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Renaming the PCA model scala parameter from right_singlar_vectors to rightSingularVectors #13

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Renaming the PCA model scala parameter from right_singlar_vectors to rightSingularVectors #13

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20 changes: 10 additions & 10 deletions ...tk-core/src/main/scala/org/trustedanalytics/daaltk/models/dimreduction/pca/PcaModel.scala
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Expand Up @@ -88,7 +88,7 @@ object PcaModel extends TkSaveableObject {
m.meanCentered,
new MllibDenseVector(m.meanVector),
new MllibDenseVector(m.singularValues),
new DenseMatrix(m.vFactorRows, m.vFactorCols, m.right_singular_vectors),
new DenseMatrix(m.vFactorRows, m.vFactorCols, m.rightSingularVectors),
m.leftSingularMatrix)

// Create PrincipalComponentsModel to return
Expand Down Expand Up @@ -141,8 +141,8 @@ case class PcaModel(svdData: SvdData) extends Serializable with Model with DaalM
/**
* Right singular vectors of the specified columns in the input frame
*/
def right_singular_vectors: Array[Array[Double]] = {
val lists = svdData.right_singular_vectors.toListOfList()
def rightSingularVectors: Array[Array[Double]] = {
val lists = svdData.rightSingularVectors.toListOfList()

lists.map(list => list.toArray).toArray
}
Expand Down Expand Up @@ -192,7 +192,7 @@ case class PcaModel(svdData: SvdData) extends Serializable with Model with DaalM
predictColumns,
meanCentered,
columnMeans)
val principalComponents = PrincipalComponentsFunctions.computePrincipalComponents(svdData.right_singular_vectors, predictC, indexedRowMatrix)
val principalComponents = PrincipalComponentsFunctions.computePrincipalComponents(svdData.rightSingularVectors, predictC, indexedRowMatrix)

val pcaColumns = for (i <- 1 to predictC) yield Column("p_" + i.toString, DataTypes.float64)
val (componentColumns, components) = tSquaredIndex match {
Expand Down Expand Up @@ -223,9 +223,9 @@ case class PcaModel(svdData: SvdData) extends Serializable with Model with DaalM
svdData.meanCentered,
svdData.meanVector.toArray,
svdData.singularValues.toArray,
svdData.right_singular_vectors.toArray,
svdData.right_singular_vectors.numRows,
svdData.right_singular_vectors.numCols,
svdData.rightSingularVectors.toArray,
svdData.rightSingularVectors.numRows,
svdData.rightSingularVectors.numCols,
svdData.leftSingularMatrix)
TkSaveLoad.saveTk(sc, path, PcaModel.formatId, PcaModel.currentFormatVersion, tkMetadata)
}
Expand All @@ -243,7 +243,7 @@ case class PcaModel(svdData: SvdData) extends Serializable with Model with DaalM
val meanCenteredVector: Array[Double] = (new DenseVector(x) - new DenseVector(columnMeans.toArray)).toArray
inputVector = new MllibDenseVector(meanCenteredVector)
}
val y = new MllibDenseMatrix(1, inputVector.size, inputVector.toArray).multiply(svdData.right_singular_vectors.asInstanceOf[MllibDenseMatrix])
val y = new MllibDenseMatrix(1, inputVector.size, inputVector.toArray).multiply(svdData.rightSingularVectors.asInstanceOf[MllibDenseMatrix])
val yArray: Array[Double] = y.values
var t_squared_index: Double = 0.0
for (i <- 0 until k) {
Expand Down Expand Up @@ -288,7 +288,7 @@ case class PcaModel(svdData: SvdData) extends Serializable with Model with DaalM
* @param meanCentered Indicator whether the columns were mean centered for training
* @param meanVector Means of the columns
* @param singularValues Singular values of the specified columns in the input frame
* @param right_singular_vectors Right singular vectors of the specified columns in the input frame
* @param rightSingularVectors Right singular vectors of the specified columns in the input frame
* @param vFactorRows Number of rows in vFactor matrix
* @param vFactorCols Number of columns in vFactor matrix
* @param leftSingularMatrix Optional RDD with left singular vectors of the specified columns in the input frame
Expand All @@ -298,7 +298,7 @@ case class PrincipalComponentsTkMetaData(k: Int,
meanCentered: Boolean,
meanVector: Array[Double],
singularValues: Array[Double],
right_singular_vectors: Array[Double],
rightSingularVectors: Array[Double],
vFactorRows: Int,
vFactorCols: Int,
leftSingularMatrix: Option[RDD[Vector]]) extends Serializable
6 changes: 3 additions & 3 deletions ...ore/src/main/scala/org/trustedanalytics/daaltk/models/dimreduction/pca/SvdAlgorithm.scala
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Expand Up @@ -221,15 +221,15 @@ case class PrincipalComponentsData(k: Int,
* @param meanCentered Indicator whether the columns were mean centered for training
* @param meanVector Means of the columns
* @param singularValues Singular values of the specified columns in the input frame
* @param right_singular_vectors Right singular vectors of the specified columns in the input frame
* @param rightSingularVectors Right singular vectors of the specified columns in the input frame
* @param leftSingularMatrix Optional RDD with left singular vectors of the specified columns in the input frame
*/
case class SvdData(k: Int,
observationColumns: Seq[String],
meanCentered: Boolean,
meanVector: Vector,
singularValues: Vector,
right_singular_vectors: Matrix,
rightSingularVectors: Matrix,
leftSingularMatrix: Option[RDD[Vector]]) {
require(observationColumns != null && observationColumns.nonEmpty, "observationColumns must not be null nor empty")
require(k >= 1, "number of Eigen values to use must be greater than equal to 1")
Expand All @@ -245,7 +245,7 @@ case class SvdData(k: Int,
meanCentered,
meanVector,
singularValues,
right_singular_vectors)
rightSingularVectors)
}
}

2 changes: 1 addition & 1 deletion ...tk-core/src/test/scala/org/trustedanalytics/daaltk/models/dimreduction/PcaModelTest.scala
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Expand Up @@ -37,7 +37,7 @@ class PcaModelTest extends TestingSparkContextWordSpec with Matchers {
Column("5", DataTypes.float32),
Column("6", DataTypes.float32)))

"PrincipalComponentsModel" should {
"PcaModel" should {

"create a DAAL PrincipalComponentsModel train and predict" in {
val rdd = sparkContext.parallelize(frameData)
Expand Down
17 changes: 0 additions & 17 deletions python/daaltk/models/dimensionality_reduction/__init__.py
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3 changes: 2 additions & 1 deletion python/daaltk/models/dimreduction/__init__.py
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Expand Up @@ -13,4 +13,5 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
#

2 changes: 1 addition & 1 deletion python/daaltk/models/dimreduction/pca.py
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Expand Up @@ -246,7 +246,7 @@ def right_singular_vectors(self):
"""
Right singular vectors of the specified columns in the input frame
"""
return [list(i) for i in list(self._scala.right_singular_vectors())]
return [list(i) for i in list(self._scala.rightSingularVectors())]

def predict(self, frame, mean_centered=True, t_squared_index=False, observation_columns=None, c=None):
"""
Expand Down