update developer and experimental tags

andrewor14 · Apr 9, 2014 · da31733 · da31733
1 parent 555e0fe
commit da31733
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diff --git a/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala b/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala
@@ -28,7 +28,7 @@ import org.apache.spark.mllib.regression._
 import org.apache.spark.rdd.RDD
 
 /**
- * <span class="badge badge-red" style="float: right;">DEVELOPER API</span>
+ * :: DeveloperApi ::
  *
  * The Java stubs necessary for the Python mllib bindings.
  */

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/classification/NaiveBayes.scala b/mllib/src/main/scala/org/apache/spark/mllib/classification/NaiveBayes.scala
@@ -27,7 +27,7 @@ import org.apache.spark.mllib.util.MLUtils
 import org.apache.spark.rdd.RDD
 
 /**
- * <span class="badge" style="float: right; background-color: #257080;">EXPERIMENTAL</span>
+ * :: Experimental ::
  *
  * Model for Naive Bayes Classifiers.
  *

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeans.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeans.scala
@@ -76,7 +76,7 @@ class KMeans private (
   }
 
   /**
-   * <span class="badge" style="float: right; background-color: #257080;">EXPERIMENTAL</span>
+   * :: Experimental ::
    *
    * Set the number of runs of the algorithm to execute in parallel. We initialize the algorithm
    * this many times with random starting conditions (configured by the initialization mode), then
@@ -398,7 +398,7 @@ object KMeans {
   }
 
   /**
-   * <span class="badge" style="float: right; background-color: #257080;">EXPERIMENTAL</span>
+   * :: Experimental ::
    */
   def main(args: Array[String]) {
     if (args.length < 4) {

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/optimization/Gradient.scala b/mllib/src/main/scala/org/apache/spark/mllib/optimization/Gradient.scala
@@ -22,7 +22,7 @@ import breeze.linalg.{axpy => brzAxpy}
 import org.apache.spark.mllib.linalg.{Vectors, Vector}
 
 /**
- * <span class="badge" style="float: right; background-color: #44751E;">DEVELOPER API</span>
+ * :: DeveloperApi ::
  *
  * Class used to compute the gradient for a loss function, given a single data point.
  */
@@ -53,7 +53,7 @@ abstract class Gradient extends Serializable {
 }
 
 /**
- * <span class="badge" style="float: right; background-color: #44751E;">DEVELOPER API</span>
+ * :: DeveloperApi ::
  *
  * Compute gradient and loss for a logistic loss function, as used in binary classification.
  * See also the documentation for the precise formulation.
@@ -96,7 +96,7 @@ class LogisticGradient extends Gradient {
 }
 
 /**
- * <span class="badge" style="float: right; background-color: #44751E;">DEVELOPER API</span>
+ * :: DeveloperApi ::
  *
  * Compute gradient and loss for a Least-squared loss function, as used in linear regression.
  * This is correct for the averaged least squares loss function (mean squared error)
@@ -130,7 +130,7 @@ class LeastSquaresGradient extends Gradient {
 }
 
 /**
- * <span class="badge" style="float: right; background-color: #44751E;">DEVELOPER API</span>
+ * :: DeveloperApi ::
  *
  * Compute gradient and loss for a Hinge loss function, as used in SVM binary classification.
  * See also the documentation for the precise formulation.

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/optimization/GradientDescent.scala b/mllib/src/main/scala/org/apache/spark/mllib/optimization/GradientDescent.scala
@@ -26,7 +26,7 @@ import org.apache.spark.rdd.RDD
 import org.apache.spark.mllib.linalg.{Vectors, Vector}
 
 /**
- * <span class="badge" style="float: right; background-color: #44751E;">DEVELOPER API</span>
+ * :: DeveloperApi ::
  *
  * Class used to solve an optimization problem using Gradient Descent.
  * @param gradient Gradient function to be used.
@@ -110,7 +110,7 @@ class GradientDescent(private var gradient: Gradient, private var updater: Updat
 }
 
 /**
- * <span class="badge" style="float: right; background-color: #44751E;">DEVELOPER API</span>
+ * :: DeveloperApi ::
  *
  * Top-level method to run gradient descent.
  */

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/optimization/Optimizer.scala b/mllib/src/main/scala/org/apache/spark/mllib/optimization/Optimizer.scala
@@ -22,7 +22,7 @@ import org.apache.spark.rdd.RDD
 import org.apache.spark.mllib.linalg.Vector
 
 /**
- * <span class="badge" style="float: right; background-color: #44751E;">DEVELOPER API</span>
+ * :: DeveloperApi ::
  *
  * Trait for optimization problem solvers.
  */

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/optimization/Updater.scala b/mllib/src/main/scala/org/apache/spark/mllib/optimization/Updater.scala
@@ -24,7 +24,7 @@ import breeze.linalg.{norm => brzNorm, axpy => brzAxpy, Vector => BV}
 import org.apache.spark.mllib.linalg.{Vectors, Vector}
 
 /**
- * <span class="badge" style="float: right; background-color: #44751E;">DEVELOPER API</span>
+ * :: DeveloperApi ::
  *
  * Class used to perform steps (weight update) using Gradient Descent methods.
  *
@@ -61,7 +61,7 @@ abstract class Updater extends Serializable {
 }
 
 /**
- * <span class="badge" style="float: right; background-color: #44751E;">DEVELOPER API</span>
+ * :: DeveloperApi ::
  *
  * A simple updater for gradient descent *without* any regularization.
  * Uses a step-size decreasing with the square root of the number of iterations.
@@ -82,7 +82,7 @@ class SimpleUpdater extends Updater {
 }
 
 /**
- * <span class="badge" style="float: right; background-color: #44751E;">DEVELOPER API</span>
+ * :: DeveloperApi ::
  *
  * Updater for L1 regularized problems.
  *          R(w) = ||w||_1
@@ -126,7 +126,7 @@ class L1Updater extends Updater {
 }
 
 /**
- * <span class="badge" style="float: right; background-color: #44751E;">DEVELOPER API</span>
+ * :: DeveloperApi ::
  *
  * Updater for L2 regularized problems.
  *          R(w) = 1/2 ||w||^2

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala b/mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala
@@ -138,7 +138,7 @@ class ALS private (
   }
 
   /**
-   * <span class="badge" style="float: right; background-color: #257080;">EXPERIMENTAL</span>
+   * :: Experimental ::
    *
    * Sets the constant used in computing confidence in implicit ALS. Default: 1.0.
    */

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala
@@ -68,7 +68,7 @@ class MatrixFactorizationModel(
   }
 
   /**
-   * <span class="badge" style="float: right; background-color: #44751E;">DEVELOPER API</span>
+   * :: DeveloperApi ::
    *
    * Predict the rating of many users for many products.
    * This is a Java stub for python predictAll()

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.scala
@@ -101,7 +101,7 @@ abstract class GeneralizedLinearAlgorithm[M <: GeneralizedLinearModel]
   }
 
   /**
-   * <span class="badge" style="float: right; background-color: #257080;">EXPERIMENTAL</span>
+   * :: Experimental ::
    *
    * Set if the algorithm should validate data before training. Default true.
    */

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala
@@ -33,7 +33,7 @@ import org.apache.spark.util.random.XORShiftRandom
 import org.apache.spark.mllib.linalg.{Vector, Vectors}
 
 /**
- * <span class="badge" style="float: right; background-color: #257080;">EXPERIMENTAL</span>
+ * :: Experimental ::
  *
  * A class that implements a decision tree algorithm for classification and regression. It
  * supports both continuous and categorical features.

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Algo.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Algo.scala
@@ -18,7 +18,7 @@
 package org.apache.spark.mllib.tree.configuration
 
 /**
- * <span class="badge" style="float: right; background-color: #257080;">EXPERIMENTAL</span>
+ * :: Experimental ::
  *
  * Enum to select the algorithm for the decision tree
  */

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/FeatureType.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/FeatureType.scala
@@ -18,7 +18,7 @@
 package org.apache.spark.mllib.tree.configuration
 
 /**
- * <span class="badge" style="float: right; background-color: #257080;">EXPERIMENTAL</span>
+ * :: Experimental ::
  *
  * Enum to describe whether a feature is "continuous" or "categorical"
  */

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/QuantileStrategy.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/QuantileStrategy.scala
@@ -18,7 +18,7 @@
 package org.apache.spark.mllib.tree.configuration
 
 /**
- * <span class="badge" style="float: right; background-color: #257080;">EXPERIMENTAL</span>
+ * :: Experimental ::
  *
  * Enum for selecting the quantile calculation strategy
  */

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Strategy.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Strategy.scala
@@ -22,7 +22,7 @@ import org.apache.spark.mllib.tree.configuration.Algo._
 import org.apache.spark.mllib.tree.configuration.QuantileStrategy._
 
 /**
- * <span class="badge" style="float: right; background-color: #257080;">EXPERIMENTAL</span>
+ * :: Experimental ::
  *
  * Stores all the configuration options for tree construction
  * @param algo classification or regression

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Entropy.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Entropy.scala
@@ -18,7 +18,7 @@
 package org.apache.spark.mllib.tree.impurity
 
 /**
- * <span class="badge" style="float: right; background-color: #257080;">EXPERIMENTAL</span>
+ * :: Experimental ::
  *
  * Class for calculating [[http://en.wikipedia.org/wiki/Binary_entropy_function entropy]] during
  * binary classification.
@@ -28,7 +28,7 @@ object Entropy extends Impurity {
    private[tree] def log2(x: Double) = scala.math.log(x) / scala.math.log(2)
 
   /**
-   * <span class="badge badge-red" style="float: right;">DEVELOPER API</span>
+   * :: DeveloperApi ::
    *
    * entropy calculation
    * @param c0 count of instances with label 0

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Gini.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Gini.scala
@@ -18,7 +18,7 @@
 package org.apache.spark.mllib.tree.impurity
 
 /**
- * <span class="badge" style="float: right; background-color: #257080;">EXPERIMENTAL</span>
+ * :: Experimental ::
  *
  * Class for calculating the
  * [[http://en.wikipedia.org/wiki/Decision_tree_learning#Gini_impurity Gini impurity]]
@@ -27,7 +27,7 @@ package org.apache.spark.mllib.tree.impurity
 object Gini extends Impurity {
 
   /**
-   * <span class="badge badge-red" style="float: right;">DEVELOPER API</span>
+   * :: DeveloperApi ::
    *
    * Gini coefficient calculation
    * @param c0 count of instances with label 0

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Impurity.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Impurity.scala
@@ -18,14 +18,14 @@
 package org.apache.spark.mllib.tree.impurity
 
 /**
- * <span class="badge" style="float: right; background-color: #257080;">EXPERIMENTAL</span>
+ * :: Experimental ::
  *
  * Trait for calculating information gain.
  */
 trait Impurity extends Serializable {
 
   /**
-   * <span class="badge badge-red" style="float: right;">DEVELOPER API</span>
+   * :: DeveloperApi ::
    *
    * information calculation for binary classification
    * @param c0 count of instances with label 0
@@ -35,7 +35,7 @@ trait Impurity extends Serializable {
   def calculate(c0 : Double, c1 : Double): Double
 
   /**
-   * <span class="badge badge-red" style="float: right;">DEVELOPER API</span>
+   * :: DeveloperApi ::
    *
    * information calculation for regression
    * @param count number of instances

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Variance.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Variance.scala
@@ -18,7 +18,7 @@
 package org.apache.spark.mllib.tree.impurity
 
 /**
- * <span class="badge" style="float: right; background-color: #257080;">EXPERIMENTAL</span>
+ * :: Experimental ::
  *
  * Class for calculating variance during regression
  */
@@ -27,7 +27,7 @@ object Variance extends Impurity {
      throw new UnsupportedOperationException("Variance.calculate")
 
   /**
-   * <span class="badge badge-red" style="float: right;">DEVELOPER API</span>
+   * :: DeveloperApi ::
    *
    * variance calculation
    * @param count number of instances

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/DecisionTreeModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/DecisionTreeModel.scala
@@ -22,7 +22,7 @@ import org.apache.spark.rdd.RDD
 import org.apache.spark.mllib.linalg.Vector
 
 /**
- * <span class="badge" style="float: right; background-color: #257080;">EXPERIMENTAL</span>
+ * :: Experimental ::
  *
  * Model to store the decision tree parameters
  * @param topNode root node

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/InformationGainStats.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/InformationGainStats.scala
@@ -18,7 +18,7 @@
 package org.apache.spark.mllib.tree.model
 
 /**
- * <span class="badge badge-red" style="float: right;">DEVELOPER API</span>
+ * :: DeveloperApi ::
  *
  * Information gain statistics for each split
  * @param gain information gain value

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Node.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Node.scala
@@ -22,7 +22,7 @@ import org.apache.spark.mllib.tree.configuration.FeatureType._
 import org.apache.spark.mllib.linalg.Vector
 
 /**
- * <span class="badge badge-red" style="float: right;">DEVELOPER API</span>
+ * :: DeveloperApi ::
  *
  * Node in a decision tree
  * @param id integer node id

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Split.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Split.scala
@@ -20,7 +20,7 @@ package org.apache.spark.mllib.tree.model
 import org.apache.spark.mllib.tree.configuration.FeatureType.FeatureType
 
 /**
- * <span class="badge badge-red" style="float: right;">DEVELOPER API</span>
+ * :: DeveloperApi ::
  *
  * Split applied to a feature
  * @param feature feature index

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/util/DataValidators.scala b/mllib/src/main/scala/org/apache/spark/mllib/util/DataValidators.scala
@@ -22,7 +22,7 @@ import org.apache.spark.rdd.RDD
 import org.apache.spark.mllib.regression.LabeledPoint
 
 /**
- * <span class="badge" style="float: right; background-color: #44751E;">DEVELOPER API</span>
+ * :: DeveloperApi ::
  *
  * A collection of methods used to validate data before applying ML algorithms.
  */

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/util/KMeansDataGenerator.scala b/mllib/src/main/scala/org/apache/spark/mllib/util/KMeansDataGenerator.scala
@@ -23,7 +23,7 @@ import org.apache.spark.SparkContext
 import org.apache.spark.rdd.RDD
 
 /**
- * <span class="badge" style="float: right; background-color: #44751E;">DEVELOPER API</span>
+ * :: DeveloperApi ::
  *
  * Generate test data for KMeans. This class first chooses k cluster centers
  * from a d-dimensional Gaussian distribution scaled by factor r and then creates a Gaussian

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/util/LinearDataGenerator.scala b/mllib/src/main/scala/org/apache/spark/mllib/util/LinearDataGenerator.scala
@@ -28,7 +28,7 @@ import org.apache.spark.mllib.linalg.Vectors
 import org.apache.spark.mllib.regression.LabeledPoint
 
 /**
- * <span class="badge" style="float: right; background-color: #44751E;">DEVELOPER API</span>
+ * :: DeveloperApi ::
  *
  * Generate sample data used for Linear Data. This class generates
  * uniformly random values for every feature and adds Gaussian noise with mean `eps` to the

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/util/LogisticRegressionDataGenerator.scala b/mllib/src/main/scala/org/apache/spark/mllib/util/LogisticRegressionDataGenerator.scala
@@ -25,7 +25,7 @@ import org.apache.spark.mllib.regression.LabeledPoint
 import org.apache.spark.mllib.linalg.Vectors
 
 /**
- * <span class="badge" style="float: right; background-color: #44751E;">DEVELOPER API</span>
+ * :: DeveloperApi ::
  *
  * Generate test data for LogisticRegression. This class chooses positive labels
  * with probability `probOne` and scales features for positive examples by `eps`.

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/util/MFDataGenerator.scala b/mllib/src/main/scala/org/apache/spark/mllib/util/MFDataGenerator.scala
@@ -25,7 +25,7 @@ import org.apache.spark.SparkContext
 import org.apache.spark.rdd.RDD
 
 /**
- * <span class="badge" style="float: right; background-color: #44751E;">DEVELOPER API</span>
+ * :: DeveloperApi ::
  *
 * Generate RDD(s) containing data for Matrix Factorization.
 *

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/util/MLUtils.scala b/mllib/src/main/scala/org/apache/spark/mllib/util/MLUtils.scala
@@ -122,7 +122,7 @@ object MLUtils {
     loadLibSVMData(sc, path, labelParser, numFeatures, sc.defaultMinSplits)
 
   /**
-   * <span class="badge" style="float: right; background-color: #257080;">EXPERIMENTAL</span>
+   * :: Experimental ::
    *
    * Load labeled data from a file. The data format used here is
    * <L>, <f1> <f2> ...
@@ -143,7 +143,7 @@ object MLUtils {
   }
 
   /**
-   * <span class="badge" style="float: right; background-color: #257080;">EXPERIMENTAL</span>
+   * :: Experimental ::
    *
    * Save labeled data to a file. The data format used here is
    * <L>, <f1> <f2> ...

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/util/SVMDataGenerator.scala b/mllib/src/main/scala/org/apache/spark/mllib/util/SVMDataGenerator.scala
@@ -27,7 +27,7 @@ import org.apache.spark.mllib.linalg.Vectors
 import org.apache.spark.mllib.regression.LabeledPoint
 
 /**
- * <span class="badge" style="float: right; background-color: #44751E;">DEVELOPER API</span>
+ * :: DeveloperApi ::
  *
  * Generate sample data used for SVM. This class generates uniform random values
  * for the features and adds Gaussian noise with weight 0.1 to generate labels.