@@ -174,7 +174,65 @@ The results of all `RunCalls` are aggregated and form the output of the total `O
### Recommendation
The main class `RecommendationTrainingConfiguration` consists of five main elements: A list of `EstimationSpecifications` to learn as approaches, the `ValidationSpecification` containing information about how the results should be validated, a list of `InputData` objects as training set, a `FeatureExtractorSpecifier` specifying how the features should be extracted for the traces and a `RecommendationAlgorithmSpecifier` defining and configuring the algorithm to use for recommendation.
#### 1. `EstimationSpecifications`
In contrast to `OptimzationConfiguration`, several different `EstimationSpecifications` from the LibReDE meta-model can be referenced in `RecommendationTrainingConfiguration`.
The estimators list contains all the candidate `EstimationSpecifications` to learn.
During the learning process, all of these are evaluated and the algorithms will try to learn which `EstimationSpecification` fits best based on the features of a specific trace.
The specifications will not be modified during that process.
Usually, the output of the optimization process will be set as the estimators list of the `RecommendationTrainingConfiguration`.
Although a list with just one element is acceptable, it does not make a lot of sense, since algorithms always have to return this specification.
#### 2. `ValidationSpecification`
The `ValidationSpecification` contains information about how to validate the results.
A `ValidationSpecification` is also required in the `OptimizationConfiguration`.
However, it is wrapped in the `OptimizationSettings` along with other settings.
The `ValidationSpecification` class is also defined in the original LibReDE meta-model, so we omit a more detailed description here.
#### 3. `TrainingData`
Similarly to the optimization a set of `InputData` objects define the training set.
The used class is directly inherited from the `OptimizationConfiguration`, so we refer to the previous section for more details.
Although the training sets do not have to be equal, most of the time the same training set can be used for optimization and recommendation.
#### 4. `FeatureExtractorSpecifier`
The feature set is not finally described by the model.
Although there exists a model which we use for all training processes, an interface called `IFeatureExtractor` can be implemented.
Every class implementing the `IFeatureExtractor` interface from the package `tools.descartes.librede.rrde.recommendation.extract` can be used during the process by putting the fully qualified class name into the `featureExtractor` field.
The recommendation algorithms work with the features provides by the `IFeatureExtractor`, but the extraction itself is handled by a different class.
This leads to more flexibility, since algorithms and features can be treated separately.
Currently only `BasicFeaturesExtractor` and `ReducedFeaturesExtractor` are implemented.
The latter one extracts the reduced and optimized feature set.
However, more extractors can easily be added.
The feature extractors are specified by an aggregation value referencing to a `Quantity` object which is parameterized by any `Dimension` extending `Time` from the original LibReDE meta-model.
By specifying this parameter the aggregation interval, very similar to the `stepSize` of the `EstimationSpecification`, can be defined.
It specifies the size of the intervals where aggregation is required for further analysis.
#### 5. `RecommendationAlgorithmSpecifier`
This class defines the `IRecommendationAlgorithm` to use for training and prediction.
Similar to `OptimizationAlgorithmSpecifier`, the String parameter `algorithmName` of `RecommendationAlgorithmSpecifier` holds the fully qualified class name of the algorithm to use.
It has to implement the `IRecommendationAlgorithm` interface from the `tools.descartes.librede.rrde.recommendation.algorithm` package.
Also similar to the previous section, sub-classes of `RecommendationAlgorithmSpecifier` can be defined to further specialize the algorithm to use and if more parameters have to be configured.
The `NeuralNetworkAlgorithmSpecifier` defines the usage of a neural net algorithm, currently implemented by the `SmileNN` class in the `tools.descartes.librede.rrde.recommendation.algorithm.impl` package.
The Integer parameter `numberOfNeurons` specifies the number of neurons to be used while creating the net.
Furthermore, a Support Vector Machine can be used by using a `SVMAlgorithmSpecifier` instance.
The parameter `gaussianSigma` refers to the `\sigma`-value of the Gaussian kernel function, while `softMarginPenalty` defines the penalty using a soft-margin classifier.
Both parameters are Double values since floating point numbers are permitted in both cases.
If the use of a Decision Tree is desired, the class `SmileTree` in the `tools.descartes.librede.rrde.recommendation.algorithm.impl` package can be used by referencing an instance of `DecisionTreeAlgorithmSpecifier` with `learningAlgorithm`.
The maximum number of nodes contained in the learned decision tree is controlled via the `maximumNumberOfNodes` Integer.
After the training, an `IRecommendationAlgorithm` is returned which can give recommendations of `EstimationSpecifications` based on a given `FeatureVector`.
In order to work best, the same `IFeatureExtractor` as for the training has to be used.
Alternatively, an `OptimizedLibredeExecutor` instance can be returned.
