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[SPARK-32310][ML][PySpark] ML params default value parity in classification, regression, clustering and fpm #29112

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[SPARK-32310][ML][PySpark] ML params default value parity in classification, regression, clustering and fpm #29112

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[SPARK-32310][ML][PySpark] ML params default value parity
huaxingao Jul 14, 2020
4bc1053
address comments
huaxingao Jul 16, 2020
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10 changes: 0 additions & 10 deletions mllib/src/main/scala/org/apache/spark/ml/classification/FMClassifier.scala
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Expand Up @@ -85,7 +85,6 @@ class FMClassifier @Since("3.0.0") (
*/
@Since("3.0.0")
def setFactorSize(value: Int): this.type = set(factorSize, value)
setDefault(factorSize -> 8)
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Where do the default params of FMClassifier move?

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I move setDefault for these params to FactorizationMachinesParams in FMRegressor when I fixed solver last time


/**
* Set whether to fit intercept term.
Expand All @@ -95,7 +94,6 @@ class FMClassifier @Since("3.0.0") (
*/
@Since("3.0.0")
def setFitIntercept(value: Boolean): this.type = set(fitIntercept, value)
setDefault(fitIntercept -> true)

/**
* Set whether to fit linear term.
Expand All @@ -105,7 +103,6 @@ class FMClassifier @Since("3.0.0") (
*/
@Since("3.0.0")
def setFitLinear(value: Boolean): this.type = set(fitLinear, value)
setDefault(fitLinear -> true)

/**
* Set the L2 regularization parameter.
Expand All @@ -115,7 +112,6 @@ class FMClassifier @Since("3.0.0") (
*/
@Since("3.0.0")
def setRegParam(value: Double): this.type = set(regParam, value)
setDefault(regParam -> 0.0)

/**
* Set the mini-batch fraction parameter.
Expand All @@ -125,7 +121,6 @@ class FMClassifier @Since("3.0.0") (
*/
@Since("3.0.0")
def setMiniBatchFraction(value: Double): this.type = set(miniBatchFraction, value)
setDefault(miniBatchFraction -> 1.0)

/**
* Set the standard deviation of initial coefficients.
Expand All @@ -135,7 +130,6 @@ class FMClassifier @Since("3.0.0") (
*/
@Since("3.0.0")
def setInitStd(value: Double): this.type = set(initStd, value)
setDefault(initStd -> 0.01)

/**
* Set the maximum number of iterations.
Expand All @@ -145,7 +139,6 @@ class FMClassifier @Since("3.0.0") (
*/
@Since("3.0.0")
def setMaxIter(value: Int): this.type = set(maxIter, value)
setDefault(maxIter -> 100)

/**
* Set the initial step size for the first step (like learning rate).
Expand All @@ -155,7 +148,6 @@ class FMClassifier @Since("3.0.0") (
*/
@Since("3.0.0")
def setStepSize(value: Double): this.type = set(stepSize, value)
setDefault(stepSize -> 1.0)

/**
* Set the convergence tolerance of iterations.
Expand All @@ -165,7 +157,6 @@ class FMClassifier @Since("3.0.0") (
*/
@Since("3.0.0")
def setTol(value: Double): this.type = set(tol, value)
setDefault(tol -> 1E-6)

/**
* Set the solver algorithm used for optimization.
Expand All @@ -176,7 +167,6 @@ class FMClassifier @Since("3.0.0") (
*/
@Since("3.0.0")
def setSolver(value: String): this.type = set(solver, value)
setDefault(solver -> AdamW)

/**
* Set the random seed for weight initialization.
Expand Down
12 changes: 3 additions & 9 deletions mllib/src/main/scala/org/apache/spark/ml/classification/LinearSVC.scala
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Expand Up @@ -55,6 +55,9 @@ private[classification] trait LinearSVCParams extends ClassifierParams with HasR
*/
final override val threshold: DoubleParam = new DoubleParam(this, "threshold",
"threshold in binary classification prediction applied to rawPrediction")

setDefault(regParam -> 0.0, maxIter -> 100, fitIntercept -> true, tol -> 1E-6,
standardization -> true, threshold -> 0.0, aggregationDepth -> 2, blockSize -> 1)
}

/**
Expand Down Expand Up @@ -82,7 +85,6 @@ class LinearSVC @Since("2.2.0") (
*/
@Since("2.2.0")
def setRegParam(value: Double): this.type = set(regParam, value)
setDefault(regParam -> 0.0)

/**
* Set the maximum number of iterations.
Expand All @@ -92,7 +94,6 @@ class LinearSVC @Since("2.2.0") (
*/
@Since("2.2.0")
def setMaxIter(value: Int): this.type = set(maxIter, value)
setDefault(maxIter -> 100)

/**
* Whether to fit an intercept term.
Expand All @@ -102,7 +103,6 @@ class LinearSVC @Since("2.2.0") (
*/
@Since("2.2.0")
def setFitIntercept(value: Boolean): this.type = set(fitIntercept, value)
setDefault(fitIntercept -> true)

/**
* Set the convergence tolerance of iterations.
Expand All @@ -113,7 +113,6 @@ class LinearSVC @Since("2.2.0") (
*/
@Since("2.2.0")
def setTol(value: Double): this.type = set(tol, value)
setDefault(tol -> 1E-6)

/**
* Whether to standardize the training features before fitting the model.
Expand All @@ -123,7 +122,6 @@ class LinearSVC @Since("2.2.0") (
*/
@Since("2.2.0")
def setStandardization(value: Boolean): this.type = set(standardization, value)
setDefault(standardization -> true)

/**
* Set the value of param [[weightCol]].
Expand All @@ -142,7 +140,6 @@ class LinearSVC @Since("2.2.0") (
*/
@Since("2.2.0")
def setThreshold(value: Double): this.type = set(threshold, value)
setDefault(threshold -> 0.0)

/**
* Suggested depth for treeAggregate (greater than or equal to 2).
Expand All @@ -154,7 +151,6 @@ class LinearSVC @Since("2.2.0") (
*/
@Since("2.2.0")
def setAggregationDepth(value: Int): this.type = set(aggregationDepth, value)
setDefault(aggregationDepth -> 2)

/**
* Set block size for stacking input data in matrices.
Expand All @@ -173,7 +169,6 @@ class LinearSVC @Since("2.2.0") (
*/
@Since("3.1.0")
def setBlockSize(value: Int): this.type = set(blockSize, value)
setDefault(blockSize -> 1)

@Since("2.2.0")
override def copy(extra: ParamMap): LinearSVC = defaultCopy(extra)
Expand Down Expand Up @@ -381,7 +376,6 @@ class LinearSVCModel private[classification] (

@Since("2.2.0")
def setThreshold(value: Double): this.type = set(threshold, value)
setDefault(threshold, 0.0)

private val margin: Vector => Double = (features) => {
BLAS.dot(features, coefficients) + intercept
Expand Down
14 changes: 4 additions & 10 deletions mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
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Expand Up @@ -243,6 +243,10 @@ private[classification] trait LogisticRegressionParams extends ProbabilisticClas
@Since("2.2.0")
def getUpperBoundsOnIntercepts: Vector = $(upperBoundsOnIntercepts)

setDefault(regParam -> 0.0, elasticNetParam -> 0.0, maxIter -> 100, tol -> 1E-6,
fitIntercept -> true, family -> "auto", standardization -> true, threshold -> 0.5,
aggregationDepth -> 2, blockSize -> 1)

protected def usingBoundConstrainedOptimization: Boolean = {
isSet(lowerBoundsOnCoefficients) || isSet(upperBoundsOnCoefficients) ||
isSet(lowerBoundsOnIntercepts) || isSet(upperBoundsOnIntercepts)
Expand Down Expand Up @@ -290,7 +294,6 @@ class LogisticRegression @Since("1.2.0") (
*/
@Since("1.2.0")
def setRegParam(value: Double): this.type = set(regParam, value)
setDefault(regParam -> 0.0)

/**
* Set the ElasticNet mixing parameter.
Expand All @@ -306,7 +309,6 @@ class LogisticRegression @Since("1.2.0") (
*/
@Since("1.4.0")
def setElasticNetParam(value: Double): this.type = set(elasticNetParam, value)
setDefault(elasticNetParam -> 0.0)

/**
* Set the maximum number of iterations.
Expand All @@ -316,7 +318,6 @@ class LogisticRegression @Since("1.2.0") (
*/
@Since("1.2.0")
def setMaxIter(value: Int): this.type = set(maxIter, value)
setDefault(maxIter -> 100)

/**
* Set the convergence tolerance of iterations.
Expand All @@ -327,7 +328,6 @@ class LogisticRegression @Since("1.2.0") (
*/
@Since("1.4.0")
def setTol(value: Double): this.type = set(tol, value)
setDefault(tol -> 1E-6)

/**
* Whether to fit an intercept term.
Expand All @@ -337,7 +337,6 @@ class LogisticRegression @Since("1.2.0") (
*/
@Since("1.4.0")
def setFitIntercept(value: Boolean): this.type = set(fitIntercept, value)
setDefault(fitIntercept -> true)

/**
* Sets the value of param [[family]].
Expand All @@ -347,7 +346,6 @@ class LogisticRegression @Since("1.2.0") (
*/
@Since("2.1.0")
def setFamily(value: String): this.type = set(family, value)
setDefault(family -> "auto")

/**
* Whether to standardize the training features before fitting the model.
Expand All @@ -361,11 +359,9 @@ class LogisticRegression @Since("1.2.0") (
*/
@Since("1.5.0")
def setStandardization(value: Boolean): this.type = set(standardization, value)
setDefault(standardization -> true)

@Since("1.5.0")
override def setThreshold(value: Double): this.type = super.setThreshold(value)
setDefault(threshold -> 0.5)

@Since("1.5.0")
override def getThreshold: Double = super.getThreshold
Expand Down Expand Up @@ -396,7 +392,6 @@ class LogisticRegression @Since("1.2.0") (
*/
@Since("2.1.0")
def setAggregationDepth(value: Int): this.type = set(aggregationDepth, value)
setDefault(aggregationDepth -> 2)

/**
* Set the lower bounds on coefficients if fitting under bound constrained optimization.
Expand Down Expand Up @@ -447,7 +442,6 @@ class LogisticRegression @Since("1.2.0") (
*/
@Since("3.1.0")
def setBlockSize(value: Int): this.type = set(blockSize, value)
setDefault(blockSize -> 1)

private def assertBoundConstrainedOptimizationParamsValid(
numCoefficientSets: Int,
Expand Down
4 changes: 2 additions & 2 deletions mllib/src/main/scala/org/apache/spark/ml/classification/NaiveBayes.scala
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Expand Up @@ -64,6 +64,8 @@ private[classification] trait NaiveBayesParams extends PredictorParams with HasW

/** @group getParam */
final def getModelType: String = $(modelType)

setDefault(smoothing -> 1.0, modelType -> NaiveBayes.Multinomial)
}

// scalastyle:off line.size.limit
Expand Down Expand Up @@ -107,7 +109,6 @@ class NaiveBayes @Since("1.5.0") (
*/
@Since("1.5.0")
def setSmoothing(value: Double): this.type = set(smoothing, value)
setDefault(smoothing -> 1.0)

/**
* Set the model type using a string (case-sensitive).
Expand All @@ -117,7 +118,6 @@ class NaiveBayes @Since("1.5.0") (
*/
@Since("1.5.0")
def setModelType(value: String): this.type = set(modelType, value)
setDefault(modelType -> Multinomial)

/**
* Sets the value of param [[weightCol]].
Expand Down
7 changes: 2 additions & 5 deletions mllib/src/main/scala/org/apache/spark/ml/clustering/BisectingKMeans.scala
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Expand Up @@ -72,6 +72,8 @@ private[clustering] trait BisectingKMeansParams extends Params with HasMaxIter
@Since("2.0.0")
def getMinDivisibleClusterSize: Double = $(minDivisibleClusterSize)

setDefault(k -> 4, maxIter -> 20, minDivisibleClusterSize -> 1.0)

/**
* Validates and transforms the input schema.
* @param schema input schema
Expand Down Expand Up @@ -226,11 +228,6 @@ class BisectingKMeans @Since("2.0.0") (
@Since("2.0.0") override val uid: String)
extends Estimator[BisectingKMeansModel] with BisectingKMeansParams with DefaultParamsWritable {

setDefault(
k -> 4,
maxIter -> 20,
minDivisibleClusterSize -> 1.0)

@Since("2.0.0")
override def copy(extra: ParamMap): BisectingKMeans = defaultCopy(extra)

Expand Down
8 changes: 2 additions & 6 deletions mllib/src/main/scala/org/apache/spark/ml/clustering/GaussianMixture.scala
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Expand Up @@ -59,6 +59,8 @@ private[clustering] trait GaussianMixtureParams extends Params with HasMaxIter w
@Since("2.0.0")
def getK: Int = $(k)

setDefault(k -> 2, maxIter -> 100, tol -> 0.01, blockSize -> 1)

/**
* Validates and transforms the input schema.
*
Expand Down Expand Up @@ -328,11 +330,6 @@ class GaussianMixture @Since("2.0.0") (
@Since("2.0.0") override val uid: String)
extends Estimator[GaussianMixtureModel] with GaussianMixtureParams with DefaultParamsWritable {

setDefault(
k -> 2,
maxIter -> 100,
tol -> 0.01)

@Since("2.0.0")
override def copy(extra: ParamMap): GaussianMixture = defaultCopy(extra)

Expand Down Expand Up @@ -392,7 +389,6 @@ class GaussianMixture @Since("2.0.0") (
*/
@Since("3.1.0")
def setBlockSize(value: Int): this.type = set(blockSize, value)
setDefault(blockSize -> 1)

/**
* Number of samples per cluster to use when initializing Gaussians.
Expand Down
11 changes: 3 additions & 8 deletions mllib/src/main/scala/org/apache/spark/ml/clustering/KMeans.scala
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Expand Up @@ -87,6 +87,9 @@ private[clustering] trait KMeansParams extends Params with HasMaxIter with HasFe
@Since("1.5.0")
def getInitSteps: Int = $(initSteps)

setDefault(k -> 2, maxIter -> 20, initMode -> MLlibKMeans.K_MEANS_PARALLEL, initSteps -> 2,
tol -> 1e-4, distanceMeasure -> DistanceMeasure.EUCLIDEAN)

/**
* Validates and transforms the input schema.
* @param schema input schema
Expand Down Expand Up @@ -271,14 +274,6 @@ class KMeans @Since("1.5.0") (
@Since("1.5.0") override val uid: String)
extends Estimator[KMeansModel] with KMeansParams with DefaultParamsWritable {

setDefault(
k -> 2,
maxIter -> 20,
initMode -> MLlibKMeans.K_MEANS_PARALLEL,
initSteps -> 2,
tol -> 1e-4,
distanceMeasure -> DistanceMeasure.EUCLIDEAN)

@Since("1.5.0")
override def copy(extra: ParamMap): KMeans = defaultCopy(extra)

Expand Down
11 changes: 5 additions & 6 deletions mllib/src/main/scala/org/apache/spark/ml/clustering/LDA.scala
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Expand Up @@ -199,8 +199,6 @@ private[clustering] trait LDAParams extends Params with HasFeaturesCol with HasM
" with estimates of the topic mixture distribution for each document (often called \"theta\"" +
" in the literature). Returns a vector of zeros for an empty document.")

setDefault(topicDistributionCol -> "topicDistribution")

/** @group getParam */
@Since("1.6.0")
def getTopicDistributionCol: String = $(topicDistributionCol)
Expand Down Expand Up @@ -315,6 +313,11 @@ private[clustering] trait LDAParams extends Params with HasFeaturesCol with HasM
@Since("2.0.0")
def getKeepLastCheckpoint: Boolean = $(keepLastCheckpoint)

setDefault(maxIter -> 20, k -> 10, optimizer -> "online", checkpointInterval -> 10,
learningOffset -> 1024, learningDecay -> 0.51, subsamplingRate -> 0.05,
optimizeDocConcentration -> true, keepLastCheckpoint -> true,
topicDistributionCol -> "topicDistribution")

/**
* Validates and transforms the input schema.
*
Expand Down Expand Up @@ -863,10 +866,6 @@ class LDA @Since("1.6.0") (
@Since("1.6.0")
def this() = this(Identifiable.randomUID("lda"))

setDefault(maxIter -> 20, k -> 10, optimizer -> "online", checkpointInterval -> 10,
learningOffset -> 1024, learningDecay -> 0.51, subsamplingRate -> 0.05,
optimizeDocConcentration -> true, keepLastCheckpoint -> true)

/**
* The features for LDA should be a `Vector` representing the word counts in a document.
* The vector should be of length vocabSize, with counts for each term (word).
Expand Down
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