MLUtils.jl

MLUtils.jl defines interfaces and implements common utilities for Machine Learning pipelines.

Features

• An extensible dataset interface (numobs and getobs).
• Data iteration and dataloaders (eachobs and DataLoader).
• Lazy data views (obsview).
• Resampling procedures (undersample and oversample).
• Train/test splits (splitobs)
• Data partitioning and aggregation tools (batch, unbatch, chunk, group_counts, group_indices).
• Folds for cross-validation (kfolds, leavepout).
• Datasets lazy tranformations (mapobs, filterobs, groupobs, joinobs, shuffleobs).
• Toy datasets for demonstration purpose.
• Other data handling utilities (flatten, normalise, unsqueeze, stack, unstack).

Examples

Let us take a look at a hello world example to get a feeling for how to use this package in a typical ML scenario.

using MLUtils

# X is a matrix of floats
# Y is a vector of strings
X, Y = load_iris()

# The iris dataset is ordered according to their labels,
# which means that we should shuffle the dataset before
# partitioning it into training- and test-set.
Xs, Ys = shuffleobs((X, Y))

# We leave out 15 % of the data for testing
cv_data, test_data = splitobs((Xs, Ys); at=0.85)

# Next we partition the data using a 10-fold scheme.
for (train_data, val_data) in kfolds(cv_data; k=10)

    # We apply a lazy transform for data augmentation
    train_data = mapobs(xy -> (xy[1] .+ 0.1 .* randn.(), xy[2]),  train_data)

    for epoch = 1:10
        # Iterate over the data using mini-batches of 5 observations each
        for (x, y) in eachobs(train_data, batchsize=5)
            # ... train supervised model on minibatches here
        end
    end
end

In the above code snippet, the inner loop for eachobs is the only place where data other than indices is actually being copied. In fact, while x and y are materialized arrays, all the rest are data views.

Related Packages

MLUtils.jl brings together functionalities previously found in LearnBase.jl , MLDataPattern.jl and MLLabelUtils.jl. These packages are now discontinued.

Other features were ported from the deep learning library Flux.jl, as they are of general use.

MLJ.jl is a more complete package for managing the whole machine learning pipeline if you are looking for a sklearn replacement.