ml_naive_bayes: Spark 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_"),
  ...
)

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.

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.
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.

Examples

Run this code

if (FALSE) {
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)
}