字符串编码预测 (StringIndexerPredictBatchOp)

Java 类名：com.alibaba.alink.operator.batch.dataproc.StringIndexerPredictBatchOp

Python 类名：StringIndexerPredictBatchOp

功能介绍

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

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

代码示例

Python 代码

from pyalink.alink import *

import pandas as pd

useLocalEnv(1)

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


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

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

predictor = StringIndexerPredictBatchOp().setSelectedCol("f0").setOutputCol("f0_indexed")

model = stringindexer.linkFrom(data)
predictor.linkFrom(model, data).print()

Java 代码

import org.apache.flink.types.Row;

import com.alibaba.alink.operator.batch.BatchOperator;
import com.alibaba.alink.operator.batch.dataproc.StringIndexerPredictBatchOp;
import com.alibaba.alink.operator.batch.dataproc.StringIndexerTrainBatchOp;
import com.alibaba.alink.operator.batch.source.MemSourceBatchOp;
import org.junit.Test;

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

public class StringIndexerPredictBatchOpTest {
	@Test
	public void testStringIndexerPredictBatchOp() throws Exception {
		List <Row> df = 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, "f0 string");
		BatchOperator <?> stringindexer = new StringIndexerTrainBatchOp()
			.setSelectedCol("f0")
			.setStringOrderType("frequency_asc");
		BatchOperator <?> predictor = new StringIndexerPredictBatchOp().setSelectedCol("f0").setOutputCol(
			"f0_indexed");
		BatchOperator model = stringindexer.linkFrom(data);
		predictor.linkFrom(model, data).print();
	}
}

运行结果

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