Moving ANN to ML package. GradientDescent constructor is now spark pr…

…ivate.
apache · Jul 29, 2015 · 374bea6 · 374bea6
1 parent 43b0ae2
commit 374bea6
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Showing 5 changed files with 7 additions and 10 deletions.
diff --git a/...g/apache/spark/mllib/ann/BreezeUtil.scala → .../org/apache/spark/ml/ann/BreezeUtil.scala b/...g/apache/spark/mllib/ann/BreezeUtil.scala → .../org/apache/spark/ml/ann/BreezeUtil.scala
@@ -15,7 +15,7 @@
  * limitations under the License.
  */
 
-package org.apache.spark.mllib.ann
+package org.apache.spark.ml.ann
 
 import breeze.linalg.{DenseMatrix => BDM, DenseVector => BDV}
 import com.github.fommil.netlib.BLAS.{getInstance => NativeBLAS}

diff --git a/...la/org/apache/spark/mllib/ann/Layer.scala → ...scala/org/apache/spark/ml/ann/Layer.scala b/...la/org/apache/spark/mllib/ann/Layer.scala → ...scala/org/apache/spark/ml/ann/Layer.scala
@@ -15,7 +15,7 @@
  * limitations under the License.
  */
 
-package org.apache.spark.mllib.ann
+package org.apache.spark.ml.ann
 
 import breeze.linalg.{*, DenseMatrix => BDM, DenseVector => BDV, Vector => BV, axpy => brzAxpy,
 sum => Bsum}
@@ -741,12 +741,12 @@ private[ann] class ANNUpdater extends Updater {
 }
 
 /**
- * Llib-style trainer class that trains a network given the data and topology
+ * MLlib-style trainer class that trains a network given the data and topology
  * @param topology topology of ANN
  * @param inputSize input size
  * @param outputSize output size
  */
-class FeedForwardTrainer (topology: Topology, val inputSize: Int,
+private[ml] class FeedForwardTrainer (topology: Topology, val inputSize: Int,
                           val outputSize: Int) extends Serializable {
 
   // TODO: what if we need to pass random seed?

diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/MultilayerPerceptronClassifier.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/MultilayerPerceptronClassifier.scala
@@ -17,14 +17,12 @@
 
 package org.apache.spark.ml.classification
 
-import breeze.linalg.{argmax => Bargmax}
-
 import org.apache.spark.annotation.Experimental
 import org.apache.spark.ml.param.shared.{HasTol, HasMaxIter, HasSeed}
 import org.apache.spark.ml.{PredictorParams, PredictionModel, Predictor}
 import org.apache.spark.ml.param.{IntParam, ParamValidators, IntArrayParam, ParamMap}
 import org.apache.spark.ml.util.Identifiable
-import org.apache.spark.mllib.ann.{FeedForwardTrainer, FeedForwardTopology}
+import org.apache.spark.ml.ann.{FeedForwardTrainer, FeedForwardTopology}
 import org.apache.spark.mllib.linalg.{Vectors, Vector}
 import org.apache.spark.mllib.regression.LabeledPoint
 import org.apache.spark.sql.DataFrame
@@ -88,7 +86,6 @@ with HasSeed with HasMaxIter with HasTol {
   setDefault(maxIter -> 100, tol -> 1e-4, layers -> Array(1, 1), blockSize -> 1)
 }
 
-
 /** Label to vector converter. */
 private object LabelConverter {
   // TODO: Use OneHotEncoder instead

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
@@ -32,7 +32,7 @@ import org.apache.spark.mllib.linalg.{Vectors, Vector}
  * @param gradient Gradient function to be used.
  * @param updater Updater to be used to update weights after every iteration.
  */
-class GradientDescent private[mllib] (private var gradient: Gradient, private var updater: Updater)
+class GradientDescent private[spark] (private var gradient: Gradient, private var updater: Updater)
   extends Optimizer with Logging {
 
   private var stepSize: Double = 1.0

diff --git a/...org/apache/spark/mllib/ann/ANNSuite.scala → ...la/org/apache/spark/ml/ann/ANNSuite.scala b/...org/apache/spark/mllib/ann/ANNSuite.scala → ...la/org/apache/spark/ml/ann/ANNSuite.scala
@@ -15,7 +15,7 @@
  * limitations under the License.
  */
 
-package org.apache.spark.mllib.ann
+package org.apache.spark.ml.ann
 
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.mllib.linalg.Vectors