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Fit and validate Neural Networks models with exploration of hyper-parameters

Source: R/tune_net.R
tune_net.Rd

Fit and validate Neural Networks models with exploration of hyper-parameters

Usage

tune_net(
  data,
  response,
  predictors,
  predictors_f = NULL,
  fit_formula = NULL,
  partition,
  grid = NULL,
  thr = NULL,
  metric = "TSS",
  n_cores = 1
)

Arguments

data

data.frame. Database with response (0,1) and predictors values.

response

character. Column name with species absence-presence data (0,1).

predictors

character. Vector with the column names of quantitative predictor variables (i.e. continuous variables). Usage predictors = c("aet", "cwd", "tmin")

predictors_f

character. Vector with the column names of qualitative predictor variables (i.e. ordinal or nominal variables type). Usage predictors_f = c("landform")

fit_formula

formula. A formula object with response and predictor variables (e.g. formula(pr_ab ~ aet + ppt_jja + pH + awc + depth + landform)). Note that the variable names used here must be consistent with those used in response, predictors, and predictors_f arguments. Defaul NULL.

partition

character. Column name with training and validation partition groups.

grid

data.frame. A data frame object with algorithm hyper-parameters values to be tested. It is recommended to generate this data.frame with the grid() function.

thr

character. Threshold used to get binary suitability values (i.e. 0,1), needed for threshold-dependent performance metrics. It is possible to use more than one threshold type. It is necessary to provide a vector for this argument. The following threshold types are available:

  • lpt: The highest threshold at which there is no omission.

  • equal_sens_spec: Threshold at which the sensitivity and specificity are equal.

  • max_sens_spec: Threshold at which the sum of the sensitivity and specificity is the highest (aka threshold that maximizes the TSS).

  • max_jaccard: The threshold at which the Jaccard index is the highest.

  • max_sorensen: The threshold at which the Sorensen index is highest.

  • max_fpb: The threshold at which FPB (F-measure on presence-background data) is highest.

  • sensitivity: Threshold based on a specified sensitivity value. Usage thr = c('sensitivity', sens='0.6') or thr = c('sensitivity'). 'sens' refers to sensitivity value. If it is not specified a sensitivity values, function will use by default 0.9.

If using more than one threshold type concatenate them, e.g., thr=c('lpt', 'max_sens_spec', 'max_jaccard'), or thr=c('lpt', 'max_sens_spec', 'sensitivity', sens='0.8'), or thr=c('lpt', 'max_sens_spec', 'sensitivity'). Function will use all thresholds if no threshold is specified.

metric

character. Performance metric used for selecting the best combination of hyper-parameter values. One of the following metrics can be used: SORENSEN, JACCARD, FPB, TSS, KAPPA, AUC, and BOYCE. TSS is used as default.

n_cores

numeric. Number of cores use for parallelization. Default 1

Value

A list object with:

  • model: A "nnet" class object from nnet package. This object can be used for predicting.

  • predictors: A tibble with quantitative (c column names) and qualitative (f column names) variables use for modeling.

  • performance: Hyper-parameters values and performance metric (see sdm_eval) for the best hyper-parameters combination.

  • hyper_performance: Performance metric (see sdm_eval) for each combination of the hyper-parameters.

  • data_ens: Predicted suitability for each test partition based on the best model. This database is used in fit_ensemble

See also

tune_gbm, tune_max, tune_raf, and tune_svm.

Examples

if (FALSE) {
data(abies)
abies

# Partitioning the data with the k-fold method

abies2 <- part_random(
  data = abies,
  pr_ab = "pr_ab",
  method = c(method = "kfold", folds = 5)
)

# pr_ab columns is species presence and absences (i.e. the response variable)
# from aet to landform are the predictors variables (landform is a qualitative variable)

# Hyper-parameter values for tuning
tune_grid <-
  expand.grid(
    size = c(2, 4, 6, 8, 10),
    decay = c(0.001, 0.05, 0.1, 1, 3, 4, 5, 10)
  )

net_t <-
  tune_net(
    data = abies2,
    response = "pr_ab",
    predictors = c(
      "aet", "cwd", "tmin", "ppt_djf",
      "ppt_jja", "pH", "awc", "depth"
    ),
    predictors_f = c("landform"),
    partition = ".part",
    grid = tune_grid,
    thr = "max_sens_spec",
    metric = "TSS",
    n_cores = 1
  )

# Outputs
net_t$model
net_t$predictors
net_t$performance
net_t$hyper_performance
net_t$data_ens
}