Java 类名：com.alibaba.alink.pipeline.regression.GeneralizedLinearRegression
Python 类名：GeneralizedLinearRegression

功能介绍

广义线性回归训练和预测

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
featureCols 特征列名 特征列名，必选 String[] ✓
labelCol 标签列名 输入表中的标签列名 String ✓
predictionCol 预测结果列名 预测结果列名 String ✓
epsilon 收敛精度 收敛精度 Double 1.0E-5
family 分布族 分布族，包含gaussian, Binomial, Poisson, Gamma and Tweedie，默认值gaussian。 String “Gamma”, “Binomial”, “Gaussian”, “Poisson”, “Tweedie” “Gaussian”
fitIntercept 是否拟合常数项 是否拟合常数项，默认是拟合 Boolean true
link 连接函数 连接函数，包含cloglog, Identity, Inverse, log, logit, power, probit和sqrt，默认值是指数分布族对应的连接函数。 String “CLogLog”, “Identity”, “Inverse”, “Log”, “Logit”, “Power”, “Probit”, “Sqrt” null
linkPower 连接函数的超参 连接函数的超参 Double 1.0
linkPredResultCol 连接函数结果的列名 连接函数结果的列名 String null
maxIter 最大迭代步数 最大迭代步数，默认为 10。 Integer 10
modelFilePath 模型的文件路径 模型的文件路径 String null
offsetCol 偏移列 偏移列 String null
overwriteSink 是否覆写已有数据 是否覆写已有数据 Boolean false
regParam l2正则系数 l2正则系数 Double 0.0
reservedCols 算法保留列名 算法保留列 String[] null
variancePower 分布族的超参 分布族的超参，默认值是0.0 Double 0.0
weightCol 权重列名 权重列对应的列名 String 所选列类型为 [BIGDECIMAL, BIGINTEGER, BYTE, DOUBLE, FLOAT, INTEGER, LONG, SHORT] null
numThreads 组件多线程线程个数 组件多线程线程个数 Integer 1
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

Python 代码

  1. from pyalink.alink import *
  3. import pandas as pd
  5. useLocalEnv(1)
  7. df = pd.DataFrame([
  8.     [1.6094,118.0000,69.0000,1.0000,2.0000],
  9.     [2.3026,58.0000,35.0000,1.0000,2.0000],
  10.     [2.7081,42.0000,26.0000,1.0000,2.0000],
  11.     [2.9957,35.0000,21.0000,1.0000,2.0000],
  12.     [3.4012,27.0000,18.0000,1.0000,2.0000],
  13.     [3.6889,25.0000,16.0000,1.0000,2.0000],
  14.     [4.0943,21.0000,13.0000,1.0000,2.0000],
  15.     [4.3820,19.0000,12.0000,1.0000,2.0000],
  16.     [4.6052,18.0000,12.0000,1.0000,2.0000]
  17. ])
  19. source = BatchOperator.fromDataframe(df, schemaStr='u double, lot1 double, lot2 double, offset double, weights double')
  21. featureColNames = ["lot1", "lot2"]
  22. labelColName = "u"
  24. # train
  25. glm = GeneralizedLinearRegression()\
  26.                 .setFamily("gamma")\
  27.                 .setLink("Log")\
  28.                 .setRegParam(0.3)\
  29.                 .setMaxIter(5)\
  30.                 .setFeatureCols(featureColNames)\
  31.                 .setLabelCol(labelColName)\
  32.                 .setPredictionCol("pred")
  34. model = glm.fit(source)
  35. predict = model.transform(source)
  36. eval2 = model.evaluate(source)
  38. predict.lazyPrint(10)
  39. eval2.print()

Java 代码

  1. import org.apache.flink.types.Row;
  3. import com.alibaba.alink.operator.batch.BatchOperator;
  4. import com.alibaba.alink.operator.batch.source.MemSourceBatchOp;
  5. import com.alibaba.alink.pipeline.regression.GeneralizedLinearRegression;
  6. import com.alibaba.alink.pipeline.regression.GeneralizedLinearRegressionModel;
  7. import org.junit.Test;
  9. import java.util.Arrays;
  10. import java.util.List;
  12. public class GeneralizedLinearRegressionTest {
  13.     @Test
  14.     public void testGeneralizedLinearRegression() throws Exception {
  15.         List <Row> df = Arrays.asList(
  16.             Row.of(1.6094, 118.0000, 69.0000, 1.0000, 2.0000),
  17.             Row.of(2.3026, 58.0000, 35.0000, 1.0000, 2.0000),
  18.             Row.of(2.7081, 42.0000, 26.0000, 1.0000, 2.0000),
  19.             Row.of(2.9957, 35.0000, 21.0000, 1.0000, 2.0000),
  20.             Row.of(3.4012, 27.0000, 18.0000, 1.0000, 2.0000),
  21.             Row.of(3.6889, 25.0000, 16.0000, 1.0000, 2.0000),
  22.             Row.of(4.0943, 21.0000, 13.0000, 1.0000, 2.0000),
  23.             Row.of(4.3820, 19.0000, 12.0000, 1.0000, 2.0000),
  24.             Row.of(4.6052, 18.0000, 12.0000, 1.0000, 2.0000)
  25.         );
  26.         BatchOperator <?> source = new MemSourceBatchOp(df,
  27.             "u double, lot1 double, lot2 double, offset double, weights double");
  28.         String[] featureColNames = new String[] {"lot1", "lot2"};
  29.         String labelColName = "u";
  30.         GeneralizedLinearRegression glm = new GeneralizedLinearRegression()
  31.             .setFamily("gamma")
  32.             .setLink("Log")
  33.             .setRegParam(0.3)
  34.             .setMaxIter(5)
  35.             .setFeatureCols(featureColNames)
  36.             .setLabelCol(labelColName)
  37.             .setPredictionCol("pred");
  38.         GeneralizedLinearRegressionModel model = glm.fit(source);
  39.         BatchOperator <?> predict = model.transform(source);
  40.         BatchOperator <?> eval2 = model.evaluate(source);
  41.         predict.lazyPrint(10);
  42.         eval2.print();
  43.     }
  44. }

运行结果

预测结果

| u | lot1 | lot2 | offset | weights | pred | | —- | —- | —- | —- | —- | —- |

| 1.6094 | 118.0000 | 69.0000 | 1.0000 | 2.0000 | 1.4601 |

| 2.3026 | 58.0000 | 35.0000 | 1.0000 | 2.0000 | 2.6396 |

| 2.7081 | 42.0000 | 26.0000 | 1.0000 | 2.0000 | 3.0847 |

| 2.9957 | 35.0000 | 21.0000 | 1.0000 | 2.0000 | 3.4135 |

| 3.4012 | 27.0000 | 18.0000 | 1.0000 | 2.0000 | 3.5215 |

| 3.6889 | 25.0000 | 16.0000 | 1.0000 | 2.0000 | 3.6901 |

| 4.0943 | 21.0000 | 13.0000 | 1.0000 | 2.0000 | 3.9275 |

| 4.3820 | 19.0000 | 12.0000 | 1.0000 | 2.0000 | 3.9891 |

| 4.6052 | 18.0000 | 12.0000 | 1.0000 | 2.0000 | 3.9581 |

评估结果

| summary | | —- |

| {“rank”:3,”degreeOfFreedom”:6,”residualDegreeOfFreeDom”:6,”residualDegreeOfFreedomNull”:8,”aic”:9702.088569686523,”dispersion”:0.016006720896643168,”deviance”:0.09638590199190827,”nullDeviance”:0.8493577599031792,”coefficients”:[0.007797743508544688,-0.031175844426488887],”intercept”:1.609524324733498,”coefficientStandardErrors”:[0.030385113783611438,0.053017230010619414,0.10937960484662312],”tValues”:[0.2566303869742456,-0.5880323136505685,14.715031444760141],”pValues”:[0.8060371545112832,0.5779564640151,6.188226474801439E-6]} |