# Overview

============================================================= LLAMA: Leveraging Learning to Automatically Manage Algorithms ============================================================= LLAMA is an R package for algorithm portfolios and selection. It does not provide any actual machine learning algorithms, but rather the infrastructure required to use those in an algorithm selection context. There are functions to create the most common types of algorithm selection models used in the literature. If you have any questions or feedback, please contact Lars Kotthoff <larsko@uwyo.edu>. Quick start =========== So you know about algorithm portfolios and selection and just want to get started. Here we go. In your R shell, type :: install.packages("llama") require(llama) to install and load LLAMA. We're going to assume that you have two input CSV files for your data -- features and times. The rows designate problem instances and the columns feature and solver names. All files have an 'ID' column that allows to link them. Load them into the data structure required by LLAMA as follows. :: data = input(read.csv("features.csv"), read.csv("times.csv")) You can also use the SAT solver data that comes with LLAMA by running :: data(satsolvers) data = satsolvers Now partition the entire set of instances into training and test sets for cross-validation. :: folds = cvFolds(data) This will give you 10 folds for cross-validation. Now we're ready to train our first model. To do that, we'll need some machine learning algorithms -- LLAMA is integrated with mlr <https://github.com/berndbischl/mlr>_ and supports all its learning algorithms. We're going to use a random forest here and train a simple classification model that predicts the best algorithm. :: model = classify(makeLearner("classif.randomForest"), folds) Great! Now let's see how well this model is doing and compare its performance to the virtual best solver (VBS) and the single best solver in terms of average misclassification penalty. :: mean(misclassificationPenalties(data, vbs)) mean(misclassificationPenalties(folds, model)) mean(misclassificationPenalties(data, singleBest)) These are the numbers I get for the satsolvers data: :virtual best: 0 :model: 74.73368 :single best: 122.3186 While we are quite far off the virtual best, our classifier beats the single best algorithm! Not bad for a model trained in a single line of code. You can use any other classification algorithms instead of a random forest of course. You can also train regression or cluster models, use different train/test splits or preprocess the data by selecting the most important features. More details in the on-line documentation and the manual. More information ================ More information can be found in the manual at http://arxiv.org/abs/1306.1031, which is also included in the R package. In addition, there are R help pages for all functions. If you find LLAMA helpful, it would be great if you could cite the manual in any publications! :: @techreport{kotthoff_llama_2013, address = {{arXiv}}, title = {{LLAMA:} Leveraging Learning to Automatically Manage Algorithms}, url = {http://arxiv.org/abs/1306.1031}, number = {{arXiv:1306.1031}}, author = {Kotthoff, Lars}, month = jun, year = {2013} }