超大ID化预测 (HugeIndexerStringPredictBatchOp)

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

Python 类名：HugeIndexerStringPredictBatchOp

功能介绍

提供字符串ID化处理功能

由StringIndexerTrainBatchOp生成词典模型，将输入数据的ID类型转化成词典模型中对应的字符串。

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
selectedCols	选择的列名	计算列对应的列名列表	String[]	✓	所选列类型为 [LONG]
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”
outputCols	输出结果列列名数组	输出结果列列名数组，可选，默认null	String[]			null
reservedCols	算法保留列名	算法保留列	String[]			null

代码示例

Python 代码

from pyalink.alink import *

import pandas as pd

useLocalEnv(1)

df = pd.DataFrame([
        ["football", "apple"],
   		["football", "apple"],
   		["football", "apple"],
   		["basketball", "apple"],
   		["basketball", "apple"],
   		["tennis", "pair"],
   		["tennis", "pair"],
   		["pingpang", "banana"],
   		["pingpang", "banana"],
   		["baseball", "banana"]
])

data = BatchOperator.fromDataframe(df, schemaStr='f0 string, f1 string')

stringindexer = StringIndexerTrainBatchOp()\
			.setSelectedCol("f0")\
			.setSelectedCols(["f1"])\
			.setStringOrderType("alphabet_asc")

predictor = HugeStringIndexerPredictBatchOp().setSelectedCols(["f0", "f1"])\
			.setOutputCols(["f0_indexed", "f1_indexed"])
		
model = stringindexer.linkFrom(data)
	
result = predictor.linkFrom(model, data)
		
indexerString = HugeIndexerStringPredictBatchOp().setSelectedCols(["f0_indexed", "f1_indexed"])\
			.setOutputCols(["f0_source", "f1_source"])
		
indexerString.linkFrom(model, result).print()

Java 代码

package com.alibaba.alink.operator.batch.dataproc;

import org.apache.flink.types.Row;

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

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

public class HugeIndexerStringPredictBatchOpTest {
	@Test
	public void testStringIndexerPredictBatchOp() throws Exception {
		List <Row> df = Arrays.asList(
			Row.of("football", "apple"),
			Row.of("football", "apple"),
			Row.of("football", "apple"),
			Row.of("basketball", "apple"),
			Row.of("basketball", "apple"),
			Row.of("tennis", "pair"),
			Row.of("tennis", "pair"),
			Row.of("pingpang", "banana"),
			Row.of("pingpang", "banana"),
			Row.of("baseball", "banana")
		);
		BatchOperator <?> data = new MemSourceBatchOp(df, "f0 string,f1 string");

		BatchOperator <?> stringindexer = new StringIndexerTrainBatchOp()
			.setSelectedCol("f0")
			.setSelectedCols("f1")
			.setStringOrderType("alphabet_asc");
		BatchOperator <?> predictor = new HugeStringIndexerPredictBatchOp().setSelectedCols("f0", "f1")
			.setOutputCols("f0_indexed", "f1_indexed");
		BatchOperator model = stringindexer.linkFrom(data);
		model.lazyPrint(10);
		BatchOperator result = predictor.linkFrom(model, data);
		result.lazyPrint(10);

		BatchOperator <?> indexerString = new HugeIndexerStringPredictBatchOp().setSelectedCols("f0_indexed", "f1_indexed")
			.setOutputCols("f0_source", "f1_source");
		indexerString.linkFrom(model, result).print();
	}
}

运行结果

f0	f1	f0_indexed	f1_indexed	f0_source	f1_source
basketball	apple	3	0	basketball	apple
football	apple	4	0	football	apple
basketball	apple	3	0	basketball	apple
pingpang	banana	6	1	pingpang	banana
football	apple	4	0	football	apple
tennis	pair	7	5	tennis	pair
tennis	pair	7	5	tennis	pair
pingpang	banana	6	1	pingpang	banana
baseball	banana	2	1	baseball	banana
football	apple	4	0	football	apple