Any plan to support rms package linear models?

[wei-wu-nyc]

Hi,
Do you have plan to support the linear models that are included in rms package? More specifically, the features I am interested in are the support for transformation of restricted cubic spline fit, through rcs() function.

Thanks.

[vruusmann]

At a first glance, the rms::lrm() model type and the rms::rcs() function type both seem doable.

Could you provide a small R code example about what functionality do you exactly need? The example could be based on the Auto-MGP dataset (mpg ~ .).

[wei-wu-nyc]

Thanks for the quick reply. I will try to come up with an example when I get a chance and will let you know. Wei

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On Mar 3, 2017, at 2:47 PM, Villu Ruusmann ***@***.***> wrote: At a first glance, the rms::lrm() model type and the rms::rcs() function type both seem doable. Could you provide a small R code example about what functionality do you exactly need? The example could be based on the Auto-MGP <https://github.com/jpmml/jpmml-r/blob/master/src/test/resources/csv/Auto.csv> dataset (mpg ~ .). — You are receiving this because you authored the thread. Reply to this email directly, view it on GitHub <#15 (comment)>, or mute the thread <https://github.com/notifications/unsubscribe-auth/AJk3ICbCdO1O8_vFivwzsU4EEQAsTMxYks5riG5fgaJpZM4MSbrV>.

[wei-wu-nyc]

Hi Villu, Attached is a simple script using the mpg data. The models used are: 1. Simple linear model of mpg~weight 2. Default rcs fit on mpg~rcs(weight) 3. rcs fit specifying number of knots 4. rcs fit specifying knots nodes. Hope this helps. Let me know if I can be of further help. Wei

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On Mar 3, 2017, at 2:47 PM, Villu Ruusmann ***@***.***> wrote: At a first glance, the rms::lrm() model type and the rms::rcs() function type both seem doable. Could you provide a small R code example about what functionality do you exactly need? The example could be based on the Auto-MGP <https://github.com/jpmml/jpmml-r/blob/master/src/test/resources/csv/Auto.csv> dataset (mpg ~ .). — You are receiving this because you authored the thread. Reply to this email directly, view it on GitHub <#15 (comment)>, or mute the thread <https://github.com/notifications/unsubscribe-auth/AJk3ICbCdO1O8_vFivwzsU4EEQAsTMxYks5riG5fgaJpZM4MSbrV>.

[vruusmann]

@wei-wu-nyc Can't find your R script.

Perhaps GitHub "ate" it because of bad file extension? You could rename it to myscript.R.txt, and re-attach via GitHub web UI.

[wei-wu-nyc]

Here is the attachment. Just to be safe. I also copied the line below.

library(rms)

mpgdata=read.csv('Auto.csv')

model1=ols(mpg~weight,data=mpgdata)
model2=ols(mpg~rcs(weight), data=mpgdata)
model3=ols(mpg~rcs(weight, nk=5), data=mpgdata)
model4=ols(mpg~rcs(weight, knots=c(2000,2500,3000,3500,4000,4500,5000)), data=mpgdata)

plot(mpgdata$weight, mpgdata$mpg)
lines(mpgdata$weight, predict(model1, mpgdata), col='red')

mpgdata=mpgdata[order(mpgdata$weight),]
lines(mpgdata$weight, predict(model2, mpgdata), col='green')
lines(mpgdata$weight, predict(model3, mpgdata), col='blue')
lines(mpgdata$weight, predict(model4, mpgdata), col='yellow')

I don't know how the over-stoke got into the text. But I think you can see the text.

r2pmml_rcs_example.R.txt

[vruusmann]

@wei-wu-nyc Thanks for clarifying the "minimum viable product".

I hope to find time to work on this next week. GitHub should keep you notified about my progress as you've been auto-subscribed to this issue.

[wei-wu-nyc]

Thanks. Looking forward to testing it out. Currently, I have to save a rms model which results to a 300+MB RData file. I have to load that into R just to do predictions on new data.

One thing to note is that when I save a rms model file, all the parameters (the explicit or implicit parameters, like the automatically determined number of knots and the knot points etc.) are saved. I am not quite sure how pmml works exactly. I suggest, these parameters should be saved together in the generated pmml file.

[vruusmann]

Currently, I have to save a rms model which results to a 300+MB RData file.

R's lm model type and all its subtypes (such as lrm) include the full training dataset. It shouldn't affect the functionality of your model in any way if you simply nullify this attribute before RDS save. A typical lm model object shouldn't exceed 1 MB then.

I am not quite sure how pmml works exactly.

During the conversion to PMML, I need to determine which fields are passed through the rcs() function. Then I need to collect the corresponding knot counts and coefficients, and generate DerivedField elements that reproduce R's knot evaluation algorithm. Should be fully compliant with the PMML specification.

[wei-wu-nyc]

Thanks you for the tip on trimming the object.

Another thing to remind you about specifics on rms package is: In rms, the linear regression model is ols() and the lrm() model is the classification model "Logistic Regression Model", hence the name.

[vruusmann]

Spline interpolation is represented by mapping a value range to a function.

PMML 4.3 doesn't have a high-level transformation for "continuous" lookup tables. Something like that could be emulated using "if" functions, but it wouldn't be too human-friendly (as "if" functions would be very deeply nested).

I've opened a feature request with the PMML working group to discuss good long-term solutions:
http://mantis.dmg.org/view.php?id=176

My suggestion is to "relax" Discretize and MapValues transformations, so that they could compute the return value dynamically. Unfortunately, the PMML working group doesn't support this suggestion, and wants to come up with a completely new transformation.

So, we're going to have to wait for a couple of weeks to get a better understanding about which way to go.

[wei-wu-nyc]

Thanks.

[vruusmann]

@wei-wu-nyc What kind of PMML consumer software would you be using? If you intend to use something that is based on the JPMML-Evaluator library, then we could go on and "relax" Discretize and MapValues transformations for the time being.

In other words, would you be happy with a "proprietary extension" that will be available in early April?

[wei-wu-nyc]

@vruusmann To tell you the truth, I haven't explored much on what PMML software we are going to use. For my purpose, I would like to output the pmml file and later loaded it either into R (supposedly if I use r2pmml package, I will be using JPMML-Evaluator library?), or our production system, which is on spark thus mostly written in SCALA. So I assume it should be able the load java library JPMML-Evaluator? And the main goal is to be able to call the prediction function without loading the original fitted R model object and the output results from either platforms should be identical or very close given the same inputs. If JPMML-Evaluator library is available on both platforms, that will work for me.