Java 类名：com.alibaba.alink.operator.stream.regression.GlmPredictStreamOp
Python 类名：GlmPredictStreamOp
GLM(Generalized Linear Model)又称为广义线性回归模型，是一种常用的统计模型，也是一种非线性模型族，许多常用的模型都属于广义线性回归。
它描述了响应和预测因子之间的非线性关系。广义线性回归模型具有线性回归模型的广义特征。响应变量遵循正态、二项式、泊松分布、伽马分布或逆高斯分布，链接函数f定义了μ和预测值的线性组合之间的关系。
GLM功能包括GLM训练，GLM预测(批和流)和GLM评估, 其中训练使用迭代最小二乘方法。
| 分布 | 连接函数 | 对应算法 |
|---|---|---|
| 二项分布 | Logit | 逻辑回归 |
| 多项分布 | Logit | softmax |
| 高斯分布 | Identity | 线性回归 |
| Poisson分布 | Log | Possion回归 |
[1] https://en.wikipedia.org/wiki/Generalized_linear_model
| 名称 | 中文名称 | 描述 | 类型 | 是否必须？ | 取值范围 | 默认值 |
|---|---|---|---|---|---|---|
| predictionCol | 预测结果列名 | 预测结果列名 | String | ✓ | ||
| linkPredResultCol | 连接函数结果的列名 | 连接函数结果的列名 | String | null | ||
| modelFilePath | 模型的文件路径 | 模型的文件路径 | String | null | ||
| reservedCols | 算法保留列名 | 算法保留列 | String[] | 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 |
from pyalink.alink import *
import pandas as pd
useLocalEnv(1)
# data
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
train = GlmTrainBatchOp()\
.setFamily("gamma")\
.setLink("Log")\
.setRegParam(0.3)\
.setMaxIter(5)\
.setFeatureCols(featureColNames)\
.setLabelCol(labelColName)
source.link(train)
# batch predict
predict = GlmPredictBatchOp()\
.setPredictionCol("pred")
predict.linkFrom(train, source)
predict.print()
# eval
eval = GlmEvaluationBatchOp()\
.setFamily("gamma")\
.setLink("Log")\
.setRegParam(0.3)\
.setMaxIter(5)\
.setFeatureCols(featureColNames)\
.setLabelCol(labelColName)
eval.linkFrom(train, source)
eval.print()
# stream predict
source_stream = StreamOperator.fromDataframe(df, schemaStr='u double, lot1 double, lot2 double, offset double, weights double')
predict_stream = GlmPredictStreamOp(train)\
.setPredictionCol("pred")
predict_stream.linkFrom(source_stream)
predict_stream.print()
StreamOperator.execute()
import org.apache.flink.types.Row;
import com.alibaba.alink.operator.batch.BatchOperator;
import com.alibaba.alink.operator.batch.regression.GlmEvaluationBatchOp;
import com.alibaba.alink.operator.batch.regression.GlmPredictBatchOp;
import com.alibaba.alink.operator.batch.regression.GlmTrainBatchOp;
import com.alibaba.alink.operator.batch.source.MemSourceBatchOp;
import com.alibaba.alink.operator.stream.StreamOperator;
import com.alibaba.alink.operator.stream.regression.GlmPredictStreamOp;
import com.alibaba.alink.operator.stream.source.MemSourceStreamOp;
import org.junit.Test;
import java.util.Arrays;
import java.util.List;
public class GlmPredictStreamOpTest {
@Test
public void testGlmPredictStreamOp() 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";
BatchOperator <?> train = new GlmTrainBatchOp()
.setFamily("gamma")
.setLink("Log")
.setRegParam(0.3)
.setMaxIter(5)
.setFeatureCols(featureColNames)
.setLabelCol(labelColName);
source.link(train);
BatchOperator <?> predict = new GlmPredictBatchOp()
.setPredictionCol("pred");
predict.linkFrom(train, source);
predict.print();
BatchOperator <?> eval = new GlmEvaluationBatchOp()
.setFamily("gamma")
.setLink("Log")
.setRegParam(0.3)
.setMaxIter(5)
.setFeatureCols(featureColNames)
.setLabelCol(labelColName);
eval.linkFrom(train, source);
eval.print();
StreamOperator <?> source_stream = new MemSourceStreamOp(df,
"u double, lot1 double, lot2 double, offset double, weights double");
StreamOperator <?> predict_stream = new GlmPredictStreamOp(train)
.setPredictionCol("pred");
predict_stream.linkFrom(source_stream);
predict_stream.print();
StreamOperator.execute();
}
}
| 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 |
{“rank”:3,“degreeOfFreedom”:6,“residualDegreeOfFreeDom”:6,“residualDegreeOfFreedomNull”:8,“aic”:9702.088569686532,“dispersion”:0.016006720896643168,“deviance”:0.0963859019919082,“nullDeviance”:0.8493577599031797,“coefficients”:[0.007797743508544201,-0.031175844426488047],“intercept”:1.609524324733497,“coefficientStandardErrors”:[0.030385113783605693,0.05301723001060941,0.10937960484661188],“tValues”:[0.25663038697427815,-0.5880323136506637,14.715031444761644],“pValues”:[0.8060371545112608,0.5779564640150403,6.188226474801439E-6]}
| u | lot1 | lot2 | offset | weights | pred |
|---|---|---|---|---|---|
| 2.7081 | 42.0000 | 26.0000 | 1.0000 | 2.0000 | 3.0847 |
| 2.9957 | 35.0000 | 21.0000 | 1.0000 | 2.0000 | 3.4135 |
| 1.6094 | 118.0000 | 69.0000 | 1.0000 | 2.0000 | 1.4601 |
| 4.0943 | 21.0000 | 13.0000 | 1.0000 | 2.0000 | 3.9275 |
| 4.3820 | 19.0000 | 12.0000 | 1.0000 | 2.0000 | 3.9891 |
| 3.4012 | 27.0000 | 18.0000 | 1.0000 | 2.0000 | 3.5215 |
| 2.3026 | 58.0000 | 35.0000 | 1.0000 | 2.0000 | 2.6396 |
| 3.6889 | 25.0000 | 16.0000 | 1.0000 | 2.0000 | 3.6901 |
| 4.6052 | 18.0000 | 12.0000 | 1.0000 | 2.0000 | 3.9581 |