jcp_showcase.jl

### A Pluto.jl notebook ###
# v0.19.16

using Markdown
using InteractiveUtils

# This Pluto notebook uses @bind for interactivity. When running this notebook outside of Pluto, the following 'mock version' of @bind gives bound variables a default value (instead of an error).
macro bind(def, element)
    quote
        local iv = try Base.loaded_modules[Base.PkgId(Base.UUID("6e696c72-6542-2067-7265-42206c756150"), "AbstractPlutoDingetjes")].Bonds.initial_value catch; b -> missing; end
        local el = $(esc(element))
        global $(esc(def)) = Core.applicable(Base.get, el) ? Base.get(el) : iv(el)
        el
    end
end

# ╔═╡ 03e3c34f-f43e-4bf8-bc39-c66dd9059c0e
begin
	using DataFrames, Flux, PlutoUI, Test
	using AtomsBase, AtomsIO
	using AtomGraphs, AtomicGraphNets, ChemistryFeaturization
end

# ╔═╡ 10a99c8e-2ef5-43f7-a82f-0f7dc30e2d6d
# If the notebook is being run on JuliaHub, first retrieve the dataset assets required for running the notebook.
if haskey(ENV, "JULIAHUB_APP_URL")
	begin
		using JuliaHubData, DataSets
		datasets = ["mp-195", "VInO4"]
		for structure in datasets
			open(IO, dataset("thazhemadam/$(structure)")) do io
				write("$(structure).cif", io)
			end
			open(IO, dataset("thazhemadam/$(structure)-image")) do io
				write("$(structure).png", io)
			end
		end
	end
end

# ╔═╡ 7234d025-4a70-4d55-a199-1c26c45ddd23
using GeometricFlux

# ╔═╡ 95450e24-a1a3-11ed-2b36-c9ef4bfe3dd0
md"""
# Chemellia Showcase!
This notebook steps through some aspects of the Chemellia ecosystem for the case of graph-based representations and models. It also highlights integrations with other Julia ecosystems and interfaces that allow Chemellia itself to be quite lightweight.
"""

# ╔═╡ 76cb16d4-61eb-4db4-8574-247078043ba1
TableOfContents()

# ╔═╡ a55e9ca6-0396-4dc1-afd8-379dc03063fe
md"""
## Building Graphs
The first step is to go from a full 3D representation of a crystal structure to the graph-based one that we'll use as input to our model. We'll start out playing with HoPt$_3$, a structure I pulled from the [Materials Project](https://materialsproject.org/) database. A unit cell looks like this:

$(PlutoUI.LocalResource("./mp-195.png"))

Let's read in the CIF file as an `AtomGraph` and take a look around...
"""

# ╔═╡ a41c48b6-3a4c-4bf5-9cfd-81388424a20d
HoPt₃ = AtomGraph("mp-195.cif")

# ╔═╡ 5b68df70-f055-4069-a35b-c173870e984e
propertynames(HoPt₃)

# ╔═╡ 71e8634e-16d9-4422-81bf-8e33f29fd487
md"It stores both the `graph` that we built, and the representation of the full 3D `structure` that was generated along the way. That structure makes use of the [AtomsBase](https://github.com/JuliaMolSim/AtomsBase.jl) interface for easy interoperability of representations. The graph is a `SimpleWeightedGraph` from the [JuliaGraphs](https://juliagraphs.org/) ecosystem, and makes use of the Graphs.jl interface. Let's visualize it:"

# ╔═╡ 10d1afae-6bf4-4914-bfaa-43f02e956c4b
visualize(HoPt₃)

# ╔═╡ f7a6ae01-2e6d-4c16-aaf8-9af17a0e59e8
md"""
(The `visualize` function in AtomGraphs.jl makes use of the GraphPlot.jl package, another package within the JuliaGraphs ecosystem.)

The AtomGraphs.jl package is capable of reading from a variety of file formats for representing atoms and molecules, and can also directly take any object that is an AtomsBase `AbstractSystem` (such as a structure used in a [DFTK](https://docs.dftk.org/stable/) or [Molly](https://github.com/JuliaMolSim/Molly.jl) calculation).

We can also do some customization of how the graphs are built. To explore that, let's look at another crystal structure, this time with a somewhat larger unit cell...

$(PlutoUI.LocalResource("./VInO4.png"))

Let's play a bit with what the cutoff radius does to the connectivity of the graph and the "apparent" coordination numbers of the atoms/nodes...
"""

# ╔═╡ 578e1a80-9499-42db-8ed3-da1e0713e604
cutoff_slider = @bind cutoff Scrubbable(2.75:0.25:4, format=".2f")

# ╔═╡ c20fb391-1765-4b29-9eb8-7f2c8f4351bf
begin
	VInO₄ = AtomGraph("VInO4.cif", cutoff_radius=cutoff)
	visualize(VInO₄)
end

# ╔═╡ 7e3ad6e5-323b-4e29-ba58-6c243a416ea9
md"""
#### Other graph-building options
* You can tweak the behavior of the cutoff method to generate edges via some more keyword arguments. `max_num_nbr` is reasonably self-explanatory. There is also `dist_decay_func`, which sets how the edge weights decay with separation distance.
* Another way to build the graph, rather than the somewhat naïve cutoff method, is to construct Voronoi tessellations of the structure and build edges only between atoms that share a face. This option is turned on by passing the keyword argument `use_voronoi=true`
"""

# ╔═╡ 660b7b62-8edf-4bdc-9d6c-c1b46002590c
md"""
## Featurization
Once we've built our graph representation the way we like it, we can featurize it, "annotating" it with information that we would want to feed into an ML model. Let's step through some of the "vocabulary" around featurization within Chemellia. In particular, it's handled within the interface provided by the ChemistryFeaturization package.

### Feature Descriptors
The main building block of a featurization is a **feature descriptor** object, which should be a subtype of `AbstractFeatureDescriptor`. For now, we'll focus `ElementFeatureDescriptor`s, which only need elemental information in order to compute their values.

To start, suppose we're interested in featurizing our graphs with information about which block of the periodic table (s, p, d, f) an element is in. We can construct the feature descriptor like so:
"""

# ╔═╡ a55da127-e349-42fe-9280-e1c868c025f7
block = ElementFeature.ElementFeatureDescriptor("Block")

# ╔═╡ c731b3fc-8b39-40a6-9b75-5e021795a495
md"""
We can "call" the feature descriptor directly on an `AtomGraph` to get its value for each atom... 
"""

# ╔═╡ 6f69dffb-57c2-410e-9e4a-9c2eb62124cf
block(HoPt₃)

# ╔═╡ 0e077478-0bf6-45ca-8969-22c4ad009b08
md"What other element features are built in?"

# ╔═╡ 9d589e76-c65c-4fc1-b013-265237677cb9
elementfeature_names

# ╔═╡ 03426b8e-4535-4e3d-bb81-73e067214100
md"The ones that just have names of orbitals are the energy levels of those orbitals in the given element. Those provide a nice opportunity to illustrate another important function..."

# ╔═╡ 0656f790-f295-48a5-8a18-f671dd1db925
begin
	sixp = ElementFeature.ElementFeatureDescriptor("6p")
	encodable_elements(sixp)
end

# ╔═╡ e18cdb44-4e20-4e93-a065-638bde8318b0
md"""
Those are the only elements for which this feature is defined! Nifty.

We can also easily define custom features. For an `ElementFeatureDescriptor`, all that's needed is a lookup table defining  Check this out:
"""

# ╔═╡ b73dc3d3-ee38-46c7-a268-e47c0d692c9a
begin
	lookup_table = DataFrame(["Ho" 73; "Pt" 92; "In" 126; "V" 40], [:Symbol, :Awesomeness])
	awesomeness = ElementFeature.ElementFeatureDescriptor("Awesomeness", lookup_table)
end

# ╔═╡ 87aedd6e-9af1-442d-93bd-6b1a45838315
awesomeness(HoPt₃)

# ╔═╡ bef802d7-9715-4c11-bb73-de2949cbc0fc
@test_throws AssertionError awesomeness(VInO₄) # The awesomeness feature isn't defined for VInO₄!

# ╔═╡ fc4625bb-9b16-4fc8-8b8e-175758115aeb
md"""
...and there you see the usefulness of the `encodable_elements` functionality.

### Codecs

The next idea we'll introduce (and one that's key to the separation of concerns within the Chemellia framework) is that of Codecs, or "encoder-decoders." The key insight behind Codecs is that the actual value of a feature is entirely distinct from the way it is encoded in order to be passed into a model, and in fact, often one might want to encode the same feature in multiple distinct ways for different applications. In addition, often the encoded version of a feature is substantially less human-readable than its actual value.

#### OneHotOneCold

As one example, consider the `OneHotOneCold` Codec. As the name suggests, this Codec performs one-hot encoding of a given feature value and decodes using a one-cold decoding scheme.

##### Categorical Values
Remember the `block` feature descriptor from before? We can very easily encode and decode this categorical feature using one-hot encoding.
"""

# ╔═╡ 17ab9f6e-abde-453e-aace-b3694eb31f64
begin
	ohoc_block = OneHotOneCold(true, ["s", "p", "d", "f"])
	local encoded_block = encode(block(HoPt₃), ohoc_block)
	local decoded_block = map(x -> decode(x, ohoc_block), encoded_block)
	@info "Feature" block(HoPt₃)
	@info "Encoded Feature" encoded_block
	@info "Decoded Feature" decoded_block
end

# ╔═╡ 4bdcc41b-083a-4687-aee5-bbc23f94363b
md"""
##### Continuous Values

Alright, we've seen that this works for a categorical variable. But, does this also work for continuous values too? Yes, it does!

Let's consider a continuous feature, Atomic mass.
"""

# ╔═╡ 5603bf38-7e37-4372-a6c1-7c8f6dd472a0
amu = ElementFeatureDescriptor("Atomic mass")

# ╔═╡ 7b6424a4-fdec-470d-9483-75a2f323d16d
md"""
Let's also define the bins with heuristically selected values, given the range of values.
"""

# ╔═╡ 19812716-75a9-4e9d-9254-c4629842430e
begin
	ohoc_amu = OneHotOneCold(false, [163, 165, 194, 197])
	local encoded_amu = encode(amu(HoPt₃), ohoc_amu)
	local decoded_amu = map(x -> decode(x, ohoc_amu), encoded_amu)
	@info "Feature" amu(HoPt₃)
	@info "Encoded Feature" encoded_amu
	@info "Decoded Feature" decoded_amu
end

# ╔═╡ 658a48c2-8409-4128-88e5-b974fe30701b
md"""
As it is with continuous values, after binning, we cannot recover the original values while decoding, only the bins into which the values were decoded themselves.
"""

# ╔═╡ f995ee00-d7c0-477a-8237-f9ddc1209468
md"""
#### Simple Codec
Let's now check out how we can write a simple Codec that performs a simple scaling and shifting operation, using a `SimpleCodec`!
We will have to define the encoding and decoding mechanism, and we can simply use `encode` and `decode` as we would normally!
"""

# ╔═╡ dd814c0a-e9d5-4749-bf8a-cb25cd7b0cab
begin
	local simple = SimpleCodec(x -> 2x, x -> x/2)
	local encoded_amu = encode(amu(HoPt₃), simple)
	local decoded_amu = map(x -> decode(x, simple), encoded_amu)
	@info "Feature" amu(HoPt₃)
	@info "Encoded Feature" encoded_amu
	@info "Decoded Feature" decoded_amu
end

# ╔═╡ 4d7c27c9-6540-4165-8fea-fbae95cdcca4
md"""
#### Direct Codec
We can also perform the same scaling operation using a `DirectCodec`, which is meant to be used for the same.
"""

# ╔═╡ a7260180-244c-4600-bd89-72e7bc857544
begin
	local direct = DirectCodec(2) # to scale by 2
	local encoded_amu = encode(amu(HoPt₃), direct)
	local decoded_amu = map(x -> decode(x, direct), encoded_amu)
	@info "Feature" amu(HoPt₃)
	@info "Encoded Feature" encoded_amu
	@info "Decoded Feature" decoded_amu
end

# ╔═╡ 2b5b1ef3-a782-43d5-9f16-20bc8303d67a
md"""#### Custom Codecs
But what if we want to write a custom Codec? It's simple! There's only two rules that your custom Codec needs to comply with.
  * Your custom Codec must be a sub-type of `AbstractCodec`.
  * Your custom Codec must have an associated `encode` and `decode` mechanism defined.
"""

# ╔═╡ 78791a9e-9537-4d66-9609-d470c90c675e
md"""
### Featurization Objects

Featurization objects store a set of feature descriptors, their associated codecs, and a procedure for combining the encoded features into whatever format they need to be in to be consumed by the model of interest. In keeping with ChemistryFeaturization being primarily an interface, concrete implementations of `AbstractFeaturization` types typically live in the repository associated with the model that they're used with, since a featurization approach is often rather specific to a model architecture.

In this example, we'll focus on `GraphNodeFeaturization` provided by the AtomicGraphNets package.

The simplest way to construct a `GraphNodeFeaturization` is just to provide a list of feature names. If they are all features that are built in to ChemistryFeaturization (that is, no additional lookup table is provided), then this is all you need!
"""

# ╔═╡ d2d30fca-8914-4853-ba20-32f50244be01
fzn = GraphNodeFeaturization(["Block", "Atomic radius"])

# ╔═╡ 26ac75f4-bab7-47e7-aad9-c6fa9e031737
md"""But we can customize things a bit more, of course! For example, if actual features and codecs aren't provided, the features will be constructed with default binnings and the results of `default_codec` will be used to populate the `codecs` field (remember that the first flag is for whether the feature is categorical valued or not):"""

# ╔═╡ 1686acf1-8d78-46a2-b1c6-925d1a3bccef
fzn.codecs

# ╔═╡ 57bae6d8-d52f-4f2c-be6e-a1b1fc017dcb
md"""Let's try building another `GraphNodeFeaturization` that includes some of the custom features we defined above (again, if we don't explicitly include codecs, the default ones will be built automatically)..."""

# ╔═╡ c77230c7-4b80-4312-b91d-2569b4d9e947
fzn_custom = GraphNodeFeaturization([block, awesomeness])

# ╔═╡ 601dcef9-a5fe-4939-888f-6acc455b852e
md"""Note that `encodable_elements` works on a featurization exactly the way you would expect (namely, it returns the intersection of the results on each feature):"""

# ╔═╡ d47cc98c-464c-4661-bcde-8d6fb6c04a86
encodable_elements(fzn_custom) # just the ones that awesomeness can encode

# ╔═╡ cb3e6815-4e48-4dff-90c3-cba87886cdab
md"""Finally, for ultimate tweakability, we can also feed in our own codecs, too!"""

# ╔═╡ 8686925b-5e74-43dd-9f55-afbf9fbef1de
fzn_morecustom = GraphNodeFeaturization([block, amu], [ohoc_block, ohoc_amu])

# ╔═╡ 6ae1ed8e-603a-4828-a562-1361516ec9ab
md"""Now let's see how we actually encode a featurization. Internally, what this function will do is call the `encode` method for each feature and then combine the results in the way specified for that featurization type. In the case of `GraphNodeFeaturization`, that simply means a concatenation."""

# ╔═╡ 5399ebb4-fed3-43dc-affe-1612a9b12bf1
encode(HoPt₃, fzn)

# ╔═╡ f99a0111-1348-4c12-8bc7-ac02ea4cee21
md"""And of course, we can also decode!"""

# ╔═╡ ad0ea23c-512c-482b-ab02-59397fe3a9ab
decode(encode(HoPt₃, fzn), fzn)

# ╔═╡ 6bf7dd34-d1f3-4145-ac95-069886c8a850
md"""
## Model Building, Evaluation, and Training
In this section, we will demonstrate how to build inputs for and architecture of an AtomicGraphNets model similar to those from `cgcnn.py`.
"""

# ╔═╡ 3bedaca0-d74b-4fa2-b910-593c8603ee33
md"""
### FeaturizedAtoms objects

FeaturizedAtoms objects encapsulate an atomic structure, a featurization scheme and the resultant encoded features that are generated by applying the featurization scheme to the atomic structure.
This mechanism ensures that we have the sufficient data (and metadata) required to effectively pre-featurize and serialize the object at given point, all while retaining the provenance of the encoded features (within reasonable limits, depending on the encoding and decoding mechanisms).
"""

# ╔═╡ 15d31f64-8d36-4b85-bb11-cd80b111b781
fg = featurize(HoPt₃, fzn)

# ╔═╡ 9dbff198-c067-4252-9673-450844810116
md"""We can `decode` a `FeaturizedAtoms` object as a single argument, since it has everything that's needed..."""

# ╔═╡ 66af0ad2-aea8-4416-8627-c0f29384c7da
decode(fg)

# ╔═╡ c0241b9c-77d1-47a0-9eb5-d65e7036e69c
md"""
### AtomicGraphNets layers and models
AtomicGraphNets provides a number of standard layers for operations such as graph convolution and pooling, as well as interfacing seamlessly with layers provided by Flux.jl.

The "workhorse" layer is the graph convolutional layer, `AGNConv`. Its action is to perform a graph convolution (using the graph Laplacian stored in the `AtomGraph` object). This is multiplied by the `convweight` matrix, added to the original input features multiplied by the `selfweight` matrix, and finally, the `bias` is added element-wise to give the final output.

We can construct one as follows, feeding the desired input and output node feature vector lengths:
"""

# ╔═╡ 53f83fcb-50e7-4d32-ab69-56fe4e54f425
la = AGNConv(14=>14)

# ╔═╡ 2d0393dc-8083-4fe9-9d5f-e9cd76af243f
md"""This can be applied directly to our featurized graph from above. The output is a copy of the graph Laplacian as well as the output feature matrix (i.e. the node feature vectors concatenated together). The Laplacian is ``passed through'' so that the full `FeaturizedAtoms` object doesn't need to be."""

# ╔═╡ 8d619f4c-beea-4917-8f71-49dd7bc82501
la(fg)

# ╔═╡ c81bd54d-c1de-44a5-b586-601310463be4
md"""We can apply it multiple times, too..."""

# ╔═╡ 82bad2b4-4598-436e-811b-fb19c9a69c9f
la(la(fg))

# ╔═╡ def924ff-4e46-4670-9f4b-112a1bf243d1
md"""Or chain multiple of them together with different sized latent spaces... (`Chain` is just Flux.jl shorthand for function composition)"""

# ╔═╡ 4f860da9-22d2-4e4b-8fa5-e9f9797039cb
begin
	c = Chain(la, AGNConv(14=>10), AGNConv(10=>6))
	c(fg)
end

# ╔═╡ 5e374c75-5fc1-4407-a667-33b1e79553a3
md"""But to build a full model, we need some other layers, too -- note that the output size of a convolutional layer will depend on the size of the input graph. We can use a *pooling* layer to get to a predictable output size. The `AGNPool` layer will apply a specified pooling function (for example, maximum or average) across nodes and reduce along the features (dim, stride, and pad will be automatically computed from the desired input and output sizes and a target ratio between dim and feature length) to get to the desired output dimensions:"""

# ╔═╡ bc5bc9a7-3e5a-47bb-bd86-bac74208df39
c2 = Chain(c, AGNPool("mean", 6, 3, 0.4))

# ╔═╡ e7a82f17-ed97-48e2-a8f4-95cb6b65a7ce
c2(fg) # output now will always be 3 x 1

# ╔═╡ 92ef3b47-c050-4a1b-bebf-11d09be0665a
md"""We can of course build up entire models "from scratch" in this way. However, AtomicGraphNets also includes some convenience functions for standard types of architectures. One of these is `build_CGCNN`, which will build a model similar to those in cgcnn.py. It takes one required argument for `input_feature_length` and a number of optional keyword arguments to make tweaks to the architecture such as number of layers, activation functions, etc. We'll utilize a few of them here."""

# ╔═╡ e6bc074f-f68f-4285-b058-7e2f6c665663
model = build_CGCNN(14, pooled_feature_length=8, num_hidden_layers=2)

# ╔═╡ 2d5a6b65-7703-4d31-8eb7-e7ef90460f65
md"""We can evaluate the model on a single input to get a prediction:"""

# ╔═╡ 58656c12-d6b3-42bb-ba58-f1d283f98614
model(fg)

# ╔═╡ 04106bba-7302-4e93-aecd-73a14d0aea03
md"""...but that doesn't mean much when we don't even know what we're predicting, and haven't done any model training! 

Training proceeds as with any Flux.jl model. Let's demonstrate how that works for a "toy" dataset..."""

# ╔═╡ 98bd6bea-9d2e-48c3-843b-e29e860fe5ae
train_set = [(fg, 1.0)]

# ╔═╡ 32df46cc-4131-40bc-a1e0-1b46a5dcb01c
begin
	opt_state = Flux.setup(Adam(), model)
	loss = Flux.Losses.mse
	
	for epoch in 1:5
	  Flux.train!(model, train_set, opt_state) do m, x, y
	    loss(m(x), y)
	  end
	end
end

# ╔═╡ 9e640d76-eaa5-45fe-b2d4-f6ee5483dfed
md"""If we now evaluate the model on that same piece of data again, we'll see the result has moved in the right direction..."""

# ╔═╡ 89a79d36-d201-43f1-8a56-c66457cc4848
model(fg)

# ╔═╡ c280f1b3-ad5e-4023-ab97-7e8f7f0b1c3b
md"""
### More Interoperability!
As another demonstration of the interoperability enabled by Chemellia and Julia in general, we will include here an example of how to use layers from the [GeometricFlux](https://github.com/FluxML/GeometricFlux.jl) package in AtomicGraphNets models through the power of multiple dispatch. As of April 2023, this functionality exists on [an unmerged branch](https://github.com/Chemellia/AtomicGraphNets.jl/blob/layer_sharing/examples/2_layer_sharing/layer_sharing.jl) of AtomicGraphNets. If and when it gets fleshed out, it will be merged in as an "officially supported" feature.
"""

# ╔═╡ b22e0107-c5fc-43e1-bcb3-cd39a66b0732
import GraphSignals: normalized_adjacency_matrix

# ╔═╡ 67b8e10f-04e2-43e6-8c34-b9168ed1b676
md"""Let's remind ourselves of the size of the encoded features in our FeaturedGraph from above, should be 4 (# atoms) x 14 (# features, 4 for block and 10 for radius)"""

# ╔═╡ f30a15f7-f330-44f4-ba1a-1299d63252c5
size(fg.encoded_features) # check size, should be 16 (# atoms) x 14 (# features, 4 for block and 10 for radius)

# ╔═╡ bc54ac22-2c74-43e7-96c4-4e65ffa849b4
md"""We know that we can apply the layer from above to this FeaturedGraph:"""

# ╔═╡ 88125ff9-0a50-4dd5-9510-37ae19d37349
la(fg) # first output is graph laplacian, second is new feature matrix

# ╔═╡ 112f5908-d8b9-4784-b3e2-64145b96702a
md"""Okay, so let's start with the `GCNConv` layer. It has a syntax that takes in a normalized adjacency matrix and a feature matrix, so we can dispatch to that
the only slight additional wrinkle is it expects the transpose in the argument, so to "translate" appropriately, we need to transpose the input and then output again. We can do this by defining the dispatch of a `GCNConv` layer on a `FeaturedGraph` object."""

# ╔═╡ a77703aa-ee96-4259-b2c7-c935c08aa661
(l::GCNConv)(fg::FeaturizedAtoms{AtomGraph{T}, GraphNodeFeaturization}) where T = Matrix(l(normalized_adjacency_matrix(fg.atoms.graph), fg.encoded_features')')

# ╔═╡ 9898c33f-e3f9-4273-9196-4cfcd7d6bc8a
md"""We'll also define the dispatch onto the tuples that AGNConv outputs so we can easily chain them together -- note that this is a slight hack and will change the behavior a bit because we'll be passing a graph Laplacian as an adjacency matrix...also we transpose it back to "default" to the AGN convention as well as passing through the Laplacian..."""

# ╔═╡ f4251f8d-4cd9-49f9-88f6-617305115417
(l::GCNConv)(t::Tuple{Matrix{R1},Matrix{R2}}) where {R1<:Real,R2<:Real} = (t[1], l(t[1], t[2]')')

# ╔═╡ 345d7e2d-a758-4282-8750-1b0ca70dcff9
md"""Let's make sure that this works for an individual layer..."""

# ╔═╡ 783d0cea-fa78-4e03-900d-63b2cfe72a9f
lg = GCNConv(14=>10)

# ╔═╡ 1744c053-a7be-47b3-a43e-ba892f47a11f
lg(fg)

# ╔═╡ 1d199188-384a-4836-a051-b93b7d75ca86
md"""...and also for a chained combination of layers..."""

# ╔═╡ 43609597-d5e3-454b-af6b-abb7f01a91ea
m = Chain(la, lg)

# ╔═╡ 0adcca11-bbca-42ca-ba38-3e7ffee703cc
m(fg)

# ╔═╡ 60394155-ba71-450e-93b2-8123b487561d
md"""But GCNConv is almost the same as AtomicGraphNets' `AGNConv` really, so it's not really all that interesting or exciting, let's try something a bit different...how about the `TopKPool` layer?

(note that this layer as constructed takes a fixed adjacency matrix, so it's likely not that useful for our purposes, this is more for demonstration...though I think it would be pretty easy to make a version that doesn't need to be that way)

As before, we first define the dispatch..."""


# ╔═╡ c5ca6518-a34b-4990-aca7-6e2ff157eb5a
(tk::TopKPool)(t::Tuple{Matrix{R1},AbstractMatrix{R2}}) where {R1<:Real,R2<:Real} = tk(t[2]')

# ╔═╡ d3d149fd-6652-444e-83fa-0701162f947a
md"""...and check that it works."""

# ╔═╡ 35603fa9-254d-4e74-a2d3-d1a72a593107
tk = TopKPool(fg.atoms.graph.weights, 4, 10) # adjacency matrix, k, input size

# ╔═╡ 68c64be8-2664-4c24-8e90-939e94102aea
m2 = Chain(la, lg, tk)

# ╔═╡ 52f90156-7640-4e74-86f0-6d352475818d
m2(fg)

# ╔═╡ 00000000-0000-0000-0000-000000000001
PLUTO_PROJECT_TOML_CONTENTS = """
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[compat]
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"""

# ╔═╡ 00000000-0000-0000-0000-000000000002
PLUTO_MANIFEST_TOML_CONTENTS = """
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[[deps.UUIDs]]
deps = ["Random", "SHA"]
uuid = "cf7118a7-6976-5b1a-9a39-7adc72f591a4"

[[deps.UnPack]]
git-tree-sha1 = "387c1f73762231e86e0c9c5443ce3b4a0a9a0c2b"
uuid = "3a884ed6-31ef-47d7-9d2a-63182c4928ed"
version = "1.0.2"

[[deps.Unicode]]
uuid = "4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"

[[deps.Unitful]]
deps = ["ConstructionBase", "Dates", "LinearAlgebra", "Random"]
git-tree-sha1 = "dba3eba51b9512695b59e0bd8263ac074c5ed2d9"
uuid = "1986cc42-f94f-5a68-af5c-568840ba703d"
version = "1.13.1"

[[deps.UnitfulAtomic]]
deps = ["Unitful"]
git-tree-sha1 = "903be579194534af1c4b4778d1ace676ca042238"
uuid = "a7773ee8-282e-5fa2-be4e-bd808c38a91a"
version = "1.0.0"

[[deps.Unmarshal]]
deps = ["JSON", "LazyJSON", "Missings", "Nullables", "Requires"]
git-tree-sha1 = "ee46863309f8f942249e1df1b74ba3088ff0f151"
uuid = "cbff2730-442d-58d7-89d1-8e530c41eb02"
version = "0.4.4"

[[deps.UnsafeAtomics]]
git-tree-sha1 = "6331ac3440856ea1988316b46045303bef658278"
uuid = "013be700-e6cd-48c3-b4a1-df204f14c38f"
version = "0.2.1"

[[deps.UnsafeAtomicsLLVM]]
deps = ["LLVM", "UnsafeAtomics"]
git-tree-sha1 = "ead6292c02aab389cb29fe64cc9375765ab1e219"
uuid = "d80eeb9a-aca5-4d75-85e5-170c8b632249"
version = "0.1.1"

[[deps.UnsafePointers]]
git-tree-sha1 = "c81331b3b2e60a982be57c046ec91f599ede674a"
uuid = "e17b2a0c-0bdf-430a-bd0c-3a23cae4ff39"
version = "1.0.0"

[[deps.VectorizationBase]]
deps = ["ArrayInterface", "CPUSummary", "HostCPUFeatures", "IfElse", "LayoutPointers", "Libdl", "LinearAlgebra", "SIMDTypes", "Static", "StaticArrayInterface"]
git-tree-sha1 = "b182207d4af54ac64cbc71797765068fdeff475d"
uuid = "3d5dd08c-fd9d-11e8-17fa-ed2836048c2f"
version = "0.21.64"

[[deps.VertexSafeGraphs]]
deps = ["Graphs"]
git-tree-sha1 = "8351f8d73d7e880bfc042a8b6922684ebeafb35c"
uuid = "19fa3120-7c27-5ec5-8db8-b0b0aa330d6f"
version = "0.2.0"

[[deps.WeakRefStrings]]
deps = ["DataAPI", "InlineStrings", "Parsers"]
git-tree-sha1 = "b1be2855ed9ed8eac54e5caff2afcdb442d52c23"
uuid = "ea10d353-3f73-51f8-a26c-33c1cb351aa5"
version = "1.4.2"

[[deps.Word2Vec]]
deps = ["LinearAlgebra", "Statistics", "Word2Vec_jll"]
git-tree-sha1 = "a4e76aeaaf2bda1556864b610051960cea642958"
uuid = "c64b6f0f-98cd-51d1-af78-58ae84944834"
version = "0.5.3"

[[deps.Word2Vec_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "264768df753f8328295d7b7cff55edc52f180284"
uuid = "9fbe4022-c126-5389-b4b2-756cc9f654d0"
version = "0.1.0+0"

[[deps.WorkerUtilities]]
git-tree-sha1 = "cd1659ba0d57b71a464a29e64dbc67cfe83d54e7"
uuid = "76eceee3-57b5-4d4a-8e66-0e911cebbf60"
version = "1.6.1"

[[deps.XML2_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Libiconv_jll", "Pkg", "Zlib_jll"]
git-tree-sha1 = "93c41695bc1c08c46c5899f4fe06d6ead504bb73"
uuid = "02c8fc9c-b97f-50b9-bbe4-9be30ff0a78a"
version = "2.10.3+0"

[[deps.XMLDict]]
deps = ["EzXML", "IterTools", "OrderedCollections"]
git-tree-sha1 = "d9a3faf078210e477b291c79117676fca54da9dd"
uuid = "228000da-037f-5747-90a9-8195ccbf91a5"
version = "0.4.1"

[[deps.XSLT_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Libgcrypt_jll", "Libgpg_error_jll", "Libiconv_jll", "Pkg", "XML2_jll", "Zlib_jll"]
git-tree-sha1 = "91844873c4085240b95e795f692c4cec4d805f8a"
uuid = "aed1982a-8fda-507f-9586-7b0439959a61"
version = "1.1.34+0"

[[deps.Xorg_libX11_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libxcb_jll", "Xorg_xtrans_jll"]
git-tree-sha1 = "5be649d550f3f4b95308bf0183b82e2582876527"
uuid = "4f6342f7-b3d2-589e-9d20-edeb45f2b2bc"
version = "1.6.9+4"

[[deps.Xorg_libXau_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "4e490d5c960c314f33885790ed410ff3a94ce67e"
uuid = "0c0b7dd1-d40b-584c-a123-a41640f87eec"
version = "1.0.9+4"

[[deps.Xorg_libXdmcp_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "4fe47bd2247248125c428978740e18a681372dd4"
uuid = "a3789734-cfe1-5b06-b2d0-1dd0d9d62d05"
version = "1.1.3+4"

[[deps.Xorg_libXext_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libX11_jll"]
git-tree-sha1 = "b7c0aa8c376b31e4852b360222848637f481f8c3"
uuid = "1082639a-0dae-5f34-9b06-72781eeb8cb3"
version = "1.3.4+4"

[[deps.Xorg_libXrender_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libX11_jll"]
git-tree-sha1 = "19560f30fd49f4d4efbe7002a1037f8c43d43b96"
uuid = "ea2f1a96-1ddc-540d-b46f-429655e07cfa"
version = "0.9.10+4"

[[deps.Xorg_libpthread_stubs_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "6783737e45d3c59a4a4c4091f5f88cdcf0908cbb"
uuid = "14d82f49-176c-5ed1-bb49-ad3f5cbd8c74"
version = "0.1.0+3"

[[deps.Xorg_libxcb_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "XSLT_jll", "Xorg_libXau_jll", "Xorg_libXdmcp_jll", "Xorg_libpthread_stubs_jll"]
git-tree-sha1 = "daf17f441228e7a3833846cd048892861cff16d6"
uuid = "c7cfdc94-dc32-55de-ac96-5a1b8d977c5b"
version = "1.13.0+3"

[[deps.Xorg_xtrans_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "79c31e7844f6ecf779705fbc12146eb190b7d845"
uuid = "c5fb5394-a638-5e4d-96e5-b29de1b5cf10"
version = "1.4.0+3"

[[deps.Xtals]]
deps = ["AtomsBase", "Bio3DView", "CSV", "DataFrames", "Graphs", "JLD2", "LinearAlgebra", "MetaGraphs", "PeriodicTable", "PrecompileSignatures", "Printf", "StaticArrays", "Unitful"]
git-tree-sha1 = "21e6b308f424ab9962cd5456448be36bdea831e1"
uuid = "ede5f01d-793e-4c47-9885-c447d1f18d6d"
version = "0.4.13"

[[deps.YAML]]
deps = ["Base64", "Dates", "Printf", "StringEncodings"]
git-tree-sha1 = "dbc7f1c0012a69486af79c8bcdb31be820670ba2"
uuid = "ddb6d928-2868-570f-bddf-ab3f9cf99eb6"
version = "0.4.8"

[[deps.ZipFile]]
deps = ["Libdl", "Printf", "Zlib_jll"]
git-tree-sha1 = "f492b7fe1698e623024e873244f10d89c95c340a"
uuid = "a5390f91-8eb1-5f08-bee0-b1d1ffed6cea"
version = "0.10.1"

[[deps.Zlib_jll]]
deps = ["Libdl"]
uuid = "83775a58-1f1d-513f-b197-d71354ab007a"
version = "1.2.12+3"

[[deps.Zygote]]
deps = ["AbstractFFTs", "ChainRules", "ChainRulesCore", "DiffRules", "Distributed", "FillArrays", "ForwardDiff", "GPUArrays", "GPUArraysCore", "IRTools", "InteractiveUtils", "LinearAlgebra", "LogExpFunctions", "MacroTools", "NaNMath", "Random", "Requires", "SnoopPrecompile", "SparseArrays", "SpecialFunctions", "Statistics", "ZygoteRules"]
git-tree-sha1 = "987ae5554ca90e837594a0f30325eeb5e7303d1e"
uuid = "e88e6eb3-aa80-5325-afca-941959d7151f"
version = "0.6.60"

[[deps.ZygoteRules]]
deps = ["ChainRulesCore", "MacroTools"]
git-tree-sha1 = "977aed5d006b840e2e40c0b48984f7463109046d"
uuid = "700de1a5-db45-46bc-99cf-38207098b444"
version = "0.2.3"

[[deps.coordgenlibs_jll]]
deps = ["Libdl", "Pkg"]
git-tree-sha1 = "95b76590b9a558b69566b59cc64415b29618113b"
uuid = "f6050b86-aaaf-512f-8549-0afff1b4d57f"
version = "1.4.0+0"

[[deps.extxyz_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "PCRE2_jll", "Pkg", "libcleri_jll"]
git-tree-sha1 = "54c88fe9d4c57d59f3f51ac09f82e1e6ff7ef6c8"
uuid = "6ecdc6fc-93a8-5528-aee3-ac7ae1c60be7"
version = "0.1.0+0"

[[deps.libblastrampoline_jll]]
deps = ["Artifacts", "Libdl", "OpenBLAS_jll"]
uuid = "8e850b90-86db-534c-a0d3-1478176c7d93"
version = "5.1.1+0"

[[deps.libcleri_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "PCRE2_jll", "Pkg"]
git-tree-sha1 = "9cf7bea1bc957a2133fe6ea57c95b36b5addc364"
uuid = "cdc7adba-bef8-5cba-a7ee-c792dee3081e"
version = "0.12.1+3"

[[deps.libinchi_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "034ee07d3b387a4ca1a153a43a0c46549b6749ba"
uuid = "172afb32-8f1c-513b-968f-184fcd77af72"
version = "1.5.1+0"

[[deps.libpng_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Zlib_jll"]
git-tree-sha1 = "94d180a6d2b5e55e447e2d27a29ed04fe79eb30c"
uuid = "b53b4c65-9356-5827-b1ea-8c7a1a84506f"
version = "1.6.38+0"

[[deps.libsodium_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "848ab3d00fe39d6fbc2a8641048f8f272af1c51e"
uuid = "a9144af2-ca23-56d9-984f-0d03f7b5ccf8"
version = "1.0.20+0"

[[deps.micromamba_jll]]
deps = ["Artifacts", "JLLWrappers", "LazyArtifacts", "Libdl"]
git-tree-sha1 = "087555b0405ed6adf526cef22b6931606b5af8ac"
uuid = "f8abcde7-e9b7-5caa-b8af-a437887ae8e4"
version = "1.4.1+0"

[[deps.nghttp2_jll]]
deps = ["Artifacts", "Libdl"]
uuid = "8e850ede-7688-5339-a07c-302acd2aaf8d"
version = "1.48.0+0"

[[deps.p7zip_jll]]
deps = ["Artifacts", "Libdl"]
uuid = "3f19e933-33d8-53b3-aaab-bd5110c3b7a0"
version = "17.4.0+0"
"""

# ╔═╡ Cell order:
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