[jira] [Comment Edited] (SPARK-19234) AFTSurvivalRegression chokes silently or with confusing errors when any labels are zero
[
https://issues.apache.org/jira/browse/SPARK-19234?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15830037#comment-15830037
]
Yanbo Liang edited comment on SPARK-19234 at 1/19/17 2:36 PM:
--
[~admackin] Nice catch. Just like [~srowen] said, the AFT survival model is
regression the log of failure time, so it's invalid when failure time is zero.
I think the correct fix should be throwing error for non-positive failure time.
Double check with R, it throw error for failure time with zero value:
{code}
data <- list(time = c(1.218, 0.0, 3.627, 0.273, 4.199), censor = c(1.0, 0.0,
0.0, 1.0, 0.0), a = c(1.56, 0.346, 1.38, 0.52, 0.795), b = c(-0.605, 2.158,
0.231, 1.151, -0.226))
model <- survreg(Surv(time, censor) ~ a + b, data)
Error in survreg(Surv(time, censor) ~ a + b, data) :
Invalid survival times for this distribution
{code}
was (Author: yanboliang):
[~admackin] Nice catch. Just like [~srowen] said, the AFT survival model is
regression the log of failure time, so it's invalid when failure time is zero.
I think the correct fix should be throwing error for non-positive failure time.
Double check with R, it throw error for zero failure time:
{code}
data <- list(time = c(1.218, 0.0, 3.627, 0.273, 4.199), censor = c(1.0, 0.0,
0.0, 1.0, 0.0), a = c(1.56, 0.346, 1.38, 0.52, 0.795), b = c(-0.605, 2.158,
0.231, 1.151, -0.226))
model <- survreg(Surv(time, censor) ~ a + b, data)
Error in survreg(Surv(time, censor) ~ a + b, data) :
Invalid survival times for this distribution
{code}
> AFTSurvivalRegression chokes silently or with confusing errors when any
> labels are zero
> ---
>
> Key: SPARK-19234
> URL: https://issues.apache.org/jira/browse/SPARK-19234
> Project: Spark
> Issue Type: Bug
> Components: ML
>Affects Versions: 2.1.0
> Environment: spark-shell or pyspark
>Reporter: Andrew MacKinlay
>Priority: Minor
> Attachments: spark-aft-failure.txt
>
>
> If you try and use AFTSurvivalRegression and any label in your input data is
> 0.0, you get coefficients of 0.0 returned, and in many cases, errors like
> this:
> {{17/01/16 15:10:50 ERROR StrongWolfeLineSearch: Encountered bad values in
> function evaluation. Decreasing step size to NaN}}
> Zero should, I think, be an allowed value for survival analysis. I don't know
> if this is a pathological case for AFT specifically as I don't know enough
> about it, but this behaviour is clearly undesirable. If you have any labels
> of 0.0, you get either a) obscure error messages, with no knowledge of the
> cause and coefficients which are all zero or b) no errors messages at all and
> coefficients of zero (arguably worse, since you don't even have console
> output to tell you something's gone awry). If AFT doesn't work with
> zero-valued labels, Spark should fail fast and let the developer know why. If
> it does, we should get results here.
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[jira] [Comment Edited] (SPARK-19234) AFTSurvivalRegression chokes silently or with confusing errors when any labels are zero
[
https://issues.apache.org/jira/browse/SPARK-19234?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15830037#comment-15830037
]
Yanbo Liang edited comment on SPARK-19234 at 1/19/17 2:33 PM:
--
[~admackin] Nice catch. Just like [~srowen] said, the AFT survival model is
regression the log of failure time, so it's invalid when failure time is zero.
I think the correct fix should be throwing error for non-positive failure time.
Double check with R, it throw error for zero failure time:
{code}
data <- list(time = c(1.218, 0.0, 3.627, 0.273, 4.199), censor = c(1.0, 0.0,
0.0, 1.0, 0.0), a = c(1.56, 0.346, 1.38, 0.52, 0.795), b = c(-0.605, 2.158,
0.231, 1.151, -0.226))
model <- survreg(Surv(time, censor) ~ a + b, data)
Error in survreg(Surv(time, censor) ~ a + b, data) :
Invalid survival times for this distribution
{code}
was (Author: yanboliang):
[~admackin] Nice catch. Just like [~srowen] side, the AFT survival model is
regression the log of failure time, so it's invalid when failure time is zero.
I think the correct fix should be throwing error for non-positive failure time.
Double check with R, it throw error for zero failure time:
{code}
data <- list(time = c(1.218, 0.0, 3.627, 0.273, 4.199), censor = c(1.0, 0.0,
0.0, 1.0, 0.0), a = c(1.56, 0.346, 1.38, 0.52, 0.795), b = c(-0.605, 2.158,
0.231, 1.151, -0.226))
model <- survreg(Surv(time, censor) ~ a + b, data)
Error in survreg(Surv(time, censor) ~ a + b, data) :
Invalid survival times for this distribution
{code}
> AFTSurvivalRegression chokes silently or with confusing errors when any
> labels are zero
> ---
>
> Key: SPARK-19234
> URL: https://issues.apache.org/jira/browse/SPARK-19234
> Project: Spark
> Issue Type: Bug
> Components: ML
>Affects Versions: 2.1.0
> Environment: spark-shell or pyspark
>Reporter: Andrew MacKinlay
>Priority: Minor
> Attachments: spark-aft-failure.txt
>
>
> If you try and use AFTSurvivalRegression and any label in your input data is
> 0.0, you get coefficients of 0.0 returned, and in many cases, errors like
> this:
> {{17/01/16 15:10:50 ERROR StrongWolfeLineSearch: Encountered bad values in
> function evaluation. Decreasing step size to NaN}}
> Zero should, I think, be an allowed value for survival analysis. I don't know
> if this is a pathological case for AFT specifically as I don't know enough
> about it, but this behaviour is clearly undesirable. If you have any labels
> of 0.0, you get either a) obscure error messages, with no knowledge of the
> cause and coefficients which are all zero or b) no errors messages at all and
> coefficients of zero (arguably worse, since you don't even have console
> output to tell you something's gone awry). If AFT doesn't work with
> zero-valued labels, Spark should fail fast and let the developer know why. If
> it does, we should get results here.
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[jira] [Comment Edited] (SPARK-19234) AFTSurvivalRegression chokes silently or with confusing errors when any labels are zero
[
https://issues.apache.org/jira/browse/SPARK-19234?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15827236#comment-15827236
]
Andrew MacKinlay edited comment on SPARK-19234 at 1/18/17 1:53 AM:
---
[~yanboliang] I presume that you are the author judging by Github commits? Do
you have an opinion on this?
was (Author: admackin):
[~yanbo] I presume that you are the author judging by Github commits? Do you
have an opinion on this?
> AFTSurvivalRegression chokes silently or with confusing errors when any
> labels are zero
> ---
>
> Key: SPARK-19234
> URL: https://issues.apache.org/jira/browse/SPARK-19234
> Project: Spark
> Issue Type: Bug
> Components: ML
>Affects Versions: 2.1.0
> Environment: spark-shell or pyspark
>Reporter: Andrew MacKinlay
>Priority: Minor
> Attachments: spark-aft-failure.txt
>
>
> If you try and use AFTSurvivalRegression and any label in your input data is
> 0.0, you get coefficients of 0.0 returned, and in many cases, errors like
> this:
> {{17/01/16 15:10:50 ERROR StrongWolfeLineSearch: Encountered bad values in
> function evaluation. Decreasing step size to NaN}}
> Zero should, I think, be an allowed value for survival analysis. I don't know
> if this is a pathological case for AFT specifically as I don't know enough
> about it, but this behaviour is clearly undesirable. If you have any labels
> of 0.0, you get either a) obscure error messages, with no knowledge of the
> cause and coefficients which are all zero or b) no errors messages at all and
> coefficients of zero (arguably worse, since you don't even have console
> output to tell you something's gone awry). If AFT doesn't work with
> zero-valued labels, Spark should fail fast and let the developer know why. If
> it does, we should get results here.
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