Merge branch 'master' into SPARK-6939

core/src/main/scala/org/apache/spark/ui/jobs/UIData.scala

@@ -24,7 +24,7 @@ import org.apache.spark.util.collection.OpenHashSet
 
 import scala.collection.mutable.HashMap
 
-private[jobs] object UIData {
+private[spark] object UIData {
 
   class ExecutorSummary {
     var taskTime : Long = 0

examples/src/main/java/org/apache/spark/examples/ml/JavaDeveloperApiExample.java

@@ -28,7 +28,6 @@
 import org.apache.spark.ml.classification.ClassificationModel;
 import org.apache.spark.ml.param.IntParam;
 import org.apache.spark.ml.param.ParamMap;
-import org.apache.spark.ml.param.Params;
 import org.apache.spark.ml.param.Params$;
 import org.apache.spark.mllib.linalg.BLAS;
 import org.apache.spark.mllib.linalg.Vector;
@@ -100,11 +99,12 @@ public static void main(String[] args) throws Exception {
 /**
  * Example of defining a type of {@link Classifier}.
  *
- * NOTE: This is private since it is an example.  In practice, you may not want it to be private.
+ * Note: Some IDEs (e.g., IntelliJ) will complain that this will not compile due to
+ *       {@link org.apache.spark.ml.param.Params#set} using incompatible return types.
+ *       However, this should still compile and run successfully.
  */
 class MyJavaLogisticRegression
-    extends Classifier<Vector, MyJavaLogisticRegression, MyJavaLogisticRegressionModel>
-    implements Params {
+    extends Classifier<Vector, MyJavaLogisticRegression, MyJavaLogisticRegressionModel> {
 
   /**
    * Param for max number of iterations
@@ -145,10 +145,12 @@ public MyJavaLogisticRegressionModel train(DataFrame dataset, ParamMap paramMap)
 /**
  * Example of defining a type of {@link ClassificationModel}.
  *
- * NOTE: This is private since it is an example.  In practice, you may not want it to be private.
+ * Note: Some IDEs (e.g., IntelliJ) will complain that this will not compile due to
+ *       {@link org.apache.spark.ml.param.Params#set} using incompatible return types.
+ *       However, this should still compile and run successfully.
  */
 class MyJavaLogisticRegressionModel
-    extends ClassificationModel<Vector, MyJavaLogisticRegressionModel> implements Params {
+    extends ClassificationModel<Vector, MyJavaLogisticRegressionModel> {
 
   private MyJavaLogisticRegression parent_;
   public MyJavaLogisticRegression parent() { return parent_; }

mllib/src/main/scala/org/apache/spark/ml/Pipeline.scala

@@ -21,7 +21,7 @@ import scala.collection.mutable.ListBuffer
 
 import org.apache.spark.Logging
 import org.apache.spark.annotation.{AlphaComponent, DeveloperApi}
-import org.apache.spark.ml.param.{Param, ParamMap}
+import org.apache.spark.ml.param.{Params, Param, ParamMap}
 import org.apache.spark.sql.DataFrame
 import org.apache.spark.sql.types.StructType
 
@@ -86,6 +86,14 @@ class Pipeline extends Estimator[PipelineModel] {
   def setStages(value: Array[PipelineStage]): this.type = { set(stages, value); this }
   def getStages: Array[PipelineStage] = getOrDefault(stages)
 
+  override def validate(paramMap: ParamMap): Unit = {
+    val map = extractParamMap(paramMap)
+    getStages.foreach {
+      case pStage: Params => pStage.validate(map)
+      case _ =>
+    }
+  }
+
   /**
    * Fits the pipeline to the input dataset with additional parameters. If a stage is an
    * [[Estimator]], its [[Estimator#fit]] method will be called on the input dataset to fit a model.
@@ -140,7 +148,7 @@ class Pipeline extends Estimator[PipelineModel] {
   override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = {
     val map = extractParamMap(paramMap)
     val theStages = map(stages)
-    require(theStages.toSet.size == theStages.size,
+    require(theStages.toSet.size == theStages.length,
       "Cannot have duplicate components in a pipeline.")
     theStages.foldLeft(schema)((cur, stage) => stage.transformSchema(cur, paramMap))
   }
@@ -157,6 +165,11 @@ class PipelineModel private[ml] (
     private[ml] val stages: Array[Transformer])
   extends Model[PipelineModel] with Logging {
 
+  override def validate(paramMap: ParamMap): Unit = {
+    val map = fittingParamMap ++ extractParamMap(paramMap)
+    stages.foreach(_.validate(map))
+  }
+
   /**
    * Gets the model produced by the input estimator. Throws an NoSuchElementException is the input
    * estimator does not exist in the pipeline.
@@ -168,7 +181,7 @@ class PipelineModel private[ml] (
     }
     if (matched.isEmpty) {
       throw new NoSuchElementException(s"Cannot find stage $stage from the pipeline.")
-    } else if (matched.size > 1) {
+    } else if (matched.length > 1) {
       throw new IllegalStateException(s"Cannot have duplicate estimators in the sample pipeline.")
     } else {
       matched.head.asInstanceOf[M]

mllib/src/main/scala/org/apache/spark/ml/classification/GBTClassifier.scala

@@ -103,21 +103,16 @@ final class GBTClassifier
    */
   val lossType: Param[String] = new Param[String](this, "lossType", "Loss function which GBT" +
     " tries to minimize (case-insensitive). Supported options:" +
-    s" ${GBTClassifier.supportedLossTypes.mkString(", ")}")
+    s" ${GBTClassifier.supportedLossTypes.mkString(", ")}",
+    (value: String) => GBTClassifier.supportedLossTypes.contains(value.toLowerCase))
 
   setDefault(lossType -> "logistic")
 
   /** @group setParam */
-  def setLossType(value: String): this.type = {
-    val lossStr = value.toLowerCase
-    require(GBTClassifier.supportedLossTypes.contains(lossStr), "GBTClassifier was given bad loss" +
-      s" type: $value. Supported options: ${GBTClassifier.supportedLossTypes.mkString(", ")}")
-    set(lossType, lossStr)
-    this
-  }
+  def setLossType(value: String): this.type = set(lossType, value)
 
   /** @group getParam */
-  def getLossType: String = getOrDefault(lossType)
+  def getLossType: String = getOrDefault(lossType).toLowerCase
 
   /** (private[ml]) Convert new loss to old loss. */
   override private[ml] def getOldLossType: OldLoss = {

mllib/src/main/scala/org/apache/spark/ml/feature/HashingTF.scala

@@ -19,7 +19,7 @@ package org.apache.spark.ml.feature
 
 import org.apache.spark.annotation.AlphaComponent
 import org.apache.spark.ml.UnaryTransformer
-import org.apache.spark.ml.param.{IntParam, ParamMap}
+import org.apache.spark.ml.param.{ParamValidators, IntParam, ParamMap}
 import org.apache.spark.mllib.feature
 import org.apache.spark.mllib.linalg.{VectorUDT, Vector}
 import org.apache.spark.sql.types.DataType
@@ -32,19 +32,21 @@ import org.apache.spark.sql.types.DataType
 class HashingTF extends UnaryTransformer[Iterable[_], Vector, HashingTF] {
 
   /**
-   * number of features
+   * Number of features.  Should be > 0.
+   * (default = 2^18^)
    * @group param
    */
-  val numFeatures = new IntParam(this, "numFeatures", "number of features")
+  val numFeatures = new IntParam(this, "numFeatures", "number of features (> 0)",
+    ParamValidators.gt(0))
+
+  setDefault(numFeatures -> (1 << 18))
 
   /** @group getParam */
   def getNumFeatures: Int = getOrDefault(numFeatures)
 
   /** @group setParam */
   def setNumFeatures(value: Int): this.type = set(numFeatures, value)
 
-  setDefault(numFeatures -> (1 << 18))
-
   override protected def createTransformFunc(paramMap: ParamMap): Iterable[_] => Vector = {
     val hashingTF = new feature.HashingTF(paramMap(numFeatures))
     hashingTF.transform

mllib/src/main/scala/org/apache/spark/ml/feature/Normalizer.scala

@@ -19,7 +19,7 @@ package org.apache.spark.ml.feature
 
 import org.apache.spark.annotation.AlphaComponent
 import org.apache.spark.ml.UnaryTransformer
-import org.apache.spark.ml.param.{DoubleParam, ParamMap}
+import org.apache.spark.ml.param.{ParamValidators, DoubleParam, ParamMap}
 import org.apache.spark.mllib.feature
 import org.apache.spark.mllib.linalg.{VectorUDT, Vector}
 import org.apache.spark.sql.types.DataType
@@ -32,19 +32,20 @@ import org.apache.spark.sql.types.DataType
 class Normalizer extends UnaryTransformer[Vector, Vector, Normalizer] {
 
   /**
-   * Normalization in L^p^ space, p = 2 by default.
+   * Normalization in L^p^ space.  Must be >= 1.
+   * (default: p = 2)
    * @group param
    */
-  val p = new DoubleParam(this, "p", "the p norm value")
+  val p = new DoubleParam(this, "p", "the p norm value", ParamValidators.gtEq(1))
+
+  setDefault(p -> 2.0)
 
   /** @group getParam */
   def getP: Double = getOrDefault(p)
 
   /** @group setParam */
   def setP(value: Double): this.type = set(p, value)
 
-  setDefault(p -> 2.0)
-
   override protected def createTransformFunc(paramMap: ParamMap): Vector => Vector = {
     val normalizer = new feature.Normalizer(paramMap(p))
     normalizer.transform

mllib/src/main/scala/org/apache/spark/ml/feature/PolynomialExpansion.scala

@@ -21,7 +21,7 @@ import scala.collection.mutable
 
 import org.apache.spark.annotation.AlphaComponent
 import org.apache.spark.ml.UnaryTransformer
-import org.apache.spark.ml.param.{IntParam, ParamMap}
+import org.apache.spark.ml.param.{ParamValidators, IntParam, ParamMap}
 import org.apache.spark.mllib.linalg._
 import org.apache.spark.sql.types.DataType
 
@@ -37,10 +37,13 @@ import org.apache.spark.sql.types.DataType
 class PolynomialExpansion extends UnaryTransformer[Vector, Vector, PolynomialExpansion] {
 
   /**
-   * The polynomial degree to expand, which should be larger than 1.
+   * The polynomial degree to expand, which should be >= 1.  A value of 1 means no expansion.
+   * Default: 2
    * @group param
    */
-  val degree = new IntParam(this, "degree", "the polynomial degree to expand")
+  val degree = new IntParam(this, "degree", "the polynomial degree to expand (>= 1)",
+    ParamValidators.gt(1))
+
   setDefault(degree -> 2)
 
   /** @group getParam */

mllib/src/main/scala/org/apache/spark/ml/feature/StandardScaler.scala

@@ -31,17 +31,19 @@ import org.apache.spark.sql.types.{StructField, StructType}
  * Params for [[StandardScaler]] and [[StandardScalerModel]].
  */
 private[feature] trait StandardScalerParams extends Params with HasInputCol with HasOutputCol {
-  
+
   /**
-   * False by default. Centers the data with mean before scaling. 
+   * Centers the data with mean before scaling.
    * It will build a dense output, so this does not work on sparse input 
    * and will raise an exception.
+   * Default: false
    * @group param
    */
   val withMean: BooleanParam = new BooleanParam(this, "withMean", "Center data with mean")
 
   /**
-   * True by default. Scales the data to unit standard deviation.
+   * Scales the data to unit standard deviation.
+   * Default: true
    * @group param
    */
   val withStd: BooleanParam = new BooleanParam(this, "withStd", "Scale to unit standard deviation")
@@ -56,7 +58,7 @@ private[feature] trait StandardScalerParams extends Params with HasInputCol with
 class StandardScaler extends Estimator[StandardScalerModel] with StandardScalerParams {
 
   setDefault(withMean -> false, withStd -> true)
-  
+
   /** @group setParam */
   def setInputCol(value: String): this.type = set(inputCol, value)
 

mllib/src/main/scala/org/apache/spark/ml/feature/Tokenizer.scala

@@ -19,7 +19,7 @@ package org.apache.spark.ml.feature
 
 import org.apache.spark.annotation.AlphaComponent
 import org.apache.spark.ml.UnaryTransformer
-import org.apache.spark.ml.param.{ParamMap, IntParam, BooleanParam, Param}
+import org.apache.spark.ml.param._
 import org.apache.spark.sql.types.{DataType, StringType, ArrayType}
 
 /**
@@ -43,20 +43,20 @@ class Tokenizer extends UnaryTransformer[String, Seq[String], Tokenizer] {
 /**
  * :: AlphaComponent ::
  * A regex based tokenizer that extracts tokens either by repeatedly matching the regex(default) 
- * or using it to split the text (set matching to false). Optional parameters also allow to fold
- * the text to lowercase prior to it being tokenized and to filer tokens using a minimal length. 
+ * or using it to split the text (set matching to false). Optional parameters also allow filtering
+ * tokens using a minimal length.
  * It returns an array of strings that can be empty.
- * The default parameters are regex = "\\p{L}+|[^\\p{L}\\s]+", matching = true, 
- * lowercase = false, minTokenLength = 1
  */
 @AlphaComponent
 class RegexTokenizer extends UnaryTransformer[String, Seq[String], RegexTokenizer] {
 
   /**
-   * param for minimum token length, default is one to avoid returning empty strings
+   * Minimum token length, >= 0.
+   * Default: 1, to avoid returning empty strings
    * @group param
    */
-  val minTokenLength: IntParam = new IntParam(this, "minLength", "minimum token length")
+  val minTokenLength: IntParam = new IntParam(this, "minLength", "minimum token length (>= 0)",
+    ParamValidators.gtEq(0))
 
   /** @group setParam */
   def setMinTokenLength(value: Int): this.type = set(minTokenLength, value)
@@ -65,7 +65,8 @@ class RegexTokenizer extends UnaryTransformer[String, Seq[String], RegexTokenize
   def getMinTokenLength: Int = getOrDefault(minTokenLength)
 
   /**
-   * param sets regex as splitting on gaps (true) or matching tokens (false)
+   * Indicates whether regex splits on gaps (true) or matching tokens (false).
+   * Default: false
    * @group param
    */
   val gaps: BooleanParam = new BooleanParam(this, "gaps", "Set regex to match gaps or tokens")
@@ -77,7 +78,8 @@ class RegexTokenizer extends UnaryTransformer[String, Seq[String], RegexTokenize
   def getGaps: Boolean = getOrDefault(gaps)
 
   /**
-   * param sets regex pattern used by tokenizer 
+   * Regex pattern used by tokenizer.
+   * Default: `"\\p{L}+|[^\\p{L}\\s]+"`
    * @group param
    */
   val pattern: Param[String] = new Param(this, "pattern", "regex pattern used for tokenizing")

mllib/src/main/scala/org/apache/spark/ml/feature/VectorIndexer.scala

@@ -22,7 +22,7 @@ import org.apache.spark.ml.util.SchemaUtils
 import org.apache.spark.ml.{Estimator, Model}
 import org.apache.spark.ml.attribute.{BinaryAttribute, NumericAttribute, NominalAttribute,
   Attribute, AttributeGroup}
-import org.apache.spark.ml.param.{IntParam, ParamMap, Params}
+import org.apache.spark.ml.param.{ParamValidators, IntParam, ParamMap, Params}
 import org.apache.spark.ml.param.shared._
 import org.apache.spark.mllib.linalg.{SparseVector, DenseVector, Vector, VectorUDT}
 import org.apache.spark.sql.{Row, DataFrame}
@@ -37,17 +37,19 @@ private[ml] trait VectorIndexerParams extends Params with HasInputCol with HasOu
   /**
    * Threshold for the number of values a categorical feature can take.
    * If a feature is found to have > maxCategories values, then it is declared continuous.
+   * Must be >= 2.
    *
    * (default = 20)
    */
   val maxCategories = new IntParam(this, "maxCategories",
-    "Threshold for the number of values a categorical feature can take." +
-      " If a feature is found to have > maxCategories values, then it is declared continuous.")
+    "Threshold for the number of values a categorical feature can take (>= 2)." +
+      " If a feature is found to have > maxCategories values, then it is declared continuous.",
+    ParamValidators.gtEq(2))
+
+  setDefault(maxCategories -> 20)
 
   /** @group getParam */
   def getMaxCategories: Int = getOrDefault(maxCategories)
-
-  setDefault(maxCategories -> 20)
 }
 
 /**
@@ -90,11 +92,7 @@ private[ml] trait VectorIndexerParams extends Params with HasInputCol with HasOu
 class VectorIndexer extends Estimator[VectorIndexerModel] with VectorIndexerParams {
 
   /** @group setParam */
-  def setMaxCategories(value: Int): this.type = {
-    require(value > 1,
-      s"DatasetIndexer given maxCategories = value, but requires maxCategories > 1.")
-    set(maxCategories, value)
-  }
+  def setMaxCategories(value: Int): this.type = set(maxCategories, value)
 
   /** @group setParam */
   def setInputCol(value: String): this.type = set(inputCol, value)