Julia下的混合集成學習包：Orchestra

Orchestra是Julia編程語言的一個異構集成學習包。它由一個統一的機器學習API驅動，是Julia下對Scikit-Learn和Carret的統一。

入門

We will cover how to predict on a dataset using Orchestra.

獲取數據

A tabular dataset will be used to obtain our instances and labels.

This will be split it into a training and test set using holdout method.

import RDatasets
using Orchestra.Util
using Orchestra.Transformers

# Obtain instances and labels
dataset = RDatasets.dataset("datasets", "iris")
instances = array(dataset[:, 1:(end-1)])
labels = array(dataset[:, end])

# Split into training and test sets
(train_ind, test_ind) = holdout(size(instances, 1), 0.3)

Create a Learner

A transformer processes instances in some form. Coincidentally, a learner is a subtype of transformer.

A transformer can be created by instantiating it, taking an options dictionary as an optional argument.

All transformers, including learners are called in the same way.

# Learner with default settings
learner = PrunedTree()

# Learner with some of the default settings overriden
learner = PrunedTree({
  :impl_options => {
    :purity_threshold => 0.5
  }
})

# All learners are called in the same way.
learner = StackEnsemble({
  :learners => [
    PrunedTree(), 
    RandomForest(),
    DecisionStumpAdaboost()
  ], 
  :stacker => RandomForest()
})

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