朴素贝叶斯预测 (NaiveBayesPredictStreamOp)

Java 类名:com.alibaba.alink.operator.stream.classification.NaiveBayesPredictStreamOp

Python 类名:NaiveBayesPredictStreamOp

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
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_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')

# stream data
streamData = StreamOperator.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 = NaiveBayesPredictStreamOp(model).setPredictionCol("pred")
predictor.linkFrom(streamData).print()
StreamOperator.execute()

Java 代码

import org.apache.flink.types.Row;

import com.alibaba.alink.operator.batch.BatchOperator;
import com.alibaba.alink.operator.batch.classification.NaiveBayesTrainBatchOp;
import com.alibaba.alink.operator.batch.source.MemSourceBatchOp;
import com.alibaba.alink.operator.stream.StreamOperator;
import com.alibaba.alink.operator.stream.classification.NaiveBayesPredictStreamOp;
import com.alibaba.alink.operator.stream.source.MemSourceStreamOp;
import org.junit.Test;

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

public class NaiveBayesPredictStreamOpTest {
	@Test
	public void testNaiveBayesPredictStreamOp() 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");
		StreamOperator <?> streamData = new MemSourceStreamOp(df_data,
			"f0 double, f1 double, f2 double, f3 double, label int");
		String[] colnames = new String[] {"f0", "f1", "f2", "f3"};
		BatchOperator <?> ns = new NaiveBayesTrainBatchOp().setFeatureCols(colnames).setLabelCol("label");
		BatchOperator <?> model = batchData.link(ns);
		StreamOperator <?> predictor = new NaiveBayesPredictStreamOp(model).setPredictionCol("pred");
		predictor.linkFrom(streamData).print();
		StreamOperator.execute();
	}
}

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