Spark ML – Naive-Bayes

R/ml_classification_naive_bayes.R

ml_naive_bayes

Description

Naive Bayes Classifiers. It supports Multinomial NB (see here) which can handle finitely supported discrete data. For example, by converting documents into TF-IDF vectors, it can be used for document classification. By making every vector a binary (0/1) data, it can also be used as Bernoulli NB (see here). The input feature values must be nonnegative.

Usage

 
ml_naive_bayes( 
  x, 
  formula = NULL, 
  model_type = "multinomial", 
  smoothing = 1, 
  thresholds = NULL, 
  weight_col = NULL, 
  features_col = "features", 
  label_col = "label", 
  prediction_col = "prediction", 
  probability_col = "probability", 
  raw_prediction_col = "rawPrediction", 
  uid = random_string("naive_bayes_"), 
  ... 
)

Arguments

Arguments	Description
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.
model_type	The model type. Supported options: `"multinomial"` and `"bernoulli"`. (default = `multinomial`)
smoothing	The (Laplace) smoothing parameter. Defaults to 1.
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.
weight_col	(Spark 2.1.0+) Weight column name. If this is not set or empty, we treat all instance weights as 1.0.
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.

Details

When x is a tbl_spark and formula (alternatively, response and features) is specified, the function returns a ml_model object wrapping a ml_pipeline_model which contains data pre-processing transformers, the ML predictor, and, for classification models, a post-processing transformer that converts predictions into class labels. For classification, an optional argument predicted_label_col (defaults to "predicted_label") can be used to specify the name of the predicted label column. In addition to the fitted ml_pipeline_model, ml_model objects also contain a ml_pipeline object where the ML predictor stage is an estimator ready to be fit against data. This is utilized by ml_save with type = "pipeline" to faciliate model refresh workflows.

Value

The object returned depends on the class of x.

spark_connection: When x is a spark_connection, the function returns an instance of a ml_estimator object. The object contains a pointer to a Spark Predictor object and can be used to compose Pipeline objects.
ml_pipeline: When x is a ml_pipeline, the function returns a ml_pipeline with the predictor appended to the pipeline.
tbl_spark: When x is a tbl_spark, a predictor is constructed then immediately fit with the input tbl_spark, returning a prediction model.
tbl_spark, with formula: specified When formula is specified, the input tbl_spark is first transformed using a RFormula transformer before being fit by the predictor. The object returned in this case is a ml_model which is a wrapper of a ml_pipeline_model.

Examples

library(sparklyr)
 
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 
 
nb_model <- iris_training %>% 
  ml_naive_bayes(Species ~ .) 
 
pred <- ml_predict(nb_model, iris_test) 
 
ml_multiclass_classification_evaluator(pred) 
#> [1] 0.9393939