@Override
public Parameters getParameters() {
Parameters parameters = super.getParameters();
parameters.set(ParamAttrs.ALPHA, alpha);
return parameters;
}
@Override
public Graph search(DataSet dataSet, Parameters parameters) {
edu.cmu.tetrad.search.Mbfs search =
new edu.cmu.tetrad.search.Mbfs(
test.getTest(dataSet, parameters), parameters.getInt("depth"));
search.setKnowledge(knowledge);
this.targetName = parameters.getString("targetName");
Node target = dataSet.getVariable(targetName);
return search.search(target);
}
public IndependenceTest getIndependenceTest() {
Object dataModel = getDataModel();
if (dataModel == null) {
dataModel = getSourceGraph();
}
Parameters params = getParams();
IndTestType testType;
if (getParams() instanceof Parameters) {
Parameters _params = params;
testType = (IndTestType) _params.get("indTestType", IndTestType.FISHER_Z);
} else {
Parameters _params = params;
testType = (IndTestType) _params.get("indTestType", IndTestType.FISHER_Z);
}
return new IndTestChooser().getTest(dataModel, params, testType);
}
/**
* Executes the algorithm, producing (at least) a result workbench. Must be implemented in the
* extending class.
*/
public void execute() {
IKnowledge knowledge = (IKnowledge) getParams().get("knowledge", new Knowledge2());
Parameters searchParams = getParams();
Parameters params = searchParams;
Graph graph;
IndependenceTest independenceTest = getIndependenceTest();
Score score = new ScoredIndTest(independenceTest);
if (independenceTest instanceof IndTestDSep) {
final DagToPag dagToPag = new DagToPag(((IndTestDSep) independenceTest).getGraph());
dagToPag.setCompleteRuleSetUsed(params.getBoolean("completeRuleSetUsed", false));
graph = dagToPag.convert();
} else {
GFci fci = new GFci(independenceTest, score);
fci.setKnowledge(knowledge);
fci.setCompleteRuleSetUsed(params.getBoolean("completeRuleSetUsed", false));
fci.setMaxPathLength(params.getInt("maxReachablePathLength", -1));
fci.setMaxDegree(params.getInt("maxIndegree"));
fci.setCompleteRuleSetUsed(false);
fci.setFaithfulnessAssumed(params.getBoolean("faithfulnessAssumed", true));
graph = fci.search();
}
if (getSourceGraph() != null) {
GraphUtils.arrangeBySourceGraph(graph, getSourceGraph());
} else if (knowledge.isDefaultToKnowledgeLayout()) {
SearchGraphUtils.arrangeByKnowledgeTiers(graph, knowledge);
} else {
GraphUtils.circleLayout(graph, 200, 200, 150);
}
setResultGraph(graph);
}
@Override
public edu.cmu.tetrad.search.IndependenceTest getTest(DataSet dataSet, Parameters parameters) {
return new IndTestMultinomialLogisticRegressionWald(
dataSet, parameters.getDouble("alpha"), false);
}
/**
* Constructs the Gui used to edit properties; called from each constructor. Constructs labels and
* text fields for editing each property and adds appropriate listeners.
*/
public void setup() {
setLayout(new BorderLayout());
JRadioButton manualRetain = new JRadioButton();
JRadioButton randomRetain = new JRadioButton();
manualRetain.setText("Manually, retaining previous values where possible.");
randomRetain.setText(
"Using a symmetric prior for each row of each conditional" + " probability table.");
ButtonGroup group = new ButtonGroup();
group.add(manualRetain);
group.add(randomRetain);
final DoubleTextField symmetricAlphaField =
new DoubleTextField(
params.getDouble("symmetricAlpha", 1.0),
5,
NumberFormatUtil.getInstance().getNumberFormat());
symmetricAlphaField.setFilter(
new DoubleTextField.Filter() {
public double filter(double value, double oldValue) {
try {
params.set("symmetricAlpha", value);
return value;
} catch (IllegalArgumentException e) {
return oldValue;
}
}
});
if (getParams().getString("initializationMode", "manualRetain").equals("manualRetain")) {
manualRetain.setSelected(true);
symmetricAlphaField.setEnabled(false);
} else if (getParams()
.getString("initializationMode", "manualRetain")
.equals("symmetricPrior")) {
randomRetain.setSelected(true);
symmetricAlphaField.setEnabled(true);
} else {
throw new IllegalStateException();
}
manualRetain.addActionListener(
new ActionListener() {
public void actionPerformed(ActionEvent e) {
getParams().set("initializationMode", "manualRetain");
symmetricAlphaField.setEnabled(false);
}
});
randomRetain.addActionListener(
new ActionListener() {
public void actionPerformed(ActionEvent e) {
getParams().set("initializationMode", "symmetricPrior");
symmetricAlphaField.setEnabled(true);
}
});
// continue workbench construction.
Box b1 = Box.createVerticalBox();
Box b2 = Box.createHorizontalBox();
b2.add(new JLabel("Pseudocounts for this Dirichlet Bayes IM should be initialized:"));
b2.add(Box.createHorizontalGlue());
Box b3 = Box.createHorizontalBox();
b3.add(manualRetain);
b3.add(Box.createHorizontalGlue());
Box b4 = Box.createHorizontalBox();
b4.add(randomRetain);
b4.add(Box.createHorizontalGlue());
Box b5 = Box.createHorizontalBox();
b5.add(Box.createRigidArea(new Dimension(30, 0)));
b5.add(new JLabel("All pseudocounts = "));
b5.add(symmetricAlphaField);
b5.add(Box.createHorizontalGlue());
b1.add(b2);
b1.add(Box.createVerticalStrut(5));
b1.add(b3);
b1.add(b4);
b1.add(b5);
b1.add(Box.createHorizontalGlue());
add(b1, BorderLayout.CENTER);
}
