Java 类名：com.alibaba.alink.pipeline.classification.OneVsRest
Python 类名：OneVsRest

功能介绍

本组件用One VS Rest策略进行多分类。

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
numClass 类别数 多分类的类别数，必选 Integer ✓
predictionCol 预测结果列名 预测结果列名 String ✓
modelFilePath 模型的文件路径 模型的文件路径 String null
overwriteSink 是否覆写已有数据 是否覆写已有数据 Boolean false
predictionDetailCol 预测详细信息列名 预测详细信息列名 String
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

代码示例

Python 代码

  1. from pyalink.alink import *
  3. import pandas as pd
  5. useLocalEnv(1)
  7. URL = "https://alink-test-data.oss-cn-hangzhou.aliyuncs.com/iris.csv";
  8. SCHEMA_STR = "sepal_length double, sepal_width double, petal_length double, petal_width double, category string";
  9. data = CsvSourceBatchOp().setFilePath(URL).setSchemaStr(SCHEMA_STR)
  11. lr = LogisticRegression() \
  12.     .setFeatureCols(["sepal_length", "sepal_width", "petal_length", "petal_width"]) \
  13.     .setLabelCol("category") \
  14.     .setPredictionCol("pred_result") \
  15.     .setMaxIter(100)
  17. oneVsRest = OneVsRest().setClassifier(lr).setNumClass(3)
  18. model = oneVsRest.fit(data)
  19. model.setPredictionCol("pred_result").setPredictionDetailCol("pred_detail")
  20. model.transform(data).print()

Java 代码

  1. import com.alibaba.alink.operator.batch.BatchOperator;
  2. import com.alibaba.alink.operator.batch.source.CsvSourceBatchOp;
  3. import com.alibaba.alink.pipeline.classification.LogisticRegression;
  4. import com.alibaba.alink.pipeline.classification.OneVsRest;
  5. import com.alibaba.alink.pipeline.classification.OneVsRestModel;
  6. import org.junit.Test;
  8. public class OneVsRestTest {
  9.     @Test
  10.     public void testOneVsRest() throws Exception {
  11.         String URL = "https://alink-test-data.oss-cn-hangzhou.aliyuncs.com/iris.csv";
  12.         String SCHEMA_STR
  13.             = "sepal_length double, sepal_width double, petal_length double, petal_width double, category string";
  14.         BatchOperator <?> data = new CsvSourceBatchOp().setFilePath(URL).setSchemaStr(SCHEMA_STR);
  15.         LogisticRegression lr = new LogisticRegression()
  16.             .setFeatureCols("sepal_length", "sepal_width", "petal_length", "petal_width")
  17.             .setLabelCol("category")
  18.             .setPredictionCol("pred_result")
  19.             .setMaxIter(100);
  20.         OneVsRest oneVsRest = new OneVsRest().setClassifier(lr).setNumClass(3);
  21.         OneVsRestModel model = oneVsRest.fit(data);
  22.         model.setPredictionCol("pred_result").setPredictionDetailCol("pred_detail");
  23.         model.transform(data).print();
  24.     }
  25. }

运行结果

| sepal_length | sepal_width | petal_length | petal_width | category | pred_result | pred_detail | | —- | —- | —- | —- | —- | —- | —- |

| 6.7000 | 3.1000 | 4.4000 | 1.4000 | Iris-versicolor | Iris-versicolor | {“Iris-versicolor”:0.9999890601537083,”Iris-virginica”:1.0939842119301402E-5,”Iris-setosa”:4.1724971938972156E-12} |

| 5.4000 | 3.0000 | 4.5000 | 1.5000 | Iris-versicolor | Iris-versicolor | {“Iris-versicolor”:0.9939699721610056,”Iris-virginica”:0.006030026623291463,”Iris-setosa”:1.2157029667713158E-9} |

| 5.4000 | 3.9000 | 1.7000 | 0.4000 | Iris-setosa | Iris-setosa | {“Iris-versicolor”:0.02236524089333592,”Iris-virginica”:0.0,”Iris-setosa”:0.9776347591066641} |

| 5.0000 | 3.4000 | 1.6000 | 0.4000 | Iris-setosa | Iris-setosa | {“Iris-versicolor”:0.07720412400682967,”Iris-virginica”:0.0,”Iris-setosa”:0.9227958759931704} |

| 5.6000 | 3.0000 | 4.5000 | 1.5000 | Iris-versicolor | Iris-versicolor | {“Iris-versicolor”:0.9961816818708689,”Iris-virginica”:0.003818317908880254,”Iris-setosa”:2.2025091271297693E-10} |

| … | … | … | … | … | … | … |