[ML] Feature Selectors

SparkML中关于特征的算法可分为：Extractors（特征提取）、Transformers（特征转换）、Selectors（特征选择）三部分。

Ref: SparkML中三种特征选择算法（VectorSlicer/RFormula/ChiSqSelector）

 

 

一、代码示范

VectorSlicer 只是根据index而“手动指定特征”的手段，不是特征选择的依据。 

RFormula 也只是根据column而“手动指定特征”的手段，不是特征选择的依据。 

VectorSlicer
from pyspark.ml.feature import VectorSlicer
from pyspark.ml.linalg import Vectors
from pyspark.sql.types import Row

df = spark.createDataFrame([
    Row(userFeatures=Vectors.sparse(3, {0: -2.0, 1: 2.3})),
    Row(userFeatures=Vectors.dense([-2.0, 2.3, 0.0]))])

df.show()
 
+--------------------+
|        userFeatures|
+--------------------+
|(3,[0,1],[-2.0,2.3])|
|      [-2.0,2.3,0.0]|
+--------------------+
slicer = VectorSlicer(inputCol="userFeatures", outputCol="features", indices=[1])
output = slicer.transform(df)

output.select("userFeatures", "features").show()
 
+--------------------+-------------+
|        userFeatures|     features|
+--------------------+-------------+
|(3,[0,1],[-2.0,2.3])|(1,[0],[2.3])|
|      [-2.0,2.3,0.0]|        [2.3]|
+--------------------+-------------+


RFormula
from pyspark.ml.feature import RFormula

dataset = spark.createDataFrame(
    [(7, "US", 18, 1.0),
     (8, "CA", 12, 0.0),
     (9, "NZ", 15, 0.0)],
    ["id", "country", "hour", "clicked"])

formula = RFormula(
    formula="clicked ~ country + hour",　　# 指定使用两个特征，country特征会自动采用one hot编码。
    featuresCol="features",
    labelCol="label")

output = formula.fit(dataset).transform(dataset)
output.select("features", "label").show()
 
+--------------+-----+
|      features|label|
+--------------+-----+
|[0.0,0.0,18.0]|  1.0|
|[0.0,1.0,12.0]|  0.0|
|[1.0,0.0,15.0]|  0.0|
+--------------+-----+


ChiSqSelector
from pyspark.ml.feature import ChiSqSelector
from pyspark.ml.linalg import Vectors

df = spark.createDataFrame([
    (7, Vectors.dense([0.0, 0.0, 18.0, 1.0]), 1.0,),
    (8, Vectors.dense([0.0, 1.0, 12.0, 0.0]), 0.0,),
    (9, Vectors.dense([1.0, 0.0, 15.0, 0.1]), 0.0,)], ["id", "features", "clicked"])

selector = ChiSqSelector(numTopFeatures=1, featuresCol="features",
                         outputCol="selectedFeatures", labelCol="clicked")

result = selector.fit(df).transform(df)

print("ChiSqSelector output with top %d features selected" % selector.getNumTopFeatures())
result.show()
 
ChiSqSelector output with top 1 features selected
+---+------------------+-------+----------------+
| id|          features|clicked|selectedFeatures|
+---+------------------+-------+----------------+
|  7|[0.0,0.0,18.0,1.0]|    1.0|          [18.0]|
|  8|[0.0,1.0,12.0,0.0]|    0.0|          [12.0]|
|  9|[1.0,0.0,15.0,0.1]|    0.0|          [15.0]|
+---+------------------+-------+----------------+

 

 

二、实践心得

参考：[Feature] Feature selection

Outline

3.1 Filter

3.1.1 方差选择法

3.1.2 相关系数法

3.1.3 卡方检验　　　　# <---- ChiSqSelector

3.1.4 互信息法

3.2 Wrapper

3.2.1 递归特征消除法

3.3 Embedded

3.3.1 基于惩罚项的特征选择法

3.3.2 基于树模型的特征选择法

  

相关系数

fraud_pd.corr('balance', 'numTrans')

n_numerical = len(numerical)
corr = []
for i in range(0, n_numerical):
    temp = [None] * i
    
    for j in range(i, n_numerical):
        temp.append(fraud_pd.corr(numerical[i], numerical[j]))
    corr.append(temp)  


print(corr)

Output: 

[[1.0,  0.00044, 0.00027],

 [None, 1.0,    -0.00028],

 [None, None,    1.0]] 

 

 

三、Embedded

Ref: [Feature] Feature selection - Embedded topic

问题，spark.ml可以lasso线性回归么？2.4.4貌似没有，但mllib里有，功能完善度不是很满意。

classification (SVMs, logistic regression)

linear regression (least squares, Lasso, ridge)

后者采样后，使用sklearn处理画出"轨迹图"。 

使用Spark SQL在DataFrame中采样构成子数据集的过程。

 

End.