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Add settings matrix rows

Usage

add_settings_matrix_rows(
  settings_matrix,
  seed = NULL,
  nrows = 0,
  min_removed_inputs = 0,
  max_removed_inputs = sum(startsWith(colnames(settings_matrix), "inc_")) - 1,
  dropout_dist = "exponential",
  min_alpha = NULL,
  max_alpha = NULL,
  min_k = NULL,
  max_k = NULL,
  min_t = NULL,
  max_t = NULL,
  alpha_values = NULL,
  k_values = NULL,
  t_values = NULL,
  possible_snf_schemes = c(1, 2, 3),
  clustering_algorithms = NULL,
  continuous_distances = NULL,
  discrete_distances = NULL,
  ordinal_distances = NULL,
  categorical_distances = NULL,
  mixed_distances = NULL,
  distance_metrics_list = NULL,
  snf_input_weights = NULL,
  snf_domain_weights = NULL,
  retry_limit = 10
)

Arguments

settings_matrix

The existing settings matrix

seed

set a seed for the random matrix generation. Setting this value will change the seed of the global environment.

nrows

Number of rows to generate for the settings matrix.

min_removed_inputs

The smallest number of input dataframes that may be randomly removed. By default, 0.

max_removed_inputs

The largest number of input dataframes that may be randomly removed. By default, this is 1 less than all the provided input dataframes in the data_list.

dropout_dist

Parameter controlling how the random removal of input dataframes should occur. Can be "none" (no input dataframes are randomly removed), "uniform" (uniformly sample between min_removed_inputs and max_removed_inputs to determine number of input dataframes to remove), or "exponential" (pick number of input dataframes to remove by sampling from min_removed_inputs to max_removed_inputs with an exponential distribution; default).

min_alpha

The minimum value that the alpha hyperparameter can have. Random assigned value of alpha for each row will be obtained by uniformly sampling numbers between min_alpha and max_alpha at intervals of 0.1. Cannot be used in conjunction with the alpha_values parameter.

max_alpha

The maximum value that the alpha hyperparameter can have. See min_alpha parameter. Cannot be used in conjunction with the alpha_values parameter.

min_k

The minimum value that the k hyperparameter can have. Random assigned value of k for each row will be obtained by uniformly sampling numbers between min_k and max_k at intervals of 1. Cannot be used in conjunction with the k_values parameter.

max_k

The maximum value that the k hyperparameter can have. See min_k parameter. Cannot be used in conjunction with the k_values parameter.

min_t

The minimum value that the t hyperparameter can have. Random assigned value of t for each row will be obtained by uniformly sampling numbers between min_t and max_t at intervals of 1. Cannot be used in conjunction with the t_values parameter.

max_t

The maximum value that the t hyperparameter can have. See min_t parameter. Cannot be used in conjunction with the t_values parameter.

alpha_values

A number or numeric vector of a set of possible values that alpha can take on. Value will be obtained by uniformly sampling the vector. Cannot be used in conjunction with the min_alpha or max_alpha parameters.

k_values

A number or numeric vector of a set of possible values that k can take on. Value will be obtained by uniformly sampling the vector. Cannot be used in conjunction with the min_k or max_k parameters.

t_values

A number or numeric vector of a set of possible values that t can take on. Value will be obtained by uniformly sampling the vector. Cannot be used in conjunction with the min_t or max_t parameters.

possible_snf_schemes

A vector containing the possible snf_schemes to uniformly randomly select from. By default, the vector contains all 3 possible schemes: c(1, 2, 3). 1 corresponds to the "individual" scheme, 2 corresponds to the "domain" scheme, and 3 corresponds to the "twostep" scheme.

clustering_algorithms

A list of clustering algorithms to uniformly randomly pick from when clustering. When not specified, randomly select between spectral clustering using the eigen-gap heuristic and spectral clustering using the rotation cost heuristic. See ?generate_clust_algs_list for more details on running custom clustering algorithms.

continuous_distances

A vector of continuous distance metrics to use when a custom distance_metrics_list is provided.

discrete_distances

A vector of categorical distance metrics to use when a custom distance_metrics_list is provided.

ordinal_distances

A vector of categorical distance metrics to use when a custom distance_metrics_list is provided.

categorical_distances

A vector of categorical distance metrics to use when a custom distance_metrics_list is provided.

mixed_distances

A vector of mixed distance metrics to use when a custom distance_metrics_list is provided.

distance_metrics_list

List containing distance metrics to vary over. See ?generate_distance_metrics_list.

snf_input_weights

Nested list containing weights for when SNF is used to merge individual input measures (see ?generate_snf_weights)

snf_domain_weights

Nested list containing weights for when SNF is used to merge domains (see ?generate_snf_weights)

retry_limit

The maximum number of attempts to generate a novel row. This function does not return matrices with identical rows. As the range of requested possible settings tightens and the number of requested rows increases, the risk of randomly generating a row that already exists increases. If a new random row has matched an existing row retry_limit number of times, the function will terminate.

Value

settings_matrix A settings matrix