orca

Adding a new method to ORCA

ORCA is designed to ease the process of adding new methods. You only need to add the new algorithm’s class file to folder src/Algorithms. After that, the method will be available to the framework and configuration files can be used to automate experiments.

Method template

The code has follow the following template, which basically satisfies the API defined in the Algorithm abstract class:

classdef NEWMETHOD < Algorithm    
    properties
        description = 'my NEWMETHOD method description';
        % Parameters to optimize and default value
        parameters = struct('k', 5);
    end

    methods    
        function obj = NEWMETHOD(varargin)
            % Process key-values pairs of parameters
            obj.parseArgs(varargin);
        end

        function [projectedTrain, predictedTrain]= privfit( obj, train, param)
            % fit the model and return prediction of train set. It is called by
            % super class Algorithm.fit() method.
            ...
            % Save the model
            obj.model = model;
        end

        function [projected, predicted] = privpredict(obj, testPatterns)
            % predict unseen patterns with 'obj.model' and return prediction and
            % projection of patterns (for threshold models)
            % It is called by super class Algorithm.predict() method.
        end
    end    
end

Where train is a structure with train.patterns being a matrix of patterns and train.targets being a vector with the corresponding labels. model class property stores the model built with the train data.

Example: adding KNN to ORCA

To illustrate the one-step process of adding a new method, we will add the KNN classifier to ORCA. Just copy the file KNN.m to folder src/Algorithms:

classdef KNN < Algorithm
    %KNN Basic k-nearest neighbors algorithm based on Euclidean distance

    properties
        description = 'k-nearest neighbors algorithm';
        % Parameters to optimize and default value
        parameters = struct('k', 5);
    end

    methods    
        function obj = KNN(varargin)
            %KNN constructs an object of the class KNN. Default k is 5
            %
            %   OBJ = KNN('k', neighbours)
            %   builds KNN with NEIGHBOURS as number of neighbours to consider
            %   to label new patterns.
            obj.parseArgs(varargin);
        end

        function [projectedTrain, predictedTrain]= privfit( obj, train, param)
            if(nargin == 3)
                obj.parameters.k = param.k;
            end

            % save train data in the model structure
            obj.model.train = train;
            obj.model.parameters = obj.parameters;
            % Predict train labels
            [projectedTrain, predictedTrain] = predict(obj, train.patterns);
        end

        function [projected, predicted] = privpredict(obj, testPatterns)
            % Variables aliases
            x = obj.model.train.patterns;
            xlabel = obj.model.train.targets;
            k = obj.model.parameters.k;

            dist = pdist2(testPatterns,x);
            % indicies of nearest neighbors
            [~,nearest] = sort(dist,2);
            % k nearest
            nearest = nearest(:,1:k);
            % mode of k nearest
            val = xlabel(nearest);
            predicted = mode(val,2);

            % dummy value for projections
            projected = -1.*ones(length(testPatterns),1);
        end
    end    
end

Then, you can define a configuration file such as knntoy.ini to describe experiments using KNN:

;Experiment ID
[knn-mae-toy]
{general-conf}
;Datasets path
basedir = ../exampledata/30-holdout
;Datasets to process (comma separated list)
datasets = toy
;Activate data standardization
standarize = true
;Number of folds for the parameters optimization
num_folds = 5
;Crossvalidation metric
cvmetric = mae

;Method: algorithm and parameter
{algorithm-parameters}
algorithm = KNN

;Method's hyper-parameter values to optimize
{algorithm-hyper-parameters-to-cv}
k = 3,5,7

To run experiments described in that file, from src folder type:

Utilities.runExperiments('../doc/addmethod/knntoy.ini')

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