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One dimensional grid search

Source: R/grid_search_1d_class.R
grid_search_1d.Rd

A one dimensional grid search calculates a performance metric for a model at evenly spaced values for a model input parameter. The "optimum" value for the parameter is suggested as the one which maximises performance, or minimises error (whichever is appropriate for the chosen metric)

Usage

grid_search_1d(
  param_to_optimise,
  search_values,
  model_index,
  factor_name,
  max_min = "min",
  ...
)

Arguments

param_to_optimise

(character) The name of the model input parameter that is the focus of the search.

search_values

(ANY) The values of the input parameter being optimised.

model_index

(numeric, integer) The index of the model in the sequence that uses the parameter being optimised.

factor_name

(character) The name of a sample-meta column to use.

max_min

(character) Maximise or minimise. Allowed values are limited to the following:

  • "max": The optimium parameter value is suggested based on maximising the performance metric.

  • "min": The optimium parameter value is suggested based on minimising the performance metric.

The default is "min".

...

Additional slots and values passed to struct_class.

Value

A grid_search_1d object with the following output slots:

results(data.frame)
metric(data.frame)
optimum_value(numeric)

Inheritance

A grid_search_1d object inherits the following struct classes:

[grid_search_1d] >> [resampler] >> [iterator] >> [struct_class]

Examples

M = grid_search_1d(
      param_to_optimise = character(0),
      search_values = numeric(0),
      model_index = numeric(0),
      factor_name = "V1",
      max_min = "min")

D = MTBLS79_DatasetExperiment()
# some preprocessing
M = pqn_norm(qc_label='QC',factor_name='Class') +
    knn_impute() +
    glog_transform(qc_label='QC',factor_name='Class') +
    filter_smeta(factor_name='Class',levels='QC',mode='exclude')
M=model_apply(M,D)
D=predicted(M)

# reduce number of features for this example
D=D[,1:10]

# optmise number of components for PLS model
I = grid_search_1d(param_to_optimise='number_components',search_values=1:5,
        model_index=2,factor_name='Class') *
        (mean_centre()+PLSDA(factor_name='Class'))
I = run(I,D,balanced_accuracy())