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Sorting out issue with calling predict on matrix #219

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merged 13 commits into from
Apr 24, 2023
+190 −164
Merged

Sorting out issue with calling predict on matrix #219

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bac03b7
removed changes related to other PR and added reformat function
pat-alt Mar 30, 2023
08316cb
fit! method through an error because matrix wasn't transposed
pat-alt Mar 30, 2023
221bc58
first go at completing remaining tasks.
pat-alt Apr 5, 2023
3c36915
currently a little clueless about this error
pat-alt Apr 5, 2023
e98182a
tests now passing
pat-alt Apr 7, 2023
0ca1d43
updated docstrings
pat-alt Apr 7, 2023
d01f536
fixed scitype warning
pat-alt Apr 12, 2023
d3942ec
test is failing
pat-alt Apr 17, 2023
f5c6b50
tests passing
pat-alt Apr 18, 2023
aa5c6ab
Update src/regressor.jl
pat-alt Apr 22, 2023
56eec63
collate function
pat-alt Apr 22, 2023
cefd9f3
tests passing
pat-alt Apr 22, 2023
49d11af
removed a redundant print statement
pat-alt Apr 23, 2023
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20 changes: 13 additions & 7 deletions src/classifier.jl
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Original file line number Diff line number Diff line change
@@ -1,6 +1,12 @@
# if `b` is a builder, then `b(model, rng, shape...)` is called to make a
# new chain, where `shape` is the return value of this method:
"""
shape(model::NeuralNetworkClassifier, X, y)

A private method that returns the shape of the input and output of the model for given data `X` and `y`.
"""
function MLJFlux.shape(model::NeuralNetworkClassifier, X, y)
X = X isa Matrix ? Tables.table(X) : X
levels = MLJModelInterface.classes(y[1])
n_output = length(levels)
n_input = Tables.schema(X).names |> length
Expand All @@ -10,7 +16,7 @@ end
# builds the end-to-end Flux chain needed, given the `model` and `shape`:
MLJFlux.build(model::NeuralNetworkClassifier, rng, shape) =
Flux.Chain(build(model.builder, rng, shape...),
model.finaliser)
model.finaliser)

# returns the model `fitresult` (see "Adding Models for General Use"
# section of the MLJ manual) which must always have the form `(chain,
Expand All @@ -19,15 +25,15 @@ MLJFlux.fitresult(model::NeuralNetworkClassifier, chain, y) =
(chain, MLJModelInterface.classes(y[1]))

function MLJModelInterface.predict(model::NeuralNetworkClassifier,
fitresult,
Xnew)
fitresult,
Xnew)
chain, levels = fitresult
X = reformat(Xnew)
probs = vcat([chain(tomat(X[:,i]))' for i in 1:size(X, 2)]...)
probs = vcat([chain(tomat(X[:, i]))' for i in 1:size(X, 2)]...)
return MLJModelInterface.UnivariateFinite(levels, probs)
end

MLJModelInterface.metadata_model(NeuralNetworkClassifier,
input=Table(Continuous),
target=AbstractVector{<:Finite},
path="MLJFlux.NeuralNetworkClassifier")
input=Union{AbstractMatrix{Continuous},Table(Continuous)},
target=AbstractVector{<:Finite},
path="MLJFlux.NeuralNetworkClassifier")
6 changes: 5 additions & 1 deletion src/core.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -145,8 +145,11 @@ nrows(y::AbstractVector) = length(y)
reformat(X) = reformat(X, scitype(X))

# ---------------------------------
# Reformatting tables
# Reformatting matrices
reformat(X, ::Type{<:AbstractMatrix}) = X'

# ---------------------------------
# Reformatting tables
reformat(X, ::Type{<:Table}) = MLJModelInterface.matrix(X)'

# ---------------------------------
Expand Down Expand Up @@ -221,6 +224,7 @@ by `model.batch_size`.)

"""
function collate(model, X, y)
y = y isa Matrix ? Tables.table(y) : y
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I'm trying to understand why this fix was needed. The nrows function is supposed to work for matrices as well as tables:

using MLJBase
julia> y = rand(2, 6)
julia> nrows(y)
2

julia> nrows(y')
6

And line 230 below should already take care of the conversion of y to a matrix, no?

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This was giving me an error previously, because the nrows method defined in the same file expects a table (or else it throws an ArgumentError. I've adjusted that and tests are passing.

row_batches = Base.Iterators.partition(1:nrows(y), model.batch_size)
Xmatrix = reformat(X)
ymatrix = reformat(y)
Expand Down
52 changes: 35 additions & 17 deletions src/regressor.jl
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Original file line number Diff line number Diff line change
@@ -1,6 +1,12 @@
# # NEURAL NETWORK REGRESSOR

"""
shape(model::NeuralNetworkRegressor, X, y)

A private method that returns the shape of the input and output of the model for given data `X` and `y`.
"""
function shape(model::NeuralNetworkRegressor, X, y)
X = X isa Matrix ? Tables.table(X) : X
n_input = Tables.schema(X).names |> length
n_ouput = 1
return (n_input, 1)
Expand All @@ -12,23 +18,30 @@ build(model::NeuralNetworkRegressor, rng, shape) =
fitresult(model::NeuralNetworkRegressor, chain, y) = (chain, nothing)

function MLJModelInterface.predict(model::NeuralNetworkRegressor,
fitresult,
Xnew)
chain = fitresult[1]
fitresult,
Xnew)
chain = fitresult[1]
Xnew_ = reformat(Xnew)
return [chain(values.(tomat(Xnew_[:,i])))[1]
for i in 1:size(Xnew_, 2)]
return [chain(values.(tomat(Xnew_[:, i])))[1]
for i in 1:size(Xnew_, 2)]
end

MLJModelInterface.metadata_model(NeuralNetworkRegressor,
input=Table(Continuous),
target=AbstractVector{<:Continuous},
path="MLJFlux.NeuralNetworkRegressor")
input=Union{AbstractMatrix{Continuous},Table(Continuous)},
target=AbstractVector{<:Continuous},
path="MLJFlux.NeuralNetworkRegressor")


# # MULTITARGET NEURAL NETWORK REGRESSOR

"""
shape(model::MultitargetNeuralNetworkRegressor, X, y)

A private method that returns the shape of the input and output of the model for given data `X` and `y`.
"""
function shape(model::MultitargetNeuralNetworkRegressor, X, y)
X = X isa Matrix ? Tables.table(X) : X
y = y isa Matrix ? Tables.table(y) : y
n_input = Tables.schema(X).names |> length
n_output = Tables.schema(y).names |> length
return (n_input, n_output)
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Expand All @@ -38,21 +51,26 @@ build(model::MultitargetNeuralNetworkRegressor, rng, shape) =
build(model.builder, rng, shape...)

function fitresult(model::MultitargetNeuralNetworkRegressor, chain, y)
target_column_names = Tables.schema(y).names
if y isa Matrix
target_column_names = nothing
else
target_column_names = Tables.schema(y).names
end
return (chain, target_column_names)
end

function MLJModelInterface.predict(model::MultitargetNeuralNetworkRegressor,
fitresult, Xnew)
chain, target_column_names = fitresult
fitresult, Xnew)
chain, target_column_names = fitresult
X = reformat(Xnew)
ypred = [chain(values.(tomat(X[:,i])))
ypred = [chain(values.(tomat(X[:, i])))
for i in 1:size(X, 2)]
return MLJModelInterface.table(reduce(hcat, y for y in ypred)',
names=target_column_names)
output = isnothing(target_column_names) ? permutedims(reduce(hcat, ypred)) :
MLJModelInterface.table(reduce(hcat, ypred)', names=target_column_names)
return output
end

MLJModelInterface.metadata_model(MultitargetNeuralNetworkRegressor,
input=Table(Continuous),
target=Table(Continuous),
path="MLJFlux.MultitargetNeuralNetworkRegressor")
input=Union{AbstractMatrix{Continuous},Table(Continuous)},
target=Table(Continuous),
path="MLJFlux.MultitargetNeuralNetworkRegressor")
188 changes: 75 additions & 113 deletions src/types.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -5,127 +5,89 @@ const MLJFluxModel = Union{MLJFluxProbabilistic,MLJFluxDeterministic}

for Model in [:NeuralNetworkClassifier, :ImageClassifier]

default_builder_ex =
Model == :ImageClassifier ? :(image_builder(VGGHack)) : Short()

ex = quote
mutable struct $Model{B,F,O,L} <: MLJFluxProbabilistic
builder::B
finaliser::F
optimiser::O # mutable struct from Flux/src/optimise/optimisers.jl
loss::L # can be called as in `loss(yhat, y)`
epochs::Int # number of epochs
batch_size::Int # size of a batch
lambda::Float64 # regularization strength
alpha::Float64 # regularizaton mix (0 for all l2, 1 for all l1)
rng::Union{AbstractRNG,Int64}
optimiser_changes_trigger_retraining::Bool
acceleration::AbstractResource # eg, `CPU1()` or `CUDALibs()`
end

function $Model(; builder::B = $default_builder_ex
, finaliser::F = Flux.softmax
, optimiser::O = Flux.Optimise.Adam()
, loss::L = Flux.crossentropy
, epochs = 10
, batch_size = 1
, lambda = 0
, alpha = 0
, rng = Random.GLOBAL_RNG
, optimiser_changes_trigger_retraining = false
, acceleration = CPU1()
) where {B,F,O,L}

model = $Model{B,F,O,L}(builder
, finaliser
, optimiser
, loss
, epochs
, batch_size
, lambda
, alpha
, rng
, optimiser_changes_trigger_retraining
, acceleration
)

message = clean!(model)
isempty(message) || @warn message

return model
end
default_builder_ex =
Model == :ImageClassifier ? :(image_builder(VGGHack)) : Short()

ex = quote
mutable struct $Model{B,F,O,L} <: MLJFluxProbabilistic
builder::B
finaliser::F
optimiser::O # mutable struct from Flux/src/optimise/optimisers.jl
loss::L # can be called as in `loss(yhat, y)`
epochs::Int # number of epochs
batch_size::Int # size of a batch
lambda::Float64 # regularization strength
alpha::Float64 # regularizaton mix (0 for all l2, 1 for all l1)
rng::Union{AbstractRNG,Int64}
optimiser_changes_trigger_retraining::Bool
acceleration::AbstractResource # eg, `CPU1()` or `CUDALibs()`
end

function $Model(; builder::B=$default_builder_ex, finaliser::F=Flux.softmax, optimiser::O=Flux.Optimise.Adam(), loss::L=Flux.crossentropy, epochs=10, batch_size=1, lambda=0, alpha=0, rng=Random.GLOBAL_RNG, optimiser_changes_trigger_retraining=false, acceleration=CPU1()
) where {B,F,O,L}

model = $Model{B,F,O,L}(builder, finaliser, optimiser, loss, epochs, batch_size, lambda, alpha, rng, optimiser_changes_trigger_retraining, acceleration
)

message = clean!(model)
isempty(message) || @warn message

return model
end
eval(ex)

end
eval(ex)

end


for Model in [:NeuralNetworkRegressor, :MultitargetNeuralNetworkRegressor]

ex = quote
mutable struct $Model{B,O,L} <: MLJFluxDeterministic
builder::B
optimiser::O # mutable struct from Flux/src/optimise/optimisers.jl
loss::L # can be called as in `loss(yhat, y)`
epochs::Int # number of epochs
batch_size::Int # size of a batch
lambda::Float64 # regularization strength
alpha::Float64 # regularizaton mix (0 for all l2, 1 for all l1)
rng::Union{AbstractRNG,Integer}
optimiser_changes_trigger_retraining::Bool
acceleration::AbstractResource # eg, `CPU1()` or `CUDALibs()`
end

function $Model(; builder::B = Linear()
, optimiser::O = Flux.Optimise.Adam()
, loss::L = Flux.mse
, epochs = 10
, batch_size = 1
, lambda = 0
, alpha = 0
, rng = Random.GLOBAL_RNG
, optimiser_changes_trigger_retraining=false
, acceleration = CPU1()
) where {B,O,L}

model = $Model{B,O,L}(builder
, optimiser
, loss
, epochs
, batch_size
, lambda
, alpha
, rng
, optimiser_changes_trigger_retraining
, acceleration)

message = clean!(model)
isempty(message) || @warn message

return model
end
ex = quote
mutable struct $Model{B,O,L} <: MLJFluxDeterministic
builder::B
optimiser::O # mutable struct from Flux/src/optimise/optimisers.jl
loss::L # can be called as in `loss(yhat, y)`
epochs::Int # number of epochs
batch_size::Int # size of a batch
lambda::Float64 # regularization strength
alpha::Float64 # regularizaton mix (0 for all l2, 1 for all l1)
rng::Union{AbstractRNG,Integer}
optimiser_changes_trigger_retraining::Bool
acceleration::AbstractResource # eg, `CPU1()` or `CUDALibs()`
end

function $Model(; builder::B=Linear(), optimiser::O=Flux.Optimise.Adam(), loss::L=Flux.mse, epochs=10, batch_size=1, lambda=0, alpha=0, rng=Random.GLOBAL_RNG, optimiser_changes_trigger_retraining=false, acceleration=CPU1()
) where {B,O,L}

model = $Model{B,O,L}(builder, optimiser, loss, epochs, batch_size, lambda, alpha, rng, optimiser_changes_trigger_retraining, acceleration)

message = clean!(model)
isempty(message) || @warn message

return model
end
eval(ex)

end
eval(ex)

end

const Regressor =
Union{NeuralNetworkRegressor, MultitargetNeuralNetworkRegressor}
Union{NeuralNetworkRegressor,MultitargetNeuralNetworkRegressor}

MMI.metadata_pkg.(
(
NeuralNetworkRegressor,
MultitargetNeuralNetworkRegressor,
NeuralNetworkClassifier,
ImageClassifier,
),
name="MLJFlux",
uuid="094fc8d1-fd35-5302-93ea-dabda2abf845",
url="https://github.com/alan-turing-institute/MLJFlux.jl",
julia=true,
license="MIT",
(
NeuralNetworkRegressor,
MultitargetNeuralNetworkRegressor,
NeuralNetworkClassifier,
ImageClassifier,
),
name="MLJFlux",
uuid="094fc8d1-fd35-5302-93ea-dabda2abf845",
url="https://github.com/alan-turing-institute/MLJFlux.jl",
julia=true,
license="MIT",
)


Expand All @@ -148,8 +110,8 @@ In MLJ or MLJBase, bind an instance `model` to data with

Here:

- `X` is any table of input features (eg, a `DataFrame`) whose columns are of scitype
`Continuous`; check column scitypes with `schema(X)`.
- `X` is either a `Matrix` or any table of input features (eg, a `DataFrame`) whose columns are of scitype
`Continuous`; check column scitypes with `schema(X)`. If `X` is a `Matrix`, it is assumed to have columns corresponding to features and rows corresponding to observations.

- `y` is the target, which can be any `AbstractVector` whose element scitype is `Multiclass`
or `OrderedFactor`; check the scitype with `scitype(y)`
Expand Down Expand Up @@ -583,8 +545,8 @@ In MLJ or MLJBase, bind an instance `model` to data with

Here:

- `X` is any table of input features (eg, a `DataFrame`) whose columns
are of scitype `Continuous`; check the column scitypes with `schema(X)`.
- `X` is either a `Matrix` or any table of input features (eg, a `DataFrame`) whose columns are of scitype
`Continuous`; check column scitypes with `schema(X)`. If `X` is a `Matrix`, it is assumed to have columns corresponding to features and rows corresponding to observations.
- `y` is the target, which can be any `AbstractVector` whose element
scitype is `Continuous`; check the scitype with `scitype(y)`

Expand Down Expand Up @@ -810,11 +772,11 @@ In MLJ or MLJBase, bind an instance `model` to data with

Here:

- `X` is any table of input features (eg, a `DataFrame`) whose columns are of scitype
`Continuous`; check column scitypes with `schema(X)`.
- `X` is either a `Matrix` or any table of input features (eg, a `DataFrame`) whose columns are of scitype
`Continuous`; check column scitypes with `schema(X)`. If `X` is a `Matrix`, it is assumed to have columns corresponding to features and rows corresponding to observations.

- `y` is the target, which can be any table of output targets whose element scitype is
`Continuous`; check column scitypes with `schema(y)`.
- `y` is the target, which can be any table or matrix of output targets whose element scitype is
`Continuous`; check column scitypes with `schema(y)`. If `y` is a `Matrix`, it is assumed to have columns corresponding to variables and rows corresponding to observations.


# Hyper-parameters
Expand Down
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