set_new_model("mlp")
set_model_mode("mlp", "classification")
set_model_mode("mlp", "regression")
# ------------------------------------------------------------------------------
set_model_engine("mlp", "classification", "keras")
set_model_engine("mlp", "regression", "keras")
set_dependency("mlp", "keras", "keras", mode = "regression")
set_dependency("mlp", "keras", "magrittr", mode = "regression")
set_dependency("mlp", "keras", "keras", mode = "classification")
set_dependency("mlp", "keras", "magrittr", mode = "classification")
set_model_arg(
model = "mlp",
eng = "keras",
parsnip = "hidden_units",
original = "hidden_units",
func = list(pkg = "dials", fun = "hidden_units"),
has_submodel = FALSE
)
set_model_arg(
model = "mlp",
eng = "keras",
parsnip = "penalty",
original = "penalty",
func = list(pkg = "dials", fun = "penalty"),
has_submodel = FALSE
)
set_model_arg(
model = "mlp",
eng = "keras",
parsnip = "dropout",
original = "dropout",
func = list(pkg = "dials", fun = "dropout"),
has_submodel = FALSE
)
set_model_arg(
model = "mlp",
eng = "keras",
parsnip = "epochs",
original = "epochs",
func = list(pkg = "dials", fun = "epochs"),
has_submodel = FALSE
)
set_model_arg(
model = "mlp",
eng = "keras",
parsnip = "activation",
original = "activation",
func = list(pkg = "dials", fun = "activation"),
has_submodel = FALSE
)
set_fit(
model = "mlp",
eng = "keras",
mode = "regression",
value = list(
interface = "matrix",
protect = c("x", "y"),
func = c(pkg = "parsnip", fun = "keras_mlp"),
defaults = list()
)
)
set_encoding(
model = "mlp",
eng = "keras",
mode = "regression",
options = list(
predictor_indicators = "traditional",
compute_intercept = TRUE,
remove_intercept = TRUE,
allow_sparse_x = FALSE
)
)
set_fit(
model = "mlp",
eng = "keras",
mode = "classification",
value = list(
interface = "matrix",
protect = c("x", "y"),
func = c(pkg = "parsnip", fun = "keras_mlp"),
defaults = list()
)
)
set_encoding(
model = "mlp",
eng = "keras",
mode = "classification",
options = list(
predictor_indicators = "traditional",
compute_intercept = TRUE,
remove_intercept = TRUE,
allow_sparse_x = FALSE
)
)
set_pred(
model = "mlp",
eng = "keras",
mode = "regression",
type = "numeric",
value = list(
pre = NULL,
post = maybe_multivariate,
func = c(fun = "predict"),
args =
list(
object = quote(object$fit),
x = quote(as.matrix(new_data))
)
)
)
set_pred(
model = "mlp",
eng = "keras",
mode = "regression",
type = "raw",
value = list(
pre = NULL,
post = NULL,
func = c(fun = "predict"),
args =
list(
object = quote(object$fit),
x = quote(as.matrix(new_data))
)
)
)
set_pred(
model = "mlp",
eng = "keras",
mode = "classification",
type = "class",
value = list(
pre = NULL,
post = NULL,
func = c(pkg = "parsnip", fun = "keras_predict_classes"),
args =
list(
object = quote(object),
x = quote(as.matrix(new_data))
)
)
)
set_pred(
model = "mlp",
eng = "keras",
mode = "classification",
type = "prob",
value = list(
pre = NULL,
post = function(x, object) {
colnames(x) <- object$lvl
x <- as_tibble(x)
x
},
func = c(fun = "predict"),
args =
list(
object = quote(object$fit),
x = quote(as.matrix(new_data))
)
)
)
set_pred(
model = "mlp",
eng = "keras",
mode = "classification",
type = "raw",
value = list(
pre = NULL,
post = NULL,
func = c(fun = "predict"),
args =
list(
object = quote(object$fit),
x = quote(as.matrix(new_data))
)
)
)
# ------------------------------------------------------------------------------
set_model_engine("mlp", "classification", "nnet")
set_model_engine("mlp", "regression", "nnet")
set_dependency("mlp", "nnet", "nnet", mode = "regression")
set_dependency("mlp", "nnet", "nnet", mode = "classification")
set_model_arg(
model = "mlp",
eng = "nnet",
parsnip = "hidden_units",
original = "size",
func = list(pkg = "dials", fun = "hidden_units"),
has_submodel = FALSE
)
set_model_arg(
model = "mlp",
eng = "nnet",
parsnip = "penalty",
original = "decay",
func = list(pkg = "dials", fun = "penalty"),
has_submodel = FALSE
)
set_model_arg(
model = "mlp",
eng = "nnet",
parsnip = "epochs",
original = "maxit",
func = list(pkg = "dials", fun = "epochs"),
has_submodel = FALSE
)
set_fit(
model = "mlp",
eng = "nnet",
mode = "regression",
value = list(
interface = "formula",
protect = c("formula", "data"),
func = c(pkg = "nnet", fun = "nnet"),
defaults = list(trace = FALSE)
)
)
set_encoding(
model = "mlp",
eng = "nnet",
mode = "regression",
options = list(
predictor_indicators = "traditional",
compute_intercept = TRUE,
remove_intercept = TRUE,
allow_sparse_x = FALSE
)
)
set_fit(
model = "mlp",
eng = "nnet",
mode = "classification",
value = list(
interface = "formula",
protect = c("formula", "data"),
func = c(pkg = "nnet", fun = "nnet"),
defaults = list(trace = FALSE)
)
)
set_encoding(
model = "mlp",
eng = "nnet",
mode = "classification",
options = list(
predictor_indicators = "traditional",
compute_intercept = TRUE,
remove_intercept = TRUE,
allow_sparse_x = FALSE
)
)
set_pred(
model = "mlp",
eng = "nnet",
mode = "regression",
type = "numeric",
value = list(
pre = NULL,
post = maybe_multivariate,
func = c(fun = "predict"),
args =
list(
object = quote(object$fit),
newdata = quote(new_data),
type = "raw"
)
)
)
set_pred(
model = "mlp",
eng = "nnet",
mode = "regression",
type = "raw",
value = list(
pre = NULL,
post = NULL,
func = c(fun = "predict"),
args =
list(
object = quote(object$fit),
newdata = quote(new_data)
)
)
)
set_pred(
model = "mlp",
eng = "nnet",
mode = "classification",
type = "class",
value = list(
pre = NULL,
post = NULL,
func = c(fun = "predict"),
args =
list(
object = quote(object$fit),
newdata = quote(new_data),
type = "class"
)
)
)
set_pred(
model = "mlp",
eng = "nnet",
mode = "classification",
type = "prob",
value = list(
pre = NULL,
post = nnet_softmax,
func = c(fun = "predict"),
args =
list(
object = quote(object$fit),
newdata = quote(new_data),
type = "raw"
)
)
)
set_pred(
model = "mlp",
eng = "nnet",
mode = "classification",
type = "raw",
value = list(
pre = NULL,
post = NULL,
func = c(fun = "predict"),
args =
list(
object = quote(object$fit),
newdata = quote(new_data)
)
)
)
## -----------------------------------------------------------------------------
set_model_engine("mlp", "classification", "brulee")
set_model_engine("mlp", "regression", "brulee")
set_dependency("mlp", "brulee", "brulee")
set_model_arg(
model = "mlp",
eng = "brulee",
parsnip = "hidden_units",
original = "hidden_units",
func = list(pkg = "dials", fun = "hidden_units"),
has_submodel = FALSE
)
set_model_arg(
model = "mlp",
eng = "brulee",
parsnip = "penalty",
original = "penalty",
func = list(pkg = "dials", fun = "penalty"),
has_submodel = FALSE
)
set_model_arg(
model = "mlp",
eng = "brulee",
parsnip = "epochs",
original = "epochs",
func = list(pkg = "dials", fun = "epochs"),
has_submodel = FALSE
)
set_model_arg(
model = "mlp",
eng = "brulee",
parsnip = "dropout",
original = "dropout",
func = list(pkg = "dials", fun = "dropout"),
has_submodel = FALSE
)
set_model_arg(
model = "mlp",
eng = "brulee",
parsnip = "learn_rate",
original = "learn_rate",
func = list(pkg = "dials", fun = "learn_rate", range = c(-2.5, -0.5)),
has_submodel = FALSE
)
set_model_arg(
model = "mlp",
eng = "brulee",
parsnip = "activation",
original = "activation",
func = list(pkg = "dials", fun = "activation", values = c('relu', 'elu', 'tanh')),
has_submodel = FALSE
)
set_fit(
model = "mlp",
eng = "brulee",
mode = "regression",
value = list(
interface = "data.frame",
protect = c("x", "y"),
func = c(pkg = "brulee", fun = "brulee_mlp"),
defaults = list()
)
)
set_encoding(
model = "mlp",
eng = "brulee",
mode = "regression",
options = list(
predictor_indicators = "none",
compute_intercept = FALSE,
remove_intercept = FALSE,
allow_sparse_x = FALSE
)
)
set_fit(
model = "mlp",
eng = "brulee",
mode = "classification",
value = list(
interface = "data.frame",
protect = c("x", "y"),
func = c(pkg = "brulee", fun = "brulee_mlp"),
defaults = list()
)
)
set_encoding(
model = "mlp",
eng = "brulee",
mode = "classification",
options = list(
predictor_indicators = "none",
compute_intercept = FALSE,
remove_intercept = FALSE,
allow_sparse_x = FALSE
)
)
set_pred(
model = "mlp",
eng = "brulee",
mode = "regression",
type = "numeric",
value = list(
pre = NULL,
post = reformat_torch_num,
func = c(fun = "predict"),
args =
list(
object = quote(object$fit),
new_data = quote(new_data),
type = "numeric"
)
)
)
set_pred(
model = "mlp",
eng = "brulee",
mode = "classification",
type = "class",
value = list(
pre = NULL,
post = NULL,
func = c(fun = "predict"),
args =
list(
object = quote(object$fit),
new_data = quote(new_data),
type = "class"
)
)
)
set_pred(
model = "mlp",
eng = "brulee",
mode = "classification",
type = "prob",
value = list(
pre = NULL,
post = NULL,
func = c(fun = "predict"),
args =
list(
object = quote(object$fit),
new_data = quote(new_data),
type = "prob"
)
)
)
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