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[SPARK-17792][ML] L-BFGS solver for linear regression does not accept general numeric label column types #15364

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[SPARK-17792][ML] L-BFGS solver for linear regression does not accept general numeric label column types #15364

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17 changes: 6 additions & 11 deletions mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala
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Original file line number Diff line number Diff line change
Expand Up @@ -189,17 +189,18 @@ class LinearRegression @Since("1.3.0") (@Since("1.3.0") override val uid: String
val numFeatures = dataset.select(col($(featuresCol))).first().getAs[Vector](0).size
val w = if (!isDefined(weightCol) || $(weightCol).isEmpty) lit(1.0) else col($(weightCol))

val instances: RDD[Instance] = dataset.select(
col($(labelCol)).cast(DoubleType), w, col($(featuresCol))).rdd.map {
case Row(label: Double, weight: Double, features: Vector) =>
Instance(label, weight, features)
}

if (($(solver) == "auto" && $(elasticNetParam) == 0.0 &&
numFeatures <= WeightedLeastSquares.MAX_NUM_FEATURES) || $(solver) == "normal") {
require($(elasticNetParam) == 0.0, "Only L2 regularization can be used when normal " +
"solver is used.'")
// For low dimensional data, WeightedLeastSquares is more efficiently since the
// training algorithm only requires one pass through the data. (SPARK-10668)
val instances: RDD[Instance] = dataset.select(
col($(labelCol)).cast(DoubleType), w, col($(featuresCol))).rdd.map {
case Row(label: Double, weight: Double, features: Vector) =>
Instance(label, weight, features)
}

val optimizer = new WeightedLeastSquares($(fitIntercept), $(regParam),
$(standardization), true)
Expand All @@ -222,12 +223,6 @@ class LinearRegression @Since("1.3.0") (@Since("1.3.0") override val uid: String
return lrModel.setSummary(trainingSummary)
}

val instances: RDD[Instance] =
dataset.select(col($(labelCol)), w, col($(featuresCol))).rdd.map {
case Row(label: Double, weight: Double, features: Vector) =>
Instance(label, weight, features)
}

val handlePersistence = dataset.rdd.getStorageLevel == StorageLevel.NONE
if (handlePersistence) instances.persist(StorageLevel.MEMORY_AND_DISK)

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8 changes: 5 additions & 3 deletions mllib/src/test/scala/org/apache/spark/ml/regression/LinearRegressionSuite.scala
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Expand Up @@ -1015,12 +1015,14 @@ class LinearRegressionSuite
}

test("should support all NumericType labels and not support other types") {
val lr = new LinearRegression().setMaxIter(1)
MLTestingUtils.checkNumericTypes[LinearRegressionModel, LinearRegression](
lr, spark, isClassification = false) { (expected, actual) =>
for (solver <- Seq("auto", "l-bfgs", "normal")) {
val lr = new LinearRegression().setMaxIter(1).setSolver(solver)
MLTestingUtils.checkNumericTypes[LinearRegressionModel, LinearRegression](
lr, spark, isClassification = false) { (expected, actual) =>
assert(expected.intercept === actual.intercept)
assert(expected.coefficients === actual.coefficients)
}
}
}
}

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