朴素贝叶斯预测 (NaiveBayesPredictBatchOp)

Java 类名：com.alibaba.alink.operator.batch.classification.NaiveBayesPredictBatchOp

Python 类名：NaiveBayesPredictBatchOp

功能介绍

使用朴素贝叶斯模型用于多分类任务的预测。

使用方式

该组件是预测组件，需要配合训练组件 NaiveBayesTrainBatchOp 使用。

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
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
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

from pyalink.alink import *

import pandas as pd

useLocalEnv(1)

df_data = pd.DataFrame([
       [1.0, 1.0, 0.0, 1.0, 1],
       [1.0, 0.0, 1.0, 1.0, 1],
       [1.0, 0.0, 1.0, 1.0, 1],
       [0.0, 1.0, 1.0, 0.0, 0],
       [0.0, 1.0, 1.0, 0.0, 0],
       [0.0, 1.0, 1.0, 0.0, 0],
       [0.0, 1.0, 1.0, 0.0, 0],
       [1.0, 1.0, 1.0, 1.0, 1],
       [0.0, 1.0, 1.0, 0.0, 0]
])

batchData = BatchOperator.fromDataframe(df_data, schemaStr='f0 double, f1 double, f2 double, f3 double, label int')

colnames = ["f0","f1","f2", "f3"]
ns = NaiveBayesTrainBatchOp().setFeatureCols(colnames).setLabelCol("label")
model = batchData.link(ns)

predictor = NaiveBayesPredictBatchOp().setPredictionCol("pred")
predictor.linkFrom(model, batchData).print()

Java 代码

import org.apache.flink.types.Row;

import com.alibaba.alink.operator.batch.BatchOperator;
import com.alibaba.alink.operator.batch.classification.NaiveBayesPredictBatchOp;
import com.alibaba.alink.operator.batch.classification.NaiveBayesTrainBatchOp;
import com.alibaba.alink.operator.batch.source.MemSourceBatchOp;
import org.junit.Test;

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

public class NaiveBayesPredictBatchOpTest {
	@Test
	public void testNaiveBayesPredictBatchOp() throws Exception {
		List <Row> df_data = Arrays.asList(
			Row.of(1.0, 1.0, 0.0, 1.0, 1),
			Row.of(1.0, 0.0, 1.0, 1.0, 1),
			Row.of(1.0, 0.0, 1.0, 1.0, 1),
			Row.of(0.0, 1.0, 1.0, 0.0, 0),
			Row.of(0.0, 1.0, 1.0, 0.0, 0),
			Row.of(0.0, 1.0, 1.0, 0.0, 0),
			Row.of(0.0, 1.0, 1.0, 0.0, 0),
			Row.of(1.0, 1.0, 1.0, 1.0, 1),
			Row.of(0.0, 1.0, 1.0, 0.0, 0)
		);
		BatchOperator <?> batchData = new MemSourceBatchOp(df_data,
			"f0 double, f1 double, f2 double, f3 double, label int");
		BatchOperator <?> ns = new NaiveBayesTrainBatchOp().setFeatureCols("f0", "f1", "f2", "f3").setLabelCol(
			"label");
		BatchOperator model = batchData.link(ns);
		BatchOperator <?> predictor = new NaiveBayesPredictBatchOp().setPredictionCol("pred");
		predictor.linkFrom(model, batchData).print();
	}
}

运行结果

f0	f1	f2	f3	label	pred
1.0	1.0	0.0	1.0	1	1
1.0	0.0	1.0	1.0	1	1
1.0	0.0	1.0	1.0	1	1
0.0	1.0	1.0	0.0	0	0
0.0	1.0	1.0	0.0	0	0
0.0	1.0	1.0	0.0	0	0
0.0	1.0	1.0	0.0	0	0
1.0	1.0	1.0	1.0	1	1
0.0	1.0	1.0	0.0	0	0