决策树训练 (DecisionTreeTrainBatchOp)

Java 类名：com.alibaba.alink.operator.batch.classification.DecisionTreeTrainBatchOp

Python 类名：DecisionTreeTrainBatchOp

功能介绍

• 决策树组件支持稠密数据格式

• 支持带样本权重的训练

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
labelCol	标签列名	输入表中的标签列名	String	✓
categoricalCols	离散特征列名	离散特征列名	String[]		所选列类型为 [BOOLEAN, DATE, DOUBLE, FLOAT, INTEGER, LONG, SHORT, STRING, TIME, TIMESTAMP]
createTreeMode	创建树的模式。	series表示每个单机创建单颗树，parallel表示并行创建单颗树。	String			“series”
featureCols	特征列名数组	特征列名数组，默认全选	String[]		所选列类型为 [BOOLEAN, DATE, DOUBLE, FLOAT, INTEGER, LONG, SHORT, STRING, TIME, TIMESTAMP]	null
maxBins	连续特征进行分箱的最大个数	连续特征进行分箱的最大个数。	Integer			128
maxDepth	树的深度限制	树的深度限制	Integer			2147483647
maxLeaves	叶节点的最多个数	叶节点的最多个数	Integer			2147483647
maxMemoryInMB	树模型中用来加和统计量的最大内存使用数	树模型中用来加和统计量的最大内存使用数	Integer			64
minInfoGain	分裂的最小增益	分裂的最小增益	Double			0.0
minSampleRatioPerChild	子节点占父节点的最小样本比例	子节点占父节点的最小样本比例	Double			0.0
minSamplesPerLeaf	叶节点的最小样本个数	叶节点的最小样本个数	Integer			2
treeType	模型中树的类型	模型中树的类型，三种选项可选：树为一种方式gini, infoGain, infoGainRatio	String		“GINI”, “INFOGAIN”, “INFOGAINRATIO”	“GINI”
weightCol	权重列名	权重列对应的列名	String		所选列类型为 [BIGDECIMAL, BIGINTEGER, BYTE, DOUBLE, FLOAT, INTEGER, LONG, SHORT]	null

代码示例

Python 代码

from pyalink.alink import *

import pandas as pd

useLocalEnv(1)

df = pd.DataFrame([
    [1.0, "A", 0, 0, 0],
    [2.0, "B", 1, 1, 0],
    [3.0, "C", 2, 2, 1],
    [4.0, "D", 3, 3, 1]
])
batchSource = BatchOperator.fromDataframe(
    df, schemaStr=' f0 double, f1 string, f2 int, f3 int, label int')
streamSource = StreamOperator.fromDataframe(
    df, schemaStr=' f0 double, f1 string, f2 int, f3 int, label int')

trainOp = DecisionTreeTrainBatchOp()\
    .setLabelCol('label')\
    .setFeatureCols(['f0', 'f1', 'f2', 'f3'])\
    .linkFrom(batchSource)
predictBatchOp = DecisionTreePredictBatchOp()\
    .setPredictionDetailCol('pred_detail')\
    .setPredictionCol('pred')
predictStreamOp = DecisionTreePredictStreamOp(trainOp)\
    .setPredictionDetailCol('pred_detail')\
    .setPredictionCol('pred')

predictBatchOp.linkFrom(trainOp, batchSource).print()
predictStreamOp.linkFrom(streamSource).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.classification.DecisionTreePredictBatchOp;
import com.alibaba.alink.operator.batch.classification.DecisionTreeTrainBatchOp;
import com.alibaba.alink.operator.batch.source.MemSourceBatchOp;
import com.alibaba.alink.operator.stream.StreamOperator;
import com.alibaba.alink.operator.stream.classification.DecisionTreePredictStreamOp;
import com.alibaba.alink.operator.stream.source.MemSourceStreamOp;
import org.junit.Test;

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

public class DecisionTreeTrainBatchOpTest {
	@Test
	public void testDecisionTreeTrainBatchOp() throws Exception {
		List <Row> df = Arrays.asList(
			Row.of(1.0, "A", 0, 0, 0),
			Row.of(2.0, "B", 1, 1, 0),
			Row.of(3.0, "C", 2, 2, 1),
			Row.of(4.0, "D", 3, 3, 1)
		);

		BatchOperator <?> batchSource = new MemSourceBatchOp(
			df, " f0 double, f1 string, f2 int, f3 int, label int");
		StreamOperator <?> streamSource = new MemSourceStreamOp(
			df, " f0 double, f1 string, f2 int, f3 int, label int");
		BatchOperator <?> trainOp = new DecisionTreeTrainBatchOp()
			.setLabelCol("label")
			.setFeatureCols("f0", "f1", "f2", "f3")
			.linkFrom(batchSource);
		BatchOperator <?> predictBatchOp = new DecisionTreePredictBatchOp()
			.setPredictionDetailCol("pred_detail")
			.setPredictionCol("pred");
		StreamOperator <?> predictStreamOp = new DecisionTreePredictStreamOp(trainOp)
			.setPredictionDetailCol("pred_detail")
			.setPredictionCol("pred");
		predictBatchOp.linkFrom(trainOp, batchSource).print();
		predictStreamOp.linkFrom(streamSource).print();
		StreamOperator.execute();
	}
}

运行结果

f0	f1	f2	f3	label	pred	pred_detail
1.0000	A	0	0	0	0	{“0”:1.0,“1”:0.0}
2.0000	B	1	1	0	0	{“0”:1.0,“1”:0.0}
3.0000	C	2	2	1	1	{“0”:0.0,“1”:1.0}
4.0000	D	3	3	1	1	{“0”:0.0,“1”:1.0}