add style check for java and scala code
This commit is contained in:
CodingCat
@@ -1,10 +1,10 @@
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/*
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Copyright (c) 2014 by Contributors
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Copyright (c) 2014 by Contributors
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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@@ -26,92 +26,93 @@ import java.util.HashMap;
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/**
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* a simple example of java wrapper for xgboost
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*
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* @author hzx
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*/
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public class BasicWalkThrough {
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public static boolean checkPredicts(float[][] fPredicts, float[][] sPredicts) {
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if(fPredicts.length != sPredicts.length) {
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return false;
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}
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for(int i=0; i<fPredicts.length; i++) {
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if(!Arrays.equals(fPredicts[i], sPredicts[i])) {
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return false;
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}
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}
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return true;
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public static boolean checkPredicts(float[][] fPredicts, float[][] sPredicts) {
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if (fPredicts.length != sPredicts.length) {
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return false;
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}
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public static void main(String[] args) throws UnsupportedEncodingException, IOException, XGBoostError {
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// load file from text file, also binary buffer generated by xgboost4j
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DMatrix trainMat = new DMatrix("../../demo/data/agaricus.txt.train");
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DMatrix testMat = new DMatrix("../../demo/data/agaricus.txt.test");
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HashMap<String, Object> params = new HashMap<String, Object>();
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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("objective", "binary:logistic");
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HashMap<String, DMatrix> watches = new HashMap<String, DMatrix>();
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watches.put("train", trainMat);
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watches.put("test", testMat);
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//set round
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int round = 2;
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//train a boost model
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Booster booster = XGBoost.train(params, trainMat, round, watches, null, null);
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//predict
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float[][] predicts = booster.predict(testMat);
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//save model to modelPath
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File file = new File("./model");
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if(!file.exists()) {
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file.mkdirs();
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}
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String modelPath = "./model/xgb.model";
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booster.saveModel(modelPath);
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//dump model
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booster.dumpModel("./model/dump.raw.txt", false);
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//dump model with feature map
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booster.dumpModel("./model/dump.nice.txt", "../../demo/data/featmap.txt", false);
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//save dmatrix into binary buffer
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testMat.saveBinary("./model/dtest.buffer");
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//reload model and data
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Booster booster2 = XGBoost.loadBoostModel(params, "./model/xgb.model");
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DMatrix testMat2 = new DMatrix("./model/dtest.buffer");
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float[][] predicts2 = booster2.predict(testMat2);
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//check the two predicts
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System.out.println(checkPredicts(predicts, predicts2));
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System.out.println("start build dmatrix from csr sparse data ...");
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//build dmatrix from CSR Sparse Matrix
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DataLoader.CSRSparseData spData = DataLoader.loadSVMFile("../../demo/data/agaricus.txt.train");
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DMatrix trainMat2 = new DMatrix(spData.rowHeaders, spData.colIndex, spData.data,
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DMatrix.SparseType.CSR);
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trainMat2.setLabel(spData.labels);
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//specify watchList
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HashMap<String, DMatrix> watches2 = new HashMap<String, DMatrix>();
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watches2.put("train", trainMat2);
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watches2.put("test", testMat2);
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Booster booster3 = XGBoost.train(params, trainMat2, round, watches2, null, null);
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float[][] predicts3 = booster3.predict(testMat2);
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//check predicts
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System.out.println(checkPredicts(predicts, predicts3));
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for (int i = 0; i < fPredicts.length; i++) {
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if (!Arrays.equals(fPredicts[i], sPredicts[i])) {
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return false;
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}
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}
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return true;
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}
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public static void main(String[] args) throws IOException, XGBoostError {
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// load file from text file, also binary buffer generated by xgboost4j
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DMatrix trainMat = new DMatrix("../../demo/data/agaricus.txt.train");
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DMatrix testMat = new DMatrix("../../demo/data/agaricus.txt.test");
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HashMap<String, Object> params = new HashMap<String, Object>();
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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("objective", "binary:logistic");
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HashMap<String, DMatrix> watches = new HashMap<String, DMatrix>();
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watches.put("train", trainMat);
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watches.put("test", testMat);
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//set round
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int round = 2;
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//train a boost model
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Booster booster = XGBoost.train(params, trainMat, round, watches, null, null);
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//predict
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float[][] predicts = booster.predict(testMat);
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//save model to modelPath
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File file = new File("./model");
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if (!file.exists()) {
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file.mkdirs();
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}
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String modelPath = "./model/xgb.model";
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booster.saveModel(modelPath);
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//dump model
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booster.dumpModel("./model/dump.raw.txt", false);
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//dump model with feature map
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booster.dumpModel("./model/dump.nice.txt", "../../demo/data/featmap.txt", false);
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//save dmatrix into binary buffer
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testMat.saveBinary("./model/dtest.buffer");
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//reload model and data
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Booster booster2 = XGBoost.loadBoostModel(params, "./model/xgb.model");
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DMatrix testMat2 = new DMatrix("./model/dtest.buffer");
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float[][] predicts2 = booster2.predict(testMat2);
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//check the two predicts
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System.out.println(checkPredicts(predicts, predicts2));
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System.out.println("start build dmatrix from csr sparse data ...");
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//build dmatrix from CSR Sparse Matrix
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DataLoader.CSRSparseData spData = DataLoader.loadSVMFile("../../demo/data/agaricus.txt.train");
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DMatrix trainMat2 = new DMatrix(spData.rowHeaders, spData.colIndex, spData.data,
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DMatrix.SparseType.CSR);
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trainMat2.setLabel(spData.labels);
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//specify watchList
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HashMap<String, DMatrix> watches2 = new HashMap<String, DMatrix>();
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watches2.put("train", trainMat2);
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watches2.put("test", testMat2);
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Booster booster3 = XGBoost.train(params, trainMat2, round, watches2, null, null);
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float[][] predicts3 = booster3.predict(testMat2);
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//check predicts
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System.out.println(checkPredicts(predicts, predicts3));
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}
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}
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@@ -1,10 +1,10 @@
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/*
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Copyright (c) 2014 by Contributors
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Copyright (c) 2014 by Contributors
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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@@ -21,38 +21,39 @@ import java.util.HashMap;
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/**
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* example for start from a initial base prediction
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*
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* @author hzx
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*/
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public class BoostFromPrediction {
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public static void main(String[] args) throws XGBoostError {
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System.out.println("start running example to start from a initial prediction");
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// load file from text file, also binary buffer generated by xgboost4j
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DMatrix trainMat = new DMatrix("../../demo/data/agaricus.txt.train");
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DMatrix testMat = new DMatrix("../../demo/data/agaricus.txt.test");
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//specify parameters
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HashMap<String, Object> params = new HashMap<String, Object>();
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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("objective", "binary:logistic");
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//specify watchList
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HashMap<String, DMatrix> watches = new HashMap<String, DMatrix>();
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watches.put("train", trainMat);
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watches.put("test", testMat);
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//train xgboost for 1 round
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Booster booster = XGBoost.train(params, trainMat, 1, watches, null, null);
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float[][] trainPred = booster.predict(trainMat, true);
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float[][] testPred = booster.predict(testMat, true);
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trainMat.setBaseMargin(trainPred);
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testMat.setBaseMargin(testPred);
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System.out.println("result of running from initial prediction");
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Booster booster2 = XGBoost.train(params, trainMat, 1, watches, null, null);
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}
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public static void main(String[] args) throws XGBoostError {
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System.out.println("start running example to start from a initial prediction");
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// load file from text file, also binary buffer generated by xgboost4j
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DMatrix trainMat = new DMatrix("../../demo/data/agaricus.txt.train");
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DMatrix testMat = new DMatrix("../../demo/data/agaricus.txt.test");
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//specify parameters
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HashMap<String, Object> params = new HashMap<String, Object>();
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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("objective", "binary:logistic");
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//specify watchList
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HashMap<String, DMatrix> watches = new HashMap<String, DMatrix>();
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watches.put("train", trainMat);
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watches.put("test", testMat);
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//train xgboost for 1 round
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Booster booster = XGBoost.train(params, trainMat, 1, watches, null, null);
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float[][] trainPred = booster.predict(trainMat, true);
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float[][] testPred = booster.predict(testMat, true);
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trainMat.setBaseMargin(trainPred);
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testMat.setBaseMargin(testPred);
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System.out.println("result of running from initial prediction");
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Booster booster2 = XGBoost.train(params, trainMat, 1, watches, null, null);
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}
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}
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@@ -1,10 +1,10 @@
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/*
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Copyright (c) 2014 by Contributors
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Copyright (c) 2014 by Contributors
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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@@ -1,10 +1,10 @@
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/*
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Copyright (c) 2014 by Contributors
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Copyright (c) 2014 by Contributors
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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@@ -29,137 +29,142 @@ import java.util.List;
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* this may make buildin evalution metric not function properly
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* for example, we are doing logistic loss, the prediction is score before logistic transformation
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* he buildin evaluation error assumes input is after logistic transformation
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* Take this in mind when you use the customization, and maybe you need write customized evaluation function
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* Take this in mind when you use the customization, and maybe you need write customized evaluation
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* function
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*
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* @author hzx
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*/
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public class CustomObjective {
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/**
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* loglikelihoode loss obj function
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*/
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public static class LogRegObj implements IObjective {
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private static final Log logger = LogFactory.getLog(LogRegObj.class);
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/**
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* loglikelihoode loss obj function
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* simple sigmoid func
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*
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* @param input
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* @return Note: this func is not concern about numerical stability, only used as example
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*/
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public static class LogRegObj implements IObjective {
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private static final Log logger = LogFactory.getLog(LogRegObj.class);
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/**
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* simple sigmoid func
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* @param input
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* @return
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* Note: this func is not concern about numerical stability, only used as example
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*/
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public float sigmoid(float input) {
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float val = (float) (1/(1+Math.exp(-input)));
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return val;
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}
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public float[][] transform(float[][] predicts) {
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int nrow = predicts.length;
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float[][] transPredicts = new float[nrow][1];
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for(int i=0; i<nrow; i++) {
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transPredicts[i][0] = sigmoid(predicts[i][0]);
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}
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return transPredicts;
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}
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@Override
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public List<float[]> getGradient(float[][] predicts, org.dmlc.xgboost4j.DMatrix dtrain) {
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int nrow = predicts.length;
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List<float[]> gradients = new ArrayList<float[]>();
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float[] labels;
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try {
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labels = dtrain.getLabel();
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} catch (XGBoostError ex) {
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logger.error(ex);
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return null;
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}
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float[] grad = new float[nrow];
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float[] hess = new float[nrow];
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float[][] transPredicts = transform(predicts);
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for(int i=0; i<nrow; i++) {
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float predict = transPredicts[i][0];
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grad[i] = predict - labels[i];
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hess[i] = predict * (1 - predict);
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}
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gradients.add(grad);
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gradients.add(hess);
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return gradients;
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}
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public float sigmoid(float input) {
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float val = (float) (1 / (1 + Math.exp(-input)));
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return val;
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}
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/**
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* user defined eval function.
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* NOTE: when you do customized loss function, the default prediction value is margin
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* this may make buildin evalution metric not function properly
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* for example, we are doing logistic loss, the prediction is score before logistic transformation
|
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* the buildin evaluation error assumes input is after logistic transformation
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* Take this in mind when you use the customization, and maybe you need write customized evaluation function
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*/
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public static class EvalError implements IEvaluation {
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private static final Log logger = LogFactory.getLog(EvalError.class);
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String evalMetric = "custom_error";
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public EvalError() {
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}
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@Override
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public String getMetric() {
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return evalMetric;
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}
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@Override
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public float eval(float[][] predicts, org.dmlc.xgboost4j.DMatrix dmat) {
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float error = 0f;
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float[] labels;
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try {
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labels = dmat.getLabel();
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} catch (XGBoostError ex) {
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logger.error(ex);
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return -1f;
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}
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int nrow = predicts.length;
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for(int i=0; i<nrow; i++) {
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if(labels[i]==0f && predicts[i][0]>0) {
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error++;
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}
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else if(labels[i]==1f && predicts[i][0]<=0) {
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error++;
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}
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}
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return error/labels.length;
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}
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}
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public static void main(String[] args) throws XGBoostError {
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//load train mat (svmlight format)
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org.dmlc.xgboost4j.DMatrix trainMat = new org.dmlc.xgboost4j.DMatrix("../../demo/data/agaricus.txt.train");
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//load valid mat (svmlight format)
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org.dmlc.xgboost4j.DMatrix testMat = new org.dmlc.xgboost4j.DMatrix("../../demo/data/agaricus.txt.test");
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HashMap<String, Object> params = new HashMap<String, Object>();
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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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public float[][] transform(float[][] predicts) {
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int nrow = predicts.length;
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float[][] transPredicts = new float[nrow][1];
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//set round
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int round = 2;
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//specify watchList
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HashMap<String, org.dmlc.xgboost4j.DMatrix> watches = new HashMap<String, org.dmlc.xgboost4j.DMatrix>();
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watches.put("train", trainMat);
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watches.put("test", testMat);
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//user define obj and eval
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IObjective obj = new LogRegObj();
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IEvaluation eval = new EvalError();
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//train a booster
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System.out.println("begin to train the booster model");
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Booster booster = XGBoost.train(params, trainMat, round, watches, obj, eval);
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for (int i = 0; i < nrow; i++) {
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transPredicts[i][0] = sigmoid(predicts[i][0]);
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}
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return transPredicts;
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}
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@Override
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public List<float[]> getGradient(float[][] predicts, org.dmlc.xgboost4j.DMatrix dtrain) {
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int nrow = predicts.length;
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List<float[]> gradients = new ArrayList<float[]>();
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float[] labels;
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try {
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labels = dtrain.getLabel();
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} catch (XGBoostError ex) {
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logger.error(ex);
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return null;
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}
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float[] grad = new float[nrow];
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float[] hess = new float[nrow];
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float[][] transPredicts = transform(predicts);
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for (int i = 0; i < nrow; i++) {
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float predict = transPredicts[i][0];
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grad[i] = predict - labels[i];
|
||||
hess[i] = predict * (1 - predict);
|
||||
}
|
||||
|
||||
gradients.add(grad);
|
||||
gradients.add(hess);
|
||||
return gradients;
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* user defined eval function.
|
||||
* NOTE: when you do customized loss function, the default prediction value is margin
|
||||
* this may make buildin evalution metric not function properly
|
||||
* for example, we are doing logistic loss, the prediction is score before logistic transformation
|
||||
* the buildin evaluation error assumes input is after logistic transformation
|
||||
* Take this in mind when you use the customization, and maybe you need write customized
|
||||
* evaluation function
|
||||
*/
|
||||
public static class EvalError implements IEvaluation {
|
||||
private static final Log logger = LogFactory.getLog(EvalError.class);
|
||||
|
||||
String evalMetric = "custom_error";
|
||||
|
||||
public EvalError() {
|
||||
}
|
||||
|
||||
@Override
|
||||
public String getMetric() {
|
||||
return evalMetric;
|
||||
}
|
||||
|
||||
@Override
|
||||
public float eval(float[][] predicts, org.dmlc.xgboost4j.DMatrix dmat) {
|
||||
float error = 0f;
|
||||
float[] labels;
|
||||
try {
|
||||
labels = dmat.getLabel();
|
||||
} catch (XGBoostError ex) {
|
||||
logger.error(ex);
|
||||
return -1f;
|
||||
}
|
||||
int nrow = predicts.length;
|
||||
for (int i = 0; i < nrow; i++) {
|
||||
if (labels[i] == 0f && predicts[i][0] > 0) {
|
||||
error++;
|
||||
} else if (labels[i] == 1f && predicts[i][0] <= 0) {
|
||||
error++;
|
||||
}
|
||||
}
|
||||
|
||||
return error / labels.length;
|
||||
}
|
||||
}
|
||||
|
||||
public static void main(String[] args) throws XGBoostError {
|
||||
//load train mat (svmlight format)
|
||||
org.dmlc.xgboost4j.DMatrix trainMat =
|
||||
new org.dmlc.xgboost4j.DMatrix("../../demo/data/agaricus.txt.train");
|
||||
//load valid mat (svmlight format)
|
||||
org.dmlc.xgboost4j.DMatrix testMat =
|
||||
new org.dmlc.xgboost4j.DMatrix("../../demo/data/agaricus.txt.test");
|
||||
|
||||
HashMap<String, Object> params = new HashMap<String, Object>();
|
||||
params.put("eta", 1.0);
|
||||
params.put("max_depth", 2);
|
||||
params.put("silent", 1);
|
||||
|
||||
|
||||
//set round
|
||||
int round = 2;
|
||||
|
||||
//specify watchList
|
||||
HashMap<String, org.dmlc.xgboost4j.DMatrix> watches =
|
||||
new HashMap<String, org.dmlc.xgboost4j.DMatrix>();
|
||||
watches.put("train", trainMat);
|
||||
watches.put("test", testMat);
|
||||
|
||||
//user define obj and eval
|
||||
IObjective obj = new LogRegObj();
|
||||
IEvaluation eval = new EvalError();
|
||||
|
||||
//train a booster
|
||||
System.out.println("begin to train the booster model");
|
||||
Booster booster = XGBoost.train(params, trainMat, round, watches, obj, eval);
|
||||
}
|
||||
}
|
||||
|
||||
@@ -1,10 +1,10 @@
|
||||
/*
|
||||
Copyright (c) 2014 by Contributors
|
||||
Copyright (c) 2014 by Contributors
|
||||
|
||||
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
|
||||
@@ -21,36 +21,38 @@ import java.util.HashMap;
|
||||
|
||||
/**
|
||||
* simple example for using external memory version
|
||||
*
|
||||
* @author hzx
|
||||
*/
|
||||
public class ExternalMemory {
|
||||
public static void main(String[] args) throws XGBoostError {
|
||||
//this is the only difference, add a # followed by a cache prefix name
|
||||
//several cache file with the prefix will be generated
|
||||
//currently only support convert from libsvm file
|
||||
DMatrix trainMat = new DMatrix("../../demo/data/agaricus.txt.train#dtrain.cache");
|
||||
DMatrix testMat = new DMatrix("../../demo/data/agaricus.txt.test#dtest.cache");
|
||||
|
||||
//specify parameters
|
||||
HashMap<String, Object> params = new HashMap<String, Object>();
|
||||
params.put("eta", 1.0);
|
||||
params.put("max_depth", 2);
|
||||
params.put("silent", 1);
|
||||
params.put("objective", "binary:logistic");
|
||||
|
||||
//performance notice: set nthread to be the number of your real cpu
|
||||
//some cpu offer two threads per core, for example, a 4 core cpu with 8 threads, in such case set nthread=4
|
||||
//param.put("nthread", num_real_cpu);
|
||||
|
||||
//specify watchList
|
||||
HashMap<String, DMatrix> watches = new HashMap<String, DMatrix>();
|
||||
watches.put("train", trainMat);
|
||||
watches.put("test", testMat);
|
||||
|
||||
//set round
|
||||
int round = 2;
|
||||
|
||||
//train a boost model
|
||||
Booster booster = XGBoost.train(params, trainMat, round, watches, null, null);
|
||||
}
|
||||
public static void main(String[] args) throws XGBoostError {
|
||||
//this is the only difference, add a # followed by a cache prefix name
|
||||
//several cache file with the prefix will be generated
|
||||
//currently only support convert from libsvm file
|
||||
DMatrix trainMat = new DMatrix("../../demo/data/agaricus.txt.train#dtrain.cache");
|
||||
DMatrix testMat = new DMatrix("../../demo/data/agaricus.txt.test#dtest.cache");
|
||||
|
||||
//specify parameters
|
||||
HashMap<String, Object> params = new HashMap<String, Object>();
|
||||
params.put("eta", 1.0);
|
||||
params.put("max_depth", 2);
|
||||
params.put("silent", 1);
|
||||
params.put("objective", "binary:logistic");
|
||||
|
||||
//performance notice: set nthread to be the number of your real cpu
|
||||
//some cpu offer two threads per core, for example, a 4 core cpu with 8 threads, in such case
|
||||
// set nthread=4
|
||||
//param.put("nthread", num_real_cpu);
|
||||
|
||||
//specify watchList
|
||||
HashMap<String, DMatrix> watches = new HashMap<String, DMatrix>();
|
||||
watches.put("train", trainMat);
|
||||
watches.put("test", testMat);
|
||||
|
||||
//set round
|
||||
int round = 2;
|
||||
|
||||
//train a boost model
|
||||
Booster booster = XGBoost.train(params, trainMat, round, watches, null, null);
|
||||
}
|
||||
}
|
||||
|
||||
@@ -1,10 +1,10 @@
|
||||
/*
|
||||
Copyright (c) 2014 by Contributors
|
||||
Copyright (c) 2014 by Contributors
|
||||
|
||||
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
|
||||
@@ -23,44 +23,45 @@ import java.util.HashMap;
|
||||
/**
|
||||
* this is an example of fit generalized linear model in xgboost
|
||||
* basically, we are using linear model, instead of tree for our boosters
|
||||
*
|
||||
* @author hzx
|
||||
*/
|
||||
public class GeneralizedLinearModel {
|
||||
public static void main(String[] args) throws XGBoostError {
|
||||
// load file from text file, also binary buffer generated by xgboost4j
|
||||
DMatrix trainMat = new DMatrix("../../demo/data/agaricus.txt.train");
|
||||
DMatrix testMat = new DMatrix("../../demo/data/agaricus.txt.test");
|
||||
|
||||
//specify parameters
|
||||
//change booster to gblinear, so that we are fitting a linear model
|
||||
// alpha is the L1 regularizer
|
||||
//lambda is the L2 regularizer
|
||||
//you can also set lambda_bias which is L2 regularizer on the bias term
|
||||
HashMap<String, Object> params = new HashMap<String, Object>();
|
||||
params.put("alpha", 0.0001);
|
||||
params.put("silent", 1);
|
||||
params.put("objective", "binary:logistic");
|
||||
params.put("booster", "gblinear");
|
||||
public static void main(String[] args) throws XGBoostError {
|
||||
// load file from text file, also binary buffer generated by xgboost4j
|
||||
DMatrix trainMat = new DMatrix("../../demo/data/agaricus.txt.train");
|
||||
DMatrix testMat = new DMatrix("../../demo/data/agaricus.txt.test");
|
||||
|
||||
//normally, you do not need to set eta (step_size)
|
||||
//XGBoost uses a parallel coordinate descent algorithm (shotgun),
|
||||
//there could be affection on convergence with parallelization on certain cases
|
||||
//setting eta to be smaller value, e.g 0.5 can make the optimization more stable
|
||||
//param.put("eta", "0.5");
|
||||
|
||||
|
||||
//specify watchList
|
||||
HashMap<String, DMatrix> watches = new HashMap<String, DMatrix>();
|
||||
watches.put("train", trainMat);
|
||||
watches.put("test", testMat);
|
||||
|
||||
//train a booster
|
||||
int round = 4;
|
||||
Booster booster = XGBoost.train(params, trainMat, round, watches, null, null);
|
||||
|
||||
float[][] predicts = booster.predict(testMat);
|
||||
|
||||
CustomEval eval = new CustomEval();
|
||||
System.out.println("error=" + eval.eval(predicts, testMat));
|
||||
}
|
||||
//specify parameters
|
||||
//change booster to gblinear, so that we are fitting a linear model
|
||||
// alpha is the L1 regularizer
|
||||
//lambda is the L2 regularizer
|
||||
//you can also set lambda_bias which is L2 regularizer on the bias term
|
||||
HashMap<String, Object> params = new HashMap<String, Object>();
|
||||
params.put("alpha", 0.0001);
|
||||
params.put("silent", 1);
|
||||
params.put("objective", "binary:logistic");
|
||||
params.put("booster", "gblinear");
|
||||
|
||||
//normally, you do not need to set eta (step_size)
|
||||
//XGBoost uses a parallel coordinate descent algorithm (shotgun),
|
||||
//there could be affection on convergence with parallelization on certain cases
|
||||
//setting eta to be smaller value, e.g 0.5 can make the optimization more stable
|
||||
//param.put("eta", "0.5");
|
||||
|
||||
|
||||
//specify watchList
|
||||
HashMap<String, DMatrix> watches = new HashMap<String, DMatrix>();
|
||||
watches.put("train", trainMat);
|
||||
watches.put("test", testMat);
|
||||
|
||||
//train a booster
|
||||
int round = 4;
|
||||
Booster booster = XGBoost.train(params, trainMat, round, watches, null, null);
|
||||
|
||||
float[][] predicts = booster.predict(testMat);
|
||||
|
||||
CustomEval eval = new CustomEval();
|
||||
System.out.println("error=" + eval.eval(predicts, testMat));
|
||||
}
|
||||
}
|
||||
|
||||
@@ -1,10 +1,10 @@
|
||||
/*
|
||||
Copyright (c) 2014 by Contributors
|
||||
Copyright (c) 2014 by Contributors
|
||||
|
||||
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
|
||||
@@ -22,41 +22,42 @@ import java.util.HashMap;
|
||||
|
||||
/**
|
||||
* predict first ntree
|
||||
*
|
||||
* @author hzx
|
||||
*/
|
||||
public class PredictFirstNtree {
|
||||
public static void main(String[] args) throws XGBoostError {
|
||||
// load file from text file, also binary buffer generated by xgboost4j
|
||||
DMatrix trainMat = new DMatrix("../../demo/data/agaricus.txt.train");
|
||||
DMatrix testMat = new DMatrix("../../demo/data/agaricus.txt.test");
|
||||
|
||||
//specify parameters
|
||||
HashMap<String, Object> params = new HashMap<String, Object>();
|
||||
public class PredictFirstNtree {
|
||||
public static void main(String[] args) throws XGBoostError {
|
||||
// load file from text file, also binary buffer generated by xgboost4j
|
||||
DMatrix trainMat = new DMatrix("../../demo/data/agaricus.txt.train");
|
||||
DMatrix testMat = new DMatrix("../../demo/data/agaricus.txt.test");
|
||||
|
||||
params.put("eta", 1.0);
|
||||
params.put("max_depth", 2);
|
||||
params.put("silent", 1);
|
||||
params.put("objective", "binary:logistic");
|
||||
//specify parameters
|
||||
HashMap<String, Object> params = new HashMap<String, Object>();
|
||||
|
||||
|
||||
//specify watchList
|
||||
HashMap<String, DMatrix> watches = new HashMap<String, DMatrix>();
|
||||
watches.put("train", trainMat);
|
||||
watches.put("test", testMat);
|
||||
params.put("eta", 1.0);
|
||||
params.put("max_depth", 2);
|
||||
params.put("silent", 1);
|
||||
params.put("objective", "binary:logistic");
|
||||
|
||||
|
||||
//train a booster
|
||||
int round = 3;
|
||||
Booster booster = XGBoost.train(params, trainMat, round, watches, null, null);
|
||||
|
||||
//predict use 1 tree
|
||||
float[][] predicts1 = booster.predict(testMat, false, 1);
|
||||
//by default all trees are used to do predict
|
||||
float[][] predicts2 = booster.predict(testMat);
|
||||
|
||||
//use a simple evaluation class to check error result
|
||||
CustomEval eval = new CustomEval();
|
||||
System.out.println("error of predicts1: " + eval.eval(predicts1, testMat));
|
||||
System.out.println("error of predicts2: " + eval.eval(predicts2, testMat));
|
||||
}
|
||||
|
||||
//specify watchList
|
||||
HashMap<String, DMatrix> watches = new HashMap<String, DMatrix>();
|
||||
watches.put("train", trainMat);
|
||||
watches.put("test", testMat);
|
||||
|
||||
|
||||
//train a booster
|
||||
int round = 3;
|
||||
Booster booster = XGBoost.train(params, trainMat, round, watches, null, null);
|
||||
|
||||
//predict use 1 tree
|
||||
float[][] predicts1 = booster.predict(testMat, false, 1);
|
||||
//by default all trees are used to do predict
|
||||
float[][] predicts2 = booster.predict(testMat);
|
||||
|
||||
//use a simple evaluation class to check error result
|
||||
CustomEval eval = new CustomEval();
|
||||
System.out.println("error of predicts1: " + eval.eval(predicts1, testMat));
|
||||
System.out.println("error of predicts2: " + eval.eval(predicts2, testMat));
|
||||
}
|
||||
}
|
||||
|
||||
@@ -1,10 +1,10 @@
|
||||
/*
|
||||
Copyright (c) 2014 by Contributors
|
||||
Copyright (c) 2014 by Contributors
|
||||
|
||||
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
|
||||
@@ -22,41 +22,42 @@ import java.util.HashMap;
|
||||
|
||||
/**
|
||||
* predict leaf indices
|
||||
*
|
||||
* @author hzx
|
||||
*/
|
||||
public class PredictLeafIndices {
|
||||
public static void main(String[] args) throws XGBoostError {
|
||||
// load file from text file, also binary buffer generated by xgboost4j
|
||||
DMatrix trainMat = new DMatrix("../../demo/data/agaricus.txt.train");
|
||||
DMatrix testMat = new DMatrix("../../demo/data/agaricus.txt.test");
|
||||
|
||||
//specify parameters
|
||||
HashMap<String, Object> params = new HashMap<String, Object>();
|
||||
params.put("eta", 1.0);
|
||||
params.put("max_depth", 2);
|
||||
params.put("silent", 1);
|
||||
params.put("objective", "binary:logistic");
|
||||
|
||||
//specify watchList
|
||||
HashMap<String, DMatrix> watches = new HashMap<String, DMatrix>();
|
||||
watches.put("train", trainMat);
|
||||
watches.put("test", testMat);
|
||||
public static void main(String[] args) throws XGBoostError {
|
||||
// load file from text file, also binary buffer generated by xgboost4j
|
||||
DMatrix trainMat = new DMatrix("../../demo/data/agaricus.txt.train");
|
||||
DMatrix testMat = new DMatrix("../../demo/data/agaricus.txt.test");
|
||||
|
||||
|
||||
//train a booster
|
||||
int round = 3;
|
||||
Booster booster = XGBoost.train(params, trainMat, round, watches, null, null);
|
||||
|
||||
//predict using first 2 tree
|
||||
float[][] leafindex = booster.predict(testMat, 2, true);
|
||||
for(float[] leafs : leafindex) {
|
||||
System.out.println(Arrays.toString(leafs));
|
||||
}
|
||||
|
||||
//predict all trees
|
||||
leafindex = booster.predict(testMat, 0, true);
|
||||
for(float[] leafs : leafindex) {
|
||||
System.out.println(Arrays.toString(leafs));
|
||||
}
|
||||
//specify parameters
|
||||
HashMap<String, Object> params = new HashMap<String, Object>();
|
||||
params.put("eta", 1.0);
|
||||
params.put("max_depth", 2);
|
||||
params.put("silent", 1);
|
||||
params.put("objective", "binary:logistic");
|
||||
|
||||
//specify watchList
|
||||
HashMap<String, DMatrix> watches = new HashMap<String, DMatrix>();
|
||||
watches.put("train", trainMat);
|
||||
watches.put("test", testMat);
|
||||
|
||||
|
||||
//train a booster
|
||||
int round = 3;
|
||||
Booster booster = XGBoost.train(params, trainMat, round, watches, null, null);
|
||||
|
||||
//predict using first 2 tree
|
||||
float[][] leafindex = booster.predict(testMat, 2, true);
|
||||
for (float[] leafs : leafindex) {
|
||||
System.out.println(Arrays.toString(leafs));
|
||||
}
|
||||
|
||||
//predict all trees
|
||||
leafindex = booster.predict(testMat, 0, true);
|
||||
for (float[] leafs : leafindex) {
|
||||
System.out.println(Arrays.toString(leafs));
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
@@ -1,10 +1,10 @@
|
||||
/*
|
||||
Copyright (c) 2014 by Contributors
|
||||
Copyright (c) 2014 by Contributors
|
||||
|
||||
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
|
||||
@@ -23,38 +23,38 @@ import org.dmlc.xgboost4j.XGBoostError;
|
||||
|
||||
/**
|
||||
* a util evaluation class for examples
|
||||
*
|
||||
* @author hzx
|
||||
*/
|
||||
public class CustomEval implements IEvaluation {
|
||||
private static final Log logger = LogFactory.getLog(CustomEval.class);
|
||||
private static final Log logger = LogFactory.getLog(CustomEval.class);
|
||||
|
||||
String evalMetric = "custom_error";
|
||||
|
||||
@Override
|
||||
public String getMetric() {
|
||||
return evalMetric;
|
||||
String evalMetric = "custom_error";
|
||||
|
||||
@Override
|
||||
public String getMetric() {
|
||||
return evalMetric;
|
||||
}
|
||||
|
||||
@Override
|
||||
public float eval(float[][] predicts, DMatrix dmat) {
|
||||
float error = 0f;
|
||||
float[] labels;
|
||||
try {
|
||||
labels = dmat.getLabel();
|
||||
} catch (XGBoostError ex) {
|
||||
logger.error(ex);
|
||||
return -1f;
|
||||
}
|
||||
int nrow = predicts.length;
|
||||
for (int i = 0; i < nrow; i++) {
|
||||
if (labels[i] == 0f && predicts[i][0] > 0.5) {
|
||||
error++;
|
||||
} else if (labels[i] == 1f && predicts[i][0] <= 0.5) {
|
||||
error++;
|
||||
}
|
||||
}
|
||||
|
||||
@Override
|
||||
public float eval(float[][] predicts, DMatrix dmat) {
|
||||
float error = 0f;
|
||||
float[] labels;
|
||||
try {
|
||||
labels = dmat.getLabel();
|
||||
} catch (XGBoostError ex) {
|
||||
logger.error(ex);
|
||||
return -1f;
|
||||
}
|
||||
int nrow = predicts.length;
|
||||
for(int i=0; i<nrow; i++) {
|
||||
if(labels[i]==0f && predicts[i][0]>0.5) {
|
||||
error++;
|
||||
}
|
||||
else if(labels[i]==1f && predicts[i][0]<=0.5) {
|
||||
error++;
|
||||
}
|
||||
}
|
||||
|
||||
return error/labels.length;
|
||||
}
|
||||
return error / labels.length;
|
||||
}
|
||||
}
|
||||
|
||||
@@ -1,10 +1,10 @@
|
||||
/*
|
||||
Copyright (c) 2014 by Contributors
|
||||
Copyright (c) 2014 by Contributors
|
||||
|
||||
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
|
||||
@@ -23,100 +23,101 @@ import java.util.List;
|
||||
|
||||
/**
|
||||
* util class for loading data
|
||||
*
|
||||
* @author hzx
|
||||
*/
|
||||
public class DataLoader {
|
||||
public static class DenseData {
|
||||
public float[] labels;
|
||||
public float[] data;
|
||||
public int nrow;
|
||||
public int ncol;
|
||||
public static class DenseData {
|
||||
public float[] labels;
|
||||
public float[] data;
|
||||
public int nrow;
|
||||
public int ncol;
|
||||
}
|
||||
|
||||
public static class CSRSparseData {
|
||||
public float[] labels;
|
||||
public float[] data;
|
||||
public long[] rowHeaders;
|
||||
public int[] colIndex;
|
||||
}
|
||||
|
||||
public static DenseData loadCSVFile(String filePath) throws IOException {
|
||||
DenseData denseData = new DenseData();
|
||||
|
||||
File f = new File(filePath);
|
||||
FileInputStream in = new FileInputStream(f);
|
||||
BufferedReader reader = new BufferedReader(new InputStreamReader(in, "UTF-8"));
|
||||
|
||||
denseData.nrow = 0;
|
||||
denseData.ncol = -1;
|
||||
String line;
|
||||
List<Float> tlabels = new ArrayList<>();
|
||||
List<Float> tdata = new ArrayList<>();
|
||||
|
||||
while ((line = reader.readLine()) != null) {
|
||||
String[] items = line.trim().split(",");
|
||||
if (items.length == 0) {
|
||||
continue;
|
||||
}
|
||||
denseData.nrow++;
|
||||
if (denseData.ncol == -1) {
|
||||
denseData.ncol = items.length - 1;
|
||||
}
|
||||
|
||||
tlabels.add(Float.valueOf(items[items.length - 1]));
|
||||
for (int i = 0; i < items.length - 1; i++) {
|
||||
tdata.add(Float.valueOf(items[i]));
|
||||
}
|
||||
}
|
||||
|
||||
public static class CSRSparseData {
|
||||
public float[] labels;
|
||||
public float[] data;
|
||||
public long[] rowHeaders;
|
||||
public int[] colIndex;
|
||||
}
|
||||
|
||||
public static DenseData loadCSVFile(String filePath) throws FileNotFoundException, UnsupportedEncodingException, IOException {
|
||||
DenseData denseData = new DenseData();
|
||||
|
||||
File f = new File(filePath);
|
||||
FileInputStream in = new FileInputStream(f);
|
||||
BufferedReader reader = new BufferedReader(new InputStreamReader(in, "UTF-8"));
|
||||
|
||||
denseData.nrow = 0;
|
||||
denseData.ncol = -1;
|
||||
String line;
|
||||
List<Float> tlabels = new ArrayList<>();
|
||||
List<Float> tdata = new ArrayList<>();
|
||||
|
||||
while((line=reader.readLine()) != null) {
|
||||
String[] items = line.trim().split(",");
|
||||
if(items.length==0) {
|
||||
continue;
|
||||
}
|
||||
denseData.nrow++;
|
||||
if(denseData.ncol == -1) {
|
||||
denseData.ncol = items.length - 1;
|
||||
}
|
||||
|
||||
tlabels.add(Float.valueOf(items[items.length-1]));
|
||||
for(int i=0; i<items.length-1; i++) {
|
||||
tdata.add(Float.valueOf(items[i]));
|
||||
}
|
||||
}
|
||||
|
||||
reader.close();
|
||||
in.close();
|
||||
|
||||
denseData.labels = ArrayUtils.toPrimitive(tlabels.toArray(new Float[tlabels.size()]));
|
||||
denseData.data = ArrayUtils.toPrimitive(tdata.toArray(new Float[tdata.size()]));
|
||||
|
||||
return denseData;
|
||||
}
|
||||
|
||||
public static CSRSparseData loadSVMFile(String filePath) throws FileNotFoundException, UnsupportedEncodingException, IOException {
|
||||
CSRSparseData spData = new CSRSparseData();
|
||||
|
||||
List<Float> tlabels = new ArrayList<>();
|
||||
List<Float> tdata = new ArrayList<>();
|
||||
List<Long> theaders = new ArrayList<>();
|
||||
List<Integer> tindex = new ArrayList<>();
|
||||
|
||||
File f = new File(filePath);
|
||||
FileInputStream in = new FileInputStream(f);
|
||||
BufferedReader reader = new BufferedReader(new InputStreamReader(in, "UTF-8"));
|
||||
|
||||
String line;
|
||||
long rowheader = 0;
|
||||
theaders.add(rowheader);
|
||||
while((line=reader.readLine()) != null) {
|
||||
String[] items = line.trim().split(" ");
|
||||
if(items.length==0) {
|
||||
continue;
|
||||
}
|
||||
|
||||
rowheader += items.length - 1;
|
||||
theaders.add(rowheader);
|
||||
tlabels.add(Float.valueOf(items[0]));
|
||||
|
||||
for(int i=1; i<items.length; i++) {
|
||||
String[] tup = items[i].split(":");
|
||||
assert tup.length == 2;
|
||||
|
||||
tdata.add(Float.valueOf(tup[1]));
|
||||
tindex.add(Integer.valueOf(tup[0]));
|
||||
}
|
||||
}
|
||||
|
||||
spData.labels = ArrayUtils.toPrimitive(tlabels.toArray(new Float[tlabels.size()]));
|
||||
spData.data = ArrayUtils.toPrimitive(tdata.toArray(new Float[tdata.size()]));
|
||||
spData.colIndex = ArrayUtils.toPrimitive(tindex.toArray(new Integer[tindex.size()]));
|
||||
spData.rowHeaders = ArrayUtils.toPrimitive(theaders.toArray(new Long[theaders.size()]));
|
||||
|
||||
return spData;
|
||||
|
||||
reader.close();
|
||||
in.close();
|
||||
|
||||
denseData.labels = ArrayUtils.toPrimitive(tlabels.toArray(new Float[tlabels.size()]));
|
||||
denseData.data = ArrayUtils.toPrimitive(tdata.toArray(new Float[tdata.size()]));
|
||||
|
||||
return denseData;
|
||||
}
|
||||
|
||||
public static CSRSparseData loadSVMFile(String filePath) throws IOException {
|
||||
CSRSparseData spData = new CSRSparseData();
|
||||
|
||||
List<Float> tlabels = new ArrayList<>();
|
||||
List<Float> tdata = new ArrayList<>();
|
||||
List<Long> theaders = new ArrayList<>();
|
||||
List<Integer> tindex = new ArrayList<>();
|
||||
|
||||
File f = new File(filePath);
|
||||
FileInputStream in = new FileInputStream(f);
|
||||
BufferedReader reader = new BufferedReader(new InputStreamReader(in, "UTF-8"));
|
||||
|
||||
String line;
|
||||
long rowheader = 0;
|
||||
theaders.add(rowheader);
|
||||
while ((line = reader.readLine()) != null) {
|
||||
String[] items = line.trim().split(" ");
|
||||
if (items.length == 0) {
|
||||
continue;
|
||||
}
|
||||
|
||||
rowheader += items.length - 1;
|
||||
theaders.add(rowheader);
|
||||
tlabels.add(Float.valueOf(items[0]));
|
||||
|
||||
for (int i = 1; i < items.length; i++) {
|
||||
String[] tup = items[i].split(":");
|
||||
assert tup.length == 2;
|
||||
|
||||
tdata.add(Float.valueOf(tup[1]));
|
||||
tindex.add(Integer.valueOf(tup[0]));
|
||||
}
|
||||
}
|
||||
|
||||
spData.labels = ArrayUtils.toPrimitive(tlabels.toArray(new Float[tlabels.size()]));
|
||||
spData.data = ArrayUtils.toPrimitive(tdata.toArray(new Float[tdata.size()]));
|
||||
spData.colIndex = ArrayUtils.toPrimitive(tindex.toArray(new Integer[tindex.size()]));
|
||||
spData.rowHeaders = ArrayUtils.toPrimitive(theaders.toArray(new Long[theaders.size()]));
|
||||
|
||||
return spData;
|
||||
}
|
||||
}
|
||||
|
||||
Reference in New Issue
Block a user