带约束的Stepwise二分类筛选预测 (ConstrainedBinarySelectorPredictBatchOp)

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

Python 类名：ConstrainedBinarySelectorPredictBatchOp

功能介绍

使用带约束的Stepwise二分类方法，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
predictionCol	预测结果列名	预测结果列名	String	✓
modelFilePath	模型的文件路径	模型的文件路径	String			null
reservedCols	算法保留列名	算法保留列	String[]			null
selectedCol	计算列对应的列名	计算列对应的列名, 默认值是null	String			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 = ConstrainedBinarySelectorTrainBatchOp()\
                .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]],"%":[],"<":[],">":[[1,3]]},"countZero":null,"elseNullSave":null}']
])
constraintData = BatchOperator.fromDataframe(constraint, schemaStr='data string')

selector.linkFrom(batchData, constraintData)

predict = ConstrainedBinarySelectorPredictBatchOp()\
            .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