Better names to calibreated linear classification models by wschin · Pull Request #3034 · dotnet/machinelearning

wschin · 2019-03-20T16:05:35Z

Fix #3016 by renaming LogisticRegressionBinaryTrainer to LbfgsLogisticRegressionTrainer. Note that for multiclass case, we have LbfgsMaximumEntropyTrainer. In addition, as SDCA (aka dual coordinate descent methods) outperforms L-BFGS when training logistic regression models, we should not make L-BFGS looks like the default trainer of logistic regression model.

This link contains our the conclusion of those new names.

…on L-BFGS

codecov · 2019-03-20T16:42:23Z

Codecov Report

❗ No coverage uploaded for pull request base (master@5f9be36). Click here to learn what that means.
The diff coverage is 96.22%.

@@            Coverage Diff            @@
##             master    #3034   +/-   ##
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  Branches          ?    16178           
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  Misses            ?    35193           
  Partials          ?     4412

Flag	Coverage Δ
#Debug	`72.52% <96.22%> (?)`
#production	`68.16% <80%> (?)`
#test	`88.72% <100%> (?)`

Impacted Files	Coverage Δ
...LogisticRegression/MulticlassLogisticRegression.cs	`65.87% <ø> (ø)`
test/Microsoft.ML.Functional.Tests/Training.cs	`100% <100%> (ø)`
...est/Microsoft.ML.StaticPipelineTesting/Training.cs	`99.28% <100%> (ø)`
...Microsoft.ML.Tests/TrainerEstimators/LbfgsTests.cs	`98.02% <100%> (ø)`
...est/Microsoft.ML.Tests/FeatureContributionTests.cs	`98.55% <100%> (ø)`
...est/Microsoft.ML.Predictor.Tests/TestPredictors.cs	`63.8% <100%> (ø)`
test/Microsoft.ML.Tests/Scenarios/OvaTest.cs	`100% <100%> (ø)`
...soft.ML.Tests/PermutationFeatureImportanceTests.cs	`100% <100%> (ø)`
.../Standard/LogisticRegression/LogisticRegression.cs	`94.95% <100%> (ø)`
test/Microsoft.ML.Functional.Tests/Evaluation.cs	`100% <100%> (ø)`
... and 4 more

artidoro · 2019-03-20T16:45:04Z

src/Microsoft.ML.StaticPipe/LbfgsStatic.cs

    /// Binary Classification trainer estimators.
    /// </summary>
-    public static class LbfgsBinaryClassificationStaticExtensions
+    public static class LbfgsBinaryExtensions


LbfgsBinaryExtensions [](start = 24, length = 21)

I think these are just static extensions, if possible could you keep static in the name of the class? #Resolved

artidoro

yaeldekel · 2019-03-20T16:49:49Z

    [TlcModule.EntryPoint(Name = "Trainers.LogisticRegressionBinaryClassifier",

Should we update the name of the entry point as well? #WontFix

Refers to: src/Microsoft.ML.StandardTrainers/Standard/LogisticRegression/LogisticRegression.cs:407 in ba22e12. [](commit_id = ba22e12, deletion_comment = False)

wschin · 2019-03-20T16:58:46Z

    [TlcModule.EntryPoint(Name = "Trainers.LogisticRegressionBinaryClassifier",

No for BC.

In reply to: 474926238 [](ancestors = 474926238)

Refers to: src/Microsoft.ML.StandardTrainers/Standard/LogisticRegression/LogisticRegression.cs:407 in ba22e12. [](commit_id = ba22e12, deletion_comment = False)

Ivanidzo4ka · 2019-03-20T17:32:16Z

src/Microsoft.ML.StandardTrainers/Standard/LogisticRegression/LogisticRegression.cs

    /// <include file='doc.xml' path='doc/members/member[@name="LBFGS"]/*' />
    /// <include file='doc.xml' path='docs/members/example[@name="LogisticRegressionBinaryClassifier"]/*' />
-    public sealed partial class LogisticRegressionBinaryTrainer : LbfgsTrainerBase<LogisticRegressionBinaryTrainer.Options,
+    public sealed partial class LbfgsLogisticRegressionTrainer : LbfgsTrainerBase<LbfgsLogisticRegressionTrainer.Options,


LbfgsLogisticRegressionTrainer [](start = 32, length = 30)

Can you call it LbfgsBinaryTrainer? #Resolved

It looks fine if we consider this trainer and its SDCA counterpart (they all solve LR). However, it will break the consistency between LBFGS trainers. Please take a look at Iteration 3.
LBFGS trainer names:
LbfgsLogisticRegressionTrainer
LbfgsPoissonRegressionTrainer
LbfgsMaximumEntropyTrainer

Note that we can't drop model names (such as LogisticRegression) because we need PoissonRegression to warn users that PoissonRegression is not a common case.

In reply to: 267460936 [](ancestors = 267460936)

@sfilipi has comment on your issue, and I share it as well.
Putting word Regression without Binary in the trainer is confusing.
If I would saw name of this trainer I would assume it's part of regression task.
Which is not.

In reply to: 267475700 [](ancestors = 267475700,267460936)

yeah, given the target user base, IMO should do a one off for this one, and keep binary in the name.

(also this feels like a classic academic vs engineering solution of a problem :D )

Also, IMO not all details need to be in the name. IMO we don't need to distingush that this is Lbfgs logistic regression through renaming. It can be in the documentations. shorter names are better...

In reply to: 267477762 [](ancestors = 267477762,267475700,267460936)

It will make the API looks like mlContext.BinaryClassification.LbfgsBinary(...) so I switch to SDCA-style name LbfgsCalibrated.

In reply to: 267481155 [](ancestors = 267481155,267477762,267475700,267460936)

yeah, given the target user base, IMO should do a one off for this one, and keep binary in the name.

(also this feels like a classic academic vs engineering solution of a problem :D )

Also, IMO not all details need to be in the name. IMO we don't need to distingush that this is Lbfgs logistic regression through renaming. It can be in the documentations. shorter names are better...

There are several trainers returning the same model types. Only this one is called LogisticRegression and this one has been outperformed by other algorithms. Thus, I want to append Lbfgs to emphasize that this is not the only trainer for logistic regression.

In reply to: 267477762 [](ancestors = 267477762,267475700,267460936)

#Resolved

Ivanidzo4ka · 2019-03-20T18:11:18Z

src/Microsoft.ML.StandardTrainers/StandardTrainersCatalog.cs

        /// </format>
        /// </example>
-        public static LogisticRegressionBinaryTrainer LogisticRegression(this BinaryClassificationCatalog.BinaryClassificationTrainers catalog,
+        public static LbfgsLogisticRegressionTrainer LbfgsLogisticRegressio(this BinaryClassificationCatalog.BinaryClassificationTrainers catalog,


LbfgsLogisticRegressio [](start = 53, length = 22)

you miss n in the end.
And I don't think this new verb any better than previous one.
More importantly I don't understand why you even changing this? We had @agoswami issue and PR regarding names, where we spend quite a lot of effort in attempt to figure out names, why you change it out of blue now? #Resolved

I don't think my changes violate his rule.

In reply to: 267478532 [](ancestors = 267478532)

This PR makes me nervous so close to ship date. Let's get consensus in #3016 before we make the change. Right now, I feel that the changes are inconsistent and unclear.

In reply to: 267481247 [](ancestors = 267481247,267478532)

rogancarr · 2019-03-20T21:36:07Z

docs/samples/Microsoft.ML.Samples/Dynamic/LogisticRegression.cs

                .Append(ml.Transforms.Concatenate("Features", "TextFeatures", "age", "fnlwgt",
                    "education-num", "capital-gain", "capital-loss", "hours-per-week"))
-                .Append(ml.BinaryClassification.Trainers.LogisticRegression());
+                .Append(ml.BinaryClassification.Trainers.LbfgsCalibrated());


LbfgsCalibrated [](start = 57, length = 15)

I don't think we should call it this. LogisticRegression is fine but LbfgsCalibrated is too esoteric. #Resolved

Plus it's not a calibrated linear model. It's fitting the to the logistic loss function. #Resolved

As long as it returns CalibratedModelParametersBase<LinearBinaryModelParameters, PlattCalibrator, I feel LbfgsCalibrated is fine. SDCA is doing the same. #Resolved

Technically true, but wouldn't it be clearer from a user perspective to call it LogisticRegression? Everybody knows what that is. Behind the scenes, many packages solve LR with variants of L-BFGS (e.g. Spark), so I don't think it would be misleading to say that it's called LogisticRegression. We can put technical details like "Implemented with L-BFGS etc." in the docs.

Look at FastTree. We don't call that CalibratedLambdaMART ;)

In reply to: 267857743 [](ancestors = 267857743)

A problem here is that L-BFGS is not good for large-scale sparse data sets. SDCA in that case is usually much faster. If we call LBFGS-LogisticRegression just LogisticRegression, external users may view LBFGS as the default solver to all logistic regression problems. #Resolved

rogancarr · 2019-03-22T18:45:01Z

docs/samples/Microsoft.ML.Samples/Dynamic/LogisticRegression.cs

                .Append(ml.Transforms.Concatenate("Features", "TextFeatures", "age", "fnlwgt",
                    "education-num", "capital-gain", "capital-loss", "hours-per-week"))
-                .Append(ml.BinaryClassification.Trainers.LogisticRegression());
+                .Append(ml.BinaryClassification.Trainers.LbfgsLogisticRegression());


How do you feel about making the other calibrated linear trainers, like SDCA into XyzLogisticRegression(). #Resolved

We can always add that at a later time. #Resolved

rogancarr

abgoswam · 2019-03-22T21:53:54Z

Here is my understanding of how we arrived at naming convention of Trainers/ModelParameters

We had couple of issues TrainersName pattern (Discussion) #2762 and More trainer related naming alignment #2938 discussing this, summarized in the comments :
- TrainersName pattern (Discussion) #2762 (comment)
- More trainer related naming alignment #2938 (comment)
MulticlassLR was discussed, and finalized while fixing issue Make Multiclass Linear Trainers Typed Based on Output Model Types. #2976

This PR proposes to modify the existing APIs as follows :

Renames several MLContext names
- LogisticRegression to LbfgsLogisticRegression
- PoissonRegression to LbfgsPoissonRegression
Renames Class names
- LogisticRegressionBinaryTrainer to LbfgsLogisticRegressionTrainer

Why do we think adding prefix "Lbfgs" in the name is a better alternative than what we have currently ? In fact to me it seems having 'Lbfgs' as prefix might throw users off

users may miss even seeing LogisticRegression if it has Lbfgs prefix .
create more doubt in their minds . W can have LogisticRegression as the API name and use documentation to clarify what the API implements behind the scene. Doesn't seem right for API design to contain implementation details.

@eerhardt @sfilipi #Resolved

Ivanidzo4ka

Let me block it for now.
I don't think we have agreement among all of us regarding how positive this change is.

eerhardt · 2019-03-22T22:13:13Z

Why do we think adding prefix "Lbfgs" in the name is a better alternative than what we have currently ? In fact to me it seems having 'Lbfgs' as prefix might throw users off

I believe @wschin answered this in the top comment to the PR:

In addition, as SDCA (aka dual coordinate descent methods) outperforms L-BFGS when training logistic regression models, we should not make L-BFGS looks like the default trainer of logistic regression model.

It seems to me that is his exact intention here. Don't use this one when you want to do LR. Or at least, don't think this is the "good"/"default" one. We have a better one. #Resolved

rogancarr · 2019-03-22T23:05:07Z

I think that it would also make sense to convert the binary SDCA trainer to be called SdcaLogisticRegression if we make this change to Lbfgs. That way, they both get the "LR Brand Name". #Resolved

abgoswam · 2019-03-22T23:21:08Z

In addition, as SDCA (aka dual coordinate descent methods) outperforms L-BFGS when training logistic regression models, we should not make L-BFGS looks like the default trainer of logistic regression model.

The PR proposes adding "LbfgsLogisticRegression" in API name itself to make it concrete that this particular API uses "Lbfgs" as the optimization algorithm. Is that the correct approach ?
Are we sure we are using "LBFGS" as the optimizing algorithm ? The internal repo refers to this paper, which leads me to believe that the optimization algo that ML.NET uses is not LBFGS itself, but rather one of its variants "OWN-QN". All the more reason to not include Lbfgs in the name of the API itself
Scikit-learn solves this by adding parameter called "solver"

solver : str, {‘newton-cg’, ‘lbfgs’, ‘liblinear’, ‘sag’, ‘saga’}, default: ‘liblinear’.
Algorithm to use in the optimization problem. #Resolved

wschin · 2019-03-23T03:13:07Z

In addition, as SDCA (aka dual coordinate descent methods) outperforms L-BFGS when training logistic regression models, we should not make L-BFGS looks like the default trainer of logistic regression model.
* The PR  proposes adding "LbfgsLogisticRegression" in API name itself to make it  concrete  that this  particular  API  uses  "Lbfgs" as the optimization algorithm.  Is that the correct approach ?

LBFGS can mean LBFGS family algorithms.

* Are we sure we are using "LBFGS" as the optimizing algorithm ?  The internal repo  refers to this [paper](https://www.microsoft.com/en-us/research/publication/scalable-training-of-l1-regularized-log-linear-models/?from=http%3A%2F%2Fresearch.microsoft.com%2Fapps%2Fpubs%2Fdefault.aspx%3Fid%3D78900),  which leads me to believe that the optimization algo that ML.NET uses is  not LBFGS itself, but rather  one of its variants "OWN-QN". All the more reason to _not_ include Lbfgs in the name of the API itself

It's a variant of LBFGS, as stated in your link.

* Scikit-learn [solves this](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) by adding parameter  called "solver"
solver : str, {‘newton-cg’, ‘lbfgs’, ‘liblinear’, ‘sag’, ‘saga’}, default: ‘liblinear’.
Algorithm to use in the optimization problem.

No. It's not necessary. L1-norm --> a variant of LBFGS, No L1-norm --> LBFGS.

#Resolved

…into sync-lbfgs-lr-me

abgoswam · 2019-03-25T16:47:24Z

Couple of notes after discussing with @wschin

Since ML.NET is type-safe, we cannot really follow the same paradigm as Scikit-Learn's paradigm in the way it allows for specifying the optimization algorithm using the solver parameter.
We can take inspiration from Spark.ML to finilize the API
- Spark.ML package exposes just 1 API
  - LogisticRegression and clarifies in documentation it uses LBFGS / OWLQN
- Spark.Mllib package exposes 2 APIs
  - LogisticRegressionWithLBFGS
  - LogisticRegressionWithSGD
SDCA has other subtelities to it e.g. supports SVM loss, calibration/non-calibration etc. We can keep the SDCA API as is. Note that the current calibrated SDCA doesn't allow user to choose another loss function, so it always produces a logistic regression model. #Resolved

revoking review

abgoswam

Remove empty line to trigger build

revoking review

rogancarr · 2019-03-25T21:29:32Z

src/Microsoft.ML.Mkl.Components/SymSgdClassificationTrainer.cs

 using Microsoft.ML.Transforms;

-[assembly: LoadableClass(typeof(SymbolicSgdTrainer), typeof(SymbolicSgdTrainer.Options),
+[assembly: LoadableClass(typeof(SymbolicSgdLogisticRegressionBinaryTrainer), typeof(SymbolicSgdLogisticRegressionBinaryTrainer.Options),


SymbolicSgdLogisticRegressionBinaryTrainer [](start = 32, length = 42)

LogisticRegressionBinaryTrainer => LbfgsLogisticRegressionTrainer. Shall SymbolicSgdBinaryTrainer => SymbolicSgdLogisticRegressionTrainer instead of SymbolicSgdLogisticRegressionBinaryTrainer? Right now, the namings don't line up. #Resolved

@rogancarr..could you cross-check ? To me the Class names do seem to match up fine, with both having the word Binary in the name

LbfgsLogisticRegressionBinaryTrainer

SymbolicSgdLogisticRegressionBinaryTrainer

In reply to: 268859710 [](ancestors = 268859710)

Yeah. Our conclusion was having LR and Binary for trainer classes and only LR for APIs.

In reply to: 268865148 [](ancestors = 268865148,268859710)

Got it. I was mixing up catalogs & classes. #Resolved

rogancarr · 2019-03-25T21:30:11Z

src/Microsoft.ML.StandardTrainers/StandardTrainersCatalog.cs


        /// <summary>
-        /// Predict a target using a linear classification model trained with <see cref="SdcaCalibratedBinaryTrainer"/>.
+        /// Predict a target using a linear classification model trained with <see cref="SdcaLogisticRegressionBinaryTrainer"/>.


SdcaLogisticRegressionBinaryTrainer [](start = 89, length = 35)

Similar to SymSgd, now Sdca has LogisticRegression and Binary. #Resolved

Yes, adding both of LogisticRegression and Binary to trainer classes was our decision. For API, we only append LogisticRegression because Binary naturally comes from their context.

In reply to: 268859905 [](ancestors = 268859905)

Sync names in logistic regression and maximum entropy trainers based …

ba22e12

…on L-BFGS

wschin added the API Issues pertaining the friendly API label Mar 20, 2019

wschin self-assigned this Mar 20, 2019

wschin requested review from artidoro, sfilipi and shmoradims March 20, 2019 16:05

artidoro reviewed Mar 20, 2019

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Add keyword Static

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Rename L-BFGS family members

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wschin added 2 commits March 20, 2019 11:18

Add missing n back

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Use SDCA-style name for LBFGS LR

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wschin added 4 commits March 21, 2019 22:36

Propose Lbfgs Logistic Regression Again

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Merge branch 'master' into sync-lbfgs-lr-me

32a5e60

Update EP

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Fix libmf

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rogancarr reviewed Mar 22, 2019

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rogancarr previously approved these changes Mar 22, 2019

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wschin requested a review from abgoswam March 22, 2019 20:57

Ivanidzo4ka self-requested a review March 22, 2019 22:05

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wschin added 2 commits March 25, 2019 08:38

Merge branch 'master' into sync-lbfgs-lr-me

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Merge branch 'sync-lbfgs-lr-me' of github.com:wschin/machinelearning …

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…into sync-lbfgs-lr-me

wschin added 2 commits March 25, 2019 13:40

Implement conclusions

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Update baseline because of class renaming

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Merge branch 'master' into sync-lbfgs-lr-me

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Fix newly-added file

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wschin force-pushed the sync-lbfgs-lr-me branch from 1a8d2da to 8af0d21 Compare March 25, 2019 21:19

wschin changed the title ~~Rename LogisticRegressionBinaryTrainer to LbfgsLogisticRegressionTrainer~~ Better names to calibreated linear classification models Mar 25, 2019

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wschin added 2 commits March 25, 2019 15:36

More new files..

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More and more new changes..

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wschin merged commit 8730c87 into dotnet:master Mar 25, 2019

wschin deleted the sync-lbfgs-lr-me branch March 25, 2019 23:47

zeahmed mentioned this pull request Mar 29, 2019

Updating .tt file due to renaming of LbfgsPoissonRegression. #3140

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