Alink在线学习(Online Learning)之Java示例【六】

最后，贴出完整代码，感兴趣的读者可以运行实验。

注意，由于示例中需要演示中间结果，有很多打印或执行的方法，我现将调用这些方法的代码设为了注释，读者可以自己释放某些代码，查看运行效果。

package com.alibaba.alink;

import com.alibaba.alink.operator.batch.BatchOperator;
import com.alibaba.alink.operator.batch.classification.LogisticRegressionTrainBatchOp;
import com.alibaba.alink.operator.batch.source.CsvSourceBatchOp;
import com.alibaba.alink.operator.stream.StreamOperator;
import com.alibaba.alink.operator.stream.dataproc.JsonValueStreamOp;
import com.alibaba.alink.operator.stream.dataproc.SplitStreamOp;
import com.alibaba.alink.operator.stream.evaluation.EvalBinaryClassStreamOp;
import com.alibaba.alink.operator.stream.onlinelearning.FtrlPredictStreamOp;
import com.alibaba.alink.operator.stream.onlinelearning.FtrlTrainStreamOp;
import com.alibaba.alink.operator.stream.source.CsvSourceStreamOp;
import com.alibaba.alink.pipeline.Pipeline;
import com.alibaba.alink.pipeline.PipelineModel;
import com.alibaba.alink.pipeline.dataproc.StandardScaler;
import com.alibaba.alink.pipeline.feature.FeatureHasher;

public class FTRLExample {

	public static void main(String[] args) throws Exception {

		//new TextSourceBatchOp()
		//	.setFilePath("http://alink-release.oss-cn-beijing.aliyuncs.com/data-files/avazu-small.csv")
		//	.firstN(10)
		//	.print();

		String schemaStr
			= "id string, click string, dt string, C1 string, banner_pos int, site_id string, site_domain string, "
			+ "site_category string, app_id string, app_domain string, app_category string, device_id string, "
			+ "device_ip string, device_model string, device_type string, device_conn_type string, C14 int, C15 int, "
			+ "C16 int, C17 int, C18 int, C19 int, C20 int, C21 int";

		CsvSourceBatchOp trainBatchData = new CsvSourceBatchOp()
			.setFilePath("http://alink-release.oss-cn-beijing.aliyuncs.com/data-files/avazu-small.csv")
			.setSchemaStr(schemaStr);

		//trainBatchData.firstN(10).print();

		String labelColName = "click";
		String[] selectedColNames = new String[] {
			"C1", "banner_pos", "site_category", "app_domain",
			"app_category", "device_type", "device_conn_type",
			"C14", "C15", "C16", "C17", "C18", "C19", "C20", "C21",
			"site_id", "site_domain", "device_id", "device_model"};

		String[] categoryColNames = new String[] {
			"C1", "banner_pos", "site_category", "app_domain",
			"app_category", "device_type", "device_conn_type",
			"site_id", "site_domain", "device_id", "device_model"};

		String[] numericalColNames = new String[] {
			"C14", "C15", "C16", "C17", "C18", "C19", "C20", "C21"};

		// result column name of feature enginerring
		String vecColName = "vec";
		int numHashFeatures = 30000;

		// setup feature enginerring pipeline
		Pipeline feature_pipeline = new Pipeline()
			.add(
				new StandardScaler()
					.setSelectedCols(numericalColNames)
			)
			.add(
				new FeatureHasher()
					.setSelectedCols(selectedColNames)
					.setCategoricalCols(categoryColNames)
					.setOutputCol(vecColName)
					.setNumFeatures(numHashFeatures)
			);

		// fit and save feature pipeline model
		String FEATURE_PIPELINE_MODEL_FILE = "/Users/yangxu/alink/data/temp/feature_pipe_model.csv";
		//feature_pipeline.fit(trainBatchData).save(FEATURE_PIPELINE_MODEL_FILE);
		//
		//BatchOperator.execute();

		// prepare stream train data
		CsvSourceStreamOp data = new CsvSourceStreamOp()
			.setFilePath("http://alink-release.oss-cn-beijing.aliyuncs.com/data-files/avazu-ctr-train-8M.csv")
			.setSchemaStr(schemaStr)
			.setIgnoreFirstLine(true);

		// split stream to train and eval data
		SplitStreamOp spliter = new SplitStreamOp().setFraction(0.5).linkFrom(data);
		StreamOperator train_stream_data = spliter;
		StreamOperator test_stream_data = spliter.getSideOutput(0);

		// load pipeline model
		PipelineModel feature_pipelineModel = PipelineModel.load(FEATURE_PIPELINE_MODEL_FILE);

		// train initial batch model
		LogisticRegressionTrainBatchOp lr = new LogisticRegressionTrainBatchOp()
			.setVectorCol(vecColName)
			.setLabelCol(labelColName)
			.setWithIntercept(true)
			.setMaxIter(10);

		BatchOperator initModel = feature_pipelineModel.transform(trainBatchData).link(lr);

		// ftrl train
		FtrlTrainStreamOp model = new FtrlTrainStreamOp(initModel)
			.setVectorCol(vecColName)
			.setLabelCol(labelColName)
			.setWithIntercept(true)
			.setAlpha(0.1)
			.setBeta(0.1)
			.setL1(0.01)
			.setL2(0.01)
			.setTimeInterval(10)
			.setVectorSize(numHashFeatures)
			.linkFrom(feature_pipelineModel.transform(train_stream_data));

		// ftrl predict
		FtrlPredictStreamOp predResult = new FtrlPredictStreamOp(initModel)
			.setVectorCol(vecColName)
			.setPredictionCol("pred")
			.setReservedCols(new String[] {labelColName})
			.setPredictionDetailCol("details")
			.linkFrom(model, feature_pipelineModel.transform(test_stream_data));

		//predResult.sample(0.0001).print();
		//
		//StreamOperator.execute();

		// ftrl eval
		predResult.link(
			new EvalBinaryClassStreamOp()
				.setLabelCol(labelColName)
				.setPredictionCol("pred")
				.setPredictionDetailCol("details")
				.setTimeInterval(10)
		).link(
			new JsonValueStreamOp()
				.setSelectedCol("Data")
				.setReservedCols(new String[] {"Statistics"})
				.setOutputCols(new String[] {"Accuracy", "AUC", "ConfusionMatrix"})
				.setJsonPath(new String[] {"$.Accuracy", "$.AUC", "$.ConfusionMatrix"})
			)
			.print();
		//StreamOperator.execute();
	}
}