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An open source Java client that gives you a simple binding to interact with BigML. You can use it to
easily create, retrieve, list, update, and delete BigML resources.
/*
Tree structure for the BigML local boosted Model
This module defines an auxiliary Tree structure that is used in the local
boosted Ensemble to predict locally or embedded into your application
without needing to send requests to BigML.io.
*/
package org.bigml.binding.localmodel;
import java.util.ArrayList;
import java.util.HashMap;
import java.util.List;
import java.util.Map;
import org.bigml.binding.MissingStrategy;
import org.bigml.binding.utils.Utils;
import org.json.simple.JSONArray;
import org.json.simple.JSONObject;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
/**
* A boosted tree-like predictive model.
*
*/
public class BoostedTree extends AbstractTree {
/**
* Logging
*/
static Logger LOGGER = LoggerFactory.getLogger(
BoostedTree.class.getName());
private final List children;
private final Double g_sum;
private final Double h_sum;
/**
* Constructor
*/
public BoostedTree(final JSONObject root, final JSONObject fields,
final Object objective) {
super(root, fields, objective);
children = new ArrayList();
JSONArray childrenObj = (JSONArray) root.get("children");
if (childrenObj != null) {
for (int i = 0; i < childrenObj.size(); i++) {
JSONObject child = (JSONObject) childrenObj.get(i);
BoostedTree childTree = new BoostedTree(child, fields, objectiveField);
children.add(childTree);
}
}
this.g_sum = ((Number) root.get("g_sum")).doubleValue();
this.h_sum = ((Number) root.get("h_sum")).doubleValue();
}
public List getChildren() {
return children;
}
/**
* Makes a prediction based on a number of field values.
*
* The input fields must be keyed by Id.
*
* .predict({"petal length": 1})
*
*/
public HashMap predict(final JSONObject inputData) {
return predict(inputData, null, MissingStrategy.LAST_PREDICTION);
}
/**
* Makes a prediction based on a number of field values.
*
* The input fields must be keyed by Id.
*
* .predict({"petal length": 1})
*
* There are two possible
* strategies to predict when the value for the splitting field
* is missing:
* 0 - LAST_PREDICTION: the last issued prediction is returned.
* 1 - PROPORTIONAL: we consider all possible outcomes and create
* an average prediction.
*
*/
public Prediction predict(final JSONObject inputData, List path,
MissingStrategy strategy) {
if (strategy == null) {
strategy = MissingStrategy.LAST_PREDICTION;
}
if( path == null ) {
path = new ArrayList();
}
if( strategy == MissingStrategy.LAST_PREDICTION ) {
if (this.children != null && this.children.size() > 0) {
for (int i = 0; i < this.children.size(); i++) {
BoostedTree child = this.children.get(i);
if( child.predicate.apply(inputData, fields) ) {
path.add(child.predicate.toRule(fields));
return child.predict(inputData, path, strategy);
}
}
}
return new Prediction(this.output, this.count, path, children);
} else if( strategy == MissingStrategy.PROPORTIONAL ) {
TreeHolder lastNode = new TreeHolder();
Map finalDistribution = predictProportional(
inputData, lastNode, path, false);
return new Prediction(finalDistribution,
((Number) finalDistribution.get("count")).longValue(),
path, lastNode.getTree().getChildren());
} else {
throw new UnsupportedOperationException(
String.format("Unsupported missing strategy %s", strategy.name()));
}
}
/**
* Makes a prediction based on a number of field values averaging
* the predictions of the leaves that fall in a subtree.
*
* Each time a splitting field has no value assigned, we consider
* both branches of the split to be true, merging their predictions.
* The function returns the merged distribution and the last node
* reached by a unique path.
*
* @param inputData
* @param path
* @param missingFound
*/
protected Map predictProportional(
final JSONObject inputData, final TreeHolder lastNode,
List path, Boolean missingFound) {
if( path == null ) {
path = new ArrayList();
}
Map finalDistribution =
new HashMap();
// We are in a leaf node... the only thing we need to do is return
// distribution of the node as a Map object
if( children.isEmpty() ) {
finalDistribution.put("g_sum", g_sum);
finalDistribution.put("h_sum", h_sum);
finalDistribution.put("count", count);
lastNode.setTree(this);
return finalDistribution;
}
String optype = (String) Utils.getJSONObject(
fields, objectiveField + ".optype");
if (isOneBranch(children, inputData) || optype.equals("text") ||
optype.equals("items")) {
for (BoostedTree child : children) {
if( child.getPredicate().apply(inputData, fields) ) {
String newRule = child.getPredicate().toRule(fields);
if( !path.contains(newRule) && !missingFound ) {
path.add(newRule);
}
return child.predictProportional(inputData, lastNode, path, missingFound);
}
}
} else {
// missing value found, the unique path stops
missingFound = true;
double gSum = 0.0;
double hSum = 0.0;
int population = 0;
for (BoostedTree child : children) {
Map distribution =
child.predictProportional(inputData, lastNode, path, missingFound);
gSum += (Double) distribution.get("g_sum");
hSum += (Double) distribution.get("h_sum");
population += ((Number) distribution.get("count")).longValue();
}
finalDistribution.put("g_sum", gSum);
finalDistribution.put("h_sum", hSum);
finalDistribution.put("count", population);
lastNode.setTree(this);
return finalDistribution;
}
return null;
}
/**
* Returns a list that includes all the leaves of the tree.
*
* @param path a List of Strings empty array where the path
* @return the list of leaf nodes
*/
protected List getLeaves(List path) {
List leaves = new ArrayList();
if( path == null ) {
path = new ArrayList();
}
if( !isPredicate() ) {
path.add(predicate.toRule(fields));
}
if( !children.isEmpty() ) {
for (BoostedTree child : children) {
leaves.addAll(child.getLeaves(path));
}
} else {
leaves.add(clone());
}
return leaves;
}
/**
* Returns a list that includes all the leaves of the tree.
*
* @return the list of leaf nodes
*/
public List getLeaves() {
return getLeaves(null);
}
/**
* Creates a copy of the current boosted tree node
*
* @return the copy of the boosted tree node
*/
protected BoostedTree clone() {
return new BoostedTree(tree, fields, objectiveField);
}
protected static class TreeHolder {
private BoostedTree tree;
public BoostedTree getTree() {
return tree;
}
public void setTree(BoostedTree tree) {
this.tree = tree;
}
}
}
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