等宽离散化训练 (EqualWidthDiscretizerTrainBatchOp)

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

Python 类名：EqualWidthDiscretizerTrainBatchOp

功能介绍

等宽离散可以计算选定数值列的分位点，每个区间都有相同的组距，也就是数据范围/组数，通过训练可以得到一系列分为点，
然后使用这些分位点进行预测。
其中可以所有列使用同一个分组数量，也可以每一列对应一个分组数量。预测结果可以是特征值或一系列0/1离散特征。

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 [BIGDECIMAL, BIGINTEGER, BYTE, DOUBLE, FLOAT, INTEGER, LONG, SHORT]
leftOpen	是否左开右闭	左开右闭为true，左闭右开为false	Boolean			true
numBuckets	quantile个数	quantile个数，对所有列有效。	Integer			2
numBucketsArray	quantile个数	quantile个数，每一列对应数组中一个元素。	Integer[]			null

代码示例

Python 代码

from pyalink.alink import *

import pandas as pd

useLocalEnv(1)

df = pd.DataFrame([
    ["a", 1, 1.1],     
    ["b", -2, 0.9],    
    ["c", 100, -0.01], 
    ["d", -99, 100.9], 
    ["a", 1, 1.1],     
    ["b", -2, 0.9],    
    ["c", 100, -0.01], 
    ["d", -99, 100.9] 
])

batchSource =  BatchOperator.fromDataframe(df,schemaStr="f_string string, f_long long, f_double double")

trainOp = EqualWidthDiscretizerTrainBatchOp(). \
setSelectedCols(['f_long', 'f_double']). \
setNumBuckets(5). \
linkFrom(batchSource)

EqualWidthDiscretizerPredictBatchOp(). \
setSelectedCols(['f_long', 'f_double']). \
linkFrom(trainOp,batchSource). \
print()

trainOp = EqualWidthDiscretizerTrainBatchOp().setSelectedCols(['f_long', 'f_double']). \
setNumBucketsArray([5,3]). \
linkFrom(batchSource)

EqualWidthDiscretizerPredictBatchOp(). \
setSelectedCols(['f_long', 'f_double']). \
linkFrom(trainOp,batchSource). \
print()

EqualWidthDiscretizerPredictBatchOp(). \
setEncode("ASSEMBLED_VECTOR"). \
setSelectedCols(['f_long', 'f_double']). \
setOutputCols(["assVec"]). \
linkFrom(trainOp,batchSource).print()

Java 代码

import org.apache.flink.types.Row;

import com.alibaba.alink.operator.batch.BatchOperator;
import com.alibaba.alink.operator.batch.feature.EqualWidthDiscretizerPredictBatchOp;
import com.alibaba.alink.operator.batch.feature.EqualWidthDiscretizerTrainBatchOp;
import com.alibaba.alink.operator.batch.source.MemSourceBatchOp;
import com.alibaba.alink.operator.stream.StreamOperator;
import com.alibaba.alink.operator.stream.feature.EqualWidthDiscretizerPredictStreamOp;
import com.alibaba.alink.operator.stream.source.MemSourceStreamOp;
import com.alibaba.alink.params.feature.HasEncodeWithoutWoe.Encode;
import org.junit.Test;

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

public class EqualWidthDiscretizerTrainBatchOpTest {
	@Test
	public void testEqualWidthDiscretizerTrainBatchOp2() throws Exception {
		List <Row> df = Arrays.asList(
			Row.of("a", 1, 1.1),
			Row.of("b", -2, 0.9),
			Row.of("c", 100, -0.01),
			Row.of("d", -99, 100.9),
			Row.of("a", 1, 1.1),
			Row.of("b", -2, 0.9),
			Row.of("c", 100, -0.01),
			Row.of("d", -99, 100.9)
		);
		BatchOperator <?> batchSource = new MemSourceBatchOp(df, "f_string string, f_long int, f_double double");

		BatchOperator <?> trainOp = new EqualWidthDiscretizerTrainBatchOp().setSelectedCols("f_long", "f_double")
			.setNumBuckets(5).linkFrom(batchSource);

		new EqualWidthDiscretizerPredictBatchOp().setSelectedCols("f_long","f_double")
			.linkFrom(trainOp, batchSource).print();

		BatchOperator trainOp2 = new EqualWidthDiscretizerTrainBatchOp().setSelectedCols("f_long", "f_double")
			.setNumBucketsArray(5,3).linkFrom(batchSource);

		new EqualWidthDiscretizerPredictBatchOp().setSelectedCols("f_long","f_double")
			.linkFrom(trainOp2,batchSource).print();

		new EqualWidthDiscretizerPredictBatchOp().setSelectedCols("f_long","f_double")
			.setEncode(Encode.ASSEMBLED_VECTOR)
			.setOutputCols("assVec")
			.linkFrom(trainOp2,batchSource).print();
	}
}

运行结果

f_string	f_long	f_double
a	2	0
b	2	0
c	4	0
d	0	4
a	2	0
b	2	0
c	4	0
d	0	4

f_string	f_long	f_double
a	2	0
b	2	0
c	4	0
d	0	2
a	2	0
b	2	0
c	4	0
d	0	2

f_string	f_long	f_double	assVec
a	1	1.1000	$8$2:1.0 5:1.0
b	-2	0.9000	$8$2:1.0 5:1.0
c	100	-0.0100	$8$5:1.0
d	-99	100.9000	$8$0:1.0
a	1	1.1000	$8$2:1.0 5:1.0
b	-2	0.9000	$8$2:1.0 5:1.0
c	100	-0.0100	$8$5:1.0
d	-99	100.9000	$8$0:1.0