Work in progress for a front-end supervised learning framework. Currently the focus is on creating a pure Julia package for SVMs in KSVM.jl
The goal of this library is manyfold:
- Education: allow the user to play around with the models, solvers, etc. for educational purposes. Provide a good base for course exercises. For example visualizing the learning curve of neural networks using different optimization algorithms.
- Research: Swap out parts of the machine learning pipeline with custom implementations without losing the ability to utilize the rest of the framework. For example to prototype new prediction models.
- Application: Porcelain interface to apply machine learning to given datasets in a convenient way. There might be multiple high-level interface for different usergroups (e.g. one that mimics R's caret package)
The following code should already work
using SupervisedLearning
using RDatasets
using UnicodePlots
data = dataset("datasets", "mtcars")
# In this case the dataset will be in-memory and encoded to -1, 1
# There will also be support for datastreaming from HDF5
problemSet = dataSource(AM ~ DRat + WT + DRat&WT, data, SignedClassEncoding)
# Convenient to use with UnicodePlots
print(barplot(classDistribution(problemSet)...))
# Methods for splitting the abstract data sets
trainSet, testSet = splitTrainTest!(problemSet, p_train = .75)
# Specifies the model and modelspecific parameter
model = Classifier.LogisticRegression(l2_coef=0.1)
# Backend for neural networks will be Mocha.jl or OnlineAI.jl
# model = Classifier.FeedForwardNeuralNetwork([4,5,7],[ReLu,ReLu,ReLu])
# train! mutates the model state
# * the do-block is the callback function which also allows for early stopping
# * In the regression case Solver.GradientDescent() will result in using Regression.jl,
# otherwise (in most deterministic cases) Optim.jl
# * There will also be stochastic gradient descent with minibatches
train!(model, trainSet, Solver.GradientDescent(), max_iter = 10000, break_every = 100) do
# You can also use the callback to execute any code
# For example to print informative messages
println("Testset accuracy: ", accuracy(model, testSet))
# You can easily store custom learning curves or other arbitrary values
# They will be linked to the correct iteration automatically
remember!(model, :testsetCost, cost(model, testSet))
end
# The loss of the training set is stored by default and can be accessed with trainingCurve
# x is a Vector{Int} of iterations with stepsize break_every,
# y is a Vector{Float64} where y[i] is the cost of the trainSet at x[i]
x, y = trainingCurve(model)
print(lineplot(x, y, title = "Learning curve for trainSet"))
# Customly stored curves can be accessed with "history"
# x is a Vector{Int} of iterations (exact values depend on when you called remember!),
# y is a Vector{Float64} where y[i] is the cost of the testSet at x[i]
x, y = history(model, :testsetCost)
print(lineplot(x, y, title = "Learning curve for testSet"))
ŷ = predict(model, testSet) # what the model says
t = groundtruth(testSet) # what it should beThis is just a rough draft and still object to change
using SupervisedLearning
using RDatasets
data = dataset("datasets", "mtcars")
# In this case the dataset will be in-memory.
# Specifying the encoding is not necessary.
# The model will select the encoding it needs automatically
# Trees for example don't need an encoding at all.
problemSet = dataSource(AM ~ DRat + WT, data)
# Methods for splitting the abstract data sets
trainSet, testSet = splitTrainTest!(problemSet, p_train = .75)
# Perform a gridsearch over an arbitrary modelspace
gsResult = gridsearch([.001, .01, .1], [.0001, .0003]) do lr, lambda
# Perform cross validation to get a good estimate for the hyperparameter performance
cvResult = crossvalidate(trainSet, k = 5) do trainFold, valFold
# Specify the model and model-specific parameters
model = Classifier.LogisticRegression(l2_coef = lambda)
# Specify the solver and solver-specific parameters
solver = Solver.NaiveGradientDescent(learning_rate = lr, normalize_gradient = false)
# train! mutates the model state
train!(model, trainFold, solver, max_iter = 1000)
# make sure to return the trained model
model
end
# You can return a model or a cvResult to gridsearch
cvResult
end
# Plot the final accuracy of all trained models using UnicodePlots
print(barplot(accuracy(gsResult, testSet)...))
# Get the best model
bestModel = gsResult.bestModel
ŷ = predict(bestModel, testSet)