Allow JVM-Package to access inplace predict method (#9167)
--------- 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
GitHub
parent
9027686cac
commit
d05ea589fb
@ -39,6 +39,21 @@ public class Booster implements Serializable, KryoSerializable {
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// handle to the booster.
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private long handle = 0;
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private int version = 0;
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/**
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* Type of prediction, used for inplace_predict.
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*/
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public enum PredictionType {
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kValue(0),
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kMargin(1);
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private Integer ptype;
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private PredictionType(final Integer ptype) {
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this.ptype = ptype;
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}
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public Integer getPType() {
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return ptype;
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}
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}
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/**
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* Create a new Booster with empty stage.
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@ -375,6 +390,97 @@ public class Booster implements Serializable, KryoSerializable {
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return predicts;
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}
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/**
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* Perform thread-safe prediction.
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*
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* @param data Flattened input matrix of features for prediction
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* @param nrow The number of preditions to make (count of input matrix rows)
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* @param ncol The number of features in the model (count of input matrix columns)
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* @param missing Value indicating missing element in the <code>data</code> input matrix
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*
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* @return predict Result matrix
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*/
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public float[][] inplace_predict(float[] data,
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int nrow,
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int ncol,
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float missing) throws XGBoostError {
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int[] iteration_range = new int[2];
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iteration_range[0] = 0;
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iteration_range[1] = 0;
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return this.inplace_predict(data, nrow, ncol,
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missing, iteration_range, PredictionType.kValue, null);
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}
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/**
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* Perform thread-safe prediction.
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*
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* @param data Flattened input matrix of features for prediction
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* @param nrow The number of preditions to make (count of input matrix rows)
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* @param ncol The number of features in the model (count of input matrix columns)
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* @param missing Value indicating missing element in the <code>data</code> input matrix
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* @param iteration_range Specifies which layer of trees are used in prediction. For
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* example, if a random forest is trained with 100 rounds.
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* Specifying `iteration_range=[10, 20)`, then only the forests
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* built during [10, 20) (half open set) rounds are used in this
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* prediction.
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*
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* @return predict Result matrix
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*/
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public float[][] inplace_predict(float[] data,
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int nrow,
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int ncol,
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float missing, int[] iteration_range) throws XGBoostError {
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return this.inplace_predict(data, nrow, ncol,
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missing, iteration_range, PredictionType.kValue, null);
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}
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/**
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* Perform thread-safe prediction.
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*
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* @param data Flattened input matrix of features for prediction
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* @param nrow The number of preditions to make (count of input matrix rows)
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* @param ncol The number of features in the model (count of input matrix columns)
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* @param missing Value indicating missing element in the <code>data</code> input matrix
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* @param iteration_range Specifies which layer of trees are used in prediction. For
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* example, if a random forest is trained with 100 rounds.
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* Specifying `iteration_range=[10, 20)`, then only the forests
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* built during [10, 20) (half open set) rounds are used in this
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* prediction.
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* @param predict_type What kind of prediction to run.
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* @return predict Result matrix
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*/
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public float[][] inplace_predict(float[] data,
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int nrow,
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int ncol,
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float missing,
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int[] iteration_range,
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PredictionType predict_type,
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float[] base_margin) throws XGBoostError {
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if (iteration_range.length != 2) {
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throw new XGBoostError(new String("Iteration range is expected to be [begin, end)."));
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}
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int ptype = predict_type.getPType();
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int begin = iteration_range[0];
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int end = iteration_range[1];
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float[][] rawPredicts = new float[1][];
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XGBoostJNI.checkCall(XGBoostJNI.XGBoosterPredictFromDense(handle, data, nrow, ncol,
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missing,
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begin, end, ptype, base_margin, rawPredicts));
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int col = rawPredicts[0].length / nrow;
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float[][] predicts = new float[nrow][col];
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int r, c;
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for (int i = 0; i < rawPredicts[0].length; i++) {
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r = i / col;
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c = i % col;
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predicts[r][c] = rawPredicts[0][i];
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}
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return predicts;
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}
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/**
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* Predict leaf indices given the data
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*
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@ -119,6 +119,10 @@ class XGBoostJNI {
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public final static native int XGBoosterPredict(long handle, long dmat, int option_mask,
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int ntree_limit, float[][] predicts);
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public final static native int XGBoosterPredictFromDense(long handle, float[] data,
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long nrow, long ncol, float missing, int iteration_begin, int iteration_end, int predict_type, float[] margin,
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float[][] predicts);
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public final static native int XGBoosterLoadModel(long handle, String fname);
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public final static native int XGBoosterSaveModel(long handle, String fname);
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@ -154,10 +158,6 @@ class XGBoostJNI {
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public final static native int XGDMatrixSetInfoFromInterface(
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long handle, String field, String json);
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@Deprecated
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public final static native int XGDeviceQuantileDMatrixCreateFromCallback(
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java.util.Iterator<ColumnBatch> iter, float missing, int nthread, int maxBin, long[] out);
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public final static native int XGQuantileDMatrixCreateFromCallback(
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java.util.Iterator<ColumnBatch> iter, java.util.Iterator<ColumnBatch> ref, String config, long[] out);
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@ -684,6 +684,85 @@ JNIEXPORT jint JNICALL Java_ml_dmlc_xgboost4j_java_XGBoostJNI_XGBoosterPredict
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return ret;
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}
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/*
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* Class: ml_dmlc_xgboost4j_java_XGBoostJNI
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* Method: XGBoosterPredictFromDense
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* Signature: (J[FJJFIII[F[[F)I
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*/
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JNIEXPORT jint JNICALL Java_ml_dmlc_xgboost4j_java_XGBoostJNI_XGBoosterPredictFromDense(
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JNIEnv *jenv, jclass jcls, jlong jhandle, jfloatArray jdata, jlong num_rows, jlong num_features,
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jfloat missing, jint iteration_begin, jint iteration_end, jint predict_type,
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jfloatArray jmargin, jobjectArray jout) {
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API_BEGIN();
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BoosterHandle handle = reinterpret_cast<BoosterHandle>(jhandle);
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/**
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* Create array interface.
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*/
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namespace linalg = xgboost::linalg;
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jfloat *data = jenv->GetFloatArrayElements(jdata, nullptr);
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xgboost::Context ctx;
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auto t_data = linalg::MakeTensorView(
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ctx.Device(),
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xgboost::common::Span{data, static_cast<std::size_t>(num_rows * num_features)}, num_rows,
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num_features);
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auto s_array = linalg::ArrayInterfaceStr(t_data);
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/**
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* Create configuration object.
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*/
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xgboost::Json config{xgboost::Object{}};
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config["cache_id"] = xgboost::Integer{};
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config["type"] = xgboost::Integer{static_cast<std::int32_t>(predict_type)};
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config["iteration_begin"] = xgboost::Integer{static_cast<xgboost::bst_layer_t>(iteration_begin)};
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config["iteration_end"] = xgboost::Integer{static_cast<xgboost::bst_layer_t>(iteration_end)};
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config["missing"] = xgboost::Number{static_cast<float>(missing)};
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config["strict_shape"] = xgboost::Boolean{true};
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std::string s_config;
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xgboost::Json::Dump(config, &s_config);
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/**
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* Handle base margin
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*/
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BoosterHandle proxy{nullptr};
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float *margin{nullptr};
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if (jmargin) {
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margin = jenv->GetFloatArrayElements(jmargin, nullptr);
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JVM_CHECK_CALL(XGProxyDMatrixCreate(&proxy));
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JVM_CHECK_CALL(
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XGDMatrixSetFloatInfo(proxy, "base_margin", margin, jenv->GetArrayLength(jmargin)));
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}
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bst_ulong const *out_shape;
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bst_ulong out_dim;
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float const *result;
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auto ret = XGBoosterPredictFromDense(handle, s_array.c_str(), s_config.c_str(), proxy, &out_shape,
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&out_dim, &result);
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jenv->ReleaseFloatArrayElements(jdata, data, 0);
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if (proxy) {
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XGDMatrixFree(proxy);
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jenv->ReleaseFloatArrayElements(jmargin, margin, 0);
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}
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if (ret != 0) {
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return ret;
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}
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std::size_t n{1};
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for (std::size_t i = 0; i < out_dim; ++i) {
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n *= out_shape[i];
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}
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jfloatArray jarray = jenv->NewFloatArray(n);
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jenv->SetFloatArrayRegion(jarray, 0, n, result);
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jenv->SetObjectArrayElement(jout, 0, jarray);
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API_END();
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}
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/*
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* Class: ml_dmlc_xgboost4j_java_XGBoostJNI
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* Method: XGBoosterLoadModel
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@ -207,6 +207,14 @@ JNIEXPORT jint JNICALL Java_ml_dmlc_xgboost4j_java_XGBoostJNI_XGBoosterEvalOneIt
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JNIEXPORT jint JNICALL Java_ml_dmlc_xgboost4j_java_XGBoostJNI_XGBoosterPredict
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(JNIEnv *, jclass, jlong, jlong, jint, jint, jobjectArray);
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/*
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* Class: ml_dmlc_xgboost4j_java_XGBoostJNI
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* Method: XGBoosterPredictFromDense
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* Signature: (J[FJJFIII[F[[F)I
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*/
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JNIEXPORT jint JNICALL Java_ml_dmlc_xgboost4j_java_XGBoostJNI_XGBoosterPredictFromDense
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(JNIEnv *, jclass, jlong, jfloatArray, jlong, jlong, jfloat, jint, jint, jint, jfloatArray, jobjectArray);
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/*
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* Class: ml_dmlc_xgboost4j_java_XGBoostJNI
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* Method: XGBoosterLoadModel
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@ -359,14 +367,6 @@ JNIEXPORT jint JNICALL Java_ml_dmlc_xgboost4j_java_XGBoostJNI_CommunicatorAllred
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JNIEXPORT jint JNICALL Java_ml_dmlc_xgboost4j_java_XGBoostJNI_XGDMatrixSetInfoFromInterface
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(JNIEnv *, jclass, jlong, jstring, jstring);
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/*
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* Class: ml_dmlc_xgboost4j_java_XGBoostJNI
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* Method: XGDeviceQuantileDMatrixCreateFromCallback
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* Signature: (Ljava/util/Iterator;FII[J)I
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*/
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JNIEXPORT jint JNICALL Java_ml_dmlc_xgboost4j_java_XGBoostJNI_XGDeviceQuantileDMatrixCreateFromCallback
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(JNIEnv *, jclass, jobject, jfloat, jint, jint, jlongArray);
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/*
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* Class: ml_dmlc_xgboost4j_java_XGBoostJNI
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* Method: XGQuantileDMatrixCreateFromCallback
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@ -15,16 +15,24 @@
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*/
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package ml.dmlc.xgboost4j.java;
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import java.io.*;
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import java.util.*;
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import junit.framework.TestCase;
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import org.junit.Assert;
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import org.junit.Test;
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import java.io.ByteArrayInputStream;
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import java.io.ByteArrayOutputStream;
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import java.io.File;
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import java.io.IOException;
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import java.util.*;
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import java.util.concurrent.*;
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import static org.junit.Assert.assertArrayEquals;
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import static org.junit.Assert.fail;
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/**
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* test cases for Booster
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* test cases for Booster Inplace Predict
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*
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* @author hzx
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* @author hzx and Sovrn
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*/
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public class BoosterImplTest {
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private String train_uri = "../../demo/data/agaricus.txt.train?indexing_mode=1&format=libsvm";
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@ -99,6 +107,179 @@ public class BoosterImplTest {
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TestCase.assertTrue(eval.eval(predicts, testMat) < 0.1f);
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}
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@Test
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public void inplacePredictTest() throws XGBoostError {
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/* Data Generation */
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// Generate a training set.
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int trainRows = 1000;
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int features = 10;
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int trainSize = trainRows * features;
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float[] trainX = generateRandomDataSet(trainSize);
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float[] trainY = generateRandomDataSet(trainRows);
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DMatrix trainingMatrix = new DMatrix(trainX, trainRows, features, Float.NaN);
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trainingMatrix.setLabel(trainY);
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// Generate a testing set
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int testRows = 10;
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int testSize = testRows * features;
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float[] testX = generateRandomDataSet(testSize);
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float[] testY = generateRandomDataSet(testRows);
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DMatrix testingMatrix = new DMatrix(testX, testRows, features, Float.NaN);
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testingMatrix.setLabel(testY);
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/* Training */
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// Set parameters
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Map<String, Object> params = new HashMap<>();
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params.put("eta", 1.0);
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params.put("max_depth",2);
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params.put("silent", 1);
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params.put("tree_method", "hist");
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Map<String, DMatrix> watches = new HashMap<>();
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watches.put("train", trainingMatrix);
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watches.put("test", testingMatrix);
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Booster booster = XGBoost.train(trainingMatrix, params, 10, watches, null, null);
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/* Prediction */
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// Standard prediction
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float[][] predictions = booster.predict(testingMatrix);
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// Inplace-prediction
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float[][] inplacePredictions = booster.inplace_predict(testX, testRows, features, Float.NaN);
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// Confirm that the two prediction results are identical
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assertArrayEquals(predictions, inplacePredictions);
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}
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@Test
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public void inplacePredictMultiPredictTest() throws InterruptedException {
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// Multithreaded, multiple prediction
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int trainRows = 1000;
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int features = 10;
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int trainSize = trainRows * features;
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int testRows = 10;
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int testSize = testRows * features;
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//Simulate multiple predictions on multiple random data sets simultaneously.
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ExecutorService executorService = Executors.newFixedThreadPool(5);
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int predictsToPerform = 100;
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for(int i = 0; i < predictsToPerform; i++) {
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executorService.submit(() -> {
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try {
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float[] trainX = generateRandomDataSet(trainSize);
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float[] trainY = generateRandomDataSet(trainRows);
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DMatrix trainingMatrix = new DMatrix(trainX, trainRows, features, Float.NaN);
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trainingMatrix.setLabel(trainY);
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float[] testX = generateRandomDataSet(testSize);
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float[] testY = generateRandomDataSet(testRows);
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DMatrix testingMatrix = new DMatrix(testX, testRows, features, Float.NaN);
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testingMatrix.setLabel(testY);
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Map<String, Object> params = new HashMap<>();
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params.put("eta", 1.0);
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params.put("max_depth", 2);
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params.put("silent", 1);
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params.put("tree_method", "hist");
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Map<String, DMatrix> watches = new HashMap<>();
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watches.put("train", trainingMatrix);
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watches.put("test", testingMatrix);
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Booster booster = XGBoost.train(trainingMatrix, params, 10, watches, null, null);
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float[][] predictions = booster.predict(testingMatrix);
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float[][] inplacePredictions = booster.inplace_predict(testX, testRows, features, Float.NaN);
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assertArrayEquals(predictions, inplacePredictions);
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} catch (XGBoostError xgBoostError) {
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fail(xgBoostError.getMessage());
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}
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});
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}
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executorService.shutdown();
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if(!executorService.awaitTermination(1, TimeUnit.MINUTES))
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executorService.shutdownNow();
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}
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@Test
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public void inplacePredictWithMarginTest() throws XGBoostError {
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//Generate a training set
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int trainRows = 1000;
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int features = 10;
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int trainSize = trainRows * features;
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float[] trainX = generateRandomDataSet(trainSize);
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float[] trainY = generateRandomDataSet(trainRows);
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DMatrix trainingMatrix = new DMatrix(trainX, trainRows, features, Float.NaN);
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trainingMatrix.setLabel(trainY);
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// Generate a testing set
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int testRows = 10;
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int testSize = testRows * features;
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float[] testX = generateRandomDataSet(testSize);
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float[] testY = generateRandomDataSet(testRows);
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DMatrix testingMatrix = new DMatrix(testX, testRows, features, Float.NaN);
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testingMatrix.setLabel(testY);
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// Set booster parameters
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Map<String, Object> params = new HashMap<>();
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params.put("eta", 1.0);
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params.put("max_depth",2);
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params.put("tree_method", "hist");
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params.put("base_score", 0.0);
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Map<String, DMatrix> watches = new HashMap<>();
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watches.put("train", trainingMatrix);
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watches.put("test", testingMatrix);
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// Train booster on training matrix.
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Booster booster = XGBoost.train(trainingMatrix, params, 10, watches, null, null);
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// Create a margin
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float[] margin = new float[testRows];
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Arrays.fill(margin, 0.5f);
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// Define an iteration range to use all training iterations, this should match
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// the without margin call
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// which defines an iteration range of [0,0)
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int[] iterationRange = new int[] { 0, 0 };
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float[][] inplacePredictionsWithMargin = booster.inplace_predict(testX,
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testRows,
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features,
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Float.NaN,
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iterationRange,
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Booster.PredictionType.kValue,
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margin);
|
||||
float[][] inplacePredictionsWithoutMargin = booster.inplace_predict(testX, testRows, features, Float.NaN);
|
||||
|
||||
for (int i = 0; i < inplacePredictionsWithoutMargin.length; i++) {
|
||||
for (int j = 0; j < inplacePredictionsWithoutMargin[i].length; j++) {
|
||||
inplacePredictionsWithoutMargin[i][j] += margin[j];
|
||||
}
|
||||
}
|
||||
for (int i = 0; i < inplacePredictionsWithoutMargin.length; i++) {
|
||||
assertArrayEquals(inplacePredictionsWithMargin[i], inplacePredictionsWithoutMargin[i], 1e-6f);
|
||||
}
|
||||
}
|
||||
|
||||
private float[] generateRandomDataSet(int size) {
|
||||
float[] newSet = new float[size];
|
||||
Random random = new Random();
|
||||
for(int i = 0; i < size; i++) {
|
||||
newSet[i] = random.nextFloat();
|
||||
}
|
||||
return newSet;
|
||||
}
|
||||
|
||||
@Test
|
||||
public void saveLoadModelWithPath() throws XGBoostError, IOException {
|
||||
DMatrix trainMat = new DMatrix(this.train_uri);
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user