同类别的组件
该文档涉及的组件
在线FM训练 (OnlineFmTrainStreamOp)
Java 类名：com.alibaba.alink.operator.stream.onlinelearning.OnlineFmTrainStreamOp
Python 类名：OnlineFmTrainStreamOp
功能介绍
OnlineFm 算法是Ftrl算法的升级，在 Ftrl 算法的基础上考虑了二阶项对模型的影响。算法支持稀疏和稠密两种类型的训练数据。
算法原理
FM模型是线性模型的升级，是在线性表达式后面加入了新的交叉项特征及对应的权值，FM模型的表达式如下所示：
这里我们使用 Ftrl 优化算法求解该模型。算法原理细节可以参考文献[1]，优化算法请参考文献[2]。
算法使用
FM算法是推荐领域被验证的效果较好的推荐方案之一，在电商、广告、视频、信息流、游戏的推荐领域有广泛应用。
- 备注 ：该组件训练的时候 FeatureCols 和 VectorCol 是两个互斥参数，只能有一个参数来描述算法的输入特征。
文献
[1] S. Rendle, “Factorization Machines,” 2010 IEEE International Conference on Data Mining, 2010, pp. 995-1000, doi: 10.1109/ICDM.2010.127.
[2] McMahan, H. Brendan, et al. “Ad click prediction: a view from the trenches.” Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining. 2013.
参数说明
| 名称 | 中文名称 | 描述 | 类型 | 是否必须？ | 取值范围 | 默认值 |
|---|---|---|---|---|---|---|
| labelCol | 标签列名 | 输入表中的标签列名 | String | ✓ | ||
| alpha | 希腊字母：阿尔法 | 经常用来表示算法特殊的参数 | Double | 0.1 | ||
| beta | 希腊字母：贝塔 | 经常用来表示算法特殊的参数 | Double | 1.0 | ||
| featureCols | 特征列名数组 | 特征列名数组，默认全选 | String[] | null | ||
| l1 | L1 正则化系数 | L1 正则化系数，默认为0.1。 | Double | x >= 0.0 | 0.1 | |
| l2 | 正则化系数 | L2 正则化系数，默认为0.1。 | Double | x >= 0.0 | 0.1 | |
| lambda0 | 常数项正则化系数 | 常数项正则化系数 | Double | 0.0 | ||
| lambda1 | 线性项正则化系数 | 线性项正则化系数 | Double | 0.0 | ||
| lambda2 | 二次项正则化系数 | 二次项正则化系数 | Double | 0.0 | ||
| miniBatchSize | Batch大小 | 表示单次OnlineFM单次迭代更新使用的样本数量，建议是并行度的整数倍. | Integer | 512 | ||
| numFactor | 因子数 | 因子数 | Integer | 10 | ||
| timeInterval | 时间间隔 | 数据流流动过程中时间的间隔 | Integer | 1800 | ||
| vectorCol | 向量列名 | 向量列对应的列名，默认值是null | String | null | ||
| withIntercept | 是否有常数项 | 是否有常数项，默认true | Boolean | true | ||
| withLinearItem | 是否含有线性项 | 是否含有线性项 | Boolean | true | ||
| modelStreamFilePath | 模型流的文件路径 | 模型流的文件路径 | String | null | ||
| modelStreamScanInterval | 扫描模型路径的时间间隔 | 描模型路径的时间间隔，单位秒 | Integer | 10 | ||
| modelStreamStartTime | 模型流的起始时间 | 模型流的起始时间。默认从当前时刻开始读。使用yyyy-mm-dd hh:mm:ss.fffffffff格式，详见Timestamp.valueOf(String s) | String | null |
代码示例
** 以下代码仅用于示意，可能需要修改部分代码或者配置环境后才能正常运行！**
Python 代码
trainData0 = RandomTableSourceBatchOp() \
.setNumCols(5) \
.setNumRows(100) \
.setOutputCols(["f0", "f1", "f2", "f3", "label"]) \
.setOutputColConfs("label:weight_set(1.0,1.0,2.0,5.0)")
model = FmClassifierTrainBatchOp() \
.setFeatureCols(["f0", "f1", "f2", "f3"]) \
.setNumEpochs(1) \
.setWithIntercept(true) \
.setWithLinearItem(true) \
.setNumFactor(10) \
.setLabelCol("label").linkFrom(trainData0)
trainData1 = RandomTableSourceStreamOp() \
.setNumCols(5) \
.setMaxRows(10000) \
.setOutputCols(["f0", "f1", "f2", "f3", "label"]) \
.setOutputColConfs("label:weight_set(1.0,1.0,2.0,5.0)") \
.setTimePerSample(0.1)
smodel = OnlineFmTrainStreamOp(model)\
.setFeatureCols(["f0", "f1", "f2", "f3"])\
.setLabelCol("label")\
.setTimeInterval(5)\
.setAlpha(0.1)\
.setBeta(0.1)\
.setL1(1.0e-4)\
.setL2(1.0e-4)\
.setWithIntercept(true)\
.setWithLinearItem(true)\
.setNumFactor(10)\
.setMiniBatchSize(4)\
.linkFrom(trainData1)
smodel.print()
StreamOperator.execute()
Java 代码
package com.alibaba.alink.operator.stream.ml.onlinelearning;
import com.alibaba.alink.operator.batch.BatchOperator;
import com.alibaba.alink.operator.batch.classification.LogisticRegressionTrainBatchOp;
import com.alibaba.alink.operator.batch.source.RandomTableSourceBatchOp;
import com.alibaba.alink.operator.stream.StreamOperator;
import com.alibaba.alink.operator.stream.onlinelearning.FtrlTrainStreamOp;
import com.alibaba.alink.operator.stream.source.RandomTableSourceStreamOp;
import org.junit.Test;
public class FtrlTrainTestTest {
@Test
public void FtrlClassification() throws Exception {
StreamOperator.setParallelism(2);
BatchOperator trainData0 = new RandomTableSourceBatchOp()
.setNumCols(5)
.setNumRows(100L)
.setOutputCols(new String[]{"f0", "f1", "f2", "f3", "label"})
.setOutputColConfs("label:weight_set(1.0,1.0,2.0,5.0)");
BatchOperator model = new FmClassifierTrainBatchOp()
.setFeatureCols(new String[]{"f0", "f1", "f2", "f3"})
.setNumEpochs(1)
.setWithIntercept(true)
.setWithLinearItem(true)
.setNumFactor(10)
.setLabelCol("label").linkFrom(trainData0);
StreamOperator trainData1 = new RandomTableSourceStreamOp()
.setNumCols(5)
.setMaxRows(100L)
.setOutputCols(new String[]{"f0", "f1", "f2", "f3", "label"})
.setOutputColConfs("label:weight_set(1.0,1.0,2.0,5.0)")
.setTimePerSample(0.1);
StreamOperator smodel = new OnlineFmTrainStreamOp(model)
.setFeatureCols(new String[]{"f0", "f1", "f2", "f3"})
.setLabelCol("label")
.setTimeInterval(5)
.setAlpha(0.1)
.setBeta(0.1)
.setL1(1.0e-4)
.setL2(1.0e-4)
.setWithIntercept(true)
.setWithLinearItem(true)
.setNumFactor(10)
.setMiniBatchSize(4)
.linkFrom(trainData1);
smodel.print();
StreamOperator.execute();
}
}
运行结果
| alinkmodelstreamtimestamp | alinkmodelstreamcount | feature_id | feature_weights | label_type |
|---|---|---|---|---|
| 2022-06-17 11:35:12.286 | 6 | null | {“vectorColName”:null,“labelColName”:“"label"”,“labelValues”:“[2.0,1.0]”,“task”:“"BINARY_CLASSIFICATION"”,“dim”:“[1,1,10]”,“vectorSize”:“4”,“lossCurve”:“[0.6307576690104946,0.34130291005290997,0.86]”,“featureColNames”:“["f0","f1","f2","f3"]”} | null |
| 2022-06-17 11:35:12.286 | 6 | 0 | [0.07401741132183576,0.06648742054692215,-0.07156106277829404,0.08270553675481662,0.03729909036368983,0.1017925835134536,0.06395293845926697,0.002710645111507162,-0.07456289813117634,-0.06074992927650826,0.20282668994956654] | null |
| 2022-06-17 11:35:12.286 | 6 | 1 | [0.06320664675357679,0.0770259532705278,-0.06979259575978414,0.101544201376189,0.04663010144478446,0.10986248522034242,0.08160172125552437,0.0050190753204412755,-0.0858894553397876,-0.07143724068176713,0.26361807219377287] | null |
| 2022-06-17 11:35:12.286 | 6 | 2 | [0.06586036786153898,0.0683656950607503,-0.07939539475173335,0.08911018406926323,0.04141014788185629,0.09345993696450056,0.07527535737550994,0.0042593545311886884,-0.08029395731770174,-0.06355289726568324,0.2563977772496345] | null |
| 2022-06-17 11:35:12.286 | 6 | 3 | [0.07867881602562854,0.07149932133476265,-0.08276899036131362,0.08616652987791219,0.03611638139965758,0.11224704359799328,0.07292359292949942,0.00669055210555388,-0.08089799819972497,-0.06221340818723913,0.23125470059102257] | null |
| 2022-06-17 11:35:12.286 | 6 | -1 | [0.2794136617284648] | null |