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step_nnmf_sparse() creates a specification of a recipe step
that will convert numeric data into one or more non-negative
components.
step_nnmf_sparse(
recipe,
...,
role = "predictor",
trained = FALSE,
num_comp = 2,
penalty = 0.001,
options = list(),
res = NULL,
prefix = "NNMF",
seed = sample.int(10^5, 1),
keep_original_cols = FALSE,
skip = FALSE,
id = rand_id("nnmf_sparse")
)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.
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.
A logical to indicate if the quantities for preprocessing have been estimated.
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.
A non-negative number used as a penalization factor for the loadings. Values are usually between zero and one.
A list of options to nmf() in the RcppML package. That
package has a separate function setRcppMLthreads() that controls the
amount of internal parallelization. Note that the argument A, k,
L1, and seed should not be passed here.
A matrix of loadings is stored here, along with the names of the
original predictors, once this preprocessing step has been trained by
prep().
A character string for the prefix of the resulting new variables. See notes below.
An integer that will be used to set the seed in isolation when computing the factorization.
A logical to keep the original variables in the
output. Defaults to FALSE.
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.
A character string that is unique to this step to identify it.
An updated version of recipe with the new step added to the
sequence of any existing operations.
When you tidy() this step, a tibble with column
terms (the selectors or variables selected) and the number of
components is returned.
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 < 10, their names will be NNMF1 - NNMF9. If
num = 101, the names would be NNMF001 - NNMF101.
Other multivariate transformation steps:
step_classdist(),
step_depth(),
step_geodist(),
step_ica(),
step_isomap(),
step_kpca_poly(),
step_kpca_rbf(),
step_kpca(),
step_mutate_at(),
step_nnmf(),
step_pca(),
step_pls(),
step_ratio(),
step_spatialsign()
# NOT RUN {
if (rlang::is_installed("RcppML")) {
library(Matrix)
library(modeldata)
data(biomass)
rec <- recipe(HHV ~ ., data = biomass) %>%
update_role(sample, new_role = "id var") %>%
update_role(dataset, new_role = "split variable") %>%
step_nnmf_sparse(
all_numeric_predictors(),
num_comp = 2,
seed = 473,
penalty = 0.01
) %>%
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()
}
# }
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