StringIndexer预测 (StringIndexerPredictStreamOp)

Java 类名：com.alibaba.alink.operator.stream.dataproc.StringIndexerPredictStreamOp

Python 类名：StringIndexerPredictStreamOp

功能介绍

基于StringIndexer模型，将一列字符串映射为整数。该组件为流式组件。

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
selectedCol	选中的列名	计算列对应的列名	String	✓	所选列类型为 [INTEGER, LONG, STRING]
handleInvalid	未知token处理策略	未知token处理策略。“keep”表示用最大id加1代替, “skip”表示补null， “error”表示抛异常	String		“KEEP”, “ERROR”, “SKIP”	“KEEP”
modelFilePath	模型的文件路径	模型的文件路径	String			null
outputCol	输出结果列	输出结果列列名，可选，默认null	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

代码示例

Python 代码

from pyalink.alink import *

import pandas as pd

useLocalEnv(1)

df_data = pd.DataFrame([
    ["football"],
    ["football"],
    ["football"],
    ["basketball"],
    ["basketball"],
    ["tennis"],
])

data = BatchOperator.fromDataframe(df_data, schemaStr='f0 string')

stream_data = StreamOperator.fromDataframe(df_data, schemaStr='f0 string')

stringindexer = StringIndexerTrainBatchOp() \
    .setSelectedCol("f0") \
    .setStringOrderType("frequency_asc")

model = stringindexer.linkFrom(data)

predictor = StringIndexerPredictStreamOp(model)\
                .setSelectedCol("f0")\
                .setOutputCol("f0_indexed")

predictor.linkFrom(stream_data).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.dataproc.StringIndexerTrainBatchOp;
import com.alibaba.alink.operator.batch.source.MemSourceBatchOp;
import com.alibaba.alink.operator.stream.StreamOperator;
import com.alibaba.alink.operator.stream.dataproc.StringIndexerPredictStreamOp;
import com.alibaba.alink.operator.stream.source.MemSourceStreamOp;
import org.junit.Test;

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

public class StringIndexerPredictStreamOpTest {
	@Test
	public void testStringIndexerPredictStreamOp() throws Exception {
		List <Row> df_data = Arrays.asList(
			Row.of("football"),
			Row.of("football"),
			Row.of("football"),
			Row.of("basketball"),
			Row.of("basketball"),
			Row.of("tennis")
		);
		BatchOperator <?> data = new MemSourceBatchOp(df_data, "f0 string");
		StreamOperator <?> stream_data = new MemSourceStreamOp(df_data, "f0 string");
		BatchOperator <?> stringindexer = new StringIndexerTrainBatchOp()
			.setSelectedCol("f0")
			.setStringOrderType("frequency_asc");
		BatchOperator model = stringindexer.linkFrom(data);
		StreamOperator <?> predictor = new StringIndexerPredictStreamOp(model)
			.setSelectedCol("f0")
			.setOutputCol("f0_indexed");
		predictor.linkFrom(stream_data).print();
		StreamOperator.execute();
	}
}

运行结果

f0	f0_indexed
basketball	1
football	2
tennis	0
basketball	1
football	2
football	2