ml_random_forest: Spark ML - Random Forest
In sparklyr: R Interface to Apache Spark

ml_random_forest_classifier

R Documentation

Spark ML – Random Forest

Description

Perform classification and regression using random forests.

Usage

ml_random_forest_classifier(
  x,
  formula = NULL,
  num_trees = 20,
  subsampling_rate = 1,
  max_depth = 5,
  min_instances_per_node = 1,
  feature_subset_strategy = "auto",
  impurity = "gini",
  min_info_gain = 0,
  max_bins = 32,
  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("random_forest_classifier_"),
  ...
)

ml_random_forest(
  x,
  formula = NULL,
  type = c("auto", "regression", "classification"),
  features_col = "features",
  label_col = "label",
  prediction_col = "prediction",
  probability_col = "probability",
  raw_prediction_col = "rawPrediction",
  feature_subset_strategy = "auto",
  impurity = "auto",
  checkpoint_interval = 10,
  max_bins = 32,
  max_depth = 5,
  num_trees = 20,
  min_info_gain = 0,
  min_instances_per_node = 1,
  subsampling_rate = 1,
  seed = NULL,
  thresholds = NULL,
  cache_node_ids = FALSE,
  max_memory_in_mb = 256,
  uid = random_string("random_forest_"),
  response = NULL,
  features = NULL,
  ...
)

ml_random_forest_regressor(
  x,
  formula = NULL,
  num_trees = 20,
  subsampling_rate = 1,
  max_depth = 5,
  min_instances_per_node = 1,
  feature_subset_strategy = "auto",
  impurity = "variance",
  min_info_gain = 0,
  max_bins = 32,
  seed = NULL,
  checkpoint_interval = 10,
  cache_node_ids = FALSE,
  max_memory_in_mb = 256,
  features_col = "features",
  label_col = "label",
  prediction_col = "prediction",
  uid = random_string("random_forest_regressor_"),
  ...
)

Arguments

`x`	A `spark_connection`, `ml_pipeline`, or a `tbl_spark`.
`formula`	Used when `x` is a `tbl_spark`. R formula as a character string or a formula. This is used to transform the input dataframe before fitting, see ft_r_formula for details.
`num_trees`	Number of trees to train (>= 1). If 1, then no bootstrapping is used. If > 1, then bootstrapping is done.
`subsampling_rate`	Fraction of the training data used for learning each decision tree, in range (0, 1]. (default = 1.0)
`max_depth`	Maximum depth of the tree (>= 0); that is, the maximum number of nodes separating any leaves from the root of the tree.
`min_instances_per_node`	Minimum number of instances each child must have after split.
`feature_subset_strategy`	The number of features to consider for splits at each tree node. See details for options.
`impurity`	Criterion used for information gain calculation. Supported: "entropy" and "gini" (default) for classification and "variance" (default) for regression. For `ml_decision_tree`, setting `"auto"` will default to the appropriate criterion based on model type.
`min_info_gain`	Minimum information gain for a split to be considered at a tree node. Should be >= 0, defaults to 0.
`max_bins`	The maximum number of bins used for discretizing continuous features and for choosing how to split on features at each node. More bins give higher granularity.
`seed`	Seed for random numbers.
`thresholds`	Thresholds in multi-class classification to adjust the probability of predicting each class. Array must have length equal to the number of classes, with values > 0 excepting that at most one value may be 0. The class with largest value `p/t` is predicted, where `p` is the original probability of that class and `t` is the class's threshold.
`checkpoint_interval`	Set checkpoint interval (>= 1) or disable checkpoint (-1). E.g. 10 means that the cache will get checkpointed every 10 iterations, defaults to 10.
`cache_node_ids`	If `FALSE`, the algorithm will pass trees to executors to match instances with nodes. If `TRUE`, the algorithm will cache node IDs for each instance. Caching can speed up training of deeper trees. Defaults to `FALSE`.
`max_memory_in_mb`	Maximum memory in MB allocated to histogram aggregation. If too small, then 1 node will be split per iteration, and its aggregates may exceed this size. Defaults to 256.
`features_col`	Features column name, as a length-one character vector. The column should be single vector column of numeric values. Usually this column is output by `ft_r_formula`.
`label_col`	Label column name. The column should be a numeric column. Usually this column is output by `ft_r_formula`.
`prediction_col`	Prediction column name.
`probability_col`	Column name for predicted class conditional probabilities.
`raw_prediction_col`	Raw prediction (a.k.a. confidence) column name.
`uid`	A character string used to uniquely identify the ML estimator.
`...`	Optional arguments; see Details.
`type`	The type of model to fit. `"regression"` treats the response as a continuous variable, while `"classification"` treats the response as a categorical variable. When `"auto"` is used, the model type is inferred based on the response variable type – if it is a numeric type, then regression is used; classification otherwise.
`response`	(Deprecated) The name of the response column (as a length-one character vector.)
`features`	(Deprecated) The name of features (terms) to use for the model fit.

Details

The supported options for feature_subset_strategy are

"auto": Choose automatically for task: If num_trees == 1, set to "all". If num_trees > 1 (forest), set to "sqrt" for classification and to "onethird" for regression.
"all": use all features
"onethird": use 1/3 of the features
"sqrt": use use sqrt(number of features)
"log2": use log2(number of features)
"n": when n is in the range (0, 1.0], use n * number of features. When n is in the range (1, number of features), use n features. (default = "auto")

ml_random_forest is a wrapper around ml_random_forest_regressor.tbl_spark and ml_random_forest_classifier.tbl_spark and calls the appropriate method based on model type.

Value

The object returned depends on the class of x. If it is a spark_connection, the function returns a ml_estimator object. If it is a ml_pipeline, it will return a pipeline with the predictor appended to it. If a tbl_spark, it will return a tbl_spark with the predictions added to it.

Examples

## Not run: 
sc <- spark_connect(master = "local")
iris_tbl <- sdf_copy_to(sc, iris, name = "iris_tbl", overwrite = TRUE)

partitions <- iris_tbl %>%
  sdf_random_split(training = 0.7, test = 0.3, seed = 1111)

iris_training <- partitions$training
iris_test <- partitions$test

rf_model <- iris_training %>%
  ml_random_forest(Species ~ ., type = "classification")

pred <- ml_predict(rf_model, iris_test)

ml_multiclass_classification_evaluator(pred)

## End(Not run)

sparklyr documentation built on May 29, 2024, 2:58 a.m.