Define configuration for generating a set of SNF-based cluster solutions

snf_config() constructs an SNF config object which inherits from classes snf_config and list. This object is used to store all settings required to transform data stored in a data_list class object into a space of cluster solutions by SNF. The SNF config object contains the following components: 1. A settings data frame (inherits from settings_df and data.frame). Data frame that stores SNF-specific hyperparameters and information about feature selection and weighting, SNF schemes, clustering algorithms, and distance metrics. Each row of the settings data frame corresponds to a distinct cluster solution. 2. A clustering algorithms list (inherits from clust_fns_list and list), which stores all clustering algorithms that the settings data frame can point to. 3. A distance metrics list (inherits from dist_metrics_list and list), which stores all distance metrics that the settings data frame can point to. 4. A weights matrix (inherits from weights_matrix, matrix, and array'), which stores the feature weights to use prior to distance calculations. Each column of the weights matrix corresponds to a different feature in the data list and each row corresponds to a different row in the settings data frame.

Usage

snf_config(
  dl = NULL,
  sdf = NULL,
  dfl = NULL,
  cfl = NULL,
  wm = NULL,
  n_solutions = 0,
  min_removed_inputs = 0,
  max_removed_inputs = length(dl) - 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,
  snf_input_weights = NULL,
  snf_domain_weights = NULL,
  retry_limit = 10,
  cnt_dist_fns = NULL,
  dsc_dist_fns = NULL,
  ord_dist_fns = NULL,
  cat_dist_fns = NULL,
  mix_dist_fns = NULL,
  automatic_standard_normalize = FALSE,
  use_default_dist_fns = FALSE,
  clust_fns = NULL,
  use_default_clust_fns = FALSE,
  weights_fill = "ones"
)

Arguments

dl: A nested list of input data from data_list().
sdf: A settings_df class object. Overrides settings data frame related parameters.
dfl: A dist_fns_list class object. Overrides distance functions list related parameters.
cfl: A clust_fns_list class object. Overrides clustering functions list related parameters.
wm: A weights_matrix class object. Overrides weights matrix related parameters.
n_solutions: Number of rows to generate for the settings data frame.
min_removed_inputs: The smallest number of input data frames that may be randomly removed. By default, 0.
max_removed_inputs: The largest number of input data frames that may be randomly removed. By default, this is 1 less than all the provided input data frames in the data list.
dropout_dist: Parameter controlling how the random removal of input data frames should occur. Can be "none" (no input data frames are randomly removed), "uniform" (uniformly sample between min_removed_inputs and max_removed_inputs to determine number of input data frames to remove), or "exponential" (pick number of input data frames to remove by sampling from min_removed_inputs to max_removed_inputs with an exponential distribution; the 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 ?clust_fns_list for more details on running custom clustering algorithms.
continuous_distances: A vector of continuous distance metrics to use when a custom dist_fns_list is provided.
discrete_distances: A vector of categorical distance metrics to use when a custom dist_fns_list is provided.
ordinal_distances: A vector of categorical distance metrics to use when a custom dist_fns_list is provided.
categorical_distances: A vector of categorical distance metrics to use when a custom dist_fns_list is provided.
mixed_distances: A vector of mixed distance metrics to use when a custom dist_fns_list is provided.
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.
cnt_dist_fns: A named list of continuous distance metric functions.
dsc_dist_fns: A named list of discrete distance metric functions.
ord_dist_fns: A named list of ordinal distance metric functions.
cat_dist_fns: A named list of categorical distance metric functions.
mix_dist_fns: A named list of mixed distance metric functions.
automatic_standard_normalize: If TRUE, will automatically use standard normalization prior to calculation of any numeric distances. This parameter overrides all other distance functions list-related parameters.
use_default_dist_fns: If TRUE, prepend the base distance metrics (euclidean distance for continuous, discrete, and ordinal data and gower distance for categorical and mixed data) to the resulting distance metrics list.
clust_fns: A list of named clustering functions
use_default_clust_fns: If TRUE, prepend the base clustering algorithms (spectral_eigen and spectral_rot, which apply spectral clustering and use the eigen-gap and rotation cost heuristics respectively for determining the number of clusters in the graph) to clust_fns.
weights_fill: String indicating what to populate generate rows with. Can be "ones" (default; fill matrix with 1), "uniform" (fill matrix with uniformly distributed random values), or "exponential" (fill matrix with exponentially distributed random values).

Value

An snf_config class object.

Examples

# Simple random config for 5 cluster solutions
input_dl <- data_list(
    list(anxiety, "anxiety", "behaviour", "ordinal"),
    list(depress, "depressed", "behaviour", "ordinal"),
    uid = "unique_id"
)
#> ℹ 38 observations dropped due to incomplete data.
my_sc <- snf_config(
    dl = input_dl,
    n_solutions = 5
)
#> ℹ No distance functions specified. Using defaults.
#> ℹ No clustering functions specified. Using defaults.

# specifying possible K range
my_sc <- snf_config(
    dl = input_dl,
    n_solutions = 5,
    min_k = 20,
    max_k = 40
)
#> ℹ No distance functions specified. Using defaults.
#> ℹ No clustering functions specified. Using defaults.

# Random feature weights across from uniform distribution
my_sc <- snf_config(
    dl = input_dl,
    n_solutions = 5,
    min_k = 20,
    max_k = 40,
    weights_fill = "uniform"
)
#> ℹ No distance functions specified. Using defaults.
#> ℹ No clustering functions specified. Using defaults.

# Specifying custom pre-built clustering and distance functions
# - Random alternation between 2-cluster and 5-cluster solutions
# - When continuous or discrete data frames are being processed,
#   randomly alternate between standardized/normalized Euclidean
#   distance vs. regular Euclidean distance
my_sc <- snf_config(
    dl = input_dl,
    n_solutions = 5,
    min_k = 20,
    max_k = 40,
    weights_fill = "uniform",
    clust_fns = list(
        "two_cluster_spectral" = spectral_two,
        "five_cluster_spectral" = spectral_five
    ),
    cnt_dist_fns = list(
         "euclidean" = euclidean_distance,
         "std_nrm_euc" = sn_euclidean_distance
    ),
    dsc_dist_fns = list(
         "euclidean" = euclidean_distance,
         "std_nrm_euc" = sn_euclidean_distance
    )
)