step_nnmf: Non-negative matrix factorization signal extraction
In recipes: Preprocessing and Feature Engineering Steps for Modeling

step_nnmf

R Documentation

Non-negative matrix factorization signal extraction

Description

step_nnmf() creates a specification of a recipe step that will convert numeric data into one or more non-negative components.

Please use step_nnmf_sparse() instead of this step function.

Usage

step_nnmf(
  recipe,
  ...,
  role = "predictor",
  trained = FALSE,
  num_comp = 2,
  num_run = 30,
  options = list(),
  res = NULL,
  columns = NULL,
  prefix = "NNMF",
  seed = sample.int(10^5, 1),
  keep_original_cols = FALSE,
  skip = FALSE,
  id = rand_id("nnmf")
)

Arguments

`recipe`	A recipe object. The step will be added to the sequence of operations for this recipe.
`...`	One or more selector functions to choose variables for this step. See `selections()` for more details.
`role`	For model terms created by this step, what analysis role should they be assigned? By default, the new columns created by this step from the original variables will be used as predictors in a model.
`trained`	A logical to indicate if the quantities for preprocessing have been estimated.
`num_comp`	The number of components to retain as new predictors. If `num_comp` is greater than the number of columns or the number of possible components, a smaller value will be used. If `num_comp = 0` is set then no transformation is done and selected variables will stay unchanged, regardless of the value of `keep_original_cols`.
`num_run`	A positive integer for the number of computations runs used to obtain a consensus projection.
`options`	A list of options to `nmf()` in the NMF package by way of the `NNMF()` function in the `dimRed` package. Note that the arguments `data` and `ndim` should not be passed here, and that NMF's parallel processing is turned off in favor of resample-level parallelization.
`res`	The `NNMF()` object is stored here once this preprocessing step has been trained by `prep()`.
`columns`	A character string of the selected variable names. This field is a placeholder and will be populated once `prep()` is used.
`prefix`	A character string that will be the prefix to the resulting new variables. See notes below.
`seed`	An integer that will be used to set the seed in isolation when computing the factorization.
`keep_original_cols`	A logical to keep the original variables in the output. Defaults to `FALSE`.
`skip`	A logical. Should the step be skipped when the recipe is baked by `bake()`? While all operations are baked when `prep()` is run, some operations may not be able to be conducted on new data (e.g. processing the outcome variable(s)). Care should be taken when using `skip = TRUE` as it may affect the computations for subsequent operations.
`id`	A character string that is unique to this step to identify it.

Details

Non-negative matrix factorization computes latent components that have non-negative values and take into account that the original data have non-negative values.

The argument num_comp controls the number of components that will be retained (the original variables that are used to derive the components are removed from the data). The new components will have names that begin with prefix and a sequence of numbers. The variable names are padded with zeros. For example, if num_comp < 10, their names will be NNMF1 - NNMF9. If num_comp = 101, the names would be NNMF1 - NNMF101.

Value

An updated version of recipe with the new step added to the sequence of any existing operations.

Tidying

When you tidy() this step, a tibble is returned with columns terms, value, component , and id:

terms: character, the selectors or variables selected
value: numeric, value of loading
component: character, name of component
id: character, id of this step

Tuning Parameters

This step has 2 tuning parameters:

num_comp: # Components (type: integer, default: 2)
num_run: Number of Computation Runs (type: integer, default: 30)

Case weights

The underlying operation does not allow for case weights.

Examples


data(biomass, package = "modeldata")

# rec <- recipe(HHV ~ ., data = biomass) %>%
#   update_role(sample, new_role = "id var") %>%
#   update_role(dataset, new_role = "split variable") %>%
#   step_nnmf(all_numeric_predictors(), num_comp = 2, seed = 473, num_run = 2) %>%
#   prep(training = biomass)
#
# bake(rec, new_data = NULL)
#
# library(ggplot2)
# bake(rec, new_data = NULL) %>%
#  ggplot(aes(x = NNMF2, y = NNMF1, col = HHV)) + geom_point()

recipes documentation built on July 4, 2024, 9:06 a.m.