polishing

Author: CarloLucibello

GNNlib/Project.toml

@@ -12,14 +12,11 @@ Graphs = "86223c79-3864-5bf0-83f7-82e725a168b6"
 KrylovKit = "0b1a1467-8014-51b9-945f-bf0ae24f4b77"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 MLUtils = "f1d291b0-491e-4a28-83b9-f70985020b54"
-MacroTools = "1914dd2f-81c6-5fcd-8719-6d5c9610ff09"
 NNlib = "872c559c-99b0-510c-b3b7-b6c96a88d5cd"
-NearestNeighbors = "b8a86587-4115-5ab1-83bc-aa920d37bbce"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 Reexport = "189a3867-3050-52da-a836-e630ba90ab69"
 SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
 Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
-StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
 
 [weakdeps]
 CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba"
@@ -38,14 +35,11 @@ KrylovKit = "0.6, 0.7"
 LinearAlgebra = "1"
 MLDatasets = "0.7"
 MLUtils = "0.4"
-MacroTools = "0.5"
 NNlib = "0.9"
-NearestNeighbors = "0.4"
 Random = "1"
 Reexport = "1"
 SparseArrays = "1"
 Statistics = "1"
-StatsBase = "0.34"
 cuDNN = "1"
 julia = "1.10"
 

GNNlib/src/GNNlib.jl

@@ -2,8 +2,6 @@ module GNNlib
 
 using Statistics: mean
 using LinearAlgebra, Random
-using Base: tail
-using MacroTools: @forward
 using MLUtils
 using NNlib
 using NNlib: scatter, gather

Project.toml

@@ -4,24 +4,17 @@ authors = ["Carlo Lucibello and contributors"]
 version = "0.6.19"
 
 [deps]
-Adapt = "79e6a3ab-5dfb-504d-930d-738a2a938a0e"
 ChainRulesCore = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4"
 DataStructures = "864edb3b-99cc-5e75-8d2d-829cb0a9cfe8"
 Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c"
 Functors = "d9f16b24-f501-4c13-a1f2-28368ffc5196"
 GNNGraphs = "aed8fd31-079b-4b5a-b342-a13352159b8c"
-Graphs = "86223c79-3864-5bf0-83f7-82e725a168b6"
-KrylovKit = "0b1a1467-8014-51b9-945f-bf0ae24f4b77"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
-MLUtils = "f1d291b0-491e-4a28-83b9-f70985020b54"
 MacroTools = "1914dd2f-81c6-5fcd-8719-6d5c9610ff09"
 NNlib = "872c559c-99b0-510c-b3b7-b6c96a88d5cd"
-NearestNeighbors = "b8a86587-4115-5ab1-83bc-aa920d37bbce"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 Reexport = "189a3867-3050-52da-a836-e630ba90ab69"
-SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
 Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
-StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
 
 [weakdeps]
 CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba"
@@ -30,26 +23,18 @@ CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba"
 GraphNeuralNetworksCUDAExt = "CUDA"
 
 [compat]
-Adapt = "3, 4"
 CUDA = "4, 5"
 ChainRulesCore = "1"
 DataStructures = "0.18"
 Flux = "0.14"
 Functors = "0.4.1"
-Graphs = "1.4"
 GNNGraphs = "1.0"
-KrylovKit = "0.6, 0.7, 0.8"
 LinearAlgebra = "1"
-MLDatasets = "0.7"
-MLUtils = "0.4"
 MacroTools = "0.5"
 NNlib = "0.9"
-NearestNeighbors = "0.4"
 Random = "1"
 Reexport = "1"
-SparseArrays = "1"
 Statistics = "1"
-StatsBase = "0.34"
 cuDNN = "1"
 julia = "1.10"
 
@@ -59,11 +44,14 @@ CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba"
 ChainRulesTestUtils = "cdddcdb0-9152-4a09-a978-84456f9df70a"
 DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
 FiniteDifferences = "26cc04aa-876d-5657-8c51-4c34ba976000"
+Graphs = "86223c79-3864-5bf0-83f7-82e725a168b6"
 InlineStrings = "842dd82b-1e85-43dc-bf29-5d0ee9dffc48"
 MLDatasets = "eb30cadb-4394-5ae3-aed4-317e484a6458"
+MLUtils = "f1d291b0-491e-4a28-83b9-f70985020b54"
+SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
 cuDNN = "02a925ec-e4fe-4b08-9a7e-0d78e3d38ccd"
 
 [targets]
-test = ["Test", "Adapt", "DataFrames", "InlineStrings", "Zygote", "FiniteDifferences", "ChainRulesTestUtils", "MLDatasets", "CUDA", "cuDNN"]
+test = ["Test", "MLUtils", "Adapt", "DataFrames", "InlineStrings", "SparseArrays", "Graphs", "Zygote", "FiniteDifferences", "ChainRulesTestUtils", "MLDatasets", "CUDA", "cuDNN"]

src/GraphNeuralNetworks.jl

@@ -2,16 +2,14 @@ module GraphNeuralNetworks
 
 using Statistics: mean
 using LinearAlgebra, Random
-using Base: tail
 using Flux
 using Flux: glorot_uniform, leakyrelu, GRUCell, @functor, batch
 using MacroTools: @forward
-using MLUtils
 using NNlib
 using NNlib: scatter, gather
 using ChainRulesCore
 using Reexport
-using SparseArrays, Graphs # not needed but if removed Documenter will complain
+using DataStructures: nlargest
 
 @reexport using GNNGraphs
 using GNNGraphs: COO_T, ADJMAT_T, SPARSE_T,

src/layers/heteroconv.jl

@@ -78,7 +78,7 @@ function _reduceby_node_t(aggr, outs, ntypes)
     end
     # workaround to provide the aggregation once per unique node type,
     # gradient is not needed
-    unique_ntypes = Flux.ChainRulesCore.ignore_derivatives() do
+    unique_ntypes = ChainRulesCore.ignore_derivatives() do
         unique(ntypes)
     end
     vals = [_reduce(node_t) for node_t in unique_ntypes]

src/layers/pool.jl

@@ -1,5 +1,3 @@
-using DataStructures: nlargest
-
 @doc raw"""
     GlobalPool(aggr)
 

test/runtests.jl

@@ -8,7 +8,6 @@ using Flux: gpu, @functor
 using LinearAlgebra, Statistics, Random
 using NNlib
 import MLUtils
-import StatsBase
 using SparseArrays
 using Graphs
 using Zygote