R/ml_classification_gbt_classifier.R

In sparklyr: R Interface to Apache Spark

#' @include ml_clustering.R
NULL

#' @rdname ml_gradient_boosted_trees
#' @template roxlate-ml-probabilistic-classifier-params
#' @export
ml_gbt_classifier <- function(
  x,
  formula = NULL,
  max_iter = 20,
  max_depth = 5,
  step_size = 0.1,
  subsampling_rate = 1,
  feature_subset_strategy = "auto",
  min_instances_per_node = 1L,
  max_bins = 32,
  min_info_gain = 0,
  loss_type = "logistic",
  seed = NULL,
  thresholds = NULL,
  checkpoint_interval = 10,
  cache_node_ids = FALSE,
  max_memory_in_mb = 256,
  features_col = "features",
  label_col = "label",
  prediction_col = "prediction",
  probability_col = "probability",
  raw_prediction_col = "rawPrediction",
  uid = random_string("gbt_classifier_"),
  ...
) {
  check_dots_used()
  UseMethod("ml_gbt_classifier")
}

#' @export
ml_gbt_classifier.spark_connection <- function(
  x,
  formula = NULL,
  max_iter = 20,
  max_depth = 5,
  step_size = 0.1,
  subsampling_rate = 1,
  feature_subset_strategy = "auto",
  min_instances_per_node = 1L,
  max_bins = 32,
  min_info_gain = 0,
  loss_type = "logistic",
  seed = NULL,
  thresholds = NULL,
  checkpoint_interval = 10,
  cache_node_ids = FALSE,
  max_memory_in_mb = 256,
  features_col = "features",
  label_col = "label",
  prediction_col = "prediction",
  probability_col = "probability",
  raw_prediction_col = "rawPrediction",
  uid = random_string("gbt_classifier_"),
  ...
) {
  .args <- list(
    max_iter = max_iter,
    max_depth = max_depth,
    step_size = step_size,
    subsampling_rate = subsampling_rate,
    feature_subset_strategy = feature_subset_strategy,
    min_instances_per_node = min_instances_per_node,
    max_bins = max_bins,
    min_info_gain = min_info_gain,
    loss_type = loss_type,
    seed = seed,
    thresholds = thresholds,
    checkpoint_interval = checkpoint_interval,
    cache_node_ids = cache_node_ids,
    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_gbt_classifier()

  stage_class <- "org.apache.spark.ml.classification.GBTClassifier"

  jobj <- (if (spark_version(x) < "2.2.0") {
    spark_pipeline_stage(
      x,
      stage_class,
      uid,
      features_col = .args[["features_col"]],
      label_col = .args[["label_col"]],
      prediction_col = .args[["prediction_col"]]
    )
  } else {
    spark_pipeline_stage(
      x,
      stage_class,
      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"]]
    )
  }) %>%
    (function(obj) {
      do.call(
        invoke,
        c(
          obj,
          "%>%",
          Filter(
            function(x) !is.null(x),
            list(
              list("setCheckpointInterval", .args[["checkpoint_interval"]]),
              list("setMaxBins", .args[["max_bins"]]),
              list("setMaxDepth", .args[["max_depth"]]),
              list("setMinInfoGain", .args[["min_info_gain"]]),
              list("setMinInstancesPerNode", .args[["min_instances_per_node"]]),
              list("setCacheNodeIds", .args[["cache_node_ids"]]),
              list("setMaxMemoryInMB", .args[["max_memory_in_mb"]]),
              list("setLossType", .args[["loss_type"]]),
              list("setMaxIter", .args[["max_iter"]]),
              list("setStepSize", .args[["step_size"]]),
              list("setSubsamplingRate", .args[["subsampling_rate"]]),
              jobj_set_param_helper(
                obj,
                "setFeatureSubsetStrategy",
                .args[["feature_subset_strategy"]],
                "2.3.0",
                "auto"
              ),
              jobj_set_param_helper(
                obj,
                "setThresholds",
                .args[["thresholds"]]
              ),
              jobj_set_param_helper(obj, "setSeed", .args[["seed"]])
            )
          )
        )
      )
    })

  new_ml_gbt_classifier(jobj)
}

#' @export
ml_gbt_classifier.ml_pipeline <- function(
  x,
  formula = NULL,
  max_iter = 20,
  max_depth = 5,
  step_size = 0.1,
  subsampling_rate = 1,
  feature_subset_strategy = "auto",
  min_instances_per_node = 1L,
  max_bins = 32,
  min_info_gain = 0,
  loss_type = "logistic",
  seed = NULL,
  thresholds = NULL,
  checkpoint_interval = 10,
  cache_node_ids = FALSE,
  max_memory_in_mb = 256,
  features_col = "features",
  label_col = "label",
  prediction_col = "prediction",
  probability_col = "probability",
  raw_prediction_col = "rawPrediction",
  uid = random_string("gbt_classifier_"),
  ...
) {
  stage <- ml_gbt_classifier.spark_connection(
    x = spark_connection(x),
    formula = formula,
    max_iter = max_iter,
    max_depth = max_depth,
    step_size = step_size,
    subsampling_rate = subsampling_rate,
    feature_subset_strategy = feature_subset_strategy,
    min_instances_per_node = min_instances_per_node,
    max_bins = max_bins,
    min_info_gain = min_info_gain,
    loss_type = loss_type,
    seed = seed,
    thresholds = thresholds,
    checkpoint_interval = checkpoint_interval,
    cache_node_ids = cache_node_ids,
    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_gbt_classifier.tbl_spark <- function(
  x,
  formula = NULL,
  max_iter = 20,
  max_depth = 5,
  step_size = 0.1,
  subsampling_rate = 1,
  feature_subset_strategy = "auto",
  min_instances_per_node = 1L,
  max_bins = 32,
  min_info_gain = 0,
  loss_type = "logistic",
  seed = NULL,
  thresholds = NULL,
  checkpoint_interval = 10,
  cache_node_ids = FALSE,
  max_memory_in_mb = 256,
  features_col = "features",
  label_col = "label",
  prediction_col = "prediction",
  probability_col = "probability",
  raw_prediction_col = "rawPrediction",
  uid = random_string("gbt_classifier_"),
  response = NULL,
  features = NULL,
  predicted_label_col = "predicted_label",
  ...
) {
  formula <- ml_standardize_formula(formula, response, features)

  stage <- ml_gbt_classifier.spark_connection(
    x = spark_connection(x),
    formula = NULL,
    max_iter = max_iter,
    max_depth = max_depth,
    step_size = step_size,
    subsampling_rate = subsampling_rate,
    feature_subset_strategy = feature_subset_strategy,
    min_instances_per_node = min_instances_per_node,
    max_bins = max_bins,
    min_info_gain = min_info_gain,
    loss_type = loss_type,
    seed = seed,
    thresholds = thresholds,
    checkpoint_interval = checkpoint_interval,
    cache_node_ids = cache_node_ids,
    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_gbt_classification,
      predictor = stage,
      formula = formula,
      dataset = x,
      features_col = features_col,
      label_col = label_col,
      predicted_label_col = predicted_label_col
    )
  }
}

# Validator
validator_ml_gbt_classifier <- function(.args) {
  .args <- ml_validate_decision_tree_args(.args)

  .args[["thresholds"]] <- cast_double_list(
    .args[["thresholds"]],
    allow_null = TRUE
  )
  .args[["max_iter"]] <- cast_scalar_integer(.args[["max_iter"]])
  .args[["step_size"]] <- cast_scalar_double(.args[["step_size"]])
  .args[["subsampling_rate"]] <- cast_scalar_double(.args[["subsampling_rate"]])
  .args[["loss_type"]] <- cast_choice(.args[["loss_type"]], "logistic")
  .args[["feature_subset_strategy"]] <- cast_string(.args[[
    "feature_subset_strategy"
  ]])
  .args
}

new_ml_gbt_classifier <- function(jobj) {
  v <- jobj %>%
    spark_connection() %>%
    spark_version()

  if (v < "2.2.0") {
    new_ml_predictor(jobj, class = "ml_gbt_classifier")
  } else {
    new_ml_probabilistic_classifier(jobj, class = "ml_gbt_classifier")
  }
}

new_ml_gbt_classification_model <- function(jobj) {
  v <- jobj %>%
    spark_connection() %>%
    spark_version()

  if (v < "2.2.0") {
    new_ml_prediction_model(
      jobj,
      # `lazy val featureImportances`
      feature_importances = possibly_null(
        ~ read_spark_vector(jobj, "featureImportances")
      ),
      num_classes = possibly_null(~ invoke(jobj, "numClasses"))(),
      # `lazy val totalNumNodes`
      total_num_nodes = function() invoke(jobj, "totalNumNodes"),
      tree_weights = invoke(jobj, "treeWeights"),
      # `def trees`
      trees = function() {
        invoke(jobj, "trees") %>%
          purrr::map(new_ml_decision_tree_regression_model)
      },
      class = "ml_multilayer_perceptron_classification_model"
    )
  } else {
    new_ml_probabilistic_classification_model(
      jobj,
      # `lazy val featureImportances`
      feature_importances = possibly_null(
        ~ read_spark_vector(jobj, "featureImportances")
      ),
      num_classes = possibly_null(~ invoke(jobj, "numClasses"))(),
      # `lazy val totalNumNodes`
      total_num_nodes = function() invoke(jobj, "totalNumNodes"),
      tree_weights = invoke(jobj, "treeWeights"),
      # `def trees`
      trees = function() {
        invoke(jobj, "trees") %>%
          purrr::map(new_ml_decision_tree_regression_model)
      },
      class = "ml_gbt_classification_model"
    )
  }
}