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Allow JVM-Package to access inplace predict method (#9167)

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---------

Co-authored-by: Stephan T. Lavavej <stl@nuwen.net>
Co-authored-by: Jiaming Yuan <jm.yuan@outlook.com>
Co-authored-by: Joe <25804777+ByteSizedJoe@users.noreply.github.com>
This commit is contained in:
Jon Yoquinto
2023-09-11 17:29:51 -06:00
committed by GitHub
parent 9027686cac
commit d05ea589fb
5 changed files with 384 additions and 18 deletions
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106
jvm-packages/xgboost4j/src/main/java/ml/dmlc/xgboost4j/java/Booster.java
View File

@@ -39,6 +39,21 @@ public class Booster implements Serializable, KryoSerializable {
// handle to the booster.
private long handle = 0;
private int version = 0;
/**
* Type of prediction, used for inplace_predict.
*/
public enum PredictionType {
kValue(0),
kMargin(1);
private Integer ptype;
private PredictionType(final Integer ptype) {
this.ptype = ptype;
}
public Integer getPType() {
return ptype;
}
}
/**
* Create a new Booster with empty stage.
@@ -375,6 +390,97 @@ public class Booster implements Serializable, KryoSerializable {
return predicts;
}
/**
* Perform thread-safe prediction.
*
* @param data Flattened input matrix of features for prediction
* @param nrow The number of preditions to make (count of input matrix rows)
* @param ncol The number of features in the model (count of input matrix columns)
* @param missing Value indicating missing element in the <code>data</code> input matrix
*
* @return predict Result matrix
*/
public float[][] inplace_predict(float[] data,
int nrow,
int ncol,
float missing) throws XGBoostError {
int[] iteration_range = new int[2];
iteration_range[0] = 0;
iteration_range[1] = 0;
return this.inplace_predict(data, nrow, ncol,
missing, iteration_range, PredictionType.kValue, null);
}
/**
* Perform thread-safe prediction.
*
* @param data Flattened input matrix of features for prediction
* @param nrow The number of preditions to make (count of input matrix rows)
* @param ncol The number of features in the model (count of input matrix columns)
* @param missing Value indicating missing element in the <code>data</code> input matrix
* @param iteration_range Specifies which layer of trees are used in prediction. For
* example, if a random forest is trained with 100 rounds.
* Specifying `iteration_range=[10, 20)`, then only the forests
* built during [10, 20) (half open set) rounds are used in this
* prediction.
*
* @return predict Result matrix
*/
public float[][] inplace_predict(float[] data,
int nrow,
int ncol,
float missing, int[] iteration_range) throws XGBoostError {
return this.inplace_predict(data, nrow, ncol,
missing, iteration_range, PredictionType.kValue, null);
}
/**
* Perform thread-safe prediction.
*
* @param data Flattened input matrix of features for prediction
* @param nrow The number of preditions to make (count of input matrix rows)
* @param ncol The number of features in the model (count of input matrix columns)
* @param missing Value indicating missing element in the <code>data</code> input matrix
* @param iteration_range Specifies which layer of trees are used in prediction. For
* example, if a random forest is trained with 100 rounds.
* Specifying `iteration_range=[10, 20)`, then only the forests
* built during [10, 20) (half open set) rounds are used in this
* prediction.
* @param predict_type What kind of prediction to run.
* @return predict Result matrix
*/
public float[][] inplace_predict(float[] data,
int nrow,
int ncol,
float missing,
int[] iteration_range,
PredictionType predict_type,
float[] base_margin) throws XGBoostError {
if (iteration_range.length != 2) {
throw new XGBoostError(new String("Iteration range is expected to be [begin, end)."));
}
int ptype = predict_type.getPType();
int begin = iteration_range[0];
int end = iteration_range[1];
float[][] rawPredicts = new float[1][];
XGBoostJNI.checkCall(XGBoostJNI.XGBoosterPredictFromDense(handle, data, nrow, ncol,
missing,
begin, end, ptype, base_margin, rawPredicts));
int col = rawPredicts[0].length / nrow;
float[][] predicts = new float[nrow][col];
int r, c;
for (int i = 0; i < rawPredicts[0].length; i++) {
r = i / col;
c = i % col;
predicts[r][c] = rawPredicts[0][i];
}
return predicts;
}
/**
* Predict leaf indices given the data
*

8
jvm-packages/xgboost4j/src/main/java/ml/dmlc/xgboost4j/java/XGBoostJNI.java
View File

@@ -119,6 +119,10 @@ class XGBoostJNI {
public final static native int XGBoosterPredict(long handle, long dmat, int option_mask,
int ntree_limit, float[][] predicts);
public final static native int XGBoosterPredictFromDense(long handle, float[] data,
long nrow, long ncol, float missing, int iteration_begin, int iteration_end, int predict_type, float[] margin,
float[][] predicts);
public final static native int XGBoosterLoadModel(long handle, String fname);
public final static native int XGBoosterSaveModel(long handle, String fname);
@@ -154,10 +158,6 @@ class XGBoostJNI {
public final static native int XGDMatrixSetInfoFromInterface(
long handle, String field, String json);
@Deprecated
public final static native int XGDeviceQuantileDMatrixCreateFromCallback(
java.util.Iterator<ColumnBatch> iter, float missing, int nthread, int maxBin, long[] out);
public final static native int XGQuantileDMatrixCreateFromCallback(
java.util.Iterator<ColumnBatch> iter, java.util.Iterator<ColumnBatch> ref, String config, long[] out);