JuliaML is a GitHub organization dedicated to building tools for machine learning in Julia. This post serves as an introduction to some of the building blocks which JuliaML provides. The two packages I'll discuss here are:

I'm assuming you have a little background in statistics/machine learning and an objective function such as

\[\frac{1}{n}\sum\_{i=1}^n f\_i(\beta)+\sum\_{j=1}^p \lambda\_j J(\beta\_j)\]is recognizable to you as a "loss" + "penalty".

An interface for dealing with loss functions. Internally, most losses are defined as distance-based (a function of \(\hat y-y\)) or margin-based (a function of \(y * \hat y\)) where \(y\) is the target and \(\hat y\) is a predicted value.

```
using LossFunctions
l = L1DistLoss()
value(l, .1, .2) # |.2 - .1|
deriv(l, .1, .2) # sign(.2 - .1)
y = randn(100)
yhat = randn(100)
value(l, y, yhat) # vector of value mapped to y[i], yhat[i]
value(l, y, yhat, AvgMode.Sum()) # sum(value(l, y, yhat)), but better
value(l, y, yhat, AvgModel.Mean()) # mean(value(l, y, yhat)), but better
```

Assume we want to use a linear transformation so that our prediction of a vector \(y\) is \(X\beta\).

Our loss function looks like \[ \frac{1}{n}\sum\_{i=1}^n f(y\_i, x\_i^T\beta), \] with gradient \[ X^T[\frac{1}{n}\sum_{i=1}^n f'(y\_i, x\_i^T\beta)]. \]

We can implement a naive, inefficient gradient descent algorithm as:

```
function f(l::Loss, x::Matrix, y::Vector; maxit=20, s=.5)
n, p = size(x)
β = zeros(p)
for i in 1:maxit
β -= (s / n) * x' * deriv(l, y, x * β)
end
β
end
```

This automatically works with whatever loss function I provide because multiple dispatch is amazing: ```
# make some fake data
x = randn(1000, 3)
y = x * [1.0, 2.0, 3.0] + randn(1000)
```

```
julia> f(L2DistLoss(), x, y)
3-element Array{Float64,1}:
0.966367
2.05046
2.94227
julia> f(L1DistLoss(), x, y)
3-element Array{Float64,1}:
1.00185
2.00302
2.95002
julia> f(HuberLoss(2.), x, y)
3-element Array{Float64,1}:
0.967642
2.0485
2.94429
```

```
θ = rand(5) # parameter vector
p = L1Penalty()
value(p, θ) # sum(abs, θ)
grad(p, θ) # the gradient, sign.(θ)
prox(p, θ, .1) # proximal mapping (soft-thresholding) with step size .1
```

Let's change our gradient descent algorithm to proximal gradient method:

```
function g(l::Loss, pen::Penalty, x::Matrix, y::Vector; maxit=20, s=.5, λ=.1)
n, p = size(x)
β = zeros(p)
for i in 1:maxit
β = prox(pen, β - (s / n) * x' * deriv(l, y, x * β), s * λ)
end
β
end
```

We can now do proximal gradient method on arbitrary loss/penalty combinations because multiple dispatch is amazing:

```
julia> g(L2DistLoss(), L1Penalty(), x, y; λ = .4)
3-element Array{Float64,1}:
0.789894
1.80736
2.81209
julia> g(L1DistLoss(), L1Penalty(), x, y; λ = .4)
3-element Array{Float64,1}:
0.0
0.464488
1.5998
julia> g(HuberLoss(2.), L1Penalty(), x, y; λ = .4)
3-element Array{Float64,1}:
0.525633
1.57115
2.57907
```

These two packages create a consistent "grammar" for losses and regularization. Julia's multiple dispatch then allows us to write general algorithms like the proximal gradient method example. This paves the way for machine learning experimentation that is unavailable in any other language that I know of.

© Josh Day. Last modified: July 16, 2020. Website built with Franklin.jl.