ml_gaussian_mixture
Spark ML -- Gaussian Mixture clustering.
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 atbl_spark.- formula
Used when
xis atbl_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; currently unused.
Value
The object returned depends on the class of x.
spark_connection: Whenxis aspark_connection, the function returns an instance of aml_estimatorobject. The object contains a pointer to a SparkEstimatorobject and can be used to composePipelineobjects.ml_pipeline: Whenxis aml_pipeline, the function returns aml_pipelinewith the clustering estimator appended to the pipeline.tbl_spark: Whenxis atbl_spark, an estimator is constructed then immediately fit with the inputtbl_spark, returning a clustering model.tbl_spark, withformulaorfeaturesspecified: Whenformulais specified, the inputtbl_sparkis first transformed using aRFormulatransformer before being fit by the estimator. The object returned in this case is aml_modelwhich is a wrapper of aml_pipeline_model. This signature does not apply toml_lda().
See Also
See http://spark.apache.org/docs/latest/ml-clustering.html for more information on the set of clustering algorithms.
Other ml clustering algorithms: ml_bisecting_kmeans,
ml_kmeans, ml_lda
Examples
# NOT RUN {
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)
# }