grid_search_1d: One dimensional grid search

In computational-metabolomics/structtoolbox: Data processing & analysis tools for Metabolomics and other omics

One dimensional grid search

Description

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())

computational-metabolomics/structtoolbox documentation built on July 2, 2024, 10:46 p.m.