nnetsauce, a package for quasi-randomized supervised learning (classification and regression), is currently available for R and Python. For more details on nnetsauce, you can read these posts.

I’ve always wanted to port nnetsauce to the Julia language. However, in the past few years, there was a little timing overhead (more precisely, a lag) when I tried to do that with Julia’s PyCall, based on my Python source code. This overhead seems to have ‘disappeared’ now.

Julia language’s nnetsauce is not a package yet, but you can already use nnetsauce in Julia.

Here’s how I did it on Ubuntu Linux:

Contents

• 1 – Install Julia
• 2 – Example using a nnetsauce classifier in Julia language

1 – Install Julia

See also: https://www.digitalocean.com/community/tutorials/how-to-install-julia-programming-language-on-ubuntu-22-04.

Run (terminal):

wget https://julialang-s3.julialang.org/bin/linux/x64/1.9/julia-1.9.4-linux-x86_64.tar.gz

Run (terminal):

tar zxvf julia-1.9.4-linux-x86_64.tar.gz

Run (terminal)(This is VSCode, but use your favorite editor here):

code ~/.bashrc

Add to .bashrc (last line):

export PATH="$PATH:julia-1.9.4/bin"

Run (terminal):

source ~/.bashrc

Run (terminal):

julia nnetsauce_example.jl

2 – Example using a nnetsauce classifier in Julia language

For Python user’s, notice that this is basically Python ^^

using Pkg
ENV["PYTHON"] = ""  # replace with your Python path
Pkg.add("PyCall")
Pkg.build("PyCall")
Pkg.add("Conda")
Pkg.build("Conda")

using PyCall
using Conda

Conda.add("pip")  # Ensure pip is installed
Conda.pip_interop(true)  # Enable pip interop
Conda.pip("install", "scikit-learn")  # Install scikit-learn
Conda.pip("install", "jax")  # /!\ Only on Linux or macOS: Install jax
Conda.pip("install", "jaxlib")  # /!\ Only on Linux or macOS: Install jaxlib
Conda.pip("install", "nnetsauce")  # Install nnetsauce
Conda.add("numpy")

np = pyimport("numpy")
ns = pyimport("nnetsauce")
sklearn = pyimport("sklearn")


# 1 - breast cancer dataset

dataset = sklearn.datasets.load_breast_cancer()

X = dataset["data"]
y = dataset["target"]

X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split(X, y, 
test_size=0.2, random_state=123)

clf = ns.Ridge2MultitaskClassifier(n_hidden_features=9, dropout=0.43, n_clusters=1, 
lambda1=1.24023438e+01, lambda2=7.30263672e+03)

@time clf.fit(X=X_train, y=y_train) # timing?

print("\n\n Model parameters: \n\n")
print(clf.get_params())

print("\n\n Testing score: \n\n") # Classifier's accuracy
print(clf.score(X_test, y_test)) # Must be: 0.9824561403508771
print("\n\n")

# 2 - wine dataset

dataset = sklearn.datasets.load_wine()

X = dataset["data"]
y = dataset["target"]

X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split(X, y, 
test_size=0.2, random_state=123)

clf = ns.Ridge2MultitaskClassifier(n_hidden_features=15,
dropout=0.1, n_clusters=3, 
type_clust="gmm")

@time clf.fit(X=X_train, y=y_train) # timing?

print("\n\n Model parameters: \n\n")
print(clf.get_params())

print("\n\n Testing score: \n\n") # Classifier's accuracy
print(clf.score(X_test, y_test)) # Must be 1.0
print("\n\n")