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[SPARK-18206][ML]Add instrumentation for MLP,NB,LDA,AFT,GLM,Isotonic,LiR #15671

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[SPARK-18206][ML]Add instrumentation for MLP,NB,LDA,AFT,GLM,Isotonic,LiR #15671

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25258ae
create pr
zhengruifeng Oct 26, 2016
2bc4e29
fix nits
zhengruifeng Oct 27, 2016
b0b2cc3
add instr for regression
zhengruifeng Oct 28, 2016
41aff47
add instr for algos
zhengruifeng Oct 28, 2016
5dc6b43
update
zhengruifeng Oct 28, 2016
457196a
remove cla.params
zhengruifeng Oct 28, 2016
22c9f0c
add numfea in rf and dt
zhengruifeng Oct 28, 2016
ff49d70
revert OvR
zhengruifeng Oct 28, 2016
e2a1546
revert trees
zhengruifeng Oct 29, 2016
867f08b
list params; correct logSuccess place; update comment of logSuccess
zhengruifeng Nov 1, 2016
d129416
select params in gbt and rf
zhengruifeng Nov 2, 2016
e62a688
fix glr,aft,lir
zhengruifeng Nov 3, 2016
19828e1
fix style
zhengruifeng Nov 4, 2016
6e27321
update als
zhengruifeng Nov 30, 2016
6d48759
fix conflict
zhengruifeng Nov 30, 2016
c172efc
fix glr
zhengruifeng Dec 30, 2016
00b91fd
fix style
zhengruifeng Dec 30, 2016
c8693d8
update
zhengruifeng Jan 5, 2017
e6b4615
Merge branch 'master' into lir_instr
zhengruifeng Jan 9, 2017
7f00d36
log quantileProbabilities.size
zhengruifeng Jan 13, 2017
c8188b0
del docConcentration
zhengruifeng Jan 13, 2017
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fix nits
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@zhengruifeng
zhengruifeng committed Jan 5, 2017
commit 2bc4e29813499a8643b30d14538ecf1431ff1f55
6 changes: 3 additions & 3 deletions mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala
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Original file line number Diff line number Diff line change
Expand Up @@ -91,7 +91,7 @@ class LinearRegression @Since("1.3.0") (@Since("1.3.0") override val uid: String
setDefault(regParam -> 0.0)

/**
* Set if we should fit the intercept
* Set if we should fit the intercept.
* Default is true.
*
* @group setParam
Expand Down Expand Up @@ -256,10 +256,10 @@ class LinearRegression @Since("1.3.0") (@Since("1.3.0") override val uid: String
val rawYStd = math.sqrt(ySummarizer.variance(0))
if (rawYStd == 0.0) {
if ($(fitIntercept) || yMean == 0.0) {
// If the rawYStd is zero and fitIntercept=true, then the intercept is yMean with
// If the rawYStd==0 and fitIntercept==true, then the intercept is yMean with
// zero coefficient; as a result, training is not needed.
// Also, if yMean==0 and rawYStd==0, all the coefficients are zero regardless of
// the fitIntercept
// the fitIntercept.
if (yMean == 0.0) {
logWarning(s"Mean and standard deviation of the label are zero, so the coefficients " +
s"and the intercept will all be zero; as a result, training is not needed.")
Expand Down