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#' @include ml_clustering.R
NULL
#' @rdname ml_decision_tree
#' @template roxlate-ml-probabilistic-classifier-params
#' @export
ml_decision_tree_classifier <- function(x, formula = NULL, max_depth = 5, max_bins = 32,
min_instances_per_node = 1, min_info_gain = 0,
impurity = "gini", seed = NULL, thresholds = NULL,
cache_node_ids = FALSE, checkpoint_interval = 10,
max_memory_in_mb = 256, features_col = "features",
label_col = "label", prediction_col = "prediction",
probability_col = "probability", raw_prediction_col = "rawPrediction",
uid = random_string("decision_tree_classifier_"), ...) {
check_dots_used()
UseMethod("ml_decision_tree_classifier")
}
#' @export
ml_decision_tree_classifier.spark_connection <- function(x, formula = NULL, max_depth = 5, max_bins = 32,
min_instances_per_node = 1, min_info_gain = 0,
impurity = "gini", seed = NULL, thresholds = NULL,
cache_node_ids = FALSE, checkpoint_interval = 10,
max_memory_in_mb = 256, features_col = "features",
label_col = "label", prediction_col = "prediction",
probability_col = "probability", raw_prediction_col = "rawPrediction",
uid = random_string("decision_tree_classifier_"), ...) {
.args <- list(
max_depth = max_depth,
max_bins = max_bins,
min_instances_per_node = min_instances_per_node,
min_info_gain = min_info_gain,
impurity = impurity,
seed = seed,
thresholds = thresholds,
cache_node_ids = cache_node_ids,
checkpoint_interval = checkpoint_interval,
max_memory_in_mb = max_memory_in_mb,
features_col = features_col,
label_col = label_col,
prediction_col = prediction_col,
probability_col = probability_col,
raw_prediction_col = raw_prediction_col
) %>%
c(rlang::dots_list(...)) %>%
validator_ml_decision_tree_classifier()
jobj <- spark_pipeline_stage(
x, "org.apache.spark.ml.classification.DecisionTreeClassifier", uid,
features_col = .args[["features_col"]], label_col = .args[["label_col"]],
prediction_col = .args[["prediction_col"]],
probability_col = .args[["probability_col"]],
raw_prediction_col = .args[["raw_prediction_col"]]
) %>%
invoke("setCheckpointInterval", .args[["checkpoint_interval"]]) %>%
invoke("setImpurity", .args[["impurity"]]) %>%
invoke("setMaxBins", .args[["max_bins"]]) %>%
invoke("setMaxDepth", .args[["max_depth"]]) %>%
invoke("setMinInfoGain", .args[["min_info_gain"]]) %>%
invoke("setMinInstancesPerNode", .args[["min_instances_per_node"]]) %>%
invoke("setCacheNodeIds", .args[["cache_node_ids"]]) %>%
invoke("setMaxMemoryInMB", .args[["max_memory_in_mb"]]) %>%
jobj_set_param("setThresholds", .args[["thresholds"]]) %>%
jobj_set_param("setSeed", .args[["seed"]])
new_ml_decision_tree_classifier(jobj)
}
#' @export
ml_decision_tree_classifier.ml_pipeline <- function(x, formula = NULL, max_depth = 5, max_bins = 32,
min_instances_per_node = 1, min_info_gain = 0,
impurity = "gini", seed = NULL, thresholds = NULL,
cache_node_ids = FALSE, checkpoint_interval = 10,
max_memory_in_mb = 256, features_col = "features",
label_col = "label", prediction_col = "prediction",
probability_col = "probability", raw_prediction_col = "rawPrediction",
uid = random_string("decision_tree_classifier_"), ...) {
stage <- ml_decision_tree_classifier.spark_connection(
x = spark_connection(x),
formula = formula,
max_depth = max_depth,
max_bins = max_bins,
min_instances_per_node = min_instances_per_node,
min_info_gain = min_info_gain,
impurity = impurity,
seed = seed,
thresholds = thresholds,
cache_node_ids = cache_node_ids,
checkpoint_interval = checkpoint_interval,
max_memory_in_mb = max_memory_in_mb,
features_col = features_col,
label_col = label_col,
prediction_col = prediction_col,
probability_col = probability_col,
raw_prediction_col = raw_prediction_col,
uid = uid,
...
)
ml_add_stage(x, stage)
}
#' @export
ml_decision_tree_classifier.tbl_spark <- function(x, formula = NULL, max_depth = 5, max_bins = 32,
min_instances_per_node = 1, min_info_gain = 0,
impurity = "gini", seed = NULL, thresholds = NULL,
cache_node_ids = FALSE, checkpoint_interval = 10,
max_memory_in_mb = 256, features_col = "features",
label_col = "label", prediction_col = "prediction",
probability_col = "probability", raw_prediction_col = "rawPrediction",
uid = random_string("decision_tree_classifier_"),
response = NULL, features = NULL,
predicted_label_col = "predicted_label", ...) {
formula <- ml_standardize_formula(formula, response, features)
stage <- ml_decision_tree_classifier.spark_connection(
x = spark_connection(x),
formula = formula,
max_depth = max_depth,
max_bins = max_bins,
min_instances_per_node = min_instances_per_node,
min_info_gain = min_info_gain,
impurity = impurity,
seed = seed,
thresholds = thresholds,
cache_node_ids = cache_node_ids,
checkpoint_interval = checkpoint_interval,
max_memory_in_mb = max_memory_in_mb,
features_col = features_col,
label_col = label_col,
prediction_col = prediction_col,
probability_col = probability_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_decision_tree_classification,
predictor = stage,
formula = formula,
dataset = x,
features_col = features_col,
label_col = label_col,
predicted_label_col = predicted_label_col
)
}
}
validator_ml_decision_tree_classifier <- function(.args) {
.args <- ml_validate_decision_tree_args(.args)
.args[["thresholds"]] <- cast_double_list(.args[["thresholds"]], allow_null = TRUE)
.args[["impurity"]] <- cast_choice(.args[["impurity"]], c("gini", "entropy"))
.args
}
new_ml_decision_tree_classifier <- function(jobj) {
new_ml_probabilistic_classifier(jobj, class = "ml_decision_tree_classifier")
}
new_ml_decision_tree_classification_model <- function(jobj) {
new_ml_probabilistic_classification_model(
jobj,
# `depth` and `featureImportances` are lazy vals in Spark.
depth = function() invoke(jobj, "depth"),
feature_importances = possibly_null(~ read_spark_vector(jobj, "featureImportances")),
# `numNodes` is a def in Spark.
num_nodes = function() invoke(jobj, "numNodes"),
class = "ml_decision_tree_classification_model"
)
}
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