ml_kmeans: K-Means Clustering Model

In danzafar/tidyspark: A Tidy Interface to Spark

Description

Fits a k-means clustering model against a spark_tbl, similarly to R's kmeans(). Users can call summary to print a summary of the fitted model, predict to make predictions on new data, and write_ml/ read_ml to save/load fitted models.

Get fitted result from a k-means model, similarly to R's fitted(). Note: A saved-loaded model does not support this method.

Usage

ml_kmeans(
  data,
  formula,
  k = 2,
  maxIter = 20,
  initMode = c("k-means||", "random"),
  seed = NULL,
  initSteps = 2,
  tol = 1e-04
)

## S4 method for signature 'KMeansModel'
summary(object)

## S4 method for signature 'KMeansModel'
fitted(object, method = c("centers", "classes"))

## S4 method for signature 'KMeansModel,character'
write_ml(object, path, overwrite = FALSE)

Arguments

data	a spark_tbl for training.
formula	a symbolic description of the model to be fitted. Currently only a few formula operators are supported, including '~', '.', ':', '+', and '-'. Note that the response variable of formula is empty in ml_kmeans.
k	number of centers.
maxIter	maximum iteration number.
initMode	the initialization algorithm chosen to fit the model.
seed	the random seed for cluster initialization.
initSteps	the number of steps for the k-means|| initialization mode. This is an advanced setting, the default of 2 is almost always enough. Must be > 0.
tol	convergence tolerance of iterations.
object	a fitted k-means model.
method	type of fitted results, "centers" for cluster centers or "classes" for assigned classes.
path	the directory where the model is saved.
overwrite	overwrites or not if the output path already exists. Default is FALSE which means throw exception if the output path exists.
...	additional argument(s) passed to the method.

Value

ml_kmeans returns a fitted k-means model.

summary returns summary information of the fitted model, which is a list. The list includes the model's k (the configured number of cluster centers), coefficients (model cluster centers), size (number of data points in each cluster), cluster (cluster centers of the transformed data), is.loaded (whether the model is loaded from a saved file), and clusterSize (the actual number of cluster centers. When using initMode = "random", clusterSize may not equal to k).

fitted returns a spark_tbl containing fitted values.

Note

summary(KMeansModel) since 2.0.0

write_ml(KMeansModel, character) since 2.0.0

Examples

## Not run: 
spark_session()
t <- as.data.frame(Titanic)
df <- spark_tbl(t)
model <- ml_kmeans(df, Class ~ Survived, k = 4, initMode = "random")
summary(model)

## End(Not run)

danzafar/tidyspark documentation built on Sept. 30, 2020, 12:19 p.m.