Add tabular model #124

manikyabard · 2021-06-13T17:43:57Z

Adds a tabular model based on the python fastai implementation.

manikyabard · 2021-06-13T17:44:33Z

The embedding layer used here is based on FluxML/Flux.jl#1516.

lorenzoh · 2021-06-15T07:26:22Z

src/models/tabularmodel.jl

+		x = tm.emb_drop(x)
+	end
+	if tm.n_cont != 0
+		if (tm.bn_cont != false)


Equivalent to if tm.bn_cont == true which is equivalent to if tm.bn_cont.

Suggested change

if (tm.bn_cont != false)

if tm.bn_cont

ToucheSir · 2021-06-15T17:07:49Z

src/models/tabularmodel.jl

+	actns = append!([], [act_cls for i in 1:(length(sizes)-1)], [nothing])
+	_layers = [linbndrop(Int64(sizes[i]), Int64(sizes[i+1]), use_bn=(use_bn && ((i!=(length(actns)-1)) || bn_final)), p=p, act=a, lin_first=lin_first) for (i, (p, a)) in enumerate(zip(push!(ps, 0.), actns))]
+	if !isnothing(y_range)
+		push!(_layers, Chain(@. x->Flux.sigmoid(x) * (y_range[2] - y_range[1]) + y_range[1]))


This doesn't need to be wrapped in a Chain. Also, is the sigmoid range meant to replace the activation of the last linear layer or to run after it?

In the case of y_range == nothing, should it be sigmoid? Right no there is no activation at all.

If it should be sigmoid, then I think it makes more sense to force y_range::Tuple and make the default as (0, 1). Just avoid nothing entirely.

No, I don't think there is an activation if y_range == nothing according to the python implementation.

ToucheSir · 2021-06-15T17:11:30Z

src/models/tabularmodel.jl

+		x = [e(x_cat[i, :]) for (i, e) in enumerate(tm.embeds)]
+		x = vcat(x...)
+		x = tm.emb_drop(x)


I wonder if there's a way to use Parallel for tm.embeds instead of writing all this out manually?

You could do Parallel(vcat, [Embedding(ni, nf) for (ni, nf) in emb_szs]). In the Chain, before the embedding layers, you would need x -> ntuple(i -> x[i, :], length(emb_szs)) to split a single x into multiple arguments.

I think splatting eachslice(x, dims=1) would work too.

Yeah I considered using Parallel first but just wasn't sure how to handle the input for that. This should simplify things a lot.

ToucheSir · 2021-06-15T17:13:16Z

src/models/tabularmodel.jl

+	n_emb = sum(size(embedlayer.weight)[1] for embedlayer in embedslist)
+	sizes = append!(zeros(0), [n_emb+n_cont], layers, [out_sz])
+	actns = append!([], [act_cls for i in 1:(length(sizes)-1)], [nothing])
+	_layers = [linbndrop(Int64(sizes[i]), Int64(sizes[i+1]), use_bn=(use_bn && ((i!=(length(actns)-1)) || bn_final)), p=p, act=a, lin_first=lin_first) for (i, (p, a)) in enumerate(zip(push!(ps, 0.), actns))]


this could benefit from splitting into a few lines. Also, do you recall why the Int64 cast was added?

I would echo this suggestion. In particular, instead of having complicated use_bn logic, just use two lines. First, iterate over the 1:(n - 1) elements, then just do push!(_layers, # last element version).

Also, I think this will be cleaner as a standard for-loop instead of a generator. Instead of pre-computing sizes, could we use Flux.outputsize inside the loop?

I think the Int64 cast was added because BatchNorm and Dense don't seem to work with floats. I could maybe restrict the types to Int64 only though.

Yeah, I can create the last linbndrop outside the loop. Although I'm not sure how the sizes for Flux.outputsize would work with the Parallel layer before it. Would it be better than using sizes?

darsnack

This is a good start, but I think we can improve the readability on top of the original Python code a lot here. In particular, I think there are two places where Parallel will intuitively express the operation and eliminate a lot of boilerplate both in the constructor and the forward pass.

darsnack · 2021-06-15T18:27:16Z

src/models/tabularmodel.jl

+		emb_szs,
+		n_cont,
+		out_sz,
+		ps::Union{Tuple, Vector, Number, Nothing}=nothing,


I would recommend pushing the isnothing etc. logic to linbndrop (it already handles the 0 case). It can be extended to handle nothing and false too.

Okay. I'm even considering removing nothing as it won't work directly with Iterators.cycle if we do use that for ps.

darsnack · 2021-06-15T18:28:50Z

src/models/tabularmodel.jl

+	if isnothing(ps)
+		ps = zeros(length(layers))
+	end
+	if ps isa Number
+		ps = fill(ps, length(layers))
+	end


In addition to the comment about isnothing(p) being part of linbndrop, this can also be deleted by doing Iterators.cycle(ps) when building _layers.

Yeah I'll try doing this.

darsnack · 2021-06-15T18:30:41Z

src/models/tabularmodel.jl

+	end
+	embedslist = [Embedding(ni, nf) for (ni, nf) in emb_szs]
+	emb_drop = Dropout(embed_p)
+	bn_cont = bn_cont ? BatchNorm(n_cont) : false


Could make this bn_cont = bn_cont ? BatchNorm(n_cont) : identity then avoid the if/else logic in the forward pass.

darsnack · 2021-06-15T18:37:35Z

src/models/tabularmodel.jl

+	actns = append!([], [act_cls for i in 1:(length(sizes)-1)], [nothing])
+	_layers = [linbndrop(Int64(sizes[i]), Int64(sizes[i+1]), use_bn=(use_bn && ((i!=(length(actns)-1)) || bn_final)), p=p, act=a, lin_first=lin_first) for (i, (p, a)) in enumerate(zip(push!(ps, 0.), actns))]
+	if !isnothing(y_range)
+		push!(_layers, Chain(@. x->Flux.sigmoid(x) * (y_range[2] - y_range[1]) + y_range[1]))


In the case of y_range == nothing, should it be sigmoid? Right no there is no activation at all.

darsnack · 2021-06-15T18:38:21Z

src/models/tabularmodel.jl

+	bn_cont = bn_cont ? BatchNorm(n_cont) : false
+	n_emb = sum(size(embedlayer.weight)[1] for embedlayer in embedslist)
+	sizes = append!(zeros(0), [n_emb+n_cont], layers, [out_sz])
+	actns = append!([], [act_cls for i in 1:(length(sizes)-1)], [nothing])


This can be avoided if you split the final layer into a separate line.

darsnack · 2021-06-15T18:50:33Z

src/models/tabularmodel.jl

+	if tm.n_emb != 0
+		x = [e(x_cat[i, :]) for (i, e) in enumerate(tm.embeds)]
+		x = vcat(x...)
+		x = tm.emb_drop(x)
+	end
+	if tm.n_cont != 0
+		if (tm.bn_cont != false)
+			x_cont = tm.bn_cont(x_cont)
+		end
+		x = tm.n_emb!=0 ? vcat(x, x_cont) : x_cont
+	end


I would suggest making the whole categorical branch/continuous branch portion of the model into a Parallel. Something like Parallel(vcat, embeds, bn_cont). It should gracefully handle the cases when either the categorical or continuous vectors are empty.

darsnack

from call

src/models/tabularmodel.jl

darsnack · 2021-06-18T16:25:26Z

src/models/tabularmodel.jl

+	_layers = []
+	for (i, (p, a)) in enumerate(zip(Iterators.cycle(ps), actns))
+		layer = linbndrop(Int64(sizes[i]), Int64(sizes[i+1]), use_bn=use_bn, p=p, act=a, lin_first=lin_first)
+		push!(_layers, layer)
+	end
+	push!(_layers, linbndrop(Int64(last(sizes)), Int64(out_sz), use_bn=bn_final, lin_first=lin_first))


Suggested change

_layers = []

for (i, (p, a)) in enumerate(zip(Iterators.cycle(ps), actns))

layer = linbndrop(Int64(sizes[i]), Int64(sizes[i+1]), use_bn=use_bn, p=p, act=a, lin_first=lin_first)

push!(_layers, layer)

end

push!(_layers, linbndrop(Int64(last(sizes)), Int64(out_sz), use_bn=bn_final, lin_first=lin_first))

n_emb = first(Flux.outputsize(embeds, (length(emb_szs), 1)))

classifiers = [

linbndrop(n_cat, first(layers); use_bn=use_bn, p=first(ps), act=act_cls, lin_first=lin_first),

[linbndrop(isize, osize; use_bn=use_bn, p=p, act=act_cls, lin_first=lin_first)

for (isize, osize, p) in zip(layers[1:(end - 2)], layers[2:(end - 1)], Iterators.cycle(Base.tail(ps)))]...,

linbndrop(last(layers)), out_sz; use_bn=bn_final, lin_first=lin_first)

]

I would go so far as to convert the comprehension into a plain for loop with push!. Not any less efficient than the splat and would help with line lengths.

Yeah maybe that's better here since we are using so many iterables in the comprehension

Seeing that Flux.outputsize isn't working with Embedding layers, I was thinking of keeping the current way of calculating n_emb for now.

lorenzoh · 2021-06-19T11:55:15Z

src/models/tabularmodel.jl

@@ -0,0 +1,52 @@
+function emb_sz_rule(n_cat)


Can we add a comment with a link to where this is taken from?

Sure I can add this link. I believe they got this formula experimentally.

Can you add it?

darsnack

Looking really good. Left a few comments.

darsnack · 2021-06-19T23:13:08Z

src/models/tabularmodel.jl

+
+    bn_cont = bn_cont ? BatchNorm(n_cont) : identity
+
+    n_emb = sum(size(embedlayer.weight)[1] for embedlayer in embedslist)


This would be clearer with Flux.outputsize

@manikyabard can you paste the error you got with Embedding and outputsize?

Sure, so if I do something like this

Flux.outputsize(Embedding(2, 3), (2,1))

the error I get is

ArgumentError: unable to check bounds for indices of type Flux.NilNumber.Nil Stacktrace: [1] checkindex(#unused#::Type{Bool}, inds::Base.OneTo{Int64}, i::Flux.NilNumber.Nil) @ Base ./abstractarray.jl:671 [2] checkindex @ ./abstractarray.jl:686 [inlined] [3] checkbounds_indices (repeats 2 times) @ ./abstractarray.jl:642 [inlined] [4] checkbounds @ ./abstractarray.jl:595 [inlined] [5] checkbounds @ ./abstractarray.jl:616 [inlined] [6] _getindex @ ./multidimensional.jl:831 [inlined] [7] getindex @ ./abstractarray.jl:1170 [inlined] [8] Embedding @ ~/.julia/packages/Flux/wii6E/src/layers/basic.jl:421 [inlined] [9] (::Embedding{Matrix{Float32}})(x::Matrix{Flux.NilNumber.Nil}) @ Flux ~/.julia/packages/Flux/wii6E/src/layers/basic.jl:422 [10] #outputsize#279 @ ~/.julia/packages/Flux/wii6E/src/outputsize.jl:93 [inlined] [11] outputsize(m::Embedding{Matrix{Float32}}, inputsizes::Tuple{Int64, Int64}) @ Flux ~/.julia/packages/Flux/wii6E/src/outputsize.jl:92 [12] top-level scope @ In[213]:1 [13] eval @ ./boot.jl:360 [inlined] [14] include_string(mapexpr::typeof(REPL.softscope), mod::Module, code::String, filename::String) @ Base ./loading.jl:1094

src/models/tabularmodel.jl

darsnack · 2021-06-19T23:16:23Z

src/models/tabularmodel.jl

+    end
+    push!(classifiers, linbndrop(last(layers), out_sz; use_bn=bn_final, lin_first=lin_first))
+
+    layers = isnothing(y_range) ? Chain(Parallel(vcat, embeds, bn_cont), classifiers...) : Chain(Parallel(vcat, embeds, bn_cont), classifiers..., @. x->Flux.sigmoid(x) * (y_range[2] - y_range[1]) + y_range[1])


Let's switch this to something like final_activation = identity as a kwarg.

Okay, then I can make a sigmoidrange function and remove the y_range part. If users want to use this then something like x -> sigmoidrange(x, 4, 6) (for getting values between 4 and 6) should work.

Yup, that's what I was thinking too

darsnack

Just some comments from the call. Also we want to refactor as TabularModel(EmbeddingBackbone, ContinuousBackbone, layers) like we discussed.

darsnack · 2021-07-02T15:26:43Z

src/models/tabularmodel.jl

+        x -> collect(eachrow(x)), 
+        x -> ntuple(i -> x[i], length(x)), 


Suggested change

x -> collect(eachrow(x)),

x -> ntuple(i -> x[i], length(x)),

x -> tuple(eachrow(x)...),

darsnack · 2021-07-02T16:01:44Z

src/models/tabularmodel.jl

+    end
+    push!(classifiers, linbndrop(last(layers), out_sz; use_bn=bn_final, lin_first=lin_first))
+    layers = Chain(
+        x -> tuple(x...),


Suggested change

x -> tuple(x...),

darsnack · 2021-07-02T16:02:03Z

src/models/tabularmodel.jl

+    push!(classifiers, linbndrop(last(layers), out_sz; use_bn=bn_final, lin_first=lin_first))
+    layers = Chain(
+        x -> tuple(x...),
+        Parallel(vcat, embeds, Chain(x -> ndims(x)==1 ? Flux.unsqueeze(x, 2) : x, bn_cont)),


Move the unsqueeze to the encode method

lorenzoh · 2021-07-12T14:52:25Z

Great to have tests! I think there're just some imports missing which is why they're failing

manikyabard · 2021-07-12T17:23:48Z

Great to have tests! I think there're just some imports missing which is why they're failing

Yeah I think it's probably because the Embedding PRs aren't merged yet.

lorenzoh · 2021-08-10T14:11:47Z

Tests are still failing because Embedding PR is not released I think

Co-authored-by: Kyle Daruwalla <daruwalla.k.public@icloud.com>

darsnack

Let's maybe knock out the final restructuring of this on the next call. I think it's possible to eliminate a lot of these keywords while maintaining flexibility (or at least restructure them so that it is clearer what each keyword does).

darsnack · 2021-08-12T16:10:29Z

src/models/tabularmodel.jl

+function classifierbackbone(
+        layers;
+        ps=0,
+        use_bn=true,
+        bn_final=false,
+        act_cls=Flux.relu,
+        lin_first=true)
+    ps = Iterators.cycle(ps)
+    classifiers = []
+
+    for (isize, osize, p) in zip(layers[1:(end-1)], layers[2:end], ps)
+        layer = linbndrop(isize, osize; use_bn=use_bn, p=p, act=act_cls, lin_first=lin_first)
+        push!(classifiers, layer)
+    end
+    Chain(classifiers...)
+end


I wasn't thinking that the linear chain should be customizable. I think this loop should get pushed into TabularModel. My suggestion was that bn_final and final_activation be lumped into a single classifier argument to TabularModel which is positional and defaults to Dense(in, out).

darsnack · 2021-08-12T16:12:17Z

src/models/tabularmodel.jl

+function continuousbackbone(n_cont)
+    n_cont > 0 ? BatchNorm(n_cont) : identity
+end


I don't know how useful it is to have this function.

darsnack · 2021-08-12T16:14:35Z

src/models/tabularmodel.jl

+        n_cont::Number,
+        out_sz::Number,
+        layers=[200, 100];
+        catdict,


I have some questions about understanding catdict that we can discuss during the call.

lorenzoh · 2021-08-15T14:20:25Z

src/models/tabularmodel.jl

@@ -0,0 +1,52 @@
+function emb_sz_rule(n_cat)


Can you add it?

src/models/tabularmodel.jl

darsnack

Mostly minor name changes to make things more readable. Only major change is to collapse catcols and cardinalitydict into a single argument. We want to optimize the API to be simple for the common case.

darsnack · 2021-08-15T15:53:00Z

src/models/tabularmodel.jl

+- `use_bn`: Boolean variable which controls whether to use batch normalization in the classifier.
+- `act_cls`: The activation function to use in the classifier layers.
+- `lin_first`: Controls if the linear layer comes before or after BatchNorm and Dropout.
+- `cardinalitydict`: An indexable collection which maps to the cardinality for each column present


Couldn't this be a vector of the cardinalities directly? Instead of having catcols be column names and cardinalitydict be a map from name => cardinality, just make catcols a vector of cardinalities.

I also think that cardinality can be confusing lingo for non-ML oriented users. Something like "vector of sizes (number of labels) for each categorical column."

src/models/tabularmodel.jl

darsnack · 2021-08-15T15:57:49Z

src/models/tabularmodel.jl

+        catcols,
+        n_cont::Number,
+        outsz::Number,
+        layers=[200, 100];


Use a tuple

src/models/tabularmodel.jl

Co-authored-by: Kyle Daruwalla <daruwalla.k.public@icloud.com>

…tent

darsnack

Small nitpicks mostly. Let's also split some of these docstrings so it is less confusing trying to explain multiple sets of arguments in a single paragraph.

darsnack · 2021-08-16T15:05:29Z

src/models/tabularmodel.jl

+"""
+    get_emb_sz(cardinalities, [size_overrides])
+    get_emb_sz(cardinalities; catcols, [size_overrides])
+
+Returns a collection of tuples containing embedding dimensions corresponding to 
+number of classes in categorical columns present in `cardinalities` and adjusting for nans. 
+
+## Keyword arguments
+
+- `size_overrides`: Depending on the method used, this could either be a collection of 
+    Integers and `nothing` or an indexable collection with column name as key and size
+    to override it with as the value. In the first case, the integer present at any index 
+    will be used to override the rule of thumb for getting embedding sizes.
+- `categorical_cols`: A collection of categorical column names.
+"""


This docstring is better off split into two separate docstrings for each method. Both will show up automatically in the docs, but you will be able to tailor the explanation of size_overrides to each method (instead of explaining both in one paragraph).

src/models/tabularmodel.jl

darsnack · 2021-08-16T15:19:50Z

src/models/tabularmodel.jl

+function tabular_embedding_backbone(embedding_sizes, dropoutprob=0.)
+    embedslist = [Flux.Embedding(ni, nf) for (ni, nf) in embedding_sizes]
+    emb_drop = dropoutprob==0. ? identity : Dropout(dropoutprob)
+    Chain(
+        x -> tuple(eachrow(x)...), 
+        Parallel(vcat, embedslist), 
+        emb_drop
+    )
+end


Suggested change

function tabular_embedding_backbone(embedding_sizes, dropoutprob=0.)

embedslist = [Flux.Embedding(ni, nf) for (ni, nf) in embedding_sizes]

emb_drop = dropoutprob==0. ? identity : Dropout(dropoutprob)

Chain(

x -> tuple(eachrow(x)...),

Parallel(vcat, embedslist),

emb_drop

)

end

function tabular_embedding_backbone(embedding_sizes, dropout_rates=0.)

embedslist = [Flux.Embedding(ni, nf) for (ni, nf) in embedding_sizes]

emb_drop = iszero(dropout_rates) ? identity : Dropout(dropout_rates)

Chain(

x -> tuple(eachrow(x)...),

Parallel(vcat, embedslist),

emb_drop

)

end

emb_drop could even be further inlined if desired. If preserving model structure is more important than a bit of lost performance, then passing active=iszero(dropout_rates) instead of using a ternary is also a valid option.

Is active an argument to something? How would that work?

active is the third arg to BatchNorm, yes. I wouldn't worry about that now though, it's a minor tweak we can always revisit later.

src/models/tabularmodel.jl

Co-authored-by: Kyle Daruwalla <daruwalla.k.public@icloud.com>

darsnack

Mostly docstring changes.

src/models/tabularmodel.jl

Co-authored-by: Kyle Daruwalla <daruwalla.k.public@icloud.com>

darsnack · 2021-08-20T15:06:48Z

@lorenzoh this is good to go. I'll let you merge to have the final skim.

manikyabard mentioned this pull request Jun 14, 2021

FastAI.jl tabular development GSoC tracking FluxML/FluxML-Community-Call-Minutes#34

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manikyabard and others added 10 commits August 12, 2021 12:09

added tabular model

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fixed batchnorm in tabular model

97546c7

added function for calculating embedding dimensions

c1bd73b

updated tabular model

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Apply suggestions from code review

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Co-authored-by: Kyle Daruwalla <daruwalla.k.public@icloud.com>

simplified tabular model

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refactored TabularModel

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updated tabular model, and added tests

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export classifierbackbone

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renamed args

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updated docstrings and embed dims calculation, made args usage consis…

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made methods concise

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updated docstrings and get_emb_sz

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minor docstring fix

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lorenzoh merged commit b306865 into FluxML:master Aug 22, 2021


		bn_cont = bn_cont ? BatchNorm(n_cont) : identity

		n_emb = sum(size(embedlayer.weight)[1] for embedlayer in embedslist)

	x -> collect(eachrow(x)),
	x -> ntuple(i -> x[i], length(x)),
	x -> tuple(eachrow(x)...),

Add tabular model #124

Add tabular model #124

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manikyabard commented Jun 13, 2021

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lorenzoh commented Jul 12, 2021

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darsnack commented Aug 20, 2021

manikyabard Jun 17, 2021 •

edited

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darsnack Jun 18, 2021 •

edited

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