NeuralNetworkBinaryClassifier

NeuralNetworkBinaryClassifier

A model type for constructing a neural network binary classifier, based on MLJFlux.jl, and implementing the MLJ model interface.

From MLJ, the type can be imported using

NeuralNetworkBinaryClassifier = @load NeuralNetworkBinaryClassifier pkg=MLJFlux

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

NeuralNetworkBinaryClassifier is for training a data-dependent Flux.jl neural network for making probabilistic predictions of a binary (Multiclass{2} or OrderedFactor{2}) target, given a table of Continuous features. Users provide a recipe for constructing the network, based on properties of the data that is encountered, by specifying an appropriate builder. See MLJFlux documentation for more on builders.

Training data

In MLJ or MLJBase, bind an instance model to data with

mach = machine(model, X, y)

Here:

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{2} or OrderedFactor{2}; check the scitype with scitype(y)

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

Hyper-parameters

builder=MLJFlux.Short(): An MLJFlux builder that constructs a neural network. Possible builders include: MLJFlux.Linear, MLJFlux.Short, and MLJFlux.MLP. See MLJFlux.jl documentation for examples of user-defined builders. See also finaliser below.
optimiser::Flux.Adam(): A Flux.Optimise optimiser. The optimiser performs the updating of the weights of the network. For further reference, see the Flux optimiser documentation. To choose a learning rate (the update rate of the optimizer), a good rule of thumb is to start out at 10e-3, and tune using powers of 10 between 1 and 1e-7.
loss=Flux.binarycrossentropy: The loss function which the network will optimize. Should be a function which can be called in the form loss(yhat, y). Possible loss functions are listed in the Flux loss function documentation. For a classification task, the most natural loss functions are:
- Flux.binarycrossentropy: Standard binary classification loss, also known as the log loss.
- Flux.logitbinarycrossentropy: Mathematically equal to crossentropy, but numerically more stable than finalising the outputs with σ and then calculating crossentropy. You will need to specify finaliser=identity to remove MLJFlux's default sigmoid finaliser, and understand that the output of predict is then unnormalized (no longer probabilistic).
- Flux.tversky_loss: Used with imbalanced data to give more weight to false negatives.
- Flux.binary_focal_loss: Used with highly imbalanced data. Weights harder examples more than easier examples.
Currently MLJ measures are not supported values of loss.
epochs::Int=10: The duration of training, in epochs. Typically, one epoch represents one pass through the complete the training dataset.
batch_size::int=1: the batch size to be used for training, representing the number of samples per update of the network weights. Typically, batch size is between 8 and 512. Increassing batch size may accelerate training if acceleration=CUDALibs() and a GPU is available.
lambda::Float64=0: The strength of the weight regularization penalty. Can be any value in the range [0, ∞).
alpha::Float64=0: The L2/L1 mix of regularization, in the range [0, 1]. A value of 0 represents L2 regularization, and a value of 1 represents L1 regularization.
rng::Union{AbstractRNG, Int64}: The random number generator or seed used during training.
optimizer_changes_trigger_retraining::Bool=false: Defines what happens when re-fitting a machine if the associated optimiser has changed. If true, the associated machine will retrain from scratch on fit! call, otherwise it will not.
acceleration::AbstractResource=CPU1(): Defines on what hardware training is done. For Training on GPU, use CUDALibs().
finaliser=Flux.σ: The final activation function of the neural network (applied after the network defined by builder). Defaults to Flux.σ.

Operations

predict(mach, Xnew): return predictions of the target given new features Xnew, which should have the same scitype as X above. Predictions are probabilistic but uncalibrated.
predict_mode(mach, Xnew): Return the modes of the probabilistic predictions returned above.

Fitted parameters

The fields of fitted_params(mach) are:

chain: The trained "chain" (Flux.jl model), namely the series of layers, functions, and activations which make up the neural network. This includes the final layer specified by finaliser (eg, softmax).

Report

The fields of report(mach) are:

training_losses: A vector of training losses (penalised if lambda != 0) in historical order, of length epochs + 1. The first element is the pre-training loss.

Examples

In this example we build a classification model using the Iris dataset. This is a very basic example, using a default builder and no standardization. For a more advanced illustration, see NeuralNetworkRegressor or ImageClassifier, and examples in the MLJFlux.jl documentation.

using MLJ, Flux
import Optimisers
import RDatasets

First, we can load the data:

mtcars = RDatasets.dataset("datasets", "mtcars");
y, X = unpack(mtcars, ==(:VS), in([:MPG, :Cyl, :Disp, :HP, :WT, :QSec]));

Note that y is a vector and X a table.

y = categorical(y) ## classifier takes catogorical input
X_f32 = Float32.(X) ## To match floating point type of the neural network layers
NeuralNetworkBinaryClassifier = @load NeuralNetworkBinaryClassifier pkg=MLJFlux
bclf = NeuralNetworkBinaryClassifier()

Next, we can train the model:

mach = machine(bclf, X_f32, y)
fit!(mach)

We can train the model in an incremental fashion, altering the learning rate as we go, provided optimizer_changes_trigger_retraining is false (the default). Here, we also change the number of (total) iterations:

julia> bclf.optimiser
Adam(0.001, (0.9, 0.999), 1.0e-8)

bclf.optimiser = Optimisers.Adam(eta = bclf.optimiser.eta * 2)
bclf.epochs = bclf.epochs + 5

fit!(mach, verbosity=2) ## trains 5 more epochs

We can inspect the mean training loss using the cross_entropy function:

training_loss = cross_entropy(predict(mach, X_f32), y)

And we can access the Flux chain (model) using fitted_params:

chain = fitted_params(mach).chain

Finally, we can see how the out-of-sample performance changes over time, using MLJ's learning_curve function:

r = range(bclf, :epochs, lower=1, upper=200, scale=:log10)
curve = learning_curve(
    bclf,
    X_f32,
    y,
    range=r,
    resampling=Holdout(fraction_train=0.7),
    measure=cross_entropy,
)
using Plots
plot(
   curve.parameter_values,
   curve.measurements,
   xlab=curve.parameter_name,
   xscale=curve.parameter_scale,
   ylab = "Cross Entropy",
)