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author | Omede Firouz <ofirouz@palantir.com> | 2015-04-07 23:36:31 -0400 |
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committer | Joseph K. Bradley <joseph@databricks.com> | 2015-04-07 23:36:31 -0400 |
commit | d138aa8ee23f4450242da3ac70a493229a90c76b (patch) | |
tree | 059bc1504106aba35d1b6cac5e8d428066f022cd /mllib | |
parent | c83e03948b184ffb3a9418fecc4d2c26ae33b057 (diff) | |
download | spark-d138aa8ee23f4450242da3ac70a493229a90c76b.tar.gz spark-d138aa8ee23f4450242da3ac70a493229a90c76b.tar.bz2 spark-d138aa8ee23f4450242da3ac70a493229a90c76b.zip |
[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
Diffstat (limited to 'mllib')
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 /** @@ -56,6 +56,9 @@ class LogisticRegression 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) override protected def train(dataset: DataFrame, paramMap: ParamMap): LogisticRegressionModel = { @@ -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") { |