/*
* Licensed to the Apache Software Foundation (ASF) under one
* or more contributor license agreements. See the NOTICE file
* distributed with this work for additional information
* regarding copyright ownership. The ASF licenses this file
* to you under the Apache License, Version 2.0 (the
* "License"); you may not use this file except in compliance
* with the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing,
* software distributed under the License is distributed on an
* "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
* KIND, either express or implied. See the License for the
* specific language governing permissions and limitations
* under the License.
*/
/*
* Copyright (c) 2010 Haifeng Li
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package hivemall.smile.regression;
import hivemall.smile.data.Attribute;
import hivemall.smile.data.Attribute.AttributeType;
import hivemall.smile.utils.SmileExtUtils;
import hivemall.utils.collections.IntArrayList;
import hivemall.utils.lang.ObjectUtils;
import hivemall.utils.lang.StringUtils;
import java.io.Externalizable;
import java.io.IOException;
import java.io.ObjectInput;
import java.io.ObjectOutput;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.List;
import java.util.PriorityQueue;
import javax.annotation.Nonnull;
import javax.annotation.Nullable;
import org.apache.hadoop.hive.ql.metadata.HiveException;
import smile.math.Math;
import smile.math.Random;
import smile.regression.GradientTreeBoost;
import smile.regression.RandomForest;
import smile.regression.Regression;
/**
* Decision tree for regression. A decision tree can be learned by splitting the training set into
* subsets based on an attribute value test. This process is repeated on each derived subset in a
* recursive manner called recursive partitioning.
* <p>
* Classification and Regression Tree techniques have a number of advantages over many of those
* alternative techniques.
* <dl>
* <dt>Simple to understand and interpret.</dt>
* <dd>In most cases, the interpretation of results summarized in a tree is very simple. This
* simplicity is useful not only for purposes of rapid classification of new observations, but can
* also often yield a much simpler "model" for explaining why observations are classified or
* predicted in a particular manner.</dd>
* <dt>Able to handle both numerical and categorical data.</dt>
* <dd>Other techniques are usually specialized in analyzing datasets that have only one type of
* variable.</dd>
* <dt>Tree methods are nonparametric and nonlinear.</dt>
* <dd>The final results of using tree methods for classification or regression can be summarized in
* a series of (usually few) logical if-then conditions (tree nodes). Therefore, there is no
* implicit assumption that the underlying relationships between the predictor variables and the
* dependent variable are linear, follow some specific non-linear link function, or that they are
* even monotonic in nature. Thus, tree methods are particularly well suited for data mining tasks,
* where there is often little a priori knowledge nor any coherent set of theories or predictions
* regarding which variables are related and how. In those types of data analytics, tree methods can
* often reveal simple relationships between just a few variables that could have easily gone
* unnoticed using other analytic techniques.</dd>
* </dl>
* One major problem with classification and regression trees is their high variance. Often a small
* change in the data can result in a very different series of splits, making interpretation
* somewhat precarious. Besides, decision-tree learners can create over-complex trees that cause
* over-fitting. Mechanisms such as pruning are necessary to avoid this problem. Another limitation
* of trees is the lack of smoothness of the prediction surface.
* <p>
* Some techniques such as bagging, boosting, and random forest use more than one decision tree for
* their analysis.
*
* @see GradientTreeBoost
* @see RandomForest
*/
public final class RegressionTree implements Regression<double[]> {
/**
* The attributes of independent variable.
*/
private final Attribute[] _attributes;
private final boolean _hasNumericType;
/**
* Variable importance. Every time a split of a node is made on variable the impurity criterion
* for the two descendant nodes is less than the parent node. Adding up the decreases for each
* individual variable over the tree gives a simple measure of variable importance.
*/
private final double[] _importance;
/**
* The root of the regression tree
*/
private final Node _root;
/**
* The maximum number of the tree depth
*/
private final int _maxDepth;
/**
* The number of instances in a node below which the tree will not split, setting S = 5
* generally gives good results.
*/
private final int _minSplit;
/**
* The minimum number of samples in a leaf node
*/
private final int _minLeafSize;
/**
* The number of input variables to be used to determine the decision at a node of the tree.
*/
private final int _numVars;
/**
* The index of training values in ascending order. Note that only numeric attributes will be
* sorted.
*/
private final int[][] _order;
private final Random _rnd;
private final NodeOutput _nodeOutput;
/**
* An interface to calculate node output. Note that samples[i] is the number of sampling of
* dataset[i]. 0 means that the datum is not included and values of greater than 1 are possible
* because of sampling with replacement.
*/
public interface NodeOutput {
/**
* Calculate the node output.
*
* @param samples the samples in the node.
* @return the node output
*/
public double calculate(int[] samples);
}
/**
* Regression tree node.
*/
public static final class Node implements Externalizable {
/**
* Predicted real value for this node.
*/
double output = 0.0;
/**
* The split feature for this node.
*/
int splitFeature = -1;
/**
* The type of split feature
*/
AttributeType splitFeatureType = null;
/**
* The split value.
*/
double splitValue = Double.NaN;
/**
* Reduction in squared error compared to parent.
*/
double splitScore = 0.0;
/**
* Children node.
*/
Node trueChild;
/**
* Children node.
*/
Node falseChild;
/**
* Predicted output for children node.
*/
double trueChildOutput = 0.0;
/**
* Predicted output for children node.
*/
double falseChildOutput = 0.0;
public Node() {}//for Externalizable
public Node(double output) {
this.output = output;
}
/**
* Evaluate the regression tree over an instance.
*/
public double predict(final double[] x) {
if (trueChild == null && falseChild == null) {
return output;
} else {
if (splitFeatureType == AttributeType.NOMINAL) {
// REVIEWME if(Math.equals(x[splitFeature], splitValue)) {
if (x[splitFeature] == splitValue) {
return trueChild.predict(x);
} else {
return falseChild.predict(x);
}
} else if (splitFeatureType == AttributeType.NUMERIC) {
if (x[splitFeature] <= splitValue) {
return trueChild.predict(x);
} else {
return falseChild.predict(x);
}
} else {
throw new IllegalStateException("Unsupported attribute type: "
+ splitFeatureType);
}
}
}
/**
* Evaluate the regression tree over an instance.
*/
public double predict(final int[] x) {
if (trueChild == null && falseChild == null) {
return output;
} else if (x[splitFeature] == (int) splitValue) {
return trueChild.predict(x);
} else {
return falseChild.predict(x);
}
}
public void jsCodegen(@Nonnull final StringBuilder builder, final int depth) {
if (trueChild == null && falseChild == null) {
indent(builder, depth);
builder.append("").append(output).append(";\n");
} else {
if (splitFeatureType == AttributeType.NOMINAL) {
indent(builder, depth);
builder.append("if(x[")
.append(splitFeature)
.append("] == ")
.append(splitValue)
.append(") {\n");
trueChild.jsCodegen(builder, depth + 1);
indent(builder, depth);
builder.append("} else {\n");
falseChild.jsCodegen(builder, depth + 1);
indent(builder, depth);
builder.append("}\n");
} else if (splitFeatureType == AttributeType.NUMERIC) {
indent(builder, depth);
builder.append("if(x[")
.append(splitFeature)
.append("] <= ")
.append(splitValue)
.append(") {\n");
trueChild.jsCodegen(builder, depth + 1);
indent(builder, depth);
builder.append("} else {\n");
falseChild.jsCodegen(builder, depth + 1);
indent(builder, depth);
builder.append("}\n");
} else {
throw new IllegalStateException("Unsupported attribute type: "
+ splitFeatureType);
}
}
}
public int opCodegen(final List<String> scripts, int depth) {
int selfDepth = 0;
final StringBuilder buf = new StringBuilder();
if (trueChild == null && falseChild == null) {
buf.append("push ").append(output);
scripts.add(buf.toString());
buf.setLength(0);
buf.append("goto last");
scripts.add(buf.toString());
selfDepth += 2;
} else {
if (splitFeatureType == AttributeType.NOMINAL) {
buf.append("push ").append("x[").append(splitFeature).append("]");
scripts.add(buf.toString());
buf.setLength(0);
buf.append("push ").append(splitValue);
scripts.add(buf.toString());
buf.setLength(0);
buf.append("ifeq ");
scripts.add(buf.toString());
depth += 3;
selfDepth += 3;
int trueDepth = trueChild.opCodegen(scripts, depth);
selfDepth += trueDepth;
scripts.set(depth - 1, "ifeq " + String.valueOf(depth + trueDepth));
int falseDepth = falseChild.opCodegen(scripts, depth + trueDepth);
selfDepth += falseDepth;
} else if (splitFeatureType == AttributeType.NUMERIC) {
buf.append("push ").append("x[").append(splitFeature).append("]");
scripts.add(buf.toString());
buf.setLength(0);
buf.append("push ").append(splitValue);
scripts.add(buf.toString());
buf.setLength(0);
buf.append("ifle ");
scripts.add(buf.toString());
depth += 3;
selfDepth += 3;
int trueDepth = trueChild.opCodegen(scripts, depth);
selfDepth += trueDepth;
scripts.set(depth - 1, "ifle " + String.valueOf(depth + trueDepth));
int falseDepth = falseChild.opCodegen(scripts, depth + trueDepth);
selfDepth += falseDepth;
} else {
throw new IllegalStateException("Unsupported attribute type: "
+ splitFeatureType);
}
}
return selfDepth;
}
@Override
public void writeExternal(ObjectOutput out) throws IOException {
out.writeDouble(output);
out.writeInt(splitFeature);
if (splitFeatureType == null) {
out.writeInt(-1);
} else {
out.writeInt(splitFeatureType.getTypeId());
}
out.writeDouble(splitValue);
if (trueChild == null) {
out.writeBoolean(false);
} else {
out.writeBoolean(true);
trueChild.writeExternal(out);
}
if (falseChild == null) {
out.writeBoolean(false);
} else {
out.writeBoolean(true);
falseChild.writeExternal(out);
}
}
@Override
public void readExternal(ObjectInput in) throws IOException, ClassNotFoundException {
this.output = in.readDouble();
this.splitFeature = in.readInt();
int typeId = in.readInt();
if (typeId == -1) {
this.splitFeatureType = null;
} else {
this.splitFeatureType = AttributeType.resolve(typeId);
}
this.splitValue = in.readDouble();
if (in.readBoolean()) {
this.trueChild = new Node();
trueChild.readExternal(in);
}
if (in.readBoolean()) {
this.falseChild = new Node();
falseChild.readExternal(in);
}
}
}
private static void indent(final StringBuilder builder, final int depth) {
for (int i = 0; i < depth; i++) {
builder.append(" ");
}
}
/**
* Regression tree node for training purpose.
*/
private final class TrainNode implements Comparable<TrainNode> {
/**
* The associated regression tree node.
*/
final Node node;
/**
* Child node that passes the test.
*/
TrainNode trueChild;
/**
* Child node that fails the test.
*/
TrainNode falseChild;
/**
* Training dataset.
*/
final double[][] x;
/**
* Training data response value.
*/
final double[] y;
int[] bags;
final int depth;
/**
* Constructor.
*/
public TrainNode(Node node, double[][] x, double[] y, int[] bags, int depth) {
this.node = node;
this.x = x;
this.y = y;
this.bags = bags;
this.depth = depth;
}
@Override
public int compareTo(final TrainNode a) {
return (int) Math.signum(a.node.splitScore - node.splitScore);
}
/**
* Calculate the node output for leaves.
*
* @param output the output calculate functor.
*/
public void calculateOutput(final NodeOutput output) {
if (node.trueChild == null && node.falseChild == null) {
int[] samples = SmileExtUtils.bagsToSamples(bags);
node.output = output.calculate(samples);
} else {
if (trueChild != null) {
trueChild.calculateOutput(output);
}
if (falseChild != null) {
falseChild.calculateOutput(output);
}
}
}
/**
* Finds the best attribute to split on at the current node. Returns true if a split exists
* to reduce squared error, false otherwise.
*/
public boolean findBestSplit() {
// avoid split if tree depth is larger than threshold
if (depth >= _maxDepth) {
return false;
}
// avoid split if the number of samples is less than threshold
final int numSamples = bags.length;
if (numSamples <= _minSplit) {
return false;
}
final double sum = node.output * numSamples;
final int p = _attributes.length;
final int[] variables = new int[p];
for (int i = 0; i < p; i++) {
variables[i] = i;
}
if (_numVars < p) {
SmileExtUtils.shuffle(variables, _rnd);
}
// Loop through features and compute the reduction of squared error,
// which is trueCount * trueMean^2 + falseCount * falseMean^2 - count * parentMean^2
final int[] samples = _hasNumericType ? SmileExtUtils.bagsToSamples(bags, x.length)
: null;
for (int j = 0; j < _numVars; j++) {
Node split = findBestSplit(numSamples, sum, variables[j], samples);
if (split.splitScore > node.splitScore) {
node.splitFeature = split.splitFeature;
node.splitFeatureType = split.splitFeatureType;
node.splitValue = split.splitValue;
node.splitScore = split.splitScore;
node.trueChildOutput = split.trueChildOutput;
node.falseChildOutput = split.falseChildOutput;
}
}
return node.splitFeature != -1;
}
/**
* Finds the best split cutoff for attribute j at the current node.
*
* @param n the number instances in this node.
* @param count the sample count in each class.
* @param impurity the impurity of this node.
* @param j the attribute to split on.
*/
private Node findBestSplit(final int n, final double sum, final int j,
@Nullable final int[] samples) {
final Node split = new Node(0.d);
if (_attributes[j].type == AttributeType.NOMINAL) {
final int m = _attributes[j].getSize();
final double[] trueSum = new double[m];
final int[] trueCount = new int[m];
for (int b = 0, size = bags.length; b < size; b++) {
int i = bags[b];
// For each true feature of this datum increment the
// sufficient statistics for the "true" branch to evaluate
// splitting on this feature.
int index = (int) x[i][j];
trueSum[index] += y[i];
++trueCount[index];
}
for (int k = 0; k < m; k++) {
final double tc = (double) trueCount[k];
final double fc = n - tc;
// skip splitting
if (tc < _minSplit || fc < _minSplit) {
continue;
}
// compute penalized means
final double trueMean = trueSum[k] / tc;
final double falseMean = (sum - trueSum[k]) / fc;
final double gain = (tc * trueMean * trueMean + fc * falseMean * falseMean) - n
* split.output * split.output;
if (gain > split.splitScore) {
// new best split
split.splitFeature = j;
split.splitFeatureType = AttributeType.NOMINAL;
split.splitValue = k;
split.splitScore = gain;
split.trueChildOutput = trueMean;
split.falseChildOutput = falseMean;
}
}
} else if (_attributes[j].type == AttributeType.NUMERIC) {
double trueSum = 0.0;
int trueCount = 0;
double prevx = Double.NaN;
for (int i : _order[j]) {
final int sample = samples[i];
if (sample > 0) {
if (Double.isNaN(prevx) || x[i][j] == prevx) {
prevx = x[i][j];
trueSum += sample * y[i];
trueCount += sample;
continue;
}
final double falseCount = n - trueCount;
// If either side is empty, skip this feature.
if (trueCount < _minSplit || falseCount < _minSplit) {
prevx = x[i][j];
trueSum += sample * y[i];
trueCount += sample;
continue;
}
// compute penalized means
final double trueMean = trueSum / trueCount;
final double falseMean = (sum - trueSum) / falseCount;
// The gain is actually -(reduction in squared error) for
// sorting in priority queue, which treats smaller number with
// higher priority.
final double gain = (trueCount * trueMean * trueMean + falseCount
* falseMean * falseMean)
- n * split.output * split.output;
if (gain > split.splitScore) {
// new best split
split.splitFeature = j;
split.splitFeatureType = AttributeType.NUMERIC;
split.splitValue = (x[i][j] + prevx) / 2;
split.splitScore = gain;
split.trueChildOutput = trueMean;
split.falseChildOutput = falseMean;
}
prevx = x[i][j];
trueSum += sample * y[i];
trueCount += sample;
}
}
} else {
throw new IllegalStateException("Unsupported attribute type: "
+ _attributes[j].type);
}
return split;
}
/**
* Split the node into two children nodes. Returns true if split success.
*/
public boolean split(final PriorityQueue<TrainNode> nextSplits) {
if (node.splitFeature < 0) {
throw new IllegalStateException("Split a node with invalid feature.");
}
// split sample bags
int childBagSize = (int) (bags.length * 0.4);
IntArrayList trueBags = new IntArrayList(childBagSize);
IntArrayList falseBags = new IntArrayList(childBagSize);
int tc = splitSamples(trueBags, falseBags);
int fc = bags.length - tc;
if (tc < _minLeafSize || fc < _minLeafSize) {
// set as a leaf node
node.splitFeature = -1;
node.splitFeatureType = null;
node.splitValue = Double.NaN;
node.splitScore = 0.0;
if (_nodeOutput == null) {
this.bags = null;
}
return false;
}
this.bags = null; // help GC for recursive call
node.trueChild = new Node(node.trueChildOutput);
this.trueChild = new TrainNode(node.trueChild, x, y, trueBags.toArray(), depth + 1);
trueBags = null; // help GC for recursive call
if (tc >= _minSplit && trueChild.findBestSplit()) {
if (nextSplits != null) {
nextSplits.add(trueChild);
} else {
trueChild.split(null);
}
}
node.falseChild = new Node(node.falseChildOutput);
this.falseChild = new TrainNode(node.falseChild, x, y, falseBags.toArray(), depth + 1);
falseBags = null; // help GC for recursive call
if (fc >= _minSplit && falseChild.findBestSplit()) {
if (nextSplits != null) {
nextSplits.add(falseChild);
} else {
falseChild.split(null);
}
}
_importance[node.splitFeature] += node.splitScore;
return true;
}
/**
* @return the number of true samples
*/
private int splitSamples(@Nonnull final IntArrayList trueBags,
@Nonnull final IntArrayList falseBags) {
int tc = 0;
if (node.splitFeatureType == AttributeType.NOMINAL) {
final int splitFeature = node.splitFeature;
final double splitValue = node.splitValue;
for (int i = 0, size = bags.length; i < size; i++) {
final int index = bags[i];
if (x[index][splitFeature] == splitValue) {
trueBags.add(index);
tc++;
} else {
falseBags.add(index);
}
}
} else if (node.splitFeatureType == AttributeType.NUMERIC) {
final int splitFeature = node.splitFeature;
final double splitValue = node.splitValue;
for (int i = 0, size = bags.length; i < size; i++) {
final int index = bags[i];
if (x[index][splitFeature] <= splitValue) {
trueBags.add(index);
tc++;
} else {
falseBags.add(index);
}
}
} else {
throw new IllegalStateException("Unsupported attribute type: "
+ node.splitFeatureType);
}
return tc;
}
}
public RegressionTree(@Nullable Attribute[] attributes, @Nonnull double[][] x,
@Nonnull double[] y, int maxLeafs) {
this(attributes, x, y, x[0].length, Integer.MAX_VALUE, maxLeafs, 5, 1, null, null, null);
}
public RegressionTree(@Nullable Attribute[] attributes, @Nonnull double[][] x,
@Nonnull double[] y, int maxLeafs, @Nullable smile.math.Random rand) {
this(attributes, x, y, x[0].length, Integer.MAX_VALUE, maxLeafs, 5, 1, null, null, rand);
}
public RegressionTree(@Nullable Attribute[] attributes, @Nonnull double[][] x,
@Nonnull double[] y, int numVars, int maxDepth, int maxLeafs, int minSplits,
int minLeafSize, @Nullable int[][] order, @Nullable int[] bags,
@Nullable smile.math.Random rand) {
this(attributes, x, y, numVars, maxDepth, maxLeafs, minSplits, minLeafSize, order, bags, null, rand);
}
/**
* Constructor. Learns a regression tree for gradient tree boosting.
*
* @param attributes the attribute properties.
* @param x the training instances.
* @param y the response variable.
* @param numVars the number of input variables to pick to split on at each node. It seems that
* dim/3 give generally good performance, where dim is the number of variables.
* @param maxLeafs the maximum number of leaf nodes in the tree.
* @param minSplits number of instances in a node below which the tree will not split, setting S
* = 5 generally gives good results.
* @param order the index of training values in ascending order. Note that only numeric
* attributes need be sorted.
* @param bags the sample set of instances for stochastic learning.
* @param output An interface to calculate node output.
*/
public RegressionTree(@Nullable Attribute[] attributes, @Nonnull double[][] x,
@Nonnull double[] y, int numVars, int maxDepth, int maxLeafs, int minSplits,
int minLeafSize, @Nullable int[][] order, @Nullable int[] bags,
@Nullable NodeOutput output, @Nullable smile.math.Random rand) {
checkArgument(x, y, numVars, maxDepth, maxLeafs, minSplits, minLeafSize);
this._attributes = SmileExtUtils.attributeTypes(attributes, x);
if (_attributes.length != x[0].length) {
throw new IllegalArgumentException("-attrs option is invliad: "
+ Arrays.toString(attributes));
}
this._hasNumericType = SmileExtUtils.containsNumericType(_attributes);
this._numVars = numVars;
this._maxDepth = maxDepth;
this._minSplit = minSplits;
this._minLeafSize = minLeafSize;
this._order = (order == null) ? SmileExtUtils.sort(_attributes, x) : order;
this._importance = new double[_attributes.length];
this._rnd = (rand == null) ? new smile.math.Random() : rand;
this._nodeOutput = output;
int n = 0;
double sum = 0.0;
if (bags == null) {
n = y.length;
bags = new int[n];
for (int i = 0; i < n; i++) {
bags[i] = i;
sum += y[i];
}
} else {
n = bags.length;
for (int i = 0; i < n; i++) {
int index = bags[i];
sum += y[index];
}
}
this._root = new Node(sum / n);
TrainNode trainRoot = new TrainNode(_root, x, y, bags, 1);
if (maxLeafs == Integer.MAX_VALUE) {
if (trainRoot.findBestSplit()) {
trainRoot.split(null);
}
} else {
// Priority queue for best-first tree growing.
PriorityQueue<TrainNode> nextSplits = new PriorityQueue<TrainNode>();
// Now add splits to the tree until max tree size is reached
if (trainRoot.findBestSplit()) {
nextSplits.add(trainRoot);
}
// Pop best leaf from priority queue, split it, and push
// children nodes into the queue if possible.
for (int leaves = 1; leaves < maxLeafs; leaves++) {
// parent is the leaf to split
TrainNode node = nextSplits.poll();
if (node == null) {
break;
}
node.split(nextSplits); // Split the parent node into two children nodes
}
}
if (output != null) {
trainRoot.calculateOutput(output);
}
}
private static void checkArgument(@Nonnull double[][] x, @Nonnull double[] y, int numVars,
int maxDepth, int maxLeafs, int minSplits, int minLeafSize) {
if (x.length != y.length) {
throw new IllegalArgumentException(String.format(
"The sizes of X and Y don't match: %d != %d", x.length, y.length));
}
if (numVars <= 0 || numVars > x[0].length) {
throw new IllegalArgumentException(
"Invalid number of variables to split on at a node of the tree: " + numVars);
}
if (maxDepth < 2) {
throw new IllegalArgumentException("maxDepth should be greater than 1: " + maxDepth);
}
if (maxLeafs < 2) {
throw new IllegalArgumentException("Invalid maximum leaves: " + maxLeafs);
}
if (minSplits < 2) {
throw new IllegalArgumentException(
"Invalid minimum number of samples required to split an internal node: "
+ minSplits);
}
if (minLeafSize < 1) {
throw new IllegalArgumentException("Invalid minimum size of leaf nodes: " + minLeafSize);
}
}
/**
* Returns the variable importance. Every time a split of a node is made on variable the
* impurity criterion for the two descendent nodes is less than the parent node. Adding up the
* decreases for each individual variable over the tree gives a simple measure of variable
* importance.
*
* @return the variable importance
*/
public double[] importance() {
return _importance;
}
@Override
public double predict(double[] x) {
return _root.predict(x);
}
public String predictJsCodegen() {
StringBuilder buf = new StringBuilder(1024);
_root.jsCodegen(buf, 0);
return buf.toString();
}
public String predictOpCodegen(@Nonnull String sep) {
List<String> opslist = new ArrayList<String>();
_root.opCodegen(opslist, 0);
opslist.add("call end");
String scripts = StringUtils.concat(opslist, sep);
return scripts;
}
@Nonnull
public byte[] predictSerCodegen(boolean compress) throws HiveException {
try {
if (compress) {
return ObjectUtils.toCompressedBytes(_root);
} else {
return ObjectUtils.toBytes(_root);
}
} catch (IOException ioe) {
throw new HiveException("IOException cause while serializing DecisionTree object", ioe);
} catch (Exception e) {
throw new HiveException("Exception cause while serializing DecisionTree object", e);
}
}
public static Node deserializeNode(final byte[] serializedObj, final int length,
final boolean compressed) throws HiveException {
final Node root = new Node();
try {
if (compressed) {
ObjectUtils.readCompressedObject(serializedObj, 0, length, root);
} else {
ObjectUtils.readObject(serializedObj, length, root);
}
} catch (IOException ioe) {
throw new HiveException("IOException cause while deserializing DecisionTree object",
ioe);
} catch (Exception e) {
throw new HiveException("Exception cause while deserializing DecisionTree object", e);
}
return root;
}
}