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Switch to DifferentiationInterface #111

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e5a85c6
Switch to DifferentiationInterface
gdalle Jul 29, 2024
d3379e6
Fix splatting
gdalle Jul 30, 2024
a708a41
Always vector
gdalle Aug 2, 2024
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6 changes: 3 additions & 3 deletions Project.toml
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Original file line number Diff line number Diff line change
@@ -1,12 +1,12 @@
name = "TMLE"
uuid = "8afdd2fb-6e73-43df-8b62-b1650cd9c8cf"
authors = ["Olivier Labayle"]
version = "0.16.1"
version = "0.16.2"

[deps]
AbstractDifferentiation = "c29ec348-61ec-40c8-8164-b8c60e9d9f3d"
CategoricalArrays = "324d7699-5711-5eae-9e2f-1d82baa6b597"
Combinatorics = "861a8166-3701-5b0c-9a16-15d98fcdc6aa"
DifferentiationInterface = "a0c0ee7d-e4b9-4e03-894e-1c5f64a51d63"
Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
GLM = "38e38edf-8417-5370-95a0-9cbb8c7f171a"
Graphs = "86223c79-3864-5bf0-83f7-82e725a168b6"
Expand Down Expand Up @@ -35,9 +35,9 @@ JSONExt = "JSON"
YAMLExt = "YAML"

[compat]
AbstractDifferentiation = "0.6.0"
CategoricalArrays = "0.10"
Combinatorics = "1.0.2"
DifferentiationInterface = "0.5"
Distributions = "0.25"
GLM = "1.8.2"
Graphs = "1.8"
Expand Down
2 changes: 1 addition & 1 deletion src/TMLE.jl
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Expand Up @@ -15,7 +15,7 @@ using Zygote
using LogExpFunctions
using PrecompileTools
using Random
import AbstractDifferentiation as AD
import DifferentiationInterface as DI
using Graphs
using MetaGraphsNext
using Combinatorics
Expand Down
16 changes: 9 additions & 7 deletions src/estimators.jl
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Expand Up @@ -95,7 +95,7 @@ ConditionalDistributionEstimator(train_validation_indices, model) =

Estimates all components of Ψ and then Ψ itself.
"""
function (estimator::Estimator)(Ψ::ComposedEstimand, dataset; cache=Dict(), verbosity=1, backend=AD.ZygoteBackend())
function (estimator::Estimator)(Ψ::ComposedEstimand, dataset; cache=Dict(), verbosity=1, backend=DI.AutoZygote())
estimates = map(Ψ.args) do estimand
estimate, _ = estimator(estimand, dataset; cache=cache, verbosity=verbosity)
estimate
Expand Down Expand Up @@ -157,23 +157,25 @@ f(x, y) = [x^2 - y, y - 3x]
compose(f, res₁, res₂)
```
"""
function compose(f, estimates...; backend=AD.ZygoteBackend())
function compose(f, estimates...; backend=DI.AutoZygote())
f₀, σ₀, n = _compose(f, estimates...; backend=backend)
estimand = ComposedEstimand(f, Tuple(e.estimand for e in estimates))
return ComposedEstimate(estimand, estimates, f₀, σ₀, n)
end

function _compose(f, estimates...; backend=AD.ZygoteBackend())
_make_vec(x::Number) = [x]
_make_vec(x::AbstractVector) = x

function _compose(f, estimates...; backend=DI.AutoZygote())
Σ = covariance_matrix(estimates...)
point_estimates = [r.estimate for r in estimates]
f₀, Js = AD.value_and_jacobian(backend, f, point_estimates...)
J = hcat(Js...)
f₀, J = DI.value_and_jacobian(_make_vec ∘ Base.splat(f), backend, point_estimates)
n = size(first(estimates).IC, 1)
σ₀ = J * Σ * J'
return collect(f₀), σ₀, n
return f₀, σ₀, n
end

function covariance_matrix(estimates...)
X = hcat([r.IC for r in estimates]...)
return cov(X, dims=1, corrected=true)
end
end
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