首页 > 代码库 > 逻辑回归特征选择
逻辑回归特征选择
特征选择很重要，除了人工选择，还可以用
其他机器学习方法，如逻辑回归、随机森林、PCA、
LDA等。
分享一下逻辑回归做特征选择
特征选择包括:
特征升维
特征降维
特征升维
如一个样本有少量特征，可以升维，更好的拟合曲线
特征X
升维X/X**2/
效果验证，做回归
加特征x**2之后的效果
特征X1、X2
升维X1/X2/X1X2/X1**2/X2**2/
特征降维
利用L1正则化做特征选择
sparkmllib代码实现
import java.io.PrintWriter
import java.util
import org.apache.spark.ml.attribute.{Attribute, AttributeGroup, NumericAttribute}
import org.apache.spark.ml.classification.{BinaryLogisticRegressionTrainingSummary, LogisticRegressionModel, LogisticRegression}
import org.apache.spark.mllib.classification.LogisticRegressionWithSGD
import org.apache.spark.mllib.linalg.Vectors
import org.apache.spark.rdd.RDD
import org.apache.spark.sql.{SQLContext, DataFrame, Row}
import org.apache.spark.sql.types.{DataTypes, StructField}
import org.apache.spark.{SparkContext, SparkConf}
object LogisticRegression {
def main(args: Array[String]) {
val conf = new SparkConf().setAppName("test").setMaster("local")
val sc = new SparkContext(conf)
val sql = new SQLContext(sc);
val df: DataFrame = sql.read.format("libsvm").load("rl.txt")
// val training = sc.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt")
val Array(train, test) = df.randomSplit(Array(0.7, 0.3),seed = 12L)
val lr = new LogisticRegression()
.setMaxIter(10)
.setRegParam(0.3)
.setElasticNetParam(1)//默认0 L2 1---》L1
// Fit the model
val lrModel: LogisticRegressionModel = lr.fit(train)
lrModel.transform(test).show(false)
// Print the coefficients and intercept for logistic regression
// coefficients 系数 intercept 截距
println(s"Coefficients: ${lrModel.coefficients} Intercept: ${lrModel.intercept}")
lrModel.write.overwrite().save("F:\\mode")
val weights: Array[Double] = lrModel.weights.toArray
val pw = new PrintWriter("F:\\weights");
//遍历
for(i<- 0 until weights.length){
//通过map得到每个下标相应的特征名
//特征名对应相应的权重
val str = weights(i)
pw.write(str.toString)
pw.println()
}
pw.flush()
pw.close()
}
}
样本lr.txt
0 1:5.1 2:3.5 3:1.4 4:0.2 0 1:4.9 2:3.0 3:1.4 4:0.2 0 1:4.7 2:3.2 3:1.3 4:0.2 0 1:4.6 2:3.1 3:1.5 4:0.2 0 1:5.0 2:3.6 3:1.4 4:0.2 0 1:5.4 2:3.9 3:1.7 4:0.4 0 1:4.6 2:3.4 3:1.4 4:0.3 0 1:5.0 2:3.4 3:1.5 4:0.2 0 1:4.4 2:2.9 3:1.4 4:0.2 0 1:4.9 2:3.1 3:1.5 4:0.1 0 1:5.4 2:3.7 3:1.5 4:0.2 0 1:4.8 2:3.4 3:1.6 4:0.2 0 1:4.8 2:3.0 3:1.4 4:0.1 0 1:4.3 2:3.0 3:1.1 4:0.1 0 1:5.8 2:4.0 3:1.2 4:0.2 0 1:5.7 2:4.4 3:1.5 4:0.4 0 1:5.4 2:3.9 3:1.3 4:0.4 0 1:5.1 2:3.5 3:1.4 4:0.3 0 1:5.7 2:3.8 3:1.7 4:0.3 0 1:5.1 2:3.8 3:1.5 4:0.3 0 1:5.4 2:3.4 3:1.7 4:0.2 0 1:5.1 2:3.7 3:1.5 4:0.4 0 1:4.6 2:3.6 3:1.0 4:0.2 0 1:5.1 2:3.3 3:1.7 4:0.5 0 1:4.8 2:3.4 3:1.9 4:0.2 0 1:5.0 2:3.0 3:1.6 4:0.2 0 1:5.0 2:3.4 3:1.6 4:0.4 0 1:5.2 2:3.5 3:1.5 4:0.2 0 1:5.2 2:3.4 3:1.4 4:0.2 0 1:4.7 2:3.2 3:1.6 4:0.2 0 1:4.8 2:3.1 3:1.6 4:0.2 0 1:5.4 2:3.4 3:1.5 4:0.4 0 1:5.2 2:4.1 3:1.5 4:0.1 0 1:5.5 2:4.2 3:1.4 4:0.2 0 1:4.9 2:3.1 3:1.5 4:0.1 0 1:5.0 2:3.2 3:1.2 4:0.2 0 1:5.5 2:3.5 3:1.3 4:0.2 0 1:4.9 2:3.1 3:1.5 4:0.1 0 1:4.4 2:3.0 3:1.3 4:0.2 0 1:5.1 2:3.4 3:1.5 4:0.2 0 1:5.0 2:3.5 3:1.3 4:0.3 0 1:4.5 2:2.3 3:1.3 4:0.3 0 1:4.4 2:3.2 3:1.3 4:0.2 0 1:5.0 2:3.5 3:1.6 4:0.6 0 1:5.1 2:3.8 3:1.9 4:0.4 0 1:4.8 2:3.0 3:1.4 4:0.3 0 1:5.1 2:3.8 3:1.6 4:0.2 0 1:4.6 2:3.2 3:1.4 4:0.2 0 1:5.3 2:3.7 3:1.5 4:0.2 0 1:5.0 2:3.3 3:1.4 4:0.2 1 1:7.0 2:3.2 3:4.7 4:1.4 1 1:6.4 2:3.2 3:4.5 4:1.5 1 1:6.9 2:3.1 3:4.9 4:1.5 1 1:5.5 2:2.3 3:4.0 4:1.3 1 1:6.5 2:2.8 3:4.6 4:1.5 1 1:5.7 2:2.8 3:4.5 4:1.3 1 1:6.3 2:3.3 3:4.7 4:1.6 1 1:4.9 2:2.4 3:3.3 4:1.0 1 1:6.6 2:2.9 3:4.6 4:1.3 1 1:5.2 2:2.7 3:3.9 4:1.4 1 1:5.0 2:2.0 3:3.5 4:1.0 1 1:5.9 2:3.0 3:4.2 4:1.5 1 1:6.0 2:2.2 3:4.0 4:1.0 1 1:6.1 2:2.9 3:4.7 4:1.4 1 1:5.6 2:2.9 3:3.6 4:1.3 1 1:6.7 2:3.1 3:4.4 4:1.4 1 1:5.6 2:3.0 3:4.5 4:1.5 1 1:5.8 2:2.7 3:4.1 4:1.0 1 1:6.2 2:2.2 3:4.5 4:1.5 1 1:5.6 2:2.5 3:3.9 4:1.1 1 1:5.9 2:3.2 3:4.8 4:1.8 1 1:6.1 2:2.8 3:4.0 4:1.3 1 1:6.3 2:2.5 3:4.9 4:1.5 1 1:6.1 2:2.8 3:4.7 4:1.2 1 1:6.4 2:2.9 3:4.3 4:1.3 1 1:6.6 2:3.0 3:4.4 4:1.4 1 1:6.8 2:2.8 3:4.8 4:1.4 1 1:6.7 2:3.0 3:5.0 4:1.7 1 1:6.0 2:2.9 3:4.5 4:1.5 1 1:5.7 2:2.6 3:3.5 4:1.0 1 1:5.5 2:2.4 3:3.8 4:1.1 1 1:5.5 2:2.4 3:3.7 4:1.0 1 1:5.8 2:2.7 3:3.9 4:1.2 1 1:6.0 2:2.7 3:5.1 4:1.6 1 1:5.4 2:3.0 3:4.5 4:1.5 1 1:6.0 2:3.4 3:4.5 4:1.6 1 1:6.7 2:3.1 3:4.7 4:1.5 1 1:6.3 2:2.3 3:4.4 4:1.3 1 1:5.6 2:3.0 3:4.1 4:1.3 1 1:5.5 2:2.5 3:4.0 4:1.3 1 1:5.5 2:2.6 3:4.4 4:1.2 1 1:6.1 2:3.0 3:4.6 4:1.4 1 1:5.8 2:2.6 3:4.0 4:1.2 1 1:5.0 2:2.3 3:3.3 4:1.0 1 1:5.6 2:2.7 3:4.2 4:1.3 1 1:5.7 2:3.0 3:4.2 4:1.2 1 1:5.7 2:2.9 3:4.2 4:1.3 1 1:6.2 2:2.9 3:4.3 4:1.3 1 1:5.1 2:2.5 3:3.0 4:1.1 1 1:5.7 2:2.8 3:4.1 4:1.3
特征选择
第一个特征权重为0，可以忽略，选择2，3，4个特征
逻辑回归特征选择
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