ml_gaussian_mixture {sparklyr} | R Documentation

## Spark ML – Gaussian Mixture clustering.

### Description

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 = 2L, max_iter = 100L,
      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; currently unused.

### 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 `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 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`

[Package sparklyr version 0.8.4 Index]