带约束的Stepwise回归筛选训练 (ConstrainedRegSelectorTrainBatchOp)

Java 类名：com.alibaba.alink.operator.batch.finance.ConstrainedRegSelectorTrainBatchOp

Python 类名：ConstrainedRegSelectorTrainBatchOp

功能介绍

使用带约束的Stepwise线性回归方法，进行特征筛选。

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	✓
alphaEntry	筛选阈值	筛选阈值	Double			0.05
alphaStay	移除阈值	移除阈值	Double			0.05
forceSelectedCols	强制选择的列	强制选择的列	int[]		所选列类型为 [BIGDECIMAL, BIGINTEGER, BYTE, DOUBLE, FLOAT, INTEGER, LONG, SHORT]
l1	L1 正则化系数	L1 正则化系数，默认为0。	Double		x >= 0.0	0.0
l2	L2 正则化系数	L2 正则化系数，默认为0。	Double		x >= 0.0	0.0
method	方法	方法	String		“FTest”, “MarginalContribution”	“FTest”
optimMethod	优化方法	优化方法	String		“SQP”, “BARRIER”	“SQP”
selectedCol	计算列对应的列名	计算列对应的列名, 默认值是null	String			null
selectedCols	选中的列名数组	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]	null

代码示例

Python 代码

from pyalink.alink import *

import pandas as pd

useLocalEnv(1)

df = pd.DataFrame([
    ["$3$0:1.0 1:7.0 2:9.0", "1.0 7.0 9.0", 1.0, 7.0, 9.0, 1.0],
    ["$3$0:1.0 1:3.0 2:3.0", "2.0 3.0 3.0", 2.0, 3.0, 3.0, 1.0],
    ["$3$0:1.0 1:2.0 2:4.0", "3.0 2.0 4.0", 3.0, 2.0, 4.0, 0.0],
    ["$3$0:1.0 1:3.0 2:4.0", "2.0 3.0 4.0", 2.0, 3.0, 4.0, 0.0],
    ["$3$0:1.0 1:3.0 2:4.0", "1.0 5.0 8.0", 1.0, 5.0, 8.0, 0.0],
    ["$3$0:1.0 1:3.0 2:4.0", "1.0 6.0 3.0", 1.0, 6.0, 3.0, 0.0]
])

batchData = BatchOperator.fromDataframe(df, schemaStr='svec string, vec string, f0 double, f1 double, f2 double, label double')

selector = ConstrainedRegSelectorTrainBatchOp()\
                .setAlphaEntry(0.65)\
                .setAlphaStay(0.7)\
                .setSelectedCol("vec")\
                .setLabelCol("label")\
                .setForceSelectedCols([1,2])

constraint = pd.DataFrame([
        ['{"featureConstraint":[],"constraintBetweenFeatures":{"name":"constraintBetweenFeatures","UP":[],"LO":[],"=":[[1,1.814],[2,0.4]],"%":[],"<":[],">":[[1,2]]},"countZero":null,"elseNullSave":null}']
])
constraintData = BatchOperator.fromDataframe(constraint, schemaStr='data string')

selector.linkFrom(batchData, constraintData)

predict = ConstrainedRegSelectorPredictBatchOp()\
            .setPredictionCol("pred")\
            .setReservedCols(["label"])

predict.linkFrom(selector, batchData).print()

运行结果

   label     pred
0    1.0  7.0 9.0
1    1.0  3.0 3.0
2    0.0  2.0 4.0
3    0.0  3.0 4.0
4    0.0  5.0 8.0
5    0.0  6.0 3.0