/// <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="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="split">the dataset split to use</param>
/// <returns>the best (lowest) average value for the hyperparameter</returns>
public static double FindMinimum(
string evaluation_measure,
string hyperparameter_name,
double[] hyperparameter_values,
RatingPredictor recommender,
ISplit<IRatings> split)
{
double min_result = double.MaxValue;
int min_i = -1;
for (int i = 0; i < hyperparameter_values.Length; i++)
{
recommender.SetProperty(hyperparameter_name, hyperparameter_values[i].ToString(CultureInfo.InvariantCulture));
double result = recommender.DoCrossValidation(split)[evaluation_measure];
if (result < min_result)
{
min_i = i;
min_result = result;
}
}
recommender.SetProperty(hyperparameter_name, hyperparameter_values[min_i].ToString(CultureInfo.InvariantCulture));
return min_result;
}
static double Run(RatingPredictor recommender, ISplit<IRatings> split, string hp_string, string evaluation_measure)
{
recommender.Configure(hp_string);
double result = recommender.DoCrossValidation(split)[evaluation_measure];
Console.Error.WriteLine("Nelder-Mead: {0}: {1}", hp_string, result.ToString(CultureInfo.InvariantCulture));
return result;
}
/// <summary>Find the the parameters resulting in the minimal results for a given evaluation measure (2D)</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_name1">the name of the first hyperparameter to optimize</param>
/// <param name="hp_values1">the values of the first hyperparameter to try out</param>
/// <param name="hp_name2">the name of the second hyperparameter to optimize</param>
/// <param name="hp_values2">the values of the second hyperparameter to try out</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 FindMinimum(
string evaluation_measure,
string hp_name1, string hp_name2,
double[] hp_values1, double[] hp_values2,
RatingPredictor recommender,
ISplit<IRatings> split)
{
double min_result = double.MaxValue;
int min_i = -1;
int min_j = -1;
for (int i = 0; i < hp_values1.Length; i++)
for (int j = 0; j < hp_values2.Length; j++)
{
recommender.SetProperty(hp_name1, hp_values1[i].ToString(CultureInfo.InvariantCulture));
recommender.SetProperty(hp_name2, hp_values2[j].ToString(CultureInfo.InvariantCulture));
Console.Error.WriteLine("reg_u={0} reg_i={1}", hp_values1[i].ToString(CultureInfo.InvariantCulture), hp_values2[j].ToString(CultureInfo.InvariantCulture)); // TODO this is not generic
double result = recommender.DoCrossValidation(split)[evaluation_measure];
if (result < min_result)
{
min_i = i;
min_j = j;
min_result = result;
}
}
// set to best hyperparameter values
recommender.SetProperty(hp_name1, hp_values1[min_i].ToString(CultureInfo.InvariantCulture));
recommender.SetProperty(hp_name2, hp_values2[min_j].ToString(CultureInfo.InvariantCulture));
return min_result;
}