[GitHub] spark pull request: [SPARK-13029][ml] fix a logistic regression is...

[asfgit]

Github user asfgit closed the pull request at:

https://github.com/apache/spark/pull/10940


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[GitHub] spark pull request: [SPARK-13029][ml] fix a logistic regression is...

[mengxr]

Github user mengxr commented on the pull request:

https://github.com/apache/spark/pull/10940#issuecomment-190593882
  
Had an offline discussion with @dbtsai and @coderxiang . We agreed to keep 
the current behavior and have it well documented. I will mark this JIRA as 
"won't" and created SPARK-13590 for documentation and logging improvement.

@coderxiang Do you mind closing this PR?


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[GitHub] spark pull request: [SPARK-13029][ml] fix a logistic regression is...

[mengxr]

Github user mengxr commented on the pull request:

https://github.com/apache/spark/pull/10940#issuecomment-190553302
  
@coderxiang @dbtsai Sorry for late response! I actually thought this PR 
already got merged ... Anyway, I tested `glmnet` and found that `glmnet` 
outputs zero coefficients for constant columns regardless of intercept, 
regularization, and standardization settings. I thought about it today and I 
feel it actually makes sense. If we have a constant column in our training 
data, do we expect it to change or stay constant in test data? If its value 
might change, we should set its coefficient to zero because we cannot estimate 
how big the change would be. If its value stays constant (or maybe users 
created this column to add bias manually), it shouldn't be regularized and 
users should really turn on `fitIntercept` instead. So my suggestion is to 
follow glmnet and set the coefficients of constant columns to zero regardless 
of other settings. If there are constant columns and `fitIntercept` is false. 
We should output a warning message. Does it sound good to you?


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[GitHub] spark pull request: [SPARK-13029][ml] fix a logistic regression is...

[dbtsai]

Github user dbtsai commented on the pull request:

https://github.com/apache/spark/pull/10940#issuecomment-176459204
  
@iyounus Ideally, it will be great that `intercept=true`, we keep the 
current behavior which is constant column doesn't have any predictive power. 


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[GitHub] spark pull request: [SPARK-13029][ml] fix a logistic regression is...

[iyounus]

Github user iyounus commented on the pull request:

https://github.com/apache/spark/pull/10940#issuecomment-176454056
  
@dbtsai Linear regression also has similar issues. There, "normal" and 
"l-gbfs" solvers treat this case differently (and incorrectly). The other 
problem there is that if intercept=true, then a constant feature column makes 
the gramian matrix singular and cholesky decomposition fails. Should I create 
separate jira for the case of constant feature?


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[GitHub] spark pull request: [SPARK-13029][ml] fix a logistic regression is...

[iyounus]

Github user iyounus commented on the pull request:

https://github.com/apache/spark/pull/10940#issuecomment-176481784
  
@dbtsai I agree that the constant feature doesn't have predictive power. 
But, the WeightedLeastSqures just throws an `AssertionError` in `lapack.dpotrs` 
(https://issues.apache.org/jira/browse/SPARK-11918), whereas the "l-bfgs" 
solver sets the coefficient to zero.



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[GitHub] spark pull request: [SPARK-13029][ml] fix a logistic regression is...

[dbtsai]

Github user dbtsai commented on the pull request:

https://github.com/apache/spark/pull/10940#issuecomment-176503318
  
@iyounus Maybe in this case, `WeightedLeastSqures ` should drop those 
columns so the model can be still trained.


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[GitHub] spark pull request: [SPARK-13029][ml] fix a logistic regression is...

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[GitHub] spark pull request: [SPARK-13029][ml] fix a logistic regression is...

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[GitHub] spark pull request: [SPARK-13029][ml] fix a logistic regression is...

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https://github.com/apache/spark/pull/10940#issuecomment-175911254
  
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[GitHub] spark pull request: [SPARK-13029][ml] fix a logistic regression is...

[SparkQA]

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[GitHub] spark pull request: [SPARK-13029][ml] fix a logistic regression is...

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[GitHub] spark pull request: [SPARK-13029][ml] fix a logistic regression is...

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[GitHub] spark pull request: [SPARK-13029][ml] fix a logistic regression is...

[coderxiang]

Github user coderxiang commented on the pull request:

https://github.com/apache/spark/pull/10940#issuecomment-175804541
  
@mengxr you mean do this locally? I was concerned this will create 
confusion since we are modifying the true value.


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[GitHub] spark pull request: [SPARK-13029][ml] fix a logistic regression is...

[mengxr]

Github user mengxr commented on the pull request:

https://github.com/apache/spark/pull/10940#issuecomment-175803323
  
@coderxiang Would the fix be cleaner if we set `featuresStd(i)` to `1.0` if 
it is `0.0`?


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[GitHub] spark pull request: [SPARK-13029][ml] fix a logistic regression is...

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https://github.com/apache/spark/pull/10940#issuecomment-175811350
  
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[GitHub] spark pull request: [SPARK-13029][ml] fix a logistic regression is...

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https://github.com/apache/spark/pull/10940#issuecomment-175811345
  
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[GitHub] spark pull request: [SPARK-13029][ml] fix a logistic regression is...

[coderxiang]

Github user coderxiang commented on a diff in the pull request:

https://github.com/apache/spark/pull/10940#discussion_r51033689
  
--- Diff: 
mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
 ---
@@ -971,8 +971,12 @@ private class LogisticAggregator(
   val margin = - {
 var sum = 0.0
 features.foreachActive { (index, value) =>
-  if (featuresStd(index) != 0.0 && value != 0.0) {
-sum += localCoefficientsArray(index) * (value / 
featuresStd(index))
+  if (value != 0.0) {
--- End diff --

Sure I'll remove, I was avoiding changing existing logic.


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[GitHub] spark pull request: [SPARK-13029][ml] fix a logistic regression is...

[SparkQA]

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https://github.com/apache/spark/pull/10940#issuecomment-175808760
  
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[GitHub] spark pull request: [SPARK-13029][ml] fix a logistic regression is...

[coderxiang]

Github user coderxiang commented on the pull request:

https://github.com/apache/spark/pull/10940#issuecomment-175811802
  
Jenkins, test this please.


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[GitHub] spark pull request: [SPARK-13029][ml] fix a logistic regression is...

[mengxr]

Github user mengxr commented on a diff in the pull request:

https://github.com/apache/spark/pull/10940#discussion_r51033447
  
--- Diff: 
mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
 ---
@@ -971,8 +971,12 @@ private class LogisticAggregator(
   val margin = - {
 var sum = 0.0
 features.foreachActive { (index, value) =>
-  if (featuresStd(index) != 0.0 && value != 0.0) {
-sum += localCoefficientsArray(index) * (value / 
featuresStd(index))
+  if (value != 0.0) {
--- End diff --

This `if` branch is not necessary.


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[GitHub] spark pull request: [SPARK-13029][ml] fix a logistic regression is...

[mengxr]

Github user mengxr commented on a diff in the pull request:

https://github.com/apache/spark/pull/10940#discussion_r51033453
  
--- Diff: 
mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
 ---
@@ -983,8 +987,12 @@ private class LogisticAggregator(
   val multiplier = weight * (1.0 / (1.0 + math.exp(margin)) - 
label)
 
   features.foreachActive { (index, value) =>
-if (featuresStd(index) != 0.0 && value != 0.0) {
-  localGradientSumArray(index) += multiplier * (value / 
featuresStd(index))
+if (value != 0.0) {
--- End diff --

same here


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[GitHub] spark pull request: [SPARK-13029][ml] fix a logistic regression is...

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[GitHub] spark pull request: [SPARK-13029][ml] fix a logistic regression is...

[mengxr]

Github user mengxr commented on a diff in the pull request:

https://github.com/apache/spark/pull/10940#discussion_r51049983
  
--- Diff: 
mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
 ---
@@ -971,8 +971,10 @@ private class LogisticAggregator(
   val margin = - {
 var sum = 0.0
 features.foreachActive { (index, value) =>
-  if (featuresStd(index) != 0.0 && value != 0.0) {
+  if (featuresStd(index) != 0.0) {
--- End diff --

The previous version is correct. Checking `value != 0.0` is much cheaper 
than computing `localCoefficientsArray(index)`.


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[GitHub] spark pull request: [SPARK-13029][ml] fix a logistic regression is...

[mengxr]

Github user mengxr commented on a diff in the pull request:

https://github.com/apache/spark/pull/10940#discussion_r51050297
  
--- Diff: 
mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala
 ---
@@ -607,6 +607,74 @@ class LogisticRegressionSuite
 assert(model2.coefficients ~= coefficientsR2 relTol 1E-2)
   }
 
+
+  test("an extra large example for review only") {
+val trainer1 = (new LogisticRegression).setFitIntercept(false)
+  .setElasticNetParam(0.0)
+  .setRegParam(1)
+  .setStandardization(false)
+  .setMaxIter(1000)
+  .setTol(1e-9)
+
+val binaryDatasetWithUniqueColumn = sqlContext.read
+  .format("libsvm")
+  .load("../data/mllib/sample_libsvm_data_with_unique_column.txt")
+
+val model1 = trainer1.fit(binaryDatasetWithUniqueColumn)
+
+val interceptR1 = 0.0
+val coefficientsR1 = Vectors.dense(0.0301002746509743, 
0.0906099616129797, 0.0954855492088332,
+  0.0243782420594917, 0.0174024017667667, -0.0006549273929309,
+  0.0637250665085166, -0.0589532651377124, 0.1383368129434264,
+  0.0665749825701113, 0.0799386779781182, 0.1198682685242071,
+  0.1802933312643371, -0.0124797701753129)
+
+assert(model1.intercept ~== interceptR1 absTol 1E-3)
+assert(model1.coefficients ~= coefficientsR1 relTol 1E-2)
+  }
+
+  test("binary logistic regression without intercept with L2 
regularizationon " +
+"data with unique column without intercept") {
+val trainer = (new LogisticRegression).setFitIntercept(false)
+  .setElasticNetParam(0.0)
+  .setRegParam(1)
+  .setStandardization(false)
+  .setMaxIter(1000)
+  .setTol(1e-9)
+
+val binaryDatasetWithUniqueColumn = sqlContext.createDataFrame(
+  sc.parallelize(
+Array(
+  LabeledPoint(label = 1.0, features = Vectors.dense(1, 1)),
+  LabeledPoint(label = 0.0, features = Vectors.dense(0, 1))
+)
+  )
+)
+
+val model = trainer.fit(binaryDatasetWithUniqueColumn)
+
+val interceptR = 0.0
+val coefficientsR = Vectors.dense(0.22478867, -0.02241016)
+
+assert(model.intercept ~== interceptR absTol 1E-3)
+assert(model.coefficients ~= coefficientsR relTol 1E-2)
+
+/*
+Use the following scikit-learn Python code to get a reference 
result:
--- End diff --

Please move the comments to the beginning of the test.


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[GitHub] spark pull request: [SPARK-13029][ml] fix a logistic regression is...

[mengxr]

Github user mengxr commented on a diff in the pull request:

https://github.com/apache/spark/pull/10940#discussion_r51050301
  
--- Diff: 
mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala
 ---
@@ -607,6 +607,74 @@ class LogisticRegressionSuite
 assert(model2.coefficients ~= coefficientsR2 relTol 1E-2)
   }
 
+
+  test("an extra large example for review only") {
+val trainer1 = (new LogisticRegression).setFitIntercept(false)
+  .setElasticNetParam(0.0)
+  .setRegParam(1)
+  .setStandardization(false)
+  .setMaxIter(1000)
+  .setTol(1e-9)
+
+val binaryDatasetWithUniqueColumn = sqlContext.read
+  .format("libsvm")
+  .load("../data/mllib/sample_libsvm_data_with_unique_column.txt")
+
+val model1 = trainer1.fit(binaryDatasetWithUniqueColumn)
+
+val interceptR1 = 0.0
+val coefficientsR1 = Vectors.dense(0.0301002746509743, 
0.0906099616129797, 0.0954855492088332,
+  0.0243782420594917, 0.0174024017667667, -0.0006549273929309,
+  0.0637250665085166, -0.0589532651377124, 0.1383368129434264,
+  0.0665749825701113, 0.0799386779781182, 0.1198682685242071,
+  0.1802933312643371, -0.0124797701753129)
+
+assert(model1.intercept ~== interceptR1 absTol 1E-3)
+assert(model1.coefficients ~= coefficientsR1 relTol 1E-2)
+  }
+
+  test("binary logistic regression without intercept with L2 
regularizationon " +
+"data with unique column without intercept") {
--- End diff --

`unique column` -> `a constant column`


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[GitHub] spark pull request: [SPARK-13029][ml] fix a logistic regression is...

[dbtsai]

Github user dbtsai commented on a diff in the pull request:

https://github.com/apache/spark/pull/10940#discussion_r51051112
  
--- Diff: 
mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
 ---
@@ -341,11 +341,11 @@ class LogisticRegression @Since("1.2.0") (
 regParamL1
   } else {
 // If `standardization` is false, we still standardize the 
data
-// to improve the rate of convergence; as a result, we 
have to
-// perform this reverse standardization by penalizing each 
component
-// differently to get effectively the same objective 
function when
+// to improve the rate of convergence unless the standard 
deviation is zero;
+// as a result, we have to perform this reverse 
standardization by penalizing
+// each component differently to get effectively the same 
objective function when
 // the training dataset is not standardized.
-if (featuresStd(index) != 0.0) regParamL1 / 
featuresStd(index) else 0.0
+if (featuresStd(index) != 0.0) regParamL1 / 
featuresStd(index) else regParamL1
--- End diff --

The constant `value` can be really large or very small negatively. The 
optimizer may not be able to converge well in this case. I don't prove or try 
it yet, but mathematically, with the following changes, this should be solving 
identical problem.

```scala
// the training dataset is not standardized.
if (featuresStd(index) != 0.0) regParamL1 / featuresStd(index) else 
regParamL1 / featuresMean(index)
```


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[GitHub] spark pull request: [SPARK-13029][ml] fix a logistic regression is...

[dbtsai]

Github user dbtsai commented on a diff in the pull request:

https://github.com/apache/spark/pull/10940#discussion_r51051223
  
--- Diff: 
mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
 ---
@@ -971,8 +971,10 @@ private class LogisticAggregator(
   val margin = - {
 var sum = 0.0
 features.foreachActive { (index, value) =>
-  if (featuresStd(index) != 0.0 && value != 0.0) {
+  if (featuresStd(index) != 0.0) {
 sum += localCoefficientsArray(index) * (value / 
featuresStd(index))
+  } else {
+sum += localCoefficientsArray(index) * value
--- End diff --

Change `value` into `1.0`


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[GitHub] spark pull request: [SPARK-13029][ml] fix a logistic regression is...

[dbtsai]

Github user dbtsai commented on a diff in the pull request:

https://github.com/apache/spark/pull/10940#discussion_r51051184
  
--- Diff: 
mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
 ---
@@ -417,7 +417,7 @@ class LogisticRegression @Since("1.2.0") (
 val rawCoefficients = state.x.toArray.clone()
 var i = 0
 while (i < numFeatures) {
-  rawCoefficients(i) *= { if (featuresStd(i) != 0.0) 1.0 / 
featuresStd(i) else 0.0 }
+  rawCoefficients(i) *= { if (featuresStd(i) != 0.0) 1.0 / 
featuresStd(i) else 1.0 }
--- End diff --

```scala
rawCoefficients(i) *= { if (featuresStd(i) != 0.0) 1.0 / featuresStd(i) 
else 1.0 / featuresMean(i) }
```


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[GitHub] spark pull request: [SPARK-13029][ml] fix a logistic regression is...

[dbtsai]

Github user dbtsai commented on a diff in the pull request:

https://github.com/apache/spark/pull/10940#discussion_r51051267
  
--- Diff: 
mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
 ---
@@ -1106,7 +1110,8 @@ private class LogisticCostFun(
 totalGradientArray(index) += regParamL2 * temp
 value * temp
   } else {
-0.0
+totalGradientArray(index) += regParamL2 * value
+value * value
--- End diff --

Change `value` into `1.0`


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[GitHub] spark pull request: [SPARK-13029][ml] fix a logistic regression is...

[dbtsai]

Github user dbtsai commented on a diff in the pull request:

https://github.com/apache/spark/pull/10940#discussion_r51051238
  
--- Diff: 
mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
 ---
@@ -983,8 +985,10 @@ private class LogisticAggregator(
   val multiplier = weight * (1.0 / (1.0 + math.exp(margin)) - 
label)
 
   features.foreachActive { (index, value) =>
-if (featuresStd(index) != 0.0 && value != 0.0) {
+if (featuresStd(index) != 0.0) {
   localGradientSumArray(index) += multiplier * (value / 
featuresStd(index))
+} else {
+  localGradientSumArray(index) += multiplier * value
--- End diff --

Change `value` into `1.0`


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[GitHub] spark pull request: [SPARK-13029][ml] fix a logistic regression is...

[coderxiang]

Github user coderxiang commented on a diff in the pull request:

https://github.com/apache/spark/pull/10940#discussion_r51052438
  
--- Diff: 
mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
 ---
@@ -341,11 +341,11 @@ class LogisticRegression @Since("1.2.0") (
 regParamL1
   } else {
 // If `standardization` is false, we still standardize the 
data
-// to improve the rate of convergence; as a result, we 
have to
-// perform this reverse standardization by penalizing each 
component
-// differently to get effectively the same objective 
function when
+// to improve the rate of convergence unless the standard 
deviation is zero;
+// as a result, we have to perform this reverse 
standardization by penalizing
+// each component differently to get effectively the same 
objective function when
 // the training dataset is not standardized.
-if (featuresStd(index) != 0.0) regParamL1 / 
featuresStd(index) else 0.0
+if (featuresStd(index) != 0.0) regParamL1 / 
featuresStd(index) else regParamL1
--- End diff --

Can you give an example that using `value` will fail to converge? 

I agree any non-zero number here can make the algorithm work, but should we 
select a particular number as the denominator, or let it be the original value? 
@mengxr what do you think?


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[GitHub] spark pull request: [SPARK-13029][ml] fix a logistic regression is...

[dbtsai]

Github user dbtsai commented on a diff in the pull request:

https://github.com/apache/spark/pull/10940#discussion_r51052743
  
--- Diff: 
mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
 ---
@@ -341,11 +341,11 @@ class LogisticRegression @Since("1.2.0") (
 regParamL1
   } else {
 // If `standardization` is false, we still standardize the 
data
-// to improve the rate of convergence; as a result, we 
have to
-// perform this reverse standardization by penalizing each 
component
-// differently to get effectively the same objective 
function when
+// to improve the rate of convergence unless the standard 
deviation is zero;
+// as a result, we have to perform this reverse 
standardization by penalizing
+// each component differently to get effectively the same 
objective function when
 // the training dataset is not standardized.
-if (featuresStd(index) != 0.0) regParamL1 / 
featuresStd(index) else 0.0
+if (featuresStd(index) != 0.0) regParamL1 / 
featuresStd(index) else regParamL1
--- End diff --

BTW, this technique is already used here by dividing the std of feature. 
Note that you need to make sure `value != 0`, and if `value == 0`, the 
coefficient of that feature will be zero. 


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[GitHub] spark pull request: [SPARK-13029][ml] fix a logistic regression is...

[coderxiang]

Github user coderxiang commented on the pull request:

https://github.com/apache/spark/pull/10940#issuecomment-175863915
  
Jenkins, test this please.


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[GitHub] spark pull request: [SPARK-13029][ml] fix a logistic regression is...

[AmplabJenkins]

Github user AmplabJenkins commented on the pull request:

https://github.com/apache/spark/pull/10940#issuecomment-175867624
  
Merged build finished. Test FAILed.


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[GitHub] spark pull request: [SPARK-13029][ml] fix a logistic regression is...

[dbtsai]

Github user dbtsai commented on the pull request:

https://github.com/apache/spark/pull/10940#issuecomment-175868432
  
Intuitively, this makes sense to me. Since when `setFitIntercept(false)`, 
those features with `std == 0` will act as the effect of intercept resulting 
non-zero coefficients. 

Can you add couple more tests as the following. Thanks.

First, adding two new datasets by zeroing out `binaryDataset` and making 
one column as non-zero constance. 

Matching the result against GLMNET like the rest of the tests when
1)  setFitIntercept(false), setStandardization(false) with/without 
regularization 
2)  setFitIntercept(false), setStandardization(true) with/without 
regularization
3)  setFitIntercept(true), setStandardization(false) with/without 
regularization
4)  setFitIntercept(true), setStandardization(true) with/without 
regularization

+cc @iyounus Linear Regression may have similar issue, if you have time, 
you may check it out. Thanks.


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[GitHub] spark pull request: [SPARK-13029][ml] fix a logistic regression is...

[dbtsai]

Github user dbtsai commented on a diff in the pull request:

https://github.com/apache/spark/pull/10940#discussion_r51054476
  
--- Diff: 
mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
 ---
@@ -341,11 +341,11 @@ class LogisticRegression @Since("1.2.0") (
 regParamL1
   } else {
 // If `standardization` is false, we still standardize the 
data
-// to improve the rate of convergence; as a result, we 
have to
-// perform this reverse standardization by penalizing each 
component
-// differently to get effectively the same objective 
function when
+// to improve the rate of convergence unless the standard 
deviation is zero;
+// as a result, we have to perform this reverse 
standardization by penalizing
+// each component differently to get effectively the same 
objective function when
 // the training dataset is not standardized.
-if (featuresStd(index) != 0.0) regParamL1 / 
featuresStd(index) else 0.0
+if (featuresStd(index) != 0.0) regParamL1 / 
featuresStd(index) else regParamL1
--- End diff --

@coderxiang You may try it out. When `value` is very small like `1e-6` as a 
constant comparing the rest of the features, the corresponding coefficient will 
be very large. In this case, the optimization on coefficients will be on 
different scales, and this often causes some convergence issue in line search. 

Similar argument can be made when the constant `value` is very large 
comparing the rest of the features. 

That's why we still standardize the features even users ask 
`standardization == false`.


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[GitHub] spark pull request: [SPARK-13029][ml] fix a logistic regression is...

[AmplabJenkins]

Github user AmplabJenkins commented on the pull request:

https://github.com/apache/spark/pull/10940#issuecomment-175867627
  
Test FAILed.
Refer to this link for build results (access rights to CI server needed): 
https://amplab.cs.berkeley.edu/jenkins//job/SparkPullRequestBuilder/50225/
Test FAILed.


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[GitHub] spark pull request: [SPARK-13029][ml] fix a logistic regression is...

[coderxiang]

Github user coderxiang commented on the pull request:

https://github.com/apache/spark/pull/10940#issuecomment-175870597
  
Jenkins, test this please.


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[GitHub] spark pull request: [SPARK-13029][ml] fix a logistic regression is...

[coderxiang]

Github user coderxiang commented on the pull request:

https://github.com/apache/spark/pull/10940#issuecomment-175870484
  
@dbtsai without regularization may let the objective being not 
strongly-convex and thus not guaranteeing the uniqueness of the solution. 


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[GitHub] spark pull request: [SPARK-13029][ml] fix a logistic regression is...

[SparkQA]

Github user SparkQA commented on the pull request:

https://github.com/apache/spark/pull/10940#issuecomment-175871497
  
**[Test build #50227 has 
started](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/50227/consoleFull)**
 for PR 10940 at commit 
[`914cffc`](https://github.com/apache/spark/commit/914cffc6f0a9e0d847f486916ff89941c55c63ce).


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[GitHub] spark pull request: [SPARK-13029][ml] fix a logistic regression is...

[dbtsai]

Github user dbtsai commented on the pull request:

https://github.com/apache/spark/pull/10940#issuecomment-175872406
  
@coderxiang I agree, without regularization, those features become 
collinear so the solution will not be unique. However, for those features with 
std != 0, the coefficients should be unique. Can you check them at least?


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Jenkins, test this please.


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https://github.com/apache/spark/pull/10940#discussion_r50957402
  
--- Diff: 
mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala
 ---
@@ -607,6 +607,73 @@ class LogisticRegressionSuite
 assert(model2.coefficients ~= coefficientsR2 relTol 1E-2)
   }
 
+
+  test("an extra large example for review only") {
--- End diff --

This test is for review only, to show an example on a larger data set. Will 
remove if merged.


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--- Diff: data/mllib/sample_libsvm_data_with_unique_column.txt ---
@@ -0,0 +1,270 @@
+1 1:0.708333 2:1 3:1 4:-0.320755 5:-0.105023 6:-1 7:1 8:-0.419847 9:-1 
10:-0.225806 12:1 13:-1 14:1
--- End diff --

This file is for review only, to show an example on a larger data set. Will 
remove if merged.


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[coderxiang]

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Jenkins, test this please.


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[GitHub] spark pull request: [SPARK-13029][ml] fix a logistic regression is...

[coderxiang]

GitHub user coderxiang opened a pull request:

https://github.com/apache/spark/pull/10940

[SPARK-13029][ml] fix a logistic regression issue when inputing data has a 
column with identical value

This is a bug that appears while fitting a Logistic Regression model with 
`.setStandardization(false)` and `setFitIntercept(false)`. If the data matrix 
has one column with identical value, the resulting model is not correct. 
Specifically, the special column will always get a weight of 0, due to the 
special check inside the code. However, the correct solution, which is unique 
for L2 logistic regression, usually has non-zero weight.

The fix is to update the special handing logic to make it compatible with 
columns with std=0.

You can merge this pull request into a Git repository by running:

$ git pull https://github.com/coderxiang/spark dev

Alternatively you can review and apply these changes as the patch at:

https://github.com/apache/spark/pull/10940.patch

To close this pull request, make a commit to your master/trunk branch
with (at least) the following in the commit message:

This closes #10940


commit 437a285bfa20d9431819bbd9e01faa2622893616
Author: Shuo Xiang 
Date:   2016-01-27T01:25:22Z

handle Logistic regression with column of unique value

commit 09e95a73392292914cedbe4b0bfc373420dc
Author: Shuo Xiang 
Date:   2016-01-27T01:25:38Z

handle Logistic regression with column of unique value




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Jenkins, retest please


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[GitHub] spark pull request: [SPARK-13029][ml] fix a logistic regression is...

[SparkQA]

Github user SparkQA commented on the pull request:

https://github.com/apache/spark/pull/10940#issuecomment-175423859
  
**[Test build #50168 has 
finished](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/50168/consoleFull)**
 for PR 10940 at commit 
[`09e95a7`](https://github.com/apache/spark/commit/09e95a73392292914cedbe4b0bfc373420dc).
 * This patch **fails PySpark unit tests**.
 * This patch merges cleanly.
 * This patch adds no public classes.


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[GitHub] spark pull request: [SPARK-13029][ml] fix a logistic regression is...

[coderxiang]

Github user coderxiang commented on the pull request:

https://github.com/apache/spark/pull/10940#issuecomment-175462272
  
Jenkins, retest please.


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