/// <summary>Find the the parameters resulting in the minimal results for a given evaluation measure using k-fold cross-validation</summary>
/// <remarks>The recommender will be set to the best parameter value after calling this method.</remarks>
/// <param name="evaluation_measure">the name of the evaluation measure</param>
/// <param name="hyperparameter_name">the name of the hyperparameter to optimize</param>
/// <param name="hyperparameter_values">the values of the hyperparameter to try out</param>
/// <param name="recommender">the recommender</param>
/// <param name="k">the number of folds to be used for cross-validation</param>
/// <returns>the best (lowest) average value for the hyperparameter</returns>
public static double FindMinimum(string evaluation_measure,
string hyperparameter_name,
double[] hyperparameter_values,
RatingPrediction.RatingPredictor recommender,
int k)
{
var data = recommender.Ratings;
var split = new RatingCrossValidationSplit(data, k);
double result = FindMinimum(evaluation_measure, hyperparameter_name, hyperparameter_values, recommender, split);
recommender.Ratings = data;
return(result);
}
/// <summary>Find the the parameters resulting in the minimal results for a given evaluation measure (1D)</summary>
/// <remarks>The recommender will be set to the best parameter value after calling this method.</remarks>
/// <param name="evaluation_measure">the name of the evaluation measure</param>
/// <param name="hp_name">the name of the hyperparameter to optimize</param>
/// <param name="hp_values">the logarithms of the values of the hyperparameter to try out</param>
/// <param name="basis">the basis to use for the logarithms</param>
/// <param name="recommender">the recommender</param>
/// <param name="split">the dataset split to use</param>
/// <returns>the best (lowest) average value for the hyperparameter</returns>
public static double FindMinimumExponential(string evaluation_measure,
string hp_name,
double[] hp_values,
double basis,
RatingPrediction.RatingPredictor recommender,
ISplit <IRatings> split)
{
var new_hp_values = new double[hp_values.Length];
for (int i = 0; i < hp_values.Length; i++)
{
new_hp_values[i] = Math.Pow(basis, hp_values[i]);
}
return(FindMinimum(evaluation_measure, hp_name, new_hp_values, recommender, split));
}
