Models

NeuroTabRegressor

NeuroTabModels.Learners.NeuroTabRegressor Type

NeuroTabRegressor(arch::Architecture; kwargs...) NeuroTabRegressor(; arch_name="NeuroTreeConfig", arch_config::AbstractDict=Dict(), kwargs...)

A model type for constructing a NeuroTabRegressor, based on NeuroTabModels.jl, and implementing both an internal API and the MLJ model interface.

Hyper-parameters

• loss=:mse: Loss to be be minimized during training. One of:

  • :mse

  • :mae

  • :logloss

  • :mlogloss

  • :gaussian_mle

• nrounds=100: Max number of rounds (epochs).

• lr=1.0f-2: Learning rate. Must be > 0. A lower eta results in slower learning, typically requiring a higher nrounds.

• wd=0.f0: Weight decay applied to the gradients by the optimizer.

• batchsize=2048: Batch size.

• seed=123: An integer used as a seed to the random number generator.

• device=:cpu: Device on which to perform the computation, either :cpu or :gpu

• gpuID=0: GPU device to use, only relveant if device = :gpu

Internal API

Do config = NeuroTabRegressor() to construct an instance with default hyper-parameters. Provide keyword arguments to override hyper-parameter defaults, as in NeuroTabRegressor(loss=:logistic, depth=5, ...).

Training model

A model is trained using fit:

m = fit(config, dtrain; feature_names, target_name, kwargs...)

Inference

Models act as a functor. returning predictions when called as a function with features as argument:

m(data)

MLJ Interface

From MLJ, the type can be imported using:

NeuroTabRegressor = @load NeuroTabRegressor pkg=NeuroTabModels

Do model = NeuroTabRegressor() to construct an instance with default hyper-parameters. Provide keyword arguments to override hyper-parameter defaults, as in NeuroTabRegressor(loss=...).

Training model

In MLJ or MLJBase, bind an instance model to data with mach = machine(model, X, y) where

• X: any table of input features (eg, a DataFrame) whose columns each have one of the following element scitypes: Continuous, Count, or <:OrderedFactor; check column scitypes with schema(X)

• y: is the target, which can be any AbstractVector whose element scitype is <:Continuous; check the scitype with scitype(y)

Train the machine using fit!(mach, rows=...).

Operations

• predict(mach, Xnew): return predictions of the target given features Xnew having the same scitype as X above.

Fitted parameters

The fields of fitted_params(mach) are:

• :fitresult: The NeuroTabModel object.

Report

The fields of report(mach) are:

• :features: The names of the features encountered in training.

Examples

Internal API

using NeuroTabModels, DataFrames
config = NeuroTabRegressor(depth=5, nrounds=10)
nobs, nfeats = 1_000, 5
dtrain = DataFrame(randn(nobs, nfeats), :auto)
dtrain.y = rand(nobs)
feature_names, target_name = names(dtrain, r"x"), "y"
m = fit(config, dtrain; feature_names, target_name)
p = m(dtrain)

MLJ Interface

using MLJBase, NeuroTabModels
m = NeuroTabRegressor(depth=5, nrounds=10)
X, y = @load_boston
mach = machine(m, X, y) |> fit!
p = predict(mach, X)

source

NeuroTabClassifier

NeuroTabModels.Learners.NeuroTabClassifier Type

NeuroTabClassifier(arch::Architecture; kwargs...) NeuroTabClassifier(; arch_name="NeuroTreeConfig", arch_config::AbstractDict=Dict(), kwargs...)

A model type for constructing a NeuroTabClassifier, based on NeuroTabModels.jl, and implementing both an internal API and the MLJ model interface.

Hyper-parameters

• nrounds=100: Max number of rounds (epochs).

• lr=1.0f-2: Learning rate. Must be > 0. A lower eta results in slower learning, typically requiring a higher nrounds.

• wd=0.f0: Weight decay applied to the gradients by the optimizer.

• batchsize=2048: Batch size.

• seed=123: An integer used as a seed to the random number generator.

• device=:cpu: Device on which to perform the computation, either :cpu or :gpu

• gpuID=0: GPU device to use, only relveant if device = :gpu

Internal API

Do config = NeuroTabClassifier() to construct an instance with default hyper-parameters. Provide keyword arguments to override hyper-parameter defaults, as in NeuroTabClassifier(depth=5, ...).

Training model

A model is trained using fit:

m = fit(config, dtrain; feature_names, target_name, kwargs...)

Inference

Models act as a functor. returning predictions when called as a function with features as argument:

m(data)

MLJ Interface

From MLJ, the type can be imported using:

NeuroTabClassifier = @load NeuroTabClassifier pkg=NeuroTabModels

Do model = NeuroTabClassifier() to construct an instance with default hyper-parameters. Provide keyword arguments to override hyper-parameter defaults, as in NeuroTabClassifier(loss=...).

Training model

In MLJ or MLJBase, bind an instance model to data with mach = machine(model, X, y) where

• X: any table of input features (eg, a DataFrame) whose columns each have one of the following element scitypes: Continuous, Count, or <:OrderedFactor; check column scitypes with schema(X)

• y: is the target, which can be any AbstractVector whose element scitype is <:Finite; check the scitype with scitype(y)

Train the machine using fit!(mach, rows=...).

Operations

• predict(mach, Xnew): return predictions of the target given features Xnew having the same scitype as X above.

Fitted parameters

The fields of fitted_params(mach) are:

• :fitresult: The NeuroTabModel object.

Report

The fields of report(mach) are:

• :features: The names of the features encountered in training.

Examples

Internal API

using NeuroTabModels, DataFrames, CategoricalArrays, Random 
config = NeuroTabClassifier(depth=5, nrounds=10)
nobs, nfeats = 1_000, 5
dtrain = DataFrame(randn(nobs, nfeats), :auto)
dtrain.y = categorical(rand(1:2, nobs))
feature_names, target_name = names(dtrain, r"x"), "y"
m = fit(config, dtrain; feature_names, target_name)
p = m(dtrain)

MLJ Interface

using MLJBase, NeuroTabModels
m = NeuroTabClassifier(depth=5, nrounds=10)
X, y = @load_crabs
mach = machine(m, X, y) |> fit!
p = predict(mach, X)

source

NeuroTabModel

NeuroTabModels.Models.NeuroTabModel Type

NeuroTabModel

source