GridSearch Class Reference

Grid search for finding suitable hyperparameters. More...

List of all members.

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).

Detailed Description

Grid search for finding suitable hyperparameters.


Member Function Documentation

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.

Parameters:
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
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 
) [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.

Parameters:
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
Returns:
the best (lowest) average value for the hyperparameter
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.

Parameters:
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
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 
) [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.

Parameters:
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
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 
) [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.

Parameters:
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
Returns:
the best (lowest) average value for the hyperparameter

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