Nothing
set_new_model("linear_reg")
set_model_mode("linear_reg", "regression")
# ------------------------------------------------------------------------------
set_model_engine("linear_reg", "regression", "lm")
set_dependency("linear_reg", "lm", "stats")
set_fit(
model = "linear_reg",
eng = "lm",
mode = "regression",
value = list(
interface = "formula",
protect = c("formula", "data", "weights"),
func = c(pkg = "stats", fun = "lm"),
defaults = list()
)
)
set_encoding(
model = "linear_reg",
eng = "lm",
mode = "regression",
options = list(
predictor_indicators = "traditional",
compute_intercept = TRUE,
remove_intercept = TRUE,
allow_sparse_x = FALSE
)
)
set_pred(
model = "linear_reg",
eng = "lm",
mode = "regression",
type = "numeric",
value = list(
pre = NULL,
post = NULL,
func = c(fun = "predict"),
args =
list(
object = expr(object$fit),
newdata = expr(new_data),
type = "response",
rankdeficient = "simple"
)
)
)
set_pred(
model = "linear_reg",
eng = "lm",
mode = "regression",
type = "conf_int",
value = list(
pre = NULL,
post = function(results, object) {
tibble::as_tibble(results) %>%
dplyr::select(-fit) %>%
setNames(c(".pred_lower", ".pred_upper"))
},
func = c(fun = "predict"),
args =
list(
object = expr(object$fit),
newdata = expr(new_data),
interval = "confidence",
level = expr(level),
type = "response"
)
)
)
set_pred(
model = "linear_reg",
eng = "lm",
mode = "regression",
type = "pred_int",
value = list(
pre = NULL,
post = function(results, object) {
tibble::as_tibble(results) %>%
dplyr::select(-fit) %>%
setNames(c(".pred_lower", ".pred_upper"))
},
func = c(fun = "predict"),
args =
list(
object = expr(object$fit),
newdata = expr(new_data),
interval = "prediction",
level = expr(level),
type = "response"
)
)
)
set_pred(
model = "linear_reg",
eng = "lm",
mode = "regression",
type = "raw",
value = list(
pre = NULL,
post = NULL,
func = c(fun = "predict"),
args = list(object = expr(object$fit), newdata = expr(new_data))
)
)
# ------------------------------------------------------------------------------
set_model_engine("linear_reg", "regression", "glm")
set_dependency("linear_reg", "glm", "stats")
set_fit(
model = "linear_reg",
eng = "glm",
mode = "regression",
value = list(
interface = "formula",
protect = c("formula", "data", "weights"),
func = c(pkg = "stats", fun = "glm"),
defaults = list(family = expr(stats::gaussian))
)
)
set_encoding(
model = "linear_reg",
eng = "glm",
mode = "regression",
options = list(
predictor_indicators = "traditional",
compute_intercept = TRUE,
remove_intercept = TRUE,
allow_sparse_x = FALSE
)
)
set_pred(
model = "linear_reg",
eng = "glm",
mode = "regression",
type = "numeric",
value = list(
pre = NULL,
post = NULL,
func = c(fun = "predict"),
args =
list(
object = expr(object$fit),
newdata = expr(new_data),
type = "response"
)
)
)
set_pred(
model = "linear_reg",
eng = "glm",
mode = "regression",
type = "conf_int",
value = list(
pre = NULL,
post = linear_lp_to_conf_int,
func = c(fun = "predict"),
args =
list(
object = quote(object$fit),
newdata = quote(new_data),
se.fit = TRUE,
type = "link"
)
)
)
set_pred(
model = "linear_reg",
eng = "glm",
mode = "regression",
type = "raw",
value = list(
pre = NULL,
post = NULL,
func = c(fun = "predict"),
args = list(object = expr(object$fit), newdata = expr(new_data))
)
)
# ------------------------------------------------------------------------------
set_model_engine("linear_reg", "regression", "glmnet")
set_dependency("linear_reg", "glmnet", "glmnet")
set_fit(
model = "linear_reg",
eng = "glmnet",
mode = "regression",
value = list(
interface = "matrix",
protect = c("x", "y", "weights"),
func = c(pkg = "glmnet", fun = "glmnet"),
defaults = list(family = "gaussian")
)
)
set_encoding(
model = "linear_reg",
eng = "glmnet",
mode = "regression",
options = list(
predictor_indicators = "traditional",
compute_intercept = TRUE,
remove_intercept = TRUE,
allow_sparse_x = TRUE
)
)
set_model_arg(
model = "linear_reg",
eng = "glmnet",
parsnip = "penalty",
original = "lambda",
func = list(pkg = "dials", fun = "penalty"),
has_submodel = TRUE
)
set_model_arg(
model = "linear_reg",
eng = "glmnet",
parsnip = "mixture",
original = "alpha",
func = list(pkg = "dials", fun = "mixture"),
has_submodel = FALSE
)
set_pred(
model = "linear_reg",
eng = "glmnet",
mode = "regression",
type = "numeric",
value = list(
pre = NULL,
post = .organize_glmnet_pred,
func = c(fun = "predict"),
args =
list(
object = expr(object$fit),
newx = expr(as.matrix(new_data[, rownames(object$fit$beta), drop = FALSE])),
type = "response",
s = expr(object$spec$args$penalty)
)
)
)
set_pred(
model = "linear_reg",
eng = "glmnet",
mode = "regression",
type = "raw",
value = list(
pre = NULL,
post = NULL,
func = c(fun = "predict"),
args =
list(object = expr(object$fit),
newx = expr(as.matrix(new_data)))
)
)
# ------------------------------------------------------------------------------
set_model_engine("linear_reg", "regression", "stan")
set_dependency("linear_reg", "stan", "rstanarm")
set_fit(
model = "linear_reg",
eng = "stan",
mode = "regression",
value = list(
interface = "formula",
protect = c("formula", "data", "weights"),
func = c(pkg = "rstanarm", fun = "stan_glm"),
defaults = list(family = expr(stats::gaussian), refresh = 0)
)
)
set_encoding(
model = "linear_reg",
eng = "stan",
mode = "regression",
options = list(
predictor_indicators = "traditional",
compute_intercept = TRUE,
remove_intercept = TRUE,
allow_sparse_x = FALSE
)
)
set_pred(
model = "linear_reg",
eng = "stan",
mode = "regression",
type = "numeric",
value = list(
pre = NULL,
post = NULL,
func = c(fun = "predict"),
args = list(object = expr(object$fit), newdata = expr(new_data))
)
)
set_pred(
model = "linear_reg",
eng = "stan",
mode = "regression",
type = "conf_int",
value = list(
pre = NULL,
post = function(results, object) {
res <-
tibble(
.pred_lower =
convert_stan_interval(
results,
level = object$spec$method$pred$conf_int$extras$level
),
.pred_upper =
convert_stan_interval(
results,
level = object$spec$method$pred$conf_int$extras$level,
lower = FALSE
),
)
if (object$spec$method$pred$conf_int$extras$std_error)
res$.std_error <- apply(results, 2, sd, na.rm = TRUE)
res
},
func = c(pkg = "parsnip", fun = "stan_conf_int"),
args =
list(
object = expr(object$fit),
newdata = expr(new_data)
)
)
)
set_pred(
model = "linear_reg",
eng = "stan",
mode = "regression",
type = "pred_int",
value = list(
pre = NULL,
post = function(results, object) {
res <-
tibble(
.pred_lower =
convert_stan_interval(
results,
level = object$spec$method$pred$pred_int$extras$level
),
.pred_upper =
convert_stan_interval(
results,
level = object$spec$method$pred$pred_int$extras$level,
lower = FALSE
),
)
if (object$spec$method$pred$pred_int$extras$std_error)
res$.std_error <- apply(results, 2, sd, na.rm = TRUE)
res
},
func = c(pkg = "rstanarm", fun = "posterior_predict"),
args =
list(
object = expr(object$fit),
newdata = expr(new_data),
seed = expr(sample.int(10^5, 1))
)
)
)
set_pred(
model = "linear_reg",
eng = "stan",
mode = "regression",
type = "raw",
value = list(
pre = NULL,
post = NULL,
func = c(fun = "predict"),
args = list(object = expr(object$fit), newdata = expr(new_data))
)
)
# ------------------------------------------------------------------------------
set_model_engine("linear_reg", "regression", "spark")
set_dependency("linear_reg", "spark", "sparklyr")
set_fit(
model = "linear_reg",
eng = "spark",
mode = "regression",
value = list(
interface = "formula",
data = c(formula = "formula", data = "x", weights = "weight_col"),
protect = c("x", "formula", "weights"),
func = c(pkg = "sparklyr", fun = "ml_linear_regression"),
defaults = list()
)
)
set_encoding(
model = "linear_reg",
eng = "spark",
mode = "regression",
options = list(
predictor_indicators = "traditional",
compute_intercept = TRUE,
remove_intercept = TRUE,
allow_sparse_x = FALSE
)
)
set_model_arg(
model = "linear_reg",
eng = "spark",
parsnip = "penalty",
original = "reg_param",
func = list(pkg = "dials", fun = "penalty"),
has_submodel = FALSE
)
set_model_arg(
model = "linear_reg",
eng = "spark",
parsnip = "mixture",
original = "elastic_net_param",
func = list(pkg = "dials", fun = "mixture"),
has_submodel = FALSE
)
set_pred(
model = "linear_reg",
eng = "spark",
mode = "regression",
type = "numeric",
value = list(
pre = NULL,
post = function(results, object) {
results <- dplyr::rename(results, pred = prediction)
results <- dplyr::select(results, pred)
results
},
func = c(pkg = "sparklyr", fun = "ml_predict"),
args = list(x = expr(object$fit), dataset = expr(new_data))
)
)
# ------------------------------------------------------------------------------
set_model_engine("linear_reg", "regression", "keras")
set_dependency("linear_reg", "keras", "keras")
set_dependency("linear_reg", "keras", "magrittr")
set_fit(
model = "linear_reg",
eng = "keras",
mode = "regression",
value = list(
interface = "matrix",
protect = c("x", "y"),
func = c(pkg = "parsnip", fun = "keras_mlp"),
defaults = list(hidden_units = 1, act = "linear")
)
)
set_encoding(
model = "linear_reg",
eng = "keras",
mode = "regression",
options = list(
predictor_indicators = "traditional",
compute_intercept = TRUE,
remove_intercept = TRUE,
allow_sparse_x = FALSE
)
)
set_model_arg(
model = "linear_reg",
eng = "keras",
parsnip = "penalty",
original = "penalty",
func = list(pkg = "dials", fun = "penalty"),
has_submodel = FALSE
)
set_pred(
model = "linear_reg",
eng = "keras",
mode = "regression",
type = "numeric",
value = list(
pre = NULL,
post = maybe_multivariate,
func = c(fun = "predict"),
args = list(object = quote(object$fit), x = quote(as.matrix(new_data)))
)
)
# ------------------------------------------------------------------------------
set_model_engine("linear_reg", "regression", "brulee")
set_dependency("linear_reg", "brulee", "brulee")
set_model_arg(
model = "linear_reg",
eng = "brulee",
parsnip = "penalty",
original = "penalty",
func = list(pkg = "dials", fun = "penalty"),
has_submodel = FALSE
)
set_model_arg(
model = "linear_reg",
eng = "brulee",
parsnip = "mixture",
original = "mixture",
func = list(pkg = "dials", fun = "mixture"),
has_submodel = FALSE
)
set_fit(
model = "linear_reg",
eng = "brulee",
mode = "regression",
value = list(
interface = "data.frame",
protect = c("x", "y"),
func = c(pkg = "brulee", fun = "brulee_linear_reg"),
defaults = list()
)
)
set_encoding(
model = "linear_reg",
eng = "brulee",
mode = "regression",
options = list(
predictor_indicators = "none",
compute_intercept = FALSE,
remove_intercept = FALSE,
allow_sparse_x = FALSE
)
)
set_pred(
model = "linear_reg",
eng = "brulee",
mode = "regression",
type = "numeric",
value = list(
pre = NULL,
post = NULL,
func = c(fun = "predict"),
args =
list(
object = quote(object$fit),
new_data = quote(new_data),
type = "numeric"
)
)
)
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