Skip to content

[SPARK-11207][ML] Add test cases for solver selection of LinearRegres… #9180

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Closed
Lewuathe wants to merge 14 commits into from
Closed

[SPARK-11207][ML] Add test cases for solver selection of LinearRegres… #9180

Changes from 1 commit
Commits
Show all changes
14 commits
Select commit Hold shift + click to select a range
11cd9c1
[SPARK-11207][ML] Add test cases for solver selection of LinearRegres…
Lewuathe Oct 20, 2015
28427d2
Fix scalastyle
Lewuathe Oct 20, 2015
f85bca6
[SPARK-11207] Improve test case with many feature datasets
Lewuathe Oct 21, 2015
22ba64e
Merge branch 'master' into SPARK-11207
Lewuathe Oct 21, 2015
f6b2256
[SPARK-11207] Remove extra lines
Lewuathe Oct 22, 2015
2082d47
[SPARK-11207] Pass sparcity to generateLinearSparseInput
Lewuathe Oct 24, 2015
003d3bd
[SPARK-11207] Add new API for generateLinearInput
Lewuathe Oct 24, 2015
0a43033
[SPARK-11207] Fix tests
Lewuathe Oct 25, 2015
59383fd
[SPARK-11207] Fix random values used by unit tests
Lewuathe Oct 25, 2015
888b216
Update Fri Oct 30 15:39:05 JST 2015
Lewuathe Oct 30, 2015
c27a4c3
Resolv conflict
Lewuathe Oct 30, 2015
97c76c9
Merge branch 'master' into SPARK-11207
Lewuathe Oct 30, 2015
74de81e
Fix style
Lewuathe Oct 30, 2015
241ec72
Minor fixed
Lewuathe Oct 30, 2015
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
Prev Previous commit
Next Next commit
[SPARK-11207] Add new API for generateLinearInput
  • Loading branch information
@Lewuathe
Lewuathe committed Oct 24, 2015
commit 003d3bd87f3936c4fd6ee0dc77ca81f3811bcbd7
36 changes: 14 additions & 22 deletions mllib/src/main/scala/org/apache/spark/mllib/util/LinearDataGenerator.scala
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -103,26 +103,10 @@ object LinearDataGenerator {
nPoints: Int,
seed: Int,
eps: Double): Seq[LabeledPoint] = {

val rnd = new Random(seed)
val x = Array.fill[Array[Double]](nPoints)(
Array.fill[Double](weights.length)(rnd.nextDouble()))

x.foreach { v =>
var i = 0
val len = v.length
while (i < len) {
v(i) = (v(i) - 0.5) * math.sqrt(12.0 * xVariance(i)) + xMean(i)
i += 1
}
}

val y = x.map { xi =>
blas.ddot(weights.length, xi, 1, weights, 1) + intercept + eps * rnd.nextGaussian()
}
y.zip(x).map(p => LabeledPoint(p._1, Vectors.dense(p._2)))
generateLinearInputInternal(intercept, weights, xMean, xVariance, nPoints, seed, eps, 0.0)
}


/**
* @param intercept Data intercept
* @param weights Weights to be applied.
Expand All @@ -133,10 +117,12 @@ object LinearDataGenerator {
* @param nPoints Number of points in sample.
* @param seed Random seed
* @param eps Epsilon scaling factor.
* @return Seq of LabeledPoint includes sparse vectors..
* @param sparcity The ratio of zero elements. If it is 0.0, LabeledPoints with
Copy link
Member

@dbtsai dbtsai Oct 29, 2015

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

Typo: sparsity

* DenseVector is returned.
* @return Seq of input.
*/
Copy link
Member

@dbtsai dbtsai Oct 23, 2015

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

How about consolidate with LinearDataGenerator, and add sparsity = 1.0 as param to control if it's sparse feature?

Copy link
Contributor Author

@Lewuathe Lewuathe Oct 24, 2015

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

Yes, I also thought it is good idea. But LinearDataGenerator is used as static object, then we have to pass sparsity as parameter to generateLinearInput. This method seems to be used a lot of suites. It is necessary to change a lot of method reference.
Therefore it might be better to do in separate JIRA. What do you thing about?

Copy link
Member

@dbtsai dbtsai Oct 24, 2015

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

Let's modify the JIRA and do it here. Basically, you can create a LinearDataGenerator with old signature calling new API for compatibility issue.

@Since("1.6.0")
def generateLinearSparseInput(
def generateLinearInputInternal(
Copy link
Member

@dbtsai dbtsai Oct 29, 2015

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

Just call it generateLinearInput without Internal.

intercept: Double,
weights: Array[Double],
xMean: Array[Double],
Expand Down Expand Up @@ -168,13 +154,19 @@ object LinearDataGenerator {
}

Copy link
Member

@dbtsai dbtsai Oct 29, 2015

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

To simplify the following code, do

y.zip(x).map { p => 
  if (sparsity == 0.0) {
    LabeledPoint(p._1, Vectors.dense(p._2))
  } else {
    LabeledPoint(p._1, Vectors.dense(p._2).toSparse)
  }
}

val sparseX = x.map { (v: Array[Double]) =>
v.zipWithIndex.filter{
v.zipWithIndex.filter {
case (d: Double, i: Int) => d != 0.0
}.map {
case (d: Double, i: Int) => (i, d)
}
}
y.zip(sparseX).map(p => LabeledPoint(p._1, Vectors.sparse(weights.length, p._2)))
if (sparcity == 0.0) {
// Return LabeledPoints with DenseVector
y.zip(x).map(p => LabeledPoint(p._1, Vectors.dense(p._2)))
} else {
// Return LabeledPoints with SparseVector
y.zip(sparseX).map(p => LabeledPoint(p._1, Vectors.sparse(weights.length, p._2)))
}
}

/**
Expand Down