IndexToString预测 (IndexToStringPredictBatchOp)

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

Python 类名：IndexToStringPredictBatchOp

功能介绍

基于 StringIndexer 模型，将一列整数映射为字符串。

在批式预测中，IndexToStringPredictBatchOp 接收两个BatchOp的输入，
第一个输入为模型（StringIndexer的getModelData()获取，或者直接输入StringIndexerTrainBatchOp），
第二个输入为要预测的数据。

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
modelName	模型名字	模型名字	String	✓
selectedCol	选中的列名	计算列对应的列名	String	✓	所选列类型为 [STRING]
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_data = pd.DataFrame([
    ["football"],
    ["football"],
    ["football"],
    ["basketball"],
    ["basketball"],
    ["tennis"],
])

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

stringIndexer = StringIndexer()\
    .setModelName("string_indexer_model")\
    .setSelectedCol("f0")\
    .setOutputCol("f0_indexed")\
    .setStringOrderType("frequency_asc").fit(train_data)

indexed = stringIndexer.transform(train_data)

indexToStrings = IndexToStringPredictBatchOp()\
    .setSelectedCol("f0_indexed")\
    .setOutputCol("f0_indxed_unindexed")

indexToStrings.linkFrom(stringIndexer.getModelData(), indexed).print()

Java 代码

import org.apache.flink.types.Row;

import com.alibaba.alink.operator.batch.BatchOperator;
import com.alibaba.alink.operator.batch.dataproc.IndexToStringPredictBatchOp;
import com.alibaba.alink.operator.batch.source.MemSourceBatchOp;
import com.alibaba.alink.pipeline.dataproc.StringIndexer;
import com.alibaba.alink.pipeline.dataproc.StringIndexerModel;
import org.junit.Test;

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

public class IndexToStringPredictStreamOpTest {

	@Test
	public void testIndexToStringPredictStreamOp() 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 <?> train_data = new MemSourceBatchOp(df_data, "f0 string");
		StringIndexerModel stringIndexer = new StringIndexer()
			.setModelName("string_indexer_model")
			.setSelectedCol("f0")
			.setOutputCol("f0_indexed")
			.setStringOrderType("frequency_asc").fit(train_data);
		BatchOperator indexed = stringIndexer.transform(train_data);
		BatchOperator <?> indexToStrings = new IndexToStringPredictBatchOp()
			.setSelectedCol("f0_indexed")
			.setOutputCol("f0_indxed_unindexed");
		indexToStrings.linkFrom(stringIndexer.getModelData(), indexed).print();
	}
}

运行结果

f0	f0_indexed	f0_indxed_unindexed
football	2	football
football	2	football
football	2	football
basketball	1	basketball
basketball	1	basketball
tennis	0	tennis