Cross特征训练 (CrossFeatureTrainBatchOp)

Java 类名：com.alibaba.alink.operator.batch.feature.CrossFeatureTrainBatchOp

Python 类名：CrossFeatureTrainBatchOp

功能介绍

将选定的特征列组合成单个向量类型的特征。

使用方式

该组件是训练组件，需要配合预测组件 CrossFeaturePredictBatch/StreamOp 使用。

为了训练模型，需要指定参与组合的特征列名（selectedCols）。

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[]	✓

代码示例

Python 代码

from pyalink.alink import *

import pandas as pd

useLocalEnv(1)

df = pd.DataFrame([
["1.0", "1.0", 1.0, 1],
["1.0", "1.0", 0.0, 1],
["1.0", "0.0", 1.0, 1],
["1.0", "0.0", 1.0, 1],
["2.0", "3.0", None, 0],
["2.0", "3.0", 1.0, 0],
["0.0", "1.0", 2.0, 0],
["0.0", "1.0", 1.0, 0]])
data = BatchOperator.fromDataframe(df, schemaStr="f0 string, f1 string, f2 double, label bigint")
train = CrossFeatureTrainBatchOp().setSelectedCols(['f0','f1','f2']).linkFrom(data)
CrossFeaturePredictBatchOp().setOutputCol("cross").linkFrom(train, data).collectToDataframe()

Java 代码

import org.apache.flink.types.Row;

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

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

public class CrossFeatureTrainBatchOpTest {
	@Test
	public void testCrossFeatureTrainBatchOp() throws Exception {
		List <Row> df = Arrays.asList(
			Row.of("1.0", "1.0", 1.0, 1),
			Row.of("1.0", "1.0", 0.0, 1),
			Row.of("1.0", "0.0", 1.0, 1),
			Row.of("1.0", "0.0", 1.0, 1),
			Row.of("2.0", "3.0", null, 0),
			Row.of("2.0", "3.0", 1.0, 0),
			Row.of("0.0", "1.0", 2.0, 0)
		);
		BatchOperator <?> data = new MemSourceBatchOp(df, "f0 string, f1 string, f2 double, label int");
		BatchOperator <?> train = new CrossFeatureTrainBatchOp().setSelectedCols("f0", "f1", "f2").linkFrom(data);
		new CrossFeaturePredictBatchOp().setOutputCol("cross").linkFrom(train, data).print();
	}
}

运行结果

f0	f1	f2	label	cross
1.0	1.0	1.0000	1	$36$0:1.0
1.0	1.0	0.0000	1	$36$9:1.0
1.0	0.0	1.0000	1	$36$6:1.0
1.0	0.0	1.0000	1	$36$6:1.0
2.0	3.0	null	0	$36$22:1.0
2.0	3.0	1.0000	0	$36$4:1.0
0.0	1.0	2.0000	0	$36$29:1.0
0.0	1.0	1.0000	0	$36$2:1.0