朴素贝叶斯文本分类预测 (NaiveBayesTextPredictStreamOp)

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

Python 类名：NaiveBayesTextPredictStreamOp

功能介绍

训练一个朴素贝叶斯文本分类模型用于多分类任务。

使用方式

该组件是预测组件，需要配合预测组件 NaiveBayesTextTrainBatchOp 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	✓
vectorCol	向量列名	向量列对应的列名	String	✓	所选列类型为 [DENSE_VECTOR, SPARSE_VECTOR, STRING, VECTOR]
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([
          ["$31$0:1.0 1:1.0 2:1.0 30:1.0","1.0  1.0  1.0  1.0", '1'],
          ["$31$0:1.0 1:1.0 2:0.0 30:1.0","1.0  1.0  0.0  1.0", '1'],
          ["$31$0:1.0 1:0.0 2:1.0 30:1.0","1.0  0.0  1.0  1.0", '1'],
          ["$31$0:1.0 1:0.0 2:1.0 30:1.0","1.0  0.0  1.0  1.0", '1'],
          ["$31$0:0.0 1:1.0 2:1.0 30:0.0","0.0  1.0  1.0  0.0", '0'],
          ["$31$0:0.0 1:1.0 2:1.0 30:0.0","0.0  1.0  1.0  0.0", '0'],
          ["$31$0:0.0 1:1.0 2:1.0 30:0.0","0.0  1.0  1.0  0.0", '0']
])

batchData = BatchOperator.fromDataframe(df_data, schemaStr='sv string, dv string, label string')

# stream data
streamData = StreamOperator.fromDataframe(df_data, schemaStr='sv string, dv string, label string')
# train op
ns = NaiveBayesTextTrainBatchOp().setVectorCol("sv").setLabelCol("label")
model = batchData.link(ns)
# predict op
predictor = NaiveBayesTextPredictStreamOp(model).setVectorCol("sv").setReservedCols(["sv", "label"]).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.NaiveBayesTextTrainBatchOp;
import com.alibaba.alink.operator.batch.source.MemSourceBatchOp;
import com.alibaba.alink.operator.stream.StreamOperator;
import com.alibaba.alink.operator.stream.classification.NaiveBayesTextPredictStreamOp;
import com.alibaba.alink.operator.stream.source.MemSourceStreamOp;
import org.junit.Test;

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

public class NaiveBayesTextPredictStreamOpTest {
	@Test
	public void testNaiveBayesTextPredictStreamOp() throws Exception {
		List <Row> df_data = Arrays.asList(
			Row.of("$31$0:1.0 1:1.0 2:1.0 30:1.0", "1.0  1.0  1.0  1.0", "1"),
			Row.of("$31$0:1.0 1:1.0 2:0.0 30:1.0", "1.0  1.0  0.0  1.0", "1"),
			Row.of("$31$0:1.0 1:0.0 2:1.0 30:1.0", "1.0  0.0  1.0  1.0", "1"),
			Row.of("$31$0:1.0 1:0.0 2:1.0 30:1.0", "1.0  0.0  1.0  1.0", "1"),
			Row.of("$31$0:0.0 1:1.0 2:1.0 30:0.0", "0.0  1.0  1.0  0.0", "0"),
			Row.of("$31$0:0.0 1:1.0 2:1.0 30:0.0", "0.0  1.0  1.0  0.0", "0"),
			Row.of("$31$0:0.0 1:1.0 2:1.0 30:0.0", "0.0  1.0  1.0  0.0", "0")
		);
		BatchOperator <?> batchData = new MemSourceBatchOp(df_data, "sv string, dv string, label string");
		StreamOperator <?> streamData = new MemSourceStreamOp(df_data, "sv string, dv string, label string");
		BatchOperator <?> ns = new NaiveBayesTextTrainBatchOp().setVectorCol("sv").setLabelCol("label");
		BatchOperator <?> model = batchData.link(ns);
		StreamOperator <?> predictor = new NaiveBayesTextPredictStreamOp(model).setVectorCol("sv").setReservedCols(
			"sv",
			"label").setPredictionCol("pred");
		predictor.linkFrom(streamData).print();
		StreamOperator.execute();
	}
}

运行结果

sv	label	pred
“$31$0:1.0 1:1.0 2:1.0 30:1.0”	1	1
“$31$0:1.0 1:1.0 2:0.0 30:1.0”	1	1
“$31$0:1.0 1:0.0 2:1.0 30:1.0”	1	1
“$31$0:1.0 1:0.0 2:1.0 30:1.0”	1	1
“$31$0:0.0 1:1.0 2:1.0 30:0.0”	0	0
“$31$0:0.0 1:1.0 2:1.0 30:0.0”	0	0
“$31$0:0.0 1:1.0 2:1.0 30:0.0”	0	0