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[SPARK-1327] GLM needs to check addIntercept for intercept and weights #236

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[SPARK-1327] GLM needs to check addIntercept for intercept and weights #236

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d7f629f
fix a bug in GLM when intercept is not used
mengxr Mar 26, 2014
0e57aa4
update Lasso and RidgeRegression to parse the weights correctly from GLM
mengxr Mar 26, 2014
a104072
remove protected to be compatible with 0.9
mengxr Mar 26, 2014
bcac1ac
add two tests to ensure {Lasso, Ridge}.setIntercept will throw an exc…
mengxr Mar 27, 2014
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21 changes: 12 additions & 9 deletions mllib/src/main/scala/org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.scala
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Original file line number Diff line number Diff line change
Expand Up @@ -136,25 +136,28 @@ abstract class GeneralizedLinearAlgorithm[M <: GeneralizedLinearModel]

// Prepend an extra variable consisting of all 1.0's for the intercept.
val data = if (addIntercept) {
input.map(labeledPoint => (labeledPoint.label, labeledPoint.features.+:(1.0)))
input.map(labeledPoint => (labeledPoint.label, 1.0 +: labeledPoint.features))
} else {
input.map(labeledPoint => (labeledPoint.label, labeledPoint.features))
}

val initialWeightsWithIntercept = if (addIntercept) {
initialWeights.+:(1.0)
0.0 +: initialWeights
} else {
initialWeights
}

val weights = optimizer.optimize(data, initialWeightsWithIntercept)
val intercept = weights(0)
val weightsScaled = weights.tail
val weightsWithIntercept = optimizer.optimize(data, initialWeightsWithIntercept)

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Should check addIntercept here.

val model = createModel(weightsScaled, intercept)
val (intercept, weights) = if (addIntercept) {
(weightsWithIntercept(0), weightsWithIntercept.tail)
} else {
(0.0, weightsWithIntercept)
}

logInfo("Final weights " + weights.mkString(","))
logInfo("Final intercept " + intercept)

logInfo("Final model weights " + model.weights.mkString(","))
logInfo("Final model intercept " + model.intercept)
model
createModel(weights, intercept)
}
}
26 changes: 25 additions & 1 deletion mllib/src/test/scala/org/apache/spark/mllib/regression/LinearRegressionSuite.scala
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Expand Up @@ -17,7 +17,6 @@

package org.apache.spark.mllib.regression

import org.scalatest.BeforeAndAfterAll
import org.scalatest.FunSuite

import org.apache.spark.mllib.util.{LinearDataGenerator, LocalSparkContext}
Expand Down Expand Up @@ -57,4 +56,29 @@ class LinearRegressionSuite extends FunSuite with LocalSparkContext {
// Test prediction on Array.
validatePrediction(validationData.map(row => model.predict(row.features)), validationData)
}

// Test if we can correctly learn Y = 10*X1 + 10*X2
test("linear regression without intercept") {
val testRDD = sc.parallelize(LinearDataGenerator.generateLinearInput(
0.0, Array(10.0, 10.0), 100, 42), 2).cache()
val linReg = new LinearRegressionWithSGD().setIntercept(false)
linReg.optimizer.setNumIterations(1000).setStepSize(1.0)

val model = linReg.run(testRDD)

assert(model.intercept === 0.0)
assert(model.weights.length === 2)
assert(model.weights(0) >= 9.0 && model.weights(0) <= 11.0)
assert(model.weights(1) >= 9.0 && model.weights(1) <= 11.0)

val validationData = LinearDataGenerator.generateLinearInput(
0.0, Array(10.0, 10.0), 100, 17)
val validationRDD = sc.parallelize(validationData, 2).cache()

// Test prediction on RDD.
validatePrediction(model.predict(validationRDD.map(_.features)).collect(), validationData)

// Test prediction on Array.
validatePrediction(validationData.map(row => model.predict(row.features)), validationData)
}
}