广义线性回归 (GeneralizedLinearRegression)

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

from pyalink.alink import *

import pandas as pd

useLocalEnv(1)

df = pd.DataFrame([
    [1.6094,118.0000,69.0000,1.0000,2.0000],
    [2.3026,58.0000,35.0000,1.0000,2.0000],
    [2.7081,42.0000,26.0000,1.0000,2.0000],
    [2.9957,35.0000,21.0000,1.0000,2.0000],
    [3.4012,27.0000,18.0000,1.0000,2.0000],
    [3.6889,25.0000,16.0000,1.0000,2.0000],
    [4.0943,21.0000,13.0000,1.0000,2.0000],
    [4.3820,19.0000,12.0000,1.0000,2.0000],
    [4.6052,18.0000,12.0000,1.0000,2.0000]
])

source = BatchOperator.fromDataframe(df, schemaStr='u double, lot1 double, lot2 double, offset double, weights double')

featureColNames = ["lot1", "lot2"]
labelColName = "u"

# train
glm = GeneralizedLinearRegression()\
                .setFamily("gamma")\
                .setLink("Log")\
                .setRegParam(0.3)\
                .setMaxIter(5)\
                .setFeatureCols(featureColNames)\
                .setLabelCol(labelColName)\
                .setPredictionCol("pred")

model = glm.fit(source)
predict = model.transform(source)
eval2 = model.evaluate(source)

predict.lazyPrint(10)
eval2.print()

Java 代码

import org.apache.flink.types.Row;

import com.alibaba.alink.operator.batch.BatchOperator;
import com.alibaba.alink.operator.batch.source.MemSourceBatchOp;
import com.alibaba.alink.pipeline.regression.GeneralizedLinearRegression;
import com.alibaba.alink.pipeline.regression.GeneralizedLinearRegressionModel;
import org.junit.Test;

import java.util.Arrays;
import java.util.List;

public class GeneralizedLinearRegressionTest {
	@Test
	public void testGeneralizedLinearRegression() throws Exception {
		List <Row> df = Arrays.asList(
			Row.of(1.6094, 118.0000, 69.0000, 1.0000, 2.0000),
			Row.of(2.3026, 58.0000, 35.0000, 1.0000, 2.0000),
			Row.of(2.7081, 42.0000, 26.0000, 1.0000, 2.0000),
			Row.of(2.9957, 35.0000, 21.0000, 1.0000, 2.0000),
			Row.of(3.4012, 27.0000, 18.0000, 1.0000, 2.0000),
			Row.of(3.6889, 25.0000, 16.0000, 1.0000, 2.0000),
			Row.of(4.0943, 21.0000, 13.0000, 1.0000, 2.0000),
			Row.of(4.3820, 19.0000, 12.0000, 1.0000, 2.0000),
			Row.of(4.6052, 18.0000, 12.0000, 1.0000, 2.0000)
		);
		BatchOperator <?> source = new MemSourceBatchOp(df,
			"u double, lot1 double, lot2 double, offset double, weights double");
		String[] featureColNames = new String[] {"lot1", "lot2"};
		String labelColName = "u";
		GeneralizedLinearRegression glm = new GeneralizedLinearRegression()
			.setFamily("gamma")
			.setLink("Log")
			.setRegParam(0.3)
			.setMaxIter(5)
			.setFeatureCols(featureColNames)
			.setLabelCol(labelColName)
			.setPredictionCol("pred");
		GeneralizedLinearRegressionModel model = glm.fit(source);
		BatchOperator <?> predict = model.transform(source);
		BatchOperator <?> eval2 = model.evaluate(source);
		predict.lazyPrint(10);
		eval2.print();
	}
}

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]}