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.
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_"),
...
)x |
A |
formula |
Used when |
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 |
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. |
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 Estimator 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 clustering estimator appended to the pipeline.
tbl_spark: When x is a tbl_spark, an estimator is constructed then
immediately fit with the input tbl_spark, returning a clustering model.
tbl_spark, with formula or features specified: When formula
is specified, the input tbl_spark is first transformed using a
RFormula transformer before being fit by
the estimator. The object returned in this case is a ml_model which is a
wrapper of a ml_pipeline_model. This signature does not apply to ml_lda().
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()
## 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) ## End(Not run)
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