ml_linear_svc: Spark ML -- LinearSVC

Description

Perform classification using linear support vector machines (SVM). This binary classifier optimizes the Hinge Loss using the OWLQN optimizer. Only supports L2 regularization currently.

Usage

ml_linear_svc(
  x,
  formula = NULL,
  fit_intercept = TRUE,
  reg_param = 0,
  max_iter = 100,
  standardization = TRUE,
  weight_col = NULL,
  tol = 1e-06,
  threshold = 0,
  aggregation_depth = 2,
  features_col = "features",
  label_col = "label",
  prediction_col = "prediction",
  raw_prediction_col = "rawPrediction",
  uid = random_string("linear_svc_"),
  ...
)

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.
fit_intercept: Boolean; should the model be fit with an intercept term?
reg_param: Regularization parameter (aka lambda)
max_iter: The maximum number of iterations to use.
standardization: Whether to standardize the training features before fitting the model.
weight_col: The name of the column to use as weights for the model fit.
tol: Param for the convergence tolerance for iterative algorithms.
threshold: in binary classification prediction, in range [0, 1].
aggregation_depth: (Spark 2.1.0+) Suggested depth for treeAggregate (>= 2).
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.
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) {
library(dplyr)

sc <- spark_connect(master = "local")
iris_tbl <- sdf_copy_to(sc, iris, name = "iris_tbl", overwrite = TRUE)

partitions <- iris_tbl %>%
  filter(Species != "setosa") %>%
  sdf_random_split(training = 0.7, test = 0.3, seed = 1111)

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

svc_model <- iris_training %>%
  ml_linear_svc(Species ~ .)

pred <- ml_predict(svc_model, iris_test)

ml_binary_classification_evaluator(pred)
}