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#' @export
#' @rdname ml-model-constructors
ml_supervised_pipeline <- function(predictor, dataset, formula, features_col, label_col) {
sc <- spark_connection(predictor)
r_formula <- ft_r_formula(sc, formula, features_col, label_col)
pipeline_model <- ml_pipeline(r_formula, predictor) %>%
ml_fit(dataset)
}
#' @export
#' @rdname ml-model-constructors
ml_clustering_pipeline <- function(predictor, dataset, formula, features_col) {
sc <- spark_connection(predictor)
pipeline <- if (spark_version(sc) < "2.0.0") {
rdf <- sdf_schema(dataset) %>%
lapply(`[[`, "name") %>%
as.data.frame(stringsAsFactors = FALSE)
features <- stats::terms(as.formula(formula), data = rdf) %>%
attr("term.labels")
vector_assembler <- ft_vector_assembler(
sc,
input_cols = features, output_col = features_col
)
ml_pipeline(vector_assembler, predictor)
} else {
r_formula <- ft_r_formula(sc, formula = formula, features_col = features_col)
ml_pipeline(r_formula, predictor)
}
pipeline %>% ml_fit(dataset)
}
ml_recommendation_pipeline <- function(predictor, dataset, formula) {
sc <- spark_connection(predictor)
r_formula <- ft_r_formula(sc, formula)
pipeline_model <- ml_pipeline(r_formula, predictor) %>%
ml_fit(dataset)
}
#' @export
#' @rdname ml-model-constructors
#' @param constructor The constructor function for the `ml_model`.
ml_construct_model_supervised <- function(constructor, predictor, formula, dataset,
features_col, label_col, ...) {
pipeline_model <- ml_supervised_pipeline(
predictor = predictor,
dataset = dataset,
formula = formula,
features_col = features_col,
label_col = label_col
)
.args <- list(
pipeline_model = pipeline_model,
formula = formula,
dataset = dataset,
features_col = features_col,
label_col = label_col,
...
)
rlang::exec(constructor, !!!.args)
}
#' @export
#' @rdname ml-model-constructors
ml_construct_model_clustering <- function(constructor, predictor, formula, dataset, features_col, ...) {
pipeline_model <- ml_clustering_pipeline(
predictor = predictor,
dataset = dataset,
formula = formula,
features_col = features_col
)
.args <- list(
pipeline_model = pipeline_model,
formula = formula,
dataset = dataset,
features_col = features_col,
...
)
rlang::exec(constructor, !!!.args)
}
ml_construct_model_recommendation <- function(constructor, predictor, formula,
dataset, ...) {
pipeline_model <- ml_recommendation_pipeline(
predictor = predictor,
dataset = dataset,
formula = formula
)
.args <- list(
pipeline_model = pipeline_model,
formula = formula,
dataset = dataset,
...
)
rlang::exec(constructor, !!!.args)
}
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