Pyspark 中的过采样或 SMOTE
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【中文标题】Pyspark 中的过采样或 SMOTE【英文标题】：Oversampling or SMOTE in Pyspark 【发布时间】：2019-05-24 23:59:03 【问题描述】：我有 7 个类，记录总数为 115，我想对这些数据运行随机森林模型。但由于数据不足以获得高精度。因此，我想对所有类应用过采样，以使多数类本身获得更高的计数，然后相应地成为少数。这在 PySpark 中可行吗？
+---------+-----+
| SubTribe|count|
+---------+-----+
| Chill| 10|
| Cool| 18|
|Adventure| 18|
| Quirk| 13|
| Mystery| 25|
| Party| 18|
|Glamorous| 13|
+---------+-----+
【问题讨论】：
【参考方案1】：这是我过去使用的 Pyspark 和 Scala smote 的另一种实现。我已经复制了代码和referenced the source，因为它很小：
Pyspark：
import random
import numpy as np
from pyspark.sql import Row
from sklearn import neighbors
from pyspark.ml.feature import VectorAssembler
def vectorizerFunction(dataInput, TargetFieldName):
if(dataInput.select(TargetFieldName).distinct().count() != 2):
raise ValueError("Target field must have only 2 distinct classes")
columnNames = list(dataInput.columns)
columnNames.remove(TargetFieldName)
dataInput = dataInput.select((','.join(columnNames)+','+TargetFieldName).split(','))
assembler=VectorAssembler(inputCols = columnNames, outputCol = 'features')
pos_vectorized = assembler.transform(dataInput)
vectorized = pos_vectorized.select('features',TargetFieldName).withColumn('label',pos_vectorized[TargetFieldName]).drop(TargetFieldName)
return vectorized
def SmoteSampling(vectorized, k = 5, minorityClass = 1, majorityClass = 0, percentageOver = 200, percentageUnder = 100):
if(percentageUnder > 100|percentageUnder < 10):
raise ValueError("Percentage Under must be in range 10 - 100");
if(percentageOver < 100):
raise ValueError("Percentage Over must be in at least 100");
dataInput_min = vectorized[vectorized['label'] == minorityClass]
dataInput_maj = vectorized[vectorized['label'] == majorityClass]
feature = dataInput_min.select('features')
feature = feature.rdd
feature = feature.map(lambda x: x[0])
feature = feature.collect()
feature = np.asarray(feature)
nbrs = neighbors.NearestNeighbors(n_neighbors=k, algorithm='auto').fit(feature)
neighbours = nbrs.kneighbors(feature)
gap = neighbours[0]
neighbours = neighbours[1]
min_rdd = dataInput_min.drop('label').rdd
pos_rddArray = min_rdd.map(lambda x : list(x))
pos_ListArray = pos_rddArray.collect()
min_Array = list(pos_ListArray)
newRows = []
nt = len(min_Array)
nexs = percentageOver/100
for i in range(nt):
for j in range(nexs):
neigh = random.randint(1,k)
difs = min_Array[neigh][0] - min_Array[i][0]
newRec = (min_Array[i][0]+random.random()*difs)
newRows.insert(0,(newRec))
newData_rdd = sc.parallelize(newRows)
newData_rdd_new = newData_rdd.map(lambda x: Row(features = x, label = 1))
new_data = newData_rdd_new.toDF()
new_data_minor = dataInput_min.unionAll(new_data)
new_data_major = dataInput_maj.sample(False, (float(percentageUnder)/float(100)))
return new_data_major.unionAll(new_data_minor)
dataInput = spark.read.format('csv').options(header='true',inferSchema='true').load("sam.csv").dropna()
SmoteSampling(vectorizerFunction(dataInput, 'Y'), k = 2, minorityClass = 1, majorityClass = 0, percentageOver = 90, percentageUnder = 5)
斯卡拉：
// Import the necessary packages
import org.apache.spark.ml.feature.BucketedRandomProjectionLSH
import org.apache.spark.ml.feature.VectorAssembler
import org.apache.spark.sql.expressions.Window
import org.apache.spark.mllib.linalg.Vectors
import org.apache.spark.sql.functions.rand
import org.apache.spark.sql.functions._
object smoteClass
def KNNCalculation(
dataFinal:org.apache.spark.sql.DataFrame,
feature:String,
reqrows:Int,
BucketLength:Int,
NumHashTables:Int):org.apache.spark.sql.DataFrame =
val b1 = dataFinal.withColumn("index", row_number().over(Window.partitionBy("label").orderBy("label")))
val brp = new BucketedRandomProjectionLSH().setBucketLength(BucketLength).setNumHashTables(NumHashTables).setInputCol(feature).setOutputCol("values")
val model = brp.fit(b1)
val transformedA = model.transform(b1)
val transformedB = model.transform(b1)
val b2 = model.approxSimilarityJoin(transformedA, transformedB, 2000000000.0)
require(b2.count > reqrows, println("Change bucket lenght or reduce the percentageOver"))
val b3 = b2.selectExpr("datasetA.index as id1",
"datasetA.feature as k1",
"datasetB.index as id2",
"datasetB.feature as k2",
"distCol").filter("distCol>0.0").orderBy("id1", "distCol").dropDuplicates().limit(reqrows)
return b3
def smoteCalc(key1: org.apache.spark.ml.linalg.Vector, key2: org.apache.spark.ml.linalg.Vector)=
val resArray = Array(key1, key2)
val res = key1.toArray.zip(key2.toArray.zip(key1.toArray).map(x => x._1 - x._2).map(_*0.2)).map(x => x._1 + x._2)
resArray :+ org.apache.spark.ml.linalg.Vectors.dense(res)
def Smote(
inputFrame:org.apache.spark.sql.DataFrame,
feature:String,
label:String,
percentOver:Int,
BucketLength:Int,
NumHashTables:Int):org.apache.spark.sql.DataFrame =
val groupedData = inputFrame.groupBy(label).count
require(groupedData.count == 2, println("Only 2 labels allowed"))
val classAll = groupedData.collect()
val minorityclass = if (classAll(0)(1).toString.toInt > classAll(1)(1).toString.toInt) classAll(1)(0).toString else classAll(0)(0).toString
val frame = inputFrame.select(feature,label).where(label + " == " + minorityclass)
val rowCount = frame.count
val reqrows = (rowCount * (percentOver/100)).toInt
val md = udf(smoteCalc _)
val b1 = KNNCalculation(frame, feature, reqrows, BucketLength, NumHashTables)
val b2 = b1.withColumn("ndtata", md($"k1", $"k2")).select("ndtata")
val b3 = b2.withColumn("AllFeatures", explode($"ndtata")).select("AllFeatures").dropDuplicates
val b4 = b3.withColumn(label, lit(minorityclass).cast(frame.schema(1).dataType))
return inputFrame.union(b4).dropDuplicates
【讨论】：
github.com/alivcor/SMORK : 这个适用于原生 Transformer Spark API【参考方案2】：也许这个项目对你的目标有用： Spark SMOTE
但我认为 115 条记录对于随机森林来说是不够的。您可以使用其他最简单的技术，例如决策树
你可以检查这个答案：
Is Random Forest suitable for very small data sets?
【讨论】：
github.com/alivcor/SMORK : 这个适用于原生 Transformer Spark API以上是关于Pyspark 中的过采样或 SMOTE的主要内容，如果未能解决你的问题，请参考以下文章