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'VariableArrayName' is not defined in all cases. It is only defined for (...) in
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I am continuing to build a simple Thurstonian model for learning to rank. The scores of two items to rank are Gaussian distributed. The means of these VectorGaussian RVs are Inner Product between a "w" parameters vector (inferred in training; fixed in prediction) and a respective features vector (given and fixed). The ranking stems from checking three cases when comparing two scores (draw, left > right, right < left) for two respective items in an example. An example is composed of a variable number of items. The dataset consists of a number of examples.
The training appears to be working as I can infer (1) the posterior for "w" which is a VectorGaussian and (2) a global, shared mean for all draw RVs (Gaussian; one per training example).
The model is written as follows:
using (Variable.ForEach(example)) { using (Variable.ForEach(item)) { var mean = Variable.InnerProduct(w, features[example][item]).Named("mean"); scores[example][item] = Variable.GaussianFromMeanAndVariance(mean, 1.0); } using (ForEachBlock pairBlock = Variable.ForEach(rank)) { var idx = pairBlock.Index; var left = scores[example][idx]; var right = scores[example][idx + 1]; using (Variable.If(Variable.IsBetween(left  right, drawMargin[example], drawMargin[example]))) ranks[example][rank] = 0; using (Variable.If(left > right + drawMargin[example])) ranks[example][rank] = 1; using (Variable.If(left < right + drawMargin[example])) ranks[example][rank] = 2; } }
The relevant model variables are defined as:
numExamples = Variable.New<int>(); example = new Range(numExamples).Named("exampleRange"); // // Jagged arrays for (items, features) // exampleSize = Variable.Array<int>(example).Named("itemSizes"); item = new Range(exampleSize[example]).Named("itemRange"); scores = Variable.Array(Variable.Array<double>(item), example).Named("scores"); features = Variable.Array(Variable.Array<Vector>(item), example).Named("features"); // // Jagged array for item pair ranks // rankSize = Variable.Array<int>(example); rank = new Range(rankSize[example]).Named("rankRange"); ranks = Variable.Array(Variable.Array<int>(rank), example).Named("pairwiseRanks"); // // Draw // drawMeanPrior = Variable.New<Gaussian>(); drawMean = Variable.Random<double, Gaussian>(drawMeanPrior); var drawMargin = Variable.Array<double>(example); using (Variable.ForEach(example)) { drawMargin[example] = Variable.GaussianFromMeanAndVariance(drawMean, 1.0).Named("drawMargin"); Variable.ConstrainPositive(drawMargin[example]); } // // Model parameters // wPrior = Variable.New<VectorGaussian>(); w = Variable.Random<Vector, VectorGaussian>(wPrior).Named("w");
At prediction stage, I observe all the variables pertaining array sizes, initial priors and feature vectors as in training. The difference this time is that (1) I observe the prior for "w" (it is trained and fixed), (2) observe draw mean prior and (3) don't observe "rank" as I want to infer it.
To infer pairwise rankings, I provide one single example that is composed of 6 feature vectors (6 items to rank). When I run the inference, I get the error:
'ranks' is not defined in all cases. It is only defined for (vbool7=true)(vbool8=true)(vbool9=true) in:
Intuitively I get that dependent on my pairwise rankings, not all "ranks" elements will be set. I am not sure how to fix this error though.
The full code is on GitHub:
https://github.com/usptact/Infer.NETLTR/tree/master/Infer.NETLTR
The data are in SVMLight format; I used LETOR MQ2007 and MQ2008 datasets for experiments and validation.
 Edited by usptact Monday, December 4, 2017 10:50 PM
Monday, December 4, 2017 10:41 PM
Answers

using (Variable.If(leftWins)) ranks[example][rank] = 1; using (Variable.IfNot(leftWins)) { using (Variable.If(rightWins)) ranks[example][rank] = 2; using(Variable.IfNot(rightWins)) ranks[example][rank] = 0; }
Tuesday, December 5, 2017 4:18 PMOwner
All replies

I searched the forum posts and found a couple of posts where it was suggested to create separate Variable<bool> variables that are used for branching. In my case I created the following three:
var isDraw = Variable.IsBetween(diff, drawMargin[example], drawMargin[example]); var leftWins = diff > drawMargin[example]; var rightWins = diff < drawMargin[example];
Then I noticed that instead of simply assigning a discrete value, people use <var>.SetTo(Variable.Constant(<x>). In my case I wrote:
using (Variable.If(isDraw)) ranks[example][idx].SetTo(Variable.Constant(0)); using (Variable.If(leftWins)) ranks[example][idx].SetTo(Variable.Constant(1)); using (Variable.If(rightWins)) ranks[example][idx].SetTo(Variable.Constant(2));
Unfortunately, I get the same error as before.
As I was trying to find the bug, I fixed the bug for observing the learned parameters "w" and "drawMargin". During prediction, these two are not inferred by the engine anymore.
Tuesday, December 5, 2017 9:54 AM 
Also tried to merge the two inner ForEach loops. Using the inner loop for the "item" range. Since the "ranks" array is one element smaller than scores, I put a branch on index (using (Variable.If(idx > 0))) around the former second block of model. This resulted in a weird schedule error. Now I am truly stuck with all options exhausted.Tuesday, December 5, 2017 10:30 AM

using (Variable.If(leftWins)) ranks[example][rank] = 1; using (Variable.IfNot(leftWins)) { using (Variable.If(rightWins)) ranks[example][rank] = 2; using(Variable.IfNot(rightWins)) ranks[example][rank] = 0; }
Tuesday, December 5, 2017 4:18 PMOwner 
Thank you, Tom! I missed this part!
After making the change, I now get a "model zero probability" error. What are the best ways to debug this issue?
My model now looks like this:
using (Variable.ForEach(example)) { using (ForEachBlock itemBlock = Variable.ForEach(item)) { var mean = Variable.InnerProduct(w, features[example][item]); scores[example][item] = Variable.GaussianFromMeanAndVariance(mean, 1.0); } using (ForEachBlock pairBlock = Variable.ForEach(rank)) { var idx = pairBlock.Index; var diff = scores[example][idx]  scores[example][idx + 1]; var isDraw = Variable.IsBetween(diff, drawMargin[example], drawMargin[example]); var leftWins = diff > drawMargin[example]; var rightWins = diff < drawMargin[example]; using (Variable.If(leftWins)) ranks[example][rank] = 1; using (Variable.IfNot(leftWins)) { using (Variable.If(rightWins)) ranks[example][rank] = 2; using (Variable.IfNot(rightWins)) ranks[example][rank] = 0; } } }
A quick search in forum Q&A shows that this may be due to some values outside the range. How can I check that which RVs get values outside the range?
UPDATE: I think I know where the issue it. The "diff" variable must include noise, e.g. have it a Gaussian RV with nonzero variance.
UPDATE 2: The problem with zero probability is elsewhere. I suspect something is wrong with getting the scores in three intervals. One of the branches is never (?) visited.
Thanks
 Edited by usptact Tuesday, December 5, 2017 7:52 PM
Tuesday, December 5, 2017 6:16 PM 
As FAQ, I switched from assigning point masses to "ranks" to slightly noisy version using SetTo() method.
var isDraw = Variable.IsBetween(diff, margin, margin); var leftWins = (diff > margin).Named("leftWins"); var rightWins = (diff < margin).Named("rightWins"); using (Variable.If(isDraw)) ranks[example][rank].SetTo(Variable.Discrete(new double[] { 0.99, 0.005, 0.005 })); using (Variable.IfNot(isDraw)) { using (Variable.If(rightWins)) ranks[example][rank].SetTo(Variable.Discrete(new double[] { 0.005, 0.005, 0.99 })); using (Variable.IfNot(rightWins)) ranks[example][rank].SetTo(Variable.Discrete(new double[] { 0.005, 0.99, 0.005 })); }
The prediction in my examples is always 0 (draw) which is incorrect but at least I get past this error.
Tuesday, December 5, 2017 8:02 PM 
When ranks is observed, this problem is typically avoided by branching on the value of ranks and imposing a constraint on diff, as opposed to branching on diff.Tuesday, December 5, 2017 8:24 PMOwner

Interesting! If I understand the constraints part correctly, you refer to Variable.ConstrainBetween(), Variable.ConstrainPositive() and Variable.ConstrainNegative(). I see that there is even a Variable.Constrain() that can accept a custom factor.
In prediction part (ranks are unobserved; w is observed), I guess I still need to do the branching based on "diff".
Tuesday, December 5, 2017 10:22 PM