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, RatingPredictor recommender, ISplit< IRatings > split)
	Find the the parameters resulting in the minimal results for a given evaluation measure (1D)
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, 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	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)
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)

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Detailed Description

Grid search for finding suitable hyperparameters.

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Member Function Documentation

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 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,
		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 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

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

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

• GridSearch.cs