From d138aa8ee23f4450242da3ac70a493229a90c76b Mon Sep 17 00:00:00 2001
From: Omede Firouz <ofirouz@palantir.com>
Date: Tue, 7 Apr 2015 23:36:31 -0400
Subject: [PATCH] [SPARK-6705][MLLIB] Add fit intercept api to ml
logisticregression
I have the fit intercept enabled by default for logistic regression, I
wonder what others think here. I understand that it enables allocation
by default which is undesirable, but one needs to have a very strong
reason for not having an intercept term enabled so it is the safer
default from a statistical sense.
Explicitly modeling the intercept by adding a column of all 1s does not
work. I believe the reason is that since the API for
LogisticRegressionWithLBFGS forces column normalization, and a column of all
1s has 0 variance so dividing by 0 kills it.
Author: Omede Firouz <ofirouz@palantir.com>
Closes #5301 from oefirouz/addIntercept and squashes the following commits:
9f1286b [Omede Firouz] [SPARK-6705][MLLIB] Add fitInterceptTerm to LogisticRegression
1d6bd6f [Omede Firouz] [SPARK-6705][MLLIB] Add a fit intercept term to ML LogisticRegression
9963509 [Omede Firouz] [MLLIB] Add fitIntercept to LogisticRegression
2257fca [Omede Firouz] [MLLIB] Add fitIntercept param to logistic regression
329c1e2 [Omede Firouz] [MLLIB] Add fit intercept term
bd9663c [Omede Firouz] [MLLIB] Add fit intercept api to ml logisticregression
---
.../spark/ml/classification/LogisticRegression.scala | 8 ++++++--
.../org/apache/spark/ml/param/sharedParams.scala | 12 ++++++++++++
.../ml/classification/LogisticRegressionSuite.scala | 9 +++++++++
3 files changed, 27 insertions(+), 2 deletions(-)
diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
index 49c00f7748..34625745dd 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
@@ -31,7 +31,7 @@ import org.apache.spark.storage.StorageLevel
* Params for logistic regression.
*/
private[classification] trait LogisticRegressionParams extends ProbabilisticClassifierParams
- with HasRegParam with HasMaxIter with HasThreshold
+ with HasRegParam with HasMaxIter with HasFitIntercept with HasThreshold
/**
@@ -55,6 +55,9 @@ class LogisticRegression
/** @group setParam */
def setMaxIter(value: Int): this.type = set(maxIter, value)
+ /** @group setParam */
+ def setFitIntercept(value: Boolean): this.type = set(fitIntercept, value)
+
/** @group setParam */
def setThreshold(value: Double): this.type = set(threshold, value)
@@ -67,7 +70,8 @@ class LogisticRegression
}
// Train model
- val lr = new LogisticRegressionWithLBFGS
+ val lr = new LogisticRegressionWithLBFGS()
+ .setIntercept(paramMap(fitIntercept))
lr.optimizer
.setRegParam(paramMap(regParam))
.setNumIterations(paramMap(maxIter))
diff --git a/mllib/src/main/scala/org/apache/spark/ml/param/sharedParams.scala b/mllib/src/main/scala/org/apache/spark/ml/param/sharedParams.scala
index 5d660d1e15..0739fdbfcb 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/param/sharedParams.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/param/sharedParams.scala
@@ -106,6 +106,18 @@ private[ml] trait HasProbabilityCol extends Params {
def getProbabilityCol: String = get(probabilityCol)
}
+private[ml] trait HasFitIntercept extends Params {
+ /**
+ * param for fitting the intercept term, defaults to true
+ * @group param
+ */
+ val fitIntercept: BooleanParam =
+ new BooleanParam(this, "fitIntercept", "indicates whether to fit an intercept term", Some(true))
+
+ /** @group getParam */
+ def getFitIntercept: Boolean = get(fitIntercept)
+}
+
private[ml] trait HasThreshold extends Params {
/**
* param for threshold in (binary) prediction
diff --git a/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala
index b3d1bfcfbe..35d8c2e16c 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala
@@ -46,6 +46,7 @@ class LogisticRegressionSuite extends FunSuite with MLlibTestSparkContext {
assert(lr.getPredictionCol == "prediction")
assert(lr.getRawPredictionCol == "rawPrediction")
assert(lr.getProbabilityCol == "probability")
+ assert(lr.getFitIntercept == true)
val model = lr.fit(dataset)
model.transform(dataset)
.select("label", "probability", "prediction", "rawPrediction")
@@ -55,6 +56,14 @@ class LogisticRegressionSuite extends FunSuite with MLlibTestSparkContext {
assert(model.getPredictionCol == "prediction")
assert(model.getRawPredictionCol == "rawPrediction")
assert(model.getProbabilityCol == "probability")
+ assert(model.intercept !== 0.0)
+ }
+
+ test("logistic regression doesn't fit intercept when fitIntercept is off") {
+ val lr = new LogisticRegression
+ lr.setFitIntercept(false)
+ val model = lr.fit(dataset)
+ assert(model.intercept === 0.0)
}
test("logistic regression with setters") {
--
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