Spark ML – Gaussian Mixture clustering. — ml_gaussian

This class performs expectation maximization for multivariate Gaussian Mixture Models (GMMs). A GMM represents a composite distribution of independent Gaussian distributions with associated "mixing" weights specifying each's contribution to the composite. Given a set of sample points, this class will maximize the log-likelihood for a mixture of k Gaussians, iterating until the log-likelihood changes by less than tol, or until it has reached the max number of iterations. While this process is generally guaranteed to converge, it is not guaranteed to find a global optimum.

Usage

ml_gaussian_mixture(
  x,
  formula = NULL,
  k = 2,
  max_iter = 100,
  tol = 0.01,
  seed = NULL,
  features_col = "features",
  prediction_col = "prediction",
  probability_col = "probability",
  uid = random_string("gaussian_mixture_"),
  ...
)

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.
k: The number of clusters to create
max_iter: The maximum number of iterations to use.
tol: Param for the convergence tolerance for iterative algorithms.
seed: A random seed. Set this value if you need your results to be reproducible across repeated calls.
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.
prediction_col: Prediction column name.
probability_col: Column name for predicted class conditional probabilities. Note: Not all models output well-calibrated probability estimates! These probabilities should be treated as confidences, not precise probabilities.
uid: A character string used to uniquely identify the ML estimator.
...: Optional arguments, see Details. #' @return 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

if (FALSE) { # \dontrun{
sc <- spark_connect(master = "local")
iris_tbl <- sdf_copy_to(sc, iris, name = "iris_tbl", overwrite = TRUE)

gmm_model <- ml_gaussian_mixture(iris_tbl, Species ~ .)
pred <- sdf_predict(iris_tbl, gmm_model)
ml_clustering_evaluator(pred)
} # }