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Posted to reviews@spark.apache.org by hhbyyh <gi...@git.apache.org> on 2016/01/18 10:27:06 UTC
[GitHub] spark pull request: [SPARK-12875] [ML] Add Weight of Evidence and ...
GitHub user hhbyyh opened a pull request:
https://github.com/apache/spark/pull/10803
[SPARK-12875] [ML] Add Weight of Evidence and Information value to Spark.ml as a feature transformer
jira: https://issues.apache.org/jira/browse/SPARK-12875
As a feature transformer, WOE and IV enable one to:
Consider each variable’s independent contribution to the outcome.
Detect linear and non-linear relationships.
Rank variables in terms of "univariate" predictive strength.
Visualize the correlations between the predictive variables and the binary outcome.
http://multithreaded.stitchfix.com/blog/2015/08/13/weight-of-evidence/ gives a good introduction to WoE and IV.
The Weight of Evidence or WoE value provides a measure of how well a grouping of feature is able to distinguish between a binary response (e.g. "good" versus "bad"), which is widely used in grouping continuous feature or mapping categorical features to continuous values. It is computed from the basic odds ratio:
(Distribution of positive Outcomes) / (Distribution of negative Outcomes)
where Distr refers to the proportion of positive or negative in the respective group, relative to the column totals.
The WoE recoding of features is particularly well suited for subsequent modeling using Logistic Regression or MLP.
In addition, the information value or IV can be computed based on WoE, which is a popular technique to select variables in a predictive model.
Next: Currently we support only calculation for categorical features. Add an estimator to estimate the proper grouping for continuous feature.
You can merge this pull request into a Git repository by running:
$ git pull https://github.com/hhbyyh/spark woe
Alternatively you can review and apply these changes as the patch at:
https://github.com/apache/spark/pull/10803.patch
To close this pull request, make a commit to your master/trunk branch
with (at least) the following in the commit message:
This closes #10803
----
commit 0b360c4f54ee23efd5c29785e77d75217b5a0893
Author: Yuhao Yang <hh...@gmail.com>
Date: 2016-01-14T09:43:52Z
draft for woe
commit a674bb0190a07c9af1f210ae7acba89d1188be57
Author: Yuhao Yang <hh...@gmail.com>
Date: 2016-01-14T15:49:05Z
add iv
commit c2beb8b51a9a80f94da9de59f56988647050addf
Author: Yuhao Yang <hh...@gmail.com>
Date: 2016-01-16T08:36:05Z
Merge remote-tracking branch 'upstream/master' into woe
commit c6239383914a4c8bde2c4afb22398399803e55b0
Author: Yuhao Yang <hh...@gmail.com>
Date: 2016-01-17T06:38:51Z
woe and ut
commit ab3a961311672d70360fd4a322c42c92945b6ca6
Author: Yuhao Yang <hh...@gmail.com>
Date: 2016-01-17T06:38:58Z
Merge remote-tracking branch 'upstream/master' into woe
commit 11f3f5a12659b0b5028f37e1542d33130ba1459e
Author: Yuhao Yang <hh...@gmail.com>
Date: 2016-01-17T16:27:31Z
add require
commit f1f118b73950415e7326e744b1b17112942976fb
Author: Yuhao Yang <hh...@gmail.com>
Date: 2016-01-18T07:02:03Z
Merge remote-tracking branch 'upstream/master' into woe
commit 8bb38abe79e03490e79cfe31b86607d93818cb27
Author: Yuhao Yang <hh...@gmail.com>
Date: 2016-01-18T09:18:27Z
style fix
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[GitHub] spark issue #10803: [SPARK-12875] [ML] Add Weight of Evidence and Informatio...
Posted by Srinivasan2Nagarajan <gi...@git.apache.org>.
Github user Srinivasan2Nagarajan commented on the issue:
https://github.com/apache/spark/pull/10803
i have attached my output file.
[export (8).zip](https://github.com/apache/spark/files/1531022/export.8.zip)
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[GitHub] spark issue #10803: [SPARK-12875] [ML] Add Weight of Evidence and Informatio...
Posted by hhbyyh <gi...@git.apache.org>.
Github user hhbyyh commented on the issue:
https://github.com/apache/spark/pull/10803
No it's not merged. Feel free to use the code as you wish.
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[GitHub] spark issue #10803: [SPARK-12875] [ML] Add Weight of Evidence and Informatio...
Posted by Srinivasan2Nagarajan <gi...@git.apache.org>.
Github user Srinivasan2Nagarajan commented on the issue:
https://github.com/apache/spark/pull/10803
Hi is this request is merged can we use this woe binning ?
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[GitHub] spark pull request: [SPARK-12875] [ML] Add Weight of Evidence and ...
Posted by AmplabJenkins <gi...@git.apache.org>.
Github user AmplabJenkins commented on the pull request:
https://github.com/apache/spark/pull/10803#issuecomment-173476492
Merged build finished. Test PASSed.
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[GitHub] spark pull request: [SPARK-12875] [ML] Add Weight of Evidence and ...
Posted by SparkQA <gi...@git.apache.org>.
Github user SparkQA commented on the pull request:
https://github.com/apache/spark/pull/10803#issuecomment-173468729
**[Test build #49858 has started](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/49858/consoleFull)** for PR 10803 at commit [`762e091`](https://github.com/apache/spark/commit/762e091014b9d5866d5e0345f4220dfbab119f5a).
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[GitHub] spark pull request: [SPARK-12875] [ML] Add Weight of Evidence and ...
Posted by AmplabJenkins <gi...@git.apache.org>.
Github user AmplabJenkins commented on the pull request:
https://github.com/apache/spark/pull/10803#issuecomment-172489015
Test PASSed.
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[GitHub] spark pull request #10803: [SPARK-12875] [ML] Add Weight of Evidence and Inf...
Posted by hhbyyh <gi...@git.apache.org>.
Github user hhbyyh closed the pull request at:
https://github.com/apache/spark/pull/10803
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[GitHub] spark pull request: [SPARK-12875] [ML] Add Weight of Evidence and ...
Posted by SparkQA <gi...@git.apache.org>.
Github user SparkQA commented on the pull request:
https://github.com/apache/spark/pull/10803#issuecomment-222228018
**[Test build #3027 has started](https://amplab.cs.berkeley.edu/jenkins/job/NewSparkPullRequestBuilder/3027/consoleFull)** for PR 10803 at commit [`762e091`](https://github.com/apache/spark/commit/762e091014b9d5866d5e0345f4220dfbab119f5a).
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[GitHub] spark issue #10803: [SPARK-12875] [ML] Add Weight of Evidence and Informatio...
Posted by Srinivasan2Nagarajan <gi...@git.apache.org>.
Github user Srinivasan2Nagarajan commented on the issue:
https://github.com/apache/spark/pull/10803
Hi yuo,
i have used this class in my code but it not given the output as i expected. the value which it produces is not continuous
val startTimeMillis = System.currentTimeMillis()
val s1 = System.nanoTime()
import org.apache.spark.sql.types.{ StringType, DoubleType, IntegerType}
import org.apache.spark.mllib.classification.{LogisticRegressionModel, LogisticRegressionWithLBFGS}
import org.apache.spark.mllib.evaluation.MulticlassMetrics
import org.apache.spark.mllib.regression.LabeledPoint
import org.apache.spark.mllib.util.MLUtils
import org.apache.spark.ml.feature.{ Tokenizer, HashingTF, IDF }
import org.apache.spark.sql
import org.apache.spark.rdd.RDD
import org.apache.spark.annotation.Since
import org.apache.spark.ml.Transformer
import org.apache.spark.ml.feature.{ VectorAssembler, StandardScaler }
import org.apache.spark.ml.param.{Param, ParamMap, ParamPair, Params}
import org.apache.spark.ml.util.Identifiable
import org.apache.spark.sql.{DataFrame, Dataset, Column}
import org.apache.spark.sql.functions._
import org.apache.spark.ml.classification.RandomForestClassifier
import org.apache.spark.ml.evaluation.BinaryClassificationEvaluator
import org.apache.spark.ml.tuning.CrossValidator
import org.apache.spark.ml.Pipeline
import org.apache.spark.mllib.linalg.{ Vector, Vectors, SparseVector }
import scala.util.parsing.combinator._
import org.apache.spark.ml.feature._
import sqlContext.implicits._
import org.apache.spark.ml.feature.Bucketizer
var dataDf = spark.sql("select (.75 * longacc_0to1point5 + 2.25 * longacc_1point5to3 + 3.75 * longacc_3to4point5 + 5.25 * longacc_4point5to6 + 6.75 * longacc_6to7point5 + 8.25 * longacc_7point5to9 + 9.75 * longacc_9to10point5 + 11.25 * longacc_10point5to12 + 12.75 * longacc_12to13point5 + 14.5 * longacc_13point5)/( longacc_0to1point5 + longacc_1point5to3 + longacc_3to4point5 + longacc_4point5to6 + longacc_6to7point5 + longacc_7point5to9 + longacc_9to10point5 + longacc_10point5to12 + longacc_12to13point5 + longacc_13point5) as Long_Acc, ((5 * Speed_FLM_0to10 + 15 * Speed_FLM_10to20 + 25 * Speed_FLM_20to30 + 40 * Speed_FLM_30to50 + 65 * Speed_FLM_50to80 + 100 * Speed_FLM_80to120+135 * Speed_FLM_120to150 + 175 * Speed_FLM_150to200 + 225 * Speed_FLM_200toInf)/( Speed_FLM_0to10 + Speed_FLM_10to20 + Speed_FLM_20to30+ Speed_FLM_30to50 + Speed_FLM_50to80 + Speed_FLM_80to120 + Speed_FLM_120to150 + Speed_FLM_150to200 + Speed_FLM_200toInf)) as Speed_FLM, ((1 * dist_0to2km + 3.5 * dist_2to5k
m + 7.5 * dist_5to10km + 15 * dist_10to20km + 35 * dist_20to50km + 100 * dist_50to150km + 175 * dist_150to500km + 600 * dist_500km)/( dist_0to2km + dist_2to5km + dist_5to10km + dist_10to20km + dist_20to50km + dist_50to150km + dist_150to500km + dist_500km)) as Dist, ((5 * Pedal_0to10perc + 15 * Pedal_10to20perc + 25 * Pedal_20to30perc + 35 * Pedal_30to40perc + 45 * Pedal_40to50perc + 55 * Pedal_50to60perc + 65* Pedal_60to70perc + 75 * Pedal_70to80perc + 85 * Pedal_80to90perc + 95 * Pedal_90perc)/ ( Pedal_0to10perc + Pedal_10to20perc + Pedal_20to30perc + Pedal_30to40perc + Pedal_40to50perc + Pedal_50to60perc + Pedal_60to70perc + Pedal_70to80perc + Pedal_80to90perc + Pedal_90perc)) as Pedal, ((1 * time_1min + 3 * time_1to5min + 7.5 * time_5to10min + 20 * time_10to30min + 45 * time_30to60min + 90 * time_60to120min + 210 * time_120to300min+400 * time_300min)/( time_1min + time_1to5min + time_5to10min + time_10to30min + time_30to60min + time_60to120min + time_120to300m
in + time_300min)) as Time, ((1 * Pauses_less_1min + 5*Pauses_1_10min+20*Pauses_10_30min + 45 * Pauses_30_60min + 90*Pauses_60_120min + 210 * Pauses_120_300min + 3750 * Pauses_300_24hr + 5040 * Pauses_24hr_1week + 21600 * Pauses_1week_1month+ 43200*Pauses_greater_30days)/(Pauses_less_1min+ Pauses_1_10min+Pauses_10_30min + Pauses_30_60min + Pauses_60_120min + Pauses_120_300min + Pauses_300_24hr + Pauses_24hr_1week + Pauses_1week_1month + Pauses_greater_30days)) as Pauses, ((750 * Speed_RPM_1000 + 1250 * Speed_RPM_1000to1500+1750 * Speed_RPM_1500to2000 + 2250 * Speed_RPM_2000to2500+2750 * Speed_RPM_2500to3000+3250 * Speed_RPM_3000to3500 + 4750 * Speed_RPM_3500to4000 + 4250 * Speed_RPM_4000to4500 + 4750 * Speed_RPM_4500to5000+5250 * Speed_RPM_5000to5500 + 5750 * Speed_RPM_5500to6000 + 6250 * Speed_RPM_6000to6500 + 6750 * Speed_RPM_6500to7000 + 7250 * Speed_RPM_7000)/( Speed_RPM_1000 + Speed_RPM_1000to1500 + Speed_RPM_1500to2000 + Speed_RPM_2000to2500 + Speed_RPM_2500to3000 + Speed_RPM
_3000to3500 + Speed_RPM_3500to4000 + Speed_RPM_4000to4500 + Speed_RPM_4500to5000 + Speed_RPM_5000to5500 + Speed_RPM_5500to6000 + Speed_RPM_6000to6500 + Speed_RPM_6500to7000 + Speed_RPM_7000)) as Speed_RPM, Speed_ReverseDirection, Speed_FLM_Standstill, Distance_Per_day, Starts_Per_Day, Driving_Time_Per_Day, Target from fisher_vin_data")
dataDf = dataDf.na.fill(0)
println("Needed Output 1 ----> " + dataDf.count())
def getMaximumValue(x: Column) = dataDf.agg(max(x)).collect().map(r => r.toSeq(0).asInstanceOf[Double]).toList(0)
def getMinimumValue(x: Column) = dataDf.agg(min(x)).collect().map(r => r.toSeq(0).asInstanceOf[Double]).toList(0)
def Normalize(x: Column): Column = {
val max_x = getMaximumValue(x.cast("double"))
val min_x = getMinimumValue(x.cast("double"))
return (x.cast("double") - min_x)/ (max_x - min_x)
}
dataDf = dataDf.withColumn("Long_Acc", Normalize(dataDf.col("Long_Acc"))).withColumn("Speed_FLM", Normalize(dataDf.col("Speed_FLM"))).withColumn("Dist", Normalize(dataDf.col("Dist"))).withColumn("Pedal", Normalize(dataDf.col("Pedal"))).withColumn("Time", Normalize(dataDf.col("Time"))).withColumn("Pauses", Normalize(dataDf.col("Pauses"))).withColumn("Speed_RPM", Normalize(dataDf.col("Speed_RPM"))).withColumn("Speed_ReverseDirection", Normalize(dataDf.col("Speed_ReverseDirection"))).withColumn("Speed_FLM_Standstill", Normalize(dataDf.col("Speed_FLM_Standstill"))).withColumn("Distance_Per_day", Normalize(dataDf.col("Distance_Per_day"))).withColumn("Starts_Per_Day", Normalize(dataDf.col("Starts_Per_Day"))).withColumn("Driving_Time_Per_Day", Normalize(dataDf.col("Driving_Time_Per_Day"))).withColumn("Target", Normalize(dataDf.col("Target")))
//------------------------------------------------------
trait HasInputCol extends Params {
final val inputCol: Param[String] = new Param[String](this, "inputCol", "input column name")
final def getInputCol: String = $(inputCol)
}
trait HasLabelCol extends Params {
final val labelCol: Param[String] = new Param[String](this, "labelCol", "label column name")
setDefault(labelCol, "label")
final def getLabelCol: String = $(labelCol)
}
trait HasOutputCol extends Params {
final val outputCol: Param[String] = new Param[String](this, "outputCol", "output column name")
setDefault(outputCol, uid + "__output")
final def getOutputCol: String = $(outputCol)
}
class WeightOfEvidence(override val uid: String) extends HasInputCol with HasLabelCol with HasOutputCol {
def this() = this(Identifiable.randomUID("woe"))
def setInputCol(value: String): this.type = set(inputCol, value)
def setLabelCol(value: String): this.type = set(labelCol, value)
def setOutputCol(value: String): this.type = set(outputCol, value)
def transform(dataset: DataFrame): DataFrame = {
//validateParams() --------------***** Important *******-----------
val sorted_dataset = dataset.sort($(inputCol))
val woeTable = WeightOfEvidence.getWoeTable(sorted_dataset, $(inputCol), $(labelCol))
val woeMap = woeTable.map(r => {
val category = r.getAs[String]($(inputCol))
val woe = r.getAs[Double]("woe")
(category, woe)
}).rdd.collectAsMap
val trans = udf { (factor: String) =>
woeMap.get(factor)
}
dataset.withColumn($(outputCol), trans(col($(inputCol))))
}
override def copy(extra: ParamMap): VectorAssembler = defaultCopy(extra)
}
object WeightOfEvidence {
def getWoeTable(dataset: DataFrame, categoryCol: String, labelCol: String): DataFrame = {
val data = dataset.select(categoryCol, labelCol)
val tmpTableName = "woe_temp"
data.createOrReplaceTempView(tmpTableName)
val err = 0.01
val query =
s"""
|SELECT
|$categoryCol,
|SUM (IF(CAST ($labelCol AS DOUBLE)=1, 1, 0)) AS 1count,
|SUM (IF(CAST ($labelCol AS DOUBLE)=0, 1, 0)) AS 0count
|FROM $tmpTableName
|GROUP BY $categoryCol
""".stripMargin
val groupResult = data.sqlContext.sql(query).cache()
val total0 = groupResult.selectExpr("SUM(0count)").first().getAs[Long](0).toDouble
val total1 = groupResult.selectExpr("SUM(1count)").first().getAs[Long](0).toDouble
groupResult.selectExpr(
categoryCol,
s"1count/$total1 AS p1",
s"0count/$total0 AS p0",
s"LOG(($err + 1count) / $total1 * $total0 / (0count + $err)) AS woe")
}
}
import org.apache.spark.sql.Row
// val test = dataDf.select("Speed_FLM").rdd.map {
// case Row(string_val: String) => (string_val, functionApplying(string_val, dataDf))
// }.toDF("Speed_FLM", "Speed_FLM_1")
// test.show()
var dataDf1 = dataDf.withColumn("Speed_FLM" , dataDf("Speed_FLM").cast(StringType) ).withColumn("Long_Acc" , dataDf("Long_Acc").cast(StringType) ).withColumn("Dist" , dataDf("Dist").cast(StringType) ).withColumn("Pedal" , dataDf("Pedal").cast(StringType) ).withColumn("Time" , dataDf("Time").cast(StringType) ).withColumn("Speed_RPM" , dataDf("Speed_RPM").cast(StringType) ).withColumn("Speed_ReverseDirection" , dataDf("Speed_ReverseDirection").cast(StringType) ).withColumn("Speed_FLM_Standstill" , dataDf("Speed_FLM_Standstill").cast(StringType) ).withColumn("Distance_Per_day" , dataDf("Distance_Per_day").cast(StringType) ).withColumn("Starts_Per_Day" , dataDf("Starts_Per_Day").cast(StringType) ).withColumn("Driving_Time_Per_Day" , dataDf("Driving_Time_Per_Day").cast(StringType) )
dataDf1.show()
val splits = Array(Double.NegativeInfinity, -12.5, 0.0, 12.5, Double.PositiveInfinity)
var jk = new WeightOfEvidence().setInputCol("Speed_FLM").setOutputCol("Weight_Of_Evidence_Speed_FLM").setLabelCol("Target").transform(dataDf1)
var bucketizer: Bucketizer = new Bucketizer().setInputCol("Weight_Of_Evidence_Speed_FLM").setOutputCol("Speed_FLM" + "_Target").setSplits(splits)
dataDf = bucketizer.transform(jk)
here is what i do im trying to replicate woe binning in R
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[GitHub] spark pull request: [SPARK-12875] [ML] Add Weight of Evidence and ...
Posted by SparkQA <gi...@git.apache.org>.
Github user SparkQA commented on the pull request:
https://github.com/apache/spark/pull/10803#issuecomment-172488890
**[Test build #49588 has finished](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/49588/consoleFull)** for PR 10803 at commit [`8bb38ab`](https://github.com/apache/spark/commit/8bb38abe79e03490e79cfe31b86607d93818cb27).
* This patch passes all tests.
* This patch merges cleanly.
* This patch adds no public classes.
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[GitHub] spark pull request: [SPARK-12875] [ML] Add Weight of Evidence and ...
Posted by SparkQA <gi...@git.apache.org>.
Github user SparkQA commented on the pull request:
https://github.com/apache/spark/pull/10803#issuecomment-172477042
**[Test build #49588 has started](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/49588/consoleFull)** for PR 10803 at commit [`8bb38ab`](https://github.com/apache/spark/commit/8bb38abe79e03490e79cfe31b86607d93818cb27).
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[GitHub] spark issue #10803: [SPARK-12875] [ML] Add Weight of Evidence and Informatio...
Posted by hhbyyh <gi...@git.apache.org>.
Github user hhbyyh commented on the issue:
https://github.com/apache/spark/pull/10803
Closing stale PR.
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[GitHub] spark pull request: [SPARK-12875] [ML] Add Weight of Evidence and ...
Posted by SparkQA <gi...@git.apache.org>.
Github user SparkQA commented on the pull request:
https://github.com/apache/spark/pull/10803#issuecomment-173476409
**[Test build #49858 has finished](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/49858/consoleFull)** for PR 10803 at commit [`762e091`](https://github.com/apache/spark/commit/762e091014b9d5866d5e0345f4220dfbab119f5a).
* This patch passes all tests.
* This patch merges cleanly.
* This patch adds no public classes.
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[GitHub] spark pull request: [SPARK-12875] [ML] Add Weight of Evidence and ...
Posted by AmplabJenkins <gi...@git.apache.org>.
Github user AmplabJenkins commented on the pull request:
https://github.com/apache/spark/pull/10803#issuecomment-173476500
Test PASSed.
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Test PASSed.
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[GitHub] spark pull request: [SPARK-12875] [ML] Add Weight of Evidence and ...
Posted by AmplabJenkins <gi...@git.apache.org>.
Github user AmplabJenkins commented on the pull request:
https://github.com/apache/spark/pull/10803#issuecomment-172489012
Merged build finished. Test PASSed.
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[GitHub] spark pull request: [SPARK-12875] [ML] Add Weight of Evidence and ...
Posted by SparkQA <gi...@git.apache.org>.
Github user SparkQA commented on the pull request:
https://github.com/apache/spark/pull/10803#issuecomment-222228358
**[Test build #3027 has finished](https://amplab.cs.berkeley.edu/jenkins/job/NewSparkPullRequestBuilder/3027/consoleFull)** for PR 10803 at commit [`762e091`](https://github.com/apache/spark/commit/762e091014b9d5866d5e0345f4220dfbab119f5a).
* This patch **fails Scala style tests**.
* This patch merges cleanly.
* This patch adds no public classes.
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