressionModelSummary;
} catch (Exception e) {
throw new MLModelBuilderException(
"An error occurred while building decision tree model: " + e.getMessage(), e);
}
}
private WorkflowEntry(String pageName, String title, Workflow workflow, WorkflowEntry parent) {
this.pageName = pageName;
this.title = title;
this.workflow = workflow;
this.id = workflow.nextId();
this.parent = parent;
if (parent != null) {
parent.children.add(this);
}
workflow.register(this);
}
/**
* This method builds a naive bayes model
*
* @param sparkContext JavaSparkContext initialized with the application
* @param modelID Model ID
* @param trainingData Training data as a JavaRDD of LabeledPoints
* @param testingData Testing data as a JavaRDD of LabeledPoints
* @param workflow Machine learning workflow
* @param mlModel Deployable machine learning model
* @throws MLModelBuilderException
*/
private ModelSummary buildNaiveBayesModel(
JavaSparkContext sparkContext,
long modelID,
JavaRDD<LabeledPoint> trainingData,
JavaRDD<LabeledPoint> testingData,
Workflow workflow,
MLModel mlModel,
SortedMap<Integer, String> includedFeatures)
throws MLModelBuilderException {
try {
Map<String, String> hyperParameters = workflow.getHyperParameters();
NaiveBayesClassifier naiveBayesClassifier = new NaiveBayesClassifier();
NaiveBayesModel naiveBayesModel =
naiveBayesClassifier.train(
trainingData, Double.parseDouble(hyperParameters.get(MLConstants.LAMBDA)));
// remove from cache
trainingData.unpersist();
// add test data to cache
testingData.cache();
JavaPairRDD<Double, Double> predictionsAndLabels =
naiveBayesClassifier.test(naiveBayesModel, testingData).cache();
ClassClassificationAndRegressionModelSummary classClassificationAndRegressionModelSummary =
SparkModelUtils.getClassClassificationModelSummary(
sparkContext, testingData, predictionsAndLabels);
// remove from cache
testingData.unpersist();
mlModel.setModel(new MLClassificationModel(naiveBayesModel));
classClassificationAndRegressionModelSummary.setFeatures(
includedFeatures.values().toArray(new String[0]));
classClassificationAndRegressionModelSummary.setAlgorithm(
SUPERVISED_ALGORITHM.NAIVE_BAYES.toString());
MulticlassMetrics multiclassMetrics =
getMulticlassMetrics(sparkContext, predictionsAndLabels);
predictionsAndLabels.unpersist();
classClassificationAndRegressionModelSummary.setMulticlassConfusionMatrix(
getMulticlassConfusionMatrix(multiclassMetrics, mlModel));
Double modelAccuracy = getModelAccuracy(multiclassMetrics);
classClassificationAndRegressionModelSummary.setModelAccuracy(modelAccuracy);
classClassificationAndRegressionModelSummary.setDatasetVersion(workflow.getDatasetVersion());
return classClassificationAndRegressionModelSummary;
} catch (Exception e) {
throw new MLModelBuilderException(
"An error occurred while building naive bayes model: " + e.getMessage(), e);
}
}
/**
* 根据工作流的定义事物返回对应示例。
*
* @param thing
* @return
*/
public static List<Workflow> getWorkflowInstances(Thing thing) {
List<Workflow> flows = new ArrayList<Workflow>();
for (String key : workflows.keySet()) {
Workflow workflow = workflows.get(key);
Thing fthing = workflow.getThing();
if (fthing == thing) {
flows.add(workflow);
}
}
return flows;
}
/**
* Start the workflow run asynchronously.
*
* @param name The name of the workflow
* @return json response containing id
*/
@Security.Authenticated(Secured.class)
public Result runWorkflow(String name) {
FormDefinition form = formDefinitionForWorkflow(name);
// Process file upload first if present in form data
Http.MultipartFormData body = request().body().asMultipartFormData();
for (Object obj : body.getFiles()) {
Http.MultipartFormData.FilePart filePart = (Http.MultipartFormData.FilePart) obj;
UserUpload userUpload = uploadFile(filePart);
BasicField fileInputField = form.getField(filePart.getKey());
fileInputField.setValue(userUpload);
}
// Set the form definition field values from the request data
Map<String, String[]> data = body.asFormUrlEncoded();
for (String key : data.keySet()) {
BasicField field = form.getField(key);
field.setValue(data.get(key));
}
// Transfer form field data to workflow settings map
Map<String, Object> settings = new HashMap<>();
for (BasicField field : form.fields) {
settings.put(field.name, field.value());
}
settings.putAll(settingsFromConfig(form));
// Update the workflow model object and persist to the db
Workflow workflow = Workflow.find.where().eq("name", form.name).findUnique();
if (workflow == null) {
workflow = new Workflow();
}
workflow.name = form.name;
workflow.title = form.title;
workflow.yamlFile = form.yamlFile;
workflow.save();
// Run the workflow
ObjectNode response = runYamlWorkflow(form.yamlFile, workflow, settings);
return redirect(routes.Application.index());
}
/**
* Runs the dialog in headless mode. The dialog will run headless while the workflow can run.
*
* @param monitor
* @param true if the workflow ran and completed correctly. False if it failed or the user
* cancelled (because user interaction was required)
*/
public boolean runHeadless(IProgressMonitor monitor) {
try {
this.headless = true;
int ticks = getWorkflowWizard().getWorkflow().getStates().length * 10;
monitor.beginTask(Messages.WorkflowWizardDialog_importTask, ticks);
// we must ensure that the contents of the dialog (shell) have been
// creates, needed for wizard pages
if (getShell() == null) {
// do in ui thread
PlatformGIS.syncInDisplayThread(
new Runnable() {
public void run() {
create();
}
});
}
Workflow pipe = getWizard().getWorkflow();
pipe.run(new SubProgressMonitor(monitor, ticks));
final boolean[] result = new boolean[] {true};
if (!pipe.isFinished()) {
// show the page corresponding to the current state
final IWizardPage page = getWizard().getPage(pipe.getCurrentState());
if (page != null) {
// ensure the page has a state if it is a DataPipelinePage
if (page instanceof WorkflowWizardPage) {
WorkflowWizardPage dpPage = (WorkflowWizardPage) page;
if (dpPage.getState() == null) dpPage.setState(pipe.getCurrentState());
}
PlatformGIS.syncInDisplayThread(
new Runnable() {
public void run() {
headless = false;
showPage(page);
if (open() == Window.CANCEL) {
result[0] = false;
}
};
});
}
}
this.headless = false;
return result[0];
} finally {
monitor.done();
}
}
/**
* 返回当前所有工作流多定义的事物列表。
*
* @return
*/
public static List<Thing> getWorkflowThings() {
List<Thing> things = new ArrayList<Thing>();
Map<String, String> thingMap = new HashMap<String, String>();
for (String key : workflows.keySet()) {
Workflow workflow = workflows.get(key);
Thing thing = workflow.getThing();
String path = thing.getMetadata().getPath();
if (thingMap.get(path) == null) {
things.add(thing);
thingMap.put(path, path);
}
}
return things;
}
public Workflow getDelete(
Session mysession, Request myrequest, Response myresponse, Configuration myconfig, DB db) {
boolean accesspermission =
RequireUser.SuperAdministrator(
text,
mysession.get("username"),
myconfig.get(db, "superadmin"),
myrequest,
myresponse,
db.getDatabase(),
mysession.get("database"));
if (!accesspermission) return new Workflow(text);
Workflow workflow = new Workflow(text);
workflow.read(db, myrequest.getParameter("id"));
return workflow;
}
@Override
public int hashCode() {
final int prime = 31;
int result = 1;
result = prime * result + (getUntilFinished() == null ? 0 : getUntilFinished().hashCode());
result = prime * result + (parent == null ? 0 : parent.hashCode());
result = prime * result + (getBlock() == null ? 0 : getBlock().hashCode());
return result;
}
@Override
public void setParent(Workflow parent) {
if (this.parent != null && this.parent != parent) {
this.parent.getControlLinks().remove(this);
}
this.parent = parent;
if (parent != null) {
parent.getControlLinks().add(this);
}
}
public static void main(String[] args) {
Power power = new Power();
power.on();
Workflow p1 = new Workflow("p1", power);
Workflow p2 = new Workflow("p2", power);
Workflow p3 = new Workflow("p3", power);
Workflow p4 = new Workflow("p4", power);
p2.waitFor(p1);
p1.start();
p2.start();
p3.start();
p4.start();
try {
Thread.sleep(100);
} catch (InterruptedException e) {
e.printStackTrace();
}
power.off();
try {
Thread.sleep(100);
} catch (InterruptedException e) {
e.printStackTrace();
}
power.on();
try {
Thread.sleep(500);
} catch (InterruptedException e) {
e.printStackTrace();
}
p3.pause(1000);
}
/** Creates some attachments and assigns them to the test workflow. */
@Test
public void testAttachments() {
final PK workflowPk = testWorkflow.getPK();
// create product attachment
final Product product = jaloSession.getProductManager().createProduct("sabbers");
assertNotNull("Product not null", product);
Map<String, Object> map = new HashMap<String, Object>();
map.put(WorkflowItemAttachment.CODE, "productTest");
map.put(WorkflowItemAttachment.ITEM, product);
map.put(WorkflowItemAttachment.WORKFLOW, testWorkflow);
final WorkflowItemAttachment attachProduct =
WorkflowManager.getInstance().createWorkflowItemAttachment(map);
assertNotNull("Attachment not null", attachProduct);
// create category attachment
final Category category =
CategoryManager.getInstance().createCategory(PK.createUUIDPK(0).getHex());
assertNotNull("Category not null", category);
map = new HashMap<String, Object>();
map.put(WorkflowItemAttachment.CODE, "categoryTest");
map.put(WorkflowItemAttachment.ITEM, category);
map.put(WorkflowItemAttachment.WORKFLOW, testWorkflow);
final WorkflowItemAttachment attachCategory =
WorkflowManager.getInstance().createWorkflowItemAttachment(map);
assertNotNull("Attachment not null", attachCategory);
final WorkflowAction action1 = getAction(ACTIONCODES.ACTION1.name());
action1.setAttachments(
Arrays.asList(new WorkflowItemAttachment[] {attachProduct, attachCategory}));
// restart
Registry.getCurrentTenant().getCache();
// check attachments
final Workflow found = JaloSession.getCurrentSession().getItem(workflowPk);
assertEquals("Excpected number of attachments", 2, found.getAttachments().size());
final WorkflowAction foundAction = getAction(ACTIONCODES.ACTION1.name());
assertEquals(
"Excpected number of attachments of action 1", 2, foundAction.getAttachments().size());
}
public Workflow getIndex(
Session mysession, Request myrequest, Response myresponse, Configuration myconfig, DB db) {
boolean accesspermission =
RequireUser.SuperAdministrator(
text,
mysession.get("username"),
myconfig.get(db, "superadmin"),
myrequest,
myresponse,
db.getDatabase(),
mysession.get("database"));
if (!accesspermission) return new Workflow(text);
String SQL = "select distinct title,action,fromstate,tostate,id from workflow";
String SQLwhere = "";
if (!myrequest.getParameter("workflow").equals("")) {
SQLwhere = " where title='" + Common.SQL_clean(myrequest.getParameter("workflow")) + "'";
}
String SQLorder = " order by title,action,fromstate,tostate,id";
SQL += SQLwhere + SQLorder;
Workflow workflow = new Workflow(text);
workflow.records(db, SQL);
return workflow;
}
@Bean
protected ReadEmailTaskJobFactory readEmailTaskJobFactory() {
return new ReadEmailTaskJobFactory( //
email.emailAccountFacade(), //
email.emailServiceFactory(), //
email.subjectHandler(), //
email.emailStore(), //
workflow
.systemWorkflowLogicBuilder() //
.build(), //
dms.defaultDmsLogic(), //
data.systemDataView(), //
email.emailTemplateLogic(), //
template.databaseTemplateEngine(), //
emailTemplateSenderFactory() //
);
}
@Bean
protected ObserverFactory observerFactory() {
return new DefaultObserverFactory( //
userStore, //
api.systemFluentApi(), //
workflow.systemWorkflowLogicBuilder().build(), //
email.emailAccountFacade(), //
email.emailTemplateLogic(), //
data.systemDataView(), //
new Supplier<CMDataView>() {
@Override
public CMDataView get() {
return user.userDataView();
}
}, //
emailTemplateSenderFactory() //
);
}
public void activate(IRequestCycle cycle) {
workflow.setCurrentEntry(this);
DataContext.bindThreadDataContext(dataContext);
cycle.getPage(pageName).validate(cycle);
cycle.activate(pageName);
}
/**
* This method builds a lasso regression model
*
* @param sparkContext JavaSparkContext initialized with the application
* @param modelID Model ID
* @param trainingData Training data as a JavaRDD of LabeledPoints
* @param testingData Testing data as a JavaRDD of LabeledPoints
* @param workflow Machine learning workflow
* @param mlModel Deployable machine learning model
* @throws MLModelBuilderException
*/
private ModelSummary buildLassoRegressionModel(
JavaSparkContext sparkContext,
long modelID,
JavaRDD<LabeledPoint> trainingData,
JavaRDD<LabeledPoint> testingData,
Workflow workflow,
MLModel mlModel,
SortedMap<Integer, String> includedFeatures)
throws MLModelBuilderException {
try {
LassoRegression lassoRegression = new LassoRegression();
Map<String, String> hyperParameters = workflow.getHyperParameters();
LassoModel lassoModel =
lassoRegression.train(
trainingData,
Integer.parseInt(hyperParameters.get(MLConstants.ITERATIONS)),
Double.parseDouble(hyperParameters.get(MLConstants.LEARNING_RATE)),
Double.parseDouble(hyperParameters.get(MLConstants.REGULARIZATION_PARAMETER)),
Double.parseDouble(hyperParameters.get(MLConstants.SGD_DATA_FRACTION)));
// remove from cache
trainingData.unpersist();
// add test data to cache
testingData.cache();
Vector weights = lassoModel.weights();
if (!isValidWeights(weights)) {
throw new MLModelBuilderException(
"Weights of the model generated are null or infinity. [Weights] "
+ vectorToString(weights));
}
JavaRDD<Tuple2<Double, Double>> predictionsAndLabels =
lassoRegression.test(lassoModel, testingData).cache();
ClassClassificationAndRegressionModelSummary regressionModelSummary =
SparkModelUtils.generateRegressionModelSummary(
sparkContext, testingData, predictionsAndLabels);
// remove from cache
testingData.unpersist();
mlModel.setModel(new MLGeneralizedLinearModel(lassoModel));
List<FeatureImportance> featureWeights =
getFeatureWeights(includedFeatures, lassoModel.weights().toArray());
regressionModelSummary.setFeatures(includedFeatures.values().toArray(new String[0]));
regressionModelSummary.setAlgorithm(SUPERVISED_ALGORITHM.LASSO_REGRESSION.toString());
regressionModelSummary.setFeatureImportance(featureWeights);
RegressionMetrics regressionMetrics =
getRegressionMetrics(sparkContext, predictionsAndLabels);
predictionsAndLabels.unpersist();
Double meanSquaredError = regressionMetrics.meanSquaredError();
regressionModelSummary.setMeanSquaredError(meanSquaredError);
regressionModelSummary.setDatasetVersion(workflow.getDatasetVersion());
return regressionModelSummary;
} catch (Exception e) {
throw new MLModelBuilderException(
"An error occurred while building lasso regression model: " + e.getMessage(), e);
}
}
/**
* This method builds a support vector machine (SVM) model
*
* @param sparkContext JavaSparkContext initialized with the application
* @param modelID Model ID
* @param trainingData Training data as a JavaRDD of LabeledPoints
* @param testingData Testing data as a JavaRDD of LabeledPoints
* @param workflow Machine learning workflow
* @param mlModel Deployable machine learning model
* @throws MLModelBuilderException
*/
private ModelSummary buildSVMModel(
JavaSparkContext sparkContext,
long modelID,
JavaRDD<LabeledPoint> trainingData,
JavaRDD<LabeledPoint> testingData,
Workflow workflow,
MLModel mlModel,
SortedMap<Integer, String> includedFeatures)
throws MLModelBuilderException {
if (getNoOfClasses(mlModel) > 2) {
throw new MLModelBuilderException(
"A binary classification algorithm cannot have more than "
+ "two distinct values in response variable.");
}
try {
SVM svm = new SVM();
Map<String, String> hyperParameters = workflow.getHyperParameters();
SVMModel svmModel =
svm.train(
trainingData,
Integer.parseInt(hyperParameters.get(MLConstants.ITERATIONS)),
hyperParameters.get(MLConstants.REGULARIZATION_TYPE),
Double.parseDouble(hyperParameters.get(MLConstants.REGULARIZATION_PARAMETER)),
Double.parseDouble(hyperParameters.get(MLConstants.LEARNING_RATE)),
Double.parseDouble(hyperParameters.get(MLConstants.SGD_DATA_FRACTION)));
// remove from cache
trainingData.unpersist();
// add test data to cache
testingData.cache();
Vector weights = svmModel.weights();
if (!isValidWeights(weights)) {
throw new MLModelBuilderException(
"Weights of the model generated are null or infinity. [Weights] "
+ vectorToString(weights));
}
// getting scores and labels without clearing threshold to get confusion matrix
JavaRDD<Tuple2<Object, Object>> scoresAndLabelsThresholded = svm.test(svmModel, testingData);
MulticlassMetrics multiclassMetrics =
new MulticlassMetrics(JavaRDD.toRDD(scoresAndLabelsThresholded));
MulticlassConfusionMatrix multiclassConfusionMatrix =
getMulticlassConfusionMatrix(multiclassMetrics, mlModel);
svmModel.clearThreshold();
JavaRDD<Tuple2<Object, Object>> scoresAndLabels = svm.test(svmModel, testingData);
ProbabilisticClassificationModelSummary probabilisticClassificationModelSummary =
SparkModelUtils.generateProbabilisticClassificationModelSummary(
sparkContext, testingData, scoresAndLabels);
// remove from cache
testingData.unpersist();
mlModel.setModel(new MLClassificationModel(svmModel));
List<FeatureImportance> featureWeights =
getFeatureWeights(includedFeatures, svmModel.weights().toArray());
probabilisticClassificationModelSummary.setFeatures(
includedFeatures.values().toArray(new String[0]));
probabilisticClassificationModelSummary.setFeatureImportance(featureWeights);
probabilisticClassificationModelSummary.setAlgorithm(SUPERVISED_ALGORITHM.SVM.toString());
probabilisticClassificationModelSummary.setMulticlassConfusionMatrix(
multiclassConfusionMatrix);
Double modelAccuracy = getModelAccuracy(multiclassMetrics);
probabilisticClassificationModelSummary.setModelAccuracy(modelAccuracy);
probabilisticClassificationModelSummary.setDatasetVersion(workflow.getDatasetVersion());
return probabilisticClassificationModelSummary;
} catch (Exception e) {
throw new MLModelBuilderException(
"An error occurred while building SVM model: " + e.getMessage(), e);
}
}
public void linkNodesToWorkflow() {
workflow.addNode(_ProposeRoute);
workflow.addNode(_EnterNumFireTruck);
workflow.addNode(_FireRouteParameters);
workflow.addNode(_AndJoin115936);
workflow.addNode(_Develop);
workflow.addNode(_EnterNumPoliceVehicle);
workflow.addNode(_PoliceRouteParameters);
workflow.addNode(_RoutesCoordinated);
workflow.addNode(_AndFork115896);
workflow.addNode(_RoutesNotCoordinated);
workflow.addNode(_AspectMarker117632);
workflow.addNode(_AspectMarker117636);
workflow.addNode(_ProcessFireRouteParameters);
workflow.addNode(_ProcessPoliceRouteParameters);
}
/** Build a supervised model. */
public MLModel build() throws MLModelBuilderException {
MLModelConfigurationContext context = getContext();
JavaSparkContext sparkContext = null;
DatabaseService databaseService = MLCoreServiceValueHolder.getInstance().getDatabaseService();
MLModel mlModel = new MLModel();
try {
sparkContext = context.getSparkContext();
Workflow workflow = context.getFacts();
long modelId = context.getModelId();
// Verify validity of response variable
String typeOfResponseVariable =
getTypeOfResponseVariable(workflow.getResponseVariable(), workflow.getFeatures());
if (typeOfResponseVariable == null) {
throw new MLModelBuilderException(
"Type of response variable cannot be null for supervised learning " + "algorithms.");
}
// Stops model building if a categorical attribute is used with numerical prediction
if (workflow.getAlgorithmClass().equals(AlgorithmType.NUMERICAL_PREDICTION.getValue())
&& typeOfResponseVariable.equals(FeatureType.CATEGORICAL)) {
throw new MLModelBuilderException(
"Categorical attribute "
+ workflow.getResponseVariable()
+ " cannot be used as the response variable of the Numerical Prediction algorithm: "
+ workflow.getAlgorithmName());
}
// generate train and test datasets by converting tokens to labeled points
int responseIndex = context.getResponseIndex();
SortedMap<Integer, String> includedFeatures =
MLUtils.getIncludedFeaturesAfterReordering(
workflow, context.getNewToOldIndicesList(), responseIndex);
// gets the pre-processed dataset
JavaRDD<LabeledPoint> labeledPoints = preProcess().cache();
JavaRDD<LabeledPoint>[] dataSplit =
labeledPoints.randomSplit(
new double[] {workflow.getTrainDataFraction(), 1 - workflow.getTrainDataFraction()},
MLConstants.RANDOM_SEED);
// remove from cache
labeledPoints.unpersist();
JavaRDD<LabeledPoint> trainingData = dataSplit[0].cache();
JavaRDD<LabeledPoint> testingData = dataSplit[1];
// create a deployable MLModel object
mlModel.setAlgorithmName(workflow.getAlgorithmName());
mlModel.setAlgorithmClass(workflow.getAlgorithmClass());
mlModel.setFeatures(workflow.getIncludedFeatures());
mlModel.setResponseVariable(workflow.getResponseVariable());
mlModel.setEncodings(context.getEncodings());
mlModel.setNewToOldIndicesList(context.getNewToOldIndicesList());
mlModel.setResponseIndex(responseIndex);
ModelSummary summaryModel = null;
Map<Integer, Integer> categoricalFeatureInfo;
// build a machine learning model according to user selected algorithm
SUPERVISED_ALGORITHM supervisedAlgorithm =
SUPERVISED_ALGORITHM.valueOf(workflow.getAlgorithmName());
switch (supervisedAlgorithm) {
case LOGISTIC_REGRESSION:
summaryModel =
buildLogisticRegressionModel(
sparkContext,
modelId,
trainingData,
testingData,
workflow,
mlModel,
includedFeatures,
true);
break;
case LOGISTIC_REGRESSION_LBFGS:
summaryModel =
buildLogisticRegressionModel(
sparkContext,
modelId,
trainingData,
testingData,
workflow,
mlModel,
includedFeatures,
false);
break;
case DECISION_TREE:
categoricalFeatureInfo = getCategoricalFeatureInfo(context.getEncodings());
summaryModel =
buildDecisionTreeModel(
sparkContext,
modelId,
trainingData,
testingData,
workflow,
mlModel,
includedFeatures,
categoricalFeatureInfo);
break;
case RANDOM_FOREST:
categoricalFeatureInfo = getCategoricalFeatureInfo(context.getEncodings());
summaryModel =
buildRandomForestTreeModel(
sparkContext,
modelId,
trainingData,
testingData,
workflow,
mlModel,
includedFeatures,
categoricalFeatureInfo);
break;
case SVM:
summaryModel =
buildSVMModel(
sparkContext,
modelId,
trainingData,
testingData,
workflow,
mlModel,
includedFeatures);
break;
case NAIVE_BAYES:
summaryModel =
buildNaiveBayesModel(
sparkContext,
modelId,
trainingData,
testingData,
workflow,
mlModel,
includedFeatures);
break;
case LINEAR_REGRESSION:
summaryModel =
buildLinearRegressionModel(
sparkContext,
modelId,
trainingData,
testingData,
workflow,
mlModel,
includedFeatures);
break;
case RIDGE_REGRESSION:
summaryModel =
buildRidgeRegressionModel(
sparkContext,
modelId,
trainingData,
testingData,
workflow,
mlModel,
includedFeatures);
break;
case LASSO_REGRESSION:
summaryModel =
buildLassoRegressionModel(
sparkContext,
modelId,
trainingData,
testingData,
workflow,
mlModel,
includedFeatures);
break;
default:
throw new AlgorithmNameException("Incorrect algorithm name");
}
// persist model summary
databaseService.updateModelSummary(modelId, summaryModel);
return mlModel;
} catch (Exception e) {
throw new MLModelBuilderException(
"An error occurred while building supervised machine learning model: " + e.getMessage(),
e);
}
}
/**
* 移除工作流。
*
* @param workflow
*/
public static void removeWorkflow(Workflow workflow) {
String id = workflow.getThing().getMetadata().getPath() + ":" + workflow.getWorkflowId();
workflows.remove(id);
}
@Override
public String apply(Workflow input) {
return input.getName();
}
@Bean
protected StartWorkflowTaskJobFactory startWorkflowTaskJobFactory() {
return new StartWorkflowTaskJobFactory(workflow.systemWorkflowLogicBuilder().build());
}
public void redirect(IRequestCycle cycle, String anchor) {
workflow.setCurrentEntry(this);
ILink link = workflowService(cycle).getLink(false, this);
throw new RedirectException(link.getURL(anchor, true));
}
public void linkStartNodesToWorkflow() {
workflow.addStartupNode(_Develop, false);
}
/**
* 放入工作流，主要是放入决定是否放入到缓存中。
*
* @param workflow
*/
public static void putWorkflow(Workflow workflow) {
String id = workflow.getThing().getMetadata().getPath() + ":" + workflow.getWorkflowId();
workflows.put(id, workflow);
}
