ALINK(三十一):特征工程(十)特征选择(二)卡方选择器 (ChiSqSelectorBatchOp)

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

Python 类名:ChiSqSelectorBatchOp

功能介绍

针对table数据,进行特征筛选

参数说明

名称

中文名称

描述

类型

是否必须?

默认值

labelCol

标签列名

输入表中的标签列名

String

 

selectedCols

选择的列名

计算列对应的列名列表

String[]

 

selectorType

筛选类型

筛选类型,包含"NumTopFeatures","percentile", "fpr", "fdr", "fwe"五种。

String

 

"NumTopFeatures"

numTopFeatures

最大的p-value列个数

最大的p-value列个数, 默认值50

Integer

 

50

percentile

筛选的百分比

筛选的百分比,默认值0.1

Double

 

0.1

fpr

p value的阈值

p value的阈值,默认值0.05

Double

 

0.05

fdr

发现阈值

发现阈值, 默认值0.05

Double

 

0.05

fwe

错误率阈值

错误率阈值, 默认值0.05

Double

 

0.05

代码示例

Python 代码

from pyalink.alink import *
import pandas as pd
useLocalEnv(1)
df = pd.DataFrame([
    ["a", 1, 1,2.0, True],
    ["c", 1, 2, -3.0, True],
    ["a", 2, 2,2.0, False],
    ["c", 0, 0, 0.0, False]
])
source = BatchOperator.fromDataframe(df, schemaStr='f_string string, f_long long, f_int int, f_double double, f_boolean boolean')
selector = ChiSqSelectorBatchOp()
            .setSelectedCols(["f_string", "f_long", "f_int", "f_double"])
            .setLabelCol("f_boolean")
            .setNumTopFeatures(2)
selector.linkFrom(source)
modelInfo: ChisqSelectorModelInfo = selector.collectModelInfo()
        
print(modelInfo.getColNames())

Java 代码

import org.apache.flink.types.Row;
import com.alibaba.alink.operator.batch.BatchOperator;
import com.alibaba.alink.operator.batch.feature.ChiSqSelectorBatchOp;
import com.alibaba.alink.operator.batch.source.MemSourceBatchOp;
import com.alibaba.alink.operator.common.feature.ChisqSelectorModelInfo;
import org.junit.Test;
import java.util.Arrays;
import java.util.List;
public class ChiSqSelectorBatchOpTest {
  @Test
  public void testChiSqSelectorBatchOp() throws Exception {
    List <Row> df = Arrays.asList(
      Row.of("a", 1L, 1, 2.0, true),
      Row.of("c", 1L, 2, -3.0, true),
      Row.of("a", 2L, 2, 2.0, false),
      Row.of("c", 0L, 0, 0.0, false)
    );
    BatchOperator <?> source = new MemSourceBatchOp(df,
      "f_string string, f_long long, f_int int, f_double double, f_boolean boolean");
    ChiSqSelectorBatchOp selector = new ChiSqSelectorBatchOp()
      .setSelectedCols("f_string", "f_long", "f_int", "f_double")
      .setLabelCol("f_boolean")
      .setNumTopFeatures(2);
    selector.linkFrom(source);
    ChisqSelectorModelInfo modelInfo = selector.collectModelInfo();
    System.out.println(modelInfo.toString());
  }
}

运行结果

------------------------- ChisqSelectorModelInfo -------------------------
Number of Selector Features: 2
Number of Features: 4
Type of Selector: NumTopFeatures
Number of Top Features: 2
Selector Indices: 
    | ColName|ChiSquare|PValue| DF|Selected|
    |--------|---------|------|---|--------|
    |  f_long|        4|0.1353|  2|    true|
    |   f_int|        2|0.3679|  2|    true|
    |f_double|        2|0.3679|  2|   false|
    |f_string|        0|     1|  1|   false|
原文地址:https://www.cnblogs.com/qiu-hua/p/14901569.html