MyMediaLite  3.11
Static Public Member Functions | List of all members
GridSearch Class Reference

Grid search for finding suitable hyperparameters More...

Static Public Member Functions

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) More...
 
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) More...
 
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 More...
 
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) More...
 
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) More...
 

Detailed Description

Grid search for finding suitable hyperparameters

Member Function Documentation

static double FindMinimum ( string  evaluation_measure,
string  hyperparameter_name,
double[]  hyperparameter_values,
RatingPredictor  recommender,
ISplit< IRatings split 
)
inlinestatic

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.

Parameters
evaluation_measurethe name of the evaluation measure
hyperparameter_namethe name of the hyperparameter to optimize
hyperparameter_valuesthe values of the hyperparameter to try out
recommenderthe recommender
splitthe dataset split to use
Returns
the best (lowest) average value for the hyperparameter
static double FindMinimum ( string  evaluation_measure,
string  hp_name1,
string  hp_name2,
double[]  hp_values1,
double[]  hp_values2,
RatingPredictor  recommender,
ISplit< IRatings split 
)
inlinestatic

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.

Parameters
evaluation_measurethe name of the evaluation measure
hp_name1the name of the first hyperparameter to optimize
hp_values1the values of the first hyperparameter to try out
hp_name2the name of the second hyperparameter to optimize
hp_values2the values of the second hyperparameter to try out
recommenderthe recommender
splitthe dataset split to use
Returns
the best (lowest) average value for the hyperparameter
static double FindMinimum ( string  evaluation_measure,
string  hyperparameter_name,
double[]  hyperparameter_values,
RatingPrediction.RatingPredictor  recommender,
uint  k 
)
inlinestatic

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.

Parameters
evaluation_measurethe name of the evaluation measure
hyperparameter_namethe name of the hyperparameter to optimize
hyperparameter_valuesthe values of the hyperparameter to try out
recommenderthe recommender
kthe number of folds to be used for cross-validation
Returns
the best (lowest) average value for the hyperparameter
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 
)
inlinestatic

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.

Parameters
evaluation_measurethe name of the evaluation measure
hp_name1the name of the first hyperparameter to optimize
hp_values1the logarithm values of the first hyperparameter to try out
hp_name2the name of the second hyperparameter to optimize
hp_values2the logarithm values of the second hyperparameter to try out
basisthe basis to use for the logarithms
recommenderthe recommender
splitthe dataset split to use
Returns
the best (lowest) average value for the hyperparameter
static double FindMinimumExponential ( string  evaluation_measure,
string  hp_name,
double[]  hp_values,
double  basis,
RatingPrediction.RatingPredictor  recommender,
ISplit< IRatings split 
)
inlinestatic

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.

Parameters
evaluation_measurethe name of the evaluation measure
hp_namethe name of the hyperparameter to optimize
hp_valuesthe logarithms of the values of the hyperparameter to try out
basisthe basis to use for the logarithms
recommenderthe recommender
splitthe dataset split to use
Returns
the best (lowest) average value for the hyperparameter

The documentation for this class was generated from the following file: