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#' Spark ML -- LinearSVC
#'
#' Perform classification using linear support vector machines (SVM). This binary classifier optimizes the Hinge Loss using the OWLQN optimizer. Only supports L2 regularization currently.
#'
#' @template roxlate-ml-algo
#' @template roxlate-ml-formula-params
#' @template roxlate-ml-linear-regression-params
#' @template roxlate-ml-predictor-params
#' @template roxlate-ml-aggregation-depth
#' @template roxlate-ml-standardization
#' @param threshold in binary classification prediction, in range [0, 1].
#' @param raw_prediction_col Raw prediction (a.k.a. confidence) column name.
#' @examples
#' \dontrun{
#' library(dplyr)
#'
#' sc <- spark_connect(master = "local")
#' iris_tbl <- sdf_copy_to(sc, iris, name = "iris_tbl", overwrite = TRUE)
#'
#' partitions <- iris_tbl %>%
#' filter(Species != "setosa") %>%
#' sdf_random_split(training = 0.7, test = 0.3, seed = 1111)
#'
#' iris_training <- partitions$training
#' iris_test <- partitions$test
#'
#' svc_model <- iris_training %>%
#' ml_linear_svc(Species ~ .)
#'
#' pred <- ml_predict(svc_model, iris_test)
#'
#' ml_binary_classification_evaluator(pred)
#' }
#'
#' @export
ml_linear_svc <- function(x, formula = NULL, fit_intercept = TRUE, reg_param = 0,
max_iter = 100, standardization = TRUE, weight_col = NULL,
tol = 1e-6, threshold = 0, aggregation_depth = 2,
features_col = "features", label_col = "label",
prediction_col = "prediction", raw_prediction_col = "rawPrediction",
uid = random_string("linear_svc_"), ...) {
check_dots_used()
UseMethod("ml_linear_svc")
}
#' @export
ml_linear_svc.spark_connection <- function(x, formula = NULL, fit_intercept = TRUE, reg_param = 0,
max_iter = 100, standardization = TRUE, weight_col = NULL,
tol = 1e-6, threshold = 0, aggregation_depth = 2,
features_col = "features", label_col = "label",
prediction_col = "prediction", raw_prediction_col = "rawPrediction",
uid = random_string("linear_svc_"), ...) {
.args <- list(
fit_intercept = fit_intercept,
reg_param = reg_param,
max_iter = max_iter,
standardization = standardization,
weight_col = weight_col,
tol = tol,
threshold = threshold,
aggregation_depth = aggregation_depth,
features_col = features_col,
label_col = label_col,
prediction_col = prediction_col,
raw_prediction_col = raw_prediction_col
) %>%
c(rlang::dots_list(...)) %>%
validator_ml_linear_svc()
sc <- spark_connection(x)
if (spark_version(sc) >= "3.0" && !is.null(.args[["weight_col"]])) {
warning("Support for `weight_col` is removed in Spark 3.0 or above because",
"it is not intended for users (see ",
"https://spark.apache.org/docs/latest/ml-migration-guide.html#breaking-changes",
"). The `weight_col` parameter will be ignored.")
.args[["weight_col"]] <- NULL
}
jobj <- spark_pipeline_stage(
x, "org.apache.spark.ml.classification.LinearSVC", uid,
features_col = .args[["features_col"]], label_col = .args[["label_col"]],
prediction_col = .args[["prediction_col"]]
) %>% (
function(obj) {
do.call(
invoke,
c(obj, "%>%", Filter(
function(x) !is.null(x),
list(
list("setRawPredictionCol", .args[["raw_prediction_col"]]),
list("setFitIntercept", .args[["fit_intercept"]]),
list("setRegParam", .args[["reg_param"]]),
list("setMaxIter", .args[["max_iter"]]),
list("setStandardization", .args[["standardization"]]),
list("setTol", .args[["tol"]]),
list("setAggregationDepth", .args[["aggregation_depth"]]),
list("setThreshold", .args[["threshold"]]),
jobj_set_param_helper(obj, "setWeightCol", .args[["weight_col"]])
)
))
)
})
new_ml_linear_svc(jobj)
}
#' @export
ml_linear_svc.ml_pipeline <- function(x, formula = NULL, fit_intercept = TRUE, reg_param = 0,
max_iter = 100, standardization = TRUE, weight_col = NULL,
tol = 1e-6, threshold = 0, aggregation_depth = 2,
features_col = "features", label_col = "label",
prediction_col = "prediction", raw_prediction_col = "rawPrediction",
uid = random_string("linear_svc_"), ...) {
stage <- ml_linear_svc.spark_connection(
x = spark_connection(x),
formula = formula,
fit_intercept = fit_intercept,
reg_param = reg_param,
max_iter = max_iter,
standardization = standardization,
weight_col = weight_col,
tol = tol,
threshold = threshold,
aggregation_depth = aggregation_depth,
features_col = features_col,
label_col = label_col,
prediction_col = prediction_col,
raw_prediction_col = raw_prediction_col,
uid = uid,
...
)
ml_add_stage(x, stage)
}
#' @export
ml_linear_svc.tbl_spark <- function(x, formula = NULL, fit_intercept = TRUE, reg_param = 0,
max_iter = 100, standardization = TRUE, weight_col = NULL,
tol = 1e-6, threshold = 0, aggregation_depth = 2,
features_col = "features", label_col = "label",
prediction_col = "prediction", raw_prediction_col = "rawPrediction",
uid = random_string("linear_svc_"), response = NULL,
features = NULL, predicted_label_col = "predicted_label", ...) {
formula <- ml_standardize_formula(formula, response, features)
stage <- ml_linear_svc.spark_connection(
x = spark_connection(x),
formula = NULL,
fit_intercept = fit_intercept,
reg_param = reg_param,
max_iter = max_iter,
standardization = standardization,
weight_col = weight_col,
tol = tol,
threshold = threshold,
aggregation_depth = aggregation_depth,
features_col = features_col,
label_col = label_col,
prediction_col = prediction_col,
raw_prediction_col = raw_prediction_col,
uid = uid,
...
)
if (is.null(formula)) {
stage %>%
ml_fit(x)
} else {
ml_construct_model_supervised(
new_ml_model_linear_svc,
predictor = stage,
formula = formula,
dataset = x,
features_col = features_col,
label_col = label_col,
predicted_label_col = predicted_label_col
)
}
}
# Validator
validator_ml_linear_svc <- function(.args) {
.args[["reg_param"]] <- cast_scalar_double(.args[["reg_param"]])
.args[["max_iter"]] <- cast_scalar_integer(.args[["max_iter"]])
.args[["fit_intercept"]] <- cast_scalar_logical(.args[["fit_intercept"]])
.args[["standardization"]] <- cast_scalar_logical(.args[["standardization"]])
.args[["tol"]] <- cast_scalar_double(.args[["tol"]])
.args[["aggregation_depth"]] <- cast_scalar_integer(.args[["aggregation_depth"]])
.args[["raw_prediction_col"]] <- cast_string(.args[["raw_prediction_col"]])
.args[["threshold"]] <- cast_scalar_double(.args[["threshold"]])
.args[["weight_col"]] <- cast_nullable_string(.args[["weight_col"]])
.args
}
# Constructors
new_ml_linear_svc <- function(jobj) {
new_ml_classifier(jobj, class = "ml_linear_svc")
}
new_ml_linear_svc_model <- function(jobj) {
new_ml_classification_model(
jobj,
coefficients = read_spark_vector(jobj, "coefficients"),
intercept = invoke(jobj, "intercept"),
threshold = invoke(jobj, "threshold"),
weight_col = possibly_null(~ invoke(jobj, "weightCol"))(),
class = "ml_linear_svc_model"
)
}
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