Grid search for finding suitable hyperparameters. More...
Static Public Member Functions | |
static double | FindMinimum (string evaluation_measure, string hyperparameter_name, double[] hyperparameter_values, RatingPrediction.RatingPredictor recommender, uint k) |
Find the the parameters resulting in the minimal results for a given evaluation measure using k-fold cross-validation. | |
static double | FindMinimum (string evaluation_measure, string hp_name1, string hp_name2, double[] hp_values1, double[] hp_values2, RatingPredictor recommender, ISplit< IRatings > split) |
Find the the parameters resulting in the minimal results for a given evaluation measure (2D). | |
static double | FindMinimum (string evaluation_measure, string hyperparameter_name, double[] hyperparameter_values, RatingPredictor recommender, ISplit< IRatings > split) |
Find the the parameters resulting in the minimal results for a given evaluation measure (1D). | |
static double | FindMinimumExponential (string evaluation_measure, string hp_name, double[] hp_values, double basis, RatingPrediction.RatingPredictor recommender, ISplit< IRatings > split) |
Find the the parameters resulting in the minimal results for a given evaluation measure (1D). | |
static double | FindMinimumExponential (string evaluation_measure, string hp_name1, string hp_name2, double[] hp_values1, double[] hp_values2, double basis, RatingPrediction.RatingPredictor recommender, ISplit< IRatings > split) |
Find the the parameters resulting in the minimal results for a given evaluation measure (2D). |
Grid search for finding suitable hyperparameters.
static double FindMinimum | ( | string | evaluation_measure, | |
string | hyperparameter_name, | |||
double[] | hyperparameter_values, | |||
RatingPrediction.RatingPredictor | recommender, | |||
uint | k | |||
) | [inline, static] |
Find the the parameters resulting in the minimal results for a given evaluation measure using k-fold cross-validation.
The recommender will be set to the best parameter value after calling this method.
evaluation_measure | the name of the evaluation measure | |
hyperparameter_name | the name of the hyperparameter to optimize | |
hyperparameter_values | the values of the hyperparameter to try out | |
recommender | the recommender | |
k | the number of folds to be used for cross-validation |
static double FindMinimum | ( | string | evaluation_measure, | |
string | hp_name1, | |||
string | hp_name2, | |||
double[] | hp_values1, | |||
double[] | hp_values2, | |||
RatingPredictor | recommender, | |||
ISplit< IRatings > | split | |||
) | [inline, static] |
Find the the parameters resulting in the minimal results for a given evaluation measure (2D).
The recommender will be set to the best parameter value after calling this method.
evaluation_measure | the name of the evaluation measure | |
hp_name1 | the name of the first hyperparameter to optimize | |
hp_values1 | the values of the first hyperparameter to try out | |
hp_name2 | the name of the second hyperparameter to optimize | |
hp_values2 | the values of the second hyperparameter to try out | |
recommender | the recommender | |
split | the dataset split to use |
static double FindMinimum | ( | string | evaluation_measure, | |
string | hyperparameter_name, | |||
double[] | hyperparameter_values, | |||
RatingPredictor | recommender, | |||
ISplit< IRatings > | split | |||
) | [inline, static] |
Find the the parameters resulting in the minimal results for a given evaluation measure (1D).
The recommender will be set to the best parameter value after calling this method.
evaluation_measure | the name of the evaluation measure | |
hyperparameter_name | the name of the hyperparameter to optimize | |
hyperparameter_values | the values of the hyperparameter to try out | |
recommender | the recommender | |
split | the dataset split to use |
static double FindMinimumExponential | ( | string | evaluation_measure, | |
string | hp_name, | |||
double[] | hp_values, | |||
double | basis, | |||
RatingPrediction.RatingPredictor | recommender, | |||
ISplit< IRatings > | split | |||
) | [inline, static] |
Find the the parameters resulting in the minimal results for a given evaluation measure (1D).
The recommender will be set to the best parameter value after calling this method.
evaluation_measure | the name of the evaluation measure | |
hp_name | the name of the hyperparameter to optimize | |
hp_values | the logarithms of the values of the hyperparameter to try out | |
basis | the basis to use for the logarithms | |
recommender | the recommender | |
split | the dataset split to use |
static double FindMinimumExponential | ( | string | evaluation_measure, | |
string | hp_name1, | |||
string | hp_name2, | |||
double[] | hp_values1, | |||
double[] | hp_values2, | |||
double | basis, | |||
RatingPrediction.RatingPredictor | recommender, | |||
ISplit< IRatings > | split | |||
) | [inline, static] |
Find the the parameters resulting in the minimal results for a given evaluation measure (2D).
The recommender will be set to the best parameter value after calling this method.
evaluation_measure | the name of the evaluation measure | |
hp_name1 | the name of the first hyperparameter to optimize | |
hp_values1 | the logarithm values of the first hyperparameter to try out | |
hp_name2 | the name of the second hyperparameter to optimize | |
hp_values2 | the logarithm values of the second hyperparameter to try out | |
basis | the basis to use for the logarithms | |
recommender | the recommender | |
split | the dataset split to use |