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

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

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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
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54 changes: 47 additions & 7 deletions mllib/src/main/scala/org/apache/spark/mllib/util/LinearDataGenerator.scala
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Original file line number Diff line number Diff line change
Expand Up @@ -77,13 +77,11 @@ object LinearDataGenerator {
nPoints: Int,
seed: Int,
eps: Double = 0.1): Seq[LabeledPoint] = {
generateLinearInput(intercept, weights,
Array.fill[Double](weights.length)(0.0),
Array.fill[Double](weights.length)(1.0 / 3.0),
nPoints, seed, eps)}
generateLinearInput(intercept, weights, Array.fill[Double](weights.length)(0.0),
Array.fill[Double](weights.length)(1.0 / 3.0), nPoints, seed, eps)
}

/**
*
* @param intercept Data intercept
* @param weights Weights to be applied.
* @param xMean the mean of the generated features. Lots of time, if the features are not properly
Expand All @@ -104,24 +102,66 @@ object LinearDataGenerator {
nPoints: Int,
seed: Int,
eps: Double): Seq[LabeledPoint] = {
generateLinearInput(intercept, weights, xMean, xVariance, nPoints, seed, eps, 0.0)
}


/**
* @param intercept Data intercept
* @param weights Weights to be applied.
* @param xMean the mean of the generated features. Lots of time, if the features are not properly
* standardized, the algorithm with poor implementation will have difficulty
* to converge.
* @param xVariance the variance of the generated features.
* @param nPoints Number of points in sample.
* @param seed Random seed
* @param eps Epsilon scaling factor.
* @param sparsity The ratio of zero elements. If it is 0.0, LabeledPoints with
* DenseVector is returned.
* @return Seq of input.
*/
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@dbtsai dbtsai Oct 23, 2015

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

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@Lewuathe Lewuathe Oct 24, 2015

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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?

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@dbtsai dbtsai Oct 24, 2015

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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 generateLinearInput(
intercept: Double,
weights: Array[Double],
xMean: Array[Double],
xVariance: Array[Double],
nPoints: Int,
seed: Int,
eps: Double,
sparsity: Double): Seq[LabeledPoint] = {
require(0.0 <= sparsity && sparsity <= 1.0)
val rnd = new Random(seed)
val x = Array.fill[Array[Double]](nPoints)(
Array.fill[Double](weights.length)(rnd.nextDouble()))

val sparseRnd = new Random(seed)
x.foreach { v =>
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@dbtsai dbtsai Oct 23, 2015

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Once you have sparsity, randomly choose n = numFeatures * (1 - sparsity) as non-zero features, and zero the rest out.

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@dbtsai dbtsai Oct 23, 2015

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You can also add the variance of sparsity such that the num of non zeros will not be constant.

var i = 0
val len = v.length
while (i < len) {
v(i) = (v(i) - 0.5) * math.sqrt(12.0 * xVariance(i)) + xMean(i)
if (sparseRnd.nextDouble() < sparsity) {
v(i) = 0.0
} else {
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)))

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@dbtsai dbtsai Oct 29, 2015

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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)
  }
}

y.zip(x).map { p =>
if (sparsity == 0.0) {
// Return LabeledPoints with DenseVector
LabeledPoint(p._1, Vectors.dense(p._2))
} else {
// Return LabeledPoints with SparseVector
LabeledPoint(p._1, Vectors.dense(p._2).toSparse)
}
}
}

/**
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