train_magmaclust {MagmaClustR}

Training MagmaClust with a Variational EM algorithm

Description

The hyper-parameters and the hyper-posterior distributions involved in MagmaClust can be learned thanks to a VEM algorithm implemented in train_magmaclust. By providing a dataset, the model hypotheses (hyper-prior mean parameters, covariance kernels and number of clusters) and initialisation values for the hyper-parameters, the function computes maximum likelihood estimates of the HPs as well as the mean and covariance parameters of the Gaussian hyper-posterior distributions of the mean processes.

Usage

train_magmaclust(
  data,
  nb_cluster = NULL,
  prior_mean_k = NULL,
  ini_hp_k = NULL,
  ini_hp_i = NULL,
  kern_k = "SE",
  kern_i = "SE",
  ini_mixture = NULL,
  common_hp_k = TRUE,
  common_hp_i = TRUE,
  grid_inputs = NULL,
  pen_diag = 1e-10,
  n_iter_max = 25,
  cv_threshold = 0.001,
  fast_approx = FALSE
)

Arguments

data

A tibble or data frame. Columns required: ID, Input , Output. Additional columns for covariates can be specified. The ID column contains the unique names/codes used to identify each individual/task (or batch of data). The Input column should define the variable that is used as reference for the observations (e.g. time for longitudinal data). The Output column specifies the observed values (the response variable). The data frame can also provide as many covariates as desired, with no constraints on the column names. These covariates are additional inputs (explanatory variables) of the models that are also observed at each reference Input.

nb_cluster

A number, indicating the number of clusters of individuals/tasks that are assumed to exist among the dataset.

prior_mean_k

The set of hyper-prior mean parameters (m_k) for the K mean GPs, one value for each cluster. cluster. This argument can be specified under various formats, such as:

• NULL (default). All hyper-prior means would be set to 0 everywhere.

• A numerical vector of the same length as the number of clusters. Each number is associated with one cluster, and considered to be the hyper-prior mean parameter of the cluster (i.e. a constant function at all Input).

• A list of functions. Each function is associated with one cluster. These functions are all evaluated at all Input values, to provide specific hyper-prior mean vectors for each cluster.

ini_hp_k

A tibble or data frame of hyper-parameters associated with kern_k, the mean process' kernel. Required column : ID. The ID column contains the unique names/codes used to identify each cluster. The other columns should be named according to the hyper-parameters that are used in kern_k.

ini_hp_i

A tibble or data frame of hyper-parameters associated with kern_i, the individual processes' kernel. Required column : ID. The ID column contains the unique names/codes used to identify each individual/task. The other columns should be named according to the hyper-parameters that are used in kern_i.

kern_k

A kernel function, associated with the mean GPs. Several popular kernels (see The Kernel Cookbook) are already implemented and can be selected within the following list:

• "SE": (default value) the Squared Exponential Kernel (also called Radial Basis Function or Gaussian kernel),

• "LIN": the Linear kernel,

• "PERIO": the Periodic kernel,

• "RQ": the Rational Quadratic kernel. Compound kernels can be created as sums or products of the above kernels. For combining kernels, simply provide a formula as a character string where elements are separated by whitespaces (e.g. "SE + PERIO"). As the elements are treated sequentially from the left to the right, the product operator '*' shall always be used before the '+' operators (e.g. 'SE * LIN + RQ' is valid whereas 'RQ + SE * LIN' is not).

kern_i

A kernel function, associated with the individual GPs. (See details above in kern_k).

ini_mixture

Initial values of the probability to belong to each cluster for each individual (ini_mixture can be used for a k-means initialisation. Used by default if NULL).

common_hp_k

A boolean indicating whether hyper-parameters are common among the mean GPs.

common_hp_i

A boolean indicating whether hyper-parameters are common among the individual GPs.

grid_inputs

A vector, indicating the grid of additional reference inputs on which the mean processes' hyper-posteriors should be evaluated.

pen_diag

A number. A jitter term, added on the diagonal to prevent numerical issues when inverting nearly singular matrices.

n_iter_max

A number, indicating the maximum number of iterations of the VEM algorithm to proceed while not reaching convergence.

cv_threshold

A number, indicating the threshold of the likelihood gain under which the VEM algorithm will stop. The convergence condition is defined as the difference of elbo between two consecutive steps, divided by the absolute value of the last one ( (ELBO_n - ELBO_{n-1}) / |ELBO_n| ).

fast_approx

A boolean, indicating whether the VEM algorithm should stop after only one iteration of the VE-step. This advanced feature is mainly used to provide a faster approximation of the model selection procedure, by preventing any optimisation over the hyper-parameters.

Details

The user can specify custom kernel functions for the argument kern_k and kern_i. The hyper-parameters used in the kernel should have explicit names, and be contained within the hp argument. hp should typically be defined as a named vector or a data frame. Although it is not mandatory for the train_magmaclust function to run, gradients be can provided within kernel function definition. See for example se_kernel to create a custom kernel function displaying an adequate format to be used in MagmaClust.

Value

A list, containing the results of the VEM algorithm used in the training step of MagmaClust. The elements of the list are:

• hp_k: A tibble containing the trained hyper-parameters for the mean process' kernel and the mixture proportions for each cluster.

• hp_i: A tibble containing the trained hyper-parameters for the individual processes' kernels.

• hyperpost: A sub-list containing the parameters of the mean processes' hyper-posterior distribution, namely:

  • mean: A list of tibbles containing, for each cluster, the hyper-posterior mean parameters evaluated at each Input.

  • cov: A list of matrices containing, for each cluster, the hyper-posterior covariance parameter of the mean process.

  • mixture: A tibble, indicating the mixture probabilities in each cluster for each individual.

• ini_args: A list containing the initial function arguments and values for the hyper-prior means, the hyper-parameters. In particular, if those arguments were set to NULL, ini_args allows us to retrieve the (randomly chosen) initialisations used during training.

• seq_elbo: A vector, containing the sequence of ELBO values associated with each iteration.

• converged: A logical value indicated whether the algorithm converged.

• training_time: Total running time of the complete training.

Examples

TRUE