Github user anantasty commented on a diff in the pull request:
https://github.com/apache/spark/pull/2451#discussion_r17802806
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/optimization/Gradient.scala ---
@@ -157,3 +157,221 @@ class HingeGradient extends Gradient {
}
}
}
+
+/**
+ * :: DeveloperApi ::
+ * Class used to compute the gradient for a loss function, given a series
of data points.
+ */
+@DeveloperApi
+abstract class MultiModelGradient extends Serializable {
+ /**
+ * Compute the gradient and loss given the features of all data points.
+ *
+ * @param data features for one data point
+ * @param label label for this data point
+ * @param weights weights/coefficients corresponding to features
+ *
+ * @return (gradient: DenseMatrix, loss: Double)
+ */
+ def compute(data: Matrix, label: DenseMatrix,
+ weights: DenseMatrix): (DenseMatrix, Matrix)
+
+ /**
+ * Compute the gradient and loss given the features of a series of data
point,
+ * add the gradient to a provided matrix to avoid creating new objects,
and return loss.
+ *
+ * @param data features for the data points
+ * @param label label for the data points
+ * @param weights weights/coefficients corresponding to features
+ * @param cumGradient the computed gradient will be added to this matrix
+ *
+ * @return loss
+ */
+ def compute(data: Matrix, label: DenseMatrix,
+ weights: DenseMatrix, cumGradient: DenseMatrix):
Matrix
+}
+
+/**
+ * :: DeveloperApi ::
+ * Compute gradient and loss for a logistic loss function, as used in
binary classification.
+ * See also the documentation for the precise formulation.
+ */
+@DeveloperApi
+class MultiModelLogisticGradient extends MultiModelGradient {
+
+ private def sigmoid(p: DenseMatrix): DenseMatrix = {
+ def takeSigmoid(p: Double): Double = {
+ 1.0 / (math.exp(-p) + 1.0)
+ }
+ p.map(takeSigmoid)
+ }
+
+ override def compute(data: Matrix, label: DenseMatrix,
+ weights: DenseMatrix): (DenseMatrix, Matrix) = {
+ val margin = data transposeMultiply weights
+ val gradient = DenseMatrix.zeros(weights.numRows, weights.numCols)
+
+ gemm(false, false, 1.0, data,
sigmoid(margin).elementWiseOperateOnColumnsInPlace(_ - _, label),
+ 0.0, gradient)
+
+ val negativeLabels = label.compare(0.0, _ == _)
+ val addMargin = margin.elementWiseOperateOnColumns(_ * _,
negativeLabels)
+
+ val loss = margin.update(v => math.log1p(math.exp(-v))).
+ elementWiseOperateInPlace(_ + _, addMargin)
+
+ val lossVector =
+ if (data.isInstanceOf[DenseMatrix]) {
+ val numFeatures = data.numRows
+ val zeroEntries = data.compare(0.0, _ == _)
+ val shouldSkip = zeroEntries.colSums.compareInPlace(numFeatures, _
== _)
+ loss.colSums(false, shouldSkip)
+ } else {
+ loss.colSums
+ }
+ (gradient, lossVector)
+ }
+
+ override def compute(data: Matrix,
+ label: DenseMatrix,
+ weights: DenseMatrix,
+ cumGradient: DenseMatrix): Matrix = {
+ val margin = data transposeMultiply weights
+ gemm(false, false, 1.0, data,
sigmoid(margin).elementWiseOperateOnColumnsInPlace(_ - _, label),
+ 1.0, cumGradient)
+
+ val negativeLabels = label.compare(0.0, _ == _)
+ val addMargin = margin.elementWiseOperateOnColumns(_ * _,
negativeLabels)
+
+ val loss = margin.update(v => math.log1p(math.exp(-v))).
+ elementWiseOperateInPlace(_ + _, addMargin)
+
+ if (data.isInstanceOf[DenseMatrix]) {
+ val numFeatures = data.numRows
+ val zeroEntries = data.compare(0.0, _ == _)
+ val shouldSkip = zeroEntries.colSums.compareInPlace(numFeatures, _
== _)
+ loss.colSums(false, shouldSkip)
+ } else {
+ loss.colSums
+ }
+ }
+}
+
+/**
+ * :: 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)
+ * L = 1/n ||A weights-y||^2
+ * See also the documentation for the precise formulation.
+ */
+@DeveloperApi
+class MultiModelLeastSquaresGradient extends MultiModelGradient {
+ override def compute(data: Matrix, label: DenseMatrix,
+ weights: DenseMatrix): (DenseMatrix, Matrix) = {
+
+ val diff = (data transposeMultiply
weights).elementWiseOperateOnColumnsInPlace(_ - _, label)
+
+ val gradient = DenseMatrix.zeros(weights.numRows, weights.numCols)
+
+ gemm(false, false, 2.0, data, diff, 0.0, gradient)
+
+ val loss = diff.update(v => v * v)
+
+ val lossVector =
+ if (data.isInstanceOf[DenseMatrix]) {
+ val numFeatures = data.numRows
+ val zeroEntries = data.compare(0.0, _ == _)
+ val shouldSkip = zeroEntries.colSums.compareInPlace(numFeatures, _
== _)
+ loss.colSums(false, shouldSkip)
+ } else {
+ loss.colSums
+ }
+ (gradient, lossVector)
+ }
+
+ override def compute(data: Matrix,
+ label: DenseMatrix,
+ weights: DenseMatrix,
+ cumGradient: DenseMatrix): Matrix = {
+ val diff = (data transposeMultiply
weights).elementWiseOperateOnColumnsInPlace(_ - _, label)
+
+ gemm(false, false, 2.0, data, diff, 1.0, cumGradient)
+ val loss = diff.update(v => v * v)
+
+ if (data.isInstanceOf[DenseMatrix]) {
+ val numFeatures = data.numRows
+ val zeroEntries = data.compare(0.0, _ == _)
+ val shouldSkip = zeroEntries.colSums.compareInPlace(numFeatures, _
== _)
+ loss.colSums(false, shouldSkip)
+ } else {
+ loss.colSums
+ }
+ }
+}
+
+
+/**
+ * :: DeveloperApi ::
+ * Compute gradient and loss for a Hinge loss function, as used in SVM
binary classification.
+ * See also the documentation for the precise formulation.
+ * NOTE: This assumes that the labels are {0,1}
+ */
+@DeveloperApi
+class MultiModelHingeGradient extends MultiModelGradient {
+ override def compute(data: Matrix, label: DenseMatrix,
+ weights: DenseMatrix): (DenseMatrix, Matrix) = {
+
+ val dotProduct = data transposeMultiply weights
+ // Our loss function with {0, 1} labels is max(0, 1 - (2y â 1)
(f_w(x)))
+ // Therefore the gradient is -(2y - 1)*x
+ val labelScaled = new DenseMatrix(1, label.numRows, label.map(_ * 2 -
1.0).values)
+
+ dotProduct.elementWiseOperateOnColumnsInPlace(_ * _, labelScaled)
+
+ val gradientMultiplier = data.elementWiseOperateOnRows(_ * _,
labelScaled.negInPlace)
+ val gradient = DenseMatrix.zeros(weights.numRows, weights.numCols)
+ val activeExamples = dotProduct.compare(1.0, _ < _) // Examples where
the hinge is active
+
+ gemm(false, false, 1.0, gradientMultiplier, activeExamples, 1.0,
gradient)
+
+ val loss = activeExamples.elementWiseOperateInPlace(_ * _,
dotProduct.update(1 - _))
+
+ val lossVector =
+ if (data.isInstanceOf[DenseMatrix]) {
+ val numFeatures = data.numRows
+ val zeroEntries = data.compare(0.0, _ == _)
+ val shouldSkip = zeroEntries.colSums.compareInPlace(numFeatures, _
== _)
+ loss.colSums(false, shouldSkip)
+ } else {
+ loss.colSums
+ }
+ (gradient, lossVector)
+ }
+
+ override def compute(data: Matrix, label: DenseMatrix,
+ weights: DenseMatrix, cumGradient: DenseMatrix):
Matrix = {
+
+ val dotProduct = data transposeMultiply weights
+ // Our loss function with {0, 1} labels is max(0, 1 - (2y â 1)
(f_w(x)))
+ // Therefore the gradient is -(2y - 1)*x
+ val labelScaled = new DenseMatrix(1, label.numRows, label.map(_ * 2 -
1.0).values)
+ dotProduct.elementWiseOperateOnColumnsInPlace(_ * _, labelScaled)
+
+ val gradientMultiplier = data.elementWiseOperateOnRows(_ * _,
labelScaled.negInPlace)
+
+ val activeExamples = dotProduct.compare(1.0, _ < _) // Examples where
the hinge is active
+
+ gemm(false, false, 1.0, gradientMultiplier, activeExamples, 1.0,
cumGradient)
+
+ val loss = activeExamples.elementWiseOperateInPlace(_ * _,
dotProduct.update(1 - _))
+
+ if (data.isInstanceOf[DenseMatrix]) {
+ val numFeatures = data.numRows
+ val zeroEntries = data.compare(0.0, _ == _)
+ val shouldSkip = zeroEntries.colSums.compareInPlace(numFeatures, _
== _)
+ loss.colSums(false, shouldSkip)
+ } else {
+ loss.colSums
+ }
+ }
+}
--- End diff --
Missing new line at end of file.
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